잔여 시간 계산에 장기 학습 프로파일 반영
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+202
-3
@@ -793,6 +793,91 @@ function battery_trend_history(int $hours = 24): array
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return $rows;
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}
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function battery_profile_history(int $days = 45): array
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{
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$days = max(3, min(90, $days));
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static $cache = [];
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$cacheKey = (string)$days;
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$now = time();
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$cacheFile = sys_get_temp_dir() . '/control-battery-profile-' . $days . 'd.json';
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if (
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isset($cache[$cacheKey])
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&& ($now - (int)$cache[$cacheKey]['time']) < 600
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&& is_array($cache[$cacheKey]['rows'])
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) {
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return $cache[$cacheKey]['rows'];
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}
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if (is_readable($cacheFile)) {
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$raw = @file_get_contents($cacheFile);
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$decoded = is_string($raw) ? json_decode($raw, true) : null;
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if (
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is_array($decoded)
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&& isset($decoded['created_at'], $decoded['rows'])
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&& is_array($decoded['rows'])
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&& ($now - (int)$decoded['created_at']) < 21600
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) {
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$cache[$cacheKey] = [
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'time' => $now,
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'rows' => $decoded['rows'],
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];
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return $decoded['rows'];
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}
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}
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$maxId = (int)db()->query("SELECT MAX(id) FROM sensor_logs")->fetchColumn();
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$minId = max(1, $maxId - 3000000);
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$stmt = db()->query("
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SELECT
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recorded_at AS time,
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battery_percent,
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battery_voltage,
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cpu_watts,
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cpu_load_1,
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1 AS samples
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FROM sensor_logs FORCE INDEX (idx_recorded_at)
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WHERE recorded_at >= DATE_SUB(NOW(), INTERVAL {$days} DAY)
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AND battery_percent IS NOT NULL
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AND id >= {$minId}
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AND MOD(id, 300) = 0
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ORDER BY recorded_at ASC
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");
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$rows = [];
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foreach ($stmt->fetchAll() as $row) {
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$time = strtotime((string)($row['time'] ?? ''));
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if ($time <= 0 || !is_numeric($row['battery_percent'] ?? null)) {
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continue;
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}
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$rows[] = [
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'time' => $time,
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'battery_percent' => (float)$row['battery_percent'],
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'battery_voltage' => is_numeric($row['battery_voltage'] ?? null) ? (float)$row['battery_voltage'] : null,
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'cpu_watts' => is_numeric($row['cpu_watts'] ?? null) ? (float)$row['cpu_watts'] : null,
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'cpu_load_1' => is_numeric($row['cpu_load_1'] ?? null) ? (float)$row['cpu_load_1'] : null,
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'samples' => (int)($row['samples'] ?? 0),
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];
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}
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$cache[$cacheKey] = [
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'time' => $now,
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'rows' => $rows,
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];
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@file_put_contents(
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$cacheFile,
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json_encode([
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'created_at' => $now,
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'days' => $days,
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'rows' => $rows,
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], JSON_UNESCAPED_UNICODE)
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);
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return $rows;
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}
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function battery_trend_candidate(array $trendRows, float $currentPercent, int $windowSeconds, ?float $recentWatts): ?array
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{
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if (count($trendRows) < 6) {
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@@ -862,6 +947,110 @@ function battery_trend_candidate(array $trendRows, float $currentPercent, int $w
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'weight' => max(0.05, $confidence),
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'avg_watts' => $windowWatts === null ? null : round($windowWatts, 3),
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'load_factor' => round($loadFactor, 3),
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'source' => 'recent_window',
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];
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}
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function battery_learned_profile_candidate(array $profileRows, float $currentPercent, ?float $recentWatts): ?array
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{
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if (count($profileRows) < 24) {
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return null;
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}
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$now = time();
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$rates = [];
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$dropSum = 0.0;
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$sampleSum = 0;
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$socBuckets = [];
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$minSoc = max(0.0, $currentPercent - 70.0);
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$maxSoc = min(100.0, $currentPercent + 8.0);
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for ($i = 1, $count = count($profileRows); $i < $count; $i++) {
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$prev = $profileRows[$i - 1];
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$row = $profileRows[$i];
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$elapsed = (int)$row['time'] - (int)$prev['time'];
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if ($elapsed < 240 || $elapsed > 900) {
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continue;
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}
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$startSoc = (float)$prev['battery_percent'];
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$endSoc = (float)$row['battery_percent'];
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$avgSoc = ($startSoc + $endSoc) / 2;
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if ($avgSoc < $minSoc || $avgSoc > $maxSoc) {
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continue;
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}
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$drop = $startSoc - $endSoc;
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if ($drop < 0.03 || $drop > 8.0) {
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continue;
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}
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$avgWatts = numeric_trimmed_average([$prev['cpu_watts'] ?? null, $row['cpu_watts'] ?? null], 0.0);
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$loadFactor = 1.0;
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if ($recentWatts !== null && $recentWatts > 0 && $avgWatts !== null && $avgWatts > 0) {
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$loadFactor = 1 + ((clamp_float($recentWatts / $avgWatts, 0.55, 1.95) - 1) * 0.5);
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}
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$socDistance = abs($currentPercent - $avgSoc);
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$socWeight = 1 / (1 + ($socDistance / 18));
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$ageDays = max(0.0, ($now - (int)$row['time']) / 86400);
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$recencyWeight = 1 / (1 + ($ageDays / 45));
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$sampleWeight = min(1.0, max(1, ((int)$prev['samples'] + (int)$row['samples']) / 2) / 30);
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$weight = max(0.01, $socWeight * $recencyWeight * $sampleWeight);
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$rates[] = [
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'rate' => ($drop / $elapsed) * $loadFactor,
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'weight' => $weight,
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'soc' => $avgSoc,
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'drop' => $drop,
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'watts' => $avgWatts,
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];
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$dropSum += $drop;
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$sampleSum += (int)$prev['samples'] + (int)$row['samples'];
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$bucket = (int)(floor($avgSoc / 10) * 10);
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$socBuckets[$bucket] = true;
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}
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if (count($rates) < 12 || $dropSum < 1.0) {
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return null;
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}
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$weightedRate = 0.0;
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$weightSum = 0.0;
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foreach ($rates as $row) {
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$weightedRate += (float)$row['rate'] * (float)$row['weight'];
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$weightSum += (float)$row['weight'];
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}
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if ($weightedRate <= 0 || $weightSum <= 0) {
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return null;
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}
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$rate = $weightedRate / $weightSum;
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$seconds = (int)round($currentPercent / $rate);
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if ($seconds <= 0) {
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return null;
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}
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$watts = numeric_trimmed_average(array_column($rates, 'watts'), 0.1);
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$confidence = min(1.0, count($rates) / 240)
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* min(1.0, $dropSum / 18)
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* min(1.0, count($socBuckets) / 5);
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return [
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'seconds' => $seconds,
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'rate_per_second' => $rate,
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'drop_percent' => round($dropSum, 3),
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'elapsed_seconds' => null,
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'window_seconds' => null,
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'sample_count' => $sampleSum,
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'confidence' => round($confidence, 4),
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'weight' => max(0.05, $confidence * 0.9),
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'avg_watts' => $watts === null ? null : round($watts, 3),
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'load_factor' => null,
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'source' => 'learned_profile',
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'intervals' => count($rates),
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'soc_buckets' => count($socBuckets),
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];
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}
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@@ -898,7 +1087,7 @@ function battery_power_fallback_estimate(float $percent, array $history): array
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];
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}
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function battery_remaining_estimate(array $battery, array $history, array $trendRows = []): array
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function battery_remaining_estimate(array $battery, array $history, array $trendRows = [], array $profileRows = []): array
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{
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$percent = $battery['percent'] ?? null;
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if ($percent === null || $percent === '' || !is_numeric($percent)) {
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@@ -922,6 +1111,11 @@ function battery_remaining_estimate(array $battery, array $history, array $trend
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}
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}
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$learned = battery_learned_profile_candidate($profileRows, $percent, $recentWatts);
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if ($learned !== null) {
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$candidates[] = $learned;
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}
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if ($candidates !== []) {
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$weightedRate = 0.0;
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$weightSum = 0.0;
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@@ -944,12 +1138,16 @@ function battery_remaining_estimate(array $battery, array $history, array $trend
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'recent_watts' => $recentWatts === null ? null : round($recentWatts, 3),
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'drop_per_hour' => round($rate * 3600, 3),
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'confidence' => round(array_sum(array_column($candidates, 'confidence')) / count($candidates), 3),
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'sources' => array_values(array_unique(array_column($candidates, 'source'))),
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'windows' => array_map(static function (array $candidate): array {
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return [
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'minutes' => (int)round($candidate['window_seconds'] / 60),
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'source' => $candidate['source'],
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'minutes' => $candidate['window_seconds'] === null ? null : (int)round($candidate['window_seconds'] / 60),
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'drop_percent' => $candidate['drop_percent'],
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'confidence' => $candidate['confidence'],
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'load_factor' => $candidate['load_factor'],
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'intervals' => $candidate['intervals'] ?? null,
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'soc_buckets' => $candidate['soc_buckets'] ?? null,
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];
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}, $candidates),
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];
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@@ -2346,7 +2544,8 @@ function collect_snapshot(bool $applyFan = true): array
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$history = add_battery_remaining_history(sensor_history(240));
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$batteryTrend = battery_trend_history(24);
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$battery['remaining'] = battery_remaining_estimate($battery, $history, $batteryTrend);
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$batteryProfile = battery_profile_history(45);
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$battery['remaining'] = battery_remaining_estimate($battery, $history, $batteryTrend, $batteryProfile);
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$processes = process_resource_data(6);
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$fanSpike = fan_spike_analysis($history, $fan, $system, $processes);
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+13
-1
@@ -699,8 +699,20 @@
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} else if (remaining.avg_watts !== undefined && remaining.avg_watts !== null) {
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lines.push(`평균 CPU 전력: ${Number(remaining.avg_watts).toFixed(3)}W`);
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}
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if (Array.isArray(remaining.sources) && remaining.sources.length > 0) {
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const labels = {
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recent_window: '최근 추세',
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learned_profile: '장기 학습',
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};
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lines.push(`계산 출처: ${remaining.sources.map(source => labels[source] || source).join(', ')}`);
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}
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if (Array.isArray(remaining.windows) && remaining.windows.length > 0) {
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lines.push(`반영 구간: ${remaining.windows.map(row => `${row.minutes}분`).join(', ')}`);
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lines.push(`반영 구간: ${remaining.windows.map(row => {
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if (row.source === 'learned_profile') {
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return `장기 ${row.intervals || 0}구간`;
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}
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return `${row.minutes}분`;
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}).join(', ')}`);
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}
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return lines.join('\n');
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