109 lines
4.5 KiB
Plaintext
109 lines
4.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 粤AGP3080 2026-08-11 用氢量差异诊断\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## tl;dr\n",
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"\n",
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"- 生产数据库当前值为 **5.992 kg**，分段关键帧合并值为 **12.051 kg**，差 **6.059 kg（+101.12%）**。\n",
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"- 根因已确认：同一车辆当天使用了3个 `source_endpoint`。现有算法按 `VIN + source_endpoint` 分组后只选择样本最多的一组，导致后续两个连续时间段被舍弃。\n",
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"- 三个来源片段按现有算法分别得到5.992、0.697、5.349 kg，合计12.038 kg；与分段关键帧结果仅差0.013 kg。\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Context & Methods\n",
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"\n",
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"诊断对象：粤AGP3080，VIN `LB9A32A25R0LS1452`，2026-08-11（Asia/Shanghai）。\n",
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"\n",
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"对比口径：生产表日结果、现有算法按连接来源分组的结果、合并全部来源后按工作段首尾5帧中位数做差的结果。加氢、异常跳变和超过5分钟的数据断点用于切段。\n",
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"\n",
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"### Key Assumptions\n",
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"\n",
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"三个端口属于同一VIN当天的连接重连，不是三个独立车辆数据源；百公里氢耗采用生产日里程502.4km。\n",
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"\n",
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"> 执行状态：生产查询已于2026-08-19完成并复核。本地缺少 `nbformat`、`jupyter` 和可用内核，因此本笔记本保存可复算代码但未在本机执行。复跑命令：`python -m jupyter nbconvert --execute --to notebook --inplace <notebook>`。\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Data\n",
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"\n",
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"| 来源连接 | 时间范围 | 原始帧 | 工作帧 | 现有算法用氢量 |\n",
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"|---|---|---:|---:|---:|\n",
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"| 8.134.95.166:54760 | 06:28:12–12:34:43 | 1542 | 1539 | 5.992kg |\n",
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"| 8.134.95.166:41692 | 12:35:13–14:38:29 | 328 | 328 | 0.697kg |\n",
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"| 8.134.95.166:37898 | 14:54:00–19:59:02 | 1357 | 1357 | 5.349kg |\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"mileage_km = 502.4\n",
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"stored_kg = 5.992\n",
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"source_consumption_kg = [5.992, 0.697, 5.349]\n",
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"segment_consumption_kg = [0.462, 2.537, 3.685, 0.000, 2.629, 2.738]\n",
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"source_sum_kg = round(sum(source_consumption_kg), 3)\n",
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"proposed_kg = round(sum(segment_consumption_kg), 3)\n",
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"gap_kg = round(proposed_kg - stored_kg, 3)\n",
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"gap_pct = round((proposed_kg / stored_kg - 1) * 100, 2)\n",
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"omitted_sources_kg = round(source_sum_kg - stored_kg, 3)\n",
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"method_residual_kg = round(proposed_kg - source_sum_kg, 3)\n",
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"current_rate = round(stored_kg / mileage_km * 100, 3)\n",
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"proposed_rate = round(proposed_kg / mileage_km * 100, 3)\n",
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"assert (source_sum_kg, proposed_kg, gap_kg) == (12.038, 12.051, 6.059)\n",
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"{\n",
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" '数据库用氢量_kg': stored_kg,\n",
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" '新算法用氢量_kg': proposed_kg,\n",
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" '差值_kg': gap_kg,\n",
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" '差值比例_pct': gap_pct,\n",
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" '被舍弃来源片段_kg': omitted_sources_kg,\n",
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" '方法残差_kg': method_residual_kg,\n",
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" '数据库百公里氢耗': current_rate,\n",
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" '新算法百公里氢耗': proposed_rate,\n",
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"}\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Takeaways\n",
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"\n",
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"1. 差值不是压力换算公式造成的；被舍弃的两个连接片段贡献6.046kg，解释总差值的99.79%。\n",
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"2. 问题在聚合粒度：`source_endpoint` 包含临时TCP端口，重连会改变端口，不能作为车辆日统计的互斥来源键。\n",
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"3. 当前生产结果只保留06:28–12:34的数据，12:35–19:59的连续有效工作数据没有进入日用氢量。\n",
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"4. 建议先按VIN和事件时间合并去重，再识别工作段；来源字段只用于追踪。\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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