950 lines
36 KiB
Python
950 lines
36 KiB
Python
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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测试部位温度记录表生成脚本（带负载状态筛选）
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- 忽略传入的 experimentProcess，自行构造固定结构的数据
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- 从 InfluxDB 查询每个测试部位在各时间点的瞬时温度值
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- 添加 load_status = 1 的筛选条件，确保只在真正采集数据时获取温度
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- 输出格式与应用中的 scriptTable 占位符兼容
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- 默认把 {scriptTable1} 放在"测试部位"所在的单元格
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环境变量：
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TABLE_TOKEN 目标占位符，默认 scriptTable1
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TABLE_START_ROW 写入起始行偏移，默认 0
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TABLE_START_COL 写入起始列偏移，默认 0
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TABLE_TIME_SLOTS 逗号分隔的时间刻度，默认 "0.5h,1h,1.5h,2h,2.5h,3h,3.5h"
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TABLE_MOTOR_SPEED 电机转速标签，默认 "980RPM"
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EXPERIMENT_START 实验开始时间（ISO 8601 格式，如 2024-01-01T10:00:00Z）
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EXPERIMENT_END 实验结束时间（ISO 8601 格式）
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INFLUX_URL InfluxDB URL
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INFLUX_ORG InfluxDB 组织
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INFLUX_TOKEN InfluxDB 令牌
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INFLUX_BUCKET InfluxDB 桶名，默认 PCM
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INFLUX_MEASUREMENT InfluxDB 测量名，默认 PCM_Measurement
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"""
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from __future__ import annotations
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import json
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import logging
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import os
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import sys
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from datetime import datetime, timedelta
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from typing import Any, Dict, List, Optional
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LOGGER = logging.getLogger(__name__)
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def _mask_secret(value: Optional[str]) -> str:
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"""掩码敏感信息"""
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if not value:
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return "<empty>"
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if len(value) <= 8:
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return "*" * len(value)
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return value[:4] + "*" * (len(value) - 8) + value[-4:]
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def _setup_logging() -> None:
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"""设置日志"""
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log_level_str = os.environ.get("TABLE_LOG_LEVEL", "DEBUG").upper()
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log_level = getattr(logging, log_level_str, logging.DEBUG)
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# 配置根日志记录器
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logging.basicConfig(
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level=log_level,
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format='%(asctime)s [%(levelname)s] %(name)s: %(message)s',
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handlers=[
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logging.StreamHandler(sys.stderr)
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]
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)
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# 如果指定了日志文件，添加文件处理器
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log_file = os.environ.get("TABLE_LOG_FILE", "").strip()
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if log_file:
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try:
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file_handler = logging.FileHandler(log_file, encoding='utf-8')
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file_handler.setLevel(log_level)
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file_handler.setFormatter(logging.Formatter(
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'%(asctime)s [%(levelname)s] %(name)s: %(message)s'
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))
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logging.getLogger().addHandler(file_handler)
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LOGGER.info("日志文件已配置: %s", log_file)
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except Exception as e:
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LOGGER.warning("配置日志文件失败: %s", e)
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def _get_influx_config() -> Dict[str, str]:
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"""获取InfluxDB配置"""
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config = {
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'url': os.environ.get("INFLUX_URL", "").strip(),
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'org': os.environ.get("INFLUX_ORG", "").strip(),
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'token': os.environ.get("INFLUX_TOKEN", "").strip(),
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'bucket': os.environ.get("INFLUX_BUCKET", "PCM").strip(),
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'measurement': os.environ.get("INFLUX_MEASUREMENT", "PCM_Measurement").strip(),
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}
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LOGGER.debug(
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"InfluxDB配置: url=%s org=%s token=%s bucket=%s measurement=%s",
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config['url'] or "<empty>",
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config['org'] or "<empty>",
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_mask_secret(config['token']),
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config['bucket'],
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config['measurement'],
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)
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return config
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def _parse_experiment_times() -> tuple[Optional[datetime], Optional[datetime]]:
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"""解析实验时间"""
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start_str = os.environ.get("EXPERIMENT_START", "").strip()
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end_str = os.environ.get("EXPERIMENT_END", "").strip()
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start_time: Optional[datetime] = None
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end_time: Optional[datetime] = None
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if start_str:
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try:
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for fmt in ["%Y-%m-%dT%H:%M:%SZ", "%Y-%m-%dT%H:%M:%S%z"]:
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try:
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start_time = datetime.strptime(start_str, fmt)
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if start_time.tzinfo is not None:
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# 转换为本地时间并去除时区信息
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start_time = start_time.astimezone(tz=None).replace(tzinfo=None)
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break
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except ValueError:
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continue
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except Exception as e:
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print(f"Warning: Failed to parse EXPERIMENT_START '{start_str}': {e}", file=sys.stderr)
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if end_str:
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try:
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for fmt in ["%Y-%m-%dT%H:%M:%SZ", "%Y-%m-%dT%H:%M:%S%z"]:
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try:
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end_time = datetime.strptime(end_str, fmt)
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if end_time.tzinfo is not None:
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# 转换为本地时间并去除时区信息
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end_time = end_time.astimezone(tz=None).replace(tzinfo=None)
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break
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except ValueError:
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continue
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except Exception as e:
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print(f"Warning: Failed to parse EXPERIMENT_END '{end_str}': {e}", file=sys.stderr)
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return start_time, end_time
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def _parse_time_slot(slot_str: str) -> float:
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"""解析时间槽字符串为小时数"""
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if not slot_str:
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return 0.0
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slot_str = slot_str.strip().lower()
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if slot_str.endswith('h'):
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try:
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return float(slot_str[:-1])
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except ValueError:
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pass
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try:
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return float(slot_str)
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except ValueError:
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pass
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return 0.0
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def _time_slots() -> List[str]:
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raw = os.environ.get("TABLE_TIME_SLOTS", "").strip()
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if not raw:
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# 根据图片，时间刻度是：0.5h, 1h, 1.5h, 2h, 2.5h, 3h, 3.5h（7列）
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return ["0.5h", "1h", "1.5h", "2h", "2.5h", "3h", "3.5h"]
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slots = [slot.strip() for slot in raw.split(",")]
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return [slot for slot in slots if slot]
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def _default_sections() -> List[Dict[str, Any]]:
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# name -> rows underneath（entries）
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# 每个 entry 对应一个测试部位，需要映射到 InfluxDB 的 field 或 tag
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return [
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{"name": "主轴承", "entries": [
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{"label": "#1", "field": "主轴承#1", "filters": {"data_type": "LSDAQ"}, "result_key": "主轴承#1"},
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{"label": "#2", "field": "主轴承#2", "filters": {"data_type": "LSDAQ"}, "result_key": "主轴承#2"},
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{"label": "#3", "field": "主轴承#3", "filters": {"data_type": "LSDAQ"}, "result_key": "主轴承#3"},
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{"label": "#4", "field": "主轴承#4", "filters": {"data_type": "LSDAQ"}, "result_key": "主轴承#4"},
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]},
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{"name": "十字头", "entries": [
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{"label": "#1", "field": "十字头#1", "filters": {"data_type": "LSDAQ"}, "result_key": "十字头#1"},
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{"label": "#2", "field": "十字头#2", "filters": {"data_type": "LSDAQ"}, "result_key": "十字头#2"},
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{"label": "#3", "field": "十字头#3", "filters": {"data_type": "LSDAQ"}, "result_key": "十字头#3"},
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]},
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{"name": "减速箱小轴承", "entries": [
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{"label": "#1（输入法兰端）", "field": "减速箱小轴承1", "filters": {"data_type": "LSDAQ"}, "result_key": "减速箱小轴承#1"},
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{"label": "#2", "field": "减速箱小轴承#2", "filters": {"data_type": "LSDAQ"}, "result_key": "减速箱小轴承#2"},
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]},
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{"name": "减速箱大轴承", "entries": [
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{"label": "#3（大端盖端）", "field": "减速箱大轴承#3", "filters": {"data_type": "LSDAQ"}, "result_key": "减速箱大轴承#3"},
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{"label": "#4", "field": "减速箱大轴承#4", "filters": {"data_type": "LSDAQ"}, "result_key": "减速箱大轴承#4"},
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]},
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{"name": "润滑油温", "entries": [
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{"label": "", "field": "mean", "filters": {"data_type": "润滑油温"}, "result_key": "润滑油温"},
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]},
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{"name": "润滑油压", "entries": [
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{"label": "(Psi)", "field": "mean", "filters": {"data_type": "润滑油压"}, "result_key": "润滑油压"},
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]},
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]
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def _query_load_status_timeline(
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start_time: datetime,
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end_time: datetime,
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influx_url: str,
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influx_org: str,
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influx_token: str,
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influx_bucket: str,
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influx_measurement: str,
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) -> List[Dict[str, Any]]:
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"""查询整个实验期间的load_status时间线数据"""
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try:
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from influxdb_client import InfluxDBClient
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import pandas as pd
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import warnings
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from influxdb_client.client.warnings import MissingPivotFunction
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except ImportError:
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LOGGER.warning("InfluxDB client not available, skip load_status timeline query")
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return []
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try:
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client = InfluxDBClient(url=influx_url, org=influx_org, token=influx_token)
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query_api = client.query_api()
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start_rfc = start_time.strftime('%Y-%m-%dT%H:%M:%SZ')
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end_rfc = end_time.strftime('%Y-%m-%dT%H:%M:%SZ')
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# 查询load_status字段的所有数据点（在Breaker数据类型中）
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flux = f'''
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from(bucket: "{influx_bucket}")
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|> range(start: {start_rfc}, stop: {end_rfc})
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|> filter(fn: (r) => r["_measurement"] == "{influx_measurement}")
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|> filter(fn: (r) => r["data_type"] == "Breaker")
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|> filter(fn: (r) => r["_field"] == "load_status")
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|> sort(columns: ["_time"])
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|> yield(name: "load_status_timeline")
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'''.strip()
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LOGGER.debug("Load status timeline query:\n%s", flux)
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", MissingPivotFunction)
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frames = query_api.query_data_frame(flux)
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if isinstance(frames, list):
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df = pd.concat(frames, ignore_index=True) if frames else pd.DataFrame()
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else:
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df = frames
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if df.empty or '_value' not in df.columns or '_time' not in df.columns:
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LOGGER.warning("No load_status timeline data found")
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return []
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# 转换为时间线数据，确保时区一致性
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timeline = []
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for _, row in df.iterrows():
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time_obj = pd.to_datetime(row['_time'])
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# 转换为本地时间，去除时区信息，与start_time/end_time保持一致
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if hasattr(time_obj, 'tz') and time_obj.tz is not None:
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# 对于pandas Timestamp，先转换为本地时区再转为Python datetime
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time_obj = time_obj.tz_convert(None).to_pydatetime()
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elif hasattr(time_obj, 'to_pydatetime'):
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# 转换为Python datetime对象
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time_obj = time_obj.to_pydatetime()
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# 确保没有时区信息
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if hasattr(time_obj, 'tzinfo') and time_obj.tzinfo is not None:
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time_obj = time_obj.replace(tzinfo=None)
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timeline.append({
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'time': time_obj,
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'load_status': float(row['_value'])
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})
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LOGGER.info("Load status timeline: %d data points from %s to %s",
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len(timeline), start_time, end_time)
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# 调试：检查时间对象类型
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if timeline:
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first_time = timeline[0]['time']
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LOGGER.debug("Timeline first time: %s (type: %s, tzinfo: %s)",
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first_time, type(first_time), getattr(first_time, 'tzinfo', None))
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LOGGER.debug("start_time: %s (type: %s, tzinfo: %s)",
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start_time, type(start_time), getattr(start_time, 'tzinfo', None))
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LOGGER.debug("end_time: %s (type: %s, tzinfo: %s)",
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end_time, type(end_time), getattr(end_time, 'tzinfo', None))
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return timeline
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except Exception as e:
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LOGGER.error("Error querying load_status timeline: %s", e)
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return []
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finally:
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try:
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client.close()
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except Exception:
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pass
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def _calculate_effective_time_points(
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start_time: datetime,
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end_time: datetime,
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time_slots: List[str],
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influx_config: Dict[str, str]
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) -> Dict[str, Optional[datetime]]:
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|
|
"""计算基于有效运行时间累计的真实时间点"""
|
|||
|
|
|
|||
|
|
# 1. 获取load_status时间线
|
|||
|
|
timeline = _query_load_status_timeline(
|
|||
|
|
start_time, end_time,
|
|||
|
|
influx_config['url'], influx_config['org'], influx_config['token'],
|
|||
|
|
influx_config['bucket'], influx_config['measurement']
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
if not timeline:
|
|||
|
|
LOGGER.warning("No load_status timeline data, fallback to original time calculation")
|
|||
|
|
# 回退到原始时间计算
|
|||
|
|
result = {}
|
|||
|
|
for slot_str in time_slots:
|
|||
|
|
slot_hours = _parse_time_slot(slot_str)
|
|||
|
|
result[slot_str] = start_time + timedelta(hours=slot_hours)
|
|||
|
|
return result
|
|||
|
|
|
|||
|
|
# 2. 计算有效运行时间段
|
|||
|
|
effective_periods = []
|
|||
|
|
current_period_start = None
|
|||
|
|
|
|||
|
|
for i, point in enumerate(timeline):
|
|||
|
|
if point['load_status'] == 1.0:
|
|||
|
|
if current_period_start is None:
|
|||
|
|
current_period_start = point['time']
|
|||
|
|
else: # load_status != 1.0
|
|||
|
|
if current_period_start is not None:
|
|||
|
|
effective_periods.append({
|
|||
|
|
'start': current_period_start,
|
|||
|
|
'end': point['time'],
|
|||
|
|
'duration_hours': (point['time'] - current_period_start).total_seconds() / 3600.0
|
|||
|
|
})
|
|||
|
|
current_period_start = None
|
|||
|
|
|
|||
|
|
# 处理最后一个周期（如果实验结束时仍在运行）
|
|||
|
|
if current_period_start is not None:
|
|||
|
|
effective_periods.append({
|
|||
|
|
'start': current_period_start,
|
|||
|
|
'end': end_time,
|
|||
|
|
'duration_hours': (end_time - current_period_start).total_seconds() / 3600.0
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
total_effective_hours = sum(period['duration_hours'] for period in effective_periods)
|
|||
|
|
LOGGER.info("Effective running periods: %d periods, total %.3f hours",
|
|||
|
|
len(effective_periods), total_effective_hours)
|
|||
|
|
|
|||
|
|
for period in effective_periods:
|
|||
|
|
LOGGER.debug("Effective period: %s → %s (%.3f hours)",
|
|||
|
|
period['start'].strftime('%H:%M:%S'),
|
|||
|
|
period['end'].strftime('%H:%M:%S'),
|
|||
|
|
period['duration_hours'])
|
|||
|
|
|
|||
|
|
# 3. 计算每个时间槽对应的真实时间点
|
|||
|
|
effective_time_points = {}
|
|||
|
|
|
|||
|
|
for slot_str in time_slots:
|
|||
|
|
target_effective_hours = _parse_time_slot(slot_str)
|
|||
|
|
|
|||
|
|
if target_effective_hours <= 0:
|
|||
|
|
effective_time_points[slot_str] = None
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
if target_effective_hours > total_effective_hours:
|
|||
|
|
LOGGER.warning("Target effective time %.3fh exceeds total effective time %.3fh for slot %s",
|
|||
|
|
target_effective_hours, total_effective_hours, slot_str)
|
|||
|
|
effective_time_points[slot_str] = None
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
# 在有效时间段中查找累计运行target_effective_hours小时的时间点
|
|||
|
|
cumulative_hours = 0.0
|
|||
|
|
target_time_point = None
|
|||
|
|
|
|||
|
|
for period in effective_periods:
|
|||
|
|
period_duration = period['duration_hours']
|
|||
|
|
|
|||
|
|
if cumulative_hours + period_duration >= target_effective_hours:
|
|||
|
|
# 目标时间点在这个周期内
|
|||
|
|
remaining_hours = target_effective_hours - cumulative_hours
|
|||
|
|
target_time_point = period['start'] + timedelta(hours=remaining_hours)
|
|||
|
|
break
|
|||
|
|
else:
|
|||
|
|
cumulative_hours += period_duration
|
|||
|
|
|
|||
|
|
effective_time_points[slot_str] = target_time_point
|
|||
|
|
|
|||
|
|
if target_time_point:
|
|||
|
|
LOGGER.info("Slot %s: effective %.3fh → actual time %s",
|
|||
|
|
slot_str, target_effective_hours, target_time_point.strftime('%H:%M:%S'))
|
|||
|
|
else:
|
|||
|
|
LOGGER.warning("Could not calculate effective time point for slot %s", slot_str)
|
|||
|
|
|
|||
|
|
return effective_time_points
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _query_influxdb_range_with_load_status(
|
|||
|
|
field_name: str,
|
|||
|
|
start_time: datetime,
|
|||
|
|
end_time: datetime,
|
|||
|
|
influx_url: str,
|
|||
|
|
influx_org: str,
|
|||
|
|
influx_token: str,
|
|||
|
|
influx_bucket: str,
|
|||
|
|
influx_measurement: str,
|
|||
|
|
filters: Optional[Dict[str, str]] = None,
|
|||
|
|
) -> Optional[float]:
|
|||
|
|
"""查询 InfluxDB 获取指定字段在时间范围内的平均值（仅当 load_status = 1 时）"""
|
|||
|
|
try:
|
|||
|
|
from influxdb_client import InfluxDBClient
|
|||
|
|
import pandas as pd
|
|||
|
|
import warnings
|
|||
|
|
from influxdb_client.client.warnings import MissingPivotFunction
|
|||
|
|
except ImportError:
|
|||
|
|
LOGGER.warning("InfluxDB client not available, skip query for field=%s", field_name)
|
|||
|
|
return None
|
|||
|
|
|
|||
|
|
try:
|
|||
|
|
client = InfluxDBClient(url=influx_url, org=influx_org, token=influx_token)
|
|||
|
|
query_api = client.query_api()
|
|||
|
|
|
|||
|
|
start_rfc = start_time.strftime('%Y-%m-%dT%H:%M:%SZ')
|
|||
|
|
end_rfc = end_time.strftime('%Y-%m-%dT%H:%M:%SZ')
|
|||
|
|
|
|||
|
|
# 构建过滤条件
|
|||
|
|
tag_filters = ""
|
|||
|
|
if filters:
|
|||
|
|
for key, value in filters.items():
|
|||
|
|
tag_filters += f'\n |> filter(fn: (r) => r["{key}"] == "{value}")'
|
|||
|
|
|
|||
|
|
# 对于环境温度，取全部非0数据的均值；其他字段仍需load_status=1筛选
|
|||
|
|
if field_name == "环境温度":
|
|||
|
|
flux = f'''
|
|||
|
|
from(bucket: "{influx_bucket}")
|
|||
|
|
|> range(start: {start_rfc}, stop: {end_rfc})
|
|||
|
|
|> filter(fn: (r) => r["_measurement"] == "{influx_measurement}")
|
|||
|
|
|> filter(fn: (r) => r["_field"] == "{field_name}")
|
|||
|
|
|> filter(fn: (r) => r["_value"] != 0.0){tag_filters}
|
|||
|
|
|> mean()
|
|||
|
|
|> yield(name: "mean_non_zero")
|
|||
|
|
'''.strip()
|
|||
|
|
else:
|
|||
|
|
flux = f'''
|
|||
|
|
from(bucket: "{influx_bucket}")
|
|||
|
|
|> range(start: {start_rfc}, stop: {end_rfc})
|
|||
|
|
|> filter(fn: (r) => r["_measurement"] == "{influx_measurement}")
|
|||
|
|
|> filter(fn: (r) => r["_field"] == "{field_name}"){tag_filters}
|
|||
|
|
|> mean()
|
|||
|
|
|> yield(name: "mean_temperature_data")
|
|||
|
|
'''.strip()
|
|||
|
|
|
|||
|
|
LOGGER.debug("Flux查询语句 (range):\n%s", flux)
|
|||
|
|
|
|||
|
|
with warnings.catch_warnings():
|
|||
|
|
warnings.simplefilter("ignore", MissingPivotFunction)
|
|||
|
|
frames = query_api.query_data_frame(flux)
|
|||
|
|
|
|||
|
|
if isinstance(frames, list):
|
|||
|
|
df = pd.concat(frames, ignore_index=True) if frames else pd.DataFrame()
|
|||
|
|
else:
|
|||
|
|
df = frames
|
|||
|
|
|
|||
|
|
if df.empty or '_value' not in df.columns:
|
|||
|
|
if field_name == "环境温度":
|
|||
|
|
LOGGER.debug("No valid range data found for field=%s (non-zero data)", field_name)
|
|||
|
|
else:
|
|||
|
|
LOGGER.debug("No valid range data found for field=%s", field_name)
|
|||
|
|
return None
|
|||
|
|
|
|||
|
|
mean_value = df['_value'].iloc[0]
|
|||
|
|
if pd.isna(mean_value):
|
|||
|
|
LOGGER.debug("Mean value is NaN for field=%s", field_name)
|
|||
|
|
return None
|
|||
|
|
|
|||
|
|
value = float(mean_value)
|
|||
|
|
if field_name == "环境温度":
|
|||
|
|
LOGGER.debug("Field=%s range_mean_value=%.3f (non-zero data)", field_name, value)
|
|||
|
|
else:
|
|||
|
|
LOGGER.debug("Field=%s range_mean_value=%.3f", field_name, value)
|
|||
|
|
return value
|
|||
|
|
except Exception as e:
|
|||
|
|
LOGGER.error("Error querying InfluxDB range for field=%s: %s", field_name, e)
|
|||
|
|
return None
|
|||
|
|
finally:
|
|||
|
|
try:
|
|||
|
|
client.close()
|
|||
|
|
except Exception:
|
|||
|
|
pass
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _query_influxdb_with_load_status(
|
|||
|
|
field_name: str,
|
|||
|
|
target_time: datetime,
|
|||
|
|
influx_url: str,
|
|||
|
|
influx_org: str,
|
|||
|
|
influx_token: str,
|
|||
|
|
influx_bucket: str,
|
|||
|
|
influx_measurement: str,
|
|||
|
|
filters: Optional[Dict[str, str]] = None,
|
|||
|
|
) -> Optional[float]:
|
|||
|
|
"""查询 InfluxDB 获取指定字段在指定时间点的瞬时值（仅当 load_status = 1 时）"""
|
|||
|
|
try:
|
|||
|
|
from influxdb_client import InfluxDBClient
|
|||
|
|
import pandas as pd
|
|||
|
|
import warnings
|
|||
|
|
from influxdb_client.client.warnings import MissingPivotFunction
|
|||
|
|
except ImportError:
|
|||
|
|
LOGGER.warning("InfluxDB client not available, skip query for field=%s", field_name)
|
|||
|
|
return None
|
|||
|
|
|
|||
|
|
try:
|
|||
|
|
client = InfluxDBClient(url=influx_url, org=influx_org, token=influx_token)
|
|||
|
|
query_api = client.query_api()
|
|||
|
|
|
|||
|
|
LOGGER.debug(
|
|||
|
|
"Querying field=%s measurement=%s target_time=%s filters=%s (with load_status=1)",
|
|||
|
|
field_name,
|
|||
|
|
influx_measurement,
|
|||
|
|
target_time.strftime('%Y-%m-%dT%H:%M:%SZ'),
|
|||
|
|
filters or {},
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 查询逻辑：查询目标时间点附近的数据，但只要 load_status = 1 的数据
|
|||
|
|
# 使用一个时间窗口来查找最接近的有效数据点
|
|||
|
|
window_minutes = 10 # 前后10分钟的窗口
|
|||
|
|
|
|||
|
|
query_start = target_time - timedelta(minutes=window_minutes)
|
|||
|
|
query_end = target_time + timedelta(minutes=window_minutes)
|
|||
|
|
|
|||
|
|
query_start_rfc = query_start.strftime('%Y-%m-%dT%H:%M:%SZ')
|
|||
|
|
query_end_rfc = query_end.strftime('%Y-%m-%dT%H:%M:%SZ')
|
|||
|
|
|
|||
|
|
# 构建过滤条件
|
|||
|
|
tag_filters = ""
|
|||
|
|
if filters:
|
|||
|
|
for key, value in filters.items():
|
|||
|
|
tag_filters += f'\n |> filter(fn: (r) => r["{key}"] == "{value}")'
|
|||
|
|
|
|||
|
|
# 查询温度数据（不需要load_status筛选，因为已经基于有效时间点查询）
|
|||
|
|
flux = f'''
|
|||
|
|
from(bucket: "{influx_bucket}")
|
|||
|
|
|> range(start: {query_start_rfc}, stop: {query_end_rfc})
|
|||
|
|
|> filter(fn: (r) => r["_measurement"] == "{influx_measurement}")
|
|||
|
|
|> filter(fn: (r) => r["_field"] == "{field_name}"){tag_filters}
|
|||
|
|
|> sort(columns: ["_time"])
|
|||
|
|
|> last()
|
|||
|
|
|> yield(name: "instantaneous_at_effective_time")
|
|||
|
|
'''.strip()
|
|||
|
|
|
|||
|
|
LOGGER.debug("Flux查询语句:\n%s", flux)
|
|||
|
|
|
|||
|
|
with warnings.catch_warnings():
|
|||
|
|
warnings.simplefilter("ignore", MissingPivotFunction)
|
|||
|
|
frames = query_api.query_data_frame(flux)
|
|||
|
|
|
|||
|
|
if isinstance(frames, list):
|
|||
|
|
df = pd.concat(frames, ignore_index=True) if frames else pd.DataFrame()
|
|||
|
|
else:
|
|||
|
|
df = frames
|
|||
|
|
|
|||
|
|
# 获取瞬时值（最近的一个有效数据点）
|
|||
|
|
if df.empty or '_value' not in df.columns:
|
|||
|
|
LOGGER.debug("No valid data found for field=%s at effective time point", field_name)
|
|||
|
|
return None
|
|||
|
|
|
|||
|
|
# 取第一行的值（因为查询已经排序并取了last()）
|
|||
|
|
instant_value = df['_value'].iloc[0]
|
|||
|
|
if pd.isna(instant_value):
|
|||
|
|
LOGGER.debug("Instantaneous value is NaN for field=%s", field_name)
|
|||
|
|
return None
|
|||
|
|
|
|||
|
|
value = float(instant_value)
|
|||
|
|
|
|||
|
|
# 如果有时间信息，记录实际的数据时间点
|
|||
|
|
if '_time' in df.columns:
|
|||
|
|
actual_time = df['_time'].iloc[0]
|
|||
|
|
LOGGER.debug("Field=%s instantaneous_value=%.3f actual_time=%s (at effective time)",
|
|||
|
|
field_name, value, actual_time)
|
|||
|
|
else:
|
|||
|
|
LOGGER.debug("Field=%s instantaneous_value=%.3f (at effective time)", field_name, value)
|
|||
|
|
|
|||
|
|
return value
|
|||
|
|
except Exception as e:
|
|||
|
|
LOGGER.error("Error querying InfluxDB for field=%s: %s", field_name, e)
|
|||
|
|
return None
|
|||
|
|
finally:
|
|||
|
|
try:
|
|||
|
|
client.close()
|
|||
|
|
except Exception:
|
|||
|
|
pass
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _load_temperature_data_with_load_status(
|
|||
|
|
time_slots: List[str],
|
|||
|
|
sections: List[Dict[str, Any]],
|
|||
|
|
start_time: Optional[datetime],
|
|||
|
|
end_time: Optional[datetime],
|
|||
|
|
) -> Dict[str, Dict[str, float]]:
|
|||
|
|
"""从 InfluxDB 查询所有测试部位在各时间点的瞬时温度值（仅当 load_status = 1 时）"""
|
|||
|
|
if not start_time or not end_time:
|
|||
|
|
LOGGER.info("Skip data query: missing start/end (%s, %s)", start_time, end_time)
|
|||
|
|
return {}
|
|||
|
|
|
|||
|
|
influx_config = _get_influx_config()
|
|||
|
|
|
|||
|
|
if not all([influx_config['url'], influx_config['org'], influx_config['token'],
|
|||
|
|
influx_config['bucket'], influx_config['measurement']]):
|
|||
|
|
LOGGER.warning(
|
|||
|
|
"Skip data query: missing Influx config url=%s bucket=%s measurement=%s",
|
|||
|
|
influx_config['url'] or "<empty>",
|
|||
|
|
influx_config['bucket'] or "<empty>",
|
|||
|
|
influx_config['measurement'] or "<empty>",
|
|||
|
|
)
|
|||
|
|
return {}
|
|||
|
|
|
|||
|
|
# 计算总时长（小时）
|
|||
|
|
total_duration = (end_time - start_time).total_seconds() / 3600.0
|
|||
|
|
LOGGER.info(
|
|||
|
|
"Fetch instantaneous temperature data (load_status=1) window=%s→%s total_hours=%.3f time_points=%s",
|
|||
|
|
start_time.isoformat(),
|
|||
|
|
end_time.isoformat(),
|
|||
|
|
total_duration,
|
|||
|
|
",".join(time_slots),
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 收集所有需要查询的字段
|
|||
|
|
query_targets: List[tuple[str, Dict[str, Any]]] = []
|
|||
|
|
for section in sections:
|
|||
|
|
entries = section.get("entries") or []
|
|||
|
|
for entry in entries:
|
|||
|
|
if isinstance(entry, dict):
|
|||
|
|
field_name = entry.get("field", "")
|
|||
|
|
if field_name:
|
|||
|
|
query_targets.append((field_name, entry))
|
|||
|
|
|
|||
|
|
if not query_targets:
|
|||
|
|
return {}
|
|||
|
|
|
|||
|
|
# 计算基于有效运行时间累计的真实时间点
|
|||
|
|
LOGGER.info("=== 开始计算有效时间点 ===")
|
|||
|
|
effective_time_points = _calculate_effective_time_points(
|
|||
|
|
start_time, end_time, time_slots, influx_config
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 为每个有效时间点查询温度数据
|
|||
|
|
temperature_data: Dict[str, Dict[str, float]] = {}
|
|||
|
|
|
|||
|
|
for idx, slot_str in enumerate(time_slots):
|
|||
|
|
target_time_point = effective_time_points.get(slot_str)
|
|||
|
|
|
|||
|
|
if target_time_point is None:
|
|||
|
|
LOGGER.warning("No effective time point calculated for slot %s, skipping", slot_str)
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
LOGGER.debug("Processing slot %s at effective time point %s",
|
|||
|
|
slot_str, target_time_point.strftime('%Y-%m-%d %H:%M:%S'))
|
|||
|
|
|
|||
|
|
for field_name, entry in query_targets:
|
|||
|
|
result_key = entry.get("result_key") or field_name
|
|||
|
|
if not result_key:
|
|||
|
|
result_key = field_name
|
|||
|
|
entry_filters = entry.get("filters") if isinstance(entry, dict) else None
|
|||
|
|
if result_key not in temperature_data:
|
|||
|
|
temperature_data[result_key] = {}
|
|||
|
|
|
|||
|
|
# 使用索引作为key，因为可能有重复的时间刻度
|
|||
|
|
slot_key = f"{idx}_{slot_str}" # 使用索引+时间刻度作为唯一key
|
|||
|
|
|
|||
|
|
# 查询瞬时值（在有效时间点）
|
|||
|
|
value = _query_influxdb_with_load_status(
|
|||
|
|
field_name,
|
|||
|
|
target_time_point,
|
|||
|
|
influx_config['url'],
|
|||
|
|
influx_config['org'],
|
|||
|
|
influx_config['token'],
|
|||
|
|
influx_config['bucket'],
|
|||
|
|
influx_config['measurement'],
|
|||
|
|
filters=entry_filters if entry_filters else None,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
if value is not None:
|
|||
|
|
temperature_data[result_key][slot_key] = value
|
|||
|
|
LOGGER.debug(
|
|||
|
|
"Slot=%s field=%s value=%.3f at effective_time=%s",
|
|||
|
|
slot_key,
|
|||
|
|
result_key,
|
|||
|
|
value,
|
|||
|
|
target_time_point.strftime('%H:%M:%S')
|
|||
|
|
)
|
|||
|
|
else:
|
|||
|
|
LOGGER.debug(
|
|||
|
|
"Slot=%s field=%s no_data at effective_time=%s",
|
|||
|
|
slot_key,
|
|||
|
|
result_key,
|
|||
|
|
target_time_point.strftime('%H:%M:%S')
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
return temperature_data
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _build_cells_with_load_status(
|
|||
|
|
time_slots: List[str],
|
|||
|
|
sections: List[Dict[str, Any]],
|
|||
|
|
motor_speed: str,
|
|||
|
|
start_time: Optional[datetime],
|
|||
|
|
end_time: Optional[datetime],
|
|||
|
|
temperature_data: Dict[str, Dict[str, float]],
|
|||
|
|
use_defaults: bool = False,
|
|||
|
|
) -> List[Dict[str, Any]]:
|
|||
|
|
"""构建单元格数据（基于 load_status = 1 的有效数据）- 与原始脚本结构完全一致"""
|
|||
|
|
cells: List[Dict[str, Any]] = []
|
|||
|
|
|
|||
|
|
def add_cell(row: int, col: int, value: str = "", rowspan: int = 1, colspan: int = 1) -> None:
|
|||
|
|
payload: Dict[str, Any] = {"row": row, "col": col, "value": value}
|
|||
|
|
if rowspan > 1:
|
|||
|
|
payload["rowspan"] = rowspan
|
|||
|
|
if colspan > 1:
|
|||
|
|
payload["colspan"] = colspan
|
|||
|
|
cells.append(payload)
|
|||
|
|
|
|||
|
|
# 模板左侧标题列已经去除，这里仅生成纯数据区，从 (0,0) 开始填入数值。
|
|||
|
|
# current_row 对应模板中的实际数据行索引。
|
|||
|
|
current_row = 0
|
|||
|
|
for section in sections:
|
|||
|
|
entries = section.get("entries") or []
|
|||
|
|
if not entries:
|
|||
|
|
continue
|
|||
|
|
# 每个测试部位子项对应模板中的一行
|
|||
|
|
for entry in entries:
|
|||
|
|
# 支持新格式（带 field 映射）和旧格式（纯字符串）
|
|||
|
|
if isinstance(entry, dict):
|
|||
|
|
field_name = entry.get("field", "")
|
|||
|
|
entry_filters = entry.get("filters")
|
|||
|
|
entry_key = entry.get("result_key") or field_name
|
|||
|
|
else:
|
|||
|
|
field_name = ""
|
|||
|
|
entry_filters = None
|
|||
|
|
entry_key = ""
|
|||
|
|
|
|||
|
|
# 仅输出数值列：列索引直接对应时间段
|
|||
|
|
# 强制填充所有列，优先使用查询数据，否则使用默认值
|
|||
|
|
if field_name:
|
|||
|
|
target_key = entry_key or field_name
|
|||
|
|
|
|||
|
|
# 遍历所有时间段列，确保每一列都有数据
|
|||
|
|
for col_idx, slot in enumerate(time_slots):
|
|||
|
|
value = None
|
|||
|
|
|
|||
|
|
# 优先使用查询到的数据
|
|||
|
|
if temperature_data:
|
|||
|
|
slot_data = temperature_data.get(target_key, {})
|
|||
|
|
if slot_data:
|
|||
|
|
slot_key = f"{col_idx}_{slot}"
|
|||
|
|
value = slot_data.get(slot_key)
|
|||
|
|
|
|||
|
|
if value is None and use_defaults:
|
|||
|
|
# 使用基础默认值 + 时间段偏移（每个时间段增加0.1度）
|
|||
|
|
default_base_value = 25.0 # 简化的默认值
|
|||
|
|
time_offset = col_idx * 0.1
|
|||
|
|
value = default_base_value + time_offset
|
|||
|
|
|
|||
|
|
if value is None:
|
|||
|
|
value_str = ""
|
|||
|
|
else:
|
|||
|
|
# 格式化为字符串（保留1位小数）
|
|||
|
|
value_str = f"{value:.1f}"
|
|||
|
|
|
|||
|
|
add_cell(current_row, col_idx, value_str)
|
|||
|
|
else:
|
|||
|
|
# 如果没有字段名，填充空字符串
|
|||
|
|
for col_idx in range(len(time_slots)):
|
|||
|
|
add_cell(current_row, col_idx, "")
|
|||
|
|
current_row += 1
|
|||
|
|
|
|||
|
|
return cells
|
|||
|
|
|
|||
|
|
|
|||
|
|
def build_temperature_table_with_load_status(_: Dict[str, Any]) -> Dict[str, Any]:
|
|||
|
|
"""构建温度表格数据（仅使用 load_status = 1 的有效数据）"""
|
|||
|
|
_setup_logging()
|
|||
|
|
|
|||
|
|
token = os.environ.get("TABLE_TOKEN", "scriptTable1")
|
|||
|
|
row_offset = int(os.environ.get("TABLE_START_ROW", "0") or 0)
|
|||
|
|
col_offset = int(os.environ.get("TABLE_START_COL", "0") or 0)
|
|||
|
|
motor_speed = os.environ.get("TABLE_MOTOR_SPEED", "980RPM")
|
|||
|
|
|
|||
|
|
# 解析实验时间范围
|
|||
|
|
start_time, end_time = _parse_experiment_times()
|
|||
|
|
|
|||
|
|
time_slots = _time_slots()
|
|||
|
|
sections = _default_sections()
|
|||
|
|
|
|||
|
|
# 查询温度数据（仅当 load_status = 1 时）
|
|||
|
|
temperature_data = _load_temperature_data_with_load_status(time_slots, sections, start_time, end_time)
|
|||
|
|
|
|||
|
|
# 始终禁止默认数据，保证查询不到值时保持空白
|
|||
|
|
use_defaults = False
|
|||
|
|
|
|||
|
|
cells = _build_cells_with_load_status(
|
|||
|
|
time_slots,
|
|||
|
|
sections,
|
|||
|
|
motor_speed,
|
|||
|
|
start_time,
|
|||
|
|
end_time,
|
|||
|
|
temperature_data,
|
|||
|
|
use_defaults=use_defaults
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 应用行偏移
|
|||
|
|
for cell in cells:
|
|||
|
|
cell["row"] += 4
|
|||
|
|
|
|||
|
|
# 添加实验时间信息（与原始脚本完全一致的逻辑）
|
|||
|
|
start_time_row = 1
|
|||
|
|
start_time_value_col = 1
|
|||
|
|
end_time_value_col = 3
|
|||
|
|
|
|||
|
|
# 获取原始时间字符串进行处理（与原始脚本保持一致）
|
|||
|
|
start_str = os.environ.get("EXPERIMENT_START", "").strip()
|
|||
|
|
if start_str and start_time:
|
|||
|
|
try:
|
|||
|
|
# 使用与原始脚本相同的时间处理逻辑
|
|||
|
|
utc_aware_dt = datetime.strptime(start_str, "%Y-%m-%dT%H:%M:%S%z")
|
|||
|
|
local_dt1 = utc_aware_dt.astimezone(tz=None)
|
|||
|
|
local_dt2 = utc_aware_dt.astimezone(tz=None) + timedelta(hours=3.5)
|
|||
|
|
start_time_value = local_dt1.strftime("%Y-%m-%d %H:%M:%S")
|
|||
|
|
end_time_value = local_dt2.strftime("%Y-%m-%d %H:%M:%S")
|
|||
|
|
cells.append({"row": start_time_row, "col": start_time_value_col, "value": start_time_value})
|
|||
|
|
cells.append({"row": start_time_row, "col": end_time_value_col, "value": end_time_value})
|
|||
|
|
except Exception as e:
|
|||
|
|
LOGGER.warning("Failed to process experiment time strings: %s", e)
|
|||
|
|
|
|||
|
|
# 查询环境温度（与原始脚本完全一致的逻辑）
|
|||
|
|
influx_url = os.environ.get("INFLUX_URL", "").strip()
|
|||
|
|
influx_org = os.environ.get("INFLUX_ORG", "").strip()
|
|||
|
|
influx_token = os.environ.get("INFLUX_TOKEN", "").strip()
|
|||
|
|
influx_bucket = os.environ.get("INFLUX_BUCKET", "PCM").strip()
|
|||
|
|
influx_measurement = os.environ.get("INFLUX_MEASUREMENT", "PCM_Measurement").strip()
|
|||
|
|
|
|||
|
|
if start_time and end_time:
|
|||
|
|
# 对于环境温度，使用时间范围查询（与原始脚本逻辑一致）
|
|||
|
|
value = _query_influxdb_range_with_load_status(
|
|||
|
|
"环境温度",
|
|||
|
|
start_time,
|
|||
|
|
end_time,
|
|||
|
|
influx_url,
|
|||
|
|
influx_org,
|
|||
|
|
influx_token,
|
|||
|
|
influx_bucket,
|
|||
|
|
influx_measurement,
|
|||
|
|
filters={"data_type": "LSDAQ"},
|
|||
|
|
)
|
|||
|
|
# 确保value不是None，避免Word COM操作异常（与原始脚本一致）
|
|||
|
|
if value is not None:
|
|||
|
|
cells.append({"row": 0, "col": 1, "value": f"{value:.1f}"})
|
|||
|
|
else:
|
|||
|
|
cells.append({"row": 0, "col": 1, "value": ""})
|
|||
|
|
|
|||
|
|
LOGGER.info(
|
|||
|
|
"Temperature table built with load_status=1 filter: token=%s cells=%d time_slots=%s",
|
|||
|
|
token,
|
|||
|
|
len(cells),
|
|||
|
|
",".join(time_slots),
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
return {
|
|||
|
|
"token": token,
|
|||
|
|
"startRow": row_offset,
|
|||
|
|
"startCol": col_offset,
|
|||
|
|
"cells": cells,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _load_payload() -> Dict[str, Any]:
|
|||
|
|
"""从标准输入或环境变量加载payload数据"""
|
|||
|
|
try:
|
|||
|
|
# 尝试从标准输入读取JSON
|
|||
|
|
try:
|
|||
|
|
import select
|
|||
|
|
if select.select([sys.stdin], [], [], 0.0)[0]:
|
|||
|
|
payload_str = sys.stdin.read().strip()
|
|||
|
|
if payload_str:
|
|||
|
|
return json.loads(payload_str)
|
|||
|
|
except ImportError:
|
|||
|
|
# Windows上select可能不可用，尝试直接读取
|
|||
|
|
import msvcrt
|
|||
|
|
if msvcrt.kbhit():
|
|||
|
|
payload_str = sys.stdin.read().strip()
|
|||
|
|
if payload_str:
|
|||
|
|
return json.loads(payload_str)
|
|||
|
|
except Exception:
|
|||
|
|
pass
|
|||
|
|
|
|||
|
|
# 如果没有标准输入，返回空字典
|
|||
|
|
return {}
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _log_environment_variables() -> None:
|
|||
|
|
"""记录相关环境变量"""
|
|||
|
|
env_vars = [
|
|||
|
|
"TABLE_TOKEN", "TABLE_START_ROW", "TABLE_START_COL", "TABLE_TIME_SLOTS", "TABLE_MOTOR_SPEED",
|
|||
|
|
"EXPERIMENT_START", "EXPERIMENT_END",
|
|||
|
|
"INFLUX_URL", "INFLUX_ORG", "INFLUX_TOKEN", "INFLUX_BUCKET", "INFLUX_MEASUREMENT"
|
|||
|
|
]
|
|||
|
|
|
|||
|
|
for var in env_vars:
|
|||
|
|
value = os.environ.get(var, "")
|
|||
|
|
if "TOKEN" in var and value:
|
|||
|
|
value = _mask_secret(value)
|
|||
|
|
LOGGER.debug("ENV %s=%s", var, value or "<empty>")
|
|||
|
|
|
|||
|
|
|
|||
|
|
def main() -> int:
|
|||
|
|
try:
|
|||
|
|
try:
|
|||
|
|
if not logging.getLogger().handlers:
|
|||
|
|
log_level_name = os.environ.get("TABLE_LOG_LEVEL", "DEBUG").strip() or "DEBUG"
|
|||
|
|
log_level = getattr(logging, log_level_name.upper(), logging.DEBUG)
|
|||
|
|
log_file_raw = os.environ.get("TABLE_LOG_FILE", "test.log").strip() or "test.log"
|
|||
|
|
log_file = os.path.abspath(log_file_raw)
|
|||
|
|
|
|||
|
|
logging.basicConfig(
|
|||
|
|
level=log_level,
|
|||
|
|
format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
|
|||
|
|
handlers=[
|
|||
|
|
logging.FileHandler(log_file, encoding="utf-8"),
|
|||
|
|
logging.StreamHandler(sys.stderr),
|
|||
|
|
],
|
|||
|
|
)
|
|||
|
|
LOGGER.info("Logging initialized -> file=%s level=%s", log_file, logging.getLevelName(log_level))
|
|||
|
|
_log_environment_variables()
|
|||
|
|
sys.stdout.reconfigure(encoding="utf-8") # type: ignore[attr-defined]
|
|||
|
|
except Exception:
|
|||
|
|
pass
|
|||
|
|
|
|||
|
|
payload = _load_payload()
|
|||
|
|
table_spec = build_temperature_table_with_load_status(payload)
|
|||
|
|
result = {"tables": [table_spec]}
|
|||
|
|
print(json.dumps(result, ensure_ascii=False))
|
|||
|
|
return 0
|
|||
|
|
except Exception as exc:
|
|||
|
|
print(f"error: {exc}", file=sys.stderr)
|
|||
|
|
return 1
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
sys.exit(main())
|