main
parent
acca3cfaf7
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a111c54582
File diff suppressed because one or more lines are too long
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@ -98,40 +98,50 @@ def _get_influx_config() -> Dict[str, str]:
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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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"""解析实验时间，前端传入本地时间，转换为UTC用于InfluxDB查询"""
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from datetime import timezone, timedelta
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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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# LOGGER.debug("原始时间字符串: START=%s, END=%s", start_str, end_str)
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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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for fmt in ["%Y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%S.%f"]:
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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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# 本地时间-8小时=UTC
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start_time = start_time - timedelta(hours=8)
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start_time = start_time.replace(tzinfo=timezone.utc)
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# LOGGER.debug("解析START: 本地=%s → UTC=%s", start_str, start_time)
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break
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except ValueError:
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continue
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if start_time is None:
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LOGGER.warning("无法解析EXPERIMENT_START: %s", start_str)
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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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LOGGER.error("解析EXPERIMENT_START失败 '%s': %s", start_str, e)
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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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for fmt in ["%Y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%S.%f"]:
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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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# 本地时间-8小时=UTC
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end_time = end_time - timedelta(hours=8)
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end_time = end_time.replace(tzinfo=timezone.utc)
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# LOGGER.debug("解析END: 本地=%s → UTC=%s", end_str, end_time)
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break
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except ValueError:
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continue
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if end_time is None:
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LOGGER.warning("无法解析EXPERIMENT_END: %s", end_str)
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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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LOGGER.error("解析EXPERIMENT_END失败 '%s': %s", end_str, e)
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return start_time, end_time
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@ -160,8 +170,8 @@ def _parse_time_slot(slot_str: str) -> float:
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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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# 时间刻度：0.5h, 1h, 1.5h, 2h, 2.5h, 3h, 3.4h（7列）
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return ["0.5h", "1h", "1.5h", "2h", "2.5h", "3h", "3.4h"]
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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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@ -175,15 +185,11 @@ def _default_sections() -> List[Dict[str, Any]]:
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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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{"label": "#5", "field": "主轴承#5", "filters": {"data_type": "LSDAQ"}, "result_key": "主轴承#5"},
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{"label": "#6", "field": "主轴承#6", "filters": {"data_type": "LSDAQ"}, "result_key": "主轴承#6"},
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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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{"label": "#4", "field": "十字头#4", "filters": {"data_type": "LSDAQ"}, "result_key": "十字头#4"},
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{"label": "#5", "field": "十字头#5", "filters": {"data_type": "LSDAQ"}, "result_key": "十字头#5"},
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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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@ -216,18 +222,20 @@ def _query_load_status_timeline(
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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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except ImportError as e:
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LOGGER.error("InfluxDB客户端导入失败: %s，请安装: pip install influxdb-client pandas", e)
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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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# 确保使用UTC时间格式查询
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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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LOGGER.debug("查询load_status时间范围: %s 到 %s", start_rfc, end_rfc)
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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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@ -253,21 +261,20 @@ from(bucket: "{influx_bucket}")
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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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# 转换为时间线数据，保持UTC时区
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from datetime import timezone
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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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# 确保转换为UTC时区的datetime对象
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if hasattr(time_obj, 'tz_localize'):
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if time_obj.tz is None:
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time_obj = time_obj.tz_localize(timezone.utc)
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else:
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time_obj = time_obj.tz_convert(timezone.utc)
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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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if hasattr(time_obj, 'to_pydatetime'):
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time_obj = time_obj.to_pydatetime()
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timeline.append({
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'time': time_obj,
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@ -352,11 +359,11 @@ def _calculate_effective_time_points(
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LOGGER.info("Effective running periods: %d periods, total %.3f hours",
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len(effective_periods), total_effective_hours)
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for period in effective_periods:
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LOGGER.debug("Effective period: %s → %s (%.3f hours)",
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period['start'].strftime('%H:%M:%S'),
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period['end'].strftime('%H:%M:%S'),
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period['duration_hours'])
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# for period in effective_periods:
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# LOGGER.debug("Effective period: %s → %s (%.3f hours)",
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# period['start'].strftime('%H:%M:%S'),
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# period['end'].strftime('%H:%M:%S'),
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# period['duration_hours'])
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# 3. 计算每个时间槽对应的真实时间点
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effective_time_points = {}
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@ -368,24 +375,10 @@ def _calculate_effective_time_points(
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effective_time_points[slot_str] = None
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continue
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# 如果目标时间 >= 总有效时间（允许小的浮点误差），使用最后一个有效时间段的结束时间
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# 这样可以处理边界情况：实验正好运行了目标时长，但由于浮点精度可能略小于目标值
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tolerance = 0.01 # 允许 0.01 小时的容差
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if target_effective_hours >= total_effective_hours - tolerance:
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if effective_periods:
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# 使用最后一个有效时间段的结束时间
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last_period = effective_periods[-1]
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target_time_point = last_period['end']
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effective_time_points[slot_str] = target_time_point
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LOGGER.info("Slot %s: effective %.3fh >= total %.3fh, using last period end time %s",
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slot_str, target_effective_hours, total_effective_hours,
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target_time_point.strftime('%H:%M:%S'))
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else:
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# 如果没有有效时间段，使用实验结束时间
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effective_time_points[slot_str] = end_time
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LOGGER.info("Slot %s: effective %.3fh >= total %.3fh, using experiment end time %s",
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slot_str, target_effective_hours, total_effective_hours,
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end_time.strftime('%H:%M:%S') if end_time else "N/A")
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if target_effective_hours > total_effective_hours:
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# LOGGER.warning("Target effective time %.3fh exceeds total effective time %.3fh for slot %s",
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# target_effective_hours, total_effective_hours, slot_str)
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effective_time_points[slot_str] = None
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continue
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# 在有效时间段中查找累计运行target_effective_hours小时的时间点
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@ -439,8 +432,11 @@ def _query_influxdb_range_with_load_status(
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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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# 确保使用UTC时间格式
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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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LOGGER.debug("查询字段 %s 时间范围: %s 到 %s", field_name, start_rfc, end_rfc)
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# 构建过滤条件
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tag_filters = ""
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@ -518,102 +514,151 @@ def _query_influxdb_with_load_status(
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influx_measurement: str,
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filters: Optional[Dict[str, str]] = None,
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) -> Optional[float]:
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"""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）"""
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"""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）
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逻辑：
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1. 在 ±window 内同时查询温度数据和 load_status 数据
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2. 对每个温度数据点，查找其最近的前一个 load_status 读数，判断是否为 1
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3. 仅保留 load_status=1 期间的温度数据点
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4. 在有效数据点中选取最接近 target_time 的瞬时值
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5. 如果当前窗口无有效数据，逐步扩大窗口重试（10→20→30min）
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"""
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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 numpy as np
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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 query for field=%s", field_name)
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return None
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client = None
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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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LOGGER.debug(
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"Querying field=%s measurement=%s target_time=%s filters=%s (with load_status=1)",
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"查询字段=%s 目标时间=%s (UTC) 过滤器=%s",
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field_name,
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influx_measurement,
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target_time.strftime('%Y-%m-%dT%H:%M:%SZ'),
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filters or {},
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)
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# 查询逻辑：查询目标时间点之前（包含目标时间点）的数据，获取最接近目标时间点的瞬时值
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# 使用实验开始时间作为查询起点，目标时间点作为查询终点，确保获取该时间点的瞬时数值
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# 需要从实验开始时间查询，因为有效时间点是基于累计运行时间计算的
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# 获取实验开始时间（需要从环境变量或传入参数获取）
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# 为了简化，我们使用一个合理的时间窗口：从目标时间点往前推足够长的时间
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# 但为了精确，我们应该查询到目标时间点为止，取最后一条
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window_minutes = 60 # 往前查询60分钟，确保能覆盖到数据
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query_start = target_time - timedelta(minutes=window_minutes)
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# 查询终点设置为目标时间点，确保获取的是该时间点或之前的数据
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query_end = target_time
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query_start_rfc = query_start.strftime('%Y-%m-%dT%H:%M:%SZ')
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query_end_rfc = query_end.strftime('%Y-%m-%dT%H:%M:%SZ')
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# 构建过滤条件
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# 构建 tag 过滤条件
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tag_filters = ""
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if filters:
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for key, value in filters.items():
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tag_filters += f'\n |> filter(fn: (r) => r["{key}"] == "{value}")'
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# 查询温度数据：查询到目标时间点为止，取最后一条（最接近目标时间点的瞬时值）
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flux = f'''
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# 逐步扩大窗口查找 load_status=1 的有效数据
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for window_minutes in [10, 20, 30]:
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query_start = target_time - timedelta(minutes=window_minutes)
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query_end = target_time + timedelta(minutes=window_minutes)
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start_rfc = query_start.strftime('%Y-%m-%dT%H:%M:%SZ')
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end_rfc = query_end.strftime('%Y-%m-%dT%H:%M:%SZ')
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LOGGER.debug("查询窗口 ±%dmin: %s 到 %s", window_minutes, start_rfc, end_rfc)
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# 查询温度数据（全部点位，按时间排序）
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temp_flux = f'''
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from(bucket: "{influx_bucket}")
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|> range(start: {query_start_rfc}, stop: {query_end_rfc})
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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["_field"] == "{field_name}"){tag_filters}
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|> sort(columns: ["_time"])
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|> last()
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|> yield(name: "instantaneous_at_effective_time")
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'''.strip()
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LOGGER.debug("Flux查询语句:\n%s", flux)
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# 查询同窗口内的 load_status 时间线
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status_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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'''.strip()
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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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LOGGER.debug("温度Flux:\n%s", temp_flux)
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LOGGER.debug("状态Flux:\n%s", status_flux)
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# 获取瞬时值（最近的一个有效数据点）
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if df.empty or '_value' not in df.columns:
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LOGGER.debug("No valid data found for field=%s at effective time point", field_name)
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return None
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# 取第一行的值（因为查询已经排序并取了last()）
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instant_value = df['_value'].iloc[0]
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if pd.isna(instant_value):
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LOGGER.debug("Instantaneous value is NaN for field=%s", field_name)
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return None
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", MissingPivotFunction)
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temp_frames = query_api.query_data_frame(temp_flux)
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status_frames = query_api.query_data_frame(status_flux)
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value = float(instant_value)
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# 如果有时间信息，记录实际的数据时间点
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if '_time' in df.columns:
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actual_time = df['_time'].iloc[0]
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LOGGER.debug("Field=%s instantaneous_value=%.3f actual_time=%s (at effective time)",
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field_name, value, actual_time)
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else:
|
||||
LOGGER.debug("Field=%s instantaneous_value=%.3f (at effective time)", field_name, value)
|
||||
|
||||
return value
|
||||
# 合并结果
|
||||
if isinstance(temp_frames, list):
|
||||
temp_df = pd.concat(temp_frames, ignore_index=True) if temp_frames else pd.DataFrame()
|
||||
else:
|
||||
temp_df = temp_frames
|
||||
if isinstance(status_frames, list):
|
||||
status_df = pd.concat(status_frames, ignore_index=True) if status_frames else pd.DataFrame()
|
||||
else:
|
||||
status_df = status_frames
|
||||
|
||||
if temp_df.empty or '_value' not in temp_df.columns or '_time' not in temp_df.columns:
|
||||
LOGGER.debug("±%dmin 窗口无温度数据 field=%s", window_minutes, field_name)
|
||||
continue
|
||||
if status_df.empty or '_value' not in status_df.columns or '_time' not in status_df.columns:
|
||||
LOGGER.debug("±%dmin 窗口无 load_status 数据 field=%s", window_minutes, field_name)
|
||||
continue
|
||||
|
||||
# 构建 load_status 时间线（转换为int64纳秒时间戳，避免类型问题）
|
||||
status_times = pd.to_datetime(status_df['_time']).values.astype('datetime64[ns]').astype(np.int64)
|
||||
status_values = status_df['_value'].values.astype(float)
|
||||
|
||||
# 对每个温度数据点，用最近的前一个 load_status 判断是否有效
|
||||
temp_df = temp_df.copy()
|
||||
temp_df['_time_ns'] = pd.to_datetime(temp_df['_time']).values.astype('datetime64[ns]').astype(np.int64)
|
||||
valid_mask = []
|
||||
|
||||
for t_ns in temp_df['_time_ns']:
|
||||
# 找 <= t_ns 的最后一个 load_status 读数
|
||||
prior_idx = np.searchsorted(status_times, t_ns, side='right') - 1
|
||||
if prior_idx >= 0:
|
||||
valid_mask.append(status_values[prior_idx] == 1.0)
|
||||
else:
|
||||
# 没有更早的读数，用最近的一个
|
||||
nearest_idx = np.argmin(np.abs(status_times - t_ns))
|
||||
valid_mask.append(status_values[nearest_idx] == 1.0)
|
||||
|
||||
valid_df = temp_df[valid_mask]
|
||||
|
||||
if valid_df.empty:
|
||||
LOGGER.debug("±%dmin 窗口内无 load_status=1 的温度数据 field=%s", window_minutes, field_name)
|
||||
continue
|
||||
|
||||
# 在有效点中选取最接近 target_time 的瞬时值
|
||||
target_ns = np.datetime64(target_time, 'ns').astype(np.int64)
|
||||
diffs = np.abs(valid_df['_time_ns'].values - target_ns)
|
||||
closest_idx = np.argmin(diffs)
|
||||
instant_value = valid_df.iloc[closest_idx]['_value']
|
||||
|
||||
if pd.isna(instant_value):
|
||||
LOGGER.debug("最近有效点值为 NaN field=%s", field_name)
|
||||
continue
|
||||
|
||||
value = float(instant_value)
|
||||
actual_time = valid_df.iloc[closest_idx]['_time']
|
||||
LOGGER.debug(
|
||||
"Field=%s value=%.3f actual_time=%s (load_status=1, ±%dmin窗口, 有效点%d/%d)",
|
||||
field_name, value, actual_time, window_minutes,
|
||||
len(valid_df), len(temp_df),
|
||||
)
|
||||
return value
|
||||
|
||||
LOGGER.warning("扩窗到±30min仍无 load_status=1 数据 field=%s", field_name)
|
||||
return None
|
||||
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
|
||||
if client:
|
||||
try:
|
||||
client.close()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def _load_temperature_data_with_load_status(
|
||||
|
|
@ -675,8 +720,12 @@ def _load_temperature_data_with_load_status(
|
|||
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.warning("No effective time point for slot %s, using simple offset", slot_str)
|
||||
slot_hours = _parse_time_slot(slot_str)
|
||||
target_time_point = start_time + timedelta(hours=slot_hours)
|
||||
if target_time_point > end_time:
|
||||
LOGGER.warning("Time point %s exceeds end time, 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'))
|
||||
|
|
@ -845,10 +894,15 @@ def build_temperature_table_with_load_status(_: Dict[str, Any]) -> Dict[str, Any
|
|||
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)
|
||||
# 尝试带时区和不带时区两种格式
|
||||
try:
|
||||
utc_aware_dt = datetime.strptime(start_str, "%Y-%m-%dT%H:%M:%S%z")
|
||||
local_dt1 = utc_aware_dt.astimezone(tz=None)
|
||||
except ValueError:
|
||||
# 不带时区，直接解析为本地时间
|
||||
local_dt1 = datetime.strptime(start_str, "%Y-%m-%dT%H:%M:%S")
|
||||
|
||||
local_dt2 = local_dt1 + 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})
|
||||
|
|
@ -878,7 +932,7 @@ def build_temperature_table_with_load_status(_: Dict[str, Any]) -> Dict[str, Any
|
|||
)
|
||||
# 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}"})
|
||||
cells.append({"row": 0, "col": 1, "value": f"{value:.1f}℃"})
|
||||
else:
|
||||
cells.append({"row": 0, "col": 1, "value": ""})
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,300 @@
|
|||
# 任务配置
|
||||
task:
|
||||
period: 210 # 单位分钟
|
||||
control_reg_addr: 1200
|
||||
# gps 配置
|
||||
gps:
|
||||
port: /dev/ttyLP4
|
||||
baudrate: 9600
|
||||
timeout: 50
|
||||
|
||||
# breaker（断路器）配置
|
||||
breaker:
|
||||
indicator:
|
||||
port: /dev/ttyUSB_LIGHT
|
||||
baudrate: 9600
|
||||
timeout: 50
|
||||
port: /dev/ttyUSB_BREAKER
|
||||
baudrate: 9600
|
||||
timeout: 10
|
||||
task_start_threshold: 5000 #int, 判定任务开始的有功功率阈值/w
|
||||
task_stop_threshold: 3000 #int, 判定任务结束的有功功率阈值/w
|
||||
duration: 3 #int，电流持续时间/s
|
||||
OVV: 300 #int, 过压值/V
|
||||
UVV: 150 #int，欠压值/V
|
||||
OCV: 10000 #int, 过流值/0.01A
|
||||
LCV: 90 #int，漏电值/1mA
|
||||
OTV: 150 #int，过温值/℃
|
||||
OPV: 13000 #int，过载有功功率/W
|
||||
OVT: 4000 #int，过压动作时间/0.1s
|
||||
UVT: 4000 #int，欠压动作时间/0.1s
|
||||
LCT: 4000 #int，漏电动作时间/0.1s
|
||||
OCT: 4000 #int，过流动作时间/0.1s
|
||||
OPT: 4000 #int，有功过载动作时间/0.1s
|
||||
OTT: 4000 #int，过温动作时间/0.1s
|
||||
# lsdaq 配置
|
||||
lsdaq:
|
||||
# Modbus-RTU 配置
|
||||
port: /dev/ttyLP3 # 串口号，如COM3或'/dev/ttyLP3'
|
||||
baudrate: 115200 # 波特率
|
||||
timeout: 1 # 超时时间(秒)
|
||||
mode: 0 # 工作模式，0-work或1-calib
|
||||
# 配置采集通道传感器类型
|
||||
# 用1位标识传感器类型，16通道16位组成1个uint16数据。CH1->CH16
|
||||
# 0：PT100; 1: 4-20mA电流型传感器;
|
||||
sensor_type: '0000 0000 0000 0000'
|
||||
sensor_Tmp_CalibParam:
|
||||
# PT100传感器对应的K值和T值
|
||||
CH1: {K2: 0, K: 0.0311314349267159, B: -536.209396150856}
|
||||
CH2: {K2: 0, K: 0.0311931732683759, B: -538.060652944714}
|
||||
CH3: {K2: 0, K: 0.0310081286336704, B: -533.809292492742}
|
||||
CH4: {K2: 0, K: 0.0312335701465264, B: -538.760137686226}
|
||||
CH5: {K2: 0, K: 0.0311077027362215, B: -536.798383628092}
|
||||
CH6: {K2: 0, K: 0.0311166612575919, B: -536.877851004226}
|
||||
CH7: {K2: 0, K: 0.0309995873314733, B: -534.326957548871}
|
||||
CH8: {K2: 0, K: 0.031204654529397, B: -539.09245272611}
|
||||
CH9: {K2: 0, K: 0.0311948194176528, B: -538.034587271267}
|
||||
CH10: {K2: 0, K: 0.030996596669504, B: -534.701789148401}
|
||||
CH11: {K2: 0, K: 0.0310055444481469, B: -535.504834782268}
|
||||
CH12: {K2: 0, K: 0.0310551132865325, B: -536.24377288418}
|
||||
CH13: {K2: 0, K: 0.0310239427307571, B: -535.807161638956}
|
||||
CH14: {K2: 0, K: 0.0313454593571509, B: -541.451349369065}
|
||||
CH15: {K2: 0, K: 0.0313738619404098, B: -541.684813710032}
|
||||
CH16: {K2: 0, K: 0.0311081891835453, B: -536.64715117882}
|
||||
sensor_Cur_CalibParam:
|
||||
# 4~20mA传感器对应的K值和T值
|
||||
CH1: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH2: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH3: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH4: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH5: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH6: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH7: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH8: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH9: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH10: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH11: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH12: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH13: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH14: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH15: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH16: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
sensor_Pres_CalibParam:
|
||||
# mA->PSI转换对应的K值和T值
|
||||
CH1: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH2: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH3: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH4: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH5: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH6: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH7: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH8: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH9: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH10: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH11: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH12: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH13: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH14: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH15: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH16: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
# 报警参数设置
|
||||
warning_param:
|
||||
# 是否启用报警
|
||||
enable: '1111 0111 1111 1111' #1-启用，0-禁用
|
||||
CH1: {lower: -20.0, upper: 110.0}
|
||||
CH2: {lower: -20.0, upper: 110.0}
|
||||
CH3: {lower: -20.0, upper: 110.0}
|
||||
CH4: {lower: -20.0, upper: 110.0}
|
||||
CH5: {lower: -20.0, upper: 110.0}
|
||||
CH6: {lower: -20.0, upper: 110.0}
|
||||
CH7: {lower: -20.0, upper: 110.0}
|
||||
CH8: {lower: -20.0, upper: 110.0}
|
||||
CH9: {lower: -20.0, upper: 110.0}
|
||||
CH10: {lower: -20.0, upper: 110.0}
|
||||
CH11: {lower: -20.0, upper: 110.0}
|
||||
CH12: {lower: -20.0, upper: 110.0}
|
||||
CH13: {lower: -20.0, upper: 110.0}
|
||||
CH14: {lower: -20.0, upper: 110.0}
|
||||
CH15: {lower: -20.0, upper: 110.0}
|
||||
CH16: {lower: -20.0, upper: 50.0}
|
||||
# 通道别名设置
|
||||
alias:
|
||||
CH1: '主轴承#1'
|
||||
CH2: '主轴承#2'
|
||||
CH3: '主轴承#3'
|
||||
CH4: '主轴承#4'
|
||||
CH5: '主轴承#5'
|
||||
CH6: '主轴承#6'
|
||||
CH7: '十字头#1'
|
||||
CH8: '十字头#2'
|
||||
CH9: '十字头#3'
|
||||
CH10: '十字头#4'
|
||||
CH11: '十字头#5'
|
||||
CH12: '减速箱小轴承1'
|
||||
CH13: '减速箱小轴承#2'
|
||||
CH14: '减速箱大轴承#3'
|
||||
CH15: '减速箱大轴承#4'
|
||||
CH16: '环境温度'
|
||||
# hsdaq 配置
|
||||
hsdaq:
|
||||
host: 192.168.0.2
|
||||
port: 8080
|
||||
local_host: 192.168.0.3
|
||||
local_port: 8080
|
||||
timeout: 50 # 单位ms
|
||||
channels: 16
|
||||
sample_time: 8 # 单位ms
|
||||
sample_period: 1000 # 单位ms
|
||||
one_sample_time: 100 # 单位us
|
||||
# 配置高频采集通道传感器类型
|
||||
# 用2位标识传感器类型，16通道32位组成1个uint32数据。CH1->CH16
|
||||
# 00：NPN或PNP型开关量; 01：电压型传感器; 10:4-20mA电流型传感器; 11：振动传感器
|
||||
sensor_type: '1010 1011 1011 1011 1011 1010 1010 0100'
|
||||
frame_size_max: 1464 # 最大包长
|
||||
file_size: 32000000 # 最大文件大小
|
||||
file_type: 0 # 0-csv或1-bin
|
||||
output_dir: data # 文件保存目录
|
||||
min_free_gb: 1 # 最小剩余磁盘空间，单位GB
|
||||
# 选择保存数据的通道，1-保存数据， 0-不保存，CH1->CH16
|
||||
save_flag: '0000 0000 0000 0000'
|
||||
daq_board_no: '2504210002'
|
||||
feature_type: "rms"
|
||||
min_vol_cur_phy_value: 0.0
|
||||
max_vol_cur_phy_value: 160.0
|
||||
vol_cur_phy_scale: 1
|
||||
mode: 0 # 工作模式，0-'work'或1-'calib'
|
||||
# 4~20mA传感器对应的K值和T值
|
||||
sensor_Cur_CalibParam:
|
||||
CH1: {K2: 0.0, K: 0.00258263, B: 0.001601482}
|
||||
CH2: {K2: 0.0, K: 0.002572228, B: 0.000180365}
|
||||
CH3: {K2: 0.0, K: 0.002577854, B: 0.003481423}
|
||||
CH4: {K2: 0.0, K: 0.002574779, B: 0.001034803}
|
||||
CH5: {K2: 0.0, K: 0.002563052, B: 0.000328752}
|
||||
CH6: {K2: 0.0, K: 0.002573372, B: -5.67175e-06}
|
||||
CH7: {K2: 0.0, K: 0.002580244, B: 0.001414032}
|
||||
CH8: {K2: 0.0, K: 0.002578148, B: 0.001619703}
|
||||
CH9: {K2: 0.0, K: 0.002582191, B: 0.000300086}
|
||||
CH10: {K2: 0.0, K: 0.002572029, B: 0.000148142}
|
||||
CH11: {K2: 0.0, K: 0.002576609, B: 0.001021399}
|
||||
# CH12: {K2: 0.0, K: 0.001928556, B: 0.003335270}
|
||||
CH12: {K2: 0.0, K: 0.01903484772, B: -78.80708088}
|
||||
CH13: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
# CH14: {K2: 0.0, K: 0.001923312, B: -0.001293004}
|
||||
CH14: {K2: 0.0, K: 0.0435167228946, B: -90.5329674119268}
|
||||
CH15: {K2: 0.0, K: 0.002580323, B: 0.000544915}
|
||||
CH16: {K2: 0.0, K: 0.002573487, B: 0.000873064}
|
||||
# 电压传感器对应的K值和T值
|
||||
sensor_Vol_CalibParam:
|
||||
CH1: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH2: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH3: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH4: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH5: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH6: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH7: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH8: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH9: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH10: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH11: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH12: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH13: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH14: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH15: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
CH16: {K2: 0.0, K: 1.0, B: 0.0}
|
||||
# 振动传感器对应的K值和T值
|
||||
sensor_Vib_CalibParam:
|
||||
CH1: {K2: 0.0, K: 0.000980181688598713, B: 0.784199472182921}
|
||||
CH2: {K2: 0.0, K: 0.000979536991333191, B: 0.758179588312897}
|
||||
CH3: {K2: 0.0, K: 0.000980321826962675, B: 0.747037511177572}
|
||||
CH4: {K2: 0.0, K: 0.000980792974240141, B: 0.757538218907948}
|
||||
CH5: {K2: 0.0, K: 0.000980973262504023, B: 0.806926311144011}
|
||||
CH6: {K2: 0.0, K: 0.000982175606057935, B: 0.785563011832194}
|
||||
CH7: {K2: 0.0, K: 0.000980663833333333, B: 0.773234764062384}
|
||||
CH8: {K2: 0.0, K: 0.000980663833333333, B: 0.773234764062384}
|
||||
CH9: {K2: 0.0, K: 0.000980663833333333, B: 0.773234764062384}
|
||||
CH10: {K2: 0.0, K: 0.000980663833333333, B: 0.773234764062384}
|
||||
CH11: {K2: 0.0, K: 0.000980663833333333, B: 0.773234764062384}
|
||||
CH12: {K2: 0.0, K: 0.000980663833333333, B: 0.773234764062384}
|
||||
CH13: {K2: 0.0, K: 0.000980663833333333, B: 0.773234764062384}
|
||||
CH14: {K2: 0.0, K: 0.000980663833333333, B: 0.773234764062384}
|
||||
CH15: {K2: 0.0, K: 0.000980663833333333, B: 0.773234764062384}
|
||||
CH16: {K2: 0.0, K: 0.000980663833333333, B: 0.773234764062384}
|
||||
# 报警参数设置
|
||||
warning_param:
|
||||
# 是否启用报警
|
||||
enable: '0000000000010100' # 1-启用，0-禁用
|
||||
CH1: {lower: 0.0, upper: 1.0}
|
||||
CH2: {lower: 0.0, upper: 1.0}
|
||||
CH3: {lower: 0.0, upper: 1.0}
|
||||
CH4: {lower: 0.0, upper: 1.0}
|
||||
CH5: {lower: 0.0, upper: 1.0}
|
||||
CH6: {lower: 0.0, upper: 1.0}
|
||||
CH7: {lower: 0.0, upper: 1.0}
|
||||
CH8: {lower: 0.0, upper: 1.0}
|
||||
CH9: {lower: 0.0, upper: 1.0}
|
||||
CH10: {lower: 0.0, upper: 1.0}
|
||||
CH11: {lower: 0.0, upper: 1.0}
|
||||
CH12: {lower: -20.0, upper: 80.0}
|
||||
CH13: {lower: 0.0, upper: 1.0}
|
||||
CH14: {lower: 10.0, upper: 100.0}
|
||||
CH15: {lower: 0.0, upper: 1.0}
|
||||
CH16: {lower: 0.0, upper: 1.0}
|
||||
calib_params:
|
||||
vibration:
|
||||
frequency: 500 # 标定时振动频率，单位Hz
|
||||
alias:
|
||||
# CH1: ''
|
||||
# CH2: ''
|
||||
# CH3: ''
|
||||
CH4: '振动1'
|
||||
# CH5: ''
|
||||
CH6: '振动2'
|
||||
# CH7: ''
|
||||
CH8: '振动3'
|
||||
# CH9: ''
|
||||
CH10: '振动4'
|
||||
# CH11: ''
|
||||
CH12: '润滑油温'
|
||||
# CH13: ''
|
||||
CH14: '润滑油压'
|
||||
# CH15: ''
|
||||
# CH16: ''
|
||||
modbus-server:
|
||||
host: 10.0.21.88
|
||||
port: 5020
|
||||
timeout: 50
|
||||
# 通过Modbus TCP协议提供gps、lsdaq和hsdaq特征值数据
|
||||
plc-server:
|
||||
host: 192.168.1.200
|
||||
port: 5020
|
||||
timeout: 500
|
||||
slave_id: 1
|
||||
# 从PLC读取的物理量
|
||||
measurements:
|
||||
pressure:
|
||||
address: 100
|
||||
type: float32
|
||||
value: 0.6914023756980896
|
||||
warning_param:
|
||||
lower: 0.0
|
||||
upper: 1.0
|
||||
enable: 1
|
||||
warning: 0
|
||||
flow:
|
||||
address: 104
|
||||
type: float32
|
||||
value: 0.2740088403224945
|
||||
warning_param:
|
||||
lower: 0.0
|
||||
upper: 1.0
|
||||
enable: 1
|
||||
warning: 0
|
||||
influxdb:
|
||||
url: http://10.0.21.88:8086
|
||||
token: 4nOdMJpKXQXAGoLDYYdRYDMxoKaEpqchzkqCQnYmgMqkQVDO3zRfaO5ifaCx90HbIRRuMZtgaUKWKNqyUD1hEg==
|
||||
org: MEASCON
|
||||
active: true
|
||||
bucket: PCM
|
||||
config-server:
|
||||
host: 0.0.0.0
|
||||
port: 5000
|
||||
|
|
@ -0,0 +1,101 @@
|
|||
# version: '1.0'
|
||||
services:
|
||||
pcmv1:
|
||||
image: pcmv1:v1.0
|
||||
container_name: pcmv1
|
||||
command: ["/bin/bash", "-c", " source .venv/bin/activate && stty -F /dev/ttyUSB_LIGHT raw && stty -F /dev/ttyUSB_BREAKER raw && python3 src/pcm-influxdb-debug.py"]
|
||||
network_mode: host
|
||||
depends_on:
|
||||
- influxdb
|
||||
privileged: true
|
||||
restart: unless-stopped
|
||||
mem_limit: 256M
|
||||
cpuset: "0"
|
||||
# ports:
|
||||
# - "0.0.0.0:5000:5000"
|
||||
volumes:
|
||||
- /home/torizon/src:/pcmv1/src
|
||||
# - /mnt/ssd_data/pcmv1:/pcmv1/data
|
||||
- /home/torizon/data:/pcmv1/data
|
||||
- /dev:/dev:ro
|
||||
tty: true
|
||||
stdin_open: true
|
||||
environment:
|
||||
- TZ=Asia/Shanghai
|
||||
deploy:
|
||||
mode: replicated
|
||||
replicas: 1
|
||||
group_add:
|
||||
- dialout
|
||||
|
||||
influxdb:
|
||||
image: influxdb:v1.0
|
||||
container_name: influxdb
|
||||
restart: unless-stopped
|
||||
# depends_on:
|
||||
# - alpine
|
||||
ports:
|
||||
- "8086:8086"
|
||||
mem_limit: 256M
|
||||
cpuset: "1"
|
||||
environment:
|
||||
DOCKER_INFLUXDB_INIT_MODE: "setup"
|
||||
DOCKER_INFLUXDB_INIT_USERNAME: "PCM"
|
||||
DOCKER_INFLUXDB_INIT_PASSWORD: "1842moon" # 请修改密码
|
||||
DOCKER_INFLUXDB_INIT_ORG: "MEASCON"
|
||||
DOCKER_INFLUXDB_INIT_BUCKET: "PCM"
|
||||
volumes:
|
||||
- "/mnt/ssd_data/influxdb:/var/lib/influxdb2" # 数据持久化
|
||||
- "/home/torizon/src/influxdb/config:/etc/influxdb2" # 配置持久化（可选）
|
||||
|
||||
# hdtestor:
|
||||
# image: hdtestor:V0.1
|
||||
# container_name: hdtestor
|
||||
# command: ["/bin/bash", "-c", "/hdtestor/scripts/auto_partition_sda.sh"]
|
||||
# network_mode: host
|
||||
# privileged: true
|
||||
# devices:
|
||||
# - "/dev:/dev"
|
||||
# restart: "no"
|
||||
# mem_limit: 256M
|
||||
# cpuset: "2"
|
||||
# volumes:
|
||||
# - /home/torizon/src/pcmv1/bash_scripts:/hdtestor/scripts
|
||||
# environment:
|
||||
# - TZ=Asia/Shanghai
|
||||
|
||||
alpine:
|
||||
image: alpine:v1.0
|
||||
container_name: alpine
|
||||
command: ["sh", "-c", "ls /app -la && /app/auto_partition_sda.sh"]
|
||||
network_mode: host
|
||||
privileged: true
|
||||
devices:
|
||||
- "/dev:/dev"
|
||||
restart: no
|
||||
mem_limit: 256M
|
||||
cpuset: "2"
|
||||
volumes:
|
||||
- /home/torizon/bash_scripts:/app
|
||||
environment:
|
||||
- TZ=Asia/Shanghai
|
||||
|
||||
# pcmv1_flask:
|
||||
# image: pcm_flask_v1:latest
|
||||
# container_name: pcm_flask_v1
|
||||
# command: python src/app.pyc
|
||||
# network_mode: host
|
||||
# privileged: true
|
||||
# restart: unless-stopped
|
||||
# mem_limit: 512M
|
||||
# cpuset: "2-3"
|
||||
# volumes:
|
||||
# - /home/torizon/app:/app
|
||||
# - /mnt/ssd_data/pcmv1:/app/data
|
||||
# tty: true
|
||||
# stdin_open: true
|
||||
# environment:
|
||||
# - TZ=Asia/Shanghai
|
||||
# deploy:
|
||||
# mode: replicated
|
||||
# replicas: 1
|
||||
|
|
@ -733,8 +733,15 @@ class SerialClient:
|
|||
self.serial = None
|
||||
|
||||
def open(self):
|
||||
self.serial = serial.Serial(self.port, self.baudrate, timeout=self.timeout)
|
||||
return self.serial.is_open
|
||||
try:
|
||||
self.serial = serial.Serial(self.port, self.baudrate, timeout=self.timeout)
|
||||
if self.logger:
|
||||
self.logger.info(f"[{nowStr()}] Serial opened: {self.port}, baudrate={self.baudrate}, timeout={self.timeout}")
|
||||
return self.serial.is_open
|
||||
except Exception as e:
|
||||
if self.logger:
|
||||
self.logger.error(f"[{nowStr()}] Serial open failed: {self.port}, err={e}")
|
||||
return False
|
||||
|
||||
def close(self):
|
||||
if self.serial and self.serial.is_open:
|
||||
|
|
@ -793,10 +800,19 @@ class IndicatorController:
|
|||
'turnOffAlarm': ['', "0105 00A1 0000", 8, 100, 1, 1, 3],
|
||||
}
|
||||
self.alarm = 0
|
||||
self.client.open()
|
||||
opened = self.client.open()
|
||||
if not opened and self.logger:
|
||||
self.logger.warning(f"[{nowStr()}] Indicator serial not open, port={config.get('port', '/dev/ttyUSB1')}")
|
||||
|
||||
def exe(self, name):
|
||||
return self.client.exeCmd(self.cmdList[name])
|
||||
ret = self.client.exeCmd(self.cmdList[name])
|
||||
if self.logger:
|
||||
if ret[0]:
|
||||
self.logger.info(f"[{nowStr()}] Indicator cmd ok: {name}")
|
||||
self.logger.debug(ret[2])
|
||||
else:
|
||||
self.logger.warning(f"[{nowStr()}] Indicator cmd failed: {name}; {ret[2]}")
|
||||
return ret
|
||||
|
||||
def alarming(self, closed):
|
||||
"""报警时：红灯亮+蜂鸣器响，绿灯灭"""
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue