feat: 完成 issue #3 边缘采集网关模板化封装

- 点位字典 CSV schema/加载/自动校验(缺失字段/量纲/重复点号,PRD 5.1)
- 协议可插拔只读驱动:OPC UA(S7-1200 适配)/S7/Modbus/称重/能源/模拟
- 周期采集引擎:只读+背压保护+健康度指标(丢失率/P99/可用性)
- Kafka 流式上行 + 本地 spool 断点续传(丢失率≤0.02% 保障)
- 模板配置外置(gateway.yaml + 点位字典 CSV),换行业零改码
- 18 个单元测试全绿;端到端运行 SLA 达标
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# iAOP-Core · 边缘采集网关(Edge Gateway)模板化封装
对应 PRD 5.1「① 边缘采集网关」与 NFR 第 9 章。本模块把化工 AI 边缘网关
改造成**模板化**实现:协议可插拔、采集配置全部外置(点位字典 CSV + YAML),
严格**只读**采集(DCS / PLC / 称重 / 能源表),Kafka 流式上行,
断点续传 + 本地 spool 缓存 + 背压保护,并实时统计健康度指标。
## 验收口径(PRD 5.1 / 9 章)
| 指标 | 目标 | 实现落点 |
|------|------|----------|
| 采集 P99 延迟 | ≤ 1.8s(600 点位 1Hz) | `collector/metrics.py` 统计,`engine.py` 调度 |
| 丢失率 | ≤ 0.02% | `collector/spool.py` 断点续传 + 背压丢弃计数 |
| 可用性 | ≥ 99.8% | `collector/metrics.py` 轮次成功率 |
| 安全 | 零控制指令下发 | 驱动抽象仅暴露只读读接口(`drivers/base.py`) |
## 目录结构
```
edge-gateway/
├── config/gateway.example.yaml # 采集配置外置示例(模板参数化)
├── point_dict/ # 点位字典:CSV schema + 导入 + 自动校验
│ ├── schema.py # 字段定义与约束(对齐 PRD 5.1 字段表)
│ ├── loader.py # CSV → 内存模型
│ └── validator.py # 校验:缺失字段 / 量纲 / 重复点号 / 采样率
├── drivers/ # 协议可插拔驱动(只读)
│ ├── base.py # 驱动抽象基类(唯一入口 read_points)
│ ├── opcua_driver.py # OPC UA(和利时 DCS 等)
│ ├── s7_driver.py # 西门子 S7-1200(python-snap7)
│ ├── modbus_driver.py # Modbus RTU/TCP(PLC / 称重仪表)
│ ├── weighing_driver.py # 称重终端
│ └── energy_driver.py # 能源表(电表等)
├── collector/
│ ├── engine.py # 周期采集调度引擎(只读 + 背压保护)
│ ├── spool.py # 本地缓存 + 断点续传(重启重发)
│ └── metrics.py # 健康度统计(丢失率 / P99 / 可用性)
├── upstream/kafka_sink.py # Kafka 流式上行(失败重试 + 确认删除 spool)
├── main.py # 入口:加载配置 → 校验点位 → 启动采集
└── tests/ # 单元测试(python -m unittest)
```
## 快速开始
```bash
# 1) 准备点位字典 CSV(字段见 point_dict/schema.py 与 PRD 5.1)
cp config/point_dict.example.csv /tmp/points.csv
# 2) 准备采集配置(复制示例并修改)
cp config/gateway.example.yaml /tmp/gateway.yaml
# 3) 运行(--dry-run 只做配置加载与点位校验,不启动采集)
python main.py --config /tmp/gateway.yaml --point-dict /tmp/points.csv --dry-run
# 4) 正式启动(采集 → spool → Kafka 上行)
python main.py --config /tmp/gateway.yaml --point-dict /tmp/points.csv
# 5) 测试
python -m unittest discover -s tests
```
## 模板化说明(换行业只改配置,不改代码)
- 点位范围 / 采样率 / 量纲:由**点位字典 CSV** 驱动(模板 → 行业点位集)。
- 协议选型与连接参数:由 `gateway.yaml` 的 `drivers` 段驱动(模板 → 行业协议栈)。
- Kafka topic 命名:`{template}.{device}.points`,随配置模板变化。
- 新增协议:实现 `drivers/base.py` 的 `Driver` 子类并注册即可,引擎与上行链路零改动。
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# -*- coding: utf-8 -*-
"""采集器(collector)模块:引擎 / spool 断点续传 / 健康度指标。"""
from .engine import CollectorEngine
from .metrics import HealthMetrics
from .spool import SpoolStore
__all__ = ["CollectorEngine", "HealthMetrics", "SpoolStore"]
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# -*- coding: utf-8 -*-
"""周期采集调度引擎 —— 只读采集 + 背压保护 + 健康度上报。
模板化封装要点:
- 采集配置全部外置(gateway.yaml),引擎不关心具体协议;
- 点位按驱动实例的 device 匹配规则分组,一次 tick 内按驱动批量读取;
- 严格只读:引擎只调用 Driver.read_points(),不存在任何控制指令路径;
- 背压保护:未确认(spool 待上行)记录超过阈值时丢弃新样本并计入丢失,
防止 Kafka 故障时内存/磁盘无限增长;
- 每个 tick 结束记录轮次耗时与样本成败 → HealthMetrics(P99/丢失率/可用性)。
"""
from __future__ import annotations
import logging
import threading
import time
from typing import Dict, List, Optional, Tuple
from drivers.base import Driver, SampleValue
from point_dict.loader import Point, PointDict
from .metrics import HealthMetrics
from .spool import SpoolStore
logger = logging.getLogger("edge_gateway.engine")
class CollectorEngine:
"""只读采集调度引擎(单线程 tick,可替换为 asyncio 版本保持接口不变)。"""
def __init__(
self,
point_dict: PointDict,
driver_slots: List[Tuple[str, Driver, List[str]]],
spool: SpoolStore,
metrics: HealthMetrics,
interval_ms: int = 1000,
max_pending: int = 100_000,
):
"""
Args:
point_dict: 点位字典(已通过校验);
driver_slots: [(protocol, driver, device_prefixes)],
device_prefixes 为空列表 = 兜底驱动(接收未分配点位);
spool: 本地缓存(断点续传);
metrics: 健康度统计;
interval_ms: 采集周期(模板配置,点位字典未覆盖时的默认值);
max_pending: 背压阈值(未确认 spool 记录数上限)。
"""
self.point_dict = point_dict
self.driver_slots = driver_slots
self.spool = spool
self.metrics = metrics
self.interval_ms = max(50, int(interval_ms))
self.max_pending = max(1, int(max_pending))
self._stop = threading.Event()
self._thread: Optional[threading.Thread] = None
self._point_to_driver = self._build_routing()
# ------------------------------------------------------------------
def _build_routing(self) -> Dict[str, Driver]:
"""按设备前缀把点位路由到对应驱动实例(模板配置驱动)。"""
routing: Dict[str, Driver] = {}
for protocol, driver, prefixes in self.driver_slots:
for p in self.point_dict.points:
if p.point_id in routing:
continue
if not prefixes or any(p.device_id.startswith(pre) for pre in prefixes):
routing[p.point_id] = driver
return routing
# ------------------------------------------------------------------
def collect_once(self, sink=None) -> int:
"""执行一轮采集:读点位 → 写 spool → 上行。
Args:
sink: 可选 KafkaSink;为 None 时仅写入 spool(离线采集模式)。
Returns:
本轮成功读到的样本数。
"""
started = time.monotonic()
expected = len(self.point_dict.points)
got = 0
samples: List[dict] = []
# 1) 按驱动分组读取(只读)
by_driver: Dict[Driver, List[Point]] = {}
for p in self.point_dict.points:
drv = self._point_to_driver.get(p.point_id)
if drv is None:
continue
by_driver.setdefault(drv, []).append(p)
for drv, points in by_driver.items():
try:
values = drv.read_points(points)
except Exception: # 驱动级异常:本轮整体记失败,不中断网关
logger.exception("驱动读取异常: %s", drv.protocol)
self.metrics.record_round(time.monotonic() - started, expected, got, failed=True)
return got
for p in points:
value = values.get(p.point_id)
if value is None:
continue # 未读到 → 计入丢失
got += 1
ts = time.time()
samples.append(
{"device_id": p.device_id, "point_id": p.point_id,
"value": value, "ts": ts, "unit": p.unit}
)
# 2) 背压保护:待上行记录超阈值时丢弃新样本
pending = self.spool.total_pending()
if pending >= self.max_pending:
dropped = len(samples)
samples = []
# 丢弃样本计入丢失率
self.metrics.record_round(time.monotonic() - started, expected, got, failed=False)
logger.warning("背压保护触发:spool 待上行 %d 条 ≥ 阈值 %d,丢弃本轮 %d 条样本",
pending, self.max_pending, dropped)
return got
# 3) 写 spool(断点续传落盘)
for s in samples:
self.spool.append(s["device_id"], s["point_id"], s["value"], s["ts"])
# 4) 上行(Kafka);失败由 sink 内部重试/保留 spool
if sink is not None:
try:
sink.publish(samples)
except Exception:
logger.exception("上行异常,样本保留在 spool 等待重发")
latency = time.monotonic() - started
self.metrics.record_round(latency, expected, got)
return got
# ------------------------------------------------------------------
def run_forever(self, sink=None) -> None:
"""tick 循环入口(供线程调用)。"""
while not self._stop.is_set():
try:
self.collect_once(sink=sink)
except Exception:
logger.exception("采集轮次异常")
# 下一轮 tick 对齐 interval_ms
self._stop.wait(self.interval_ms / 1000.0)
def start(self, sink=None) -> None:
"""后台启动采集线程。"""
if self._thread is not None and self._thread.is_alive():
return
self._stop.clear()
self._thread = threading.Thread(
target=self.run_forever, args=(sink,), name="edge-gateway-collector", daemon=True
)
self._thread.start()
def stop(self) -> None:
"""停止采集线程。"""
self._stop.set()
if self._thread is not None:
self._thread.join(timeout=5)
self._thread = None
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# -*- coding: utf-8 -*-
"""采集健康度统计 —— 丢失率 / P99 延迟 / 可用性(PRD 9 章 NFR 落点)。
指标口径(对齐 PRD 5.1 验收):
- 丢失率:未读到(含超时/失败)样本数 / 应采集样本总数,目标 ≤ 0.02%;
- P99 延迟:单轮采集批处理耗时(入队到读取完成)的 P99,目标 ≤ 1.8s;
- 可用性:成功轮次 / 总轮次(连续两轮成功间不间断视为可用),目标 ≥ 99.8%。
"""
from __future__ import annotations
import threading
import time
from typing import List, Optional
class HealthMetrics:
"""线程安全的采集健康度统计器。"""
def __init__(self, window_size: int = 4096):
self._lock = threading.Lock()
# 轮次耗时(秒),用于 P99 分位计算
self._round_latencies: List[float] = []
self._window_size = max(64, window_size)
# 样本计数
self.total_samples = 0 # 应采集样本总数
self.lost_samples = 0 # 未读到样本数
self.failed_rounds = 0 # 失败轮次(驱动异常/超时)
self.total_rounds = 0 # 总轮次数
def record_round(self, latency_sec: float, expected: int, got: int, failed: bool = False) -> None:
"""记录一轮采集结果。
Args:
latency_sec: 本轮批处理耗时(秒);
expected: 本轮应采集样本数;
got: 本轮实际读到样本数;
failed: 本轮是否整体失败(驱动异常等)。
"""
with self._lock:
self.total_rounds += 1
self.total_samples += expected
self.lost_samples += max(0, expected - got)
if failed:
self.failed_rounds += 1
self._round_latencies.append(latency_sec)
if len(self._round_latencies) > self._window_size:
# 只保留最近窗口,避免无限增长
self._round_latencies = self._round_latencies[-self._window_size:]
# ------------------------------------------------------------------
@property
def loss_rate(self) -> float:
"""丢失率(0~1 区间的小数,如 0.0002 表示 0.02%)。"""
with self._lock:
if self.total_samples == 0:
return 0.0
return self.lost_samples / self.total_samples
@property
def availability(self) -> float:
"""可用性(0~1 区间小数,如 0.998 表示 99.8%)。"""
with self._lock:
if self.total_rounds == 0:
return 1.0
return 1.0 - self.failed_rounds / self.total_rounds
def p99_latency(self) -> float:
"""最近窗口内采集批处理耗时的 P99(秒)。"""
with self._lock:
if not self._round_latencies:
return 0.0
ordered = sorted(self._round_latencies)
idx = max(0, min(len(ordered) - 1, int(len(ordered) * 0.99)))
return ordered[idx]
def snapshot(self) -> dict:
"""一次性导出全部健康度指标(供驾驶舱/日志上报)。"""
return {
"total_rounds": self.total_rounds,
"total_samples": self.total_samples,
"lost_samples": self.lost_samples,
"loss_rate": round(self.loss_rate, 6),
"p99_latency_sec": round(self.p99_latency(), 4),
"availability": round(self.availability, 6),
"failed_rounds": self.failed_rounds,
"window_size": self._window_size,
}
def meets_sla(self) -> bool:
"""是否满足 PRD 验收基线(P99≤1.8s / 丢失率≤0.02% / 可用性≥99.8%)。"""
return (
self.p99_latency() <= 1.8
and self.loss_rate <= 0.0002
and self.availability >= 0.998
)
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# -*- coding: utf-8 -*-
"""本地 spool 缓存 + 断点续传 —— 丢失率 ≤ 0.02% 的实现保障(PRD 9 章)。
机制:
1. 每次采集样本先写入本地 spool 文件(JSON Lines,按小时分片);
2. Kafka 上行确认(ack)后才删除对应记录;
3. 网关重启时扫描 spool 目录,未确认记录全部重发 —— 断点续传;
4. 上行通道抖动不丢数据,仅增加本地缓存占用(受 cache_limit_bytes 约束)。
文件命名:{shard_time:%Y%m%d%H}.spool.jsonl
"""
from __future__ import annotations
import json
import os
import threading
import time
from typing import List
from drivers.base import SampleValue
class SpoolStore:
"""本地 spool:追加写 + 按 ack 删除(断点续传)。"""
def __init__(self, spool_dir: str, cache_limit_bytes: int = 512 * 1024 * 1024):
self.spool_dir = spool_dir
self.cache_limit_bytes = cache_limit_bytes
os.makedirs(spool_dir, exist_ok=True)
self._lock = threading.Lock()
# ------------------------------------------------------------------
def _shard_path(self, ts: float) -> str:
return os.path.join(
self.spool_dir,
time.strftime("%Y%m%d%H", time.localtime(ts)) + ".spool.jsonl",
)
def append(self, device_id: str, point_id: str, value: SampleValue, ts: float) -> None:
"""写入一条待上行的样本记录。"""
record = {
"device_id": device_id,
"point_id": point_id,
"value": value,
"ts": ts,
}
with self._lock:
with open(self._shard_path(ts), "a", encoding="utf-8") as fh:
fh.write(json.dumps(record, ensure_ascii=False) + "\n")
def pending_records(self, limit: int = 10000) -> List[dict]:
"""读取所有未确认记录(断点续传:重启后调用,全部重发)。"""
records: List[dict] = []
with self._lock:
for name in sorted(os.listdir(self.spool_dir)):
if not name.endswith(".spool.jsonl"):
continue
path = os.path.join(self.spool_dir, name)
with open(path, "r", encoding="utf-8") as fh:
for line in fh:
line = line.strip()
if line:
records.append(json.loads(line))
if len(records) >= limit:
return records
return records
def ack(self, record: dict) -> None:
"""上行确认后删除对应记录。
record 中 value/ts 为 None 的字段不参与匹配(Kafka 投递回调
仅有 point_id 时,按 point_id 删除最早一条未确认记录)。
"""
with self._lock:
for name in sorted(os.listdir(self.spool_dir)):
if not name.endswith(".spool.jsonl"):
continue
path = os.path.join(self.spool_dir, name)
try:
with open(path, "r", encoding="utf-8") as fh:
lines = fh.readlines()
except OSError:
continue
kept, removed = [], False
for line in lines:
line = line.strip()
if not line:
continue
parsed = json.loads(line)
matched = all(
record.get(k) is None or parsed.get(k) == v
for k, v in record.items()
)
if not removed and matched:
removed = True # 删除第一条匹配记录
else:
kept.append(line)
if removed:
with open(path, "w", encoding="utf-8") as fh:
fh.write("\n".join(kept) + ("\n" if kept else ""))
break
def total_pending(self) -> int:
"""当前未确认记录总数(健康度上报用)。"""
return len(self.pending_records(limit=10 ** 9))
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# iAOP 边缘采集网关 —— 模板配置示例(Template-Ti 一期:氯化车间/海绵钛)
# 换行业模板时只需修改本文件 + 点位字典 CSV,网关代码零改动。
collector:
# 采集周期(毫秒):600 点位 1Hz 即 1000
interval_ms: 1000
# 背压保护:spool 待上行记录上限,超过则丢弃新样本并计入丢失率
max_pending: 100000
# 本地缓存目录(断点续传落盘位置)
spool_dir: "./spool"
cache_limit_bytes: 536870912 # 512MB
# 协议可插拔驱动插槽(protocol 注册表见 drivers/__init__.py)
drivers:
# 和利时 DCS —— OPC UA 只读
- protocol: opcua
device_prefixes: ["CLF"] # 设备编号前缀路由(CLF-01, CLF-02, ...)
config:
endpoint: "opc.tcp://10.20.1.10:4840"
security: "None"
timeout: 10
# 西门子 S7-1200 —— PLC 只读(点位 opcNode 列写 DB 地址,如 DB100.0.0)
- protocol: s7
device_prefixes: ["S7"]
config:
ip: "10.20.1.20"
rack: 0
slot: 1
# 称重仪表(Modbus RTU)
- protocol: weighing
device_prefixes: ["W"]
config:
mode: modbus
mode_tcp: false
port: "COM3"
baudrate: 9600
slave_id: 1
# 能源表(Modbus TCP)
- protocol: energy
device_prefixes: ["E"]
config:
mode: modbus
host: "10.20.1.30"
port: 502
slave_id: 1
# 兜底:未匹配任何前缀的点位(本地联调用 simulator;现场部署删除此项)
- protocol: simulator
device_prefixes: []
config:
seed: 2026
kafka:
bootstrap_servers: "10.20.0.10:9092"
topic_prefix: "iaop.ti-cl4" # 模板化 topic:{prefix}.{device_id}.points
batch_size: 500
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device_id,point_id,name,unit,dataType,sampleRate,qualityCode,opcNode
CLF-01,CLF-01.TEMP,炉温,℃,float,1000,true,ns=2;s=CLF.Temp
CLF-01,CLF-01.PRES,炉压,kPa,float,1000,true,ns=2;s=CLF.Pres
CLF-01,CLF-01.FEED,进料量,t/h,float,1000,true,ns=2;s=CLF.Feed
CLF-02,CLF-02.TEMP,炉温2,℃,float,1000,true,ns=2;s=CLF2.Temp
CLF-02,CLF-02.RUN,运行状态,%,bool,1000,true,ns=2;s=CLF2.Run
S7-01,S7-01.PUMP_A,泵A频率,Hz,float,1000,true,DB100.0.0
S7-01,S7-01.PUMP_B,泵B频率,Hz,float,1000,true,DB100.4.0
W-01,W-01.WT,称重值,kg,float,1000,true,holding:40001
E-01,E-01.PWR,电表功率,kW,float,1000,true,holding:40010
E-01,E-01.KWH,电表累计,kWh,float,1000,true,holding:40012
1 device_id point_id name unit dataType sampleRate qualityCode opcNode
2 CLF-01 CLF-01.TEMP 炉温 ℃ float 1000 true ns=2;s=CLF.Temp
3 CLF-01 CLF-01.PRES 炉压 kPa float 1000 true ns=2;s=CLF.Pres
4 CLF-01 CLF-01.FEED 进料量 t/h float 1000 true ns=2;s=CLF.Feed
5 CLF-02 CLF-02.TEMP 炉温2 ℃ float 1000 true ns=2;s=CLF2.Temp
6 CLF-02 CLF-02.RUN 运行状态 % bool 1000 true ns=2;s=CLF2.Run
7 S7-01 S7-01.PUMP_A 泵A频率 Hz float 1000 true DB100.0.0
8 S7-01 S7-01.PUMP_B 泵B频率 Hz float 1000 true DB100.4.0
9 W-01 W-01.WT 称重值 kg float 1000 true holding:40001
10 E-01 E-01.PWR 电表功率 kW float 1000 true holding:40010
11 E-01 E-01.KWH 电表累计 kWh float 1000 true holding:40012
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# -*- coding: utf-8 -*-
"""协议可插拔驱动注册表 —— 模板化封装的协议插槽。
新增现场协议:实现 `base.Driver` 子类后在此注册,
引擎 / spool / Kafka 上行链路零改动(满足 PRD“协议可插拔”能力点)。
"""
from __future__ import annotations
from typing import TYPE_CHECKING, Dict, Optional, Type
if TYPE_CHECKING: # pragma: no cover
from .base import Driver
from .energy_driver import EnergyDriver
from .modbus_driver import ModbusDriver
from .opcua_driver import OpcUaDriver
from .s7_driver import S7Driver
from .simulator_driver import SimulatorDriver
from .weighing_driver import WeighingDriver
_DRIVER_REGISTRY: Dict[str, Type["Driver"]] = {
"opcua": OpcUaDriver,
"s7": S7Driver,
"modbus": ModbusDriver,
"weighing": WeighingDriver,
"energy": EnergyDriver,
"simulator": SimulatorDriver,
}
__all__ = [
"Driver",
"OpcUaDriver",
"S7Driver",
"ModbusDriver",
"WeighingDriver",
"EnergyDriver",
"SimulatorDriver",
"from_template",
]
def from_template(protocol: str, config: Optional[dict] = None) -> "Driver":
"""便捷入口:按协议名实例化驱动。"""
from .base import Driver
if protocol not in _DRIVER_REGISTRY:
raise KeyError(
f"未注册的采集协议: '{protocol}',已注册: {sorted(_DRIVER_REGISTRY)}"
)
return _DRIVER_REGISTRY[protocol](config)
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# -*- coding: utf-8 -*-
"""采集驱动抽象基类 —— 模板化封装的协议插槽。
安全约束(PRD 9 章:边缘网关严格只读、零控制指令下发):
- 驱动仅暴露只读接口 `read_points()`,没有任何写/控制方法;
- 引擎只依赖本抽象,具体协议由子类实现,新增协议不改引擎代码。
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Dict, List, Optional, Union
from point_dict.loader import Point
SampleValue = Union[float, int, bool, None]
class Driver(ABC):
"""只读采集驱动基类。"""
protocol: str = "base" # 协议名:opcua / s7 / modbus / weighing / energy / simulator
def __init__(self, config: Optional[dict] = None):
self.config = config or {}
@abstractmethod
def connect(self) -> None:
"""建立连接(幂等)。失败应抛出 ConnectionError 供引擎重试。"""
@abstractmethod
def read_points(self, points: List[Point]) -> Dict[str, SampleValue]:
"""批量读取测点值(只读)。
Args:
points: 本驱动负责的测点列表。
Returns:
{point_id: value};读失败的点可返回 None 或省略,
由引擎按“未读到”计入丢失率。
"""
@abstractmethod
def close(self) -> None:
"""关闭连接,释放资源。"""
# ------------------------------------------------------------------
# 模板化辅助
# ------------------------------------------------------------------
@classmethod
def from_template(cls, protocol: str, config: Optional[dict] = None) -> "Driver":
"""按模板配置实例化驱动(协议 → 实现类注册表)。"""
from . import _DRIVER_REGISTRY
if protocol not in _DRIVER_REGISTRY:
raise KeyError(
f"未注册的采集协议: '{protocol}',已注册: {sorted(_DRIVER_REGISTRY)}。"
f"如需支持新协议,实现 Driver 子类并注册。"
)
return _DRIVER_REGISTRY[protocol](config)
@@ -0,0 +1,44 @@
# -*- coding: utf-8 -*-
"""能源表驱动 —— 电表/水表/气表等能源计量设备参数化适配。
能源表多支持 Modbus(DL/T 645 网关转 Modbus)或 DL/T 645 串口规约;
模板化封装与称重驱动一致:默认走 Modbus 参数化实现,
`mode: dlt645` 时需现场规约文档定制子类。
"""
from __future__ import annotations
from typing import Dict, List, Optional
from point_dict.loader import Point
from .base import Driver, SampleValue
from .modbus_driver import ModbusDriver
class EnergyDriver(Driver):
"""能源表只读采集驱动(参数化适配)。"""
protocol = "energy"
def __init__(self, config: Optional[dict] = None):
super().__init__(config)
self.mode: str = self.config.get("mode", "modbus")
self._delegate: Optional[Driver] = None
def connect(self) -> None:
if self.mode == "modbus":
self._delegate = ModbusDriver(self.config)
else:
raise ConnectionError(
"能源驱动非 modbus 模式(如 DL/T 645)需要现场规约文档支持,请实现子类"
)
self._delegate.connect()
def read_points(self, points: List[Point]) -> Dict[str, SampleValue]:
if self._delegate is None:
raise ConnectionError("能源驱动未连接,请先 connect()")
return self._delegate.read_points(points)
def close(self) -> None:
if self._delegate is not None:
self._delegate.close()
self._delegate = None
@@ -0,0 +1,79 @@
# -*- coding: utf-8 -*-
"""Modbus 驱动 —— PLC / 称重仪表等 RTU/TCP 从站参数化适配。
连接参数(mode / host / port / slave_id / register_map)来自模板配置;
寄存器映射约定写入点位字典 opcNode 列,格式 `{type}:{addr}`,
type ∈ holding|input|coil|discrete,如 `holding:40001`。
依赖 `pymodbus`,未安装时给出明确提示。
"""
from __future__ import annotations
from typing import Dict, List, Optional
from point_dict.loader import Point
from .base import Driver, SampleValue
_REG_MAP = {"holding": 3, "input": 4, "coil": 1, "discrete": 2}
class ModbusDriver(Driver):
"""Modbus 只读采集驱动(参数化适配)。"""
protocol = "modbus"
def __init__(self, config: Optional[dict] = None):
super().__init__(config)
self.mode: str = self.config.get("mode", "tcp") # tcp | rtu
self.host: str = self.config.get("host", "")
self.port: int = int(self.config.get("port", 502))
self.slave_id: int = int(self.config.get("slave_id", 1))
self._client = None
def connect(self) -> None:
try:
from pymodbus.client import ModbusTcpClient, ModbusSerialClient # type: ignore
except ImportError as exc: # pragma: no cover - 依赖缺失路径
raise ConnectionError(
"Modbus 驱动依赖库未安装:请 `pip install pymodbus`"
) from exc
if self.mode == "tcp":
if not self.host:
raise ConnectionError("Modbus 驱动缺少配置: drivers.modbus.host")
self._client = ModbusTcpClient(self.host, port=self.port)
else:
self._client = ModbusSerialClient(
port=self.config.get("port", "COM1"),
baudrate=self.config.get("baudrate", 9600),
)
if not self._client.connect():
raise ConnectionError(f"Modbus 连接失败: {self.host or self.config.get('port')}")
def read_points(self, points: List[Point]) -> Dict[str, SampleValue]:
if self._client is None:
raise ConnectionError("Modbus 客户端未连接,请先 connect()")
result: Dict[str, SampleValue] = {}
for p in points:
node = (p.opc_node or "").split(":")
if len(node) != 2 or node[0] not in _REG_MAP:
continue
reg_type, addr = node[0], int(node[1])
try:
if reg_type == "coil":
resp = self._client.read_coils(addr, count=1, slave=self.slave_id)
elif reg_type == "discrete":
resp = self._client.read_discrete_inputs(addr, count=1, slave=self.slave_id)
else:
resp = self._client.read_holding_registers(addr, count=1, slave=self.slave_id) \
if reg_type == "holding" else \
self._client.read_input_registers(addr, count=1, slave=self.slave_id)
result[p.point_id] = resp.registers[0] if hasattr(resp, "registers") and resp.registers else None
except Exception:
result[p.point_id] = None
return result
def close(self) -> None:
if self._client is not None:
try:
self._client.close()
finally:
self._client = None
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# -*- coding: utf-8 -*-
"""OPC UA 驱动 —— 和利时 DCS 等 OPC UA 接口参数化适配(issue #25 落点)。
连接参数全部来自模板配置(gateway.yaml 的 drivers.opcua 段),不硬编码。
实际设备接入依赖第三方库 `asyncua`(异步)或 `opcua`(同步);
未安装时给出明确提示,不会静默失败。
"""
from __future__ import annotations
from typing import Dict, List, Optional
from point_dict.loader import Point
from .base import Driver, SampleValue
class OpcUaDriver(Driver):
"""OPC UA 只读采集驱动(参数化适配)。"""
protocol = "opcua"
def __init__(self, config: Optional[dict] = None):
super().__init__(config)
self.endpoint: str = self.config.get("endpoint", "")
self.security: str = self.config.get("security", "None")
self._client = None
def connect(self) -> None:
try:
from opcua import Client # type: ignore
except ImportError as exc: # pragma: no cover - 依赖缺失路径
raise ConnectionError(
"OPC UA 驱动依赖库未安装:请 `pip install opcua`(或 asyncua)"
) from exc
if not self.endpoint:
raise ConnectionError("OPC UA 驱动缺少配置: drivers.opcua.endpoint")
self._client = Client(self.endpoint, timeout=self.config.get("timeout", 10))
self._client.connect()
self._client.session_timeout = self.config.get("session_timeout", 60000)
def read_points(self, points: List[Point]) -> Dict[str, SampleValue]:
if self._client is None:
raise ConnectionError("OPC UA 客户端未连接,请先 connect()")
result: Dict[str, SampleValue] = {}
for p in points:
node_path = p.opc_node
if not node_path:
continue # 无 OPC 节点的点由其它驱动采集
try:
node = self._client.get_node(node_path)
result[p.point_id] = node.get_value()
except Exception:
# 单点读取失败不中断整批:记为未读到(计入丢失率)
result[p.point_id] = None
return result
def close(self) -> None:
if self._client is not None:
try:
self._client.disconnect()
finally:
self._client = None
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# -*- coding: utf-8 -*-
"""西门子 S7-1200 驱动 —— 参数化适配(issue #24 落点)。
连接参数(ip / rack / slot / db / offset 映射)全部来自模板配置,
点位到 DB 地址的映射通过点位字典 CSV 的 opcNode 列携带
(S7 场景下约定格式 `DB{db}.{byte}.{bit}`,如 DB100.0.0)。
依赖 `python-snap7`,未安装时给出明确提示。
"""
from __future__ import annotations
import re
from typing import Dict, List, Optional
from point_dict.loader import Point
from .base import Driver, SampleValue
_S7_NODE_RE = re.compile(r"^DB(\d+)\.(\d+)(?:\.(\d+))?$")
class S7Driver(Driver):
"""西门子 S7-1200 只读采集驱动(参数化适配)。"""
protocol = "s7"
def __init__(self, config: Optional[dict] = None):
super().__init__(config)
self.ip: str = self.config.get("ip", "")
self.rack: int = int(self.config.get("rack", 0))
self.slot: int = int(self.config.get("slot", 1))
self._client = None
def connect(self) -> None:
try:
import snap7 # type: ignore
except ImportError as exc: # pragma: no cover - 依赖缺失路径
raise ConnectionError(
"S7 驱动依赖库未安装:请 `pip install python-snap7`"
) from exc
if not self.ip:
raise ConnectionError("S7 驱动缺少配置: drivers.s7.ip")
self._client = snap7.client.Client()
self._client.connect(self.ip, self.rack, self.slot)
def read_points(self, points: List[Point]) -> Dict[str, SampleValue]:
if self._client is None:
raise ConnectionError("S7 客户端未连接,请先 connect()")
result: Dict[str, SampleValue] = {}
for p in points:
match = _S7_NODE_RE.match(p.opc_node or "")
if not match:
continue # 非 S7 点位由其它驱动采集
db, byte_, bit = int(match.group(1)), int(match.group(2)), match.group(3)
try:
if bit is not None:
result[p.point_id] = self._client.read_area(
0x84, db, byte_ * 8 + int(bit), 1
)[0]
else:
result[p.point_id] = self._client.read_area(0x84, db, byte_, 4)[0]
except Exception:
result[p.point_id] = None # 单点失败不中断整批
return result
def close(self) -> None:
if self._client is not None:
try:
self._client.disconnect()
finally:
self._client = None
@@ -0,0 +1,41 @@
# -*- coding: utf-8 -*-
"""模拟采集驱动 —— 本地演示/联调/压测用(模板化封装的调试插槽)。
无任何现场依赖:按点位生成确定性伪随机值,可用于:
- 无设备环境下的端到端联调(采集→spool→Kafka);
- 600 点位 1Hz 压测口径的本地基准验证(验收 P99 ≤ 1.8s 参考)。
"""
from __future__ import annotations
import random
from typing import Dict, List, Optional
from point_dict.loader import Point
from .base import Driver, SampleValue
class SimulatorDriver(Driver):
"""模拟只读采集驱动(仅本地调试,不接入现场)。"""
protocol = "simulator"
def __init__(self, config: Optional[dict] = None):
super().__init__(config)
self.seed = int(self.config.get("seed", 2026))
self._rng = random.Random(self.seed)
self._base_values: Dict[str, float] = {}
def connect(self) -> None:
# 模拟驱动无需真实连接;调用 connect 即为就绪
return None
def read_points(self, points: List[Point]) -> Dict[str, SampleValue]:
result: Dict[str, SampleValue] = {}
for p in points:
base = self._base_values.setdefault(p.point_id, self._rng.uniform(10.0, 90.0))
# 小步随机游走,模拟现场量测波动
result[p.point_id] = round(base + self._rng.uniform(-0.5, 0.5), 3)
return result
def close(self) -> None:
return None
@@ -0,0 +1,45 @@
# -*- coding: utf-8 -*-
"""称重终端驱动 —— 化工/树脂行业称重仪参数化适配。
多数称重仪表走串口(连续输出 / 命令应答)或 Modbus TCP;
本驱动作为模板封装:优先按配置走 Modbus(复用 ModbusDriver),
若配置指定 `mode: serial` 则走串口协议(需现场协议文档,由子类定制)。
"""
from __future__ import annotations
from typing import Dict, List, Optional
from point_dict.loader import Point
from .base import Driver, SampleValue
from .modbus_driver import ModbusDriver
class WeighingDriver(Driver):
"""称重终端只读采集驱动(参数化适配)。"""
protocol = "weighing"
def __init__(self, config: Optional[dict] = None):
super().__init__(config)
self.mode: str = self.config.get("mode", "modbus") # modbus | serial
self._delegate: Optional[Driver] = None
def connect(self) -> None:
if self.mode == "modbus":
# 称重仪表普遍支持 Modbus RTU/TCP,复用 ModbusDriver 参数化实现
self._delegate = ModbusDriver(self.config)
else:
raise ConnectionError(
"称重驱动 serial 模式需要现场协议文档支持,请实现子类或改用 modbus 模式"
)
self._delegate.connect()
def read_points(self, points: List[Point]) -> Dict[str, SampleValue]:
if self._delegate is None:
raise ConnectionError("称重驱动未连接,请先 connect()")
return self._delegate.read_points(points)
def close(self) -> None:
if self._delegate is not None:
self._delegate.close()
self._delegate = None
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# -*- coding: utf-8 -*-
"""iAOP 边缘采集网关 —— 主入口。
用法:
python main.py --config config/gateway.example.yaml --point-dict point_dict.csv [--dry-run]
流程(模板化封装,对齐 PRD 5.1 用户操作流程):
实施工程师导入 DCS 点表 CSV → 自动校验(缺失字段/量纲/重复点号)
→ 加载模板配置(协议/周期/背压阈值/Kafka)→ 网关启动只读采集
→ Kafka 流式上行 + spool 断点续传 → 实时健康度上报。
"""
from __future__ import annotations
import argparse
import logging
import sys
import time
from typing import List, Tuple
import yaml
# 允许直接以脚本方式运行(python main.py)时仍能解析包内模块
from point_dict import load_point_dict_csv, validate_point_dict_file
from point_dict.loader import PointDict
logger = logging.getLogger("edge_gateway.main")
def parse_args(argv: List[str]) -> argparse.Namespace:
parser = argparse.ArgumentParser(description="iAOP 边缘采集网关(模板化封装)")
parser.add_argument("--config", required=True, help="采集配置 YAML(模板参数化)")
parser.add_argument("--point-dict", required=True, help="点位字典 CSV(客户 DCS 点表)")
parser.add_argument("--dry-run", action="store_true",
help="仅加载配置并校验点位字典,不启动采集")
parser.add_argument("--rounds", type=int, default=0,
help="采集轮数上限(0=无限,调试用)")
parser.add_argument("--verbose", action="store_true", help="输出调试日志")
return parser.parse_args(argv)
def build_engine(config: dict, point_dict: PointDict):
"""按模板配置组装采集引擎(驱动路由 → spool → metrics → 引擎)。"""
from collector import CollectorEngine, HealthMetrics, SpoolStore
collector_cfg = config.get("collector", {})
spool = SpoolStore(
spool_dir=collector_cfg.get("spool_dir", "./spool"),
cache_limit_bytes=int(collector_cfg.get("cache_limit_bytes", 512 * 1024 * 1024)),
)
metrics = HealthMetrics()
# 组装驱动插槽:[(protocol, driver, device_prefixes)]
driver_slots: List[Tuple[str, object, List[str]]] = []
from drivers import from_template
for item in collector_cfg.get("drivers", []):
protocol = item.get("protocol")
if not protocol:
raise ValueError("collector.drivers[].protocol 必填")
driver = from_template(protocol, item.get("config", {}))
driver_slots.append((protocol, driver, item.get("device_prefixes", [])))
if not driver_slots:
# 模板配置未声明驱动时默认走模拟驱动(本地联调),避免空跑
from drivers import SimulatorDriver
driver_slots.append(("simulator", SimulatorDriver(), []))
engine = CollectorEngine(
point_dict=point_dict,
driver_slots=driver_slots,
spool=spool,
metrics=metrics,
interval_ms=int(collector_cfg.get("interval_ms", 1000)),
max_pending=int(collector_cfg.get("max_pending", 100_000)),
)
return engine, spool, metrics
def build_sink(config: dict, spool) -> object:
"""按模板配置组装 Kafka 上行通道。"""
from upstream import KafkaSink
kafka_cfg = config.get("kafka", {})
return KafkaSink(
bootstrap_servers=kafka_cfg.get("bootstrap_servers", "127.0.0.1:9092"),
topic_prefix=kafka_cfg.get("topic_prefix", "iaop"),
spool=spool,
batch_size=int(kafka_cfg.get("batch_size", 500)),
)
def main(argv: List[str]) -> int:
args = parse_args(argv)
logging.basicConfig(
level=logging.DEBUG if args.verbose else logging.INFO,
format="%(asctime)s %(levelname)s %(name)s: %(message)s",
)
# 1) 加载模板配置
with open(args.config, "r", encoding="utf-8") as fh:
config = yaml.safe_load(fh) or {}
# 2) 加载 + 自动校验点位字典(缺失字段/量纲/重复点号)
report = validate_point_dict_file(args.point_dict)
logger.info("点位字典校验: %s", report.summary())
if not report.ok:
for issue in report.issues[:20]:
logger.error(" [%s] %s", issue.code, issue.message)
if len(report.issues) > 20:
logger.error(" ... 共 %d 条问题", len(report.issues))
print(f"点位字典校验失败:{report.summary()}")
return 2
point_dict = load_point_dict_csv(args.point_dict)
logger.info("点位字典加载完成:%d 个测点", len(point_dict))
if args.dry_run:
print("dry-run 通过:配置与点位字典校验 OK,未启动采集")
return 0
# 3) 组装并启动
engine, spool, metrics = build_engine(config, point_dict)
sink = build_sink(config, spool)
# 断点续传:启动时先重发上次未确认记录
pending = spool.pending_records(limit=10 ** 9)
if pending:
logger.info("检测到 %d 条未确认 spool 记录,启动续传", len(pending))
sink.publish(pending)
engine.start(sink=sink)
logger.info("采集网关已启动:%d 测点 @ %dms,Kafka=%s",
len(point_dict), engine.interval_ms, config.get("kafka", {}).get("bootstrap_servers"))
try:
rounds = 0
while True:
time.sleep(5)
rounds += 5
snap = metrics.snapshot()
logger.info("健康度: 轮次=%d 样本=%d 丢失率=%.4f%% P99=%.3fs 可用性=%.4f%%",
snap["total_rounds"], snap["total_samples"],
snap["loss_rate"] * 100, snap["p99_latency_sec"],
snap["availability"] * 100)
if args.rounds and rounds >= args.rounds:
break
except KeyboardInterrupt:
pass
finally:
engine.stop()
sink.close()
snap = metrics.snapshot()
print(f"SLA 达标(P99≤1.8s/丢失率≤0.02%/可用性≥99.8%): {metrics.meets_sla()}")
print(f"健康度快照: {snap}")
return 0
if __name__ == "__main__":
sys.exit(main(sys.argv[1:]))
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# -*- coding: utf-8 -*-
"""点位字典(Point Dictionary)模块:CSV schema + 加载 + 自动校验。"""
from .loader import Point, PointDict, load_point_dict_csv
from .schema import CSV_HEADERS, VALID_DATA_TYPES, VALID_UNITS
from .validator import ValidationIssue, ValidationReport, validate_point_dict, validate_point_dict_file
__all__ = [
"Point",
"PointDict",
"load_point_dict_csv",
"CSV_HEADERS",
"VALID_DATA_TYPES",
"VALID_UNITS",
"ValidationIssue",
"ValidationReport",
"validate_point_dict",
"validate_point_dict_file",
]
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# -*- coding: utf-8 -*-
"""点位字典 CSV 加载器:CSV → 内存模型(Point 记录列表)。
复用化工 AI 边缘网关的点位字典机制,改为模板化读取:
- 表头必须与 schema.CSV_HEADERS 一致(列顺序不重要,按列名匹配)。
- 行为宽松:缺失列/多余列由校验器(validator.py)统一报告,加载器不做丢弃。
"""
from __future__ import annotations
import csv
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional
from . import schema
@dataclass
class Point:
"""单条测点记录(对应点位字典 CSV 一行)。"""
device_id: str
point_id: str
name: str
unit: str
data_type: str
sample_rate: int
quality_code: bool = True
opc_node: Optional[str] = None
row_number: int = 0 # CSV 行号(从 2 开始,表头为第 1 行),用于报错定位
@property
def topic(self) -> str:
"""Kafka 上行 topic(模板化:按设备聚合)。"""
return f"{self.device_id}.points"
class PointDict:
"""点位字典内存模型。"""
def __init__(self, points: List[Point]):
self.points = points
def by_point_id(self) -> Dict[str, Point]:
return {p.point_id: p for p in self.points}
def by_device_id(self) -> Dict[str, List[Point]]:
grouped: Dict[str, List[Point]] = {}
for p in self.points:
grouped.setdefault(p.device_id, []).append(p)
return grouped
def __len__(self) -> int:
return len(self.points)
def _to_bool(raw: str) -> bool:
"""宽松解析布尔列(true/false/1/0/yes/no,大小写不敏感)。"""
return raw.strip().lower() in ("1", "true", "yes", "y", "on")
def load_point_dict_csv(path: str) -> PointDict:
"""从 CSV 文件加载点位字典。
Args:
path: CSV 文件路径(UTF-8,含表头,表头列名对齐 schema.CSV_HEADERS)。
Returns:
PointDict:点位内存模型。结构/取值问题不在此抛出,
统一由 validator.validate_point_dict() 报告(便于聚合展示全部错误)。
"""
points: List[Point] = []
with open(path, "r", encoding="utf-8-sig") as fh:
reader = csv.DictReader(fh)
fieldnames = list(reader.fieldnames or [])
for row_number, row in enumerate(reader, start=2):
sample_rate_raw = (row.get("sampleRate") or "").strip()
try:
sample_rate = int(float(sample_rate_raw)) if sample_rate_raw else 0
except ValueError:
sample_rate = 0
points.append(
Point(
device_id=(row.get("device_id") or "").strip(),
point_id=(row.get("point_id") or "").strip(),
name=(row.get("name") or "").strip(),
unit=(row.get("unit") or "").strip(),
data_type=(row.get("dataType") or "").strip(),
sample_rate=sample_rate,
quality_code=_to_bool(row["qualityCode"]) if (row.get("qualityCode") or "").strip() else True,
opc_node=((row.get("opcNode") or "").strip() or None),
row_number=row_number,
)
)
return PointDict(points)
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# -*- coding: utf-8 -*-
"""点位字典 CSV schema —— 对齐 PRD 5.1「边缘采集网关」字段规范表。
字段表(PRD 5.1):
device_id string 必填,唯一 设备编号,如 CLF-01
point_id string 必填,唯一 测点编号,如 CLF-01.TEMP
name string 必填 中文名,如 炉温
unit enum 必填 ℃ / kPa / m³/h / % / ...
dataType enum 必填 float / int / bool
sampleRate int 必填, >0 采集周期(ms)
qualityCode bool 默认 true 是否启用质量码
opcNode string 选填 OPC UA 节点路径
"""
from __future__ import annotations
from typing import List
# 合法量纲集合(可按行业模板扩展;此处覆盖化工/氯化车间常用量纲)
VALID_UNITS: List[str] = [
"℃", "kPa", "MPa", "m³/h", "m3/h", "%", "kg", "t", "t/h", "m³", "m3",
"A", "V", "Hz", "kW", "kWh", "Pa", "bar", "mm", "L", "L/min", "m/s",
]
# 合法数据类型集合
VALID_DATA_TYPES: List[str] = ["float", "int", "bool"]
# 必填字段
REQUIRED_FIELDS: List[str] = ["device_id", "point_id", "name", "unit", "dataType", "sampleRate"]
# CSV 表头(列顺序固定,便于实施工程师对照 DCS 点表填写)
CSV_HEADERS: List[str] = [
"device_id", "point_id", "name", "unit", "dataType", "sampleRate",
"qualityCode", "opcNode",
]
def is_valid_unit(unit: str) -> bool:
"""量纲合法性校验(大小写不敏感)。"""
if not unit or unit != unit.strip():
return False
return unit in VALID_UNITS
def is_valid_data_type(dtype: str) -> bool:
"""数据类型合法性校验。"""
return dtype in VALID_DATA_TYPES
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# -*- coding: utf-8 -*-
"""点位字典自动校验器。
校验维度(对齐 issue #3 与 PRD 5.1):
1. 缺失字段:必填列缺失 / 必填值为空;
2. 量纲:unit 不在合法量纲集合;
3. 重复点号:point_id 重复(同一测点被定义两次);
4. 采样率:sampleRate 必须为正整数;
5. 数据类型:dataType 必须为 float/int/bool;
6. 表头:CSV 缺少必填列。
注:同一设备下多个测点行是正常场景(如 CLF-01 的炉温/炉压),
因此 device_id 重复不做行级报错。
返回校验报告(逐条错误 + 汇总),不抛异常,便于配置台一次性展示全部问题。
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Dict, List
from . import schema
from .loader import Point, PointDict
@dataclass
class ValidationIssue:
"""单条校验问题。"""
code: str # 错误码:missing_field / bad_unit / dup_point / ...
row: int # CSV 行号(表头为 1,数据从 2 起;表头级问题 row=1)
message: str # 人类可读描述
@dataclass
class ValidationReport:
"""校验报告。"""
issues: List[ValidationIssue] = field(default_factory=list)
@property
def ok(self) -> bool:
return not self.issues
def summary(self) -> str:
if self.ok:
return "点位字典校验通过"
by_code: Dict[str, int] = {}
for it in self.issues:
by_code[it.code] = by_code.get(it.code, 0) + 1
detail = ", ".join(f"{code}×{n}" for code, n in sorted(by_code.items()))
return f"点位字典校验失败(共 {len(self.issues)} 条):{detail}"
def _add_missing_header_issues(report: ValidationReport, headers: List[str]) -> None:
missing = [f for f in schema.REQUIRED_FIELDS if f not in headers]
for f in missing:
report.issues.append(
ValidationIssue(code="missing_column", row=1, message=f"CSV 缺少必填列: {f}")
)
def validate_point_dict(point_dict: PointDict, headers: List[str]) -> ValidationReport:
"""校验点位字典,返回报告。
Args:
point_dict: 加载后的点位字典;
headers: CSV 表头(用于检查缺失列)。
"""
report = ValidationReport()
_add_missing_header_issues(report, headers)
seen_point_ids: Dict[str, int] = {}
for p in point_dict.points:
# 1) 缺失字段
for f in schema.REQUIRED_FIELDS:
value = getattr(p, {
"dataType": "data_type",
"sampleRate": "sample_rate",
}.get(f, f))
if value is None or (isinstance(value, str) and value == ""):
report.issues.append(
ValidationIssue(code="missing_field", row=p.row_number,
message=f"第{p.row_number}行 必填字段缺失: {f}")
)
# 2) 量纲
if p.unit and not schema.is_valid_unit(p.unit):
report.issues.append(
ValidationIssue(code="bad_unit", row=p.row_number,
message=f"第{p.row_number}行 非法量纲: '{p.unit}'(合法值见 schema.VALID_UNITS)")
)
# 3) 数据类型
if p.data_type and not schema.is_valid_data_type(p.data_type):
report.issues.append(
ValidationIssue(code="bad_data_type", row=p.row_number,
message=f"第{p.row_number}行 非法数据类型: '{p.data_type}'(须为 float/int/bool)")
)
# 4) 采样率
if p.sample_rate <= 0:
report.issues.append(
ValidationIssue(code="bad_sample_rate", row=p.row_number,
message=f"第{p.row_number}行 sampleRate 必须为正整数(ms),当前: {p.sample_rate}")
)
# 5) 重复点号(同一测点被定义两次)
if p.point_id:
if p.point_id in seen_point_ids:
report.issues.append(
ValidationIssue(code="dup_point", row=p.row_number,
message=f"第{p.row_number}行 重复测点 point_id: '{p.point_id}'(首次出现于第{seen_point_ids[p.point_id]}行)")
)
else:
seen_point_ids[p.point_id] = p.row_number
return report
def validate_point_dict_file(path: str) -> ValidationReport:
"""便捷入口:加载 + 校验一个 CSV 文件。"""
from .loader import load_point_dict_csv
point_dict = load_point_dict_csv(path)
headers: List[str] = []
with open(path, "r", encoding="utf-8-sig") as fh:
import csv
reader = csv.DictReader(fh)
headers = list(reader.fieldnames or [])
return validate_point_dict(point_dict, headers)
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# iAOP 边缘采集网关依赖(模板化封装最小集)
# 现场协议驱动按需安装:opcua / python-snap7 / pymodbus / confluent-kafka
PyYAML>=6.0 # 模板配置解析
confluent-kafka>=2.0 ; python_version >= "3.8" # Kafka 上行(缺失时降级 spool-only)
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# -*- coding: utf-8 -*-
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# -*- coding: utf-8 -*-
"""采集引擎 / 健康度指标 / spool 断点续传 端到端测试(模拟驱动)。"""
import os
import tempfile
import time
import unittest
from collector.engine import CollectorEngine
from collector.metrics import HealthMetrics
from collector.spool import SpoolStore
from drivers import SimulatorDriver
from point_dict.loader import Point, PointDict
def make_point_dict(n: int = 10) -> PointDict:
points = []
for i in range(n):
points.append(
Point(
device_id=f"CLF-{i // 5 + 1:02d}",
point_id=f"CLF-{i // 5 + 1:02d}.P{i:03d}",
name=f"测点{i}",
unit="℃",
data_type="float",
sample_rate=1000,
quality_code=True,
row_number=i + 2,
)
)
return PointDict(points)
class EngineMetricsTest(unittest.TestCase):
def setUp(self):
self._tmp = tempfile.mkdtemp()
def test_single_round_all_read(self):
pd = make_point_dict(10)
spool = SpoolStore(os.path.join(self._tmp, "spool"))
metrics = HealthMetrics()
engine = CollectorEngine(
point_dict=pd,
driver_slots=[("simulator", SimulatorDriver(), [])],
spool=spool,
metrics=metrics,
interval_ms=1000,
)
got = engine.collect_once()
self.assertEqual(got, 10)
self.assertEqual(metrics.total_samples, 10)
self.assertEqual(metrics.lost_samples, 0)
self.assertLessEqual(metrics.loss_rate, 0.0002) # ≤0.02%
self.assertLessEqual(metrics.p99_latency(), 1.8) # P99 ≤ 1.8s
self.assertGreaterEqual(metrics.availability, 0.998)
self.assertTrue(metrics.meets_sla())
# spool 已落盘
self.assertEqual(spool.total_pending(), 10)
def test_failed_driver_counts_round(self):
"""驱动抛异常 → 该轮记为失败轮次,可用性下降。"""
class BoomDriver(SimulatorDriver):
protocol = "boom"
def read_points(self, points):
raise RuntimeError("simulated failure")
pd = make_point_dict(5)
spool = SpoolStore(os.path.join(self._tmp, "spool"))
metrics = HealthMetrics()
engine = CollectorEngine(
point_dict=pd,
driver_slots=[("boom", BoomDriver(), [])],
spool=spool,
metrics=metrics,
)
got = engine.collect_once()
self.assertEqual(got, 0)
self.assertEqual(metrics.failed_rounds, 1)
self.assertEqual(metrics.total_rounds, 1)
self.assertEqual(metrics.availability, 0.0)
def test_spool_ack_removes_record(self):
spool = SpoolStore(os.path.join(self._tmp, "spool"))
spool.append("CLF-01", "CLF-01.TEMP", 85.5, time.time())
self.assertEqual(spool.total_pending(), 1)
spool.ack({"device_id": None, "point_id": "CLF-01.TEMP", "value": None, "ts": None})
self.assertEqual(spool.total_pending(), 0)
def test_spool_pending_replay_after_restart(self):
"""断点续传:spool 未确认记录重启后可全部重发。"""
spool = SpoolStore(os.path.join(self._tmp, "spool"))
ts = time.time()
spool.append("CLF-01", "CLF-01.TEMP", 1.0, ts)
spool.append("CLF-01", "CLF-01.PRES", 2.0, ts)
# 模拟网关重启:新实例读取同一 spool 目录
spool2 = SpoolStore(os.path.join(self._tmp, "spool"))
pending = spool2.pending_records()
self.assertEqual(len(pending), 2)
ids = {r["point_id"] for r in pending}
self.assertEqual(ids, {"CLF-01.TEMP", "CLF-01.PRES"})
def test_metrics_p99_window(self):
metrics = HealthMetrics(window_size=4)
for i in range(10):
metrics.record_round(latency_sec=0.1 + i * 0.05, expected=10, got=10)
self.assertLessEqual(metrics.p99_latency(), 0.6)
self.assertEqual(metrics.total_samples, 100)
if __name__ == "__main__":
unittest.main()
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# -*- coding: utf-8 -*-
"""点位字典加载器测试。"""
import os
import tempfile
import unittest
from point_dict.loader import load_point_dict_csv
GOOD_CSV = """device_id,point_id,name,unit,dataType,sampleRate,qualityCode,opcNode
CLF-01,CLF-01.TEMP,炉温,℃,float,1000,true,ns=2;s=CLF.Temp
CLF-01,CLF-01.PRES,炉压,kPa,float,1000,,ns=2;s=CLF.Pres
"""
class LoaderTest(unittest.TestCase):
def setUp(self):
self._tmp = tempfile.mkdtemp()
self.path = os.path.join(self._tmp, "points.csv")
def _write(self, content):
with open(self.path, "w", encoding="utf-8") as fh:
fh.write(content)
return self.path
def test_load_valid_csv(self):
path = self._write(GOOD_CSV)
pd = load_point_dict_csv(path)
self.assertEqual(len(pd), 2)
p0 = pd.points[0]
self.assertEqual(p0.device_id, "CLF-01")
self.assertEqual(p0.point_id, "CLF-01.TEMP")
self.assertEqual(p0.unit, "℃")
self.assertEqual(p0.data_type, "float")
self.assertEqual(p0.sample_rate, 1000)
self.assertTrue(p0.quality_code)
self.assertEqual(p0.opc_node, "ns=2;s=CLF.Temp")
self.assertEqual(p0.row_number, 2)
def test_quality_code_default_true(self):
"""qualityCode 列留空时默认 true。"""
path = self._write(GOOD_CSV)
pd = load_point_dict_csv(path)
self.assertTrue(pd.points[1].quality_code)
def test_utf8_bom_tolerated(self):
path = self._write("\ufeff" + GOOD_CSV)
pd = load_point_dict_csv(path)
self.assertEqual(len(pd), 2)
def test_grouping_by_device(self):
path = self._write(GOOD_CSV)
pd = load_point_dict_csv(path)
grouped = pd.by_device_id()
self.assertEqual(set(grouped.keys()), {"CLF-01"})
self.assertEqual(len(grouped["CLF-01"]), 2)
if __name__ == "__main__":
unittest.main()
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# -*- coding: utf-8 -*-
"""点位字典自动校验器测试(缺失字段/量纲/重复点号/采样率/表头)。"""
import os
import tempfile
import unittest
from point_dict.validator import validate_point_dict_file
GOOD_CSV = """device_id,point_id,name,unit,dataType,sampleRate,qualityCode,opcNode
CLF-01,CLF-01.TEMP,炉温,℃,float,1000,true,ns=2;s=CLF.Temp
CLF-01,CLF-01.PRES,炉压,kPa,float,1000,true,ns=2;s=CLF.Pres
"""
class ValidatorTest(unittest.TestCase):
def setUp(self):
self._tmp = tempfile.mkdtemp()
self.path = os.path.join(self._tmp, "points.csv")
def _write(self, content):
with open(self.path, "w", encoding="utf-8") as fh:
fh.write(content)
return self.path
def test_valid_dict_passes(self):
report = validate_point_dict_file(self._write(GOOD_CSV))
self.assertTrue(report.ok, report.summary())
self.assertEqual(report.issues, [])
def test_missing_required_field(self):
csv = """device_id,point_id,name,unit,dataType,sampleRate,qualityCode,opcNode
CLF-01,,炉温,℃,float,1000,true,ns=2
"""
report = validate_point_dict_file(self._write(csv))
self.assertFalse(report.ok)
codes = [i.code for i in report.issues]
self.assertIn("missing_field", codes)
def test_bad_unit(self):
csv = """device_id,point_id,name,unit,dataType,sampleRate,qualityCode,opcNode
CLF-01,CLF-01.TEMP,炉温,摄氏度,float,1000,true,
"""
report = validate_point_dict_file(self._write(csv))
codes = [i.code for i in report.issues]
self.assertIn("bad_unit", codes)
def test_duplicate_point_id(self):
csv = """device_id,point_id,name,unit,dataType,sampleRate,qualityCode,opcNode
CLF-01,CLF-01.TEMP,炉温,℃,float,1000,true,
CLF-02,CLF-01.TEMP,炉温2,℃,float,1000,true,
"""
report = validate_point_dict_file(self._write(csv))
codes = [i.code for i in report.issues]
self.assertIn("dup_point", codes)
def test_multiple_points_same_device_ok(self):
"""同一设备下多个测点行是正常场景,不报错。"""
csv = """device_id,point_id,name,unit,dataType,sampleRate,qualityCode,opcNode
CLF-01,CLF-01.TEMP,炉温,℃,float,1000,true,
CLF-01,CLF-01.PRES,炉压,kPa,float,1000,true,
"""
report = validate_point_dict_file(self._write(csv))
self.assertTrue(report.ok, report.summary())
def test_bad_sample_rate(self):
csv = """device_id,point_id,name,unit,dataType,sampleRate,qualityCode,opcNode
CLF-01,CLF-01.TEMP,炉温,℃,float,0,true,
"""
report = validate_point_dict_file(self._write(csv))
codes = [i.code for i in report.issues]
self.assertIn("bad_sample_rate", codes)
def test_bad_data_type(self):
csv = """device_id,point_id,name,unit,dataType,sampleRate,qualityCode,opcNode
CLF-01,CLF-01.TEMP,炉温,℃,double,1000,true,
"""
report = validate_point_dict_file(self._write(csv))
codes = [i.code for i in report.issues]
self.assertIn("bad_data_type", codes)
def test_missing_header_column(self):
csv = """device_id,point_id,name,dataType,sampleRate,qualityCode,opcNode
CLF-01,CLF-01.TEMP,炉温,float,1000,true,
"""
report = validate_point_dict_file(self._write(csv))
codes = [i.code for i in report.issues]
self.assertIn("missing_column", codes)
def test_multiple_issues_aggregated(self):
csv = """device_id,point_id,name,unit,dataType,sampleRate,qualityCode,opcNode
CLF-01,CLF-01.TEMP,炉温,摄氏度,float,0,true,
CLF-01,CLF-01.TEMP,炉温2,℃,float,1000,true,
"""
report = validate_point_dict_file(self._write(csv))
self.assertGreaterEqual(len(report.issues), 3) # bad_unit + bad_sample_rate + dup_point
self.assertFalse(report.ok)
self.assertIn("点位字典校验失败", report.summary())
if __name__ == "__main__":
unittest.main()
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# -*- coding: utf-8 -*-
"""上行(upstream)模块:Kafka 流式上行 + 断点续传。"""
from .kafka_sink import KafkaSink
__all__ = ["KafkaSink"]
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# -*- coding: utf-8 -*-
"""Kafka 流式上行 —— 模板化封装(topic 命名随模板变化)+ 断点续传确认。
设计:
- topic 模板:`{topic_prefix}.{device_id}.points`(如 `ti-cl4.CLF-01.points`);
- 每条样本先写 spool(engine 已写),发送成功后 ack 删除;
- Kafka 不可用时:生产者切换为降级模式,样本保留在 spool,
下次 publish 时从 pending 重发(断点续传,丢失率受保障);
- 依赖 `confluent-kafka`;未安装时自动降级为 NullProducer(本地联调用)。
"""
from __future__ import annotations
import logging
import threading
import time
from typing import List, Optional
from collector.spool import SpoolStore
logger = logging.getLogger("edge_gateway.kafka_sink")
class KafkaSink:
"""Kafka 上行通道。"""
def __init__(
self,
bootstrap_servers: str,
topic_prefix: str,
spool: SpoolStore,
batch_size: int = 500,
):
self.bootstrap_servers = bootstrap_servers
self.topic_prefix = topic_prefix
self.spool = spool
self.batch_size = max(1, batch_size)
self._producer = None
self._degraded = False # True = Kafka 不可用,仅保留 spool
self._lock = threading.Lock()
self._sent = 0
self._failed = 0
self._connect()
# ------------------------------------------------------------------
def _connect(self) -> None:
"""尝试连接 Kafka;失败则降级(不阻断采集)。"""
try:
from confluent_kafka import Producer # type: ignore
except ImportError:
logger.warning("confluent-kafka 未安装,Kafka 上行降级为 spool-only 模式")
self._degraded = True
return
try:
self._producer = Producer({"bootstrap.servers": self.bootstrap_servers})
except Exception as exc:
logger.warning("Kafka 初始化失败(%s),降级为 spool-only 模式", exc)
self._degraded = True
def _topic(self, device_id: str) -> str:
return f"{self.topic_prefix}.{device_id}.points"
# ------------------------------------------------------------------
def publish(self, samples: List[dict]) -> int:
"""上行一批样本;Kafka 发送成功的记录从 spool ack 删除。
Returns:
本轮成功上行条数。
"""
if self._degraded:
# 降级模式:样本已在 spool,等待 Kafka 恢复后重发
return 0
assert self._producer is not None
ok = 0
for s in samples:
topic = self._topic(s["device_id"])
record = {"device_id": s["device_id"], "point_id": s["point_id"],
"value": s["value"], "ts": s["ts"]}
try:
self._producer.produce(
topic,
key=s["point_id"].encode("utf-8"),
value=__import__("json").dumps(record, ensure_ascii=False).encode("utf-8"),
callback=self._on_delivery,
)
# 已进入 producer 缓冲即视为“已提交”,flush 时确认删除
self._sent += 1
ok += 1
except Exception as exc:
self._failed += 1
logger.warning("Kafka 发送失败(%s),样本保留在 spool", exc)
self._flush()
return ok
def _flush(self) -> None:
try:
if self._producer is not None:
self._producer.flush(timeout=5)
except Exception as exc:
logger.warning("Kafka flush 异常(%s)", exc)
def _on_delivery(self, err, msg) -> None: # pragma: no cover - 回调路径
"""投递确认回调:确认成功则 ack 删除 spool 记录(断点续传核心)。"""
if err is not None:
self._failed += 1
logger.warning("Kafka 投递失败: %s", err)
return
try:
point_id = msg.key().decode("utf-8") if msg.key() else ""
# 投递成功即按 point_id ack 最早一条未确认记录(幂等消费容忍轻微重发)
self.spool.ack({"device_id": None, "point_id": point_id, "value": None, "ts": None})
except Exception:
pass # ack 失败仅导致重发,幂等消费可容忍
# ------------------------------------------------------------------
@property
def stats(self) -> dict:
return {"sent": self._sent, "failed": self._failed, "degraded": self._degraded}
def close(self) -> None:
self._flush()
if self._producer is not None:
try:
self._producer.flush(timeout=5)
except Exception:
pass