DualFlow/models/detection.py

import os
import re
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties

# -------------------------- 彻底解决字体警告问题 --------------------------
def setup_chinese_font():
    try:
        # 尝试加载Windows常见中文字体
        font = FontProperties(fname="C:/Windows/Fonts/simhei.ttf")  # 黑体
        plt.rcParams["font.family"] = font.get_name()
    except:
        try:
            # 尝试加载macOS常见中文字体
            font = FontProperties(fname="/System/Library/Fonts/PingFang.ttc")  # 苹方
            plt.rcParams["font.family"] = font.get_name()
        except:
            try:
                # 尝试加载Linux常见中文字体
                font = FontProperties(fname="/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf")
                plt.rcParams["font.family"] = font.get_name()
            except:
                # 无中文字体时使用默认英文无衬线字体（无警告）
                plt.rcParams["font.family"] = ["DejaVu Sans", "sans-serif"]
    plt.rcParams["axes.unicode_minus"] = False  # 解决负号显示问题

# 初始化字体配置（无警告）
setup_chinese_font()

# -------------------------- 核心配置：需画图的传感器白名单（新增4个水质传感器） --------------------------
PLOT_WHITELIST = [
    # 1. 跨膜压差（4个）
    "C.M.UF1_DB@press_PV",
    "C.M.UF2_DB@press_PV",
    "C.M.UF3_DB@press_PV",
    "C.M.UF4_DB@press_PV",
    # 2. 一段压差（4个）
    "C.M.RO1_DB@DPT_1",
    "C.M.RO2_DB@DPT_1",
    "C.M.RO3_DB@DPT_1",
    "C.M.RO4_DB@DPT_1",
    # 3. 二段压差（4个）
    "C.M.RO1_DB@DPT_2",
    "C.M.RO2_DB@DPT_2",
    "C.M.RO3_DB@DPT_2",
    "C.M.RO4_DB@DPT_2",
    # 4. 总进水电导（1个）
    "C.M.RO_Cond_ZJS@out",
    # 5. 产水电导（4个）
    "C.M.RO1_Cond_CS@out",
    "C.M.RO2_Cond_CS@out",
    "C.M.RO3_Cond_CS@out",
    "C.M.RO4_Cond_CS@out",
    # 6. 新增需画图的水质传感器（4个）
    "C.M.RO_PH_ZJS@out",    # 进水pH：不在6-9为异常
    "C.M.UF_Tur_ZJS@out",   # 进水浊度：大于10为异常
    "C.M.UF_Tur_ZCS@out",   # 产水浊度：大于0.15为异常
    "C.M.PH_WGS@out"        # 外供水pH：不在6-10为异常（已修改阈值）
]

# -------------------------- 传感器异常检测类 --------------------------
class DirectSensorAnomalyDetector:
    """直接传感器异常检测器类：包含压差、电导、水质传感器画图，标准按要求配置"""
    def __init__(self, data_dir="datasets_xishan", result_dir="direct_detection_results"):
        self.data_dir = data_dir  # 数据文件存放目录
        self.result_dir = result_dir  # 结果保存目录
        self.fig_dir = os.path.join(result_dir, "figures")  # 图表保存目录
        os.makedirs(self.result_dir, exist_ok=True)
        os.makedirs(self.fig_dir, exist_ok=True)  # 自动创建图表目录
        
        # 核心配置：仅保留有明确阈值的传感器（无阈值的已删除）
        self.sensor_config = self._build_sensor_config()
        
        # 存储结果
        self.results = {
            "raw_data": {},          # 原始数据（仅用于统计）
            "valid_data": {},        # 筛选后的有效数据（核心分析数据）
            "anomalies": {},         # 异常标记（True=异常）
            "threshold_summary": []  # 阈值总结列表
        }

    def _build_sensor_config(self):
        """构建传感器配置：包含所有明确阈值，重点确保新增水质传感器标准正确"""
        config = {}
        
        # 1. 超滤(UF)相关传感器 - 跨膜压差标准：超过0.06Mpa为异常
        uf_vars = [
            "C.M.UF1_DB@press_PV",  # 跨膜压差：超过0.06Mpa为异常
            "C.M.UF2_DB@press_PV",
            "C.M.UF3_DB@press_PV",
            "C.M.UF4_DB@press_PV",
            "C.M.UF1_FT_JS@out",    # 进水流量：低于基准80%或110%-120%
            "C.M.UF2_FT_JS@out",
            "C.M.UF3_FT_JS@out",
            "C.M.UF4_FT_JS@out",
            "UF1_FluxF",            # 膜通量：49.5-60.5lmh
            "UF2_FluxF",
            "UF3_FluxF",
            "UF4_FluxF",
            "UF1Per",               # 渗透率：247.5-302.5lmh/bar
            "UF2Per",
            "UF3Per",
            "UF4Per",
            "C.M.UF1_DW@press_PV",  # 反洗压差：偏离正常±10%-20%
            "C.M.UF2_DW@press_PV",
            "C.M.UF3_DW@press_PV",
            "C.M.UF4_DW@press_PV",
            "C.M.UF1_PT_CS@out",    # 产水压力：偏离正常±10%-20%
            "C.M.UF2_PT_CS@out",
            "C.M.UF3_PT_CS@out",
            "C.M.UF4_PT_CS@out",
            "C.M.UF_FT_ZCS@out",    # 总产水流量：偏离正常±10%-20%
            "C.M.UF_PT_ZCS@out",    # 总产水压力：偏离正常±10%-20%
            "C.M.UF_PT_ZJS@out",    # 总进水压力：偏离正常±10%-20%
            "C.M.UF_FT_FX@out"      # 反洗流量：偏离正常±10%-20%
        ]
        for var in uf_vars:
            if "DB@press_PV" in var and "DW" not in var:  # 跨膜压差（非反洗）
                desc = "跨膜压差：超过0.06Mpa为异常"
            elif "FluxF" in var:
                desc = "膜运行通量：49.5-60.5lmh"
            elif "FT_JS" in var:
                desc = "进水流量：低于基准80%或110%-120%"
            elif "Per" in var:
                desc = "渗透率：247.5-302.5lmh/bar"
            else:
                desc = "正常范围±10%-20%"
            config[var] = {"source": "UFExcel", "desc": desc}
        
        # 2. 反渗透(RO)相关传感器 - 段间压差、电导标准按要求配置
        ro_vars = [
            "C.M.RO1_DB@DPT_1",     # 一段压差：超过0.3Mpa为异常（已修改阈值）
            "C.M.RO2_DB@DPT_1",
            "C.M.RO3_DB@DPT_1",
            "C.M.RO4_DB@DPT_1",
            "C.M.RO1_DB@DPT_2",     # 二段压差：超过0.3Mpa为异常（已修改阈值）
            "C.M.RO2_DB@DPT_2",
            "C.M.RO3_DB@DPT_2",
            "C.M.RO4_DB@DPT_2",
            "C.M.RO1_PT_JS@out",    # 一段进水压力：偏离正常±10%-20%
            "C.M.RO2_PT_JS@out",
            "C.M.RO3_PT_JS@out",
            "C.M.RO4_PT_JS@out",
            "C.M.RO1_FT_NS@out",    # 浓水流量：偏离正常±10%-20%
            "C.M.RO2_FT_NS@out",
            "C.M.RO3_FT_NS@out",
            "C.M.RO4_FT_NS@out",
            "C.M.RO_Cond_ZJS@out",  # 总进水电导：超过4000μs/cm为异常
            "C.M.RO1_Cond_CS@out",  # 产水电导：超过250μs/cm为异常
            "C.M.RO2_Cond_CS@out",
            "C.M.RO3_Cond_CS@out",
            "C.M.RO4_Cond_CS@out",
            "RO1_FluxF",            # 膜通量：19.8-24.2lmh
            "RO2_FluxF",
            "RO3_FluxF",
            "RO4_FluxF",
            "RO1HSL",               # 回收率：70%-80%
            "RO2HSL",
            "RO3HSL",
            "RO4HSL",
            "RO1_TYL",              # 脱盐率：≥97%
            "RO2_TYL",
            "RO3_TYL",
            "RO4_TYL",
            "C.M.RO_PT_ZCS@out",    # 总产水压力：偏离正常±10%-20%
            "C.M.RO1_PT_CS@out",    # 产水压力：偏离正常±10%-20%
            "C.M.RO2_PT_CS@out",
            "C.M.RO3_PT_CS@out",
            "C.M.RO4_PT_CS@out"
        ]
        for var in ro_vars:
            if "DB@DPT_1" in var or "DB@DPT_2" in var:  # 一段/二段压差（已修改阈值）
                desc = "段间压差：超过0.3Mpa为异常"  # 原阈值0.12Mpa修改为0.3Mpa
            elif "Cond_ZJS" in var:  # 总进水电导
                desc = "总进水电导：超过4000μs/cm为异常"
            elif "Cond_CS" in var:  # 产水电导
                desc = "产水电导：超过250μs/cm为异常"
            elif "HSL" in var:
                desc = "回收率：70%-80%"
            elif "TYL" in var:
                desc = "脱盐率：≥97%"
            elif "FluxF" in var:
                desc = "膜运行通量：19.8-24.2lmh"
            else:
                desc = "正常范围±10%-20%"
            config[var] = {"source": "ROExcel", "desc": desc}
        
        # 3. 水质类传感器 - 重点确保新增4个传感器标准正确
        water_quality_vars = {
            "C.M.RO_TT_ZJS@out": "水温：5-35℃",
            "C.M.RO_PH_ZJS@out": "进水PH：不在6.0-9.0为异常",  # 新增画图
            "C.M.RO_ORP_ZJS@out": "进水ORP：±300mv",
            "C.M.UF_Tur_ZJS@out": "进水浊度：大于10NTU为异常",  # 新增画图
            "C.M.UF_Tur_ZCS@out": "产水浊度：大于0.15NTU为异常",  # 新增画图
            "C.M.ZH_PH@out": "中和池PH：6.0-9.0",
            "C.M.ZH_ORP@out": "中和池ORP：±300mv",
            "C.M.PH_CIP@out": "CIP清洗液PH：酸1.5-3.0/碱10-12.5",
            "C.M.PH_WGS@out": "外供水PH：不在6.0-10.0为异常"  # 原阈值6.0-9.0修改为6.0-10.0
        }
        for var, desc in water_quality_vars.items():
            config[var] = {"source": "Excel水质规则", "desc": desc}
        
        # 4. 液位类传感器 - 标准不变
        level_vars = {
            "C.M.LT_JSC@out": "超滤原水池液位：3.0-5.0m",
            "C.M.LT_FXSC@out": "反洗水池液位：3.0-5.0m",
            "C.M.LT_QSC@out": "清水池液位：3.0-5.0m",
            "C.M.LT_ZHC@out": "中和池液位：3.0-5.0m",
            "C.M.LT_HCl@out": "盐酸药箱液位：0.2-1.1m",
            "C.M.LT_NaOH@out": "氢氧化钠药箱液位：0.2-1.1m",
            "C.M.LT_NaClO@out": "次氯酸钠药箱液位：0.2-1.1m",
            "C.M.LT_PAC@out": "絮凝剂药箱液位：0.2-1.1m",
            "C.M.LT_HYJ1@out": "还原剂药箱液位：0.2-1.1m",
            "C.M.LT_HYJ2@out": "还原剂药箱液位：0.2-1.1m",
            "C.M.LT_ZGJ@out": "阻垢剂药箱液位：0.2-1.1m",
            "C.M.LT_SJJ@out": "杀菌剂药箱液位：0.2-1.1m"
        }
        for var, desc in level_vars.items():
            config[var] = {"source": "Excel液位规则", "desc": desc}
        
        # 5. 泵/风机频率传感器 - 标准不变
        frequency_vars = [
            "C.M.UF_GSB1_fre@out",  # 运行频率：35-65Hz
            "C.M.UF_GSB2_fre@out",
            "C.M.UF_GSB3_fre@out",
            "C.M.UF_GSB4_fre@out",
            "C.M.UF_FXB1_fre@out",
            "C.M.UF_FXB2_fre@out",
            "C.M.UF_FXB3_fre@out",
            "C.M.RO_GYB1_fre@out",
            "C.M.RO_GYB2_fre@out",
            "C.M.RO_GYB3_fre@out",
            "C.M.RO_GYB4_fre@out",
            "C.M.RO_DJB1_fre@out",
            "C.M.RO_DJB2_fre@out",
            "C.M.RO_DJB3_fre@out",
            "C.M.RO_DJB4_fre@out",
            "C.M.RO_WGB1_fre@out",
            "C.M.RO_WGB2_fre@out",
            "C.M.RO_WGB3_fre@out",
            "C.M.CIP_QXB1_fre@out",
            "C.M.CIP_QXB2_fre@out",
            "C.M.JYB2_ZGJ1_fre@out",
            "C.M.JYB2_ZGJ2_fre@out",
            "C.M.JYB2_ZGJ3_fre@out",
            "C.M.JYB2_ZGJ4_fre@out"
        ]
        for var in frequency_vars:
            config[var] = {
                "source": "设备默认规则", 
                "desc": "运行频率：35-65Hz"
            }
        
        # 6. 补充异常规则变量 - 标准不变
        supplemental_vars = {
            "water_in": "超滤总进水量：≥600",
            "C.M.FT_ZJS@out": "总进水流量：≥600",
            "RO_TCHFlow": "RO总产水流量：≥550",
            "RO1_CSFlow": "RO1产水流量：≥150",
            "RO2_CSFlow": "RO2产水流量：≥150",
            "RO3_CSFlow": "RO3产水流量：≥150",
            "RO4_CSFlow": "RO4产水流量：≥150",
            "C.M.UF_ORP_ZCS@out": "超滤总产水ORP：≤100",
            "C.M.UF_PH_ZCS@out": "超滤总产水PH：6.0-9.0",
            "C.M.PT_KYJ@out": "压缩空气压力：≥0.5",
            "C.M.UF_Cl_ZCS@out": "超滤总产水余氯：≤0",
            "RO_TotalFlow": "RO总进水流量：≥700",
            "QSWGB_Flow": "清水外供泵出水流量：≥200"
        }
        for var, desc in supplemental_vars.items():
            config[var] = {"source": "补充规则", "desc": desc}
        
        return config

    def filter_valid_data(self, raw_data):
        """筛选有效数据：剔除NaN、负值、无穷大（无效数据）"""
        valid_data = np.where(
            (raw_data > 0) & (~np.isnan(raw_data)) & (~np.isinf(raw_data)),
            raw_data,
            np.nan  # 无效数据标记为NaN（画图时自动不显示）
        )
        return valid_data

    def _parse_threshold(self, desc):
        """解析阈值描述：支持超过阈值、范围、小于阈值等所有场景"""
        # 处理"超过X为异常"格式（跨膜压差、段间压差、电导）
        over_match = re.search(r'超过(\d+\.?\d*)', desc)
        if over_match:
            return ("over", float(over_match.group(1)))
        
        # 处理"不在X-Y为异常"格式（pH值）
        not_range_match = re.search(r'不在(\d+\.?\d*)-(\d+\.?\d*)', desc)
        if not_range_match:
            return ("not_range", float(not_range_match.group(1)), float(not_range_match.group(2)))
        
        # 处理"大于X为异常"格式（浊度）
        gt_match = re.search(r'大于(\d+\.?\d*)', desc)
        if gt_match:
            return ("gt", float(gt_match.group(1)))
        
        # 处理范围格式（A-B）
        range_match = re.search(r'(\d+\.?\d*)-(\d+\.?\d*)', desc)
        if range_match:
            return (float(range_match.group(1)), float(range_match.group(2)))
        
        # 处理"<X"格式
        lt_match = re.search(r'<(\d+\.?\d*)', desc)
        if lt_match:
            return (0.0, float(lt_match.group(1)) - 1e-9)
        
        # 处理"≥X"格式
        ge_match = re.search(r'≥(\d+\.?\d*)', desc)
        if ge_match:
            return (float(ge_match.group(1)), float('inf'))
        
        # 处理"≤X"格式
        le_match = re.search(r'≤(\d+\.?\d*)', desc)
        if le_match:
            return (float('-inf'), float(le_match.group(1)))
        
        # 处理"±X"格式
        pm_match = re.search(r'±(\d+\.?\d*)', desc)
        if pm_match:
            val = float(pm_match.group(1))
            return (-val, val)
        
        # 处理双范围（酸A-B/碱C-D）
        double_range_match = re.search(r'酸(\d+\.?\d*)-(\d+\.?\d*)/碱(\d+\.?\d*)-(\d+\.?\d*)', desc)
        if double_range_match:
            a, b, c, d = map(float, double_range_match.groups())
            return ((a, b), (c, d))
        
        # 处理百分比范围（低于基准80%或110%-120%）
        if "低于基准80%" in desc and "110%-120%" in desc:
            return ("baseline_80_110_120",)
        
        # 处理±10%-20%
        if "±10%" in desc or "±20%" in desc:
            return ("percent_range", desc)
        
        return (None, None)

    def rule_based_detect(self, var_name, valid_data):
        """基于规则的异常检测：支持新增水质传感器的异常逻辑"""
        config = self.sensor_config[var_name]
        desc = config["desc"]
        thresholds = self._parse_threshold(desc)
        
        # 跳过无效数据（NaN）
        valid_mask = ~np.isnan(valid_data)
        anomalies = np.zeros_like(valid_data, dtype=bool)
        
        if not np.any(valid_mask):
            print(f" 变量 {var_name} 无有效数据，跳过检测")
            return anomalies
        
        # 1. 超过X为异常（跨膜压差、段间压差、电导）
        if isinstance(thresholds, tuple) and thresholds[0] == "over":
            threshold_val = thresholds[1]
            anomalies[valid_mask] = valid_data[valid_mask] > threshold_val
        
        # 2. 不在X-Y为异常（pH值）
        elif isinstance(thresholds, tuple) and thresholds[0] == "not_range":
            low, high = thresholds[1], thresholds[2]
            anomalies[valid_mask] = (valid_data[valid_mask] < low) | (valid_data[valid_mask] > high)
        
        # 3. 大于X为异常（浊度）
        elif isinstance(thresholds, tuple) and thresholds[0] == "gt":
            threshold_val = thresholds[1]
            anomalies[valid_mask] = valid_data[valid_mask] > threshold_val
        
        # 4. 双范围处理（CIP清洗液PH）
        elif isinstance(thresholds, tuple) and len(thresholds) == 2 and all(isinstance(t, tuple) for t in thresholds):
            (a, b), (c, d) = thresholds
            normal = ((valid_data >= a) & (valid_data <= b)) | ((valid_data >= c) & (valid_data <= d))
            anomalies[valid_mask] = ~normal[valid_mask]
        
        # 5. 基准百分比范围（低于80%或110%-120%）
        elif thresholds == ("baseline_80_110_120",):
            baseline = np.percentile(valid_data[valid_mask], 50)
            low_threshold = baseline * 0.8
            high_low = baseline * 1.1
            high_high = baseline * 1.2
            anomalies[valid_mask] = (valid_data[valid_mask] < low_threshold) | \
                                   ((valid_data[valid_mask] >= high_low) & (valid_data[valid_mask] <= high_high))
        
        # 6. 百分比范围（±10%-20%）
        elif isinstance(thresholds, tuple) and thresholds[0] == "percent_range":
            percent = 0.1 if "±10%" in config["desc"] else 0.2
            mean_val = np.mean(valid_data[valid_mask])
            low = mean_val * (1 - percent)
            high = mean_val * (1 + percent)
            anomalies[valid_mask] = (valid_data[valid_mask] < low) | (valid_data[valid_mask] > high)
        
        # 7. 普通范围处理
        elif thresholds[0] is not None and thresholds[1] is not None:
            lower, upper = thresholds
            anomalies[valid_mask] = (valid_data[valid_mask] < lower) | (valid_data[valid_mask] > upper)
        
        return anomalies

    def load_raw_data(self):
        """加载所有CSV数据，仅保留有明确阈值的传感器"""
        file_paths = [os.path.join(self.data_dir, f"data_process_{i}.csv") for i in range(1, 105)]
        existing_files = [p for p in file_paths if os.path.exists(p)]
        
        if not existing_files:
            raise FileNotFoundError(f" 未在 {self.data_dir} 目录找到数据文件（需命名为data_process_1~104.csv）")
        
        print(f"找到 {len(existing_files)} 个数据文件，加载中...")
        for file in existing_files:
            try:
                df = pd.read_csv(file).iloc[:, 1:]  # 剔除第一列时间列
                for var_name in self.sensor_config.keys():
                    if var_name not in df.columns:
                        continue
                    
                    # 合并原始数据
                    raw_data = df[var_name].values
                    if var_name not in self.results["raw_data"]:
                        self.results["raw_data"][var_name] = []
                    self.results["raw_data"][var_name].append(raw_data)
            except Exception as e:
                print(f" 加载文件 {os.path.basename(file)} 出错：{str(e)}，跳过")
        
        # 转换为numpy数组
        for var_name in self.results["raw_data"]:
            self.results["raw_data"][var_name] = np.concatenate(self.results["raw_data"][var_name])

    def plot_anomalies(self, var_name):
        """画图：有效数据+异常数据+阈值线（适配所有新增传感器类型）"""
        valid_data = self.results["valid_data"][var_name]
        anomalies = self.results["anomalies"][var_name]
        config = self.sensor_config[var_name]
        
        # 生成时间轴（按数据点顺序，单位：小时）
        time = np.arange(len(valid_data)) / 60  # 假设1分钟1个数据点，转换为小时
        
        # 筛选有效数据和异常数据的索引
        valid_mask = ~np.isnan(valid_data)
        anomaly_mask = anomalies & valid_mask
        
        # 创建图表
        plt.figure(figsize=(12, 6))
        
        # 1. 绘制有效数据（蓝色实线）
        plt.plot(time[valid_mask], valid_data[valid_mask], 'b-', alpha=0.8, label='有效数据')
        
        # 2. 绘制异常数据（红色圆点，突出显示）- 不显示异常点个数
        if np.any(anomaly_mask):
            plt.scatter(time[anomaly_mask], valid_data[anomaly_mask], 
                       color='red', s=20, zorder=5, label='异常点')  # 修改此处，去掉个数统计
        
        # 3. 绘制阈值线（根据阈值类型适配）
        thresholds = self._parse_threshold(config["desc"])
        if isinstance(thresholds, tuple):
            # 超过X为异常（红色虚线）
            if thresholds[0] == "over":
                threshold_val = thresholds[1]
                plt.axhline(y=threshold_val, color='red', linestyle='--', alpha=0.8, 
                           label=f'异常阈值：{threshold_val}（超过为异常）')
            
            # 不在X-Y为异常（绿色虚线：上下限）
            elif thresholds[0] == "not_range":
                low, high = thresholds[1], thresholds[2]
                plt.axhline(y=low, color='green', linestyle='--', alpha=0.8, label=f'正常下限：{low}')
                plt.axhline(y=high, color='green', linestyle='--', alpha=0.8, label=f'正常上限：{high}')
                plt.fill_between(time, low, high, alpha=0.1, color='green', label='正常范围')
            
            # 大于X为异常（红色虚线）
            elif thresholds[0] == "gt":
                threshold_val = thresholds[1]
                plt.axhline(y=threshold_val, color='red', linestyle='--', alpha=0.8, 
                           label=f'异常阈值：{threshold_val}（大于为异常）')
            
            # 双范围（CIP清洗液PH）
            elif len(thresholds) == 2 and all(isinstance(t, tuple) for t in thresholds):
                (a, b), (c, d) = thresholds
                plt.axhline(y=a, color='g', linestyle='--', alpha=0.6, label=f'酸下限：{a}')
                plt.axhline(y=b, color='g', linestyle='--', alpha=0.6, label=f'酸上限：{b}')
                plt.axhline(y=c, color='g', linestyle='--', alpha=0.6, label=f'碱下限：{c}')
                plt.axhline(y=d, color='g', linestyle='--', alpha=0.6, label=f'碱上限：{d}')
            
            # 普通范围
            elif thresholds[0] is not None and thresholds[1] is not None:
                lower, upper = thresholds
                if lower != float('-inf'):
                    plt.axhline(y=lower, color='g', linestyle='--', alpha=0.6, label=f'正常下限：{lower}')
                if upper != float('inf'):
                    plt.axhline(y=upper, color='g', linestyle='--', alpha=0.6, label=f'正常上限：{upper}')
            
            # 百分比范围
            elif thresholds[0] == "percent_range":
                percent = 0.1 if "±10%" in config["desc"] else 0.2
                mean_val = np.mean(valid_data[valid_mask])
                low = mean_val * (1 - percent)
                high = mean_val * (1 + percent)
                plt.axhline(y=low, color='g', linestyle='--', alpha=0.6, label=f'正常下限：{low:.2f}')
                plt.axhline(y=high, color='g', linestyle='--', alpha=0.6, label=f'正常上限：{high:.2f}')
        
        # 图表美化
        plt.title(f'{var_name}\n异常标准：{config["desc"]}', fontsize=12, pad=20)
        plt.xlabel('时间（小时）', fontsize=10)
        plt.ylabel('数值', fontsize=10)
        plt.legend(loc='upper right', fontsize=9)
        plt.grid(alpha=0.3)
        plt.tight_layout()
        
        # 保存图表
        fig_path = os.path.join(self.fig_dir, f'{var_name}_异常检测图.png')
        plt.savefig(fig_path, dpi=300, bbox_inches='tight', facecolor='white')
        plt.close()
        print(f" 图表保存：{fig_path}")

    def run_detection(self):
        """完整检测流程：加载数据→筛选有效数据→异常检测→指定传感器画图→生成报告"""
        # 1. 加载原始数据
        self.load_raw_data()
        
        # 2. 逐传感器处理
        processed_count = 0
        abnormal_count = 0
        plotted_count = 0  # 统计画图的传感器数量
        for var_name, config in self.sensor_config.items():
            if var_name not in self.results["raw_data"]:
                print(f"变量 {var_name} 无数据，跳过")
                continue
            
            print(f"\n 处理传感器：{var_name}")
            raw_data = self.results["raw_data"][var_name]
            
            # 3. 筛选有效数据
            valid_data = self.filter_valid_data(raw_data)
            self.results["valid_data"][var_name] = valid_data
            valid_count = np.sum(~np.isnan(valid_data))
            print(f"   有效数据量：{valid_count}/{len(raw_data)}")
            
            if valid_count == 0:
                continue
            
            # 4. 异常检测
            anomalies = self.rule_based_detect(var_name, valid_data)
            self.results["anomalies"][var_name] = anomalies
            processed_count += 1
            
            # 统计异常
            anomaly_count = np.sum(anomalies)
            anomaly_ratio = (anomaly_count / valid_count) * 100 if valid_count > 0 else 0.0
            if anomaly_count > 0:
                abnormal_count += 1
                print(f"    异常数据：{anomaly_count} 条（占比 {anomaly_ratio:.2f}%）")
            else:
                print(f"   无异常数据")
            
            # 5. 仅对白名单中的传感器画图（包含新增的4个水质传感器）
            if var_name in PLOT_WHITELIST:
                self.plot_anomalies(var_name)
                plotted_count += 1
            
            # 记录总结
            self.results["threshold_summary"].append({
                "传感器名称": var_name.split("@")[0] if "@" in var_name else var_name,
                "变量标识": var_name,
                "阈值来源": config["source"],
                "异常标准": config["desc"],
                "原始数据总量": len(raw_data),
                "有效数据量": valid_count,
                "异常数据量": anomaly_count,
                "异常比例(%)": round(anomaly_ratio, 2),
                "是否画图": "是" if var_name in PLOT_WHITELIST else "否"
            })
        
        # 6. 生成总结报告
        self._generate_summary_report(processed_count, abnormal_count, plotted_count)
        return self.results

    def _generate_summary_report(self, processed_count, abnormal_count, plotted_count):
        """生成检测总结报告"""
        summary_df = pd.DataFrame(self.results["threshold_summary"])
        summary_path = os.path.join(self.result_dir, "传感器异常检测总结报告.csv")
        summary_df.to_csv(summary_path, index=False, encoding="utf-8-sig")
        
        # 打印汇总信息
        total_sensors = len(self.sensor_config)
        print(f"\n" + "="*60)
        print(f" 检测总结")
        print(f"="*60)
        print(f"总传感器数量（有明确阈值）：{total_sensors}")
        print(f"成功处理的传感器：{processed_count}")
        print(f"存在异常的传感器：{abnormal_count}")
        print(f"生成图表的传感器：{plotted_count}（压差类+电导类+水质类）")
        print(f"  - 跨膜压差：4个")
        print(f"  - 一段/二段压差：8个")
        print(f"  - 电导类：5个")
        print(f"  - 水质类（pH+浊度）：4个")
        print(f"结果目录：{os.path.abspath(self.result_dir)}")
        print(f"图表目录：{os.path.abspath(self.fig_dir)}")
        print(f"总结报告：{os.path.abspath(summary_path)}")
        print(f"="*60)

if __name__ == "__main__":
    try:
        # 初始化检测器并执行
        detector = DirectSensorAnomalyDetector(
            data_dir="datasets_xishan",  # 数据目录（需自行确保存在）
            result_dir="direct_detection_results"  # 结果保存目录
        )
        detector.run_detection()
        print("\n 检测完成！已生成压差、电导、水质传感器的异常检测图表")
    except Exception as e:
        print(f"\n 检测出错：{str(e)}")