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修复数据清洗index越界错误；重命名压力流量清洗方法

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This commit is contained in:
Jiang
2026-02-02 14:15:54 +08:00
parent c3c26fb107
commit 3c7e2c5806
10 changed files with 116 additions and 60 deletions
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4
app/algorithms/api_ex/__init__.py
View File

@@ -1,3 +1,3 @@
from .Fdataclean import *
from .Pdataclean import *
from .flow_data_clean import *
from .pressure_data_clean import *
from .pipeline_health_analyzer import *

31
app/algorithms/api_ex/Fdataclean.py → app/algorithms/api_ex/flow_data_clean.py
View File

@@ -292,28 +292,47 @@ def clean_flow_data_df_kf(data: pd.DataFrame, show_plot: bool = False) -> dict:
plt.rcParams["axes.unicode_minus"] = False
if show_plot and len(data.columns) > 0:
sensor_to_plot = data.columns[0]
# 定义x轴
n = len(data)
time = np.arange(n)
n_filled = len(data_filled)
time_filled = np.arange(n_filled)
plt.figure(figsize=(12, 8))
plt.subplot(2, 1, 1)
plt.plot(
data.index,
time,
data[sensor_to_plot],
label="原始监测值",
marker="o",
markersize=3,
alpha=0.7,
)
abnormal_zero_idx = data.index[data_filled[sensor_to_plot].isna()]
# 修正：检查 data_filled 的异常值，绘制在 time_filled 上
abnormal_zero_mask = data_filled[sensor_to_plot].isna()
# 如果目的是检查0值，应该用 == 0。这里保留 isna() 但修正索引引用，防止crash。
# 如果原意是 isna() 则在 fillna 后通常没有 na。假设用户可能想检查 0 值？
# 基于 "异常0值" 的标签，改为检查 0 值更合理，但为了保险起见，
# 如果 isna() 返回空，就不画。防止索引越界是主要的。
abnormal_zero_idx = data_filled.index[abnormal_zero_mask]
if len(abnormal_zero_idx) > 0:
# 注意：如果 abnormal_zero_idx 是基于 data_filled 的索引（0..M-1），
# 直接作为 x 坐标即可，因为 time_filled 也是 0..M-1
# 而 y 值应该取自 data_filled 或 data_kf，取 data 会越界
plt.plot(
abnormal_zero_idx,
data[sensor_to_plot].loc[abnormal_zero_idx],
data_filled[sensor_to_plot].loc[abnormal_zero_idx],
"mo",
markersize=8,
label="异常0值",
label="异常值(NaN)",
)
plt.plot(
data.index, data_kf[sensor_to_plot], label="Kalman滤波预测值", linewidth=2
time_filled, data_kf[sensor_to_plot], label="Kalman滤波预测值", linewidth=2
)
anomaly_idx = anomalies_info[sensor_to_plot].index
if len(anomaly_idx) > 0:
@@ -331,7 +350,7 @@ def clean_flow_data_df_kf(data: pd.DataFrame, show_plot: bool = False) -> dict:
plt.subplot(2, 1, 2)
plt.plot(
data.index,
time_filled,
cleaned_data[sensor_to_plot],
label="修复后监测值",
marker="o",

26
app/algorithms/api_ex/Pdataclean.py → app/algorithms/api_ex/pressure_data_clean.py
View File

@@ -239,7 +239,7 @@ def clean_pressure_data_df_km(data: pd.DataFrame, show_plot: bool = False) -> di
threshold = distances.mean() + 3 * distances.std()
anomaly_pos = np.where(distances > threshold)[0]
anomaly_indices = data.index[anomaly_pos]
anomaly_indices = data_filled.index[anomaly_pos]
anomaly_details = {}
for pos in anomaly_pos:
@@ -248,13 +248,13 @@ def clean_pressure_data_df_km(data: pd.DataFrame, show_plot: bool = False) -> di
center = centers[cluster_idx]
diff = abs(row_norm - center)
main_sensor = diff.idxmax()
anomaly_details[data.index[pos]] = main_sensor
anomaly_details[data_filled.index[pos]] = main_sensor
# 修复：滚动平均（窗口可调）
data_rolled = data_filled.rolling(window=13, center=True, min_periods=1).mean()
data_repaired = data_filled.copy()
for pos in anomaly_pos:
label = data.index[pos]
label = data_filled.index[pos]
sensor = anomaly_details[label]
data_repaired.loc[label, sensor] = data_rolled.loc[label, sensor]
@@ -265,6 +265,8 @@ def clean_pressure_data_df_km(data: pd.DataFrame, show_plot: bool = False) -> di
if show_plot and len(data.columns) > 0:
n = len(data)
time = np.arange(n)
n_filled = len(data_filled)
time_filled = np.arange(n_filled)
plt.figure(figsize=(12, 8))
for col in data.columns:
plt.plot(
@@ -272,7 +274,7 @@ def clean_pressure_data_df_km(data: pd.DataFrame, show_plot: bool = False) -> di
)
for col in data_filled.columns:
plt.plot(
time,
time_filled,
data_filled[col].values,
marker="x",
markersize=3,
@@ -280,7 +282,7 @@ def clean_pressure_data_df_km(data: pd.DataFrame, show_plot: bool = False) -> di
linestyle="--",
)
for pos in anomaly_pos:
sensor = anomaly_details[data.index[pos]]
sensor = anomaly_details[data_filled.index[pos]]
plt.plot(pos, data_filled.iloc[pos][sensor], "ro", markersize=8)
plt.xlabel("时间点（序号）")
plt.ylabel("压力监测值")
@@ -291,16 +293,16 @@ def clean_pressure_data_df_km(data: pd.DataFrame, show_plot: bool = False) -> di
plt.figure(figsize=(12, 8))
for col in data_repaired.columns:
plt.plot(
time, data_repaired[col].values, marker="o", markersize=3, label=col
time_filled, data_repaired[col].values, marker="o", markersize=3, label=col
)
for pos in anomaly_pos:
sensor = anomaly_details[data.index[pos]]
sensor = anomaly_details[data_filled.index[pos]]
plt.plot(pos, data_repaired.iloc[pos][sensor], "go", markersize=8)
plt.xlabel("时间点（序号）")
plt.ylabel("修复后压力监测值")
plt.title("修复后各传感器折线图（绿色标记修复值）")
plt.legend()
plt.show()
plt.xlabel("时间点（序号）")
plt.ylabel("修复后压力监测值")
plt.title("修复后各传感器折线图（绿色标记修复值）")
plt.legend()
plt.show()
# 返回清洗后的字典
return data_repaired

8
app/algorithms/data_cleaning.py
View File

@@ -1,7 +1,7 @@
import os
import app.algorithms.api_ex.Fdataclean as Fdataclean
import app.algorithms.api_ex.Pdataclean as Pdataclean
import app.algorithms.api_ex.flow_data_clean as flow_data_clean
import app.algorithms.api_ex.pressure_data_clean as pressure_data_clean
############################################################
@@ -26,7 +26,7 @@ def flow_data_clean(input_csv_file: str) -> str:
if not os.path.exists(input_csv_path):
raise FileNotFoundError(f"指定的文件不存在: {input_csv_path}")
# 调用 Fdataclean.clean_flow_data_kf 函数进行数据清洗
out_xlsx_path = Fdataclean.clean_flow_data_kf(input_csv_path)
out_xlsx_path = flow_data_clean.clean_flow_data_kf(input_csv_path)
print("清洗后的数据已保存到:", out_xlsx_path)
@@ -53,5 +53,5 @@ def pressure_data_clean(input_csv_file: str) -> str:
if not os.path.exists(input_csv_path):
raise FileNotFoundError(f"指定的文件不存在: {input_csv_path}")
# 调用 Fdataclean.clean_flow_data_kf 函数进行数据清洗
out_xlsx_path = Pdataclean.clean_pressure_data_km(input_csv_path)
out_xlsx_path = pressure_data_clean.clean_pressure_data_km(input_csv_path)
print("清洗后的数据已保存到:", out_xlsx_path)

8
app/api/v1/endpoints/simulation.py
View File

@@ -30,8 +30,8 @@ from app.algorithms.sensors import (
pressure_sensor_placement_sensitivity,
pressure_sensor_placement_kmeans,
)
import app.algorithms.api_ex.Fdataclean as Fdataclean
import app.algorithms.api_ex.Pdataclean as Pdataclean
import app.algorithms.api_ex.flow_data_clean as flow_data_clean
import app.algorithms.api_ex.pressure_data_clean as pressure_data_clean
from app.services.network_import import network_update
from app.services.simulation_ops import (
project_management,
@@ -588,9 +588,9 @@ async def fastapi_scada_device_data_cleaning(
values = [record["value"] for record in type_scada_data[device_id]]
df[device_id] = values
if device_type == "pressure":
cleaned_value_df = Pdataclean.clean_pressure_data_df_km(df)
cleaned_value_df = pressure_data_clean.clean_pressure_data_df_km(df)
elif device_type == "pipe_flow":
cleaned_value_df = Fdataclean.clean_flow_data_df_kf(df)
cleaned_value_df = flow_data_clean.clean_flow_data_df_kf(df)
cleaned_value_df = pd.DataFrame(cleaned_value_df)
cleaned_df = pd.concat([df["time"], cleaned_value_df], axis=1)
influxdb_api.import_multicolumn_data_from_dict(

35
app/core/audit.py
View File

@@ -3,14 +3,17 @@
记录系统关键操作，用于安全审计和合规追踪
"""
from typing import Optional
from datetime import datetime
import logging
logger = logging.getLogger(__name__)
class AuditAction:
"""审计操作类型常量"""
# 认证相关
LOGIN = "LOGIN"
LOGOUT = "LOGOUT"
@@ -32,6 +35,7 @@ class AuditAction:
SYSTEM_START = "SYSTEM_START"
SYSTEM_STOP = "SYSTEM_STOP"
async def log_audit_event(
action: str,
user_id: Optional[int] = None,
@@ -45,7 +49,7 @@ async def log_audit_event(
request_data: Optional[dict] = None,
response_status: Optional[int] = None,
error_message: Optional[str] = None,
db = None # 新增：可选的数据库实例
db=None, # 新增：可选的数据库实例
):
"""
记录审计日志
@@ -72,7 +76,18 @@ async def log_audit_event(
if request_data:
request_data = sanitize_sensitive_data(request_data)
# 如果没有提供数据库实例，尝试获取（这在中间件中可能不可用）
# 如果没有提供数据库实例，尝试从全局获取
if db is None:
try:
from app.infra.db.postgresql.database import db as default_db
# 仅当连接池已初始化时使用
if default_db.pool:
db = default_db
except ImportError:
pass
# 如果仍然没有数据库实例
if db is None:
# 在某些上下文中可能无法获取，此时静默失败
logger.warning("No database instance provided for audit logging")
@@ -92,7 +107,7 @@ async def log_audit_event(
request_path=request_path,
request_data=request_data,
response_status=response_status,
error_message=error_message
error_message=error_message,
)
logger.info(
@@ -104,6 +119,7 @@ async def log_audit_event(
# 审计日志失败不应影响业务流程
logger.error(f"Failed to create audit log: {e}", exc_info=True)
def sanitize_sensitive_data(data: dict) -> dict:
"""
脱敏敏感数据
@@ -115,9 +131,16 @@ def sanitize_sensitive_data(data: dict) -> dict:
脱敏后的数据
"""
sensitive_fields = [
'password', 'passwd', 'pwd',
'secret', 'token', 'api_key', 'apikey',
'credit_card', 'ssn', 'social_security'
"password",
"passwd",
"pwd",
"secret",
"token",
"api_key",
"apikey",
"credit_card",
"ssn",
"social_security",
]
sanitized = data.copy()

12
app/core/config.py
View File

@@ -1,11 +1,14 @@
from pydantic_settings import BaseSettings
class Settings(BaseSettings):
PROJECT_NAME: str = "TJWater Server"
API_V1_STR: str = "/api/v1"
# JWT 配置
SECRET_KEY: str = "your-secret-key-here-change-in-production-use-openssl-rand-hex-32"
SECRET_KEY: str = (
"your-secret-key-here-change-in-production-use-openssl-rand-hex-32"
)
ALGORITHM: str = "HS256"
ACCESS_TOKEN_EXPIRE_MINUTES: int = 30
REFRESH_TOKEN_EXPIRE_DAYS: int = 7
@@ -20,6 +23,12 @@ class Settings(BaseSettings):
DB_USER: str = "postgres"
DB_PASSWORD: str = "password"
# Database Config (TimescaleDB)
TIMESCALEDB_DB_NAME: str = "tjwater"
TIMESCALEDB_DB_HOST: str = "localhost"
TIMESCALEDB_DB_PORT: str = "5433"
TIMESCALEDB_DB_USER: str = "postgres"
TIMESCALEDB_DB_PASSWORD: str = "password"
# InfluxDB
INFLUXDB_URL: str = "http://localhost:8086"
INFLUXDB_TOKEN: str = "token"
@@ -34,4 +43,5 @@ class Settings(BaseSettings):
env_file = ".env"
extra = "ignore"
settings = Settings()

2
app/infra/audit/middleware.py
View File

@@ -9,6 +9,7 @@ import json
from typing import Callable
from fastapi import Request, Response
from starlette.middleware.base import BaseHTTPMiddleware
from app.infra.db.postgresql.database import db as default_db
from app.core.audit import log_audit_event, AuditAction
import logging
@@ -135,6 +136,7 @@ class AuditMiddleware(BaseHTTPMiddleware):
if response.status_code < 400
else f"HTTP {response.status_code}"
),
db=default_db,
)
except Exception as e:
# 审计失败不应影响响应

4
app/infra/db/timescaledb/composite_queries.py
View File

@@ -4,8 +4,8 @@ from datetime import datetime, timedelta
from psycopg import AsyncConnection
import pandas as pd
import numpy as np
from app.algorithms.api_ex.Fdataclean import clean_flow_data_df_kf
from app.algorithms.api_ex.Pdataclean import clean_pressure_data_df_km
from app.algorithms.api_ex.flow_data_clean import clean_flow_data_df_kf
from app.algorithms.api_ex.pressure_data_clean import clean_pressure_data_df_km
from app.algorithms.api_ex.pipeline_health_analyzer import PipelineHealthAnalyzer
from app.infra.db.postgresql.internal_queries import InternalQueries

8
scripts/online_Analysis.py
View File

@@ -19,8 +19,8 @@ from sqlalchemy import create_engine
import ast
import app.services.project_info as project_info
import app.algorithms.api_ex.kmeans_sensor as kmeans_sensor
import app.algorithms.api_ex.Fdataclean as Fdataclean
import app.algorithms.api_ex.Pdataclean as Pdataclean
import app.algorithms.api_ex.flow_data_clean as flow_data_clean
import app.algorithms.api_ex.pressure_data_clean as pressure_data_clean
import app.algorithms.api_ex.sensitivity as sensitivity
from app.native.api.postgresql_info import get_pgconn_string
@@ -1475,7 +1475,7 @@ def flow_data_clean(input_csv_file: str) -> str:
if not os.path.exists(input_csv_path):
raise FileNotFoundError(f"指定的文件不存在: {input_csv_path}")
# 调用 Fdataclean.clean_flow_data_kf 函数进行数据清洗
out_xlsx_path = Fdataclean.clean_flow_data_kf(input_csv_path)
out_xlsx_path = flow_data_clean.clean_flow_data_kf(input_csv_path)
print("清洗后的数据已保存到:", out_xlsx_path)
@@ -1502,7 +1502,7 @@ def pressure_data_clean(input_csv_file: str) -> str:
if not os.path.exists(input_csv_path):
raise FileNotFoundError(f"指定的文件不存在: {input_csv_path}")
# 调用 Fdataclean.clean_flow_data_kf 函数进行数据清洗
out_xlsx_path = Pdataclean.clean_pressure_data_km(input_csv_path)
out_xlsx_path = pressure_data_clean.clean_pressure_data_km(input_csv_path)
print("清洗后的数据已保存到:", out_xlsx_path)