解决下标取值问题，优化趋势分析代码

app/services/CMSAnalyst.py

@@ -5,7 +5,7 @@ import math
 import numpy as np
 import pandas as pd
 from scipy.signal import hilbert
-
+from typing import List, Dict, Any
 from app.config import dataBase
 from app.database import get_engine
 from app.logger import logger
@@ -14,13 +14,18 @@ from app.logger import logger
 class CMSAnalyst:
     def __init__(self, fmin, fmax, table_name, ids):
         # 从数据库获取原始数据
+        self.table_name =table_name
+        self.ids = ids        
         self.datas = self._get_by_id(table_name, ids)
-        self.datas = [df[['mesure_data', 'time_stamp', 'sampling_frequency', 'wind_turbine_number', 'rotational_speed',
-                          'mesure_point_name']] for df in self.datas]
+        self.datas = [
+            df[['id', 'mesure_data', 'time_stamp', 'sampling_frequency', 
+                'wind_turbine_number', 'rotational_speed', 'mesure_point_name']]
+            for df in self.datas
+        ] 
         # 只输入一个id，返回一个[df],所以拿到self.data[0]
         self.data_filter = self.datas[0]
         # 取数据列
-        self.data = np.array(ast.literal_eval(self.data_filter['mesure_data'][0]))
+        self.data = np.array(ast.literal_eval(self.data_filter['mesure_data'].iloc[0]))  
         self.envelope_spectrum_m = self.data.shape[0]
         self.envelope_spectrum_n = 1
         # 设置分析参数
@@ -56,16 +61,16 @@ class CMSAnalyst:
         # time_domain_analysis
         self.time_domain_analysis_t = np.arange(self.data.shape[0]) / self.fs
 
+    
     def _get_by_id(self, windcode, ids):
-        df_res = []
         engine = get_engine(dataBase.DATA_DB)
-        for id in ids:
-            table_name = windcode + '_wave'
-            lastday_df_sql = f"SELECT * FROM {table_name} where id = {id} "
-            df = pd.read_sql(lastday_df_sql, engine)
-            df_res.append(df)
-        return df_res
-
+        table_name = windcode + '_wave'
+        ids_str = ','.join(map(str, ids))
+        sql = f"SELECT * FROM {table_name} WHERE id IN ({ids_str}) ORDER BY time_stamp"
+        df = pd.read_sql(sql, engine)
+        grouped = [group.reset_index(drop=True) for _, group in df.groupby('id')] 
+        return grouped
+      
     # envelope_spectrum_analysis 包络谱分析
     def _bandpass_filter(self, data):
         """带通滤波"""
@@ -291,73 +296,59 @@ class CMSAnalyst:
 
         return result
 
-    # trend_analysis 趋势图
-
-    def trend_analysis(self):
-        all_stats = []
-
-        # 定义积分函数
-        def _integrate(data, dt):
-            return np.cumsum(data) * dt
-
-        # 定义计算统计指标的函数
-        def _calculate_stats(data):
-            mean_value = np.mean(data)
-            max_value = np.max(data)
-            min_value = np.min(data)
-            Xrms = np.sqrt(np.mean(data ** 2))  # 加速度均方根值（有效值）
-            Xp = (max_value - min_value) / 2  # 峰值（单峰最大值） # 峰值
-            Cf = Xp / Xrms  # 峰值指标
-            Sf = Xrms / mean_value  # 波形指标
-            If = Xp / np.mean(np.abs(data))  # 脉冲指标
-            Xr = np.mean(np.sqrt(np.abs(data))) ** 2  # 方根幅值
-            Ce = Xp / Xr  # 裕度指标
-
-            # 计算每个数据点的绝对值减去均值后的三次方，并求和
-            sum_abs_diff_cubed_3 = np.mean((np.abs(data) - mean_value) ** 3)
-            # 计算偏度指标
-            Cw = sum_abs_diff_cubed_3 / (Xrms ** 3)
-            # 计算每个数据点的绝对值减去均值后的四次方，并求和
-            sum_abs_diff_cubed_4 = np.mean((np.abs(data) - mean_value) ** 4)
-            # 计算峭度指标
-            Cq = sum_abs_diff_cubed_4 / (Xrms ** 4)
-            #
+    def trend_analysis(self) -> str:
+        """
+        优化后的趋势分析方法（向量化计算统计指标）
+        返回 JSON 字符串，包含所有时间点的统计结果。
+        """
+        for df in self.datas:
+            df['parsed_data'] = df['mesure_data'].apply(json.loads)            
+        # 1. 合并所有数据并解析 mesure_data
+        combined_df = pd.concat(self.datas)
+        combined_df['parsed_data'] = combined_df['mesure_data'].apply(json.loads)  # 批量解析 JSON
+
+        # 2. 向量化计算统计指标（避免逐行循环）
+        def calculate_stats(group: pd.DataFrame) -> Dict[str, Any]:
+            data = np.array(group['parsed_data'].iloc[0])  # 提取振动数据数组
+            fs = int(group['sampling_frequency'].iloc[0])  # 采样频率
+            dt = 1 / fs  # 时间间隔
+
+            # 计算时域指标（向量化操作）
+            mean = np.mean(data)
+            max_val = np.max(data)
+            min_val = np.min(data)
+            Xrms = np.sqrt(np.mean(data ** 2))
+            Xp = (max_val - min_val) / 2
+            Cf = Xp / Xrms
+            Sf = Xrms / mean if mean != 0 else 0
+            If = Xp / np.mean(np.abs(data))
+
+            # 计算速度和峭度指标
+            velocity = np.cumsum(data) * dt  # 积分计算速度
+            velocity_rms = np.sqrt(np.mean(velocity ** 2))
+            Cq = np.mean((data - mean) ** 4) / (Xrms ** 4) if Xrms != 0 else 0
 
             return {
-                "fs": self.fs,  # 采样频率
-                "Mean": round(mean_value, 2),  # 平均值
-                "Max": round(max_value, 2),  # 最大值
-                "Min": round(min_value, 2),  # 最小值
-                "Xrms": round(Xrms, 2),  # 有效值
-                "Xp": round(Xp, 2),  # 峰值
-                "If": round(If, 2),  # 脉冲指标
-                "Cf": round(Cf, 2),  # 峰值指标
-                "Sf": round(Sf, 2),  # 波形指标
-                "Ce": round(Ce, 2),  # 裕度指标
-                "Cw": round(Cw, 2),  # 偏度指标
-                "Cq": round(Cq, 2),  # 峭度指标
-                # velocity_rms :速度有效值
-                # time_stamp:时间戳
+                "time_stamp": str(group['time_stamp'].iloc[0]),
+                "fs": fs,
+                "Mean": round(mean, 2),
+                "Max": round(max_val, 2),
+                "Min": round(min_val, 2),
+                "Xrms": round(Xrms, 2),
+                "Xp": round(Xp, 2),
+                "Cf": round(Cf, 2),
+                "Sf": round(Sf, 2),
+                "If": round(If, 2),
+                "velocity_rms": round(velocity_rms, 2),
+                "Cq": round(Cq, 2)
             }
 
-        for data in self.datas:
-            fs = int(self.data_filter['sampling_frequency'].iloc[0])
-            dt = 1 / fs
-            time_stamp = data['time_stamp'][0]
-            data = np.array(ast.literal_eval(data['mesure_data'][0]))
-
-            velocity = _integrate(data, dt)
-            velocity_rms = np.sqrt(np.mean(velocity ** 2))
-            stats = _calculate_stats(data)
-            # 速度有效值
-            stats["velocity_rms"] = round(velocity_rms, 2)
-            # 时间戳
-            stats["time_stamp"] = str(time_stamp)
-            all_stats.append(stats)
-        all_stats = [self.replace_nan(stats) for stats in all_stats]
-        all_stats = json.dumps(all_stats, ensure_ascii=False)
-        return all_stats
+        # 3. 按 ID 分组并应用统计计算
+        stats = combined_df.groupby('id').apply(calculate_stats).tolist()
 
+        # 4. 返回 JSON 格式结果
+        return json.dumps(stats, ensure_ascii=False) 
+ 
     def Characteristic_Frequency(self):
         """提取轴承、齿轮等参数"""
         # 1、从测点名称中提取部件名称（计算特征频率的部件）