CMS/cms_class_20241201.py

import numpy as np
from scipy.signal import hilbert
from scipy.fft import ifft
import plotly.graph_objs as go
import pandas as pd
from sqlalchemy import create_engine, text
import sqlalchemy
from typing import Dict,Any
import json
import ast
import math

'''
# 输入：
{
    "ids":[12345,121212],
    "windCode":"xxxx",
    "analysisType":"xxxxx",
    "fmin":int(xxxx) (None),
    "fmax":"int(xxxx) (None),
}

[{id:xxxx,"time":xxx},{}]

id[123456]

# 通过id，读取mysql，获取数据
engine = create_engine('mysql+pymysql://root:admin123456@192.168.50.235:30306/energy_data')

def get_by_id(table_name,id):
    lastday_df_sql = f"SELECT * FROM {table_name} where id = {id} "
    # print(lastday_df_sql)
    df = pd.read_sql(lastday_df_sql, engine)
    return df

select distinct id, timeStamp from table_name group by ids

ids  time 
1   xxx
2   xxxx

df_data = []    
# for id in ids:
# sql_data = get_by_id('SKF001_wave',id)
# df_data.append(sql_data)
# print(sql_data)

[df1,df2]
'''


'''
数据库字段：
"samplingFrequency"
"timeStamp"
"mesureData"
'''
# %%

# %%

# 主要的类
class CMSAnalyst:
    def __init__(self, fmin, fmax, table_name, ids):
        self.table_name =table_name
        self.ids = ids
        # envelope_spectrum_analysis
        # datas是[df1,df2,.....]
        #从数据库获取原始数据
        self.datas = self._get_by_id(table_name,ids)
        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]
        # print("mesure_data sample:", self.data_filter['mesure_data'].iloc[0])  # 打印第一条数据
        self.data = np.array(ast.literal_eval(self.data_filter['mesure_data'].iloc[0]))        
        # print(self.data_filter)
        # 取数据列
        self.data = np.array(ast.literal_eval(self.data_filter['mesure_data'][0]))
        self.envelope_spectrum_m = self.data.shape[0]
        self.envelope_spectrum_n = 1
        #设置分析参数
        self.fs = int(self.data_filter['sampling_frequency'].iloc[0])
        self.envelope_spectrum_t = np.arange(self.envelope_spectrum_m) / self.fs
        self.fmin = fmin if fmin is not None else 0 
        self.fmax = fmax if fmax is not None else float('inf') 
        self.envelope_spectrum_y = self._bandpass_filter(self.data)
        self.f, self.HP = self._calculate_envelope_spectrum(self.envelope_spectrum_y)
        #设备信息
        self.wind_code = self.data_filter['wind_turbine_number'].iloc[0]
        self.rpm_Gen = self.data_filter['rotational_speed'].iloc[0]
        self.mesure_point_name = self.data_filter['mesure_point_name'].iloc[0]
        self.fn_Gen = round(self.rpm_Gen/60,2)

        self.CF = self.Characteristic_Frequency()
        print(self.CF)
        self.CF = pd.DataFrame(self.CF,index=[0])
        print(self.CF)
        print(self.rpm_Gen)
        #if self.CF['type'].iloc[0] == 'bearing':
        n_rolls_m = self.CF['n_rolls'].iloc[0]
        d_rolls_m = self.CF['d_rolls'].iloc[0]
        D_diameter_m = self.CF['D_diameter'].iloc[0]
        theta_deg_m = self.CF['theta_deg'].iloc[0]
        print(n_rolls_m)
        print(d_rolls_m)
        print(D_diameter_m)
        print(theta_deg_m)
        self.bearing_frequencies = self.calculate_bearing_frequencies(n_rolls_m, d_rolls_m, D_diameter_m, theta_deg_m, self.rpm_Gen)
        print(self.bearing_frequencies)
        self.bearing_frequencies = pd.DataFrame(self.bearing_frequencies,index=[0])
        print(self.bearing_frequencies)
        # frequency_domain_analysis
        (
            self.frequency_domain_analysis_t,
            self.frequency_domain_analysis_f,
            self.frequency_domain_analysis_m,
            self.frequency_domain_analysis_mag,
            self.frequency_domain_analysis_Xrms,
        ) = self._calculate_spectrum(self.data)

        # 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 = create_engine('mysql+pymysql://root:admin123456@192.168.50.235:30306/energy_data_prod')
    #     engine = create_engine('mysql+pymysql://root:admin123456@106.120.102.238:10336/energy_data_prod')
    #     for id in ids:
    #         table_name=windcode+'_wave'
    #         lastday_df_sql = f"SELECT * FROM {table_name} where id = {id} "
    #         # print(lastday_df_sql)
    #         df = pd.read_sql(lastday_df_sql, engine)
    #         df_res.append(df)
    #     return df_res
    
    # def _get_by_id(self, windcode, ids):
    #     #engine = create_engine('mysql+pymysql://root:admin123456@106.120.102.238:10336/energy_data_prod')
    #     engine = create_engine('mysql+pymysql://root:admin123456@192.168.50.235:30306/energy_data_prod')
    #     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)
        
    #     # 按ID分组返回
    #     grouped = [group for _, group in df.groupby('id')]
    #     return grouped        
    def _get_by_id(self, windcode, ids):
        engine = create_engine('mysql+pymysql://root:admin123456@192.168.50.235:30306/energy_data_prod')
        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"
        print("Executing SQL:", sql)  # 打印 SQL
        df = pd.read_sql(sql, engine)
        print("Returned DataFrame shape:", df.shape)  # 检查返回的数据量
        grouped = [group 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"""
        
        m= data.shape[0]
        ni = round(self.fmin * self.envelope_spectrum_m / self.fs + 1)
        # na = round(self.fmax * self.envelope_spectrum_m / self.fs + 1)
        if self.fmax == float('inf'):
            na = m
        else:
            na = round(self.fmax * m / self.fs + 1)
        col = 1
        y = np.zeros((self.envelope_spectrum_m, col))
        # for p in range(col):
        #     print(data.shape,p)
        z = np.fft.fft(data)
        a = np.zeros(self.envelope_spectrum_m, dtype=complex)
        a[ni:na] = z[ni:na]
        a[self.envelope_spectrum_m - na + 1 : self.envelope_spectrum_m - ni + 1] = z[
            self.envelope_spectrum_m - na + 1 : self.envelope_spectrum_m - ni + 1
        ]
        z = np.fft.ifft(a)
        y[:, 0] = np.real(z)

        return y

    def _calculate_envelope_spectrum(self, y):
        """计算包络谱"""
        m, n = y.shape
        HP = np.zeros((m, n))
        col = 1
        for p in range(col):
            H = np.abs(hilbert(y[:, p] - np.mean(y[:, p])))
            HP[:, p] = np.abs(np.fft.fft(H - np.mean(H))) * 2 / m
        f = np.fft.fftfreq(m, d=1 / self.fs)
        return f, HP


    def envelope_spectrum(self):
        """绘制包络谱"""
        # 只取正频率部分
        positive_frequencies = self.f[: self.envelope_spectrum_m // 2]
        positive_HP = self.HP[: self.envelope_spectrum_m // 2, 0]

        x = positive_frequencies
        y = positive_HP
        title = "包络谱"
        xaxis = "频率(Hz)"
        yaxis = "加速度(m/s^2)"
        Xrms = np.sqrt(np.mean(y**2))  # 加速度均方根值（有效值）
        rpm_Gen = round(self.rpm_Gen, 2)
        BPFI_1X = round(self.bearing_frequencies['BPFI'].iloc[0], 2)
        BPFO_1X = round(self.bearing_frequencies['BPFO'].iloc[0], 2)
        BSF_1X = round(self.bearing_frequencies['BSF'].iloc[0], 2)
        FTF_1X = round(self.bearing_frequencies['FTF'].iloc[0], 2)
        fn_Gen = round(self.fn_Gen, 2)
        _3P_1X = round(self.fn_Gen, 2) * 3

        # if self.CF['type'].iloc[0] == 'bearing':
        result = {
            "fs":self.fs,
            "Xrms":round(Xrms, 2),
            "x":list(x),
            "y":list(y),
            "title":title,
            "xaxis":xaxis,
            "yaxis":yaxis,
            "rpm_Gen": round(rpm_Gen, 2),  # 转速r/min
            "BPFI":  [{"Xaxis": BPFI_1X ,"val": "1BPFI"},{"Xaxis": BPFI_1X*2 ,"val": "2BPFI"},
                        {"Xaxis": BPFI_1X*3, "val": "3BPFI"}, {"Xaxis": BPFI_1X*4, "val": "4BPFI"},
                        {"Xaxis": BPFI_1X*5, "val": "5BPFI"}, {"Xaxis": BPFI_1X*6, "val": "6BPFI"}],
            "BPFO": [{"Xaxis": BPFO_1X ,"val": "1BPFO"},{"Xaxis": BPFO_1X*2 ,"val": "2BPFO"},
                        {"Xaxis": BPFO_1X*3, "val": "3BPFO"}, {"Xaxis": BPFO_1X*4, "val": "4BPFO"},
                        {"Xaxis": BPFO_1X*5, "val": "5BPFO"}, {"Xaxis": BPFO_1X*6, "val": "6BPFO"}],
            "BSF": [{"Xaxis": BSF_1X ,"val": "1BSF"},{"Xaxis": BSF_1X*2 ,"val": "2BSF"},
                        {"Xaxis": BSF_1X*3, "val": "3BSF"}, {"Xaxis": BSF_1X*4, "val": "4BSF"},
                        {"Xaxis": BSF_1X*5, "val": "5BSF"}, {"Xaxis": BSF_1X*6, "val": "6BSF"}],
            "FTF": [{"Xaxis": FTF_1X ,"val": "1FTF"},{"Xaxis": FTF_1X*2 ,"val": "2FTF"},
                        {"Xaxis": FTF_1X*3, "val": "3FTF"}, {"Xaxis": FTF_1X*4, "val": "4FTF"},
                        {"Xaxis": FTF_1X*5, "val": "5FTF"}, {"Xaxis": FTF_1X*6, "val": "6FTF"}],
            "fn_Gen":[{"Xaxis": fn_Gen ,"val": "1X"},{"Xaxis": fn_Gen*2 ,"val": "2X"},
                        {"Xaxis": fn_Gen*3, "val": "3X"}, {"Xaxis": fn_Gen*4, "val": "4X"},
                        {"Xaxis": fn_Gen*5, "val": "5X"}, {"Xaxis": fn_Gen*6, "val": "6X"}],
            "B3P":_3P_1X,
        }
        # result = json.dumps(result, ensure_ascii=False)
        result = self.replace_nan(result)

        return result


    # frequency_domain_analysis 频谱分析

    def _calculate_spectrum(self, data):
        """计算频谱"""
        m = data.shape[0]
        n = 1
        t = np.arange(m) / self.fs
        mag = np.zeros((m, n))
        Xrms = np.sqrt(np.mean(data**2))  # 加速度均方根值（有效值）
        # col=1
        # for p in range(col):
        mag = np.abs(np.fft.fft(data - np.mean(data))) * 2 / m
        f = np.fft.fftfreq(m, d=1 / self.fs)
        return t, f, m, mag, Xrms

    def frequency_domain(self):
        """绘制频域波形参数"""
        # 只取正频率部分
        positive_frequencies = self.frequency_domain_analysis_f[
            : self.frequency_domain_analysis_m // 2
        ]
        positive_mag = self.frequency_domain_analysis_mag[
            : self.frequency_domain_analysis_m // 2
        ]

        x = positive_frequencies
        y = positive_mag
        title = "频域信号"
        xaxis = "频率(Hz)"
        yaxis = "加速度(m/s^2)"
        Xrms = self.frequency_domain_analysis_Xrms

        rpm_Gen = round(self.rpm_Gen, 2)
        BPFI_1X = round(self.bearing_frequencies['BPFI'].iloc[0], 2)
        BPFO_1X =  round(self.bearing_frequencies['BPFO'].iloc[0], 2)
        BSF_1X =  round(self.bearing_frequencies['BSF'].iloc[0], 2)
        FTF_1X = round(self.bearing_frequencies['FTF'].iloc[0], 2)
        fn_Gen = round(self.fn_Gen, 2)
        _3P_1X = round(self.fn_Gen, 2) * 3

        # if self.CF['type'].iloc[0] == 'bearing':
        result = {
            "fs":self.fs,
            "Xrms":round(Xrms, 2),
            "x":list(x),
            "y":list(y),
            "title":title,
            "xaxis":xaxis,
            "yaxis":yaxis,
            "rpm_Gen": round(rpm_Gen, 2),  # 转速r/min
            "BPFI":  [{"Xaxis": BPFI_1X ,"val": "1BPFI"},{"Xaxis": BPFI_1X*2 ,"val": "2BPFI"},
                        {"Xaxis": BPFI_1X*3, "val": "3BPFI"}, {"Xaxis": BPFI_1X*4, "val": "4BPFI"},
                        {"Xaxis": BPFI_1X*5, "val": "5BPFI"}, {"Xaxis": BPFI_1X*6, "val": "6BPFI"}],
            "BPFO": [{"Xaxis": BPFO_1X ,"val": "1BPFO"},{"Xaxis": BPFO_1X*2 ,"val": "2BPFO"},
                        {"Xaxis": BPFO_1X*3, "val": "3BPFO"}, {"Xaxis": BPFO_1X*4, "val": "4BPFO"},
                        {"Xaxis": BPFO_1X*5, "val": "5BPFO"}, {"Xaxis": BPFO_1X*6, "val": "6BPFO"}],
            "BSF": [{"Xaxis": BSF_1X ,"val": "1BSF"},{"Xaxis": BSF_1X*2 ,"val": "2BSF"},
                        {"Xaxis": BSF_1X*3, "val": "3BSF"}, {"Xaxis": BSF_1X*4, "val": "4BSF"},
                        {"Xaxis": BSF_1X*5, "val": "5BSF"}, {"Xaxis": BSF_1X*6, "val": "6BSF"}],
            "FTF": [{"Xaxis": FTF_1X ,"val": "1FTF"},{"Xaxis": FTF_1X*2 ,"val": "2FTF"},
                        {"Xaxis": FTF_1X*3, "val": "3FTF"}, {"Xaxis": FTF_1X*4, "val": "4FTF"},
                        {"Xaxis": FTF_1X*5, "val": "5FTF"}, {"Xaxis": FTF_1X*6, "val": "6FTF"}],
            "fn_Gen":[{"Xaxis": fn_Gen ,"val": "1X"},{"Xaxis": fn_Gen*2 ,"val": "2X"},
                        {"Xaxis": fn_Gen*3, "val": "3X"}, {"Xaxis": fn_Gen*4, "val": "4X"},
                        {"Xaxis": fn_Gen*5, "val": "5X"}, {"Xaxis": fn_Gen*6, "val": "6X"}],
            "B3P":_3P_1X,
        }
        result = self.replace_nan(result)       
        result = json.dumps(result, ensure_ascii=False)
        return result

    # time_domain_analysis 时域分析
    def time_domain(self):
        """绘制时域波形参数"""        
        x = self.time_domain_analysis_t
        y = self.data
        rpm_Gen =self.rpm_Gen
        title = "时间域信号"
        xaxis = "时间(s)"
        yaxis = "加速度(m/s^2)"
        # 图片右侧统计量
        mean_value = np.mean(y)#平均值
        max_value = np.max(y)#最大值
        min_value = np.min(y)#最小值
        Xrms = np.sqrt(np.mean(y**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
        Xpp=max_value-min_value#峰峰值
        Cf = Xp / Xrms  # 峰值指标
        Sf = Xrms / mean_value  # 波形指标
        If = Xp / np.mean(np.abs(y))  # 脉冲指标
        Xr = np.mean(np.sqrt(np.abs(y))) ** 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(y) - 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(y) - mean_value) ** 4)
        # 计算峭度指标
        Cq = sum_abs_diff_cubed_4 / (Xrms**4)
        result = {
                 
            "x":list(x),
            "y":list(y),
            "title":title,
            "xaxis":xaxis,
            "yaxis":yaxis,
            "fs":self.fs,
            "Xrms":round(Xrms, 2),#有效值
            "mean_value":round(mean_value, 2),# 均值 
            "max_value":round(max_value, 2),# 最大值 
            "min_value":round(min_value, 2),  # 最小值          
            "Xp":round(Xp, 2),# 峰值
            "Xpp":round(Xpp, 2),# 峰峰值
            "Cf":round(Cf, 2),# 峰值指标
            "Sf":round(Sf, 2),# 波形因子
            "If":round(If, 2),# 脉冲指标
            "Ce":round(Ce, 2),# 裕度指标
            "Cw":round(Cw, 2) ,# 偏度指标
            "Cq":round(Cq, 2) ,# 峭度指标
            "rpm_Gen": round(rpm_Gen, 2),  # 转速r/min

        }
        result = self.replace_nan(result)
        result = json.dumps(result, ensure_ascii=False)

        return result

    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))
            Xr = np.mean(np.sqrt(np.abs(data))) ** 2
            Ce = Xp / Xr
            
            # 计算偏度和峭度

 
            # 计算速度有效值
            velocity = np.cumsum(data) * dt
            velocity_rms = np.sqrt(np.mean(velocity**2))
            Cw = np.mean((data - mean) ** 3) / (Xrms ** 3) if Xrms != 0 else 0
            Cq = np.mean((data - mean) ** 4) / (Xrms ** 4) if Xrms != 0 else 0                     
            return {
                "fs": fs,
                "Mean": round(mean, 2),
                "Max": round(max_val, 2),
                "Min": round(min_val, 2),
                "Xrms": round(Xrms, 2),
                "Xp": round(Xp, 2),
                "If": round(If, 2),
                "Cf": round(Cf, 2),
                "Sf": round(Sf, 2),
                "Ce": round(Ce, 2),
                "Cw": round(Cw, 2),
                "Cq": round(Cq, 2),
                "velocity_rms": round(velocity_rms, 2),
                "time_stamp": str(group['time_stamp'].iloc[0])
            }
        
 
        # 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"""
        str1 = self.mesure_point_name
        print(str1)
        # 2、连接233的数据库'energy_show'，从表'wind_engine_group'查找风机编号'engine_code'对应的机型编号'mill_type_code'
        engine_code = self.wind_code
        print(engine_code)
        Engine = create_engine('mysql+pymysql://admin:admin123456@192.168.50.233:3306/energy_show')
        #Engine = create_engine('mysql+pymysql://admin:admin123456@106.120.102.238:16306/energy_show')        
        # df_sql2 = f"SELECT * FROM {'wind_engine_group'} where engine_code = {'engine_code'} "
        df_sql2 = f"SELECT * FROM wind_engine_group WHERE engine_code = '{engine_code}'"
        df2 = pd.read_sql(df_sql2, Engine)
        mill_type_code = df2['mill_type_code'].iloc[0]
        print(mill_type_code)

        # # 3、从表'unit_bearings'中通过机型编号'mill_type_code'查找部件'brand'、'model'的参数信息
        # 3、从相关的表中通过机型编号'mill_type_code'或者齿轮箱编号gearbox_code查找部件'brand'、'model'的参数信息
        #unit_bearings主轴承参数表 关键词"main_bearing"
        if 'main_bearing' in str1:
            print("main_bearing")
            df_sql3 = f"SELECT * FROM unit_bearings WHERE mill_type_code = '{mill_type_code}' "            
            df3 = pd.read_sql(df_sql3, Engine)
            if df3.empty:
                print("警告: 没有找到有效的机型信息")   
            if 'front' in str1:
                brand = 'front_bearing' + '_brand'  
                model = 'front_bearing' + '_model'  
                front_has_value = not pd.isna(df3[brand].iloc[0]) and not pd.isna(df3[model].iloc[0]) 
                if not  front_has_value:
                    print("警告: 没有找到有效的品牌信息")
            elif 'rear' in str1:
                brand = 'rear_bearing' + '_brand'  
                model = 'rear_bearing' + '_model'  
                end_has_value = not pd.isna(df3[brand].iloc[0]) and not pd.isna(df3[model].iloc[0])       
                if not  end_has_value:
                    print("警告: 没有找到有效的品牌信息")                
            else:
                # 当没有指定 front 或 end 时，自动选择有值的轴承信息
                front_brand_col = 'front_bearing_brand'
                front_model_col = 'front_bearing_model'
                rear_brand_col = 'rear_bearing_brand'
                rear_model_col = 'rear_bearing_model'
                # 检查 front_bearing 是否有值
                front_has_value = not pd.isna(df3[front_brand_col].iloc[0]) and not pd.isna(df3[front_model_col].iloc[0])                
                # 检查 end_bearing 是否有值
                end_has_value = not pd.isna(df3[rear_brand_col].iloc[0]) and not pd.isna(df3[rear_model_col].iloc[0])               
                # 根据检查结果选择合适的列
                if front_has_value and end_has_value:
                    # 如果两者都有值，默认选择 front
                    brand = front_brand_col
                    model = front_model_col
                elif front_has_value:
                    brand = front_brand_col
                    model = front_model_col
                elif end_has_value:
                    brand = rear_brand_col
                    model = rear_model_col
                else:
                    # 如果两者都没有有效值，设置默认值或抛出异常
                    print("警告: 没有找到有效的轴承信息")
                    brand = front_brand_col  # 默认使用 front
                    model = front_model_col  # 默认使用 front
            print(brand)
            _brand = df3[brand].iloc[0]
            _model = df3[model].iloc[0]
            print(_brand)
            print(_model)     
        #unit_dynamo 发电机参数表 关键词generator stator
        elif 'generator'in str1 or 'stator' in str1:
            print("generator or 'stator'")
            # df_sql3 = f"SELECT * FROM {'unit_dynamo'} where mill_type_code = {'mill_type_code'} "
            df_sql3 = f"SELECT * FROM unit_dynamo WHERE mill_type_code = '{mill_type_code}' "
            df3 = pd.read_sql(df_sql3, Engine)
            if 'non' in str1:
                brand = 'non_drive_end_bearing' + '_brand'  
                model = 'non_drive_end_bearing' + '_model'  
            else:
                brand = 'drive_end_bearing' + '_brand'  
                model = 'drive_end_bearing' + '_model'              
            print(brand)
            _brand = df3[brand].iloc[0]
            _model = df3[model].iloc[0]
            print(_brand)
            print(_model)       

         #齿轮箱区分行星轮/平行轮 和 轴承两个表
        elif 'gearbox' in str1:
            print("gearbox")
            #根据mill_type_code从unit_gearbox表中获得gearbox_code
            df_sql3 = f"SELECT * FROM unit_gearbox WHERE mill_type_code = '{mill_type_code}' "
            df3 = pd.read_sql(df_sql3, Engine)
            gearbox_code =df3['code'].iloc[0]
            print(gearbox_code)         
            #Engine33 = create_engine('mysql+pymysql://admin:admin123456@106.120.102.238:16306/energy_show')
             #如果是行星轮/平行轮 则从unit_gearbox_structure 表中取数据
            if 'planet'in str1 or 'sun' in str1:
                print("'planet' or 'sun' ")
                gearbox_structure =1 if 'planet'in str1 else 2
                planetary_gear_grade =1
                if 'first' in str1:
                    planetary_gear_grade =1
                elif 'second'in str1:
                    planetary_gear_grade =2
                elif 'third'in str1:
                    planetary_gear_grade =3
                # df_sql33 = f"SELECT * FROM unit_gearbox_structure WHERE gearbox_code = '{gearbox_code}' "
                df_sql33 = f"""
                SELECT bearing_brand, bearing_model 
                FROM unit_gearbox_structure 
                    WHERE gearbox_code = '{gearbox_code}' 
                    AND gearbox_structure = '{gearbox_structure}'
                    AND planetary_gear_grade = '{planetary_gear_grade}'
                    """
                df33 = pd.read_sql(df_sql33, Engine)
                if df33.empty:
                    print("unit_gearbox_structure没有该测点的参数")
                else:
                    brand = 'bearing' + '_brand'  
                    model = 'bearing' + '_model'  
                    print(brand)
                    _brand = df33[brand].iloc[0]
                    _model = df33[model].iloc[0]
                    has_value = not pd.isna(df33[brand].iloc[0]) and not pd.isna(df33[model].iloc[0])
                    if has_value:   
                        print(_brand)
                        print(_model)   
                    else:
                        print("警告: 没有找到有效的轴承信息")
             #如果是齿轮箱轴承 则从unit_gearbox_bearings 表中取数据
            elif 'shaft' in str1 or'input' in str1:
                print("'shaft'or'input'")
                # df_sql33 = f"SELECT * FROM unit_gearbox_bearings WHERE gearbox_code = '{gearbox_code}' "
                # df33 = pd.read_sql(df_sql33, Engine33)
                #高速轴 低速中间轴 取bearing_rs/gs均可
                parallel_wheel_grade=1
                if 'low_speed' in str1:
                    parallel_wheel_grade =1                
                elif 'low_speed_intermediate' in str1:
                    parallel_wheel_grade =2
                elif 'high_speed' in str1:
                    parallel_wheel_grade =3    
                # df_sql33 = f"SELECT * FROM unit_gearbox_bearings WHERE gearbox_code = '{gearbox_code}' "
                df_sql33 = f"""
                SELECT bearing_rs_brand, bearing_rs_model, bearing_gs_brand, bearing_gs_model 
                FROM unit_gearbox_bearings 
                WHERE gearbox_code = '{gearbox_code}'
                AND parallel_wheel_grade = '{parallel_wheel_grade}'
                """
                df33 = pd.read_sql(df_sql33, Engine)
                if not df33.empty:
                    if 'high_speed' in str1 or 'low_speed_intermediate' in str1:
                        rs_brand = 'bearing_rs' + '_brand'  
                        rs_model = 'bearing_rs' + '_model'  
                        gs_brand = 'bearing_gs' + '_brand'  
                        gs_model = 'bearing_gs' + '_model'  
                        rs_has_value = not pd.isna(df33[rs_brand].iloc[0]) and not pd.isna(df33[rs_model].iloc[0])     
                        gs_has_value = not pd.isna(df33[gs_brand].iloc[0]) and not pd.isna(df33[gs_model].iloc[0])    
                        if rs_has_value and gs_has_value:
                            brand = rs_brand
                            model = rs_model
                        elif rs_has_value:
                            brand = rs_brand
                            model = rs_model
                        elif gs_has_value:
                            brand = gs_brand
                            model = gs_model
                        else:
                            print("警告: 没有找到有效的品牌信息")
                            brand = rs_brand 
                            model = rs_model 
                    #低速轴 取bearing_model          
                    elif 'low_speed'in str1:
                        brand = 'bearing' + '_brand'  
                        model = 'bearing' + '_model'
                else:
                    print("警告: 没有找到有效的轴承信息")     
                print(brand)
                _brand = df33[brand].iloc[0]
                _model = df33[model].iloc[0]
                print(_brand)
                print(_model)   


        # 4、从表'unit_dict_brand_model'中通过'_brand'、'_model'查找部件的参数信息

        df_sql4 = f"SELECT * FROM unit_dict_brand_model where manufacture = %s AND model_number = %s"
        params = [(_brand, _model)]
        df4 = pd.read_sql(df_sql4, Engine, params=params)
        n_rolls = df4['rolls_number'].iloc[0]
        d_rolls = df4['rolls_diameter'].iloc[0]
        D_diameter = df4['circle_diameter'].iloc[0]
        theta_deg = df4['theta_deg'].iloc[0]
        result = {
            "type":'bearing',
            "n_rolls":round(n_rolls, 2),
            "d_rolls":round(d_rolls, 2),
            "D_diameter":round(D_diameter, 2),
            "theta_deg":round(theta_deg, 2),
        }
    # result = json.dumps(result, ensure_ascii=False)
        return result
    def calculate_bearing_frequencies(self, n, d, D, theta_deg, rpm):
        """
        计算轴承各部件特征频率

        参数：
        n (int): 滚动体数量
        d (float): 滚动体直径（单位：mm）
        D (float): 轴承节圆直径（滚动体中心圆直径，单位：mm）
        theta_deg (float): 接触角（单位：度）
        rpm (float): 转速（转/分钟）

        返回：
        dict: 包含各特征频率的字典（单位：Hz）
        """
        # 转换角度为弧度
        theta = math.radians(theta_deg)

        # 转换直径单位为米（保持单位一致性，实际计算中比值抵消单位影响）
        # 注意：由于公式中使用的是比值，单位可以保持mm不需要转换
        ratio = d / D

        # 基础频率计算（转/秒）
        f_r = rpm / 60.0

        # 计算各特征频率
        BPFI = n / 2 * (1 + ratio * math.cos(theta)) * f_r  # 内圈故障频率
        BPFO = n / 2 * (1 - ratio * math.cos(theta)) * f_r  # 外圈故障频率
        BSF = (D / (2 * d)) * (1 - (ratio ** 2) * (math.cos(theta) ** 2)) * f_r  # 滚动体故障频率
        FTF = 0.5 * (1 - ratio * math.cos(theta)) * f_r  # 保持架故障频率

        return {
            "BPFI": round(BPFI, 2),
            "BPFO": round(BPFO, 2),
            "BSF": round(BSF, 2),
            "FTF": round(FTF, 2),

        }
    
    #检查返回结果是否有nan 若有，则替换成none
    def replace_nan(self, obj):
        if isinstance(obj, dict):
            return {k: self.replace_nan(v) for k, v in obj.items()}
        elif isinstance(obj, list):
            return [self.replace_nan(item) for item in obj]
        elif isinstance(obj, float) and math.isnan(obj):
            return None
        return obj

if __name__ == "__main__":
    # table_name = "SKF001_wave"
    # ids = [67803,67804]
    # fmin, fmax = None, None

    cms = CMSAnalyst(fmin, fmax,table_name,ids)
    time_domain = cms.time_domain()
    # print(time_domain)

'''
    trace = go.Scatter(
        x=time_domain['x'],
        y=time_domain['y'],
        mode="lines",
        name=time_domain['title'],
    )
    layout = go.Layout(
        title= time_domain['title'],
        xaxis=dict(title=time_domain["xaxis"]),
        yaxis=dict(title=time_domain["yaxis"]),
    )
    fig = go.Figure(data=[trace], layout=layout)
    fig.show()
'''

    # data_path_lsit = ["test1.csv", "test2.csv"]
    # trend_analysis_test = cms.trend_analysis(data_path_lsit, fmin, fmax)
    # print(trend_analysis_test)