
import mitosheet
import numpy as np
import pandas as pd
import plotly as py
import cufflinks as cf
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff

cf.set_config_file(
    offline=True, 
    world_readable=True, 
    theme='white',        # 设置绘图风格
)

import warnings
warnings.filterwarnings("ignore")

import sklearn
import graphviz
from sklearn import tree
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, roc_auc_score

from colorama import Fore
def color(text):
    return Fore.RED + text + Fore.RESET


file_path = './附件2(Attachment 2)2022-51MCM-Problem B.xlsx'
sheet1 = pd.read_excel(
    io=file_path, 
    index_col=None, 
    sheet_name='温度(temperature)', )
sheet2 = pd.read_excel(
    io=file_path, 
    index_col=None, 
    sheet_name='产品质量(quality of the products)', )
sheet3 = pd.read_excel(
    io=file_path, 
    index_col=None, 
    sheet_name='原矿参数(mineral parameter)', )
sheet4 = pd.read_excel(
    io=file_path, 
    index_col=None, 
    sheet_name='过程数据(process parameter)', )


# todo 找到有效的温度数据
sheet1_time_string = sheet1.iloc[:, 0].astype('string')

cond1 = sheet1_time_string.apply(lambda x: x[14: 16]) == "50"
data_part1 = sheet1[cond1].iloc[:-2, :]

exp_date1 = [
    "2022-02-03 20:50:00", 
    "2022-02-26 13:50:00", 
    "2022-03-21 06:50:00", 
    "2022-04-04 10:50:00", "2022-04-04 15:50:00", 
    "2022-03-10 10:50:00", "2022-03-10 11:50:00", "2022-03-10 12:50:00", 
]
cond1 = sheet1_time_string.apply(lambda x: x in exp_date1)
data_part1 = data_part1[cond1.apply(lambda x: not x)]
data_part1.index = [i for i in range(len(data_part1))]
print(data_part1.shape)
# data_part1
# mitosheet.sheet(data_part1, analysis_to_replay="id-conydfcblv")

(1725, 3)


# todo 找到有效的产品质量数据
sheet2_time_string = sheet2.iloc[:, 0].astype('string')

exp_date2 = [
    "2022-02-20 23:50:00", "2022-02-21 00:50:00", "2022-02-21 01:50:00", 
    
    "2022-02-21 09:50:00", "2022-02-21 10:50:00", "2022-02-21 02:50:00", "2022-02-21 03:50:00", "2022-02-21 04:50:00", 
    "2022-02-21 05:50:00", "2022-02-21 06:50:00", "2022-02-21 07:50:00", "2022-02-21 08:50:00", 
    
    "2022-02-26 06:50:00", "2022-02-26 07:50:00", "2022-02-26 08:50:00", "2022-02-26 09:50:00", "2022-02-26 10:50:00",
    
    "2022-04-08 00:50:00", "2022-04-08 01:50:00", 
]

cond2 = sheet2_time_string.apply(lambda x: x in exp_date2)
data_part2_ = sheet2[cond2.apply(lambda x: not x)].iloc[2:, :]
data_part2_.index = [i for i in range(len(data_part2_))]
print(data_part2_.shape)
# data_part2_
# mitosheet.sheet(data_part2_, analysis_to_replay="id-etdktbfykz")

(1725, 5)


data_part2_


# todo 计算合格数量、合格率
cond10 = 77.78 < data_part2_.iloc[:, 1]
cond11 = data_part2_.iloc[:, 1] < 80.33
cond2 = data_part2_.iloc[:, 2] < 24.15
cond3 = data_part2_.iloc[:, 3] < 17.15
cond4 = data_part2_.iloc[:, 4] < 15.62
# print("合格率：", len(data_part2_[cond10][cond11][cond2][cond3][cond4]) / len(data_part2_))

# todo 找到产品是否合格
def is_qualified(x):
    return 77.78 < x[1] < 80.33 and x[2] < 24.15 and x[3] < 17.15 and x[4] < 15.62
data_part2 = pd.DataFrame(data_part2_.apply(is_qualified, axis=1))
data_part2.columns = ['是否合格']
# print(len(data_part2))
# print(data_part2.sum() / len(data_part2))

print(data_part2.shape)
print(data_part2.sum(), data_part2.sum() / len(data_part2))
# data_part2
# mitosheet.sheet(data_part2, analysis_to_replay="id-mkgwgrqoel")

(1725, 1)
是否合格    472
dtype: int64 是否合格    0.273623
dtype: float64


# todo 找到原矿参数数据 3
cnt = data_part1.iloc[:, 0].astype('string').apply(lambda x: x[5: 10])
time_cnt = []
for i in pd.DataFrame(cnt).groupby(by='时间 (Time)'):
    time_cnt.append(len(i[1]))
data_part3 = pd.DataFrame(np.repeat(sheet3.iloc[:-4, :].values, time_cnt, axis=0), columns=sheet3.columns)
print(data_part3.shape)
# data_part3
# mitosheet.sheet(data_part3, analysis_to_replay="id-rvruqimmlr")

(1725, 5)


cols = ['时间 (Time)', "过程数据3 (Process parameter 3)", "过程数据4 (Process parameter 4)"]
proc_data = pd.DataFrame(sheet4)
proc_data.iplot(x='时间 (Time)')
print("相关系数：")
proc_data.iloc[:, 1:].corr()

相关系数：


def norm(data):
    return (data - data.min()) / (data.max() - data.min())

data_part4 = sheet4.apply(lambda x: (x[3] + x[4]), axis=1)
data_part4 = pd.concat([sheet4.iloc[:, 0], data_part4], axis=1).rename(columns={0: "原矿质量"})
print(data_part4.shape)
# data_part4
# mitosheet.sheet(data_part4, analysis_to_replay="id-lntnexsmmk")

(619, 2)


exp_date4 = []
for i in exp_date1 + exp_date2:
    exp_date4.append(i[:-5] + "30")
# print(exp_date4)

sheet4_time_string = data_part4.iloc[:, 0].astype('string')
cond4 = sheet4_time_string.apply(lambda x: x[: -3] not in exp_date4)

data_part4_need = data_part4[cond4]
data_part4_need = data_part4_need.iloc[:-33, :]

for _ in range(5):
    data_part4_need.drop(index=np.random.randint(0, len(data_part4_need)), inplace=True)
data_part4_need.index = [i for i in range(len(data_part4_need))]

data_part4_need = pd.DataFrame(np.repeat(data_part4_need.values, 3, axis=0), columns=data_part4_need.columns)
print(data_part4_need.shape)
# data_part4_need
# mitosheet.sheet(data_part4_need, analysis_to_replay="id-owygulbcev")

(1725, 2)


data_part4_need


X = pd.concat([data_part1.iloc[:, 1:], data_part3.iloc[:, 1:], data_part4_need.iloc[:, 1:]], axis=1)
Ys = data_part2

X

Ys


X.to_csv("quention3-X_data.csv")
Ys.to_csv("quention3-Y_data.csv")


cond = (pd.notna(X).iloc[:, 0] == True)
remain_index = X[cond].index


X = X[cond]
Y = Ys[cond].replace(to_replace=[True, False], value=[1, 0])
print(X.shape, Y.shape)

(1640, 7) (1640, 1)

X

Y


def norm(data):
    return (data - data.min()) / (data.max() - data.min())

# data_part4 = sheet4.apply(lambda x: (x[3] + x[4]), axis=1)
# data_part4 = pd.concat([sheet4.iloc[:, 0], data_part4], axis=1).rename(columns={0: "原矿质量"})
# print(data_part4.shape)
data_part4 = sheet4[['时间 (Time)', '过程数据3 (Process parameter 3)', '过程数据4 (Process parameter 4)']]
data_part4_need = sheet4[['时间 (Time)', '过程数据3 (Process parameter 3)', '过程数据4 (Process parameter 4)']]
data_part4_need


exp_date4 = []
for i in exp_date1 + exp_date2:
    exp_date4.append(i[:-5] + "30")
# print(exp_date4)

sheet4_time_string = data_part4.iloc[:, 0].astype('string')
cond4 = sheet4_time_string.apply(lambda x: x[: -3] not in exp_date4)

data_part4_need = data_part4[cond4]
data_part4_need = data_part4_need.iloc[:-33, :]

for _ in range(5):
    data_part4_need.drop(index=np.random.randint(0, len(data_part4_need)), inplace=True)
data_part4_need.index = [i for i in range(len(data_part4_need))]

data_part4_need = pd.DataFrame(np.repeat(data_part4_need.values, 3, axis=0), columns=data_part4_need.columns)
print(data_part4_need.shape)
data_part4_need
# data_part4_need
# mitosheet.sheet(data_part4_need, analysis_to_replay="id-owygulbcev")

(1725, 3)


X = pd.concat([data_part1.iloc[:, 1:], data_part3.iloc[:, 1:], data_part4_need.iloc[:, 1:]], axis=1)
Ys = data_part2_.iloc[:, 1:]


cond = (pd.notna(X).iloc[:, 0] == True)
remain_index = X[cond].index


X = X[cond]
Y = Ys[cond].replace(to_replace=[True, False], value=[1, 0])
print(X.shape, Y.shape)

(1640, 8) (1640, 4)

X

Y


from copy import copy

def get_data(X=X, num=10):
    """
    :param X: pd.DataFrame
    """
    x_example = np.squeeze(X.sample(1).astype(float).values)
    data = []
    x1, x2, x3, x4, x5, x6, x7, x8 = [[] for i in range(8)]
    low, high = list(X.min()), list(X.max())
    for i in range(X.shape[1]):
        x_examples = copy(x_example).repeat(num).reshape(-1, num).T
        x_examples[:, i] = np.linspace(low[i], high[i], num)
        data.append(x_examples)
    return data


def get_ypred(models, x):
    ys = []
    for model in models:
        ys.append(list(model.predict(x)))
    return ys


# 温度1、2、原矿参数1、2、3、4、过程参数3、4
from xgboost import XGBRegressor as XGBC 

num = 100
x_data_s = get_data(num=num)  # (8, n, 8)

# todo train
model1 = XGBC()
model2 = XGBC()
model3 = XGBC()
model4 = XGBC()
ypreds = []
for i in range(Y.shape[1]):
    xtrain, xtest, ytrain, ytest = train_test_split(
        np.array(X, dtype=float), 
        np.array(Y, dtype=float)[:, i], 
        test_size=0.3, 
        shuffle=True, 
    )
    exec(f'model{i+1}.fit(xtrain, ytrain)')
    exec(f'ypred = model{i+1}.predict(xtest)')
    ypreds.append(list(ypred))
pd.DataFrame(ypreds).T
# print(model1, model2, model3, model4)

# todo Sensitivity analysis
y_preds_s = []  # (8, n, 4)
for i in range(len(x_data_s)):
    x_data = x_data_s[i]  # (n, m) = (8, n, m)[i]
    ypreds = get_ypred([model1, model2, model3, model4], x_data)
    y_preds_s.append(ypreds)  # (8, n, 4)

# args = ['温度1', '温度2', '原矿参数1', '原矿参数2', '原矿参数3', '原矿参数4', '过程参数3', '过程参数4']
args = ["系统I温度", "系统II温度", "原矿参数1", "原矿参数2", "原矿参数3", "原矿参数4", "过程数据3", "过程数据4"]
yargs = ['指标A', '指标B', '指标C', '指标D']
for i in range(len(args)):
    arg = args[i]
    traces = []
    y_preds = pd.DataFrame(y_preds_s[i], index=yargs).T  # (n, 4) = (8, n, 4)[i]
    x_preds = pd.DataFrame(np.linspace(X.min()[i], X.max()[i], num), columns=['x'])
    y_preds = pd.concat([x_preds, y_preds], axis=1)
    y_preds.iplot(
        x='x', y=yargs,
        title="XGBoost模型——" + arg + "的灵敏性分析", 
    )
    y_preds.figure(
        x='x', y=yargs,
        title="XGBoost模型——" + arg + "的灵敏性分析",
    ).write_image('./img/问题4-' + "XGBoost模型——" + arg + "的灵敏性分析.svg")

X

Y


index_num = Y.shape[1]
index_name = ["指标A", "指标B", "指标C", "指标D"]
index_colors = ["red", "lightpink", "darkorange", "khaki", "green", "lightgreen", "blue", "lightblue"]

data_to_predict = np.array(
    [[1404.89,859.77,52.75,96.87,46.61,22.91, ], 
    [1151.75,859.77,52.75,96.87,46.61,22.91, ],], 
)


# 4-10
mineral_param410 = np.array([56.27, 111.38, 47.52, 20.26])
process_param410 = np.array(sheet4[-8:])[:, 3:]
data_to_predict410 = np.concatenate([mineral_param410.repeat(8).reshape(-1, 8).T, process_param410], axis=1)
pd.DataFrame(data_to_predict410, columns=['原矿参数1', '原矿参数2', '原矿参数3', '原矿参数4', '过程数据1', '过程数据2'])
# 4-11
mineral_param411 = np.array([56.71, 111.46, 46.67, 18.48])
process_param411 = np.array(sheet4[-16:-8])[:, 3:]
data_to_predict411 = np.concatenate([mineral_param411.repeat(8).reshape(-1, 8).T, process_param411], axis=1)
pd.DataFrame(data_to_predict411, columns=['原矿参数1', '原矿参数2', '原矿参数3', '原矿参数4', '过程数据1', '过程数据2'])


xtrain

array([[ 719.44,  684.02,   56.19, ...,   19.33,  275.99,  148.99],
       [ 862.01,  799.5 ,   56.43, ...,   19.88,  252.85,  167.57],
       [1376.17,  881.89,   62.29, ...,   16.36,  275.99,  135.63],
       ...,
       [1403.97, 1039.93,   54.41, ...,   19.31,  247.16,  148.38],
       [ 593.45,  623.25,   53.74, ...,   21.16,  289.26,  139.25],
       [1149.46,  805.52,   55.12, ...,   20.26,  283.34,  146.55]])


ytrain

array([12.4 , 14.37, 18.62, ..., 12.94, 21.26, 13.83])


model1 = XGBC()
model2 = XGBC()
model3 = XGBC()
model4 = XGBC()

xtrain, xtest, ytrain, ytest = train_test_split(
    np.array(X, dtype=float), 
    np.array(Y, dtype=float), 
    test_size=0.3, 
    shuffle=True, 
)

for i in range(Y.shape[1]):
    exec(f'model{i+1}.fit(xtrain, ytrain[:, {i}])')


from numba import jit

def get_preds(models, x):
    ypreds = []
    for model in models:
        ypred = list(model.predict(x))
        ypreds.append(ypred)
    return ypreds
def check_pass(y, th):
    boolean = list(map(lambda x: 77.78 < x[0] < 80.33 and x[1] < 24.15 and x[2] < 17.15 and x[3] < 15.62, y))
    rate = sum(boolean) / len(boolean)
    flag = True if rate >= th else False
    return [flag, rate, boolean]

@jit
def run_tuning(models, data_to_predict, th, X=X):
    degree_ans_list = []
    index_ans_list = []
    step=10
    
    low, high = list(X.min())[:2], list(X.max())[:2]
    degree1_low, degree2_low = low
    degree1_high, degree2_high = high
    degree1 = list(np.arange(degree1_low, degree1_high, step=step))
    degree2 = list(np.arange(degree2_low, degree2_high, step=step))
    
    degree = []
    for i in degree1:
        for j in degree2:
            degree.append([i, j])
    degree = np.array(degree)
    l = len(degree)
    print(l)
    
    for j in range(l):
        x = degree[j].repeat(8).reshape(-1, 8).T
        x = np.concatenate([x, data_to_predict], axis=1)
        ypreds = np.array(get_preds(models, x)).T
        check = check_pass(ypreds, th)
        if check[0]:
            print('.', end='')
#             print('one more ans!')
#             print(j, ":")
#             print("合格", check[2])
#             print("合格率", check[1])
#             print()
            index_ans_list.append(list(ypreds))
            degree_ans_list.append(list(degree[j]))
        if j % (l // 10) == 0:
            print(j, '/', l)
    return degree_ans_list, index_ans_list


degree_ans410, index410 = run_tuning([model1, model2, model3, model4], data_to_predict410, 0.8)
pd.DataFrame(degree_ans410)

9520
0 / 9520
........................952 / 9520
1904 / 9520
.................................2856 / 9520
............................................................................3808 / 9520
............4760 / 9520
..5712 / 9520
.6664 / 9520
.7616 / 9520
8568 / 9520
...............................................................................................................................


degree_ans411, index411 = run_tuning([model1, model2, model3, model4], data_to_predict411, 0.99)
pd.DataFrame(degree_ans411)

9520
0 / 9520
952 / 9520
1904 / 9520
2856 / 9520
3808 / 9520
4760 / 9520
5712 / 9520
6664 / 9520
7616 / 9520
8568 / 9520
...............................


from hmz.math_model.evaluate import TOPSIS

def get_max_score_of_degree(X=np.array(index410), degree=degree_ans410):
    fin_scores = []
    for i in range(8):
        topsis410 = TOPSIS(pd.DataFrame(X[:, i, :]))
        fin_score = topsis410.score()
        fin_scores.append(list(np.squeeze(fin_score.values)))
    
    finscores410 = np.array(fin_scores).sum(0) / 8
    maxscoresarg410 = np.argsort(finscores410)[-1]
    
    return degree[maxscoresarg410]


# 4-10
ans_410 = get_max_score_of_degree(np.array(index410), degree_ans410)
# 4-11
ans_411 = get_max_score_of_degree(np.array(index411), degree_ans411)
# 以下是 hmz 这个(自己写的)库的内部输出，暂时无法关闭，直接忽略即可（后期完善）

原始数据:
             0          1          2          3
0    79.764633  24.048122  10.924195  12.420654
1    79.738739  24.074551  10.801689  12.330379
2    80.097137  23.670055  10.979162  12.430060
3    80.071259  23.696484  10.856656  12.339784
4    78.260117  23.674995  10.928226  12.427949
..         ...        ...        ...        ...
271  79.706123  22.745268  10.015746  12.681078
272  79.207275  24.111877  10.268134  12.406067
273  79.357895  23.362579  10.069987  11.202466
274  79.273247  23.998615   9.844916  12.590789
275  79.273247  23.967379   9.780243  12.157454

[276 rows x 4 columns]

去量纲 (求和归一化) 后的数据:
            0         1         2         3
0    0.003644  0.003640  0.003691  0.003611
1    0.003643  0.003644  0.003650  0.003584
2    0.003659  0.003583  0.003709  0.003613
3    0.003658  0.003587  0.003668  0.003587
4    0.003575  0.003583  0.003692  0.003613
..        ...       ...       ...       ...
271  0.003641  0.003443  0.003384  0.003686
272  0.003618  0.003649  0.003469  0.003606
273  0.003625  0.003536  0.003402  0.003257
274  0.003621  0.003632  0.003326  0.003660
275  0.003621  0.003628  0.003304  0.003534

[276 rows x 4 columns]

数据信息熵权重:
                       0         1         2         3
entropy weight  0.008933  0.019355  0.626799  0.344913

指标权重:
                       0         1         2         3
entropy weight  0.008933  0.019355  0.626799  0.344913

未归一化得分:
     final score
0        0.63752
1        0.58710
2        0.65494
3        0.60538
4        0.63959
..           ...
271      0.39127
272      0.42393
273      0.21122
274      0.33990
275      0.25708

[276 rows x 1 columns]

归一化后最终得分:
     final score
0        0.00402
1        0.00370
2        0.00413
3        0.00382
4        0.00403
..           ...
271      0.00247
272      0.00267
273      0.00133
274      0.00214
275      0.00162

[276 rows x 1 columns]

原始数据:
             0          1          2          3
0    79.600266  24.490633  10.898557  12.116571
1    79.574371  24.517061  10.881515  12.026297
2    79.932770  24.112566  10.953524  12.125978
3    79.906891  24.138994  10.936481  12.035701
4    78.188751  24.088915  10.990034  12.045560
..         ...        ...        ...        ...
271  79.551483  22.650713  10.020551  12.208196
272  79.115501  24.017323  10.256428  11.681862
273  79.266121  23.302156  10.041856  10.478261
274  79.181473  23.938192   9.816785  11.614734
275  79.181473  23.906956   9.752111  11.181397

[276 rows x 4 columns]

去量纲 (求和归一化) 后的数据:
            0         1         2         3
0    0.003641  0.003706  0.003693  0.003470
1    0.003639  0.003710  0.003687  0.003444
2    0.003656  0.003649  0.003712  0.003473
3    0.003655  0.003653  0.003706  0.003447
4    0.003576  0.003645  0.003724  0.003450
..        ...       ...       ...       ...
271  0.003638  0.003427  0.003396  0.003497
272  0.003618  0.003634  0.003475  0.003346
273  0.003625  0.003526  0.003403  0.003001
274  0.003621  0.003622  0.003327  0.003327
275  0.003621  0.003617  0.003305  0.003202

[276 rows x 4 columns]

数据信息熵权重:
                       0         1        2         3
entropy weight  0.003594  0.010783  0.23734  0.748282

指标权重:
                       0         1        2         3
entropy weight  0.003594  0.010783  0.23734  0.748282

未归一化得分:
     final score
0        0.42399
1        0.40155
2        0.42705
3        0.40464
4        0.40783
..           ...
271      0.43617
272      0.30765
273      0.03426
274      0.28637
275      0.17745

[276 rows x 1 columns]

归一化后最终得分:
     final score
0        0.00278
1        0.00264
2        0.00280
3        0.00266
4        0.00268
..           ...
271      0.00286
272      0.00202
273      0.00023
274      0.00188
275      0.00117

[276 rows x 1 columns]

原始数据:
             0          1          2          3
0    79.740768  23.913185  11.225509  13.524830
1    79.714890  23.939613  11.103003  13.434555
2    80.073288  23.535118  11.243889  13.534236
3    80.047394  23.561546  11.121383  13.443959
4    78.233246  23.545671  11.207811  13.604304
..         ...        ...        ...        ...
271  79.654060  22.587654   9.934772  14.695075
272  79.116150  23.954264  10.187160  13.727303
273  79.266769  23.204966   9.989013  12.523702
274  79.182121  23.841002   9.763942  13.912024
275  79.182121  23.809765   9.699268  13.478691

[276 rows x 4 columns]

去量纲 (求和归一化) 后的数据:
            0         1         2         3
0    0.003646  0.003617  0.003774  0.003390
1    0.003645  0.003621  0.003733  0.003367
2    0.003662  0.003560  0.003780  0.003392
3    0.003660  0.003564  0.003739  0.003370
4    0.003577  0.003562  0.003768  0.003410
..        ...       ...       ...       ...
271  0.003642  0.003417  0.003340  0.003683
272  0.003618  0.003624  0.003425  0.003441
273  0.003625  0.003510  0.003358  0.003139
274  0.003621  0.003606  0.003283  0.003487
275  0.003621  0.003602  0.003261  0.003378

[276 rows x 4 columns]

数据信息熵权重:
                       0         1         2         3
entropy weight  0.002584  0.010084  0.228769  0.758563

指标权重:
                       0         1         2         3
entropy weight  0.002584  0.010084  0.228769  0.758563

未归一化得分:
     final score
0        0.31380
1        0.29273
2        0.31608
3        0.29501
4        0.33027
..           ...
271      0.55358
272      0.34449
273      0.07980
274      0.38180
275      0.28634

[276 rows x 1 columns]

归一化后最终得分:
     final score
0        0.00223
1        0.00208
2        0.00225
3        0.00210
4        0.00235
..           ...
271      0.00394
272      0.00245
273      0.00057
274      0.00272
275      0.00204

[276 rows x 1 columns]

原始数据:
             0          1          2          3
0    79.606781  23.618134  11.215198  12.258276
1    79.580887  23.644562  11.198155  12.430330
2    79.731613  23.240067  11.233578  12.401795
3    79.705719  23.266495  11.216536  12.573850
4    77.916214  23.617666  11.284947  12.922679
..         ...        ...        ...        ...
271  79.501251  22.483269   9.760892  13.166908
272  79.139244  23.849878   9.996769  12.611273
273  79.215889  23.134712   9.782197  11.407672
274  79.063530  23.770748   9.557126  12.544144
275  79.063530  23.739511   9.492453  12.110809

[276 rows x 4 columns]

去量纲 (求和归一化) 后的数据:
            0         1         2         3
0    0.003656  0.003589  0.003784  0.003308
1    0.003655  0.003593  0.003778  0.003355
2    0.003662  0.003531  0.003790  0.003347
3    0.003661  0.003535  0.003784  0.003394
4    0.003579  0.003589  0.003807  0.003488
..        ...       ...       ...       ...
271  0.003651  0.003416  0.003293  0.003554
272  0.003635  0.003624  0.003373  0.003404
273  0.003638  0.003515  0.003300  0.003079
274  0.003631  0.003612  0.003224  0.003386
275  0.003631  0.003607  0.003203  0.003269

[276 rows x 4 columns]

数据信息熵权重:
                       0         1         2         3
entropy weight  0.003402  0.022324  0.341235  0.633039

指标权重:
                       0         1         2         3
entropy weight  0.003402  0.022324  0.341235  0.633039

未归一化得分:
     final score
0        0.29916
1        0.32953
2        0.32638
3        0.35865
4        0.43489
..           ...
271      0.42199
272      0.30312
273      0.04756
274      0.27393
275      0.17076

[276 rows x 1 columns]

归一化后最终得分:
     final score
0        0.00210
1        0.00231
2        0.00229
3        0.00251
4        0.00305
..           ...
271      0.00296
272      0.00212
273      0.00033
274      0.00192
275      0.00120

[276 rows x 1 columns]

原始数据:
             0          1          2          3
0    79.467545  23.922579  10.629008  12.349236
1    79.466187  23.949007  10.738872  12.521290
2    80.215500  23.544512  10.672751  12.609084
3    80.214142  23.570940  10.782615  12.781138
4    78.483383  24.591230  10.601325  12.926341
..         ...        ...        ...        ...
271  79.882248  22.919056   9.674474  12.869946
272  79.520241  24.038708   9.926862  12.712962
273  79.596886  23.393278   9.716475  11.632993
274  79.488197  23.927557   9.431499  12.474279
275  79.488197  23.896320   9.366826  12.040943

[276 rows x 4 columns]

去量纲 (求和归一化) 后的数据:
            0         1         2         3
0    0.003614  0.003637  0.003670  0.003495
1    0.003614  0.003641  0.003708  0.003543
2    0.003648  0.003579  0.003685  0.003568
3    0.003648  0.003584  0.003723  0.003617
4    0.003569  0.003739  0.003661  0.003658
..        ...       ...       ...       ...
271  0.003633  0.003484  0.003341  0.003642
272  0.003616  0.003655  0.003428  0.003598
273  0.003620  0.003556  0.003355  0.003292
274  0.003615  0.003638  0.003257  0.003530
275  0.003615  0.003633  0.003234  0.003408

[276 rows x 4 columns]

数据信息熵权重:
                       0         1         2         3
entropy weight  0.009891  0.024854  0.452447  0.512808

指标权重:
                       0         1         2         3
entropy weight  0.009891  0.024854  0.452447  0.512807

未归一化得分:
     final score
0        0.49478
1        0.55639
2        0.57097
3        0.63442
4        0.64968
..           ...
271      0.48653
272      0.48287
273      0.15226
274      0.35844
275      0.23083

[276 rows x 1 columns]

归一化后最终得分:
     final score
0        0.00303
1        0.00341
2        0.00350
3        0.00389
4        0.00398
..           ...
271      0.00298
272      0.00296
273      0.00093
274      0.00220
275      0.00141

[276 rows x 1 columns]

原始数据:
             0          1          2          3
0    79.152390  23.962713  10.792052  11.870848
1    79.126495  23.989141  10.901916  12.042902
2    79.894821  23.584646  10.835795  12.130696
3    79.868942  23.611074  10.945659  12.302751
4    77.915634  24.023657  10.832456  12.405244
..         ...        ...        ...        ...
271  79.634178  22.988487   9.914741  11.913091
272  79.272171  24.080072  10.167129  11.891924
273  79.348816  23.434643   9.956742  10.811954
274  79.240128  24.004358   9.671766  11.653240
275  79.240128  23.973122   9.607093  11.219906

[276 rows x 4 columns]

去量纲 (求和归一化) 后的数据:
            0         1         2         3
0    0.003623  0.003643  0.003707  0.003434
1    0.003621  0.003647  0.003745  0.003483
2    0.003657  0.003586  0.003722  0.003509
3    0.003655  0.003590  0.003760  0.003558
4    0.003566  0.003652  0.003721  0.003588
..        ...       ...       ...       ...
271  0.003645  0.003495  0.003406  0.003446
272  0.003628  0.003661  0.003493  0.003440
273  0.003632  0.003563  0.003420  0.003127
274  0.003627  0.003649  0.003322  0.003371
275  0.003627  0.003645  0.003300  0.003245

[276 rows x 4 columns]

数据信息熵权重:
                       0         1         2         3
entropy weight  0.004221  0.008194  0.241341  0.746245

指标权重:
                       0         1         2         3
entropy weight  0.004221  0.008194  0.241341  0.746245

未归一化得分:
     final score
0        0.33205
1        0.38287
2        0.40637
3        0.45754
4        0.48512
..           ...
271      0.32732
272      0.32495
273      0.03964
274      0.24855
275      0.12078

[276 rows x 1 columns]

归一化后最终得分:
     final score
0        0.00234
1        0.00269
2        0.00286
3        0.00322
4        0.00341
..           ...
271      0.00230
272      0.00229
273      0.00028
274      0.00175
275      0.00085

[276 rows x 1 columns]

原始数据:
             0          1          2          3
0    79.766800  23.505770  10.758327  12.246002
1    79.740921  23.532198  10.741285  12.155725
2    80.092941  23.127703  10.813294  12.389522
3    80.067047  23.154131  10.796252  12.299245
4    78.518219  23.394386  10.781718  12.351813
..         ...        ...        ...        ...
271  79.951462  22.446222   9.813476  12.953490
272  79.515480  23.812832  10.049353  12.291340
273  79.528938  23.097666   9.834781  11.087739
274  79.444283  23.733702   9.609710  12.224212
275  79.444283  23.702465   9.545036  11.790875

[276 rows x 4 columns]

去量纲 (求和归一化) 后的数据:
            0         1         2         3
0    0.003638  0.003568  0.003650  0.003496
1    0.003636  0.003572  0.003644  0.003470
2    0.003652  0.003510  0.003669  0.003537
3    0.003651  0.003514  0.003663  0.003511
4    0.003581  0.003551  0.003658  0.003526
..        ...       ...       ...       ...
271  0.003646  0.003407  0.003329  0.003698
272  0.003626  0.003614  0.003409  0.003509
273  0.003627  0.003506  0.003337  0.003165
274  0.003623  0.003602  0.003260  0.003490
275  0.003623  0.003598  0.003238  0.003366

[276 rows x 4 columns]

数据信息熵权重:
                       0         1         2        3
entropy weight  0.006855  0.032661  0.424194  0.53629

指标权重:
                       0         1         2        3
entropy weight  0.006855  0.032661  0.424194  0.53629

未归一化得分:
     final score
0        0.49450
1        0.46930
2        0.53825
3        0.51262
4        0.52476
..           ...
271      0.55364
272      0.42187
273      0.09449
274      0.36380
275      0.24268

[276 rows x 1 columns]

归一化后最终得分:
     final score
0        0.00302
1        0.00286
2        0.00329
3        0.00313
4        0.00320
..           ...
271      0.00338
272      0.00258
273      0.00058
274      0.00222
275      0.00148

[276 rows x 1 columns]

原始数据:
             0          1          2          3
0    80.131454  23.536272  10.793567  12.626909
1    80.105576  23.562700  10.671061  12.786196
2    80.463974  23.158203  10.848534  12.636315
3    80.438080  23.184631  10.726027  12.795602
4    79.228745  23.020424  10.528901  12.879308
..         ...        ...        ...        ...
271  79.805542  21.876425  10.021272  13.181669
272  79.273712  23.070349  10.265304  13.318564
273  79.408134  22.457951  10.176111  12.541399
274  79.487961  23.053745   9.948914  13.070413
275  79.532158  22.857344   9.772976  12.868498

[276 rows x 4 columns]

去量纲 (求和归一化) 后的数据:
            0         1         2         3
0    0.003654  0.003631  0.003627  0.003407
1    0.003653  0.003636  0.003586  0.003450
2    0.003670  0.003573  0.003646  0.003410
3    0.003668  0.003577  0.003605  0.003453
4    0.003613  0.003552  0.003539  0.003476
..        ...       ...       ...       ...
271  0.003639  0.003375  0.003368  0.003557
272  0.003615  0.003560  0.003450  0.003594
273  0.003621  0.003465  0.003420  0.003384
274  0.003625  0.003557  0.003344  0.003527
275  0.003627  0.003527  0.003284  0.003473

[276 rows x 4 columns]

数据信息熵权重:
                       0         1         2         3
entropy weight  0.004338  0.045683  0.254575  0.695405

指标权重:
                       0         1         2         3
entropy weight  0.004338  0.045683  0.254575  0.695405

未归一化得分:
     final score
0        0.40211
1        0.44345
2        0.40567
3        0.44695
4        0.46589
..           ...
271      0.53761
272      0.58179
273      0.36386
274      0.50673
275      0.44802

[276 rows x 1 columns]

归一化后最终得分:
     final score
0        0.00237
1        0.00261
2        0.00239
3        0.00263
4        0.00274
..           ...
271      0.00317
272      0.00343
273      0.00214
274      0.00299
275      0.00264

[276 rows x 1 columns]

原始数据:
            0          1          2          3
0   79.829887  23.005432  10.628290  12.378992
1   79.717094  23.316051  10.221199  12.596301
2   80.276855  23.106533   9.718573  12.566521
3   79.717728  23.127262  10.040483  13.009837
4   79.767410  23.098364  10.040483  12.179219
5   79.537994  23.214788  10.110101  12.239650
6   79.537994  23.214788  10.150516  12.239650
7   79.499802  23.379076  10.192090  12.286936
8   79.538872  23.124134  10.269629  12.267331
9   79.503799  23.189041  10.134656  12.533977
10  80.105553  22.968794  10.279283  12.527067
11  79.263229  23.492081  10.308683  12.428029
12  79.263229  23.174654  10.142380  12.583354
13  79.220306  23.349674  10.308239  12.508672
14  79.381683  23.415661  10.192671  12.702552
15  79.308861  23.147036  10.205710  12.702552
16  79.541870  23.244547  10.161258  12.872066
17  79.518311  23.029554  10.213611  12.672649
18  79.537651  22.907923  10.159216  12.672649
19  79.587311  22.949686  10.159216  12.852307
20  79.563240  22.739334  10.096224  12.920726
21  79.563240  22.780781  10.043509  12.974807
22  79.563240  22.654457  10.152330  12.974807
23  79.546707  22.621225   9.933713  12.974807
24  79.496216  22.922806  10.013960  12.622882
25  79.472511  22.676493  10.013960  12.622882
26  79.754745  22.701401   9.961439  12.647752
27  80.131577  22.724527  10.018920  13.002899
28  80.118332  22.658957   9.893783  12.145371
29  80.099403  22.709785  10.294139  12.260662
30  79.814041  23.184008  10.336141  11.023709

去量纲 (求和归一化) 后的数据:
           0         1         2         3
0   0.032336  0.032228  0.033806  0.031823
1   0.032290  0.032663  0.032511  0.032382
2   0.032517  0.032370  0.030912  0.032305
3   0.032290  0.032399  0.031936  0.033445
4   0.032310  0.032358  0.031936  0.031310
5   0.032218  0.032522  0.032157  0.031465
6   0.032218  0.032522  0.032286  0.031465
7   0.032202  0.032752  0.032418  0.031587
8   0.032218  0.032395  0.032665  0.031536
9   0.032204  0.032485  0.032235  0.032222
10  0.032447  0.032177  0.032696  0.032204
11  0.032106  0.032910  0.032789  0.031949
12  0.032106  0.032465  0.032260  0.032349
13  0.032089  0.032710  0.032788  0.032157
14  0.032154  0.032803  0.032420  0.032655
15  0.032125  0.032427  0.032461  0.032655
16  0.032219  0.032563  0.032320  0.033091
17  0.032210  0.032262  0.032487  0.032578
18  0.032217  0.032092  0.032314  0.032578
19  0.032238  0.032150  0.032314  0.033040
20  0.032228  0.031855  0.032113  0.033216
21  0.032228  0.031914  0.031946  0.033355
22  0.032228  0.031737  0.032292  0.033355
23  0.032221  0.031690  0.031596  0.033355
24  0.032201  0.032112  0.031852  0.032450
25  0.032191  0.031767  0.031852  0.032450
26  0.032305  0.031802  0.031685  0.032514
27  0.032458  0.031835  0.031867  0.033427
28  0.032453  0.031743  0.031469  0.031223
29  0.032445  0.031814  0.032743  0.031519
30  0.032329  0.032478  0.032876  0.028339

数据信息熵权重:
                       0         1         2         3
entropy weight  0.008357  0.089755  0.190963  0.710925

指标权重:
                       0         1         2         3
entropy weight  0.008357  0.089755  0.190963  0.710925

未归一化得分:
    final score
0       0.68494
1       0.78679
2       0.75225
3       0.92357
4       0.57805
5       0.60948
6       0.61023
7       0.63478
8       0.62568
9       0.75366
10      0.75286
11      0.70673
12      0.77750
13      0.74618
14      0.83673
15      0.83597
16      0.90742
17      0.82135
18      0.81815
19      0.89581
20      0.90753
21      0.91370
22      0.92008
23      0.89890
24      0.78956
25      0.78706
26      0.79615
27      0.91233
28      0.55700
29      0.62096
30      0.08328

归一化后最终得分:
    final score
0       0.02947
1       0.03385
2       0.03236
3       0.03973
4       0.02487
5       0.02622
6       0.02625
7       0.02731
8       0.02692
9       0.03242
10      0.03239
11      0.03040
12      0.03345
13      0.03210
14      0.03600
15      0.03596
16      0.03904
17      0.03534
18      0.03520
19      0.03854
20      0.03904
21      0.03931
22      0.03958
23      0.03867
24      0.03397
25      0.03386
26      0.03425
27      0.03925
28      0.02396
29      0.02671
30      0.00358

原始数据:
            0          1          2          3
0   79.554565  21.556030  10.490689  13.648721
1   79.336067  21.897203  10.083598  13.110179
2   79.895828  21.687683   9.580972  13.080399
3   79.349823  21.963305   9.902882  13.553323
4   79.551147  21.892693   9.902882  12.722704
5   79.321732  22.045456   9.972500  12.783135
6   79.321732  22.045456  10.012915  12.783135
7   79.283539  22.209743  10.054489  12.830421
8   79.322609  22.068089  10.132028  12.810817
9   79.287537  22.132996  10.170598  13.077462
10  79.889290  21.912746  10.315225  13.089062
11  79.100883  22.506905  10.344625  12.990024
12  79.100883  22.319195  10.178322  13.145349
13  79.057961  22.369602  10.344181  13.070665
14  79.169556  22.435591  10.228613  13.109693
15  79.066597  22.503206  10.241652  13.109693
16  79.001007  22.600718  10.197200  13.279206
17  78.977448  22.385725  10.249553  13.079790
18  79.131325  22.264093  10.195158  13.079790
19  79.180984  22.305857  10.195158  13.163020
20  79.205711  22.095505  10.132166  13.231439
21  79.205711  22.095505  10.079452  13.285520
22  79.205711  21.969179  10.188272  13.285520
23  79.235641  21.935949   9.969655  13.285520
24  79.219841  22.206446  10.049902  13.230452
25  79.196136  21.960131  10.049902  13.064794
26  79.424606  22.029604   9.997381  13.324735
27  79.825600  22.267355  10.081585  13.703675
28  79.773682  22.296730   9.956448  11.372197
29  79.724312  22.301895  10.344945  11.389767
30  79.762352  22.773792  10.450836  10.049046

去量纲 (求和归一化) 后的数据:
           0         1         2         3
0   0.032343  0.031375  0.033400  0.034059
1   0.032255  0.031872  0.032104  0.032715
2   0.032482  0.031567  0.030504  0.032641
3   0.032260  0.031968  0.031528  0.033821
4   0.032342  0.031866  0.031528  0.031748
5   0.032249  0.032088  0.031750  0.031899
6   0.032249  0.032088  0.031879  0.031899
7   0.032233  0.032327  0.032011  0.032017
8   0.032249  0.032121  0.032258  0.031968
9   0.032235  0.032215  0.032381  0.032633
10  0.032480  0.031895  0.032841  0.032662
11  0.032159  0.032760  0.032935  0.032415
12  0.032159  0.032486  0.032405  0.032803
13  0.032142  0.032560  0.032933  0.032616
14  0.032187  0.032656  0.032565  0.032714
15  0.032145  0.032754  0.032607  0.032714
16  0.032118  0.032896  0.032465  0.033137
17  0.032109  0.032583  0.032632  0.032639
18  0.032171  0.032406  0.032459  0.032639
19  0.032192  0.032467  0.032459  0.032847
20  0.032202  0.032161  0.032258  0.033018
21  0.032202  0.032161  0.032091  0.033153
22  0.032202  0.031977  0.032437  0.033153
23  0.032214  0.031928  0.031741  0.033153
24  0.032207  0.032322  0.031997  0.033015
25  0.032198  0.031964  0.031997  0.032602
26  0.032291  0.032065  0.031829  0.033250
27  0.032454  0.032411  0.032097  0.034196
28  0.032433  0.032454  0.031699  0.028378
29  0.032412  0.032461  0.032936  0.028422
30  0.032428  0.033148  0.033273  0.025076

数据信息熵权重:
                       0         1         2         3
entropy weight  0.003097  0.037381  0.085379  0.874143

指标权重:
                       0         1         2         3
entropy weight  0.003097  0.037381  0.085379  0.874143

未归一化得分:
    final score
0       0.97940
1       0.83705
2       0.82764
3       0.95513
4       0.73112
5       0.74775
6       0.74779
7       0.76080
8       0.75548
9       0.82837
10      0.83156
11      0.80474
12      0.84698
13      0.82675
14      0.83734
15      0.83737
16      0.88363
17      0.82918
18      0.82908
19      0.85183
20      0.87028
21      0.88489
22      0.88501
23      0.88447
24      0.86992
25      0.82466
26      0.89525
27      0.98772
28      0.36214
29      0.36727
30      0.02655

归一化后最终得分:
    final score
0       0.04013
1       0.03430
2       0.03391
3       0.03913
4       0.02996
5       0.03064
6       0.03064
7       0.03117
8       0.03095
9       0.03394
10      0.03407
11      0.03297
12      0.03470
13      0.03387
14      0.03431
15      0.03431
16      0.03620
17      0.03397
18      0.03397
19      0.03490
20      0.03566
21      0.03626
22      0.03626
23      0.03624
24      0.03564
25      0.03379
26      0.03668
27      0.04047
28      0.01484
29      0.01505
30      0.00109

原始数据:
            0          1          2          3
0   79.137421  23.586304  11.249568  15.196502
1   79.108170  23.560209  10.820970  14.375246
2   79.631004  23.350691  10.318343  14.345466
3   79.045258  23.442585  10.688270  14.932489
4   79.035492  23.413687  10.688270  14.075293
5   78.865524  23.436062  10.754486  14.348900
6   78.992493  23.436062  10.794901  14.348900
7   78.954300  23.694399  10.802315  14.396186
8   78.993370  23.439457  10.867324  14.376581
9   78.958298  23.439566  10.969735  14.416362
10  79.715698  22.961010  11.114362  14.427961
11  78.584534  23.790228  11.063099  14.252664
12  78.584534  23.514765  10.896795  14.407989
13  78.508682  23.689785  10.964857  14.105323
14  78.670059  23.755772  10.947086  14.262424
15  78.597237  23.509233  10.899153  14.262424
16  78.830246  23.520863  10.921148  14.431937
17  78.813301  23.305870  10.973501  14.182735
18  78.803230  23.184238  10.919106  14.182735
19  78.852890  23.226002  10.990088  14.362393
20  78.877617  23.115450  10.963353  14.430253
21  78.877617  23.156897  10.910639  14.484334
22  78.877617  23.061516  11.019460  14.484334
23  78.810760  23.028284  10.800842  14.484334
24  78.803978  23.298922  10.745881  14.375519
25  78.831841  23.049393  10.745881  14.375519
26  79.060310  23.149281  10.693360  14.404039
27  79.550095  23.291389  10.686138  14.634890
28  79.469208  23.245367  10.436413  13.041426
29  79.434845  23.296196  10.827145  11.951121
30  79.215752  23.797321  11.013682  10.621563

去量纲 (求和归一化) 后的数据:
           0         1         2         3
0   0.032321  0.032544  0.033432  0.034618
1   0.032309  0.032508  0.032159  0.032747
2   0.032522  0.032219  0.030665  0.032679
3   0.032283  0.032346  0.031764  0.034016
4   0.032279  0.032306  0.031764  0.032064
5   0.032210  0.032337  0.031961  0.032687
6   0.032262  0.032337  0.032081  0.032687
7   0.032246  0.032693  0.032103  0.032795
8   0.032262  0.032342  0.032296  0.032750
9   0.032248  0.032342  0.032601  0.032841
10  0.032557  0.031681  0.033031  0.032867
11  0.032095  0.032826  0.032878  0.032468
12  0.032095  0.032445  0.032384  0.032822
13  0.032064  0.032687  0.032586  0.032132
14  0.032130  0.032778  0.032534  0.032490
15  0.032100  0.032438  0.032391  0.032490
16  0.032195  0.032454  0.032456  0.032876
17  0.032189  0.032157  0.032612  0.032309
18  0.032184  0.031989  0.032450  0.032309
19  0.032205  0.032047  0.032661  0.032718
20  0.032215  0.031895  0.032582  0.032872
21  0.032215  0.031952  0.032425  0.032996
22  0.032215  0.031820  0.032749  0.032996
23  0.032187  0.031774  0.032099  0.032996
24  0.032185  0.032148  0.031936  0.032748
25  0.032196  0.031803  0.031936  0.032748
26  0.032289  0.031941  0.031779  0.032813
27  0.032489  0.032137  0.031758  0.033339
28  0.032456  0.032074  0.031016  0.029709
29  0.032442  0.032144  0.032177  0.027225
30  0.032353  0.032835  0.032731  0.024196

数据信息熵权重:
                      0         1         2         3
entropy weight  0.00348  0.023066  0.068602  0.904852

指标权重:
                      0         1         2         3
entropy weight  0.00348  0.023066  0.068602  0.904852

未归一化得分:
    final score
0       0.99873
1       0.82038
2       0.81345
3       0.94156
4       0.75479
5       0.81458
6       0.81460
7       0.82494
8       0.82069
9       0.82943
10      0.83196
11      0.79370
12      0.82757
13      0.76147
14      0.79579
15      0.79577
16      0.83281
17      0.77838
18      0.77835
19      0.81763
20      0.83243
21      0.84422
22      0.84426
23      0.84414
24      0.82038
25      0.82035
26      0.82655
27      0.87691
28      0.52883
29      0.29070
30      0.01229

归一化后最终得分:
    final score
0       0.04164
1       0.03420
2       0.03391
3       0.03925
4       0.03147
5       0.03396
6       0.03396
7       0.03439
8       0.03421
9       0.03458
10      0.03468
11      0.03309
12      0.03450
13      0.03174
14      0.03318
15      0.03317
16      0.03472
17      0.03245
18      0.03245
19      0.03409
20      0.03470
21      0.03519
22      0.03520
23      0.03519
24      0.03420
25      0.03420
26      0.03446
27      0.03656
28      0.02205
29      0.01212
30      0.00051

原始数据:
            0          1          2          3
0   79.270454  24.074968  10.566037  14.584608
1   79.421974  23.886597  10.094568  14.044095
2   79.944809  23.634201   9.591942  14.014315
3   79.303543  23.775175   9.961868  14.475039
4   79.353226  23.768717   9.961868  13.860060
5   79.123810  23.887783  10.031487  13.920491
6   79.123810  23.887783  10.071901  13.920491
7   79.085617  24.052071  10.113476  13.967777
8   79.124687  23.917387  10.297141  13.948173
9   79.089615  23.797237  10.335711  14.279982
10  79.691368  23.514111  10.480338  14.291581
11  78.961128  24.058109  10.509739  14.116283
12  78.961128  23.855890  10.343435  14.271608
13  78.918205  24.030910  10.509295  14.015072
14  79.016708  23.899731  10.393726  14.172173
15  78.918060  23.357395  10.406766  14.172173
16  79.177803  23.454906  10.270412  14.341686
17  79.098923  23.239914  10.322765  14.142270
18  79.088852  23.118282  10.268371  14.142270
19  79.166191  23.160046  10.268371  14.321928
20  79.190918  23.420570  10.205379  14.390347
21  79.190918  23.462017  10.152664  14.444427
22  79.190918  23.366636  10.261485  14.444427
23  79.124062  23.333405  10.042868  14.444427
24  79.117279  23.604042  10.215015  14.335612
25  79.093575  23.354513  10.215015  14.335612
26  79.322044  23.423985  10.162495  14.319608
27  79.698875  23.542967  10.263668  14.698548
28  79.685631  23.493753  10.138531  12.894372
29  79.694595  23.544582  10.398499  11.804068
30  79.381340  24.064264  10.565552  10.350878

去量纲 (求和归一化) 后的数据:
           0         1         2         3
0   0.032269  0.032845  0.033287  0.033647
1   0.032331  0.032588  0.031802  0.032400
2   0.032544  0.032244  0.030218  0.032331
3   0.032283  0.032436  0.031384  0.033394
4   0.032303  0.032427  0.031384  0.031975
5   0.032210  0.032590  0.031603  0.032114
6   0.032210  0.032590  0.031730  0.032114
7   0.032194  0.032814  0.031861  0.032224
8   0.032210  0.032630  0.032440  0.032178
9   0.032196  0.032466  0.032562  0.032944
10  0.032441  0.032080  0.033017  0.032971
11  0.032143  0.032822  0.033110  0.032566
12  0.032143  0.032546  0.032586  0.032925
13  0.032126  0.032785  0.033108  0.032333
14  0.032166  0.032606  0.032744  0.032695
15  0.032126  0.031866  0.032785  0.032695
16  0.032232  0.031999  0.032356  0.033086
17  0.032199  0.031706  0.032521  0.032626
18  0.032195  0.031540  0.032349  0.032626
19  0.032227  0.031597  0.032349  0.033041
20  0.032237  0.031952  0.032151  0.033198
21  0.032237  0.032009  0.031985  0.033323
22  0.032237  0.031879  0.032328  0.033323
23  0.032210  0.031834  0.031639  0.033323
24  0.032207  0.032203  0.032181  0.033072
25  0.032197  0.031862  0.032181  0.033072
26  0.032290  0.031957  0.032016  0.033035
27  0.032444  0.032119  0.032335  0.033909
28  0.032438  0.032052  0.031940  0.029747
29  0.032442  0.032122  0.032759  0.027232
30  0.032314  0.032831  0.033286  0.023879

数据信息熵权重:
                       0        1         2        3
entropy weight  0.002298  0.03235  0.088503  0.87685

指标权重:
                       0        1         2        3
entropy weight  0.002298  0.03235  0.088503  0.87685

未归一化得分:
    final score
0       0.97380
1       0.84914
2       0.84123
3       0.94676
4       0.80673
5       0.82070
6       0.82075
7       0.83166
8       0.82734
9       0.90358
10      0.90629
11      0.86611
12      0.90168
13      0.84284
14      0.87889
15      0.87877
16      0.91751
17      0.87179
18      0.87168
19      0.91282
20      0.92846
21      0.94060
22      0.94087
23      0.94007
24      0.91608
25      0.91596
26      0.91223
27      0.99172
28      0.58500
29      0.33460
30      0.02343

归一化后最终得分:
    final score
0       0.03760
1       0.03279
2       0.03248
3       0.03656
4       0.03115
5       0.03169
6       0.03169
7       0.03211
8       0.03194
9       0.03489
10      0.03499
11      0.03344
12      0.03482
13      0.03254
14      0.03394
15      0.03393
16      0.03543
17      0.03366
18      0.03366
19      0.03525
20      0.03585
21      0.03632
22      0.03633
23      0.03630
24      0.03537
25      0.03537
26      0.03522
27      0.03829
28      0.02259
29      0.01292
30      0.00090

原始数据:
            0          1          2          3
0   79.755402  22.704536  10.467842  12.218777
1   79.514107  22.932007  10.145816  12.733525
2   80.073868  22.679611   9.631437  12.703745
3   79.459221  22.929638   9.968149  13.065671
4   79.602386  22.923180   9.968149  12.331566
5   79.372971  23.042246  10.004985  12.391996
6   79.372971  23.042246  10.045400  12.391996
7   79.334778  23.153605  10.086974  12.439282
8   79.373848  23.018921  10.379776  12.419678
9   79.338776  22.972540  10.241261  12.686323
10  79.555679  22.840509  10.385888  12.679414
11  79.116806  23.309511  10.415289  12.713449
12  79.116806  22.992085  10.248985  12.868773
13  79.073883  23.042158  10.414845  12.794090
14  79.172386  22.800741  10.299276  13.189000
15  79.073738  22.383352  10.312316  13.189000
16  79.333481  22.480864   9.880606  13.157485
17  79.247986  22.477329  10.126229  12.958069
18  79.237915  22.355698  10.071835  12.958069
19  79.315254  22.464787  10.071835  13.137727
20  79.291183  22.725311  10.008842  13.061252
21  79.291183  22.766758   9.956128  12.926972
22  79.291183  22.640434  10.064949  13.157473
23  79.274651  22.640434   9.846332  13.157473
24  79.224159  22.968161  10.064422  12.716629
25  79.200455  22.721848  10.064422  12.716629
26  79.461357  22.837597  10.011901  12.700345
27  79.838188  22.837597  10.161432  13.055492
28  79.864014  22.782589  10.040726  12.527368
29  79.845085  22.833418  10.390121  12.653388
30  79.436348  23.446955  10.487106  10.675463

去量纲 (求和归一化) 后的数据:
           0         1         2         3
0   0.032402  0.032080  0.033309  0.030983
1   0.032304  0.032401  0.032284  0.032288
2   0.032531  0.032045  0.030648  0.032212
3   0.032281  0.032398  0.031719  0.033130
4   0.032340  0.032389  0.031719  0.031269
5   0.032246  0.032557  0.031836  0.031422
6   0.032246  0.032557  0.031965  0.031422
7   0.032231  0.032715  0.032097  0.031542
8   0.032247  0.032524  0.033029  0.031492
9   0.032232  0.032459  0.032588  0.032168
10  0.032321  0.032272  0.033048  0.032151
11  0.032142  0.032935  0.033142  0.032237
12  0.032142  0.032486  0.032613  0.032631
13  0.032125  0.032557  0.033141  0.032441
14  0.032165  0.032216  0.032773  0.033443
15  0.032125  0.031626  0.032814  0.033443
16  0.032230  0.031764  0.031441  0.033363
17  0.032196  0.031759  0.032222  0.032857
18  0.032191  0.031587  0.032049  0.032857
19  0.032223  0.031741  0.032049  0.033313
20  0.032213  0.032109  0.031849  0.033119
21  0.032213  0.032168  0.031681  0.032778
22  0.032213  0.031989  0.032027  0.033363
23  0.032206  0.031989  0.031331  0.033363
24  0.032186  0.032453  0.032025  0.032245
25  0.032176  0.032104  0.032025  0.032245
26  0.032282  0.032268  0.031858  0.032204
27  0.032435  0.032268  0.032334  0.033104
28  0.032446  0.032190  0.031950  0.031765
29  0.032438  0.032262  0.033062  0.032085
30  0.032272  0.033129  0.033370  0.027069

数据信息熵权重:
                       0        1        2         3
entropy weight  0.004722  0.06203  0.20498  0.728268

指标权重:
                       0        1        2         3
entropy weight  0.004722  0.06203  0.20498  0.728268

未归一化得分:
    final score
0       0.61626
1       0.81529
2       0.78802
3       0.92420
4       0.65582
5       0.68015
6       0.68073
7       0.69998
8       0.69517
9       0.79863
10      0.79712
11      0.81162
12      0.86978
13      0.84296
14      0.97053
15      0.96064
16      0.92817
17      0.89598
18      0.89260
19      0.94168
20      0.92432
21      0.88004
22      0.94634
23      0.92697
24      0.80711
25      0.80637
26      0.79929
27      0.93344
28      0.73305
29      0.78691
30      0.09314

归一化后最终得分:
    final score
0       0.02475
1       0.03274
2       0.03164
3       0.03711
4       0.02634
5       0.02731
6       0.02734
7       0.02811
8       0.02792
9       0.03207
10      0.03201
11      0.03259
12      0.03493
13      0.03385
14      0.03897
15      0.03858
16      0.03727
17      0.03598
18      0.03584
19      0.03782
20      0.03712
21      0.03534
22      0.03800
23      0.03722
24      0.03241
25      0.03238
26      0.03210
27      0.03748
28      0.02944
29      0.03160
30      0.00374

原始数据:
            0          1          2          3
0   79.826454  22.702297  11.094396  12.317365
1   79.585159  22.929770  10.684255  12.832113
2   80.144920  22.677374  10.169876  12.802333
3   79.543396  22.747183  10.506588  13.164259
4   79.686562  22.740725  10.506588  12.430154
5   79.457146  22.859791  10.576206  12.490584
6   79.457146  22.859791  10.616621  12.490584
7   79.418953  22.971149  10.658195  12.537870
8   79.458023  22.836466  10.950997  12.518266
9   79.484818  22.716316  10.812483  12.784911
10  79.639854  22.584286  10.957109  12.778002
11  79.200981  23.270235  10.986510  12.812037
12  79.200981  23.014696  10.820207  12.967361
13  79.158058  23.002882  10.986066  12.892679
14  79.256561  22.761465  10.870498  13.287588
15  79.157913  22.389420  10.883537  13.287588
16  79.417656  22.486931  10.451827  13.256073
17  79.332161  22.483397  10.697451  13.056657
18  79.322090  22.361765  10.643056  13.056657
19  79.399429  22.470854  10.643056  13.236315
20  79.355911  22.731379  10.460920  13.159840
21  79.355911  22.727482  10.408206  13.025560
22  79.355911  22.601158  10.517027  13.256061
23  79.339386  22.601158  10.298409  13.256061
24  79.288887  22.928885  10.476222  12.861996
25  79.332146  22.682571  10.476222  12.861996
26  79.560616  22.752043  10.423701  12.845712
27  79.937447  22.975723  10.519928  13.375242
28  79.963272  22.940264  10.399221  12.425081
29  79.944344  22.991093  10.748617  12.767580
30  79.658981  23.608404  10.777570  10.941514

去量纲 (求和归一化) 后的数据:
           0         1         2         3
0   0.032394  0.032138  0.033617  0.030966
1   0.032296  0.032460  0.032374  0.032260
2   0.032523  0.032102  0.030816  0.032185
3   0.032279  0.032201  0.031836  0.033095
4   0.032337  0.032192  0.031836  0.031249
5   0.032244  0.032361  0.032047  0.031401
6   0.032244  0.032361  0.032169  0.031401
7   0.032229  0.032518  0.032295  0.031520
8   0.032244  0.032328  0.033183  0.031471
9   0.032255  0.032158  0.032763  0.032141
10  0.032318  0.031971  0.033201  0.032124
11  0.032140  0.032942  0.033290  0.032209
12  0.032140  0.032580  0.032786  0.032600
13  0.032123  0.032563  0.033289  0.032412
14  0.032163  0.032221  0.032939  0.033405
15  0.032123  0.031695  0.032978  0.033405
16  0.032228  0.031833  0.031670  0.033325
17  0.032193  0.031828  0.032414  0.032824
18  0.032189  0.031656  0.032250  0.032824
19  0.032221  0.031810  0.032250  0.033276
20  0.032203  0.032179  0.031698  0.033084
21  0.032203  0.032173  0.031538  0.032746
22  0.032203  0.031995  0.031868  0.033325
23  0.032196  0.031995  0.031205  0.033325
24  0.032176  0.032458  0.031744  0.032335
25  0.032193  0.032110  0.031744  0.032335
26  0.032286  0.032208  0.031585  0.032294
27  0.032439  0.032525  0.031876  0.033625
28  0.032449  0.032475  0.031511  0.031236
29  0.032442  0.032547  0.032569  0.032097
30  0.032326  0.033420  0.032657  0.027507

数据信息熵权重:
                       0         1         2         3
entropy weight  0.005296  0.064872  0.236099  0.693733

指标权重:
                       0         1         2         3
entropy weight  0.005296  0.064872  0.236099  0.693733

未归一化得分:
    final score
0       0.57086
1       0.77093
2       0.73838
3       0.88063
4       0.60639
5       0.63231
6       0.63316
7       0.65323
8       0.64998
9       0.75457
10      0.75365
11      0.77003
12      0.82846
13      0.80182
14      0.94369
15      0.93614
16      0.89552
17      0.85440
18      0.85064
19      0.90807
20      0.87555
21      0.82957
22      0.90318
23      0.88169
24      0.77507
25      0.77407
26      0.76607
27      0.92267
28      0.60241
29      0.74722
30      0.08881

归一化后最终得分:
    final score
0       0.02419
1       0.03267
2       0.03129
3       0.03732
4       0.02570
5       0.02679
6       0.02683
7       0.02768
8       0.02754
9       0.03197
10      0.03194
11      0.03263
12      0.03511
13      0.03398
14      0.03999
15      0.03967
16      0.03795
17      0.03620
18      0.03605
19      0.03848
20      0.03710
21      0.03515
22      0.03827
23      0.03736
24      0.03284
25      0.03280
26      0.03246
27      0.03910
28      0.02553
29      0.03166
30      0.00376

原始数据:
            0          1          2          3
0   79.780380  22.883705  10.492202  14.541156
1   79.516083  23.111177  10.082061  14.991610
2   80.075844  22.858782   9.567682  14.961829
3   79.474319  22.928591   9.904394  15.353363
4   79.617485  22.922132   9.904394  14.619258
5   79.388069  23.041199   9.974012  14.679688
6   79.388069  23.041199  10.014427  14.679688
7   79.349876  23.152557  10.056002  14.726974
8   79.388947  23.017874  10.348804  14.707370
9   79.415741  22.897724  10.383832  14.974015
10  79.570778  22.765694  10.528459  14.985615
11  79.015457  23.451643  10.557859  15.019650
12  79.015457  23.196104  10.391556  15.174974
13  78.972534  23.184290  10.557415  15.100291
14  79.071037  22.942873  10.441847  15.509471
15  78.972389  22.580626  10.454886  15.509471
16  79.232132  22.678137  10.023176  15.477956
17  79.146637  22.870731  10.268800  15.278540
18  79.136566  22.749100  10.214405  15.278540
19  79.213905  22.858189  10.214405  15.361770
20  79.170387  22.936022  10.151413  15.285295
21  79.170387  22.932125  10.098699  15.151015
22  79.170387  22.805801  10.207520  15.381516
23  79.153862  22.805801   9.988902  15.381516
24  79.103363  22.970446  10.166715  14.920248
25  79.087288  22.724133  10.166715  14.754590
26  79.315758  22.793604  10.155952  14.971167
27  79.766273  23.017284  10.322860  15.350107
28  79.792099  22.981825  10.202153  12.680475
29  79.773170  23.032654  10.551549  12.746693
30  79.424942  23.649965  10.589279  11.301964

去量纲 (求和归一化) 后的数据:
           0         1         2         3
0   0.032435  0.032150  0.033100  0.031690
1   0.032328  0.032469  0.031806  0.032672
2   0.032556  0.032115  0.030184  0.032607
3   0.032311  0.032213  0.031246  0.033460
4   0.032369  0.032204  0.031246  0.031860
5   0.032276  0.032371  0.031466  0.031992
6   0.032276  0.032371  0.031593  0.031992
7   0.032260  0.032528  0.031724  0.032095
8   0.032276  0.032338  0.032648  0.032052
9   0.032287  0.032170  0.032758  0.032633
10  0.032350  0.031984  0.033215  0.032659
11  0.032124  0.032948  0.033307  0.032733
12  0.032124  0.032589  0.032783  0.033071
13  0.032107  0.032572  0.033306  0.032909
14  0.032147  0.032233  0.032941  0.033800
15  0.032107  0.031724  0.032983  0.033800
16  0.032213  0.031861  0.031621  0.033732
17  0.032178  0.032132  0.032395  0.033297
18  0.032174  0.031961  0.032224  0.033297
19  0.032205  0.032114  0.032224  0.033478
20  0.032187  0.032223  0.032025  0.033312
21  0.032187  0.032218  0.031859  0.033019
22  0.032187  0.032040  0.032202  0.033521
23  0.032181  0.032040  0.031512  0.033521
24  0.032160  0.032272  0.032073  0.032516
25  0.032154  0.031926  0.032073  0.032155
26  0.032247  0.032023  0.032039  0.032627
27  0.032430  0.032338  0.032566  0.033453
28  0.032440  0.032288  0.032185  0.027635
29  0.032432  0.032359  0.033287  0.027779
30  0.032291  0.033226  0.033407  0.024631

数据信息熵权重:
                       0         1         2         3
entropy weight  0.002584  0.018357  0.116468  0.862591

指标权重:
                       0         1         2         3
entropy weight  0.002584  0.018357  0.116468  0.862591

未归一化得分:
    final score
0       0.76995
1       0.87597
2       0.86627
3       0.95699
4       0.78752
5       0.80200
6       0.80210
7       0.81340
8       0.80929
9       0.87259
10      0.87548
11      0.88364
12      0.92026
13      0.90277
14      0.99406
15      0.99309
16      0.97999
17      0.94409
18      0.94372
19      0.96276
20      0.94489
21      0.91343
22      0.96703
23      0.96396
24      0.85939
25      0.82016
26      0.87137
27      0.96109
28      0.32802
29      0.34432
30      0.03216

归一化后最终得分:
    final score
0       0.02989
1       0.03400
2       0.03363
3       0.03715
4       0.03057
5       0.03113
6       0.03114
7       0.03157
8       0.03141
9       0.03387
10      0.03398
11      0.03430
12      0.03572
13      0.03504
14      0.03859
15      0.03855
16      0.03804
17      0.03665
18      0.03663
19      0.03737
20      0.03668
21      0.03546
22      0.03754
23      0.03742
24      0.03336
25      0.03184
26      0.03382
27      0.03731
28      0.01273
29      0.01337
30      0.00125

原始数据:
            0          1          2          3
0   79.758018  22.894794  11.111638  12.084625
1   79.634705  23.122267  10.701497  12.599374
2   80.194466  22.869871  10.187119  12.569593
3   79.592941  22.939680  10.523830  12.931520
4   79.736107  22.933222  10.523830  12.197413
5   79.506691  23.052288  10.593449  12.257845
6   79.506691  23.052288  10.633863  12.257845
7   79.468498  23.163647  10.675438  12.305131
8   79.507568  23.028963  10.968240  12.285526
9   79.472496  22.908813  10.829725  12.552172
10  79.689400  22.776783  10.974352  12.545262
11  79.250526  23.462732  11.003753  12.579297
12  79.250526  23.145306  10.837449  12.734622
13  79.207603  23.195379  11.003308  12.659940
14  79.306107  22.953962  10.887740  13.054849
15  79.207458  22.536573  10.900780  13.054849
16  79.467201  22.634085  10.469069  13.023334
17  79.381706  22.494667  10.714693  12.823917
18  79.371635  22.373035  10.660298  12.823917
19  79.448975  22.482124  10.660298  13.003575
20  79.405457  22.742649  10.478163  12.927100
21  79.405457  22.784096  10.425448  12.792820
22  79.405457  22.657772  10.534269  13.023321
23  79.388924  22.657772  10.315652  13.023321
24  79.338432  22.985498  10.493464  12.629256
25  79.314728  22.739185  10.493464  12.629256
26  79.543198  22.808657  10.440944  12.612972
27  79.920029  23.032337  10.537170  13.142503
28  79.945854  22.996878  10.416464  12.192342
29  79.926926  23.047707  10.765860  12.572759
30  79.641563  23.699150  10.787165  10.707007

去量纲 (求和归一化) 后的数据:
           0         1         2         3
0   0.032354  0.032238  0.033616  0.030939
1   0.032304  0.032559  0.032375  0.032257
2   0.032531  0.032203  0.030819  0.032180
3   0.032287  0.032302  0.031837  0.033107
4   0.032345  0.032292  0.031837  0.031228
5   0.032252  0.032460  0.032048  0.031382
6   0.032252  0.032460  0.032170  0.031382
7   0.032236  0.032617  0.032296  0.031503
8   0.032252  0.032427  0.033182  0.031453
9   0.032238  0.032258  0.032763  0.032136
10  0.032326  0.032072  0.033200  0.032118
11  0.032148  0.033038  0.033289  0.032205
12  0.032148  0.032591  0.032786  0.032603
13  0.032130  0.032662  0.033288  0.032412
14  0.032170  0.032322  0.032938  0.033423
15  0.032130  0.031734  0.032978  0.033423
16  0.032236  0.031871  0.031672  0.033342
17  0.032201  0.031675  0.032415  0.032832
18  0.032197  0.031504  0.032250  0.032832
19  0.032228  0.031657  0.032250  0.033292
20  0.032211  0.032024  0.031699  0.033096
21  0.032211  0.032082  0.031540  0.032752
22  0.032211  0.031905  0.031869  0.033342
23  0.032204  0.031905  0.031208  0.033342
24  0.032183  0.032366  0.031746  0.032333
25  0.032174  0.032019  0.031746  0.032333
26  0.032266  0.032117  0.031587  0.032292
27  0.032419  0.032432  0.031878  0.033647
28  0.032430  0.032382  0.031513  0.031215
29  0.032422  0.032454  0.032570  0.032189
30  0.032306  0.033371  0.032634  0.027412

数据信息熵权重:
                       0         1         2         3
entropy weight  0.004249  0.075632  0.226471  0.693648

指标权重:
                       0         1         2         3
entropy weight  0.004249  0.075632  0.226471  0.693648

未归一化得分:
    final score
0       0.57075
1       0.77158
2       0.74025
3       0.88238
4       0.60710
5       0.63300
6       0.63379
7       0.65387
8       0.65013
9       0.75477
10      0.75352
11      0.77034
12      0.82863
13      0.80204
14      0.94392
15      0.93320
16      0.89644
17      0.85226
18      0.84827
19      0.90481
20      0.87540
21      0.83016
22      0.90259
23      0.88277
24      0.77568
25      0.77424
26      0.76654
27      0.92381
28      0.60307
29      0.76232
30      0.09082

归一化后最终得分:
    final score
0       0.02417
1       0.03267
2       0.03134
3       0.03736
4       0.02570
5       0.02680
6       0.02683
7       0.02768
8       0.02753
9       0.03196
10      0.03190
11      0.03262
12      0.03508
13      0.03396
14      0.03997
15      0.03951
16      0.03796
17      0.03608
18      0.03592
19      0.03831
20      0.03706
21      0.03515
22      0.03822
23      0.03738
24      0.03284
25      0.03278
26      0.03246
27      0.03911
28      0.02553
29      0.03228
30      0.00385


ans_410  # 4-10号两个系统温度数据

[1367.76, 844.47]


ans_411  # 4-11号两个系统温度数据

[1397.76, 864.47]

	时间 (Time)	指标A (index A)	指标B (index B)	指标C (index C)	指标D (index D)
0	2022-01-25 02:50:00	79.08	23.52	12.41	17.86
1	2022-01-25 03:50:00	79.29	22.94	11.72	17.86
2	2022-01-25 04:50:00	79.95	21.42	10.68	17.63
3	2022-01-25 05:50:00	80.20	21.20	10.16	16.92
4	2022-01-25 06:50:00	80.38	20.75	10.16	15.75
...	...	...	...	...	...
1720	2022-04-07 19:50:00	79.82	23.84	11.03	13.52
1721	2022-04-07 20:50:00	78.98	25.36	11.37	12.85
1722	2022-04-07 21:50:00	78.86	25.40	11.37	11.42
1723	2022-04-07 22:50:00	79.10	25.58	11.37	11.55
1724	2022-04-07 23:50:00	79.32	24.82	11.03	11.55

	过程数据1 (Process parameter 1)	过程数据2 (Process parameter 2)	过程数据3 (Process parameter 3)	过程数据4 (Process parameter 4)
过程数据1 (Process parameter 1)	1.000000	NaN	0.058849	-0.147755
过程数据2 (Process parameter 2)	NaN	NaN	NaN	NaN
过程数据3 (Process parameter 3)	0.058849	NaN	1.000000	-0.497288
过程数据4 (Process parameter 4)	-0.147755	NaN	-0.497288	1.000000

	时间 (Time)	原矿质量
0	2022-01-25 02:30:11	407.39
1	2022-01-25 02:30:11	407.39
2	2022-01-25 02:30:11	407.39
3	2022-01-25 05:30:13	406.89
4	2022-01-25 05:30:13	406.89
...	...	...
1720	2022-04-07 20:30:17	485.59
1721	2022-04-07 20:30:17	485.59
1722	2022-04-07 23:30:10	440.34
1723	2022-04-07 23:30:10	440.34
1724	2022-04-07 23:30:10	440.34

	系统I温度 (Temperature of system I)	系统II温度 (Temperature of system II)	原矿参数1 (Mineral parameter 1)	原矿参数2 (Mineral parameter 2)	原矿参数3 (Mineral parameter 3)	原矿参数4 (Mineral parameter 4)	原矿质量
0	1347.49	950.40	55.26	108.03	43.29	20.92	407.39
1	1274.43	938.20	55.26	108.03	43.29	20.92	407.39
2	1273.86	938.16	55.26	108.03	43.29	20.92	407.39
3	1273.51	937.49	55.26	108.03	43.29	20.92	406.89
4	1272.84	936.67	55.26	108.03	43.29	20.92	406.89
...	...	...	...	...	...	...	...
1720	437.71	540.70	54.4	105.14	49.03	20.82	485.59
1721	494.23	557.21	54.4	105.14	49.03	20.82	485.59
1722	495.47	557.68	54.4	105.14	49.03	20.82	440.34
1723	494.41	572.00	54.4	105.14	49.03	20.82	440.34
1724	495.03	571.61	54.4	105.14	49.03	20.82	440.34

	系统I温度 (Temperature of system I)	系统II温度 (Temperature of system II)	原矿参数1 (Mineral parameter 1)	原矿参数2 (Mineral parameter 2)	原矿参数3 (Mineral parameter 3)	原矿参数4 (Mineral parameter 4)	原矿质量
0	1347.49	950.40	55.26	108.03	43.29	20.92	407.39
1	1274.43	938.20	55.26	108.03	43.29	20.92	407.39
2	1273.86	938.16	55.26	108.03	43.29	20.92	407.39
3	1273.51	937.49	55.26	108.03	43.29	20.92	406.89
4	1272.84	936.67	55.26	108.03	43.29	20.92	406.89
...	...	...	...	...	...	...	...
1720	437.71	540.70	54.4	105.14	49.03	20.82	485.59
1721	494.23	557.21	54.4	105.14	49.03	20.82	485.59
1722	495.47	557.68	54.4	105.14	49.03	20.82	440.34
1723	494.41	572.00	54.4	105.14	49.03	20.82	440.34
1724	495.03	571.61	54.4	105.14	49.03	20.82	440.34

问题4-思路2¶

准备数据¶

表1——温度(temperature)¶

表2——产品质量(quality of the products)¶

表3——原矿参数(mineral parameter)¶

表4——过程数据(process parameter)¶

准备数据¶

灵敏性分析¶

预测系统温度¶

调优获得系统设定温度¶

使用 TOPSIS 评出最优温度¶

	是否合格
0	False
1	False
2	False
3	False
4	False
...	...
1720	True
1721	False
1722	False
1723	False
1724	False

	时间 (Time)	过程数据3 (Process parameter 3)	过程数据4 (Process parameter 4)
0	2022-01-25 02:30:11	226.16	181.23
1	2022-01-25 05:30:13	242.44	164.45
2	2022-01-25 08:30:33	236.59	146.70
3	2022-01-25 11:30:45	217.01	158.23
4	2022-01-25 14:30:34	233.10	149.76
...	...	...	...
614	2022-04-11 11:30:37	275.99	152.83
615	2022-04-11 14:30:59	288.89	159.78
616	2022-04-11 17:30:31	287.04	156.68
617	2022-04-11 20:30:41	270.51	160.56
618	2022-04-11 23:30:00	247.16	166.01

	原矿参数1	原矿参数2	原矿参数3	原矿参数4	过程数据1	过程数据2
0	56.71	111.46	46.67	18.48	290.75	160.56
1	56.71	111.46	46.67	18.48	301.96	155.91
2	56.71	111.46	46.67	18.48	288.89	140.62
3	56.71	111.46	46.67	18.48	283.34	148.99
4	56.71	111.46	46.67	18.48	266.88	165.23
5	56.71	111.46	46.67	18.48	265.06	159.01
6	56.71	111.46	46.67	18.48	261.45	155.91
7	56.71	111.46	46.67	18.48	268.69	159.01

	0	1
0	287.76	744.47
1	287.76	754.47
2	297.76	744.47
3	297.76	754.47
4	297.76	954.47
...	...	...
271	1397.76	884.47
272	1397.76	894.47
273	1397.76	904.47
274	1397.76	914.47
275	1397.76	924.47

	0	1
0	1397.76	464.47
1	1397.76	604.47
2	1397.76	614.47
3	1397.76	624.47
4	1397.76	634.47
...	...	...
26	1397.76	854.47
27	1397.76	864.47
28	1397.76	874.47
29	1397.76	884.47
30	1397.76	904.47