import pandas as pd
from statsmodels.tsa.statespace.sarimax import SARIMAX
#from prophet import Prophet
#from pmdarima import auto_arima
from statsmodels.tsa.holtwinters import ExponentialSmoothing as ES1
from statsmodels.tsa.api import ExponentialSmoothing as ES2
from statsmodels.tsa.ar_model import AutoReg
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.seasonal import STL
from statsmodels.tsa.api import Holt
import numpy as np
from sklearn.svm import SVR
# import pandas as pd
# from statsmodels.tsa.statespace.sarimax import SARIMAX
# from prophet import Prophet
# from pmdarima import auto_arima
# from statsmodels.tsa.holtwinters import ExponentialSmoothing as ES1
# from statsmodels.tsa.api import ExponentialSmoothing as ES2
# import numpy as np
# from sklearn.svm import SVR
df = xl('A1:B73', headers=True)
将数字列转换为时间戳
df['年月'] = pd.to_datetime(df['年月'], unit='s')
将时间戳列转换为时间字符串
df['年月'] = df['年月'].dt.strftime('%Y-%m-%d')

销售数据 = df[['年月','本月实际销售金额']]
#print(data)

所有预测 = pd.DataFrame()
for i in range(30, len(销售数据)):

数据子集 = 销售数据.iloc[:i+1].copy()
# 获取最后一个销售额
最后销售额 = [数据子集['本月实际销售金额'].iloc[-1]]

# 获取最后一个年月
最后年月 = [数据子集.index[-1]]

# SARIMA
SARIMA模型 = SARIMAX(数据子集['本月实际销售金额'], order=(1, 1, 1), seasonal_order=(1, 1, 1, 12))
SARIMA模型拟合 = SARIMA模型.fit(disp=False)
SARIMA预测 = SARIMA模型拟合.forecast(steps=1)

# Prophet
#数据_Prophet = 数据子集.reset_index().rename(columns={'年月': 'ds', '本月实际销售金额': 'y'})
#Prophet模型 = Prophet()
#Prophet模型.fit(数据_Prophet)
#未来 = Prophet模型.make_future_dataframe(periods=1, freq='MS')
#Prophet预测 = Prophet模型.predict(未来)
#Prophet预测数据 = Prophet预测[['ds', 'yhat']].iloc[-1:]

# ARIMA
#ARIMA模型 = auto_arima(数据子集['本月实际销售金额'], seasonal=True, m=12)
#ARIMA预测 = ARIMA模型.predict(n_periods=1)

# ExponentialSmoothing方法1
ES1模型 = ES1(数据子集['本月实际销售金额'], trend='add', seasonal='add', seasonal_periods=12)
ES1模型拟合 = ES1模型.fit()
ES1预测 = ES1模型拟合.forecast(steps=1)

# # ExponentialSmoothing方法2
# ES2模型 = ES2(数据子集['本月实际销售金额'], trend='add', seasonal='add', seasonal_periods=12)
# ES2模型拟合 = ES2模型.fit()
# ES2预测 = ES2模型拟合.forecast(steps=6)

# SVM
SVM模型 = SVR()
时间序列 = np.arange(len(数据子集)).reshape(-1, 1)
SVM模型.fit(时间序列, 数据子集['本月实际销售金额'].values)
SVM预测 = SVM模型.predict(np.array([np.arange(len(数据子集), len(数据子集)+1)]).T)

# # 移动平均预测
# 数据子集['移动平均'] = 数据子集['本月实际销售金额'].rolling(window=36).mean()
# 预测_移动平均 = [数据子集['移动平均'].iloc[-1]] * 6  # 使用最后一个移动平均值作为预测值

# 自回归模型（AR）
AR模型 = AutoReg(数据子集['本月实际销售金额'], lags=1)
AR模型拟合 = AR模型.fit()
AR预测 = AR模型拟合.predict(start=len(数据子集), end=len(数据子集) + 0)

# 移动平均模型（MA）
MA模型 = ARIMA(数据子集['本月实际销售金额'], order=(0, 0, 1))
MA模型拟合 = MA模型.fit()
MA预测 = MA模型拟合.forecast(steps=1)

# 季节性分解时间序列（STL）
#stl = STL(数据子集['本月实际销售金额'], seasonal=13)
#stl_result = stl.fit()
#deseasonalized_data = 数据子集['本月实际销售金额'] - stl_result.seasonal
#STL_ARIMA模型 = ARIMA(deseasonalized_data, order=(1, 1, 0))
#STL_ARIMA模型拟合 = STL_ARIMA模型.fit()
#STL_ARIMA预测 = STL_ARIMA模型拟合.forecast(steps=1)
#STL预测 = STL_ARIMA预测 + stl_result.seasonal.iloc[-1:].values

# Holt-Winters方法（三重指数平滑）
HW模型 = ES1(数据子集['本月实际销售金额'], trend='add', seasonal='mul', seasonal_periods=12)
HW模型拟合 = HW模型.fit()
HW预测 = HW模型拟合.forecast(steps=1)

# ... 在此添加其他方法的代码 ...

预测数据 = pd.DataFrame({
    '数据子集最后年月': 最后年月,
    #'预测的年月': [数据子集.index[-1] + pd.DateOffset(months=1)],
    '销售金额': 最后销售额,
    #'SARIMA预测销售金额累计值': [sum(SARIMA预测)],
    #'Prophet预测销售金额累计值': [Prophet预测数据['yhat'].sum()],
    #'ARIMA预测销售金额累计值': [sum(ARIMA预测)],
    'ExponentialSmoothing方法1预测销售金额累计值': [sum(ES1预测)],
    # 'ExponentialSmoothing方法2预测销售金额累计值': [sum(ES2预测)],
    'SVM预测销售金额累计值': [sum(SVM预测)],
    # '移动平均预测销售金额累计值': [sum(预测_移动平均)]
    'AR预测销售金额累计值': [sum(AR预测)],
    'MA预测销售金额累计值': [sum(MA预测)],
    #'STL预测销售金额累计值': [sum(STL预测)],
    'Holt-Winters预测销售金额累计值': [sum(HW预测)],
    # ... 在此添加其他方法的预测结果 ...
})

#所有预测 = 所有预测.append(预测数据)
#所有预测 = 所有预测.append(预测数据, ignore_index=True) # GPT4
#所有预测 = 所有预测.append(预测数据, ignore_index=True) #coze
所有预测 = pd.concat([所有预测, 预测数据], ignore_index=True)

print(所有预测)

所有预测.to_excel('预测销售数据-1个月-2023.6.2-2.xlsx', index=False)

/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/statespace/sarimax.py:1009: UserWarning: Non-invertible starting seasonal moving average Using zeros as starting parameters.
warn('Non-invertible starting seasonal moving average'
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/statespace/sarimax.py:1009: UserWarning: Non-invertible starting seasonal moving average Using zeros as starting parameters.
warn('Non-invertible starting seasonal moving average'
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/statespace/sarimax.py:1009: UserWarning: Non-invertible starting seasonal moving average Using zeros as starting parameters.
warn('Non-invertible starting seasonal moving average'
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/statespace/sarimax.py:1009: UserWarning: Non-invertible starting seasonal moving average Using zeros as starting parameters.
warn('Non-invertible starting seasonal moving average'
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
warnings.warn(
dG9vIG1hbnkgb3V0cHV0cy4uLmlnbm9yZWQ=

"too many outputs...ignored"
这段消息是指出现了太多的输出，但被忽略了。
如果您的预期是获取某个特定输出或者做了某个特定操作后出现了这条消息，那可能意味着系统忽略了一部分输出信息。

/opt/conda/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.

warnings.warn(

Traceback (most recent call last):

File "<wps>", line 132, in <module>

File "/opt/conda/lib/python3.11/site-packages/pandas/core/generic.py", line 2345, in to_excel

formatter.write(

File "/opt/conda/lib/python3.11/site-packages/pandas/io/formats/excel.py", line 946, in write

writer = ExcelWriter( # type: ignore[abstract]

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "/opt/conda/lib/python3.11/site-packages/pandas/io/excel/_openpyxl.py", line 61, in init

super().init(

File "/opt/conda/lib/python3.11/site-packages/pandas/io/excel/_base.py", line 1263, in init

self._handles = get_handle(

^^^^^^^^^^^

File "/opt/conda/lib/python3.11/site-packages/pandas/io/common.py", line 872, in get_handle

handle = open(handle, ioargs.mode)

^^^^^^^^^^^^^^^^^^^^^^^^^

PermissionError: disabled for open 预测销售数据-1个月-2023.6.2-2.xlsx

你在尝试以写模式打开一个文件时出现了权限错误。通常这是因为文件可能被其他程序锁定，或者由于文件系统权限限制导致程序无法访问文件。

这里有几个可以尝试的方法来解决这个问题：
确保该文件没有被其他程序占用。可以尝试关闭所有其他程序，然后再次运行你的代码。

检查你的程序是否具有足够的权限来写入文件。确保你当前的用户具有对文件所在目录的写入权限。

如果你使用的是Jupyter notebook或者类似的交互式环境，尝试重启环境后再次运行代码。

如果你在使用共享文件系统，确保其他用户没有锁定该文件。

import pandas as pd

from statsmodels.tsa.statespace.sarimax import SARIMAX

#from prophet import Prophet

#from pmdarima import auto_arima

from statsmodels.tsa.holtwinters import ExponentialSmoothing as ES1

from statsmodels.tsa.api import ExponentialSmoothing as ES2

from statsmodels.tsa.ar_model import AutoReg

from statsmodels.tsa.arima.model import ARIMA

from statsmodels.tsa.seasonal import STL

from statsmodels.tsa.api import Holt

import numpy as np

from sklearn.svm import SVR

import pandas as pd
from statsmodels.tsa.statespace.sarimax import SARIMAX
from prophet import Prophet
from pmdarima import auto_arima
from statsmodels.tsa.holtwinters import ExponentialSmoothing as ES1
from statsmodels.tsa.api import ExponentialSmoothing as ES2
import numpy as np
from sklearn.svm import SVR
df = xl('A1:B73', headers=True)
将数字列转换为时间戳
df['年月'] = pd.to_datetime(df['年月'], unit='s')
将时间戳列转换为时间字符串
df['年月'] = df['年月'].dt.strftime('%Y-%m-%d')

销售数据 = df[['年月','本月实际销售金额']]
#print(data)

所有预测 = pd.DataFrame()
for i in range(30, len(销售数据)):

数据子集 = 销售数据.iloc[:i+1].copy()
# 获取最后一个销售额
最后销售额 = [数据子集['本月实际销售金额'].iloc[-1]]

# 获取最后一个年月
最后年月 = [数据子集.index[-1]]

# SARIMA
SARIMA模型 = SARIMAX(数据子集['本月实际销售金额'], order=(1, 1, 1), seasonal_order=(1, 1, 1, 12))
SARIMA模型拟合 = SARIMA模型.fit(disp=False)
SARIMA预测 = SARIMA模型拟合.forecast(steps=1)

# Prophet
#数据_Prophet = 数据子集.reset_index().rename(columns={'年月': 'ds', '本月实际销售金额': 'y'})
#Prophet模型 = Prophet()
#Prophet模型.fit(数据_Prophet)
#未来 = Prophet模型.make_future_dataframe(periods=1, freq='MS')
#Prophet预测 = Prophet模型.predict(未来)
#Prophet预测数据 = Prophet预测[['ds', 'yhat']].iloc[-1:]

# ARIMA
#ARIMA模型 = auto_arima(数据子集['本月实际销售金额'], seasonal=True, m=12)
#ARIMA预测 = ARIMA模型.predict(n_periods=1)

# ExponentialSmoothing方法1
ES1模型 = ES1(数据子集['本月实际销售金额'], trend='add', seasonal='add', seasonal_periods=12)
ES1模型拟合 = ES1模型.fit()
ES1预测 = ES1模型拟合.forecast(steps=1)

# # ExponentialSmoothing方法2
# ES2模型 = ES2(数据子集['本月实际销售金额'], trend='add', seasonal='add', seasonal_periods=12)
# ES2模型拟合 = ES2模型.fit()
# ES2预测 = ES2模型拟合.forecast(steps=6)

# SVM
SVM模型 = SVR()
时间序列 = np.arange(len(数据子集)).reshape(-1, 1)
SVM模型.fit(时间序列, 数据子集['本月实际销售金额'].values)
SVM预测 = SVM模型.predict(np.array([np.arange(len(数据子集), len(数据子集)+1)]).T)

# # 移动平均预测
# 数据子集['移动平均'] = 数据子集['本月实际销售金额'].rolling(window=36).mean()
# 预测_移动平均 = [数据子集['移动平均'].iloc[-1]] * 6  # 使用最后一个移动平均值作为预测值

# 自回归模型（AR）
AR模型 = AutoReg(数据子集['本月实际销售金额'], lags=1)
AR模型拟合 = AR模型.fit()
AR预测 = AR模型拟合.predict(start=len(数据子集), end=len(数据子集) + 0)

# 移动平均模型（MA）
MA模型 = ARIMA(数据子集['本月实际销售金额'], order=(0, 0, 1))
MA模型拟合 = MA模型.fit()
MA预测 = MA模型拟合.forecast(steps=1)

# 季节性分解时间序列（STL）
#stl = STL(数据子集['本月实际销售金额'], seasonal=13)
#stl_result = stl.fit()
#deseasonalized_data = 数据子集['本月实际销售金额'] - stl_result.seasonal
#STL_ARIMA模型 = ARIMA(deseasonalized_data, order=(1, 1, 0))
#STL_ARIMA模型拟合 = STL_ARIMA模型.fit()
#STL_ARIMA预测 = STL_ARIMA模型拟合.forecast(steps=1)
#STL预测 = STL_ARIMA预测 + stl_result.seasonal.iloc[-1:].values

# Holt-Winters方法（三重指数平滑）
HW模型 = ES1(数据子集['本月实际销售金额'], trend='add', seasonal='mul', seasonal_periods=12)
HW模型拟合 = HW模型.fit()
HW预测 = HW模型拟合.forecast(steps=1)

# ... 在此添加其他方法的代码 ...

预测数据 = pd.DataFrame({
    '数据子集最后年月': 最后年月,
    #'预测的年月': [数据子集.index[-1] + pd.DateOffset(months=1)],
    '销售金额': 最后销售额,
    #'SARIMA预测销售金额累计值': [sum(SARIMA预测)],
    #'Prophet预测销售金额累计值': [Prophet预测数据['yhat'].sum()],
    #'ARIMA预测销售金额累计值': [sum(ARIMA预测)],
    'ExponentialSmoothing方法1预测销售金额累计值': [sum(ES1预测)],
    # 'ExponentialSmoothing方法2预测销售金额累计值': [sum(ES2预测)],
    'SVM预测销售金额累计值': [sum(SVM预测)],
    # '移动平均预测销售金额累计值': [sum(预测_移动平均)]
    'AR预测销售金额累计值': [sum(AR预测)],
    'MA预测销售金额累计值': [sum(MA预测)],
    #'STL预测销售金额累计值': [sum(STL预测)],
    'Holt-Winters预测销售金额累计值': [sum(HW预测)],
    # ... 在此添加其他方法的预测结果 ...
})

#所有预测 = 所有预测.append(预测数据)
#所有预测 = 所有预测.append(预测数据, ignore_index=True) # GPT4
#所有预测 = 所有预测.append(预测数据, ignore_index=True) #coze
所有预测 = pd.concat([所有预测, 预测数据], ignore_index=True)

#print(所有预测)

#所有预测.to_excel('预测销售数据-1个月-2023.6.2-2.xlsx', index=False)

#write_xl(所有预测, "A1", sheet_name="工作表2")

print(所有预测)

import wps365
dir(wps365)

/*
* 提示：该行代码过长，系统自动注释不进行高亮。一键复制会移除系统注释 
* ['ApiClient', 'ApiResponseHandler', 'Configuration', 'CustomApi', 'DefaultApi', 'GptSummary', 'HttpException', 'LOGGER_FILE_PATH', 'MOCK_API', 'MockClient', 'MockController', 'MockException', 'MockResponseHandler', 'RELEASE_API', 'ReleaseClient', 'TEST_API', 'TestClient', 'V5Api', 'WPS365Api', 'WPS365MockApi', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', 'absolute_import', 'batch_create_chat_member', 'batch_delete_chat_member', 'chat_llm', 'client', 'connection', 'create_chat', 'create_chat_message', 'create_event', 'create_file', 'custom_api', 'datetime_to_str', 'default', 'dns_resolver', 'get_all_chat_messages', 'get_all_recent_chat', 'get_chat_by_user_name', 'get_event', 'get_event_list', 'get_user_busy_time_list', 'get_user_free_time_list', 'get_users_common_free_time_list', 'gpt_api', 'host_map', 'init_client', 'json', 'list_files', 'mock', 'origin_create_connection', 'patched_create_connection', 'prompt', 'rest', 'search_all_messages', 'search_event_list', 'search_file_list', 'search_one_chat', 'search_one_event', 'search_one_file', 'search_one_user', 'search_user_list', 'show_chat', 'show_chat_list', 'show_chat_messages', 'show_check_free_time_reject', 'show_check_free_time_resolve', 'show_create_event_response', 'show_download_info', 'show_event', 'show_event_list', 'show_file_list', 'show_help_info', 'show_search_messages', 'show_time_list', 'show_user', 'show_user_list', 'string_to_timestamp', 'sumarize', 'summarize_chat_messages', 'swagger_client', 'timestamp_to_datetime', 'timestamp_to_str', 'trans_to_common_messages', 'transform_event', 'update_event', 'utils', 'v5_copy_file', 'v5_link_info', 'v7_batch_create_chat_member', 'v7_batch_create_event_attendee', 'v7_batch_create_file_permission', 'v7_batch_create_group_member', 'v7_batch_create_latest_items', 'v7_batch_delete_chat_member', 'v7_batch_delete_event_attendee', 'v7_batch_delete_file_permissions', 'v7_batch_delete_group_member', 'v7_batch_read_group_member', 'v7_batch_update_chat_member_banned', 'v7_batch_update_file_permissions', 'v7_change_pptx_theme', 'v7_commit_upload', 'v7_copy_file', 'v7_create_calendar', 'v7_create_calendar_permission', 'v7_create_chat', 'v7_create_chat_message', 'v7_create_dept', 'v7_create_dept_member', 'v7_create_doclib', 'v7_create_drive', 'v7_create_event', 'v7_create_file', 'v7_create_file_with_content', 'v7_create_group', 'v7_create_group_member', 'v7_create_mail_draft', 'v7_create_meeting', 'v7_create_user', 'v7_current_company', 'v7_current_user', 'v7_delete_calendar', 'v7_delete_calendar_permission', 'v7_delete_chat', 'v7_delete_dept', 'v7_delete_dept_member', 'v7_delete_doclib', 'v7_delete_event', 'v7_delete_file', 'v7_delete_file_permission', 'v7_delete_group', 'v7_delete_group_member', 'v7_delete_meeting', 'v7_delete_recent_chat', 'v7_delete_user', 'v7_download', 'v7_export_pxf_from_file', 'v7_export_pxf_from_kdc', 'v7_generate_pptx_diagram', 'v7_generate_slide', 'v7_generate_slides', 'v7_generate_slides_from_file', 'v7_get_calendar', 'v7_get_calendar_list', 'v7_get_calendar_permissions', 'v7_get_chat', 'v7_get_chat_member_list', 'v7_get_chat_message_list', 'v7_get_current_company', 'v7_get_current_session_info', 'v7_get_current_user', 'v7_get_dept', 'v7_get_dept_children', 'v7_get_dept_members', 'v7_get_docer_resource', 'v7_get_docer_resource_file', 'v7_get_doclib_meta', 'v7_get_drive_meta', 'v7_get_event', 'v7_get_event_attendee', 'v7_get_event_list', 'v7_get_file_content', 'v7_get_file_meta', 'v7_get_file_meta_directly', 'v7_get_file_path', 'v7_get_free_busy_list', 'v7_get_group', 'v7_get_group_list', 'v7_get_group_member', 'v7_get_group_member_list', 'v7_get_human_enum_tree', 'v7_get_human_enum_trees', 'v7_get_human_groups', 'v7_get_human_string_values', 'v7_get_human_tags', 'v7_get_link_meta', 'v7_get_mail_folder', 'v7_get_mail_folder_children', 'v7_get_mail_folders', 'v7_get_mail_message', 'v7_get_mail_messages', 'v7_get_mail_messages_in_folder', 'v7_get_mailboxes', 'v7_get_meeting', 'v7_get_meeting_participants', 'v7_get_meetings', 'v7_get_partner_dept_members', 'v7_get_partner_depts', 'v7_get_partners', 'v7_get_recent_chat', 'v7_get_recent_chat_list', 'v7_get_record', 'v7_get_records', 'v7_get_recv_links', 'v7_get_root_dept', 'v7_get_send_links', 'v7_get_summaries', 'v7_get_summary', 'v7_get_transcript', 'v7_get_transcripts', 'v7_get_user', 'v7_get_user_depts', 'v7_get_user_list', 'v7_get_users_file_acl', 'v7_list_docer_categories', 'v7_list_docer_resources', 'v7_list_doclibs', 'v7_list_drives', 'v7_list_files', 'v7_list_latest_items', 'v7_llm', 'v7_match_human_groups', 'v7_match_human_tags', 'v7_media_tools_cancel_task', 'v7_media_tools_create_task', 'v7_media_tools_query_task', 'v7_media_tools_upload_file', 'v7_merge_forward_chat_message', 'v7_move_file', 'v7_open_file_link', 'v7_put_meeting_participants', 'v7_recall_chat_message', 'v7_rename_file', 'v7_request_upload', 'v7_restore_group', 'v7_search_chats', 'v7_search_dept', 'v7_search_files', 'v7_search_group_members', 'v7_search_groups', 'v7_search_messages', 'v7_search_user', 'v7_send_mail_message', 'v7_translate_picture', 'v7_translate_text', 'v7_update_calendar', 'v7_update_calendar_permission', 'v7_update_chat', 'v7_update_chat_member', 'v7_update_dept', 'v7_update_event', 'v7_update_file', 'v7_update_file_permission', 'v7_update_group_member', 'v7_update_group_member_role', 'v7_update_group_owner', 'v7_update_link', 'v7_update_meeting', 'v7_update_recent_chat', 'v7_update_user']
*/