targets.py 13 KB

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  1. # -*- coding: utf-8 -*-
  2. from abc import abstractmethod
  3. from typing import Dict, Tuple, Optional
  4. import arrow
  5. import numpy as np
  6. import pandas as pd
  7. from httpx import AsyncClient
  8. from loguru import logger
  9. from app.controllers.controller import Controller
  10. from app.resources.params import (
  11. TEMPERATURE_RELATED_FEEDBACK_WEIGHT,
  12. TEMPERATURE_RELATED_FEEDBACK,
  13. CO2_RELATED_FEEDBACK_WEIGHT,
  14. SWITCH_RELATED_FEEDBACK
  15. )
  16. from app.services.platform import DataPlatformService
  17. from app.services.transfer import SpaceInfoService, Season
  18. from app.utils.date import get_time_str, get_quarter_minutes, TIME_FMT
  19. class TargetController(Controller):
  20. def __init__(
  21. self,
  22. realtime_data: float,
  23. feedback: Dict,
  24. is_customized: bool,
  25. is_temporary: bool,
  26. current_targets: pd.DataFrame,
  27. ) -> None:
  28. super(TargetController, self).__init__()
  29. self._realtime_data = realtime_data
  30. self._feedback = feedback
  31. self._is_customized = is_customized
  32. self._is_temporary = is_temporary
  33. self._current_targets = current_targets
  34. self._now_time = arrow.get(get_time_str(), TIME_FMT).time().strftime('%H%M%S')
  35. self._quarter_time = get_quarter_minutes(get_time_str())
  36. async def calculate_diff(self, weight: Dict) -> float:
  37. related_feedback = [v for k, v in self._feedback.items() if k in weight]
  38. related_feedback = np.array(related_feedback)
  39. weight = np.array(list(weight.values()))
  40. feedback_count = related_feedback.sum()
  41. diff = 0
  42. if feedback_count > 0:
  43. diff = np.dot(related_feedback, weight) / feedback_count
  44. return diff
  45. @abstractmethod
  46. async def init_temporary(self):
  47. pass
  48. @abstractmethod
  49. async def get_targets(self) -> float:
  50. pass
  51. async def generate_temporary(self, lower, upper):
  52. now_str = get_time_str()
  53. time_index = arrow.get(arrow.get(now_str, TIME_FMT).shift(minutes=15).timestamp
  54. // (15 * 60) * (15 * 60)).time().strftime('%H%M%S')
  55. result = {time_index: [lower, upper]}
  56. self._results.update({'temporary_targets': result})
  57. async def readjust_global(self, latest_change: float, previous_changes: pd.DataFrame):
  58. previous_changes = pd.concat([
  59. pd.DataFrame({'timestamp': [self._now_time], 'value': [latest_change]}),
  60. previous_changes,
  61. ])
  62. previous_changes.reset_index(inplace=True)
  63. previous_changes['weight1'] = previous_changes['index'].apply(lambda x: (1 / (x + 1)) ** 3)
  64. new_targets = []
  65. time_index = self._current_targets.reset_index()['time']
  66. for item in time_index:
  67. previous_changes['delta'] = previous_changes['timestamp'].apply(
  68. lambda x: abs(arrow.get(str(x), 'HHmmss') - arrow.get(item, 'HHmmss')).seconds // (15 * 60)
  69. )
  70. previous_changes['weight2'] = previous_changes['delta'].apply(lambda x: 0.5 ** x)
  71. previous_changes['weight'] = previous_changes['weight1'] * previous_changes['weight2']
  72. new_targets.append(
  73. (previous_changes['value'] * previous_changes['weight']).sum() / previous_changes['weight'].sum()
  74. )
  75. self._current_targets['new_targets'] = new_targets
  76. @abstractmethod
  77. async def run(self):
  78. pass
  79. class TemperatureTargetController(TargetController):
  80. def __init__(
  81. self,
  82. realtime_data: float,
  83. feedback: Dict,
  84. is_customized: bool,
  85. is_temporary: bool,
  86. current_targets: pd.DataFrame,
  87. season: Season,
  88. previous_changes: Optional[pd.DataFrame] = None
  89. ) -> None:
  90. super(TemperatureTargetController, self).__init__(
  91. realtime_data,
  92. feedback,
  93. is_customized,
  94. is_temporary,
  95. current_targets
  96. )
  97. self._season = season
  98. self._previous_changes = previous_changes
  99. @staticmethod
  100. def _cut(value: float) -> float:
  101. _LOWER_LIMIT = 22.0
  102. _UPPER_LIMIT = 28.0
  103. value = min(value, _UPPER_LIMIT)
  104. value = max(value, _LOWER_LIMIT)
  105. return value
  106. async def init_temporary(self) -> Tuple[float, float]:
  107. _VAR = 2
  108. _RANGE = 1
  109. new_target = 24.0
  110. new_lower_bound, new_upper_bound = new_target - 1.0, new_target + 1.0
  111. if not np.isnan(self._realtime_data):
  112. if self._season == Season.cooling:
  113. if ('a little hot' in self._feedback
  114. or 'so hot' in self._feedback
  115. or 'switch on' in self._feedback):
  116. mid = self._realtime_data - _VAR
  117. new_lower_bound = mid - _RANGE
  118. new_upper_bound = mid + _RANGE
  119. elif self._season == Season.heating:
  120. if ('a little cold' in self._feedback
  121. or 'so cold' in self._feedback
  122. or 'switch on' in self._feedback):
  123. mid = self._realtime_data + _VAR
  124. new_lower_bound = mid - _RANGE
  125. new_upper_bound = mid + _RANGE
  126. return self._cut(new_lower_bound), self._cut(new_upper_bound)
  127. async def get_targets(self) -> float:
  128. current_lower_target = self._current_targets['temperatureMin'].loc[self._quarter_time]
  129. current_upper_target = self._current_targets['temperatureMax'].loc[self._quarter_time]
  130. if np.isnan(current_lower_target):
  131. current_lower_target = 23.0
  132. if np.isnan(current_upper_target):
  133. current_upper_target = 25.0
  134. return (current_lower_target + current_upper_target) / 2
  135. async def readjust_current(self, current: float, diff: float) -> float:
  136. _RANGE = 2
  137. new_target = current
  138. if np.isnan(self._realtime_data):
  139. new_target += diff
  140. else:
  141. if self._season == Season.cooling:
  142. standard = current + 1.0
  143. elif self._season == Season.heating:
  144. standard = current - 1.0
  145. else:
  146. standard = current
  147. if (diff > 0 and self._realtime_data + _RANGE > standard
  148. or diff < 0 and self._realtime_data - _RANGE < standard):
  149. new_target += diff
  150. return new_target
  151. async def generate_global(self):
  152. _RANGE = 1
  153. new_targets = self._current_targets['new_targets'].apply(lambda x: [self._cut(x - _RANGE),
  154. self._cut(x + _RANGE)])
  155. time_index = self._current_targets.reset_index()['time']
  156. result = {}
  157. for i in range(len(time_index)):
  158. result.update({time_index[i]: new_targets[i]})
  159. self._results.update({'global_targets': result})
  160. async def run(self):
  161. diff = await self.calculate_diff(TEMPERATURE_RELATED_FEEDBACK_WEIGHT)
  162. if diff != 0:
  163. if not self._is_customized:
  164. lower_bound, upper_bound = await self.init_temporary()
  165. await self.generate_temporary(lower_bound, upper_bound)
  166. else:
  167. current_target = await self.get_targets()
  168. new_target = await self.readjust_current(current_target, diff)
  169. if not self._is_temporary:
  170. self._results.update({'latest_change': new_target})
  171. await self.readjust_global(new_target, self._previous_changes)
  172. await self.generate_global()
  173. else:
  174. await self.generate_temporary(self._cut(new_target) - 1.0, self._cut(new_target + 1.0))
  175. else:
  176. return
  177. class Co2TargetController(TargetController):
  178. def __init__(
  179. self,
  180. realtime_data: float,
  181. feedback: Dict,
  182. is_customized: bool,
  183. is_temporary: bool,
  184. current_targets: pd.DataFrame,
  185. previous_changes: Optional[pd.DataFrame] = None
  186. ) -> None:
  187. super(Co2TargetController, self).__init__(
  188. realtime_data,
  189. feedback,
  190. is_customized,
  191. is_temporary,
  192. current_targets
  193. )
  194. self._previous_changes = previous_changes
  195. @staticmethod
  196. def _cut(value: float) -> float:
  197. _UPPER_LIMIT = 1000.0
  198. value = min(value, _UPPER_LIMIT)
  199. return value
  200. async def init_temporary(self) -> float:
  201. new_target = 1000
  202. diff = await self.calculate_diff(CO2_RELATED_FEEDBACK_WEIGHT)
  203. if not np.isnan(self._realtime_data):
  204. new_target += diff
  205. return self._cut(new_target)
  206. async def get_targets(self) -> float:
  207. current_upper_target = self._current_targets['co2Max'].loc[self._quarter_time]
  208. if np.isnan(current_upper_target):
  209. current_upper_target = 500.0
  210. return current_upper_target
  211. async def readjust_current(self, lower: float, upper: float, diff: float) -> float:
  212. new_target = upper - lower
  213. if np.isnan(self._realtime_data):
  214. new_target += diff
  215. else:
  216. if (diff > 50 and self._realtime_data + 100 > upper
  217. or diff < -50 and self._realtime_data - 100 < upper):
  218. new_target = self._realtime_data + diff
  219. return self._cut(new_target)
  220. async def generate_global(self):
  221. new_targets = self._current_targets['new_targets'].apply(lambda x: [0, x])
  222. time_index = self._current_targets.reset_index()['time']
  223. result = {}
  224. for i in range(len(time_index)):
  225. result.update({time_index[i]: new_targets[i]})
  226. self._results.update({'global_targets': result})
  227. async def run(self):
  228. diff = await self.calculate_diff(CO2_RELATED_FEEDBACK_WEIGHT)
  229. if diff != 0:
  230. if not self._is_customized:
  231. upper_bound = await self.init_temporary()
  232. await self.generate_temporary(0, upper_bound)
  233. else:
  234. current_upper = await self.get_targets()
  235. upper_bound = await self.readjust_current(0, current_upper, diff)
  236. if not self._is_temporary:
  237. self._results.update({'latest_change': upper_bound})
  238. await self.readjust_global(upper_bound, self._previous_changes)
  239. await self.generate_global()
  240. else:
  241. await self.generate_temporary(0, upper_bound)
  242. else:
  243. return
  244. @logger.catch()
  245. async def readjust_all_target(project_id: str, space_id: str, wechat_time: str):
  246. async with AsyncClient() as client:
  247. transfer = SpaceInfoService(client, project_id, space_id)
  248. platform = DataPlatformService(client, project_id)
  249. realtime_temperature = await platform.get_realtime_temperature(space_id)
  250. logger.debug(realtime_temperature)
  251. current_targets = await transfer.get_custom_target()
  252. logger.debug(current_targets)
  253. feedback = await transfer.get_feedback(wechat_time)
  254. logger.debug(feedback)
  255. is_customized = await transfer.is_customized()
  256. logger.debug(is_customized)
  257. is_temporary = await transfer.is_temporary()
  258. logger.debug(is_temporary)
  259. season = await transfer.get_season()
  260. logger.debug(season)
  261. previous_changes = await transfer.env_database_get()
  262. logger.debug(previous_changes)
  263. if feedback.get('switch off') and feedback.get('switch off') > 0:
  264. need_switch_off = True
  265. for item in SWITCH_RELATED_FEEDBACK:
  266. if feedback.get(item) and feedback.get(item) > 0:
  267. need_switch_off = False
  268. break
  269. else:
  270. need_switch_off = False
  271. need_run_room_control = False
  272. if need_switch_off:
  273. async with AsyncClient() as client:
  274. transfer = SpaceInfoService(client, project_id, space_id)
  275. await transfer.set_temporary_custom()
  276. return need_run_room_control
  277. temperature_results = {}
  278. for item in TEMPERATURE_RELATED_FEEDBACK:
  279. if feedback.get(item) and feedback.get(item) > 0:
  280. temperature_controller = TemperatureTargetController(
  281. realtime_temperature,
  282. feedback,
  283. is_customized,
  284. is_temporary,
  285. current_targets[['temperatureMin', 'temperatureMax']].copy(),
  286. season,
  287. previous_changes['temperature']
  288. )
  289. await temperature_controller.run()
  290. temperature_results = temperature_controller.get_results()
  291. break
  292. if temperature_results:
  293. need_run_room_control = True
  294. async with AsyncClient() as client:
  295. transfer = SpaceInfoService(client, project_id, space_id)
  296. if temperature_results.get('temporary_targets'):
  297. logger.debug(temperature_results.get('temporary_targets'))
  298. await transfer.set_custom_target('temperature', temperature_results.get('temporary_targets'), '0')
  299. if temperature_results.get('global_targets'):
  300. logger.debug(temperature_results.get('global_targets'))
  301. await transfer.set_custom_target('temperature', temperature_results.get('global_targets'), '1')
  302. if temperature_results.get('latest_change'):
  303. logger.debug(temperature_results.get('latest_change'))
  304. await transfer.env_database_set('temperature', temperature_results.get('latest_change'))
  305. return need_run_room_control