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buliding_temoperature_control.py
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buliding_temoperature_control.py
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import copy
from STLnested_tool import Phi
from STLnested_tool import Probability_Phi
from STLnested_tool import Probability
# 给两个列表集合取交集，考虑R以及\运算
def jiao(region1, region2):
# 用来给两个集合取交集，考虑全集R，R的存法是region = [['R']]
if region1[0] == 'R':
region = region2
elif region2[0] == 'R':
region = region1
else: # 这种情况是两个都不是R，正常取交集即可
if region1[0] == 'not':
if region2[0] == 'not':
region = ['not', list(set(region1[1]) | set(region2[1]))]
else:
region = [list(set(region2[0]) - set(region1[1]))]
elif region2[0] == 'not': # 这时候region1肯定不是not了
region = [list(set(region1[0]) - set(region2[1]))]
else: # 俩都不是not不是R，正常取交集
# print(region1)
# print(region2)
region = [list(set(region1[0]) & set(region2[0]))]
return region
# 给两个列表取并集，考虑R以及\运算
def bing(region1, region2):
# 用来给两个集合取并集，考虑全集R，R的存法是region = ['R'] 存了一个字符R
if region1[0] == 'R':
region = region1
elif region2[0] == 'R':
region = region2
else: # 这种情况是两个都不是R，正常取并集即可
if region1[0] == 'not':
if region2[0] == 'not': # 都是not
region = ['not', list(set(region1[1]) & set(region2[1]))]
else: # 1是not，2不是
region = ['not', list(set(region1[1]) - set(region2[0]))]
elif region2[0] == 'not': # 这时候region1肯定不是not了
region = [list(set(region2[1]) - set(region1[0]))]
else: # 俩都不是not不是R，正常取并集
# print(region1)
# print(region2)
region = [list(set(region1[0]) | set(region2[0]))]
return region
class Feasible_Set():
# 用来存储X_k^I
def __init__(self, k, probability_phi, X):
self.k = k
self.I = probability_phi
self.X = X
# 重写print
def __repr__(self):
if self.X == ['R']:
prt = f"{self.X}"
else:
low = min(self.X[0])
high = max(self.X[0])
prt = f"[{low}, {high}]"
# if self.X == ['R']:
# prt = f"{self.X}"
# else:
# self.X[0].sort()
# prt = f"{self.X[0]}"
return prt
def fanwei(low, high):
# 将连续的范围转换成离散的取值列表，目前的精度为0.1
x = round(low,1)
list = []
while(x <= high):
list.append(x)
x = x + 0.1
x = round(x, 1)
return list
def temperature_dynamic(x_k,u_k):
# 建筑温度变化的数学模型
t_s = 1
a_e = 0.06
a_H = 0.08
T_h = 55 # 加热温度
T_e = 0 # 外界环境温度
x_k1 = x_k + t_s * (a_e*(T_e - x_k)+a_H*(T_h-x_k)*u_k)
return x_k1
def temperature_dynamic_backward(x_k1,u_k):
# 建筑温度变化的数学模型的逆计算
t_s = 1
a_e = 0.06
a_H = 0.08
T_h = 55 # 加热温度
T_e = 0 # 外界环境温度
x_k = (x_k1-t_s*a_e*T_e-t_s*a_H*T_h*u_k)/(1-t_s*a_e-t_s*a_H*u_k)
return x_k
def one_step_set_temperature(fanwei_k):
low_k = fanwei_k[0]
high_k = fanwei_k[-1]
low_k1 = temperature_dynamic(low_k, 0)
high_k1 = temperature_dynamic(high_k, 1)
fanwei_k1 = fanwei(low_k1,high_k1)
return fanwei_k1
def one_step_set_temperature_backward(fanwei_k1):
# low_k1 = fanwei_k1[0]
# high_k1 = fanwei_k1[-1]
low_k1 = min(fanwei_k1)
high_k1 = max(fanwei_k1)
low_k = temperature_dynamic_backward(low_k1, 1)
high_k = temperature_dynamic_backward(high_k1, 0)
# print(low_k1,high_k1)
# print(low_k, high_k)
fanwei_k = fanwei(low_k, high_k)
return [fanwei_k]
def feasible_set(task):
end = task.effective_horizon()[1]
feasible_set_list = []
for i in range(end+2):
feasible_set_list += [[]]
I_every_k = task.potential_set()
I_every_k_phi = []
for i in range(end+2):
I_every_k_phi.append([])
for i in range(end+2):
for I_k in I_every_k[i]:
p_phi = Probability_Phi(None, None, I_k)
I_every_k_phi[i] += [p_phi]
# if p_phi.sat == False:
# I_every_k_phi[i] += [p_phi]
print(I_every_k_phi)
# print(I_every_k[end+1])
for I_T1 in I_every_k_phi[end+1]:
# print(I_T1)
feasible_set_list[end+1] += [Feasible_Set(end+1, I_T1, ['R'])]
k = end
while(k>=0):
print(k)
print(I_every_k_phi[k])
for I_k in I_every_k_phi[k]:
# 已经T了就别算了，反正是R
if I_k.sat == True:
continue
# 算某个I的X_k_I，需要算其所有后继集
X_k_I_list = []
linshi_list = []
for I_k1 in I_every_k_phi[k+1]:
# print(I_k)
# print(I_k1)
if task.is_successor_phi(I_k, I_k1):
consistent_region = task.consistent_region(I_k.P_list, I_k1.P_list)
for X_k1 in feasible_set_list[k+1]:
if X_k1.I.is_equal(I_k1):
if X_k1.X != ['R']:
# print(f"here{X_k1.X}")
one_step_set = one_step_set_temperature_backward(X_k1.X[0])
else:
one_step_set = ['R']
# print(f"交:{one_step_set}和{consistent_region}")
# print(f"结果是:{jiao(consistent_region, one_step_set)}")
linshi_list += [jiao(consistent_region, one_step_set)]
# print(f"所有后继集:{linshi_list}")
X_k_I_list = linshi_list[0]
for list1 in linshi_list:
X_k_I_list = bing(X_k_I_list,list1)
# print(X_k_I_list)
if X_k_I_list != [[]]:
feasible_set_list[k] += [Feasible_Set(k, I_k, X_k_I_list)]
k = k-1
return feasible_set_list
if __name__ == "__main__":
# print(temperature_dynamic(20,0))
# print(fanwei(10,15))
# print(one_step_set_temperature(fanwei(10,15)))
o1 = Phi([0], [5], ['G'], [fanwei(20,25)])
# print(o1)
# task = Phi([0,13],[5,15], ['U','G'], [o1,fanwei(27,30)], [fanwei(0,50),None]) # 在5分钟里到达[20,25]且到达后再维持5分钟
# task = Phi([0], [5], ['U'], [o1], [fanwei(0, 50)]) # 在5分钟里到达[20,25]且到达后再维持5分钟
task = Phi([13], [15], ['G'], [fanwei(27, 30)]) # 在5分钟里到达[20,25]且到达后再维持5分钟
print(task)
print(task.effective_horizon())
I_every_k = task.potential_set()
print(I_every_k)
feasible_set_task = feasible_set(task)
for i in range(16):
print(f"k={i}")
print(f"{len(I_every_k[i])}, {len(feasible_set_task[i])}")
print(I_every_k[i])
print(feasible_set_task[i])