278 lines
10 KiB
Python
278 lines
10 KiB
Python
"""
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Title: Controller
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Developer:
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Sang Inn Woo, Ph.D. @ Incheon National University
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Starting Date: 2022-11-10
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"""
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import psycopg2 as pg2
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import numpy as np
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import settle_prediction_steps_main
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import matplotlib.pyplot as plt
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import sys
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import pdb
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'''
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apptb_surset01
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cons_code: names of monitoring points
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apptb_surset02
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cons_code: names of monitoring points
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amount_cum_sub: accumulated settlement
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fill_height: height of surcharge fill
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nod: number of date
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'''
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def settlement_prediction(business_code, cons_code):
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# connect the database
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#connection = pg2.connect("host=localhost dbname=postgres user=postgres password=lab36981 port=5432") # SW local
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#connection = pg2.connect("host=localhost dbname=sgis user=postgres password=postgres port=5434") # ICTWay local
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# connect the database
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try:
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# 디비엔텍 DB서버
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connection = pg2.connect("host=10.dbnt.co.kr dbname=sgis_new user=smartgeoinfo password=Smartgeoinfo1! port=55432")
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print(f"[OK] DB 접속 성공: {connection.get_dsn_parameters()['host']}")
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except pg2.OperationalError as e:
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print(f"[Error] DB 접속 실패: {e}")
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sys.exit(1)
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# set cursor
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cursor = connection.cursor()
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# select monitoring data for the monitoring point
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postgres_select_query = """SELECT amount_cum_sub, fill_height, nod FROM apptb_surset02 WHERE business_code='""" + business_code \
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+ """' and cons_code='""" + cons_code + """' ORDER BY nod ASC"""
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'''
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변경사항
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(amount_cum_sub * -1) --> amount_cum_sub
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침하예측은 부호의 영향을 크게 받습니다.
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현재 개발이 침하량 양수를 기준으로 되어 있어, 양수를 넘겨 줘야 합니다.
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여기서는 일단 데이터 그대로 받습니다.
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'''
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cursor.execute(postgres_select_query)
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monitoring_record = cursor.fetchall()
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# initialize time, surcharge, and settlement lists
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time = []
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surcharge = []
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settlement = []
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# fill lists
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for row in monitoring_record:
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# DB 값이 NULL(None)이면 건너뜀 (에러 방지)
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if row[0] is None or row[1] is None or row[2] is None:
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continue
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# [로그 추가] 침하량이 0인 데이터 찾기
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if float(row[0]) == 0:
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print(f"[DB_CHECK] Zero settlement found - Business: {business_code}, Cons: {cons_code}, Day(nod): {row[2]}")
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settlement.append(float(row[0]))
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surcharge.append(float(row[1]))
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time.append(float(row[2]))
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# convert lists to np arrays
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settlement = np.array(settlement)
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surcharge = np.array(surcharge)
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time = np.array(time)
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# adjust the sign
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sgn = 1
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if np.average(settlement) < 0:
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sgn = -1
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settlement = sgn * settlement
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'''
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변경사항
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침하 예측은 부호의 영향을 크게 받습니다.
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만일 평균 침하량의 부호가 음수라면,
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침하량이 음수로 기입되어있다고 보고,
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양수로 변환시킵니다.
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'''
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# run the settlement prediction and get results
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results = (settle_prediction_steps_main.
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run_settle_prediction(point_name=cons_code, np_time=time,
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np_surcharge=surcharge, np_settlement=settlement,
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final_step_predict_percent=90,
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additional_predict_percent=300,
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asaoka_interval=3))
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# prediction method code
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# 1: original hyperbolic method (쌍곡선법)
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# 2: nonlinear hyperbolic method (비선형 쌍곡선법)
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# 3: weighted nonlinear hyperbolic method (가중 비선형 쌍곡선법)
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# 4: Asaoka method (아사오카법)
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# 5: Step loading (단계성토 고려법)
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'''
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time_hyper, sp_hyper_original,
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time_hyper, sp_hyper_nonlinear,
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time_hyper, sp_hyper_weight_nonlinear,
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time_asaoka, sp_asaoka,
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time[step_start_index[0]:], -sp_step[step_start_index[0]:],
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'''
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for i in range(5):
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# if there are prediction data for the given data point, delete it first
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postgres_delete_query = """DELETE FROM apptb_pred02_no""" + str(i + 1) \
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+ """ WHERE business_code='""" + business_code \
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+ """' and cons_code='""" + cons_code + """'"""
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cursor.execute(postgres_delete_query)
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connection.commit()
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# get time and settlement arrays
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time = results[2 * i]
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predicted_settlement = results[2 * i + 1]
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# for each prediction time
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for j in range(len(time)):
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# construct insert query
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postgres_insert_query \
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= """INSERT INTO apptb_pred02_no""" + str(i + 1) + """ """ \
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+ """(business_code, cons_code, prediction_progress_days, predicted_settlement, prediction_method) """ \
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+ """VALUES (%s, %s, %s, %s, %s)"""
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# set data to insert
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#record_to_insert = (business_code, cons_code, time[j], -predicted_settlement[j], i + 1)
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record_to_insert = (business_code, cons_code, float(time[j]), float(-predicted_settlement[j]), i + 1)
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'''
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변경사항
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predicted_settlement[j] --> -predicted_settlement[j]
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여기서 침하예측값의 부호를 음수로 설정해서 DB에 저장합니다.
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'''
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# execute the insert query
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cursor.execute(postgres_insert_query, record_to_insert)
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# commit changes
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connection.commit()
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def read_database_and_plot(business_code, cons_code):
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# connect the database
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connection = pg2.connect("host=localhost dbname=postgres user=postgres password=lab36981 port=5432") #SW local
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#connection = pg2.connect("host=192.168.0.72 dbname=sgis user=sgis password=sgis port=5432") # ICTWay internal
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# set cursor
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cursor = connection.cursor()
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# select monitoring data for the monitoring point
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postgres_select_query = ("""SELECT amount_cum_sub, fill_height, nod FROM apptb_surset02 WHERE business_code='"""
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+ business_code + """' and cons_code='""" + cons_code + """' ORDER BY nod ASC""")
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cursor.execute(postgres_select_query)
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monitoring_record = cursor.fetchall()
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# initialize time, surcharge, and settlement lists
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time_monitored = []
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surcharge_monitored = []
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settlement_monitored = []
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# fill lists
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for row in monitoring_record:
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settlement_monitored.append(float(row[0]))
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surcharge_monitored.append(float(row[1]))
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time_monitored.append(float(row[2]))
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# convert lists to np arrays
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settlement_monitored = np.array(settlement_monitored)
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surcharge_monitored = np.array(surcharge_monitored)
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time_monitored = np.array(time_monitored)
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# prediction method code
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# 1: original hyperbolic method
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# 2: nonlinear hyperbolic method
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# 3: weighted nonlinear hyperbolic method
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# 4: Asaoka method
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# 5: Step loading
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time_predicted_series = []
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settlement_predicted_series = []
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for i in range(5):
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# temporarily set the prediction method as 0
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postgres_select_query = """SELECT prediction_progress_days, predicted_settlement """ \
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+ """FROM apptb_pred02_no""" + str(i + 1) \
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+ """ WHERE business_code='""" + business_code \
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+ """' and cons_code='""" + cons_code \
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+ """' ORDER BY prediction_progress_days ASC"""
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# select predicted data for the monitoring point
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cursor.execute(postgres_select_query)
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prediction_record = cursor.fetchall()
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# initialize time, surcharge, and settlement lists
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time_predicted = []
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settlement_predicted = []
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# fill lists
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for row in prediction_record:
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time_predicted.append(float(row[0]))
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settlement_predicted.append(float(row[1]))
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# add lists to series
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time_predicted_series.append(np.array(time_predicted))
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settlement_predicted_series.append(np.array(settlement_predicted))
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# 그래프 크기, 서브 그래프 개수 및 비율 설정
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fig, axes = plt.subplots(2, 1, figsize=(8, 6), gridspec_kw={'height_ratios': [1, 3]})
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# 성토고 그래프 표시
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axes[0].plot(time_monitored, surcharge_monitored, color='black', label='surcharge height')
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# 성토고 그래프 설정
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axes[0].set_ylabel("Surcharge height (m)", fontsize=10)
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axes[0].set_xlim(left=0)
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axes[0].set_xlim(right=np.max(time_predicted))
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axes[0].grid(color="gray", alpha=.5, linestyle='--')
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axes[0].tick_params(direction='in')
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# 계측 및 예측 침하량 표시
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axes[1].scatter(time_monitored, -settlement_monitored, s=30,
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facecolors='white', edgecolors='grey', label='measured data')
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axes[1].plot(time_predicted_series[0], settlement_predicted_series[0],
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linestyle='--', color='red', label='Original Hyperbolic')
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axes[1].plot(time_predicted_series[1], settlement_predicted_series[1],
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linestyle='--', color='blue', label='Nonlinear Hyperbolic')
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axes[1].plot(time_predicted_series[2], settlement_predicted_series[2],
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linestyle='--', color='black', label='Weighted Nonlinear Hyperbolic')
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axes[1].plot(time_predicted_series[3], settlement_predicted_series[3],
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linestyle='--', color='olive', label='Asaoka')
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axes[1].plot(time_predicted_series[4], settlement_predicted_series[4],
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linestyle='--', color='navy', label='Step Loading Hyperbolic')
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axes[0].set_ylabel("Settlement (cm)", fontsize=10)
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axes[1].legend()
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axes[1].set_ylim(top=0)
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axes[1].set_xlim(left=0)
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axes[1].set_xlim(right=np.max(time_predicted))
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plt.show()
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# script to call: python3 controller.py [business_code] [cons_code]
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# for example: python3 controller.py 221222SA0003 CONS001
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if __name__ == '__main__':
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# Example site 1: 231229SA0001, CONS017
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# Example site 2: 231229SA0006, CONS061
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args = sys.argv[1:]
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business_code = args[0]
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cons_code = args[1]
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settlement_prediction(business_code = business_code, cons_code = cons_code)
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print("The settlement prediction is over.")
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#read_database_and_plot(business_code=business_code, cons_code=cons_code)
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#print("Visualization is over.") |