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update efdh foh

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MrWaradana 1 month ago
parent effe624bd4
commit aae078bdfc
6 changed files with 632 additions and 203 deletions
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2
src/equipment/model.py
Unescape Escape View File

@ -94,3 +94,5 @@ class EquipmentTransactionRecords(Base, DefaultMixin, IdentityMixin):
eac_annual_acq_cost = Column(Float, nullable=False) eac_annual_acq_cost = Column(Float, nullable=False)
eac_disposal_cost = Column(Float, nullable=False) eac_disposal_cost = Column(Float, nullable=False)
eac_eac = Column(Float, nullable=False) eac_eac = Column(Float, nullable=False)
efdh_equivalent_forced_derating_hours = Column(Float, nullable=False)
foh_forced_outage_hours = Column(Float, nullable=False)

2
src/equipment/schema.py
Unescape Escape View File

@ -72,6 +72,8 @@ class MasterBase(DefaultBase):
eac_annual_acq_cost: Optional[float] = Field(None, nullable=True, le=MAX_PRICE) eac_annual_acq_cost: Optional[float] = Field(None, nullable=True, le=MAX_PRICE)
eac_disposal_cost: Optional[float] = Field(None, nullable=True, le=MAX_PRICE) eac_disposal_cost: Optional[float] = Field(None, nullable=True, le=MAX_PRICE)
eac_eac: Optional[float] = Field(None, nullable=True, le=MAX_PRICE) eac_eac: Optional[float] = Field(None, nullable=True, le=MAX_PRICE)
efdh_equivalent_forced_derating_hours: Optional[float] = Field(None, nullable=True)
foh_forced_outage_hours: Optional[float] = Field(None, nullable=True)
class EquipmentCreate(EquipmentBase): class EquipmentCreate(EquipmentBase):

6
src/modules/equipment/Eac.py
Unescape Escape View File

@ -150,11 +150,11 @@ class Eac:
# Nilai proyeksi yang didiskontokan menggunakan offset eksponen dari urutan aktual terakhir # Nilai proyeksi yang didiskontokan menggunakan offset eksponen dari urutan aktual terakhir
# sehingga offset tahun berlanjut dari aktual yang sudah diproses. # sehingga offset tahun berlanjut dari aktual yang sudah diproses.
# Rumus NPV: NPV = Σ [Ct / (1 + r)^t] # Rumus NPV: NPV = Σ [Ct / (1 + r)^t]
# dimana Ct = cash flow pada periode t, r = inflation_rate, t = periode # dimana Ct = cash flow pada periode t, r = disc_rate, t = periode
# value adalah rc_total_cost pada periode t # value adalah rc_total_cost pada periode t
# 1 + inflation_rate ** (last_seq + i + 1) adalah perhitungan diskonto dengan offset waktu # 1 + disc_rate ** (last_seq + i + 1) adalah perhitungan diskonto dengan offset waktu
discounted_proj = sum( discounted_proj = sum(
(float(value) / ((1 + inflation_rate) ** (last_seq + i + 1))) (float(value) / ((1 + disc_rate) ** (last_seq + i + 1)))
for i, value in enumerate(cumulative_values) for i, value in enumerate(cumulative_values)
) )

51
src/modules/equipment/Prediksi.py
Unescape Escape View File

@ -334,9 +334,11 @@ class Prediksi:
finally: finally:
if connection: if connection:
connection.close() connection.close()
def __get_asset_criticality_params(self, equipment_id): def __get_asset_criticality_params(self, equipment_id):
try: try:
connections = get_connection() connections = get_connection()
efdh_foh_sum = None
connection = ( connection = (
connections[0] if isinstance(connections, tuple) else connections connections[0] if isinstance(connections, tuple) else connections
) )
@ -356,8 +358,8 @@ class Prediksi:
asset_crit_bpp_pembangkit, asset_crit_bpp_pembangkit,
asset_crit_dmn_daya_mampu_netto, asset_crit_dmn_daya_mampu_netto,
asset_crit_marginal_cost, asset_crit_marginal_cost,
asset_crit_efdh_equivalent_force_derated_hours, asset_crit_efdh_equivalent_forced_derated_hours,
asset_crit_foh_force_outage_hours, asset_crit_foh_forced_outage_hours,
asset_crit_extra_fuel_cost asset_crit_extra_fuel_cost
FROM lcc_ms_year_data FROM lcc_ms_year_data
) t ) t
@ -392,12 +394,33 @@ class Prediksi:
part2 = max(0.0, (dmn - ens)) * extra_fuel part2 = max(0.0, (dmn - ens)) * extra_fuel
asset_criticality = part1 + part2 asset_criticality = part1 + part2
efdh = _f("asset_crit_efdh_equivalent_force_derated_hours") # EFDH efdh = _f("asset_crit_efdh_equivalent_forced_derated_hours") # EFDH per Year
foh = _f("asset_crit_foh_force_outage_hours") foh = _f("asset_crit_foh_forced_outage_hours") # FOH per Year
query_each_equipment = """
SELECT
efdh_equivalent_forced_derated_hours,
foh_forced_outage_hours
FROM lcc_ms_equipment_data
WHERE assetnum = %s
"""
cursor.execute(query_each_equipment, (equipment_id,))
result_eq = cursor.fetchone()
if result_eq:
eq_efdh = float(result_eq.get("efdh_equivalent_force_derated_hours") or 0.0) # EFDH per Equipment
eq_foh = float(result_eq.get("foh_force_outage_hours") or 0.0) # FOH per Equipment
efdh_foh_equipment = eq_efdh + eq_foh
# if efdh_foh_equipment == 0:
# efdh_foh_sum = efdh + foh
# else:
efdh_foh_sum = efdh_foh_equipment
return { return {
"asset_criticality": asset_criticality, "asset_criticality": asset_criticality,
"efdh_oh_sum": efdh + foh, "efdh_foh_sum": efdh_foh_sum,
} }
except Exception as e: except Exception as e:
@ -430,7 +453,8 @@ class Prediksi:
raw_oh_interval, raw_oh_material_cost, raw_oh_labor_time, raw_oh_labor_human, raw_oh_interval, raw_oh_material_cost, raw_oh_labor_time, raw_oh_labor_human,
raw_predictive_interval, raw_predictive_material_cost, raw_predictive_labor_time, raw_predictive_labor_human, raw_predictive_interval, raw_predictive_material_cost, raw_predictive_labor_time, raw_predictive_labor_human,
"raw_loss_output_MW" as raw_loss_output_mw, raw_loss_output_price, "raw_loss_output_MW" as raw_loss_output_mw, raw_loss_output_price,
raw_operational_cost, raw_maintenance_cost raw_operational_cost, raw_maintenance_cost,
efdh_equivalent_forced_derated_hours, foh_forced_outage_hours,
FROM lcc_equipment_tr_data FROM lcc_equipment_tr_data
WHERE assetnum = %s; WHERE assetnum = %s;
''' '''
@ -500,6 +524,9 @@ class Prediksi:
raw_operational_cost = float(r.get("raw_operational_cost") or 0.0) raw_operational_cost = float(r.get("raw_operational_cost") or 0.0)
raw_maintenance_cost = float(r.get("raw_maintenance_cost") or 0.0) raw_maintenance_cost = float(r.get("raw_maintenance_cost") or 0.0)
efdh_equivalent_forced_derated_hours = float(r.get("efdh_equivalent_forced_derated_hours") or 0.0)
foh_forced_outage_hours = float(r.get("foh_forced_outage_hours") or 0.0)
rc_cm_material_cost = float(r.get("rc_cm_material_cost") or 0.0) rc_cm_material_cost = float(r.get("rc_cm_material_cost") or 0.0)
# compute per-column costs using helpers # compute per-column costs using helpers
@ -541,17 +568,19 @@ class Prediksi:
asset_criticality_value = 0.0 asset_criticality_value = 0.0
# Simplify extraction and avoid repeating the multiplication # Simplify extraction and avoid repeating the multiplication
ac = asset_criticality_data if isinstance(asset_criticality_data, dict) else {} ac = asset_criticality_data if isinstance(asset_criticality_data, dict) else {}
try:
efdh_oh_sum = float(ac.get("efdh_oh_sum", 0.0)) efdh_foh_sum = efdh_equivalent_forced_derated_hours + foh_forced_outage_hours if efdh_equivalent_forced_derated_hours and foh_forced_outage_hours else 0.0
except Exception: # try:
efdh_oh_sum = 0.0 # efdh_foh_sum = float(ac.get("efdh_foh_sum", 0.0))
# except Exception:
# efdh_foh_sum = 0.0
try: try:
asset_criticality_value = float(ac.get("asset_criticality", 0.0)) asset_criticality_value = float(ac.get("asset_criticality", 0.0))
except Exception: except Exception:
asset_criticality_value = 0.0 asset_criticality_value = 0.0
# single multiplier used for all RC groups # single multiplier used for all RC groups
ac_multiplier = efdh_oh_sum * asset_criticality_value ac_multiplier = efdh_foh_sum * asset_criticality_value
total = rc_total_cost( total = rc_total_cost(
rc_cm=rc_cm_material + rc_cm_labor + ac_multiplier, rc_cm=rc_cm_material + rc_cm_labor + ac_multiplier,

764
src/modules/equipment/insert_actual_data.py
Unescape Escape View File

@ -1,4 +1,6 @@
import asyncio import asyncio
import pandas as pd
from decimal import Decimal, InvalidOperation
import psycopg2 import psycopg2
from psycopg2.extras import DictCursor from psycopg2.extras import DictCursor
from uuid import uuid4 from uuid import uuid4
@ -141,6 +143,237 @@ def get_data_tahun(cursor):
cursor.execute(query) cursor.execute(query)
return cursor.fetchall() return cursor.fetchall()
def _parse_decimal(value: str, decimal_separator: str = ".") -> Decimal:
"""Parse numeric strings that may use comma decimal separators."""
if value is None:
return Decimal("0")
sanitized = value.strip()
if not sanitized:
return Decimal("0")
sanitized = sanitized.replace(" ", "")
if decimal_separator == ",":
sanitized = sanitized.replace(".", "").replace(",", ".")
else:
sanitized = sanitized.replace(",", "")
try:
return Decimal(sanitized)
except (InvalidOperation, ValueError):
print(f"Unable to parse numeric value '{value}', defaulting to 0.")
return Decimal("0")
def _normalize_key(key: str) -> str:
if not key:
return ""
cleaned = key.strip().lstrip("\ufeff").lower()
for char in (" ", ".", "-", "\t"):
cleaned = cleaned.replace(char, "_")
while "__" in cleaned:
cleaned = cleaned.replace("__", "_")
return cleaned
def _load_acquisition_cost_lookup(csv_path: str) -> dict:
if not os.path.exists(csv_path):
print(f"CSV file not found at {csv_path}")
return {}
try:
df = pd.read_csv(csv_path, sep=";", dtype=str, keep_default_na=False, encoding="utf-8")
except Exception as exc:
print(f"Failed to read CSV file {csv_path}: {exc}")
return {}
df.columns = [_normalize_key(col) for col in df.columns]
required_cols = {"location_tag", "proportion", "category_no", "acquisition_cost"}
missing_cols = required_cols - set(df.columns)
if missing_cols:
print(f"CSV file is missing required columns: {', '.join(sorted(missing_cols))}")
return {}
lookup = {}
for _, row in df.iterrows():
raw_tag = (row.get("location_tag") or "").strip()
location_tag = raw_tag.upper()
if not location_tag:
continue
lookup[location_tag] = {
"proportion": _parse_decimal(row.get("proportion"), decimal_separator=","),
"category_no": _parse_decimal(row.get("category_no"), decimal_separator="."),
"acquisition_cost": _parse_decimal(row.get("acquisition_cost"), decimal_separator="."),
"raw_location_tag": raw_tag,
}
return lookup
def _build_tr_row_values(
data_cm_row,
data_pm_row,
data_oh_row,
data_predictive_row,
data_tahunan_row,
):
"""Return sanitized numeric values for equipment transaction rows."""
def _safe_value(row, key):
if not row:
return 0
value = row.get(key)
return value if value is not None else 0
has_recursive_data = any(
row for row in (data_cm_row, data_pm_row, data_oh_row, data_predictive_row)
)
if not has_recursive_data:
return {
"raw_cm_interval": 0,
"raw_cm_material_cost": 0,
"raw_cm_labor_time": 0,
"raw_cm_labor_human": 0,
"raw_pm_interval": 0,
"raw_pm_material_cost": 0,
"raw_pm_labor_time": 0,
"raw_pm_labor_human": 0,
"raw_oh_interval": 0,
"raw_oh_material_cost": 0,
"raw_oh_labor_time": 0,
"raw_oh_labor_human": 0,
"raw_predictive_interval": 0,
"raw_predictive_material_cost": 0,
"raw_predictive_labor_time": 0,
"raw_predictive_labor_human": 0,
"raw_loss_output_MW": 0,
"raw_loss_output_price": 0,
"rc_cm_material_cost": 0,
"rc_cm_labor_cost": 0,
"rc_pm_material_cost": 0,
"rc_pm_labor_cost": 0,
"rc_oh_material_cost": 0,
"rc_oh_labor_cost": 0,
"rc_predictive_labor_cost": 0,
}
raw_cm_interval = _safe_value(data_cm_row, "raw_cm_interval")
raw_cm_material_cost_total = _safe_value(data_cm_row, "raw_cm_material_cost")
raw_cm_material_cost = (
raw_cm_material_cost_total / raw_cm_interval if raw_cm_interval else 0
)
raw_cm_labor_time = _safe_value(data_cm_row, "raw_cm_labor_time")
raw_cm_labor_human = _safe_value(data_cm_row, "raw_cm_labor_human")
raw_pm_interval = _safe_value(data_pm_row, "raw_pm_interval")
raw_pm_material_cost = _safe_value(data_pm_row, "raw_pm_material_cost")
raw_pm_labor_time = _safe_value(data_pm_row, "raw_pm_labor_time")
raw_pm_labor_human = _safe_value(data_pm_row, "raw_pm_labor_human")
raw_oh_interval = _safe_value(data_oh_row, "raw_oh_interval")
raw_oh_material_cost = _safe_value(data_oh_row, "raw_oh_material_cost")
raw_oh_labor_time = _safe_value(data_oh_row, "raw_oh_labor_time")
raw_oh_labor_human = _safe_value(data_oh_row, "raw_oh_labor_human")
raw_pdm_interval = _safe_value(data_predictive_row, "raw_predictive_interval")
raw_pdm_material_cost = _safe_value(
data_predictive_row, "raw_predictive_material_cost"
)
raw_pdm_labor_time = _safe_value(
data_predictive_row, "raw_predictive_labor_time"
)
raw_pdm_labor_human = _safe_value(
data_predictive_row, "raw_predictive_labor_human"
)
raw_loss_output_MW = (
data_tahunan_row.get("total_lost")
if data_tahunan_row and data_tahunan_row.get("total_lost") is not None
else 0
)
raw_loss_output_price = (
data_tahunan_row.get("rp_per_kwh")
if data_tahunan_row and data_tahunan_row.get("rp_per_kwh") is not None
else 0
)
man_hour_value = (
data_tahunan_row.get("man_hour")
if data_tahunan_row and data_tahunan_row.get("man_hour") is not None
else None
)
rc_cm_material_cost = raw_cm_material_cost_total
rc_cm_labor_cost = (
data_cm_row.get("raw_cm_labor_time")
* data_cm_row.get("rc_cm_labor_human")
* man_hour_value
if data_cm_row
and data_cm_row.get("rc_cm_labor_cost")
and data_cm_row.get("rc_cm_labor_human")
and man_hour_value is not None
else 0
)
rc_pm_material_cost = raw_pm_material_cost
rc_pm_labor_cost = (
data_pm_row.get("raw_pm_labor_time")
* data_pm_row.get("rc_pm_labor_human")
* man_hour_value
if data_pm_row
and data_pm_row.get("rc_pm_labor_cost")
and data_pm_row.get("rc_pm_labor_human")
and man_hour_value is not None
else 0
)
rc_oh_material_cost = raw_oh_material_cost
rc_oh_labor_cost = (
data_oh_row.get("raw_oh_labor_time")
* data_oh_row.get("rc_oh_labor_human")
* man_hour_value
if data_oh_row
and data_oh_row.get("rc_oh_labor_cost")
and data_oh_row.get("rc_oh_labor_human")
and man_hour_value is not None
else 0
)
rc_predictive_labor_cost = (
data_predictive_row.get("raw_predictive_labor_human") * man_hour_value
if data_predictive_row
and data_predictive_row.get("rc_predictive_labor_cost")
and man_hour_value is not None
else 0
)
return {
"raw_cm_interval": raw_cm_interval,
"raw_cm_material_cost": raw_cm_material_cost,
"raw_cm_labor_time": raw_cm_labor_time,
"raw_cm_labor_human": raw_cm_labor_human,
"raw_pm_interval": raw_pm_interval,
"raw_pm_material_cost": raw_pm_material_cost,
"raw_pm_labor_time": raw_pm_labor_time,
"raw_pm_labor_human": raw_pm_labor_human,
"raw_oh_interval": raw_oh_interval,
"raw_oh_material_cost": raw_oh_material_cost,
"raw_oh_labor_time": raw_oh_labor_time,
"raw_oh_labor_human": raw_oh_labor_human,
"raw_predictive_interval": raw_pdm_interval,
"raw_predictive_material_cost": raw_pdm_material_cost,
"raw_predictive_labor_time": raw_pdm_labor_time,
"raw_predictive_labor_human": raw_pdm_labor_human,
"raw_loss_output_MW": raw_loss_output_MW,
"raw_loss_output_price": raw_loss_output_price,
"rc_cm_material_cost": rc_cm_material_cost,
"rc_cm_labor_cost": rc_cm_labor_cost,
"rc_pm_material_cost": rc_pm_material_cost,
"rc_pm_labor_cost": rc_pm_labor_cost,
"rc_oh_material_cost": rc_oh_material_cost,
"rc_oh_labor_cost": rc_oh_labor_cost,
"rc_predictive_labor_cost": rc_predictive_labor_cost,
}
async def insert_ms_equipment_data(): async def insert_ms_equipment_data():
connection = None connection = None
try: try:
@ -234,9 +467,9 @@ async def insert_lcca_maximo_corrective_data():
cursor_db_app.execute(check_data_query) cursor_db_app.execute(check_data_query)
data_count = cursor_db_app.fetchone()[0] data_count = cursor_db_app.fetchone()[0]
if data_count > 0: # if data_count > 0:
truncate_query = "TRUNCATE TABLE lcc_equipment_tr_data" # truncate_query = "TRUNCATE TABLE lcc_equipment_tr_data"
cursor_db_app.execute(truncate_query) # cursor_db_app.execute(truncate_query)
query_main = "SELECT DISTINCT(assetnum) FROM ms_equipment_master" query_main = "SELECT DISTINCT(assetnum) FROM ms_equipment_master"
cursor_db_app.execute(query_main) cursor_db_app.execute(query_main)
@ -359,6 +592,203 @@ async def insert_lcca_maximo_corrective_data():
connection_wo_db.close() connection_wo_db.close()
production_connection.close() production_connection.close()
async def insert_acquisition_cost_data():
connection = None
connection_wo_db = None
cursor = None
try:
connection, connection_wo_db = get_connection()
if connection is None or connection_wo_db is None:
print("Database connection failed.")
return
start_time = datetime.now()
print(f"Start insert_acquisition_cost_data at {start_time.isoformat()}")
# Ambil data dari tabel lcc_ms_equipment_data join dengan ms_equipment_master dari pada kolom assetnum dengan
# select assetnum dan location_tag
location_tag_query = """
SELECT em.assetnum, em.location_tag, em.name
FROM lcc_ms_equipment_data AS ed
JOIN ms_equipment_master AS em ON ed.assetnum = em.assetnum;
"""
cursor = connection.cursor(cursor_factory=DictCursor)
cursor.execute(location_tag_query)
location_tag_results = cursor.fetchall()
if not location_tag_results:
print("No equipment data found to update.")
return
csv_path = os.path.join(os.path.dirname(__file__), "acquisition_cost.csv")
csv_lookup = _load_acquisition_cost_lookup(csv_path)
if not csv_lookup:
print("CSV file does not contain any usable rows.")
return
update_query = """
UPDATE lcc_ms_equipment_data
SET proportion = %s,
category_no = %s,
acquisition_cost = %s,
updated_at = NOW()
WHERE assetnum = %s
"""
updated_assets = 0
skipped_missing_csv = 0
skipped_missing_tag = 0
progress_rows = []
processed_csv_tags = set()
for idx, row in enumerate(location_tag_results, start=1):
assetnum = row["assetnum"]
location_tag_value = row["location_tag"]
equipment_name = row.get("name")
normalized_tag = (location_tag_value or "").strip().upper()
if not normalized_tag:
skipped_missing_tag += 1
print(f"[{idx}] Skipping asset {assetnum}: missing location_tag")
progress_rows.append(
{
"assetnum": assetnum,
"location_tag": location_tag_value or "",
"name": equipment_name or "",
"status": "missing_tag",
}
)
continue
csv_row = csv_lookup.get(normalized_tag)
if not csv_row:
skipped_missing_csv += 1
print(f"[{idx}] No CSV match for asset {assetnum} (location_tag={location_tag_value})")
progress_rows.append(
{
"assetnum": assetnum,
"location_tag": location_tag_value or "",
"name": equipment_name or "",
"status": "no_csv_match",
}
)
continue
processed_csv_tags.add(normalized_tag)
try:
cursor.execute(
update_query,
(
csv_row["proportion"],
csv_row["category_no"],
csv_row["acquisition_cost"],
assetnum,
),
)
if cursor.rowcount:
updated_assets += 1
progress_rows.append(
{
"assetnum": assetnum,
"location_tag": location_tag_value or "",
"name": equipment_name or "",
"status": "updated",
}
)
else:
progress_rows.append(
{
"assetnum": assetnum,
"location_tag": location_tag_value or "",
"name": equipment_name or "",
"status": "to_do",
}
)
except Exception as exc:
try:
connection.rollback()
except Exception:
pass
print(f"[{idx}] Error updating asset {assetnum}: {exc}")
progress_rows.append(
{
"assetnum": assetnum,
"location_tag": location_tag_value or "",
"name": equipment_name or "",
"status": "to_do",
}
)
continue
if idx % 100 == 0:
try:
connection.commit()
except Exception:
connection.rollback()
print(
f"Processed {idx} assets so far. Updated {updated_assets}, "
f"no CSV match {skipped_missing_csv}, missing tag {skipped_missing_tag}."
)
# Capture CSV rows that never matched any asset so the checklist highlights remaining work.
unused_csv_tags = [
(tag, data)
for tag, data in csv_lookup.items()
if tag not in processed_csv_tags
]
if unused_csv_tags:
for unused_tag, csv_row in unused_csv_tags:
progress_rows.append(
{
"assetnum": "",
"location_tag": csv_row.get("raw_location_tag") or unused_tag,
"name": "",
"status": "csv_unprocessed",
}
)
if progress_rows:
progress_df = pd.DataFrame(progress_rows)
progress_csv_path = os.path.join(
os.path.dirname(__file__), "acquisition_cost_progress.csv"
)
try:
progress_df.to_csv(progress_csv_path, index=False)
print(f"Progress checklist saved to {progress_csv_path}")
except Exception as exc:
print(f"Failed to write progress checklist CSV: {exc}")
try:
connection.commit()
except Exception as exc:
print(f"Commit failed: {exc}")
connection.rollback()
duration = datetime.now() - start_time
print(
f"Finished insert_acquisition_cost_data in {duration.total_seconds():.2f}s. "
f"Updated {updated_assets} assets, missing CSV {skipped_missing_csv}, missing tag {skipped_missing_tag}."
)
except Exception as e:
print("Error saat menjalankan insert_acquisition_cost_data:", e)
finally:
if cursor:
try:
cursor.close()
except Exception:
pass
if connection or connection_wo_db:
try:
connection.close()
except Exception:
pass
try:
connection_wo_db.close()
except Exception:
pass
async def query_data(): async def query_data():
connection = None connection = None
@ -375,9 +805,71 @@ async def query_data():
# Membuat cursor menggunakan DictCursor # Membuat cursor menggunakan DictCursor
cursor = connection.cursor(cursor_factory=DictCursor) cursor = connection.cursor(cursor_factory=DictCursor)
cursor_wo = connection_production_wo.cursor(cursor_factory=DictCursor) cursor_wo = connection_production_wo.cursor(cursor_factory=DictCursor)
insert_query = """
INSERT INTO lcc_equipment_tr_data (
id, assetnum, tahun, seq, is_actual,
raw_cm_interval, raw_cm_material_cost, raw_cm_labor_time, raw_cm_labor_human,
raw_pm_interval, raw_pm_material_cost, raw_pm_labor_time, raw_pm_labor_human,
raw_oh_interval, raw_oh_material_cost, raw_oh_labor_time, raw_oh_labor_human,
raw_predictive_interval, raw_predictive_material_cost, raw_predictive_labor_time, raw_predictive_labor_human,
"raw_loss_output_MW", raw_loss_output_price,
"rc_cm_material_cost", "rc_cm_labor_cost",
"rc_pm_material_cost", "rc_pm_labor_cost",
"rc_oh_material_cost", "rc_oh_labor_cost",
"rc_predictive_labor_cost",
created_by, created_at
) VALUES (
%s, %s, %s, %s, %s,
%s, %s, %s, %s,
%s, %s, %s, %s,
%s, %s, %s, %s,
%s, %s, %s, %s,
%s, %s,
%s, %s,
%s, %s,
%s, %s,
%s,
'Sys', NOW()
)
"""
update_query = """
UPDATE lcc_equipment_tr_data
SET seq = %s,
is_actual = %s,
raw_cm_interval = %s,
raw_cm_material_cost = %s,
raw_cm_labor_time = %s,
raw_cm_labor_human = %s,
raw_pm_interval = %s,
raw_pm_material_cost = %s,
raw_pm_labor_time = %s,
raw_pm_labor_human = %s,
raw_oh_interval = %s,
raw_oh_material_cost = %s,
raw_oh_labor_time = %s,
raw_oh_labor_human = %s,
raw_predictive_interval = %s,
raw_predictive_material_cost = %s,
raw_predictive_labor_time = %s,
raw_predictive_labor_human = %s,
"raw_loss_output_MW" = %s,
raw_loss_output_price = %s,
"rc_cm_material_cost" = %s,
"rc_cm_labor_cost" = %s,
"rc_pm_material_cost" = %s,
"rc_pm_labor_cost" = %s,
"rc_oh_material_cost" = %s,
"rc_oh_labor_cost" = %s,
"rc_predictive_labor_cost" = %s,
updated_by = 'Sys',
updated_at = NOW()
WHERE assetnum = %s AND tahun = %s
"""
# TRUNCATE DATA # TRUNCATE DATA
truncate_query = "TRUNCATE TABLE lcc_equipment_tr_data RESTART IDENTITY" # truncate_query = "TRUNCATE TABLE lcc_equipment_tr_data RESTART IDENTITY"
cursor.execute(truncate_query) # cursor.execute(truncate_query)
# Query untuk mendapatkan semua data dari tabel `lcc_ms_equipment_data` # Query untuk mendapatkan semua data dari tabel `lcc_ms_equipment_data`
# query_main = "SELECT * FROM lcc_ms_equipment_data" # query_main = "SELECT * FROM lcc_ms_equipment_data"
@ -462,189 +954,87 @@ async def query_data():
print(f"Error checking data for assetnum {assetnum}: {e}") print(f"Error checking data for assetnum {assetnum}: {e}")
continue continue
if not data_exists: row_values = _build_tr_row_values(
# Insert data jika belum ada data_cm_row,
if not data_cm_row and not data_pm_row and not data_oh_row and not data_predictive_row: data_pm_row,
# Jika data recursive_row tidak ada data_oh_row,
insert_query = """ data_predictive_row,
INSERT INTO lcc_equipment_tr_data ( data_tahunan_row,
id, assetnum, tahun, seq, is_actual, )
raw_cm_interval, raw_cm_material_cost, raw_cm_labor_time, raw_cm_labor_human
, raw_pm_interval, raw_pm_material_cost, raw_pm_labor_time, raw_pm_labor_human
, raw_oh_interval, raw_oh_material_cost, raw_oh_labor_time, raw_oh_labor_human
, raw_predictive_interval, raw_predictive_material_cost, raw_predictive_labor_time, raw_predictive_labor_human
, "raw_loss_output_MW", raw_loss_output_price
, rc_cm_material_cost, rc_cm_labor_cost
, rc_pm_material_cost, rc_pm_labor_cost
, rc_oh_material_cost, rc_oh_labor_cost
, rc_predictive_labor_cost
, created_by, created_at
) VALUES (
%s, %s, %s, %s, %s,
%s, %s, %s, %s,
%s, %s, %s, %s,
%s, %s, %s, %s,
%s, %s, %s, %s,
%s, %s
, %s, %s
, %s, %s
, %s, %s
, %s
, 'Sys', NOW()
)
"""
cursor.execute(
insert_query,
(
str(uuid4()), # id
assetnum, # assetnum
year, # tahun
seq, # seq
1, # is_actual
0, # raw_cm_interval (minimal 1 karena minimal 1x OH)
0, # raw_cm_material_cost
0, # raw_cm_labor_time
0, # raw_cm_labor_human
0, # raw_pm_interval set default 1
0, # raw_pm_material_cost
0, # raw_pm_labor_time
0, # raw_pm_labor_human
0, # raw_oh_interval set default 1
0, # raw_oh_material_cost
0, # raw_oh_labor_time
0, # raw_oh_labor_human
0, # raw_predictive_interval set default 1
0, # raw_predictive_material_cost
0, # raw_predictive_labor_time
0, # raw_predictive_labor_human
( # "raw_loss_output_MW"
# data_tahunan_row["total_lost"]
0
if data_tahunan_row
else 0
),
( # raw_loss_output_price
# data_tahunan_row["rp_per_kwh"]
0
if data_tahunan_row
else 0
),
0, # rc_cm_material_cost
0, # rc_cm_labor_cost
0, # rc_pm_material_cost
0, # rc_pm_labor_cost
0, # rc_oh_material_cost
0, # rc_oh_labor_cost
0, # rc_predictive_labor_cost
),
)
inserted_this_asset += 1
total_inserted += 1
# print minimal per-year insert log
# print(f"Inserted default data for {assetnum} year {year}")
else:
# Jika data recursive_row ada
insert_query = """
INSERT INTO lcc_equipment_tr_data (
id, assetnum, tahun, seq, is_actual,
raw_cm_interval, raw_cm_material_cost, raw_cm_labor_time, raw_cm_labor_human
, raw_pm_interval, raw_pm_material_cost, raw_pm_labor_time, raw_pm_labor_human
, raw_oh_interval, raw_oh_material_cost, raw_oh_labor_time, raw_oh_labor_human
, raw_predictive_interval, raw_predictive_material_cost, raw_predictive_labor_time, raw_predictive_labor_human
, "raw_loss_output_MW", raw_loss_output_price
, "rc_cm_material_cost", "rc_cm_labor_cost"
, "rc_pm_material_cost", "rc_pm_labor_cost"
, "rc_oh_material_cost", "rc_oh_labor_cost"
, "rc_predictive_labor_cost"
, created_by, created_at
) VALUES (
%s, %s, %s, %s, %s,
%s, %s, %s, %s,
%s, %s, %s, %s,
%s, %s, %s, %s,
%s, %s, %s, %s,
%s, %s,
%s, %s,
%s, %s,
%s, %s,
%s,
'Sys', NOW()
)
"""
# Normalize row values to avoid inserting NULL and avoid division by zero
raw_cm_interval = data_cm_row.get("raw_cm_interval") if data_cm_row and data_cm_row.get("raw_cm_interval") is not None else 0
raw_cm_material_cost = data_cm_row.get("raw_cm_material_cost") if data_cm_row and data_cm_row.get("raw_cm_material_cost") is not None else 0
avg_cm_material_cost = (raw_cm_material_cost / raw_cm_interval) if raw_cm_interval else 0
raw_cm_labor_time = data_cm_row.get("raw_cm_labor_time") if data_cm_row and data_cm_row.get("raw_cm_labor_time") is not None else 0
raw_cm_labor_human = data_cm_row.get("raw_cm_labor_human") if data_cm_row and data_cm_row.get("raw_cm_labor_human") is not None else 0
raw_pm_interval = data_pm_row.get("raw_pm_interval") if data_pm_row and data_pm_row.get("raw_pm_interval") is not None else 0
raw_pm_material_cost = data_pm_row.get("raw_pm_material_cost") if data_pm_row and data_pm_row.get("raw_pm_material_cost") is not None else 0
raw_pm_labor_time = data_pm_row.get("raw_pm_labor_time") if data_pm_row and data_pm_row.get("raw_pm_labor_time") is not None else 0
raw_pm_labor_human = data_pm_row.get("raw_pm_labor_human") if data_pm_row and data_pm_row.get("raw_pm_labor_human") is not None else 0
raw_oh_interval = data_oh_row.get("raw_oh_interval") if data_oh_row and data_oh_row.get("raw_oh_interval") is not None else 0
raw_oh_material_cost = data_oh_row.get("raw_oh_material_cost") if data_oh_row and data_oh_row.get("raw_oh_material_cost") is not None else 0
raw_oh_labor_time = data_oh_row.get("raw_oh_labor_time") if data_oh_row and data_oh_row.get("raw_oh_labor_time") is not None else 0
raw_oh_labor_human = data_oh_row.get("raw_oh_labor_human") if data_oh_row and data_oh_row.get("raw_oh_labor_human") is not None else 0
raw_pdm_interval = data_predictive_row.get("raw_predictive_interval") if data_predictive_row and data_predictive_row.get("raw_predictive_interval") is not None else 0
raw_pdm_material_cost = data_predictive_row.get("raw_predictive_material_cost") if data_predictive_row and data_predictive_row.get("raw_predictive_material_cost") is not None else 0
raw_pdm_labor_time = data_predictive_row.get("raw_predictive_labor_time") if data_predictive_row and data_predictive_row.get("raw_predictive_labor_time") is not None else 0
raw_pdm_labor_human = data_predictive_row.get("raw_predictive_labor_human") if data_predictive_row and data_predictive_row.get("raw_predictive_labor_human") is not None else 0
raw_loss_output_MW = data_tahunan_row.get("total_lost") if data_tahunan_row and data_tahunan_row.get("total_lost") is not None else 0
raw_loss_output_price = data_tahunan_row.get("rp_per_kwh") if data_tahunan_row and data_tahunan_row.get("rp_per_kwh") is not None else 0
rc_cm_material_cost = data_cm_row.get("raw_cm_material_cost") if data_cm_row and data_cm_row.get("raw_cm_material_cost") is not None else 0
rc_cm_labor_cost = data_cm_row.get("raw_cm_labor_time")*data_cm_row.get("rc_cm_labor_human")*data_tahunan_row.get("man_hour") if data_cm_row and data_cm_row.get("rc_cm_labor_cost") and data_cm_row.get("rc_cm_labor_human") and data_tahunan_row.get("man_hour") is not None else 0
rc_pm_material_cost = data_pm_row.get("raw_pm_material_cost") if data_pm_row and data_pm_row.get("raw_pm_material_cost") is not None else 0
rc_pm_labor_cost = data_pm_row.get("raw_pm_labor_time")*data_pm_row.get("rc_pm_labor_human")*data_tahunan_row.get("man_hour") if data_pm_row and data_pm_row.get("rc_pm_labor_cost") and data_pm_row.get("rc_pm_labor_human") and data_tahunan_row.get("man_hour") is not None else 0
rc_oh_material_cost = data_oh_row.get("raw_oh_material_cost") if data_oh_row and data_oh_row.get("raw_oh_material_cost") is not None else 0
rc_oh_labor_cost = data_oh_row.get("raw_oh_labor_time")*data_oh_row.get("rc_oh_labor_human")*data_tahunan_row.get("man_hour") if data_oh_row and data_oh_row.get("rc_oh_labor_cost") and data_oh_row.get("rc_oh_labor_human") and data_tahunan_row.get("man_hour") is not None else 0
rc_predictive_labor_cost = data_predictive_row.get("raw_predictive_labor_human")*data_tahunan_row.get("man_hour") if data_predictive_row and data_predictive_row.get("rc_predictive_labor_cost") and data_tahunan_row.get("man_hour") is not None else 0
cursor.execute(
insert_query,
(
str(uuid4()), # id
assetnum, # assetnum
year, # tahun
seq, # seq
1, # is_actual
raw_cm_interval, # raw_cm_interval
avg_cm_material_cost, # avg raw_cm_material_cost per interval
raw_cm_labor_time, # raw_cm_labor_time
raw_cm_labor_human, # raw_cm_labor_human
raw_pm_interval, # raw_pm_interval
raw_pm_material_cost, # raw_pm_material_cost
raw_pm_labor_time, # raw_pm_labor_time
raw_pm_labor_human,
raw_oh_interval,
raw_oh_material_cost,
raw_oh_labor_time,
raw_oh_labor_human,
raw_pdm_interval,
raw_pdm_material_cost,
raw_pdm_labor_time,
raw_pdm_labor_human,
raw_loss_output_MW,
raw_loss_output_price,
rc_cm_material_cost,
rc_cm_labor_cost,
rc_pm_material_cost,
rc_pm_labor_cost,
rc_oh_material_cost,
rc_oh_labor_cost,
rc_predictive_labor_cost,
),
)
inserted_this_asset += 1
total_inserted += 1
if not data_exists:
cursor.execute(
insert_query,
(
str(uuid4()),
assetnum,
year,
seq,
1,
row_values["raw_cm_interval"],
row_values["raw_cm_material_cost"],
row_values["raw_cm_labor_time"],
row_values["raw_cm_labor_human"],
row_values["raw_pm_interval"],
row_values["raw_pm_material_cost"],
row_values["raw_pm_labor_time"],
row_values["raw_pm_labor_human"],
row_values["raw_oh_interval"],
row_values["raw_oh_material_cost"],
row_values["raw_oh_labor_time"],
row_values["raw_oh_labor_human"],
row_values["raw_predictive_interval"],
row_values["raw_predictive_material_cost"],
row_values["raw_predictive_labor_time"],
row_values["raw_predictive_labor_human"],
row_values["raw_loss_output_MW"],
row_values["raw_loss_output_price"],
row_values["rc_cm_material_cost"],
row_values["rc_cm_labor_cost"],
row_values["rc_pm_material_cost"],
row_values["rc_pm_labor_cost"],
row_values["rc_oh_material_cost"],
row_values["rc_oh_labor_cost"],
row_values["rc_predictive_labor_cost"],
),
)
inserted_this_asset += 1
total_inserted += 1
else:
cursor.execute(
update_query,
(
seq,
1,
row_values["raw_cm_interval"],
row_values["raw_cm_material_cost"],
row_values["raw_cm_labor_time"],
row_values["raw_cm_labor_human"],
row_values["raw_pm_interval"],
row_values["raw_pm_material_cost"],
row_values["raw_pm_labor_time"],
row_values["raw_pm_labor_human"],
row_values["raw_oh_interval"],
row_values["raw_oh_material_cost"],
row_values["raw_oh_labor_time"],
row_values["raw_oh_labor_human"],
row_values["raw_predictive_interval"],
row_values["raw_predictive_material_cost"],
row_values["raw_predictive_labor_time"],
row_values["raw_predictive_labor_human"],
row_values["raw_loss_output_MW"],
row_values["raw_loss_output_price"],
row_values["rc_cm_material_cost"],
row_values["rc_cm_labor_cost"],
row_values["rc_pm_material_cost"],
row_values["rc_pm_labor_cost"],
row_values["rc_oh_material_cost"],
row_values["rc_oh_labor_cost"],
row_values["rc_predictive_labor_cost"],
assetnum,
year,
),
)
seq = seq + 1 seq = seq + 1
# commit per asset to persist progress and free transaction # commit per asset to persist progress and free transaction

10
src/modules/equipment/run.py
Unescape Escape View File

@ -3,12 +3,12 @@ import time
# prefer package-relative imports, but allow running this file directly as a script # prefer package-relative imports, but allow running this file directly as a script
try: try:
from src.modules.equipment.insert_actual_data import query_data, insert_lcca_maximo_corrective_data, insert_ms_equipment_data from src.modules.equipment.insert_actual_data import query_data, insert_lcca_maximo_corrective_data, insert_ms_equipment_data, insert_acquisition_cost_data
from src.modules.equipment.Prediksi import Prediksi, main as predict_run from src.modules.equipment.Prediksi import Prediksi, main as predict_run
from src.modules.equipment.Eac import Eac, main as eac_run from src.modules.equipment.Eac import Eac, main as eac_run
except ImportError: except ImportError:
# fallback when there's no parent package (e.g., python run.py) # fallback when there's no parent package (e.g., python run.py)
from insert_actual_data import query_data, insert_lcca_maximo_corrective_data, insert_ms_equipment_data from insert_actual_data import query_data, insert_lcca_maximo_corrective_data, insert_ms_equipment_data, insert_acquisition_cost_data
from Prediksi import Prediksi, main as predict_run from Prediksi import Prediksi, main as predict_run
from Eac import Eac, main as eac_run from Eac import Eac, main as eac_run
@ -23,6 +23,12 @@ async def main():
# print(f"Error in query_data: {str(e)}") # print(f"Error in query_data: {str(e)}")
# return # return
# try:
# await insert_acquisition_cost_data()
# except Exception as e:
# print(f"Error in insert_acquisition_cost_data: {str(e)}")
# return
# try: # try:
# await predict_run() # await predict_run()
# except Exception as e: # except Exception as e:

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