optimizer-framework/scripts/data_model_loop.py at master · ksericpro/optimizer-framework · GitHub

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import psycopg2

import pandas as pd

import math

from api.logger_config import logger

from api.db_config import get_db_params

DB_PARAMS = get_db_params()

def run_data_model_loop():

"""

Analyzes historical performance to derive current-day parameters for the optimizer.

This fulfills the requirement of the 'Data Model' in the system architecture.

"""

try:

conn = psycopg2.connect(**DB_PARAMS)

# 1. Fetch aggregate stats for the last 7 days

query = """

SELECT

driver_id,

AVG(total_orders_completed) as avg_orders,

AVG(average_service_time) as avg_service,

AVG(efficiency_score) as avg_efficiency

FROM performance_metrics

WHERE date >= CURRENT_DATE - INTERVAL '7 days'

GROUP BY driver_id

"""

stats = pd.read_sql(query, conn)

if stats.empty:

logger.warning("No recent performance data found. Skipping Data Model update.")

return {"status": "success", "drivers_updated": 0}

cur = conn.cursor()

logger.info(f"Running Data Model Loop for {len(stats)} drivers...")

updated_count = 0

for _, row in stats.iterrows():

base_capacity = row['avg_orders']

efficiency_boost = 1.1 if row['avg_efficiency'] > 0.85 else 1.0

new_max_jobs = math.ceil(base_capacity * efficiency_boost)

cur.execute(

"UPDATE drivers SET max_jobs_per_day = %s WHERE id = %s",

(new_max_jobs, row['driver_id'])

)

updated_count += 1

conn.commit()

cur.close()

conn.close()

logger.info(f"Data Model Loop complete. Updated {updated_count} drivers.")

return {"status": "success", "drivers_updated": updated_count}

except Exception as e:

return {"status": "error", "message": str(e)}

if __name__ == "__main__":

run_data_model_loop()

optimizer-framework/scripts/data_model_loop.py at master · ksericpro/optimizer-framework · GitHub

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