diff --git a/Dockerfile b/Dockerfile index 7587a1e..808cb71 100644 --- a/Dockerfile +++ b/Dockerfile @@ -2,7 +2,7 @@ # 接收构建参数 BASE_IMAGE，由 docker_deply.sh 传入 ARG BASE_IMAGE=python:3.9-slim FROM ${BASE_IMAGE} - +# FROM docker.m.daocloud.io/python:3.9-slim # 设置工作目录 WORKDIR /app diff --git a/__pycache__/main.cpython-312.pyc b/__pycache__/main.cpython-312.pyc index 229c646..d4e429b 100644 Binary files a/__pycache__/main.cpython-312.pyc and b/__pycache__/main.cpython-312.pyc differ diff --git a/__pycache__/main.cpython-313.pyc b/__pycache__/main.cpython-313.pyc index 8eb6081..086e800 100644 Binary files a/__pycache__/main.cpython-313.pyc and b/__pycache__/main.cpython-313.pyc differ diff --git a/docker_deply.sh b/docker_deply.sh index 23bbd6b..21e1a83 100755 --- a/docker_deply.sh +++ b/docker_deply.sh @@ -37,7 +37,9 @@ PLATFORM="linux/amd64" # 默认架构 ARCH_SUFFIX="" # 架构后缀，用于区分不同架构的tar文件 # 默认使用华为云源 (AMD64速度快) BASE_IMAGE="swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/python:3.9-slim" - +if [ "$PLATFORM" = "linux/arm64" ]; then + BASE_IMAGE="docker.m.daocloud.io/python:3.9-slim" +fi # 颜色输出 RED='\033[0;31m' GREEN='\033[0;32m' diff --git a/main.py b/main.py index c833469..0606b00 100644 --- a/main.py +++ b/main.py @@ -9,6 +9,7 @@ from typing import Optional, List, Dict import random import uuid import os +import difflib from dotenv import load_dotenv load_dotenv() @@ -54,36 +55,42 @@ def assign_seat(cur, user_industry: str) -> str: """ Allocate a seat based on: 1. Even distribution (11 tables, max 12 per table) + - Prioritize filling empty tables first (min count) + - If counts are equal, fill sequentially (Table 1 before Table 2) 2. Mix industries (try to put user in a table where their industry is least represented) + - Uses natural language similarity to judge industry overlap """ TOTAL_TABLES = 11 MAX_PER_TABLE = 12 # Initialize table stats - # tables = { 1: {'count': 0, 'industries': []}, ... } tables = {i: {'count': 0, 'industries': []} for i in range(1, TOTAL_TABLES + 1)} # Fetch current seating status - # We join checkin_info with gsdh_data to get industries of people ALREADY SEATED + # Uses aggregation for efficiency as requested + # We use array_agg to collect industries for the diversity check in one query query = """ - SELECT ci.location, gd.industry_company + SELECT ci.location, COUNT(ci.gsdh_id), array_agg(gd.industry_company) FROM checkin_info ci - JOIN gsdh_data gd ON ci.gsdh_id = gd.new_id + LEFT JOIN gsdh_data gd ON ci.gsdh_id = gd.new_id WHERE ci.location IS NOT NULL AND ci.location LIKE '第%桌' + GROUP BY ci.location """ cur.execute(query) rows = cur.fetchall() for row in rows: - loc = row[0] # e.g. "第1桌" - ind = row[1] + loc = row[0] + count = row[1] + industries = row[2] if row[2] else [] + try: # Extract table number table_num = int(loc.replace("第", "").replace("桌", "")) if 1 <= table_num <= TOTAL_TABLES: - tables[table_num]['count'] += 1 - if ind: - tables[table_num]['industries'].append(ind) + tables[table_num]['count'] = count + # Filter out None values from industries + tables[table_num]['industries'] = [ind for ind in industries if ind] except ValueError: continue @@ -93,33 +100,39 @@ def assign_seat(cur, user_industry: str) -> str: if not available_tables: return "自由席" # Fallback if all full - # Strategy 1: Find tables with Minimum Count (Even Distribution) + # Strategy 1: Find tables with Minimum Count + # This automatically handles "Prioritize filling TOTAL_TABLES" because empty tables have count 0 min_count = min(t[1]['count'] for t in available_tables) - candidates_step1 = [t for t in available_tables if t[1]['count'] == min_count] + candidates = [t for t in available_tables if t[1]['count'] == min_count] - # Strategy 2: Among candidates, find best for diversity - # We want a table where user_industry is NOT present, or present least often - best_table = None + # Sort by table number to ensure sequential filling if counts are equal (Requirement: 顺序分配) + candidates.sort(key=lambda x: x[0]) - # If user has no industry info, just pick random from candidates + # Strategy 2: Optimize for Industry Diversity if not user_industry: - best_table = random.choice(candidates_step1)[0] + # If no industry info, just pick the first one (Sequential) + best_table = candidates[0][0] else: - # Score candidates: lower score is better (score = count of this industry in that table) + # Calculate similarity scores + # Score = sum of similarity with existing users + # Lower score is better (more unique) scored_candidates = [] - for table_id, stats in candidates_step1: - # Simple fuzzy check: count how many times user_industry appears in stats['industries'] - # We use simple string containment - industry_count = sum(1 for existing_ind in stats['industries'] if existing_ind and user_industry in existing_ind) - scored_candidates.append((table_id, industry_count)) - # Sort by industry count (asc) - scored_candidates.sort(key=lambda x: x[1]) + for table_id, stats in candidates: + total_similarity = 0.0 + for existing_ind in stats['industries']: + if existing_ind: + # Use difflib for fuzzy matching (0.0 to 1.0) + # This helps understand "Natural Language" industries better than exact match + sim = difflib.SequenceMatcher(None, user_industry, existing_ind).ratio() + total_similarity += sim + + scored_candidates.append((table_id, total_similarity)) - # Pick the one with least collision - min_collision = scored_candidates[0][1] - final_candidates = [x[0] for x in scored_candidates if x[1] == min_collision] - best_table = random.choice(final_candidates) + # Sort by similarity score (asc), then by table_id (asc) + scored_candidates.sort(key=lambda x: (x[1], x[0])) + + best_table = scored_candidates[0][0] return f"第{best_table}桌"