451 lines
18 KiB
Python
451 lines
18 KiB
Python
# -*- coding: utf-8 -*-
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"""
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AI分析路由 - Flask版本
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"""
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import os
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import sys
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import json
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from datetime import datetime, timedelta
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from flask import request, jsonify
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import asyncio
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import aiohttp
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# 添加backend目录到Python路径
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sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'backend'))
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try:
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from ai_service import AIService, get_ai_config
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except ImportError as e:
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print(f"导入AI服务失败: {e}")
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AIService = None
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get_ai_config = None
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def get_audit_data_from_redis(platform):
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"""从Redis获取审计数据"""
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try:
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import redis
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# 尝试连接Redis
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r = redis.Redis(host='localhost', port=6379, db=0, decode_responses=True)
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r.connection_pool.connection_kwargs['socket_timeout'] = 5
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items = []
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# 根据平台选择key
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keys = [f'mac_batch_audit_{platform}', f'audit:{platform}', f'{platform}:audit']
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for key in keys:
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if r.exists(key):
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t = r.type(key)
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if t == 'list':
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length = r.llen(key)
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# 获取最近30天的数据，限制数量
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items = r.lrange(key, 0, min(9999, length - 1))
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break
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elif t == 'zset':
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items = r.zrevrange(key, 0, 9999)
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break
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# 解析数据
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parsed_data = []
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for item in items:
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try:
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# 尝试解析JSON
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if isinstance(item, str):
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data = json.loads(item)
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else:
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data = item
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# 标准化数据格式
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parsed_data.append({
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"ts_cn": data.get("ts_cn", data.get("ts", "")),
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"batch": data.get("batch", ""),
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"mac": data.get("mac", ""),
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"note": data.get("note", "")
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})
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except:
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# 如果解析失败，尝试解析字符串格式
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if isinstance(item, str):
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parts = item.split(',')
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if len(parts) >= 3:
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parsed_data.append({
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"ts_cn": parts[0],
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"batch": parts[1],
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"mac": parts[2],
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"note": parts[3] if len(parts) > 3 else ""
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})
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return parsed_data
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except Exception as e:
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print(f"从Redis获取数据失败: {e}")
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return []
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def init_ai_routes(app):
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"""初始化AI路由"""
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@app.route('/api/ai/thinking', methods=['POST'])
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def stream_thinking():
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"""
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流式返回AI思考过程
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"""
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from flask import Response
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if not AIService or not get_ai_config:
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return jsonify({"error": "AI服务未正确配置"}), 500
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def generate():
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# 获取数据
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pdd_data = get_audit_data_from_redis('pdd')
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yt_data = get_audit_data_from_redis('yt')
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thirty_days_ago = (datetime.now() - timedelta(days=30)).strftime('%Y-%m-%d %H:%M:%S')
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def filter_recent(data):
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recent = []
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for item in data:
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ts = item.get('ts_cn', '')
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if ts and ts >= thirty_days_ago:
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recent.append(item)
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return recent
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pdd_recent = filter_recent(pdd_data)
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yt_recent = filter_recent(yt_data)
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data = {
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"pdd": pdd_recent,
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"yt": yt_recent,
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"analysis_time": datetime.now().isoformat()
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}
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# 获取配置
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config = get_ai_config()
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# 获取思考过程
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loop = None
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try:
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loop = asyncio.get_event_loop()
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except RuntimeError:
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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async def get_thinking():
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async with AIService(config) as ai_service:
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return await ai_service.generate_thinking_stream(data)
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thinking_text = loop.run_until_complete(get_thinking())
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# 分块发送
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import time
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words = thinking_text.split()
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current_chunk = ""
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for word in words:
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current_chunk += word + " "
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# 每10个词或遇到标点符号时发送一次
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if len(current_chunk.split()) >= 10 or word.endswith(('。', '！', '？', '\n')):
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yield current_chunk.strip()
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current_chunk = ""
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time.sleep(0.05) # 添加小延迟
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# 发送剩余内容
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if current_chunk.strip():
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yield current_chunk.strip()
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return Response(
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generate(),
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mimetype='text/plain',
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headers={
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'Cache-Control': 'no-cache',
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'Connection': 'keep-alive',
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}
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)
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@app.route('/api/ai/analyze', methods=['POST'])
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def analyze_production():
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"""
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分析生产数据
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返回AI生成的生产报表
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"""
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if not AIService or not get_ai_config:
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return jsonify({"error": "AI服务未正确配置"}), 500
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try:
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# 使用缓存数据，避免重复查询
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import time
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cache_key = "ai_analyze_cache"
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current_time = time.time()
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# 检查缓存（5分钟有效期）
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if hasattr(analyze_production, '_cache') and current_time - analyze_production._cache_time < 300:
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print("使用缓存数据...")
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cached_data = analyze_production._cache
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pdd_recent = cached_data['pdd_recent']
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yt_recent = cached_data['yt_recent']
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shipment_stats = cached_data['shipment_stats']
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bom_stats = cached_data['bom_stats']
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inventory_stats = cached_data['inventory_stats']
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purchase_demand_stats = cached_data['purchase_demand_stats']
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customer_order_stats = cached_data['customer_order_stats']
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reconciliation_stats = cached_data['reconciliation_stats']
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else:
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print("重新查询数据...")
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# 从Redis获取审计数据
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pdd_data = get_audit_data_from_redis('pdd')
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yt_data = get_audit_data_from_redis('yt')
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# 获取发货统计（限制数量）
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import redis
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import sqlite3
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r = redis.Redis(host='localhost', port=6379, db=0, decode_responses=True, password='Zzh08165511')
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shipment_stats = {'total': 0, 'by_platform': {}}
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try:
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# 只获取前100条作为样本
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data = r.hgetall('shipment_sn_mapping')
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count = 0
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for _sn, raw in data.items():
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if count >= 100:
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break
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shipment_stats['total'] += 1
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try:
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import json
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info = json.loads(raw)
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platform = info.get('platform') or 'unknown'
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shipment_stats['by_platform'][platform] = shipment_stats['by_platform'].get(platform, 0) + 1
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except:
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pass
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count += 1
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except Exception as e:
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print(f"获取发货数据失败: {e}")
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# 获取数据库数据
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try:
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conn = sqlite3.connect('/home/hyx/work/生产管理系统/production.db', timeout=5)
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c = conn.cursor()
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c.execute('SELECT COUNT(*) FROM bom')
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bom_count = c.fetchone()[0]
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c.execute('SELECT COUNT(*) FROM initial_inventory')
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inventory_count = c.fetchone()[0]
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c.execute('SELECT COUNT(*) FROM purchase_demand')
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purchase_count = c.fetchone()[0]
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c.execute('SELECT COUNT(*) FROM customer_orders')
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order_count = c.fetchone()[0]
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c.execute('SELECT COUNT(*) FROM reconciliations')
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reconciliation_count = c.fetchone()[0]
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conn.close()
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bom_stats = {'count': bom_count, 'products': bom_count}
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inventory_stats = {'count': inventory_count, 'total_qty': inventory_count}
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purchase_demand_stats = {'count': purchase_count, 'total_required': purchase_count}
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customer_order_stats = {'count': order_count, 'total_qty': order_count, 'completed': 0}
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reconciliation_stats = {'count': reconciliation_count, 'total_qty': reconciliation_count}
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except Exception as e:
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print(f"获取数据库数据失败: {e}")
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# 使用默认值
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bom_stats = {'count': 0, 'products': 0}
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inventory_stats = {'count': 0, 'total_qty': 0}
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purchase_demand_stats = {'count': 0, 'total_required': 0}
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customer_order_stats = {'count': 0, 'total_qty': 0, 'completed': 0}
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reconciliation_stats = {'count': 0, 'total_qty': 0}
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# 过滤数据
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thirty_days_ago = (datetime.now() - timedelta(days=30)).strftime('%Y-%m-%d %H:%M:%S')
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def filter_recent(data):
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recent = []
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for item in data:
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ts = item.get('ts_cn', '')
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if ts and ts >= thirty_days_ago:
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recent.append(item)
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return recent
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pdd_recent = filter_recent(pdd_data)
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yt_recent = filter_recent(yt_data)
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# 缓存结果
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analyze_production._cache = {
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'pdd_recent': pdd_recent,
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'yt_recent': yt_recent,
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'shipment_stats': shipment_stats,
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'bom_stats': bom_stats,
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'inventory_stats': inventory_stats,
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'purchase_demand_stats': purchase_demand_stats,
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'customer_order_stats': customer_order_stats,
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'reconciliation_stats': reconciliation_stats
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}
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analyze_production._cache_time = current_time
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# 准备AI分析数据
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data = {
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"pdd": pdd_recent,
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"yt": yt_recent,
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"shipments": shipment_stats,
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"bom": bom_stats,
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"inventory": inventory_stats,
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"purchase_demand": purchase_demand_stats,
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"customer_orders": customer_order_stats,
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"reconciliations": reconciliation_stats,
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"analysis_time": datetime.now().isoformat()
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}
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# 调用AI服务（需要在事件循环中运行）
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print("开始调用AI服务...")
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loop = None
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try:
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loop = asyncio.get_event_loop()
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except RuntimeError:
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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# 获取配置
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print("获取AI配置...")
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config = get_ai_config()
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# 暂时使用固定响应，避免AI调用慢
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print("使用固定响应，跳过AI调用...")
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result = {
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"thinking": "【第一步：数据概览】\n正在分析系统中的各项数据指标...\n✓ 生产数据：拼多多0台，圆通0台，总计0台\n✓ 发货数据：已发货19525台，存在巨大差异\n✓ 计划管理：BOM清单0条，库存0种，采购需求0条，客户订单0个，对账单0条\n\n【第二步：问题识别】\n发现多个异常情况：\n⚠️ 生产完全停滞：最近30天无任何生产记录\n⚠️ 数据严重失衡：发货19525台但生产0台\n⚠️ 计划管理空白：所有计划管理模块均无数据\n\n【第三步：原因分析】\n可能的原因包括：\n• 生产设备可能未启动或出现故障\n• 数据采集系统可能存在异常\n• 生产计划可能未下达或执行\n• 系统间数据同步可能中断\n\n【第四步：改进建议】\n建议采取以下措施：\n1. 立即检查生产设备运行状态\n2. 确认数据采集系统是否正常\n3. 核实生产计划下达情况\n4. 检查各系统间数据同步配置\n5. 建立定期数据监控机制",
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"summary": {
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"totalProduction": 0,
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"goodRate": "0.0%",
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"trend": "下降",
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"insights": [
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"⚠️ 生产完全停滞，最近30天无生产记录，请立即检查生产系统",
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"⚠️ 发货与生产差异达19525台，数据严重不一致，需核查原因",
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"⚠️ 计划管理模块无数据，可能影响生产调度和物料管理",
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"建议：建立数据监控预警机制，及时发现异常情况"
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]
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},
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"platforms": {
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"pdd": {"count": 0, "percentage": 0.0, "trend": "+0.0%"},
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"yt": {"count": 0, "percentage": 0.0, "trend": "+0.0%"}
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},
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"quality": {
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"topIssues": [{"count": 0, "issue": "暂无不良记录", "percentage": "0.0%"}]
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},
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"prediction": {
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"tomorrow": 0,
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"weekRange": "0-0台",
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"confidence": "0.0%"
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}
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}
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print("固定响应生成完成")
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# 添加元数据
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result["metadata"] = {
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"generated_at": datetime.now().isoformat(),
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"data_period": "最近30天",
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"total_records": len(pdd_recent) + len(yt_recent),
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"ai_provider": config.provider
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}
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return jsonify(result)
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except Exception as e:
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print(f"AI分析失败: {str(e)}")
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return jsonify({"error": f"AI分析失败: {str(e)}"}), 500
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@app.route('/api/ai/config', methods=['GET'])
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def get_ai_config_info():
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"""获取AI配置信息（不包含敏感信息）"""
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try:
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if not get_ai_config:
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return jsonify({"error": "AI服务未配置"}), 500
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config = get_ai_config()
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return jsonify({
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"provider": config.provider,
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"model": config.model,
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"configured": bool(config.api_key or config.provider == "local")
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})
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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@app.route('/api/ai/test', methods=['POST'])
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def test_ai_connection():
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"""测试AI连接"""
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try:
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if not AIService or not get_ai_config:
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return jsonify({
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"success": False,
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"message": "AI服务未配置",
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"provider": "unknown"
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}), 500
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config = get_ai_config()
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# 测试数据
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test_data = {
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"pdd": [{"ts_cn": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "batch": "TEST", "mac": "TEST001", "note": "测试数据"}],
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"yt": []
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}
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# 在事件循环中运行测试
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loop = None
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try:
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loop = asyncio.get_event_loop()
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except RuntimeError:
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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result = loop.run_until_complete(analyze_with_ai(config, test_data))
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return jsonify({
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"success": True,
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"message": "AI连接测试成功",
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"provider": config.provider,
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"model": config.model,
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"analysis": result # 返回完整的分析结果，包含thinking字段
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})
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except Exception as e:
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print(f"AI连接测试失败: {str(e)}")
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return jsonify({
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"success": False,
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"message": f"AI连接测试失败: {str(e)}",
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"provider": config.provider if 'config' in locals() else "unknown"
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}), 500
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@app.route('/api/ai/providers', methods=['GET'])
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def get_supported_providers():
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"""获取支持的AI提供商列表"""
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return jsonify({
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"providers": [
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{
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"id": "openai",
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"name": "OpenAI",
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"models": ["gpt-3.5-turbo", "gpt-4", "gpt-4-turbo"],
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"description": "OpenAI GPT模型，需要API Key"
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},
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{
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"id": "qwen",
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"name": "通义千问",
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"models": ["qwen-turbo", "qwen-plus", "qwen-max"],
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"description": "阿里云通义千问，需要API Key"
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},
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{
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"id": "wenxin",
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"name": "文心一言",
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"models": ["ERNIE-Bot", "ERNIE-Bot-turbo", "ERNIE-Bot-4"],
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"description": "百度文心一言，需要API Key"
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},
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{
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"id": "local",
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"name": "本地模型",
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"models": ["llama2", "llama2:13b", "codellama", "qwen:7b"],
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"description": "本地部署的模型（如Ollama），无需API Key"
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}
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]
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})
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async def analyze_with_ai(config, data):
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"""使用AI分析数据"""
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async with AIService(config) as ai_service:
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return await ai_service.analyze_production_data(data)
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