441 lines
18 KiB
Python
441 lines
18 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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AI分析服务
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支持多种AI接口：OpenAI、通义千问、文心一言、本地模型等
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"""
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import json
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import asyncio
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional, Union
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import aiohttp
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from dataclasses import dataclass
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import os
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@dataclass
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class AIConfig:
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"""AI配置"""
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provider: str # openai, qwen, wenxin, local
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api_key: str
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base_url: Optional[str] = None
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model: str = "gpt-3.5-turbo"
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temperature: float = 0.7
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max_tokens: int = 2000
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class AIService:
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"""AI分析服务"""
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def __init__(self, config: AIConfig):
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self.config = config
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self.session = None
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async def __aenter__(self):
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self.session = aiohttp.ClientSession()
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return self
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async def __aexit__(self, exc_type, exc_val, exc_tb):
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if self.session:
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await self.session.close()
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async def analyze_production_data(self, data: Dict) -> Dict:
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"""分析生产数据"""
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# 构建分析提示词
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prompt = self._build_analysis_prompt(data)
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# 调用AI接口
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response = await self._call_ai(prompt)
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# 解析AI返回的结果
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return self._parse_ai_response(response)
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async def generate_thinking_stream(self, data: Dict) -> str:
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"""生成思考过程（用于流式输出）"""
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# 构建一个专门用于思考过程的prompt
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thinking_prompt = f"""
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请分析以下生产数据，并详细展示你的思考过程。请按照以下步骤进行分析：
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数据情况：
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- 拼多多数据：{len(data.get('pdd', []))} 条记录
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- 圆通数据：{len(data.get('yt', []))} 条记录
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- 分析时间：{data.get('analysis_time', '未知')}
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请按照以下格式输出思考过程：
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第一步：数据概览 - 描述看到的基础数据情况
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第二步：规律发现 - 分析数据中的模式和趋势
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第三步：原因推断 - 解释为什么会出现这些规律
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第四步：结论形成 - 总结关键发现和建议
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请详细描述每个步骤的思考内容，让用户了解AI是如何分析数据的。
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"""
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# 调用AI获取思考过程
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response = await self._call_ai(thinking_prompt)
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# 提取思考过程部分
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if "第一步：" in response:
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# 提取从第一步开始的内容
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start_idx = response.find("第一步：")
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thinking = response[start_idx:]
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else:
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thinking = response
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return thinking
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def _build_analysis_prompt(self, data: Dict) -> str:
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"""构建分析提示词"""
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# 获取最近30天的数据
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today = datetime.now()
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thirty_days_ago = today - timedelta(days=30)
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# 统计数据
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pdd_data = data.get('pdd', [])
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yt_data = data.get('yt', [])
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shipment_stats = data.get('shipments', {'total': 0, 'by_platform': {}})
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bom_stats = data.get('bom', {'count': 0, 'products': 0})
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inventory_stats = data.get('inventory', {'count': 0, 'total_qty': 0})
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purchase_demand_stats = data.get('purchase_demand', {'count': 0, 'total_required': 0})
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customer_order_stats = data.get('customer_orders', {'count': 0, 'total_qty': 0, 'completed': 0})
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reconciliation_stats = data.get('reconciliations', {'count': 0, 'total_qty': 0})
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# 计算关键指标
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total_pdd = len(pdd_data)
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total_yt = len(yt_data)
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total_production = total_pdd + total_yt
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# 发货数据
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total_shipments = shipment_stats.get('total', 0)
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shipment_by_platform = shipment_stats.get('by_platform', {})
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# 计划管理数据
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bom_count = bom_stats.get('count', 0) or 0
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bom_products = bom_stats.get('products', 0) or 0
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inventory_count = inventory_stats.get('count', 0) or 0
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inventory_qty = inventory_stats.get('total_qty', 0) or 0
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purchase_demand_count = purchase_demand_stats.get('count', 0) or 0
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purchase_required = purchase_demand_stats.get('total_required', 0) or 0
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customer_order_count = customer_order_stats.get('count', 0) or 0
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customer_order_qty = customer_order_stats.get('total_qty', 0) or 0
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customer_order_completed = customer_order_stats.get('completed', 0) or 0
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reconciliation_count = reconciliation_stats.get('count', 0) or 0
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reconciliation_qty = reconciliation_stats.get('total_qty', 0) or 0
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# 计算订单完成率
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order_completion_rate = (customer_order_completed / customer_order_count * 100) if customer_order_count > 0 else 0
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# 计算良品率（假设最后100条数据中有良品标记）
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recent_data = (pdd_data + yt_data)[-100:]
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good_count = sum(1 for item in recent_data if '不良' not in item.get('note', ''))
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good_rate = (good_count / len(recent_data) * 100) if recent_data else 0
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# 计算趋势（最近7天 vs 之前7天）
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seven_days_ago = today - timedelta(days=7)
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recent_pdd = len([d for d in pdd_data if datetime.strptime(d['ts_cn'], '%Y-%m-%d %H:%M:%S') > seven_days_ago])
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recent_yt = len([d for d in yt_data if datetime.strptime(d['ts_cn'], '%Y-%m-%d %H:%M:%S') > seven_days_ago])
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fourteen_days_ago = today - timedelta(days=14)
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prev_pdd = len([d for d in pdd_data if fourteen_days_ago < datetime.strptime(d['ts_cn'], '%Y-%m-%d %H:%M:%S') <= seven_days_ago])
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prev_yt = len([d for d in yt_data if fourteen_days_ago < datetime.strptime(d['ts_cn'], '%Y-%m-%d %H:%M:%S') <= seven_days_ago])
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trend = "上升" if (recent_pdd + recent_yt) > (prev_pdd + prev_yt) else "下降"
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# 统计不良类型
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defect_types = {}
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for item in recent_data:
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note = item.get('note', '')
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if '不良' in note:
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defect_type = note.split('：')[-1] if '：' in note else note
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defect_types[defect_type] = defect_types.get(defect_type, 0) + 1
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top_defects = sorted(defect_types.items(), key=lambda x: x[1], reverse=True)[:3]
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# 计算趋势百分比
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pdd_trend = ((recent_pdd - prev_pdd) / prev_pdd * 100) if prev_pdd > 0 else 0
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yt_trend = ((recent_yt - prev_yt) / prev_yt * 100) if prev_yt > 0 else 0
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# 计算百分比
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pdd_percentage = (total_pdd/total_production*100) if total_production > 0 else 0
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yt_percentage = (total_yt/total_production*100) if total_production > 0 else 0
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# 计算生产与发货的差异
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production_shipment_gap = total_production - total_shipments
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gap_percentage = (production_shipment_gap / total_production * 100) if total_production > 0 else 0
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prompt = f"""
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作为生产管理专家，请分析以下生产数据并提供专业洞察：
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【基础数据】
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- 统计周期：最近30天
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- 总产量：{total_production} 台
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- 良品率：{good_rate:.1f}%
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- 产量趋势：{trend.lower()}
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【平台分布】
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- 拼多多：{total_pdd} 台 ({pdd_percentage:.1f}%)
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- 圆通：{total_yt} 台 ({yt_percentage:.1f}%)
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【发货统计】
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- 总发货量：{total_shipments} 台
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- 发货平台分布：{shipment_by_platform}
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- 生产与发货差异：{production_shipment_gap} 台 ({gap_percentage:.1f}%)
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【计划管理】
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- BOM物料清单：{bom_count} 条记录，涉及 {bom_products} 个产品
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- 期初库存：{inventory_count} 种物料，总库存量 {inventory_qty}
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- 采购需求：{purchase_demand_count} 条需求，总需求量 {purchase_required}
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- 客户订单：{customer_order_count} 个订单，总订单量 {customer_order_qty} 台，完成率 {order_completion_rate:.1f}%
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- 对账单：{reconciliation_count} 条记录，总对账量 {reconciliation_qty} 台
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【质量分析】
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主要不良问题：
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{chr(10).join([f"- {d[0]}：{d[1]} 次" for d in top_defects]) if top_defects else "- 暂无不良记录"}
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【最近7天产量】
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- 拼多多：{recent_pdd} 台
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- 圆通：{recent_yt} 台
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请以JSON格式返回分析结果，包含以下字段。**重要提示：对于需要特别注意的问题，请在insights中使用"⚠️"标记**：
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{{
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"thinking": "第一步：数据概览 - 描述看到的基础数据情况\\n第二步：规律发现 - 分析数据中的模式和趋势\\n第三步：原因推断 - 解释为什么会出现这些规律\\n第四步：结论形成 - 总结关键发现和建议",
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"summary": {{
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"totalProduction": {total_production},
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"goodRate": "{good_rate:.1f}%",
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"trend": "{trend.lower()}",
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"insights": [
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"洞察1：具体分析产量变化原因（如有问题用⚠️标记）",
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"洞察2：质量管控建议（如有问题用⚠️标记）",
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"洞察3：生产优化建议"
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]
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}},
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"platforms": {{
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"pdd": {{
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"count": {total_pdd},
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"percentage": {pdd_percentage:.1f},
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"trend": "{pdd_trend:+.1f}%"
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}},
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"yt": {{
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"count": {total_yt},
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"percentage": {yt_percentage:.1f},
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"trend": "{yt_trend:+.1f}%"
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}}
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}},
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"quality": {{
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"topIssues": [
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{{"issue": "问题1", "count": 数量, "percentage": "百分比"}}
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]
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}},
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"prediction": {{
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"tomorrow": 预测明日产量,
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"weekRange": "本周预测范围",
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"confidence": "置信度百分比"
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}}
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}}
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重要提示：
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1. thinking字段必须简洁，控制在200字以内，用分步骤的方式展示
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2. 每一步都要简明扼要，突出重点
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3. 先描述数据特征，再分析问题，最后给出建议
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4. 洞察要具体、实用，基于实际数据
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5. 预测要基于历史趋势
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6. 返回标准JSON格式，不要包含其他文字
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"""
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return prompt
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async def _call_ai(self, prompt: str) -> str:
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"""调用AI接口"""
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if self.config.provider == "openai":
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return await self._call_openai(prompt)
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elif self.config.provider == "qwen":
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return await self._call_qwen(prompt)
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elif self.config.provider == "wenxin":
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return await self._call_wenxin(prompt)
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elif self.config.provider == "local":
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return await self._call_local(prompt)
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else:
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raise ValueError(f"不支持的AI提供商: {self.config.provider}")
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async def _call_openai(self, prompt: str) -> str:
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"""调用OpenAI API"""
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headers = {
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"Authorization": f"Bearer {self.config.api_key}",
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"Content-Type": "application/json"
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}
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data = {
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"model": self.config.model,
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"messages": [
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{"role": "system", "content": "你是一个专业的生产管理数据分析专家。"},
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{"role": "user", "content": prompt}
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],
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"temperature": self.config.temperature,
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"max_tokens": self.config.max_tokens
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}
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url = self.config.base_url or "https://api.openai.com/v1/chat/completions"
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async with self.session.post(url, headers=headers, json=data) as response:
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if response.status == 200:
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result = await response.json()
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return result["choices"][0]["message"]["content"]
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else:
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raise Exception(f"OpenAI API调用失败: {response.status}")
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async def _call_qwen(self, prompt: str) -> str:
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"""调用通义千问API（使用OpenAI兼容接口）"""
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headers = {
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"Authorization": f"Bearer {self.config.api_key}",
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"Content-Type": "application/json"
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}
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data = {
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"model": self.config.model or "qwen-turbo",
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"messages": [
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{"role": "system", "content": "你是一个专业的生产管理数据分析专家。"},
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{"role": "user", "content": prompt}
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],
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"temperature": self.config.temperature,
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"max_tokens": 1000 # 减少token数量
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}
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# 使用兼容模式的URL
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if self.config.base_url and self.config.base_url.endswith('/chat/completions'):
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url = self.config.base_url
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else:
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url = (self.config.base_url or "https://dashscope.aliyuncs.com/compatible-mode/v1") + "/chat/completions"
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async with self.session.post(url, headers=headers, json=data) as response:
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if response.status == 200:
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result = await response.json()
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return result["choices"][0]["message"]["content"]
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else:
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error_text = await response.text()
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raise Exception(f"通义千问API调用失败: {response.status} - {error_text}")
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async def _call_wenxin(self, prompt: str) -> str:
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"""调用文心一言API"""
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# 实现文心一言API调用
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access_token = await self._get_wenxin_access_token()
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headers = {
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"Content-Type": "application/json"
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}
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data = {
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"messages": [
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{"role": "user", "content": prompt}
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],
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"temperature": self.config.temperature,
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"max_output_tokens": self.config.max_tokens
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}
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url = f"https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions?access_token={access_token}"
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async with self.session.post(url, headers=headers, json=data) as response:
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if response.status == 200:
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result = await response.json()
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return result["result"]
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else:
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raise Exception(f"文心一言API调用失败: {response.status}")
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async def _call_local(self, prompt: str) -> str:
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"""调用本地模型（如Ollama）"""
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headers = {
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"Content-Type": "application/json"
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}
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data = {
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"model": self.config.model,
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"prompt": prompt,
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"stream": False,
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"options": {
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"temperature": self.config.temperature,
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"num_predict": self.config.max_tokens
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}
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}
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url = self.config.base_url or "http://localhost:11434/api/generate"
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async with self.session.post(url, headers=headers, json=data) as response:
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if response.status == 200:
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result = await response.json()
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return result["response"]
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else:
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raise Exception(f"本地模型API调用失败: {response.status}")
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async def _get_wenxin_access_token(self) -> str:
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"""获取文心一言access token"""
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# 这里需要实现获取access token的逻辑
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# 参考百度AI文档
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pass
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def _parse_ai_response(self, response: str) -> Dict:
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"""解析AI响应"""
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try:
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# 尝试解析JSON
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# 如果响应包含```json，需要提取
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if "```json" in response:
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start = response.find("```json") + 7
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end = response.find("```", start)
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json_str = response[start:end].strip()
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else:
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json_str = response.strip()
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return json.loads(json_str)
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except json.JSONDecodeError:
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# 如果解析失败，返回默认结构
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return {
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"summary": {
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"totalProduction": 0,
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"goodRate": "0%",
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"trend": "stable",
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"insights": ["AI分析暂时不可用，请稍后重试"]
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},
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"platforms": {"pdd": {"count": 0, "percentage": 0, "trend": "+0%"}},
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"quality": {"topIssues": []},
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"prediction": {"tomorrow": 0, "weekRange": "0-0", "confidence": "0%"}
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}
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# 配置示例
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def get_ai_config() -> AIConfig:
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"""获取AI配置"""
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# 从环境变量或配置文件读取
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provider = os.getenv("AI_PROVIDER", "openai") # openai, qwen, wenxin, local
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if provider == "openai":
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return AIConfig(
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provider="openai",
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api_key=os.getenv("OPENAI_API_KEY", ""),
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base_url=os.getenv("OPENAI_BASE_URL"),
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model="gpt-3.5-turbo"
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)
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elif provider == "qwen":
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return AIConfig(
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provider="qwen",
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api_key=os.getenv("QWEN_API_KEY", ""),
|
||
base_url=os.getenv("QWEN_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
|
||
model="qwen-turbo"
|
||
)
|
||
elif provider == "wenxin":
|
||
return AIConfig(
|
||
provider="wenxin",
|
||
api_key=os.getenv("WENXIN_API_KEY", ""),
|
||
model="ERNIE-Bot"
|
||
)
|
||
elif provider == "local":
|
||
return AIConfig(
|
||
provider="local",
|
||
api_key="",
|
||
base_url=os.getenv("LOCAL_AI_URL", "http://localhost:11434/api/generate"),
|
||
model="llama2"
|
||
)
|
||
else:
|
||
raise ValueError(f"不支持的AI提供商: {provider}")
|
||
|
||
# 使用示例
|
||
async def analyze_production(data: Dict) -> Dict:
|
||
"""分析生产数据"""
|
||
config = get_ai_config()
|
||
async with AIService(config) as ai_service:
|
||
return await ai_service.analyze_production_data(data)
|