575 lines
20 KiB
Python
575 lines
20 KiB
Python
"""智能定价服务
|
||
|
||
实现智能定价核心业务逻辑，包含：
|
||
- 综合定价计算
|
||
- AI 定价建议生成（遵循瑞小美 AI 接入规范）
|
||
- 定价策略模拟
|
||
- 定价报告导出
|
||
"""
|
||
|
||
import json
|
||
from datetime import datetime
|
||
from decimal import Decimal
|
||
from typing import Optional, List, Dict, Any, AsyncIterator
|
||
|
||
from sqlalchemy import select, func
|
||
from sqlalchemy.ext.asyncio import AsyncSession
|
||
from sqlalchemy.orm import selectinload
|
||
|
||
from app.models import (
|
||
Project,
|
||
ProjectCostSummary,
|
||
MarketAnalysisResult,
|
||
PricingPlan,
|
||
)
|
||
from app.schemas.pricing import (
|
||
StrategyType,
|
||
PricingSuggestions,
|
||
StrategySuggestion,
|
||
MarketReference,
|
||
AIAdvice,
|
||
AIUsage,
|
||
GeneratePricingResponse,
|
||
StrategySimulationResult,
|
||
SimulateStrategyResponse,
|
||
)
|
||
from app.services.ai_service_wrapper import AIServiceWrapper
|
||
from app.services.cost_service import CostService
|
||
from app.services.market_service import MarketService
|
||
|
||
# 导入提示词
|
||
import sys
|
||
import os
|
||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../prompts'))
|
||
from prompts.pricing_advice_prompts import SYSTEM_PROMPT, USER_PROMPT, PROMPT_META
|
||
|
||
|
||
class PricingService:
|
||
"""智能定价服务"""
|
||
|
||
# 各策略的利润率范围
|
||
STRATEGY_MARGINS = {
|
||
StrategyType.TRAFFIC: (0.10, 0.20), # 引流款：10%-20%
|
||
StrategyType.PROFIT: (0.40, 0.60), # 利润款：40%-60%
|
||
StrategyType.PREMIUM: (0.60, 0.80), # 高端款：60%-80%
|
||
}
|
||
|
||
STRATEGY_NAMES = {
|
||
StrategyType.TRAFFIC: "引流款",
|
||
StrategyType.PROFIT: "利润款",
|
||
StrategyType.PREMIUM: "高端款",
|
||
}
|
||
|
||
def __init__(self, db: AsyncSession):
|
||
self.db = db
|
||
self.cost_service = CostService(db)
|
||
self.market_service = MarketService(db)
|
||
|
||
async def get_project_with_cost(self, project_id: int) -> tuple[Project, Optional[ProjectCostSummary]]:
|
||
"""获取项目及其成本汇总"""
|
||
result = await self.db.execute(
|
||
select(Project).options(
|
||
selectinload(Project.cost_summary)
|
||
).where(Project.id == project_id)
|
||
)
|
||
project = result.scalar_one_or_none()
|
||
|
||
if not project:
|
||
raise ValueError(f"项目不存在: {project_id}")
|
||
|
||
return project, project.cost_summary
|
||
|
||
async def get_market_reference(self, project_id: int) -> Optional[MarketReference]:
|
||
"""获取市场参考数据"""
|
||
result = await self.db.execute(
|
||
select(MarketAnalysisResult).where(
|
||
MarketAnalysisResult.project_id == project_id
|
||
).order_by(MarketAnalysisResult.analysis_date.desc()).limit(1)
|
||
)
|
||
market_result = result.scalar_one_or_none()
|
||
|
||
if market_result:
|
||
return MarketReference(
|
||
min=float(market_result.market_min_price),
|
||
max=float(market_result.market_max_price),
|
||
avg=float(market_result.market_avg_price),
|
||
)
|
||
return None
|
||
|
||
def calculate_strategy_price(
|
||
self,
|
||
base_cost: float,
|
||
strategy: StrategyType,
|
||
target_margin: Optional[float] = None,
|
||
market_ref: Optional[MarketReference] = None
|
||
) -> StrategySuggestion:
|
||
"""计算单个策略的定价建议
|
||
|
||
Args:
|
||
base_cost: 基础成本
|
||
strategy: 策略类型
|
||
target_margin: 自定义目标毛利率（可选）
|
||
market_ref: 市场参考（可选）
|
||
|
||
Returns:
|
||
策略定价建议
|
||
"""
|
||
min_margin, max_margin = self.STRATEGY_MARGINS[strategy]
|
||
|
||
# 使用策略默认的中间利润率，或自定义目标
|
||
if target_margin is not None:
|
||
margin = target_margin / 100
|
||
else:
|
||
margin = (min_margin + max_margin) / 2
|
||
|
||
# 成本加成定价法：价格 = 成本 / (1 - 毛利率)
|
||
suggested_price = base_cost / (1 - margin)
|
||
|
||
# 如果有市场参考，调整价格
|
||
if market_ref:
|
||
if strategy == StrategyType.TRAFFIC:
|
||
# 引流款：取市场最低价和成本定价的较低者
|
||
market_low = market_ref.min * 0.9
|
||
suggested_price = min(suggested_price, market_low)
|
||
elif strategy == StrategyType.PREMIUM:
|
||
# 高端款：取市场高位
|
||
market_high = market_ref.max * 1.1
|
||
suggested_price = max(suggested_price, market_high * 0.9)
|
||
|
||
# 确保价格不低于成本（边界处理：零成本时设置最低价格）
|
||
if base_cost == 0:
|
||
# 零成本时，使用市场参考或设置一个最低保护价格
|
||
if market_ref:
|
||
suggested_price = market_ref.avg * 0.8
|
||
else:
|
||
suggested_price = 1.0 # 最低保护价格
|
||
else:
|
||
suggested_price = max(suggested_price, base_cost * 1.05)
|
||
|
||
# 计算实际毛利率（防止除零）
|
||
if suggested_price > 0:
|
||
actual_margin = (suggested_price - base_cost) / suggested_price * 100
|
||
else:
|
||
actual_margin = 0
|
||
|
||
descriptions = {
|
||
StrategyType.TRAFFIC: "低于市场均价，适合引流获客、新店开业、淡季促销",
|
||
StrategyType.PROFIT: "接近市场均价，平衡利润与竞争力，适合日常经营",
|
||
StrategyType.PREMIUM: "定位高端，高利润空间，需配套优质服务和品牌溢价",
|
||
}
|
||
|
||
return StrategySuggestion(
|
||
strategy=self.STRATEGY_NAMES[strategy],
|
||
suggested_price=round(suggested_price, 2),
|
||
margin=round(actual_margin, 1),
|
||
description=descriptions[strategy],
|
||
)
|
||
|
||
def calculate_all_strategies(
|
||
self,
|
||
base_cost: float,
|
||
target_margin: float,
|
||
market_ref: Optional[MarketReference] = None,
|
||
strategies: Optional[List[StrategyType]] = None
|
||
) -> PricingSuggestions:
|
||
"""计算所有策略的定价建议"""
|
||
if strategies is None:
|
||
strategies = list(StrategyType)
|
||
|
||
suggestions = PricingSuggestions()
|
||
|
||
for strategy in strategies:
|
||
suggestion = self.calculate_strategy_price(
|
||
base_cost=base_cost,
|
||
strategy=strategy,
|
||
target_margin=target_margin if strategy == StrategyType.PROFIT else None,
|
||
market_ref=market_ref,
|
||
)
|
||
|
||
if strategy == StrategyType.TRAFFIC:
|
||
suggestions.traffic = suggestion
|
||
elif strategy == StrategyType.PROFIT:
|
||
suggestions.profit = suggestion
|
||
elif strategy == StrategyType.PREMIUM:
|
||
suggestions.premium = suggestion
|
||
|
||
return suggestions
|
||
|
||
async def generate_pricing_advice(
|
||
self,
|
||
project_id: int,
|
||
target_margin: float = 50,
|
||
strategies: Optional[List[StrategyType]] = None,
|
||
stream: bool = False,
|
||
) -> GeneratePricingResponse:
|
||
"""生成智能定价建议
|
||
|
||
遵循瑞小美 AI 接入规范：
|
||
- 通过 AIServiceWrapper 调用
|
||
- 必须传入 prompt_name（用于统计）
|
||
|
||
Args:
|
||
project_id: 项目ID
|
||
target_margin: 目标毛利率
|
||
strategies: 要计算的策略列表
|
||
stream: 是否流式输出（此方法返回完整结果，流式由路由处理）
|
||
|
||
Returns:
|
||
定价建议响应
|
||
"""
|
||
# 获取项目和成本数据
|
||
project, cost_summary = await self.get_project_with_cost(project_id)
|
||
|
||
if not cost_summary:
|
||
# 如果没有成本汇总，先计算
|
||
cost_result = await self.cost_service.calculate_project_cost(project_id)
|
||
base_cost = cost_result.total_cost
|
||
else:
|
||
base_cost = float(cost_summary.total_cost)
|
||
|
||
# 获取市场参考
|
||
market_ref = await self.get_market_reference(project_id)
|
||
|
||
# 计算各策略价格
|
||
pricing_suggestions = self.calculate_all_strategies(
|
||
base_cost=base_cost,
|
||
target_margin=target_margin,
|
||
market_ref=market_ref,
|
||
strategies=strategies,
|
||
)
|
||
|
||
# 构建 AI 输入数据
|
||
cost_data = self._format_cost_data(cost_summary)
|
||
market_data = self._format_market_data(market_ref)
|
||
|
||
# 调用 AI 生成建议（遵循规范）
|
||
ai_service = AIServiceWrapper(db_session=self.db)
|
||
|
||
messages = [
|
||
{"role": "system", "content": SYSTEM_PROMPT},
|
||
{"role": "user", "content": USER_PROMPT.format(
|
||
project_name=project.project_name,
|
||
cost_data=cost_data,
|
||
market_data=market_data,
|
||
target_margin=target_margin,
|
||
)},
|
||
]
|
||
|
||
ai_advice = None
|
||
ai_usage = None
|
||
|
||
try:
|
||
response = await ai_service.chat(
|
||
messages=messages,
|
||
prompt_name=PROMPT_META["name"], # 必填！用于调用统计
|
||
)
|
||
|
||
ai_advice = AIAdvice(
|
||
summary=self._extract_section(response.content, "推荐方案") or response.content[:200],
|
||
cost_analysis=self._extract_section(response.content, "成本") or "",
|
||
market_analysis=self._extract_section(response.content, "市场") or "",
|
||
risk_notes=self._extract_section(response.content, "风险") or "",
|
||
recommendations=self._extract_recommendations(response.content),
|
||
)
|
||
|
||
ai_usage = AIUsage(
|
||
provider=response.provider,
|
||
model=response.model,
|
||
tokens=response.total_tokens,
|
||
latency_ms=response.latency_ms,
|
||
)
|
||
except Exception as e:
|
||
# AI 调用失败不影响基本定价计算
|
||
print(f"AI 调用失败: {e}")
|
||
|
||
return GeneratePricingResponse(
|
||
project_id=project_id,
|
||
project_name=project.project_name,
|
||
cost_base=base_cost,
|
||
market_reference=market_ref,
|
||
pricing_suggestions=pricing_suggestions,
|
||
ai_advice=ai_advice,
|
||
ai_usage=ai_usage,
|
||
)
|
||
|
||
async def generate_pricing_advice_stream(
|
||
self,
|
||
project_id: int,
|
||
target_margin: float = 50,
|
||
) -> AsyncIterator[str]:
|
||
"""流式生成定价建议
|
||
|
||
Args:
|
||
project_id: 项目ID
|
||
target_margin: 目标毛利率
|
||
|
||
Yields:
|
||
SSE 格式的响应片段
|
||
"""
|
||
# 获取基础数据
|
||
project, cost_summary = await self.get_project_with_cost(project_id)
|
||
|
||
if not cost_summary:
|
||
cost_result = await self.cost_service.calculate_project_cost(project_id)
|
||
base_cost = cost_result.total_cost
|
||
else:
|
||
base_cost = float(cost_summary.total_cost)
|
||
|
||
market_ref = await self.get_market_reference(project_id)
|
||
|
||
# 先返回基础定价计算结果
|
||
pricing_suggestions = self.calculate_all_strategies(
|
||
base_cost=base_cost,
|
||
target_margin=target_margin,
|
||
market_ref=market_ref,
|
||
)
|
||
|
||
# 发送初始数据
|
||
initial_data = {
|
||
"type": "init",
|
||
"project_name": project.project_name,
|
||
"cost_base": base_cost,
|
||
"market_reference": market_ref.model_dump() if market_ref else None,
|
||
"pricing_suggestions": pricing_suggestions.model_dump(),
|
||
}
|
||
yield f"data: {json.dumps(initial_data, ensure_ascii=False)}\n\n"
|
||
|
||
# 构建 AI 输入
|
||
cost_data = self._format_cost_data(cost_summary)
|
||
market_data = self._format_market_data(market_ref)
|
||
|
||
ai_service = AIServiceWrapper(db_session=self.db)
|
||
|
||
messages = [
|
||
{"role": "system", "content": SYSTEM_PROMPT},
|
||
{"role": "user", "content": USER_PROMPT.format(
|
||
project_name=project.project_name,
|
||
cost_data=cost_data,
|
||
market_data=market_data,
|
||
target_margin=target_margin,
|
||
)},
|
||
]
|
||
|
||
# 流式返回 AI 建议
|
||
try:
|
||
async for chunk in ai_service.chat_stream(
|
||
messages=messages,
|
||
prompt_name=PROMPT_META["name"],
|
||
):
|
||
yield f"data: {json.dumps({'type': 'chunk', 'content': chunk}, ensure_ascii=False)}\n\n"
|
||
except Exception as e:
|
||
yield f"data: {json.dumps({'type': 'error', 'message': str(e)}, ensure_ascii=False)}\n\n"
|
||
|
||
# 发送完成信号
|
||
yield f"data: {json.dumps({'type': 'done'}, ensure_ascii=False)}\n\n"
|
||
|
||
async def simulate_strategies(
|
||
self,
|
||
project_id: int,
|
||
strategies: List[StrategyType],
|
||
target_margin: float = 50,
|
||
) -> SimulateStrategyResponse:
|
||
"""模拟定价策略
|
||
|
||
Args:
|
||
project_id: 项目ID
|
||
strategies: 要模拟的策略列表
|
||
target_margin: 目标毛利率
|
||
|
||
Returns:
|
||
策略模拟结果
|
||
"""
|
||
project, cost_summary = await self.get_project_with_cost(project_id)
|
||
|
||
if not cost_summary:
|
||
cost_result = await self.cost_service.calculate_project_cost(project_id)
|
||
base_cost = cost_result.total_cost
|
||
else:
|
||
base_cost = float(cost_summary.total_cost)
|
||
|
||
market_ref = await self.get_market_reference(project_id)
|
||
|
||
results = []
|
||
for strategy in strategies:
|
||
suggestion = self.calculate_strategy_price(
|
||
base_cost=base_cost,
|
||
strategy=strategy,
|
||
target_margin=target_margin if strategy == StrategyType.PROFIT else None,
|
||
market_ref=market_ref,
|
||
)
|
||
|
||
# 确定市场位置
|
||
market_position = "中等"
|
||
if market_ref:
|
||
if suggestion.suggested_price < market_ref.avg * 0.8:
|
||
market_position = "低于市场均价"
|
||
elif suggestion.suggested_price > market_ref.avg * 1.2:
|
||
market_position = "高于市场均价"
|
||
else:
|
||
market_position = "接近市场均价"
|
||
|
||
results.append(StrategySimulationResult(
|
||
strategy_type=strategy.value,
|
||
strategy_name=self.STRATEGY_NAMES[strategy],
|
||
suggested_price=suggestion.suggested_price,
|
||
margin=suggestion.margin,
|
||
profit_per_unit=round(suggestion.suggested_price - base_cost, 2),
|
||
market_position=market_position,
|
||
))
|
||
|
||
return SimulateStrategyResponse(
|
||
project_id=project_id,
|
||
project_name=project.project_name,
|
||
base_cost=base_cost,
|
||
results=results,
|
||
)
|
||
|
||
async def create_pricing_plan(
|
||
self,
|
||
project_id: int,
|
||
plan_name: str,
|
||
strategy_type: StrategyType,
|
||
target_margin: float,
|
||
created_by: Optional[int] = None,
|
||
) -> PricingPlan:
|
||
"""创建定价方案
|
||
|
||
Args:
|
||
project_id: 项目ID
|
||
plan_name: 方案名称
|
||
strategy_type: 策略类型
|
||
target_margin: 目标毛利率
|
||
created_by: 创建人ID
|
||
|
||
Returns:
|
||
创建的定价方案
|
||
"""
|
||
# 获取成本数据
|
||
project, cost_summary = await self.get_project_with_cost(project_id)
|
||
|
||
if not cost_summary:
|
||
cost_result = await self.cost_service.calculate_project_cost(project_id)
|
||
base_cost = Decimal(str(cost_result.total_cost))
|
||
else:
|
||
base_cost = cost_summary.total_cost
|
||
|
||
# 获取市场参考
|
||
market_ref = await self.get_market_reference(project_id)
|
||
|
||
# 计算建议价格
|
||
suggestion = self.calculate_strategy_price(
|
||
base_cost=float(base_cost),
|
||
strategy=strategy_type,
|
||
target_margin=target_margin if strategy_type == StrategyType.PROFIT else None,
|
||
market_ref=market_ref,
|
||
)
|
||
|
||
# 创建定价方案
|
||
pricing_plan = PricingPlan(
|
||
project_id=project_id,
|
||
plan_name=plan_name,
|
||
strategy_type=strategy_type.value,
|
||
base_cost=base_cost,
|
||
target_margin=Decimal(str(target_margin)),
|
||
suggested_price=Decimal(str(suggestion.suggested_price)),
|
||
is_active=True,
|
||
created_by=created_by,
|
||
)
|
||
|
||
self.db.add(pricing_plan)
|
||
await self.db.flush()
|
||
await self.db.refresh(pricing_plan)
|
||
|
||
return pricing_plan
|
||
|
||
async def update_pricing_plan(
|
||
self,
|
||
plan_id: int,
|
||
**kwargs
|
||
) -> PricingPlan:
|
||
"""更新定价方案"""
|
||
result = await self.db.execute(
|
||
select(PricingPlan).where(PricingPlan.id == plan_id)
|
||
)
|
||
plan = result.scalar_one_or_none()
|
||
|
||
if not plan:
|
||
raise ValueError(f"定价方案不存在: {plan_id}")
|
||
|
||
for key, value in kwargs.items():
|
||
if value is not None and hasattr(plan, key):
|
||
if key in ['target_margin', 'final_price', 'base_cost', 'suggested_price']:
|
||
setattr(plan, key, Decimal(str(value)))
|
||
else:
|
||
setattr(plan, key, value)
|
||
|
||
await self.db.flush()
|
||
await self.db.refresh(plan)
|
||
|
||
return plan
|
||
|
||
async def save_ai_advice(self, plan_id: int, advice: str) -> None:
|
||
"""保存 AI 建议到定价方案"""
|
||
result = await self.db.execute(
|
||
select(PricingPlan).where(PricingPlan.id == plan_id)
|
||
)
|
||
plan = result.scalar_one_or_none()
|
||
|
||
if plan:
|
||
plan.ai_advice = advice
|
||
await self.db.flush()
|
||
|
||
def _format_cost_data(self, cost_summary: Optional[ProjectCostSummary]) -> str:
|
||
"""格式化成本数据用于 AI 输入"""
|
||
if not cost_summary:
|
||
return "暂无成本数据"
|
||
|
||
return f"""- 耗材成本：{float(cost_summary.material_cost):.2f} 元
|
||
- 设备折旧：{float(cost_summary.equipment_cost):.2f} 元
|
||
- 人工成本：{float(cost_summary.labor_cost):.2f} 元
|
||
- 固定成本分摊：{float(cost_summary.fixed_cost_allocation):.2f} 元
|
||
- **总成本（最低成本线）：{float(cost_summary.total_cost):.2f} 元**"""
|
||
|
||
def _format_market_data(self, market_ref: Optional[MarketReference]) -> str:
|
||
"""格式化市场数据用于 AI 输入"""
|
||
if not market_ref:
|
||
return "暂无市场行情数据"
|
||
|
||
return f"""- 市场最低价：{market_ref.min:.2f} 元
|
||
- 市场最高价：{market_ref.max:.2f} 元
|
||
- 市场均价：{market_ref.avg:.2f} 元"""
|
||
|
||
def _extract_section(self, content: str, keyword: str) -> Optional[str]:
|
||
"""从 AI 响应中提取特定部分"""
|
||
lines = content.split('\n')
|
||
in_section = False
|
||
section_lines = []
|
||
|
||
for line in lines:
|
||
if keyword in line and ('#' in line or '**' in line):
|
||
in_section = True
|
||
continue
|
||
elif in_section:
|
||
if line.startswith('#') or (line.startswith('**') and line.endswith('**')):
|
||
break
|
||
section_lines.append(line)
|
||
|
||
return '\n'.join(section_lines).strip() if section_lines else None
|
||
|
||
def _extract_recommendations(self, content: str) -> List[str]:
|
||
"""从 AI 响应中提取建议列表"""
|
||
recommendations = []
|
||
lines = content.split('\n')
|
||
|
||
for line in lines:
|
||
line = line.strip()
|
||
if line.startswith('- ') or line.startswith('* ') or line.startswith('• '):
|
||
recommendations.append(line[2:].strip())
|
||
elif line and line[0].isdigit() and '.' in line:
|
||
# 处理 "1. xxx" 格式
|
||
parts = line.split('.', 1)
|
||
if len(parts) > 1:
|
||
recommendations.append(parts[1].strip())
|
||
|
||
return recommendations[:5] # 最多返回5条
|