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update bug

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dby 2025-10-30 22:43:24 +08:00
parent ccefd36160
commit cc7701236a
11 changed files with 9251 additions and 62 deletions
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7
app/api/v1/app_valuations/app_valuations.py
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@ -389,7 +389,8 @@ async def _extract_calculation_params_b1(data: UserValuationCreate) -> Dict[str,
# followers 非当前计算用键，先移除避免干扰
# click_count 与 view_count 目前未参与计算，先移除
'sales_volume': safe_float(data.sales_volume),#
'link_views': safe_float(data.link_views),
# 政策乘数B13相关参数
'implementation_stage': implementation_stage,
'funding_support': funding_support
@ -434,8 +435,8 @@ async def _extract_calculation_params_b2(data: UserValuationCreate) -> Dict[str,
# 以下三项需由后续模型/服务计算；此处提供默认可计算占位
# 历史传承度HI(用户填写)
historical_inheritance = sum([safe_float(i) for i in data.historical_evidence])
structure_complexity = 0.75 # TODO 需要依赖图片和模型 结构复杂度SC(系统计算)
normalized_entropy = 0.85 # TODO 需要依赖图片和模型 归一化信息熵H(系统计算)
structure_complexity = 1.5 # 默认值 纹样基因熵值B22(系统计算)
normalized_entropy = 9 # 默认值 归一化信息熵H(系统计算)
return {
"inheritor_level_coefficient": inheritor_level_coefficient,

11
app/utils/calculation_engine/cultural_value_b2/sub_formulas/living_heritage_b21.py
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@ -127,10 +127,10 @@ if __name__ == "__main__":
inheritor_level = "国家级传承人" # 传承人等级 (用户填写)
cross_border_depth = 50.0
# 教学传播频次数据
offline_sessions = 10 # 线下传习次数（符合标准：传承人主导、时长≥2小时、参与人数≥5人）
douyin_views = 1000000 # 抖音播放量
kuaishou_views = 500000 # 快手播放量
bilibili_views = 200000 # B站播放量
offline_sessions = 20 # 线下传习次数（符合标准：传承人主导、时长≥2小时、参与人数≥5人）
douyin_views = 10000000 # 抖音播放量
kuaishou_views = 0 # 快手播放量
bilibili_views = 0 # B站播放量
@ -142,10 +142,11 @@ if __name__ == "__main__":
kuaishou_views=kuaishou_views,
bilibili_views=bilibili_views
)
print(teaching_frequency)
# 计算活态传承系数B21
living_heritage_b21 = calculator.calculate_living_heritage_b21(
inheritor_level_coefficient, teaching_frequency, cross_border_depth
1, teaching_frequency, 0.3
)
print(f"传承人等级系数: {inheritor_level_coefficient:.2f}")

2
app/utils/calculation_engine/cultural_value_b2/sub_formulas/pattern_gene_b22.py
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@ -134,7 +134,7 @@ if __name__ == "__main__":
# 计算纹样基因值B22
pattern_gene_b22 = calculator.calculate_pattern_gene_b22(
structure_complexity, normalized_entropy, historical_inheritance
1.5, 9, historical_inheritance
)
print(f"结构复杂度SC: {structure_complexity:.4f}")

51
app/utils/calculation_engine/economic_value_b1/economic_value_b1.py
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@ -88,18 +88,28 @@ class EconomicValueB1Calculator:
# )
# 计算基础价值B11
basic_value_b11 = self.basic_value_calculator.calculate_basic_value_b11(
financial_value,
legal_strength,
financial_value, # 财务价值F
legal_strength, # 法律强度L
development_potential,
input_data["industry_coefficient"]
)
# 计算流量因子B12
social_media_spread_s3 = self.traffic_factor_calculator.calculate_interaction_index(
# 计算互动量指数
interaction_index = self.traffic_factor_calculator.calculate_interaction_index(
input_data["likes"],
input_data["comments"],
input_data["shares"],
)
# 计算覆盖人群指数
coverage_index = self.traffic_factor_calculator.calculate_coverage_index(0)
# 计算转化率
conversion_efficiency = self.traffic_factor_calculator.calculate_conversion_efficiency(
input_data["sales_volume"], input_data["link_views"])
social_media_spread_s3 = self.traffic_factor_calculator.calculate_social_media_spread_s3(interaction_index,
coverage_index,
conversion_efficiency)
traffic_factor_b12 = self.traffic_factor_calculator.calculate_traffic_factor_b12(
input_data['search_index_s1'],
input_data['industry_average_s2'],
@ -164,28 +174,29 @@ if __name__ == "__main__":
'''
input_data = {
# 基础价值B11相关参数
'three_year_income': [1000, 2000, 2222],
'patent_score': 1, # 专利分
'popularity_score': 4.0, # 普及地域分值
'infringement_score': 1.0, # 侵权分
'innovation_ratio': 600.0,
'esg_score':10.0,
'industry_coefficient':12.0,
'three_year_income': [2000, 2400, 2600],
'patent_score': 0, # 专利分
'patent_count': 0, # 专利分
'popularity_score': 10.0, # 普及地域分值
'infringement_score': 0, # 侵权分
'innovation_ratio': 1200 / 3000,
'esg_score': 7.0,
'industry_coefficient': -0.0625, # 行业系数
# 流量因子B12相关参数
'search_index_s1': 4500.0,
'industry_average_s2': 5000.0,
'search_index_s1': 0,
'industry_average_s2': 15000, # 行业均值
# 'social_media_spread_s3': social_media_spread_s3,
'likes': 4, # 点赞
'comments': 5, # 评论
'shares': 6, # 转发
'followers': 7, # 粉丝数
'likes': 3933333.33, # 点赞
'comments': 3933333.33, # 评论
'shares': 3933333.33, # 转发
'followers': 15000, # 粉丝数
'click_count': 1000,# 点击量
'view_count': 100, # 内容浏览量
"sales_volume": 5000,
"link_views": 20000,
# 政策乘数B13相关参数
'policy_match_score': 10.0, # 政策匹配度
'policy_match_score': 7.0, # 政策匹配度
'implementation_stage': 10.0, # 实施阶段评分
'funding_support': 10.0 # 资金支持度
}

34
app/utils/calculation_engine/economic_value_b1/sub_formulas/basic_value_b11.py
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@ -67,6 +67,8 @@ class BasicValueB11Calculator:
# 如果没有提供增长率，则根据三年数据计算
if growth_rate is None:
growth_rate = self._calculate_growth_rate(annual_revenue_3_years)
growth_factor = (1 + growth_rate) ** 5
result = avg_annual_revenue * growth_factor / 5
financial_value = (avg_annual_revenue * math.pow(1 + growth_rate, 5)) / 5
@ -261,7 +263,7 @@ if __name__ == "__main__":
calculator = BasicValueB11Calculator()
# 示例数据
annual_revenue = [100, 120, 150] # 近三年收益，单位：万元 (用户填写)
annual_revenue = [2000,2400,2600] # 近三年收益，单位：万元 (用户填写)
patent_remaining_years = 8 # 专利剩余年限 (用户填写)
region_coverage = "全国覆盖" # 普及地域 (用户填写)
infringement_status = "无侵权记录" # 侵权记录 (用户填写)
@ -276,17 +278,19 @@ if __name__ == "__main__":
patent_score = calculate_patent_score(patent_remaining_years)
popularity_score = calculate_popularity_score(region_coverage)
infringement_score = calculate_infringement_score(infringement_status)
legal_strength = calculator.calculate_legal_strength_l(patent_score, popularity_score, infringement_score)
# legal_strength = calculator.calculate_legal_strength_l(patent_score, popularity_score, infringement_score)
legal_strength = calculator.calculate_legal_strength_l(0, 10, 0)
patent_usage_score = calculate_patent_usage_score(patent_count)
development_potential = calculator.calculate_development_potential_d(patent_usage_score, esg_score,
innovation_ratio)
# development_potential = calculator.calculate_development_potential_d(patent_usage_score, esg_score,
# innovation_ratio)
development_potential = calculator.calculate_development_potential_d(0, 7,
1200/3000)
industry_coefficient = calculator.calculate_industry_coefficient_i(target_industry_roe, benchmark_industry_roe)
# 计算基础价值B11
basic_value = calculator.calculate_basic_value_b11(
financial_value, legal_strength, development_potential, industry_coefficient
770.0, legal_strength, development_potential, -0.0625
)
print(f"财务价值F: {financial_value:.2f}")
@ -294,3 +298,21 @@ if __name__ == "__main__":
print(f"发展潜力D: {development_potential:.2f}")
print(f"行业系数I: {industry_coefficient:.4f}")
print(f"基础价值B11: {basic_value:.2f}")
# initial_value = 2333
# growth_rate = 0.14 # 14%
# years = 5
#
# # 计算 (1+14%)^5
# growth_factor = (1 + growth_rate) ** years
#
# # 计算 2333 * (1+14%)^5 / 5
# result = initial_value * growth_factor / 5
# print(f"2333 * (1+14%)^5 / 5 = {result:.2f}")
#
# 也可以分步显示计算过程
# print("\n计算过程：")
# print(f"增长率: {growth_rate*100}%")
# print(f"(1+14%)^5 = {growth_factor:.4f}")
# print(f"2333 × {growth_factor:.4f} = {initial_value * growth_factor:.2f}")
# print(f"再除以5: {initial_value * growth_factor:.2f} ÷ 5 = {result:.2f}")

2
app/utils/calculation_engine/economic_value_b1/sub_formulas/policy_multiplier_b13.py
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@ -121,7 +121,7 @@ if __name__ == "__main__":
# 计算政策契合度评分P
policy_compatibility_score = calculator.calculate_policy_compatibility_score(
policy_match_score, implementation_stage_score, funding_support_score
7, 10, 10
)
# 计算政策乘数B13

16
app/utils/calculation_engine/economic_value_b1/sub_formulas/traffic_factor_b12.py
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@ -101,9 +101,10 @@ class TrafficFactorB12Calculator:
returns:
float: 覆盖人群指数
"""
#
#
if followers == 0:
return 0
coverage_index = followers / 10000.0
return coverage_index
def calculate_conversion_efficiency(self,
@ -116,7 +117,7 @@ class TrafficFactorB12Calculator:
转化效率 = 商品链接点击量 / 内容浏览量
args:
click_count: 商品链接点击量 (用户填写)
click_count: 商品链接点击量/销售 (用户填写)
view_count: 内容浏览量 (用户填写)
returns:
@ -287,13 +288,16 @@ if __name__ == "__main__":
search_index_s1 = calculate_search_index_s1(baidu_index, wechat_index, weibo_index)
interaction_index, coverage_index = processor.calculate_multi_platform_interaction(platform_data)
conversion_efficiency = calculator.calculate_conversion_efficiency(click_count, view_count)
# 互动量指数 × 0.4 + 覆盖人群指数 × 0.3 + 转化效率 × 0.3
# social_media_spread_s3 = calculator.calculate_social_media_spread_s3(
# interaction_index, coverage_index, conversion_efficiency
# )
social_media_spread_s3 = calculator.calculate_social_media_spread_s3(
interaction_index, coverage_index, conversion_efficiency
11800, 1.5, 0.25
)
# 计算流量因子B12
traffic_factor = calculator.calculate_traffic_factor_b12(
search_index_s1, industry_average, social_media_spread_s3
search_index_s1, 15000, social_media_spread_s3
)
print(f"近30天搜索指数S1: {search_index_s1:.2f}")

2
app/utils/calculation_engine/final_value_ab/final_value_a.py
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@ -212,7 +212,7 @@ if __name__ == "__main__":
'model_data': {
'economic_data': {
# 基础价值B11相关参数
'three_year_income': [1000, 2000, 2222],
'three_year_income': [2000,2400,2600],
'patent_score': 1, # 专利分 (0-10)
'popularity_score': 4.0, # 普及地域分 (0-10)
'infringement_score': 1.0, # 侵权分 (0-10)

29
app/utils/calculation_engine/final_value_ab/model_value_b.py
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@ -1,4 +1,3 @@
"""
模型估值B计算模块
@ -21,7 +20,7 @@ except ImportError:
class ModelValueBCalculator:
"""模型估值B计算器"""
def __init__(self):
"""初始化计算器"""
self.economic_value_calculator = EconomicValueB1Calculator()
@ -29,9 +28,9 @@ class ModelValueBCalculator:
self.risk_adjustment_calculator = RiskAdjustmentB3Calculator()
def calculate_model_value_b(self,
economic_value_b1: float,
cultural_value_b2: float,
risk_value_b3: float) -> float:
economic_value_b1: float,
cultural_value_b2: float,
risk_value_b3: float) -> float:
"""
模型估值B = （经济价值B1*0.7+文化价值B2*0.3）*风险调整系数B3
@ -44,9 +43,9 @@ class ModelValueBCalculator:
float: 模型估值B
"""
model_value = (economic_value_b1 * 0.7 + cultural_value_b2 * 0.3) * risk_value_b3
return model_value
def calculate_complete_model_value_b(self, input_data: Dict) -> Dict:
"""
计算完整的模型估值B，包含所有子公式
@ -62,7 +61,7 @@ class ModelValueBCalculator:
input_data['economic_data']
)
economic_value_b1 = economic_result['economic_value_b1']
# 计算文化价值B2
cultural_result = self.cultural_value_calculator.calculate_complete_cultural_value_b2(
input_data['cultural_data']
@ -79,7 +78,7 @@ class ModelValueBCalculator:
cultural_value_b2,
risk_value_b3
)
return {
'economic_value_b1': economic_value_b1,
'cultural_value_b2': cultural_value_b2,
@ -92,7 +91,7 @@ class ModelValueBCalculator:
if __name__ == "__main__":
# 创建计算器实例
calculator = ModelValueBCalculator()
# 示例数据
input_data = {
# 经济价值B1相关数据
@ -104,9 +103,11 @@ if __name__ == "__main__":
'search_index_s1': 4500.0,
'industry_average_s2': 5000.0,
'social_media_spread_s3': 1.07,
'policy_compatibility_score': 9.1
'policy_compatibility_score': 9.1,
'sales_volume': 99, # 销售量
'link_views': 88 # 浏览量
},
# 文化价值B2相关数据
'cultural_data': {
'inheritor_level_coefficient': 10.0,
@ -117,10 +118,10 @@ if __name__ == "__main__":
'historical_inheritance': 0.80
}
}
# 计算模型估值B
result = calculator.calculate_complete_model_value_b(input_data)
print("模型估值B计算结果:")
print(f"经济价值B1: {result['economic_value_b1']:.2f}")
print(f"文化价值B2: {result['cultural_value_b2']:.4f}")

10
app/utils/calculation_engine/risk_adjustment_b3/sub_formulas/risk_adjustment_b3.py
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@ -48,7 +48,7 @@ class RiskAdjustmentB3Calculator:
float: 风险评分总和R
"""
risk_score_sum = market_risk * 0.3 + legal_risk * 0.4 + inheritance_risk * 0.3
risk_score_sum = (market_risk * 0.3 + legal_risk * 0.4 + inheritance_risk * 0.3)/10
return risk_score_sum
@ -179,14 +179,14 @@ if __name__ == "__main__":
# 示例数据
# 市场风险：过去30天价格数据
highest_price = 100.0 # 过去30天最高价格 (用户填写)
lowest_price = 95.0 # 过去30天最低价格 (用户填写)
highest_price = 340 # 过去30天最高价格 (用户填写)
lowest_price = 300 # 过去30天最低价格 (用户填写)
# 法律风险：诉讼状态
lawsuit_status = "无诉讼" # 诉讼状态 (API获取)
lawsuit_status = 10 # 诉讼状态 (API获取)
# 传承风险：传承人年龄
inheritor_ages = [45, 60, 75] # 传承人年龄列表 (用户填写)
inheritor_ages = [100,20,5] # 传承人年龄列表 (用户填写)
# 计算各项风险评分
market_risk = calculator.calculate_market_risk(highest_price, lowest_price)

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web/package-lock.json generated Normal file
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