update bug

app/api/v1/app_valuations/app_valuations.py

@@ -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)
+    structure_complexity = 1.5 # 默认值  纹样基因熵值B22(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)
+    normalized_entropy = 9     #  默认值  归一化信息熵H(系统计算)
 
     return {
         "inheritor_level_coefficient": inheritor_level_coefficient,

app/utils/calculation_engine/cultural_value_b2/sub_formulas/living_heritage_b21.py

@@ -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）
+    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 = 1000000  # 抖音播放量
+    douyin_views = 10000000  # 抖音播放量
-    kuaishou_views = 500000  # 快手播放量
+    kuaishou_views = 0  # 快手播放量
-    bilibili_views = 200000  # B站播放量
+    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}")

app/utils/calculation_engine/cultural_value_b2/sub_formulas/pattern_gene_b22.py

@@ -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}")

app/utils/calculation_engine/economic_value_b1/economic_value_b1.py

@@ -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,
+            financial_value,  # 财务价值F
-            legal_strength,
+            legal_strength,  # 法律强度L
             development_potential,
             input_data["industry_coefficient"]
         )
 
-        # 计算流量因子B12
+        # 计算互动量指数
-        social_media_spread_s3 = self.traffic_factor_calculator.calculate_interaction_index(
+        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],
+        'three_year_income': [2000, 2400, 2600],
-        'patent_score': 1,  # 专利分
+        'patent_score': 0,  # 专利分
-        'popularity_score': 4.0,  # 普及地域分值
+        'patent_count': 0,  # 专利分
-        'infringement_score': 1.0,  # 侵权分
+        'popularity_score': 10.0,  # 普及地域分值
-        'innovation_ratio': 600.0,
+        'infringement_score': 0,  # 侵权分
-        'esg_score':10.0,
+        'innovation_ratio': 1200 / 3000,
-        'industry_coefficient':12.0,
+        '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,
+        'search_index_s1': 0,
-        'industry_average_s2': 5000.0,
+        'industry_average_s2': 15000,  # 行业均值
         # 'social_media_spread_s3': social_media_spread_s3,
-        'likes': 4, # 点赞
+        'likes': 3933333.33,  # 点赞
-        'comments': 5, # 评论
+        'comments': 3933333.33,  # 评论
-        'shares': 6, # 转发
+        'shares': 3933333.33,  # 转发
-        'followers': 7, # 粉丝数
+        'followers': 15000,  # 粉丝数
 
-        'click_count': 1000,# 点击量
+        "sales_volume": 5000,
-        'view_count': 100, # 内容浏览量
+        "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
     }

app/utils/calculation_engine/economic_value_b1/sub_formulas/basic_value_b11.py

@@ -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,
+    # development_potential = calculator.calculate_development_potential_d(patent_usage_score, esg_score,
-                                                                         innovation_ratio)
+    #                                                                      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}")

app/utils/calculation_engine/economic_value_b1/sub_formulas/policy_multiplier_b13.py

@@ -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

app/utils/calculation_engine/economic_value_b1/sub_formulas/traffic_factor_b12.py

@@ -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}")

app/utils/calculation_engine/final_value_ab/final_value_a.py

@@ -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)

app/utils/calculation_engine/final_value_ab/model_value_b.py

@@ -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,
+                                economic_value_b1: float,
-                               cultural_value_b2: float,
+                                cultural_value_b2: float,
-                               risk_value_b3: float) -> 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}")

app/utils/calculation_engine/risk_adjustment_b3/sub_formulas/risk_adjustment_b3.py

@@ -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)
+    highest_price = 340  # 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)
+    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)