宠物行业AI赋能S2B2C转型

import torch
from transformers import ViTForImageClassification, AutoProcessor
from pet_diagnosis import SymptomAnalyzer  # 假设的病理知识库

# 多模态特征提取
def multi_modal_analysis(image_path, text_description):
    # 视觉特征提取
    processor = AutoProcessor.from_pretrained("google/vit-base-patch16-224")
    model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224")
    image = Image.open(image_path)
    inputs = processor(images=image, return_tensors="pt")
    visual_features = model(**inputs).logits
    
    # 文本特征提取
    text_encoder = torch.load('pet_bert.pth')  # 预训练文本模型
    text_features = text_encoder.encode(text_description)
    
    # 多模态融合
    fused_features = torch.cat([visual_features, text_features], dim=1)
    diagnosis = SymptomAnalyzer.predict(fused_features)
    return diagnosis

# 示例使用
result = multi_modal_analysis("skin_lesion.jpg", "猫咪持续抓挠患处3天")
print(f"AI诊断建议：{result}")

from fbprophet import Prophet
import pandas as pd

# 历史销售数据预处理
data = pd.read_csv('pet_food_sales.csv')
data['ds'] = pd.to_datetime(data['date'])
data['y'] = data['sales_volume']

# 多变量预测模型
model = Prophet()
model.add_regressor('weather_temp')  # 天气因子
model.add_regressor('social_media_mentions')  # 社交媒体热度
model.fit(data)

# 生成未来预测
future = model.make_future_dataframe(periods=30)
future['weather_temp'] = [25]*30  # 天气预报数据
future['social_media_mentions'] = get_social_trends()  # 实时爬取社交数据

forecast = model.predict(future)
plot_forecast(forecast)  # 可视化库存建议

from sentence_transformers import SentenceTransformer
from sklearn.neighbors import NearestNeighbors

# 多模态嵌入模型
multimodal_model = SentenceTransformer('clip-ViT-B-32')

# 生成商品特征矩阵 
product_data = {
    "冻干鸡肉粮": ("product_image.jpg", "高蛋白无谷配方"),
    "智能饮水机": ("waterer.jpg", "流动水循环系统")
}

embeddings = []
for desc, (img_path, text) in product_data.items():
    image_emb = multimodal_model.encode(Image.open(img_path))
    text_emb = multimodal_model.encode(text)
    combined_emb = np.concatenate([image_emb, text_emb])
    embeddings.append(combined_emb)

# 构建推荐索引
nn = NearestNeighbors(n_neighbors=3).fit(embeddings)

# 用户特征匹配
user_pet = ("波斯猫", "皮肤敏感", "activity_log.csv")
user_emb = multimodal_model.encode(user_pet[1])  # 文本特征编码
_, indices = nn.kneighbors([user_emb])

print("推荐商品：", [list(product_data.keys())[i] for i in indices[0]])

python

class PetKG:     def __init__(self):         self.entities = {             'breed': {'波斯猫': {'寿命': '15-20年', '常见疾病': ['多囊肾']}},             'disease': {'皮肤病': {'症状': ['瘙痒', '脱毛'], '治疗方案': [...]}}         }          def query(self, entity_type, entity_name):         return self.entities.get(entity_type, {}).get(entity_name, {})