AI 创作日记 | 门店巡检的重构：DeepSeek多模态技术落地纪实

class DeepSeekInspector:
    def __init__(self):
        self.vision_model = load_model('deepseek-vision-v3')
        self.nlp_engine = load_model('deepseek-nlp-pro')
        self.fusion_layer = self._build_fusion_network()

    def _build_fusion_network(self):
        # 多模态特征融合层
        return tf.keras.layers.Concatenate()(
            [self.vision_model.output, 
             self.nlp_engine.output]
        )

    def analyze_frame(self, img, audio_text):
        """处理单帧巡检数据"""
        visual_feat = self.vision_model.preprocess(img)
        text_feat = self.nlp_engine.encode(audio_text)
        fused_feat = self.fusion_layer([visual_feat, text_feat])
        return self._make_decision(fused_feat)

    def _make_decision(self, features):
        # 三级异常检测逻辑
        if features['shelf_confidence'] < 0.7:
            return {'alert': '缺货预警', 'level': 1}
        elif features['price_diff'] > 0.15:
            return {'alert': '价签异常', 'level': 2}
        elif features['sentiment'] < -0.8:
            return {'alert': '客诉风险', 'level': 3}
        return {'status': '正常'}

# 使用示例
inspector = DeepSeekInspector()
result = inspector.analyze_frame(camera_img, "顾客说找不到促销商品")
print(result)  # 输出：{'alert': '缺货预警', 'level': 1}

import cv2
import numpy as np
from deepseek_vision import ShelfAnalyzer

class ShelfInspector:
    def __init__(self):
        self.analyzer = ShelfAnalyzer.from_pretrained("deepseek/retail-vision-v3")
        
    def detect_abnormalities(self, img_path):
        """货架医生的X光眼"""
        img = cv2.imread(img_path)
        results = self.analyzer.analyze(
            image=img,
            tasks=['expired', 'misplaced', 'empty_space']
        )
        
        # 生成热力图
        heatmap = self._create_heatmap(results['empty_space'])
        return {
            'alert_level': self._calculate_risk(results),
            'heatmap': heatmap,
            'details': results
        }
    
    def _create_heatmap(self, empty_data):
        """空位可视化警报"""
        # 实现细节简化
        return cv2.applyColorMap(empty_data, cv2.COLORMAP_JET)

# 使用示例
inspector = ShelfInspector()
result = inspector.detect_abnormalities("shelf_001.jpg")
cv2.imwrite("heatmap.jpg", result['heatmap'])

from transformers import AutoModel, AutoTokenizer
import torch

class MultimodalDecisionEngine:
    def __init__(self):
        self.tokenizer = AutoTokenizer.from_pretrained("deepseek/mm-retail-v2")
        self.model = AutoModel.from_pretrained("deepseek/mm-retail-v2")
        
    def analyze_scene(self, image, text_desc, sensor_data):
        """六边形战士的分析逻辑"""
        inputs = self.tokenizer(
            text=text_desc,
            images=image,
            sensors=sensor_data,
            return_tensors="pt"
        )
        
        with torch.no_grad():
            outputs = self.model(**inputs)
        
        return self._interpret_outputs(outputs)
    
    def _interpret_outputs(self, outputs):
        """模型输出翻译官"""
        # 实现细节简化
        return {
            'action': 'restock' if outputs.logits[0] > 0.7 else 'normal',
            'confidence': float(torch.sigmoid(outputs.logits))
        }

{
    "temperature": 18.5, 
    "humidity": 45,
    "shelf_vibration": 0.02
}

# 伪代码：确保多模态数据时间戳对齐
def time_align(data_streams):
    window_size = 500  # 毫秒
    return synchronize(video_feed, audio_stream, sensor_log, window_size)

return {
    'action': 'restock' if outputs.logits[0] > 0.7 else 'normal',
    'confidence': float(torch.sigmoid(outputs.logits))
}

class CrossModalValidator:
    def __init__(self):
        self.vision_model = load_vision_model()
        self.audio_model = load_audio_model()
    
    def validate(self, image, audio):
        # 视觉验证
        vis_pred = self.vision_model(image)
        # 音频验证 
        audio_pred = self.audio_model(audio)
        # 动态权重融合
        if vis_pred.confidence < 0.7:
            return audio_pred.adjust(vis_pred)
        return vis_pred

# 使用示例：检测冷藏柜状态
validator = CrossModalValidator()
status = validator.validate(
    fridge_image, 
    fridge_audio
)
print(f"运行状态：{status}")

class InspectionEvolution:
    def __init__(self):
        self.data_lake = []
        self.model_version = "v1.2"
    
    def collect_feedback(self, alert_result, actual_result):
        """构建数据闭环"""
        self.data_lake.append({
            'prediction': alert_result,
            'ground_truth': actual_result,
            'timestamp': datetime.now()
        })
    
    def retrain_model(self):
        """月度模型迭代"""
        if len(self.data_lake) > 10000:
            self._version_upgrade()
            return f"Model upgraded to {self.model_version}"
        return "Data insufficient for retraining"

# 自动化演进示例
evolution_system = InspectionEvolution()
evolution_system.collect_feedback('缺货预警', '实际缺货')
print(evolution_system.retrain_model())

def collect_feedback(self, alert_result, actual_result):
    """构建数据闭环"""
    self.data_lake.append({
        'prediction': alert_result,
        'ground_truth': actual_result,
        'timestamp': datetime.now()
    })

def retrain_model(self):
    """月度模型迭代"""
    if len(self.data_lake) > 10000:
        self._version_upgrade()
        return f"Model upgraded to {self.model_version}"
    return "Data insufficient for retraining"

# 建议实现的版本升级逻辑
def _version_upgrade(self):
    self.model_version = f"v{float(self.model_version[1:])+0.1}"
    # 此处应添加实际训练代码
    # 例如：self.model = train_new_model(self.data_lake)

def adaptive_light_compensation(img):
    # 动态光照补偿算法
    lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
    l, a, b = cv2.split(lab)
    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
    limg = clahe.apply(l)
    return cv2.cvtColor(cv2.merge((limg,a,b)), cv2.COLOR_LAB2BGR)