AI 创作日记 | 解码新零售基因，DeepSeek 树状推理机制如何颠覆传统决策逻辑

# 传统决策树示例
from sklearn.tree import DecisionTreeClassifier

# 静态规则建模
clf = DecisionTreeClassifier(
    max_depth=3,
    criterion='gini' 
)

# DeepSeek动态推理树结构
class DeepSeekDecisionNode:
    def __init__(self, condition_fn, children=None):
        self.condition = condition_fn  # 动态条件函数
        self.children = children or []  # 多级子节点
        
    def evaluate(self, context):
        # 实时环境感知
        branch = self.condition(context)
        return self.children[branch].evaluate(context)

class DecisionTreeNode:
    def __init__(self, depth, max_depth):
        self.children = []
        self.depth = depth
        self.max_depth = max_depth
        
    def expand(self, scenario):
        """树节点扩展方法"""
        if self.depth >= self.max_depth:
            return
            
        # 分支1：成本维度分析
        cost_branch = CostAnalyzer(scenario).generate_child()
        self.children.append(cost_branch)
        
        # 分支2：时效维度分析  
        time_branch = TimeEvaluator(scenario).generate_child()
        self.children.append(time_branch)
        
        # 分支3：风险维度分析
        risk_branch = RiskAssessor(scenario).generate_child()
        self.children.append(risk_branch)

class DeepSeekDecisionTree:
    def __init__(self, root_scenario):
        self.root = DecisionTreeNode(depth=0, max_depth=5)
        self._build_tree(root_scenario)
        
    def _build_tree(self, scenario):
        """深度优先构建决策树"""
        stack = [(self.root, scenario)]
        while stack:
            node, current_scenario = stack.pop()
            node.expand(current_scenario)
            for child in node.children:
                if child.depth < child.max_depth:
                    stack.append((child, update_scenario(current_scenario)))

class DecisionTreeGenerator:
    def __init__(self, max_depth=5):
        self.max_depth = max_depth
        self.paths = []
        
    def generate_paths(self, current_path, depth):
        if depth == self.max_depth:
            self.paths.append(current_path)
            return
            
        # 生成决策分支：库存调拨方向/数量/优先级
        for action in ['hold', 'transfer_in', 'transfer_out']:
            new_path = current_path.copy()
            new_path.append({
                'action': action,
                'qty_range': self._calc_qty_range(action),
                'priority': self._calc_priority(action)
            })
            self.generate_paths(new_path, depth+1)
            
    def _calc_qty_range(self, action):
        # 基于实时库存动态计算可行区间
        return (0, 1000) if action == 'hold' else (100, 500)
        
    def _calc_priority(self, action):
        # 结合销售预测计算动作优先级
        return {'transfer_out': 0.8, 'transfer_in': 0.6, 'hold': 0.3}[action]

V = α*(1 - C/C_max) + β*(T_min/T) + γ*R  
其中：  
C: 预估总成本（含运输、仓储、机会成本）  
T: 预计到货时间  
R: 风险系数（0-1）  
α+β+γ=1 动态权重根据策略调整

import numpy as np

def evaluate_path(node):
    """多路径价值评估函数"""
    # 动态权重调整（示例：促销期间时效权重提升）
    if is_promotion_period():
        alpha, beta, gamma = 0.4, 0.5, 0.1  
    else:
        alpha, beta, gamma = 0.6, 0.3, 0.1
        
    cost = node.cost / MAX_COST  # 成本归一化
    time = MIN_TIME / node.time  # 时效系数
    risk = node.risk_score
    
    return alpha*(1 - cost) + beta*time + gamma*risk

def parallel_evaluation(tree):
    """多路径并行评估"""
    from concurrent.futures import ThreadPoolExecutor
    
    with ThreadPoolExecutor(max_workers=8) as executor:
        futures = {executor.submit(evaluate_path, node): node 
                  for node in get_leaves(tree.root)}
        
        results = []
        for future in futures:
            score = future.result()
            results.append( (futures[future], score) )
            
    return sorted(results, key=lambda x: x[1], reverse=True)

def intelligent_pruning(node, best_score, threshold=0.15):
    """智能剪枝算法"""
    if not node.children:
        return
        
    # 计算子节点最大潜在价值
    max_child = max([child.upper_bound for child in node.children])
    
    # 剪枝条件判断
    if (max_child - best_score) / best_score < threshold:
        node.children = []  # 执行剪枝
        return True
    return False

def dynamic_routing(ctx):
    # 环境感知层
    urgency_level = calculate_urgency(ctx.order)
    ctx.update(logistics.get_real_time_info())
    
    # 树状推理执行
    router = InventoryRouter(root_node)
    candidate_plans = router.decide(ctx)
    
    # 多路径验证
    validated_plans = []
    for plan in candidate_plans:
        is_valid, score = validate_plan(plan, ctx.constraints)
        if is_valid:
            validated_plans.append( (plan, score) )
    
    # 选择最优方案
    best_plan = max(validated_plans, key=lambda x: x[1])[0]
    
    # 生成执行指令
    return generate_operation_order(best_plan)

CACHE = LRUCache(capacity=100)

def get_decision_tree(scenario):
    signature = scenario.signature()
    if signature in CACHE:
        return CACHE[signature]
    else:
        tree = DeepSeekDecisionTree(scenario)
        CACHE.put(signature, tree)
        return tree