how/script/data_processing/build_persona_graph.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
构建人设图谱

================================================================================
输入文件:
================================================================================
1. pattern聚合结果.json     - 分类节点、标签节点、属于/包含边
2. dimension_associations_analysis.json    - 分类共现边（跨点）
3. intra_dimension_associations_analysis.json - 分类共现边（点内）
4. 历史帖子解构目录/*.json  - 标签共现边

================================================================================
输出文件: 人设图谱.json
================================================================================
{
    "meta": {                    # 元信息
        "description": "...",
        "account": "账号名",
        "createdAt": "时间戳",
        "stats": { ... }         # 统计信息
    },
    "nodes": {                   # 节点字典 (nodeId -> nodeData)
        "{domain}:{dimension}:{type}:{name}": {
            "name": "显示名称",
            "type": "人设|灵感点|目的点|关键点|分类|标签",
            "domain": "人设",
            "dimension": "人设|灵感点|目的点|关键点",
            "detail": { ... }
        }
    },
    "edges": {                   # 边字典 (edgeId -> edgeData)
        "{source}|{type}|{target}": {
            "source": "源节点ID",
            "target": "目标节点ID",
            "type": "属于|包含|标签共现|分类共现|分类共现",
            "score": 0.5,
            "detail": { ... }
        }
    },
    "index": {                   # 游走索引
        "outEdges": { nodeId: { edgeType: [{ target, score }] } },
        "inEdges": { nodeId: { edgeType: [{ source, score }] } }
    },
    "tree": { ... }              # 嵌套树结构（从根节点沿"包含"边构建）
}

================================================================================
核心逻辑:
================================================================================
1. 提取节点
   - 从 pattern 提取分类节点（按维度分组的层级分类）
   - 从 pattern 提取标签节点（具体特征标签）
   - 添加根节点（人设）和维度节点（灵感点/目的点/关键点）

2. 提取边
   - 属于/包含边：根据节点的 parentPath 构建层级关系
   - 分类共现边（跨点）：从关联分析结果提取
   - 分类共现边（点内）：从点内关联分析提取
   - 标签共现边：遍历历史帖子，统计标签同现

3. 构建索引
   - outEdges: 从该节点出发能到达的节点
   - inEdges: 能到达该节点的源节点

4. 构建树
   - 从根节点开始，沿"包含"边递归构建嵌套树结构

================================================================================
节点ID格式: {domain}:{dimension}:{type}:{name}
================================================================================
- 根节点:   人设:人设:人设:人设
- 维度节点: 人设:灵感点:灵感点:灵感点
- 分类节点: 人设:灵感点:分类:视觉呈现
- 标签节点: 人设:灵感点:标签:手绘风格

================================================================================
边类型:
================================================================================
- 属于:         子节点 -> 父节点（层级关系）
- 包含:         父节点 -> 子节点（层级关系）
- 标签共现:     标签 <-> 标签（同一帖子出现）
- 分类共现:     分类 <-> 分类（跨维度共现）
- 分类共现: 分类 <-> 分类（点内组合共现）

================================================================================
图游走函数:
================================================================================
1. walk_graph(index, start_node, edge_types, direction, min_score)
   - 从起始节点出发，按边类型序列游走N步
   - 示例: walk_graph(index, "人设:灵感点:标签:手绘风格", ["属于", "分类共现"])
   - 返回: 到达的节点ID集合

2. get_neighbors(index, node_id, edge_type, direction, min_score)
   - 获取节点的邻居
   - 示例: get_neighbors(index, "人设:灵感点:分类:视觉呈现", "包含")
   - 返回: 邻居列表 [{"target": "...", "score": 0.5}, ...]

================================================================================
"""

import json
from pathlib import Path
from typing import Dict, List, Set, Any
from datetime import datetime
import sys

# 添加项目根目录到路径
project_root = Path(__file__).parent.parent.parent
sys.path.insert(0, str(project_root))

from script.data_processing.path_config import PathConfig


# ==================== 节点和边构建工具 ====================

def build_node_id(domain: str, dimension: str, node_type: str, name: str) -> str:
    """构建节点ID"""
    return f"{domain}:{dimension}:{node_type}:{name}"


def build_edge_id(source: str, edge_type: str, target: str) -> str:
    """构建边ID"""
    return f"{source}|{edge_type}|{target}"


def create_node(
    domain: str,
    dimension: str,
    node_type: str,
    name: str,
    detail: Dict = None
) -> Dict:
    """创建节点"""
    return {
        "name": name,
        "type": node_type,
        "dimension": dimension,
        "domain": domain,
        "detail": detail or {}
    }


def create_edge(
    source: str,
    target: str,
    edge_type: str,
    score: float = None,
    detail: Dict = None
) -> Dict:
    """创建边"""
    return {
        "source": source,
        "target": target,
        "type": edge_type,
        "score": score,
        "detail": detail or {}
    }


# ==================== 从 pattern 提取分类节点 ====================

def extract_category_nodes_from_pattern(
    pattern_data: Dict,
    dimension_key: str,
    dimension_name: str
) -> Dict[str, Dict]:
    """
    从 pattern 聚合结果中提取分类节点

    Returns:
        { nodeId: nodeData }
    """
    nodes = {}

    if dimension_key not in pattern_data:
        return nodes

    def collect_sources_recursively(node: Dict) -> List[Dict]:
        """递归收集节点及其所有子节点的特征来源"""
        sources = []

        if "特征列表" in node:
            for feature in node["特征列表"]:
                source = {
                    "pointName": feature.get("所属点", ""),
                    "pointDesc": feature.get("点描述", ""),
                    "postId": feature.get("帖子id", "")
                }
                sources.append(source)

        for key, value in node.items():
            if key in ["特征列表", "_meta", "帖子数", "特征数", "帖子列表"]:
                continue
            if isinstance(value, dict):
                sources.extend(collect_sources_recursively(value))

        return sources

    def traverse_node(node: Dict, parent_path: List[str]):
        """递归遍历节点"""
        for key, value in node.items():
            if key in ["特征列表", "_meta", "帖子数", "特征数", "帖子列表"]:
                continue

            if isinstance(value, dict):
                current_path = parent_path + [key]

                # 收集帖子ID列表（递归收集当前节点及所有子节点的帖子ID，去重）
                all_sources = collect_sources_recursively(value)
                unique_post_ids = list(set(s.get("postId", "") for s in all_sources if s.get("postId")))

                # 构建节点
                node_id = build_node_id("人设", dimension_name, "分类", key)
                nodes[node_id] = create_node(
                    domain="人设",
                    dimension=dimension_name,
                    node_type="分类",
                    name=key,
                    detail={
                        "parentPath": parent_path.copy(),
                        "postIds": unique_post_ids,
                        "postCount": len(unique_post_ids)
                    }
                )

                # 递归处理子节点
                traverse_node(value, current_path)

    traverse_node(pattern_data[dimension_key], [])
    return nodes


# ==================== 从 pattern 提取标签节点 ====================

def extract_tag_nodes_from_pattern(
    pattern_data: Dict,
    dimension_key: str,
    dimension_name: str
) -> Dict[str, Dict]:
    """
    从 pattern 聚合结果中提取标签节点

    Returns:
        { nodeId: nodeData }
    """
    nodes = {}
    tag_map = {}  # 用于合并同名标签: tagId -> { postIds, parentPath }

    if dimension_key not in pattern_data:
        return nodes

    def traverse_node(node: Dict, parent_path: List[str]):
        """递归遍历节点"""
        # 处理特征列表（标签）
        if "特征列表" in node:
            for feature in node["特征列表"]:
                tag_name = feature.get("特征名称", "")
                if not tag_name:
                    continue

                post_id = feature.get("帖子id", "")
                tag_id = build_node_id("人设", dimension_name, "标签", tag_name)

                if tag_id not in tag_map:
                    tag_map[tag_id] = {
                        "name": tag_name,
                        "postIds": set(),
                        "parentPath": parent_path.copy()
                    }

                if post_id:
                    tag_map[tag_id]["postIds"].add(post_id)

        # 递归处理子节点
        for key, value in node.items():
            if key in ["特征列表", "_meta", "帖子数", "特征数", "帖子列表"]:
                continue

            if isinstance(value, dict):
                current_path = parent_path + [key]
                traverse_node(value, current_path)

    traverse_node(pattern_data[dimension_key], [])

    # 转换为节点
    for tag_id, tag_info in tag_map.items():
        nodes[tag_id] = create_node(
            domain="人设",
            dimension=dimension_name,
            node_type="标签",
            name=tag_info["name"],
            detail={
                "parentPath": tag_info["parentPath"],
                "postIds": list(tag_info["postIds"]),
                "postCount": len(tag_info["postIds"])
            }
        )

    return nodes


# ==================== 从 pattern 提取属于/包含边 ====================

def extract_belong_contain_edges(
    pattern_data: Dict,
    dimension_key: str,
    dimension_name: str,
    nodes: Dict[str, Dict]
) -> Dict[str, Dict]:
    """
    从 pattern 聚合结果中提取属于/包含边

    Returns:
        { edgeId: edgeData }
    """
    edges = {}

    if dimension_key not in pattern_data:
        return edges

    # 构建分类名称到ID的映射
    category_name_to_id = {}
    for node_id, node_data in nodes.items():
        if node_data["type"] == "分类" and node_data["dimension"] == dimension_name:
            category_name_to_id[node_data["name"]] = node_id

    # 为每个节点创建属于边（子→父）
    for node_id, node_data in nodes.items():
        if node_data["dimension"] != dimension_name:
            continue

        parent_path = node_data["detail"].get("parentPath", [])
        if not parent_path:
            continue

        # 取最后一个作为直接父分类
        parent_name = parent_path[-1]
        parent_id = category_name_to_id.get(parent_name)

        if parent_id:
            # 获取 source 和 target 的 postIds
            child_post_ids = node_data["detail"].get("postIds", [])
            parent_post_ids = nodes.get(parent_id, {}).get("detail", {}).get("postIds", [])

            # 属于边：子 → 父
            edge_id = build_edge_id(node_id, "属于", parent_id)
            edges[edge_id] = create_edge(
                source=node_id,
                target=parent_id,
                edge_type="属于",
                score=1.0,
                detail={
                    "sourcePostIds": child_post_ids,
                    "targetPostIds": parent_post_ids
                }
            )

            # 包含边：父 → 子
            edge_id_contain = build_edge_id(parent_id, "包含", node_id)
            edges[edge_id_contain] = create_edge(
                source=parent_id,
                target=node_id,
                edge_type="包含",
                score=1.0,
                detail={
                    "sourcePostIds": parent_post_ids,
                    "targetPostIds": child_post_ids
                }
            )

    return edges


# ==================== 从关联分析提取分类共现边（跨点）====================

def extract_category_cooccur_edges(associations_data: Dict, nodes: Dict[str, Dict]) -> Dict[str, Dict]:
    """
    从 dimension_associations_analysis.json 中提取分类共现边（跨点）

    Args:
        associations_data: 关联分析数据
        nodes: 已构建的节点数据（用于获取节点的 postIds）

    Returns:
        { edgeId: edgeData }
    """
    edges = {}

    if "单维度关联分析" not in associations_data:
        return edges

    single_dim = associations_data["单维度关联分析"]

    # 维度映射
    dimension_map = {
        "灵感点维度": "灵感点",
        "目的点维度": "目的点",
        "关键点维度": "关键点"
    }

    def get_last_segment(path: str) -> str:
        """获取路径的最后一段"""
        return path.split("/")[-1]

    for dim_key, dim_data in single_dim.items():
        if dim_key not in dimension_map:
            continue

        source_dimension = dimension_map[dim_key]

        for direction_key, direction_data in dim_data.items():
            if direction_key == "说明" or "→" not in direction_key:
                continue

            for source_path, source_info in direction_data.items():
                source_name = get_last_segment(source_path)
                source_node_id = build_node_id("人设", source_dimension, "分类", source_name)

                for field_name, associations in source_info.items():
                    if not field_name.startswith("与") or not field_name.endswith("的关联"):
                        continue

                    target_dimension = field_name[1:-3]

                    if not isinstance(associations, list):
                        continue

                    for assoc in associations:
                        target_path = assoc.get("目标分类", "")
                        if not target_path:
                            continue

                        target_name = get_last_segment(target_path)
                        target_node_id = build_node_id("人设", target_dimension, "分类", target_name)

                        # 使用 Jaccard 作为 score
                        jaccard = assoc.get("Jaccard相似度", 0)

                        # 获取 source 和 target 的 postIds
                        source_post_ids = nodes.get(source_node_id, {}).get("detail", {}).get("postIds", [])
                        target_post_ids = nodes.get(target_node_id, {}).get("detail", {}).get("postIds", [])

                        edge_id = build_edge_id(source_node_id, "分类共现", target_node_id)
                        edges[edge_id] = create_edge(
                            source=source_node_id,
                            target=target_node_id,
                            edge_type="分类共现",
                            score=jaccard,
                            detail={
                                "postIds": assoc.get("共同帖子ID", []),
                                "postCount": assoc.get("共同帖子数", 0),
                                "jaccard": jaccard,
                                "overlapCoef": assoc.get("重叠系数", 0),
                                "sourcePostIds": source_post_ids,
                                "targetPostIds": target_post_ids
                            }
                        )

    return edges


# ==================== 从关联分析提取分类共现边（点内）====================

def extract_intra_category_cooccur_edges(intra_data: Dict, nodes: Dict[str, Dict]) -> Dict[str, Dict]:
    """
    从 intra_dimension_associations_analysis.json 中提取点内分类共现边

    Args:
        intra_data: 点内关联分析数据
        nodes: 已构建的节点数据（用于获取节点的 postIds）

    Returns:
        { edgeId: edgeData }
    """
    edges = {}

    if "叶子分类组合聚类" not in intra_data:
        return edges

    clusters_by_dim = intra_data["叶子分类组合聚类"]

    for dimension, clusters in clusters_by_dim.items():
        if dimension not in ("灵感点", "目的点", "关键点"):
            continue

        for cluster_key, cluster_data in clusters.items():
            leaf_categories = cluster_data.get("叶子分类组合", [])
            point_count = cluster_data.get("点数", 0)
            point_details = cluster_data.get("点详情列表", [])

            # 提取点名称列表
            point_names = [p.get("点名称", "") for p in point_details if p.get("点名称")]

            # 两两组合生成共现边
            for i in range(len(leaf_categories)):
                for j in range(i + 1, len(leaf_categories)):
                    cat1 = leaf_categories[i]
                    cat2 = leaf_categories[j]

                    cat1_id = build_node_id("人设", dimension, "分类", cat1)
                    cat2_id = build_node_id("人设", dimension, "分类", cat2)

                    # 确保顺序一致（按字典序）
                    if cat1_id > cat2_id:
                        cat1_id, cat2_id = cat2_id, cat1_id

                    edge_id = build_edge_id(cat1_id, "分类共现", cat2_id)

                    if edge_id in edges:
                        # 累加
                        edges[edge_id]["detail"]["pointCount"] += point_count
                        edges[edge_id]["detail"]["pointNames"].extend(point_names)
                    else:
                        # 获取 source 和 target 的 postIds
                        cat1_post_ids = nodes.get(cat1_id, {}).get("detail", {}).get("postIds", [])
                        cat2_post_ids = nodes.get(cat2_id, {}).get("detail", {}).get("postIds", [])

                        # 计算 Jaccard（基于帖子）
                        cat1_set = set(cat1_post_ids)
                        cat2_set = set(cat2_post_ids)
                        intersection = cat1_set & cat2_set
                        union = cat1_set | cat2_set
                        jaccard = round(len(intersection) / len(union), 4) if union else 0

                        edges[edge_id] = create_edge(
                            source=cat1_id,
                            target=cat2_id,
                            edge_type="分类共现",
                            score=jaccard,
                            detail={
                                "postIds": list(intersection),
                                "postCount": len(intersection),
                                "jaccard": jaccard,
                                "pointCount": point_count,
                                "pointNames": point_names.copy(),
                                "sourcePostIds": cat1_post_ids,
                                "targetPostIds": cat2_post_ids
                            }
                        )

    return edges


# ==================== 从历史帖子提取标签共现边 ====================

def extract_tag_cooccur_edges(historical_posts_dir: Path, nodes: Dict[str, Dict]) -> Dict[str, Dict]:
    """
    从历史帖子解构结果中提取标签共现边

    Args:
        historical_posts_dir: 历史帖子目录
        nodes: 已构建的节点数据（用于获取标签的 postIds 计算 Jaccard）

    Returns:
        { edgeId: edgeData }
    """
    edges = {}
    cooccur_map = {}  # (tag1_id, tag2_id) -> { postIds: set() }

    if not historical_posts_dir.exists():
        print(f"  警告: 历史帖子目录不存在: {historical_posts_dir}")
        return edges

    json_files = list(historical_posts_dir.glob("*.json"))
    print(f"  找到 {len(json_files)} 个历史帖子文件")

    def extract_post_id_from_filename(filename: str) -> str:
        """从文件名中提取帖子ID"""
        import re
        match = re.match(r'^([^_]+)_', filename)
        return match.group(1) if match else ""

    def extract_tags_from_post(post_data: Dict) -> Dict[str, List[str]]:
        """从帖子解构结果中提取所有标签"""
        tags_by_dimension = {
            "灵感点": [],
            "目的点": [],
            "关键点": []
        }

        if "三点解构" not in post_data:
            return tags_by_dimension

        three_points = post_data["三点解构"]

        # 灵感点
        if "灵感点" in three_points:
            inspiration = three_points["灵感点"]
            for section in ["全新内容", "共性差异", "共性内容"]:
                if section in inspiration and isinstance(inspiration[section], list):
                    for item in inspiration[section]:
                        if "提取的特征" in item and isinstance(item["提取的特征"], list):
                            for feature in item["提取的特征"]:
                                tag_name = feature.get("特征名称", "")
                                if tag_name:
                                    tags_by_dimension["灵感点"].append(tag_name)

        # 目的点
        if "目的点" in three_points:
            purpose = three_points["目的点"]
            if "purposes" in purpose and isinstance(purpose["purposes"], list):
                for item in purpose["purposes"]:
                    if "提取的特征" in item and isinstance(item["提取的特征"], list):
                        for feature in item["提取的特征"]:
                            tag_name = feature.get("特征名称", "")
                            if tag_name:
                                tags_by_dimension["目的点"].append(tag_name)

        # 关键点
        if "关键点" in three_points:
            key_points = three_points["关键点"]
            if "key_points" in key_points and isinstance(key_points["key_points"], list):
                for item in key_points["key_points"]:
                    if "提取的特征" in item and isinstance(item["提取的特征"], list):
                        for feature in item["提取的特征"]:
                            tag_name = feature.get("特征名称", "")
                            if tag_name:
                                tags_by_dimension["关键点"].append(tag_name)

        return tags_by_dimension

    # 遍历所有帖子文件
    for file_path in json_files:
        post_id = extract_post_id_from_filename(file_path.name)
        if not post_id:
            continue

        try:
            with open(file_path, "r", encoding="utf-8") as f:
                post_data = json.load(f)

            tags_by_dimension = extract_tags_from_post(post_data)

            # 对每个维度内的标签两两组合
            for dimension, tags in tags_by_dimension.items():
                unique_tags = list(set(tags))
                for i in range(len(unique_tags)):
                    for j in range(i + 1, len(unique_tags)):
                        tag1 = unique_tags[i]
                        tag2 = unique_tags[j]

                        tag1_id = build_node_id("人设", dimension, "标签", tag1)
                        tag2_id = build_node_id("人设", dimension, "标签", tag2)

                        # 确保顺序一致
                        if tag1_id > tag2_id:
                            tag1_id, tag2_id = tag2_id, tag1_id

                        key = (tag1_id, tag2_id)

                        if key not in cooccur_map:
                            cooccur_map[key] = {"postIds": set()}

                        cooccur_map[key]["postIds"].add(post_id)

        except Exception as e:
            print(f"  警告: 处理文件 {file_path.name} 时出错: {e}")

    # 转换为边
    for (tag1_id, tag2_id), info in cooccur_map.items():
        cooccur_post_ids = list(info["postIds"])
        cooccur_count = len(cooccur_post_ids)

        # 获取两个标签的帖子集合，计算 Jaccard
        tag1_post_ids = nodes.get(tag1_id, {}).get("detail", {}).get("postIds", [])
        tag2_post_ids = nodes.get(tag2_id, {}).get("detail", {}).get("postIds", [])

        union_count = len(set(tag1_post_ids) | set(tag2_post_ids))
        jaccard = round(cooccur_count / union_count, 4) if union_count > 0 else 0

        edge_id = build_edge_id(tag1_id, "标签共现", tag2_id)
        edges[edge_id] = create_edge(
            source=tag1_id,
            target=tag2_id,
            edge_type="标签共现",
            score=jaccard,
            detail={
                "postIds": cooccur_post_ids,
                "postCount": cooccur_count,
                "jaccard": jaccard,
                "sourcePostIds": tag1_post_ids,
                "targetPostIds": tag2_post_ids
            }
        )

    return edges


# ==================== 构建嵌套树结构 ====================

def build_nested_tree(nodes: Dict[str, Dict], edges: Dict[str, Dict]) -> Dict:
    """
    从根节点开始，沿"包含"边递归构建嵌套树结构

    包含边：父节点 -> 子节点
    从根节点开始，递归找所有包含的子节点

    Returns:
        嵌套的树结构
    """
    # 从"包含"边构建 父节点 -> [子节点] 的映射
    parent_to_children = {}  # parent_id -> [child_id, ...]

    for edge_id, edge_data in edges.items():
        if edge_data["type"] == "包含":
            parent_id = edge_data["source"]
            child_id = edge_data["target"]

            if parent_id not in parent_to_children:
                parent_to_children[parent_id] = []
            parent_to_children[parent_id].append(child_id)

    # 递归构建子树
    def build_subtree(node_id: str) -> Dict:
        node_data = nodes[node_id]

        subtree = {
            "id": node_id,
            "name": node_data["name"],
            "type": node_data["type"],
            "domain": node_data["domain"],
            "dimension": node_data["dimension"],
            "detail": node_data.get("detail", {}),
            "children": []
        }

        # 获取子节点
        child_ids = parent_to_children.get(node_id, [])

        for child_id in child_ids:
            if child_id in nodes:
                subtree["children"].append(build_subtree(child_id))

        return subtree

    # 从根节点开始构建
    root_id = "人设:人设:人设:人设"
    return build_subtree(root_id)


# ==================== 图游走工具 ====================

def walk_graph(
    index: Dict,
    start_node: str,
    edge_types: List[str],
    direction: str = "out",
    min_score: float = None
) -> Set[str]:
    """
    从起始节点出发，按指定边类型序列游走N步

    Args:
        index: 游走索引 {"outEdges": {...}, "inEdges": {...}}
        start_node: 起始节点ID
        edge_types: 边类型序列，如 ["属于", "分类共现"]
        direction: 游走方向 "out"(沿出边) / "in"(沿入边)
        min_score: 最小分数过滤

    Returns:
        到达的节点ID集合

    Example:
        # 从标签出发，沿"属于"边走1步，再沿"分类共现"边走1步
        result = walk_graph(
            index,
            "人设:灵感点:标签:手绘风格",
            ["属于", "分类共现"]
        )
    """
    edge_index = index["outEdges"] if direction == "out" else index["inEdges"]
    target_key = "target" if direction == "out" else "source"

    current_nodes = {start_node}

    for edge_type in edge_types:
        next_nodes = set()
        for node in current_nodes:
            neighbors = edge_index.get(node, {}).get(edge_type, [])
            for neighbor in neighbors:
                # 分数过滤
                if min_score is not None and neighbor.get("score", 0) < min_score:
                    continue
                next_nodes.add(neighbor[target_key])
        current_nodes = next_nodes

        if not current_nodes:
            break

    return current_nodes


def get_neighbors(
    index: Dict,
    node_id: str,
    edge_type: str = None,
    direction: str = "out",
    min_score: float = None
) -> List[Dict]:
    """
    获取节点的邻居

    Args:
        index: 游走索引
        node_id: 节点ID
        edge_type: 边类型（可选，不指定则返回所有类型）
        direction: 方向 "out" / "in"
        min_score: 最小分数过滤

    Returns:
        邻居列表 [{"target": "...", "score": 0.5}, ...]
    """
    edge_index = index["outEdges"] if direction == "out" else index["inEdges"]
    node_edges = edge_index.get(node_id, {})

    if edge_type:
        neighbors = node_edges.get(edge_type, [])
    else:
        neighbors = []
        for edges in node_edges.values():
            neighbors.extend(edges)

    if min_score is not None:
        neighbors = [n for n in neighbors if n.get("score", 0) >= min_score]

    return neighbors


# ==================== 构建索引 ====================

def build_index(edges: Dict[str, Dict]) -> Dict:
    """
    构建游走索引

    Returns:
        {
            "outEdges": { nodeId: { edgeType: [{ target, score }] } },
            "inEdges": { nodeId: { edgeType: [{ source, score }] } }
        }
    """
    out_edges = {}
    in_edges = {}

    for edge_id, edge_data in edges.items():
        source = edge_data["source"]
        target = edge_data["target"]
        edge_type = edge_data["type"]
        score = edge_data["score"]

        # outEdges
        if source not in out_edges:
            out_edges[source] = {}
        if edge_type not in out_edges[source]:
            out_edges[source][edge_type] = []
        out_edges[source][edge_type].append({
            "target": target,
            "score": score
        })

        # inEdges
        if target not in in_edges:
            in_edges[target] = {}
        if edge_type not in in_edges[target]:
            in_edges[target][edge_type] = []
        in_edges[target][edge_type].append({
            "source": source,
            "score": score
        })

    return {
        "outEdges": out_edges,
        "inEdges": in_edges
    }


# ==================== 主函数 ====================

def main():
    config = PathConfig()
    config.ensure_dirs()

    print(f"账号: {config.account_name}")
    print(f"输出版本: {config.output_version}")
    print()

    # 输入文件路径
    pattern_file = config.pattern_cluster_file
    associations_file = config.account_dir / "pattern相关文件/optimization/dimension_associations_analysis.json"
    intra_associations_file = config.account_dir / "pattern相关文件/optimization/intra_dimension_associations_analysis.json"
    historical_posts_dir = config.historical_posts_dir

    # 输出文件路径
    output_file = config.intermediate_dir / "人设图谱.json"

    print("输入文件:")
    print(f"  pattern聚合文件: {pattern_file}")
    print(f"  跨点关联分析文件: {associations_file}")
    print(f"  点内关联分析文件: {intra_associations_file}")
    print(f"  历史帖子目录: {historical_posts_dir}")
    print(f"\n输出文件: {output_file}")
    print()

    # ===== 读取数据 =====
    print("=" * 60)
    print("读取数据...")

    print("  读取 pattern 聚合结果...")
    with open(pattern_file, "r", encoding="utf-8") as f:
        pattern_data = json.load(f)

    print("  读取跨点关联分析结果...")
    with open(associations_file, "r", encoding="utf-8") as f:
        associations_data = json.load(f)

    print("  读取点内关联分析结果...")
    with open(intra_associations_file, "r", encoding="utf-8") as f:
        intra_associations_data = json.load(f)

    # ===== 提取节点 =====
    print("\n" + "=" * 60)
    print("提取节点...")

    all_nodes = {}

    dimension_mapping = {
        "灵感点列表": "灵感点",
        "目的点": "目的点",
        "关键点列表": "关键点"
    }

    # 分类节点
    print("\n提取分类节点:")
    for dim_key, dim_name in dimension_mapping.items():
        category_nodes = extract_category_nodes_from_pattern(pattern_data, dim_key, dim_name)
        all_nodes.update(category_nodes)
        print(f"  {dim_name}: {len(category_nodes)} 个")

    # 标签节点
    print("\n提取标签节点:")
    for dim_key, dim_name in dimension_mapping.items():
        tag_nodes = extract_tag_nodes_from_pattern(pattern_data, dim_key, dim_name)
        all_nodes.update(tag_nodes)
        print(f"  {dim_name}: {len(tag_nodes)} 个")

    # 统计
    category_count = sum(1 for n in all_nodes.values() if n["type"] == "分类")
    tag_count = sum(1 for n in all_nodes.values() if n["type"] == "标签")
    print(f"\n节点总计: {len(all_nodes)} (分类: {category_count}, 标签: {tag_count})")

    # ===== 提取边 =====
    print("\n" + "=" * 60)
    print("提取边...")

    all_edges = {}

    # 属于/包含边
    print("\n提取属于/包含边:")
    for dim_key, dim_name in dimension_mapping.items():
        belong_contain_edges = extract_belong_contain_edges(pattern_data, dim_key, dim_name, all_nodes)
        all_edges.update(belong_contain_edges)
    belong_count = sum(1 for e in all_edges.values() if e["type"] == "属于")
    contain_count = sum(1 for e in all_edges.values() if e["type"] == "包含")
    print(f"  属于边: {belong_count}, 包含边: {contain_count}")

    # 分类共现边（跨点）
    print("\n提取分类共现边（跨点）:")
    category_cooccur_edges = extract_category_cooccur_edges(associations_data, all_nodes)
    all_edges.update(category_cooccur_edges)
    print(f"  分类共现边: {len(category_cooccur_edges)}")

    # 分类共现边（点内）
    print("\n提取分类共现边（点内）:")
    intra_category_edges = extract_intra_category_cooccur_edges(intra_associations_data, all_nodes)
    all_edges.update(intra_category_edges)
    print(f"  分类共现边: {len(intra_category_edges)}")

    # 标签共现边
    print("\n提取标签共现边:")
    tag_cooccur_edges = extract_tag_cooccur_edges(historical_posts_dir, all_nodes)
    all_edges.update(tag_cooccur_edges)
    print(f"  标签共现边: {len(tag_cooccur_edges)}")

    # ===== 添加根节点和维度节点 =====
    print("\n添加根节点和维度节点:")

    # 收集所有帖子ID（用于根节点）
    all_post_ids_for_root = set()
    for node in all_nodes.values():
        post_ids = node["detail"].get("postIds", [])
        all_post_ids_for_root.update(post_ids)

    # 根节点
    root_id = "人设:人设:人设:人设"
    root_post_ids = list(all_post_ids_for_root)
    all_nodes[root_id] = create_node(
        domain="人设",
        dimension="人设",
        node_type="人设",
        name="人设",
        detail={
            "postIds": root_post_ids,
            "postCount": len(root_post_ids)
        }
    )

    # 维度节点 + 边
    dimensions = ["灵感点", "目的点", "关键点"]
    for dim in dimensions:
        # 收集该维度下所有节点的帖子ID
        dim_post_ids = set()
        for node in all_nodes.values():
            if node["dimension"] == dim:
                post_ids = node["detail"].get("postIds", [])
                dim_post_ids.update(post_ids)
        dim_post_ids_list = list(dim_post_ids)

        dim_id = f"人设:{dim}:{dim}:{dim}"
        all_nodes[dim_id] = create_node(
            domain="人设",
            dimension=dim,
            node_type=dim,
            name=dim,
            detail={
                "postIds": dim_post_ids_list,
                "postCount": len(dim_post_ids_list)
            }
        )

        # 维度 -> 根 的属于边
        edge_id = build_edge_id(dim_id, "属于", root_id)
        all_edges[edge_id] = create_edge(
            source=dim_id,
            target=root_id,
            edge_type="属于",
            score=1.0,
            detail={
                "sourcePostIds": dim_post_ids_list,
                "targetPostIds": root_post_ids
            }
        )

        # 根 -> 维度 的包含边
        edge_id_contain = build_edge_id(root_id, "包含", dim_id)
        all_edges[edge_id_contain] = create_edge(
            source=root_id,
            target=dim_id,
            edge_type="包含",
            score=1.0,
            detail={
                "sourcePostIds": root_post_ids,
                "targetPostIds": dim_post_ids_list
            }
        )

        # 找该维度下的顶级分类（没有父节点的分类），添加边
        dim_categories = [
            (nid, ndata) for nid, ndata in all_nodes.items()
            if ndata["dimension"] == dim and ndata["type"] == "分类"
            and not ndata["detail"].get("parentPath")
        ]

        for cat_id, cat_data in dim_categories:
            cat_post_ids = cat_data["detail"].get("postIds", [])

            # 顶级分类 -> 维度 的属于边
            edge_id = build_edge_id(cat_id, "属于", dim_id)
            all_edges[edge_id] = create_edge(
                source=cat_id,
                target=dim_id,
                edge_type="属于",
                score=1.0,
                detail={
                    "sourcePostIds": cat_post_ids,
                    "targetPostIds": dim_post_ids_list
                }
            )

            # 维度 -> 顶级分类 的包含边
            edge_id_contain = build_edge_id(dim_id, "包含", cat_id)
            all_edges[edge_id_contain] = create_edge(
                source=dim_id,
                target=cat_id,
                edge_type="包含",
                score=1.0,
                detail={
                    "sourcePostIds": dim_post_ids_list,
                    "targetPostIds": cat_post_ids
                }
            )

    print(f"  添加节点: 1 根节点 + 3 维度节点 = 4")
    print(f"  添加边: 根↔维度 6条 + 维度↔顶级分类")

    # 边统计
    edge_type_counts = {}
    for edge in all_edges.values():
        t = edge["type"]
        edge_type_counts[t] = edge_type_counts.get(t, 0) + 1

    print(f"\n边总计: {len(all_edges)}")
    for t, count in sorted(edge_type_counts.items(), key=lambda x: -x[1]):
        print(f"  {t}: {count}")

    # ===== 计算节点概率 =====
    print("\n" + "=" * 60)
    print("计算节点概率...")

    # 1. 计算总帖子数（所有帖子ID的并集）
    all_post_ids = set()
    for node in all_nodes.values():
        post_ids = node["detail"].get("postIds", [])
        all_post_ids.update(post_ids)
    total_post_count = len(all_post_ids)
    print(f"  总帖子数: {total_post_count}")

    # 2. 为每个节点计算概率
    for node_id, node in all_nodes.items():
        post_count = node["detail"].get("postCount", 0)

        # 全局概率
        if total_post_count > 0:
            node["detail"]["probGlobal"] = round(post_count / total_post_count, 4)
        else:
            node["detail"]["probGlobal"] = 0

        # 相对父节点的概率
        # 通过"属于"边找父节点
        parent_edge_id = None
        for edge_id, edge in all_edges.items():
            if edge["source"] == node_id and edge["type"] == "属于":
                parent_node_id = edge["target"]
                parent_node = all_nodes.get(parent_node_id)
                if parent_node:
                    parent_post_count = parent_node["detail"].get("postCount", 0)
                    if parent_post_count > 0:
                        node["detail"]["probToParent"] = round(post_count / parent_post_count, 4)
                    else:
                        node["detail"]["probToParent"] = 0
                break
        else:
            # 没有父节点（根节点）
            node["detail"]["probToParent"] = 1.0

    print(f"  已为 {len(all_nodes)} 个节点计算概率")

    # 3. 更新"包含"边的分数（使用子节点的 probToParent）
    contain_edge_updated = 0
    for edge_id, edge in all_edges.items():
        if edge["type"] == "包含":
            target_node = all_nodes.get(edge["target"])
            if target_node:
                edge["score"] = target_node["detail"].get("probToParent", 1.0)
                contain_edge_updated += 1
    print(f"  已更新 {contain_edge_updated} 条包含边的分数")

    # ===== 构建索引 =====
    print("\n" + "=" * 60)
    print("构建索引...")
    index = build_index(all_edges)
    print(f"  outEdges 节点数: {len(index['outEdges'])}")
    print(f"  inEdges 节点数: {len(index['inEdges'])}")

    # ===== 构建嵌套树 =====
    print("\n" + "=" * 60)
    print("构建嵌套树...")
    tree = build_nested_tree(all_nodes, all_edges)

    # 统计树节点数
    def count_tree_nodes(node):
        count = 1
        for child in node.get("children", []):
            count += count_tree_nodes(child)
        return count

    tree_node_count = count_tree_nodes(tree)
    print(f"  树节点数: {tree_node_count}")

    # ===== 统计各维度 =====
    dimension_stats = {}
    for dim_name in ["灵感点", "目的点", "关键点"]:
        dim_categories = sum(1 for n in all_nodes.values() if n["type"] == "分类" and n["dimension"] == dim_name)
        dim_tags = sum(1 for n in all_nodes.values() if n["type"] == "标签" and n["dimension"] == dim_name)
        dimension_stats[dim_name] = {
            "categoryCount": dim_categories,
            "tagCount": dim_tags
        }

    # ===== 构建输出 =====
    print("\n" + "=" * 60)
    print("保存结果...")

    output_data = {
        "meta": {
            "description": "人设图谱数据",
            "account": config.account_name,
            "createdAt": datetime.now().isoformat(),
            "stats": {
                "nodeCount": len(all_nodes),
                "edgeCount": len(all_edges),
                "categoryCount": category_count,
                "tagCount": tag_count,
                "treeNodeCount": tree_node_count,
                "dimensions": dimension_stats,
                "edgeTypes": edge_type_counts
            }
        },
        "nodes": all_nodes,
        "edges": all_edges,
        "index": index,
        "tree": tree
    }

    with open(output_file, "w", encoding="utf-8") as f:
        json.dump(output_data, f, ensure_ascii=False, indent=2)

    print(f"\n输出文件: {output_file}")
    print("\n" + "=" * 60)
    print("完成!")


if __name__ == "__main__":
    main()