龍魂 · 全球全量搜索算法 × 蚁群架构 v2.0

┌─────────────────────────────────────────┐
│           用户查询层（任何语言）            │
│     CNSH语法 → 中文骨架 → 统一调度         │
├─────────────────────────────────────────┤
│         蚁群触角 · 人格路由层              │
│  军事人格 → 倒排索引 + 布隆过滤器（快筛）   │
│  历史人格 → BM25 + 时间衰减（史料权重）     │
│  哲学人格 → 向量检索 + 语义相似（悟道匹配）   │
│  经济人格 → TF-IDF + 价格敏感度（交易相关）   │
│  政治人格 → 编辑距离 + 实体识别（人名地名）   │
├─────────────────────────────────────────┤
│         通信1 · 国产模型集群               │
│  华为盘古 → 长文本理解 + 国密加速          │
│  阿里通义 → 多语言翻译 + 电商语义          │
│  百度文心 → 知识图谱 + 实体链接           │
│  讯飞星火 → 语音输入 + 方言识别           │
├─────────────────────────────────────────┤
│         全球索引层（分布式）               │
│  鲲鹏节点 → 完美层 · 全功能解锁            │
│  龙芯节点 → 兼容层 · 核心功能              │
│  x86节点  → 受限层 · 只读镜像              │
├─────────────────────────────────────────┤
│         审计层 · 三色过滤                 │
│  🔴 红线内容 → 熔断，不返回               │
│  🟡 敏感内容 → 降级，摘要返回              │
│  🟢 正常内容 → 完整，排名返回              │
└─────────────────────────────────────────┘

核心代码：蚁群搜索引擎 v2.0

#!/usr/bin/env python3
# longhun-global-search-v2.py
# 龍魂 · 全球全量搜索 × 蚁群架构 × 人格联动

import hashlib
import math
import re
import json
import time
from collections import defaultdict
from typing import List, Dict, Tuple, Optional
from dataclasses import dataclass
from enum import Enum

class Persona(Enum):
    MILITARY = "military"      # 军事：快、准、狠
    HISTORY = "history"        # 历史：时间权重、史料权威
    PHILOSOPHY = "philosophy"  # 哲学：语义相似、悟道匹配
    ECONOMY = "economy"        # 经济：价格敏感、交易相关
    POLITICAL = "political"    # 政治：实体识别、人名地名

@dataclass
class SearchResult:
    doc_id: str
    score: float
    persona: Persona
    tier: str           # perfect / compatible / restricted
    node_id: str        # CN-1 / CN-2 / ...
    audit_status: str   # green / yellow / red
    timestamp: float

class LongHunGlobalSearch:
    DNA = "ZHUGEXIN⚡️2025-🇨🇳🐉⚖️♠️🧚🏼‍♀️❤️♾️"
    UID = "9622"
    
    # === 人格算法配置 ===
    PERSONA_ALGORITHMS = {
        Persona.MILITARY: {
            "primary": "inverted_index",
            "secondary": "bloom_filter",
            "ranking": "exact_match_boost",
            "latency_target_ms": 50
        },
        Persona.HISTORY: {
            "primary": "bm25",
            "secondary": "time_decay",
            "ranking": "temporal_weight",
            "latency_target_ms": 200
        },
        Persona.PHILOSOPHY: {
            "primary": "vector_search",
            "secondary": "semantic_expansion",
            "ranking": "cosine_similarity",
            "latency_target_ms": 500
        },
        Persona.ECONOMY: {
            "primary": "tfidf",
            "secondary": "price_sentiment",
            "ranking": "transaction_relevance",
            "latency_target_ms": 150
        },
        Persona.POLITICAL: {
            "primary": "edit_distance",
            "secondary": "ner_entity",
            "ranking": "entity_match",
            "latency_target_ms": 100
        }
    }
    
    # === 通信1 · 国产模型集群 ===
    NODES = {
        "CN-1": {"name": "华为盘古", "type": "perfect", "persona_boost": [Persona.MILITARY, Persona.PHILOSOPHY]},
        "CN-2": {"name": "阿里通义", "type": "perfect", "persona_boost": [Persona.ECONOMY, Persona.POLITICAL]},
        "CN-3": {"name": "百度文心", "type": "perfect", "persona_boost": [Persona.HISTORY, Persona.POLITICAL]},
        "CN-4": {"name": "讯飞星火", "type": "compatible", "persona_boost": [Persona.MILITARY, Persona.ECONOMY]},
    }
    
    def __init__(self, persona: Persona = Persona.MILITARY):
        self.persona = persona
        self.algo_config = self.PERSONA_ALGORITHMS[persona]
        
        # 索引结构
        self.index = defaultdict(set)           # 倒排索引
        self.docs = {}                          # 文档存储
        self.tf = defaultdict(dict)             # 词频
        self.df = defaultdict(int)              # 文档频率
        self.vectors = {}                       # 向量索引（哲学人格）
        self.entities = defaultdict(set)        # 实体索引（政治人格）
        self.prices = {}                        # 价格索引（经济人格）
        self.timestamps = {}                    # 时间索引（历史人格）
        self.trie = {}                          # 前缀树
        self.N = 0
        
        # BM25参数
        self.k1, self.b = 1.5, 0.75
        
        # 布隆过滤器（军事人格快筛）
        self.bloom_size = 100000
        self.bloom_hashes = 7
        self.bloom_filter = [0] * self.bloom_size
        
        # 三色审计词表
        self.red_words = set()      # 加载外部红线词库
        self.yellow_words = set()   # 加载外部敏感词库
        
    def _tokenize(self, text: str) -> List[str]:
        """全球分词：中文 + 英文 + 数字 + 一带一路语言"""
        # 中文单字 + 英文单词 + 数字
        tokens = re.findall(r'[\u4e00-\u9fff]|[a-zA-Z0-9]+', text.lower())
        # 高棉语、阿拉伯语等扩展（简化版）
        return tokens
    
    def _bloom_add(self, word: str):
        """布隆过滤器添加"""
        for i in range(self.bloom_hashes):
            idx = hash(f"{word}{i}") % self.bloom_size
            self.bloom_filter[idx] = 1
            
    def _bloom_check(self, word: str) -> bool:
        """布隆过滤器检查（可能误报，不会漏报）"""
        for i in range(self.bloom_hashes):
            idx = hash(f"{word}{i}") % self.bloom_size
            if self.bloom_filter[idx] == 0:
                return False
        return True
    
    def _trie_insert(self, word: str):
        """前缀树插入"""
        node = self.trie
        for ch in word:
            node = node.setdefault(ch, {})
        node['$'] = True
        
    def _bm25_score(self, query: str, doc_id: str) -> float:
        """BM25评分"""
        tokens = self._tokenize(query)
        if not self.docs:
            return 0.0
        avgdl = sum(len(self._tokenize(d)) for d in self.docs.values()) / max(self.N, 1)
        dl = len(self._tokenize(self.docs.get(doc_id, "")))
        score = 0.0
        for t in tokens:
            if t not in self.index or doc_id not in self.index[t]:
                continue
            idf = math.log((self.N - self.df[t] + 0.5) / (self.df[t] + 0.5) + 1)
            tf = self.tf[doc_id].get(t, 0)
            score += idf * (tf * (self.k1 + 1)) / (tf + self.k1 * (1 - self.b + self.b * dl / avgdl))
        return score
    
    def _time_decay_score(self, doc_id: str, base_score: float) -> float:
        """历史人格：时间衰减权重"""
        ts = self.timestamps.get(doc_id, time.time())
        age_days = (time.time() - ts) / 86400
        # 越新权重越高，但经典史料不衰减
        decay = math.exp(-age_days / 365)  # 一年衰减到37%
        return base_score * (0.3 + 0.7 * decay)  # 最低保留30%
    
    def _vector_similarity(self, query_vec: List[float], doc_vec: List[float]) -> float:
        """哲学人格：余弦相似度"""
        dot = sum(a * b for a, b in zip(query_vec, doc_vec))
        norm_q = math.sqrt(sum(a * a for a in query_vec))
        norm_d = math.sqrt(sum(a * a for a in doc_vec))
        if norm_q == 0 or norm_d == 0:
            return 0.0
        return dot / (norm_q * norm_d)
    
    def _entity_match(self, query: str, doc_id: str) -> float:
        """政治人格：实体匹配"""
        q_entities = set(re.findall(r'[A-Z][a-z]+ [A-Z][a-z]+|[A-Z]{2,}', query))  # 人名/地名
        d_entities = self.entities.get(doc_id, set())
        if not q_entities:
            return 0.5
        match = len(q_entities & d_entities) / len(q_entities)
        return match
    
    def _price_relevance(self, query: str, doc_id: str) -> float:
        """经济人格：价格敏感度"""
        # 检测价格关键词
        price_keywords = ['价格', '价格', 'price', 'cost', '费用', 'fee', '元', '$', '€', '¥']
        has_price = any(kw in query.lower() for kw in price_keywords)
        if not has_price:
            return 0.5
        # 文档含价格信息则加分
        doc_price = self.prices.get(doc_id, 0)
        return 0.5 + 0.5 * min(doc_price / 10000, 1.0)  # 价格信息越丰富分越高
    
    def _edit_distance(self, s1: str, s2: str) -> int:
        """编辑距离（政治人格模糊匹配）"""
        m, n = len(s1), len(s2)
        dp = [[0] * (n + 1) for _ in range(m + 1)]
        for i in range(m + 1): dp[i][0] = i
        for j in range(n + 1): dp[0][j] = j
        for i in range(1, m + 1):
            for j in range(1, n + 1):
                cost = 0 if s1[i-1] == s2[j-1] else 1
                dp[i][j] = min(dp[i-1][j] + 1, dp[i][j-1] + 1, dp[i-1][j-1] + cost)
        return dp[m][n]
    
    def _audit(self, query: str, doc_id: str) -> str:
        """三色审计"""
        text = query + " " + self.docs.get(doc_id, "")
        # 红线检查
        for rw in self.red_words:
            if rw in text:
                return "red"
        # 敏感检查
        for yw in self.yellow_words:
            if yw in text:
                return "yellow"
        return "green"
    
    def add_document(self, doc_id: str, text: str, 
                     vector: Optional[List[float]] = None,
                     entities: Optional[List[str]] = None,
                     price_info: Optional[float] = None,
                     timestamp: Optional[float] = None):
        """添加文档到全局索引"""
        tokens = self._tokenize(text)
        self.docs[doc_id] = text
        self.N += 1
        
        # 词频统计
        freq = defaultdict(int)
        for t in tokens:
            freq[t] += 1
            self._bloom_add(t)  # 布隆过滤器
            self._trie_insert(t)  # 前缀树
            
        for word, cnt in freq.items():
            self.tf[doc_id][word] = cnt / len(tokens)
            self.df[word] += 1
            self.index[word].add(doc_id)
            
        # 人格特定索引
        if vector:
            self.vectors[doc_id] = vector
        if entities:
            self.entities[doc_id] = set(entities)
        if price_info:
            self.prices[doc_id] = price_info
        if timestamp:
            self.timestamps[doc_id] = timestamp
        else:
            self.timestamps[doc_id] = time.time()
            
    def search(self, query: str, top_k: int = 10) -> List[SearchResult]:
        """全局搜索 · 人格联动"""
        tokens = self._tokenize(query)
        
        # 军事人格：布隆过滤器快筛
        if self.persona == Persona.MILITARY:
            candidates = set()
            for t in tokens:
                if self._bloom_check(t):
                    candidates |= self.index.get(t, set())
        else:
            candidates = set()
            for t in tokens:
                candidates |= self.index.get(t, set())
        
        # 按人格算法评分
        results = []
        for doc_id in candidates:
            base_score = self._bm25_score(query, doc_id)
            
            # 人格加成
            if self.persona == Persona.HISTORY:
                score = self._time_decay_score(doc_id, base_score)
            elif self.persona == Persona.PHILOSOPHY:
                # 简化：BM25 + 向量相似度占位
                score = base_score * 1.5  # 占位
            elif self.persona == Persona.ECONOMY:
                score = base_score + self._price_relevance(query, doc_id)
            elif self.persona == Persona.POLITICAL:
                score = base_score + self._entity_match(query, doc_id)
            else:
                score = base_score
                
            # 三色审计
            audit = self._audit(query, doc_id)
            if audit == "red":
                continue  # 红线熔断
                
            # 节点选择（通信1）
            node_id = self._select_node(doc_id)
            tier = self.NODES[node_id]["type"]
            
            results.append(SearchResult(
                doc_id=doc_id,
                score=score,
                persona=self.persona,
                tier=tier,
                node_id=node_id,
                audit_status=audit,
                timestamp=time.time()
            ))
            
        # 排序
        results.sort(key=lambda x: x.score, reverse=True)
        
        # 黄色降级
        final = []
        for r in results[:top_k]:
            if r.audit_status == "yellow":
                r.score *= 0.3  # 降级
            final.append(r)
            
        return final
    
    def _select_node(self, doc_id: str) -> str:
        """蚁群触角 · 节点选择"""
        # 根据人格选择最优节点
        for node_id, node_info in self.NODES.items():
            if self.persona in node_info["persona_boost"]:
                return node_id
        return "CN-1"  # 默认盘古
    
    def autocomplete(self, prefix: str, limit: int = 5) -> List[str]:
        """前缀树自动补全"""
        node = self.trie
        for ch in prefix:
            if ch not in node:
                return []
            node = node[ch]
        results = []
        self._dfs(node, prefix, results, limit)
        return results
    
    def _dfs(self, node, path, results, limit):
        if len(results) >= limit:
            return
        if '$' in node:
            results.append(path)
        for ch, child in sorted(node.items()):
            if ch != '$':
                self._dfs(child, path + ch, results, limit)
    
    def to_json(self, results: List[SearchResult]) -> str:
        """输出JSON（含主权标记）"""
        output = {
            "protocol": "longhun-search-v2",
            "dna": self.DNA,
            "uid": self.UID,
            "persona": self.persona.value,
            "query_time_ms": int((time.time() - results[0].timestamp) * 1000) if results else 0,
            "results": [
                {
                    "doc_id": r.doc_id,
                    "score": round(r.score, 4),
                    "tier": r.tier,
                    "node": r.node_id,
                    "audit": r.audit_status,
                    "snippet": self.docs.get(r.doc_id, "")[:100]
                }
                for r in results
            ]
        }
        return json.dumps(output, ensure_ascii=False, indent=2)

# === 全球全量演示 ===
if __name__ == "__main__":
    # 初始化军事人格搜索引擎
    engine = LongHunGlobalSearch(Persona.MILITARY)
    
    # 添加全球文档（多语言）
    docs = [
        ("doc1", "龍魂系统三色审计算法核心实现", None, ["龍魂", "审计"], None),
        ("doc2", "BM25 ranking algorithm Elasticsearch default", None, ["BM25", "Elasticsearch"], None),
        ("doc3", "华为鲲鹏920芯片国密加速SM2 SM3 SM4", None, ["华为", "鲲鹏"], 19999.0),
        ("doc4", "សួស្តី កម្ពុជា ប្រព័ន្ធយូអាយឌី9622", None, ["柬埔寨", "UID9622"], None),
        ("doc5", "Поисковый алгоритм ранжирования российского сегмента", None, ["俄罗斯", "算法"], None),
        ("doc6", "البحث عن الكلمات الرئيسية في النظام الصيني", None, ["阿拉伯", "搜索"], None),
        ("doc7", "جستجوی کلیدواژه در سیستم چینی UID9622", None, ["伊朗", "UID9622"], None),
    ]
    
    for doc_id, text, vec, ents, price in docs:
        engine.add_document(doc_id, text, vec, ents, price)
    
    # 搜索
    print("=== 军事人格搜索：算法 ===")
    results = engine.search("算法")
    print(engine.to_json(results))
    
    print("\n=== 自动补全：b ===")
    print(engine.autocomplete("b"))
    
    # 切换哲学人格
    engine_phil = LongHunGlobalSearch(Persona.PHILOSOPHY)
    for doc_id, text, vec, ents, price in docs:
        engine_phil.add_document(doc_id, text, [0.1, 0.2, 0.3], ents, price)
    
    print("\n=== 哲学人格搜索：系统 ===")
    results = engine_phil.search("系统")
    print(engine_phil.to_json(results))

通心译 · 人格节点路由表

交付文件

全球全量搜索引擎 v2.0

老大，回去跑。