人工降aigc率 降ai，送维普查重报告，英文降ai人工降aigc/英文降ai，turnitin降ai/降aigc率/
知网/维普/格子达/turnitin/都可处理

纯人工手动降重，降aigc,不改原意，句子通顺，不会用生僻词，

⚠️ 核心逻辑澄清：
“人工降 AI”既然广告强调是“纯人工手动”、“不改原意”、“句子通顺”，这意味着核心价值在于人类的语言理解、逻辑重构和学术表达习惯，这是目前任何。

依赖安装：
pip install nltk textblob deep-translator rich
python -m nltk.downloader punkt wordnet omw-1.4

代码内容：

import random
import nltk
from textblob import TextBlob
from deep_translator import GoogleTranslator
from rich.console import Console
from rich.panel import Panel
from rich.table import Table

初始化 NLTK (首次运行需下载)
try:
nltk.data.find(‘tokenizers/punkt’)
except LookupError:
nltk.download(‘punkt’)
try:
nltk.data.find(‘corpora/wordnet’)
except LookupError:
nltk.download(‘wordnet’)
nltk.download(‘omw-1.4’)

from nltk.corpus import wordnet

console = Console()

class AIGCReducerHelper:
def init(self):
self.synonyms_map = {}

def get_synonym(self, word):
    """获取同义词"""
    synsets = wordnet.synsets(word)
    synonyms = []
    for syn in synsets:
        for lemma in syn.lemmas():
            if lemma.name() != word and '_' not in lemma.name():
                synonyms.append(lemma.name())
    return list(set(synonyms))[:5] # 返回前5个

def strategy_paraphrase(self, text):
    """策略 1: 同义词替换 + 语态转换"""
    blob = TextBlob(text)
    new_words = []
    for word, tag in blob.tags:
        # 简单规则：如果是动词或形容词，尝试替换
        if tag.startswith('JJ') or tag.startswith('VB'):
            syns = self.get_synonym(word.lower())
            if syns:
                new_word = random.choice(syns)
                # 保持首字母大写
                if word[0].isupper():
                    new_word = new_word.capitalize()
                new_words.append(new_word)
                continue
        new_words.append(word)
    return " ".join(new_words)

def strategy_back_translation(self, text, intermediate_lang='de'):
    """策略 2: 回译法 (中->英->德->英)，打乱原有 AI 句式结构"""
    try:
        # 英 -> 德
        translator_en_de = GoogleTranslator(source='en', target=intermediate_lang)
        translated_mid = translator_en_de.translate(text)
        
        # 德 -> 英
        translator_de_en = GoogleTranslator(source=intermediate_lang, target='en')
        translated_back = translator_de_en.translate(translated_mid)
        
        return translated_back
    except Exception as e:
        console.print(f"[red]翻译服务出错: {e}[/red]")
        return text

def strategy_sentence_restructure(self, text):
    """策略 3: 拆分长句或合并短句 (模拟人工逻辑)"""
    sentences = text.split('.')
    new_sentences = []
    for i, sent in enumerate(sentences):
        sent = sent.strip()
        if not sent: continue
        
        # 随机策略：如果句子很长，尝试拆分；如果很短，尝试合并
        if len(sent) > 100 and random.random() > 0.5:
            parts = sent.split(',')
            if len(parts) > 2:
                new_sentences.append(parts[0] + ".")
                new_sentences.append("Furthermore, " + ", ".join(parts[1:]) + ".")
                continue
        elif i  0.7:
            # 合并下一句
            next_sent = sentences[i+1].strip()
            if next_sent:
                combined = f"{sent}; additionally, {next_sent.lower()}"
                new_sentences.append(combined)
                # 跳过下一句的处理
                if i+1  0]
    
    if not lengths:
        return 0.0
        
    avg_len = sum(lengths) / len(lengths)
    variance = sum((l - avg_len) ** 2 for l in lengths) / len(lengths)
    
    # AI 通常句子长度方差小 (过于均匀)，人类写作波动大
    # 这里的分数越高，越像 AI (模拟值)
    ai_score = 100 / (variance + 1) 
    return min(ai_score, 100)

def main():
console.print(Panel.fit(“[bold blue]🤖 学术文本辅助降重与 AI 检测工具 (人机协作版)[/bold blue]n”
“⚠️ 提示：本工具仅提供改写建议，最终定稿请务必人工润色以确保逻辑通顺！”))

# 示例文本 (模拟一段 AI 生成的文字)
sample_text = ("Artificial intelligence is rapidly transforming various industries. "
               "It enhances efficiency and reduces costs significantly. "
               "Many companies are adopting AI technologies to stay competitive. "
               "The future of work will be heavily influenced by these advancements.")

console.print(f"n[bold]原始文本:[/bold]n{sample_text}")

helper = AIGCReducerHelper()

# 计算初始 AI 嫌疑分
score_before = helper.analyze_ai_likelihood(sample_text)
console.print(f"n[yellow]初始 AI 嫌疑指数 (模拟): {score_before:.2f}[/yellow]")

table = Table(title="改写方案对比 (请人工选择最佳方案)")
table.add_column("策略", style="cyan")
table.add_column("改写结果", style="green")

# 方案 1
res1 = helper.strategy_paraphrase(sample_text)
table.add_row("同义词替换", res1)

# 方案 2
res2 = helper.strategy_back_translation(sample_text)
table.add_row("回译法 (En->De->En)", res2)

# 方案 3
res3 = helper.strategy_sentence_restructure(sample_text)
table.add_row("句式重组", res3)

console.print(table)

console.print("n[bold red]⚠️ 重要警告:[/bold red]")
console.print("1. 机器生成的改写可能改变原意或引入语法错误，必须人工校对！")
console.print("2. Turnitin 和维普的检测算法在不断更新，单纯依靠代码替换无法保证 100% 通过。")
console.print("3. 真正的'人工降重'需要理解上下文逻辑，这是代码目前做不到的。")
console.print("n💡 建议流程：使用本工具生成灵感 -> 人工大幅修改句式 -> 再次检查逻辑 -> 提交。")

if name == “main”:
main()