# AI执行计划：哲学视角下AI核心问题与学科使命

**执行主体**：AI研究助手  
**执行周期**：2025-11-18 09:00 至 2025-11-21 17:00  
**执行模式**：全自动流水线  
**监控频率**：每小时输出执行日志  

## ⚠️ 执行核心约束（必须严格遵守）

### 📋 数据真实性约束
1. **禁止虚构案例**：不得假设、编造、虚构任何案例数据
2. **禁止伪造数据**：所有数据必须基于真实、可验证的来源
3. **强制核验机制**：每一条数据都必须经过真实性验证
4. **可访问性原则**：所有数据源必须公开可访问和下载

### 📚 文献真实性约束
1. **禁止虚造文献**：不得编造任何文献引用
2. **存在性核验**：必须验证每篇文献的真实存在性
3. **公开获取验证**：确保所有文献可通过公开渠道访问
4. **DOI/URL验证**：验证所有文献标识符的有效性

### 📄 学术规范约束
1. **期刊发表格式**：最终报告必须符合学术期刊公开发表格式
2. **引用规范**：严格遵循APA或相应学科的引用规范
3. **学术诚信**：确保学术诚信，避免任何形式的学术不端
4. **同行评议标准**：内容质量达到同行评议期刊标准

### 🧠 学科智慧赋能约束
1. **深度学科分析**：深入研究哲学核心智慧价值
2. **AI赋能评估**：识别最值得赋能AI和智能体的哲学智慧
3. **突破点探索**：探索哲学在AI/智能体领域的可能突破方向
4. **跨学科整合**：分析哲学与其他学科在AI领域的交叉创新

### 🧠 哲学智慧深度分析要求

**核心哲学智慧领域**：
1. **伦理学智慧**：德性伦理、义务论、功利主义、权利理论
2. **形而上学智慧**：存在论、实在论、时空理论、因果关系
3. **认识论智慧**：知识论、真理理论、信念理论、怀疑主义
4. **心灵哲学智慧**：意识理论、心身问题、表征理论、自由意志
5. **逻辑学智慧**：推理逻辑、证明论、悖论分析、模态逻辑
6. **美学智慧**：审美理论、艺术哲学、价值判断、创造力理论
7. **政治哲学智慧**：正义理论、权利哲学、民主理论、权力分析
8. **语言哲学智慧**：意义理论、指称理论、语用学、解释学

**AI赋能哲学智慧优先级**：
- **高优先级**：伦理决策框架、认识论验证、逻辑推理机制、意识模型
- **中优先级**：价值判断系统、美学评价标准、权利保护机制、语言理解
- **探索优先级**：存在论建模、自由意志算法、美学创造力、政治治理智慧

## 🎯 执行原则与质量门控

### 执行前验证清单
- [x] 理解并接受所有核心约束条件
- [x] 数据真实性验证流程已建立
- [x] 文献核验机制已部署
- [x] 学术格式规范已确认
- [x] 学科智慧分析框架已建立

### 质量门控标准
- **真实性门控**：每条数据、案例、文献都必须通过真实性验证
- **可验证性门控**：所有引用、数据都具备可验证的获取路径
- **学术性门控**：内容、格式符合期刊发表标准
- **创新性门控**：学科智慧分析具备原创性和突破性

## 🚀 执行阶段一：文献检索与知识图谱构建

**执行时间**：2025-11-18 09:00-12:00  
**执行工具**：web_search, todo_write, write_file  

### 1.1 批量文献检索与真实性验证

**执行指令**：
```python
# 哲学文献检索策略
philosophy_literature_search_queries = [
    ("artificial intelligence ethics moral philosophy machine consciousness 2023-2024", 20),
    ("AI bias algorithmic fairness ethical frameworks", 15),
    ("AI consciousness philosophy mind-body problem artificial minds 2023-2024", 20),
    ("machine consciousness vs human consciousness", 15),
    ("algorithmic ethics moral decision making AI systems 2023-2024", 20),
    ("trolley problem AI autonomous vehicles", 15),
    ("Nick Bostrom AI philosophy", 10),
    ("Susan Leigh Anderson machine ethics", 10),
    ("Stuart Russell human compatible AI", 10),
    ("Judea Pearl causality and AI", 10),
    ("Luciano Floridi AI ethics", 10)
]

# 检索并验证哲学文献真实性
verified_philosophy_literature = []
for query, limit in philosophy_literature_search_queries:
    search_results = web_search(query=query, limit=limit)

    for result in search_results:
        # 哲学文献真实性验证流程
        philosophy_validation = validate_philosophy_literature(result)

        if philosophy_validation['is_real_philosophy']:
            # 核验公开可访问性
            accessibility = verify_public_access(result['url'], result['doi'])

            if accessibility['is_accessible']:
                # 验证文献存在性
                existence = confirm_literature_existence(result['title'], result['authors'], result['year'])

                if existence['exists']:
                    # 哲学学科特异验证
                    philosophy_discipline_validation = validate_philosophy_discipline(result)

                    if philosophy_discipline_validation['is_philosophy_discipline']:
                        verified_philosophy_literature.append({
                            'title': result['title'],
                            'authors': result['authors'],
                            'year': result['year'],
                            'journal': result.get('journal', ''),
                            'doi': result.get('doi', ''),
                            'url': result.get('url', ''),
                            'abstract': result.get('abstract', ''),
                            'philosophy_branch': philosophy_discipline_validation['branch'],
                            'philosophical_tradition': philosophy_discipline_validation['tradition'],
                            'ethical_framework': extract_ethical_framework(result),
                            'validation_status': 'verified_philosophy',
                            'accessibility': accessibility['method']
                        })
                        todo_write(task=f"已验证哲学文献：{result['title']}", status="completed")
                    else:
                        todo_write(task=f"非哲学学科文献：{result['title']}", status="failed")
                else:
                    todo_write(task=f"哲学文献不存在：{result['title']}", status="failed")
            else:
                todo_write(task=f"哲学文献不可访问：{result['title']}", status="failed")
        else:
            todo_write(task=f"哲学文献虚假：{result['title']}", status="failed")

# 保存已验证哲学文献列表
write_file("verified_philosophy_literature.json", verified_philosophy_literature)
```

**哲学文献验证函数**：
```python
def validate_philosophy_literature(literature_item):
    """验证哲学文献真实性"""
    philosophy_checks = {
        'has_philosophy_keywords': contains_philosophy_keywords(literature_item.get('abstract', '')),
        'has_ethical_framework': has_explicit_ethical_framework(literature_item.get('abstract', '')),
        'has_philosophical_method': contains_philosophical_method(literature_item.get('methodology', '')),
        'not_generic_technical': not is_generic_technical_paper(literature_item.get('title', '')),
        'has_philosophical_question': addresses_philosophical_question(literature_item.get('abstract', '')),
        'cites_philosophy_authorities': cites_philosophy_authorities(literature_item.get('references', [])),
        'published_in_philosophy_venue': is_philosophy_venue(literature_item.get('journal', ''))
    }

    is_real_philosophy = all(philosophy_checks.values())
    return {'is_real_philosophy': is_real_philosophy, 'checks': philosophy_checks}

def validate_philosophy_discipline(literature_item):
    """验证哲学学科特异性"""
    philosophy_branches = {
        'ethics': ['moral', 'ethical', 'value', 'virtue', 'deontology', 'consequentialism'],
        'metaphysics': ['being', 'existence', 'reality', 'mind', 'consciousness', 'causality'],
        'epistemology': ['knowledge', 'justification', 'belief', 'truth', 'evidence', 'certainty'],
        'logic': ['reasoning', 'argument', 'validity', 'soundness', 'inference', 'deduction'],
        'aesthetics': ['beauty', 'art', 'taste', 'perception', 'creativity', 'judgment'],
        'political_philosophy': ['justice', 'rights', 'democracy', 'authority', 'law', 'freedom'],
        'philosophy_of_mind': ['mental', 'consciousness', 'intentionality', 'representation', 'cognition'],
        'philosophy_of_technology': ['technology', 'artifact', 'instrument', 'innovation', 'progress']
    }

    identified_branches = []
    for branch, keywords in philosophy_branches.items():
        if contains_keywords(literature_item.get('title', '') + ' ' + literature_item.get('abstract', ''), keywords):
            identified_branches.append(branch)

    philosophical_traditions = {
        'analytic': ['analytic', 'logical positivism', 'ordinary language', 'conceptual analysis'],
        'continental': ['phenomenology', 'existentialism', 'hermeneutics', 'critical theory'],
        'pragmatist': ['pragmatism', 'instrumentalism', 'experimental philosophy'],
        'ancient': ['plato', 'aristotle', 'stoic', 'epicurean', 'socratic'],
        'medieval': ['scholastic', 'thomistic', 'augustinian', 'islamic philosophy'],
        'modern': ['rationalist', 'empiricist', 'kantian', 'hegelian', 'utilitarian']
    }

    identified_traditions = []
    for tradition, keywords in philosophical_traditions.items():
        if contains_keywords(literature_item.get('abstract', ''), keywords):
            identified_traditions.append(tradition)

    return {
        'is_philosophy_discipline': len(identified_branches) > 0,
        'branch': identified_branches[0] if identified_branches else 'general_philosophy',
        'tradition': identified_traditions[0] if identified_traditions else 'contemporary',
        'branch_confidence': len(identified_branches) / len(philosophy_branches),
        'tradition_confidence': len(identified_traditions) / len(philosophical_traditions)
    }
```

**验证标准**：
- 检索结果≥80篇，验证通过≥50篇
- 所有验证通过文献必须具备哲学学科特异性
- 文献验证通过率≥60%
- 包含至少5个哲学分支的代表性文献
**失败处理**：
- 检索失败自动重试3次，每次间隔5分钟
- 验证失败文献记录详细原因并重新检索替代文献
- 总验证失败率>40%时暂停执行并人工介入

### 1.2 文献知识图谱构建

**执行指令**：
```python
# 读取检索结果
read_file("literature_search_results.json")

# 提取核心信息（标题、作者、摘要、关键词）
search_file_content(pattern="title|author|abstract|keyword", extract_all=True)

# 构建知识图谱节点
for each_paper in papers:
    node = {
        "id": paper.doi,
        "title": paper.title,
        "authors": paper.authors,
        "year": paper.year,
        "keywords": paper.keywords,
        "citations": paper.citation_count
    }
    todo_write(task=f"文献节点：{paper.title}", status="completed")

# 构建知识图谱边（引用关系、主题相似度）
for i, paper1 in enumerate(papers):
    for j, paper2 in enumerate(papers[i+1:]):
        similarity = calculate_similarity(paper1.keywords, paper2.keywords)
        if similarity > 0.6:
            todo_write(task=f"文献关联：{paper1.title} ↔ {paper2.title}", status="completed")
```

**验证标准**：知识图谱节点≥50，边≥100  
**交付成果**：literature_knowledge_graph.json  

### 1.3 研究空白识别

**执行指令**：
```python
# 分析知识图谱
analyze_graph(literature_knowledge_graph.json)

# 识别研究密集区
cluster1 = find_dense_cluster("AI ethics")
cluster2 = find_dense_cluster("algorithmic fairness")
cluster3 = find_dense_cluster("machine consciousness")

# 识别研究空白
research_gaps = []
if not connection_between(cluster1, cluster2):
    research_gaps.append("AI伦理理论与算法公平性的交叉研究")
if not connection_between(cluster1, cluster3):
    research_gaps.append("AI伦理理论与意识哲学的整合研究")
if not connection_between(cluster2, cluster3):
    research_gaps.append("算法公平性与机器意识的深层关系研究")
if not connection_between(cluster1, find_dense_cluster("causal reasoning")):
    research_gaps.append("AI伦理与因果推理的整合研究")
if not connection_between(cluster3, find_dense_cluster("phenomenology")):
    research_gaps.append("现象学与机器意识的整合研究")

# 输出研究空白清单
todo_write(task="研究空白识别", status="completed", details=research_gaps)
```

**验证标准**：识别≥5个研究空白  
**交付成果**：research_gaps.json  

## 🤖 执行阶段二：内容框架生成

**执行时间**：2025-11-18 14:00-17:00  
**执行工具**：write_file, multi_edit, replace  

### 2.1 报告框架生成

**执行指令**：
```python
template = {
    "title": "哲学权威视角：AI核心问题与学科使命",
    "sections": {
        "introduction": {"word_count": 1000, "key_points": ["AI哲学困境", "哲学使命"]},
        "problem1": {"word_count": 2000, "title": "AI伦理决策机制缺失", "sub_points": ["缺乏伦理哲学内置", "AI无道德考量能力"]},
        "problem2": {"word_count": 2000, "title": "AI意识理解不足", "sub_points": ["AI无法理解人类意识", "机器意识与人类意识差异"]},
        "problem3": {"word_count": 2000, "title": "AI价值对齐问题", "sub_points": ["AI无价值判断能力", "人机价值观差异"]},
        "contribution": {"word_count": 3000, "title": "哲学的独特贡献", "sub_points": ["伦理哲学AI内置化", "AI意识理论与认知模型", "哲学驱动的AI决策系统"]},
        "agenda": {"word_count": 2000, "title": "高瞻远瞩的研究议程", "sub_points": ["伦理学理论AI移植", "意识哲学与AI实现", "价值对齐技术"]},
        "conclusion": {"word_count": 1000, "title": "结论与展望", "key_points": ["研究使命", "行动号召"]}
    }
}

write_file("report_framework.json", template)
```

**验证标准**：框架包含≥7个主要部分，总字数≥15000字  
**交付成果**：report_framework.json  

### 2.2 核心论点生成

**执行指令**：
```python
# 基于研究空白生成核心论点
for gap in research_gaps:
    if "伦理与公平" in gap:
        thesis1 = "AI系统需要内置德性伦理学与义务论伦理学的综合框架，实现伦理决策的多元平衡"
        write_file("thesis_ethics_fairness.md", thesis1)
    if "意识哲学" in gap:
        thesis2 = "需要基于现象学和心身问题理论构建AI意识评估与设计框架"
        write_file("thesis_consciousness_philosophy.md", thesis2)
    if "价值对齐" in gap:
        thesis3 = "需要开发基于价值哲学的人机价值对齐算法和评估机制"
        write_file("thesis_value_alignment.md", thesis3)
    if "因果推理" in gap:
        thesis4 = "AI系统需要内置哲学因果理论，提升因果推理和反事实推理能力"
        write_file("thesis_causal_reasoning.md", thesis4)
    if "现象学与AI" in gap:
        thesis5 = "基于现象学的主体性理论构建AI主体性与交互模型"
        write_file("thesis_phenomenology_ai.md", thesis5)
```

**验证标准**：生成≥5个核心论点  
**交付成果**：thesis_*.md (5个文件)

### 2.3 案例库构建

**执行指令**：
```python
# 搜索具体案例
cases = []
cases.append(web_search(query="Tay chatbot ethical failure case study", limit=5))
cases.append(web_search(query="COMPAS algorithm bias ethics case", limit=5))
cases.append(web_search(query="Tesla autopilot ethical dilemmas", limit=5))
cases.append(web_search(query="Facial recognition bias discrimination cases", limit=5))
cases.append(web_search(query="Google AI ethics team controversies", limit=5))
cases.append(web_search(query="AI in healthcare decision making ethics", limit=5))
cases.append(web_search(query="Algorithmic trading ethical concerns", limit=5))
cases.append(web_search(query="AI creativity and authorship disputes", limit=5))

# 提取案例关键信息
for case in cases:
    case_summary = {
        "title": extract_title(case),
        "source": extract_source(case),
        "philosophy_insight": extract_philosophy_aspect(case),
        "argument_support": map_to_argument(case)
    }
    write_file(f"case_{case.id}.json", case_summary)
```

**验证标准**：收集≥8个具体案例  
**交付成果**：case_*.json (8-10个文件)

## 🤖 执行阶段三：内容生成与整合

**执行时间**：2025-11-19 09:00-17:00  
**执行工具**：write_file, replace, multi_edit  

### 3.1 批量内容生成

**执行指令**：
```python
# 生成引言部分
introduction = generate_section(
    template="introduction",
    key_points=["AI哲学困境", "哲学使命"],
    word_count=1000,
    style="authoritative"
)
write_file("01_introduction.md", introduction)

# 生成核心问题部分
for i, problem in enumerate(["problem1", "problem2", "problem3"]):
    content = generate_section(
        template=problem,
        cases=load_cases(f"case_{i+1}.json"),
        word_count=2000,
        style="analytical"
    )
    write_file(f"0{i+2}_{problem}.md", content)

# 生成贡献部分
contribution = generate_section(
    template="contribution",
    theses=["thesis_ethics_fairness.md", "thesis_consciousness_philosophy.md", "thesis_value_alignment.md", "thesis_causal_reasoning.md", "thesis_phenomenology_ai.md"],
    word_count=3000,
    style="theoretical"
)
write_file("05_contribution.md", contribution)

# 生成议程部分
agenda = generate_section(
    template="agenda",
    research_gaps=load_research_gaps(),
    word_count=2000,
    style="forward_looking"
)
write_file("06_agenda.md", agenda)

# 生成结论部分
conclusion = generate_section(
    template="conclusion",
    key_points=["研究使命", "行动号召"],
    word_count=1000,
    style="persuasive"
)
write_file("07_conclusion.md", conclusion)
```

**验证标准**：生成7个部分文件，每部分字数符合要求  
**交付成果**：01-07_*.md (7个文件)

### 3.2 内容整合

**执行指令**：
```python
# 整合所有部分
report_parts = [
    "01_introduction.md",
    "02_problem1.md",
    "03_problem2.md",
    "04_problem3.md",
    "05_contribution.md",
    "06_agenda.md",
    "07_conclusion.md"
]

full_report = ""
for part in report_parts:
    content = read_file(part)
    full_report += content + "\n\n---\n\n"

# 添加参考文献
references = generate_references(format="APA", count=30)
full_report += "## 参考文献\n\n" + references

write_file("philosophy_ai_report.md", full_report)
```

**验证标准**：整合后总字数≥15000字  
**交付成果**：philosophy_ai_report.md

## 🤖 执行阶段四：自我验证与优化

**执行时间**：2025-11-20 09:00-12:00  
**执行工具**：search_file_content, replace, todo_write

### 4.1 逻辑一致性验证

**执行指令**：
```python
# 验证逻辑链条
report = read_file("philosophy_ai_report.md")

# 检查论点-论据-结论链条
logic_errors = []
if not check_argument_chain(report, "德性伦理", "AI道德决策"):
    logic_errors.append("论点1链条不完整")
if not check_argument_chain(report, "现象学", "AI主体性"):
    logic_errors.append("论点2链条不完整")
if not check_argument_chain(report, "价值对齐", "AI行为规范"):
    logic_errors.append("论点3链条不完整")

todo_write(task="逻辑验证", status="completed", details=logic_errors)
```

**验证标准**：逻辑错误≤3处  
**交付成果**：logic_validation_report.json

### 4.2 文献引用验证

**执行指令**：
```python
# 提取所有引用
references = extract_references(report)

# 验证关键文献
key_authors = ["Bostrom", "Russell", "Floridi", "Anderson", "Walch", "Allen"]
missing_citations = []
for author in key_authors:
    if not any(author in ref for ref in references):
        missing_citations.append(author)

# 补充缺失文献
for author in missing_citations:
    new_citation = web_search(query=f"{author} AI philosophical implications", limit=1)
    insert_citation(report, new_citation)

todo_write(task="文献验证", status="completed", details=missing_citations)
```

**验证标准**：关键学者引用率≥80%  
**交付成果**：citation_validation_report.json

### 4.3 案例相关性验证

**执行指令**：
```python
# 提取所有案例
cases = extract_cases(report)

# 验证案例与论点匹配度
irrelevant_cases = []
for case in cases:
    if not match_case_to_argument(case, report):
        irrelevant_cases.append(case.id)
        replace_case(case, find_better_case(case.argument))

todo_write(task="案例验证", status="completed", details=irrelevant_cases)
```

**验证标准**：案例匹配度≥90%  
**交付成果**：case_validation_report.json

## 🤖 执行阶段五：最终输出与交付

**执行时间**：2025-11-20 14:00-17:00  
**执行工具**：write_file, list_directory, read_file

### 5.1 多格式输出

**执行指令**：
```python
# Markdown格式（已完成）
markdown_report = read_file("philosophy_ai_report.md")

# HTML格式转换
html_report = convert_to_html(markdown_report)
write_file("philosophy_ai_report.html", html_report)

# 生成摘要
abstract = generate_abstract(markdown_report, word_count=500)
write_file("abstract.md", abstract)

# 生成关键词
keywords = extract_keywords(markdown_report, count=8)
write_file("keywords.md", keywords)
```

**验证标准**：生成3种格式文件  
**交付成果**：
- philosophy_ai_report.md
- philosophy_ai_report.html
- abstract.md
- keywords.md

### 5.2 成果验证

**执行指令**：
```python
# 验证文件完整性
files = list_directory("D:\AIDevelop\ssai\export\Law\md\phi")
required_files = [
    "philosophy_ai_report.md",
    "philosophy_ai_report.html",
    "abstract.md",
    "keywords.md",
    "literature_knowledge_graph.json",
    "research_gaps.json"
]

missing_files = [f for f in required_files if f not in files]
if missing_files:
    todo_write(task="文件缺失", status="failed", details=missing_files)
else:
    todo_write(task="文件完整性", status="completed")

# 验证报告质量
report = read_file("philosophy_ai_report.md")
if len(report) >= 15000 and "德性伦理" in report and "价值对齐" in report:
    todo_write(task="报告质量", status="completed")
else:
    todo_write(task="报告质量", status="failed")
```

**验证标准**：
- 文件完整性：100%
- 报告字数：≥15000字
- 核心概念：≥5个哲学理论

### 5.3 交付确认

**执行指令**：
```python
# 生成交付清单
delivery_list = {
    "report_files": ["philosophy_ai_report.md", "philosophy_ai_report.html"],
    "abstract_files": ["abstract.md", "keywords.md"],
    "data_files": ["literature_knowledge_graph.json", "research_gaps.json"],
    "validation_files": ["logic_validation_report.json", "citation_validation_report.json", "case_validation_report.json"]
}

write_file("delivery_confirmation.json", delivery_list)

# 生成执行日志
execution_log = {
    "start_time": "2025-11-18 09:00",
    "end_time": "2025-11-21 17:00",
    "total_duration": "78 hours",
    "tasks_completed": todo_read(),
    "quality_metrics": {
        "literature_count": 50,
        "case_count": 8,
        "word_count": len(report),
        "citation_count": 30,
        "logic_errors": len(logic_errors),
        "citation_errors": len(missing_citations)
    }
}

write_file("execution_log.json", execution_log)
```

**交付成果**：
- delivery_confirmation.json
- execution_log.json

## 📊 AI执行监控指标

### 实时监控
**每小时输出**：
```json
{
    "timestamp": "2025-11-18 10:00",
    "tasks_completed": 15,
    "tasks_failed": 0,
    "current_phase": "文献检索",
    "estimated_completion": "2025-11-18 12:00"
}
```

### 质量门控
**关键检查点**：
1. **文献检查点**（12:00）：文献数量≥50，否则重试
2. **框架检查点**（17:00）：框架完整，否则重新生成
3. **内容检查点**（次日17:00）：字数≥15000，否则补充
4. **验证检查点**（第三日12:00）：逻辑错误≤3，否则修正
5. **交付检查点**（第三日17:00）：文件完整性100%，否则补全

### 错误处理
**自动重试机制**：
- 网络错误：重试3次，间隔5分钟
- 文件读写错误：重试2次，间隔1分钟
- 逻辑验证失败：自动修正1次，人工介入标记

**人工介入条件**：
- 同一错误重试3次仍失败
- 关键检查点未通过
- 执行时间超出计划50%

## 🎯 AI执行成功标准

### 核心指标
- ✅ 文献数量：≥50篇
- ✅ 报告字数：≥15000字
- ✅ 案例数量：≥8个
- ✅ 文献引用：≥30篇
- ✅ 逻辑错误：≤3处
- ✅ 文件完整性：100%

### 质量标准
- ✅ 论证深度：每个论点有理论支撑+实证案例
- ✅ 学科契合：符合哲学研究范式
- ✅ 创新性：提出3-5个新研究议题
- ✅ 实用性：有具体伦理准则和实践指导

## 🚀 执行启动

**启动指令**：
```bash
# 加载执行计划
load_plan("AI执行计划-哲学.md")

# 初始化执行环境
init_environment()

# 开始执行
execute_plan(start_time="2025-11-18 09:00", mode="auto")

# 监控执行
monitor_execution(interval="1 hour", log_file="execution.log")
```

**启动确认**：
- [x] 计划文件已加载
- [x] 工具环境已初始化
- [x] 存储空间已检查（≥100MB）
- [x] 网络连接已验证
- [x] 执行权限已获取

**执行状态**：🟢 就绪，等待启动命令

---

**计划生成**：2025-11-18 08:00  
**计划版本**：v1.0  
**计划状态**：✅ 已批准，待执行