# 量子神经网络模拟器
class QuantumNeuralNetwork:
    def __init__(self, num_qubits=8, classical_layers=3):
        self.num_qubits = num_qubits
        self.classical_layers = classical_layers
        self.quantum_circuit = self._initialize_quantum_circuit()
        self.classical_network = self._initialize_classical_network()
        
    def _initialize_quantum_circuit(self):
        """初始化量子电路"""
        circuit = {
            "qubits": self.num_qubits,
            "gates": ["Hadamard", "CNOT", "RX", "RY", "RZ", "Toffoli"],
            "depth": 50,
            "entanglement": "full",
            "measurement_basis": ["X", "Y", "Z"]
        }
        return circuit
    
    def _initialize_classical_network(self):
        """初始化经典神经网络"""
        network = {
            "layers": [
                {"type": "Dense", "units": 128, "activation": "relu"},
                {"type": "Dense", "units": 64, "activation": "relu"},
                {"type": "Dense", "units": 32, "activation": "relu"},
                {"type": "Output", "units": 10, "activation": "softmax"}
            ],
            "optimizer": "Adam",
            "loss": "categorical_crossentropy"
        }
        return network
    
    def hybrid_training(self, dataset, epochs=100):
        """混合训练过程"""
        training_log = {
            "phase1": {"量子预处理": "数据编码到量子态", "时间": "2-4小时"},
            "phase2": {"量子特征提取": "量子电路执行", "时间": "1-2小时"},
            "phase3": {"经典网络训练": "梯度下降优化", "时间": "3-6小时"},
            "phase4": {"量子-经典反馈": "参数同步更新", "时间": "1-2小时"}
        }
        
        results = {
            "准确率提升": "比纯经典网络高15-25%",
            "训练时间减少": "比纯量子网络快10倍",
            "能耗降低": "减少40-60%",
            "泛化能力": "提升30%"
        }
        
        return training_log, results
    
    def quantum_speedup_analysis(self, problem_size):
        """量子加速分析"""
        speedup_data = {
            "小规模问题": {"经典时间": "10分钟", "量子时间": "2分钟", "加速比": "5x"},
            "中规模问题": {"经典时间": "2小时", "量子时间": "15分钟", "加速比": "8x"},
            "大规模问题": {"经典时间": "24小时", "量子时间": "1.5小时", "加速比": "16x"},
            "超大规模问题": {"经典时间": "7天", "量子时间": "6小时", "加速比": "28x"}
        }
        
        return speedup_data.get(problem_size, {"加速比": "需具体分析"})

# 使用示例
qnn = QuantumNeuralNetwork(num_qubits=12, classical_layers=4)
print("量子神经网络配置:")
print(f"量子比特数: {qnn.num_qubits}")
print(f"量子门类型: {', '.join(qnn.quantum_circuit['gates'][:3])}")
print(f"经典网络层数: {len(qnn.classical_network['layers'])}")

training_log, results = qnn.hybrid_training("ImageNet子集", epochs=50)
print("\n混合训练结果:")
for key, value in results.items():
    print(f"{key}: {value}")

print("\n量子加速分析:")
speedup = qnn.quantum_speedup_analysis("大规模问题")
for key, value in speedup.items():
    print(f"{key}: {value}")

# 神经形态芯片模拟器
class NeuroMorphicChip:
    def __init__(self, num_neurons=1000000, num_synapses=10000000):
        self.num_neurons = num_neurons
        self.num_synapses = num_synapses
        self.architecture = self._define_architecture()
        self.learning_rules = self._define_learning_rules()
        
    def _define_architecture(self):
        """定义芯片架构"""
        architecture = {
            "神经元类型": ["兴奋性", "抑制性", "调节性"],
            "突触类型": ["电突触", "化学突触", "可塑性突触"],
            "连接模式": ["全连接", "小世界网络", "层级结构"],
            "脉冲编码": ["频率编码", "时间编码", "群体编码"],
            "功耗特征": {
                "静态功耗": "5mW/cm²",
                "动态功耗": "50mW/cm² @ 1GHz",
                "能效比": "100TOPS/W"
            }
        }
        return architecture
    
    def _define_learning_rules(self):
        """定义学习规则"""
        rules = {
            "STDP": {
                "描述": "脉冲时间依赖可塑性",
                "公式": "Δw = A⁺e^(-Δt/τ⁺) if Δt>0 else A⁻e^(Δt/τ⁻)",
                "参数": {"A⁺": 0.01, "A⁻": -0.012, "τ⁺": 20, "τ⁻": 20}
            },
            "Hebbian": {
                "描述": "赫布学习规则",
                "公式": "Δw = η * x * y",
                "参数": {"η": 0.001}
            },
            "BCM": {
                "描述": "BCM理论",
                "公式": "Δw = φ(θ, y) * x",
                "参数": {"θ": "滑动阈值"}
            }
        }
        return rules
    
    def simulate_learning(self, task, duration_ms=1000):
        """模拟学习过程"""
        simulation = {
            "任务类型": task,
            "模拟时长": f"{duration_ms}ms",
            "脉冲数量": self._calculate_spike_count(task, duration_ms),
            "突触变化": self._calculate_synapse_changes(task),
            "能耗分析": self._calculate_energy_consumption(duration_ms)
        }
        
        # 学习效果评估
        performance = {
            "模式识别准确率": "98.2%",
            "学习速度": "比传统AI快100倍",
            "能耗效率": "比GPU低1000倍",
            "鲁棒性": "噪声容忍度提升40%"
        }
        
        return simulation, performance
    
    def _calculate_spike_count(self, task, duration_ms):
        """计算脉冲数量"""
        spike_rates = {
            "图像识别": 50,  # Hz
            "语音识别": 100,
            "运动控制": 200,
            "强化学习": 150
        }
        rate = spike_rates.get(task, 80)
        return int(rate * self.num_neurons * duration_ms / 1000)
    
    def _calculate_synapse_changes(self, task):
        """计算突触变化"""
        changes = {
            "图像识别": "12.5%突触权重更新",
            "语音识别": "8.3%突触权重更新",
            "运动控制": "15.7%突触权重更新",
            "强化学习": "20.1%突触权重更新"
        }
        return changes.get(task, "10%突触权重更新")
    
    def _calculate_energy_consumption(self, duration_ms):
        """计算能耗"""
        static_power = 0.005  # W/cm²
        dynamic_power = 0.05  # W/cm²
        area = 1.0  # cm²
        
        static_energy = static_power * area * duration_ms / 1000
        dynamic_energy = dynamic_power * area * duration_ms / 1000
        
        return {
            "静态能耗": f"{static_energy:.3f}J",
            "动态能耗": f"{dynamic_energy:.3f}J",
            "总能耗": f"{static_energy + dynamic_energy:.3f}J"
        }
    
    def compare_with_traditional_ai(self, task="图像识别"):
        """与传统AI对比"""
        comparison = {
            "能耗": {"神经形态": "0.05J", "传统AI": "50J", "优势": "1000x"},
            "延迟": {"神经形态": "5ms", "传统AI": "50ms", "优势": "10x"},
            "学习样本数": {"神经形态": "100", "传统AI": "10000", "优势": "100x"},
            "硬件成本": {"神经形态": "$100", "传统AI": "$10000", "优势": "100x"}
        }
        
        return comparison

# 使用示例
neurochip = NeuroMorphicChip(num_neurons=500000, num_synapses=5000000)
print("神经形态芯片配置:")
print(f"神经元数量: {neurochip.num_neurons:,}")
print(f"突触数量: {neurochip.num_synapses:,}")
print(f"支持的学习规则: {', '.join(neurochip.learning_rules.keys())}")

simulation, performance = neurochip.simulate_learning("图像识别", duration_ms=500)
print("\n学习模拟结果:")
for key, value in simulation.items():
    print(f"{key}: {value}")

print("\n性能评估:")
for key, value in performance.items():
    print(f"{key}: {value}")

print("\n与传统AI对比:")
comparison = neurochip.compare_with_traditional_ai()
for metric, data in comparison.items():
    print(f"{metric}: 神经形态={data['神经形态']}, 传统AI={data['传统AI']}, 优势={data['优势']}")

# 多模态生成模型
class MultiModalGenerator:
    def __init__(self):
        self.modalities = ["text", "image", "audio", "video", "3d"]
        self.fusion_methods = self._define_fusion_methods()
        self.generation_capabilities = self._define_capabilities()
        
    def _define_fusion_methods(self):
        """定义融合方法"""
        methods = {
            "早期融合": {
                "描述": "在输入层融合",
                "优点": ["信息完整", "交互充分"],
                "缺点": ["计算量大", "对齐困难"]
            },
            "晚期融合": {
                "描述": "在输出层融合",
                "优点": ["模块化", "易于训练"],
                "缺点": ["信息损失", "协调困难"]
            },
            "混合融合": {
                "描述": "多层次融合",
                "优点": ["平衡性能", "灵活性强"],
                "缺点": ["设计复杂", "调参困难"]
            },
            "注意力融合": {
                "描述": "基于注意力的动态融合",
                "优点": ["自适应", "高效"],
                "缺点": ["训练不稳定", "需要大量数据"]
            }
        }
        return methods
    
    def _define_capabilities(self):
        """定义生成能力"""
        capabilities = {
            "text_to_image": {
                "分辨率": "4096x4096",
                "风格控制": ["写实", "卡通", "油画", "水彩"],
                "细节控制": ["高", "中", "低"],
                "生成时间": "2-5秒"
            },
            "image_to_text": {
                "描述长度": "50-500字",
                "情感分析": "支持",
                "物体识别": "1000+类别",
                "场景理解": "深度语义"
            },
            "audio_to_video": {
                "视频长度": "最长60秒",
                "帧率": "30fps",
                "分辨率": "1920x1080",
                "唇形同步": "准确率98%"
            },
            "cross_modal": {
                "文本+图像→视频": "支持",
                "音频+文本→图像": "支持",
                "视频→音频+文本": "支持",
                "3D+文本→视频": "支持"
            }
        }
        return capabilities
    
    def generate_content(self, source_modality, target_modality, input_data):
        """生成内容"""
        generation = {
            "输入模态": source_modality,
            "输出模态": target_modality,
            "输入描述": self._describe_input(input_data),
            "生成参数": self._get_generation_params(source_modality, target_modality),
            "质量评估": self._assess_quality(source_modality, target_modality)
        }
        
        # 生成示例
        examples = {
            ("text", "image"): "一只穿着宇航服的猫在月球上喝咖啡",
            ("image", "text"): "描述这幅画的场景和情感",
            ("audio", "video"): "根据音乐生成舞蹈视频",
            ("text", "3d"): "生成一个未来城市的3D模型"
        }
        
        example = examples.get((source_modality, target_modality), "多模态转换")
        
        return generation, example
    
    def _describe_input(self, input_data):
        """描述输入"""
        if isinstance(input_data, str):
            return f"文本输入: {input_data[:50]}..."
        elif isinstance(input_data, dict):
            return f"结构化数据: {list(input_data.keys())}"
        else:
            return "多媒体输入"
    
    def _get_generation_params(self, source, target):
        """获取生成参数"""
        params = {
            "temperature": 0.7,
            "top_p": 0.9,
            "guidance_scale": 7.5,
            "num_inference_steps": 50,
            "seed": 42
        }
        
        # 根据不同任务调整参数
        if source == "text" and target == "image":
            params.update({"guidance_scale": 8.0, "num_inference_steps": 75})
        elif source == "audio" and target == "video":
            params.update({"temperature": 0.8, "num_inference_steps": 100})
        
        return params
    
    def _assess_quality(self, source, target):
        """评估质量"""
        quality_metrics = {
            "text_to_image": {
                "图像质量": "FID: 8.2",
                "语义一致性": "CLIP Score: 0.85",
                "多样性": "LPIPS: 0.65"
            },
            "image_to_text": {
                "BLEU-4": "0.45",
                "ROUGE-L": "0.52",
                "CIDEr": "1.25"
            },
            "audio_to_video": {
                "PSNR": "32.5 dB",
                "SSIM": "0.92",
                "唇形同步准确率": "97.8%"
            }
        }
        
        key = f"{source}_to_{target}"
        return quality_metrics.get(key, {"质量评估": "需具体测试"})
    
    def evaluate_fusion_performance(self):
        """评估融合性能"""
        evaluation = {
            "单模态基准": {
                "文本生成": "BLEU-4: 0.48",
                "图像生成": "FID: 9.5",
                "音频生成": "MOS: 4.2",
                "视频生成": "FVD: 125"
            },
            "多模态融合": {
                "文本+图像": "FID: 7.8 (提升18%)",
                "音频+视频": "FVD: 98 (提升22%)",
                "全模态": "综合得分: 0.85"
            },
            "创新指标": {
                "跨模态一致性": "0.92",
                "内容多样性": "0.78",
                "创意新颖性": "0.81"
            }
        }
        
        return evaluation

# 使用示例
generator = MultiModalGenerator()
print("多模态生成模型能力:")
for capability, details in generator.generation_capabilities.items():
    print(f"\n{capability}:")
    for key, value in details.items():
        if isinstance(value, list):
            print(f"  {key}: {', '.join(value[:2])}...")
        else:
            print(f"  {key}: {value}")

print("\n文本到图像生成示例:")
generation, example = generator.generate_content("text", "image", "一只穿着宇航服的猫在月球上喝咖啡")
for key, value in generation.items():
    print(f"{key}: {value}")
print(f"示例: {example}")

print("\n融合性能评估:")
evaluation = generator.evaluate_fusion_performance()
for category, metrics in evaluation.items():
    print(f"\n{category}:")
    for metric, value in metrics.items():
        print(f"  {metric}: {value}")

# 自监督学习实验框架
class SelfSupervisedExperiment:
    def __init__(self):
        self.augmentation_methods = self._define_augmentations()
        self.contrastive_losses = self._define_losses()
        self.evaluation_metrics = self._define_metrics()
        
    def _define_augmentations(self):
        """定义数据增强方法"""
        augmentations = {
            "图像": ["随机裁剪", "颜色抖动", "高斯模糊", "随机旋转", "CutMix", "MixUp"],
            "文本": ["随机掩码", "词序打乱", "同义词替换", "回译", "句子删除"],
            "音频": ["时间拉伸", "音高变换", "背景噪声", "时间掩码", "频率掩码"],
            "视频": ["帧采样", "时间裁剪", "空间裁剪", "速度变换", "颜色变换"]
        }
        return augmentations
    
    def _define_losses(self):
        """定义对比损失函数"""
        losses = {
            "InfoNCE": {
                "公式": "L = -log(exp(sim(z_i, z_j)/τ) / Σ exp(sim(z_i, z_k)/τ))",
                "温度参数": "τ = 0.07",
                "优点": ["理论完备", "实践有效"],
                "缺点": ["需要负样本", "计算量大"]
            },
            "SimCLR": {
                "公式": "类似InfoNCE，加强数据增强",
                "温度参数": "τ = 0.5",
                "优点": ["简单有效", "可扩展性强"],
                "缺点": ["批大小敏感", "内存需求大"]
            },
            "BYOL": {
                "公式": "L = 2 - 2·<z_i, z_j>/(||z_i||·||z_j||)",
                "温度参数": "无",
                "优点": ["无需负样本", "训练稳定"],
                "缺点": ["需要动量编码器", "可能崩溃"]
            },
            "SwAV": {
                "公式": "基于聚类的对比学习",
                "温度参数": "τ = 0.1",
                "优点": ["在线聚类", "效率高"],
                "缺点": ["聚类数敏感", "实现复杂"]
            }
        }
        return losses
    
    def _define_metrics(self):
        """定义评估指标"""
        metrics = {
            "线性评估": {
                "ImageNet准确率": "Top-1: 78.5%, Top-5: 94.2%",
                "下游任务迁移": "平均提升15-25%",
                "少样本学习": "1-shot准确率: 65.3%"
            },
            "表征质量": {
                "对齐性": "0.92",
                "均匀性": "0.85",
                "可解释性": "0.78"
            },
            "计算效率": {
                "训练时间": "比监督学习快3倍",
                "数据效率": "仅需10%标注数据",
                "推理速度": "与监督模型相当"
            }
        }
        return metrics
    
    def run_experiment(self, dataset, method="SimCLR", epochs=100):
        """运行实验"""
        experiment = {
            "数据集": dataset,
            "方法": method,
            "训练轮数": epochs,
            "硬件配置": "8×A100 GPU, 512GB内存",
            "训练时间": f"{epochs * 2}小时"
        }
        
        # 实验结果
        results = {
            "预训练效果": self._get_pretrain_results(method, dataset),
            "下游任务": self._get_downstream_results(method),
            "消融实验": self._get_ablation_study(method),
            "创新发现": self._get_innovations(method)
        }
        
        return experiment, results
    
    def _get_pretrain_results(self, method, dataset):
        """获取预训练结果"""
        results_map = {
            "SimCLR": {
                "ImageNet": "Top-1: 76.5%",
                "CIFAR-100": "Top-1: 82.3%",
                "Places365": "Top-1: 58.7%"
            },
            "BYOL": {
                "ImageNet": "Top-1: 77.8%",
                "CIFAR-100": "Top-1: 83.1%",
                "Places365": "Top-1: 59.2%"
            },
            "SwAV": {
                "ImageNet": "Top-1: 78.5%",
                "CIFAR-100": "Top-1": "83.7%",
                "Places365": "Top-1: "59.8%"
            }
        }
        
        return results_map.get(method, {}).get(dataset, "待测试")
    
    def _get_downstream_results(self, method):
        """获取下游任务结果"""
        tasks = {
            "目标检测": {"mAP": "45.2", "提升": "+3.5%"},
            "语义分割": {"mIoU": "52.8", "提升": "+4.2%"},
            "实例分割": {"AP": "38.7", "提升": "+3.8%"},
            "姿态估计": ["PCK@0.2: 88.5", "提升: +2.7%"]
        }
        
        # 不同方法的表现差异
        method_boost = {"SimCLR": 1.0, "BYOL": 1.05, "SwAV": 1.08}
        boost = method_boost.get(method, 1.0)
        
        boosted_tasks = {}
        for task, metrics in tasks.items():
            boosted_tasks[task] = {}
            for key, value in metrics.items():
                if "提升" in key:
                    boosted_tasks[task][key] = value
                else:
                    # 应用提升系数
                    if ":" in value:
                        metric_name, metric_value = value.split(":")
                        try:
                            num_value = float(metric_value.strip())
                            boosted_value = num_value * boost
                            boosted_tasks[task][key] = f"{metric_name}: {boosted_value:.1f}"
                        except:
                            boosted_tasks[task][key] = value
                    else:
                        boosted_tasks[task][key] = value
        
        return boosted_tasks
    
    def _get_ablation_study(self, method):
        """获取消融实验结果"""
        studies = {
            "数据增强": {
                "基础增强": "Top-1: 70.2%",
                "+颜色抖动": "Top-1: 72.5% (+2.3%)",
                "+随机裁剪": "Top-1: 74.1% (+1.6%)",
                "+全部增强": "Top-1: 76.5% (+2.4%)"
            },
            "损失函数": {
                "InfoNCE": "Top-1: 75.8%",
                "NT-Xent": "Top-1: 76.2% (+0.4%)",
                "改良版本": "Top-1: 76.5% (+0.3%)"
            },
            "训练策略": {
                "标准训练": "Top-1: 74.3%",
                "+学习率预热": "Top-1: 75.1% (+0.8%)",
                "+梯度裁剪": "Top-1: 75.6% (+0.5%)",
                "+全部策略": "Top-1: 76.5% (+0.9%)"
            }
        }
        
        return studies
    
    def _get_innovations(self, method):
        """获取创新发现"""
        innovations = {
            "SimCLR": [
                "大批次训练的关键性",
                "数据增强组合的重要性",
                "投影头设计的优化",
                "温度参数的敏感度分析"
            ],
            "BYOL": [
                "无需负样本的可行性",
                "动量编码器的稳定性",
                "预测头的设计原则",
                "训练崩溃的预防机制"
            ],
            "SwAV": [
                "在线聚类的有效性",
                "多裁剪策略的优势",
                "原型向量的学习",
                " sinkhorn算法的应用"
            ]
        }
        
        return innovations.get(method, ["方法特定的创新发现"])

# 使用示例
experiment = SelfSupervisedExperiment()
print("自监督学习实验框架:")
print(f"数据增强方法: {len(experiment.augmentation_methods['图像'])}种图像增强")
print(f"对比损失函数: {', '.join(experiment.contrastive_losses.keys())}")

exp_config, results = experiment.run_experiment("ImageNet", method="SwAV", epochs=200)
print("\n实验配置:")
for key, value in exp_config.items():
    print(f"{key}: {value}")

print("\n实验结果 - 预训练效果:")
for dataset, accuracy in results["预训练效果"].items():
    print(f"{dataset}: {accuracy}")

print("\n实验结果 - 下游任务:")
for task, metrics in results["下游任务"].items():
    print(f"{task}: {metrics}")

# 联邦学习隐私实验
class FederatedPrivacyExperiment:
    def __init__(self):
        self.privacy_techniques = self._define_techniques()
        self.attack_methods = self._define_attacks()
        self.evaluation_framework = self._define_evaluation()
        
    def _define_techniques(self):
        """定义隐私保护技术"""
        techniques = {
            "差分隐私": {
                "原理": "添加噪声保护个体数据",
                "参数": ["ε-差分隐私", "δ-松弛项"],
                "实现": ["高斯噪声", "拉普拉斯噪声", "指数机制"],
                "优点": ["理论保证", "易于实现"],
                "缺点": ["效用损失", "噪声累积"]
            },
            "同态加密": {
                "原理": "密文上直接计算",
                "方案": ["Paillier", "BFV", "CKKS"],
                "性能": ["计算开销大", "通信开销大"],
                "优点": ["强安全性", "精确计算"],
                "缺点": ["效率低", "实现复杂"]
            },
            "安全多方计算": {
                "原理": "多方协同计算不泄露输入",
                "协议": ["GMW", "BGW", "SPDZ"],
                "设置": ["诚实多数", "恶意安全"],
                "优点": ["通用性", "强安全"],
                "缺点": ["通信复杂", "效率问题"]
            },
            "联邦平均": {
                "原理": "模型参数平均",
                "变体": ["FedAvg", "FedProx", "FedNova"],
                "隐私": ["参数泄露风险", "需要额外保护"],
                "优点": ["简单高效", "广泛使用"],
                "缺点": ["隐私较弱", "收敛问题"]
            }
        }
        return techniques
    
    def _define_attacks(self):
        """定义攻击方法"""
        attacks = {
            "成员推理攻击": {
                "目标": "判断数据是否在训练集中",
                "方法": ["影子模型", "置信度分析", "损失值分析"],
                "防御": ["差分隐私", "正则化", "模型压缩"]
            },
            "属性推理攻击": {
                "目标": "推断训练数据属性",
                "方法": ["属性分类器", "相关性分析", "模型反演"],
                "防御": ["属性隐藏", "噪声添加", "模型混淆"]
            },
            "模型窃取攻击": {
                "目标": "复制目标模型",
                "方法": ["查询攻击", "蒸馏攻击", "替代模型"],
                "防御": ["查询限制", "输出扰动", "水印技术"]
            },
            "数据重构攻击": {
                "目标": "重构原始训练数据",
                "方法": ["模型反演", "生成对抗", "梯度泄露"],
                "防御": ["梯度裁剪", "安全聚合", "同态加密"]
            }
        }
        return attacks
    
    def _define_evaluation(self):
        """定义评估框架"""
        evaluation = {
            "隐私度量": {
                "差分隐私预算": "ε = 1.0, δ = 1e-5",
                "成员推理准确率": "< 55% (随机水平)",
                "属性泄露率": "< 5%",
                "数据重构质量": "PSNR < 20dB"
            },
            "效用度量": {
                "模型准确率": "与中心化训练相差<3%",
                "收敛速度": "比中心化慢<2倍",
                "通信开销": "比中心化多<5倍",
                "计算开销": "比中心化多<10倍"
            },
            "安全等级": {
                "L1-基础防护": ["差分隐私", "梯度裁剪"],
                "L2-中级防护": ["安全聚合", "同态加密"],
                "L3-高级防护": ["安全多方计算", "可信执行环境"],
                "L4-军事级": ["全同态加密", "零知识证明"]
            }
        }
        return evaluation
    
    def run_privacy_experiment(self, scenario, technique, attack_strength="中等"):
        """运行隐私实验"""
        experiment = {
            "场景": scenario,
            "隐私技术": technique,
            "攻击强度": attack_strength,
            "参与方数量": self._get_participant_count(scenario),
            "数据分布": self._get_data_distribution(scenario)
        }
        
        # 实验结果
        results = {
            "隐私保护效果": self._evaluate_privacy(technique, attack_strength),
            "模型效用保持": self._evaluate_utility(technique, scenario),
            "系统开销分析": self._evaluate_overhead(technique),
            "攻防对抗结果": self._evaluate_attack_defense(technique, attack_strength)
        }
        
        return experiment, results
    
    def _get_participant_count(self, scenario):
        """获取参与方数量"""
        counts = {
            "医疗数据联邦": "10-100家医院",
            "金融风控联邦": "50-500家银行",
            "物联网设备联邦": "1000-10000台设备",
            "移动用户联邦": "百万级用户"
        }
        return counts.get(scenario, "10-100参与方")
    
    def _get_data_distribution(self, scenario):
        """获取数据分布"""
        distributions = {
            "医疗数据联邦": "非独立同分布，各医院专科不同",
            "金融风控联邦": "独立同分布，但数据量差异大",
            "物联网设备联邦": "高度非独立同分布，设备异构",
            "移动用户联邦": "独立同分布，用户行为相似"
        }
        return distributions.get(scenario, "独立同分布")
    
    def _evaluate_privacy(self, technique, attack_strength):
        """评估隐私保护效果"""
        # 不同技术的隐私保护强度
        privacy_strength = {
            "差分隐私": {"弱攻击": 0.95, "中等攻击": 0.85, "强攻击": 0.70},
            "同态加密": {"弱攻击": 0.99, "中等攻击": 0.98, "强攻击": 0.95},
            "安全多方计算": {"弱攻击": 0.99, "中等攻击": 0.99, "强攻击": 0.97},
            "联邦平均": {"弱攻击": 0.60, "中等攻击": 0.40, "强攻击": 0.20}
        }
        
        strength = privacy_strength.get(technique, {}).get(attack_strength, 0.50)
        
        return {
            "隐私保护强度": f"{strength:.2f}/1.0",
            "差分隐私预算": "ε=1.0" if technique == "差分隐私" else "不适用",
            "加密强度": "256位" if "加密" in technique else "无",
            "理论保证": "有" if technique in ["差分隐私", "同态加密", "安全多方计算"] else "无"
        }
    
    def _evaluate_utility(self, technique, scenario):
        """评估模型效用"""
        # 基准准确率
        baseline_acc = {
            "医疗数据联邦": "92.5%",
            "金融风控联邦": "88.3%",
            "物联网设备联邦": "85.7%",
            "移动用户联邦": "94.2%"
        }
        
        baseline = baseline_acc.get(scenario, "90.0%")
        
        # 不同技术的效用损失
        utility_loss = {
            "差分隐私": 0.03,  # 3%损失
            "同态加密": 0.01,  # 1%损失
            "安全多方计算": 0.02,  # 2%损失
            "联邦平均": 0.05   # 5%损失
        }
        
        loss = utility_loss.get(technique, 0.04)
        
        # 计算最终准确率
        try:
            base_acc = float(baseline.replace("%", ""))
            final_acc = base_acc * (1 - loss)
            accuracy = f"{final_acc:.1f}%"
        except:
            accuracy = f"{baseline} (估算)"
        
        return {
            "模型准确率": accuracy,
            "相对损失": f"{loss*100:.1f}%",
            "收敛轮数": self._get_convergence_rounds(technique),
            "稳定性": self._get_stability(technique)
        }
    
    def _get_convergence_rounds(self, technique):
        """获取收敛轮数"""
        rounds = {
            "差分隐私": "150-200轮",
            "同态加密": "200-300轮",
            "安全多方计算": "250-350轮",
            "联邦平均": "100-150轮"
        }
        return rounds.get(technique, "150-250轮")
    
    def _get_stability(self, technique):
        """获取稳定性"""
        stability = {
            "差分隐私": "中等（噪声影响）",
            "同态加密": "高（精确计算）",
            "安全多方计算": "高（协议保证）",
            "联邦平均": "低（客户端漂移）"
        }
        return stability.get(technique, "中等")
    
    def _evaluate_overhead(self, technique):
        """评估系统开销"""
        overhead = {
            "差分隐私": {
                "计算开销": "增加10-20%",
                "通信开销": "基本不变",
                "存储开销": "基本不变",
                "总延迟": "增加15-25%"
            },
            "同态加密": {
                "计算开销": "增加100-1000倍",
                "通信开销": "增加2-5倍",
                "存储开销": "增加3-10倍",
                "总延迟": "增加50-200倍"
            },
            "安全多方计算": {
                "计算开销": "增加50-200倍",
                "通信开销": "增加10-100倍",
                "存储开销": "增加5-20倍",
                "总延迟": "增加100-500倍"
            },
            "联邦平均": {
                "计算开销": "基本不变",
                "通信开销": "增加2-10倍",
                "存储开销": "基本不变",
                "总延迟": "增加5-20倍"
            }
        }
        
        return overhead.get(technique, {"各项开销": "需具体评估"})
    
    def _evaluate_attack_defense(self, technique, attack_strength):
        """评估攻防对抗"""
        defense_success = {
            "差分隐私": {"弱攻击": 0.95, "中等攻击": 0.85, "强攻击": 0.65},
            "同态加密": {"弱攻击": 0.99, "中等攻击": 0.97, "强攻击": 0.92},
            "安全多方计算": {"弱攻击": 0.99, "中等攻击": 0.98, "强攻击": 0.95},
            "联邦平均": {"弱攻击": 0.70, "中等攻击": 0.45, "强攻击": 0.20}
        }
        
        success_rate = defense_success.get(technique, {}).get(attack_strength, 0.50)
        
        return {
            "防御成功率": f"{success_rate*100:.1f}%",
            "攻击检测率": f"{(success_rate+0.1)*100:.1f}%",
            "恢复能力": "强" if success_rate > 0.8 else "中等" if success_rate > 0.6 else "弱",
            "建议组合": self._recommend_combination(technique)
        }
    
    def _recommend_combination(self, technique):
        """推荐技术组合"""
        combinations = {
            "差分隐私": "差分隐私 + 安全聚合 + 梯度裁剪",
            "同态加密": "同态加密 + 模型压缩 + 批量处理",
            "安全多方计算": "安全多方计算 + 差分隐私 + 可信执行环境",
            "联邦平均": "联邦平均 + 差分隐私 + 安全聚合"
        }
        return combinations.get(technique, "根据场景定制")

# 使用示例
privacy_experiment = FederatedPrivacyExperiment()
print("联邦学习隐私实验框架:")
print(f"隐私保护技术: {', '.join(privacy_experiment.privacy_techniques.keys())}")
print(f"攻击方法: {', '.join(privacy_experiment.attack_methods.keys())}")

exp_config, results = privacy_experiment.run_privacy_experiment(
    scenario="医疗数据联邦",
    technique="差分隐私",
    attack_strength="中等"
)

print("\n实验配置:")
for key, value in exp_config.items():
    print(f"{key}: {value}")

print("\n隐私保护效果:")
for metric, value in results["隐私保护效果"].items():
    print(f"{metric}: {value}")

print("\n模型效用保持:")
for metric, value in results["模型效用保持"].items():
    print(f"{metric}: {value}")

print("\n系统开销分析:")
for metric, value in results["系统开销分析"].items():
    print(f"{metric}: {value}")

# 实验数据分析工具
class ExperimentAnalyzer:
    def __init__(self):
        self.experiments = self._load_experiment_data()
        self.metrics = self._define_metrics()
        self.visualization_tools = self._define_visualization()
        
    def _load_experiment_data(self):
        """加载实验数据"""
        experiments = {
            "量子神经网络": {
                "准确率": 92.5,
                "训练时间": 4.2,  # 小时
                "能耗": 0.05,     # kWh
                "硬件成本": 150000,  # 美元
                "创新指数": 0.88
            },
            "神经形态芯片": {
                "准确率": 94.2,
                "训练时间": 0.5,   # 小时
                "能耗": 0.002,    # kWh
                "硬件成本": 5000,   # 美元
                "创新指数": 0.92
            },
            "多模态生成": {
                "准确率": 89.7,
                "训练时间": 12.5,  # 小时
                "能耗": 2.5,      # kWh
                "硬件成本": 80000,  # 美元
                "创新指数": 0.85
            },
            "自监督学习": {
                "准确率": 91.3,
                "训练时间": 8.3,   # 小时
                "能耗": 1.2,      # kWh
                "硬件成本": 60000,  # 美元
                "创新指数": 0.79
            },
            "联邦隐私": {
                "准确率": 88.5,
                "训练时间": 15.2,  # 小时
                "能耗": 0.8,      # kWh
                "硬件成本": 30000,  # 美元
                "创新指数": 0.83
            }
        }
        return experiments
    
    def _define_metrics(self):
        """定义评估指标"""
        metrics = {
            "技术成熟度": {
                "权重": 0.25,
                "计算": lambda exp: (exp["准确率"]/100 * 0.4 + 
                                   (1 - min(exp["训练时间"]/24, 1)) * 0.3 +
                                   (1 - min(exp["能耗"]/5, 1)) * 0.3) * 100
            },
            "经济效益": {
                "权重": 0.20,
                "计算": lambda exp: (1 - min(exp["硬件成本"]/200000, 1)) * 100
            },
            "创新价值": {
                "权重": 0.30,
                "计算": lambda exp: exp["创新指数"] * 100
            },
            "应用潜力": {
                "权重": 0.25,
                "计算": lambda exp: (exp["准确率"] * 0.6 + 
                                   exp["创新指数"] * 100 * 0.4) / 2
            }
        }
        return metrics
    
    def _define_visualization(self):
        """定义可视化工具"""
        tools = {
            "雷达图": "多维度对比",
            "柱状图": "单项指标对比",
            "折线图": "趋势分析",
            "散点图": "相关性分析",
            "热力图": "矩阵比较"
        }
        return tools
    
    def analyze_experiments(self):
        """分析所有实验"""
        analysis = {}
        
        for exp_name, exp_data in self.experiments.items():
            scores = {}
            total_score = 0
            
            for metric_name, metric_info in self.metrics.items():
                weight = metric_info["权重"]
                score = metric_info["计算"](exp_data)
                weighted_score = score * weight
                
                scores[metric_name] = {
                    "原始分": f"{score:.1f}",
                    "加权分": f"{weighted_score:.1f}"
                }
                total_score += weighted_score
            
            analysis[exp_name] = {
                "各项得分": scores,
                "总分": f"{total_score:.1f}",
                "排名": 0  # 稍后计算
            }
        
        # 计算排名
        sorted_exps = sorted(analysis.items(), 
                           key=lambda x: float(x[1]["总分"]), 
                           reverse=True)
        
        for rank, (exp_name, _) in enumerate(sorted_exps, 1):
            analysis[exp_name]["排名"] = rank
        
        return analysis
    
    def generate_comparison_report(self):
        """生成对比报告"""
        analysis = self.analyze_experiments()
        
        report = "AI前沿实验综合评估报告\n"
        report += "=" * 60 + "\n\n"
        
        report += "🏆 实验排名:\n"
        for exp_name, data in sorted(analysis.items(), 
                                   key=lambda x: x[1]["排名"]):
            report += f"{data['排名']}. {exp_name}: {data['总分']}分\n"
        
        report += "\n📊 详细分析:\n"
        for exp_name, data in analysis.items():
            report += f"\n{exp_name} (排名: {data['排名']}, 总分: {data['总分']}):\n"
            for metric, scores in data["各项得分"].items():
                report += f"  {metric}: {scores['原始分']} (加权: {scores['加权分']})\n"
        
        # 技术趋势分析
        report += "\n📈 技术趋势分析:\n"
        trends = {
            "量子计算": "处于早期阶段，潜力巨大但成本高",
            "神经形态": "快速发展期，能效优势明显",
            "多模态": "成熟应用期，创意价值突出",
            "自监督": "理论研究期，数据效率高",
            "联邦学习": "产业落地期，隐私需求强"
        }
        
        for tech, trend in trends.items():
            report += f"  • {tech}: {trend}\n"
        
        # 投资建议
        report += "\n💰 投资建议:\n"
        recommendations = {
            1: "神经形态芯片 - 短期回报高，应用广泛",
            2: "多模态生成 - 创意产业潜力大",
            3: "联邦隐私 - 合规需求增长快",
            4: "自监督学习 - 长期技术储备",
            5: "量子神经网络 - 高风险高回报"
        }
        
        for rank, rec in recommendations.items():
            report += f"  {rank}. {rec}\n"
        
        return report
    
    def identify_research_gaps(self):
        """识别研究空白"""
        gaps = [
            {
                "领域": "量子-经典混合算法",
                "现状": "理论初步，实践不足",
                "机会": "开发实用化混合算法",
                "难度": "高",
                "时间窗口": "3-5年"
            },
            {
                "领域": "神经形态芯片编程",
                "现状": "硬件领先，软件滞后",
                "机会": "建立完整软件生态",
                "难度": "中",
                "时间窗口": "2-3年"
            },
            {
                "领域": "多模态评估标准",
                "现状": "缺乏统一评估体系",
                "机会": "制定行业标准",
                "难度": "中",
                "时间窗口": "1-2年"
            },
            {
                "领域": "隐私-效用平衡",
                "现状": "两者往往对立",
                "机会": "开发平衡算法",
                "难度": "高",
                "时间窗口": "2-4年"
            },
            {
                "领域": "实验可复现性",
                "现状": "复现困难，标准不一",
                "机会": "建立开源基准",
                "难度": "低",
                "时间窗口": "1年"
            }
        ]
        
        return gaps

# 使用示例
analyzer = ExperimentAnalyzer()
print(analyzer.generate_comparison_report())

print("\n🔍 研究空白识别:")
gaps = analyzer.identify_research_gaps()
for i, gap in enumerate(gaps, 1):
    print(f"\n{i}. {gap['领域']}:")
    print(f"   现状: {gap['现状']}")
    print(f"   机会: {gap['机会']}")
    print(f"   难度: {gap['难度']}, 时间窗口: {gap['时间窗口']}")

# 技术转化分析
class TechnologyTransfer:
    def __init__(self):
        self.maturity_levels = self._define_maturity()
        self.conversion_paths = self._define_paths()
        self.success_factors = self._define_factors()
        
    def _define_maturity(self):
        """定义技术成熟度"""
        levels = {
            "TRL1": {"名称": "基础原理", "描述": "科学原理发现", "投资需求": "$1-10M"},
            "TRL2": {"名称": "技术概念", "描述": "应用概念提出", "投资需求": "$10-50M"},
            "TRL3": {"名称": "实验验证", "描述": "关键功能验证", "投资需求": "$50-100M"},
            "TRL4": {"名称": "实验室原型", "描述": "实验室环境验证", "投资需求": "$100-200M"},
            "TRL5": {"名称": "模拟环境", "描述": "相关环境验证", "投资需求": "$200-500M"},
            "TRL6": {"名称": "工程原型", "描述": "工程环境演示", "投资需求": "$500M-1B"},
            "TRL7": {"名称": "系统原型", "描述": "操作环境演示", "投资需求": "$1-2B"},
            "TRL8": {"名称": "完成认证", "描述": "系统完成认证", "投资需求": "$2-5B"},
            "TRL9": {"名称": "实际应用", "描述": "成功任务运行", "投资需求": "$5B+"}
        }
        return levels
    
    def _define_paths(self):
        """定义转化路径"""
        paths = {
            "学术转化": {
                "步骤": ["论文发表", "专利申请", "技术许可", "初创公司"],
                "时间": "3-5年",
                "成功率": "10-20%",
                "典型案例": ["Google Brain", "OpenAI"]
            },
            "产业合作": {
                "步骤": ["联合研究", "技术转让", "产品集成", "市场推广"],
                "时间": "2-4年",
                "成功率": "30-50%",
                "典型案例": ["MIT-IBM Watson", "Stanford-Google"]
            },
            "创业孵化": {
                "步骤": ["概念验证", "团队组建", "天使投资", "产品开发"],
                "时间": "1-3年",
                "成功率": "5-15%",
                "典型案例": ["DeepMind", "Anthropic"]
            },
            "开源生态": {
                "步骤": ["代码开源", "社区建设", "生态发展", "商业支持"],
                "时间": "2-5年",
                "成功率": "20-40%",
                "典型案例": ["TensorFlow", "PyTorch"]
            }
        }
        return paths
    
    def _define_factors(self):
        """定义成功因素"""
        factors = {
            "技术因素": ["创新性", "实用性", "可扩展性", "专利保护"],
            "市场因素": ["需求强度", "竞争格局", "市场规模", "增长潜力"],
            "团队因素": ["技术能力", "商业经验", "执行能力", "网络资源"],
            "资源因素": ["资金支持", "基础设施", "数据资源", "合作伙伴"]
        }
        return factors
    
    def analyze_transfer_potential(self, technology, current_tri="TRL3"):
        """分析转化潜力"""
        analysis = {
            "技术评估": self._assess_technology(technology, current_tri),
            "市场分析": self._analyze_market(technology),
            "路径推荐": self._recommend_paths(technology, current_tri),
            "风险评估": self._assess_risks(technology),
            "投资建议": self._provide_investment_advice(technology, current_tri)
        }
        
        return analysis
    
    def _assess_technology(self, technology, current_tri):
        """评估技术"""
        tech_assessment = {
            "量子神经网络": {
                "创新性": "极高",
                "实用性": "中等",
                "成熟度": "早期",
                "专利情况": "基础专利多",
                "技术壁垒": "极高"
            },
            "神经形态芯片": {
                "创新性": "高",
                "实用性": "高",
                "成熟度": "中期",
                "专利情况": "核心专利集中",
                "技术壁垒": "高"
            },
            "多模态生成": {
                "创新性": "高",
                "实用性": "极高",
                "成熟度": "中后期",
                "专利情况": "应用专利多",
                "技术壁垒": "中等"
            }
        }
        
        return tech_assessment.get(technology, {
            "创新性": "需评估",
            "实用性": "需评估",
            "成熟度": current_tri,
            "专利情况": "未知",
            "技术壁垒": "未知"
        })
    
    def _analyze_market(self, technology):
        """分析市场"""
        market_analysis = {
            "量子神经网络": {
                "市场规模": "$50B (2030年)",
                "年增长率": "45%",
                "主要应用": ["药物发现", "材料科学", "金融建模"],
                "竞争格局": "蓝海市场",
                "客户需求": "高性能计算需求"
            },
            "神经形态芯片": {
                "市场规模": "$30B (2030年)",
                "年增长率": "60%",
                "主要应用": ["边缘计算", "物联网", "机器人"],
                "竞争格局": "多家竞争",
                "客户需求": "低功耗实时处理"
            },
            "多模态生成": {
                "市场规模": "$100B (2030年)",
                "年增长率": "35%",
                "主要应用": ["内容创作", "教育培训", "娱乐媒体"],
                "竞争格局": "激烈竞争",
                "客户需求": "创意工具需求"
            }
        }
        
        return market_analysis.get(technology, {
            "市场规模": "待调研",
            "年增长率": "待调研",
            "主要应用": ["待确定"],
            "竞争格局": "未知",
            "客户需求": "待验证"
        })
    
    def _recommend_paths(self, technology, current_tri):
        """推荐转化路径"""
        recommendations = {
            "量子神经网络": {
                "短期": "学术转化 + 政府合作",
                "中期": "产业合作 + 创业孵化",
                "长期": "建立技术标准 + 生态建设",
                "优先级": ["基础研究", "硬件开发", "算法优化"]
            },
            "神经形态芯片": {
                "短期": "创业孵化 + 风险投资",
                "中期": "产业合作 + 产品开发",
                "长期": "市场拓展 + 生态建设",
                "优先级": ["芯片设计", "软件开发", "应用验证"]
            },
            "多模态生成": {
                "短期": "开源生态 + 社区建设",
                "中期": "产品开发 + 市场推广",
                "长期": "平台建设 + 生态扩张",
                "优先级": ["模型优化", "用户体验", "商业变现"]
            }
        }
        
        return recommendations.get(technology, {
            "短期": "技术验证",
            "中期": "产品原型",
            "长期": "市场推广",
            "优先级": ["技术完善", "团队建设", "资金筹集"]
        })
    
    def _assess_risks(self, technology):
        """评估风险"""
        risks = {
            "量子神经网络": {
                "技术风险": "高（量子硬件不稳定）",
                "市场风险": "中（需求不明确）",
                "竞争风险": "中（巨头布局）",
                "政策风险": "低（政府支持）",
                "资金风险": "高（投入巨大）"
            },
            "神经形态芯片": {
                "技术风险": "中（工艺挑战）",
                "市场风险": "低（需求明确）",
                "竞争风险": "高（激烈竞争）",
                "政策风险": "低（产业政策）",
                "资金风险": "中（需要持续投入）"
            },
            "多模态生成": {
                "技术风险": "低（技术相对成熟）",
                "市场风险": "中（竞争激烈）",
                "竞争风险": "高（巨头主导）",
                "政策风险": "中（内容监管）",
                "资金风险": "低（相对较低）"
            }
        }
        
        return risks.get(technology, {
            "技术风险": "需评估",
            "市场风险": "需评估",
            "竞争风险": "需评估",
            "政策风险": "需评估",
            "资金风险": "需评估"
        })
    
    def _provide_investment_advice(self, technology, current_tri):
        """提供投资建议"""
        advice = {
            "量子神经网络": {
                "投资阶段": "早期风险投资",
                "投资金额": "$10-50M",
                "投资回报期": "7-10年",
                "预期回报": "10-50倍",
                "退出方式": ["IPO", "并购", "技术许可"]
            },
            "神经形态芯片": {
                "投资阶段": "成长期投资",
                "投资金额": "$50-200M",
                "投资回报期": "5-7年",
                "预期回报": "5-20倍",
                "退出方式": ["IPO", "战略投资", "并购"]
            },
            "多模态生成": {
                "投资阶段": "扩张期投资",
                "投资金额": "$20-100M",
                "投资回报期": "3-5年",
                "预期回报": "3-10倍",
                "退出方式": ["并购", "战略投资", "持续经营"]
            }
        }
        
        return advice.get(technology, {
            "投资阶段": "根据成熟度确定",
            "投资金额": "根据需求确定",
            "投资回报期": "5-8年",
            "预期回报": "3-15倍",
            "退出方式": ["多种选择"]
        })
    
    def generate_transfer_plan(self, technology, current_tri="TRL3"):
        """生成转化计划"""
        analysis = self.analyze_transfer_potential(technology, current_tri)
        
        plan = f"{technology}技术转化计划\n"
        plan += "=" * 60 + "\n\n"
        
        plan += "🔬 技术评估:\n"
        for key, value in analysis["技术评估"].items():
            plan += f"  {key}: {value}\n"
        
        plan += "\n📈 市场分析:\n"
        for key, value in analysis["市场分析"].items():
            if isinstance(value, list):
                plan += f"  {key}: {', '.join(value[:3])}\n"
            else:
                plan += f"  {key}: {value}\n"
        
        plan += "\n🛣️ 转化路径:\n"
        for timeframe, path in analysis["路径推荐"].items():
            if timeframe != "优先级":
                plan += f"  {timeframe}: {path}\n"
        
        plan += "\n⚠️ 风险评估:\n"
        for risk, level in analysis["风险评估"].items():
            plan += f"  {risk}: {level}\n"
        
        plan += "\n💰 投资建议:\n"
        for advice, detail in analysis["投资建议"].items():
            if isinstance(detail, list):
                plan += f"  {advice}: {', '.join(detail)}\n"
            else:
                plan += f"  {advice}: {detail}\n"
        
        # 行动计划
        plan += "\n🎯 三年行动计划:\n"
        years = {
            "第一年": ["完善技术", "组建团队", "种子融资"],
            "第二年": ["产品原型", "客户验证", "A轮融资"],
            "第三年": ["市场推广", "规模扩张", "B轮融资"]
        }
        
        for year, actions in years.items():
            plan += f"  {year}: {', '.join(actions)}\n"
        
        return plan

# 使用示例
transfer = TechnologyTransfer()
print("技术成熟度等级:")
for level, info in list(transfer.maturity_levels.items())[:3]:
    print(f"{level}: {info['名称']} - {info['描述']}")

print("\n" + transfer.generate_transfer_plan("神经形态芯片", "TRL4"))

# 未来研究路线图
class FutureResearchRoadmap:
    def __init__(self):
        self.research_directions = self._define_directions()
        self.grand_challenges = self._define_challenges()
        self.collaboration_opportunities = self._define_collaborations()
        
    def _define_directions(self):
        """定义研究方向"""
        directions = {
            "基础理论": [
                "AI的数学基础",
                "学习理论突破",
                "因果推理框架",
                "可解释性理论"
            ],
            "核心技术": [
                "下一代神经网络",
                "神经符号AI",
                "具身智能",
                "通用人工智能"
            ],
            "交叉学科": [
                "AI for Science",
                "AI + 生物",
                "AI + 材料",
                "AI + 能源"
            ],
            "伦理治理": [
                "AI对齐研究",
                "价值学习",
                "安全框架",
                "治理机制"
            ]
        }
        return directions
    
    def _define_challenges(self):
        """定义重大挑战"""
        challenges = [
            {
                "挑战": "实现通用人工智能",
                "难度": "极高",
                "时间表": "10-30年",
                "关键突破": ["世界模型", "推理能力", "自我改进"]
            },
            {
                "挑战": "解决AI对齐问题",
                "难度": "高",
                "时间表": "5-15年",
                "关键突破": ["价值学习", "可解释性", "安全机制"]
            },
            {
                "挑战": "实现可持续AI",
                "难度": "中高",
                "时间表": "3-10年",
                "关键突破": ["能效提升", "绿色计算", "循环经济"]
            },
            {
                "挑战": "建立AI治理体系",
                "难度": "中",
                "时间表": "2-5年",
                "关键突破": ["国际标准", "监管框架", "伦理准则"]
            }
        ]
        return challenges
    
    def _define_collaborations(self):
        """定义合作机会"""
        collaborations = {
            "学术合作": [
                "跨国研究联盟",
                "开放科学倡议",
                "数据共享平台",
                "基准测试社区"
            ],
            "产业合作": [
                "技术转移中心",
                "联合实验室",
                "产业联盟",
                "标准制定组织"
            ],
            "政府合作": [
                "国家AI计划",
                "监管沙盒",
                "公共数据开放",
                "人才培养计划"
            ],
            "社会合作": [
                "公众参与",
                "伦理审查",
                "影响评估",
                "教育普及"
            ]
        }
        return collaborations
    
    def generate_roadmap(self, timeframe="2026-2030"):
        """生成路线图"""
        roadmap = f"AI前沿研究路线图 ({timeframe})\n"
        roadmap += "=" * 60 + "\n\n"
        
        roadmap += "🧭 研究方向:\n"
        for category, directions in self.research_directions.items():
            roadmap += f"\n{category}:\n"
            for direction in directions[:2]:
                roadmap += f"  • {direction}\n"
        
        roadmap += "\n🏔️ 重大挑战:\n"
        for i, challenge in enumerate(self.grand_challenges, 1):
            roadmap += f"\n{i}. {challenge['挑战']}:\n"
            roadmap += f"   难度: {challenge['难度']}, 时间表: {challenge['时间表']}\n"
            roadmap += f"   关键突破: {', '.join(challenge['关键突破'][:2])}\n"
        
        roadmap += "\n🤝 合作机会:\n"
        for category, opportunities in self.collaboration_opportunities.items():
            roadmap += f"\n{category}:\n"
            for opportunity in opportunities[:2]:
                roadmap += f"  • {opportunity}\n"
        
        # 资源需求
        roadmap += "\n💰 资源需求:\n"
        resources = {
            "2026": {"资金": "$50B", "人才": "100万", "计算": "100 ZFLOPS"},
            "2028": {"资金": "$100B", "人才": "200万", "计算": "1 YFLOPS"},
            "2030": {"资金": "$200B", "人才": "500万", "计算": "10 YFLOPS"}
        }
        
        for year, needs in resources.items():
            roadmap += f"  {year}: 资金{needs['资金']}, 人才{needs['人才']}, 算力{needs['计算']}\n"
        
        return roadmap

# 使用示例
roadmap = FutureResearchRoadmap()
print(roadmap.generate_roadmap("2026-2030"))