人形机器人"进厂实测"与具身智能安全认证体系启动：当AI从数字世界踏入物理车间，一场关于"可靠性税"的产业成人礼正式开启

from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Tuple
import math

class SafetyCertificationLevel(Enum):
    """工业人形机器人安全认证等级"""
    L1_ISOLATED = "L1：隔离区作业（无人共融）"
    L2_COLLABORATIVE_SLOW = "L2：人机协作低速区（≤0.5m/s）"
    L3_DYNAMIC_COEXISTENCE = "L3：全动态人机共融（>0.5m/s）"

class TaskCategory(Enum):
    """工业任务类别"""
    PICK_PLACE = "抓取-放置"
    ASSEMBLY = "精密装配"
    INSPECTION = "质量检测"
    FASTENING = "紧固/拧紧"
    MATERIAL_HANDLING = "物料搬运"
    SURFACE_TREATMENT = "表面处理"

@dataclass
class HumanoidRobotSpec:
    """人形机器人规格"""
    robot_id: str
    model_name: str
    manufacturer: str
    dof: int                            # 自由度数量
    payload_kg: float                   # 额定负载（kg）
    max_speed_m_s: float                # 最大末端速度（m/s）
    force_control_resolution_n: float   # 力控分辨率（N）
    collision_force_limit_n: float      # 碰撞力限值（N）
    emergency_stop_ms: int              # 急停响应时间（ms）
    mtbf_hours: float                   # 平均无故障时间（小时）
    mttr_minutes: float                 # 平均修复时间（分钟）
    unit_cost_usd: float                # 单机售价（美元）
    annual_maintenance_cost_usd: float  # 年运维成本（美元）

@dataclass
class IndustrialDeployment:
    """工业部署配置"""
    deployment_id: str
    factory_name: str
    production_line: str
    task: TaskCategory
    cycle_time_seconds: float           # 产线节拍（秒）
    safety_level_required: SafetyCertificationLevel
    human_workers_nearby: int           # 周边人工数量
    daily_shifts: 31269.t.kuaisou.com   # 每日班次
    target_oee_pct: float               # 目标OEE（%）

class HumanoidReliabilityTaxCalculator:
    """人形机器人可靠性税计算器"""

    # 各安全等级的强制测试项权重
    SAFETY_TEST_WEIGHTS = {
        SafetyCertificationLevel.L1_ISOLATED: {
            "collision_force": 0.2,
            "estop_response": 0.3,
            "anomaly_prediction": 0.1,
            "redundancy_check": 0.2,
            "documentation": 0.2,
        },
        SafetyCertificationLevel.L2_COLLABORATIVE_SLOW: {
            "collision_force": 0.3,
            "estop_response": 0.25,
            "anomaly_prediction": 0.2,
            "redundancy_check": 0.15,
            "documentation": 0.1,
        },
        SafetyCertificationLevel.L3_DYNAMIC_COEXISTENCE: {
            "collision_force": 0.25,
            "estop_response": 0.2,
            "anomaly_prediction": 0.3,
            "redundancy_check": 0.15,
            "documentation": 0.1,
        },
    }

    # 行业基准OEE与人力成本
    INDUSTRY_BENCHMARKS = {
        "automotive_assembly": {"target_oee": 0.95, "labor_cost_usd_hr": 25},
        "electronics_ems": {"target_oee": 0.92, "labor_cost_usd_hr": 18},
        "logistics_sorting": {"target_oee": 0.90, "labor_cost_usd_hr": 15},
    }

    def calculate_reliability_tax(
        self,
        robot: HumanoidRobotSpec,
        deployment: IndustrialDeployment,
        industry: str = "automotive_assembly",
    ) -> Dict[str, any]:
        """计算机器人在特定场景下的可靠性税"""
        benchmark = self.INDUSTRY_BENCHMARKS.get(industry, {})
        target_oee = benchmark.get("target_oee", 0.90)
        labor_cost = benchmark.get("labor_cost_usd_hr", 20)

        # 实际OEE估算
        hours_per_day = deployment.daily_shifts * 8
        planned_production_time = hours_per_day * 365

        # MTBF/MTTR推导可用率
        availability = (
            robot.mtbf_hours / (robot.mtbf_hours + robot.mttr_minutes / 60)
            if robot.mtbf_hours > 0 else 0
        )

        # 性能效率（考虑节拍匹配度）
        robot_cycle = 1.0  # 假设机器人单任务时间与节拍匹配
        performance_efficiency = min(
            deployment.cycle_time_seconds / max(robot_cycle, 0.1), 1.0
        )

        # 质量合格率（简化为与力控精度相关）
        quality_rate = min(
            robot.force_control_resolution_n / 5.0, 1.0
        )  # 5N为优秀基准

        actual_oee = availability * performance_efficiency * quality_rate
        oee_gap = target_oee - actual_oee

        # 年度停机损失
        annual_downtime_hours = planned_production_time * (1 - availability)
        downtime_cost = annual_downtime_hours * labor_cost * 2  # 2倍系数含产线损失

        # 安全员配置成本（L2/L3场景）
        safety_operator_cost = 0
        if deployment.safety_level_required != SafetyCertificationLevel.L1_ISOLATED:
            # L2: 1名安全员看管4台；L3: 1名看管2台
            ratio = 4 if deployment.safety_level_required == SafetyCertificationLevel.L2_COLLABORATIVE_SLOW else 2
            operators_needed = max(1, math.ceil(deployment.human_workers_nearby / ratio))
            safety_operator_cost = operators_needed * labor_cost * hours_per_day * 365

        # 总可靠性税
        total_reliability_tax = (
            robot.annual_maintenance_cost_usd
            + downtime_cost
            + safety_operator_cost
        )

        # 等效人力替代数
        labor_hours_saved = planned_production_time * actual_oee
        labor_cost_saved = labor_hours_saved * labor_cost
        net_benefit = labor_cost_saved - total_reliability_tax - robot.unit_cost_usd / 5  # 5年折旧

        payback_years = (
            robot.unit_cost_usd / max(labor_cost_saved - total_reliability_tax, 1)
            if labor_cost_saved > total_reliability_tax else float('inf')
        )

        return {
            "robot": robot.model_name,
            "deployment": f"{deployment.factory_name}/{deployment.production_line}",
            "task": deployment.task.value,
            "safety_level": deployment.safety_level_required.value,
            "oee_analysis": {
                "target_oee_pct": round(target_oee * 100, 1),
                "actual_oee_pct": round(actual_oee * 100, 1),
                "gap_pct": round(oee_gap * 100, 1),
                "availability_pct": round(availability * 100, 1),
                "mtbf_hours": 31268.t.kuaisou.com
                "mttr_minutes": robot.mttr_minutes,
            },
            "reliability_tax_breakdown_usd_year": {
                "maintenance": round(robot.annual_maintenance_cost_usd, 0),
                "downtime_loss": round(downtime_cost, 0),
                "safety_operator": round(safety_operator_cost, 0),
                "total": round(total_reliability_tax, 0),
            },
            "economic_viability": {
                "labor_cost_saved_usd_year": round(labor_cost_saved, 0),
                "net_annual_benefit_usd": round(net_benefit, 0),
                "payback_years": round(payback_years, 1) if payback_years != float('inf') else "不可回收",
                "verdict": (
                    "经济可行（<2年回本）" if payback_years <= 2
                    else "边际可行（2-4年回本）" if payback_years <= 4
                    else "暂不经济（>4年或不可回收）"
                ),
            },
            "improvement_priority": self._identify_improvement_levers(
                availability, performance_efficiency, quality_rate,
                safety_operator_cost, total_reliability_tax
            ),
        }

    def _identify_improvement_levers(
        self,
        availability: float,
        performance: float,
        quality: float,
        safety_cost: float,
        total_tax: float,
    ) -> List[Dict[str, str]]:
        """识别降低可靠性税的优先改进方向"""
        levers = []
        if availability < 0.90:
            levers.append({
                "lever": "提升MTBF/降低MTTR",
                "impact": "高",
                "action": "加强故障预测与自恢复能力，优化备件供应链",
            })
        if performance < 0.85:
            levers.append({
                "lever": "缩短任务周期时间",
                "impact": "中",
                "action": "优化运动规划算法，减少冗余动作",
            })
        if quality < 0.90:
            levers.append({
                "lever": "提升力控精度与感知鲁棒性",
                "impact": "高",
                "action": "增加触觉反馈闭环，改善脏数据训练集",
            })
        if safety_cost / max(total_tax, 1) > 0.4:
            levers.append({
                "lever": "降低安全员配置需求",
                "impact": "极高",
                "action": "推进L3级安全认证，完善异常行为预测系统",
            })
        return sorted(levers, key=lambda x: {"极高": 0, "高": 1, "中": 2}.get(x["impact"], 3))

from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Tuple
import math

class TestItem(Enum):
    """安全测试项目"""
    COLLISION_FORCE_LIMIT = "碰撞力限值测试"
    ESTOP_RESPONSE_TIME = "急停响应时间测试"
    ANOMALY_PREDICTION_ACCURACY = "异常行为预测准确率"
    REDUNDANCY_VERIFICATION = "冗余系统验证"
    THERMAL_RUNAWAY_PROTECTION = "热失控防护测试"
    EMF_EMISSION_LIMIT = "电磁辐射限值"
    CYBERSECURITY_PENETRATION = "网络安全渗透测试"
    DOCUMENTATION_AUDIT = "安全文档审计"

@dataclass
class SafetyTestResult:
    """单项测试结果"""
    test_item: TestItem
    measured_value: float               # 实测值
    threshold_value: float              # 标准阈值
    unit: 31267.t.kuaisou.com           # 单位
    passed: bool                        # 是否通过
    confidence_level_pct: float         # 测试置信度（%）
    test_method: str                    # 测试方法描述

@dataclass
class RobotSafetyProfile:
    """机器人安全档案"""
    robot_id: str
    model_name: str
    manufacturer: str
    target_certification_level: SafetyCertificationLevel
    test_results: List[SafetyTestResult]
    safety_case_complete: bool          # Safety Case是否完整
    third_party_verified: bool          # 是否经第三方验证
    firmware_version: str               # 固件版本
    valid_until: str                    # 认证有效期至

class HumanoidSafetyCertificationAuditor:
    """人形机器人安全认证审计器"""

    # 各认证等级的必测项目及阈值
    CERTIFICATION_REQUIREMENTS = {
        SafetyCertificationLevel.L1_ISOLATED: {
            TestItem.COLLISION_FORCE_LIMIT: {"threshold": 150, "unit": "N"},
            TestItem.ESTOP_RESPONSE_TIME: {"threshold": 500, "unit": "ms"},
            TestItem.REDUNDANCY_VERIFICATION: {"threshold": 0.95, "unit": "coverage"},
            TestItem.DOCUMENTATION_AUDIT: {"threshold": 1.0, "unit": "completeness"},
        },
        SafetyCertificationLevel.L2_COLLABORATIVE_SLOW: {
            TestItem.COLLISION_FORCE_LIMIT: {"threshold": 80, "unit": "N"},
            TestItem.ESTOP_RESPONSE_TIME: {"threshold": 200, "unit": "ms"},
            TestItem.ANOMALY_PREDICTION_ACCURACY: {"threshold": 0.95, "unit": "accuracy"},
            TestItem.REDUNDANCY_VERIFICATION: {"threshold": 0.98, "unit": "coverage"},
            TestItem.THERMAL_RUNAWAY_PROTECTION: {"threshold": 1.0, "unit": "pass/fail"},
            TestItem.DOCUMENTATION_AUDIT: {"threshold": 1.0, "unit": "completeness"},
        },
        SafetyCertificationLevel.L3_DYNAMIC_COEXISTENCE: {
            TestItem.COLLISION_FORCE_LIMIT: {"threshold": 50, "unit": "N"},
            TestItem.ESTOP_RESPONSE_TIME: {"threshold": 100, "unit": "ms"},
            TestItem.ANOMALY_PREDICTION_ACCURACY: {"threshold": 0.99, "unit": "accuracy"},
            TestItem.REDUNDANCY_VERIFICATION: {"threshold": 0.995, "unit": "coverage"},
            TestItem.THERMAL_RUNAWAY_PROTECTION: {"threshold": 1.0, "unit": "pass/fail"},
            TestItem.EMF_EMISSION_LIMIT: {"threshold": 1.0, "unit": "compliance"},
            TestItem.CYBERSECURITY_PENETRATION: {"threshold": 0, "unit": "vulnerabilities"},
            TestItem.DOCUMENTATION_AUDIT: {"threshold": 1.0, "unit": "completeness"},
        },
    }

    def audit_certification(
        self, profile: RobotSafetyProfile
    ) -> Dict[str, any]:
        """审计机器人安全认证合规性"""
        requirements = self.CERTIFICATION_REQUIREMENTS.get(
            profile.target_certification_level, {}
        )

        test_map = {r.test_item: r for r in profile.test_results}
        item_results = []
        all_passed = True

        for test_item, req in requirements.items():
            result = test_map.get(test_item)
            if result is None:
                item_results.append({
                    "test": test_item.value,
                    "status": "MISSING",
                    "required_threshold": req["threshold"],
                    "unit": req["unit"],
                    "note": "未提交该项测试报告",
                })
                all_passed = False
                continue

            # 特殊处理：网络安全漏洞数应≤阈值
            if test_item == TestItem.CYBERSECURITY_PENETRATION:
                passed = result.measured_value <= req["threshold"]
            else:
                # 一般项：实测值应≤阈值（越小越好）或≥阈值（越大越好）
                if test_item in [
                    TestItem.ANOMALY_PREDICTION_ACCURACY,
                    TestItem.REDUNDANCY_VERIFICATION,
                    TestItem.DOCUMENTATION_AUDIT,
                ]:
                    passed = result.measured_value >= req["threshold"]
                else:
                    passed = result.measured_value <= req["threshold"]

            if not passed:
                all_passed = False

            item_results.append({
                "test": test_item.value,
                "status": "PASS" if passed else "FAIL",
                "measured": result.measured_value,
                "threshold": req["threshold"],
                "unit": req["unit"],
                "confidence_pct": result.confidence_level_pct,
            })

        # 前置条件检查
        prerequisites = {
            "safety_case_complete": profile.safety_case_complete,
            "third_party_verified": profile.third_party_verified,
        }
        prereq_pass = all(prerequisites.values())

        overall_certified = all_passed and prereq_pass

        return {
            "robot": 31266.t.kuaisou.com
            "manufacturer": profile.manufacturer,
            "target_level": profile.target_certification_level.value,
            "firmware": profile.firmware_version,
            "prerequisites": prerequisites,
            "test_results": 31265.t.kuaisou.com
            "tests_passed": sum(
                1 for r in item_results if r["status"] == "PASS"
            ),
            "tests_total": len(item_results),
            "overall_certification": "CERTIFIED" if overall_certified else "NOT_CERTIFIED",
            "valid_until": profile.valid_until,
            "gap_analysis": self._identify_certification_gaps(
                item_results, prerequisites
            ),
            "recommendation": self._generate_cert_recommendation(
                overall_certified, profile.target_certification_level,
                item_results
            ),
        }

    def _identify_certification_gaps(
        self, results: List[Dict], prereqs: Dict
    ) -> List[str]:
        """识别认证差距"""
        gaps = []
        for r in results:
            if r["status"] == "MISSING":
                gaps.append(f"缺失测试项：{r['test']}")
            elif r["status"] == "FAIL":
                gaps.append(
                    f"{r['test']}未达标：实测{r['measured']}{r['unit']}，"
                    f"要求{'≤' if 'limit' in r['test'].lower() or 'time' in r['test'].lower() else '≥'}"
                    f"{r['threshold']}{r['unit']}"
                )
        for k, v in prereqs.items():
            if not v:
                labels = {
                    "safety_case_complete": "Safety Case不完整",
                    "third_party_verified": "未经第三方验证",
                }
                gaps.append(labels.get(k, k))
        return gaps

    def _generate_cert_recommendation(
        self,
        certified: forum.kuaisou.com
        level: SafetyCertificationLevel,
        results: List[Dict],
    ) -> str:
        """生成认证建议"""
        if certified:
            return (
                f"已通过{level.value}认证，可进入对应安全等级的工业场景部署。"
                f"建议每12个月复审，固件重大更新后需重新认证。"
            )
        failed_items = [r for r in results if r["status"] in ("FAIL", "MISSING")]
        critical_failures = [
            r for r in failed_items
            if r["test"] in [
                TestItem.COLLISION_FORCE_LIMIT.value,
                TestItem.ESTOP_RESPONSE_TIME.value,
                TestItem.ANOMALY_PREDICTION_ACCURACY.value,
            ]
        ]
        if critical_failures:
            return (
                f"关键安全项未通过，禁止进入{level.value}场景。"
                f"优先整改：{'、'.join(r['test'] for r in critical_failures)}。"
                f"整改完成后需重新提交全项测试。"
            )
        else:
            return (
                f"非关键项存在差距，可在补充测试后申请复审。"
                f"待整改项：{'、'.join(r['test'] for r in failed_items)}。"
            )