""" Comprehensive Contingency Planning System for Mission Failure Scenarios """ import json import math import datetime import dataclasses import itertools from typing import Dict, List, Optional, Any, Tuple @dataclasses.dataclass class FailureScenario: name: str description: str probability: float impact_level: str detection_methods: List[str] immediate_actions: List[str] short_term_actions: List[str] long_term_actions: List[str] resources_required: Dict[str, float] recovery_time: str success_probability: float @dataclasses.dataclass class ContingencyAction: action_id: str description: str priority: int time_to_complete: int # minutes dependencies: List[str] resources: Dict[str, float] success_rate: float class ContingencyPlanningSystem: """Advanced contingency planning with automated response generation""" def __init__(self): self.failure_scenarios = { "PROPULSION_PARTIAL_FAILURE": { "name": "Partial Propulsion System Failure", "description": "One or more engines fail, reducing thrust capability", "probability": 0.15, "impact_level": "HIGH", "detection_methods": ["thrust_monitoring", "vibration_analysis", "pressure_sensors"], "immediate_actions": [ "Isolate failed engine", "Redistribute fuel flow", "Recalculate thrust vector", "Update trajectory profile" ], "short_term_actions": [ "Verify backup engines operational", "Adjust mission timeline", "Calculate new orbital parameters", "Inform mission control" ], "long_term_actions": [ "Analyze failure root cause", "Update propulsion procedures", "Modify mission requirements", "Implement redundancy improvements" ], "resources_required": { "fuel_reserve": 20.0, "power": 15.0, "crew_time": 2.0 }, "recovery_time": "2-8 hours", "success_probability": 0.75 }, "COMMUNICATION_COMPLETE_LOSS": { "name": "Complete Communication System Failure", "description": "Loss of all communication links with ground control", "probability": 0.08, "impact_level": "HIGH", "detection_methods": ["link_quality_monitor", "signal_strength", "antenna_health"], "immediate_actions": [ "Activate autonomous mode", "Switch to backup communication systems", "Store critical telemetry data", "Execute pre-programmed mission profile" ], "short_term_actions": [ "Attempt antenna realignment", "Deploy emergency beacon", "Utilize satellite relay systems", "Conserve power for communication attempts" ], "long_term_actions": [ "Analyze communication failure patterns", "Update redundancy procedures", "Enhance autonomous systems", "Improve emergency beacon design" ], "resources_required": { "power": 25.0, "data_storage": 10.0, "crew_time": 1.5 }, "recovery_time": "4-24 hours", "success_probability": 0.65 }, "POWER_SYSTEM_CRITICAL_FAILURE": { "name": "Critical Power System Failure", "description": "Primary power generation failure, relying on backup systems", "probability": 0.12, "impact_level": "CRITICAL", "detection_methods": ["voltage_monitoring", "current_sensors", "solar_panel_efficiency"], "immediate_actions": [ "Switch to backup power sources", "Shed non-essential loads", "Activate emergency power protocols", "Prioritize life support systems" ], "short_term_actions": [ "Assess battery capacity", "Implement power conservation measures", "Modify mission timeline", "Configure minimal operations mode" ], "long_term_actions": [ "Repair or replace failed components", "Enhance power system redundancy", "Improve battery management", "Update power failure procedures" ], "resources_required": { "battery_capacity": 80.0, "fuel_reserve": 10.0, "crew_time": 3.0 }, "recovery_time": "6-48 hours", "success_probability": 0.55 }, "THERMAL_CONTROL_FAILURE": { "name": "Thermal Control System Failure", "description": "Loss of temperature regulation capability", "probability": 0.10, "impact_level": "MEDIUM", "detection_methods": ["temperature_sensors", "radiator_efficiency", "heater_status"], "immediate_actions": [ "Activate redundant thermal control", "Adjust spacecraft orientation", "Minimize heat-generating activities", "Monitor critical component temperatures" ], "short_term_actions": [ "Implement emergency cooling procedures", "Relocate critical equipment to thermal zones", "Adjust operational schedules", "Use passive thermal control methods" ], "long_term_actions": [ "Repair thermal control components", "Enhance system redundancy", "Improve thermal modeling", "Update failure response procedures" ], "resources_required": { "power": 20.0, "propellant": 5.0, "crew_time": 2.5 }, "recovery_time": "1-12 hours", "success_probability": 0.80 }, "LIFE_SUPPORT_DEGRADATION": { "name": "Life Support System Degradation", "description": "Reduced capability in life support systems", "probability": 0.05, "impact_level": "CRITICAL", "detection_methods": ["air_quality_sensors", "pressure_monitors", "co2_scrubbers"], "immediate_actions": [ "Activate backup life support systems", "Implement emergency oxygen procedures", "Increase air circulation", "Monitor crew vital signs" ], "short_term_actions": [ "Repair failed components", "Conserve life support resources", "Implement rationing procedures", "Prepare emergency evacuation scenarios" ], "long_term_actions": [ "Replace failed components", "Enhance system redundancy", "Improve maintenance procedures", "Update emergency protocols" ], "resources_required": { "oxygen_reserve": 30.0, "power": 10.0, "crew_time": 4.0 }, "recovery_time": "1-6 hours", "success_probability": 0.85 }, "NAVIGATION_SYSTEM_FAILURE": { "name": "Navigation System Failure", "description": "Loss of primary navigation capability", "probability": 0.07, "impact_level": "HIGH", "detection_methods": ["gps_lock_status", "gyro_health", "star_tracker_accuracy"], "immediate_actions": [ "Switch to backup navigation systems", "Activate inertial navigation mode", "Use celestial navigation backup", "Maintain current trajectory" ], "short_term_actions": [ "Recalculate position using multiple methods", "Update navigation algorithms", "Implement manual navigation procedures", "Coordinate with ground for position updates" ], "long_term_actions": [ "Repair or replace navigation components", "Enhance system redundancy", "Improve autonomous navigation", "Update contingency procedures" ], "resources_required": { "power": 15.0, "computation": 20.0, "crew_time": 2.0 }, "recovery_time": "2-6 hours", "success_probability": 0.70 } } self.action_library = self._build_action_library() self.contingency_plans = {} self.execution_history = [] def _build_action_library(self) -> Dict[str, ContingencyAction]: """Build comprehensive library of contingency actions""" actions = { "ISOLATE_FAILED_SYSTEM": ContingencyAction( action_id="ISO001", description="Isolate failed system to prevent cascading failures", priority=1, time_to_complete=5, dependencies=[], resources={"power": 5.0, "crew_time": 0.5}, success_rate=0.95 ), "ACTIVATE_BACKUP_SYSTEM": ContingencyAction( action_id="BKP001", description="Activate backup/redundant systems", priority=2, time_to_complete=10, dependencies=["ISO001"], resources={"power": 10.0, "crew_time": 1.0}, success_rate=0.90 ), "RECALCULATE_TRAJECTORY": ContingencyAction( action_id="TRAJ001", description="Recalculate mission trajectory with new constraints", priority=3, time_to_complete=15, dependencies=["BKP001"], resources={"computation": 30.0, "crew_time": 0.5}, success_rate=0.85 ), "CONSERVE_RESOURCES": ContingencyAction( action_id="CON001", description="Implement resource conservation measures", priority=2, time_to_complete=8, dependencies=[], resources={"crew_time": 1.0}, success_rate=0.88 ), "EMERGENCY_COMMUNICATION": ContingencyAction( action_id="COM001", description="Establish emergency communication links", priority=4, time_to_complete=20, dependencies=["CON001"], resources={"power": 15.0, "crew_time": 2.0}, success_rate=0.75 ), "SYSTEM_REPAIR": ContingencyAction( action_id="REP001", description="Repair failed system components", priority=5, time_to_complete=60, dependencies=["ISO001", "BKP001"], resources={"power": 25.0, "crew_time": 4.0, "spare_parts": 1.0}, success_rate=0.70 ), "CREW_SAFETY_PROTOCOLS": ContingencyAction( action_id="SAF001", description="Activate crew safety and emergency protocols", priority=1, time_to_complete=3, dependencies=[], resources={"crew_time": 0.2}, success_rate=0.98 ), "MISSION_MODIFICATION": ContingencyAction( action_id="MOD001", description="Modify mission profile to accommodate failures", priority=4, time_to_complete=30, dependencies=["TRAJ001"], resources={"computation": 40.0, "crew_time": 2.0}, success_rate=0.80 ) } return actions def generate_contingency_plan(self, scenario_name: str, available_resources: Dict[str, float]) -> Dict[str, Any]: """Generate optimized contingency plan for specific failure scenario""" if scenario_name not in self.failure_scenarios: return {"error": f"Unknown failure scenario: {scenario_name}"} scenario = self.failure_scenarios[scenario_name] # Select appropriate actions based on scenario action_sequence = self._select_actions_for_scenario(scenario_name, available_resources) # Calculate total resources and time total_resources = self._calculate_total_resources(action_sequence) total_time = sum(action.time_to_complete for action in action_sequence) success_probability = self._calculate_plan_success_probability(action_sequence) # Generate detailed plan plan = { "scenario_name": scenario_name, "scenario_description": scenario["description"], "impact_level": scenario["impact_level"], "probability": scenario["probability"], "timestamp": datetime.datetime.now().isoformat(), "action_sequence": [ { "step": i + 1, "action_id": action.action_id, "description": action.description, "priority": action.priority, "time_to_complete": action.time_to_complete, "dependencies": action.dependencies, "success_rate": action.success_rate } for i, action in enumerate(action_sequence) ], "total_time_required": total_time, "total_resources_required": total_resources, "success_probability": success_probability, "critical_path": self._identify_critical_path(action_sequence), "alternative_options": self._generate_alternative_options(scenario_name, available_resources) } self.contingency_plans[scenario_name] = plan return plan def _select_actions_for_scenario(self, scenario_name: str, available_resources: Dict[str, float]) -> List[ContingencyAction]: """Select optimal sequence of actions for given scenario""" scenario = self.failure_scenarios[scenario_name] selected_actions = [] # Core actions that are always included based on scenario core_actions = { "PROPULSION_PARTIAL_FAILURE": ["ISO001", "BKP001", "TRAJ001", "MOD001"], "COMMUNICATION_COMPLETE_LOSS": ["SAF001", "CON001", "BKP001", "COM001"], "POWER_SYSTEM_CRITICAL_FAILURE": ["SAF001", "CON001", "BKP001", "MOD001"], "THERMAL_CONTROL_FAILURE": ["ISO001", "BKP001", "CON001"], "LIFE_SUPPORT_DEGRADATION": ["SAF001", "BKP001", "CON001", "REP001"], "NAVIGATION_SYSTEM_FAILURE": ["ISO001", "BKP001", "TRAJ001"] } action_ids = core_actions.get(scenario_name, []) # Add actions based on available resources for action_id in action_ids: if action_id in self.action_library: action = self.action_library[action_id] # Check if resources are available resources_available = True for resource, amount in action.resources.items(): if available_resources.get(resource, 0) < amount: resources_available = False break if resources_available: selected_actions.append(action) # Sort actions by priority and dependencies selected_actions = self._sort_actions_by_priority(selected_actions) return selected_actions def _sort_actions_by_priority(self, actions: List[ContingencyAction]) -> List[ContingencyAction]: """Sort actions based on priority and dependencies""" sorted_actions = [] remaining_actions = actions.copy() while remaining_actions: # Find actions with no unsatisfied dependencies ready_actions = [] for action in remaining_actions: dependencies_satisfied = all( dep.action_id in [a.action_id for a in sorted_actions] for dep in self.action_library.values() if dep.action_id in action.dependencies ) if dependencies_satisfied: ready_actions.append(action) if not ready_actions: # If no ready actions, break to avoid infinite loop sorted_actions.extend(remaining_actions) break # Sort ready actions by priority (lower number = higher priority) ready_actions.sort(key=lambda x: x.priority) # Add the highest priority ready action selected_action = ready_actions[0] sorted_actions.append(selected_action) remaining_actions.remove(selected_action) return sorted_actions def _calculate_total_resources(self, actions: List[ContingencyAction]) -> Dict[str, float]: """Calculate total resources required for action sequence""" total_resources = {} for action in actions: for resource, amount in action.resources.items(): total_resources[resource] = total_resources.get(resource, 0) + amount return total_resources def _calculate_plan_success_probability(self, actions: List[ContingencyAction]) -> float: """Calculate overall plan success probability""" if not actions: return 0.0 # Multiply individual success rates overall_probability = 1.0 for action in actions: overall_probability *= action.success_rate return overall_probability def _identify_critical_path(self, actions: List[ContingencyAction]) -> List[str]: """Identify critical path in action sequence""" return [action.action_id for action in actions if action.priority <= 2] def _generate_alternative_options(self, scenario_name: str, available_resources: Dict[str, float]) -> List[Dict[str, Any]]: """Generate alternative contingency options""" alternatives = [] # Generate minimal response option (highest priority actions only) minimal_actions = [a for a in self.action_library.values() if a.priority == 1] if minimal_actions: alternatives.append({ "option_name": "Minimal Response", "description": "Execute only critical priority 1 actions", "actions": [a.action_id for a in minimal_actions], "success_probability": self._calculate_plan_success_probability(minimal_actions), "time_required": sum(a.time_to_complete for a in minimal_actions) }) # Generate resource-conservative option conservative_actions = [ a for a in self.action_library.values() if a.resources.get("crew_time", 0) <= 2.0 and a.priority <= 3 ] if conservative_actions: alternatives.append({ "option_name": "Resource Conservative", "description": "Minimize crew time and resource usage", "actions": [a.action_id for a in conservative_actions], "success_probability": self._calculate_plan_success_probability(conservative_actions), "time_required": sum(a.time_to_complete for a in conservative_actions) }) return alternatives def execute_contingency_plan(self, plan_name: str, available_resources: Dict[str, float]) -> Dict[str, Any]: """Execute a contingency plan and track results""" if plan_name not in self.contingency_plans: return {"error": f"No contingency plan found for: {plan_name}"} plan = self.contingency_plans[plan_name] execution_log = { "plan_name": plan_name, "execution_start": datetime.datetime.now().isoformat(), "actions_executed": [], "total_time_elapsed": 0, "resources_consumed": {}, "execution_status": "IN_PROGRESS" } current_time = 0 for action_step in plan["action_sequence"]: action_id = action_step["action_id"] if action_id in self.action_library: action = self.action_library[action_id] # Simulate action execution current_time += action.time_to_complete # Update resource consumption for resource, amount in action.resources.items(): execution_log["resources_consumed"][resource] = \ execution_log["resources_consumed"].get(resource, 0) + amount # Log action execution action_log = { "action_id": action_id, "execution_time": current_time, "success": True, # Simulated success "notes": f"Executed {action.description}" } execution_log["actions_executed"].append(action_log) execution_log["total_time_elapsed"] = current_time execution_log["execution_end"] = datetime.datetime.now().isoformat() execution_log["execution_status"] = "COMPLETED" self.execution_history.append(execution_log) return execution_log def get_contingency_readiness_assessment(self) -> Dict[str, Any]: """Assess overall contingency readiness""" assessment = { "timestamp": datetime.datetime.now().isoformat(), "total_scenarios": len(self.failure_scenarios), "prepared_scenarios": len(self.contingency_plans), "action_library_size": len(self.action_library), "execution_history_size": len(self.execution_history), "readiness_score": 0.0, "high_risk_scenarios": [], "recommendations": [] } # Calculate readiness score assessment["readiness_score"] = (len(self.contingency_plans) / len(self.failure_scenarios)) * 100 # Identify high-risk scenarios for scenario_name, scenario in self.failure_scenarios.items(): if scenario["impact_level"] == "CRITICAL" and scenario_name not in self.contingency_plans: assessment["high_risk_scenarios"].append(scenario_name) # Generate recommendations if assessment["readiness_score"] < 80: assessment["recommendations"].append("Generate contingency plans for remaining scenarios") if assessment["high_risk_scenarios"]: assessment["recommendations"].append( f"Prioritize plans for high-risk scenarios: {', '.join(assessment['high_risk_scenarios'])}" ) return assessment # Test the contingency planning system def test_contingency_system(): """Test the contingency planning system""" contingency_system = ContingencyPlanningSystem() # Sample available resources available_resources = { "power": 100.0, "crew_time": 8.0, "fuel_reserve": 50.0, "computation": 50.0 } # Generate contingency plan for critical scenario plan = contingency_system.generate_contingency_plan("POWER_SYSTEM_CRITICAL_FAILURE", available_resources) print("Contingency Plan Generated:") print(f"Scenario: {plan['scenario_name']}") print(f"Impact Level: {plan['impact_level']}") print(f"Total Time: {plan['total_time_required']} minutes") print(f"Success Probability: {plan['success_probability']:.2f}") print(f"Number of Actions: {len(plan['action_sequence'])}") # Execute the plan execution = contingency_system.execute_contingency_plan("POWER_SYSTEM_CRITICAL_FAILURE", available_resources) print(f"\nPlan Execution:") print(f"Status: {execution['execution_status']}") print(f"Total Time: {execution['total_time_elapsed']} minutes") print(f"Actions Completed: {len(execution['actions_executed'])}") # Get readiness assessment readiness = contingency_system.get_contingency_readiness_assessment() print(f"\nReadiness Assessment:") print(f"Readiness Score: {readiness['readiness_score']:.1f}%") print(f"High Risk Scenarios: {len(readiness['high_risk_scenarios'])}") return True if __name__ == "__main__": test_contingency_system()