Kaizen OS Complete Lab Architecture

class ComputeOrchestrator:
    """
    Lab 3 manages compute allocation for Lab 2 (Thought Broker)
    """

    def __init__(self):
        self.providers = {
            "openai": {"cost": 0.01, "latency": 500,  "quality": 0.95},
            "anthropic": {"cost": 0.015, "latency": 600, "quality": 0.97},
            "google": {"cost": 0.008, "latency": 700,  "quality": 0.93},
            "deepseek": {"cost": 0.002, "latency": 1000, "quality": 0.88},
            "local": {"cost": 0.0001, "latency": 2000, "quality": 0.75}
        }

        self.citizen_quotas = {}  # Track usage per citizen

    def route_query(self, query, citizen_did, priority="standard"):
        """
        Allocate compute resources based on query characteristics
        """
        # Check citizen quota
        if self.citizen_quotas.get(citizen_did, 0) > MONTHLY_QUOTA:
            return self.route_to_local_model(query)  # Fallback to local

        # Determine requirements
        if priority == "urgent":
            # Minimize latency (emergency medical question, etc.)
            provider = min(self.providers.items(), key=lambda x: x[1]["latency"])
        elif query.requires_high_quality():
            # Maximize quality (complex analysis, important decision)
            provider = max(self.providers.items(), key=lambda x: x[1]["quality"])
        else:
            # Minimize cost (routine query)
            provider = min(self.providers.items(), key=lambda x: x[1]["cost"])

        # Update quota
        self.citizen_quotas[citizen_did] += provider[1]["cost"]

        return provider[0]  # Return provider name

    def load_balance(self):
        """
        Distribute queries across providers to avoid bottlenecks
        """
        # If OpenAI is overloaded, route to Anthropic/Google
        # If all cloud providers down, fall back to local models
        pass

class EnergyRouter:
    """
    Optimize energy flows for Hive energy systems (Lab 5 infrastructure)
    """

    def __init__(self, hive_id):
        self.hive_id = hive_id
        self.solar_panels = get_hive_solar_capacity(hive_id)
        self.batteries = get_hive_battery_capacity(hive_id)
        self.grid_connection = get_grid_connection_status(hive_id)

    def optimize_energy_flow(self, hour):
        """
        Decide: Charge batteries? Sell to grid? Power Hive? Import from grid?
        """
        current_generation = self.solar_panels.current_output(hour)
        current_demand = self.estimate_hive_demand(hour)
        battery_charge = self.batteries.current_charge()
        grid_price = self.get_current_grid_price(hour)

        # Decision tree
        if current_generation > current_demand:
            # Surplus energy
            surplus = current_generation - current_demand

            if battery_charge < 0.80:
                # Batteries not full → Charge them
                self.batteries.charge(surplus)
            elif grid_price > EXPORT_THRESHOLD:
                # High grid prices → Sell surplus
                gic_earned = self.export_to_grid(surplus, grid_price)
                credit_citizen_accounts(self.hive_id, gic_earned)
            else:
                # Low prices → Store for later
                self.batteries.charge(surplus)

        else:
            # Deficit energy
            deficit = current_demand - current_generation

            if battery_charge > 0.20:
                # Batteries have charge → Use them
                self.batteries.discharge(deficit)
            elif grid_price < IMPORT_THRESHOLD:
                # Cheap grid power → Import
                self.import_from_grid(deficit, grid_price)
            else:
                # Expensive grid power → Demand response
                self.reduce_non_essential_load(deficit)

    def emergency_islanding(self):
        """
        Grid failure → Switch to microgrid mode
        """
        self.disconnect_from_grid()
        self.prioritize_essential_loads()  # Clinic, water, food storage
        self.coordinate_with_neighboring_hives()  # Share power if possible

class SupplyChainCoordinator:
    """
    Coordinate physical goods flow (Lab 5 life infrastructure)
    """

    def route_food_delivery(self, harvest_data):
        """
        Farm harvested food → Which Hive needs it most?
        """
        # Get current inventory at all Hive food co-ops
        hive_inventories = get_all_hive_food_inventory()

        # Calculate needs (based on member count + current stock)
        hive_needs = {
            hive_id: calculate_food_need(hive_id, harvest_data.crop_type)
            for hive_id in hive_inventories.keys()
        }

        # Optimize delivery route (traveling salesman)
        delivery_route = optimize_route(
            origin=harvest_data.farm_location,
            destinations=hive_needs.keys(),
            weights=hive_needs.values()
        )

        # Dispatch delivery (volunteer driver earns GIC)
        assign_driver(delivery_route, compensation=100)  # GIC for delivery

        # Update inventories
        for hive_id, quantity in hive_needs.items():
            update_inventory(hive_id, harvest_data.crop_type, +quantity)

        # Compensate farmer
        total_gic = sum(hive_needs.values()) * PRICE_PER_KG
        pay_farmer(harvest_data.farmer_did, total_gic)

    def allocate_water_rights(self, water_source_id):
        """
        Drought conditions → Who gets water?
        """
        available_water = get_current_water_level(water_source_id)

        # Priority ranking
        allocations = {
            "drinking_water": 0.50,    # Humans first
            "food_production": 0.30,   # Agriculture second
            "industry": 0.15,          # Manufacturing third
            "luxury": 0.05             # Lawns, golf courses last
        }

        for category, percentage in allocations.items():
            allocated_volume = available_water * percentage
            distribute_water(category, allocated_volume)

    def assign_housing(self, citizen_did):
        """
        New citizen needs housing → Match to available co-housing unit
        """
        citizen_profile = get_citizen_profile(citizen_did)
        available_units = get_vacant_housing_units()

        # Match based on preferences + compatibility
        best_match = find_best_housing_match(
            citizen_profile,
            available_units,
            criteria=["location", "amenities", "community_vibe"]
        )

        # Assign unit
        assign_housing_unit(citizen_did, best_match.unit_id)

        # Set up rent-to-own plan
        initialize_equity_tracking(citizen_did, best_match.unit_id)

class FinancialFlowCoordinator:
    """
    Handle all GIC transactions (connects Labs 1, 5, 7)
    """

    def process_epoch_mint(self, agent_id):
        """
        Every 90 days: Founding Agents mint GIC
        """
        if not epoch_ready(agent_id):
            raise EpochNotReady("Must wait 90 days between mints")

        agent_data = get_founding_agent_data(agent_id)
        mint_amount = agent_data.epoch_mint_cap

        # Mint to agent wallet
        gic_mint(agent_data.wallet, mint_amount)

        # Auto-donate to PublicGoodsPool
        donate_amount = mint_amount * (agent_data.donate_bps / 10000)
        gic_transfer(agent_data.wallet, PUBLIC_GOODS_POOL, donate_amount)

        # Log to Civic Ledger
        log_epoch_mint(agent_id, mint_amount, donate_amount)

    def distribute_ubi(self):
        """
        Monthly: Distribute UBI to all citizens
        """
        eligible_citizens = get_all_citizens_with_gi_above_threshold(0.90)

        for citizen in eligible_citizens:
            base_ubi = calculate_base_ubi(current_month)
            integrity_bonus = calculate_integrity_bonus(citizen.gi_score)
            total_ubi = base_ubi + integrity_bonus

            # Transfer GIC
            gic_transfer(UBI_POOL, citizen.wallet, total_ubi)

            # Log
            log_ubi_payment(citizen.did, total_ubi, citizen.gi_score)

    def process_debt_repayment(self):
        """
        Quarterly: Convert GIC surplus → USD → Pay down national debt
        """
        surplus = calculate_system_surplus()
        gic_to_convert = surplus * 0.10  # 10% of surplus to debt repayment

        # Convert GIC → USDT → USD
        usdt_received = gic_to_usdt_swap(gic_to_convert)
        usd_received = usdt_to_usd_transfer(usdt_received)

        # Purchase Treasury bonds (retire debt)
        bonds_purchased = buy_treasury_bonds(usd_received)

        # Log impact
        log_debt_repayment(usd_received, bonds_purchased)

    def balance_reserves(self):
        """
        Daily: Rebalance BTC Vault + USD Credit Bridge
        """
        btc_vault_level = get_btc_vault_balance()
        usd_credit_level = get_usd_credit_balance()

        # Target: 15% BTC, 10% USD Credit, 5% PublicGoodsPool
        if btc_vault_level < TARGET_BTC_PERCENTAGE:
            convert_gic_to_btc(deficit_amount)

        if usd_credit_level < TARGET_USD_PERCENTAGE:
            convert_gic_to_usd(deficit_amount)

class LaborMarketCoordinator:
    """
    Match citizen skills → Available opportunities
    """

    def match_jobs(self, citizen_did):
        """
        Find work opportunities aligned with citizen's skills + interests
        """
        citizen = get_citizen_profile(citizen_did)
        skills = citizen.oaa_certifications + citizen.work_history
        interests = analyze_reflections_for_interests(citizen.eomm_history)

        # Available opportunities
        opportunities = get_all_open_opportunities(hive_id=citizen.hive)

        # Score matches
        matches = [
            {
                "opportunity": opp,
                "skill_match": calculate_skill_overlap(skills, opp.requirements),
                "interest_match": calculate_interest_alignment(interests, opp.description),
                "gic_per_hour": opp.compensation
            }
            for opp in opportunities
        ]

        # Rank by total score
        ranked_matches = sorted(matches, key=lambda x: x["skill_match"] + x["interest_match"], reverse=True)

        return ranked_matches[:10]  # Top 10 recommendations

    def coordinate_shifts(self, service_type):
        """
        Example: Healthcare clinic needs coverage 24/7
        """
        if service_type == "healthcare":
            # Get available nurses + community health workers
            available_staff = get_available_healthcare_workers(hive_id)

            # Generate optimal schedule
            schedule = generate_shift_schedule(
                staff=available_staff,
                coverage_requirements={"morning": 2, "afternoon": 2, "evening": 1, "night": 1},
                constraints=["no_back_to_back_nights", "prefer_consistent_days"]
            )

            # Notify staff
            for shift in schedule:
                notify_worker(shift.worker_did, shift.details)
                confirm_shift(shift.worker_did, shift.shift_id)

class MaintenanceCoordinator:
    """
    Keep Hive infrastructure running smoothly
    """

    def monitor_equipment(self):
        """
        IoT sensors → Predict failures before they happen
        """
        equipment = get_all_hive_equipment()

        for device in equipment:
            sensor_data = device.get_sensor_readings()

            # AI model predicts failure risk
            failure_risk = predict_failure_probability(
                device_type=device.type,
                age=device.age,
                sensor_data=sensor_data,
                maintenance_history=device.maintenance_log
            )

            if failure_risk > 0.70:
                # High risk → Schedule immediate maintenance
                create_maintenance_ticket(
                    device_id=device.id,
                    priority="high",
                    description=f"Predicted failure risk: {failure_risk}",
                    assign_to="next_available_technician"
                )

            elif failure_risk > 0.40:
                # Medium risk → Schedule routine maintenance
                create_maintenance_ticket(
                    device_id=device.id,
                    priority="medium",
                    schedule_for="next_monthly_maintenance_window"
                )

    def optimize_repair_routes(self, maintenance_tickets):
        """
        Multiple repairs needed → Optimize technician route
        """
        # Traveling salesman problem
        optimal_route = calculate_optimal_repair_route(
            technician_location=get_technician_location(),
            repair_sites=[ticket.location for ticket in maintenance_tickets],
            priorities=[ticket.priority for ticket in maintenance_tickets]
        )

        # Assign route to technician
        assign_repair_route(technician_did, optimal_route)