> ## Documentation Index
> Fetch the complete documentation index at: https://resq-dependabot-github-actions-github-actions-478e18be3d.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Models

<a id="resq_mcp.dtsop.models" />

# resq\_mcp.dtsop.models

DTSOP domain models for the ResQ MCP server.

<a id="resq_mcp.dtsop.models.annotations" />

## annotations

<a id="resq_mcp.dtsop.models.Literal" />

## Literal

<a id="resq_mcp.dtsop.models.BaseModel" />

## BaseModel

<a id="resq_mcp.dtsop.models.SimulationRequest" />

## SimulationRequest Objects

```python theme={null}
class SimulationRequest(BaseModel)
```

Request for high-fidelity physics simulation in digital twin.

Part of DTSOP system. Triggers physics-based simulation in Unity/Unreal
Engine for accurate disaster propagation modeling and strategy validation.

**Attributes**:

* `scenario_id` - Unique scenario identifier for this simulation.
* `sector_id` - Geographic sector to simulate.
* `disaster_type` - Type of disaster to model (e.g., "flood", "wildfire").
* `parameters` - Simulation parameters (e.g., \{"wind\_speed": 15.5, "water\_level": 2.3}).
* `priority` - Processing priority (standard queued, urgent fast-tracked).

**Notes**:

Simulations run asynchronously. Monitor progress via the returned
simulation ID and resource subscription (resq://simulations/\{id}).

<a id="resq_mcp.dtsop.models.SimulationRequest.scenario_id" />

#### scenario\_id

<a id="resq_mcp.dtsop.models.SimulationRequest.sector_id" />

#### sector\_id

<a id="resq_mcp.dtsop.models.SimulationRequest.disaster_type" />

#### disaster\_type

<a id="resq_mcp.dtsop.models.SimulationRequest.parameters" />

#### parameters

e.g., wind\_speed, water\_level

<a id="resq_mcp.dtsop.models.SimulationRequest.priority" />

#### priority

<a id="resq_mcp.dtsop.models.OptimizationStrategy" />

## OptimizationStrategy Objects

```python theme={null}
class OptimizationStrategy(BaseModel)
```

Reinforcement learning-optimized deployment and evacuation strategy.

Part of DTSOP system. Generated by RL agents trained on thousands of
simulated disaster scenarios to optimize resource allocation and
evacuation routing under various constraints.

**Attributes**:

* `strategy_id` - Unique strategy identifier (e.g., "STRAT-X1Y2Z3W4").
* `related_alert_id` - Pre-alert or incident ID this strategy addresses.
* `recommended_deployment` - Mapping of drone types to recommended counts
  (e.g., \{"surveillance": 2, "payload": 1}).
* `evacuation_routes` - Ordered list of recommended evacuation routes.
* `estimated_success_rate` - Predicted success probability (0.0 to 1.0)
  based on simulation outcomes.
* `simulation_proof_url` - NeoFS/IPFS URL for simulation evidence and logs.

**Notes**:

Success rate derived from Monte Carlo simulations across varying
disaster intensities and communication scenarios.

<a id="resq_mcp.dtsop.models.OptimizationStrategy.strategy_id" />

#### strategy\_id

<a id="resq_mcp.dtsop.models.OptimizationStrategy.related_alert_id" />

#### related\_alert\_id

<a id="resq_mcp.dtsop.models.OptimizationStrategy.recommended_deployment" />

#### recommended\_deployment

drone\_type -> count

<a id="resq_mcp.dtsop.models.OptimizationStrategy.evacuation_routes" />

#### evacuation\_routes

<a id="resq_mcp.dtsop.models.OptimizationStrategy.estimated_success_rate" />

#### estimated\_success\_rate

<a id="resq_mcp.dtsop.models.OptimizationStrategy.simulation_proof_url" />

#### simulation\_proof\_url