Метод increment_hunger_vector()

In-Memory Twin Registry: Массовый пакетный инкремент метаболического истощения

1. Functional Purpose

The increment_hunger_vector method serves as the primary metabolic and physiological driver within the system runtime. Executed as a batch operation in memory by the macro-orchestrator on every intra-day tick (t), this method forces a linear increment of the dynamic hunger parameter ((M_{})) for all currently active digital twin states. This continuous drift shifts the probability density calculated by the stochastic engine toward food-related intents.

2. Mathematical Modeling of Metabolic Drift

To simulate realistic human behavior, the simulator requires a baseline continuous internal trigger. Without external interventions or a consumption action, the metabolic exhaustion of each twin accumulates linearly over time. On each micro-tick \(t\), the method recalculates the state vector element as follows:

\[M_{\text{hunger}}(t) = \min(1.0, M_{\text{hunger}}(t - 1) + \Delta m)\]

Where: * \(\Delta m\) (\(rate\)) — the constant step of metabolic depletion per simulation tick (calculated by the orchestrator based on time discretization, e.g., 15 simulated minutes). * \(1.0\) — the strict upper boundary representing total starvation, forcing the maximum probability scale into the logistical normalization phase.

3. Method Specification (IT Contract)

Call Type: Iterative, batch memory update (RAM-Batch Write).
Executor: In-Memory Twin Registry Service.

3.1. Input Parameters (Call Arguments)

Parameter	Data Type	Required	Description
`rate`	Float	Yes	Metabolic depletion coefficient added to the vector on each tick.

3.2. Output Data (Return Value)

void — The method mutates the properties of existing objects within the active In-Memory memory pool in-place.

4. Operational Flow Diagram (Mermaid)

graph TD
    A[Orchestrator: Tick t] --> B[Call increment_hunger_vector]
    B --> C[Loop through all user_id keys in RAM]
    C --> D["New M_hunger = min(1.0, old + rate)"]
    D --> E[Atoimic pointer rewrite in memory]
    E --> F{Next user_id?}
    F -- Yes --> C
    F -- No --> G[Batch metabolic step complete]

5. Production Prototype Implementation (Python)

class RegistryHungerIncrementer:
    def __init__(self, states_storage: dict):
        # Bind by reference to the centralized In-Memory dictionary
        self._states = states_storage

    def increment_hunger_vector(self, rate: float) -> None:
        """
        Performs a high-speed batch increment of metabolic parameters
        across all digital twin vectors locked in host RAM.
        """
        if rate <= 0.0:
            raise ValueError("Коэффициент метаболического сдвига должен быть строго больше нуля.")

        for user_id, twin_state in self._states.items():
            # Calculate linear increment with upper saturation limit
            new_hunger = min(1.0, twin_state.m_hunger + rate)
            
            # Atomic swap of the property using Pydantic update mechanics
            self._states[user_id] = twin_state.copy(update={"m_hunger": new_hunger})