> For the complete documentation index, see [llms.txt](https://docs.owlverse.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.owlverse.ai/technical-framework-and-algorithmic-logic/advanced-compute-task-structure-preview-version.md).

# Advanced Compute Task Structure (Preview Version)

Each **Owl compute node** dynamically generates compute tasks based on current system status and predefined strategies. These tasks (ComputeTask) are used for:

* **Sentiment Monitoring** (e.g., community emotions, FOMO/FUD signals)
* **Whale Transaction Tracing** (real-time tracking of large on-chain flows)
* **Trending Token Extraction** (detecting CA activity surges)
* **Token Graph Building** (creating token interaction and exposure maps)
* **AI Model Sample Preparation** (for LLMs and custom predictive models)

#### 🔧 Task Generation Logic

```python
    def __init__(self, task_type, data_source, params, priority=0):
        self.task_id = generate_task_id()
        self.task_type = task_type  # e.g., 'sentiment', 'whale_trace'
        self.data_source = data_source  # ['ethereum', 'twitter', 'solana']
        self.params = params  # e.g., CA list, time range
        self.priority = priority
        self.timestamp = now()
```

***

### 🤖 Model Triggering & Smart Response (LLM & Strategy Engine)

Once a task is completed, the result is routed into the **intelligent preprocessing channel** and triggers a specialized strategy model (e.g., `TrendX-AlphaCore-v2`):

```python
def route_to_model(task_result):
    model = model_registry.get(task_result.task_type)
    response = model.process(task_result.data)

    if task_result.task_type == "sentiment":
        return parse_sentiment_signal(response)
    elif task_result.task_type == "whale_trace":
        return generate_alert(response)
```

The model may use multi-layer neural networks:

```python
class AlphaCoreModel:
    def process(self, data):
        vectorized = self.vectorize(data)
        signal = self.infer(vectorized)
        return signal

    def infer(self, x):
        return sigmoid(W3 * relu(W2 * relu(W1 * x + b1) + b2) + b3)
```

***

### 🧩 Data Fusion → On-chain Trigger

All outputs are processed through the **DFG (Data Fusion Graph)** engine, which merges sentiment, whale activity, and contract data to generate composite scores.

```python
def generate_fusion_graph(sentiment, whale_flow, ca_activity):
    fusion_score = sentiment["score"] * 0.6 + whale_flow["weight"] * 0.3 + ca_activity["rank"] * 0.1
    if fusion_score > 0.8:
        trigger_alert(ca_activity["contract"])
```

Alerts can be pushed to Telegram bots or committed directly on-chain:

```python
def trigger_alert(contract):
    tx = build_contract_alert_tx(contract)
    send_to_blockchain(tx)
```

***

### 📡 API Example (ComputeNode ↔ Owlverse Server)

```json
POST /compute/task
{
  "wallet": "0x123...",
  "task_type": "sentiment",
  "ca": ["0x456...", "0x789..."],
  "time_range": "3600s"
}

GET /compute/result?task_id=abc123
{
  "task_id": "abc123",
  "result": {
    "score": 0.92,
    "trend": "positive",
    "top_keywords": ["buy", "moon", "whale"]
  }
}
```


---

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