submission

[zysymu]

No description provided.

[github-actions]

AI Detection Analysis 🔍

Confidence Score: 90%

Reasoning: This pull request contains a comprehensive and well-organized submission that implements an elevator simulation system with event-driven architecture, data collection, and test cases. The system is designed for both demonstration and data generation for machine learning purposes. The code files are syntactically consistent, modular, consistently formatted, and contain rich inline documentation. The README is clearly structured with distinct sections, markdown formatting, and example outputs—all demonstrated in a tone common to LLM-generated technical writing (friendly, pedagogical, but precise). Furthermore, the design includes mock scenarios, parameterization, use of abstraction (e.g., Elevator, Building, Simulator), and coverage of business rule logic, which suggests a high-level overview rather than an implementation built progressively by a human over time.

Key Indicators:

• The README exhibits the LLM pattern of narrative scaffolded explanations with bullet points, code snippets, and example outputs—even simulated terminal output, which is often synthesized by AI models.
• Highly consistent, formal docstrings and inline comments in Python code that follow best practices rigorously—seen in all modules (elevator logic, data collection, simulation runner).
• The SQLite schema includes very clean, ML-consumable fields, indicating a generative model may have been prompted with "simulate elevator data for ML model."
• Topics like SCAN algorithm and 80% capacity logic are explained in a style that is slightly overly expository—typical of AI explanations rather than real-world engineering documentation.
• Tests cover an exhaustive set of edge and integration cases, implemented in a way consistent with AI-written test scaffolds.
• There's little evidence of iterative build-up, debugging artifacts, or human-specific inconsistencies (naming drift, ad-hoc solutions) that typically appear in complex human-authored systems.

Based on all of these elements and the coherence throughout the many files, it's highly likely an AI model generated this PR or contributed significantly to authoring it.

⚠️ Warning: High confidence that this PR was generated by AI