The performance of pretrained models heavily depends on the quantity and quality of available data . Small models have fewer parameters and limited representational capacity, so with insufficient data, they struggle to learn generalized features. This often leads to underfitting or overfitting. As noted in technical documentation (e.g., Hugging Face), local adaptation typically requires large, diverse datasets—something small models handle poorly.

Government documents emphasize “one-form application, one-set materials.” Form-level optimization enables cross-department workflow consolidation.

In enterprise data governance, the process owner is responsible for how data is handled within a business process, while the data owner is accountable for the overall quality, integrity, and security of that data. Therefore, process owners should report their data-management work and issues to the data owner.

City perception focuses on real-time sensing of running status via IoT and sensors. Typical scenes are intelligent connected transport (A) and urban lifeline monitoring (water, power, gas, etc.) (C). OA and project approval (B, D) are process/office systems rather than direct sensing scenarios.

One-Network Unified Management is built around a closed loop of discover → dispatch → handle → analyze, forming four centers that support continuous improvement in city governance.

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A city digital platform must support heterogeneous compute (CPU, GPU, etc.) , elastic scheduling for load changes, cloud–edge collaboration to optimize latency and bandwidth, and a multi-algorithm repository to manage, store, and iterate bureau-specific models.

IOC integrates real-time monitoring, cross-department coordination, emergency command, and big-data–based decision support, thus improving monitoring, governance, emergency response, and policymaking.

Intelligent connectivity (V2X, cloud, etc.) enhances perception and decision-making , supports new auto-industry growth , and helps optimize traffic flow , so it is considered a key solution in all three areas.

The description matches data aggregation/ingestion , which focuses on bringing multi-source, heterogeneous data (structured and unstructured) into a unified platform across scenarios. Sharing, governance, and asset management are later stages built on this aggregation.