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https://github.com/deepmodeling/Uni-Lab-OS
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* Add post process station and related resources - Created JSON configuration for post_process_station and its child post_process_deck. - Added YAML definitions for post_process_station, bottle carriers, bottles, and deck resources. - Implemented Python classes for bottle carriers, bottles, decks, and warehouses to manage resources in the post process. - Established a factory method for creating warehouses with customizable dimensions and layouts. - Defined the structure and behavior of the post_process_deck and its associated warehouses. * feat(post_process): add post_process_station and related warehouse functionality - Introduced post_process_station.json to define the post-processing station structure. - Implemented post_process_warehouse.py to create warehouse configurations with customizable layouts. - Added warehouses.py for specific warehouse configurations (4x3x1). - Updated post_process_station.yaml to reflect new module paths for OpcUaClient. - Refactored bottle carriers and bottles YAML files to point to the new module paths. - Adjusted deck.yaml to align with the new organizational structure for post_process_deck. * Add PLC communication guide for AI4M Add a comprehensive developer guide (docs/developer_guide/add_PLC.md) describing the PLC integration standard used by Uni-Lab for workstation devices, using the AI4M implementation as reference. Covers rationale for using OPC UA, the opcua_nodes_*.csv node-table format, communication base classes (BaseOpcUaClient / OpcUaClientWithSubscription), data types, and subscription/cache/reconnect behavior. Documents driver patterns for AI4MDevice, three handshake paradigms (pulse, parameter handshake, id-based), registry/graph configuration (YAML/JSON), debugging tips (KEPServerEX sim, standalone run), and a checklist for onboarding new PLC-controlled equipment.
