让AI助手能安全操控工业设备，通过协议适配与模拟测试验证可靠性。
IndustriConnect: MCP Adapters and Mock-First Evaluation for AI-Assisted Industrial Operations
- 设计MCP适配器将工业协议转为AI可调用工具，保留安全控制机制。
- 在7种故障、12种压力场景下完成2820次调用，正常任务全成功，故障处理有结构化反馈。
- 支持本地模拟测试和会话级恢复，适合工业AI系统开发者与运维团队使用。
AI助手虽能分解多步骤流程，但无法原生支持工业协议（如Modbus、MQTT/Sparkplug B、OPC UA）。本文提出INDUSTRICONNECT原型系统，包含一套模型上下文协议（MCP）适配器，将工业操作转化为可发现的AI工具，同时保留协议特性的连接性与安全控制。系统采用统一响应格式和“先模拟后接入”工作流，可在本地验证适配器行为。通过覆盖正常、故障注入、压力及恢复场景的确定性基准测试，共执行870次实验（其中480次正常、210次故障注入、120次压力测试、60次恢复测试），累计2820次工具调用，涵盖7个故障场景和12个压力场景。结果表明：正常任务全部成功；故障场景验证了适配器级别的uint16范围校验；压力场景识别出并发边界；所有三种协议均实现端点重启后的同会话恢复。实验结果提供了关于适配器正确性、并发行为和结构化错误处理的实证依据。
原文摘要 · Abstract (English)
AI assistants can decompose multi-step workflows, but they do not natively speak industrial protocols such as Modbus, MQTT/Sparkplug B, or OPC UA, so this paper presents INDUSTRICONNECT, a prototype suite of Model Context Protocol (MCP) adapters that expose industrial operations as schema-discoverable AI tools while preserving protocol-specific connectivity and safety controls; the system uses a common response envelope and a mock-first workflow so adapter behavior can be exercised locally before connecting to plant equipment, and a deterministic benchmark covering normal, fault-injected, stress, and recovery scenarios evaluates the flagship adapters, comprising 870 runs (480 normal, 210 fault-injected, 120 stress, 60 recovery trials) and 2820 tool calls across 7 fault scenarios and 12 stress scenarios, where the normal suite achieved full success, the fault suite confirmed structured error handling with adapter-level uint16 range validation, the stress suite identified concurrency boundaries, and same-session recovery after endpoint restart is demonstrated for all three protocols, with results providing evidence spanning adapter correctness, concurrency behavior, and structured error handling for AI-assisted industrial operations.
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