
从上游生产系统获取实时需求信号。
评估自主移动机器人的电池电量和健康指标。
计算 AMR 和单元的最佳任务-资源映射。
根据当前负载条件将资源分配给工作站。
记录性能数据,以便进行持续的系统改进。

Ensure all hardware and software prerequisites are validated prior to system activation.
Validate bandwidth capacity supports real-time AI inference requirements without packet loss.
Confirm UPS systems and grid connections can sustain peak load during simultaneous operation.
Ensure all endpoints meet enterprise security standards before connecting to production networks.
Verify ETL processes are ready to ingest telemetry data for analytics and reporting.
Secure sign-off from operations and IT leadership regarding resource budgeting and scope.
Confirm all hardware meets local safety and environmental compliance regulations.
Deploy a single unit cluster to validate allocation logic under controlled conditions.
Expand deployment across multiple sites while monitoring resource contention metrics.
Refine allocation algorithms based on collected telemetry and operational feedback.
资源利用率:衡量在特定时间窗口内,可用的机器人积极分配给生产任务的百分比。
任务完成时间:跟踪自主移动机器人和固定自动化单元从任务分配到完成的平均持续时间。
电池效率评分:计算 AMR 在标准运行程序期间的每单位距离的能量消耗。
Manages distributed workloads across robotic nodes dynamically based on task priority.
Processes real-time sensor data locally to minimize latency and bandwidth consumption.
Provides standardized endpoints for tracking resource usage and health status of all units.
Optimizes power draw across the fleet to extend operational uptime and reduce costs.
Define acceptable thresholds for AI inference delay to prevent task failure.
Establish manual override procedures if automated resource allocation fails.
Assess third-party API stability and potential lock-in scenarios early in planning.
Schedule maintenance windows to minimize disruption during system updates.