Metaverse-enabled virtual fencing and remote management system for livestock

Abstract

A livestock management system consisting of wearable GPS / IoT trackers assigned to the animals, edge controllers and cloud services that run AI models to analyze behavior and provide recommendations on grazing patterns, a virtual fence module to define and enforce dynamically adaptable geofences via interfaces attached to animals, and a metaverse visualization interface for remote monitoring and control of boundaries and herd movements.

Description

AREA OF INVENTION

[0001] The invention relates to precision devices for animal husbandry that integrate IoT location sensors, AI behavior analysis and software-defined virtual fences with immersive fem interfaces to optimize grazing, animal welfare monitoring and operational processes on a large scale. BACKGROUND OF THE INVENTION

[0002] Intelligent agricultural systems that combine sensors, AI, and connected applications improve resource utilization and operational decisions. Similar AIoT patterns that have proven successful in park and urban infrastructure can be applied to spatial optimization and real-time control problems in agriculture, provided latency and reliability requirements are met at the network edge and in the cloud. Advances in AI networking and metaverse-level visualization enable digital twins that map physical assets for situational awareness, planning, and control. Modern network architectures increasingly incorporate quantum-safe and robust security architectures for long-lasting IoT deployments that must withstand ever-evolving threats.For swift enforcement in Germany, device / system claims with functional modules are suitable for utility model protection, while purely methodological claims are generally excluded. This leads to a product-oriented formulation for virtual fences and remote management devices. SUMMARY OF THE INVENTION

[0003] The invention provides a metaverse-based system for managing pasture areas. It comprises lightweight GPS / communication trackers for animals, edge controllers, and cloud services with AI models for position and behavior analysis and grazing pattern recommendations, as well as a virtual fence module. This module visualizes and monitors dynamically adjustable boundaries using harmless stimuli via collars or location-based guidance. Visualization is provided in an intuitive user interface for remote monitoring and control. The system replaces most physical fences with software-defined boundaries and routes, supports the early detection of health problems and anomalies based on behavioral traits, and is scalable for pasture areas and herds. Policy-based scenarios for rotational grazing and pasture recovery are visualized in a persistent digital twin.A security and device management layer deploys the trackers, authenticates data streams, and logs decisions for audits. The system is compatible with robust AIoT architectures for managing critical infrastructure in distributed environments. DETAILED DESCRIPTION

[0004] Each animal wears a lightweight GPS / IoT tracker that reports position, movement characteristics, temperature (optional), and battery status at configurable intervals. Edge aggregators collect local data with low latency and synchronize it with cloud services that host the herd's digital twin for visualization and control within the metaverse. AI models at the edge infer behavioral states (grazing, resting, wandering, restlessness) from trajectories and inertia patterns, detect anomalies indicative of health problems, and calculate recommended grazing allocations and movement guidelines based on grazing maps, biomass targets, and animal welfare policies. Cloud-based planners schedule rotational grazing and boundary adjustments with transparent overlays on the 3D interface. The virtual fence module defines geofences and corridors as geodata polygons displayed in the digital twin.Rule compliance is monitored by tracker-equipped collars or signals that deliver graduated, animal-friendly stimuli (e.g., audio or vibration) as soon as animals approach the boundaries. The system adjusts thresholds using AI feedback to minimize stress and ensure adherence to the rules. Interactions are logged in the virtual system for later review. The Metaverse user interface displays real-time herd positions, grazing intensity heatmaps, predicted grazing recovery, and boundary checks. Operators adjust virtual fences, plan rotations, and assign sub-herds to paddocks using intuitive tools with policy checks to prevent overgrazing and protect sensitive areas. Changes are propagated to end devices within defined SLAs.Reliability is ensured through the autonomy of the end devices: In the event of a backhaul connection failure, the local controllers temporarily store policies, continue geofencing monitoring, and buffer telemetry data for later synchronization—a typical pattern for robust AIoT implementations that require continuous operation despite temporary network outages. Security adheres to the best practices of critical IoT systems: authenticated tracker deployment, encrypted telemetry, rotating keys, and quantum-resistant key exchanges on management channels to future-proof Ranches' long-lasting infrastructure against the cryptographic risks typical of city-scale IoT.Sustainability and animal welfare metrics—distance traveled, time in shade, water access, pasture utilization, and anomaly alerts—are calculated and displayed alongside historical trends to enable evidence-based management. APIs export data to farm management systems and regulatory bodies to document animal welfare and health practices. The architecture scales horizontally by adding trackers and edge gateways per paddock, with site-wide dashboards for large operations. Firmware and model updates are orchestrated remotely, with staggered rollouts, rollbacks, and compliance logging to ensure security and traceability across all devices and policies.For protection under German utility model law, the object is described as a device comprising trackers, controllers, virtual border monitoring and metaverse visualization, which corresponds to product-oriented claims suitable for rapid registration.

Claims

[1] A livestock management system consisting of wearable GPS / IoT trackers assigned to the animals, edge controllers and cloud services running AI models to analyze behavior and provide recommendations on grazing patterns, a virtual fence module to define and enforce dynamically adaptable geofences via interfaces attached to animals, and a metaverse visualization interface for remote monitoring and control of boundaries and herd movements. [2] System according to claim 1, wherein the edge autonomy maintains geofence enforcement and telemetry buffering in case of connection loss, and cloud planners synchronize updated boundaries and schedules with edge controllers, with policy validation for pasture restoration and animal welfare restrictions. [3] System according to claim 1 or 2, wherein the metaverse interface generates a digital twin of paddocks, watering places, shaded areas and herd positions, thus enabling interactive boundary editing, route assignment and anomaly detection with live overlays of grazing intensity and health alerts. [4] System according to any of the preceding claims, wherein security and device management functions provide trackers, authenticate telemetry data and rotate cryptographic keys using robust or quantum-resistant key generation for long-lasting IoT implementations, and dashboards enable the operation of multiple sites with audit logs and remote firmware / model updates.