Data Processing Applications TRIZ Cases

Hybrid edge-cloud workload allocation balances data control, processing capacity, and carbon impact in building management computing.

When a master DER fails in islanded microgrid operation, the server reassigns control to the storage unit with the highest remaining capacity.

A single sensor plus equipment identifiers builds a virtual coating line model to track multiple units with lower wiring complexity and cost.

Voice and visual elevator announcements help users recognize autonomous vehicle boarding even in noisy buildings, reducing delays.

Height-based safety zones let robots detect vulnerable people and reroute or slow down in real time to reduce collision risk.

Flowback and real-time well data feed a machine learning model that predicts choke valve failure early, reducing shutdown and safety risks.

ML models parse and correlate air route traffic data to predict sector counts and support flight plan adjustments that reduce delays.

Adaptive IIoT sampling uses production, model, and equipment data to target part inspections and cut inspection effort and cost.

Predictive productivity maps and transport availability guide harvester routes to cut non-harvesting time and improve field throughput.

An LLM agent coordinates expert models across P&IDs, narratives, and tables to find contradictions and build consistent structured data.

Two linked remote diagnostic paths keep vehicle data and control signals flowing, enabling self-diagnosis with real-time technician support.

Dynamic workload allocation across edge and off-premises building devices improves software testing, response efficiency, and carbon-aware computing.

Operation data from home appliances trains AI models to detect new trouble patterns more accurately than fixed rule-based diagnosis.

A drone switches between Li-Fi and Wi-Fi while checking trip energy and conditions to keep remote data flowing or divert to charging.

Real-time biometric and image analysis detects passenger tension or emergencies and triggers relief actions or flight path changes.

Dimension reduction and influence scoring isolate the process parameters most linked to product defects, speeding fault localization.

A BIM-based building view highlights hidden or unknown equipment locations from real-time and static data, giving a clear overview of problem areas.

Airborne LiDAR maps storm-damaged utility networks in 3D to pinpoint downed poles and wires for faster repair prioritization.

Stored user ID and berth position let a marina ferry self-navigate to the right watercraft while blocking unauthorized use.

Integrated geographic simulation layers reveal cross-sector infrastructure dependencies and vulnerabilities for faster disruption response.

Load receipts, off-load weights, and remaining site capacity are used to route excavation assets and avoid overfilled destination sites.

Automatically generated PLC code sequences valves, conveyors, and engines to reduce manual errors in factory material flow control.

ML-built process graphs match isomorphic production steps across naming variations, helping reuse manufacturing equipment with minimal retooling.

Balances secure on-premises processing with cloud capacity by partitioning building workloads and shifting noncritical tasks to lower-carbon times.

An interactive AI control approach captures KPI targets, process variables, and equipment constraints to improve energy, quality, and stability.

User feedback and historical asset data help an LLM refine prescriptive alerts without machine-specific rules or maintenance-system integration.

An LLM agent coordinates multi-source EPC engineering data and expert tools to detect contradictions and produce consistent structured output.

A crane or excavator carries a 2D camera to capture site images for fast 3D models or orthophotos without UAV or LIDAR complexity.

Location and asset ID let a central server verify presence, then apply the right license and configuration without manual code entry.

Configurable multi-tenant tools and AI analytics let manufacturers customize data, reports, and schedules without losing cloud integration.

Weighted consolidation of defect-cause models helps generate faster, more consistent remediation measures without relying on individual experience.

Tenant-level AI-assisted configuration of databases, templates, and reports expands industrial data integration without sacrificing cloud scalability.

Visual codes on passenger devices enable secure UAM boarding and confirm identity while transferring flight and UAS configuration data.

Dynamic relay selection separates management and communication roles, keeping remote vehicle support scalable and operating through relay failures.

Iterative water-level trajectory optimization cuts invalid calculations in cascade reservoir scheduling while stabilizing total hydropower output.

Metaheuristic vehicle routing plans multi-drone inspection checkpoints to improve coverage, battery use, and collision avoidance.

Workpiece-specific data sets combine sensor and facility parameters to reveal systematic production faults and guide automated quality optimization.

UAVs pick up unassigned packages first, then receive task updates after identification to reduce loading delays and battery waste.

Haul-vehicle arrival timing guides unmanned water sprinkling to suppress mine dust without disrupting site productivity.

Route guidance is timed to a predicted safe vehicle position, so drivers can change routes without late or unsafe instructions.

Autonomous 3D point-cloud scanning updates BIM/CIM object positions from target markers, cutting manual site photography and worker exposure.

A graph neural network scheduler estimates job rewards from machine state to cut weighted completion time and adapt to dynamic arrivals.

Dynamic relay selection and centralized resource management keep moving-body remote support fast and stable as scale changes or devices fail.

A device twin consolidates application access to industrial automation devices, cutting OT network traffic and improving response times.

Operator location tracking cancels unneeded equipment alerts once one responder is moving, reducing wasted trips and preserving work efficiency.

Telemetry-driven recommendations match asset issues to software upgrades and maintenance actions, helping reduce downtime and failure risk.

An HMM-based approach combines smart meter data with external factors to infer device-level household power states without smart plugs.

Wearable worker tracking feeds real-time productivity and safety data to adjust line speed, reducing defects, fatigue, and injury risk.

Device twins consolidate app access to industrial automation devices, cutting OT network traffic and improving response times.

Passenger feedback is correlated with flight and weather data to identify discomfort causes and trigger rerouting or cabin-condition changes.

Maximum demand power controller adjusts device operation times to distribute peak electrical load across the grid.

Approximating continuous values at discrete timestamps resolves timestamp discrepancies that cause incorrect query results.

Automated verification adjusts statement requirements by urgency level, resolving manual supervision delays in gas network operations.

A portable electronic device retrieves time-correlated data from bulletin services to associate metadata with captured media content.