Data Processing Applications TRIZ Cases

Real-time work machine position tracking lets autonomous vehicles choose pickup targets, cutting operator wait time and site disruption.

ID-linked history and position checks help reused vehicle batteries keep accurate voltage prediction and prevent unauthorized secondary use.

When Level 4 driving reaches ODD limits, secure mobile and server links enable trip initiation plus safe remote handoff, parking, and summoning.

Calendar integration lets a ridehail app schedule and dispatch autonomous vehicles automatically, removing manual trip planning for events.

When a vehicle sensor fails, coordinated handover to another sensor keeps sensing data flowing and supports safer, more reliable driving.

Sensor-map matching flags when autonomous driving data is unreliable, preventing wrong map updates during GPS signal loss.

Production vehicles compare new perception outputs with baseline worldviews to update ADS models locally and cut development time and cost.

Wireless ECU update delivery uses an in-vehicle gateway and server to replace wired servicing and coordinate multi-vendor updates across fleets.

Complex agricultural labels are converted into machine-readable procedures and mapped prescriptions to improve application accuracy and compliance.

Priority-tagged secure IP packets let active grid elements send real-time power and curtailment data without losing reliability.

A master control unit writes and verifies programs across multiple ECUs before final assembly, cutting vehicle production time and checks.

CO2 emissions from battery manufacturing are apportioned by SOH at recycling, creating a fairer burden split between automakers and recyclers.

Combining vital signs with internal crash injury signals lets the vehicle rank life-criticality and support faster, better-targeted rescue.

Dynamic SoS upper thresholds guide charging and discharging to cut power dissipation and improve energy storage utilization.

Deep learning on CAN, vibration, and noise data enables bidirectional vehicle diagnostics that cut unnecessary repairs and improve driver guidance.

Uses smartphone heading, GPS, and accelerometer data to distinguish driver phone handling from passenger use during vehicle turns.

Event timing analysis reveals facility operation pattern changes without direct power metering, helping revise energy-saving control plans.

Real-time string current and irradiance analysis identifies abnormal photovoltaic array states more accurately than infrared image methods.

A formal ontology maps driving scenes to ODD definitions, reducing mischaracterization and improving autonomous vehicle safety checks.

Continuous in-flight analysis of aircraft system data enables earlier failure prediction, corrective action, and safer operation.

An optimization algorithm uses facility peak shaving assets to cut maximum power exchange and support lower demand-charge contract limits.

CO2 emissions from battery manufacturing are split by battery deterioration at recycling, giving recyclers a fair share of the burden.

Presence sensors and external microphones let a vehicle detect spoken emergency requests outside the cabin and place a hands-free call.

Unsupervised clustering partitions gross building load data into time-based usage patterns, revealing causal energy waste in complex facilities.

Processes IED energy signals to detect and quantify changes, link them to likely loads, and surface actionable electrical issues.

Dynamic scheduling uses weather, occupancy, and energy pricing data to keep property temperatures comfortable at lower cost.

Automated planning, die transport, inspection, and packaging turn manual stamping lines into adaptive unmanned production with consistent part quality.

Pressure sensors and control valves balance local water pressure zones to maintain minimum service pressure while reducing leaks and pipe fatigue.

Measured deviations and correlational impact values guide die parameter adjustment, cutting debugging time and resource waste while supporting stable production.

Separating measurement and system control on linked buses speeds signal processing and simplifies functional safety verification.

Captured panel identity, dismantling data, and recycling results are stored on a blockchain ledger to make solar panel recycling supervision credible.

Multi-source gas and discharge data drive cleaning parameters and robot control to prevent impurity buildup from degrading filtration and gas quality.

Separate safety radio and gaming Wi-Fi links let kart requests be validated by position before speed changes or effects are triggered.

Cloud long-term scheduling and local backup control improve compressor switching, energy use, and safety under predicted demand.

Historical and current field data predict harvester and grain cart routes to cut transfer delays, downtime, and crop quality loss.

Blockchain oracle data validates product and pre-product plans across production entities to support secure re-planning and resource control.

XR overlays on a wearable terminal highlight autonomous traveling devices whose capabilities match user service requests, speeding selection.

Image analysis and sensor data predict gas tank aging and damage, reducing manual inspection time while improving maintenance accuracy.

Criticality-based code segmentation turns plant control logic into deployable services across PLC, edge, and cloud environments while preserving reliability.

Sensor-based scoring maps suitable and unsuitable mowing zones where GNSS blockage, slope, and obstacles affect autonomous operation.

Autonomous multivariate analysis surfaces hidden near-misses and anomalous process shifts before alarms, helping plants reduce downtime and accidents.

Position tracking identifies the first operator moving to faulty equipment, then cancels other alerts to avoid redundant response effort.

Real-time sensors and ML predict acoustic and electromagnetic impacts, then adjust offshore operations to protect aquatic ecosystems.

Drone imagery and generative AI turn disaster damage data into real-time 3D maps, speeding accurate assessment for rescue and recovery.

AI combines workpiece and system parameters to verify defects more reliably and trigger targeted process corrections in production.

Real-time sensor feedback and gas material models adjust pipeline conditions to limit hydrates, avoid phase shifts, and reduce fracture risk.

Pilot intent inferred from FMS inputs lets an EFB deliver turbulence-mitigation suggestions faster than full FMS feature updates.

Machine learning combines sensor and production target data to classify production machine downtime causes and speed operator response.

Equipment-linked registration data and usage records make shared component shape data more reliable across different mounters, cutting verification work.

Dynamic package identification lets UAVs pick up unassigned loads first, reducing loading time, idle battery drain, and delivery delays.