Data Processing Applications TRIZ Cases

Four composable digital twin cores cut IIoT deployment time while enabling complex event processing and actionable asset insights.

When multiple machines stop, downstream productivity estimation helps teams recover the equipment that most improves subsequent-process output.

A cloud-coordinated IIoT platform breaks data silos, compares target and standard factories, and adjusts energy distribution in real time.

Sensor-equipped picker carts and a field computer predict when and where full containers need pickup, cutting walking time during harvest.

A layered card mat lets folded card sections fit small electronic cutters, enabling precise large customized card cutting for home use.

Adaptive data retention and collection frequency help smart gas pipeline IoT maintenance preserve storage performance and improve maintenance accuracy.

Predictive fleet scheduling assigns autonomous vehicles to future states to avoid battery depletion and depot congestion while meeting demand.

Correlating machine and environmental sensor data helps flag quality issues early, reducing lag in root-cause detection and response.

Quantitative capacity models compare multiple fault countermeasures across evaluation indexes, cutting factory planning time and effort.

Automated SAT generation converts target specifications into inspection criteria, speeding attraction component checks while maintaining accuracy.

Priority-based floor sequencing helps logistics robots deliver across multiple building floors while reducing congestion and delivery risk.

Real-time load receipts and destination capacity tracking help route excavated material efficiently, cutting haul costs and environmental impact.

By excluding outliers and condition-change data, this case clarifies true production capability and speeds improvement prioritization.

Nutrients are split into fast, medium, and slow digestion fractions to improve delivery timing, availability, growth, and gut health.

Social graph data guides drone image capture by face, location, and time while reducing manual setup and improving shared drone use.

Adaptive obstacle avoidance creates a closed mowing route that cuts unnecessary travel, power use, and coverage inefficiency.

Attachment-state monitoring prompts operators to add or remove temporary communication devices, preventing remote control setup errors.

GUI-based goal-state specification makes hierarchical workflow planning less error-prone while preserving abstraction and plan feasibility.

Intermediate representations align data from different apparatuses, correcting facility differences while preserving confidentiality for shared prediction models.

By pairing tolerance-extreme parts with matching counterparts, this case raises component combination rate while keeping dimensions within target range.

RFID tag reading on moving medical devices replaces manual checks and removes mismatched units from high-throughput production lines.

Pre-positioned loading bridges align with forward and rear aircraft doors, cutting docking adjustment time and speeding passenger transfer.

Power-quality sensing at pivot or utility disconnect points predicts irrigation component failures early, reducing field breakdown delays and repair costs.

Georeferenced visual indicators combine machine position with field data to reveal crop, pest, and fertilizer anomalies in real time.

Crowd-sourced telemetry and workload data identify lower-load nearby systems where failing hardware can be reused to extend component life.

Real-time calorific value sensing adjusts gas pressure parameters to stabilize flow and gas use efficiency across changing supply conditions.

Instrumented picker carts and a field computer predict full-container timing and dispatch robots early to cut picker walking time.

Machine-readable tool IDs and workstation data updates enable continuous composite-part flow while cutting cycle time and work in progress.

Model-based reinforcement learning uses sensor feedback and computational graphs to replace static greenhouse rules with adaptive control.

Planning data is converted into machine-ready processing steps, reducing manual intervention and improving switchgear manufacturing accuracy.

Machine learning uses historic activity data to recommend sensor types and locations within workflow boundaries for more accurate KPI monitoring.

Continuous PSD-based sensor analysis tracks wind turbine tower modal changes offshore, helping maintain reliable control as site conditions evolve.

Coordinated setup and material delivery timing keeps substrate lines ready at production start, reducing standby time and in-process inventory.

Predictive data ranking and set-point deviation maps help operators spot machine drift in real time and prevent paper-making breaks.

Moving road vehicles let delivery drones land for wireless charging and UAV-to-UAV servicing, cutting downtime without returning to base.

Guided smartphone imaging captures key surfaces with reference aids to build a 3D model for accurate remote key duplication.

Grid-based clustering of valve process signals improves defect detection accuracy and speed while reducing inspector-dependent errors.

Automated wellness indexing combines sensor and occupant data to assess building health risks and trigger remediation actions.

Fuses soil salinity, moisture, and nutrient data with a knowledge graph to recommend irrigation and fertilization plans for saline-alkali land.

Aerial vehicles relay GNSS-based position data to farm machines in poor-connectivity areas, enabling reliable location tracking and yield analysis.

A virtual plant mirrors actual plant status to compare worker actions with operation plans, improving training realism and evaluation accuracy.

Sensors on an under-canopy unmanned vehicle identify forest objects and mark trees consistently, cutting harvest planning time and cost.

A shared action plan is translated into controller-specific control data, enabling coordinated facility control without modifying mixed controllers.

Graphical comparison of stored sheet-metal drawings highlights geometric differences, helping operators sort similar workpieces faster and with fewer errors.

Deep learning updates field travel paths in real time to account for terrain, weather, and machine limits, cutting fuel use and work time.

Deep learning updates field travel paths from recorded machine routes to cut fuel use, soil compaction, and operating time.

Historical usage and operation data drive predictive maintenance and traffic regulation to cut gas station failure risk with less disruption.

On-demand imager activation on boarding-type mobile objects improves target search while reducing user discomfort from continuous photographing.

Machine-readable codes link weld training results to LMS activities, making high-quality performance data easy to share, access, and compare.

A variable safety area and movement alarm cut unnecessary emergency stops for automated replacement units moving along production lines.

Automated system replaces manual verification to reduce errors while improving labor efficiency in biological fluid tracking.

A nondestructive data pipeline tags raw agricultural sensor data with identifiers to enable efficient re-translation and normalization.

An evergreen index pairs candidate topic models with hierarchical topics to categorize digital information.

Periodic location updates resolve misrouting bottlenecks by dynamically shifting calls to suitable PSAPs as callers move, reducing response delays.

A controller compares current gaming media amounts against initial login values to trigger display messages.

Aggregating diverse electromagnetic field sources into a unified database improves modeling accuracy and identifies biological hazards.

Cloud computing system updates service level agreements based on actual usage levels to select compliant services from primary and secondary clouds.

Applying a susceptible-infected-recovered model to bucketed social media items identifies breakout points and reduces false positives in virality prediction.

Optical sensor arrays detect light pulses to enable offline communication and authentication without cellular networks.

A cognitive computing system monitors user interactions to learn preferences and prioritize relevant social media posts.

An image managing apparatus identifies connected medical imaging devices and transfers files to recording systems.

Grouped photo library presents recommended files within the social network interface for direct selection and upload.

System monitors location and check-in status to send automatic distress notifications, eliminating manual calls during incapacitation.

A user state determination device analyzes moving history information to identify specific locations where users likely lost articles.

A caregiver location system switches tag transceivers between low and high power modes based on movement detection.

SOAP-based alert notifications traverse firewalls and VPNs to deliver reliable management signals across distributed networks.

Segmenting the display into a coded image and an uncoded remaining image resolves user confusion about which specific area received the information code.

Security panel echoes its user interface to a support site using a lightweight binary protocol, eliminating on-site installer visits.

Machine learning models process SCADA data to identify redundant air quality monitors, reducing instrument costs while maintaining emission detection accuracy.

A central host system activates pre-printed lottery tickets at the point of sale via electronic communication with vending terminals.

An automated content syndication service retrieves, tags, and indexes media items from multiple sources to deliver fresh content without manual maintenance.

A relationship analytics platform measures interactive effort and personal growth using continuous user feedback.