Data Processing Applications TRIZ Cases

Time-series operating data is matched to modeled process patterns to identify preparatory, main, and troubleshooting states for accurate revenue management.

A unified semantic model maps KPI variables to production-line data sources, cutting configuration effort while keeping data acquisition flexible.

Dual IC tags on screws and tray positions let the server verify correct placement and alert workers before fastening errors occur.

A two-stage abnormality check flags system-level anomalies first, then isolates abnormal elements with lower calculation and memory load.

Dynamic risk evaluation lets production equipment switch to lower-risk items or components automatically, reducing non-usable output.

Unique match codes and geo-fenced pickup zones let riders and drivers pair directly during mass egress, cutting confusion and wait times.

A virtual factory meta model automates PFMEA across changing production steps to maintain quality evaluation speed and accuracy.

Assigned uncertainty data for each module enables runtime propagation of input errors into output confidence in flexible modular plants.

Sensor-driven blended scheduling combines usage and operating data to predict generator maintenance, cut downtime, and avoid unnoticed faults.

Dynamic schedule changes advance, delay, or insert autonomous vehicles based on boarding demand while maintaining stable spacing and service.

Main and secondary setting versions with status-based access cut commissioning delays while preventing unsafe overwrites in container lines.

Runtime uncertainty propagation links each modular entity to output confidence, helping flexible plants adapt configurations without losing precision.

Machine learning recommends catheter instruments and use forms from patient and operation data to improve selection accuracy and reduce clinician workload.

Linked sector simulation layers reveal cross-infrastructure vulnerabilities and cascading effects under cyberattack and disaster scenarios.

Independent IIoT data paths match failure codes to self-repair instructions, cutting manual troubleshooting and line stoppages.

A site management vehicle coordinates autonomous construction implements with obstacle detection and human override to reduce crowded, hazardous work zones.

Historical stream matching adjusts boundary values from embedded metadata, reducing nuisance alarms, downtime, and energy waste.

A sensor-equipped mobile machine maps work surfaces and guides a slave machine, improving obstacle avoidance, monitoring accuracy, and safety.

Assembly data from control modules is checked against reference patterns to detect errors and guide module removal, replacement, or addition.

Past alert cases, operator actions, and resulting facility states are analyzed to recommend faster, verified responses to sensor alerts.

Coordinated networked and master-slave control aligns drilling subsystems in real time to stabilize mud circulation and prevent hardware damage.

Preprocessed georeferenced field data is overlaid on agricultural surfaces to replace slow spreadsheet correlation and support faster farm decisions.

A five-platform Industrial IoT automates balance-rate calculation across production lines, cutting manual workload, errors, and adjustment time.

A BACnet gateway links IoT fixtures to building systems for scheduled control, real-time monitoring, and predictive maintenance alerts.

A neural network separates tool sounds from actuator noise in mixed manufacturing acoustics to improve anomaly detection and task control.

Selective reception checks and retransmission keep industrial machine update data consistent while cutting redundant client communications.

Monte Carlo state modeling and Benders decomposition improve energy storage planning when power-gas coupling and gas-side failures affect grid reliability.

Continuous error log collection and selective analysis turn machine faults into real-time alerts and more accurate maintenance scheduling.

Augmented reality links machine data points to collection devices, simplifying accurate setup for industrial machine monitoring.

Aerial drones relay and normalize yield data from farm machinery, reducing USB-based data loss and enabling real-time decisions in low-connectivity fields.

Historical and real-time log analysis builds a process model to detect abnormal manufacturing events without added sensor infrastructure.

A grid map of nozzle, header, and feeder combinations turns manufacturing logs into fast fault isolation for suction-step productivity losses.

NLP-based threat categorization, geofencing, and expiry prediction help building security teams prioritize risks with less manual alarm review.

Fusing asset events from disparate databases into consolidated records cuts redundancy, simplifies retrieval, and speeds critical fault response.

Automated IoT data collection and deep learning unify measurement and indicator data across bases for faster quality issue prediction.

Tracks nutrient content and movement between farm locations using sensor and GPS data to improve nutrient utilization and reduce waste.

A drone lands on and rides with a vehicle to extend range, conserve battery power, and stay within flight regulation limits.

Accentuated production chart displays isolate retention periods across mixed-model step sequences, making productivity issues easier to spot.

Tool-log comparisons reveal IC recipe similarity without accessing restricted recipe data, reducing engineering time and improving tool monitoring.

Orthographic images and structured work data create shareable trench excavation plans with route lines, sequence points, and tolerances for precise execution.

Distance and bearing checks against road links score probe points, filter low-quality data, and improve traffic analysis confidence.

SLAM maps, design drawings, and robot-cleaner images build a 3D home view for precise in-vehicle appliance control.

By matching model parameters to real plant measurements, this case improves facility state evaluation and detects deterioration earlier.

Multi-resolution analysis and deep learning separate environmental drivers from soil gas variation to detect early CO2 storage leakage.

Combining additive and removal machining data, this case generates viable process patterns that cut trial-and-error, time, and cost.

Real-time flow sensing and incremental valve throttling keep water use within limits despite pressure changes while avoiding full shut-off.

Wireless status monitoring links lubricating oil injectors to terminals and servers, reducing manual checks in hard-to-reach machinery.

Production vehicles compare baseline and test worldviews to weakly annotate sensor data, cutting manual labeling time and cost for ADS perception.

Stored machining performance data is used to sequence additive and subtractive steps, helping inexperienced users choose manufacturable process patterns.

Automated vehicles geocode target data with GPS, sensors, and feature matching to map remote assets faster without manual collection.