Data Processing Applications TRIZ Cases

A reconfigurable vehicle software module replaces hardwired upfitter integration, cutting upgrade effort while maintaining compatibility with new upfit modules.

Machine learning predicts steel pipe out-of-roundness after expansion, helping optimize bending conditions to cut lead time and cost.

Iterative coding and adaptation schemes let one IIoT production line framework fit multiple products while cutting development cost.

Random package pickup with real-time ID scanning updates each UAV's delivery task, reducing loading delays, idling, and battery waste.

Coordinated vehicle position control reserves traffic slots so cars can cross level intersections without stopping, reducing congestion and travel time.

Local verification of licensed and actual relay parameters blocks unsafe substation operation without relying on internet license servers.

Environmental sensor data is used to select relevant production sensors and adjust measurement cycles and sensitivity automatically.

Discrete simulation shortens semi-fixed planning periods while keeping fixed-plan preparation ready and avoiding overlap with fluid work.

Game actions are converted into control patterns that synchronize adult toy stimulation across users, improving consistency and engagement.

Rule-based workspace validation blocks invalid plant connections and supports simulation of load, storage, and resource allocation under pricing constraints.

AI-equipped drones and robots identify plant issues across large fields, replacing labor-intensive grower spot checks with scalable monitoring.

Real-time consumer context and feedback guide a self-driving vehicle to demonstrate the most relevant features during a pending transaction.

Prediction error and contribution analysis reveal which manufacturing conditions caused metal material quality abnormalities over time.

Mobile base stations and robots maintain worker safety monitoring and alerts where GPS and wireless links are unreliable.

Staggered vehicle starts and adaptive progress targets reduce bottleneck queuing and keep fleet mission cycles moving faster.

Segmented image matching narrows board-pack searches to relevant groups, cutting processing time while preserving identification accuracy.

An aerial vehicle relays GNSS-based position data to farm machinery in poor-connectivity fields, avoiding USB transfer loss and enabling timely decisions.

A dual Product Controller and Operation Controller loop keeps product state aligned with sensor feedback, enabling flexible manufacturing without machine rework.

Hierarchical classification groups link facility power data across buildings, workplaces, and equipment to reveal unnecessary energy use.

Autonomous or remote vehicle driving removes driver handoff and fixed conveyors, cutting transfer time and improving factory layout flexibility.

Real-time wellness indexing combines occupancy, air, water, and health data to detect building health risks and guide remediation.

Operational state data reshapes alarm rules so OT monitoring can separate operator actions from attacks and cut false alarms.

Decentralized blockchain control secures autonomous smart grid transactions and settlement while reducing SCADA cyber vulnerabilities.

RFID-tagged component reels link unique IDs to cloud records, improving inventory control, shipping accuracy, and part selection.

Machine-learning on vehicle assignment and position logs predicts future congestion in target areas, enabling better route and vehicle selection.

A gateway checks work orders and converts raw automation data into abstracted or anonymized outputs for secure, policy-compliant cloud access.

Grouping HMI parts with identical attributes lets one PLC signal update multiple display states, cutting control logic and communication load.

Map-defined start and end points let a terminal extract only route-specific work vehicle operation data, cutting mining evaluation time and labor.

Predicted demand guides UAV pre-staging and payload reconfiguration, cutting extra flight legs, delays, and delivery costs.

A telematics-guided vehicle carries and coordinates drone takeoff and landing to extend UAS range, cut battery use, and avoid restricted zones.

Coupled encoding and dynamic DRL rewards preserve continuous-discrete feature relations for adaptive manufacturing data characterization.

Restricting boarding after a designated stop lets depot-bound circuit vehicles still deliver passengers to their intended alighting stop.

An in-room signaling and detection setup shares occupancy status with authorized staff to prevent unwanted entry without exposing guest privacy.

Private in-room status signaling lets staff request access without corridor panels, reducing unwanted entries and protecting guest privacy.

Priority-based field access resolves task conflicts between agricultural machines, reducing delays and improving operational efficiency.

Machine learning tunes textile production settings from trial runs and detected effects to cut waste and keep quality stable across changing conditions.

A networked virtual experiment platform synchronizes teacher and student actions to cut licensing cost and enable safe remote lab participation.

Machine learning labels industrial datapoints with location, device, and unit IDs to cut expert analysis time and improve monitoring accuracy.

A time-chart approach maps synchronizing and acknowledgment signals into sequence programs for accurate interlocking control of multiple production units.

A ceiling-mounted router moves on rails to avoid crop blockage, improving greenhouse wireless reliability without dense fixed router installation.

GPS geofences associate plant and machine checkpoints to measure full haul-truck cycle time and filter false checkpoint detections.

LSTM-based error trend analysis predicts livestock house sensor remaining life, enabling timely maintenance and stable environment data collection.

A virtual single-stage plant model simplifies multistage pre-blending, improves spec compliance, and supports efficient product routing.

Event-triggered ripple control lets power and water devices request help only when local limits are reached, improving disruption resilience.

Real-time machine state analysis flags when thermal displacement or tool-wear actions are needed, easing operator burden while protecting dimensional accuracy.

Semantic ID mapping aligns semi-structured AAS fields with fixed database fields, enabling unified asset collection and remote control.

Builds hierarchical fault trees from component links and stored causal data to avoid unrelated factors and missing failure paths.

Coordinated ore flow targets across blasting, crushing, transport, and storage reduce interruptions and avoid local suboptimization in mines.

Machine learning links object state, device conditions, and prior outcomes to estimate process settings that stabilize multi-step manufacturing quality.

Only failure-relevant logs from upstream and downstream equipment are sent for diagnosis, preserving confidential production data.