Data Processing Applications TRIZ Cases

Finite-element simulation and GRA-PAM RBF modeling improve tunnel water inrush prediction without drainage-driven rock instability.

Preconfigured failure codes and self-repair instructions let assembly line equipment recover faster with less manual troubleshooting and downtime.

Illuminance-based detachment detection lets a mobile asset sensing terminal enter sleep mode, cutting transmission power and battery replacements.

Predicting route-based assistance probability and priority helps reserve operators early while avoiding wasted preparation and delayed support.

Geofenced work zones and event-based deployment let autonomous machines avoid collisions, cut idle time, and keep site tasks moving.

Usage history guides work machine assignment toward less-used units, leveling wear and aligning maintenance and replacement timing.

A five-platform IIoT layout classifies inspection data at sub-platform level to cut processing burden, cost, and robot response delays.

An IoT-controlled emergency gas skid switches between tank and pipeline supply to match demand, improving outage response and lowering operating cost.

Edge devices analyze KPIs locally, request cloud context data, and download updated apps to cut network load and speed custom industrial analytics.

Mapped region assignment lets multiple mowers share large lawns by battery status, cutting mowing time and operator intervention.

A map-based GUI turns user-defined agricultural data operations into an executable operation tree for faster geospatial analysis and visualization.

Location-aware mobile authentication limits hot water settings by access point, reducing scald risk while allowing higher temperatures where needed.

SEC curves from flow and power data let compressor systems benchmark energy use and verify optimization savings within short time intervals.

Workpiece and facility parameters are fused into AI-driven datasets to expose systematic production faults and support real-time process correction.

Calculates gaps between production line models and measured machine data to generate adjustment plans that improve throughput and cost alignment.

Biometric and site sensor data from smart belts let an AI server detect burial alarms and generate rescue scenarios for faster construction response.

AI combines workpiece and system parameters with non-contact sensing to detect systematic production defects and improve treatment quality.

Multiple slide-table screw holes and an adjustment block expand stroke tuning while avoiding stopper bolt buckling and excess protrusion.

Allocating line power by each product's total work time improves per-unit CO2 accuracy in mixed-flow production without complex monitoring.

Sensor-driven coordination assigns mowers, walkers, or robots to target areas automatically, reducing manual work distribution delays.

Time-series quality views linked to production members help separate simultaneous PCB faults and trace temporal error patterns.

Driving video is compared with stored danger zone images to guide nearby shared mobility users along safer paths with fewer false alerts.

Transfer learning converts data between manufacturing equipment to cut model-building experiments while preserving quality prediction accuracy.

Multiple undersampled signals with different delays let machine learning detect mechanical abnormalities reliably without high-frequency sensing.

Waiting-period prioritization and call cancellation feedback cut unnecessary operator assignments in vehicle remote assistance.

Blockchain-secured production nodes and neural network matching improve initiation, milestone tracking, and bottleneck detection across supply chains.

Comparing allocated and unallocated vehicle monitoring data helps assign the right monitor, reducing operator stress while maintaining coverage.

Feature vectors are projected onto a learned subspace so anomalous objects can be flagged before DNN predictions mislead vehicles or robots.

Relative standard deviation and trend-coefficient analysis predict abnormal component behavior early, reducing downtime and replacement delays.

Real-time site audio and images let a server route workers to the best time-zone support terminal with automatic translation for 24-hour maintenance.

Pretrained machine learning generates welding procedure specifications from material and thickness data, reducing manual WPS time and errors.

By comparing an existing PLC setup with prebuilt case configurations, this page shows how new factory automation layouts are proposed with less design effort.

Motion documents, image recognition, and pass-rate scoring quantify worker action deviations to improve standardized work in digital factories.

Prebuilt evaluation models are matched to raw material properties, cutting model generation time and processing load in facility state evaluation.

A five-platform IIoT layout automates parameter updates across production line devices to cut manual effort and keep product quality consistent.

Visual floor-map heat maps let users adjust device zones by time and percentage, cutting energy cost without cumbersome manual settings.

An AI agent combines KPI targets, process variables, and equipment constraints to generate chiller and pump setpoints for industrial optimization.

Reinforcement learning and digital twins replace static order dressing rules to cut off-spec output from wear and material variation.

Adjustable electromagnetic fields boost plant yield, stress tolerance, and pest resistance without fertilizers, pesticides, or GMO changes.

Floor-zoned IoT pressure control adjusts in-home gas by height and real-time data to avoid excess pipeline burden and unsafe overpressure.

Marked coating materials link to cloud-stored processing data, simplifying batch handling and enabling automatic machine parameter updates.

Sensor data and AI flag fuel leaks, excesses, and tank issues early, then trigger workflows and alerts for faster response.

A shared field reference position aligns route start points to keep agricultural material supply consistent across multiple work routes.

An aerial edge-computing vehicle collects, normalizes, and relays harvest data where rural connectivity is weak, reducing loss and improving yield accuracy.

AI-equipped UAVs classify security events, identify intruders, and guide law enforcement with real-time alerts and video.

Balances maintenance cost and equipment reliability by optimizing future operating, maintenance, and replacement decisions for building equipment.

A common start reference and plant interval calculation keep agricultural material supply aligned across multiple work routes.

Multi-layer parameter sharing cuts the cost of managing chamber-specific inference models in substrate manufacturing processes.

Plans AGV stowage and transport paths from material size and machine clearance data to avoid collisions in changing industrial layouts.

Probability density modeling flags abnormal setup times from production data, helping manufacturers cut lead time and target setup inefficiencies.