Resource Allocation TRIZ Cases

A shared AUTOSAR basic software partition lets multi-core ECUs run components in parallel with lower memory overhead and simpler communication.

A local automation unit handles time-critical control while external computing adds complex analytics without missing the sampling cycle.

By splitting one control process across cooperating PLC masters, this case avoids network division while meeting cycle-time limits.

A cascaded PID scheme reallocates CPU resources by pool size and policy feedback to maintain service levels and improve utilization.

A vehicle-edge-cloud compute pool assigns autonomous driving workloads by latency and performance needs to cut delay and use resources efficiently.

Temperature-based switch timing fully discharges residual capacitor charge, preventing low-temperature restart failures in CPU power units.

By grouping similar IoT devices from work profiles, the network identifies underperforming units and recommends replacement or reconfiguration.

Prebuilt cloud and system configuration templates automate control system setup, cutting manual errors, deployment time, and update effort.

Multi-level compute and memory units raise neural network computing density while lean control and dynamic allocation cut power use.

A centralized SDA control plane monitors execution, network, and security events, then triggers coordinated remediation across distributed nodes.

Predictive thermal models and global scheduling shift workloads and cooling effort to meet server heat limits with less data center energy waste.

Precomputed sensor and command predictions let a client keep real-time operation when server responses are delayed by network latency.

Dynamic selection of communication programs by destination keeps PLC data routing reliable without adding replacement hardware.

Edge devices filter and analyze plant-floor data locally, then send selected data to the cloud for cross-facility analytics and control.

Dynamic module coefficients adjust power by operation rate, suppressing semiconductor heating without uniform performance loss.

Cloud-based data models let PLCs exchange MQTT or AMQP data with remote sensors and actuators without changing the automation program.

A virtual clock estimates user-program execution time across control unit variants, helping verify real-time limits before hardware selection.