Controller Memory Self-Diagnosis Segmentation
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Solution Overview
Problem
Existing memory self-diagnosis functions uniformly diagnose all data sets for errors, leading to excessive processing and potential unnecessary shutdowns, as they do not differentiate between error-permitting and error-free data sets.
Innovation Solution
A controller that enables dynamic setting of the memory self-diagnosis function based on data importance, allowing selective error detection and processing within specified memory ranges, thereby avoiding excessive error processing and maintaining operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the memory self-diagnosis function is uniformly applied to all data sets, then error detection capability is improved, but excessive processing occurs leading to unnecessary shutdowns and reduced productivity
Solution Approach 1:
The patent segments the memory space into multiple regions with different error tolerance attributes. The diagnosis unit is divided into pluralities corresponding to each memory region, allowing independent error detection settings for each segment. This enables critical data regions to have strict error checking while non-critical regions tolerate errors, resolving the contradiction between comprehensive error detection and excessive processing.
Solution Approach 2:
The patent applies local quality by assigning different error detection policies to different memory regions based on their specific requirements. Each memory region can be configured with appropriate error tolerance levels, allowing the system to maintain high reliability for critical data while accepting errors in less important areas, thereby avoiding unnecessary shutdowns and maintaining productivity.
2Reliability
If the memory self-diagnosis function is applied to all data sets, then data integrity is improved, but processing complexity and cost increase
Solution Approach 1:
The patent reduces processing complexity by segmenting the diagnosis function into multiple independent units, each handling a specific memory region. This modular approach allows the system to activate only the necessary diagnosis units based on data importance, avoiding the complexity of uniformly processing all data sets while maintaining data integrity for critical regions.
3Reliability
If error detection is performed on all memory data, then safety is improved, but unnecessary shutdowns occur reducing operational efficiency
Solution Approach 1:
The patent applies local quality by configuring different error tolerance attributes for different memory regions. Critical safety-related data regions have strict error detection that triggers shutdowns only when necessary, while non-critical regions allow errors without triggering shutdowns. This resolves the contradiction by maintaining functional safety for critical operations while avoiding unnecessary shutdowns that reduce operational efficiency.
Data Source
AI summary
A controller sets the processing in accordance with an error that may occur in data. A controller for controlling a machine or a facility includes a storage unit, a diagnosis unit that diagnoses the presence of an error in data written in a memory space of the storage unit or data read from the memory space, and a processing unit that performs processing in accordance with a diagnosis result obtained by the diagnosis unit. The processing unit performs appropriate processing when an error is detected in data within a set range of the memory space in which the diagnosis unit is to be enabled.