Industrial Controller Program Datatype Optimization for Memory Efficiency
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Solution Overview
Problem
Engineering programs for industrial control systems face challenges in efficiently assigning datatypes to variables, leading to suboptimal memory utilization and computational performance due to mismatches between bit size and value ranges, which can result in program crashes or memory wastage.
Innovation Solution
An automated system that generates an abstract syntax tree from the engineering program, determines a memory efficiency parameter using machine learning, and optimizes datatype assignments to align bit sizes with value ranges, employing a machine learning model to suggest optimal datatypes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the bitsize of the assigned datatype is increased to accommodate the full range of values required by the variable, then program reliability is improved, but memory utilization deteriorates
Solution Approach 1:
The system automatically analyzes the actual value ranges used by variables in the engineering program and dynamically adjusts the bitsize parameter of datatypes to match the minimum required size. This ensures sufficient memory capacity for reliability while eliminating excessive allocation that would waste memory resources.
Solution Approach 2:
The patent implements an automated datatype optimization system that performs self-analysis of variable value ranges and self-adjustment of bitsize parameters without requiring manual intervention. The system serves itself by detecting mismatches and correcting them automatically, improving both reliability and memory utilization.
2Quantity of substance
If the bitsize of the assigned datatype is decreased to reduce memory usage, then memory utilization is improved, but program reliability deteriorates
Solution Approach 1:
The system calculates the minimum necessary bitsize by analyzing the actual value ranges of variables and adjusts the datatype parameter to this optimal size. This prevents both over-allocation and under-allocation, ensuring memory efficiency while maintaining sufficient capacity for reliable operation.
Solution Approach 2:
The automated system performs self-diagnosis of datatype-variable mismatches and self-correction by adjusting bitsize parameters. It detects when datatypes are oversized or undersized and automatically adjusts them to appropriate sizes, ensuring both memory efficiency and operational reliability.
3Adaptability or versatility
If manual assignment of datatypes to variables is performed, then adaptability to specific requirements is improved, but productivity deteriorates
Solution Approach 1:
The system performs self-analysis of variable value ranges and self-assignment of appropriate datatypes, replacing the manual process. This automated self-service capability maintains adaptability to specific project requirements while dramatically improving productivity by eliminating time-consuming manual analysis and assignment.
Solution Approach 2:
The patent replaces the manual mechanical process of datatype assignment with an automated computational system. The machine learning model and analysis algorithms substitute for human engineers in analyzing variable ranges and selecting datatypes, maintaining adaptability while improving productivity through automation.
4Manufacturing precision
If extensive manual analysis is performed for datatype assignment, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The system replaces manual analysis with automated computational analysis using machine learning models and algorithms. This substitution maintains or improves datatype assignment precision through systematic analysis of variable value ranges while reducing the time required from extensive manual review to automated rapid assessment.
Solution Approach 2:
The system performs preliminary automated analysis of variable value ranges and datatype requirements before the actual programming work begins. By pre-identifying appropriate datatypes and detecting potential mismatches early, it ensures precision in datatype assignment while eliminating the need for time-consuming manual analysis during the programming process.
Data Source
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AI summary
The present invention discloses a method for optimizing engineering programs for controller devices within industrial plants. The method comprises reception of an engineering program associated with the industrial plant. The method furtehr comprises determination of a first value for a memory efficiency parameter associated with the engineering program. The method further comprises changing a datatype of at least one variable of the plurality of variables from at least one first datatype to at least one second datatype. The method further comprises determining a second value for the memory efficiency parameter associated with the modified engineering program. The method further comprises deploying the modified engineering program to a controller device of the industrial plant based on the first value and the second value of the efficiency parameter.