Data Memory Extraction Strategy for Low-Latency Production Control
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Solution Overview
Problem
Extracting data from large data memories for controlling multiple stations or systems is inefficient in terms of resources and costs, particularly in multi-stage production processes.
Innovation Solution
Developing an optimal extraction strategy based on current use case and available resources, integrating filtering and standardization operations, and dividing the extraction process into temporal portions to adapt dynamically to the relevant circumstances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data are extracted from a common data memory containing large amounts of data for controlling multiple stations, then all required data can be obtained, but resource consumption and costs increase significantly
Solution Approach 1:
The patent segments the data memory into multiple topic-specific partitions (e.g., production data, quality data, maintenance data) and further divides data requests into specific topic filters. This allows the system to extract only relevant data from specific partitions rather than scanning the entire data memory, significantly reducing resource consumption while ensuring all required data for a given purpose is obtained.
2Loss of information
If all data are extracted from the data memory, then complete information is available, but extraction time and latencies increase
Solution Approach 1:
The patent extracts only the specific data needed for a given purpose by applying topic filters to partitioned data. Instead of extracting all data from the data memory, the system selectively retrieves only relevant records based on the request parameters (e.g., extracting only production data for a specific station or time period), thereby minimizing extraction time and latency while preserving information completeness for the intended use case.
3Ease of manufacture
If a simple extraction method is used, then implementation is easy, but resource efficiency and cost-effectiveness are poor
Solution Approach 1:
The patent implements a dynamic extraction strategy where the data retrieval process adapts based on request parameters. The system dynamically determines which partitions to access and what filters to apply based on the specific extraction needs (e.g., time ranges, station identifiers, data types). This dynamic approach maintains implementation simplicity through standardized interfaces while achieving high resource efficiency by avoiding unnecessary data access.
Data Source
AI summary
A method for extracting data from a data memory. The method includes: providing data; storing the data in a data memory; developing an optimal extraction strategy based on a current use case and/or available resources; extracting data from the data memory based on the optimal extraction strategy; and providing the extracted data.