Flow Data Compression Using Piecewise Linear Reference Points
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for compressing flow data do not achieve high enough compression rates, resulting in a significant requirement for storage space.
Innovation Solution
A method involving the construction of non-intersecting line segments based on flow data points and a maximum error, followed by determining a target piecewise linear function, which represents the flow data using reference points of continuity and discontinuity, ensuring a minimum quantity of parameters and thus the lowest storage requirement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing compression methods are used on flow data, then some compression is achieved, but the compression rate is not high enough and storage space requirements remain significant
Solution Approach 1:
The patent divides the flow data into multiple non-overlapping time windows and processes each window independently to generate piecewise linear approximation functions. This segmentation allows the system to handle large volumes of flow data efficiently by breaking them into manageable segments, achieving high compression rates while maintaining storage efficiency.
Solution Approach 2:
The patent transforms the original flow data parameters (time-series values) into a piecewise linear function representation with significantly fewer parameters. By changing from storing all raw data points to storing only the essential parameters of linear functions (slopes and intercepts) that approximate the data within error margins, the storage space requirement is dramatically reduced while maintaining acceptable accuracy.
2Measurement precision
If more data points are stored to maintain accuracy, then measurement precision is improved, but storage space requirements increase
Solution Approach 1:
The patent applies partial action by storing only the essential parameters needed to reconstruct the flow data within a predefined error margin. Instead of storing all data points or using overly complex approximation models, the system uses simple piecewise linear functions with minimal parameters, achieving the required accuracy level with minimal storage space.
3Quantity of substance
If complex compression algorithms are used to achieve higher compression rates, then storage space requirements are reduced, but processing complexity increases
Solution Approach 1:
The patent uses simple, computationally inexpensive piecewise linear functions as disposable approximation models for each time window. These simple linear functions are easy to compute and store, providing an efficient balance between compression rate and processing complexity without requiring complex algorithms.
Data Source
Figure 1(a)~2
Figure 3
Figure 4
AI summary
Embodiments of the present invention provide a method for compressing flow data, including: constructing multiple line segments according to flow data and a predefined maximum error that are acquired; determining a target piecewise linear function according to the multiple line segments, where the target piecewise linear function includes multiple linear functions, and an intersection set of value ranges of independent variables of every two linear functions among the multiple linear functions includes a maximum of one value; and outputting a reference data point according to the target piecewise linear function, where the reference data point includes a point of continuity and a point of discontinuity of the target piecewise linear function. In this way, in the embodiments of the present invention, multiple line segments are constructed according to multiple data points and a maximum error, a target piecewise linear function is further determined according to the multiple line segments, and a point of continuity and a point of discontinuity of the target piecewise linear function are used to represent compressed flow data. The method of the embodiments of the present invention can ensure that the target piecewise linear function has a minimum quantity of parameters, which therefore has the lowest requirement on storage space.