Flow Data Compression Using Piecewise Linear Segmentation
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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 the determination of a target piecewise linear function, which represents the flow data with a minimum quantity of parameters, ensuring the lowest storage requirement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing compression methods are used, then flow data can be compressed, but the compression rate is not high enough and storage space requirement remains significant
Solution Approach 1:
The patent segments the flow data into multiple non-intersecting line segments that approximate the original data curve. Each line segment is defined by start and end points, creating a piecewise linear representation that reduces the number of parameters needed to store the data while maintaining accuracy within a specified error threshold.
Solution Approach 2:
The patent transforms the storage representation from storing all original data points to storing only the critical parameters of the piecewise linear function (start and end points of each line segment). This parameter transformation achieves minimum storage requirement by capturing essential information while discarding redundant data.
2Measurement precision
If more parameters are used to represent flow data, then data accuracy is improved, but storage space requirement increases
Solution Approach 1:
The patent uses partial action by selecting only the essential parameters (start and end points of line segments) needed to represent the flow data within the maximum error threshold. Instead of storing all parameters of every data point, it stores minimal sufficient information to reconstruct the data with acceptable accuracy.
Solution Approach 2:
The patent extracts only the critical information from the original flow data - specifically the coordinates of inflection points and endpoints that define the piecewise linear function. This extraction process separates essential data from redundant data, achieving compact representation while preserving accuracy.
Data Source
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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.