Multi-dimensional time series data dynamic evolution analysis method and device, medium and electronic equipment

By constructing a three-dimensional density field and gravity model, the problem of quantitative analysis of the interaction between data clusters in multidimensional time series data was solved, realizing dynamic visualization and three-dimensional presentation of data clusters, and improving analysis efficiency and interactivity.

CN121765196BActive Publication Date: 2026-05-05JIANGXI QIUSHI INST OF ADVANCED STUDIES
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Patent Information

Application Number
CN202610243061.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-03-02
Publication Date
2026-05-05
Estimated Expiration
2046-03-02

AI Technical Summary

Technical Problem

Existing technologies struggle to achieve intuitive, dynamic, and quantifiable analysis of interactions between data clusters in multidimensional time-series data. In particular, there is a lack of effective quantification methods during the dynamic evolution of high-dimensional data, and traditional methods are difficult to meet real-time requirements.

Method used

By constructing a three-dimensional density field, using a kernel density estimation algorithm to identify data clusters and calculate cluster quality, and combining a similarity gravity model to quantify the correlation strength between clusters, a dynamic evolution view of data clusters is generated.

Benefits of technology

It enables a three-dimensional presentation and dynamic visualization of multi-dimensional data, which can intuitively show the life cycle and interaction of data clusters, improving analysis efficiency and interactivity.

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Abstract

A multi-dimensional time series data dynamic evolution analysis method, device, medium and electronic equipment, the method comprises the steps of: obtaining and structuring pre-processing time series data; each of the time series data is quantified and attributed in multiple dimensions, and a three-dimensional data node containing spatial coordinates, initial weight and timestamp is constructed; based on the set of three-dimensional data nodes in a specific time window, a three-dimensional data density field is constructed which can represent the data hotspot distribution and intensity in the time window; in the three-dimensional data density field, the density peak area is identified as the core data cluster, and the cluster quality is calculated according to its density and coverage, and the association strength between different core data clusters is calculated; the sequence of the state snapshots of the continuous multiple time window data fields is serialized to form a dynamic evolution view which can show the birth and death of data hotspots, the fusion and splitting process of data clusters. The abstract multi-dimensional data is converted into intuitive three-dimensional density field.
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