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Time slice division method and system for adjoint analysis

A time slice and time series technology, applied in the field of data analysis, can solve problems such as redundancy, and achieve the effect of meeting business needs and efficiently dividing time slices

Inactive Publication Date: 2020-06-05
CHENGDU SEFON SOFTWARE CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method avoids data omission, but therefore adds a lot of time slice overlap, resulting in a lot of redundancy

Method used

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  • Time slice division method and system for adjoint analysis

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0047] Such as figure 1 As shown, a time slice division method with adjoint analysis, including the following steps:

[0048] S1. Numerical processing of time series data;

[0049] S2. Select at least one Linkage to establish a hierarchical clustering model;

[0050] S3. Setting the termination time threshold of the hierarchical clustering model;

[0051] S4. Using a hierarchical clustering model to train the data obtained in step S1 to obtain clustering fragments of time series data.

Embodiment 2

[0053]In this embodiment, on the basis of Embodiment 1, the Linkage in step S2 includes at least one of ward Linkage, completeLinkage, average Linkage, and single Linkage.

[0054] The method for selecting at least one Linkage to establish a hierarchical clustering model in the step S2 includes the following steps:

[0055] S201, analyzing the distribution of time series data time points in step S1;

[0056] S202. Extracting data whose distribution in the time series data is different from the overall distribution;

[0057] S203, select Linkage for the data extracted in step S202 to establish a hierarchical clustering model, and select Linkage for other unextracted data to establish a hierarchical clustering model;

[0058] S204. Combine the hierarchical clustering models established in step S203 to obtain a hierarchical clustering model including at least one Linkage.

Embodiment 3

[0060] In this embodiment, on the basis of Embodiment 1, the method for setting the termination time threshold of the hierarchical clustering model in step S3 includes the following steps:

[0061] S301. Determine the type of problem to be analyzed in the accompanying analysis;

[0062] S302. Read the Linkage type selected in step S2;

[0063] S303. Read the corresponding termination time threshold from the external database according to the problem type to be analyzed and the selected Linkage type.

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Abstract

The invention discloses an adjoint analysis time slice division method and system, and provides an adjoint analysis time slice division algorithm based on a hierarchical clustering method. The continuity of time is difficult to clearly divide, so that time slice division is a difficult point in the accompanying analysis problem. The method adopts an unsupervised machine learning algorithm to divide time slices, and is convenient and efficient. According to the method, the slice division effect can be controlled by using a time distance threshold without presetting the fragmentation number, multiple calculation modes are provided at the same time, the algorithm is flexible and controllable, the actual combat requirements are highly met, and multiple actual combat scenes are met.

Description

technical field [0001] The invention relates to the field of data analysis, in particular to a time slice division method and system for accompanying analysis. Background technique [0002] Data mining is a popular profession, and the mining of spatiotemporal trajectories plays a pivotal role as one of its important branches. A spatiotemporal trajectory is a recorded sequence of the position and time of a moving object. As an important spatiotemporal object data type, the application of spatiotemporal trajectory covers many aspects such as human behavior, transportation and logistics. By analyzing various spatio-temporal trajectory data, the similarity anomaly features in spatio-temporal trajectory data can be obtained, which helps to discover meaningful trajectory patterns. Adjoint mode is a kind of space-time trajectory mode, which has important applications in traffic management, resource allocation and other fields. [0003] The basis of accompanying analysis is to pl...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06Q50/26
CPCG06Q50/26G06F18/23G06F18/241
Inventor 张艳清查文宇王纯斌王伟才殷腾蛟潘小东
Owner CHENGDU SEFON SOFTWARE CO LTD
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