Data fusion method and device

Abstract

The invention provides a data fusion method and device, and the method comprises the steps: obtaining a plurality of pieces of sample data, and determining the weight of each piece of sample data in the plurality of pieces of sample data based on a moment when a selected moment is associated with each piece of sample data in the plurality of pieces of sample data; selecting sample data from the plurality of sample data to form an effective sample data set; and based on each piece of sample data in the effective sample data set and the weight of each piece of sample data, determining fusion data of the plurality of pieces of sample data at a selected moment. By the adoption of the mode, weights can be allocated to each piece of sample data based on the timeliness of the sample data; moreover, the effective sample data set can be obtained by eliminating abnormal data, so that the fusion data determined based on each sample data in the effective sample data set and the weight of each sample data can reflect the real situation more accurately, and the accuracy of data fusion is improved.

Description

technical field [0001] The present application relates to the technical field of data processing, and in particular, to a data fusion method and device. Background technique [0002] With the advent of the era of big data, the amount of data is increasing day by day. When performing data analysis and processing, it is often necessary to extract effective data from massive data, and then analyze and process the effective data. The data fusion technology can adapt to the needs of massive data processing, but how to ensure the accuracy of the fused data is very important. [0003] At present, the traditional method of data fusion is to linearly average multiple data to be fused to obtain fused data. However, in fact, there may be abnormal data in the multiple data to be fused, and the importance of different data is not the same. Directly performing a linear average on all the data may make it difficult for the fused data to accurately reflect the real situation, which in turn...

AI Technical Summary

Problems solved by technology

However, in fact, there may be abnormal data in the multiple data to be fused, and the importance of different data is not the same. Directly performing a linear average on all the data may make it difficult for the fused data to accurately reflect the real situation, which in turn leads to It is difficult to achieve the expected results in subsequent data analysis and processing

Images