Supplementary method for incomplete ordered key value type working condition data, and method for converting incomplete ordered key value type working condition data into category set

Abstract

The invention provides a supplementary method for incomplete ordered key value type working condition data, and a method for converting the incomplete ordered key value type working condition data into a category set, and combines two methods. The supplementary method for the incomplete ordered key value type working condition data comprises the following steps that: obtaining the incomplete ordered key value type working condition data; processing the incomplete working condition data; obtaining integrated ordered key value type working condition data; obtaining the integrated working condition data; setting M pieces of category sets, and selecting M pieces of category set centers; calculating a distance value of the integrated working condition and each category set; judging whether M isequal to 2, and if M is equal to 2, finishing category set conversion; otherwise, calculating the ratio of a data size to a total data size in the M pieces of category sets; and judging whether the ratio exceeds 1 / 2 of the category set or not, if the ratio exceeds 1 / 2 of the category set, dividing the category set into two category sets, and otherwise, finishing the category set conversion. By use of the supplementary method, under a situation of incomplete data, the incomplete ordered key value type working condition data can be supplemented, all supplemented data can be converted into the category set, and data supplementary accuracy and data to category set integrity can be improved.

Description

technical field [0001] The invention relates to the technical field of working condition data processing, in particular to a supplementary method for incomplete ordered key-value working condition data and a method for converting category sets. Background technique [0002] In the existing data collection process, some samples in the data set are missing due to errors in data measurement, limitations in data acquisition, and deviations in understanding the data. For example, the description of cultural relics in archeology may be vague, or some record items cannot be obtained; when collecting data in industry, the collection of data may fail due to environmental problems, large errors in data measurement or results with Random noise data, these may cause some attributes missing in the field data. If the data is incomplete, it will often affect the next step of work, so the processing of incomplete data is very important, but it is also relatively complicated. [0003] For ...

AI Technical Summary

Problems solved by technology

[0002] In the existing data collection process, some samples in the data set are missing due to errors in data measurement, limitations in data acquisition, and deviations in understanding the data.

For example, the description of cultural relics in archeology may be vague, or some record items cannot be obtained; when collecting data in industry, the collection of data may fail due to environmental problems, large errors in data measurement or results with Data with random noise, which may cause some attributes to be missing in field data

If the data is incomplete, it will often affect the next step of work, so the processing of incomplete data is very important, but also more complicated

[0003] For the collected working condition data, due to technical reasons, it is difficult to obtain complete data

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