Attendance identification algorithm based on bitmap

Abstract

The invention discloses an attendance identification algorithm based on a bitmap, which comprises the following steps: identifying that the attendance state of the day is normal by adopting a binary symbol X, identifying that the attendance state of the day is abnormal by adopting different binary symbols Y, and sequentially corresponding to one binary symbol every day to form a binary sequence of each worker in an attendance period; when the attendance state is changed from an abnormal state to a normal state, taking the attendance state as rectification, using a binary symbol X for identification on the rectification day, using a binary symbol Y for identification on the day before rectification, and obtaining attendance identification data corresponding to each worker and storing in a database; acquiring a character string in attendance identification data from a database, converting the character string into a bitmap, and counting an abnormal date, a rectification date and a rectification day number based on binary symbols on the bitmap. According to the technical scheme, the occupied storage space is greatly reduced; the data processing logic is simple and efficient, and the processing efficiency of related statistics is greatly improved; storage is simple and processing is rapid.

Description

Technical field [0001] The present invention relates to the field of data processing, particularly a bitmap based attendance identifier algorithm. Background technique [0002] In the construction industry, it is often necessary to identify the construction workers' attendance state. The common state of the common attendance logo and statistical items include: attendance timeout and not attendance, for example, work for more than 1 day, it is deemed to be attendance timeout; After more than 7 days, it is not possible to do not attendance. The rectification of the abnormal attendance state is made of normal state; for example, workers attending get off work at the attendance timeout, and the many days of unresolved workers are deemed to have been rectified; and the number of days whose abnormal state changes to normal states is the number of days. For example, workers attending attendance timeout will go to work for 6 days, and the number of days is 6. [0003] In the conventional...

AI Technical Summary

Problems solved by technology

[0005] (2) Statistical logic is complex and inefficient

If you want to make statistics based on identification records, you need to perform range queries on massive relational data, which is inefficient

[0006] (3) Poor ability to deal with data changes

There is a lag in uploading attendance, and the order cannot be guaranteed, so frequent changes to identification and statistics are required, and traditional processing methods cannot cope with scenarios where data changes frequently

Images