Implementation method for data quality control
A data quality control and implementation method technology, applied in the field of data quality control, can solve problems such as inability to perform data optimization, data alarm and warning, etc., and achieve the effect of optimizing processing and optimizing sequences
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Embodiment 1
[0074] as attached figure 1 The shown flow chart of a method for implementing a data quality control method of the present invention includes:
[0075] Step 100: Obtain the target attribute of the target data, perform sequence extraction on the target data according to the target attribute, and obtain sequence data;
[0076] Step 101: Determine the association relationship between the sequence data, and perform quality supervision and measurement on the sequence data based on the quality control algorithm and the association relationship, and determine the low-quality sequence;
[0077] Step 102: Optimizing the low-quality sequence according to a preset optimized sequence library to obtain an optimized sequence;
[0078] Step 103: Verify whether the optimized sequence meets the control standard, and give an alarm to the optimized sequence that does not meet the control standard.
[0079] The principle of the above technical solution is: in the process of data quality control...
Embodiment 2
[0082] As an embodiment of the present invention, the target attributes of the acquired target data include:
[0083] Determining the space complexity of various types of data in the target data (that is, the measurement of the storage space occupied by various types of data in the target data), and based on the space complexity, determining the space attribute of the target data;
[0084] Determining the information entropy of all types of data in the target data (that is, the quantitative measurement of various types of data in the target data), performing gradient division on the entropy value of the information entropy, and determining the target data based on the gradient of the entropy value The entropy value attribute;
[0085] Determine the degree of correlation of various types of data in the target data (that is, the Mahalanobis distance between various types of data in the target data), and determine the relationship attributes of the target data based on the degree...
Embodiment 3
[0090] As an embodiment of the present invention: performing sequence extraction on the target data according to the target attribute to obtain sequence data includes:
[0091] generating a corresponding sequence code in the target data based on the target attribute;
[0092] Counting the sequence codes, and generating a key-value sequence of the sequence codes through a key-value function;
[0093] According to the key-value sequence, data corresponding to the key-value sequence in the target data is determined to generate sequence data.
[0094] The principle of the above-mentioned technical solution is: after the sequence data is determined, the present invention can digitize the target data because the target attribute has been determined, and the target data after digitization can be coded by sequence, and the sequence code is performed in the form of computer language Numericalization, finally determine the key value of the sequence data through the target data after di...
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