CN115081828A - Automatic attribution method for data quality problem and storage medium - Google Patents
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Abstract
The invention discloses an automatic attribution method and a storage medium for data quality problems, wherein the method comprises the following steps: acquiring a plurality of index data to be monitored; checking whether the data values of the index data are located in a first preset interval one by one, and if not, marking the data values as abnormal index data; executing the following steps on each abnormal index data: acquiring a blood-related link of the index data, acquiring a table name and a field of each link node, checking whether a data value corresponding to each link node is located in a second preset interval one by one, and if not, marking the data value as abnormal link node data; determining abnormal source link node data of each abnormal index data; and calculating the correlation degree of each abnormal source link node data and each influence factor, and screening the influence factors with the correlation degree higher than a preset threshold value as the influence factors possibly causing index data abnormity. The method can automatically screen the data with quality problems and automatically analyze reasons, has high efficiency and is not easy to miss.
Description
Technical Field
The invention mainly relates to the technical field of power data quality analysis, in particular to an automatic attribution method and a storage medium for data quality problems.
Background
The power grid enterprises generally need to analyze the data of the electric power data and summarize the electric power operation condition. Due to the diversity, complexity and versatility of the power data, some erroneous data values are prone to occur. If no error is found, the electric power data with the error data value is analyzed, only an error conclusion can be obtained, and the actual electric power management condition cannot be obtained. Therefore, the power grid enterprise usually performs quality monitoring analysis on the power data, and the traditional analysis mode is as follows: after the related power data are arranged into a table or converted into a visual chart, an analyst manually checks whether quality problems exist in each analyzed data value, and if a certain data value has quality problems, the analyst needs to further analyze whether quality problems exist in data generated by each data processing node on a data link of the data value. The analysis mode has low efficiency, omission is easily caused by negligence of analysts, whether data are abnormal or not is judged mainly by depending on personal experience of analysts, and the judgment standards of different analysts on the data abnormality are possibly different, so that the analysis results are different.
Disclosure of Invention
The invention aims to provide an automatic attribution method for data quality problems and a computer readable storage medium storing a computer program capable of realizing the attribution method when executed.
In order to solve the above technical problem, a first aspect of the present invention provides an automatic attribution method for data quality problems, including the following steps:
A. acquiring a plurality of index data to be monitored;
B. checking whether the data values of the index data are located in a first preset interval corresponding to the index data one by one, and if not, marking the index data as abnormal index data;
C. acquiring table names and fields of all abnormal index data, and executing the following steps on each abnormal index data to trace and acquire abnormal link node data of the abnormal index data: according to the table name and the field of the abnormal index data, inquiring a data link network to obtain a blood-related link of the abnormal index data; obtaining the table name and the field of each link node in the blood margin link by using a GraphSAGE algorithm; checking whether the data values corresponding to the table names and the fields of the link nodes are located in a second preset interval corresponding to the data of the link nodes one by one, and if not, marking the data values corresponding to the table names and the fields of the link nodes as abnormal link node data;
D. determining abnormal source link node data of each abnormal index data according to the data flow direction of the blood-related link of each abnormal index data and the abnormal link node data of each abnormal index data;
E. and calculating the correlation between the abnormal source link node data of each abnormal index data and each influence factor in the prestored influence factor set, and screening the influence factors of which the correlation is higher than a preset threshold value as the influence factors possibly causing the index data to be abnormal.
Optionally, in the step E, specifically, a causal relationship test method, a mutual information method, a chi-square test method, and a linear correlation coefficient method are respectively used to calculate correlation degrees between the abnormal source link node data and each influence factor in the pre-stored influence factor set, an influence factor with a correlation degree higher than a preset threshold is screened out from the correlation degrees of each influence factor calculated by each algorithm, whether there is an influence factor with a frequency higher than or equal to a preset frequency is determined, and if yes, the influence factor is used as an influence factor that may cause the index data to be abnormal.
Optionally, in the step E, if it is determined that the index data is abnormal, all the screened influence factors are used as influence factors which may cause the abnormality of the index data.
Optionally, the preset number of times is 2.
Optionally, the first preset interval corresponding to the index data is specifically a preset interval of a normal distribution curve corresponding to the index data, and the second preset interval corresponding to the data of the link node is specifically a preset interval of a normal distribution curve corresponding to the data of the link node.
Optionally, the preset interval of the normal distribution curve is specifically set according to a normal minimum value and a normal maximum value of data corresponding to the normal distribution curve.
Optionally, the preset threshold is 80%.
A second aspect of the invention provides a computer-readable storage medium having stored thereon an executable computer program which, when executed, implements an automatic attribution method of data quality issues as described above.
The automatic attribution method for the data quality problem judges whether the index data is abnormal or not by checking whether the index data is located in a preset interval corresponding to the index data or not, automatically acquires a blood-border link of the data and acquires a table name and a field of each link node in the blood-border link by using a GraphSAGE algorithm after judging that the data is abnormal, then judges whether a data value corresponding to the table name and the field of each link node is abnormal or not, then automatically determines abnormal source link node data according to the data flow direction, then calculates the correlation degree of the abnormal source link node data and each influence factor in a prestored influence factor set, and then screens out the influence factors possibly causing the index data to be abnormal. Therefore, the method can automatically screen the data with quality problems and automatically trace the problem source to analyze the reason causing the quality problems of the data, and is high in efficiency and not easy to miss.
Detailed Description
The invention is described in further detail below with reference to specific embodiments.
Because the quality monitoring analysis of the power data manually is inefficient and easy to miss, the embodiment provides an automatic attribution method for the data quality problem executed by a computer processor, the method is stored in a computer readable storage medium in the form of a computer program, and the computer processor executes the computer program in the computer readable storage medium so as to realize the automatic attribution method for the data quality problem, and the specific steps are as follows:
the computer processor firstly obtains a plurality of index data to be monitored X1, X2, X3, X4 and X5, wherein each index data comprises a table name and a field of the index and a corresponding data value. The power data of a general power grid enterprise meet the characteristic of normal distribution and fall into an interval centered by a normal distribution curve, and if a certain power data is not in the interval, the power data is abnormal, namely, the quality problem exists. It should be noted that, for different power data, the corresponding normal distribution curves are different, and the corresponding intervals are also different in size. Therefore, the present embodiment provides a normal distribution check interface, and the computer processor calls the normal distribution check interface to check whether the data values of the index data X1, X2, X3, X4, and X5 are within the preset interval (i.e. the first preset interval) of the normal distribution curve corresponding to the index data. The preset interval of the normal distribution curve corresponding to the index data X1 is the maximum value X1 under the normal condition according to the index data X1 max And minimum value X1 min The preset interval of the normal distribution curve corresponding to the index data X1 is [ X1 ] max ，X1 min ]. Similarly, the preset interval of the normal distribution curve corresponding to the index data X3 is the maximum value X3 under the normal condition according to the index data X3 max And minimum value X3 min The preset interval of the normal distribution curve corresponding to the index data X3 is [ X3 ] max ，X3 min ]. By analogy, the preset interval of the normal distribution curve corresponding to the index data X5 is [ X5 ] max ，X5 min ]. In this embodiment, it is verified that the data values of the index data X1, X3, and X5 are not within the preset interval of the corresponding normal distribution curve, which means that the data values of the index data X1, X3, and X5 are abnormal, and the index data X1, X3 are used to determine the data values of the index data X1, X3, and X5X5 is marked as abnormal index data and put into the abnormal data set T1; in this embodiment, it is verified that the index data X2 and X4 are within the preset interval of the corresponding normal distribution curve, which means that the data values of the index data X2 and X4 are normal, and the index data X2 and X4 are not processed.
After the computer processor checks all the index data X1, X2, X3, X4, and X5 to be monitored, table names and fields of all the abnormal index data X1, X3, and X5 in the abnormal data set T1 are obtained, and the following steps are performed on each abnormal index data to trace back the abnormal link node data of the abnormal index data, specifically: for the abnormal index data X1, the computer processor firstly queries a data link network prestored in a database according to the table name and the field of the abnormal index data X1 to obtain a blood-related link of the abnormal index data X1; and then, obtaining the table name and the field of each link node in the blood margin link by using a GraphSAGE algorithm, and calling the normal distribution check interface to check whether the data values corresponding to the table name and the field of each link node are positioned in a preset interval (namely a second preset interval) of a normal distribution curve corresponding to the data of the link node one by one. The principle of setting the preset intervals of the normal distribution curves corresponding to the index data X1, X3, and X5 is the same, and the preset interval of the normal distribution curve corresponding to the data of each link node is set according to the maximum value and the minimum value of the data of the link node under normal conditions. If the link node is not in the preset interval, the data value corresponding to the table name and the field of the link node is abnormal, marking the data value corresponding to the table name and the field of the link node as abnormal link node data and putting the abnormal link node data into an abnormal data set T2; if the data value corresponding to the table name and the field of the link node is normal within the preset interval, no processing is performed. Similarly, for the abnormal index data X3, the computer processor firstly queries a data link network prestored in the database according to the table name and the field of the abnormal index data X3 to obtain a blood-related link of the abnormal index data X3; and then, obtaining the table name and the field of each link node in the blood margin link by using a GraphSAGE algorithm, and calling the normal distribution check interface to check whether the data values corresponding to the table name and the field of each link node are positioned in a preset interval of a normal distribution curve corresponding to the data of the link node one by one. If the data values corresponding to the table name and the field of the link node are not in the preset interval, which means that the data values corresponding to the table name and the field of the link node are abnormal, marking the data values corresponding to the table name and the field of the link node as abnormal link node data and putting the abnormal link node data into an abnormal data set T2; and if the data values corresponding to the table name and the field of the link node are in the preset interval, the data values corresponding to the table name and the field of the link node are normal, and no processing is performed. By analogy, the computer processor traces back to the abnormal index data X5 to obtain the abnormal link node data of the abnormal index data X5 and puts into the abnormal data set T2.
After checking out the abnormal link node data of the blood-edge links of the abnormal index data X1, X3 and X5, the computer processor determines the abnormal source link node data of the abnormal index data X1, X3 and X5 according to the data flow direction of the blood-edge links of the abnormal index data X1, X3 and X5 and the abnormal link node data of the abnormal index data X1, X3 and X5. Influence factor sets causing various data quality problems are prestored in a database, and for abnormal source link node data of abnormal index data X1, a computer processor calculates the correlation degree between the abnormal source link node data of the abnormal index data X1 and each influence factor in the prestored influence factor sets by respectively adopting a causal relationship test method, a mutual information method, a chi-square test method and a linear correlation coefficient method, then selects influence factors with the correlation degree higher than 80% (namely a preset threshold value is set to be 80%) from among the correlation degrees of the influence factors respectively calculated by various algorithms, and supposing that influence factors with the correlation degree higher than 80% selected by adopting the causal relationship test method are M1, M2 and M3, influence factors with the correlation degree higher than 80% selected by adopting the mutual information method are M2, M3 and M4, and influence factors with the correlation degree higher than 80% selected by adopting the chi-square test method are M2, M2, M5, M6, M7, the influence factors with correlation higher than 80% screened by the linear correlation coefficient method include M3 and M5, so that among the screened influence factors, the influence factor M2 appears 3 times, the influence factor M3 appears 3 times, and the influence factor M5 appears 2 times, that is, the frequency of appearance of the influence factors M2, M3, M5 is higher than or equal to 2 times (that is, the preset frequency is 2 times), and then the influence factors M2, M3, M5 are used as the influence factors which may cause the abnormality of the index data X1. For the abnormal source link node data of the abnormal index data X3, the computer processor respectively adopts a causal relationship test method, a mutual information method, a chi-square test method and a linear correlation coefficient method to calculate the correlation degree of the abnormal source link node data of the abnormal index data X3 and each influence factor in a prestored influence factor set, then selects the influence factor with the correlation degree higher than 80% (namely the preset threshold is set as 80%) from the correlation degrees of each influence factor respectively calculated by various algorithms, supposing that the influence factors with the correlation degree higher than 80% selected by adopting the causal relationship test method are M8 and M9, the influence factors with the correlation degree higher than 80% selected by adopting the mutual information method are M8, the influence factors with the correlation degree higher than 80% selected by adopting the chi-square test method are M9 and M10, and the influence factors with the correlation degree higher than 80% selected by adopting the linear correlation coefficient method are M9 and M9, M11, if the influence factor M9 and the influence factor M8 occur 3 times among the selected influence factors, that is, the frequency of occurrence of the influence factors M8 and M9 is higher than or equal to 2 times (that is, the preset frequency is 2 times), the influence factors M8 and M9 are used as influence factors that may cause abnormality of the index data X3. Similarly, for the abnormal source link node data of the abnormal index data X5, the computer processor calculates the correlation degree between the abnormal source link node data of the abnormal index data X5 and each influence factor in the prestored influence factor set by respectively adopting a causal relationship test method, a mutual information method, a chi-square test method and a linear correlation coefficient method, selects an influence factor with a correlation degree higher than 80% from the correlation degrees of the influence factors calculated by respectively adopting various algorithms, supposes that the influence factors with a correlation degree higher than 80% selected by adopting the causal relationship test method are M12 and M13, the influence factors with a correlation degree higher than 80% selected by adopting the mutual information method are M14, the influence factors with a correlation degree higher than 80% selected by adopting the chi-square test method are M15 and M16, the influence factors with a correlation degree higher than 80% selected by adopting the linear correlation coefficient method are M17, then, of the above influence factors that are selected, influence factors M12, M13, M14, M15, M16, and M17 all appear only once, and the number of occurrences is less than 2, and then the selected influence factors M12, M13, M14, M15, M16, and M17 are used as influence factors that may cause abnormality of the index data X5. So far, the computer processor automatically screens the index data X1, X3 and X5 with quality problems and the link node data with quality problems on the blood-related links of the index data X1, X3 and X5, and automatically analyzes the influence factors which may cause the quality problems of the data, namely analyzes the reasons which may cause the quality problems of the data, thereby improving the analysis efficiency. The analyst can make further analysis and diagnosis according to the automatically screened abnormal data and the influence factors analyzed correspondingly.
The above description is only the embodiments of the present invention, and the scope of protection is not limited thereto. The insubstantial changes or substitutions will now be made by those skilled in the art based on the teachings of the present invention, which fall within the scope of the claims.
Claims (8)
1. An automatic attribution method of data quality problems is characterized by comprising the following steps:
A. acquiring a plurality of index data to be monitored;
B. checking whether the data values of the index data are located in a first preset interval corresponding to the index data one by one, and if not, marking the index data as abnormal index data;
C. acquiring table names and fields of all abnormal index data, and executing the following steps on each abnormal index data to trace and acquire abnormal link node data of the abnormal index data: according to the table name and the field of the abnormal index data, inquiring a data link network to obtain a blood-related link of the abnormal index data; obtaining the table name and the field of each link node in the blood margin link by using a GraphSAGE algorithm; checking whether the data values corresponding to the table names and the fields of the link nodes are located in a second preset interval corresponding to the data of the link nodes one by one, and if not, marking the data values corresponding to the table names and the fields of the link nodes as abnormal link node data;
D. determining abnormal source link node data of each abnormal index data according to the data flow direction of the blood-related link of each abnormal index data and the abnormal link node data of each abnormal index data;
E. and calculating the correlation between the abnormal source link node data of each abnormal index data and each influence factor in the prestored influence factor set, and screening the influence factors of which the correlation is higher than a preset threshold value as the influence factors possibly causing the index data to be abnormal.
2. The method of automatic attribution of data quality problems as claimed in claim 1, wherein: and E, specifically, calculating the correlation degrees of the abnormal source link node data and each influence factor in the pre-stored influence factor set by respectively adopting a causal relationship test method, a mutual information method, a chi-square test method and a linear correlation coefficient method, screening the influence factors of which the correlation degrees are higher than a preset threshold value from among the correlation degrees of the influence factors calculated by various algorithms respectively, judging whether the influence factors of which the occurrence times are higher than or equal to the preset times exist, and if so, taking the influence factors as the influence factors possibly causing the index data to be abnormal.
3. The method of automatic attribution of data quality problems as claimed in claim 2, wherein: and E, if judging that the data is not abnormal, taking all screened influence factors as influence factors which possibly cause the abnormality of the index data.
4. The method of automatic attribution of data quality problems according to claim 2, wherein: the preset number of times is 2.
5. The method of automatic attribution of data quality problems as claimed in claim 1, wherein: the first preset interval corresponding to the index data is specifically a preset interval of a normal distribution curve corresponding to the index data, and the second preset interval corresponding to the data of the link node is specifically a preset interval of a normal distribution curve corresponding to the data of the link node.
6. The method of automatic attribution of data quality problems according to claim 5, wherein: the preset interval of the normal distribution curve is specifically set according to a normal minimum value and a normal maximum value of data corresponding to the normal distribution curve.
7. The method of automatic attribution of data quality problems as claimed in claim 1, wherein: the preset threshold is 80%.
8. A computer-readable storage medium having stored thereon an executable computer program, the computer program comprising: the computer program when executed implements an automatic attribution method of a data quality issue as claimed in any one of claims 1 to 7.
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| CN115912359A (en) * | 2023-02-23 | 2023-04-04 | 豪派(陕西)电子科技有限公司 | Digitalized potential safety hazard identification, investigation and treatment method based on big data |
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