A multi-dimensional data structure integrated interaction method

Abstract

The present invention provides a multi-dimensional data structure integrated interactive method, which relates to the field of data processing technology, and includes the following steps: step S10, collecting, entering and marking initial data; step S20, performing pre-verification and launching invalid data verification; step S30, extracting features from data and classifying them; step S40, judging whether the data in each classification is abnormal, and outputting the judgment result; step S50, based on the judgment result and data marking, tracing the data, outputting the tracing result, and providing feedback to the user. When there are several data interfaces, the abnormal part in the data can be judged, so that the user can quickly trace back from a large number of data and a large number of data interfaces, so that the reasons for data abnormality and invalidity can be verified from the root, and the practicality is better.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a multi-dimensional data structure integrated interaction method. Background Art

[0002] In terms of risk control, anti-fraud and credit investigation, financial enterprises mostly obtain user information data through multiple different data interfaces. After testing the validity of the data, they interact with the data interfaces and provide feedback on data quality.

[0003] Each data interface will provide a massive amount of raw data to the interactive data pool, among which there will be a large amount of invalid data, erroneous data or abnormal data. In the existing multidimensional data interaction methods, usually only data validity verification can be performed, and then the invalid or erroneous data will be deleted, but it is difficult to determine the abnormal data in the data and trace it back. Therefore, in the existing interaction methods, it is difficult to determine the reasons for data anomalies or invalidity, and then to verify the reasons for data anomalies and invalidity at the root. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a multi-dimensional data structure integrated interaction method.

[0005] The present invention solves the above technical problems through the following technical means: a multi-dimensional data structure integrated interaction method, comprising the following steps: step S10, collecting, entering and marking initial data; step S20, performing pre-verification and invalid data verification; step S30, extracting features from the data and classifying them; step S40, judging whether the data in each classification is abnormal, and outputting the judgment result; step S50, tracing the data based on the judgment result and data marking, outputting the tracing result, and providing feedback to the user.

[0006] Furthermore, the step S10 includes: step S11, receiving original data from several different data interfaces, and marking the data based on blockchain technology; step S12, establishing a corresponding storage database based on the original data; step S13, entering the marked original data into the database and the memory.

[0007] Furthermore, in step S20, data pre-verification uses MD5 checksum and digital signature method to derive valid data and invalid data.

[0008] Furthermore, in step S30, the classifier involved is a naive Bayes classifier.

[0009] Furthermore, the step S30 includes: step S31, classifying the data through the machine learning model and outputting the classification results; step S32, based on the classification results in step S31, evaluating through the expert evaluation model, outputting the evaluation results, and based on the evaluation results, making targeted improvements to the machine learning model.

[0010] Furthermore, the step S40 includes: step S41, defining abnormal parameters of the data and performing abnormal value calculation; step S42, comparing the abnormal value with the threshold range and outputting the comparison result.

[0011] Furthermore, the S50 includes step S51, comparing the abnormal value with the threshold range, outputting the detection result, and determining whether the data is normal data or abnormal data.

[0012] Furthermore, if the output result of step S51 indicates that the data is abnormal data, step S51 is followed by: step S52, establishing an abnormal data pool for storing the output abnormal data; step S53, encapsulating the stored abnormal data based on a common template, and outputting the encapsulated abnormal data; step S54, calling the abnormal data, marking the data tags on the abnormal data through a comparison module, verifying the original source, and outputting the verification result; step S55, calling the verification result in step S54, and providing feedback to the user.

[0013] Furthermore, if the output result of step S51 indicates that the data is normal data, step S51 is followed by step S56, establishing a normal data pool, storing and packaging the normal data to form packaged data; and step S57, calling the packaged data and providing feedback to the user.

[0014] Furthermore, after step S55 and step S57, step S66 is included to generate a work log.

[0015] Beneficial effects of the present invention:

[0016] In the present invention, when there are several data interfaces, the abnormal part in the data can be judged, so that the user can quickly trace back from a large number of data and a large number of data interfaces, so as to verify the reasons for data anomalies and invalidity from the root, which is more practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a schematic diagram of the flow structure of the data interaction method of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be an intermediate element at the same time. Example

[0020] like Figure 1 As shown, the multi-dimensional data structure integrated interaction method described in this embodiment includes the following steps:

[0021] Step S10, collect and input the initial data, and mark the data source; specifically, step S10 includes the following steps:

[0022] Step S11, receiving raw data from several different data interfaces, marking the data based on blockchain technology, and fixing the data transfer path;

[0023] Among them, these data interfaces are the original sources of data. If there is an error or abnormality in the data, the source of these data can be traced to confirm which data interface has the error.

[0024] Step S12, based on the original data directly received from the data interface, a corresponding storage database is established; it is used to store the original data and facilitate later calls; through this mark, it is easy for users to understand the source of the data;

[0025] Step S13, the marked original data is entered into the database. Meanwhile, the database is also configured with a memory, and the storage can be completed after being stored in the database; therefore, the user can directly call it from the memory when necessary.

[0026] Step S20, perform pre-verification, carry out invalid data verification, and exclude meaningless data;

[0027] During use, there will be a certain amount of invalid data or meaningless data in the data received from the data interface. Such data needs to be excluded to prevent interference with data processing and interaction.

[0028] The main part of pre-verification is validity testing, exporting valid data and invalid data, and configuring the valid data that has passed the verification into the corresponding database and storage;

[0029] When conducting validity testing, MD5 checksum and digital signature methods can be used; this type of detection is fast and can be adapted to a variety of data. After invalid data is screened out, it can reduce the interference of invalid data. After verification is completed, two parts, valid data and invalid data, will be exported. The valid data that has passed the verification will be configured into the corresponding database and storage, and invalid or even meaningless data will be screened out and no longer used.

[0030] Step S30, extracting features from the data, and classifying using a classifier based on the extracted features;

[0031] When in use, by using the classifier to classify the data, it is convenient for subsequent data processing and data call to prevent data from being disorganized; wherein, the classifier can be a naive Bayes classifier. Furthermore, several databases and corresponding storage devices can be established to store several groups of classified data one by one.

[0032] Specifically, the step S30 includes:

[0033] Step S31, classifying the data through a machine learning model, distinguishing the data according to their common features, and outputting the classification results;

[0034] Step S32, based on the classification result in step S31, evaluate through the expert evaluation model and output the evaluation result; if the evaluation result is no longer within the threshold range, based on the evaluation result, make targeted improvements to the machine learning model, so as to make the classification effect of the machine learning model better.

[0035] Step S40, determining whether the data in each category is abnormal, and outputting the determination result;

[0036] Specifically, the step S40 includes:

[0037] Step S41, define the abnormal parameters of the data, call valid data from each category, and start the abnormal value calculation;

[0038] Step S42, comparing the calculated abnormal value with the threshold range pre-configured by the user, determining whether it is within the threshold range, and outputting the comparison result;

[0039] Based on step S42, it can be concluded whether the called data is abnormal. In the above steps, the processing and transfer of data are marked and fixed based on the blockchain, so that the data transfer path can be traced.

[0040] Step S50, based on the comparison result outputted in step S40 and the data tag, determining the original source of the data, outputting the tracing result, and providing feedback to the user;

[0041] Specifically, the S50 includes the following steps:

[0042] Step S51, when the calculated abnormal value is outside the preset threshold range, the detected data is abnormal data, and the detection result is output;

[0043] Step S52, accordingly, establishing an abnormal data pool for storing the output abnormal data;

[0044] Step S53, based on the general template, encapsulate the stored abnormal data, output the encapsulated abnormal data, and store the encapsulated result in a corresponding database or storage;

[0045] Step S54, calling the abnormal data, and marking the data tags on the abnormal data through the comparison module to confirm the original source of the abnormal data;

[0046] Based on the original source of the data, the corresponding data interface can be confirmed, and it can be known which data interface outputs abnormal data and needs to be maintained or repaired. Based on this judgment, the verification result is generated;

[0047] Step S55, calling the verification result in step S54, outputting the verification result, and providing feedback to the user;

[0048] Correspondingly, if it is confirmed that the calculated abnormal value is within the preset threshold range, the calculated data is normal data, and the detection result is output;

[0049] Step S56, accordingly, a normal data pool is established, and the normal data is stored, stored and packaged;

[0050] Step S57, calling the encapsulated data in step S56 and providing feedback to the user;

[0051] After step S55 and step S57, the steps include:

[0052] Step S66, generating a work log to facilitate user verification and call.

[0053] When in use, the data transfer path is marked based on the blockchain. When the data output at the data interface is detected, the data can be verified, classified and detected to filter out abnormal data. After encapsulation, the user can quickly determine which data interface it comes from based on the marks and fixed paths on the abnormal data, and can conveniently complete data traceability, so that the user can inspect or maintain the data interface and complete the entire data interaction process. Compared with the existing interaction method that can only perform preliminary validity verification on the data, in this solution, when there are several data interfaces, the abnormal part of the data can be judged, so that the user can quickly trace back from a large number of data and a large number of data interfaces, so as to verify the causes of data anomalies and invalidity from the root, which is more practical.

[0054] It should be noted that, in this article, if there are first and second, etc., relational terms are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0055] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-dimensional data structure integrated interaction method, characterized in that: The following steps are included: Step S10, collecting, entering and marking initial data; Step S20, perform pre-verification and carry out invalid data verification; Step S30, extracting features from the data and classifying them; Step S40, determining whether the data in each category is abnormal, and outputting the determination result; Step S50, based on the judgment result and the data mark, data tracing is performed, the tracing result is output, and feedback is given to the user; The step S10 comprises: Step S11, receiving raw data from several different data interfaces, and marking the data based on blockchain technology; Step S12, establishing a corresponding storage database based on the original data; Step S13, entering the marked original data into the database and the memory; The step S40 includes: Step S41, defining abnormal parameters of the data and performing abnormal value calculation; Step S42, comparing the abnormal value with the threshold range and outputting the comparison result; The step S50 determines the original source of the data based on the comparison result outputted from the step S40 and the data tag, outputs the traceability result, and provides feedback to the user; Specifically, the S50 includes the following steps: Step S51, when the calculated abnormal value is outside the preset threshold range, the detected data is abnormal data, and the detection result is output; Step S52, accordingly, establishing an abnormal data pool for storing the output abnormal data; Step S53, based on the general template, encapsulate the stored abnormal data, output the encapsulated abnormal data, and store the encapsulated result in a corresponding database or storage; Step S54, calling the abnormal data, and marking the data tags on the abnormal data through the comparison module to confirm the original source of the abnormal data; Based on the original source of the data, the corresponding data interface can be confirmed, and it can be known which data interface outputs abnormal data and needs to be maintained. Based on this judgment, the verification result is generated; Step S55, calling the verification result in step S54, outputting the verification result, and providing feedback to the user.

2. A multi-dimensional data structure integrated interaction method according to claim 1, characterized in that: In step S20, data pre-verification uses MD5 checksum and digital signature method to derive valid data and invalid data.

3. A multi-dimensional data structure integrated interaction method according to claim 1, characterized in that: In step S30, the classifier involved is a naive Bayes classifier.

4. A multi-dimensional data structure integrated interaction method according to claim 3, characterized in that: The step S30 includes: Step S31, classifying the data through a machine learning model and outputting the classification results; Step S32, based on the classification result in step S31, an evaluation is performed through an expert evaluation model, the evaluation result is output, and based on the evaluation result, targeted improvements are made to the machine learning model.

5. A multi-dimensional data structure integrated interaction method according to claim 4, characterized in that: If the output result of step S51 indicates that the data is normal data, step S51 further includes: Step S56, establishing a normal data pool, storing and encapsulating the normal data into it, and forming encapsulated data; Step S57, calling the encapsulated data in step S56 and providing feedback to the user.

6. A multi-dimensional data structure integrated interaction method according to claim 5, characterized in that: After step S55 and step S57, step S66 is included to generate a work log.

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