An electronic certificate quality detection and analysis system

Abstract

The present invention discloses an electronic certificate quality detection and analysis system, and relates to the technical field of file detection, which includes a data persistence module and a business module, wherein: the data persistence module includes a relational database and an ElasticSearch cluster, the relational database stores the necessary data required for certificate detection, and ElasticSearch stores the detection results after the certificate detection; the business module includes an interactive submodule, MQ, and a detection cluster including multiple detection submodules, and a user imports an external sample into a relational database, customizes the regular verification rules corresponding to the standard certificate type, sends a detection request to MQ, and requests ElasticSearch to obtain statistical detection results through the interactive submodule. MQ is responsible for receiving the detection request and distributing it to the detection cluster, and the detection cluster calls the certificate library interface through an identifier, obtains the certificate information and the regular verification rules, performs the certificate detection, and the detection results are serialized and persisted in ElasticSearch for the interactive submodule to perform statistical analysis. The present invention can meet the unified detection of large amounts of certificates.

Description

Technical Field

[0001] The present invention relates to the technical field of document detection, in particular to an electronic certificate quality detection and analysis system. Background Art

[0002] The current certificate database generally has large stock data and complex structured data. When quality testing is required for stock data, the interface + relational database storage solution is usually adopted. The specific process of this solution is: obtain the metadata, face information, and attachment information of the certificate through a unified information retrieval interface; then, according to the electronic certificate engineering standards published by the government service platform, adopt a regular verification method to check the obtained certificate metadata, face information, and certificate attachments; then organize the inspection results into structured data and persist them in a relational database, providing a basis for the subsequent electronic certificate engineering standards to manage non-standard data.

[0003] The above solution has two obvious defects:

[0004] (1) At present, the inventory of licenses and certificates in various provinces across the country is too large, and the demand for single testing is very large. Using a single interface for testing has high requirements on the response time and concurrency of the interface, which is difficult to implement;

[0005] (2) The daily testing demand in each province is very high. In some cases, a license is tested twice or even multiple times. This will result in a large amount of structured data of the test results that needs to be persisted. Traditional relational databases have poor real-time statistical performance when faced with this amount of data. Summary of the invention

[0006] In view of the needs and deficiencies of current technological development, the present invention provides an electronic certificate quality detection and analysis system.

[0007] The electronic certificate quality detection and analysis system of the present invention solves the above technical problems by adopting the following technical solutions:

[0008] An electronic certificate quality detection and analysis system includes two parts: a data persistence module and a business module.

[0009] The data persistence module stores the necessary data required for license testing and the test results after the license testing;

[0010] The business module stores the main entrance for electronic certificate detection, the electronic certificate structured data detection method, and the detection result statistical analysis method. It is used to interact with users, distribute detection requests, detect certificate data, and request the data persistence module to store the obtained statistical detection results.

[0011] Specifically, the necessary data required for the license test includes license sample data, standard license type information, definition agency information, regular verification rules corresponding to the face items, and test batch information.

[0012] The certificate sample data comes from the certificate library, including the certificate metadata, face data, and certificate attachments. The certificate data belonging to the same sample have the same certificate identifier;

[0013] Standard certificate type information includes certificate type code and certificate type name; through the certificate type code, the regular verification rule of each certificate can be associated, thereby realizing different standard detections for different types of certificates;

[0014] The definition institution information includes the definition institution name and the unified social credit code of the definition institution;

[0015] Regular verification rules corresponding to the face-to-face items, that is, regular verification rules defined for the certificate metadata, face-to-face item data, and structured data in the OFD format file according to the electronic certificate engineering standards issued by the State Council, are used to verify the structured data of the certificate;

[0016] The test batch information is the batch number used to distinguish different batches of test records and is used to distinguish the test results each time.

[0017] Preferably, the involved certificate sample data, certificate type information, definition agency information, regular verification rules corresponding to the face items, and test batch information will change according to changes in the test batch and national standards for the test certificate.

[0018] Specifically, the data persistence module involved includes a relational database and a cluster containing multiple ElasticSearchs. The relational database stores the necessary data required for license detection, and ElasticSearch stores the detection results after the license detection.

[0019] The index design of ElasticSearch includes three parts: metadata, face data detection results, and file information detection results.

[0020] The metadata is designed as an Object, which contains three types of license index information: license type code, license definition agency, and license district. The type is keyword and is used for statistical aggregation.

[0021] The design of the face data detection result and the file information detection result is an Array respectively. The Array consists of multiple Objects. Each Object consists of a face item name and a face item detection status, and the type is keyword; the face item name in the Object consists of the face item name specified in the published national standard, and the face item detection status is used to map the detection result information of the face item; the number of Objects is determined by the number of face items specified in the published national standard.

[0022] More specifically, the index structure of the ElasticSearch involved adopts a flat data structure that includes all statistical dimensions.

[0023] More specifically, an ElasticSearch index is created by date using an ElasticSearch template, and the index name is a unified prefix + date timestamp.

[0024] More specifically, when requesting ElasticSearch to perform an aggregate query, the requested index path uses the template name, and ElasticSearch automatically routes the request to each server in the ElasticSearch cluster and returns the statistical results.

[0025] More specifically, the business modules involved include an interaction submodule, MQ, and a detection cluster including multiple detection submodules;

[0026] The user imports external samples into the relational database of the data persistence module through the interactive submodule. At the same time, the user customizes the regular verification rules corresponding to the standard certificate type through the interactive submodule, sends a detection request to MQ, and requests ElasticSearch to obtain statistical detection results;

[0027] MQ is responsible for receiving detection requests from the interaction submodule and distributing them to the detection cluster to reduce the request peak.

[0028] The detection cluster receives the detection request distributed in MQ, and calls the interface for obtaining certificate information and certificate attachments provided by the certificate library through the certificate identifier in the detection request to obtain the certificate information that needs to be detected; then it obtains the corresponding regular verification rules from the relational database according to the certificate type code and performs detection; after the detection is completed, the detection results are serialized and persisted in ElasticSearch for statistical analysis by the interactive sub-module.

[0029] More specifically, the customized regular validation rules include metadata regular validation rules, face item regular validation rules and attachment regular validation rules;

[0030] The statistical detection results are obtained by the interactive submodule requesting ElasticSearch to parse the returned structured data and obtain the statistical results of each batch detection.

[0031] More specifically, the test results are serialized and persisted in ElasticSearch. During this process, if an exception occurs, the entire process is rolled back, logs are recorded, and the results are pushed again to ensure data consistency.

[0032] Compared with the prior art, the electronic certificate quality detection and analysis system of the present invention has the following beneficial effects:

[0033] (1) The present invention can perform unified detection on the certificates in the certificate database, can meet the requirements of certificate detection under large data volumes and large requests, and has the advantage of fast response;

[0034] (2) The present invention introduces MQ and ElasticSearch, distributes requests through MQ, and distributes a large number of requests to various detection sub-modules of the detection cluster to achieve peak shaving. The detection results are stored through ElasticSearch and provided to the interactive sub-module for statistical analysis, ultimately achieving a second-level response to large amounts of data retrieval and aggregate statistics to achieve data analysis of license detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Attached Figure 1 It is a model connection block diagram of the present invention;

[0036] Attached Figure 2 It is a flow chart of the detection submodule outputting the detection result of the present invention. DETAILED DESCRIPTION

[0037] In order to make the technical solution, the technical problem solved and the technical effect of the present invention more clearly understood, the technical solution of the present invention is clearly and completely described below in conjunction with specific embodiments. Example

[0038] Combined with Figure 1 , 2 ,This embodiment proposes an electronic certificate quality detection and analysis system, which includes a data persistence module and a business module.

[0039] The data persistence module includes a relational database and a cluster containing multiple ElasticSearch instances.

[0040] The relational database stores the necessary data required for certificate and license testing. The necessary data required for certificate and license testing include certificate and license sample data, standard certificate and license type information, definition agency information, regular verification rules corresponding to the face-to-face items, and test batch information, among which: the certificate and license sample data comes from the certificate and license library, including the certificate and license metadata, face-to-face data, and certificate and license attachments. The certificate and license data belonging to the same sample have the same certificate and license identification; the standard certificate and license type information includes the certificate and license type code and the certificate and license type name; through the certificate and license type code, the regular verification rules of each certificate and license can be associated, thereby realizing different standard tests for different types of certificates and licenses; the definition agency information includes the definition agency name and the unified social credit code of the definition agency; the regular verification rules corresponding to the face-to-face items, that is, the regular verification rules defined for the certificate and license metadata, face-to-face item data, and structured data in the OFD format file according to the electronic certificate and license engineering standards issued by the State Council, are used to verify the structured data of the certificate and license; the test batch information is the batch number for distinguishing different batches of test records, which is used to distinguish the test results of each time. It should be added that the certificate sample data, certificate type information, definition agency information, regular verification rules corresponding to the face items, and test batch information will change according to changes in the test batch and national standards for test certificates.

[0041] ElasticSearch stores the test results after the license test. The index design of ElasticSearch includes three parts: metadata, face data test results, and file information test results, among which:

[0042] The metadata is designed as an Object, which contains three types of license index information: license type code, license definition agency, and license district. The type is keyword and is used for statistical aggregation.

[0043] The design of the face data detection result and the file information detection result is an Array respectively. The Array consists of multiple Objects. Each Object consists of a face item name and a face item detection status, and the type is keyword; the face item name in the Object consists of the face item name specified in the published national standard, and the face item detection status is used to map the detection result information of the face item; the number of Objects is determined by the number of face items specified in the published national standard.

[0044] In this embodiment, the index structure of ElasticSearch adopts a flat data structure, which includes all statistical dimensions. Usually, the ElasticSearch index is established by date using the ElasticSearch template, and the index name is a unified prefix + date timestamp. When requesting ElasticSearch for an aggregate query, the requested index path uses the template name, and ElasticSearch automatically routes the request to each server in the ElasticSearch cluster and returns the statistical results.

[0045] Combined with Figure 1 , 2 The business module stores the main entrance of electronic certificate detection, the electronic certificate structured data detection method, and the detection result statistical analysis method. The business module includes an interactive submodule, MQ, and a detection cluster containing multiple detection submodules. The user imports external samples into the relational database of the data persistence module through the interactive submodule. At the same time, the user customizes the regular verification rules corresponding to the standard certificate type through the interactive submodule, sends a detection request to MQ, and requests ElasticSearch to obtain statistical detection results. Among them, the customized regular verification rules include metadata regular verification rules, face item regular verification rules, and attachment regular verification rules. The statistical detection results are structured data returned by the interactive submodule requesting ElasticSearch to parse and obtain the statistical results of each batch of detections. MQ is responsible for receiving the detection requests from the interactive submodule and distributing them to the detection cluster to achieve request peak shaving. The detection cluster receives the detection requests distributed in MQ, and calls the interface for obtaining certificate information and certificate attachments provided by the certificate library through the certificate identifier in the detection request to obtain the certificate information that needs to be detected. Then, the corresponding regular verification rules are obtained from the relational database according to the certificate type code for detection. After the detection is completed, the detection results are serialized and persisted in ElasticSearch for statistical analysis by the interactive submodule.

[0046] The test results are serialized and persisted in ElasticSearch. During this process, if an exception occurs, the entire process is rolled back, logs are recorded, and the results are pushed again to ensure data consistency.

[0047] In summary, the electronic certificate quality detection and analysis system of the present invention can be used to uniformly detect the certificates in the certificate library, and can meet the certificate detection requirements under large data volumes and large request problems.

[0048] The above specific examples are used to explain the principles and implementation methods of the present invention in detail. These examples are only used to help understand the core technical content of the present invention. Based on the above specific embodiments of the present invention, any improvements and modifications made to the present invention by technicians in this technical field without departing from the principles of the present invention should fall within the scope of patent protection of the present invention.

Claims

1. An electronic certificate quality detection and analysis system, characterized in that ,It includes two parts: data persistence module and business module, among which, The data persistence module includes a relational database and a cluster including multiple ElasticSearchs, wherein the relational database stores the necessary data required for license detection, and the ElasticSearch stores the detection results after the license detection; the index design of the ElasticSearch includes three parts: metadata, face data detection results, and file information detection results, wherein: the metadata is designed as an Object, including three types of license index information: license type code, license definition agency, and license district, the type is keyword, and is used for statistical aggregation; the design of the face data detection result and the file information detection result is respectively an Array, the Array consists of a plurality of Objects, each Object consists of a face item name and a face item detection status, the type is keyword; the face item name in the Object consists of the face item name specified in the published national standard, and the face item detection status is used to map the face item detection result information; the number of Objects is determined by the number of face items specified in the published national standard; The business module includes an interactive submodule, MQ, and a detection cluster including multiple detection submodules; the user imports external samples into the relational database of the data persistence module through the interactive submodule. At the same time, the user customizes the regular verification rules corresponding to the standard certificate type through the interactive submodule, sends a detection request to MQ, and requests ElasticSearch to obtain statistical detection results; MQ is responsible for receiving the detection requests from the interactive submodule and distributing them to the detection cluster to achieve request peak shaving; the detection cluster receives the detection requests distributed in MQ, calls the interface for obtaining certificate information and certificate attachments provided by the certificate library through the certificate identifier in the detection request, and obtains the certificate information that needs to be detected; then, the corresponding regular verification rules are obtained from the relational database according to the certificate type code for detection; after the detection is completed, the detection results are serialized and persisted in ElasticSearch for statistical analysis by the interactive submodule.

2. The electronic certificate quality detection and analysis system according to claim 1, characterized in that: The necessary data required for license testing includes license sample data, standard license type information, definition agency information, regular verification rules corresponding to the face items, and test batch information. The certificate sample data comes from the certificate library, including the certificate metadata, face data, and certificate attachments. The certificate data belonging to the same sample have the same certificate identifier; Standard certificate type information includes certificate type code and certificate type name; through the certificate type code, the regular verification rule of each certificate can be associated, thereby realizing different standard detections for different types of certificates; The definition institution information includes the definition institution name and the unified social credit code of the definition institution; Regular verification rules corresponding to the face-to-face items, that is, regular verification rules defined for the certificate metadata, face-to-face item data, and structured data in the OFD format file according to the electronic certificate engineering standards issued by the State Council, are used to verify the structured data of the certificate; The test batch information is the batch number used to distinguish different batches of test records and is used to distinguish the test results each time.

3. The electronic certificate quality detection and analysis system according to claim 2 is characterized in that: The certificate sample data, certificate type information, definition agency information, regular verification rules corresponding to the face items, and test batch information will change according to changes in the test batch and national standards for test certificates.

4. The electronic certificate quality detection and analysis system according to claim 2, characterized in that: The index structure of the ElasticSearch adopts a flat data structure, which includes all statistical dimensions.

5. The electronic certificate quality detection and analysis system according to claim 2 is characterized in that: Use the ElasticSearch template to create an ElasticSearch index by date, and the index name is a unified prefix + date timestamp.

6. The electronic certificate quality detection and analysis system according to claim 2, characterized in that: When requesting ElasticSearch to perform an aggregate query, the requested index path uses the template name, and ElasticSearch automatically routes the request to each server in the ElasticSearch cluster and returns the statistical results.

7. The electronic certificate quality detection and analysis system according to claim 2, characterized in that: Customized regular validation rules include metadata regular validation rules, face item regular validation rules and attachment regular validation rules; The statistical detection results are obtained by the interactive submodule requesting ElasticSearch to parse the returned structured data and obtain the statistical results of each batch detection.

8. The electronic certificate quality detection and analysis system according to claim 2, characterized in that: The test results are serialized and persisted in ElasticSearch. During this process, if an exception occurs, the entire process is rolled back, logs are recorded, and the results are pushed again to ensure data consistency.

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