CN117171367B - Specification detection method for specified attribute values of different database tables - Google Patents
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Abstract
The invention provides a specification detection method for specified attribute values of different database tables, which comprises the following steps: acquiring a designated attribute value and designated attribute value limiting content of a database table; acquiring a specified attribute value and a setting specification standard corresponding to the specified attribute value limiting content; generating a knowledge graph for detection based on the specified attribute value, the limited content of the specified attribute value and the comprehensive setting standard; and detecting the knowledge graph to be detected by using a preset standard detection model to obtain a standard detection result. According to the method, the knowledge graph for detection is generated based on the specified attribute values of the database tables, the limited content of the specified attribute values and the set standard, and the detection is carried out by using the standard detection model, so that the efficiency and the accuracy of standard detection of the specified attribute values of the database tables are improved, and the efficient processing of different database tables is facilitated.
Description
Technical Field
The invention relates to the technical field of database processing, in particular to a specification detection method for specified attribute values of different database tables.
Background
A database TABLE (TABLE) is an object in a database for storing data, is a collection of structured data, and is the basis of the entire database system; a table is defined as a collection of columns in which data is organized in a row and column format, each column in the table designed to store some type of information (e.g., date, name, dollar amount, or number); each column has its own attribute values that define the types of data and other constraints that the column can store; as in MySQL data tables, common table attribute values are: integer type, variable length string type, fixed point number type and floating point number type; common attribute value restrictions include primary key, foreign key, unique key, the value in the column cannot be null, if any value in the column is not specified, default values are used; these restrictions will help ensure that the data in the table is always valid, clean and useful.
The existing database products all support structured query language SQL as a standard processing language; SQL itself has different standard versions, resulting in different kinds of database tables in database products, and different attribute values and attribute value limits corresponding to the database tables; the standard detection of the attribute values mostly depends on the existing database or utilizes compiled detection sentences to detect, thereby increasing the detection workload, reducing the detection efficiency and being unfavorable for updating and expanding the application of the detection.
The patent application document with the application number of CN202211396817.6 discloses a method and a device for detecting SQL sentences, wherein the method is used for analyzing source codes to be detected in a development stage to obtain SQL sentences to be detected in the source codes to be detected, generating random data and random variables according to the SQL sentences to be detected and filling the random data and the random variables into SQL databases and parameters to be filled in the SQL sentences to be detected respectively, so that the filled SQL sentences are operated based on the filled SQL databases to generate operation results; the patent application document improves the timeliness of SQL statement problem discovery and reduces the cost of SQL statement repair by advancing the detection of SQL statements to a system development stage, and realizes the pre-running of all SQL statements in an SQL database by generating random data and random variables, thereby realizing the detection of all SQL statements; however, the detection method relies on a preset SQL database to detect, a database with numerous and complicated data needs to be designed in advance, the operation efficiency is not high enough, and the detection accuracy is difficult to ensure.
Patent application document with application number of CN202310141430.4 discloses a database compatibility detection method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: constructing a target virtual database corresponding to the application system, wherein the target virtual database is obtained by simulating a target real database based on a container mode and is not imported into a database table structure corresponding to the application system in advance; extracting a target structured query statement for database operation from source codes corresponding to an application system; and detecting the compatibility of the target real database to the structured query statement by running the target structured query statement on the target virtual database. The method realizes the rapid detection of database compatibility, rapidly adapts to a target structured query language compatible with a target real database, and simplifies the database deployment mode of an application system; the application crisis detects database compatibility through the target structured query statement, and the situation that the detection execution efficiency is low and the detection accuracy is not high exists.
Therefore, it is necessary to provide a specification detection method for specified attribute values of different database tables.
Disclosure of Invention
The invention provides a specification detection method for specified attribute values of different database tables, which is used for generating a knowledge graph for detection based on the specified attribute values of the database tables, limiting contents of the specified attribute values and setting specification standards, and detecting by using a specification detection model, so that the efficiency and accuracy of specification detection of the specified attribute values of the database tables are improved, and the efficient processing of the different database tables is facilitated.
The invention provides a specification detection method for specified attribute values of different database tables, which comprises the following steps:
s1: acquiring a designated attribute value and designated attribute value limiting content of a database table;
s2: acquiring a specified attribute value and a setting specification standard corresponding to the specified attribute value limiting content;
s3: generating a knowledge graph for detection based on the specified attribute value, the limited content of the specified attribute value and the set specification standard;
s4: and detecting the knowledge graph to be detected by using a preset standard detection model to obtain a standard detection result.
Further, S1 includes:
s101, acquiring a plurality of different creation script languages of database tables;
s102, based on a preset retrieval extraction template, retrieving and extracting in the established script language according to the preset keywords of the appointed attribute value to obtain the appointed attribute value and the appointed attribute value limiting content.
Further, S2 includes:
s201, acquiring a first setting specification standard corresponding to a specified attribute value and a second setting specification standard corresponding to the limiting content of the specified attribute value;
s202, summarizing the first setting standard specification and the second setting standard specification to generate the setting standard specification.
Further, S3 includes:
based on the knowledge graph technology, the specified attribute value limiting content and the set specification standard are taken as entities, and the knowledge graph for detection is generated according to the relation among the specified attribute value, the specified attribute value limiting content and the set specification standard.
Further, S4 includes:
s401, constructing a standard detection model;
and S402, detecting the to-be-detected knowledge graph based on the standard detection model to obtain a standard detection result.
Further, S401 includes:
s4011: acquiring a plurality of specified attribute values for testing and limited contents of the specified attribute values for testing;
s4012: acquiring a test attribute value and a test designated attribute value to limit keywords in the content, and searching for a plurality of times in a to-be-detected knowledge graph based on the keywords;
s4013: based on the search flows of S4011 and S4012, a search program is designed and written, and a specification detection model is generated according to the search program.
Further, S402 includes:
s4021: searching the knowledge graph to be detected by using a standard detection model to obtain a plurality of recommended results; the recommendation result comprises a plurality of content items for setting standard standards;
s4022: acquiring a difference degree value of a content item and a set standard;
s4023: and acquiring a matched standard detection result based on a matching corresponding relation library of the preset difference degree value and the standard detection result.
Further, S4022 includes:
s4022-1: splitting the content item according to the first setting standard and the second setting standard to obtain a first content item and a second content item;
s4022-2: comparing the difference degree of the first content item and the first setting standard according to a preset calculation formula of the difference degree value to obtain a first difference degree value; comparing the difference degree of the second content item and the second setting standard to obtain a second difference degree value;
s4022-3: and respectively weighting and accumulating the first difference degree value and the second difference degree value, and then averaging to obtain the difference degree value.
Further, S5, analyzing negative influence factor data influencing the quality evaluation of the database table according to the standard detection result; the method comprises the following specific steps:
s501: acquiring a historical difference degree value and acquiring an influence value of the historical difference degree value on the standardization degree of the database table; constructing a first matching corresponding relation table of the difference degree value and the influence value;
s502: according to the first matching corresponding relation table, a first influence value corresponding to the first difference degree value and a second influence value corresponding to the second difference degree value are respectively obtained;
s503: acquiring a history influence value, and calculating the duty ratio of the acquired history influence value in negative influence factors of the quality evaluation of the database table; constructing a second matching corresponding relation table of the history influence value and the duty ratio;
s504: according to the second matching corresponding relation table, a first duty ratio corresponding to the first influence value and a second duty ratio corresponding to the second influence value are respectively obtained;
s505: summarizing the first duty ratio and the second duty ratio to generate a comprehensive duty ratio; negative impact factor data for quality assessment of the database tables is generated based on the composite duty cycle.
Further, S6, based on a preset micro-service architecture, carrying out standard detection on the appointed attribute value of the database table, modifying the database table aiming at the detection abnormality, modifying compiling execution and carrying out subsequent execution of the database table for continuous processing;
s601: establishing a micro-service architecture for database table detection processing; the micro services in the micro service architecture comprise standard detection micro services, detection modification micro services, modification correction micro services and database table follow-up micro services; the system comprises a standard detection micro-service, a modification micro-service, a database table follow-up processing micro-service and a database table follow-up processing micro-service, wherein the standard detection micro-service is used for carrying out standard detection on a designated attribute value of a database table, the modification micro-service is used for modifying non-standard content of the designated attribute value of the database table, the modification micro-service is used for compiling and executing a script of the modified database table, and the database table follow-up processing micro-service is used for carrying out follow-up processing on the database table without abnormality in standard detection;
s602: acquiring a plurality of calling instructions in a preset calling instruction library;
s603: calling the micro service to process the flow according to the calling instruction; the flow process comprises the following steps: according to the standard detection result, if the standard detection result is abnormal, invoking a subsequent processing micro-service of the database table to perform subsequent processing; if the standard detection result is abnormal, calling the detection modification micro-service, modifying and checking the micro-service to execute modification and compiling execution, calling the standard detection micro-service again to perform standard detection on the database table with successful compiling execution, and if the standard detection result is not abnormal, calling the database table to perform subsequent processing on the micro-service; and if the standard detection result is abnormal, calling the detection modification micro-service again, modifying the correction micro-service to execute modification and compiling execution until the standard detection result is abnormal.
Compared with the prior art, the invention has the following advantages and beneficial effects: according to the method, the knowledge graph for detection is generated based on the specified attribute values of the database tables, the limited content of the specified attribute values and the set standard, and the detection is carried out by using the standard detection model, so that the efficiency and the accuracy of standard detection of the specified attribute values of the database tables are improved, and the efficient processing of different database tables is facilitated.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a method for detecting specification of specified attribute values of different database tables;
FIG. 2 is a schematic diagram of method steps for obtaining a specified attribute value and a specified attribute value limit content of a database table;
fig. 3 is a schematic diagram of method steps for obtaining a specified attribute value and a set specification standard corresponding to the specified attribute value limit content.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The invention provides a specification detection method for specified attribute values of different database tables, as shown in fig. 1, comprising the following steps: s1: acquiring a designated attribute value and designated attribute value limiting content of a database table;
s2: acquiring a specified attribute value and a setting specification standard corresponding to the specified attribute value limiting content;
s3: generating a knowledge graph for detection based on the specified attribute value, the limited content of the specified attribute value and the set specification standard;
s4: and detecting the knowledge graph to be detected by using a preset standard detection model to obtain a standard detection result.
The working principle of the technical scheme is as follows: s1: acquiring a designated attribute value and designated attribute value limiting content of a database table;
s2: acquiring a specified attribute value and a setting specification standard corresponding to the specified attribute value limiting content;
s3: generating a knowledge graph for detection based on the specified attribute value, the limited content of the specified attribute value and the set specification standard;
s4: and detecting the knowledge graph to be detected by using a preset standard detection model to obtain a standard detection result.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the knowledge graph for detection is generated based on the specified attribute value of the database table, the limited content of the specified attribute value and the set standard, and the detection is performed by using the standard detection model, so that the efficiency and the accuracy of the standard detection of the specified attribute value of the database table are improved, and the efficient processing of different database tables is facilitated.
In one embodiment, as shown in fig. 2, S1 includes:
s101, acquiring a plurality of different creation script languages of database tables;
s102, based on a preset retrieval extraction template, retrieving and extracting in the established script language according to the preset keywords of the appointed attribute value to obtain the appointed attribute value and the appointed attribute value limiting content.
The working principle of the technical scheme is as follows: s1 comprises the following steps:
s101, acquiring a plurality of different creation script languages of database tables;
s102, based on a preset retrieval extraction template, retrieving and extracting in the established script language according to the preset keywords of the appointed attribute value to obtain the appointed attribute value and the appointed attribute value limiting content.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the specified attribute value and the specified attribute value limiting content can be ensured to be obtained rapidly and efficiently by utilizing the retrieval and extraction template to carry out retrieval and extraction.
In one embodiment, as shown in fig. 3, S2 includes:
s201, acquiring a first setting specification standard corresponding to a specified attribute value and a second setting specification standard corresponding to the limiting content of the specified attribute value;
s202, summarizing the first setting standard specification and the second setting standard specification to generate the setting standard specification.
The working principle of the technical scheme is as follows: s2 comprises the following steps:
s201, acquiring a first setting specification standard corresponding to a specified attribute value and a second setting specification standard corresponding to the limiting content of the specified attribute value;
s202, summarizing the first setting standard specification and the second setting standard specification to generate the setting standard specification.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the generation setting standard specification is provided, and necessary conditions are provided for subsequent specification detection.
In one embodiment, S3 comprises:
based on the knowledge graph technology, the specified attribute value limiting content and the set specification standard are taken as entities, and the knowledge graph for detection is generated according to the relation among the specified attribute value, the specified attribute value limiting content and the set specification standard.
The working principle of the technical scheme is as follows: s3 comprises the following steps:
based on the knowledge graph technology, the specified attribute value limiting content and the set specification standard are taken as entities, and the knowledge graph for detection is generated according to the relation among the specified attribute value, the specified attribute value limiting content and the set specification standard.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, conditions are provided for subsequent standard detection by generating the knowledge graph for detection.
In one embodiment, S4 comprises:
s401, constructing a standard detection model;
and S402, detecting the to-be-detected knowledge graph based on the standard detection model to obtain a standard detection result.
The working principle of the technical scheme is as follows: s4 comprises the following steps:
s401, constructing a standard detection model;
and S402, detecting the to-be-detected knowledge graph based on the standard detection model to obtain a standard detection result.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the high-efficiency execution of the standard detection and the accuracy of the standard detection result can be ensured by using the standard detection model for detection.
In one embodiment, S401 includes:
s4011: acquiring a plurality of specified attribute values for testing and limited contents of the specified attribute values for testing;
s4012: acquiring a test attribute value and a test designated attribute value to limit keywords in the content, and searching for a plurality of times in a to-be-detected knowledge graph based on the keywords;
s4013: based on the search flows of S4011 and S4012, a search program is designed and written, and a specification detection model is generated according to the search program.
The working principle of the technical scheme is as follows: s401 includes:
s4011: acquiring a plurality of specified attribute values for testing and limited contents of the specified attribute values for testing;
s4012: acquiring a test attribute value and a test designated attribute value to limit keywords in the content, and searching for a plurality of times in a to-be-detected knowledge graph based on the keywords;
s4013: based on the search flows of S4011 and S4012, a search program is designed and written, and a specification detection model is generated according to the search program.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the search program is designed and written through the process of searching in the knowledge graph to be detected, so that the quality of the standard detection model generation is ensured.
In one embodiment, S402 includes:
s4021: searching the knowledge graph to be detected by using a standard detection model to obtain a plurality of recommended results; the recommendation result comprises a plurality of content items for setting standard standards;
s4022: acquiring a difference degree value of a content item and a set standard;
s4023: and acquiring a matched standard detection result based on a matching corresponding relation library of the preset difference degree value and the standard detection result.
The working principle of the technical scheme is as follows: s402 includes:
s4021: searching the knowledge graph to be detected by using a standard detection model to obtain a plurality of recommended results; the recommendation result comprises a plurality of content items for setting standard standards;
s4022: acquiring a difference degree value of a content item and a set standard;
s4023: and acquiring a matched standard detection result based on a matching corresponding relation library of the preset difference degree value and the standard detection result.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the accurate standard detection result can be ensured to be obtained by searching the knowledge graph to be detected by using the standard detection model.
In one embodiment, S4022 includes:
s4022-1: splitting the content item according to the first setting standard and the second setting standard to obtain a first content item and a second content item;
s4022-2: comparing the difference degree of the first content item and the first setting standard according to a preset calculation formula of the difference degree value to obtain a first difference degree value; comparing the difference degree of the second content item and the second setting standard to obtain a second difference degree value;
s4022-3: and respectively weighting and accumulating the first difference degree value and the second difference degree value, and then averaging to obtain the difference degree value.
The working principle of the technical scheme is as follows: s4022 includes:
s4022-1: splitting the content item according to the first setting standard and the second setting standard to obtain a first content item and a second content item;
s4022-2: comparing the difference degree of the first content item and the first setting standard according to a preset calculation formula of the difference degree value to obtain a first difference degree value; comparing the difference degree of the second content item and the second setting standard to obtain a second difference degree value;
s4022-3: and respectively weighting and accumulating the first difference degree value and the second difference degree value, and then averaging to obtain the difference degree value.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the accuracy and rationality of the difference degree value can be ensured through splitting the content item.
In one embodiment, the method further comprises S5, analyzing negative influence factor data influencing quality evaluation of the database table according to the standard detection result; the method comprises the following specific steps:
s501: acquiring a historical difference degree value and acquiring an influence value of the historical difference degree value on the standardization degree of the database table; constructing a first matching corresponding relation table of the difference degree value and the influence value;
s502: according to the first matching corresponding relation table, a first influence value corresponding to the first difference degree value and a second influence value corresponding to the second difference degree value are respectively obtained;
s503: acquiring a history influence value, and calculating the duty ratio of the acquired history influence value in negative influence factors of the quality evaluation of the database table; constructing a second matching corresponding relation table of the history influence value and the duty ratio;
s504: according to the second matching corresponding relation table, a first duty ratio corresponding to the first influence value and a second duty ratio corresponding to the second influence value are respectively obtained;
s505: summarizing the first duty ratio and the second duty ratio to generate a comprehensive duty ratio; negative impact factor data for quality assessment of the database tables is generated based on the composite duty cycle.
The working principle of the technical scheme is as follows: s5, analyzing negative influence factor data influencing the quality evaluation of the database table according to the standard detection result; the method comprises the following specific steps:
s501: acquiring a historical difference degree value and acquiring an influence value of the historical difference degree value on the standardization degree of the database table; constructing a first matching corresponding relation table of the difference degree value and the influence value;
s502: according to the first matching corresponding relation table, a first influence value corresponding to the first difference degree value and a second influence value corresponding to the second difference degree value are respectively obtained;
s503: acquiring a history influence value, and calculating the duty ratio of the acquired history influence value in negative influence factors of the quality evaluation of the database table; constructing a second matching corresponding relation table of the history influence value and the duty ratio;
s504: according to the second matching corresponding relation table, a first duty ratio corresponding to the first influence value and a second duty ratio corresponding to the second influence value are respectively obtained;
s505: summarizing the first duty ratio and the second duty ratio to generate a comprehensive duty ratio; negative impact factor data for quality assessment of the database tables is generated based on the composite duty cycle.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the negative influence factor data influencing the quality evaluation of the database table is analyzed according to the standard detection result, so that the negative influence factor data influencing the quality evaluation of the database table is generated, and powerful reference data can be provided for the quality evaluation of the database table.
In one embodiment, the method further comprises S6, based on a preset micro-service architecture, performing specification detection on the appointed attribute value of the database table, modifying the database table aiming at the detection abnormality, modifying compiling execution and performing subsequent execution of the database table for continuous processing;
s601: establishing a micro-service architecture for database table detection processing; the micro services in the micro service architecture comprise standard detection micro services, detection modification micro services, modification correction micro services and database table follow-up micro services; the system comprises a standard detection micro-service, a modification micro-service, a database table follow-up processing micro-service and a database table follow-up processing micro-service, wherein the standard detection micro-service is used for carrying out standard detection on a designated attribute value of a database table, the modification micro-service is used for modifying non-standard content of the designated attribute value of the database table, the modification micro-service is used for compiling and executing a script of the modified database table, and the database table follow-up processing micro-service is used for carrying out follow-up processing on the database table without abnormality in standard detection;
s602: acquiring a plurality of calling instructions in a preset calling instruction library;
s603: calling the micro service to process the flow according to the calling instruction; the flow process comprises the following steps: according to the standard detection result, if the standard detection result is abnormal, invoking a subsequent processing micro-service of the database table to perform subsequent processing; if the standard detection result is abnormal, calling the detection modification micro-service, modifying and checking the micro-service to execute modification and compiling execution, calling the standard detection micro-service again to perform standard detection on the database table with successful compiling execution, and if the standard detection result is not abnormal, calling the database table to perform subsequent processing on the micro-service; and if the standard detection result is abnormal, calling the detection modification micro-service again, modifying the correction micro-service to execute modification and compiling execution until the standard detection result is abnormal.
The working principle of the technical scheme is as follows: s6, based on a preset micro-service architecture, carrying out standard detection on the appointed attribute value of the database table, modifying the database table aiming at detection abnormality, modifying compiling execution and carrying out subsequent execution of the database table for continuous processing;
s601: establishing a micro-service architecture for database table detection processing; the micro services in the micro service architecture comprise standard detection micro services, detection modification micro services, modification correction micro services and database table follow-up micro services; the system comprises a standard detection micro-service, a modification micro-service, a database table follow-up processing micro-service and a database table follow-up processing micro-service, wherein the standard detection micro-service is used for carrying out standard detection on a designated attribute value of a database table, the modification micro-service is used for modifying non-standard content of the designated attribute value of the database table, the modification micro-service is used for compiling and executing a script of the modified database table, and the database table follow-up processing micro-service is used for carrying out follow-up processing on the database table without abnormality in standard detection;
s602: acquiring a plurality of calling instructions in a preset calling instruction library;
s603: calling the micro service to process the flow according to the calling instruction; the flow process comprises the following steps: according to the standard detection result, if the standard detection result is abnormal, invoking a subsequent processing micro-service of the database table to perform subsequent processing; if the standard detection result is abnormal, calling the detection modification micro-service, modifying and checking the micro-service to execute modification and compiling execution, calling the standard detection micro-service again to perform standard detection on the database table with successful compiling execution, and if the standard detection result is not abnormal, calling the database table to perform subsequent processing on the micro-service; and if the standard detection result is abnormal, calling the detection modification micro-service again, modifying the correction micro-service to execute modification and compiling execution until the standard detection result is abnormal.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, batch detection processing can be performed on different database tables by constructing the micro-service architecture, and the detection processing efficiency is improved.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (6)
1. A method for canonical detection of specified attribute values for different database tables, comprising:
s1: acquiring a designated attribute value and designated attribute value limiting content of a database table;
s2: acquiring a specified attribute value and a setting specification standard corresponding to the specified attribute value limiting content;
s3: generating a knowledge graph for detection based on the specified attribute value, the limited content of the specified attribute value and the set specification standard;
s4: detecting the knowledge graph to be detected by using a preset standard detection model to obtain a standard detection result;
s4 comprises the following steps:
s401, constructing a standard detection model;
s402, detecting a to-be-detected knowledge graph based on a standard detection model to obtain a standard detection result;
s401 includes:
s4011: acquiring a plurality of specified attribute values for testing and limited contents of the specified attribute values for testing;
s4012: acquiring a test attribute value and a test designated attribute value to limit keywords in the content, and searching for a plurality of times in a to-be-detected knowledge graph based on the keywords;
s4013: designing and writing a search program based on the search flow of S4011 and S4012, and generating a standard detection model according to the search program;
s402 includes:
s4021: searching the knowledge graph to be detected by using a standard detection model to obtain a plurality of recommended results; the recommendation result comprises a plurality of content items for setting standard standards;
s4022: acquiring a difference degree value of a content item and a set standard;
s4023: acquiring a matched standard detection result based on a matching corresponding relation library of a preset difference degree value and the standard detection result;
s4022 includes:
s4022-1: splitting the content item according to the first setting standard and the second setting standard to obtain a first content item and a second content item;
s4022-2: comparing the difference degree of the first content item and the first setting standard according to a preset calculation formula of the difference degree value to obtain a first difference degree value; comparing the difference degree of the second content item and the second setting standard to obtain a second difference degree value;
s4022-3: and respectively weighting and accumulating the first difference degree value and the second difference degree value, and then averaging to obtain the difference degree value.
2. The method of claim 1, wherein S1 comprises:
s101, acquiring a plurality of different creation script languages of database tables;
s102, based on a preset retrieval extraction template, retrieving and extracting in the established script language according to the preset keywords of the appointed attribute value to obtain the appointed attribute value and the appointed attribute value limiting content.
3. The method of claim 1, wherein S2 comprises:
s201, acquiring a first setting specification standard corresponding to a specified attribute value and a second setting specification standard corresponding to the limiting content of the specified attribute value;
s202, summarizing the first setting standard specification and the second setting standard specification to generate the setting standard specification.
4. The method of claim 1, wherein S3 comprises:
based on the knowledge graph technology, the specified attribute value limiting content and the set specification standard are taken as entities, and the knowledge graph for detection is generated according to the relation among the specified attribute value, the specified attribute value limiting content and the set specification standard.
5. The method for detecting specification of specified attribute values of different database tables according to claim 1, further comprising S5 analyzing negative influence factor data affecting quality evaluation of the database tables according to the specification detection result; the method comprises the following specific steps:
s501: acquiring a historical difference degree value and acquiring an influence value of the historical difference degree value on the standardization degree of the database table; constructing a first matching corresponding relation table of the difference degree value and the influence value;
s502: according to the first matching corresponding relation table, a first influence value corresponding to the first difference degree value and a second influence value corresponding to the second difference degree value are respectively obtained;
s503: acquiring a history influence value, and calculating the duty ratio of the acquired history influence value in negative influence factors of the quality evaluation of the database table; constructing a second matching corresponding relation table of the history influence value and the duty ratio;
s504: according to the second matching corresponding relation table, a first duty ratio corresponding to the first influence value and a second duty ratio corresponding to the second influence value are respectively obtained;
s505: summarizing the first duty ratio and the second duty ratio to generate a comprehensive duty ratio; negative impact factor data for quality assessment of the database tables is generated based on the composite duty cycle.
6. The method for detecting specification of specified attribute values of different database tables according to claim 1, further comprising S6, based on a preset micro-service architecture, performing specification detection on the specified attribute values of the database tables, performing modification of the database tables for detection anomalies, performing modification compilation execution, and performing subsequent execution of the database tables for coherent processing;
s601: establishing a micro-service architecture for database table detection processing; the micro services in the micro service architecture comprise standard detection micro services, detection modification micro services, modification correction micro services and database table follow-up micro services; the system comprises a standard detection micro-service, a modification micro-service, a database table follow-up processing micro-service and a database table follow-up processing micro-service, wherein the standard detection micro-service is used for carrying out standard detection on a designated attribute value of a database table, the modification micro-service is used for modifying non-standard content of the designated attribute value of the database table, the modification micro-service is used for compiling and executing a script of the modified database table, and the database table follow-up processing micro-service is used for carrying out follow-up processing on the database table without abnormality in standard detection;
s602: acquiring a plurality of calling instructions in a preset calling instruction library;
s603: calling the micro service to process the flow according to the calling instruction; the flow process comprises the following steps: according to the standard detection result, if the standard detection result is abnormal, invoking a subsequent processing micro-service of the database table to perform subsequent processing; if the standard detection result is abnormal, calling the detection modification micro-service, modifying and checking the micro-service to execute modification and compiling execution, calling the standard detection micro-service again to perform standard detection on the database table with successful compiling execution, and if the standard detection result is not abnormal, calling the database table to perform subsequent processing on the micro-service; and if the standard detection result is abnormal, calling the detection modification micro-service again, modifying the correction micro-service to execute modification and compiling execution until the standard detection result is abnormal.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104866593A (en) * | 2015-05-29 | 2015-08-26 | 中国电子科技集团公司第二十八研究所 | Database searching method based on knowledge graph |
| CN106445795A (en) * | 2016-09-26 | 2017-02-22 | 中国工商银行股份有限公司 | Method and device for detecting efficiency of database SQL |
| CN110362660A (en) * | 2019-07-23 | 2019-10-22 | 重庆邮电大学 | A kind of Quality of electronic products automatic testing method of knowledge based map |
| CN111930518A (en) * | 2020-09-22 | 2020-11-13 | 北京东方通科技股份有限公司 | Knowledge graph representation learning-oriented distributed framework construction method |
| CN112148735A (en) * | 2020-09-23 | 2020-12-29 | 上海爱数信息技术股份有限公司 | Construction method for structured form data knowledge graph |
| CN112181936A (en) * | 2019-07-03 | 2021-01-05 | 北京京东尚科信息技术有限公司 | Database detection method and device |
| CN113971236A (en) * | 2020-07-23 | 2022-01-25 | 北京金山数字娱乐科技有限公司 | Data monitoring method and device of knowledge graph |
| WO2022267865A1 (en) * | 2021-06-24 | 2022-12-29 | 中兴通讯股份有限公司 | Workflow creation method and system, and electronic device and computer-readable storage medium |
| CN116089260A (en) * | 2022-11-09 | 2023-05-09 | 福建福诺移动通信技术有限公司 | SQL sentence detection method and device |
| CN116701648A (en) * | 2023-05-29 | 2023-09-05 | 中铁第四勘察设计院集团有限公司 | Mapping knowledge graph and schema design method based on standard specification |
-
2023
- 2023-09-26 CN CN202311248690.8A patent/CN117171367B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104866593A (en) * | 2015-05-29 | 2015-08-26 | 中国电子科技集团公司第二十八研究所 | Database searching method based on knowledge graph |
| CN106445795A (en) * | 2016-09-26 | 2017-02-22 | 中国工商银行股份有限公司 | Method and device for detecting efficiency of database SQL |
| CN112181936A (en) * | 2019-07-03 | 2021-01-05 | 北京京东尚科信息技术有限公司 | Database detection method and device |
| CN110362660A (en) * | 2019-07-23 | 2019-10-22 | 重庆邮电大学 | A kind of Quality of electronic products automatic testing method of knowledge based map |
| CN113971236A (en) * | 2020-07-23 | 2022-01-25 | 北京金山数字娱乐科技有限公司 | Data monitoring method and device of knowledge graph |
| CN111930518A (en) * | 2020-09-22 | 2020-11-13 | 北京东方通科技股份有限公司 | Knowledge graph representation learning-oriented distributed framework construction method |
| CN112148735A (en) * | 2020-09-23 | 2020-12-29 | 上海爱数信息技术股份有限公司 | Construction method for structured form data knowledge graph |
| WO2022267865A1 (en) * | 2021-06-24 | 2022-12-29 | 中兴通讯股份有限公司 | Workflow creation method and system, and electronic device and computer-readable storage medium |
| CN116089260A (en) * | 2022-11-09 | 2023-05-09 | 福建福诺移动通信技术有限公司 | SQL sentence detection method and device |
| CN116701648A (en) * | 2023-05-29 | 2023-09-05 | 中铁第四勘察设计院集团有限公司 | Mapping knowledge graph and schema design method based on standard specification |
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