CN116501757A - ER diagram-based simulation data construction method and device - Google Patents

Abstract

The invention provides a simulated data construction method and device based on an ER diagram, wherein the method comprises the following steps: acquiring an ER diagram, analyzing the ER diagram, and generating SQL table information corresponding to the ER diagram; performing visual rendering on the ER diagram according to the SQL table information to obtain a visual interface corresponding to the ER diagram; receiving a rule configuration instruction, and configuring data on the visual interface to generate a rule according to the rule configuration instruction; and generating simulation data according to the data generation rule. According to the invention, the simulation data is generated by configuring the data generation rule of the entity in the ER diagram on line, so that the visual configuration of the data generation rule on the operation interface is realized, the adjustment is convenient, the simulation data conforming to the corresponding service scene can be generated through simple interface operation, and the efficiency of generating the simulation data is improved.

Description

A method and device for constructing analog data based on ER diagram

technical field

The invention relates to the field of system testing and related information technology processing, in particular to a method and device for constructing simulated data based on ER diagrams.

Background technique

Information systems need data support in experiments, trials, and drills, but it is often not appropriate to use real data. Typical reasons are as follows: 1. For security reasons, real data is not allowed. In order to solve this problem, some simple transformations are often used to disguise the real data in some occasions, but the disguised data can be restored to real data through inverse transformation, which still has potential safety hazards. At the same time, some disguised data will reduce the rationality of the data and affect the application effect. Second, due to time or cost requirements, it is difficult to use real data. Real data needs to be obtained according to a certain process, with the support of corresponding personnel, equipment guarantee, and minimum time guarantee. In the case of time constraints or limited investment, it is difficult to obtain real data that meets the requirements. 3. Different tasks have specific data requirements, and real data cannot meet the application. The test, trial, and drill tasks of the information system have clear objectives and need to be supported by data suitable for the objectives. Usually, the existing real data is not necessarily based on the data requirements of this task, and there are deviations from the actual data requirements in terms of completeness, timeliness, usability, relevance, and utility. In view of the above reasons, there is an increasingly urgent need for those skilled in the art to quickly generate simulated data to meet actual application scenarios, and urgently need to be solved. Reasonable models and algorithms should be used to quickly generate simulated data by means of computer simulation to meet the actual needs of information system experiments, trials, and drills.

In the existing technologies for quickly generating simulated data, it is common practice to configure relevant business rules and directly insert relevant data into the database according to the ER diagram designed by the user database. However, in this method, the business rules are manually configured, and the configuration process is cumbersome and cannot be modified quickly, which reduces the efficiency of generating simulation data.

Therefore, there are defects in the prior art and need to be improved and developed.

Contents of the invention

The technical problem to be solved by the present invention is to provide a method and device for constructing test scene data based on ER diagrams in view of the above-mentioned defects of the prior art, aiming at solving the problem of manually configuring business rules based on the ER diagrams designed by the user database in the prior art , the configuration process is cumbersome and cannot be modified quickly, which reduces the efficiency of generating simulation data.

The technical solution adopted by the present invention to solve technical problems is as follows:

A method for constructing simulation data based on ER diagrams, including:

Obtaining an ER diagram, analyzing the ER diagram, and generating SQL table information corresponding to the ER diagram;

performing visual rendering on the ER diagram according to the SQL table information, to obtain a visual interface corresponding to the ER diagram;

receiving a rule configuration instruction, and configuring data generation rules on the visual interface according to the rule configuration instruction;

Simulation data is generated according to the data generation rules.

In an implementation manner, the acquisition of the ER diagram, parsing the ER diagram, and generating SQL table information corresponding to the ER diagram include:

Obtaining the ER diagram, using the DDL plug-in to analyze the ER diagram, and generating SQL table information corresponding to the ER diagram;

Wherein, the SQL table information is the original entity information; or, the SQL table information includes: the original entity information and the foreign key of the parent entity of the original entity; or, the SQL table information includes: the foreign keys of all entities involved in the relationship Attribute information for keys and relationships.

In an implementation manner, the ER diagram is visually rendered according to the SQL table information to obtain a visual interface corresponding to the ER diagram, including:

Visually rendering the entity table structure in the ER diagram and the relationship between the entity tables according to the SQL table information to obtain a visual interface corresponding to the ER diagram;

Wherein, the visual interface includes: a scene list area, an operation area, and a parameter configuration area; the scene list area is used to create various business scenarios; the operation area includes: an operation button list, an operation canvas and a running log list, and the Function buttons are set on the operation button list, the operation canvas is used to configure and view the data generation rules of the ER diagram, and the log list is used to view the generation log of the simulation data corresponding to the ER diagram; The parameter configuration area is used to configure entity attribute generation rules and relationship attribute generation rules corresponding to the ER diagram.

In an implementation manner, the receiving rule configuration instruction, before configuring the data generation rules on the visual interface according to the rule configuration instruction, further includes:

Metadata information is stored in advance, and the metadata information is obtained according to the data standard corresponding to the current scene service, and the data standard includes metadata standard and/or dictionary table standard.

In an implementation manner, the receiving a rule configuration instruction, and configuring a data generation rule on the visual interface according to the rule configuration instruction includes:

receiving a rule configuration instruction, and configuring data generation rules on the visual interface according to the metadata information and the rule configuration instruction;

Wherein, the data generation rules include: entity attribute rules corresponding to the ER diagram, correlation degree parameters between entities, and target data types.

In one implementation, the entity attribute rules include: simulated data generation rules, normal data settings, and abnormal data settings; the simulated data generation rules include: fixed values, random values, regular expressions, incremental, and lexicon generation and custom functions.

Specifically, users can customize functions on the configuration list page of the present invention to meet specific rule requirements, making the application scope wider and more targeted.

In an implementation manner, the steps of configuring the parameters of the degree of association between entities include:

Obtain the database table corresponding to the ER diagram, the database table includes a relational database SQL table and a non-relational database SQL table;

Analyzing the SQL statement in the database table, determining the association relationship between the database tables according to the type of the associated field in the SQL statement, the association relationship of the database table includes a foreign key relationship, a same-dimensional relationship and a master-child relationship;

receiving a user setting instruction, and setting the association relationship on the visual interface;

According to the association relationship, the generation order of the simulation data generated by each database table is arranged.

In an implementation manner, arranging the generation order of the simulation data generated by each database table according to the association relationship includes:

The database table in the same-dimensional relationship is used as the same-dimensional table, the database table in the master-sub-relationship is used as the main-subtable, the sub-table in the main-subtable is attached to the main table to hide, and the foreign key table of the sub-table is used as the main table the foreign key table;

Assigning the same initial label to each database table, and updating the initial label according to the quantity of all foreign key tables of the database table, to obtain the final label;

The order of the final labels from small to large is used as the generation order of the simulated data for each database table.

In an implementation manner, the configuration of the target data type includes: setting a data generation rate, setting normal data, setting abnormal data, and setting a data format.

In an implementation manner, generating simulation data according to the data generation rules includes:

Generating simulated data for each of the database tables sequentially according to the generation sequence;

After the simulation data of the main table is generated, the foreign key value is randomly obtained from the primary key of the main table.

The present invention also provides a device for constructing simulation data based on ER diagrams, including:

An analysis module, configured to obtain an ER diagram, analyze the ER diagram, and generate SQL table information corresponding to the ER diagram;

A rendering module, configured to visually render the ER diagram according to the SQL table information, to obtain a visual interface corresponding to the ER diagram;

A configuration module, configured to receive a rule configuration instruction, and configure data generation rules on the visual interface according to the rule configuration instruction;

A generation module, configured to generate simulation data according to the data generation rules.

The present invention also provides a terminal, which includes: a memory, a processor, and an ER diagram-based simulation data construction program that is stored on the memory and can run on the processor, and the ER diagram-based simulation data When the construction program is executed by the processor, the steps of the above-mentioned ER diagram-based simulation data construction method are realized.

The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program can be executed to implement the method for constructing simulation data based on an ER diagram as described above. step.

A method and device for constructing simulated data based on an ER diagram provided by the present invention, the method includes: obtaining an ER diagram, analyzing the ER diagram, and generating SQL table information corresponding to the ER diagram; according to the SQL Performing visual rendering of the ER diagram based on table information to obtain a visual interface corresponding to the ER diagram; receiving a rule configuration instruction, configuring a data generation rule on the visualization interface according to the rule configuration instruction; generating a data generation rule according to the data generation rule mock data. The present invention generates simulated data by configuring the data generation rules of the entities in the ER diagram online, realizes the visual configuration of the data generation rules on the operation interface, facilitates adjustment, and can generate simulated data conforming to corresponding business scenarios through simple interface operations, improving to improve the efficiency of generating simulated data.

Description of drawings

Fig. 1 is an overall flow chart of constructing simulation data based on ER diagram in the present invention.

Fig. 2 is a flow chart of a preferred embodiment of the simulation data construction method based on the ER diagram in the present invention.

Fig. 3 is a layout diagram of a visual operation interface in the present invention.

Fig. 4 is a schematic diagram of the specific operation of the visualized operation interface in the present invention.

Fig. 5 is a function principle block diagram of a preferred embodiment of the device for constructing analog data based on ER diagram in the present invention.

Fig. 6 is a functional block diagram of the terminal in the present invention.

Detailed ways

In order to make the object, technical solution and advantages of the present invention more clear and definite, the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

Information systems need data support in experiments, trials, and drills, but it is often not appropriate to use real data. Typical reasons are as follows: 1. For security reasons, real data is not allowed. In order to solve this problem, some simple transformations are often used to disguise the real data in some occasions, but the disguised data can be restored to real data through inverse transformation, which still has potential safety hazards. At the same time, some disguised data will reduce the rationality of the data and affect the application effect. Second, due to time or cost requirements, it is difficult to use real data. Real data needs to be obtained according to a certain process, with the support of corresponding personnel, equipment guarantee, and minimum time guarantee. In the case of time constraints or limited investment, it is difficult to obtain real data that meets the requirements. 3. Different tasks have specific data requirements, and real data cannot meet the application. The test, trial, and drill tasks of the information system have clear objectives and need to be supported by data suitable for the objectives. Usually, the existing real data is not necessarily based on the data requirements of this task, and there are deviations from the actual data requirements in terms of completeness, timeliness, usability, relevance, and utility. In view of the above reasons, there is an increasingly urgent need for those skilled in the art to quickly generate simulated data to meet actual application scenarios, and urgently need to be solved. Reasonable models and algorithms should be used to quickly generate simulated data by means of computer simulation to meet the actual needs of information system experiments, trials, and drills.

Among the existing technologies for quickly generating a large amount of simulated data, a common method is to generate data by simulating real user scenarios through interfaces written by developers. This method is more appropriate in some respects, because writing The code simulates the user scenario, the business data is complete, and the data will not be lost during the data transfer process. However, because it needs to write codes to process each step of the business process, interface definition, signature, encryption, etc., the workload is heavy and cumbersome. In addition, its scalability is poor, and a set of code needs to be written for a business scenario.

Another common method is to configure relevant business rules based on the ER diagram designed by the user database, and directly insert relevant data into the database. However, in this method, business rules are manually configured, and the configuration process is cumbersome. It cannot be adjusted in time according to changes in business rules, and a large amount of test data that meets the test scenario cannot be quickly generated.

The present invention provides a method for constructing simulation data based on ER diagrams, which is used to solve the following requirements:

(1) Provide ER diagram upload. Users upload ER diagrams of different types of databases, configure target data types and generation rules, and the system dynamically adapts to target data source types to automatically generate business simulation data.

(2) Support function plug-ins. Supports a variety of rules for generating simulated data, such as fixed values, random values, regular expressions, and incremental values; supports selection of thesaurus to generate random values within a specific range; supports thesaurus, table design, and field information sharing; supports direct use Ready-made lexicons to build dictionary tables; support to generate simulated data in various formats, such as json, xml, sql, cvs.

At the same time, the present invention provides a method for constructing simulated data based on ER diagrams, which supports the following three application scenarios:

(1) Test scene. During the test, it is necessary to verify the function or performance of the data backend. At this time, it is necessary to reduce the coupling with the data generator. This method is used to continuously and uninterruptedly generate correct data and abnormal data under special circumstances.

(2) Continuous integration scenarios. In the continuous integration scenario, one or more modules form a platform, which requires a steady stream of data to enter the continuous integration environment to automate testing and iteration work.

(3) Production scenarios. After a project is tested and iterated and released to the production environment, continuous function or usability monitoring is usually required. Various normal or abnormal data need to be produced continuously and stably according to certain rules and definitions and sent back to the platform. According to the business requirements in the continuous integration scenario, production verification can be carried out only through certain rule configurations to meet this requirement.

As shown in FIG. 1 , the process of constructing simulation data based on ER diagrams in the present invention mainly includes parsing ER diagrams 12 , rendering ER diagrams 13 , combing metadata 14 , configuration rules 15 , and simulation data 16 .

The present invention provides a method for constructing simulated data based on ER diagrams. SQL is generated by parsing ER diagram metadata, and combined with G6 and Vue3 frameworks, the ER diagrams are visualized and rendered; data rules and entities and entities in ER diagrams are configured online Association rules for relationships, configuration of output data source types, and business rules for constructing simulated data. Finally, according to the business rules, dynamically adapt, complete data format conversion, and generate simulated data. The interface configuration of business rules is realized, which is convenient for adjustment. Through Simple interface operations can generate a large amount of test data that conforms to the test scenario, and the purpose of quickly generating test data can be achieved.

Please refer to FIG. 2 . FIG. 2 is a flow chart of the ER diagram-based simulation data construction method in the present invention. As shown in Figure 2, the ER diagram-based simulation data construction method described in the embodiment of the present invention includes the following steps:

Step S100, acquiring an ER diagram, analyzing the ER diagram, and generating SQL table information corresponding to the ER diagram.

Specifically, the ER diagram is the design of the database, using the relationship between the graphs to reflect the relationship between various tables. The parsing of the ER diagram to generate the SQL table information corresponding to the ER diagram is completed by using a DDL plug-in. The invention utilizes the DDL plug-in to complete the conversion of ER diagrams into SQL table information, can quickly complete the conversion, supports the analysis of ER diagrams designed by clients required by different database types, realizes the standardization of ER diagram metadata, and has good compatibility.

In an implementation manner, the step S100 specifically includes:

Obtaining the ER diagram, using the DDL plug-in to analyze the ER diagram, and generating SQL table information corresponding to the ER diagram;

Wherein, the SQL table information is the original entity information; or, the SQL table information includes: the original entity information and the foreign key of the parent entity of the original entity; or, the SQL table information includes: the foreign keys of all entities involved in the relationship Attribute information for keys and relationships.

Specifically, SQL in relational databases obtained from ER diagrams can generally be divided into three categories:

(1) The converted SQL table contains the same information content as the original entity. This type of transformation is generally applicable to:

Entities at either end of a binary "many-to-many" relationship;

In the binary "one-to-many" relationship, the entity at the "one" end;

In a binary "one-to-one" relationship, an entity at one end;

In the binary "many-to-many" regression relationship, entities at either end (note: both ends of the relationship point to the same entity);

Entities at either end of a ternary or n-ary relationship;

In a hierarchical generalization relationship, the superclass entity.

(2) The converted SQL table not only contains the information content of the original entity, but also contains the foreign key of the parent entity of the original entity. This type of transformation is generally applicable to:

In the binary "one-to-many" relationship, the entity at the "many" end;

In a binary "one-to-one" relationship, an entity at one end;

Entities at either end of a binary "one-to-one" or "one-to-many" regression relationship.

This conversion is one of the common methods for processing relationships, that is, adding foreign key information pointing to the primary key in the parent table in the child table.

(3) The SQL table converted from the "relationship", which contains the foreign keys of all entities involved in the "relationship" and the attribute information of the "relationship" itself. This type of transformation is generally applicable to:

Binary "many-to-many" relationship;

Binary "many-to-many" regression relationship;

ternary or n-ary relations;

This transformation is another commonly used relational approach. For the "many-to-many" relationship, it needs to be defined as an independent table containing the primary keys of two related entities, and the table can also contain the attribute information of the relationship.

The present invention supports the above three types of transformations, and generates SQL table information corresponding to the ER diagram. Among them, the basic conversion steps from ER diagram to SQL table are as follows:

(1) Convert each entity into a table, which contains key and non-key attributes.

In entity conversion, if there is a "one-to-many" relationship between two entities, add the primary key of the "one" end entity to the "many" end entity table as a foreign key. If there is a "one-to-one" relationship between two entities, put the primary key of a "one" entity into the other entity table as a foreign key, and the entity that adds the foreign key can theoretically be optional, but generally follow the following principles : According to the most natural parent-child relationship between entities, put the key of the parent entity into the child entity; another strategy is based on efficiency, adding foreign keys to tables with fewer rows.

Convert each entity in the generalization hierarchy into a table. Each table will contain the keys of the superclass entities. In fact, the primary key of the subclass entity is also a foreign key. The superclass table also contains non-key attributes common to all related entities, and the other tables contain nonkey attributes specific to each subclass entity.

The converted SQL table may contain not null, unique, foreign key and other constraints. Each table must have a primary key (primary key), the primary key implies not null and unique constraints.

(2) Convert each "many-to-many" binary or binary regression relationship into a table, which contains the key of the entity and the attribute of the relationship.

In the "many-to-many" binary relationship transformation, each "many-to-many" binary relationship can be transformed into

A table that contains the keys of the two entities and the attributes of the relationship.

The converted SQL table may contain not null constraints. The reason why the unique constraint is not used here is that the primary key of the relational table is composed of the foreign keys of each entity, and the unique constraint is already implied.

(3) Convert ternary and higher (n-ary) relationships into a table.

In ternary relationship conversion, each ternary (or n-ary) relationship is transformed into a table, which contains n primary keys of related entities and attributes of the relationship.

The converted table must contain not null constraints. The primary key of a relational table is composed of the foreign keys of each entity. An n-ary relational table has n foreign keys. In addition to primary key constraints, other candidate keys (candidate keys) should also be added with unique constraints.

At the same time, the processing rules for NULL values in the conversion process are as follows:

(1) When the relationship between entities is optional, the foreign key column in the SQL table is allowed to be NULL;

(2) When the relationship between entities is mandatory, the foreign key column in the SQL table is not allowed to be NULL;

(3) In the SQL table converted from the "many-to-many" relationship, any foreign key column in it is not allowed to be NULL.

The present invention completes the generation of SQL table information corresponding to the ER diagram through the above rules and steps, supports multiple relationship processing methods, has a wide range of applicable scenarios, and can meet complex business requirements.

As shown in Figure 2, the described ER diagram-based simulation data construction method also includes the following steps:

Step S200, performing visual rendering on the ER diagram according to the SQL table information, to obtain a visual interface corresponding to the ER diagram.

Specifically, the present invention uses the combination of G6, X6, Vuedraggble, and jsplumb to perform visual rendering of ER diagrams, supports uploading of ER diagrams of different types of databases, and has high compatibility.

In one embodiment, the step S200 specifically includes:

Visually rendering the entity table structure in the ER diagram and the relationship between the entity tables according to the SQL table information to obtain a visual interface corresponding to the ER diagram;

The visual interface includes: a scene list area, an operation area, and a parameter configuration area; the scene list area is used to create various business scenarios; the operation area includes: an operation button list, an operation canvas and a running log list, and the operation button Function buttons are set on the list, the operation canvas is used to configure and view the data generation rules of the ER diagram, and the log list is used to view the generation log of the simulation data corresponding to the ER diagram; the The parameter configuration area is used to configure the entity attribute generation rules and relationship attribute generation rules corresponding to the ER diagram.

Specifically, as shown in Figure 3, the ER diagram visualization IDE consists of three parts, namely the scene list area on the left, the operation area in the middle, and the parameter configuration area on the right.

As shown in Figure 4, the specific functions of each part are as follows:

(1) Scene area. The scene area contains a scene list, and its function is to maintain different scene functions, such as creating traffic scenes and medical insurance scenes.

(2) Operating area. The operation area consists of a list of operation buttons, an operation canvas, and a list of running logs. The operation button list mainly includes function buttons such as zoom in, zoom out, run, and select, which are used to control the operation; the operation canvas is used to configure and view the generation rules of entity/relationship attributes in the ER diagram; the log list is used to view the simulated data of the ER diagram log.

(3) Parameter configuration area. It mainly realizes the configuration of the generation rules of different attributes of each entity and relationship in the ER diagram. The parameter configuration is rendered by the database backend configuration, and the user only needs to fill in the relevant parameter values.

The present invention obtains the visual interface corresponding to the ER diagram through visual rendering of the ER diagram. The invention supports entity table structure rendering, can display table structure, field type, primary key, default value, index, remark information, etc. according to different database types, and supports entity table relationship rendering at the same time, and is easy to operate.

The receiving rule configuration instruction, before configuring data generation rules on the visual interface according to the rule configuration instruction, further includes:

Metadata information is stored in advance, and the metadata information is obtained according to the data standard corresponding to the current scene service, and the data standard includes metadata standard and/or dictionary table standard.

Specifically, metadata is the concept of information and the connection between information. To simulate the data close to the business, according to various standards of business data in the user's actual scene, such as metadata standards, dictionary table standards, etc., it is necessary to Metadata combing of entities and relationships between entities.

Such as the entity student table in the ER diagram, the attributes include student_id, sex, student_name, city, etc. This information is the metadata describing a student table. According to the user's metadata standard, sort out the real business meaning represented by each attribute, such as city represents the city field, sort out the value range and business rules in the actual business.

Through the sorting of metadata, it can meet the needs of business and data understanding, quickly understand the upstream and downstream relationship of data and its own meaning, reduce the time cost of data research, and improve efficiency; can clearly understand the ins and outs of data flow in ER diagram , business processing rules, conversion status, etc.

The present invention pre-stores metadata information, and the metadata information is combed manually and coded, which is accurate and reliable, and facilitates the use of the metadata information later.

As shown in Figure 1, the described ER diagram-based simulation data construction method also includes the following steps:

Step S300, receiving a rule configuration instruction, and configuring data generation rules on the visual interface according to the rule configuration instruction.

Specifically, the rule configuration instruction is generated by the user operating in the configuration area of the visual interface.

In an implementation manner, the step S300 specifically includes:

receiving a rule configuration instruction, and configuring data generation rules on the visual interface according to the metadata information and the rule configuration instruction;

Wherein, the data generation rules include: entity attribute rules corresponding to the ER diagram, correlation degree parameters between entities, and target data types.

Specifically, according to the sorted out metadata information and rule configuration instructions, configure the data generation rules of each entity/relationship in the configuration area of the visual interface. The present invention configures the data generation rules in the configuration area of the visual interface without manually configuring the rules, so that the configuration process is simple and convenient, and can be adjusted in time according to changes in business rules.

In one implementation, the entity attribute rules include: simulated data generation rules, normal data settings, and abnormal data settings; the simulated data generation rules include: fixed values, random values, regular expressions, incremental, and lexicon generation and custom functions.

Specifically, the simulated data generation rules are set on the configuration list page of the visual interface, such as the generation rules for ID numbers of specified regions, users of specified genders, and so on. The operation is intuitive and the process is simple, which can meet the business tests of different scenarios. The simulated data generation rules include fixed value, random value, regular expression, increment, lexicon generation and custom function. Supports function plug-ins, including: support for selecting thesaurus to generate random values within a specific range; support for thesaurus, table design, and field information sharing; support for directly using ready-made thesaurus to create dictionary tables; support for generating simulations in multiple formats Data, such as json, xml, sql, cvs; support selection, field dictionary customization, and user uploading of custom functions. By configuring business rules on the interface and supporting function plug-ins, it is convenient for users to choose to use, easy to expand, and improve the applicability of data.

Both normal data and abnormal data are set. The normal data meets the normal usage scenarios of the business process, and the abnormal data meets the abnormal usage scenarios of the business process. By setting and generating normal data and abnormal data respectively, test scenarios can be fully covered.

In an implementation manner, the steps of configuring the parameters of the degree of association between entities include:

A1. Obtain the database table corresponding to the ER diagram, and the database table includes a relational database SQL table and a non-relational database SQL table;

A2. The SQL statement in the database table is analyzed, and the association relationship between the database tables is determined according to the type of the associated field in the SQL statement, and the association relationship of the database table includes a foreign key relationship, a same-dimensional relationship and a master-child relationship relation;

A3. Receive a user setting instruction, and set the association relationship on the visual interface;

A4. Arranging the generation sequence of the simulated data generated by each database table according to the association relationship.

Among them, the relationship between database tables can be divided into three types: foreign key relationship, same-dimensional relationship, and master-child relationship. The association relationship can be distinguished according to whether the associated field is the primary key (or part of the primary key) of the table. The associated fields are obtained from the analysis of the relationship between entities on the ER diagram.

Specifically, analyze the JOIN clause according to the SQL statement, and determine what kind of association it is based on whether the JOIN field is the primary key or part of the primary key of the table. If the JOIN field is the primary key of the table, it is a full connection to obtain common data; if the JOIN field is not the primary key of the table, it is a left connection or a right connection. When the same table is a subtable of two tables at the same time, the two main tables are regarded as the foreign key tables of the subtables instead of being treated as the main subtable, so as to ensure that there are no subtables with multiple main tables.

It should be noted that some foreign keys are implicit relationships, such as the date and area code in the ID number. If there is an action to provide these parts of information from the ID number in the calculation, it needs to be manually operated on the visual interface to make these implicit Finding out the association relationship is equivalent to splitting a field such as an ID number into several sections.

The invention configures the correlation degree parameters between entities on the visual interface, so that each database table automatically generates the generation sequence of the simulated data, and can respond to configuration modification in time. The present invention simulates data according to the actual business ER relationship diagram to generate business simulation data, which can better reflect the association of entity attributes and the association between entities, and the business data has strong association.

In an implementation manner, arranging the generation order of the simulation data generated by each database table according to the association relationship includes:

B1. Using the database table in the same-dimensional relationship as the same-dimensional table, using the database table in the master-sub-relationship as the main sub-table, the sub-table in the main-sub-table is attached to the main table to hide, and the foreign key table of the sub-table is used as The foreign key table of the main table;

B2. Assign the same initial label to each database table, and update the initial label according to the quantity of all foreign key tables of the database table to obtain the final label;

B3. The order of the final labels from small to large is used as the generation order of the simulated data generated by each database table.

Specifically, after the association relationship is set, the order of generating data can be arranged. Treat the same-dimension table as a logical table together, and the sub-tables in the main and sub-tables are attached to the main table and hidden first, and the other foreign key tables of the sub-tables are also regarded as the foreign key tables of the main table, and wait for the main table to be processed. Then process the child table. Label each table with the number 1. For each table, if the maximum number of labels of all foreign key tables it has is n, change its own label to n+1, and repeat this action until all labels do not change. The present invention automatically arranges the order of generating data by setting the association relationship, so that the data can be quickly generated after setting the rules, and can be changed in time according to the change of the configuration rules.

In an implementation manner, the configuration of the target data type includes: setting a data generation rate, setting normal data, setting abnormal data, and setting a data format.

Specifically, by setting the data generation rate in the configuration list of the visual interface, the rate of data generation can be set according to actual needs, so as to avoid affecting the performance of the operating system due to the generated data. Set normal data and abnormal data to make it more comprehensive to meet the test scenario. Set the data format in the configuration list of the visual interface, which can support json, xml, sql, cvs, etc., which facilitates the connection of subsequent test data with other test systems and test environments.

As shown in Figure 1, the described ER diagram-based simulation data construction method also includes the following steps:

Step S400, generating simulation data according to the data generation rule.

Specifically, after the rule configuration is completed on the configuration list page of the visual interface, it can respond immediately and generate simulation data. When the configuration rules are modified, the modified simulation data can be generated in time. After the user uploads ER diagrams of different types of databases and configures the generation rules, the target data source type can be dynamically adapted to automatically generate business simulation data. For example, it provides Mysql ER diagrams and data that can be dynamically converted to Opengauss data sources to meet various application scenarios. At the same time, the present invention provides online ER diagram visualization, and can observe the data generation status in real time on the component operation log list interface of the visualization interface, such as table generation traffic, error information, and the like. Through the real-time observation of the data generation status, the status of the generated data can be judged in time to facilitate the next operation. If the generation speed is slow or fast, it can be adjusted in time according to the actual situation. If there is an error message in the generated data, you can stop in time and check the cause of the error. It avoids the waste of resources or the situation that the data is not up to expectations due to the inability to observe the data generation status in real time, which facilitates the use of users.

In an implementation manner, the generating simulation data according to the data generation rules includes:

C1. Generating simulated data for each of the database tables sequentially according to the generation sequence;

C2. After the simulation data of the main table is generated, randomly obtain the foreign key value from the primary key of the main table.

Specifically, the data is generated in ascending order of table labels. When generating data for a table labeled n+1, the labels of the foreign key tables it references do not exceed n. If it has already been generated, its foreign key value is randomly selected from the primary keys of the generated main tables; the same dimension table can be generated together, and the sub-table will wait for the completion of the main table before generating. The present invention simulates data according to the actual business ER relationship diagram to generate business simulation data, the business data has strong correlation and the operation is simple.

Further, as shown in Figure 5, based on the above-mentioned ER diagram-based simulation data construction method, the present invention also provides a corresponding ER diagram-based simulation data construction device, including:

The parsing module 100 is configured to obtain an ER diagram, analyze the ER diagram, and generate SQL table information corresponding to the ER diagram;

The rendering module 200 is configured to visually render the ER diagram according to the SQL table information, to obtain a visual interface corresponding to the ER diagram;

The configuration module 300 is configured to receive a rule configuration instruction, and configure data generation rules on the visual interface according to the rule configuration instruction;

A generation module 400, configured to generate simulation data according to the data generation rules.

As shown in Figure 6, the present invention also provides a terminal, including: a memory 20, a processor 10, and an ER diagram-based simulation data construction program 30 stored on the memory 20 and operable on the processor 10 , when the ER diagram-based simulation data construction program 30 is executed by the processor 10, the steps of the above-mentioned ER diagram-based simulation data construction method are realized.

The present invention also provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and the computer program can be executed to realize the steps of the method for constructing simulation data based on an ER diagram as described above.

In summary, the present invention provides a method and device for constructing simulated data based on ER diagrams. The method includes: obtaining ER diagrams, analyzing the ER diagrams, and generating SQL table information corresponding to the ER diagrams. Perform visual rendering on the ER diagram according to the SQL table information to obtain a visual interface corresponding to the ER diagram; receive a rule configuration instruction, configure data generation rules on the visualization interface according to the rule configuration instruction; Generate simulation data using the above data generation rules. The present invention configures the data rules of the entities in the ER diagram online, the association rules of the relationship between entities, configures the output data source type, and constructs the business rules of the simulated data, and finally generates the simulated data according to the business rules, realizing the operation of the business rules Visual configuration on the interface is convenient for adjustment, and a large amount of simulated data in line with business scenarios can be generated through simple interface operations, and simulated data can be quickly generated. The invention has strong business data correlation, supports ER diagram analysis of different database types, ER diagram dynamically adapts to different data sources, supports function plug-in, data can be observed, is convenient to use, intuitive, strong compatibility, and has a wide range of applications.

It should be understood that the application of the present invention is not limited to the above examples, and those skilled in the art can make improvements or changes according to the above descriptions, and all these improvements and changes should belong to the scope of protection of the appended claims of the present invention.

Claims (13)

1. A method of modeling data construction based on an ER diagram, comprising:

acquiring an ER diagram, analyzing the ER diagram, and generating SQL table information corresponding to the ER diagram;

performing visual rendering on the ER diagram according to the SQL table information to obtain a visual interface corresponding to the ER diagram;

receiving a rule configuration instruction, and configuring data on the visual interface to generate a rule according to the rule configuration instruction;

and generating simulation data according to the data generation rule.

2. The ER graph-based simulated data construction method of claim 1, wherein the obtaining the ER graph, parsing the ER graph, and generating SQL table information corresponding to the ER graph, comprises:

acquiring an ER diagram, analyzing the ER diagram by using a DDL plug-in unit, and generating SQL table information corresponding to the ER diagram;

the SQL table information is original entity information; alternatively, the SQL table information includes: original entity information and an external key of an original entity father entity; alternatively, the SQL table information includes: foreign keys of all entities related to the relationship and attribute information of the relationship.

3. The ER graph-based simulated data construction method of claim 1, wherein performing visual rendering on the ER graph according to the SQL table information to obtain a visual interface corresponding to the ER graph, comprises:

performing visual rendering on the relation between the entity table structure and the entity table in the ER diagram according to the SQL table information to obtain a visual interface corresponding to the ER diagram;

wherein the visual interface comprises: a scene list area, an operation area and a parameter configuration area; the scene list area is used for creating various business scenes; the operation region includes: the device comprises an operation button list, an operation canvas and a running log list, wherein the operation button list is provided with function buttons, the operation canvas is used for configuring and checking data generation rules of the ER diagram, and the log list is used for checking generation logs of simulation data corresponding to the ER diagram; the parameter configuration area is used for configuring entity attribute generation rules and relationship attribute generation rules corresponding to the ER diagram.

4. The ER graph-based simulated data construction method of claim 1, wherein said receiving a rule configuration instruction, prior to configuring a data generation rule on said visual interface in accordance with said rule configuration instruction, further comprises:

metadata information is stored in advance, wherein the metadata information is obtained according to a data standard corresponding to the current scene service, and the data standard comprises a metadata standard and/or a dictionary table standard.

5. The ER graph-based simulated data construction method of claim 4, wherein said receiving a rule configuration instruction, configuring data generation rules on said visual interface in accordance with said rule configuration instruction, comprises:

receiving a rule configuration instruction, and configuring data on the visual interface according to the metadata information and the rule configuration instruction to generate a rule;

wherein the data generation rule includes: and the entity attribute rule corresponding to the ER diagram, the association degree parameter between the entities and the target data type.

6. The ER graph-based simulated data construction method of claim 5, wherein said entity attribute rules comprise: simulating data generation rules, normal data settings and abnormal data settings; the simulation data generation rule includes: fixed values, random values, regular expressions, increments, lexicon generation, and custom functions.

7. The ER diagram-based simulation data construction method of claim 5, wherein the configuring of the association parameter between the entities comprises:

acquiring a database table corresponding to the ER diagram, wherein the database table comprises a relational database SQL table and a non-relational database SQL table;

analyzing the SQL sentences in the database tables, and determining the association relation among the database tables according to the types of the association fields in the SQL sentences, wherein the association relation of the database tables comprises an external key relation, a same-dimensional relation and a main sub relation;

receiving a user setting instruction, and setting the association relation on the visual interface;

and scheduling the generation sequence of the simulation data generated by each database table according to the association relation.

8. The ER graph-based simulation data construction method of claim 7, wherein the scheduling of the generation order of the respective database tables to generate the simulation data according to the association relation comprises:

taking a database table in a same-dimensional relationship as a same-dimensional table, taking a database table in a main-sub relationship as a main sub table, hiding the sub table in the main sub table by attaching to the main table, and taking an external key table of the sub table as an external key table of the main table;

assigning the same initial label to each database table, and updating the initial label according to the number of all external key tables of the database table to obtain a final label;

the order of the final marks from small to large is used as the generation order of the simulation data generated by each database table.

9. The ER graph-based simulated data construction method of claim 5, wherein the configuration of the target data type comprises: setting a data generation rate, setting normal data, setting abnormal data, and setting a data format.

10. The ER graph-based simulation data construction method of claim 8, wherein generating simulation data according to the data generation rule comprises:

generating simulation data for each database table in turn according to the generation order;

and after the simulation data of the main table are generated, randomly acquiring an external key value from the main key of the main table.

11. An ER diagram-based analog data construction apparatus, comprising:

the analysis module is used for acquiring an ER diagram, analyzing the ER diagram and generating SQL table information corresponding to the ER diagram;

the rendering module is used for performing visual rendering on the ER diagram according to the SQL table information to obtain a visual interface corresponding to the ER diagram;

the configuration module is used for receiving a rule configuration instruction, and configuring data on the visual interface to generate a rule according to the rule configuration instruction;

and the generation module is used for generating simulation data according to the data generation rule.

12. A terminal, comprising: memory, processor and store on the memory and can be run on the processor based on ER diagram simulation data construction procedure, the simulation data construction procedure based on ER diagram is carried out by the processor and realizes the steps of the simulation data construction method based on ER diagram according to any one of claims 1-10.

13. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program executable for implementing the steps of the ER graph-based simulation data construction method according to any one of claims 1 to 10.