CN114925143A - Method, device, equipment, medium and product for describing logical model blood relationship - Google Patents
Method, device, equipment, medium and product for describing logical model blood relationship Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment, a medium and a product for describing a blood relationship of a logic model. The invention relates to the technical field of big data. The method comprises the following steps: acquiring attribute information of a logic model; determining a conversion rule between attributes with a blood relationship in the attributes of the logic model according to the attribute information of the logic model; and describing the conversion rule through a preset SQL language to obtain a blood relationship description result. The method and the device solve the problems of high cost, difficult maintenance and the like of the logic model blood relationship analysis, can improve the accuracy of the blood relationship analysis and reduce the acquisition cost while standardizing the logic model blood relationship description.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a method, a device, equipment, a medium and a product for describing a blood relationship of a logic model.
Background
In the current big data era, new data can be generated among huge and complex data information through the combination and conversion of the benzoin. The new data from its generation, process fusion, circulation to final extinction will have necessary links with the original data before processing, which are the relationship of blood relationship. If deep analysis can be carried out on the blood relationship, the method is favorable for positioning errors in the program, analyzing the service difference, tracking the index fluctuation, greatly improving the data quality and optimizing the user data experience.
At present, in the prior art, the blood relationship between tables and fields, that is, the blood relationship between physical models, is mostly obtained by analyzing the etl script or analyzing the database log. However, there is a lack of powerful means of acquisition and maintenance of the genetic relationship between logical models, and existing solutions typically involve manual maintenance and analysis by hand.
The bloody border relationship of the existing manual maintenance logic model is mostly obtained from descriptive language of the source caliber of the logic model. If the source caliber is analyzed among tens of millions of tables, the source and the destination of the data of the tables are searched, blood relationship analysis not only needs to pay huge labor cost, but also is easy to lose the relationship, and the correctness is difficult to guarantee.
Disclosure of Invention
The invention provides a method, a device, equipment, a medium and a product for describing a logical model blood relationship, which are used for solving the problems of high cost, difficult maintenance and the like of the logical model blood relationship analysis, improving the accuracy of the blood relationship analysis and reducing the acquisition cost while standardizing the logical model blood relationship description.
According to an aspect of the present invention, there is provided a method for describing a logical model blood relationship, the method comprising:
acquiring attribute information of a logic model;
determining a conversion rule between attributes with blood relationship in the attributes of the logic model according to the attribute information of the logic model;
and describing the conversion rule through a preset SQL language to obtain a blood relationship description result.
According to another aspect of the present invention, there is provided an apparatus for describing relationship of logical model blood relationship, the apparatus comprising:
the attribute information acquisition module is used for acquiring the attribute information of the logic model;
the conversion rule determining module is used for determining a conversion rule between attributes with a blood relationship in the attributes of the logic model according to the attribute information of the logic model;
and the blood relationship description result generation module is used for describing the conversion rule through a preset SQL language to obtain a blood relationship description result.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform a method for logical model context description according to any of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement a method for describing a logical model blood relationship according to any one of the embodiments of the present invention when the computer instructions are executed.
According to another aspect of the invention, a computer program product is provided, comprising a computer program which, when executed by a processor, implements a method of describing logical model kindred relationships as described in any of the embodiments of the invention.
According to the technical scheme of the embodiment of the invention, the conversion rule between attributes with the blood relationship in the attributes of the logic model is determined according to the attribute information of the logic model. And describing the conversion rule through SQL language to obtain a normative blood relationship description result. The technical scheme can solve the problems of high cost, difficult maintenance and the like of logic model blood relationship analysis, improve the accuracy of blood relationship analysis and reduce the acquisition cost while standardizing the logic model blood relationship description.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1A is a flow chart of a method for describing a logical model blood relationship according to an embodiment of the present invention;
FIG. 1B is a schematic diagram of a logical model attribute table context analysis according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for describing relationship between logical model blood vessels according to a second embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an apparatus for describing relationship between logical model blood vessels according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing the method for describing logical model blood relationship according to the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. According to the technical scheme, the data acquisition, storage, use, processing and the like meet relevant regulations of national laws and regulations.
Example one
Fig. 1A is a flowchart of a method for describing a logical model blood relationship according to an embodiment of the present invention, where this embodiment is applicable to a description scenario of a logical model blood relationship, and the method can be executed by a device for describing a logical model blood relationship, where the device can be implemented in hardware and/or software, and the device can be configured in an electronic device. As shown in fig. 1A, the method includes:
and S110, acquiring attribute information of the logic model.
The present scheme may be performed by a blood relationship analysis system. The blood relationship analysis system may obtain attribute information of the logical model to be analyzed. Wherein the logical model may be an instantiation of a conceptual model. Specifically, the conceptual model may be a model of entity composition that is extracted through analysis and summarization according to product requirements and used for describing business requirements. For example, there are entities in the sales business such as "customer", "order", "merchant", and "merchandise". The conceptual model may be that "merchant" and "customer" sign an "order" for the fact that "goods" were purchased.
It will be appreciated that to implement what is described by the conceptual model, a more specific analysis of the requirements, i.e., the process of building the logical model, is required. Still by way of example, a "customer" may include contact, contact phone, address, etc. attribute information, a "good" may include name, specification, unit price, etc. attribute information, an "order" may include date, time, etc. attribute information, and a "business" may include business scope, contact phone, address, etc. attribute information. There is a link between the "order" and the attribute information of the "customer", "merchant", and "goods". The business system needs to establish a data table to realize the functions to be realized by the sales business, for example, the business system can establish a customer information table, a merchant information table, a commodity information table and an order table, and by establishing the data tables, the business system can realize the functions of customer information maintenance, merchant information maintenance, commodity information maintenance, sales order establishment and the like.
It should be noted that the logical model may be one, and the attribute information obtained from the logical model may be used to analyze the data relationship inside the logical model. The number of the logic models can be multiple, the attribute information of the multiple logic models can be obtained, the data blood relationship in the logic models can be analyzed, and the data blood relationship between the logic models can also be analyzed.
And S120, determining a conversion rule between attributes with a blood relationship in the attributes of the logic model according to the attribute information of the logic model.
The blood relationship analysis system can determine which attributes in the logic model have blood relationship according to the attribute information of the logic model. Wherein the blood relationship is a link between related data obtained in the process of data tracing. After finding attributes for which a kindred relationship exists, the kindred relationship analysis system may further determine a transformation rule between attributes for which a kindred relationship exists, which may also be referred to as a kindred caliber between attributes. The conversion rules may include rules such as data access criteria and access logic, for example, to access female clients whose daily consumption is less than 100 yuan and whose age is more than 18 years old from the client information table, the access criteria may be to meet constraints of daily consumption, age and gender, and the access logic may access data according to gender first, then access data sequentially according to age and daily consumption on the basis of data access, and finally obtain target data. The data taking logic may also obtain three sets of taken data according to daily consumption, age and gender, and perform intersection operation on the three sets of taken data to obtain the target data.
FIG. 1B is a schematic diagram of a logical model attribute table context relationship analysis according to an embodiment of the present invention. The blood relationship analysis system can further determine the conversion rule between the attribute tables according to the data source relationship between the attribute tables. As shown in fig. 1B, regarding the attribute table F, the source data table is the attribute table a, and the blood relationship analysis system can analyze the rules of the attribute table F, such as the access criteria and the access logic, in the attribute table a, so as to determine the conversion rule from the attribute table a to the attribute table F.
And S130, describing the conversion rule through a preset SQL language to obtain a blood relationship description result.
The kindred relationship analysis system may allow a user to describe the transformation rules using the SQL language. Specifically, the blood relationship analysis system can select partial SQL sentences to construct an SQL language description library. The user can select reasonable sentences in the SQL language description library to describe the conversion rules.
Specifically, the description of the conversion rule through a preset SQL language includes at least one of the following cases:
describing the source logic model attribute of the current logic model attribute through a SELECT statement;
describing a source logic model of the current logic model through an FROM statement;
describing the incidence relation between the source logic models of the current logic model through INNER/LEFT/RIGHT/FULL/SELF JOIN statements;
describing the filtering relation of the source logic model through a WHERE statement;
describing the grouping relation among the source logic models through a GROUP BY statement;
describing the ordering relation of the source logic models after combination through an ORDER BY statement;
a parallel combination of multiple source logical relationships is described by a UNION/UNION ALL statement.
As will be readily appreciated, the source logical model is the source model of the current logical model data. It should be noted that the SQL statements may be used alone or in combination, and the blood relationship analysis system may introduce a new SQL statement to describe the transformation rule according to actual needs. The wishing relation analysis system can also construct a function for realizing a specific function based on the SQL statement so as to adapt to a conversion rule of complex logic.
The method and the device can set the corresponding SQL description sentences aiming at various conditions in the conversion rule, and are favorable for realizing the standard and accurate conversion rule description.
After all the conversion rules related to the logic model are described by the SQL language, the blood relationship analysis system can obtain the blood relationship description result of the logic model to be analyzed.
In this embodiment, optionally, after obtaining the blood relationship description result, the method further includes:
analyzing a blood relationship link with the attribute of the blood relationship in the attribute of the logic model according to the blood relationship description result;
establishing a blood margin view of the logic model according to the source information acquired from the blood margin link; the source information includes source component information, source table information, and source field relationship information.
It can be understood that, after obtaining the blood relationship description results of all logic models to be analyzed, the blood relationship analysis system analyzes the blood relationship description results, for example, the blood relationship description results may be analyzed through SQL language compiling and analyzing software. According to the analysis result, the blood relationship analysis system can generate blood relationship links among attributes with blood relationship in the logical model attributes, the blood relationship links can be data streams among the attributes with blood relationship in a single logical model or multiple logical models, and the blood relationship among the attributes can be macroscopically displayed. As shown in fig. 1B, attribute table a- > attribute table F- > attribute table I- > attribute table is a blood-related link.
From the blood relationship link, the blood relationship analysis system can acquire the source information of each attribute or each logic model, and further construct a blood relationship view of the logic model according to information such as a source component, a source table and a source field. Fig. 1B can be regarded as a simple illustration of a blood margin view, which may be composed of a plurality of blood margin links and include rich blood margin relationship information.
The scheme generates a blood relationship view by analyzing the blood relationship description result. The scheme can display the blood relationship in the form of a blood relationship view, so that the blood relationship of the logic model is more specific and clear, and more intuitive blood relationship recognition is brought to a user.
In one possible solution, optionally, after determining that the conversion rule between attributes of the blood relationship exists in the attributes of the logical model, the method further includes:
describing the conversion rule through a preset spoken description template to obtain a spoken description result of the blood relationship;
and analyzing the spoken description result according to a preset analysis rule to generate a blood relationship description result based on the SQL language.
For non-technical personnel, the description of the conversion rule by using the SQL language has certain difficulty. In order to facilitate the description of non-technical personnel, the blood relationship analysis system correspondingly develops a spoken description template according to each SQL statement. For example, for a statement SELECT field FROM table, the spoken description may be < pick > [ table. Specifically, the spoken description template may be represented as the following table:
table 1:
after the spoken language description is completed, the blood relationship analysis system can analyze the spoken language description result according to a predetermined analysis rule. The parsing rule may be a mapping rule between a spoken description and an SQL language. According to the analysis rule, the blood relationship analysis system can translate the spoken description into the SQL language description, so that a blood relationship description result based on the SQL language is obtained.
The spoken description template provided by the scheme is user-friendly to non-technical personnel. The spoken description is converted into the SQL description through the analysis rule, and the blood relationship description result based on the SQL can be generated quickly and accurately.
According to the technical scheme, the conversion rule between attributes with blood relationship in the attributes of the logic model is determined through the attribute information of the logic model. And describing the conversion rule through an SQL language to obtain a normative blood relationship description result. According to the technical scheme, the problems of high cost, difficulty in maintenance and the like of logic model blood relationship analysis can be solved, the accuracy of blood relationship analysis can be improved while the description of the blood relationship of the logic model is standardized, and the acquisition cost is reduced.
Example two
Fig. 2 is a flowchart of a method for describing relationship between logical model blood vessels according to a second embodiment of the present invention. The present embodiment is detailed based on the above-described embodiments. As shown in fig. 2, the method includes:
and S210, acquiring the attribute information of the logic model.
S220, determining a conversion rule between attributes with a blood relationship in the attributes of the logic model according to the attribute information of the logic model.
And S230, describing the conversion rule through a preset SQL language to obtain a blood relationship description result.
S240, if the establishment event of the non-strategy field in the physical model is detected, determining the conversion rule description associated with the logic model attribute corresponding to the non-strategy field in the blood relationship description result.
The physical model may be a model that implements a logical model on a specific physical medium. Such as: the database uses SQL Server 2000, and the physical model can build the database on the database Server by writing a specific SQL script. From a logical model to a physical model, it can be understood as a design and development process that is increasingly refined from abstract to concrete. The physical model is accompanied by the establishment of fields in the process of establishing. The fields in the physical model may include policy fields and non-policy fields. The policy field may be a field generated in a physics process such as creation time and creator, and the non-policy field is a field in the physics model having a mapping relationship with the logical model attribute.
When the blood relationship analysis system detects the establishment event of the non-strategy field in the physical model, the possibility that the non-strategy field has blood relationship is shown. The blood relationship analysis system can extract a conversion rule associated with the logic model attribute from the blood relationship description result according to the mapping relationship between the non-policy field and the logic model attribute.
S250, extracting the entity name and the attribute name of the logic model according to the analysis result described by the conversion rule; and determining the name of the physical table and the name of the field in the physical model according to the mapping relation between the attribute and the non-policy field.
The blood relationship analysis system can analyze the conversion rule description in the blood relationship description result so as to obtain the entity name and the attribute name of the logic model. For example, through the analysis of the SELECT field a FROM table b, the entity name b and the attribute name a of the logic model can be obtained. It will be appreciated that entities in the logical model have a mapping relationship to tables in the physical model, and attributes in the logical model have a mapping relationship to fields in the physical model. Through the mapping relation between the attribute and the non-policy field, the blood relationship analysis system can determine the name of the field according to the determination of the non-policy field in the physical model, and further determine the name of the physical table according to the affiliated relation between the field and the physical table.
S260, replacing the entity name in the conversion rule description by the physical table name, and replacing the attribute name in the conversion rule description by the field name to obtain the conversion rule description between the non-policy fields with the blood relationship in the physical model field.
The blood relationship analysis system can replace the entity name in the conversion rule description of the logic model with the physical table name and replace the attribute name with the field name so as to obtain the conversion rule description between the non-policy fields of the physical model. Similarly, the blood relationship analysis system may also replace the entity name in the blood relationship description result of the logical model with the physical table name, and replace the attribute name with the field name to obtain the blood relationship description result of the physical model. It should be noted that, the attributes in the logical model and the fields in the physical model not only have a one-to-one simple mapping relationship, but also may have a one-to-many equal complex mapping relationship, so that name replacement for the conversion rule description in the blood relationship description result is more rigorous and accurate.
And S270, generating a blood relationship description result of the physical model according to the conversion rule description.
It is easy to understand that similar to the blood relationship analysis process of the logical model, the blood relationship description result of the physical model can also be obtained based on the conversion rule description of the physical model.
In this embodiment, optionally, after obtaining a conversion rule description between non-policy fields in which a blood relationship exists in the physical model field, the method further includes:
determining a source physical table and a source field of each non-policy field according to an analysis result described by the conversion rule;
and establishing a blood vessel edge view of the physical model according to the source physical table and the source field.
In a process similar to the process of constructing the blood relationship view of the logic model, the blood relationship analysis system can obtain the source physical tables and the source fields of the non-strategy fields according to the analysis result described by the conversion rule, so that the blood relationship view of the physical model is constructed.
The scheme can display the blood relationship of the physical model in the form of the blood relationship view, brings more visual blood relationship understanding for users, and is favorable for comparing the blood relationship of the physical model with the blood relationship of the logic model.
In a possible solution, optionally, the determining, according to the mapping relationship between the attribute and the non-policy field, a physical table name and a field name in a physical model includes:
and if the attributes and the non-policy fields have mapping relations, determining the physical table name and the field name in the physical model according to a user selection result.
In the case where there is a one-to-many mapping relationship between attributes and non-policy fields, the blood relationship analysis system may list a plurality of non-policy fields for selection by the user. And determining the field name in the physical model according to the user selection result, and further determining the physical table name.
According to the scheme, under the condition that one-to-many mapping relations exist between the attribute fields and the non-policy fields, the field names and the physical table names in the physical model can be accurately determined, and the physical model can be attached to obtain a reliable blood relationship description result.
In a preferred embodiment, after generating the blood-related relationship description result of the physical model, the method further comprises:
based on product demand information and development design information, supplementing conversion rule description between attributes with blood relationship in the attributes of the logic model and mapping relationship between non-strategy fields and the attributes on the basis of the current logic model;
and generating a conversion rule description between the non-policy fields with the blood relationship according to the conversion rule description and the mapping relationship so as to supplement the blood relationship description result of the physical model.
According to the scheme, after the preliminary construction of the blood relationship description result of the physical model is finished, the blood relationship description result is perfected according to original requirement information and design information, so that the construction of a comprehensive and complete blood relationship is facilitated, and the loss and leakage of the blood relationship are avoided.
According to the technical scheme, based on the natural connection relation between the physical model and the logic model, in the physical model establishing process, the blood relationship description result of the physical model is obtained through the blood relationship description result of the logic model. The technical scheme can ensure the unification of the blood relationship between the physical model and the logical model and can reduce the workload of maintaining the relationship between the two blood relationships.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a description apparatus of a logical model blood relationship according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes:
an attribute information obtaining module 310, configured to obtain attribute information of the logic model;
a conversion rule determining module 320, configured to determine, according to the attribute information of the logic model, a conversion rule between attributes having a blood relationship in the attributes of the logic model;
the blood relationship description result generating module 330 is configured to describe the conversion rule through a preset SQL language to obtain a blood relationship description result.
In this embodiment, optionally, the blood relationship description result generating module 330 is specifically configured to:
describing the source logic model attribute of the current logic model attribute through a SELECT statement;
describing a source logic model of the current logic model through an FROM statement;
describing the incidence relation between the source logic models of the current logic model through INNER/LEFT/RIGHT/FULL/SELF JOIN statements;
describing the filtering relation of the source logic model through a WHERE statement;
describing the grouping relation among the source logic models through a GROUP BY statement;
describing the ordering relation after the source logic models are combined through an ORDER BY statement;
a parallel combination of multiple source logical relationships is described by a UNION/UNION ALL statement.
In this scheme, optionally, the apparatus further includes:
the blood relationship link analysis module is used for analyzing blood relationship links with blood relationship attributes in the logical model attributes according to the blood relationship description result;
the blood margin view building module is used for building a blood margin view of the logic model according to the source information acquired from the blood margin link; the source information includes source component information, source table information and source field relation information.
In a preferred aspect, the apparatus further comprises:
the spoken language description result generation module is used for describing the conversion rule through a preset spoken language description template to obtain a spoken language description result of the blood relationship;
and the spoken language description result conversion module is used for analyzing the spoken language description result according to a preset analysis rule to generate a blood relationship description result based on the SQL language.
In one possible solution, optionally, the apparatus further includes:
a conversion rule description determining module, configured to determine, in the blood relationship description result, a conversion rule description associated with a logical model attribute corresponding to a non-policy field if an establishment event of the non-policy field in the physical model is detected; the non-strategy field is a field which has a mapping relation with the logic model attribute in the physical model;
a physical table name and field name determining module, configured to extract an entity name and an attribute name of the logical model according to an analysis result described by the conversion rule; determining a physical table name and a field name in a physical model according to the mapping relation between the attribute and the non-strategy field;
and the conversion rule description generation module is used for replacing the entity name in the conversion rule description with the physical table name and replacing the attribute name in the conversion rule description with the field name to obtain the conversion rule description between the non-policy fields with the blood relationship in the physical model field.
On the basis of the above scheme, optionally, the apparatus further includes:
a source physical table and source field determining module, configured to determine a source physical table and a source field of each non-policy field according to an analysis result described by the conversion rule;
and the physical model blood relationship view construction module is used for establishing the blood relationship view of the physical model according to the source physical table and the source field.
In this scheme, optionally, the physical table name and field name determining module is specifically configured to:
and if the attributes have a mapping relation with a plurality of non-strategy fields, determining the physical table name and the field name in the physical model according to the user selection result.
Optionally, the apparatus further comprises:
and the physical model blood relationship description result generation module is used for generating a blood relationship description result of the physical model according to the conversion rule description.
On the basis of the above scheme, optionally, the apparatus further includes:
the mapping relation supplement module is used for supplementing conversion rule description between attributes with blood relationship in the attributes of the logic model and the mapping relation between non-strategy fields and the attributes on the basis of the current logic model on the basis of product demand information and development design information;
and the blood relationship description result supplementing module is used for generating conversion rule description between non-strategy fields with blood relationship according to the conversion rule description and the mapping relationship so as to supplement the blood relationship description result of the physical model.
The device for describing the logical model blood relationship provided by the embodiment of the invention can execute the method for describing the logical model blood relationship provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example four
FIG. 4 illustrates a block diagram of an electronic device 410 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 410 includes at least one processor 411, and a memory communicatively connected to the at least one processor 411, such as a Read Only Memory (ROM)412, a Random Access Memory (RAM)413, and the like, wherein the memory stores computer programs executable by the at least one processor, and the processor 411 may perform various appropriate actions and processes according to the computer programs stored in the Read Only Memory (ROM)412 or the computer programs loaded from the storage unit 418 into the Random Access Memory (RAM) 413. In the RAM 413, various programs and data necessary for the operation of the electronic device 410 can also be stored. The processor 411, the ROM 412, and the RAM 413 are connected to each other through a bus 414. An input/output (I/O) interface 415 is also connected to bus 414.
A number of components in the electronic device 410 are connected to the I/O interface 415, including: an input unit 416 such as a keyboard, a mouse, or the like; an output unit 417 such as various types of displays, speakers, and the like; a storage unit 418, such as a magnetic disk, optical disk, or the like; and a communication unit 419 such as a network card, modem, wireless communication transceiver, or the like. The communication unit 419 allows the electronic device 410 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
In some embodiments, the method of describing logical model bloodletting relationships may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program may be loaded and/or installed onto electronic device 410 via ROM 412 and/or communications unit 419. When loaded into RAM 413 and executed by processor 411, may perform one or more steps of the method of description of logical model bloodletting relations described above. Alternatively, in other embodiments, the processor 411 may be configured by any other suitable means (e.g., by means of firmware) to perform the method of describing logical model kindred relationships.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
EXAMPLE five
Embodiments of the present invention further provide a computer program product, including a computer program, which, when being executed by a processor, implements the method for describing the relationship between logical model and blood vessels as provided in any of the embodiments of the present application.
Computer program product in implementing the computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired result of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (13)
1. A method for describing a logical model blood relationship, the method comprising:
acquiring attribute information of a logic model;
determining a conversion rule between attributes with a blood relationship in the attributes of the logic model according to the attribute information of the logic model;
and describing the conversion rule through a preset SQL language to obtain a blood relationship description result.
2. The method according to claim 1, wherein the description of the conversion rule through a preset SQL language includes at least one of the following cases:
describing the source logic model attribute of the current logic model attribute through a SELECT statement;
describing a source logic model of the current logic model through an FROM statement;
describing the incidence relation between the source logic models of the current logic model through INNER/LEFT/RIGHT/FULL/SELF JOIN statements;
describing the filtering relation of the source logic model through a WHERE statement;
describing the grouping relation among the source logic models through a GROUP BY statement;
describing the ordering relation of the source logic models after combination through an ORDER BY statement;
a parallel combination of multiple source logical relationships is described by a UNION/UNION ALL statement.
3. The method of claim 1, wherein after obtaining the kindred description result, the method further comprises:
analyzing a blood relationship link with the attribute of the blood relationship in the attribute of the logic model according to the blood relationship description result;
establishing a blood margin view of the logic model according to the source information acquired from the blood margin link; the source information includes source component information, source table information, and source field relationship information.
4. The method of claim 1, wherein after determining a conversion rule between attributes for which a kindred relationship exists among the logical model attributes, the method further comprises:
describing the conversion rule by presetting a spoken description template to obtain a spoken description result of the blood relationship;
and analyzing the spoken description result according to a preset analysis rule to generate a blood relationship description result based on the SQL language.
5. The method of claim 1, wherein after obtaining the kindred description result, the method further comprises:
if an establishment event of a non-policy field in a physical model is detected, determining a conversion rule description associated with a logic model attribute corresponding to the non-policy field in the blood relationship description result; wherein the non-policy field is a field in the physical model, which has a mapping relation with the logical model attribute;
extracting the entity name and the attribute name of the logic model according to the analysis result described by the conversion rule; determining the name of a physical table and the name of a field in a physical model according to the mapping relation between the attribute and the non-policy field;
and replacing the entity name in the conversion rule description by the physical table name, and replacing the attribute name in the conversion rule description by the field name to obtain the conversion rule description between the non-policy fields with the blood relationship in the physical model field.
6. The method of claim 5, wherein after obtaining a transformation rule description between non-policy fields in the physical model field for which a kindred relationship exists, the method further comprises:
determining a source physical table and a source field of each non-policy field according to an analysis result described by the conversion rule;
and establishing a blood relationship view of the physical model according to the source physical table and the source field.
7. The method of claim 5, wherein determining the physical table name and the field name in the physical model according to the mapping relationship between the attribute and the non-policy field comprises:
and if the attributes and the non-policy fields have mapping relations, determining the physical table name and the field name in the physical model according to a user selection result.
8. The method of claim 5, wherein after obtaining a description of a transformation rule between non-policy fields in which a kindred relationship exists in the physical model field, the method further comprises:
and generating a blood relationship description result of the physical model according to the conversion rule description.
9. The method of claim 8, wherein after generating the kindred description of the physical model, the method further comprises:
supplementing conversion rule description between attributes with blood relationship in the attributes of the logic model and mapping relationship between non-strategy fields and the attributes on the basis of the current logic model based on product demand information and development design information;
and generating a conversion rule description between the non-policy fields with the blood relationship according to the conversion rule description and the mapping relationship so as to supplement the blood relationship description result of the physical model.
10. An apparatus for describing a logical model blood relationship, the apparatus comprising:
the attribute information acquisition module is used for acquiring the attribute information of the logic model;
the conversion rule determining module is used for determining a conversion rule between attributes with a blood relationship in the attributes of the logic model according to the attribute information of the logic model;
and the blood relationship description result generation module is used for describing the conversion rule through a preset SQL language to obtain a blood relationship description result.
11. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of describing logical model bloodletting relationships of any one of claims 1-9.
12. A computer-readable storage medium, having stored thereon computer instructions for causing a processor to execute a method for describing logical model blood relationship according to any one of claims 1-9.
13. A computer program product, characterized in that the computer program product comprises a computer program which, when being executed by a processor, carries out the method of description of logical model consanguinity relations according to any one of claims 1-9.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115203277A (en) * | 2022-09-19 | 2022-10-18 | 北京必盈特信息技术有限公司 | Data decision method and device |
| CN116303370A (en) * | 2023-05-17 | 2023-06-23 | 建信金融科技有限责任公司 | Script blood margin analysis method, script blood margin analysis device, storage medium, script blood margin analysis equipment and script blood margin analysis product |
| CN117131791A (en) * | 2023-10-27 | 2023-11-28 | 德特赛维技术有限公司 | Model evaluation method, system and storage medium based on big data platform |
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2022
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115203277A (en) * | 2022-09-19 | 2022-10-18 | 北京必盈特信息技术有限公司 | Data decision method and device |
| CN116303370A (en) * | 2023-05-17 | 2023-06-23 | 建信金融科技有限责任公司 | Script blood margin analysis method, script blood margin analysis device, storage medium, script blood margin analysis equipment and script blood margin analysis product |
| CN116303370B (en) * | 2023-05-17 | 2023-08-15 | 建信金融科技有限责任公司 | Script blood margin analysis method, script blood margin analysis device, storage medium, script blood margin analysis equipment and script blood margin analysis product |
| CN117131791A (en) * | 2023-10-27 | 2023-11-28 | 德特赛维技术有限公司 | Model evaluation method, system and storage medium based on big data platform |
| CN117131791B (en) * | 2023-10-27 | 2024-01-23 | 德特赛维技术有限公司 | Model evaluation method, system and storage medium based on big data platform |
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