CN110502538B - Method, system, equipment and storage medium for portrait tag generation logic mapping - Google Patents
Method, system, equipment and storage medium for portrait tag generation logic mapping Download PDFInfo
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Abstract
The embodiment of the invention provides a method, a system, a computer device and a computer storage medium for generating logical mapping of portrait labels, wherein the method comprises the following steps: acquiring a plurality of fuzzy matching tasks associated with the ith portrait tag from a database management platform; acquiring a database name and a task running state corresponding to each fuzzy matching task; selecting one of the target fuzzy matching tasks; converting sql sentences in the fuzzy matching task scripts corresponding to the target fuzzy matching tasks into corresponding grammar trees; analyzing the grammar tree to obtain an (i+1) th table name and an (i+1) th portrait tag of a previous stage associated with the (i) th portrait tag; repeating until an nth portrait tag of the unassociated fuzzy matching task is obtained; and establishing a relation chain between the ith portrait label and the (i+1) th to nth table names. The embodiment of the invention can automatically process the query of the fuzzy matching task of the portrait tag in batches, and reduce the error rate of manual query.
Description
Technical Field
The embodiment of the invention relates to the field of databases, in particular to a method, a system, equipment and a storage medium for generating logical mapping of portrait labels.
Background
The task on which the current search portrait tag depends can only be manually inquired through a database management platform, and the sql processing logic of the tag in the current script file needs to be manually checked, and the related tag and table name of the previous stage are traced back; the query is repeated until the queried table is an ODS table (a base table processed in the data warehouse process); and then manually recording all the inquiry paths. Each tag search takes more than 10 minutes, and the operations take time and labor, and manual recording is prone to error.
Disclosure of Invention
In view of the above, an object of an embodiment of the present invention is to provide a method, a system, an apparatus, and a storage medium for generating logical mapping for portrait labels, which can automatically process queries of fuzzy matching tasks of portrait labels in batches, and reduce an error rate of human queries.
In order to achieve the above object, an embodiment of the present invention provides a method for generating a logical mapping for portrait tag, including:
s100, acquiring a task list from a database management platform, wherein the task list comprises a plurality of fuzzy matching tasks associated with the ith portrait tag;
s102, acquiring a database name and a task running state corresponding to each fuzzy matching task in the task list;
s104, selecting one target fuzzy matching task according to the database name and the task running state corresponding to each fuzzy matching task;
s106, converting the sql statement in the fuzzy matching task script corresponding to the target fuzzy matching task into a corresponding grammar tree;
s108, analyzing the grammar tree to obtain an i+1th table name and an i+1th portrait tag of the upper level associated with the i portrait tag;
repeating the steps S100-S108 until i=n to obtain an nth portrait tag, wherein the nth portrait tag has no associated fuzzy matching task;
and establishing a relation chain between the ith portrait label and the ith (+1) to nth table names, wherein each table name of the ith (+1) to nth table names is used as a relation chain link point in the relation chain and is configured in the relation chain according to the sequence.
Further, the step of obtaining the task list from the database management platform includes:
logging on a database management platform through a crawler interface simulation: constructing a user name parameter and a password parameter, and simulating a Web post request to log in a database management platform;
inputting an ith table name associated with the ith portrait tag to the database management platform;
and acquiring task lists matched with the ith table names from a plurality of databases associated with a database management platform through fuzzy matching task scripts.
Further, the step of obtaining the task list matched with the ith table name from a plurality of databases associated with a database management platform through fuzzy matching task scripts comprises the following steps:
querying the plurality of databases for one or more tasks having the same string as the i-th table name;
and establishing a task list according to the one or more tasks.
Further, the method also comprises the step of judging whether each image label is associated with a fuzzy matching task:
inquiring a table type corresponding to an ith table name of the ith portrait tag;
and when the table type corresponding to the ith table name is a basic table processed in the data warehouse process, determining that the ith portrait tag has no fuzzy matching task.
To achieve the above object, an embodiment of the present invention further provides a system for generating a logical mapping for portrait tag, including:
the first acquisition module is used for acquiring a task list from the database management platform, wherein the task list comprises a plurality of fuzzy matching tasks associated with the ith portrait tag;
the second acquisition module is used for acquiring the database name and the task running state corresponding to each fuzzy matching task in the task list;
the selection module is used for selecting one of the target fuzzy matching tasks according to the database name and the task running state corresponding to each fuzzy matching task;
the conversion module is used for converting the sql statement in the fuzzy matching task script corresponding to the target fuzzy matching task into a corresponding grammar tree;
the parsing module is used for parsing the grammar tree to obtain an (i+1) th table name and an (i+1) th portrait tag of the upper level associated with the (i) th portrait tag;
the judging and executing module is used for repeatedly executing the fuzzy matching task until the nth portrait label is obtained when i=n, wherein the nth portrait label is irrelevant to the fuzzy matching task;
the building module is used for building a relation chain between the ith portrait label and the ith+1 to nth table names, wherein each table name of the ith+1 to nth table names is used as a relation chain link point in the relation chain and is configured in the relation chain according to the sequence.
Further, the first acquisition module is further configured to:
logging on a database management platform through a crawler interface simulation: constructing a user name parameter and a password parameter, and simulating a Web post request to log in a database management platform;
inputting an ith table name associated with the ith portrait tag to the database management platform;
and acquiring task lists matched with the ith table names from a plurality of databases associated with a database management platform through scripts of fuzzy matching tasks.
Further, the first acquisition module is further configured to:
querying the plurality of databases for one or more tasks having the same string as the i-th table name;
and establishing a task list according to the one or more tasks.
Further, the judgment execution module is further configured to:
inquiring a table type corresponding to an ith table name of the ith portrait tag;
and when the table type corresponding to the ith table name is a basic table processed in the data warehouse process, determining that the ith portrait tag has no fuzzy matching task.
To achieve the above object, an embodiment of the present invention further provides a computer device, where the computer device includes a memory and a processor, where the memory stores a system for generating a logical mapping of portrait labels that can be executed on the processor, and where the system for generating a logical mapping of portrait labels is executed by the processor to implement the steps of the method for generating a logical mapping of portrait labels as described above.
To achieve the above object, an embodiment of the present invention further provides a computer-readable storage medium having stored therein a computer program executable by at least one processor to cause the at least one processor to perform the steps of the method of image tag generation logic mapping as described above.
According to the portrait tag generation logic mapping method, system, equipment and storage medium, simulation login is carried out on a database management platform, a plurality of fuzzy matching tasks associated with the ith portrait tag are obtained from the database management platform, and then a previous task is further inquired through a table associated with the ith portrait tag until the associated fuzzy matching task cannot be inquired. The embodiment of the invention can automatically process the query of the fuzzy matching task of the portrait tag in batches, and reduce the error rate of human query.
Drawings
FIG. 1 is a flowchart of a first embodiment of a method for creating logical mappings for portrait tags according to an embodiment of the present invention.
Fig. 2 is a flowchart of step S100 according to an embodiment of the present invention.
Fig. 3 is a flowchart of step S110 according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of program modules of a second embodiment of a system for creating logical mappings for portrait tags according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of a hardware structure of a third embodiment of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the detailed description and specific examples, while indicating the embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the embodiments of the present invention.
Example 1
Referring to FIG. 1, a flowchart illustrating steps in a method for portrait tag generation logic mapping according to a first embodiment of the present invention is shown. It will be appreciated that the flow charts in the method embodiments are not intended to limit the order in which the steps are performed. An exemplary description will be made below with the server as an execution subject. Specifically, the following is described.
Step S100, a task list is obtained from a database management platform, wherein the task list comprises a plurality of fuzzy matching tasks associated with the ith portrait tag.
Illustratively, referring to FIG. 2, the step of obtaining a task list from the database management platform further comprises:
step S100A, logging on a database management platform through a crawler interface in a simulation mode: and constructing a user name parameter and a password parameter, and simulating a Web post request to log in a database management platform. The database management platform can record login information (such as login state) of a user for the first time, and perform simulated login operation according to the login information when the user is detected to login again, so that the problem that the user needs to login again when a task is checked later is avoided.
Step S100B, inputting an ith table name associated with the ith portrait tag to the database management platform.
And step S100C, acquiring a task list matched with the ith table name from a plurality of databases associated with a database management platform through fuzzy matching task scripts.
Illustratively, step S100B further includes:
querying the plurality of databases for one or more tasks having the same string as the i-th table name;
and establishing a task list according to the one or more tasks.
Specifically, the ith portrait tag refers to a behavior tag of a user, such as the number of activities of the user in a certain piece of software in the last month.
Step S102, obtaining a database name and a task running state corresponding to each fuzzy matching task in the task list.
Specifically, program source files, running state monitoring and scheduling can be performed through the database management platform. The list names of the task lists are sent to the server of the database management platform, the server returns the relevant task list, and fuzzy matching means matching only if the fact that the same component character string as the list names is queried.
Step S104, selecting one of the target fuzzy matching tasks according to the database name and the task running state corresponding to each fuzzy matching task.
Specifically, if the database name corresponding to each fuzzy matching task in the task list is sx_xx_safe, and only one task running state of the fuzzy matching task in the task list is in running and in issuing (corresponding, the task running states of other fuzzy matching tasks in the task list are all in a stop or offline state), then the task in running and in issuing is the target fuzzy matching task. It will be appreciated that the target fuzzy matching task is a fuzzy matching task uniquely associated with the ith portrait tag.
And S106, converting the sql statement in the fuzzy matching task script corresponding to the target fuzzy matching task into a corresponding grammar tree.
Specifically, if the target fuzzy matching task is: the daily activity times of all users for 30 days are summed up, and the parsed grammar tree nodes are as follows:
select- -keywords;
a clients, sum (day_count) as Month_act_count-task list;
from- -keywords;
dual—table name;
group by- -keywords;
where day <30- -constraint (judgment).
By translating the sql statement into a syntax tree, the processing logic of the ith portrait tag can be better parsed.
Step S108, analyzing the grammar tree to obtain the (i+1) th table name and the (i+1) th portrait tag of the upper level associated with the (i) th portrait tag.
Specifically, the i-th portrait tag moth_act_count is obtained by processing the i-th portrait tag by the processing logic, so that the i+1th table name of the upper stage associated with the i-th portrait tag is dual, and the i+1th portrait tag is day_count.
Step S110, repeating the steps S100 to S108 until the n-th portrait tag is obtained when i=n, where the n-th portrait tag has no associated fuzzy matching task.
Illustratively, referring to fig. 3, the method further includes the step of determining whether each image tag is associated with a fuzzy matching task:
step S110A, inquiring a table type corresponding to an ith table name of an ith portrait tag;
in step S110B, when the table type corresponding to the i-th table name is a base table (ODS table, operational Data Store) processed in the data warehouse process, it is determined that the i-th portrait tag has no fuzzy matching task.
Specifically, when no fuzzy matching task is found in the ith portrait tag, stopping the query. A large number of portrait tags can be queried to increase the processing rate.
Step S112, building a relation chain between the ith portrait label and the (i+1) th to nth table names, wherein each of the (i+1) th to nth table names is used as a relation chain link point in the relation chain and is configured in the relation chain according to the sequence.
Specifically, for example: in the table named seq_icmp_old_client_day, there is a portrait tag MONTH_HONGBAO_CNT_1 (the number of red packets in the last MONTH of activity of the client), when the portrait tag is abnormal, the user verifies by obtaining the processing logic of the portrait tag, so that a database management is required to be logged in to search for the fuzzy matched task hive_seq_icmp_old_client_day of the portrait tag after the processing logic, and the portrait tag is found by summing up the day_hongbao_count in the table seq_icmp_old_client_ jgj of the previous task through the syntax tree analyzed by the sql statement of the task script, thereby backtracking; the resulting logical map is as follows:
the table is a basic table processed in the data warehouse process of fuzzy matching of the ith portrait tag, and reflects the logic mapping process of the ith portrait tag.
Example two
With continued reference to FIG. 4, a program module diagram of a second embodiment of a system for portrait tag generation logic mapping according to an embodiment of the present invention is shown. In this embodiment, the system 20 for generating logical mappings of portrait tags may include or be partitioned into one or more program modules, one or more program modules being stored in a storage medium and executed by one or more processors to accomplish the embodiments of the present invention and to implement the methods for generating logical mappings of portrait tags described above. Program modules depicted in the embodiments of the present invention are directed to a series of computer program instruction segments capable of performing particular functions, and are more suitable than programs themselves for describing the execution of system 20 for portrait tag generation logic mapping in a storage medium. The following description will specifically describe functions of each program module of the present embodiment:
a first obtaining module 200, configured to obtain a task list from a database management platform, where the task list includes a plurality of fuzzy matching tasks associated with the ith portrait tag.
The first acquisition module 200 is also exemplary for:
logging on a database management platform through a crawler interface simulation: and constructing a user name parameter and a password parameter, and simulating a Web post request to log in a database management platform. The database management platform is used for monitoring and scheduling program source files and running states. The database management platform can record login information (such as login state) of a user for the first time, and perform simulated login operation according to the login information when the user is detected to login again, so that the problem that the user needs to login again when a task is checked later is avoided.
And inputting an ith table name associated with the ith portrait tag to the database management platform.
And acquiring task lists matched with the ith table names from a plurality of databases associated with a database management platform through scripts of fuzzy matching tasks.
The first acquisition module 200 is also exemplary for:
querying the plurality of databases for one or more tasks having the same string as the i-th table name;
and establishing a task list according to the one or more tasks.
Specifically, the ith portrait tag refers to a behavior tag of a user, such as the number of activities of the user in a certain piece of software in the last month.
And the second obtaining module 201 is configured to obtain a database name and a task running state corresponding to each fuzzy matching task in the task list.
Specifically, program source files, running state monitoring and scheduling can be performed through the database management platform. The list names of the task lists are sent to the server of the database management platform, the server returns the relevant task list, and fuzzy matching means matching only if the fact that the same component character string as the list names is queried.
And the selection module 202 is configured to select one of the target fuzzy matching tasks according to the database name and the task running state corresponding to each fuzzy matching task.
Specifically, if the database name corresponding to each fuzzy matching task in the task list is sx_xx_safe, and only one task running state of the fuzzy matching task in the task list is in running and in issuing (corresponding, the task running states of other fuzzy matching tasks in the task list are all in a stop or offline state), then the task in running and in issuing is the target fuzzy matching task. It will be appreciated that the target fuzzy matching task is a fuzzy matching task uniquely associated with the ith portrait tag.
And the conversion module 203 is configured to convert the sql statement in the fuzzy matching task script corresponding to the target fuzzy matching task into a corresponding syntax tree.
Specifically, the target fuzzy matching task is as follows: the total number of activities per day for all users over 30 days is summed up, and the nodes of the parsed syntax tree are as follows:
select- -keywords;
a label list of clientno, sum (day_count) as mole_act_count;
from- -keywords;
dual—table name;
group by- -keywords;
where day <30- -constraint.
By translating the sql statement into a syntax tree, the processing logic of the ith portrait tag can be better parsed.
And a parsing module 204, configured to parse the language-law book to obtain an i+1th table name and an i+1th image tag of a previous level associated with the i-th image tag.
Specifically, since the i-th portrait tag moth_act_count is processed by day_count (processing logic) by the analysis, the i+1th table name of the upper stage associated with the i-th portrait tag is dual, and the i+1th portrait tag is day_count.
And the judging and executing module 205 is configured to repeatedly execute the fuzzy matching task until the nth portrait tag is obtained when i=n, where the nth portrait tag has no associated fuzzy matching task.
Illustratively, the decision performing module 205 is further configured to:
inquiring a table type corresponding to an ith table name of the ith portrait tag;
and when the table type corresponding to the ith table name is a basic table (ODS table, operational Data Store) processed in the data warehouse process, determining that the ith portrait tag has no fuzzy matching task, and stopping inquiring.
Specifically, when no fuzzy matching task is found in the ith portrait tag, stopping the query. Can perform the query of a large quantity of portrait labels to improve the processing rate
A building module 206, configured to build a relationship chain between the ith portrait tag and the (i+1) -th table names, where each of the (i+1) -th table names is configured in the relationship chain according to a sequence as a relationship link point in the relationship chain.
Specifically, for example: in the table named seq_icmp_old_client_day, there is a portrait tag MONTH_HONGBAO_CNT_1 (the number of red packets in the last MONTH of activity of the client), when the portrait tag is abnormal, the user verifies by obtaining the processing logic of the portrait tag, so that a database management is required to be logged in to search for the fuzzy matched task hive_seq_icmp_old_client_day of the portrait tag after the processing logic, and the portrait tag is found by summing up the day_hongbao_count in the table seq_icmp_old_client_ jgj of the previous task through the syntax tree analyzed by the sql statement of the task script, thereby backtracking; the resulting logical map is as follows:
the table is a basic table processed in the data warehouse process of fuzzy matching of the ith portrait tag, and reflects the logic mapping process of the ith portrait tag.
Example III
Fig. 5 is a schematic hardware architecture of a computer device according to a third embodiment of the invention. In this embodiment, the computer device 2 is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction. The computer device 2 may be a rack server, a blade server, a tower server, or a rack server (including a stand-alone server, or a server cluster made up of multiple servers), or the like. As shown in fig. 5, the computer device 2 includes, but is not limited to, at least a memory 21, a processor 22, a network interface 23, and a system 20 for generating logical mappings of portrait tags, which are communicatively coupled to each other via a system bus. Wherein:
in this embodiment, the memory 21 includes at least one type of computer-readable storage medium including flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the memory 21 may be an internal storage unit of the computer device 2, such as a hard disk or a memory of the computer device 2. In other embodiments, the memory 21 may also be an external storage device of the computer device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the computer device 20. Of course, the memory 21 may also include both internal storage units of the computer device 2 and external storage devices. In this embodiment, the memory 21 is typically used to store an operating system installed on the computer device 2 and various application software, such as program code of the portrait tag generating logic mapping system 20 of the second embodiment. Further, the memory 21 may be used to temporarily store various types of data that have been output or are to be output.
The processor 22 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 22 is generally used to control the overall operation of the computer device 20. In this embodiment, the processor 22 is configured to execute the program code stored in the memory 21 or process data, for example, to execute the system 20 for generating the logical mapping for the portrait tag, so as to implement the method for generating the logical mapping for the portrait tag according to the first embodiment.
The network interface 23 may comprise a wireless network interface or a wired network interface, which network interface 23 is typically used for establishing a communication connection between the server 2 and other electronic devices. For example, the network interface 23 is used to connect the server 2 to an external terminal through a network, establish a data transmission channel and a communication connection between the server 2 and the external terminal, and the like. The network may be an Intranet (Intranet), the Internet (Internet), a global system for mobile communications (Global System of Mobile communication, GSM), wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA), a 4G network, a 5G network, bluetooth (Bluetooth), wi-Fi, or other wireless or wired network.
It is noted that fig. 5 only shows a computer device 2 having components 20-23, but it is understood that not all of the illustrated components are required to be implemented, and that more or fewer components may alternatively be implemented.
In this embodiment, the system 20 for generating logical mappings from portrait tags stored in the memory 21 may also be divided into one or more program modules, which are stored in the memory 21 and executed by one or more processors (the processor 22 in this embodiment) to complete the embodiment of the present invention.
For example, fig. 4 shows a schematic program module of a second embodiment of the system 20 for implementing the portrait tag generating logic mapping, where the system 20 for implementing the portrait tag generating logic mapping may be divided into a first obtaining module 200, a second obtaining module 201, a selecting module 202, a converting module 203, an analyzing module 204, a judging executing module 205, and a building module 206. Program modules depicted in the exemplary embodiment of the present invention are directed to a series of computer program instruction segments capable of performing particular functions, and more particularly, to a program that is adapted to describe the execution of the system 20 for generating logical mappings of portrayal labels in the computer device 2. The specific functions of the program modules 200-206 are described in detail in the second embodiment, and are not described herein.
Example IV
The present embodiment also provides a computer-readable storage medium such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, which when executed by a processor, performs the corresponding functions. The computer readable storage medium of the present embodiment is for storing a system 20 for portrait tag generation logic mapping, which when executed by a processor implements the portrait tag generation logic mapping method of the first embodiment.
According to the portrait tag generation logic mapping method, system, equipment and storage medium, simulation login is carried out on a database management platform, a plurality of fuzzy matching tasks associated with the ith portrait tag are obtained from the database management platform, and then a previous task is further inquired through a table associated with the ith portrait tag until the associated fuzzy matching task cannot be inquired. The embodiment of the invention can automatically process the query of the fuzzy matching task of the portrait tag in batches, and reduce the error rate of manual query; when the embodiment of the invention is used for carrying out the fuzzy matching task of 800 portrait labels, the time can be shortened to 2 hours, and the time is saved.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment.
The foregoing description is only the preferred embodiments of the present invention, and is not intended to limit the scope of the embodiments of the present invention, but rather the equivalent structures or equivalent flow changes made by the descriptions of the embodiments of the present invention and the contents of the drawings, or the direct or indirect application in other related technical fields, are all included in the scope of the embodiments of the present invention.
Claims (6)
1. A method of portrait tag generation logic mapping, comprising:
s100, acquiring a task list from a database management platform, wherein the task list comprises a plurality of fuzzy matching tasks associated with an ith portrait tag;
s102, acquiring a database name and a task running state corresponding to each fuzzy matching task in the task list;
s104, selecting one target fuzzy matching task according to the database name and the task running state corresponding to each fuzzy matching task;
s106, converting the sql statement in the fuzzy matching task script corresponding to the target fuzzy matching task into a corresponding grammar tree;
s108, analyzing the grammar tree to obtain an i+1th table name and an i+1th portrait tag of the upper level associated with the i portrait tag;
repeating the steps S100-S108 until i=n to obtain an nth portrait tag, wherein the nth portrait tag has no associated fuzzy matching task;
establishing a relation chain between the ith portrait label and the ith+1 to nth table names, wherein each table name of the ith+1 to nth table names is used as a relation chain link point in the relation chain and is configured in the relation chain according to the sequence;
the step of obtaining the task list from the database management platform comprises the following steps:
logging on a database management platform through a crawler interface simulation: constructing a user name parameter and a password parameter, and simulating a Web post request to log in a database management platform;
inputting an ith table name associated with the ith portrait tag to the database management platform;
acquiring task lists matched with the ith table names from a plurality of databases associated with a database management platform through fuzzy matching task scripts;
the step of obtaining a task list matched with the ith table name from a plurality of databases associated with a database management platform through fuzzy matching task scripts comprises the following steps:
querying the plurality of databases for one or more tasks having the same string as the i-th table name;
and establishing a task list according to the one or more tasks.
2. The method of claim 1, further comprising the step of determining whether each image tag is associated with a fuzzy matching task:
inquiring a table type corresponding to an ith table name of the ith portrait tag;
and when the table type corresponding to the ith table name is a basic table processed in the data warehouse process, determining that the ith portrait tag has no fuzzy matching task.
3. A system for portrait tag generation logic mapping, comprising:
the first acquisition module is used for acquiring a task list from the database management platform, wherein the task list comprises a plurality of fuzzy matching tasks associated with the ith portrait tag;
the second acquisition module is used for acquiring the database name and the task running state corresponding to each fuzzy matching task in the task list;
the selection module is used for selecting one of the target fuzzy matching tasks according to the database name and the task running state corresponding to each fuzzy matching task;
the conversion module is used for converting the sql statement in the fuzzy matching task script corresponding to the target fuzzy matching task into a corresponding grammar tree;
the parsing module is used for parsing the grammar tree to obtain an (i+1) th table name and an (i+1) th portrait tag of the upper level associated with the (i) th portrait tag;
the judging and executing module is used for repeatedly executing the fuzzy matching task until the nth portrait label is obtained when i=n, wherein the nth portrait label is irrelevant to the fuzzy matching task;
the building module is used for building a relation chain between the ith portrait label and the (i+1) th to nth table names, wherein each of the (i+1) th to nth table names is used as a relation chain link point in the relation chain and is configured in the relation chain according to the sequence;
the first acquisition module is further configured to:
logging on a database management platform through a crawler interface simulation: constructing a user name parameter and a password parameter, and simulating a Web post request to log in a database management platform;
inputting an ith table name associated with the ith portrait tag to the database management platform;
acquiring a task list matched with the i table name from a plurality of databases associated with a database management platform through scripts of fuzzy matching tasks;
the first acquisition module is further configured to:
querying the plurality of databases for one or more tasks having the same string as the i-th table name;
and establishing a task list according to the one or more tasks.
4. The system of claim 3, wherein the determination execution module is further configured to:
inquiring a table type corresponding to an ith table name of the ith portrait tag;
and when the table type corresponding to the ith table name is a basic table processed in the data warehouse process, determining that the ith portrait tag has no fuzzy matching task.
5. A computer device comprising a memory, a processor, the memory having stored thereon a system operable on the processor to generate a logical mapping for portrait labels, the system to generate a logical mapping for portrait labels when executed by the processor implementing the steps of the method to generate a logical mapping for portrait labels according to any one of claims 1-2.
6. A computer-readable storage medium, in which a computer program is stored, the computer program being executable by at least one processor to cause the at least one processor to perform the steps of the method of generating a logical mapping of portrait labels according to any one of claims 1-2.
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| CN111709843B (en) * | 2020-05-09 | 2023-07-28 | 中国人民财产保险股份有限公司 | Customer portrait generation method and device and electronic equipment |
| CN113760960A (en) * | 2020-06-01 | 2021-12-07 | 北京搜狗科技发展有限公司 | Information generation method and device for generating information |
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