CN116450664A - Data processing method, device, equipment and storage medium - Google Patents
Data processing method, device, equipment and storage medium Download PDFInfo
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The application relates to the field of data processing, and discloses a data processing method, a device, equipment and a storage medium, wherein the method comprises the following steps: acquiring corresponding system demand data when the performance system executes a change task, constructing a mapping relation between the system demand data and the change task completion time, and generating a first timeline; analyzing the blood relationship of the system demand data; according to the blood edge relation and the system demand data, changing downstream data in the blood edge relation, and recording the data changing completion time; constructing a mapping relation between the changed downstream data, a change task and data change completion time, and generating a second time line; and generating event records corresponding to the change tasks according to the first time line and the second time line. According to the system and the method, the blood-margin relation of the system demand data is analyzed, and the data required to be changed in the downstream data source of the performance system are synchronously and rapidly updated based on the blood-margin relation, so that the data and the system updating efficiency are improved.
Description
Technical Field
The present disclosure relates to the field of data processing, and in particular, to a data processing method, apparatus, device, and storage medium.
Background
With the development of digitization, the processing of performance data of employees by enterprises is generally based on a set performance system. The performance system is one of the entries of the relevant performance data of a company manager, the performance rules are the management basis of the performance system, and the iterative updating of the performance system is based on the system requirements and updates the performance rules in the performance system. Therefore, the establishment of the related rules requires strict early investigation and calculation processes, and the change of the rules also requires long-time game.
In the existing performance system updating process, according to system changing requirements, changing tasks are executed to correspondingly update each item of data source, and further iterative updating of the performance system and performance rules thereof is achieved. In the process of updating multiple data sources, because the relevance among the data sources is difficult to directly develop, the data related to the demand change task in each data source is difficult to synchronously update according to the system demand, but the data related to the system change demand in each data source is required to be sequentially searched and updated one by one according to the lifecycle process of the iterative update of the system, so that the data update efficiency and the data management efficiency are low.
Disclosure of Invention
In view of the above, in order to solve the deficiencies of the prior art, the present application provides a data processing method, apparatus, device and storage medium applicable to fields such as financial science and technology or other fields.
In a first aspect, the present application provides a data processing method, including:
acquiring corresponding system demand data when the performance system executes the change task, constructing a mapping relation between the system demand data and the change task completion time, and generating a first timeline;
analyzing the blood relationship of the system demand data; the blood-edge relationship is used for representing the association relationship between upstream data and downstream data, and the system demand data is used as the upstream data in the blood-edge relationship;
according to the blood edge relation and the system demand data, changing downstream data in the blood edge relation, and recording data changing completion time;
constructing a mapping relation between the changed downstream data, the changing task and the data changing completion time, and generating a second time line;
and generating event records corresponding to the change tasks according to the first time line and the second time line.
In an alternative embodiment, the analyzing the blood relationship of the system demand data includes:
identifying data dimensions corresponding to the system demand data, and acquiring association relations between the data dimensions and a plurality of preset data dimensions;
and acquiring target data under each data dimension corresponding to the association relation from the performance system, determining association information between the target data and the system demand data, and taking the association information as a blood margin relation.
In an optional embodiment, the identifying the data dimension corresponding to the system requirement data, and obtaining the association relationship between the data dimension and a plurality of preset data dimensions includes:
performing word segmentation processing on the system demand data to obtain a first keyword;
identifying the semantics of each first keyword, and determining the data dimension corresponding to the first keyword according to the semantics;
and determining the association relation between the data dimension and a plurality of preset data dimensions according to a preset data dimension association table.
In an optional embodiment, the identifying the semantics of each first keyword, and determining the data dimension corresponding to the first keyword according to the semantics includes:
according to the preset semantic tags of the data dimensions, calculating semantic similarity between the semantics of the first keywords and the semantic tags of the data dimensions;
if the semantic similarity is greater than or equal to a preset similarity threshold, determining that the first keyword belongs to the corresponding data dimension;
and if the semantic similarity is smaller than the preset similarity threshold, determining that the first keyword is not affiliated to the corresponding data dimension.
In an optional embodiment, the obtaining, from the performance system, target data under each data dimension corresponding to the association relationship, determining association information between the target data and the system demand data, and taking the association information as a blood relationship, includes:
performing word segmentation processing on the system demand data to obtain second keywords;
searching a target field with a mapping relation with each second keyword from a preset field mapping table correspondingly; the target field is a second keyword corresponding to each data belonging to each data dimension;
and determining the blood relationship between the target data and the system demand data according to the target field and the mapping relationship thereof.
In an optional embodiment, the generating, according to the first timeline and the second timeline, an event record corresponding to the change task includes:
and sequencing each data contained in the first time line and the second time line in a waterfall flow form according to the time sequence of the corresponding mapping, and correspondingly drawing to generate an event record corresponding to the change task.
In an optional embodiment, after the generating the event record corresponding to the change task according to the first timeline and the second timeline, the method further includes:
updating the current version number of the performance system;
and constructing an association relation between the current version number and the event record, and generating an update log of the performance system according to the association relation.
In a second aspect, the present application provides a data processing apparatus comprising:
the system comprises an acquisition module, a first time line and a second time line, wherein the acquisition module is used for acquiring system demand data corresponding to a performance system when the performance system executes a change task, constructing a mapping relation between the system demand data and the change task completion time, and generating the first time line;
the analysis module is used for analyzing the blood relationship of the system demand data; the blood-edge relationship is used for representing the association relationship between upstream data and downstream data, and the system demand data is used as the upstream data in the blood-edge relationship;
the change module is used for changing downstream data in the blood edge relation according to the blood edge relation and the system demand data, and recording data change completion time;
the construction module is used for constructing the mapping relation between the changed downstream data, the changing task and the data changing completion time to generate a second time line;
and the generation module is used for generating an event record corresponding to the change task according to the first time line and the second time line.
In a third aspect, the present application provides a computer device comprising a memory storing a computer program and at least one processor for executing the computer program to implement the aforementioned data processing method.
In a fourth aspect, the present application provides a computer storage medium storing a computer program which, when executed, implements a data processing method according to the foregoing.
The embodiment of the application has the following beneficial effects:
the application provides a data processing method, which comprises the steps of obtaining system demand data corresponding to a performance system when executing a change task, constructing a mapping relation between the system demand data and the change task completion time, and generating a first time line; analyzing the blood relationship of the system demand data; the blood-edge relationship is used for representing the association relationship between upstream data and downstream data, and the system demand data is used as the upstream data in the blood-edge relationship; according to the blood edge relation and the system demand data, changing downstream data in the blood edge relation, and recording the data changing completion time; constructing a mapping relation between the changed downstream data, a change task and data change completion time, and generating a second time line; and generating event records corresponding to the change tasks according to the first time line and the second time line. According to the system and the method, the blood-margin relation of the system demand data is analyzed, and the data required to be changed in the downstream data source of the performance system are synchronously and rapidly updated based on the blood-margin relation, so that the data and the system updating efficiency are improved.
Drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings that are required for the embodiments will be briefly described, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope of protection of the present application. Like elements are numbered alike in the various figures.
FIG. 1 shows a schematic diagram of a first implementation of a data processing method in an embodiment of the present application;
FIG. 2 shows a schematic diagram of a second implementation of the data processing method in the examples of the present application;
FIG. 3 shows a schematic diagram of a third implementation of the data processing method in the examples of the present application;
FIG. 4 shows a schematic diagram of a fourth implementation of the data processing method in the examples of the present application;
FIG. 5 shows a schematic diagram of a fifth implementation of the data processing method in the examples of the present application;
FIG. 6 shows a schematic diagram of a sixth implementation of the data processing method in the example of the present application;
fig. 7 shows a schematic structural diagram of a data processing apparatus in an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments.
The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.
In the following, the terms "comprises", "comprising", "having" and their cognate terms may be used in various embodiments of the present application are intended only to refer to a particular feature, number, step, operation, element, component, or combination of the foregoing, and should not be interpreted as first excluding the existence of or increasing the likelihood of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.
Furthermore, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of this application belong. The terms (such as those defined in commonly used dictionaries) will be interpreted as having a meaning that is identical to the meaning of the context in the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein in connection with the various embodiments.
After the performance system is online, the performance system needs to be continuously perfected and updated in order to be better suitable for actual application scenes and business requirements, and then the performance system is subjected to system requirement change meeting the actual business requirements. Because the performance system is put into use online, in order to facilitate the user to use the performance system quickly and efficiently, the update of the performance system needs to be performed quickly.
Specifically, referring to fig. 1, the data processing method is described in detail below.
S10, system demand data corresponding to the performance system executing the changing task is obtained, a mapping relation between the system demand data and the changing task completion time is constructed, and a first timeline is generated.
After the system update requirement of the performance system for a certain function or a certain data source is determined, executing a change task according to the system update requirement, simultaneously acquiring system requirement data corresponding to the execution process of the change task, and recording the completion time of executing the change task.
Further, a mapping between the system demand data and the executed change task and the change task completion time is constructed and used as upstream data of the current performance system change, and a first timeline is correspondingly generated, so that a user can conveniently check specific data change conditions. The system requirement data may be single data or a set of multiple data, which is not limited herein.
S20, analyzing the blood relationship of the system demand data.
When the performance system creates data, an association relation is established for the data fields corresponding to each created data, so that the association relation is stored in a database.
Specifically, the performance system characterizes the mapping relation between the data fields of each system data by setting a field mapping table when constructing each system data. Wherein, each data field and its mapping relation are recorded in the field mapping table, and one data field can establish mapping relation with the other one or more data fields.
The change task is only to update the data directly related to the system update requirement in the performance system, but the data in other data sources related to the system update requirement are also required to be updated synchronously, so that the data in other data sources related to the system update requirement are correspondingly updated by analyzing the blood relationship of the system requirement data.
Analyzing the blood-edge relationship of the system demand data, namely analyzing the association relationship corresponding to the system demand data, wherein the system demand data is used as upstream data in the blood-edge relationship, and the blood-edge relationship is used for representing the association relationship between the upstream data and the downstream data.
S30, changing downstream data in the blood-edge relationship according to the blood-edge relationship and the system demand data, and recording the data changing completion time.
In the performance system, system data with a blood-edge relation with the system demand data is searched, the system data is used as downstream data in the blood-edge relation, after the upstream data (namely the system demand data) in the blood-edge relation is changed, the downstream data in the blood-edge relation is synchronously changed, and when the downstream data is changed, the data change completion time is recorded.
S40, constructing a mapping relation between the changed downstream data, the changing task and the data changing completion time, and generating a second time line.
After the downstream data is correspondingly changed, a mapping relation is established among the downstream data, the changing task and the data changing completion time, and a second time line is correspondingly drawn according to the mapping relation and the data changing completion time, wherein the second time line is used for displaying the downstream data which is correspondingly changed at a certain time point when the changing task is executed.
S50, generating event records corresponding to the change tasks according to the first time line and the second time line.
For example, the data contained in the first time line and the second time line are sequenced in a waterfall flow form according to the time sequence of the corresponding mapping, and the event records corresponding to the change tasks are correspondingly drawn and generated.
It can be understood that the system demand data in the first time line and the downstream data in the second time line are sequentially ordered according to the corresponding time sequence of the completion time of the change task and the completion time of the data change task, and then the ordering result is presented through the waterfall flow type page layout, so that the event record corresponding to the change task is correspondingly drawn and generated. The event records are presented in a waterfall flow type page, and the waterfall flow type page is displayed by means of a performance system.
The event record corresponds to a task execution log when the performance system executes the change task, and the event record details all the upstream data and the downstream data changed when the performance system executes the change task and the blood relationship between the data.
Furthermore, the user can check the corresponding event record when the current performance system executes the change task through the performance system, and the event record is displayed in a waterfall flow type page, so that the user can check conveniently, and meanwhile, the data management efficiency is improved.
As an alternative embodiment, as shown in fig. 2, S20 specifically includes the following steps:
s21, identifying data dimensions corresponding to system demand data, and acquiring association relations between the data dimensions and a plurality of preset data dimensions.
S22, acquiring target data under each data dimension corresponding to the association relationship from the performance system, determining association information between the target data and system demand data, and taking the association information as a blood relationship.
The dimension is a classification field, and further, the data dimension is used for representing attribute information corresponding to certain data, and different data dimensions represent data attributes corresponding to different data, for example, the data dimension can be specifically divided into a time dimension, a place dimension, a user name dimension, and the like.
Illustratively, a plurality of data dimensions are preset to categorize individual data contained in the performance system according to the plurality of data dimensions. Each data dimension is associated according to a service requirement or other actual requirement conditions, that is, an association relationship is constructed between each data dimension according to requirements such as the service requirement, and one data dimension can be associated with one or more other data dimensions.
Specifically, when the performance system sets the data dimensions, the mapping relation among the data dimensions is represented by setting a data dimension association table. Each data dimension and the mapping relation thereof are recorded in the data dimension association table, and one data dimension can establish the mapping relation with the other data dimension or data dimensions.
Furthermore, the system demand data specifically corresponds to one or more data dimensions, and the system data belonging to each data dimension is based on the association relationship among the data dimensions, so that the blood relationship of each system data is correspondingly formed.
It can be understood that the data dimension to which the system demand data corresponds is identified through the semantics of the system demand data, so that the blood-margin relationship of the system demand data and the target data with the blood-margin relationship with the system demand data are determined according to the association relationship between the data dimensions.
As an alternative embodiment, as shown in fig. 3, S21 specifically includes the following steps:
s211, performing word segmentation processing on the system demand data to obtain a first keyword.
S212, identifying the semantics of each first keyword, and determining the data dimension corresponding to the first keywords according to the semantics.
S213, according to a preset data dimension association table, determining association relations between the data dimensions and a plurality of preset data dimensions.
For example, the word segmentation processing is performed on the system requirement data through a preset word segmentation tool or word segmentation algorithm so as to correspondingly obtain corresponding data fields. The first keyword is a field used for representing attribute information in the data field. It should be noted that the word segmentation tool or the word segmentation algorithm may be set or selected according to actual requirements, which is not limited herein.
For example, if the composition of the system demand data is: the tableA_Adress_aaa is subjected to word segmentation processing to obtain a plurality of data fields: tableA, adress, aaa; the data field "table a" is a data table name stored in the system requirement data, the data field "Adress" is a field representing data attribute information, and the data field "aaa" is a specific data description.
Further, a data field representing attribute information is extracted from the plurality of data fields and used as a first keyword, namely, a first keyword is determined to be a data field 'Adress', and then the data dimension to which the system demand data correspondingly belongs is determined by identifying the semantics of the first keyword.
According to the preset data dimension association table, the association relation between the data dimension corresponding to the first keyword and the remainder dimension can be obtained through searching.
As an optional implementation manner, as shown in fig. 4, in S212, the "determining the data dimension corresponding to the system requirement data according to the semantics" specifically includes the following steps:
s2121, calculating semantic similarity between the semantic of each first keyword and the semantic label of each data dimension according to the preset semantic label of each data dimension.
S2122, judging whether the semantic similarity is larger than or equal to a preset similarity threshold.
S2123, if the semantic similarity is greater than or equal to a preset similarity threshold, determining that the first keyword belongs to the corresponding data dimension.
S2124, if the semantic similarity is smaller than a preset similarity threshold, determining that the first keyword is not affiliated to the corresponding data dimension.
Presetting semantic tags for each data dimension, wherein each data dimension corresponds to one semantic tag; the semantic tags are used for representing attribute information. For example, the semantic tag may be a "time value", such that a target field with a semantic "time value" or a similar semantic is retrieved from the semantic tag.
Further, according to the semantic tags of the data dimensions, the semantics of the first keywords and the semantic tags are matched to correspondingly determine the data dimensions corresponding to the first keywords.
It can be understood that calculating the semantic similarity between the semantic of the first keyword and the semantic tag, and when the semantic similarity is greater than or equal to a preset similarity threshold, indicating that attribute information represented by the semantic of the first keyword is consistent with the semantic tag, and indicating that the data dimension corresponding to the semantic tag is the data dimension corresponding to the first keyword; that is, the first keyword is affiliated to the data dimension.
Otherwise, if the semantic similarity is smaller than a preset similarity threshold, the attribute information represented by the semantic meaning of the first keyword is inconsistent with the semantic label, which indicates that the first keyword is not affiliated to the data dimension, and the semantic similarity between the semantic meaning of the first keyword and the semantic label of the residual data dimension can be calculated.
It should be noted that the preset similarity threshold may be set according to actual requirements, which is not limited herein, for example, the value range of the similarity threshold may be (0.7,1).
As an alternative embodiment, as shown in fig. 5, S22 specifically includes the following steps:
s221, performing word segmentation on the system demand data to obtain second keywords.
S222, correspondingly searching target fields with mapping relations between the second keywords from a preset field mapping table.
S223, determining the blood-edge relation between the target data and the system demand data according to the target field and the mapping relation thereof.
For example, the word segmentation processing is performed on the system requirement data through a preset word segmentation tool or word segmentation algorithm so as to correspondingly obtain corresponding data fields. The second keyword is a target field used for representing the data description in the data field, that is, the target field is used as the second keyword corresponding to each data belonging to each data dimension. It should be noted that the word segmentation tool or the word segmentation algorithm may be set or selected according to actual requirements, which is not limited herein.
For example, if the system demand data is: the data field can be obtained after the tableA_Adress_aaa is subjected to word segmentation processing: tableA, adress, aaa; the data field "table a" is a data table name stored in the system requirement data, the data field "Adress" is a field representing data attribute information, and the data field "aaa" is a specific data description. Further, the first keyword is the data field "Adress".
Further, searching a target field with a mapping relation with the second keyword from a preset field mapping table, wherein the mapping relation between the second keyword and the target field is the blood-margin relation of the second keyword.
As an alternative implementation manner, as shown in fig. 6, this embodiment further specifically includes the following steps:
and S60, updating the current version number of the performance system.
And S70, constructing an association relation between the current version number and the event record, and generating an update log of the performance system according to the association relation.
After the update of the downstream data is completed, the current version number of the performance system is synchronously updated. It will be appreciated that after the update of the upstream data and downstream data is completed, the iterative update of the performance system is completed.
And establishing an association relation between the current version number of the performance system and the event record corresponding to the change task so as to generate an update log, and facilitating users (such as system staff) to check iterative update conditions corresponding to the performance system at regular time.
According to the embodiment of the invention, the data required to be changed in the downstream data source in the performance system is synchronously and rapidly updated based on the blood-edge relation by analyzing the blood-edge relation of the system demand data, so that the data and the system updating efficiency are improved. Further, after the data is updated, the data updating is associated with the use version of the performance system, so that a user can conveniently check specific system demand change details, and further, each time the demand data of the performance system is changed, the iteration updating of the performance system is completed, and fuzzy management of the system demand change history is avoided.
Referring to fig. 7, the present application provides a data processing apparatus, including:
an obtaining module 71, configured to obtain system requirement data corresponding to when the performance system executes a change task, and construct a mapping relationship between the system requirement data and the change task, and between the system requirement data and a change task completion time, so as to generate a first timeline;
an analysis module 72 for analyzing the blood relationship of the system demand data; the blood-edge relationship is used for representing the association relationship between upstream data and downstream data, and the system demand data is used as the upstream data in the blood-edge relationship;
a changing module 73, configured to change downstream data in the blood edge relationship according to the blood edge relationship and the system demand data, and record a data change completion time;
a construction module 74, configured to construct a mapping relationship between the changed downstream data and the change task and the data change completion time, and generate a second timeline;
and the generating module 75 is configured to generate an event record corresponding to the change task according to the first timeline and the second timeline.
The above-described data processing apparatus corresponds to the data processing method of the above-described embodiment; any of the alternatives in the above embodiments are also applicable to the present embodiment and will not be described in detail here.
The present application also provides a computer device comprising a memory storing a computer program and at least one processor for executing the computer program to implement the data processing method of the above embodiments.
The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, at least one application program required for a function; the storage data area may store data created from the use of the computer device (such as system demand data, blood relationship, event records, etc.), and so forth. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
The present application also provides a computer storage medium storing computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the steps of the data processing method of the above embodiments.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners as well. The apparatus embodiments described above are merely illustrative, for example, of the flow diagrams and block diagrams in the figures, which illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules or units in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a smart phone, a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application.
Claims (10)
1. A method of data processing, comprising:
acquiring corresponding system demand data when a performance system executes a change task, constructing a mapping relation between the system demand data and the change task completion time, and generating a first timeline;
analyzing the blood relationship of the system demand data; the blood-edge relationship is used for representing the association relationship between upstream data and downstream data, and the system demand data is used as the upstream data in the blood-edge relationship;
according to the blood edge relation and the system demand data, changing downstream data in the blood edge relation, and recording data changing completion time;
constructing a mapping relation between the changed downstream data, the changing task and the data changing completion time, and generating a second time line;
and generating event records corresponding to the change tasks according to the first time line and the second time line.
2. The data processing method of claim 1, wherein said analyzing the blood relationship of the system demand data comprises:
identifying data dimensions corresponding to the system demand data, and acquiring association relations between the data dimensions and a plurality of preset data dimensions;
and acquiring target data under each data dimension corresponding to the association relation from the performance system, determining association information between the target data and the system demand data, and taking the association information as a blood margin relation.
3. The data processing method according to claim 2, wherein the identifying the data dimension corresponding to the system demand data, and obtaining the association relationship between the data dimension and the preset plurality of data dimensions, includes:
performing word segmentation processing on the system demand data to obtain a first keyword;
identifying the semantics of each first keyword, and determining the data dimension corresponding to the first keyword according to the semantics;
and determining the association relation between the data dimension and a plurality of preset data dimensions according to a preset data dimension association table.
4. A data processing method according to claim 3, wherein the identifying the semantics of each of the first keywords, and determining the data dimension corresponding to the first keywords according to the semantics, comprises:
according to the preset semantic tags of the data dimensions, calculating semantic similarity between the semantics of the first keywords and the semantic tags of the data dimensions;
if the semantic similarity is greater than or equal to a preset similarity threshold, determining that the first keyword belongs to the corresponding data dimension;
and if the semantic similarity is smaller than the preset similarity threshold, determining that the first keyword is not affiliated to the corresponding data dimension.
5. The data processing method according to claim 2, wherein the obtaining target data under each data dimension corresponding to the association relationship from the performance system, determining association information between the target data and the system demand data, and taking the association information as a blood relationship, includes:
performing word segmentation processing on the system demand data to obtain second keywords;
searching a target field with a mapping relation with each second keyword from a preset field mapping table correspondingly; the target field is a second keyword corresponding to each data belonging to each data dimension;
and determining the blood relationship between the target data and the system demand data according to the target field and the mapping relationship thereof.
6. The method according to claim 1, wherein generating an event record corresponding to the change task according to the first timeline and the second timeline includes:
and sequencing each data contained in the first time line and the second time line in a waterfall flow form according to the time sequence of the corresponding mapping, and correspondingly drawing to generate an event record corresponding to the change task.
7. The data processing method according to claim 1, further comprising, after the event record corresponding to the change task is generated according to the first timeline and the second timeline:
updating the current version number of the performance system;
and constructing an association relation between the current version number and the event record, and generating an update log of the performance system according to the association relation.
8. A data processing apparatus, comprising:
the system comprises an acquisition module, a first time line and a second time line, wherein the acquisition module is used for acquiring system demand data corresponding to a performance system when the performance system executes a change task, constructing a mapping relation between the system demand data and the change task completion time, and generating the first time line;
the analysis module is used for analyzing the blood relationship of the system demand data; the blood-edge relationship is used for representing the association relationship between upstream data and downstream data, and the system demand data is used as the upstream data in the blood-edge relationship;
the change module is used for changing downstream data in the blood edge relation according to the blood edge relation and the system demand data, and recording data change completion time;
the construction module is used for constructing the mapping relation between the changed downstream data, the changing task and the data changing completion time to generate a second time line;
and the generation module is used for generating an event record corresponding to the change task according to the first time line and the second time line.
9. A computer device, characterized in that it comprises a memory storing a computer program and at least one processor for executing the computer program to implement the data processing method of any of claims 1-7.
10. A computer storage medium, characterized in that it stores a computer program which, when executed, implements the data processing method according to any one of claims 1-7.
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| CN117055977A (en) * | 2023-10-13 | 2023-11-14 | 深圳易伙科技有限责任公司 | Method and device for linking data between code-free applications |
| CN117055977B (en) * | 2023-10-13 | 2024-01-26 | 深圳易伙科技有限责任公司 | Method and device for linking data between code-free applications |
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