CN115619510A - Data processing method, device, equipment and storage medium - Google Patents

Data processing method, device, equipment and storage medium Download PDF

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CN115619510A
CN115619510A CN202211130994.XA CN202211130994A CN115619510A CN 115619510 A CN115619510 A CN 115619510A CN 202211130994 A CN202211130994 A CN 202211130994A CN 115619510 A CN115619510 A CN 115619510A
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data
object type
summarized
attribute array
type attribute
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邓蒙
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Ping An Property and Casualty Insurance Company of China Ltd
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Ping An Property and Casualty Insurance Company of China Ltd
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Abstract

An embodiment of the application provides a data processing method, a data processing device, a data processing apparatus and a storage medium, wherein the method comprises the following steps: acquiring data to be summarized, and determining a first data object type of the data to be summarized; traversing a predetermined total object type attribute array based on the first data object type to obtain a first object type attribute array corresponding to the data to be summarized; and acquiring a first summary list of the data to be summarized from the first object type attribute array. The method aims to realize the universality of data summarization so as to improve the data summarization efficiency.

Description

Data processing method, device, equipment and storage medium
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a data processing method, apparatus, device, and storage medium.
Background
With the continuous development and growth of business scale of enterprises, the amount of data needing to be summarized and processed is larger and larger. At present, in order to summarize a large amount of data, intelligent analysis is often performed by means of computer program codes, however, in the prior art, a method of traversing data attributes is adopted to classify and summarize a large amount of data. Different judgment conditions need to be set in the process of traversing the data attributes, and superposition calculation needs to be carried out in the process of summarizing, so that the logic implementation process of computer program codes is complex, and universality cannot be realized aiming at different business summarizing requirements. Therefore, the existing data summarization process cannot be used universally and has low efficiency.
Disclosure of Invention
In view of this, embodiments of the present application provide a data processing method, an apparatus, a device, and a storage medium, so as to solve the problem that a data summarization process in the prior art cannot be general, and aim to improve data summarization efficiency.
A first aspect of an embodiment of the present application provides a data processing method, where the method includes:
acquiring data to be summarized, and determining a first data object type of the data to be summarized;
traversing a predetermined total object type attribute array based on the first data object type to obtain a first object type attribute array corresponding to the data to be summarized;
and acquiring a first summary list of the data to be summarized from the first object type attribute array.
A second aspect of the embodiments of the present application provides a data processing apparatus, including:
the determining module is used for acquiring data to be summarized and determining a first data object type of the data to be summarized;
the obtaining module is used for traversing a predetermined total object type attribute array based on the first data object type to obtain a first object type attribute array corresponding to the data to be summarized;
and the obtaining module is used for obtaining a first summary list of the data to be summarized from the first object type attribute array.
A third aspect of embodiments of the present application provides a data processing apparatus, which includes a memory, a processor, and a computer program stored in the memory and executable on the on-line claim settlement apparatus, where the processor implements the steps of the data processing method provided in the first aspect when executing the computer program.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the steps of the data processing method provided by the first aspect.
Compared with the prior art, the data processing method provided by the embodiment of the application comprises the steps of firstly obtaining data to be summarized, and determining a first data object type of the data to be summarized; then traversing a predetermined total object type attribute array based on the first data object type to obtain a first object type attribute array corresponding to the data to be summarized; and finally, acquiring a first summary list of the data to be summarized from the first object type attribute array. The first object type attribute array corresponding to the first data object type is determined in the predetermined total object type attribute array by determining the first data object type of the data to be summarized, so that the first summarizing list of the data to be summarized is obtained from the first object type attribute array, the rewriting of computer program codes for summarizing the data is avoided, the predetermined total object type attribute array has universality, and the efficiency of summarizing the data can be improved.
The advantageous effects provided by the second aspect to the fourth aspect of the embodiments of the present application are the same as those provided by the first aspect of the embodiments of the present application, and are not described herein again.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the embodiments or the prior art description will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings may be obtained according to these drawings without inventive labor.
FIG. 1 is a flowchart illustrating an implementation of a data summarization method according to an embodiment of the present application;
fig. 2 is a schematic view of an application scenario of a data summarization method according to an embodiment of the present application;
FIG. 3 is a schematic flow chart illustrating an implementation of a data processing method according to another embodiment of the present application;
fig. 4 is a block diagram of a data processing apparatus according to an embodiment of the present application;
fig. 5 is a block diagram of a data processing device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.
The data processing method according to the embodiment of the present application may be implemented by a data processing device. The data processing device includes but is not limited to a terminal or a server. The server may be a single server or a cloud server cluster, and the terminal may be a personal digital device, a notebook, a desktop computer, a smart wearable device, a robot, or the like. And is not particularly limited herein.
The data processing method comprises the steps of firstly obtaining data to be summarized, and determining a first data object type of the data to be summarized; then traversing a predetermined total object type attribute array based on the first data object type to obtain a first object type attribute array corresponding to the data to be summarized; and finally, acquiring a first summary list of the data to be summarized from the first object type attribute array. The first object type attribute array corresponding to the first data object type is determined in the predetermined total object type attribute array by determining the first data object type of the data to be summarized, so that the first summarizing list of the data to be summarized is obtained from the first object type attribute array, the rewriting of computer program codes for summarizing the data is avoided, the predetermined total object type attribute array has universality, and the efficiency of summarizing the data can be improved.
The data summarization method provided by the embodiment of the present application is described below in detail with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a flowchart illustrating an implementation of a data summarization method according to an embodiment of the present disclosure. The data summarization method provided by the embodiment of the application can be implemented by a terminal or a server.
Or, the data summarization method provided by the embodiment of the present application may be implemented by executing the terminal and the server together.
Exemplarily, as shown in fig. 2, fig. 2 is a schematic view of an application scenario of the data summarization method according to an embodiment of the present application. As can be seen from fig. 2, in this embodiment, a terminal acquires data to be summarized, sends the acquired data to be summarized to a server, and the server determines a first data object type of the data to be summarized; traversing a predetermined total object type attribute array based on the first data object type to obtain a first object type attribute array corresponding to the data to be summarized; and acquiring a first summary list of the data to be summarized from the first object type attribute array. Further, after the server obtains the first summary list of the data to be summarized, the server can send the first summary list to the terminal, so that the terminal displays the first summary category through a preset interface.
It should be understood that in this application scenario, the computing power of the corresponding terminal is limited, and in order to improve the efficiency of obtaining the data summarization list, the terminal sends the data to be summarized to the server, and the server performs data summarization processing.
In some application scenarios, when the computing power of the terminal is strong, the terminal can independently complete the data summarization process. That is, the process of summarizing the data may be independently completed by the terminal, may be independently completed by the server, or may be interactively completed by the server and the terminal. It is not limited to the details given herein
As shown in fig. 1, the data processing method provided in this embodiment includes steps S101 to S103. The details are as follows:
s101, acquiring data to be summarized, and determining a first data object type of the data to be summarized.
Specifically, the data to be summarized is data associated with business inside the enterprise, such as financial data, human resource data, business data, market layout data, and the like. In a specific implementation, the corresponding data to be summarized may be obtained from a database storing the data to be summarized, for example, in an embodiment, the data to be summarized is financial data, and the corresponding financial data may be obtained from a database of a financial transaction system.
After the data to be summarized are obtained, in order to visually display details of the data to be summarized, first data object types of the data to be summarized are determined. The first data object type is a data type meeting the current business requirement. For example, if the current service requirement is the expense detail of the department a within the preset time length, the corresponding first data type is expense data of different service dimensions of the department a within the preset time length.
Illustratively, the acquiring data to be summarized and determining a first data object type of the data to be summarized include: and acquiring data to be summarized, and performing service dimension classification on the data to be summarized according to the current service requirement to obtain the first data object type.
The service dimension refers to a data dimension corresponding to a service scene, for example, the current service requirement is expense details of a department A within a preset time length, the corresponding service dimension includes accommodation dimension, catering dimension, traffic dimension and the like, the summarized data are classified according to different service dimensions, and first data types under different service dimensions are obtained, for example, accommodation expense data, catering expense data, traffic expense data and the like. Data to be summarized are classified into data object types under different service dimensions, and then data summarization can be carried out based on the data object types, so that the complexity of data summarization can be effectively reduced, and the data summarization efficiency is improved.
And S102, traversing a predetermined total object type attribute array based on the first data object type to obtain a first object type attribute array corresponding to the data to be summarized.
In this embodiment, the object type attribute array is an array formed by data corresponding to a determined data object type, with the data object type as a variable. All data lists corresponding to the corresponding data object types are included in the array. The data list corresponding to the corresponding data object type can be quickly acquired by predetermining the object type attribute array, and data summarization and display are carried out based on the data list.
It should be understood that the total object type attribute array includes a second object type attribute array corresponding to each of the different second data object types. The second data object type may include the first data object type or may not include the first data object type, if the second data object type includes the first data object type, the total object type attribute array is directly traversed, the first object type attribute array corresponding to the first data object type is obtained, if the second data object type does not include the first data object type, the corresponding first object type attribute array can be generated, and the first object type attribute array can be stored in the total object type attribute array so as to be directly multiplexed next time. In addition, the second object type attribute array corresponding to each different second data object type included in the total object type attribute array may also be edited, for example, a failed second object type attribute array is replaced, or new data is added to the second object type attribute array, so that along with the change of the service requirement, the total object type attribute array can provide an object type attribute array meeting different service requirements, and the simplification of the data summarizing process is realized.
In an embodiment, after traversing the predetermined total object type attribute array based on the first data object type, the method further includes: if the first object type attribute array matched with the first data object type does not exist in the predetermined total object type attribute array, taking the first data object type as a second keyword; performing cluster analysis on the data to be summarized based on the second keyword to obtain an array corresponding to the second keyword; and taking the array corresponding to the second keyword as a first object type attribute array corresponding to the first data object type.
In a specific implementation, after the setting the array corresponding to the second keyword as the first object type attribute array corresponding to the first data object type, the method further includes: and adding the first object type attribute array to a total object type attribute array, and updating the total object attribute array.
S103, acquiring a first summary list of the data to be summarized from the first object type attribute array.
In specific implementation, the first object type attribute array is used for storing data lists under each business dimension, such as an accommodation cost detail list, a traffic cost detail list, a catering cost detail list and the like. The first summary list may be a data list in a single service dimension, or may be a summary of data lists in multiple service dimensions, and is specifically determined according to a current service requirement. Specifically, the summarizing of the data lists in the multiple service dimensions may be to splice the data lists corresponding to the multiple service dimensions according to a preset rule, or may also be to summarize the data lists corresponding to the multiple service dimensions again based on the time information, and the like, which is not limited herein.
As can be seen from the above analysis, in the data processing method provided in the embodiment of the present application, first, by acquiring data to be summarized, a first data object type of the data to be summarized is determined; then traversing a predetermined total object type attribute array based on the first data object type to obtain a first object type attribute array corresponding to the data to be summarized; and finally, acquiring a first summary list of the data to be summarized from the first object type attribute array. The first object type attribute array corresponding to the first data object type is determined in the predetermined total object type attribute array by determining the first data object type of the data to be summarized, so that the first summarizing list of the data to be summarized is obtained from the first object type attribute array, the rewriting of computer program codes for summarizing the data is avoided, the predetermined total object type attribute array has universality, and the efficiency of summarizing the data can be improved.
Referring to fig. 3, fig. 3 is a schematic view illustrating an implementation flow of a data processing method according to another embodiment of the present application. As shown in fig. 3, compared with the data processing method shown in fig. 1, the data processing method provided in this embodiment has the same specific implementation procedures as S301 and S101, and as S306 to S307 and S102 to S103, except that S302 to S305 are further included before S306. The details are as follows:
s301, obtaining data to be summarized, and determining a first data object type of the data to be summarized.
S302, acquiring different service data based on preset service dimensions.
The preset business dimension may be predetermined based on business data inside an enterprise, and the correspondence may be different dimensions of the business data corresponding to the business data to be summarized, for example, sales details, cost details, tax details, and the like, which need to be summarized. It should be understood that different business dimensions correspond to different business data, and business data of an enterprise can be reasonably divided based on different preset business dimensions, so that the summary of all business data is reduced, and the data summary process is simplified.
And S303, clustering the different service data to obtain second data object types of different preset categories.
Specifically, clustering processing is performed on different service data based on data categories to obtain preset category data corresponding to the different service data under preset service dimensions, clustering results of the same preset category data under the preset service dimensions are formed, and the clustering results of the same preset category data are respectively second data object types of different preset categories. For example, the preset category data is expense data, personnel structure data, tax data, and the like. And taking the clustering result of the expense data as a second data object type of a preset expense category, taking the personnel structure data as a second data object type of a preset personnel structure category, and taking the tax data as a second data object type of a preset tax category.
S304, respectively creating a second object type attribute array of each second data object type.
In an embodiment, the creating a second object type attribute array of each second data object type respectively includes: respectively taking each second data object type as a first keyword, and respectively performing data clustering analysis based on each first keyword to obtain an array corresponding to each second data object type; generating values of the second data object type based on the arrays, and creating key-value pairs of the first keywords and the values; and taking each key value pair as a second object type attribute array of each second data object type.
In one embodiment, said generating values for said second data object type based on each of said arrays comprises: determining a summary variable of each array based on a preset service type; respectively summarizing the data corresponding to the second data object types under different service types based on the summarizing variables to obtain a summarizing list of the data corresponding to the second data object types under different service types; and respectively setting each summary list as the value of each second data object type.
S305, obtaining the total object type attribute array according to all the second object type attribute arrays, wherein the total object type attribute array comprises second summary lists corresponding to the second object type attribute arrays.
And S306, traversing a predetermined total object type attribute array based on the first data object type to obtain a first object type attribute array corresponding to the data to be summarized.
S307, a first summary list of the data to be summarized is obtained from the first object type attribute array.
As can be seen from the above analysis, in the data processing method provided in the embodiment of the present application, data to be summarized are first obtained, and a first data object type of the data to be summarized is determined; then traversing a predetermined total object type attribute array based on the first data object type to obtain a first object type attribute array corresponding to the data to be summarized; and finally, acquiring a first summary list of the data to be summarized from the first object type attribute array. The first object type attribute array corresponding to the first data object type is determined in the predetermined total object type attribute array by determining the first data object type of the data to be summarized, so that the first summarizing list of the data to be summarized is obtained from the first object type attribute array, the rewriting of computer program codes for summarizing the data is avoided, the predetermined total object type attribute array has universality, and the efficiency of summarizing the data can be improved.
The data processing method provided by the present application is exemplarily explained by the embodiments of fig. 1 to 3. The following describes, by way of example, a device and an apparatus to which the data processing method provided in the embodiment of the present application is applied, with reference to fig. 4 and 5.
Referring to fig. 4, fig. 4 is a block diagram of a data processing apparatus according to an embodiment of the present disclosure. The data processing apparatus in this embodiment may include modules integrated in a data processing device, for executing the steps in the above method embodiments. Please refer to fig. 1 to fig. 3 for the corresponding embodiments. For convenience of explanation, only the portions related to the present embodiment are shown. Referring to fig. 4, the data processing apparatus 400 includes:
a first determining module 401, configured to obtain data to be summarized, and determine a first data object type of the data to be summarized;
a first obtaining module 402, configured to traverse a predetermined total object type attribute array based on the first data object type, to obtain a first object type attribute array corresponding to the data to be summarized;
a first obtaining module 403, configured to obtain a first summary list of the data to be summarized from the first object type attribute array.
In an embodiment, the apparatus 400 further includes:
the second acquisition module is used for acquiring different service data based on the preset service dimensionality;
the second obtaining module is used for clustering the different service data to obtain second data object types of different preset categories;
a creating module, configured to create a second object type attribute array of each second data object type respectively;
and the third obtaining module is used for obtaining the total object type attribute array according to all the second object type attribute arrays, and the total object type attribute array comprises a second summary list corresponding to each second object type attribute array.
In one embodiment, the creating module includes:
the obtaining unit is used for respectively taking each second data object type as a first keyword, and respectively carrying out data clustering analysis on the basis of each first keyword to obtain an array corresponding to each second data object type;
and the creating unit is used for generating values of the second data object type based on the arrays, creating key value pairs of the first keywords and the values, and taking the key value pairs as second object type attribute arrays of the second data object types respectively.
In an embodiment, the creating unit includes:
the determining subunit is used for determining the summarizing variable of each array based on a preset service type;
the obtaining subunit is configured to, based on each of the summarizing variables, respectively, perform summarization on data corresponding to each of the second data object types under different service categories to obtain a summarization list of the data corresponding to each of the second data object types under different service categories; and respectively taking each summary list as the value of each second data object type.
In an embodiment, the apparatus 400 further includes:
the clustering module is used for taking the first data object type as a second keyword if the first object type attribute array matched with the first data object type does not exist in the predetermined total object type attribute array; performing cluster analysis on the data to be summarized based on the second keyword to obtain an array corresponding to the second keyword; and taking the array corresponding to the second keyword as a first object type attribute array corresponding to the first data object type.
In an embodiment, the apparatus 400 further includes:
and the updating module is used for adding the first object type attribute array to a total object type attribute array and updating the total object attribute array.
In an embodiment, the first determining module is specifically configured to:
and acquiring data to be summarized, and performing service dimension classification on the data to be summarized according to the current service requirement to obtain the first data object type.
It should be understood that, in the structural block diagram of the data processing apparatus 400 shown in fig. 4, each module may be integrated in a data processing device, and is used to execute each step in the embodiment corresponding to fig. 1 or fig. 3, and for each step in the embodiment corresponding to fig. 1 or fig. 3, the detailed explanation is already performed in the above embodiment, and specific reference is made to the relevant description in the embodiment corresponding to fig. 1 or fig. 3, which is not repeated herein.
A data processing device capable of integrating the data processing apparatus 400 is exemplified below.
Referring to fig. 5, fig. 5 is a block diagram of a data processing device according to an embodiment of the present disclosure. As shown in fig. 5, the data processing apparatus 500 of this embodiment includes: a processor 510, a memory 520, and a computer program 530, such as a data processing program, stored in the memory 520 and executable on the processor 510. The processor 510, when executing the computer program 530, implements the steps in the various embodiments of the data processing methods described above, such as the steps shown in fig. 1 or fig. 3. Alternatively, when the processor 510 executes the computer program 530, the functions of the modules or units in the embodiment corresponding to fig. 4, for example, the functions of the modules 401 to 403 shown in fig. 4, are implemented, for which specific reference is made to the relevant description in the embodiment corresponding to fig. 4, which is not repeated herein.
Illustratively, the computer program 530 may be divided into one or more units, which are stored in the memory 520 and executed by the processor 510 to accomplish the present application. The unit or units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 530 in the data processing device 500. For example, the computer program 530 may be partitioned to include: the device comprises a determining module, an obtaining module and an obtaining module; the specific functions of the modules are described in fig. 4.
The data processing apparatus 500 may include, but is not limited to, a processor 510, a memory 520. Those skilled in the art will appreciate that fig. 5 is only an example of the data processing device 500 and does not constitute a limitation of the data processing device 500, and may include more or less components than those shown, or some of the components may be combined, or different components, for example, the data processing device 500 may also include an input output device, a network access device, a bus, etc.
The Processor 510 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein, in one embodiment, the processor is configured to execute a computer program stored in the memory to implement the steps of:
acquiring data to be summarized, and determining a first data object type of the data to be summarized;
traversing a predetermined total object type attribute array based on the first data object type to obtain a first object type attribute array corresponding to the data to be summarized;
and acquiring a first summary list of the data to be summarized from the first object type attribute array.
In an embodiment, before said traversing the predetermined array of total object type attributes based on the first data object type, further comprising:
acquiring different service data based on preset service dimensions;
clustering the different service data to obtain second data object types of different preset categories;
respectively creating a second object type attribute array of each second data object type;
and obtaining the total object type attribute array according to all the second object type attribute arrays, wherein the total object type attribute array comprises a second summary list corresponding to each second object type attribute array.
In an embodiment, the creating a second object type attribute array of each second data object type respectively includes:
respectively taking each second data object type as a first keyword, and respectively performing data clustering analysis based on each first keyword to obtain an array corresponding to each second data object type;
generating values of the second data object type based on the arrays, and creating key-value pairs of the first keywords and the values;
and taking each key value pair as a second object type attribute array of each second data object type.
In one embodiment, said generating values for said second data object type based on each of said arrays comprises:
determining a summary variable of each array based on a preset service type;
respectively summarizing the data corresponding to the second data object types under different service types based on the summarizing variables to obtain a summarizing list of the data corresponding to the second data object types under different service types;
and respectively taking each summary list as the value of each second data object type.
In an embodiment, after said traversing the predetermined array of total object type attributes based on the first data object type, further comprising:
if the first object type attribute array matched with the first data object type does not exist in the predetermined total object type attribute array, taking the first data object type as a second keyword;
performing cluster analysis on the data to be summarized based on the second keyword to obtain an array corresponding to the second keyword;
and taking the array corresponding to the second keyword as a first object type attribute array corresponding to the first data object type.
In an embodiment, after the taking the array corresponding to the second keyword as the first object type attribute array corresponding to the first data object type, the method further includes:
and adding the first object type attribute array to a total object type attribute array, and updating the total object attribute array.
In an embodiment, the obtaining of the data to be summarized and the determining of the first data object type of the data to be summarized include:
and acquiring data to be summarized, and performing service dimension classification on the data to be summarized according to the current service requirement to obtain the first data object type.
The memory 520 may be an internal storage unit of the data processing device 500, such as a hard disk or a memory of the data processing device 500. The memory 520 may also be an external storage device of the data processing device 500, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the data processing device 500. Further, the memory 520 may also include both an internal storage unit and an external storage device of the data processing apparatus 500. The memory 520 is used for storing the computer programs and other programs and data required by the data processing device 500. The memory 520 may also be used to temporarily store data that has been output or is to be output.
In an embodiment of the present application, a computer-readable storage medium is further provided, where the computer-readable storage medium stores a computer program, the computer program includes program instructions, and the processor executes the program instructions to implement the steps of the data processing method provided in each of the foregoing embodiments of the present application.
The computer-readable storage medium may be an internal storage unit of the data processing apparatus described in the foregoing embodiment, for example, a hard disk or a memory of the data processing apparatus. The computer readable storage medium may also be an external storage device of the data processing apparatus, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the data processing apparatus.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present disclosure, and they should be construed as being included in the present disclosure.

Claims (10)

1. A method of data processing, the method comprising:
acquiring data to be summarized, and determining a first data object type of the data to be summarized;
traversing a predetermined total object type attribute array based on the first data object type to obtain a first object type attribute array corresponding to the data to be summarized;
and acquiring a first summary list of the data to be summarized from the first object type attribute array.
2. The method of claim 1, prior to said traversing a predetermined array of total object type attributes based on said first data object type, further comprising:
acquiring different service data based on preset service dimensionality;
clustering the different service data to obtain second data object types of different preset categories;
respectively creating a second object type attribute array of each second data object type;
and obtaining the total object type attribute array according to all the second object type attribute arrays, wherein the total object type attribute array comprises a second summary list corresponding to each second object type attribute array.
3. The method of claim 2, wherein said separately creating a second object type attribute array for each of said second data object types comprises:
respectively taking each second data object type as a first keyword, and respectively carrying out data clustering analysis on the basis of each first keyword to obtain an array corresponding to each second data object type;
generating values of the second data object type based on the arrays, and creating key-value pairs of the first keywords and the values;
and taking each key value pair as a second object type attribute array of each second data object type.
4. The method of claim 3, wherein said generating values for said second data object type based on each of said arrays comprises:
determining a summary variable of each array based on a preset service type;
respectively summarizing the data corresponding to the second data object types under different service types based on the summarizing variables to obtain a summarizing list of the data corresponding to the second data object types under different service types;
and respectively taking each summary list as the value of each second data object type.
5. The method of claim 1, after said traversing a predetermined array of total object type attributes based on the first data object type, further comprising:
if the first object type attribute array matched with the first data object type does not exist in the predetermined total object type attribute array, taking the first data object type as a second keyword;
performing cluster analysis on the data to be summarized based on the second keyword to obtain an array corresponding to the second keyword;
and taking the array corresponding to the second keyword as a first object type attribute array corresponding to the first data object type.
6. The method of claim 5, wherein after the taking the array corresponding to the second key as the first object type attribute array corresponding to the first data object type, further comprising:
and adding the first object type attribute array to a total object type attribute array, and updating the total object attribute array.
7. The method of claim 1, wherein said obtaining data to be summarized, determining a first data object type of said data to be summarized, comprises:
and acquiring data to be summarized, and performing service dimension classification on the data to be summarized according to the current service requirement to obtain the first data object type.
8. A data processing apparatus, characterized in that the apparatus comprises:
the determining module is used for acquiring data to be summarized and determining a first data object type of the data to be summarized;
the obtaining module is used for traversing a predetermined total object type attribute array based on the first data object type to obtain a first object type attribute array corresponding to the data to be summarized;
and the obtaining module is used for obtaining a first summary list of the data to be summarized from the first object type attribute array.
9. A data processing device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the data processing method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the data processing method according to any one of claims 1 to 7.
CN202211130994.XA 2022-09-16 2022-09-16 Data processing method, device, equipment and storage medium Pending CN115619510A (en)

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CN202211130994.XA CN115619510A (en) 2022-09-16 2022-09-16 Data processing method, device, equipment and storage medium

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CN202211130994.XA CN115619510A (en) 2022-09-16 2022-09-16 Data processing method, device, equipment and storage medium

Publications (1)

Publication Number Publication Date
CN115619510A true CN115619510A (en) 2023-01-17

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