CN114416850A

CN114416850A - Multi-dimensional data processing method and device, electronic equipment and storage medium

Info

Publication number: CN114416850A
Application number: CN202210090776.1A
Authority: CN
Inventors: 汪尚泉
Original assignee: Ping An Technology Shenzhen Co Ltd
Current assignee: Ping An Technology Shenzhen Co Ltd
Priority date: 2022-01-26
Filing date: 2022-01-26
Publication date: 2022-04-29

Abstract

The invention relates to the technical field of artificial intelligence, and provides a multi-dimensional data processing method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: extracting a plurality of target indexes from multi-dimensional data to be processed based on business requirements, and performing theme classification on the plurality of target indexes; extracting an atomic table from the multi-dimensional data based on a plurality of target indexes; classifying the atomic tables to obtain a plurality of target atomic tables; grouping and aggregating each target atomic table by adopting a grouping function to obtain a summary table of each target atomic table; and processing a plurality of summary tables of a plurality of target atomic tables to obtain a result table. According to the invention, the atomic tables are classified, and the grouping function is adopted to group and aggregate each target atomic table, so that different atomic tables do not need to be spliced together, the task waiting time is eliminated, and the data processing efficiency and accuracy are improved.

Description

Multi-dimensional data processing method and device, electronic equipment and storage medium

Technical Field

The invention relates to the technical field of artificial intelligence, in particular to a multi-dimensional data processing method and device, electronic equipment and a storage medium.

Background

With the continuous improvement of a service system, service requirements are gradually increased, service personnel cannot confirm all requirements at one time aiming at multi-dimensional data, meanwhile, interface tables are increased due to the fact that the dimensions are multiple, and in the prior art, atomic tables related to the service requirements are spliced into a wide table, so that subsequent logic processing is facilitated.

However, in the prior art, the atomic tables related to the service requirements are spliced into one wide table, and with the increase of the later requirements, the problem of inconsistent output time in the wide table is caused, so that the task waiting is caused, the output time of the downstream task is influenced, and the data processing efficiency and the accuracy are low.

Therefore, it is necessary to provide a method for processing data rapidly and accurately.

Disclosure of Invention

In view of the above, there is a need for a method, an apparatus, an electronic device, and a storage medium for processing multidimensional data, in which the atomic tables are classified and each target atomic table is grouped and aggregated by using a grouping function, so that different atomic tables do not need to be spliced together, task waiting time is eliminated, and data processing efficiency and accuracy are improved.

A first aspect of the present invention provides a multidimensional data processing method, the method comprising:

analyzing the received data processing request to acquire service requirements and multi-dimensional data to be processed;

extracting a plurality of target indexes from the multi-dimensional data to be processed based on the service demands, and performing theme classification on the plurality of target indexes;

extracting an atomic table from the multi-dimensional data to be processed based on the target indexes;

classifying the atomic tables to obtain a plurality of target atomic tables;

grouping and aggregating each target atomic table by adopting a grouping function to obtain a summary table of each target atomic table;

and processing a plurality of summary tables of the target atomic tables to obtain a result table.

Optionally, the extracting, from the multidimensional data to be processed, a plurality of target indicators based on the business demand includes:

acquiring a corresponding service theme from a preset database according to the service requirement;

extracting a plurality of preset first indexes from the multidimensional data to be processed according to the service theme, and extracting a plurality of preset second indexes from the multidimensional data to be processed according to associated target indexes in a front-end page corresponding to the service theme;

and determining the preset first indexes and the preset second indexes as a plurality of target indexes corresponding to the service demand.

Optionally, the subject classifying the plurality of target indicators includes:

analyzing the business meaning of each target index and the processing logic of the front-end page corresponding to each target index;

and performing theme classification on the plurality of target indexes based on the business meaning of each target index and the processing logic of each target index.

Optionally, the classifying the atom tables to obtain a plurality of target atom tables includes:

judging whether the atomic list meets preset classification conditions or not;

when the atomic table meets preset classification conditions, classifying the atomic table to obtain a plurality of target atomic tables; or

And when the atomic table does not meet the preset classification condition, determining the atomic table as a target atomic table.

Optionally, when the atomic table meets a preset classification condition, classifying the atomic table to obtain a plurality of target atomic tables includes:

when target indexes of a plurality of subjects exist in the atomic table and the numerical output time difference values among the target indexes of the plurality of subjects exist in a plurality of preset threshold value ranges, recording data of the same subject and numerical output time difference values in the same threshold value range in the atomic table in the same atomic table to obtain a target atomic table.

Optionally, after the classifying the atom table to obtain a plurality of target atom tables, the method further includes:

extracting a plurality of preset dimension tables from the summary table of each target atom table, creating a driving table according to the combination of the preset dimension tables, associating the driving table serving as a main table with the preset dimension tables in the summary table, and adding the associated driving table into the summary table.

Optionally, the grouping and aggregating each target atom table by using a grouping function to obtain a summary table of each target atom table includes:

acquiring an index requirement corresponding to each target index from the service requirement;

and grouping and aggregating each target atomic table according to the index requirement of each target index to obtain a summary table of each target atomic table.

A second aspect of the present invention provides a multidimensional data processing apparatus, the apparatus comprising:

the analysis module is used for analyzing the received data processing request to acquire the service requirement and the multi-dimensional data to be processed;

the theme classification module is used for extracting a plurality of target indexes from the multi-dimensional data to be processed based on the service demands and performing theme classification on the plurality of target indexes;

the extraction module is used for extracting an atomic table from the multi-dimensional data to be processed based on the target indexes;

the classification module is used for classifying the atomic tables to obtain a plurality of target atomic tables;

the grouping and aggregation module is used for grouping and aggregating each target atomic table by adopting a grouping function to obtain a summary table of each target atomic table;

and the processing module is used for processing the plurality of summary tables of the plurality of target atomic tables to obtain a result table.

A third aspect of the invention provides an electronic device comprising a processor and a memory, the processor being configured to implement the multidimensional data processing method when executing a computer program stored in the memory.

A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the multidimensional data processing method.

In summary, according to the multidimensional data processing method, the multidimensional data processing device, the electronic device, and the storage medium of the present invention, a plurality of target indexes are extracted from the multidimensional data to be processed based on the service requirement, the plurality of target indexes are subject-classified, the target indexes of the same subject are determined as a subject class, and the subject of each target index is considered when subsequently classifying the atomic list, so that the efficiency of classifying the atomic list is improved. And extracting an atomic table from the multi-dimensional data to be processed based on the target indexes, classifying the atomic table to obtain a plurality of target atomic tables, and improving the data processing efficiency. Grouping and polymerizing each target atomic table by adopting a grouping function to obtain a summary table of each target atomic table, processing the plurality of summary tables of the plurality of target atomic tables to obtain a result table, and grouping and polymerizing the preset dimension tables in each target atomic table again according to new service requirements without re-extracting the target atomic tables when different preset dimension tables are newly added to different versions of the same index service to obtain a new summary table, so that the data processing efficiency is improved, and the display efficiency of the result table is further improved.

Drawings

Fig. 1 is a flowchart of a multidimensional data processing method according to an embodiment of the present invention.

FIG. 2 is a diagram illustrating a conventional multidimensional data processing method according to an embodiment of the present invention;

FIG. 3 is a diagram illustrating a multi-dimensional data processing method according to an embodiment of the present invention;

fig. 4 is a structural diagram of a multi-dimensional data processing apparatus according to a second embodiment of the present invention.

Fig. 5 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.

Detailed Description

In order that the above objects, features and advantages of the present invention can be more clearly understood, a detailed description of the present invention will be given below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflict.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

Example one

In this embodiment, the multidimensional data processing method may be applied to an electronic device, and for an electronic device that needs multidimensional data processing, the multidimensional data processing function provided by the method of the present invention may be directly integrated on the electronic device, or may be run in the electronic device in the form of a Software Development Kit (SDK).

The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.

The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning, deep learning and the like.

As shown in fig. 1, the multidimensional data processing method specifically includes the following steps, and the order of the steps in the flowchart may be changed and some may be omitted according to different requirements.

And S11, analyzing the received data processing request, and acquiring the service requirement and the multi-dimensional data to be processed.

In this embodiment, when a user performs multidimensional data processing, a data processing request is initiated to a server through a client, specifically, the client may be a smart phone, an IPAD, or other existing intelligent devices, the server may be a data processing subsystem, and in a data processing process, for example, the client may send the data processing request to the data processing subsystem, and the data processing subsystem is configured to receive the data processing request sent by the client.

In this embodiment, when the data processing subsystem receives a data processing request, the data processing request is analyzed to obtain a service requirement and multidimensional data to be processed, where the multidimensional data to be processed refers to data corresponding to a plurality of target indexes included in the data processing request, and specifically, the service requirement includes an index requirement corresponding to each target index and an index display requirement of a front-end page, for example, the service requirement may be to obtain human resource information in an enterprise, obtain human resource information in the enterprise, and need to obtain a plurality of target indexes, for example: the obtained target indexes are as follows: the number of the workers, the number of the diamonds and the total income, and the index requirements corresponding to the workers are as follows: acquiring the information of the personnel at work in M years, wherein the index requirements corresponding to the number of the diamond people are as follows: acquiring the number of diamonds in the third quarter of M years; the index requirements corresponding to the total income are as follows: all income information of each worker in M years, the index display requirement of the front-end page corresponding to the worker is as follows: displaying the total number of the staff, the names of the staff and the positions of the staff; the index display requirements of the front page corresponding to the number of diamond people are as follows: displaying the marketing volume per month in the third quarter of M years for each diamond person; the index display requirements of the front-end page corresponding to the total income number are as follows: displaying the quarterly bonus of each of the present staff, displaying the bonus of the member, displaying the training allowance, displaying the management allowance, and displaying the wage of each of the present staff.

S12, extracting a plurality of target indexes from the multi-dimensional data to be processed based on the business requirements, and performing theme classification on the plurality of target indexes.

In this embodiment, the service requirement is to obtain human resource information in an enterprise, obtain a corresponding service theme as a human resource theme based on the service requirement, and extract a plurality of preset first indexes from the multidimensional data to be processed based on the human resource theme, where the preset first indexes may be, for example: the number of the workers and diamonds and the total income; and extracting a plurality of preset second indexes from the multi-dimensional data to be processed according to the associated target indexes in the front-end page corresponding to the service theme, where for example, the preset second indexes may be: the position of the staff, the name of the staff.

In this embodiment, the multidimensional data to be processed includes a plurality of target indicators, where the plurality of target indicators include a plurality of preset first indicators and a plurality of preset second indicators.

In an optional embodiment, the extracting, from the multidimensional data to be processed, a plurality of target indicators based on the business demand includes:

In this embodiment, the service theme refers to a service activity corresponding to the data processing request, for example, the service theme is a human resource theme, an insurance service theme, and the like.

In an optional embodiment, the subject classifying the plurality of target indicators includes:

In this embodiment, the front-end page displays the target index corresponding to the service theme and other target indexes associated with the target index.

In this embodiment, each topic includes a plurality of target indicators; the processing logic refers to a logical processing relationship between each target index and the target index associated with the target index, for example, total income is quarterly bonus, increased membership bonus, training allowance, management allowance and wage, wherein the total income represents the target index, and the quarterly bonus, the increased membership bonus, the training allowance, the management allowance and the wage represent the target index associated with the total income.

In this embodiment, when the total income index is obtained, the target index corresponding to the total income index is divided into income topics.

Illustratively, the revenue topic contains a plurality of target metrics: total income, quarterly bonus, membership bonus, training allowance, management allowance, payroll, etc.; the premium theme includes target indicators such as total premium and performance of new contracts.

In the embodiment, in the process of processing multi-dimensional data to be processed, if all indexes of each dimension are in the same table, if one index is delayed, the timeliness of the whole report is delayed, and the display of a front-end page is affected.

S13, extracting an atomic list from the multi-dimensional data to be processed based on the target indexes.

In this embodiment, the dimensions in the atomic table set for different business requirements are different, for example, for a human resource theme, 7 dimensions are preset in the atomic table: a person dimension, a group dimension, a department dimension, a zone dimension, a tertiary mechanism dimension, a secondary mechanism dimension, a war zone dimension.

In an optional embodiment, the extracting an atom table from the multidimensional data to be processed based on the plurality of target indexes includes:

acquiring the table name, field information and access logic of the atomic table corresponding to each target index;

and traversing the multi-dimensional data to extract the atomic table according to the table name, the field information and the access logic of the atomic table corresponding to each target index.

In this embodiment, each target indicator corresponds to one atomic table, specifically, the atomic table corresponding to each target indicator includes information such as a table name, field information, and access logic, for example, for the total revenue of the target indicator, the table name of the corresponding atomic table: the quarter prize in 2017 and the field information are the quarter prizes, the number taking logic is to obtain the information in the atomic table, and aiming at the total income of the target indexes: the corresponding number taking logic is to obtain the name of the quarter, the amount of the quarter and the information of the season award personnel.

And S14, classifying the atom tables to obtain a plurality of target atom tables.

In this embodiment, the target atom table is obtained by classifying according to a preset classification condition.

In an optional embodiment, the classifying the atom table to obtain a plurality of target atom tables includes:

judging whether the atomic list meets preset classification conditions or not;

Specifically, the preset classification conditions are as follows: the atomic meter has a plurality of target indexes of subjects, and the time difference of the number output among the plurality of target indexes in the atomic meter has a plurality of preset threshold value ranges.

In this embodiment, the counting time refers to a time when each target index is recorded for the first time.

Further, when the atomic table meets a preset classification condition, classifying the atomic table to obtain a plurality of target atomic tables includes:

when target indexes of a plurality of subjects exist in the atomic table and the numerical output time difference values among the target indexes exist in a plurality of preset threshold value ranges, recording data of the same subject and numerical output time difference values in the same threshold value range in the atomic table in the same atomic table to obtain a target atomic table.

In this embodiment, in the prior art, the related sub-atomic tables are all spliced in one atomic table according to the business requirements, and as the subsequent requirements increase, the number of fields of each atomic table also increases, and meanwhile, since the output time of the target indexes of the sub-atomic tables in the atomic table is different, data processing can be performed only after the atomic table task of the latest target index runs out, so that the output time of a downstream task is delayed, and the data processing efficiency is low.

In the embodiment, the atomic tables are classified to judge whether the atomic tables meet preset classification conditions, specifically, whether the atomic tables are split or not can be quickly determined by judging whether target indexes with different themes exist in the same table or not and whether the time difference of the number output between a plurality of target indexes exists in a plurality of preset threshold ranges, when the atomic tables are determined to be split, data with the same theme and the time difference of the number output in the same threshold range in the atomic tables are recorded in the same atomic table without splicing different atomic tables together, although the number of the atomic tables is increased, field data of each target atomic table is few, a script is simple, task waiting time is greatly eliminated, efficiency of taking data downstream is improved, and the problem of low data processing efficiency caused by time delay of the number output of a downstream task due to splicing different atomic tables together in the prior art is solved, the data processing efficiency is improved.

And S15, grouping and aggregating each target atomic table by adopting a grouping function to obtain a summary table of each target atomic table.

In this embodiment, each target atomic table includes a plurality of preset dimension tables, each target atomic table is grouped and aggregated by using a grouping function, so as to obtain a plurality of dimension tables of actual requirements in the service requirements, and the plurality of dimension tables are aggregated to obtain a summary table of each target atomic table.

In an optional embodiment, the grouping and aggregating each target atom table by using a grouping function to obtain a summary table of each target atom table includes:

In this embodiment, each target atomic table includes a plurality of preset dimension tables, and the service requirements are different for each level in the target atomic table, and different indexes are added to the multidimensional tables of the summary table according to their respective needs during processing, so that the service requirements can be met at the initial stage of the project, but in the process of project advancement, the same index service adds different preset dimension tables in different versions, and the original tables with new dimensions added to the atomic table need to be frequently modified, so that the script maintenance efficiency is low as the tables are increased, and further the data processing efficiency is low.

In this embodiment, each target atomic table includes all preset dimension tables, and data of the preset dimension tables corresponding to the service requirements are grouped and aggregated by using a grouping function according to the service requirements to obtain a summary table of each target atomic table.

In an optional embodiment, after the classifying the atom table to obtain a plurality of target atom tables, the method further includes:

In this embodiment, a driving table is created in the summary table of each target atom table, the driving table is used as a main table and is associated with a plurality of preset dimension table tables in the summary table to be added to the summary table, the plurality of preset dimension tables of each target atom table are optimized into two tables, the output time period of the target atom table is improved, and the data processing efficiency is further improved.

And S16, processing the plurality of summary tables of the plurality of target atom tables to obtain a result table.

In this embodiment, the result table includes a display index of a front-end page.

In an optional embodiment, the processing the plurality of summary tables of the plurality of target atom tables to obtain the result table includes:

acquiring an index display requirement of a front-end page from the service requirement;

and splicing the plurality of summary tables of the plurality of target atomic tables according to the index display requirement of the front-end page to obtain a result table, and displaying the result table on the front-end page corresponding to the business theme according to a preset display mode.

In this embodiment, since the target atomic table includes all the summary tables of the preset dimension tables, when the result table of the front-end page is displayed, the multiple summary tables of the multiple target atomic tables need to be spliced according to the index display requirement of the front-end page, the target atomic tables do not need to be re-extracted, and the data processing efficiency is improved.

In this embodiment, the display mode may be preset, for example, the display may be performed in a default mode of the front-end page, or may be performed in a customized mode in the business needs.

Referring to fig. 2, a schematic diagram of a conventional multidimensional data processing method is shown, for example, a target atomic table includes a target atomic table 1, a target atomic table 2, and a target atomic table 3, where each target atomic table includes target indexes of different topics and a plurality of target indexes with large numerical time difference values, and the target atomic table is preset with 7 dimensions: the method comprises the steps of obtaining a human dimension, a group dimension, a department dimension, a zone dimension, a three-level mechanism dimension, a two-level mechanism dimension and a war zone dimension, and splicing a target atomic table 1, a target atomic table 2 and a target atomic table 3 into a summary table, wherein one summary table comprises 7 preset dimension tables, a result table is obtained according to the summary table, and the result table also comprises 7 preset dimension tables.

Fig. 3 is a schematic diagram of a multidimensional data processing method provided in an embodiment of the present invention, for example, an atomic table is extracted from the multidimensional data based on a plurality of target indexes, and the atomic table is classified to obtain a plurality of target atomic tables: the method comprises a target atom table 1 and a target atom table 2, wherein each target atom table only contains data with the same theme and the same export time difference value within the same threshold range, grouping and aggregation are carried out on each target atom table to obtain a summary table of each target atom table, only two tables are arranged in the summary table of each target atom table, the summary table of the target atom table 1 and the summary table of the target atom table 2 are processed, and the obtained result table is only two tables.

In summary, in the multidimensional data processing method described in this embodiment, a plurality of target indexes are extracted from the multidimensional data to be processed based on the service requirement, the plurality of target indexes are subject-classified, the target indexes of the same subject are determined as a subject category, and when subsequently performing atomic meter classification, the subject of each target index is considered, so that the atomic meter classification efficiency is improved. And extracting an atomic table from the multi-dimensional data to be processed based on the target indexes, classifying the atomic table to obtain a plurality of target atomic tables, solving the problem of low data processing efficiency caused by downstream task output time delay due to the fact that different atomic tables are spliced together, and improving the data processing efficiency. Grouping and aggregating each target atomic table by adopting a grouping function to obtain a summary table of each target atomic table, processing the summary tables of the target atomic tables to obtain a result table, displaying the result table on the front-end page, and grouping and aggregating the preset dimension tables in each target atomic table again according to new service requirements to obtain a new summary table without re-extracting the target atomic table when different preset dimension tables are newly added to different versions of the same index service.

Example two

In some embodiments, the multidimensional data processing apparatus 40 may comprise a plurality of functional modules comprised of program code segments. The program codes of the various program segments in the multidimensional data processing apparatus 40 can be stored in a memory of an electronic device and executed by the at least one processor to perform the functions of multidimensional data processing (described in detail in fig. 1 to 3).

In this embodiment, the multi-dimensional data processing apparatus 40 may be divided into a plurality of functional modules according to the functions performed by the apparatus. The functional module may include: parsing module 401, topic classification module 402, extraction module 403, classification module 404, grouping aggregation module 405, and processing module 406. The module referred to herein is a series of computer readable instruction segments stored in a memory that can be executed by at least one processor and that can perform a fixed function. In the present embodiment, the functions of the modules will be described in detail in the following embodiments.

The analysis module 401 is configured to analyze the received data processing request, and obtain a service requirement and multidimensional data to be processed.

In this embodiment, when the data processing subsystem receives a data processing request, the data processing request is analyzed to obtain a service requirement and multidimensional data to be processed, where the multidimensional data to be processed refers to data corresponding to multiple indexes included in the data processing request, and specifically, the service requirement includes an index requirement corresponding to each target index and an index display requirement of a front-end page, for example, the service requirement may be to obtain human resource information in an enterprise, obtain human resource information in the enterprise, and need to obtain multiple target indexes, for example: the obtained target indexes are as follows: the number of the workers, the number of the diamonds and the total income, and the index requirements corresponding to the workers are as follows: acquiring the information of the personnel at work in M years, wherein the index requirements corresponding to the number of the diamond people are as follows: acquiring the number of diamonds in the third quarter of M years; the index requirements corresponding to the total income are as follows: all income information of each worker in M years, the index display requirement of the front-end page corresponding to the worker is as follows: displaying the total number of the staff, the names of the staff and the positions of the staff; the index display requirements of the front page corresponding to the diamond are as follows: displaying the marketing volume per month in the third quarter of M years for each diamond person; the index display requirements of the front-end page corresponding to the total income are as follows: displaying the quarterly bonus of each of the present staff, displaying the bonus of the member, displaying the training allowance, displaying the management allowance, and displaying the wage of each of the present staff.

A topic classification module 402, configured to extract a plurality of target indicators from the to-be-processed multidimensional data based on the business demand, and perform topic classification on the plurality of target indicators.

In this embodiment, the multidimensional data to be processed includes a plurality of target indicators, where the plurality of target indicators include a plurality of preset first indicators and a plurality of preset second indicators. In an optional embodiment, the extracting, by the topic classification module 402, a plurality of target indexes from the multidimensional data to be processed based on the business requirement includes:

In an optional embodiment, the topic classification module 402 topic-classifies the plurality of target indicators including:

Illustratively, if the revenue topic contains multiple target metrics: total income, quarterly bonus, membership bonus, training allowance, management allowance, payroll, etc.; the premium theme includes other target indicators such as total premium and performance of new contracts.

An extracting module 403, configured to extract an atomic table from the to-be-processed multidimensional data based on the multiple target indicators.

In an optional embodiment, the extracting module 403 extracts an atomic table from the multidimensional data to be processed based on the target indexes includes:

A classification module 404, configured to classify the atomic list to obtain a plurality of target atomic lists.

In an alternative embodiment, the classifying module 404 classifies the atom list to obtain a plurality of target atom lists, including:

judging whether the atomic list meets preset classification conditions or not;

Specifically, the preset classification conditions are as follows:

the atomic meter has a plurality of target indexes of subjects, and the time difference of the number output among the plurality of target indexes in the atomic meter has a plurality of preset threshold value ranges.

And a grouping and aggregating module 405, configured to perform grouping and aggregating on each target atom table by using a grouping function, so as to obtain a summary table of each target atom table.

In an optional embodiment, the grouping and aggregating module 405 performs grouping and aggregating on each target atomic table by using a grouping function, and obtaining a summary table of each target atomic table includes:

In an optional embodiment, after the classifying the atom tables to obtain a plurality of target atom tables, the extracting module 403 is further configured to extract a plurality of preset dimension tables from a summary table of each target atom table, create a driving table according to a combination of the preset dimension tables, and add the driving table as a main table to the summary table in association with the preset dimension tables in the summary table.

And a processing module 406, configured to process the multiple summary tables of the multiple target atomic tables to obtain a result table.

In an alternative embodiment, the processing module 406 processes the plurality of summary tables of the plurality of target atom tables to obtain a result table, including:

In summary, in the multidimensional data processing apparatus described in this embodiment, a plurality of target indexes are extracted from the multidimensional data to be processed based on the service requirement, the plurality of target indexes are subject-classified, the target indexes of the same subject are determined as a subject category, and when subsequently performing atomic meter classification, the subject of each target index is considered, so that the atomic meter classification efficiency is improved. And extracting an atomic table from the multi-dimensional data to be processed based on the target indexes, classifying the atomic table to obtain a plurality of target atomic tables, solving the problem of low data processing efficiency caused by downstream task output time delay due to the fact that different atomic tables are spliced together, and improving the data processing efficiency. Grouping and aggregating each target atomic table by adopting a grouping function to obtain a summary table of each target atomic table, processing the summary tables of the target atomic tables to obtain a result table, displaying the result table on the front-end page, and grouping and aggregating the preset dimension tables in each target atomic table again according to new service requirements to obtain a new summary table without re-extracting the target atomic table when different preset dimension tables are newly added to different versions of the same index service.

EXAMPLE III

Fig. 5 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention. In the preferred embodiment of the present invention, the electronic device 5 comprises a memory 51, at least one processor 52, at least one communication bus 53 and a transceiver 54.

It will be appreciated by those skilled in the art that the configuration of the electronic device shown in fig. 5 does not constitute a limitation of the embodiment of the present invention, and may be a bus-type configuration or a star-type configuration, and the electronic device 5 may include more or less hardware or software than those shown, or different component arrangements.

In some embodiments, the electronic device 5 is an electronic device capable of automatically performing numerical calculation and/or information processing according to instructions set or stored in advance, and the hardware thereof includes but is not limited to a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The electronic device 5 may also include a client device, which includes, but is not limited to, any electronic product that can interact with a client through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device, for example, a personal computer, a tablet computer, a smart phone, a digital camera, and the like.

It should be noted that the electronic device 5 is only an example, and other existing or future electronic products, such as those that can be adapted to the present invention, should also be included in the scope of the present invention, and are included herein by reference.

In some embodiments, the memory 51 is used for storing program codes and various data, such as the multidimensional data processing device 40 installed in the electronic equipment 5, and realizes high-speed and automatic access to programs or data during the operation of the electronic equipment 5. The Memory 51 includes a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an electronically Erasable rewritable Read-Only Memory (Electrically-Erasable Programmable Read-Only Memory (EEPROM)), an optical Read-Only disk (CD-ROM) or other optical disk Memory, a magnetic disk Memory, a tape Memory, or any other medium readable by a computer capable of carrying or storing data.

In some embodiments, the at least one processor 52 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The at least one processor 52 is a Control Unit (Control Unit) of the electronic device 5, connects various components of the electronic device 5 by using various interfaces and lines, and executes various functions and processes data of the electronic device 5 by running or executing programs or modules stored in the memory 51 and calling data stored in the memory 51.

In some embodiments, the at least one communication bus 53 is arranged to enable connection communication between the memory 51 and the at least one processor 52, etc.

Although not shown, the electronic device 5 may further include a power source (such as a battery) for supplying power to each component, and optionally, the power source may be logically connected to the at least one processor 52 through a power management device, so as to implement functions of managing charging, discharging, and power consumption through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 5 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.

It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.

The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, an electronic device, or a network device) or a processor (processor) to execute parts of the methods according to the embodiments of the present invention.

In a further embodiment, in conjunction with fig. 4, the at least one processor 52 may execute operating devices of the electronic device 5, as well as installed various types of applications (e.g., the multi-dimensional data processing device 40), program code, and the like, such as the various modules described above.

The memory 51 has program code stored therein, and the at least one processor 52 can call the program code stored in the memory 51 to perform related functions. For example, the modules illustrated in fig. 4 are program codes stored in the memory 51 and executed by the at least one processor 52, so as to implement the functions of the modules for the purpose of multidimensional data processing.

Illustratively, the program code may be divided into one or more modules/units, which are stored in the memory 51 and executed by the processor 52 to accomplish the present application. The one or more modules/units may be a series of computer readable instruction segments capable of performing certain functions, which are used to describe the execution of the program code in the electronic device 5. For example, the program code may be partitioned into a parsing module 401, a topic classification module 402, an extraction module 403, a classification module 404, a grouping aggregation module 405, and a processing module 406.

In one embodiment of the invention, the memory 51 stores a plurality of computer-readable instructions that are executed by the at least one processor 52 to implement the functionality of multi-dimensional data processing.

Specifically, the method for implementing the instruction by the at least one processor 52 may refer to the description of the relevant steps in the embodiments corresponding to fig. 1 to fig. 3, which is not repeated herein.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or that the singular does not exclude the plural. A plurality of units or means recited in the present invention may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.

Claims

1. A method of multidimensional data processing, the method comprising:

classifying the atomic tables to obtain a plurality of target atomic tables;

2. The multidimensional data processing method of claim 1, wherein the extracting a plurality of target metrics from the multidimensional data to be processed based on the business demand comprises:

3. The multidimensional data processing method of claim 1, wherein the thematic classification of the plurality of target metrics comprises:

4. The multidimensional data processing method of claim 1, wherein said classifying the atomic list to obtain a plurality of target atomic lists comprises:

judging whether the atomic list meets preset classification conditions or not;

5. The multidimensional data processing method of claim 4, wherein when the atomic list meets a preset classification condition, classifying the atomic list to obtain a plurality of target atomic lists comprises:

6. The method of multidimensional data processing of claim 1, wherein after said classifying said atom list resulting in a plurality of target atom lists, said method further comprises:

7. The multidimensional data processing method of claim 1, wherein the grouping and aggregating each target atomic table by using a grouping function to obtain a summary table of each target atomic table comprises:

8. A multidimensional data processing apparatus, the apparatus comprising:

9. An electronic device, characterized in that the electronic device comprises a processor and a memory, the processor being configured to implement the multidimensional data processing method as claimed in any one of claims 1 to 7 when executing a computer program stored in the memory.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a multidimensional data processing method as claimed in any one of claims 1 to 7.