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

Abstract

The present invention relates to the field of artificial intelligence technology, and provides a multidimensional data processing method, device, electronic device and storage medium, the method comprising: extracting multiple target indicators from the multidimensional data to be processed based on business needs, and subject-classifying the multiple target indicators; extracting atomic tables from the multidimensional data based on the multiple target indicators; classifying the atomic tables to obtain multiple target atomic tables; using a grouping function to group and aggregate each target atomic table to obtain a summary table of each target atomic table; processing multiple summary tables of multiple target atomic tables to obtain a result table. The present invention classifies the atomic tables and uses a grouping function to group and aggregate each target atomic table, without splicing different atomic tables together, eliminating task waiting time, and improving data processing efficiency and accuracy.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a multi-dimensional data processing method, device, electronic device and storage medium. Background Art

[0002] As business systems continue to improve, business needs are gradually increasing. For multi-dimensional data, business personnel cannot confirm all needs at one time. At the same time, due to the large number of dimensions, the number of interface tables also increases. The existing technology concatenates the atomic tables involved in business needs into a wide table to facilitate subsequent logical processing.

[0003] However, the existing technology splices the atomic tables involved in business needs into a wide table. As the subsequent needs increase, the problem of inconsistent data output time in the wide table arises, causing tasks to wait, affecting the data output time of downstream tasks, and resulting in low data processing efficiency and accuracy.

[0004] Therefore, it is necessary to propose a method that can process data quickly and accurately. Summary of the invention

[0005] In view of the above content, it is necessary to propose a multi-dimensional data processing method, device, electronic device and storage medium. By classifying the atomic tables and using a grouping function to group and aggregate each target atomic table, there is no need to splice different atomic tables together, thereby eliminating task waiting time and improving data processing efficiency and accuracy.

[0006] A first aspect of the present invention provides a multi-dimensional data processing method, the method comprising:

[0007] Parse the received data processing requests to obtain business requirements and multi-dimensional data to be processed;

[0008] Extracting multiple target indicators from the multi-dimensional data to be processed based on the business needs, and subject-classifying the multiple target indicators;

[0009] Extracting an atom table from the multi-dimensional data to be processed based on the multiple target indicators;

[0010] Classifying the atom tables to obtain a plurality of target atom tables;

[0011] Using a grouping function to group and aggregate each of the target atomic tables to obtain a summary table of each of the target atomic tables;

[0012] The multiple summary tables of the multiple target atomic tables are processed to obtain a result table.

[0013] Optionally, extracting a plurality of target indicators from the multi-dimensional data to be processed based on the business requirements includes:

[0014] Acquire corresponding business topics from a preset database according to the business requirements;

[0015] Extracting a plurality of preset first indicators from the multi-dimensional data to be processed according to the business theme, and extracting a plurality of preset second indicators from the multi-dimensional data to be processed according to the target indicators associated with the front-end page corresponding to the business theme;

[0016] The multiple preset first indicators and the multiple preset second indicators are determined as multiple target indicators corresponding to the business needs.

[0017] Optionally, the subject classification of the multiple target indicators includes:

[0018] Analyze the business meaning of each target indicator and the processing logic of the front-end page corresponding to each target indicator;

[0019] The multiple target indicators are subject-classified based on the business meaning of each target indicator and the processing logic of each target indicator.

[0020] Optionally, the classifying the atom tables to obtain a plurality of target atom tables comprises:

[0021] Determining whether the atomic table meets a preset classification condition;

[0022] When the atom table meets a preset classification condition, classify the atom table to obtain a plurality of target atom tables; or

[0023] When the atom table does not meet the preset classification condition, the atom table is determined as a target atom table.

[0024] Optionally, when the atom table satisfies a preset classification condition, classifying the atom table to obtain a plurality of target atom tables includes:

[0025] When there are target indicators of multiple subjects in the atomic table, and there are multiple preset threshold ranges for the output time difference between the target indicators of multiple subjects in the atomic table, the data with the same subject and the output time difference within the same threshold range in the atomic table are recorded in the same atomic table to obtain a target atomic table.

[0026] Optionally, after classifying the atom tables to obtain a plurality of target atom tables, the method further includes:

[0027] A plurality of preset dimension tables are extracted from the summary table of each of the target atomic tables, a driving table is created according to the combination of the plurality of preset dimension tables, and the driving table is associated with the plurality of preset dimension tables in the summary table as a main table, and the associated driving table is added to the summary table.

[0028] Optionally, the adopting a grouping function to group and aggregate each of the target atom tables to obtain a summary table of each of the target atom tables includes:

[0029] Obtaining an indicator requirement corresponding to each of the target indicators from the business requirements;

[0030] Each of the target atomic tables is grouped and aggregated according to the indicator requirement of each of the target indicators to obtain a summary table of each of the target atomic tables.

[0031] A second aspect of the present invention provides a multi-dimensional data processing device, the device comprising:

[0032] The parsing module is used to parse the received data processing requests and obtain the business requirements and multi-dimensional data to be processed;

[0033] A subject classification module, used to extract multiple target indicators from the multi-dimensional data to be processed based on the business needs, and to subject-classify the multiple target indicators;

[0034] An extraction module, configured to extract an atom table from the multi-dimensional data to be processed based on the multiple target indicators;

[0035] A classification module, used for classifying the atom table to obtain a plurality of target atom tables;

[0036] A grouping and aggregation module, used for grouping and aggregating each of the target atomic tables using a grouping function to obtain a summary table of each of the target atomic tables;

[0037] A processing module is used to process the multiple summary tables of the multiple target atomic tables to obtain a result table.

[0038] A third aspect of the present invention provides an electronic device, comprising a processor and a memory, wherein the processor is configured to implement the multi-dimensional data processing method when executing a computer program stored in the memory.

[0039] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, and the computer program implements the multi-dimensional data processing method when executed by a processor.

[0040] In summary, the multidimensional data processing method, device, electronic device and storage medium of the present invention extract multiple target indicators from the multidimensional data to be processed based on the business needs, and classify the multiple target indicators by subject. By classifying the multiple target indicators by subject, the target indicators of the same subject are determined as a subject category. When the atomic table is subsequently classified, the subject of each target indicator is taken into account, thereby improving the classification efficiency of the atomic table. Atomic tables are extracted from the multidimensional data to be processed based on the multiple target indicators, and the atomic tables are classified to obtain multiple target atomic tables, thereby improving the data processing efficiency. A grouping function is used to group and aggregate each of the target atomic tables to obtain a summary table of each of the target atomic tables, and multiple summary tables of the multiple target atomic tables are processed to obtain a result table. When different preset dimension tables are added in different versions for the same indicator business, there is no need to re-extract the target atomic table. It is only necessary to re-group and aggregate the preset dimension tables in each of the target atomic tables according to the new business needs to obtain a new summary table, thereby improving the data processing efficiency and thus improving the display efficiency of the result table. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flowchart of a multi-dimensional data processing method provided in Example 1 of the present invention.

[0042] Figure 2 is a schematic diagram of an existing multi-dimensional data processing method provided by Embodiment 1 of the present invention;

[0043] Figure 3 is a schematic diagram of a multi-dimensional data processing method provided in Embodiment 1 of the present invention;

[0044] Figure 4 It is a structural diagram of a multi-dimensional data processing device provided in Embodiment 2 of the present invention.

[0045] Figure 5 It is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0046] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0048] Embodiment 1

[0049] Figure 1 It is a flowchart of a multi-dimensional data processing method provided in Example 1 of the present invention.

[0050] In this embodiment, the multi-dimensional data processing method can be applied to electronic devices. For electronic devices that need to perform multi-dimensional data processing, the multi-dimensional data processing function provided by the method of the present invention can be directly integrated into the electronic device, or run in the electronic device in the form of a software development kit (SDK).

[0051] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0052] The basic technologies of artificial intelligence generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. Artificial intelligence software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, as well as machine learning and deep learning.

[0053] like Figure 1 As shown, the multi-dimensional data processing method specifically includes the following steps. According to different requirements, the order of the steps in the flowchart can be changed, and some can be omitted.

[0054] S11, parsing the received data processing request to obtain business requirements and multi-dimensional data to be processed.

[0055] In this embodiment, when the user performs multi-dimensional data processing, the user initiates a data processing request to the server through the client. Specifically, the client can be a smart phone, IPAD or other existing smart devices, and the server can be a data processing subsystem. During the data processing process, the client can send a data processing request to the data processing subsystem, and the data processing subsystem is used to receive the data processing request sent by the client.

[0056] In this embodiment, when the data processing subsystem receives a data processing request, it parses the data processing request to obtain business requirements and multi-dimensional data to be processed, wherein the multi-dimensional data to be processed refers to data corresponding to multiple target indicators included in the data processing request. Specifically, the business requirements include indicator requirements corresponding to each target indicator and indicator display requirements of the front-end page. For example, the business requirement may be to obtain human resources information in an enterprise. To obtain human resources information in an enterprise, it is necessary to obtain multiple target indicators. For example, the target indicators to be obtained are: employees, number of diamonds, and total income. The indicator requirements corresponding to employees are: obtain For the information of employees in year M, the indicator requirement for the number of diamonds is: obtain the number of diamonds in the third quarter of year M; the indicator requirement for the total income is: all income information of each employee in year M, and the indicator display requirements of the front-end page corresponding to the employees are: display the total number of employees, display the names of employees, and display the positions of employees; the indicator display requirement of the front-end page corresponding to the number of diamonds is: display the marketing volume of each diamond employee in each month in the third quarter of year M; the indicator display requirements of the front-end page corresponding to the total income number are: display the quarterly bonus of each employee, display the recruitment bonus, display the training allowance, display the management allowance, and display the salary of each employee.

[0057] S12: extracting a plurality of target indicators from the multi-dimensional data to be processed based on the business requirements, and subject-classifying the plurality of target indicators.

[0058] In this embodiment, the business demand is to obtain human resources information in an enterprise, and the corresponding business theme obtained based on the business demand is a human resources theme. Based on the human resources theme, multiple preset first indicators are extracted from the multi-dimensional data to be processed, for example, the preset first indicators may be: employees, number of diamonds, and total income; and multiple preset second indicators are extracted from the multi-dimensional data to be processed according to the target indicators associated with the front-end page corresponding to the business theme, for example, the preset second indicators may be: the positions of employees and the names of employees.

[0059] In this embodiment, the multidimensional data to be processed includes a plurality of target indicators, wherein the plurality of target indicators include a plurality of preset first indicators and a plurality of preset second indicators.

[0060] In an optional embodiment, extracting a plurality of target indicators from the multi-dimensional data to be processed based on the business requirements includes:

[0061] Acquire corresponding business topics from a preset database according to the business requirements;

[0062] Extracting a plurality of preset first indicators from the multi-dimensional data to be processed according to the business theme, and extracting a plurality of preset second indicators from the multi-dimensional data to be processed according to the target indicators associated with the front-end page corresponding to the business theme;

[0063] The multiple preset first indicators and the multiple preset second indicators are determined as multiple target indicators corresponding to the business needs.

[0064] In this embodiment, the business subject refers to the business activity corresponding to the data processing request, for example, the business subject is a human resources subject, an insurance business subject, etc.

[0065] In an optional embodiment, the subject classification of the multiple target indicators includes:

[0066] Analyze the business meaning of each target indicator and the processing logic of the front-end page corresponding to each target indicator;

[0067] The multiple target indicators are subject-classified based on the business meaning of each target indicator and the processing logic of each target indicator.

[0068] In this embodiment, the front-end page displays the target indicator corresponding to the business theme and other target indicators associated with the target indicator.

[0069] In this embodiment, each theme contains multiple target indicators; the processing logic refers to the logical processing relationship between each target indicator and the target indicator associated with each target indicator, for example, total income = quarterly bonus + recruitment bonus + training allowance + management allowance + salary, where total income represents the target indicator, and quarterly bonus, recruitment bonus, training allowance, management allowance and salary are represented as target indicators associated with total income.

[0070] In this embodiment, when the total revenue indicator is obtained, the target indicator corresponding to the total revenue indicator is divided into the revenue theme.

[0071] For example, the income theme includes multiple target indicators: total income, quarterly bonus, recruitment bonus, training allowance, management allowance, salary, etc.; the premium theme includes target indicators such as total premium and new contract performance.

[0072] In this embodiment, during the processing of the multi-dimensional data to be processed, if all indicators of each dimension are in the same table, if one indicator is delayed, the timeliness of the entire report will be delayed, affecting the display of the front-end page. In this embodiment, by classifying the multiple target indicators by subject, the target indicators of the same subject are determined as a subject category, and the subject of each target indicator is taken into consideration during the subsequent atomic table classification, thereby improving the classification efficiency of the atomic table.

[0073] S13, extracting an atomic table from the multi-dimensional data to be processed based on the multiple target indicators.

[0074] In this embodiment, different dimensions are set in the atomic table for different business needs. For example, for the human resources theme, seven dimensions are preset in the atomic table: person dimension, group dimension, department dimension, district dimension, third-level institution dimension, second-level institution dimension, and large region dimension.

[0075] In an optional embodiment, extracting an atomic table from the to-be-processed multi-dimensional data based on the multiple target indicators includes:

[0076] Obtain the table name, field information and data retrieval logic of the atomic table corresponding to each of the target indicators;

[0077] According to the table name, field information and data retrieval logic of the atomic table corresponding to each of the target indicators, the multi-dimensional data is traversed to extract the atomic table.

[0078] In this embodiment, each target indicator corresponds to an atomic table. Specifically, the atomic table corresponding to each target indicator includes information such as table name, field information and data retrieval logic. For example, for the target indicator total revenue, the corresponding atomic table has the table name: 2017 quarterly bonus, and the field information is quarterly bonus. The data retrieval logic refers to obtaining the information in the atomic table. For the target indicator total revenue: the corresponding data retrieval logic is to obtain the quarter name, quarter amount, and quarterly bonus personnel information.

[0079] S14, classifying the atom tables to obtain a plurality of target atom tables.

[0080] In this embodiment, the target atom table refers to a table obtained by classification according to preset classification conditions.

[0081] In an optional embodiment, classifying the atom tables to obtain a plurality of target atom tables comprises:

[0082] Determining whether the atomic table meets a preset classification condition;

[0083] When the atom table meets a preset classification condition, classify the atom table to obtain a plurality of target atom tables; or

[0084] When the atom table does not meet the preset classification condition, the atom table is determined as a target atom table.

[0085] Specifically, the preset classification conditions are: there are target indicators of multiple subjects in the atomic table, and there are multiple preset threshold ranges for the output time difference between the multiple target indicators in the atomic table.

[0086] In this embodiment, the output time refers to the time when each target indicator is first recorded.

[0087] Furthermore, when the atom table meets a preset classification condition, the atom table is classified to obtain a plurality of target atom tables, including:

[0088] When there are target indicators of multiple subjects in the atomic table, and there are multiple preset threshold ranges for the output time difference between the multiple target indicators in the atomic table, the data with the same subject and the output time difference within the same threshold range in the atomic table are recorded in the same atomic table to obtain a target atomic table.

[0089] In this embodiment, the prior art combines all the involved sub-atomic tables into one atomic table according to business needs. As the subsequent needs increase, the number of fields in each atomic table also increases. At the same time, since the target indicators of the sub-atomic tables in the atomic table have different output times, data processing can only be performed after the atomic table task of the latest target indicator is completed, which causes a delay in the output time of downstream tasks and leads to low data processing efficiency.

[0090] In this embodiment, by classifying the atomic table, it is determined whether the atomic table meets the preset classification conditions. Specifically, by determining whether target indicators of different themes are in the same table, and whether the output time difference between multiple target indicators exists in multiple preset threshold ranges, it can be quickly determined whether to split the atomic table. When it is determined to split the atomic table, the data with the same theme and output time difference in the same threshold range in the atomic table are recorded in the same atomic table, and there is no need to splice different atomic tables together. Although the number of atomic tables is increased, the field data of each target atomic table is very small, the script is simple, the task waiting time is greatly eliminated, and the efficiency of downstream obtaining data is improved. The problem of low data processing efficiency caused by splicing different atomic tables together and causing downstream task output time delay in the prior art is solved, thereby improving data processing efficiency.

[0091] S15, using a grouping function to group and aggregate each of the target atom tables to obtain a summary table of each of the target atom tables.

[0092] In this embodiment, since each of the target atomic tables contains multiple preset dimension tables, each of the target atomic tables is grouped and aggregated by using a grouping function to obtain multiple dimension tables actually required in the business needs, and the multiple dimension tables are aggregated to obtain a summary table of each of the target atomic tables.

[0093] In an optional embodiment, the adopting a grouping function to group and aggregate each of the target atom tables to obtain a summary table of each of the target atom tables includes:

[0094] Obtaining an indicator requirement corresponding to each of the target indicators from the business requirements;

[0095] Each of the target atomic tables is grouped and aggregated according to the indicator requirement of each of the target indicators to obtain a summary table of each of the target atomic tables.

[0096] In this embodiment, each target atomic table contains multiple preset dimension tables, and business needs have different requirements for each level in the target atomic table. The multi-dimensional tables of the summary table add different indicators according to their respective needs during processing, which can meet business needs in the early stage of the project. However, in the course of project advancement, the same indicator business will add different preset dimension tables in different versions, and the original atomic table needs to be frequently modified to add new dimension tables. As the number of tables increases, the script maintenance efficiency is low, which in turn leads to low data processing efficiency.

[0097] In this embodiment, since each target atomic table contains all preset dimension tables, the data of the preset dimension tables corresponding to the business needs are grouped and aggregated by using a grouping function according to business needs to obtain a summary table of each target atomic table. When different preset dimension tables are added to the same indicator business in different versions, there is no need to re-extract the target atomic table. Instead, the preset dimension tables in each target atomic table need only be re-grouped and aggregated according to the new business needs to obtain a new summary table, thereby improving data processing efficiency.

[0098] In an optional embodiment, after classifying the atom tables to obtain a plurality of target atom tables, the method further includes:

[0099] A plurality of preset dimension tables are extracted from the summary table of each of the target atomic tables, a driving table is created according to the combination of the plurality of preset dimension tables, and the driving table is associated with the plurality of preset dimension tables in the summary table as a main table, and the associated driving table is added to the summary table.

[0100] In this embodiment, a driving table is created in the summary table of each target atomic table, and the driving table is associated with multiple preset dimension tables in the summary table as a main table and added to the summary table. The multiple preset dimension tables of each target atomic table are optimized into two tables, which improves the data output timeliness of the target atomic table and further improves the data processing efficiency.

[0101] S16, processing the multiple summary tables of the multiple target atom tables to obtain a result table.

[0102] In this embodiment, the result table includes display indicators of the front-end page.

[0103] In an optional embodiment, the processing of the multiple summary tables of the multiple target atomic tables to obtain a result table includes:

[0104] Obtain the indicator display requirements of the front-end page from the business requirements;

[0105] According to the indicator display requirements of the front-end page, multiple summary tables of the multiple target atomic tables are spliced ​​to obtain a result table, and the result table is displayed on the front-end page corresponding to the business theme according to a preset display method.

[0106] In this embodiment, since the target atomic table contains summary tables of all preset dimension tables, when displaying the result table of the front-end page, it is necessary to splice multiple summary tables of multiple target atomic tables according to the indicator display requirements of the front-end page. There is no need to re-extract the target atomic table, thereby improving data processing efficiency.

[0107] In this embodiment, the display method can be preset. For example, it can be displayed in the default method of the front-end page, or it can be displayed in a customized way according to business needs.

[0108] See also Figure 2 A schematic diagram of an existing multi-dimensional 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, wherein each target atomic table includes target indicators of different themes and multiple target indicators with large time differences in output, and at the same time, seven dimensions are preset in the target atomic table: person dimension, group dimension, department dimension, district dimension, third-level institution dimension, second-level institution dimension, and large regional dimension, and the target atomic table 1, target atomic table 2 and target atomic table 3 are spliced ​​into a summary table, wherein one summary table includes seven preset dimension tables, and a result table is obtained according to the summary table, and the result table also includes seven preset dimension tables.

[0109] Figure 3It is a schematic diagram of the multidimensional data processing method provided in Example 1 of the present invention. For example, based on multiple target indicators, atomic tables are extracted from the multidimensional data, and the atomic tables are classified to obtain multiple target atomic tables: target atomic table 1 and target atomic table 2, wherein each target atomic table only contains data with the same subject and data output time difference within the same threshold range, and each target atomic table is grouped and aggregated to obtain a summary table of each target atomic table, wherein there are only two tables in the summary table of each target atomic table, and the summary table of target atomic table 1 and the summary table of target atomic table 2 are processed to obtain only two result tables, which improves the table output efficiency compared with 7 tables in the prior art, thereby improving the data processing efficiency.

[0110] In summary, the multidimensional data processing method described in this embodiment extracts multiple target indicators from the multidimensional data to be processed based on the business needs, and classifies the multiple target indicators by subject. By classifying the multiple target indicators by subject, the target indicators of the same subject are determined as a subject category. When the atomic table is subsequently classified, the subject of each target indicator is taken into consideration, thereby improving the classification efficiency of the atomic table. Atomic tables are extracted from the multidimensional data to be processed based on the multiple target indicators, and the atomic tables are classified to obtain multiple target atomic tables, thereby solving the problem of low data processing efficiency caused by delays in the output time of downstream tasks caused by splicing different atomic tables together, thereby improving data processing efficiency. A grouping function is used to group and aggregate each of the target atomic tables to obtain a summary table of each of the target atomic tables, multiple summary tables of the multiple target atomic tables are processed to obtain a result table, and the result table is displayed on the front-end page. When different preset dimension tables are added to the same indicator business in different versions, there is no need to re-extract the target atomic table. It is only necessary to re-group and aggregate the preset dimension tables in each of the target atomic tables according to the new business needs to obtain a new summary table, which improves data processing efficiency and thereby improves the display efficiency of the result table.

[0111] Embodiment 2

[0112] Figure 4 It is a structural diagram of a multi-dimensional data processing device provided in Embodiment 2 of the present invention.

[0113] In some embodiments, the multi-dimensional data processing device 40 may include a plurality of functional modules composed of program code segments. The program code of each program segment in the multi-dimensional data processing device 40 may be stored in a memory of the electronic device and executed by the at least one processor to perform (see Figures 1 to 3 Description) Functions for multi-dimensional data processing.

[0114] In this embodiment, the multi-dimensional data processing device 40 can be divided into multiple functional modules according to the functions it performs. The functional modules may include: 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. The module referred to in the present invention refers to a series of computer-readable instruction segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0115] The parsing module 401 is used to parse the received data processing request to obtain business requirements and multi-dimensional data to be processed.

[0116] In this embodiment, when the user performs multi-dimensional data processing, the user initiates a data processing request to the server through the client. Specifically, the client can be a smart phone, IPAD or other existing smart devices, and the server can be a data processing subsystem. During the data processing process, the client can send a data processing request to the data processing subsystem, and the data processing subsystem is used to receive the data processing request sent by the client.

[0117] In this embodiment, when the data processing subsystem receives a data processing request, it parses the data processing request to obtain business requirements and multi-dimensional data to be processed, wherein the multi-dimensional data to be processed refers to data corresponding to multiple indicators included in the data processing request. Specifically, the business requirements include indicator requirements corresponding to each target indicator and indicator display requirements of the front-end page. For example, the business requirement may be to obtain human resource information in an enterprise. To obtain human resource information in an enterprise, it is necessary to obtain multiple target indicators. For example, the target indicators to be obtained are: employees, number of diamonds, and total income. The indicator requirements corresponding to employees are: Take the information of employees in year M, and the indicator demand corresponding to the number of diamonds is: get the number of diamonds in the third quarter of year M; the indicator demand corresponding to the total income is: all income information of each employee in year M, and the indicator display demand of the front-end page corresponding to the employees is: display the total number of employees, display the names of employees, and display the positions of employees; the indicator display demand of the front-end page corresponding to diamonds is: display the marketing volume of each diamond employee in each month in the third quarter of year M; the indicator display demand of the front-end page corresponding to the total income is: display the quarterly bonus of each employee, display the recruitment bonus, display the training allowance, display the management allowance, and display the salary of each employee.

[0118] The subject classification module 402 is used to extract multiple target indicators from the multi-dimensional data to be processed based on the business needs, and to subject-classify the multiple target indicators.

[0119] In this embodiment, the business demand is to obtain human resources information in an enterprise, and the corresponding business theme obtained based on the business demand is a human resources theme. Based on the human resources theme, multiple preset first indicators are extracted from the multi-dimensional data to be processed, for example, the preset first indicators may be: employees, number of diamonds, and total income; and multiple preset second indicators are extracted from the multi-dimensional data to be processed according to the target indicators associated with the front-end page corresponding to the business theme, for example, the preset second indicators may be: the positions of employees and the names of employees.

[0120] In this embodiment, the multidimensional data to be processed includes multiple target indicators, wherein the multiple target indicators include multiple preset first indicators and multiple preset second indicators. In an optional embodiment, the topic classification module 402 extracts multiple target indicators from the multidimensional data to be processed based on the business needs, including:

[0121] Acquire corresponding business topics from a preset database according to the business requirements;

[0122] Extracting a plurality of preset first indicators from the multi-dimensional data to be processed according to the business theme, and extracting a plurality of preset second indicators from the multi-dimensional data to be processed according to the target indicators associated with the front-end page corresponding to the business theme;

[0123] The multiple preset first indicators and the multiple preset second indicators are determined as multiple target indicators corresponding to the business needs.

[0124] In this embodiment, the business subject refers to the business activity corresponding to the data processing request, for example, the business subject is a human resources subject, an insurance business subject, etc.

[0125] In an optional embodiment, the subject classification module 402 classifies the multiple target indicators by subject, including:

[0126] Analyze the business meaning of each target indicator and the processing logic of the front-end page corresponding to each target indicator;

[0127] The multiple target indicators are subject-classified based on the business meaning of each target indicator and the processing logic of each target indicator.

[0128] In this embodiment, the front-end page displays the target indicator corresponding to the business theme and other target indicators associated with the target indicator.

[0129] In this embodiment, each theme contains multiple target indicators; the processing logic refers to the logical processing relationship between each target indicator and the target indicator associated with each target indicator, for example, total income = quarterly bonus + recruitment bonus + training allowance + management allowance + salary, where total income represents the target indicator, and quarterly bonus, recruitment bonus, training allowance, management allowance and salary are represented as target indicators associated with total income.

[0130] In this embodiment, when the total revenue indicator is obtained, the target indicator corresponding to the total revenue indicator is divided into the revenue theme.

[0131] For example, if the income theme includes multiple target indicators: total income, quarterly bonus, recruitment bonus, training allowance, management allowance, salary, etc.; the premium theme includes other target indicators such as total premium and new contract performance.

[0132] In this embodiment, during the processing of the multi-dimensional data to be processed, if all indicators of each dimension are in the same table, if one indicator is delayed, the timeliness of the entire report will be delayed, affecting the display of the front-end page. In this embodiment, by classifying the multiple target indicators by subject, the target indicators of the same subject are determined as a subject category, and the subject of each target indicator is taken into consideration during the subsequent atomic table classification, thereby improving the classification efficiency of the atomic table.

[0133] The extraction module 403 is used to extract an atom table from the multi-dimensional data to be processed based on the multiple target indicators.

[0134] In this embodiment, different dimensions are set in the atomic table for different business needs. For example, for the human resources theme, seven dimensions are preset in the atomic table: person dimension, group dimension, department dimension, district dimension, third-level institution dimension, second-level institution dimension, and large region dimension.

[0135] In an optional embodiment, the extraction module 403 extracts the atomic table from the multi-dimensional data to be processed based on the multiple target indicators, including:

[0136] Obtain the table name, field information and data retrieval logic of the atomic table corresponding to each of the target indicators;

[0137] According to the table name, field information and data retrieval logic of the atomic table corresponding to each of the target indicators, the multi-dimensional data is traversed to extract the atomic table.

[0138] In this embodiment, each target indicator corresponds to an atomic table. Specifically, the atomic table corresponding to each target indicator includes information such as table name, field information and data retrieval logic. For example, for the target indicator total revenue, the corresponding atomic table has the table name: 2017 quarterly bonus, and the field information is quarterly bonus. The data retrieval logic refers to obtaining the information in the atomic table. For the target indicator total revenue: the corresponding data retrieval logic is to obtain the quarter name, quarter amount, and quarterly bonus personnel information.

[0139] The classification module 404 is used to classify the atom table to obtain multiple target atom tables.

[0140] In this embodiment, the target atom table refers to a table obtained by classification according to preset classification conditions.

[0141] In an optional embodiment, the classification module 404 classifies the atom tables to obtain a plurality of target atom tables including:

[0142] Determining whether the atomic table meets a preset classification condition;

[0143] When the atom table meets a preset classification condition, classify the atom table to obtain a plurality of target atom tables; or

[0144] When the atom table does not meet the preset classification condition, the atom table is determined as a target atom table.

[0145] Specifically, the preset classification conditions are:

[0146] There are target indicators of multiple subjects in the atomic table, and there are multiple preset threshold ranges for the output time difference between the multiple target indicators in the atomic table.

[0147] In this embodiment, the output time refers to the time when each target indicator is first recorded.

[0148] Furthermore, when the atom table meets a preset classification condition, the atom table is classified to obtain a plurality of target atom tables, including:

[0149] When there are target indicators of multiple subjects in the atomic table, and there are multiple preset threshold ranges for the output time difference between the multiple target indicators in the atomic table, the data with the same subject and the output time difference within the same threshold range in the atomic table are recorded in the same atomic table to obtain a target atomic table.

[0150] In this embodiment, the prior art combines all the involved sub-atomic tables into one atomic table according to business needs. As the subsequent needs increase, the number of fields in each atomic table also increases. At the same time, since the target indicators of the sub-atomic tables in the atomic table have different output times, data processing can only be performed after the atomic table task of the latest target indicator is completed, which causes a delay in the output time of downstream tasks and leads to low data processing efficiency.

[0151] In this embodiment, by classifying the atomic table, it is determined whether the atomic table meets the preset classification conditions. Specifically, by determining whether target indicators of different themes are in the same table, and whether the output time difference between multiple target indicators exists in multiple preset threshold ranges, it can be quickly determined whether to split the atomic table. When it is determined to split the atomic table, the data with the same theme and output time difference in the same threshold range in the atomic table are recorded in the same atomic table, and there is no need to splice different atomic tables together. Although the number of atomic tables is increased, the field data of each target atomic table is very small, the script is simple, the task waiting time is greatly eliminated, and the efficiency of downstream obtaining data is improved. The problem of low data processing efficiency caused by splicing different atomic tables together and causing downstream task output time delay in the prior art is solved, thereby improving data processing efficiency.

[0152] The grouping and aggregation module 405 is used to group and aggregate each of the target atomic tables using a grouping function to obtain a summary table of each of the target atomic tables.

[0153] In this embodiment, since each of the target atomic tables contains multiple preset dimension tables, each of the target atomic tables is grouped and aggregated by using a grouping function to obtain multiple dimension tables actually required in the business needs, and the multiple dimension tables are aggregated to obtain a summary table of each of the target atomic tables.

[0154] In an optional embodiment, the grouping and aggregation module 405 uses a grouping function to group and aggregate each of the target atomic tables, and obtains a summary table of each of the target atomic tables including:

[0155] Obtaining an indicator requirement corresponding to each of the target indicators from the business requirements;

[0156] Each of the target atomic tables is grouped and aggregated according to the indicator requirement of each of the target indicators to obtain a summary table of each of the target atomic tables.

[0157] In this embodiment, each target atomic table contains multiple preset dimension tables, and business needs have different requirements for each level in the target atomic table. The multi-dimensional tables of the summary table add different indicators according to their respective needs during processing, which can meet business needs in the early stage of the project. However, in the course of project advancement, the same indicator business will add different preset dimension tables in different versions, and the original atomic table needs to be frequently modified to add new dimension tables. As the number of tables increases, the script maintenance efficiency is low, which in turn leads to low data processing efficiency.

[0158] In this embodiment, since each target atomic table contains all preset dimension tables, the data of the preset dimension tables corresponding to the business needs are grouped and aggregated by using a grouping function according to business needs to obtain a summary table of each target atomic table. When different preset dimension tables are added to the same indicator business in different versions, there is no need to re-extract the target atomic table. Instead, the preset dimension tables in each target atomic table need only be re-grouped and aggregated according to the new business needs to obtain a new summary table, thereby improving data processing efficiency.

[0159] In an optional embodiment, after classifying the atomic tables to obtain multiple target atomic tables, the extraction module 403 is further used to extract multiple preset dimension tables from the summary table of each target atomic table, create a driving table according to the combination of the multiple preset dimension tables, and associate the driving table as a main table with the multiple preset dimension tables in the summary table and add it to the summary table.

[0160] In this embodiment, a driving table is created in the summary table of each target atomic table, and the driving table is associated with multiple preset dimension tables in the summary table as a main table and added to the summary table. The multiple preset dimension tables of each target atomic table are optimized into two tables, which improves the data output timeliness of the target atomic table and further improves the data processing efficiency.

[0161] The processing module 406 is used to process the multiple summary tables of the multiple target atomic tables to obtain a result table.

[0162] In this embodiment, the result table includes display indicators of the front-end page.

[0163] In an optional embodiment, the processing module 406 processes the multiple summary tables of the multiple target atomic tables to obtain a result table including:

[0164] Obtain the indicator display requirements of the front-end page from the business requirements;

[0165] According to the indicator display requirements of the front-end page, multiple summary tables of the multiple target atomic tables are spliced ​​to obtain a result table, and the result table is displayed on the front-end page corresponding to the business theme according to a preset display method.

[0166] In this embodiment, since the target atomic table contains summary tables of all preset dimension tables, when displaying the result table of the front-end page, it is necessary to splice multiple summary tables of multiple target atomic tables according to the indicator display requirements of the front-end page. There is no need to re-extract the target atomic table, thereby improving data processing efficiency.

[0167] In this embodiment, the display method can be preset. For example, it can be displayed in the default method of the front-end page, or it can be displayed in a customized way according to business needs.

[0168] See also Figure 2 A schematic diagram of an existing multi-dimensional 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, wherein each target atomic table includes target indicators of different themes and multiple target indicators with large time differences in output, and at the same time, seven dimensions are preset in the target atomic table: person dimension, group dimension, department dimension, district dimension, third-level institution dimension, second-level institution dimension, and large regional dimension, and the target atomic table 1, target atomic table 2 and target atomic table 3 are spliced ​​into a summary table, wherein one summary table includes seven preset dimension tables, and a result table is obtained according to the summary table, and the result table also includes seven preset dimension tables.

[0169] Figure 3 It is a schematic diagram of the multidimensional data processing method provided in Example 1 of the present invention. For example, based on multiple target indicators, atomic tables are extracted from the multidimensional data, and the atomic tables are classified to obtain multiple target atomic tables: target atomic table 1 and target atomic table 2, wherein each target atomic table only contains data with the same subject and data output time difference within the same threshold range, and each target atomic table is grouped and aggregated to obtain a summary table of each target atomic table, wherein there are only two tables in the summary table of each target atomic table, and the summary table of target atomic table 1 and the summary table of target atomic table 2 are processed to obtain only two result tables, which improves the table output efficiency compared with 7 tables in the prior art, thereby improving the data processing efficiency.

[0170] In summary, the multidimensional data processing device described in this embodiment extracts multiple target indicators from the multidimensional data to be processed based on the business needs, and classifies the multiple target indicators by subject. By classifying the multiple target indicators by subject, the target indicators of the same subject are determined as a subject category. When the atomic table is subsequently classified, the subject of each target indicator is taken into consideration, thereby improving the classification efficiency of the atomic table. Atomic tables are extracted from the multidimensional data to be processed based on the multiple target indicators, and the atomic tables are classified to obtain multiple target atomic tables, thereby solving the problem of low data processing efficiency caused by delays in the output time of downstream tasks caused by splicing different atomic tables together, thereby improving data processing efficiency. A grouping function is used to group and aggregate each of the target atomic tables to obtain a summary table of each of the target atomic tables, multiple summary tables of the multiple target atomic tables are processed to obtain a result table, and the result table is displayed on the front-end page. When different preset dimension tables are added to the same indicator business in different versions, there is no need to re-extract the target atomic table. It is only necessary to re-group and aggregate the preset dimension tables in each of the target atomic tables according to the new business needs to obtain a new summary table, which improves data processing efficiency and thereby improves the display efficiency of the result table.

[0171] Embodiment 3

[0172] See also Figure 5 FIG. 2 is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention. In a preferred embodiment of the present invention, the electronic device 5 includes a memory 51 , at least one processor 52 , at least one communication bus 53 and a transceiver 54 .

[0173] Those skilled in the art should understand that Figure 5 The structure of the electronic device shown does not constitute a limitation of the embodiments of the present invention, and may be a bus structure or a star structure. The electronic device 5 may also include more or less other hardware or software than shown in the figure, or a different arrangement of components.

[0174] In some embodiments, the electronic device 5 is an electronic device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits, programmable gate arrays, digital processors, and embedded devices. The electronic device 5 may also include client devices, which include but are not limited to any electronic product that can interact with a client through a keyboard, mouse, remote control, touchpad, or voice-controlled device, such as a personal computer, tablet computer, smart phone, digital camera, etc.

[0175] It should be noted that the electronic device 5 is only an example, and other existing or future electronic products that are suitable for the present invention should also be included in the protection scope of the present invention and included here by reference.

[0176] In some embodiments, the memory 51 is used to store program codes and various data, such as the multi-dimensional data processing device 40 installed in the electronic device 5, and to achieve high-speed and automatic access to programs or data during the operation of the electronic device 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 programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0177] In some embodiments, the at least one processor 52 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips, etc. The at least one processor 52 is the control core (Control Unit) of the electronic device 5, and uses various interfaces and lines to connect various components of the entire electronic device 5, and executes various functions of the electronic device 5 and processes data by running or executing programs or modules stored in the memory 51, and calling data stored in the memory 51.

[0178] In some embodiments, the at least one communication bus 53 is configured to implement connection and communication between the memory 51 and the at least one processor 52, etc.

[0179] Although not shown, the electronic device 5 may also include a power source (such as a battery) for supplying power to various components. Optionally, the power source may be logically connected to the at least one processor 52 through a power management device, so that the power management device can manage charging, discharging, and power consumption. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 5 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0180] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0181] The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, electronic device, or network device, etc.) or a processor to execute a part of the method described in each embodiment of the present invention.

[0182] In a further embodiment, in combination Figure 4 The at least one processor 52 can execute the operating device of the electronic device 5 and various installed application programs (such as the multi-dimensional data processing device 40), program codes, etc., for example, the above-mentioned modules.

[0183] The memory 51 stores program codes, and the at least one processor 52 can call the program codes stored in the memory 51 to execute related functions. For example, Figure 4 The modules described in the figure are program codes stored in the memory 51 and are executed by the at least one processor 52, thereby realizing the functions of the modules and achieving the purpose of multi-dimensional data processing.

[0184] Exemplarily, 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 complete the present application. The one or more modules / units may be a series of computer-readable instruction segments capable of completing specific functions, which are used to describe the execution process of the program code in the electronic device 5. For example, the program code may be divided 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.

[0185] In one embodiment of the present invention, the memory 51 stores a plurality of computer-readable instructions, and the plurality of computer-readable instructions are executed by the at least one processor 52 to implement the function of multi-dimensional data processing.

[0186] Specifically, the specific implementation method of the at least one processor 52 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0187] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0188] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0189] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0190] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "including" does not exclude other units or, and the singular does not exclude the plural. The multiple units or devices stated in the present invention can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A multi-dimensional data processing method, characterized in that: The method comprises: Parse the received data processing requests to obtain business requirements and multi-dimensional data to be processed; Extracting multiple target indicators from the multi-dimensional data to be processed based on the business needs, and subject-classifying the multiple target indicators, including: analyzing the business meaning of each target indicator and the processing logic of the front-end page corresponding to each target indicator; subject-classifying the multiple target indicators based on the business meaning of each target indicator and the processing logic of each target indicator, wherein the processing logic refers to the logical processing relationship between each target indicator and each target indicator associated with each target indicator, and the business need is to obtain human resources information in the enterprise; Extracting an atomic table from the multi-dimensional data to be processed based on the multiple target indicators, including: obtaining a table name, field information, and data acquisition logic of the atomic table corresponding to each of the target indicators; traversing the multi-dimensional data to extract an atomic table according to the table name, field information, and data acquisition logic of the atomic table corresponding to each of the target indicators, wherein the dimensions in the atomic table are pre-set according to the corresponding subject; Classifying the atom table to obtain multiple target atom tables, including: determining whether the atom table meets a preset classification condition; when the atom table meets the preset classification condition, classifying the atom table to obtain multiple target atom tables, including: when there are target indicators of multiple subjects in the atom table, and the output time difference between the target indicators of multiple subjects in the atom table exists in multiple preset threshold ranges, recording the data of the same subject and the output time difference within the same threshold range in the atom table, to obtain multiple target atom tables; Using a grouping function to group and aggregate each of the target atomic tables to obtain a summary table of each of the target atomic tables; The multiple summary tables of the multiple target atomic tables are processed to obtain a result table.

2. The multi-dimensional data processing method according to claim 1, characterized in that: The extracting a plurality of target indicators from the multi-dimensional data to be processed based on the business requirements includes: Acquire corresponding business topics from a preset database according to the business requirements; Extracting a plurality of preset first indicators from the multi-dimensional data to be processed according to the business theme, and extracting a plurality of preset second indicators from the multi-dimensional data to be processed according to the target indicators associated with the front-end page corresponding to the business theme; The multiple preset first indicators and the multiple preset second indicators are determined as multiple target indicators corresponding to the business needs.

3. The multi-dimensional data processing method according to claim 1, characterized in that: The method further comprises: When the atom table does not meet the preset classification condition, the atom table is determined as a target atom table.

4. The multi-dimensional data processing method according to claim 1, characterized in that: After classifying the atom tables to obtain a plurality of target atom tables, the method further includes: A plurality of preset dimension tables are extracted from the summary table of each of the target atomic tables, a driving table is created according to the combination of the plurality of preset dimension tables, and the driving table is associated with the plurality of preset dimension tables in the summary table as a main table, and the associated driving table is added to the summary table.

5. The multi-dimensional data processing method according to claim 1, characterized in that: The grouping function is used to group and aggregate each of the target atomic tables to obtain a summary table of each of the target atomic tables, including: Obtaining an indicator requirement corresponding to each of the target indicators from the business requirements; Each of the target atomic tables is grouped and aggregated according to the indicator requirement of each of the target indicators to obtain a summary table of each of the target atomic tables.

6. A multi-dimensional data processing device, characterized in that: The device is used to implement the multi-dimensional data processing method according to any one of claims 1 to 5, and the device includes: The parsing module is used to parse the received data processing requests and obtain the business requirements and multi-dimensional data to be processed; A subject classification module, used to extract multiple target indicators from the multi-dimensional data to be processed based on the business needs, and to subject-classify the multiple target indicators; An extraction module, configured to extract an atom table from the multi-dimensional data to be processed based on the multiple target indicators; A classification module, used for classifying the atom table to obtain a plurality of target atom tables; A grouping and aggregation module, used for grouping and aggregating each of the target atomic tables using a grouping function to obtain a summary table of each of the target atomic tables; A processing module is used to process the multiple summary tables of the multiple target atomic tables to obtain a result table.

7. An electronic device, characterized in that: The electronic device comprises a processor and a memory, and the processor is used to implement the multi-dimensional data processing method according to any one of claims 1 to 5 when executing a computer program stored in the memory.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-dimensional data processing method according to any one of claims 1 to 5 is implemented.

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