Data processing method, device, electronic device and computer readable medium

Abstract

The present application discloses a data processing method, device, electronic device and computer-readable medium, which relates to the field of big data processing technology. The method includes: in response to a data processing request, if the data processing request contains a new identifier, obtaining a new data item corresponding to the new identifier, obtaining new basic configuration data bound to the new data item, using the new basic configuration data as a label of the new data item, generating a new data item portrait, displaying all data item portraits that need to be displayed, including the new data item portrait; summarizing the displayed data item portraits and grouping them according to preset dimensions, obtaining each grouped data item portrait and obtaining corresponding portrait index data, querying corresponding configuration item data according to the portrait index data; extracting the header field in the portrait index data according to the configuration item data; querying the corresponding matching module data based on the header field, and then generating report statistical performance data. Improve data processing efficiency and accuracy.

Description

Technical Field

[0001] The present application relates to the field of big data processing technology, and in particular to a data processing method, device, electronic device and computer-readable medium. Background Art

[0002] At present, with the development of digital transformation of banks and changes in customer service behaviors, branch management faces new changes and challenges. In the new service environment, branches are required to integrate online and offline to help digital marketing, and the activity data is timely and efficient, effectively supporting the front-line precision marketing to acquire and activate customers. At present, the frequency of customers coming to bank branches has dropped significantly, and the contact points between banks and customers are gradually decreasing. It is difficult for managers to grasp the performance indicators of branches and the personal performance of outsourced service personnel in a refined manner, and settlement data lacks effective support. It is difficult for staff to grasp customer information and the completion of performance indicators in real time, and the marketing direction and enthusiasm are limited. Using a multi-dimensional association table, a new type of association table must be added for each transaction detail, resulting in a long transaction line. If the amount of performance data is large, the real-time data processing efficiency is insufficient. Summary of the invention

[0003] In view of this, the embodiments of the present application provide a data processing method, device, electronic device and computer-readable medium, which can solve the problem of the existing use of multi-dimensional association relationship tables, where a new type of association relationship table needs to be added for each transaction detail, resulting in long transaction lines and insufficient real-time data processing efficiency when the amount of performance data is large.

[0004] To achieve the above objective, according to one aspect of an embodiment of the present application, a data processing method is provided, including:

[0005] In response to the data processing request, the data item portraits for display are summarized and grouped according to the preset dimensions to obtain the data item portraits of each group; before summarizing the data item portraits for display, the method further includes: if the data processing request includes a new identifier, obtaining the new data item corresponding to the new identifier, obtaining the new basic configuration data bound to the new data item, using the new basic configuration data as a label for the new data item to generate the new data item portrait, and displaying all the data item portraits that need to be displayed including the new data item portrait;

[0006] Obtain the portrait indicator data corresponding to each grouped data item portrait, and query the corresponding configuration item data based on the portrait indicator data;

[0007] Extract the header fields in the portrait indicator data based on the configuration item data;

[0008] Based on the header fields, query the corresponding matching module data;

[0009] Generate reports and statistical performance data based on header fields and matching module data.

[0010] Optionally, a summary of the data items displayed may include:

[0011] Determine the data items that need to be displayed corresponding to the data processing request, and obtain basic configuration data of the data items that need to be displayed;

[0012] Generate and display data item portraits based on data items and basic configuration data;

[0013] A portrait of the data items displayed in summary.

[0014] Optionally, the summarized data item portraits are grouped according to preset dimensions to obtain respective grouped data item portraits, including:

[0015] Obtain the application identifier corresponding to the data processing request, and obtain the achievable function data corresponding to the application identifier;

[0016] The preset dimensions are determined based on the achievable functional data, and the summarized data item portraits are grouped according to the preset dimensions to obtain portraits of each grouped data item.

[0017] Optionally, the portrait indicator data corresponding to each grouped data item portrait is obtained, including:

[0018] Obtain the portrait indicator data associated with the achievable functional data corresponding to each grouped data item portrait.

[0019] Optionally, according to the configuration item data, extract the header fields in the profile indicator data, including:

[0020] Determine the fields in the profile indicator data that are common to the configuration item data, and determine the common fields as header fields;

[0021] Extract the header fields from the portrait indicator data.

[0022] Optionally, based on the header field, query the corresponding matching module data, including:

[0023] Query the data with correlation with the header field higher than the preset threshold from the data item configuration basic module as matching module data.

[0024] In addition, the present application also provides a data processing device, including:

[0025] The grouping unit is configured to respond to the data processing request, summarize the data item portraits for display, and group the summarized data item portraits according to preset dimensions to obtain the portraits of each grouped data item; before summarizing the displayed data item portraits, it also includes: if the data processing request contains a new identifier, obtain the new data item corresponding to the new identifier, obtain the new basic configuration data bound to the new data item, use the new basic configuration data as a label of the new data item to generate the new data item portrait, and display all data item portraits that need to be displayed including the new data item portrait;

[0026] A first query unit is configured to obtain portrait index data corresponding to each grouped data item portrait, and query corresponding configuration item data according to the portrait index data;

[0027] An extraction unit is configured to extract a header field in the profile indicator data according to the configuration item data;

[0028] A second query unit is configured to query corresponding matching module data based on the header field;

[0029] The generating unit is configured to generate report statistical performance data based on the header fields and matching module data.

[0030] Optionally, the grouping unit is further configured to:

[0031] Determine the data items that need to be displayed corresponding to the data processing request, and obtain basic configuration data of the data items that need to be displayed;

[0032] Generate and display data item portraits based on data items and basic configuration data;

[0033] A portrait of the data items displayed in summary.

[0034] Optionally, the grouping unit is further configured to:

[0035] Obtain the application identifier corresponding to the data processing request, and obtain the achievable function data corresponding to the application identifier;

[0036] The preset dimensions are determined based on the achievable functional data, and the summarized data item portraits are grouped according to the preset dimensions to obtain portraits of each grouped data item.

[0037] Optionally, the first query unit is further configured to:

[0038] Obtain the portrait indicator data associated with the achievable functional data corresponding to each grouped data item portrait.

[0039] Optionally, the extraction unit is further configured to:

[0040] Determine the fields in the profile indicator data that are common to the configuration item data, and determine the common fields as header fields;

[0041] Extract the header fields from the portrait indicator data.

[0042] Optionally, the second query unit is further configured to:

[0043] Query the data with correlation with the header field higher than the preset threshold from the data item configuration basic module as matching module data.

[0044] In addition, the present application also provides a data processing electronic device, including: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by one or more processors, the one or more processors implement the data processing method as described above.

[0045] In addition, the present application also provides a computer-readable medium on which a computer program is stored, and when the program is executed by a processor, the data processing method as described above is implemented.

[0046] To achieve the above objective, according to another aspect of the embodiments of the present application, a computer program product is provided.

[0047] A computer program product according to an embodiment of the present application includes a computer program, and when the program is executed by a processor, the data processing method provided by the embodiment of the present application is implemented.

[0048] One embodiment of the above invention has the following advantages or beneficial effects: the present application summarizes and displays data item portraits in response to data processing requests, and groups the summarized data item portraits according to preset dimensions to obtain each grouped data item portrait; before summarizing and displaying the data item portraits, the method also includes: if the data processing request contains a new identifier, obtain the new data item corresponding to the new identifier, obtain the new basic configuration data bound to the new data item, use the new basic configuration data as a label of the new data item to generate the new data item portrait, and display all data item portraits that need to be displayed including the new data item portrait; obtain the portrait index data corresponding to each grouped data item portrait, query the corresponding configuration item data according to the portrait index data; extract the header field in the portrait index data according to the configuration item data; query the corresponding matching module data based on the header field; generate report statistical performance data based on the header field and the matching module data. Improve data processing efficiency and accuracy.

[0049] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to better understand the present application and do not constitute an improper limitation on the present application.

[0051] Figure 1 is a schematic diagram of the main flow of a data processing method according to an embodiment of the present application;

[0052] Figure 2 is a schematic diagram of the main flow of a data processing method according to an embodiment of the present application;

[0053] Figure 3 is a schematic diagram of a basic module for configuring data items of a data processing method according to an embodiment of the present application;

[0054] Figure 4 is a schematic diagram of main units of a data processing device according to an embodiment of the present application;

[0055] Figure 5 is an exemplary system architecture diagram to which the embodiments of the present application can be applied;

[0056] Figure 6 It is a structural diagram of a computer system of a terminal device or server suitable for implementing an embodiment of the present application. DETAILED DESCRIPTION

[0057] The following is a description of the exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, and they should be considered as merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of known functions and structures is omitted in the following description. It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of the present application are in compliance with the relevant provisions of national laws and regulations. It should be noted that in the embodiments of the present application, certain software, components, models, and other existing solutions in the industry may be mentioned, which should be considered as exemplary, and their purpose is only to illustrate the feasibility of the implementation of the technical solution of the present application, but it does not mean that the applicant has or must have used the solution. In the technical solution of the present application, the collection, analysis, use, transmission, storage, and other aspects of the user's personal information involved are in compliance with the provisions of relevant laws and regulations, and are used for legal and reasonable purposes, and are not shared, leaked, or sold outside of these legal uses, and are subject to supervision and management by regulatory authorities. Take necessary measures to prevent illegal access to such personal information, ensure that persons with access to personal information comply with relevant laws and regulations, and ensure the security of personal information. Once such personal information is no longer needed, risks should be minimized by limiting or even prohibiting data collection and / or deleting data.

[0058] When used, including in certain related applications, user privacy is protected by de-identifying data, such as by removing specific identifiers when used, controlling the amount or specificity of data stored, controlling how data is stored, and / or other methods of de-identification.

[0059] Figure 1 is a schematic diagram of the main flow of a data processing method according to an embodiment of the present application, such as Figure 1 As shown, the data processing method mainly includes the following steps S101 to S105.

[0060] Step S101, in response to a data processing request, summarize the data item portraits for display, and group the summarized data item portraits according to preset dimensions to obtain each grouped data item portrait; before summarizing the data item portraits for display, the method also includes: if the data processing request contains a new identifier, obtain the new data item corresponding to the new identifier, obtain the new basic configuration data bound to the new data item, use the new basic configuration data as a label for the new data item to generate a new data item portrait, and display all data item portraits that need to be displayed including the new data item portrait.

[0061] In this embodiment, the execution subject of the data processing method (for example, a server) can receive the data processing request through a wired connection or a wireless connection. After receiving the data processing request, the execution subject can identify the displayed data item portraits and summarize the displayed data item portraits.

[0062] Before summarizing and displaying the data item portraits, the data processing method further includes: if the data processing request contains a new identifier, that is, a new data item portrait to be displayed needs to be added, then the execution subject can obtain the new data item corresponding to the new identifier, and obtain the new basic configuration data bound to the new data item. For example, the new basic configuration data may include display type (direct display, spliced ​​display), display subtype (direct matching, indirect matching), data display type (text, time formatting), and use the new basic configuration data as a label for the new data item, associate the label with the corresponding new data item to generate a new data item portrait, and display all data item portraits that need to be displayed, including the new data item portrait. This improves the flexibility of data processing.

[0063] Specifically, the summarized data item portraits are grouped according to preset dimensions to obtain each grouped data item portrait, including: obtaining an application identifier corresponding to the data processing request, and obtaining achievable function data corresponding to the application identifier; determining the preset dimensions based on the achievable function data, and grouping the summarized data item portraits according to the preset dimensions to obtain each grouped data item portrait.

[0064] The achievable function data corresponding to the application identifier may include data such as instant communication function, payment function, shopping function, etc. The preset dimensions determined according to the achievable function data may be instant communication applications, payment applications, shopping applications, etc. The execution entity may group the aggregated data item portraits according to the preset dimensions, and mark each group with corresponding labels (for example, display type (direct display, spliced ​​display), display subtype (direct match, indirect match, direct display), data display type (text, time formatting), spliced ​​content, default content, text color, background color) to accurately obtain the portraits of each grouped data item.

[0065] Step S102, obtaining the portrait index data corresponding to each grouped data item portrait, and querying the corresponding configuration item data according to the portrait index data.

[0066] Specifically, obtaining the portrait indicator data corresponding to each grouped data item portrait includes: obtaining the portrait indicator data associated with the achievable function data corresponding to each grouped data item portrait. For example, when the achievable function data corresponding to a grouped data item portrait is payment function data, the associated portrait indicator data may include card binding status, number of payments, card binding date, and other data.

[0067] According to the determined portrait indicator data such as the card binding status, the number of payments, the card binding date, etc., the configuration item data containing one or more of these portrait indicator data are queried.

[0068] Step S103, extracting the header fields in the portrait indicator data according to the configuration item data.

[0069] Specifically, according to the configuration item data, the header fields in the portrait indicator data are extracted, including: determining the fields in the portrait indicator data that are shared with the configuration item data, and determining the shared fields as header fields; extracting the header fields in the portrait indicator data.

[0070] The header fields can be associated with specific performance unit price configurations. The executing entity can determine the fields in the portrait indicator data that are shared with the configuration item data, and determine the shared fields as header fields; accurately extract the header fields in the portrait indicator data.

[0071] In some embodiments, the execution subject may also extract the header fields in the portrait indicator data by calculating the similarity according to the header data configured in the configuration item to improve the efficiency and accuracy of extracting the header fields in the portrait indicator data. For example, the header fields may include company, organization, region, month, etc.

[0072] Step S104: query corresponding matching module data based on the header field.

[0073] Specifically, based on the header field, the corresponding matching module data is queried, including: querying the data with a correlation with the header field higher than a preset threshold from the data item configuration basic module as the matching module data.

[0074] The header field can be associated with one or more configurations and modules in the data item configuration basic module. The execution entity can obtain the functional data and definition data of each configuration and module included in the data item configuration basic module, calculate the similarity between the header field and the functional data and definition data, and use the corresponding data of the configuration or module corresponding to the data with a similarity higher than a preset threshold as the matching module data.

[0075] Step S105, generating a report statistical performance data based on the header fields and matching module data.

[0076] The executing entity can call the logic processing performance statistics module to accurately and intuitively generate report statistics performance data based on the header fields and matching module data.

[0077] This embodiment responds to data processing requests, summarizes the displayed data item portraits, groups the summarized data item portraits according to preset dimensions, and obtains each grouped data item portrait; obtains the portrait index data corresponding to each grouped data item portrait, queries the corresponding configuration item data according to the portrait index data; extracts the header field in the portrait index data according to the configuration item data; queries the corresponding matching module data based on the header field; and generates report statistics performance data based on the header field and the matching module data. This improves data processing efficiency and accuracy.

[0078] Figure 2 is a schematic diagram of the main flow of a data processing method according to an embodiment of the present application, such as Figure 2 As shown, the data processing method mainly includes the following steps S201 to S208.

[0079] Step S201, in response to a data processing request, determining a data item that needs to be displayed corresponding to the data processing request, and acquiring basic configuration data of the data item that needs to be displayed.

[0080] The executing entity can call the business pricing item configuration module to configure related data items, configure performance unit prices, and configure pricing unit price data. It can divide and bind basic data configuration items by company, contract, organization, region, time and month dimensions, set measurement and assessment unit prices, and performance rules.

[0081] After the execution subject receives the data processing request, it can determine the data items that need to be displayed corresponding to the data processing request (that is, the associated data items of the configuration) according to the preset configuration, and perform word segmentation processing on the data items that need to be displayed to obtain each word segment, and perform similarity matching with the basic configuration data based on the obtained each word segment, so as to accurately and quickly obtain the basic configuration data of the matched data items that need to be displayed.

[0082] Step S202: Generate and display a data item portrait based on the data item and basic configuration data.

[0083] The executing entity can use the basic configuration data corresponding to the data item as the label of the data item, associate the label with the corresponding data item to obtain and display the data item portrait.

[0084] When generating a data item portrait, the executing entity can deduplicate the labels associated with the data item to ensure the accuracy of the generated data item portrait.

[0085] Step S203, summarize and display the data item portraits; before summarizing and displaying the data item portraits, the method also includes: if the data processing request contains a new identifier, obtain the new data item corresponding to the new identifier, obtain the new basic configuration data bound to the new data item, use the new basic configuration data as a label for the new data item to generate a new data item portrait, and display all data item portraits that need to be displayed including the new data item portrait.

[0086] Before the data item portraits are summarized and displayed, the data processing method also includes: if the data processing request contains a new identifier, such as 1 or XZ, the embodiment of the present application does not specifically limit the form and content of the new identifier, obtain the new data item corresponding to the new identifier, for example, the new data item can correspond to one or more of consumption data, performance data, financial management data, etc., obtain the new basic configuration data bound to the new data item, for example, the new basic configuration data can include data such as text color, background color, sorting, splicing content, default content, etc., use the new basic configuration data as a label for the new data item, connect each new data in the new data item with the corresponding label to generate a new data item portrait, and display all data item portraits that need to be displayed, including the new data item portrait. To improve the flexibility of data processing. In response to detecting the presence of a label, it can be determined that a data item portrait has been generated, and the generated data item portrait detected is summarized and displayed. The generation of data item portraits is detected in real time and summarized and displayed, improving the efficiency of data processing.

[0087] Step S204, grouping the summarized data item portraits according to preset dimensions to obtain each grouped data item portrait.

[0088] In some embodiments, the preset dimension may include an application type, and the execution subject may group the summarized data item portraits according to the application type to accurately obtain each grouped data item portrait. The execution subject may obtain the application type identifier from the label of the data item portrait, and determine the application type corresponding to the corresponding data item portrait according to the application type identifier, so as to group the data item portraits based on the application type to accurately obtain the grouped data item portraits.

[0089] Step S205, obtaining the portrait index data corresponding to each grouped data item portrait, and querying the corresponding configuration item data according to the portrait index data.

[0090] The executing entity can determine the data corresponding to the label associated with the specific performance unit price in each grouped data item portrait as the portrait indicator data, and query the corresponding configuration item data based on the determined portrait indicator data, thereby improving the efficiency and accuracy of determining the portrait indicator data, so as to improve the efficiency and accuracy of the corresponding data processing.

[0091] Step S206, extracting the header fields in the portrait indicator data according to the configuration item data.

[0092] The execution subject can extract the header fields in the portrait indicator data by calculating the cosine similarity according to the header data configured in the configuration item to improve the efficiency and accuracy of extracting the header fields in the portrait indicator data. For example, the header fields may include company, organization, region, month, etc.

[0093] Step S207: query corresponding matching module data based on the header field.

[0094] Based on the header fields and data item configurations corresponding to the basic module, the module content, a query statement or model prompt word is generated, and the query statement is executed to accurately and quickly query the corresponding matching module data, or the model prompt word is input into the pre-trained query model to output the corresponding matching module data. This improves the efficiency and accuracy of querying the matching module data corresponding to the header fields.

[0095] Step S208, generating a report statistical performance data based on the header fields and matching module data.

[0096] The executing entity can input the header fields and matching module data into the report generation model to accurately generate report statistical performance data.

[0097] Figure 3 1 is a schematic diagram of a basic module for configuring data items in a data processing method according to an embodiment of the present application. Figure 3As shown, the execution body of the data processing method of the embodiment of the present application includes a data item configuration basic module, which can realize the portrait module data configuration and portrait screening group configuration, including a dynamic display matching module, a business pricing item configuration module, a pricing item attribution module, a pricing item performance processing module, a report dynamic header column module, a parsing header processing module and a logic processing performance statistics module. The embodiment of the present application adopts a multi-dimensional configuration to make the display of indicator items and report statistics, and the combination of compliance conditions dynamic, so as to be more suitable for employee performance measurement in various dimensions without losing accuracy.

[0098] Specifically, the grouping unit in the execution body may include a business pricing item configuration module, and the grouping unit may implement the portrait module data configuration and the portrait screening group configuration. The first query unit of the execution body may include a pricing item attribution module. The extraction unit of the execution body may include a report dynamic header column module. The second query unit of the execution body may include a parsing header processing module. The generation unit in the execution body may include a pricing item performance processing module, a dynamic display matching module, and a logic processing performance statistics module. It can be understood that the dynamic display matching module may also be located in the grouping unit.

[0099] In the embodiment of the present application, a basic module for data item configuration can be set. Performance data item KEY, data item type, update time type (monthly update, annual update, one-time update). The indicator item portrait configuration module is set for the data item display content, and the portrait module data configuration and portrait screening group configuration can be implemented: configure the portrait display of the related data items, and create a portrait display table. Add a new portrait type, bind the basic configuration of the data item, display type (direct display, splicing display), display subtype (direct match, indirect match, direct display), data display type (text, time formatting), splicing content, default content, text color, background color, data item ID to be summed, and sorting. Through the dynamic display matching module, that is, the summary classification portrait display module, you can establish a portrait screening group, create a portrait group table, a sub-group table, and a related data joint table: performance data group association binding portrait configuration, and also support 3-layer sub-group creation, and the sub-group items can continue to bind the corresponding portrait indicator data. For example: system label: sub-group: A application, B application, C application, etc. The corresponding sub-group binding portrait performance data association: A application card binding status, A application payment number, A application card binding date, etc., can allow branch personnel to see the dynamic performance indicators more clearly. Data item pricing data processing module, can include business pricing item configuration module, pricing item attribution module, pricing item performance processing module: configure associated data items, performance unit price configuration, create performance pricing table. Configure pricing unit price data, and bind data basic configuration items according to company, contract, organization, region, time and month dimensions. Set measurement assessment unit price and performance rules. After receiving the performance data, query the configuration item data, match multiple data compliance conditions, dynamically associate and bind specific performance details, and generate the corresponding performance detail data specific unit price, performance attribution, and update time. To meet the performance division of large data volume, performance detail data is stored in the warehouse and divided into tables on a monthly basis. Report dynamic header column module: configure report dynamic header. Create a report setting module. The header is associated with the specific performance unit price configuration, and the corresponding company, organization, region, and month items are displayed as the report statistics header. Parsing header processing module: parse the configuration items through the report configuration table, query and summarize the data item columns that need to be counted, including the data item ID, the performance company, organization, region, time and month to which the data item belongs, and the corresponding performance unit price, and complete the assembly of the report header basic data module. Logical processing performance statistics module: query the corresponding matching module data according to the dynamic column, and generate report statistics performance data. It can realize the division of performance types into module display and performance measurement statistics. Support dynamic data item configuration and association. The processing of performance measurement details is more accurate, and the statistics of each dimension are fully covered. If the business wants to add a new performance item, it only needs to configure and process it according to the corresponding module. It solves the problem that managers are difficult to finely grasp the performance indicators of outlets and the personal performance of outsourced service personnel. It enables staff to grasp customer information and performance indicator completion status, marketing direction, and promote enthusiasm in real time.

[0100] Figure 4 Schematic diagram of the main units of the data processing device according to the embodiment of the present application. Figure 4 As shown, the data processing device 400 includes a grouping unit 401 , a first query unit 402 , an extracting unit 403 , a second query unit 404 and a generating unit 405 .

[0101] The grouping unit 401 is configured to respond to the data processing request, summarize the data item portraits for display, and group the summarized data item portraits according to preset dimensions to obtain each grouped data item portrait; before summarizing the data item portraits for display, it also includes: if the data processing request contains a new identifier, obtain the new data item corresponding to the new identifier, obtain the new basic configuration data bound to the new data item, use the new basic configuration data as a label for the new data item to generate the new data item portrait, and display all data item portraits that need to be displayed including the new data item portrait.

[0102] The first query unit 402 is configured to obtain portrait index data corresponding to each grouped data item portrait, and query corresponding configuration item data according to the portrait index data.

[0103] The extraction unit 403 is configured to extract the header field in the portrait indicator data according to the configuration item data.

[0104] The second query unit 404 is configured to query corresponding matching module data based on the header field.

[0105] The generating unit 405 is configured to generate report statistical performance data based on the header fields and matching module data.

[0106] In some embodiments, the grouping unit 401 is further configured to: determine the data items that need to be displayed corresponding to the data processing request, and obtain the basic configuration data of the data items that need to be displayed; generate and display data item portraits based on the data items and the basic configuration data; and summarize the displayed data item portraits.

[0107] In some embodiments, the grouping unit 401 is further configured to: obtain an application identifier corresponding to a data processing request, obtain achievable function data corresponding to the application identifier; determine a preset dimension based on the achievable function data, and group the summarized data item portraits according to the preset dimension to obtain each grouped data item portrait.

[0108] In some embodiments, the first query unit 402 is further configured to: obtain portrait indicator data associated with the achievable functional data corresponding to the portrait of each grouped data item.

[0109] In some embodiments, the extraction unit 403 is further configured to: determine the fields in the portrait indicator data that are common with the configuration item data, and determine the common fields as header fields; and extract the header fields in the portrait indicator data.

[0110] In some embodiments, the second query unit 404 is further configured to query data having a correlation with the header field higher than a preset threshold from the data item configuration basic module as matching module data.

[0111] It should be noted that the data processing method and the data processing device of the present application have a corresponding relationship in terms of specific implementation contents, so the repeated contents will not be described again.

[0112] Figure 5 An exemplary system architecture 500 to which the data processing method or data processing device according to the embodiment of the present application can be applied is shown.

[0113] like Figure 5 As shown, system architecture 500 may include terminal devices 501, 502, 503, a network 504 and a server 505. Network 504 is used to provide a medium for communication links between terminal devices 501, 502, 503 and server 505. Network 504 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0114] Users can use terminal devices 501, 502, 503 to interact with server 505 through network 504 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 501, 502, 503, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0115] The terminal devices 501 , 502 , and 503 may be various electronic devices having a data processing screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0116] The server 505 may be a server that provides various services, such as a background management server that provides support for data processing requests submitted by users using terminal devices 501, 502, and 503 (only as an example). The background management server may respond to the data processing request, summarize the displayed data item portraits, and group the summarized data item portraits according to preset dimensions to obtain each grouped data item portrait; before summarizing the displayed data item portraits, the data processing method also includes: if the data processing request contains a new identifier, obtain the new data item corresponding to the new identifier, obtain the new basic configuration data bound to the new data item, use the new basic configuration data as a label of the new data item to generate the new data item portrait, and display all data item portraits that need to be displayed including the new data item portrait; obtain the portrait index data corresponding to each grouped data item portrait, and query the corresponding configuration item data according to the portrait index data; extract the header field in the portrait index data according to the configuration item data; query the corresponding matching module data based on the header field; generate report statistics performance data based on the header field and the matching module data. Improve data processing efficiency and accuracy.

[0117] It should be noted that the data processing method provided in the embodiment of the present application is generally executed by the server 505 , and accordingly, the data processing device is generally set in the server 505 .

[0118] It should be understood that Figure 5 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0119] Reference below Figure 6 , which shows a schematic diagram of the structure of a computer system 600 of a terminal device suitable for implementing an embodiment of the present application. Figure 6 The terminal device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0120] like Figure 6 As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the computer system 600 are also stored. The CPU 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0121] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage section 608 as needed.

[0122] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the above-mentioned functions defined in the system of the present application are executed.

[0123] It should be noted that the computer-readable medium shown in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. Computer-readable storage media may include, for example, but are not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0124] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the above-mentioned module, program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0125] The units involved in the embodiments described in the present application may be implemented by software or hardware. The units described may also be set in a processor, for example, it may be described as: a processor includes a grouping unit, a first query unit, an extraction unit, a second query unit, and a generation unit. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0126] As another aspect, the present application also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently and not be assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device responds to the data processing request, summarizes the displayed data item portraits, and groups the summarized data item portraits according to preset dimensions to obtain each grouped data item portrait; before summarizing the displayed data item portraits, the data processing method also includes: if the data processing request contains a new identifier, obtain the new data item corresponding to the new identifier, obtain the new basic configuration data bound to the new data item, use the new basic configuration data as a label of the new data item, so as to generate the new data item portrait, and display all data item portraits that need to be displayed including the new data item portrait; obtain the portrait index data corresponding to each grouped data item portrait, and query the corresponding configuration item data according to the portrait index data; extract the header field in the portrait index data according to the configuration item data; query the corresponding matching module data based on the header field; generate report statistical performance data based on the header field and the matching module data.

[0127] The computer program product of the present application includes a computer program, and the computer program implements the data processing method in the embodiment of the present application when executed by a processor.

[0128] According to the technical solution of the embodiment of the present application, the data processing efficiency and accuracy can be improved.

[0129] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A data processing method, characterized in that: include: In response to the data processing request, the displayed data item portraits are summarized, and the summarized data item portraits are grouped according to preset dimensions to obtain respective grouped data item portraits; Before the data item portraits are displayed in the summary, the method further includes: if the data processing request includes a new identifier, obtaining a new data item corresponding to the new identifier, obtaining new basic configuration data bound to the new data item, using the new basic configuration data as a label for the new data item to generate a new data item portrait, and displaying all data item portraits that need to be displayed including the new data item portrait; Obtain portrait indicator data corresponding to each of the grouped data item portraits, and query corresponding configuration item data according to the portrait indicator data; Extracting the header fields in the portrait indicator data according to the configuration item data; Based on the header field, query corresponding matching module data; Based on the header fields and the matching module data, a report is generated to statistically analyze performance data.

2. The method according to claim 1, characterized in that The data item portraits displayed in the summary include: Determine the data item that needs to be displayed corresponding to the data processing request, and obtain basic configuration data of the data item that needs to be displayed; Based on the data item and the basic configuration data, generate and display a data item portrait; A portrait of the data items displayed in a summary.

3. The method according to claim 1, characterized in that The step of grouping the summarized data item portraits according to preset dimensions to obtain each grouped data item portrait includes: Obtaining an application identifier corresponding to the data processing request, and obtaining achievable function data corresponding to the application identifier; A preset dimension is determined based on the achievable functional data, and the summarized data item portraits are grouped according to the preset dimension to obtain each grouped data item portrait.

4. The method according to claim 1, characterized in that The obtaining of the portrait indicator data corresponding to each of the grouped data item portraits includes: Obtain the portrait indicator data associated with the achievable functional data corresponding to the portraits of each grouped data item.

5. The method according to claim 1, characterized in that The step of extracting the header fields in the portrait indicator data according to the configuration item data includes: Determine the fields in the portrait indicator data that are common to the configuration item data, and determine the common fields as header fields; Extract the header field in the portrait indicator data.

6. The method according to claim 1, characterized in that The querying corresponding matching module data based on the header field includes: The data whose correlation with the header field is higher than a preset threshold is queried from the data item configuration basic module to serve as matching module data.

7. A data processing device, characterized in that: include: a grouping unit configured to, in response to a data processing request, summarize the displayed data item portraits, and group the summarized data item portraits according to a preset dimension to obtain respective grouped data item portraits; Before the data item portraits are displayed in the summary, the process further includes: if the data processing request includes a new identifier, obtaining a new data item corresponding to the new identifier, obtaining new basic configuration data bound to the new data item, using the new basic configuration data as a label for the new data item to generate a new data item portrait, and displaying all data item portraits that need to be displayed, including the new data item portrait; A first query unit is configured to obtain portrait index data corresponding to each of the grouped data item portraits, and query corresponding configuration item data according to the portrait index data; an extraction unit, configured to extract a header field in the portrait indicator data according to the configuration item data; A second query unit is configured to query corresponding matching module data based on the header field; The generating unit is configured to generate report statistical performance data based on the table header fields and the matching module data.

8. The device according to claim 7, characterized in that The grouping unit is further configured to: Determine the data item that needs to be displayed corresponding to the data processing request, and obtain basic configuration data of the data item that needs to be displayed; Based on the data item and the basic configuration data, generate and display a data item portrait; A portrait of the data items displayed in a summary.

9. The device according to claim 7, characterized in that The grouping unit is further configured to: Obtaining an application identifier corresponding to the data processing request, and obtaining achievable function data corresponding to the application identifier; A preset dimension is determined based on the achievable functional data, and the summarized data item portraits are grouped according to the preset dimension to obtain each grouped data item portrait.

10. The device according to claim 7, characterized in that The first query unit is further configured to: Obtain the portrait indicator data associated with the achievable functional data corresponding to the portraits of each grouped data item.

11. The device according to claim 7, characterized in that The extraction unit is further configured to: Determine the fields in the portrait indicator data that are common to the configuration item data, and determine the common fields as header fields; Extract the header field in the portrait indicator data.

12. The device according to claim 7, characterized in that The second query unit is further configured to: The data whose correlation with the header field is higher than a preset threshold is queried from the data item configuration basic module to serve as matching module data.

13. A data processing electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

14. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

Images