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

Abstract

The present disclosure provides a data processing method and apparatus, an electronic device, and a computer-readable storage medium, which can be applied to the fields of big data technology and finance. The data processing method includes: in response to detecting a data query instruction for a data lake, executing the data query instruction to obtain reference object data, wherein the reference object data is used to represent first object data related to a focus operation of a target object, where the focus operation is an operation generated by the target object during the process of using a program product; determining local object data to be processed based on the reference object data and a local object data set of a local database; obtaining target local object data and target stock object data based on application-side object data, the local object data to be processed, and stock object data in the data lake, wherein the target stock object data includes the target local object data, and both the target local object data and the target stock object data are consistent with the application-side object data.

Description

Technical Field

[0001] The present disclosure relates to the fields of big data technology and finance, and more specifically, to a data processing method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] With the development of data processing technology, various structured data, semi-structured data, unstructured data and binary data are growing exponentially. Traditional databases find it difficult to store and analyze the content of this data. Therefore, big data technologies such as data lakes are usually used to process this data.

[0003] In the process of implementing the concepts of the present disclosure, the inventors discovered that there are at least the following problems in the related art: in the event of network jitter, it is difficult to ensure the accuracy of object data in the local database and the data lake. Summary of the Invention

[0004] In view of this, the present disclosure provides a data processing method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product.

[0005] According to one aspect of the present disclosure, there is provided a data processing method, comprising:

[0006] In response to detecting a data query instruction for the data lake, executing the data query instruction to obtain reference object data, wherein the reference object data is used to represent first object data related to a focus operation of a target object, where the focus operation is an operation generated by the target object during the process of utilizing the program product;

[0007] Determining local object data to be processed based on the reference object data and a local object data set in the local database, wherein the local object data set is used to represent second object data related to the focus operation of the target object; and

[0008] Target local object data and target stock object data are obtained based on the application-side object data, the above-mentioned local object data to be processed, and the stock object data in the above-mentioned data lake, wherein the above-mentioned target stock object data includes the above-mentioned target local object data, and the above-mentioned target local object data and the above-mentioned target stock object data are consistent with the above-mentioned application-side object data.

[0009] According to an embodiment of the present disclosure, the above-mentioned reference object data includes a reference platform object identifier and a reference private domain object identifier.

[0010] The local object data set includes a first local object data set and a second local object data set;

[0011] The first local object data set includes a first local private domain object identifier and a first local platform object identifier;

[0012] According to an embodiment of the present disclosure, determining the local object data to be processed based on the reference object data and the local object data set includes:

[0013] When it is determined that the target first local platform object identifier does not match the above-mentioned reference platform object identifier, the above-mentioned local object data to be processed is obtained based on the above-mentioned reference object data and the above-mentioned second local object data set, wherein the above-mentioned target first local platform object identifier is the first local platform object identifier associated with the target first local private domain object identifier, and the above-mentioned target first local private domain object identifier is the first local private domain object identifier in the above-mentioned first local object data set that matches the above-mentioned reference private domain object identifier.

[0014] According to an embodiment of the present disclosure, the local object dataset further includes a third local object dataset;

[0015] The above method further includes:

[0016] When it is determined that the target first local platform object identifier matches the reference platform object identifier, the local object data to be processed is obtained according to the reference object data, the second local object data set and the third local object data set.

[0017] According to an embodiment of the present disclosure, the second local object data set includes a second local private domain object identifier.

[0018] According to an embodiment of the present disclosure, obtaining the local object data to be processed based on the reference object data and the second local object data set includes:

[0019] The local object data to be processed is obtained according to the second local object data corresponding to the target second local private domain object identifier, wherein the target second local private domain object identifier is the second local private domain object identifier in the second local object data set that matches the reference private domain object identifier.

[0020] According to an embodiment of the present disclosure, the second local object data further includes an attention state identifier, and the attention state identifier is used to represent the attention state corresponding to the attention operation.

[0021] According to an embodiment of the present disclosure, obtaining the local object data to be processed based on the second local object data corresponding to the target second local private domain object identifier includes:

[0022] The attention state identifier in the second local object data corresponding to the target second local private domain object identifier is set as an abnormal attention state identifier to obtain the local object data to be processed.

[0023] According to an embodiment of the present disclosure, the first local object data further includes a first local public domain object identifier.

[0024] The second local object data includes a second local public domain object identifier;

[0025] The third local object data set includes at least one third local object data, and the third local object data includes a third local private domain object identifier and a third local public domain object identifier.

[0026] According to an embodiment of the present disclosure, obtaining the local object data to be processed based on the reference object data, the second local object data set, and the third local object data set includes:

[0027] When it is determined that the target third local private domain object identifier does not match the above-mentioned reference private domain object identifier, the attention status identifier in the second local object data corresponding to the target second local public domain object identifier is set to an abnormal attention status identifier to obtain the above-mentioned local object data to be processed, wherein the above-mentioned target third local private domain object identifier is a third local private domain object identifier associated with the target third local public domain object identifier, the above-mentioned target third local public domain object identifier is a third local public domain object identifier in the above-mentioned third local object data set that matches the above-mentioned reference public domain object identifier, and the above-mentioned target second local public domain object identifier is a second local public domain object identifier in the above-mentioned second local object data set that matches the above-mentioned reference public domain object identifier.

[0028] According to an embodiment of the present disclosure, the above method may further include:

[0029] When it is determined that the target third local private domain object identifier matches the reference private domain object identifier, the attention status identifier in the second local data corresponding to the target second local public domain object identifier is set to the attention status identifier to obtain the local object data to be processed.

[0030] According to an embodiment of the present disclosure, when it is determined that the target third local private domain object identifier does not match the reference private domain object identifier, the method further includes:

[0031] The target third local private domain object identifier is modified to the reference private domain object identifier.

[0032] According to an embodiment of the present disclosure, the above-mentioned local object data to be processed includes first local object data to be processed and second local object data to be processed.

[0033] According to an embodiment of the present disclosure, obtaining target local object data and target stock object data based on the application-side object data, the to-be-processed local object data, and the stock object data in the data lake includes:

[0034] Obtaining third local object data to be processed according to the application-side object data and the first local object data to be processed; and

[0035] The target local object data and the target stock object data are obtained according to the second local object data to be processed, the third local object data to be processed, and the stock object data in the data lake.

[0036] According to an embodiment of the present disclosure, the first local object data to be processed is the local object data to be processed whose attention status identifier is an already-attended status identifier.

[0037] According to an embodiment of the present disclosure, obtaining the third local object data to be processed based on the application-side object data and the first local object data to be processed includes:

[0038] If it is determined that the attention status identifier in the first local object data to be processed matches the attention status identifier in the application-side object data, determining the first local object data to be processed as the third local object data to be processed; and

[0039] When it is determined that the attention status identifier in the above-mentioned first local object data to be processed does not match the attention status identifier in the above-mentioned application-side object data, the attention status identifier in the above-mentioned first local object data to be processed is set to the non-attention status identifier to obtain the above-mentioned third local object data to be processed.

[0040] According to an embodiment of the present disclosure, obtaining the target local object data and the target stock object data based on the second local object data to be processed, the third local object data to be processed, and the stock object data in the data lake includes:

[0041] Obtaining object data to be updated and fixed object data based on the second local object data to be processed, the third local object data to be processed, and the existing object data in the data lake, wherein the object data to be updated includes at least one of object data to be added, object data to be modified, and object data to be deleted;

[0042] Performing an update operation on the object data to be updated to obtain updated object data;

[0043] Obtaining the target inventory object data according to the updated object data and the fixed object data;

[0044] Deleting the local object data to be processed whose attention status identifier is an abnormal attention status identifier in the second local object data to be processed to obtain fourth local object data to be processed; and

[0045] The target local object data is obtained according to the third local object data to be processed and the fourth local object data to be processed.

[0046] According to an embodiment of the present disclosure, the first local private domain object identifier is determined based on the first local public domain object identifier.

[0047] According to another aspect of the present disclosure, there is provided a data processing apparatus, comprising:

[0048] a first obtaining module configured to, in response to detecting a data query instruction for the data lake, execute the data query instruction to obtain reference object data, wherein the reference object data is used to represent first object data related to a focus operation of a target object, where the focus operation is an operation generated by the target object during the process of utilizing the program product;

[0049] A first determining module is configured to determine local object data to be processed based on the reference object data and a local object data set in a local database, wherein the local object data set is used to represent second object data related to the focus operation of the target object; and

[0050] The second acquisition module is used to obtain target local object data and target stock object data based on the application-side object data, the above-mentioned local object data to be processed, and the stock object data in the above-mentioned data lake, wherein the above-mentioned target stock object data includes the above-mentioned target local object data, and the above-mentioned target local object data and the above-mentioned target stock object data are consistent with the above-mentioned application-side object data.

[0051] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0052] one or more processors;

[0053] a memory for storing one or more instructions,

[0054] When the one or more instructions are executed by the one or more processors, the one or more processors implement the method described in the present disclosure.

[0055] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which executable instructions are stored. When the executable instructions are executed by a processor, the processor implements the method described in the present disclosure.

[0056] According to another aspect of the present disclosure, a computer program product is provided. The computer program product includes computer-executable instructions. When the computer-executable instructions are executed, they are used to implement the method described in the present disclosure.

[0057] According to the embodiments of the present disclosure, by determining the local object data to be processed based on the reference object data and the local object data set of the local database, and then obtaining the target local object data of the local database and the target stock object data of the data lake that are consistent with the application-side object data based on the application-side object data, the technical problem of the difficulty in ensuring the accuracy of the object data in the local database and the data lake in the related technologies is at least partially overcome, and the consistency of the object data in the local database and the data lake with the object data on the application side is ensured, thereby improving the accuracy of the object data in the local database and the data lake. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0059] Figure 1 The system architecture to which the data processing method according to the embodiment of the present disclosure can be applied is schematically shown;

[0060] Figure 2 The following schematically shows a flow chart of a data processing method according to an embodiment of the present disclosure;

[0061] Figure 3 A flowchart of a method for obtaining local object data to be processed according to an embodiment of the present disclosure is schematically shown;

[0062] Figure 4 Schematically shows a flow chart of a method for obtaining local object data to be processed according to another embodiment of the present disclosure;

[0063] Figure 5 The following schematically shows a flow chart of a method for obtaining target local object data and target stock object data according to an embodiment of the present disclosure;

[0064] Figure 6 A flowchart of a method for obtaining target local object data and target stock object data according to another embodiment of the present disclosure is schematically shown;

[0065] Figure 7A block diagram schematically shows a data processing device according to an embodiment of the present disclosure; and

[0066] Figure 8 A block diagram schematically shows an electronic device suitable for implementing a data processing method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0067] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0068] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0069] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0070] When expressions such as “at least one of A, B, and C, etc.” are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (e.g., “a system having at least one of A, B, and C” should include but is not limited to systems having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, and C, etc.). When expressions such as “at least one of A, B, or C, etc.” are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (e.g., “a system having at least one of A, B, or C” should include but is not limited to systems having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, and C, etc.).

[0071] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved comply with the provisions of relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good morals.

[0072] In the technical solution disclosed herein, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0073] A data lake can refer to a system used to store data in its original format. Essentially, a data lake is an enterprise data architecture approach. Physically, a data lake is a data storage platform that centrally stores massive amounts of data from multiple sources and types within an enterprise, supporting rapid data processing and analysis. A data lake can be either object blocks or files.

[0074] A data lake is typically a single repository for all of an enterprise's data. This includes copies of raw data generated by the original system as well as transformed data used for various tasks, such as reporting, visualization, advanced analytics, and machine learning. Data lakes can be built in an enterprise's on-premises data center or in the cloud.

[0075] Data lakes can be used to store structured data, such as rows and columns; semi-structured data, such as CSV (Comma-Separated Values), logs, XML (Extensible Markup Language), and JSON (JavaScript Object Notation); unstructured data, such as email (Electronic Mail), documents, and PDF (Portable Document Format); and binary data, such as images, audio, and video.

[0076] However, in the process of implementing the concepts disclosed herein, the inventors discovered that there are at least the following problems in the related art: in the event of network jitter, the data in the local database and the data stored in the data lake will be inconsistent with the application side, making it difficult to ensure the accuracy of the object data in the local database and the data lake.

[0077] In order to at least partially solve the technical problems existing in the related art, the present disclosure provides a data processing method and device, an electronic device, a computer-readable storage medium and a computer program product, which can be applied to the field of big data technology and the financial field. The data processing method includes: in response to detecting a data query instruction for a data lake, executing the data query instruction to obtain reference object data, wherein the reference object data is used to represent first object data related to a focus operation of a target object, and the focus operation is an operation generated by the target object in the process of using the program product; determining the local object data to be processed based on the reference object data and a local object data set of a local database, wherein the local object data set is used to represent second object data related to the focus operation of the target object; obtaining target local object data and target stock object data based on application-side object data, the local object data to be processed and the stock object data in the data lake, wherein the target stock object data includes the target local object data, and the target local object data and the target stock object data are both consistent with the application-side object data.

[0078] It should be noted that the data processing methods and apparatuses provided in the embodiments of the present disclosure can be used in the fields of big data technology and finance, for example, in data synchronization between a local database and a data lake. The data processing methods and apparatuses provided in the embodiments of the present disclosure can also be used in any field other than big data technology and finance, for example, in the field of information security. The application fields of the data processing methods and apparatuses provided in the embodiments of the present disclosure are not limited.

[0079] Figure 1 The system architecture to which the data processing method according to the embodiment of the present disclosure can be applied is schematically shown. Figure 1 The examples shown are merely examples of system architectures to which the embodiments of the present disclosure may be applied, to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure may not be used in other devices, systems, environments or scenarios.

[0080] like Figure 1 As shown, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a server 104, and a cloud platform 105. The terminal devices 101, 102, 103 and the server 104, and the server 104 and the cloud platform 105 may communicate via a network, which may include various connection types, such as wired and / or wireless communication links.

[0081] Users can use terminal devices 101, 102, and 103 to interact with server 104 over the network to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software (for example only).

[0082] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0083] Server 104 may be a server that provides various services, such as a backend management server (for example only) that supports websites browsed by users using terminal devices 101, 102, and 103. The backend management server may analyze and process received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal device. Server 104 may be provided with a local database.

[0084] The data lake may include a local data lake and a cloud data lake. The local data lake may be set up on the server 104. In response to detecting a data query instruction for the local data lake, the server 104 may call the local data lake set up on the server 104 to execute the data query instruction for the local data lake and obtain the reference object data.

[0085] Alternatively, the cloud data lake can be set up on the cloud platform 105. In response to detecting a data query instruction for the cloud data lake, the cloud data lake set up on the cloud platform 105 can be called by the server 104 to execute the data query instruction for the cloud data lake to obtain the reference object data.

[0086] It should be noted that the data processing method provided in the embodiment of the present disclosure can generally be executed by the server 104. Accordingly, the data processing device provided in the embodiment of the present disclosure can generally be set in the server 104. The data processing method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 104 and can communicate with the terminal devices 101, 102, 103 and / or the server 104. Accordingly, the data processing device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 104 and can communicate with the terminal devices 101, 102, 103 and / or the server 104.

[0087] Alternatively, the data processing method provided in the embodiment of the present disclosure may also be generally executed by the terminal device 101, 102, or 103. Accordingly, the data processing apparatus provided in the embodiment of the present disclosure may also be provided in the terminal device 101, 102, or 103.

[0088] For example, the local object dataset may be originally stored in any one of the terminal devices 101, 102, or 103 (for example, the terminal device 101, but not limited thereto), or stored in an external storage device and imported into the terminal device 101. The terminal device 101 may then locally execute the data processing method provided in the embodiments of the present disclosure, or send the local object dataset to another terminal device, server, or server cluster, and the other terminal device, server, or server cluster that receives the local object dataset may execute the data processing method provided in the embodiments of the present disclosure.

[0089] It should be understood that Figure 1 The number of terminal devices, servers, and cloud platforms in the embodiment is merely illustrative. Any number of terminal devices, servers, and cloud platforms may be used as required.

[0090] It should be noted that the sequence numbers of the operations in the following method are only used to indicate the operation for the purpose of description, and should not be regarded as indicating the order in which the operations should be performed. Unless explicitly stated, the method does not need to be performed in the order shown.

[0091] Figure 2 The flowchart of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0092] like Figure 2 As shown, the data processing method 200 may include operations S210 to S230.

[0093] In operation S210 , in response to detecting a data query instruction for the data lake, the data query instruction is executed to obtain reference object data. The reference object data is used to represent first object data related to a focus operation of a target object, where the focus operation is an operation generated by the target object during the process of utilizing the program product.

[0094] In operation S220, local object data to be processed is determined based on the reference object data and a local object data set in the local database. The local object data set is used to represent second object data related to the operation of interest of the target object.

[0095] In operation S230, target local object data and target stock object data are obtained based on the application-side object data, the local object data to be processed, and the stock object data in the data lake. The target stock object data includes the target local object data, and both the target local object data and the target stock object data are consistent with the application-side object data.

[0096] According to the embodiments of the present disclosure, a data lake can be understood as a distributed database that stores data. The data lake offers good query performance and the ability to access data in real time. A data lake can be used to store real-time data. Because historical data and real-time data may have different formats, historical data can be converted to the same format as real-time data before being stored in the data lake.

[0097] According to an embodiment of the present disclosure, the code for generating a data query instruction can be pre-written into a script. When a data query is required, the user can run the script through a terminal device to generate a data query instruction, and can send the data query instruction to a server so that the server executes the data query instruction and obtains the reference object data.

[0098] According to embodiments of the present disclosure, target objects may include users. Program products may include official accounts, mini-programs, PC (Personal Computer) websites, and H5 (HTML (Hyper Text Markup Language) 5) websites. Reference object data may be used to represent first object data related to a user's interest operation generated while utilizing the program product.

[0099] According to an embodiment of the present disclosure, the text format of the reference object data may be any of the following: TXT (text document), RTF (Rich Text Format), DOC (Document), or XLS (Microsoft Excel). The reference object data may include a reference platform object identifier and a reference private domain object identifier.

[0100] According to an embodiment of the present disclosure, a local database may be used to store historical data. The local object dataset of the local database may include a first local object dataset, a second local object dataset, and a third local object dataset. The local object dataset may be used to represent second object data related to a user's interest operation generated during the use of the program product.

[0101] According to an embodiment of the present disclosure, local object data to be processed can be determined based on reference object data and a local object data set in a local database. The local object data to be processed may include first local object data to be processed and second local object data to be processed. The first local object data to be processed may include local object data to be processed whose attention status is identified as an attended state identifier. The second local object data to be processed may include local object data to be processed whose attention status is identified as an unattended state identifier or an abnormal attended state identifier.

[0102] According to embodiments of the present disclosure, application-side object data may include application-side object data related to a target object. Existing object data may include data related to the target object stored in the data lake before a certain time point. Based on the application-side object data, the local object data to be processed, and the existing object data in the data lake, target local object data consistent with the application-side object data and target existing object data including the target local object data can be obtained.

[0103] According to the embodiments of the present disclosure, by determining the local object data to be processed based on the reference object data and the local object data set of the local database, and then obtaining the target local object data of the local database and the target stock object data of the data lake that are consistent with the application-side object data based on the application-side object data, the technical problem of the difficulty in ensuring the accuracy of the object data in the local database and the data lake in the related technologies is at least partially overcome, and the consistency of the object data in the local database and the data lake with the object data on the application side is ensured, thereby improving the accuracy of the object data in the local database and the data lake.

[0104] Reference below Figures 3 to 6 , the data processing method 200 according to an embodiment of the present invention is further described.

[0105] Figure 3 A flowchart of a method for obtaining local object data to be processed according to an embodiment of the present disclosure is schematically shown.

[0106] like Figure 3 As shown, operation S220 may include operations S321 to S322.

[0107] In operation S321, if it is determined that the target first local platform object identifier does not match the reference platform object identifier, local object data to be processed is obtained based on the reference object data and the second local object data set. The target first local platform object identifier is a first local platform object identifier associated with the target first local private domain object identifier, and the target first local private domain object identifier is a first local private domain object identifier in the first local object data set that matches the reference private domain object identifier.

[0108] In operation S322 , when it is determined that the target first local platform object identifier matches the reference platform object identifier, local object data to be processed is obtained according to the reference object data, the second local object data set, and the third local object data set.

[0109] According to an embodiment of the present disclosure, operation S220 may include only operation S321 or only operation S322.

[0110] According to an embodiment of the present disclosure, the second local object data set includes a second local private domain object identifier.

[0111] According to an embodiment of the present disclosure, operation S321 may include the following operations.

[0112] The local object data to be processed is obtained according to the second local object data corresponding to the target second local private domain object identifier, wherein the target second local private domain object identifier is a second local private domain object identifier in the second local object data set that matches the reference private domain object identifier.

[0113] According to an embodiment of the present disclosure, the second local object data further includes an attention state identifier, which is used to represent an attention state corresponding to the attention operation.

[0114] According to an embodiment of the present disclosure, obtaining the local object data to be processed according to the second local object data corresponding to the target second local private domain object identifier may include the following operations.

[0115] The attention state identifier in the second local object data corresponding to the target second local private domain object identifier is set as an abnormal attention state identifier to obtain the local object data to be processed.

[0116] According to an embodiment of the present disclosure, the reference object data may include a reference platform object identifier and a reference private domain object identifier. The reference platform object identifier can be used to represent the unique identifier for the same user under the same open platform on the application side. The reference public domain object identifier can be used to represent the unique identifier for different public accounts or mini-programs under the same open platform. The same reference platform object identifier can correspond to multiple reference public domain object identifiers. The reference private domain object identifier can be used to represent that the application side can calculate the public domain object identifier generated by each user's follow-up operation according to a preset algorithm to generate a unique hash value mark for the user.

[0117] According to an embodiment of the present disclosure, the first local object data further includes a first local public domain object identifier. The second local object data includes a second local public domain object identifier. The third local object data set includes at least one third local object data, the third local object data including a third local private domain object identifier and a third local public domain object identifier.

[0118] According to an embodiment of the present disclosure, operation S322 may include the following operations.

[0119] If it is determined that the target third local private domain object identifier does not match the reference private domain object identifier, the attention status identifier in the second local object data corresponding to the target second local public domain object identifier is set to an abnormal attention status identifier, thereby obtaining the local object data to be processed. The target third local private domain object identifier is a third local private domain object identifier associated with the target third local public domain object identifier, the target third local public domain object identifier is a third local public domain object identifier in the third local object data set that matches the reference public domain object identifier, and the target second local public domain object identifier is a second local public domain object identifier in the second local object data set that matches the reference public domain object identifier.

[0120] According to an embodiment of the present disclosure, operation S322 may further include the following operations.

[0121] When it is determined that the target third local private domain object identifier matches the reference private domain object identifier, the attention state identifier in the second local data corresponding to the target second local public domain object identifier is set as the attention state identifier to obtain the local object data to be processed.

[0122] According to an embodiment of the present disclosure, when it is determined that the target third local private domain object identifier does not match the reference private domain object identifier, the following operations may be further included.

[0123] The target third local private domain object identifier is modified to the reference private domain object identifier.

[0124] According to an embodiment of the present disclosure, the first local private domain object identifier is determined according to the first local public domain object identifier.

[0125] According to an embodiment of the present disclosure, business classification may include different categories of program products divided according to target objects, such as personal, merchant, and enterprise categories. You can apply for public accounts with different target objects as the main body to correspond to different businesses on the open platform. As shown in Table 1 below, the correspondence between business classification, APPID, and APPsecret can be recorded in database table 1 (table_1) of the local database. NULL in Table 1 represents an empty field.

[0126] For example, you can apply for a public account a corresponding to Class A business with an individual as the main body, and record the corresponding relationship in database table 1 of the local database: Class A business, APPID_a and APPsecret_a; you can apply for a public account b corresponding to Class B business with an enterprise as the main body, and record the corresponding relationship in database table 1 of the local database: Class B business, APPID_b and APPsecret_b.

[0127] According to the embodiments of the present disclosure, it is also possible to register and authenticate a mini-program based on any official account. For example, mini-program c is derived under official account a. Mini-program c corresponds to a Class C service, and the corresponding relationship can be recorded in local database 1: Class C service, APPID_c, and APPsecret_c. As shown in Table 1 below, the corresponding relationship between service classification, APPID, and APPsecret can be recorded in Database Table 1 of the local database.

[0128] According to an embodiment of the present disclosure, various public accounts and mini-programs can be bound to the same open platform. After the binding is completed, when a user follows a public account or mini-program, a corresponding record can be made in the local database. For users who follow any public account or mini-program under the same open platform, the application side can generate a unique platform object identifier, namely UnionId. Depending on the public account or mini-program followed, the same user can generate different public domain object identifiers, namely OpenId.

[0129] According to the embodiments of the present disclosure, since different users perform different follow operations on different public accounts or mini-programs, there are differences in the data recorded in the database table. If the public domain object identifier is directly used to identify the user, it is easy to cause data unauthorized access and there is a security risk. Therefore, the application side can calculate the public domain object identifier generated by each user's follow operation according to the preset algorithm to generate a unique hash value mark for the user as the private domain object identifier, namely PrivateId. The preset algorithm can be set according to actual needs. For example, the preset algorithm can include independent hashing, repeated hashing or combined hashing, etc., which are not limited here.

[0130] According to an embodiment of the present disclosure, as shown in Table 1 below, a record of the correspondence between the user's private domain object identifier, the APPID of the official account or mini-program, the public domain object identifier, and the attention status identifier can be added to database table 2 (table_2). The attention status identifier can include any one of the following: an attention status identifier, an unattention status identifier, and an abnormal attention status identifier. Database table 2 can be entered into the lake. The first entry into the lake is the stock entry into the lake, and the subsequent entry into the lake is the incremental entry into the lake, that is, the data newly added to database table 2 every day can be entered into the lake.

[0131] According to an embodiment of the present disclosure, as shown in Table 1 below, after a user performs a follow operation, a record of the corresponding relationship between the user's platform object identifier and private domain object identifier can be added to database table 3 (table_3). Since the platform object identifier for the same user on the same platform is constant (i.e., it does not change due to the user following or unfollowing), while the public domain object identifier does change due to the user following or unfollowing, the data in database table 3 can remain accurate and serve as source data for subsequent online transactions conducted by the user.

[0132] According to an embodiment of the present disclosure, as shown in Table 1 below, a record of the correspondence between the APPID, public domain object identifier, and private domain object identifier of the official account or mini program can be added to database table 4 (table_4).

[0133] For example, when a user initiates the process of following a public account, the user's follow status identifier can be generated and saved in database table 2, the generated Unionid can be saved in database table 3, and the generated Openid can be saved in database table 4.

[0134] Table name Field 1 Field 2 Field 3 Field 4 Field 5 Primary Key Database Table 1 Auto-increment ID Business Classification APPID APPsecret NULL Field 2 Database Table 2 Auto-increment ID PrivateId APPID OpenId Attention status indicator Fields 2 and 3 Database Table 3 Auto-increment ID UnionId PrivateId NULL NULL Fields 1 and 2 Database Table 4 Auto-increment ID OpenId APPID PrivateId NULL Fields 1 and 2 Data Lake Table Auto-increment ID OpenId APPID PrivateId U, I, D marking Fields 2, 3, and 4

[0135] Table 1

[0136] According to an embodiment of the present disclosure, the local object dataset may include a first local object dataset, a second local object dataset, and a third local object dataset.

[0137] According to an embodiment of the present disclosure, the first local object dataset may include database table 3 as shown in Table 1 above. The first local object dataset may include a first local platform object identifier (i.e., UnionId in field 2 of database table 3) and a first local private domain object identifier (i.e., PrivateId in field 3 of database table 3). The first local private domain object identifier is determined based on the first local public domain object identifier.

[0138] According to an embodiment of the present disclosure, the second local object dataset may include database table 2 as shown in Table 1 above. The second local object dataset may include a second local public domain object identifier (i.e., OpenId in field 4 of database table 2) and a second local private domain object identifier (i.e., PrivateId in field 2 of database table 2).

[0139] According to an embodiment of the present disclosure, the third local object dataset may include database table 4 as shown in Table 1 above. The third local object dataset may include a third local public domain object identifier (i.e., OpenId in field 2 of database table 4) and a third local private domain object identifier (i.e., PrivateId in field 4 of database table 4).

[0140] According to an embodiment of the present disclosure, a data query instruction can be executed to filter out the first object data corresponding to the APPID, public domain object identifier and private domain object identifier of the official account or mini program related to the follow operation of the target object in the data lake table shown in Table 1 above.

[0141] For example, you can filter the data in the data lake table based on query conditions such as entry time, APPID, and OpenID, and export the results to the reference object data file X.txt.

[0142] According to an embodiment of the present disclosure, after the first object data is obtained through screening, reference object data in text format may be obtained based on the first object data. The reference object data may include a reference platform object identifier and a reference private domain object identifier.

[0143] According to an embodiment of the present disclosure, after obtaining the reference object data, a first local private domain object identifier in the first local object data that matches the reference private domain object identifier can be determined as a target first local private domain object identifier based on the reference private domain object identifier. Based on the target first local private domain object identifier, a first local platform object identifier associated with the target first local private domain object identifier can be determined as a target first local platform object identifier. It can be determined whether the target first local platform object identifier matches the reference platform object identifier.

[0144] For example, PrivateId in the reference object data file X.txt can be used as a query condition. By using the query statement: select UnionId from table_3where PrivateId in X.txt, a query is performed in the local database to check whether a corresponding UnionId exists in database table 3 (table_3).

[0145] According to an embodiment of the present disclosure, when it is determined that the target first local platform object identifier does not match the reference platform object identifier, it is possible to query whether there is a first local private domain object identifier corresponding to the reference private domain object identifier in the database table 3 shown in Table 1 above based on the reference private domain object identifier.

[0146] According to an embodiment of the present disclosure, if it is determined that the first local private domain object identifier corresponding to the reference private domain object identifier does not exist in database table 3, the second local private domain object identifier in the second local object data that matches the reference private domain object identifier can be determined as the target second local private domain object identifier. After determining the target second local private domain object identifier, the attention status identifier in the second local object data corresponding to the target second local private domain object identifier can be set to an abnormal attention status identifier to obtain the local object data to be processed.

[0147] According to an embodiment of the present disclosure, when it is determined that the target first local platform object identifier matches the reference platform object identifier, it is possible to query whether there is a first local private domain object identifier corresponding to the reference private domain object identifier in database Table 3 shown in Table 1 above based on the reference private domain object identifier.

[0148] According to an embodiment of the present disclosure, if it is determined that a first local private domain object identifier corresponding to the reference private domain object identifier exists in database table 3, a third local public domain object identifier that matches the reference public domain object identifier in the third local object data set may be determined as a target third local public domain object identifier, and a third local private domain object identifier associated with the target third local public domain object identifier may be determined as a target third local private domain object identifier. After determining the target third local private domain object identifier, it may be determined whether the target third local private domain object identifier matches the reference private domain object identifier.

[0149] For example, if a corresponding UnionId is found in database table 3, the PrivateId in the reference object data file X.txt can be used as the query condition. The query statement is: select PrivateId from table_4where OpenId in X.txt to query whether the corresponding OpenId exists in database table 4 (table_4) in the local database.

[0150] According to an embodiment of the present disclosure, when it is determined that the target third local private domain object identifier does not match the reference private domain object identifier, the second local public domain object identifier that matches the reference public domain object identifier in the second local object data can be determined as the target second local public domain object identifier, and the attention status identifier in the second local object data corresponding to the target second local public domain object identifier can be set to an abnormal attention status identifier to obtain the local object data to be processed.

[0151] For example, if no corresponding UnionId is found in database table 3, it means that the data may not generate data records on the application side due to network jitter or other reasons. Therefore, the attention status flag of the data of the PrivateId in database table 2 can be set to "abnormal attention status flag". The abnormal attention status flag can be used to indicate data that needs to be cleaned.

[0152] According to an embodiment of the present disclosure, when it is determined that the target third local private domain object identifier matches the reference private domain object identifier, the second local public domain object identifier that matches the reference public domain object identifier in the second local object data can be determined as the target second local public domain object identifier, and the attention status identifier in the second local object data corresponding to the target second local public domain object identifier can be set to the attention status identifier to obtain the local object data to be processed.

[0153] For example, if a UnionId corresponding to a PrivateId is found in database table 4, the data lake and the local database record are consistent. Therefore, the attention status flag of the data with the PrivateId in database table 2 can be set to "already paid attention to." The "already paid attention to" status flag can be used to indicate that the data has been paid attention to.

[0154] According to an embodiment of the present disclosure, if it is determined that the target third local private domain object identifier does not match the reference private domain object identifier, the target third local private domain object identifier can be modified to the reference private domain object identifier. For example, the PrivateId in database table 4 can be updated to the PrivateId corresponding to the data item in database table 3.

[0155] According to an embodiment of the present disclosure, the local object data to be processed is determined based on the reference object data and the local object data set in the local database. Because the local object data to be processed is obtained by determining whether the target first local platform object identifier matches the reference platform object identifier, and determining whether the target third local private domain object identifier matches the reference private domain object identifier, the accuracy of the object data in the local database and the data lake can be guaranteed.

[0156] Figure 4 A flowchart of a method for obtaining local object data to be processed according to another embodiment of the present disclosure is schematically shown.

[0157] like Figure 4 As shown, the method 400 for obtaining the local object data to be processed may include S410 to S460.

[0158] In operation S410 , in response to detecting a data query instruction for the data lake, the data query instruction is executed to obtain reference object data.

[0159] In operation S420 , it may be determined whether the target first native platform object identifier matches the reference platform object identifier.

[0160] If it is determined that the target first local platform object identifier does not match the reference platform object identifier, operation S430 may be performed. In operation S430, the attention state identifier in the second local object data corresponding to the target second local private domain object identifier may be set to an abnormal attention state identifier to obtain the local object data to be processed.

[0161] If it is determined that the target first local platform object identifier matches the reference platform object identifier, operation S440 may be performed. In operation S440, it may be determined whether the target third local private domain object identifier matches the reference private domain object identifier.

[0162] If it is determined that the target third local private domain object identifier does not match the reference private domain object identifier, operation S450 may be executed. In operation S450, the attention state identifier in the second local object data corresponding to the target second local public domain object identifier may be set as an abnormal attention state identifier to obtain the local object data to be processed.

[0163] If it is determined that the target third local private domain object identifier matches the reference private domain object identifier, operation S460 may be executed. In operation S460, the attention status flag in the second local data corresponding to the target second local public domain object identifier may be set to the attention status flag to obtain the local object data to be processed.

[0164] Figure 5 A flowchart of a method for obtaining target local object data and target stock object data according to an embodiment of the present disclosure is schematically shown.

[0165] like Figure 5 As shown, operation S230 may include operations S531 to S532.

[0166] In operation S531 , third local object data to be processed is obtained according to the application-side object data and the first local object data to be processed.

[0167] In operation S532 , target local object data and target stock object data are obtained according to the second local object data to be processed, the third local object data to be processed, and the stock object data in the data lake.

[0168] According to an embodiment of the present disclosure, operation S531 may include the following operations.

[0169] If it is determined that the attention status identifier in the first local object data to be processed matches the attention status identifier in the application-side object data, the first local object data to be processed is determined as the third local object data to be processed. If it is determined that the attention status identifier in the first local object data to be processed does not match the attention status identifier in the application-side object data, the attention status identifier in the first local object data to be processed is set to the unattention status identifier, thereby obtaining the third local object data to be processed.

[0170] According to an embodiment of the present disclosure, operation S532 may include the following operations.

[0171] Based on the second local object data to be processed, the third local object data to be processed, and the existing object data in the data lake, object data to be updated and fixed object data are obtained. The object data to be updated includes at least one of object data to be added, object data to be modified, and object data to be deleted. An update operation is performed on the object data to be updated to obtain updated object data. Based on the updated object data and the fixed object data, target existing object data is obtained. A deletion operation is performed on the local object data to be processed whose attention status is an abnormal attention status in the second local object data to be processed to obtain fourth local object data to be processed. Based on the third local object data to be processed and the fourth local object data to be processed, target local object data is obtained.

[0172] According to an embodiment of the present disclosure, the local object data to be processed may include first local object data to be processed and second local object data to be processed. The first local object data to be processed may include local object data to be processed whose attention status is identified as an attended state identifier. The second local object data to be processed may include local object data to be processed whose attention status is identified as an unattended state identifier or an abnormal attended state identifier.

[0173] According to an embodiment of the present disclosure, it is possible to determine whether the attention status identifier in the first local object data to be processed matches the attention status identifier in the application-side object data. For example, based on the data in database table 2, the application-side interface can be called to check each data item with the attention status identifier set as the attention status identifier. If the two items match, the attention status identifier of the data item does not need to be adjusted; if the two items do not match, the attention status identifier of the data item can be updated to the unattention status identifier.

[0174] According to an embodiment of the present disclosure, the second local object data to be processed, the third local object data to be processed, and the existing object data in the data lake can be compared to obtain the object data to be updated and the fixed object data. The object data to be updated can include at least one of object data to be added, object data to be modified, and object data to be deleted.

[0175] According to an embodiment of the present disclosure, as shown in Table 1, the U flag (Update) in field 5 of the data lake table can be used to identify object data to be modified. The I flag (Insert) can be used to identify object data to be added. The D flag (Delete) can be used to identify object data to be deleted.

[0176] According to the embodiments of the present disclosure, since the accuracy of the object data in the data lake can be guaranteed, the object data in the data lake can be mined according to actual business needs to accurately analyze user behavior, etc.

[0177] According to the embodiments of the present disclosure, since the local object data to be processed whose attention status is marked as an abnormal attention status in the second local object data to be processed can be deleted, it is possible to clean the abnormal data in the local database and the data lake. In addition, by obtaining the third local object data to be processed based on the application-side object data and the first local object data to be processed, and then obtaining the target local object data and the target existing object data based on the second local object data to be processed, the third local object data to be processed, and the existing object data in the data lake, the consistency of the object data in the local database and the data lake with the application-side object data is ensured, thereby improving the accuracy of the data.

[0178] Figure 6 A flowchart of a method for obtaining target local object data and target stock object data according to another embodiment of the present disclosure is schematically shown.

[0179] like Figure 6 As shown, the method 600 for obtaining target local object data and target stock object data may include S610 to S650.

[0180] In operation S610 , local object data to be processed is determined based on reference object data and a local object data set in a local database.

[0181] In operation S620 , it is determined whether the attention status identifier in the first to-be-processed local object data matches the attention status identifier in the application-side object data.

[0182] If it is determined that the attention status identifier in the first local object data to be processed matches the attention status identifier in the application-side object data, operation S630 may be performed. In operation S630, the first local object data to be processed is determined as the third local object data to be processed.

[0183] If it is determined that the attention status identifier in the first local object data to be processed does not match the attention status identifier in the application-side object data, operation S640 may be executed. In operation S640, the attention status identifier in the first local object data to be processed is set to the non-attention status identifier to obtain third local object data to be processed.

[0184] After obtaining the third local object data to be processed, operation S650 may be performed. In operation S650, target local object data and target stock object data are obtained based on the second local object data to be processed, the third local object data to be processed, and the stock object data in the data lake.

[0185] The above are merely exemplary embodiments, but are not limited thereto. Other data processing methods known in the art may also be included as long as they can process data.

[0186] Figure 7 The block diagram schematically shows a data processing device according to an embodiment of the present disclosure.

[0187] like Figure 7 As shown, the data processing device 700 may include a first obtaining module 701 , a first determining module 702 , and a second obtaining module 703 .

[0188] The first obtaining module 701 is configured to, in response to detecting a data query instruction for the data lake, execute the data query instruction to obtain reference object data. The reference object data is used to represent first object data related to a focus operation of a target object, where the focus operation is an operation generated by the target object during the process of utilizing the program product.

[0189] The first determining module 702 is configured to determine the local object data to be processed based on the reference object data and the local object data set in the local database. The local object data set is used to represent the second object data related to the focus operation of the target object.

[0190] The second obtaining module 703 is configured to obtain target local object data and target stock object data based on the application-side object data, the local object data to be processed, and the stock object data in the data lake. The target stock object data includes the target local object data, and both the target local object data and the target stock object data are consistent with the application-side object data.

[0191] According to an embodiment of the present disclosure, the reference object data includes a reference platform object identifier and a reference private domain object identifier.

[0192] According to an embodiment of the present disclosure, the local object dataset includes a first local object dataset and a second local object dataset.

[0193] According to an embodiment of the present disclosure, the first local object data set includes a first local private domain object identifier and a first local platform object identifier.

[0194] According to an embodiment of the present disclosure, the first determining module 702 may include a first obtaining submodule.

[0195] A first obtaining submodule is configured to obtain, when it is determined that the target first local platform object identifier does not match the reference platform object identifier, local object data to be processed based on the reference object data and the second local object dataset. The target first local platform object identifier is a first local platform object identifier associated with the target first local private domain object identifier, and the target first local private domain object identifier is a first local private domain object identifier in the first local object dataset that matches the reference private domain object identifier.

[0196] According to an embodiment of the present disclosure, the local object dataset further includes a third local object dataset. The first determining module 702 may further include a second obtaining submodule.

[0197] The second obtaining submodule is configured to obtain the local object data to be processed according to the reference object data, the second local object data set and the third local object data set when it is determined that the target first local platform object identifier matches the reference platform object identifier.

[0198] According to an embodiment of the present disclosure, the second local object data set includes a second local private domain object identifier.

[0199] According to an embodiment of the present disclosure, the first obtaining submodule may include a first obtaining unit.

[0200] The first obtaining unit is configured to obtain the local object data to be processed based on the second local object data corresponding to the target second local private domain object identifier, wherein the target second local private domain object identifier is a second local private domain object identifier in the second local object data set that matches the reference private domain object identifier.

[0201] According to an embodiment of the present disclosure, the second local object data further includes an attention state identifier, which is used to represent an attention state corresponding to the attention operation.

[0202] According to an embodiment of the present disclosure, the first obtaining unit may include a first obtaining sub-unit.

[0203] The first obtaining subunit is configured to set the attention state identifier in the second local object data corresponding to the target second local private domain object identifier as an abnormal attention state identifier, and obtain the local object data to be processed.

[0204] According to an embodiment of the present disclosure, the first local object data further includes a first local public domain object identifier.

[0205] According to an embodiment of the present disclosure, the second local object data includes a second local public domain object identifier.

[0206] According to an embodiment of the present disclosure, the third local object data set includes at least one third local object data, and the third local object data includes a third local private domain object identifier and a third local public domain object identifier.

[0207] According to an embodiment of the present disclosure, the second obtaining submodule may include a first setting unit.

[0208] The first setting unit is configured to, if it is determined that the target third local private domain object identifier does not match the reference private domain object identifier, set the attention status identifier in the second local object data corresponding to the target second local public domain object identifier to an abnormal attention status identifier, thereby obtaining the local object data to be processed. The target third local private domain object identifier is a third local private domain object identifier associated with the target third local public domain object identifier, the target third local public domain object identifier is a third local public domain object identifier in the third local object data set that matches the reference public domain object identifier, and the target second local public domain object identifier is a second local public domain object identifier in the second local object data set that matches the reference public domain object identifier.

[0209] According to an embodiment of the present disclosure, the second obtaining submodule may further include a second setting unit.

[0210] The second setting unit is configured to, when it is determined that the target third local private domain object identifier matches the reference private domain object identifier, set the attention status identifier in the second local data corresponding to the target second local public domain object identifier to the attended status identifier, thereby obtaining the local object data to be processed.

[0211] According to an embodiment of the present disclosure, when it is determined that the target third local private domain object identifier does not match the reference private domain object identifier, the second obtaining submodule may further include a modifying unit.

[0212] The modification unit is configured to modify the target third local private domain object identifier into a reference private domain object identifier.

[0213] According to an embodiment of the present disclosure, the local object data to be processed includes first local object data to be processed and second local object data to be processed.

[0214] According to an embodiment of the present disclosure, the second obtaining module 703 may include a third obtaining submodule and a fourth obtaining submodule.

[0215] The third obtaining submodule is configured to obtain third local object data to be processed according to the application-side object data and the first local object data to be processed.

[0216] The fourth obtaining submodule is used to obtain target local object data and target existing object data based on the second local object data to be processed, the third local object data to be processed, and the existing object data in the data lake.

[0217] According to an embodiment of the present disclosure, the first local object data to be processed is the local object data to be processed whose attention status identifier is the attended status identifier.

[0218] According to an embodiment of the present disclosure, the third obtaining submodule may include a determining unit and a third setting unit.

[0219] a determining unit, configured to, when it is determined that the attention status identifier in the first local object data to be processed matches the attention status identifier in the application-side object data, determine the first local object data to be processed as the third local object data to be processed; and

[0220] The third setting unit is used to set the attention status identifier in the first local object data to be processed to the non-attention status identifier when it is determined that the attention status identifier in the first local object data to be processed does not match the attention status identifier in the application side object data, so as to obtain the third local object data to be processed.

[0221] According to an embodiment of the present disclosure, the fourth obtaining submodule may include a second obtaining unit, an updating unit, a third obtaining unit, a deleting unit, and a fourth obtaining unit.

[0222] The second obtaining unit is configured to obtain object data to be updated and fixed object data based on the second local object data to be processed, the third local object data to be processed, and the existing object data in the data lake. The object data to be updated includes at least one of object data to be added, object data to be modified, and object data to be deleted.

[0223] The updating unit is used to perform an updating operation on the object data to be updated to obtain the updated object data.

[0224] The third obtaining unit is configured to obtain target inventory object data according to the updated object data and the fixed object data.

[0225] The deleting unit is configured to delete the local object data to be processed whose attention status identifier is an abnormal attention status identifier in the second local object data to be processed, to obtain fourth local object data to be processed.

[0226] The fourth obtaining unit is configured to obtain target local object data according to the third local object data to be processed and the fourth local object data to be processed.

[0227] According to an embodiment of the present disclosure, the first local private domain object identifier is determined according to the first local public domain object identifier.

[0228] According to the modules, submodules, units, and subunits of the embodiments of the present invention, any multiple or at least part of the functions of any multiple thereof can be implemented in one module. According to the modules, submodules, units, and subunits of the embodiments of the present invention, any one or more thereof can be split into multiple modules for implementation. According to the modules, submodules, units, and subunits of the embodiments of the present invention, any one or more thereof can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware of any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation modes of software, hardware, and firmware or in an appropriate combination of any of them. Alternatively, according to the modules, submodules, units, and subunits of the embodiments of the present invention, one or more thereof can be at least partially implemented as a computer program module, which can perform the corresponding function when the computer program module is run.

[0229] For example, any multiple of the first obtaining module 701, the first determining module 702, and the second obtaining module 703 can be combined in one module / unit / subunit to be implemented, or any one of the modules / units / subunits can be split into multiple modules / units / subunits. Alternatively, at least part of the functions of one or more modules / units / subunits in these modules / units / subunits can be combined with at least part of the functions of other modules / units / subunits and implemented in one module / unit / subunit. According to an embodiment of the present disclosure, at least one of the first obtaining module 701, the first determining module 702, and the second obtaining module 703 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation modes of software, hardware, and firmware or in a suitable combination of any of them. Alternatively, at least one of the first obtaining module 701 , the first determining module 702 , and the second obtaining module 703 may be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function may be executed.

[0230] It should be noted that the data processing device part in the embodiment of the present disclosure corresponds to the data processing method part in the embodiment of the present disclosure. The description of the data processing device part specifically refers to the data processing method part and will not be repeated here.

[0231] Figure 8A block diagram schematically shows an electronic device suitable for implementing a data processing method according to an embodiment of the present disclosure. Figure 8 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0232] like Figure 8 As shown, the computer electronic device 800 according to an embodiment of the present disclosure includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage part 809 into a random access memory (RAM) 803. The processor 801 may, for example, include a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include an onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for executing different actions of the method flow according to an embodiment of the present disclosure.

[0233] Various programs and data required for the operation of the electronic device 800 are stored in the RAM 803. The processor 801, ROM 802, and RAM 803 are connected to each other via a bus 804. The processor 801 executes the various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 802 and / or RAM 803. It should be noted that the programs may also be stored in one or more memories other than the ROM 802 and RAM 803. The processor 801 may also execute the various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0234] According to an embodiment of the present disclosure, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to the bus 804. The electronic device 800 may further include one or more of the following components connected to the I / O interface 805: an input portion 806 including a keyboard, a mouse, etc.; an output portion 807 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage portion 808 including a hard disk; and a communication portion 809 including a network interface card such as a LAN card or a modem. The communication portion 809 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed in the drive 810 as needed, so that a computer program read therefrom can be installed into the storage portion 808 as needed.

[0235] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage 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 809, and / or installed from the removable medium 811. When the computer program is executed by the processor 801, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, device, apparatus, module, unit, etc. described above can be implemented by a computer program module.

[0236] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0237] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: 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), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0238] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 802 and / or the RAM 803 described above and / or one or more memories other than the ROM 802 and the RAM 803 .

[0239] An embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method provided by the embodiment of the present disclosure. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the data processing method provided by the embodiment of the present disclosure.

[0240] When the computer program is executed by the processor 801, the above functions defined in the system / device of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0241] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 809, and / or installed from a removable medium 811. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0242] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0243] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes may occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession may actually be executed substantially in parallel, or they may 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 flowchart, as well as the combination of boxes in the block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or may be implemented using a combination of dedicated hardware and computer instructions. It will be understood by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or coupled in various ways, and all such combinations and / or couplings fall within the scope of the present disclosure.

[0244] The embodiments of the present disclosure are described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be used in combination to advantage. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A data processing method, comprising: In response to detecting a data query instruction for the data lake, executing the data query instruction to obtain reference object data, wherein the reference object data is used to represent first object data related to a focus operation of a target object, the focus operation being an operation generated by the target object during a process of utilizing a program product; Determining local object data to be processed based on the reference object data and a local object data set in a local database, wherein the local object data set is used to represent second object data related to the focus operation of the target object; and Target local object data and target stock object data are obtained according to the application-side object data, the local object data to be processed, and the stock object data in the data lake, wherein the target stock object data includes the target local object data, and the target local object data and the target stock object data are both consistent with the application-side object data.

2. The method according to claim 1, wherein The reference object data includes a reference platform object identifier and a reference private domain object identifier; Wherein, the local object dataset includes a first local object dataset and a second local object dataset; The first local object data set includes a first local private domain object identifier and a first local platform object identifier; The step of determining the local object data to be processed based on the reference object data and the local object data set includes: When it is determined that the target first local platform object identifier does not match the reference platform object identifier, the local object data to be processed is obtained based on the reference object data and the second local object data set, wherein the target first local platform object identifier is the first local platform object identifier associated with the target first local private domain object identifier, and the target first local private domain object identifier is the first local private domain object identifier in the first local object data set that matches the reference private domain object identifier.

3. The method according to claim 2, wherein: The local object dataset further includes a third local object dataset; The method further comprises: When it is determined that the target first local platform object identifier matches the reference platform object identifier, the local object data to be processed is obtained according to the reference object data, the second local object data set, and the third local object data set.

4. The method according to claim 2, wherein: The second local object data set includes a second local private domain object identifier; The step of obtaining the to-be-processed local object data according to the reference object data and the second local object data set includes: The local object data to be processed is obtained according to the second local object data corresponding to the target second local private domain object identifier, wherein the target second local private domain object identifier is the second local private domain object identifier in the second local object data set that matches the reference private domain object identifier.

5. The method according to claim 4, wherein The second local object data further includes an attention state identifier, where the attention state identifier is used to represent an attention state corresponding to the attention operation; The obtaining of the to-be-processed local object data according to the second local object data corresponding to the target second local private domain object identifier includes: The attention state identifier in the second local object data corresponding to the target second local private domain object identifier is set as an abnormal attention state identifier to obtain the local object data to be processed.

6. The method according to claim 3, wherein: The first local object data further includes a first local public domain object identifier; Wherein, the second local object data includes a second local public domain object identifier; The third local object data set includes at least one third local object data, and the third local object data includes a third local private domain object identifier and a third local public domain object identifier; The obtaining of the to-be-processed local object data according to the reference object data, the second local object data set, and the third local object data set includes: When it is determined that the target third local private domain object identifier does not match the reference private domain object identifier, the attention status identifier in the second local object data corresponding to the target second local public domain object identifier is set to an abnormal attention status identifier to obtain the local object data to be processed, wherein the target third local private domain object identifier is a third local private domain object identifier associated with the target third local public domain object identifier, the target third local public domain object identifier is a third local public domain object identifier that matches the reference public domain object identifier in the third local object data set, the target second local public domain object identifier is a second local public domain object identifier that matches the reference public domain object identifier in the second local object data set, and the reference public domain object identifier is used to represent a unique identifier for different official accounts or mini-programs under the same open platform.

7. The method according to claim 6, further comprising: When it is determined that the target third local private domain object identifier matches the reference private domain object identifier, the attention status identifier in the second local data corresponding to the target second local public domain object identifier is set as the attention status identifier to obtain the local object data to be processed.

8. The method according to claim 6, wherein: In the case where it is determined that the target third local private domain object identifier does not match the reference private domain object identifier, the method further includes: The target third local private domain object identifier is modified to the reference private domain object identifier.

9. The method according to any one of claims 1 to 5, wherein The local object data to be processed includes first local object data to be processed and second local object data to be processed; The step of obtaining target local object data and target stock object data based on the application-side object data, the local object data to be processed, and the stock object data in the data lake includes: Obtaining third local object data to be processed according to the application-side object data and the first local object data to be processed; and The target local object data and the target stock object data are obtained according to the second local object data to be processed, the third local object data to be processed, and the stock object data in the data lake.

10. The method according to claim 9, wherein: The first local object data to be processed is the local object data to be processed whose attention status identifier is an attended status identifier; The obtaining of third local object data to be processed according to the application-side object data and the first local object data to be processed includes: If it is determined that the attention status identifier in the first local object data to be processed matches the attention status identifier in the application-side object data, determining the first local object data to be processed as the third local object data to be processed; and When it is determined that the attention status identifier in the first local object data to be processed does not match the attention status identifier in the application-side object data, the attention status identifier in the first local object data to be processed is set to the non-attention status identifier to obtain the third local object data to be processed.

11. The method according to claim 9, wherein: The obtaining, according to the second to-be-processed local object data, the third to-be-processed local object data, and the stock object data in the data lake, the target local object data and the target stock object data includes: Obtaining object data to be updated and fixed object data based on the second local object data to be processed, the third local object data to be processed, and the existing object data in the data lake, wherein the object data to be updated includes at least one of object data to be added, object data to be modified, and object data to be deleted; performing an update operation on the object data to be updated to obtain updated object data; Obtaining the target inventory object data according to the updated object data and the fixed object data; Deleting the local object data to be processed whose attention status identifier is an abnormal attention status identifier in the second local object data to be processed to obtain fourth local object data to be processed; and The target local object data is obtained according to the third local object data to be processed and the fourth local object data to be processed.

12. The method according to claim 6, wherein: The first local private domain object identifier is determined according to the first local public domain object identifier.

13. A data processing device comprising: a first obtaining module configured to, in response to detecting a data query instruction for the data lake, execute the data query instruction to obtain reference object data, wherein the reference object data is used to represent first object data related to a focus operation of a target object, where the focus operation is an operation generated by the target object during the process of utilizing a program product; A first determining module is configured to determine local object data to be processed based on the reference object data and a local object dataset in a local database, wherein the local object dataset is used to represent second object data related to the focus operation of the target object; and The second acquisition module is used to obtain target local object data and target stock object data based on the application-side object data, the local object data to be processed, and the stock object data in the data lake, wherein the target stock object data includes the target local object data, and the target local object data and the target stock object data are consistent with the application-side object data.

14. An electronic device comprising: one or more processors; a memory for storing one or more instructions, When the one or more instructions are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 12.

15. A computer-readable storage medium having executable instructions stored thereon, wherein when the executable instructions are executed by a processor, the processor is enabled to implement the method according to any one of claims 1 to 12.

16. A computer program product, comprising computer-executable instructions, wherein the computer-executable instructions are used to implement the method according to any one of claims 1 to 12 when executed.

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