Data mining, multimedia task processing method, device, electronic equipment and medium

Abstract

The present disclosure provides a data mining and multimedia task processing method, device, electronic device and medium, which relates to the field of data processing technology, especially to the field of big data technology, data mining technology and artificial intelligence technology, and can be applied to scenarios such as UGC. The specific implementation scheme is: based on the incremental data of the first predetermined time period, determine the object incremental data corresponding to the first object; based on the first object identification information corresponding to the first object, determine the object full data corresponding to the first object from the full data of the second predetermined time period; based on the object incremental data and the object full data corresponding to the first object, obtain the object aggregate data corresponding to the first object; determine the full data of the third predetermined time period from other full data and the object aggregate data corresponding to the first object, and the other full data is at least part of the full data of the second predetermined time period except the object full data.

Description

Technical Field

[0001] The present disclosure relates to the fields of data processing technology, particularly big data technology, data mining technology, and artificial intelligence technology, and can be applied to scenarios such as User Generated Content (UGC). Specifically, it relates to a data mining and multimedia task processing method, device, electronic device, and medium. Background Art

[0002] With the development of computer technology, all walks of life have generated massive amounts of data. Data mining can be performed on massive amounts of data to obtain the potential value of the data. Summary of the Invention

[0003] The present disclosure provides a data mining and multimedia task processing method, device, electronic device and medium.

[0004] According to one aspect of the present disclosure, a data mining method is provided, comprising: determining object incremental data corresponding to a first object based on incremental data of a first predetermined time period; determining object full data corresponding to the first object from full data of a second predetermined time period based on first object identification information corresponding to the first object, wherein the first predetermined time period is a predetermined time period after the second predetermined time period; obtaining object aggregate data corresponding to the first object based on the object incremental data and object full data corresponding to the first object; and determining full data of a third predetermined time period from other full data and object aggregate data corresponding to the first object, wherein the third predetermined time period includes the first predetermined time period, there is an overlap between the third predetermined time period and the second predetermined time period, and the other full data is at least part of the full data of the second predetermined time period except the object full data.

[0005] According to another aspect of the present disclosure, a multimedia task processing method is provided, including: determining the full amount of data of the above-mentioned multimedia task in a fourth predetermined time period, wherein the full amount of data of the above-mentioned fourth predetermined time period is obtained using the above-mentioned method according to the present disclosure; and processing the above-mentioned multimedia task using the full amount of data of the above-mentioned predetermined time period.

[0006] According to another aspect of the present disclosure, a data mining device is provided, including: a first determination module for determining object incremental data corresponding to a first object based on incremental data of a first predetermined time period; a second determination module for determining object full data corresponding to the first object from full data of a second predetermined time period based on first object identification information corresponding to the first object, wherein the first predetermined time period is a predetermined time period after the second predetermined time period; an acquisition module for obtaining object aggregate data corresponding to the first object based on object incremental data and object full data corresponding to the first object; and a third determination module for determining full data of a third predetermined time period from other full data and object aggregate data corresponding to the first object, wherein the third predetermined time period includes the first predetermined time period, there is an overlap between the third predetermined time period and the second predetermined time period, and the other full data is at least part of the full data of the second predetermined time period except the full data of the object.

[0007] According to another aspect of the present disclosure, a multimedia task processing device is provided, including: a fifth determination module, used to determine the full amount of data of the above-mentioned multimedia task in a fourth predetermined time period, wherein the full amount of data of the above-mentioned fourth predetermined time period is obtained using the above-mentioned device according to the present disclosure; and a processing module, used to process the above-mentioned multimedia task using the full amount of data of the above-mentioned fourth predetermined time period.

[0008] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described above.

[0009] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the above method.

[0010] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, which implements the above method when executed by a processor.

[0011] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings are used to better understand the present invention and do not constitute a limitation of the present invention.

[0013] Figure 1 Schematically illustrates an exemplary system architecture to which the content processing method and apparatus according to an embodiment of the present disclosure can be applied;

[0014] Figure 2 The following schematically shows a flow chart of a content processing method according to an embodiment of the present disclosure;

[0015] Figure 3A The following schematically illustrates a principle diagram of a data mining method according to an embodiment of the present disclosure;

[0016] Figure 3B Schematically illustrates a schematic example diagram of a first predetermined time period, a second predetermined time period, and a third predetermined time period according to an embodiment of the present disclosure;

[0017] Figure 4 The flowchart of the multimedia task processing method according to the embodiment of the present disclosure is schematically shown;

[0018] Figure 5 An exemplary diagram of a multimedia task processing method according to an embodiment of the present disclosure is schematically shown;

[0019] Figure 6 A block diagram of a data mining device according to an embodiment of the present disclosure is schematically shown;

[0020] Figure 7 A block diagram schematically illustrates a multimedia task processing device according to an embodiment of the present disclosure; and

[0021] Figure 8 The block diagram schematically shows an electronic device suitable for implementing a data mining method and a multimedia task processing method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0023] Figure 1 An exemplary system architecture to which the data mining method, multimedia task processing method, and apparatus according to an embodiment of the present disclosure can be applied is schematically shown.

[0024] It should be noted that Figure 1The 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 imply that the embodiments of the present disclosure may not be applied to other devices, systems, environments, or scenarios. For example, in another embodiment, an exemplary system architecture to which the data mining method, multimedia task processing method, and apparatus may be applied may include a terminal device, but the terminal device may implement the data mining method, multimedia task processing method, and apparatus provided by the embodiments of the present disclosure without interacting with a server.

[0025] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as at least one of a wired and wireless communication link. The terminal device may include at least one of the first terminal device 101, the second terminal device 102, and the third terminal device 103.

[0026] A user can use at least one of the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. At least one of the first terminal device 101, the second terminal device 102, and the third terminal device 103 can be installed with various communication client applications, such as at least one of a knowledge reading application, a web browser application, a search application, an instant messaging tool, an email client, and a social platform software.

[0027] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with display screens and supporting web browsing. For example, the electronic device can include at least one of a smartphone, a tablet computer, a laptop computer, and a desktop computer.

[0028] Server 105 can be a server that provides various services. For example, server 105 can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system. It solves the problems of traditional physical hosts and VPS services (Virtual Private Servers), such as difficult management and poor business scalability.

[0029] It should be noted that the data mining method and multimedia task processing method provided in the embodiments of the present disclosure can generally be executed by one of the first terminal device 101, the second terminal device 102, and the third terminal device 103. Correspondingly, the data mining apparatus and multimedia task processing apparatus provided in the embodiments of the present disclosure can also be set in one of the first terminal device 101, the second terminal device 102, and the third terminal device 103.

[0030] Alternatively, the data mining method and multimedia task processing method provided by the embodiments of the present disclosure may also be generally executed by the server 105. Accordingly, the data mining device and multimedia task processing device provided by the embodiments of the present disclosure may generally be set in the server 105. The data mining method and multimedia task processing method provided by the embodiments of the present disclosure may also be executed by a server or server cluster that is different from the server 105 and can communicate with at least one of the first terminal device 101, the second terminal device 102, the third terminal device 103 and the server 105. Accordingly, the data mining device and multimedia task processing device provided by the embodiments of the present disclosure may also be set in a server or server cluster that is different from the server 105 and can communicate with at least one of the first terminal device 101, the second terminal device 102, the third terminal device 103 and the server 105.

[0031] It should be understood that Figure 1 The number of the first terminal device, the second terminal device, the third terminal device, the network and the server is only illustrative. According to the implementation requirements, there can be any number of the first terminal device, the second terminal device, the third terminal device, the network and the server.

[0032] 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.

[0033] Figure 2 The flowchart of the data mining method according to the embodiment of the present disclosure is schematically shown.

[0034] like Figure 2 As shown, the method 200 includes operations S210 to S240.

[0035] In operation S210, object incremental data corresponding to a first object is determined based on incremental data of a first predetermined period of time.

[0036] In operation S220, the object full data corresponding to the first object is determined from the full data of the second predetermined time period according to the first object identification information corresponding to the first object.

[0037] In operation S230 , object aggregate data corresponding to the first object is obtained based on the object incremental data and the object full data corresponding to the first object.

[0038] In operation S240 , full data for a third predetermined period is determined from other full data and the object aggregate data corresponding to the first object.

[0039] According to an embodiment of the present disclosure, the first predetermined time period may be a predetermined time period after the second predetermined time period. The third predetermined time period may include the first predetermined time period. The third predetermined time period may overlap with the second predetermined time period. The other full data may be at least a portion of the full data of the second predetermined time period, excluding the full object data.

[0040] According to embodiments of the present disclosure, the data may be data that can be applied to various application tasks. The application tasks may include at least one of the following: resource recommendation tasks, resource retrieval tasks, natural language processing tasks, financial risk control tasks, industrial quality inspection tasks, autonomous driving tasks, medical treatment tasks, platform architecture tasks, cloud service tasks, metaverse tasks, augmented reality tasks, virtual reality tasks, and mixed reality tasks.

[0041] According to embodiments of the present disclosure, resource recommendation tasks may include at least one of the following: video resource recommendation tasks, audio resource recommendation tasks, and text resource recommendation tasks. Depending on the resource's application scenario, resource recommendation tasks may include at least one of the following: financial product recommendation tasks, route recommendation tasks, object recommendation tasks, music recommendation tasks, film and television drama recommendation tasks, game recommendation tasks, food recommendation tasks, scenic spot recommendation tasks, accommodation recommendation tasks, and game recommendation tasks.

[0042] According to embodiments of the present disclosure, natural language processing tasks may include any of the following: information extraction tasks, sentiment analysis tasks, knowledge reasoning tasks, intent recognition tasks, text classification tasks, text summarization tasks, article generation tasks, machine translation tasks, coreference resolution tasks, and speech evaluation tasks. Information extraction tasks may include any of the following: relationship extraction tasks, named entity recognition tasks, and event extraction tasks. Machine translation tasks may include at least one of the following: text translation tasks and speech translation tasks.

[0043] According to embodiments of the present disclosure, financial risk control tasks may include at least one of the following: credit assessment tasks, anti-fraud tasks, credit granting tasks, and default tasks. Industrial quality inspection tasks may include at least one of the following: defect detection tasks and anomaly detection tasks. Defect detection tasks may include at least one of the following: small component detection tasks, instrument defect detection tasks, hot-rolled steel plate defect detection tasks, cold-rolled steel plate defect detection tasks, and automotive parts defect detection tasks.

[0044] According to embodiments of the present disclosure, autonomous driving tasks may include at least one of the following: lane segmentation, high-precision map generation, point cloud data stitching, trajectory optimization, and obstacle detection. Metaverse tasks may include at least one of the following: virtual object generation and 3D reconstruction.

[0045] According to an embodiment of the present disclosure, a medical treatment task may include at least one of the following: a medical image recognition task, a medical image segmentation task, and a medical image classification task. A medical image recognition task may include at least one of the following: a non-whole body part medical image recognition task and a whole body part medical image recognition task. For example, a non-whole body part medical image recognition task may include at least one of the following: an abdominal image recognition task, an eye image recognition task, a brain image recognition task, and a lung image recognition task. A medical image segmentation task may include at least one of the following: a non-whole body part medical image segmentation task and a whole body part medical image segmentation task. For example, a non-whole body part medical image segmentation task may include at least one of the following: an abdominal image segmentation task, an eye image segmentation task, a brain image segmentation task, and a lung image segmentation task. A medical image classification task may include at least one of the following: a non-whole body part medical image classification task and a whole body part medical image classification task. An object detection task for a medical image may include at least one of the following: a non-whole body part medical image classification task and a whole body part medical image classification task.

[0046] According to embodiments of the present disclosure, data may refer to multimedia data, depending on the type of data in an application task. Multimedia data may include at least one of the following: image data, audio data, and text data. Image data may include at least one of the following: static image data and dynamic image data. Dynamic image data may include video frames from a video. Static image data may include at least one of the following: static text image data and static non-text image data. Static text image data may include at least one of the following: static document text image data and static scene text image data. Dynamic image data may include at least one of the following: dynamic text image data and dynamic non-text image data. Dynamic text image data may include at least one of the following: dynamic document text image data and dynamic scene text image data. Document text images may refer to text images with neat layout, controlled lighting, and a relatively simple background. Scene text images may refer to text images with a relatively complex background, diverse text forms, and uncontrolled lighting. Text forms may include at least one of the following: text color, size, font, orientation, and irregular layout. Irregular layout may include at least one of bending, tilting, wrinkling, deformation, and incompleteness.

[0047] According to an embodiment of the present disclosure, data may include user-generated content (UGC). UGC can be an object displaying or providing original content to other objects through an Internet platform. For example, original content can be referred to as a resource. Resources can include at least one of the following: image data, text data, and audio data. An object can be a resource producer or a resource consumer. A resource consumption object can refer to an object that consumes resources. A resource production object can refer to an object that produces resources. An Internet platform can include applications. The above-mentioned multimedia data can be UGC data.

[0048] According to an embodiment of the present disclosure, incremental data may refer to data added to full data within a predetermined time period. Full data may refer to existing data within a predetermined data range. Full data may include full data of user-generated content (UGC). Incremental data may include incremental data of UGC. Full data may include full data of at least one object. Full object data may refer to full data corresponding to collection index information. Full object data may be determined based on full data of at least one single object. Full data of a single object may correspond to object identification information. Incremental data may include at least one object incremental data. Object incremental data may include at least one single object incremental data. Object incremental data may correspond to object identification information. Object identification information may be used to characterize an object. Object identification information may include one of object identity information and object account identification information.

[0049] According to an embodiment of the present disclosure, the set index information may be determined based on the object identification information. For example, the set index information may be obtained by mapping the object identification information. For example, the object identification information of a single object full data belonging to the same object full data may be mapped to obtain the same set index information.

[0050] According to an embodiment of the present disclosure, when the object is a first object, the object incremental data corresponding to the first object may refer to the first object incremental data. The object full data corresponding to the first object may be determined based on the full data of at least one single object. The at least one single object full data may include the first object full data and the full data of other objects. The first object full data may refer to the full data corresponding to the first object. The other object full data may be the full data corresponding to other objects. The other objects may refer to objects with the same set index information as that corresponding to the first object.

[0051] According to an embodiment of the present disclosure, the object aggregate data corresponding to the first object may be obtained based on the object incremental data and the object full data corresponding to the first object. Other full data may be at least part of the data in the full data of the second predetermined time period except the object full data corresponding to the first object. At least part of the data may include the object full data corresponding to other objects. For example, other full data may be part of the data in the full data of the second predetermined time period except the object full data corresponding to the first object. Alternatively, other full data may be all data in the full data of the second predetermined time period except the object full data corresponding to the first object.

[0052] According to an embodiment of the present disclosure, the duration represented by the third predetermined time period may be the same as or different from the duration represented by the second predetermined time period. The first predetermined time period, the second predetermined time period, and the third predetermined time period may be configured based on actual business needs and are not limited here. It is sufficient to ensure that the first predetermined time period is a predetermined time period after the second predetermined time period, the third predetermined time period includes the first predetermined time period, and there is overlap between the third predetermined time period and the second predetermined time period. For example, the first predetermined time period, the second predetermined time period, and the third predetermined time period may be determined based on the data update cycle.

[0053] For example, the first predetermined time period may be from 2022-11-01 00:00 00 to 2022-11-01 23:59 59. The second predetermined time period may be from 2022-10-15 00:00 00 to 2022-10-31 23:59 59. The third predetermined time period may be from 2022-10-16 00:00 00 to 2022-11-01 23:59 59.

[0054] For example, the first predetermined time period may be from 2022-10-01 00:00 00 to 2022-10-01 23:59 59. The second predetermined time period may be from 2022-09-22 00:00 00 to 2022-09-30 23:59 59. The third predetermined time period may be from 2022-09-25 00:00 00 to 2022-10-01 23:59 59.

[0055] According to an embodiment of the present disclosure, a data interface can be called in response to detecting a data acquisition instruction. A sample data set and a set of original label values ​​corresponding to the sample data set are acquired from a data source using the data interface. The data source may include at least one of the following: a local database, a cloud database, and the Internet. The data acquisition instruction may be generated in one of the following ways: the data acquisition instruction may be generated and sent by other electronic devices. Alternatively, the data acquisition instruction may be automatically generated when it is detected that a trigger condition is met. For example, it may be determined that the trigger condition is met when it is detected that incremental data appears. Alternatively, the data acquisition instruction may be generated when it is detected that a data acquisition operation is triggered. The data acquisition operation may be triggered by an operating body. The operating body may include one of the following: an object and a stylus.

[0056] According to an embodiment of the present disclosure, determining the object incremental data corresponding to the first object for the first predetermined time period based on the incremental data for the first predetermined time period may include: determining at least one single object incremental data corresponding to the first object from the incremental data for the first predetermined time period based on the first object identification information of the first object. Based on the at least one single object incremental data corresponding to the first object, the object incremental data corresponding to the first object for the first predetermined time period is obtained. For example, the at least one single object incremental data corresponding to the first object may be merged to obtain the object incremental data corresponding to the first object for the first predetermined time period. Alternatively, at least one target single object incremental data may be determined from the at least one single object incremental data corresponding to the first object. Based on the at least one target single object incremental data, the object incremental data corresponding to the first object for the first predetermined time period is obtained.

[0057] According to an embodiment of the present disclosure, the full object data corresponding to the first object can be determined from the full data of the second predetermined time period based on the first object identification information corresponding to the first object, which may include: the full data of the second predetermined time period may include at least one full object data. The full object data can be represented by set index information. Based on the object identification information corresponding to the first object, set index information matching the first object identification information can be determined from the set index information corresponding to at least one full object data. The full object data corresponding to the set index information matching the first object identification information is determined as the full object data corresponding to the first object. Alternatively, based on the first object identification information corresponding to the first object, the set index information corresponding to the first object is determined. Based on the set index information, a data set corresponding to the set index information is determined from at least one data set. The at least one data set includes the full data of the second predetermined time period. The data included in the data set corresponding to the set index information is determined as the full object data corresponding to the first object.

[0058] According to an embodiment of the present disclosure, after determining the object incremental data and the object full data corresponding to the first object, the object incremental data and the object full data corresponding to the first object may be merged to obtain object aggregate data corresponding to the first object. Alternatively, the object incremental data corresponding to the first object may be processed to obtain intermediate object incremental data corresponding to the first object. The intermediate object incremental data and the object full data corresponding to the first object are merged to obtain object aggregate data corresponding to the first object. Processing the object incremental data corresponding to the first object to obtain the intermediate object incremental data corresponding to the first object may include: preprocessing the object incremental data corresponding to the first object to obtain the intermediate object incremental data corresponding to the first object. Preprocessing may include at least one of the following: data cleaning, data conversion, data integration, and data specification.

[0059] According to an embodiment of the present disclosure, after determining the object aggregate data corresponding to the first object, the full data corresponding to the third predetermined time period can be determined from other full data and the object aggregate data corresponding to the first object. For example, if it is determined that the first intermediate full data corresponding to the third predetermined time period contains empty data corresponding to the second object identification information, the first intermediate full data corresponding to the second object identification information is deleted to obtain the second intermediate full data corresponding to the third predetermined time period. Based on the second intermediate full data corresponding to the third predetermined time period, the full data corresponding to the third predetermined time period is obtained. If it is determined that the first intermediate full data corresponding to the third predetermined time period does not contain empty data corresponding to the second object identification information in the first intermediate full data corresponding to the third predetermined time period, in response to determining that the first intermediate full data corresponding to the fourth object contains empty data in the first intermediate full data corresponding to the third predetermined time period, the first intermediate full data corresponding to the fourth object is deleted to obtain the full data corresponding to the third predetermined time period. In response to determining that the first intermediate full data corresponding to the third predetermined time period does not contain the first intermediate full data corresponding to the fourth object, which is empty data, the first intermediate full data corresponding to the third predetermined time period is determined as the full data corresponding to the third predetermined time period.

[0060] According to an embodiment of the present disclosure, if it is determined that the full data for the second predetermined time period is the first time the full data is acquired, the full data of at least one single object can be merged based on the object identification information to obtain the full data of the object corresponding to the object identification information. The full data for the second predetermined time period is obtained based on the full data of the object corresponding to the at least one object identification information. If it is determined that the full data for the second predetermined time period is not the first time the full data is acquired, the full data for the second predetermined time period can be directly acquired. For example, the full data for the second predetermined time period can be directly acquired from the cache.

[0061] For example, in the case where the application task is a resource recommendation task, the data may include data for implementing resource recommendation. The data for implementing resource recommendation may include at least one of the following: first object basic data and object social interaction data, etc. The data may include at least one of full data and incremental data. The first object basic data may be used to describe the basic information of the object. Object social interaction data may be used for interactive behavior data between at least one of an object and a resource and an object and another object. For example, the first object basic data may include at least one of the following: object account data, object gender data, object age data, and object interest data, etc. Object social interaction data may include at least one of the following: attention data, reward data, comment data, barrage data, like data, collection data, sharing data, and forwarding data, etc.

[0062] For example, when the application task is a financial risk control task, the data may include data used to implement financial risk control. The data used to implement financial risk control may include at least one of the following: second object basic data, object behavior data, object credit assessment data, object authorization data, and external access data. The data may include at least one of full data and incremental data. The second object basic data may be used to describe the basic information of the object. Object behavior data may refer to data generated by the object when using an application. The application may include an application corresponding to financial risk control. Object credit assessment data may refer to data used to assess the credit of the object. Object authorization data may refer to data authorized by the object. For example, object authorization data may include authorization data for the permissions of the application. External access data may refer to data corresponding to financial risk control obtained from the outside. For example, external access data may include credit assessment data of an external financial institution, etc.

[0063] For example, if the application task is an article generation task, the data may include data used to implement article generation. The data used to implement article generation may include at least one of the following: main body data and summary data. The data may include at least one of full data and incremental data. Main body data may refer to data related to the main text information of the material. Summary data may refer to data related to the outline information and concatenation information of the material. Summary data may include at least one of the following: title data, summary data, and clue data.

[0064] For example, if the application task is a text translation task, the data may include data used to implement text translation. The data used to implement text translation may include at least one of the following: source text data and target text data. The data may include at least one of full data and incremental data. If the application task is a speech translation task, the data used to implement speech translation may include at least one of the following: source speech data and target speech data. The data may include at least one of full data and incremental data.

[0065] According to embodiments of the present disclosure, the data mining methods of the embodiments of the present disclosure can be performed by an electronic device. For example, the electronic device can be a server or a terminal device. The electronic device can include at least one processor. The processor can be used to perform the data mining methods provided by the embodiments of the present disclosure. For example, the data mining methods provided by the embodiments of the present disclosure can be performed using a single processor, or can be performed in parallel using multiple processors.

[0066] According to an embodiment of the present disclosure, since the object aggregate data of the first object is determined based on the object incremental data of the first object in the first predetermined time period and the object full data of the second predetermined time period, the object incremental data of the first object is determined based on the incremental data of the first predetermined time period, and the object full data of the first object is determined from the full data of the second predetermined time period based on the first object identification information of the first object, thus achieving the determination of the object aggregate data based on the incremental data. In addition, since the full data of the third predetermined time period is determined based on other full data and the object aggregate data of the first object, thus achieving the full data of the third predetermined time period based on the object incremental data of the first predetermined time period, reducing the amount of data mining, thereby reducing resource consumption, and improving data mining efficiency. Since the amount of data mining is reduced, the amount of data processing of electronic devices such as processors is reduced, and the processing efficiency of electronic devices such as processors is improved. In addition, the effect of improving the internal performance of electronic devices in accordance with natural laws is achieved, thereby enhancing the core competitiveness of electronic devices.

[0067] According to an embodiment of the present disclosure, operation S210 may include the following operations.

[0068] At least one single object incremental data corresponding to the first object is determined based on the incremental data of the first predetermined time period, and the at least one single object incremental data corresponding to the first object is merged to obtain object incremental data corresponding to the first object.

[0069] According to an embodiment of the present disclosure, the incremental data may include at least one object incremental data. The object incremental data may include at least one single object incremental data. The single object incremental data has object identification information corresponding to the single object incremental data. Based on the first object identification information, at least one single object incremental data corresponding to the first object identification information can be determined from the incremental data of the first predetermined time period. The at least one single object incremental data corresponding to the first object identification information is then merged to obtain the object incremental data corresponding to the first object.

[0070] For example, if the first object identification information of the first object is "1112", the incremental data may include five single object incremental data, such as single object incremental data A1, single object incremental data A2, single object incremental data A3, single object incremental data A4 and single object incremental data A5. The object identification information of the single object incremental data A1 is "1112". The object identification information of the single object incremental data A2 is "1111". The object identification information of the single object incremental data A3 is "1112". The object identification information of the single object incremental data A4 is "1112". The object identification information of the single object incremental data A5 is "1113". Since the object identification information of the single object incremental data A1, the single object incremental data A3 and the single object incremental data A4 are all "1112", and "1112" is the first object identification information, therefore, according to the first object identification information, from the incremental data, it is determined that the single object incremental data corresponding to the first object identification information includes the single object incremental data A1, the single object incremental data A3 and the single object incremental data A4. The single object incremental data A1, the single object incremental data A3 and the single object incremental data A4 are merged to obtain the object incremental data corresponding to the first object.

[0071] According to an embodiment of the present disclosure, at least one single object incremental data corresponding to the first object is determined based on the incremental data of the first predetermined time period, and the at least one single object incremental data corresponding to the first object is merged to obtain the object incremental data corresponding to the first object, thereby achieving more accurate determination of the object incremental data corresponding to the first object.

[0072] According to an embodiment of the present disclosure, operation S220 may include the following operations.

[0073] Determine, based on first object identification information corresponding to the first object, set index information corresponding to the first object. Determine, based on the set index information, a data set corresponding to the set index information from at least one data set. Determine the data included in the data set corresponding to the set index information as the full object data corresponding to the first object.

[0074] According to an embodiment of the present disclosure, at least one data set may include a full amount of data for the second predetermined time period.

[0075] According to embodiments of the present disclosure, set index information can be used to characterize data sets. At least one data set can include the full object data corresponding to each of the at least one data set. The number of data sets can be configured based on actual business needs and is not limited here. The full object data can be stored in the data set. The number of full data of a single object included in the data set can be configured based on actual business needs and is not limited here. The set index information can be determined based on object identification information.

[0076] According to an embodiment of the present disclosure, determining the set index information corresponding to the first object based on the first object identification information corresponding to the first object may include: performing mapping processing on the first object identification information corresponding to the first object to obtain mapping information corresponding to the first object. For example, the first object identification information corresponding to the first object may be processed using a digest algorithm to obtain mapping information corresponding to the first object. The digest algorithm may include at least one of the following: a message digest algorithm, a secure hash algorithm (SHA), and a message authentication code (MAC) algorithm. Based on the mapping information corresponding to the first object, the set index information corresponding to the first object is obtained. The set index information corresponding to the first object may be referred to as target set index information.

[0077] According to an embodiment of the present disclosure, determining, based on first object identification information corresponding to the first object, set index information corresponding to the first object may include: determining at least one target object identifier from multiple object identifiers included in the first object identification information corresponding to the first object; and obtaining, based on the at least one target object identifier, the set index information corresponding to the first object.

[0078] According to an embodiment of the present disclosure, a data set may have set index information corresponding to the data set. Set index information matching target set index information may be determined from at least one set index information. The data set corresponding to the set index information matching the target set index information is determined as a target data set. Data included in the target data set is determined to be the full amount of object data corresponding to the first object.

[0079] According to an embodiment of the present disclosure, since the data set and the set index information correspond to each other, by determining the data set corresponding to the set index information from at least one data set, the data included in the data set corresponding to the set index information is determined as the full object data corresponding to the first object. Compared with directly determining the full object data corresponding to the first object from the full data, the scope of searching for the full object data corresponding to the first object is narrowed, thereby improving the efficiency of determining the full object data corresponding to the first object and achieving relatively rapid determination of the full object data corresponding to the first object.

[0080] According to an embodiment of the present disclosure, the above data mining method may further include the following operations.

[0081] Store the data set in a file.

[0082] According to an embodiment of the present disclosure, a data set may be stored in a file corresponding to the data set.

[0083] According to an embodiment of the present disclosure, the first object identification information may include multiple object identifiers.

[0084] According to an embodiment of the present disclosure, determining the set index information corresponding to the first object according to the first object identification information corresponding to the first object may include the following operations.

[0085] At least one target object identifier is determined from a plurality of object identifiers corresponding to the first object, and set index information corresponding to the first object is obtained according to the at least one target object identifier.

[0086] According to embodiments of the present disclosure, multiple object identifiers may be sorted. A predetermined number of object identifiers before sorting may be determined as at least one target object identifier. The predetermined number may be configured based on actual business needs and is not limited herein. For example, the predetermined number may be 2.

[0087] According to an embodiment of the present disclosure, obtaining set index information corresponding to a first object based on at least one target object identifier may include: combining the at least one target object identifier to obtain the set index information corresponding to the first object. Alternatively, determining the at least one target object identifier as the set index information corresponding to the first object.

[0088] For example, the first object identification information is "1234567891." The first object identification information may include 10 object identifiers, such as object identifier "1," object identifier "2," object identifier "3," object identifier "4," object identifier "5," object identifier "6," object identifier "7," object identifier "8," object identifier "9," and object identifier "1." Two target object identifiers may be determined from the 10 object identifiers. The two target object identifiers may be the first two object identifiers in the 10 object identifiers. Thus, the two target object identifiers are object identifier "1" and object identifier "2."

[0089] According to an embodiment of the present disclosure, operation S230 may include the following operations.

[0090] The object incremental data and the object full data corresponding to the first object are merged to obtain the object aggregate data corresponding to the first object.

[0091] According to an embodiment of the present disclosure, object incremental data corresponding to a first object may be added to object full data corresponding to the first object to obtain object aggregate data corresponding to the first object. The object full data corresponding to the first object may include single object full data corresponding to the first object. Adding the object incremental data corresponding to the first object to the object full data corresponding to the first object to obtain object aggregate data corresponding to the first object may include: adding the object incremental data corresponding to the first object to the single object full data corresponding to the first object to obtain new single object full data corresponding to the first object. Based on the new single object full data corresponding to the first object and other single object full data, object aggregate data corresponding to the first object is obtained. Other single object full data may refer to at least part of the data in the object full data corresponding to the first object except the single object full data corresponding to the first object.

[0092] According to an embodiment of the present disclosure, merging object incremental data and object full data corresponding to a first object to obtain object aggregate data corresponding to the first object may include the following operations.

[0093] If it is determined that the full object data corresponding to the first object is empty data, the object incremental data corresponding to the first object is determined as the object aggregate data corresponding to the first object. If it is determined that the full object data corresponding to the first object is not empty data, the object incremental data corresponding to the first object is added to the full object data corresponding to the first object to obtain the object aggregate data corresponding to the first object.

[0094] According to an embodiment of the present disclosure, the fact that the full object data corresponding to the first object is empty data may mean that the full object data corresponding to the first object does not exist in the full data. For example, if no set index information matching the set index information corresponding to the first object is found in at least one set index information, it can be said that the full object data corresponding to the first object is empty data. In the case where it is determined that the full object data corresponding to the first object is empty data, the object incremental data corresponding to the first object can be determined as the object aggregate data corresponding to the first object.

[0095] According to an embodiment of the present disclosure, the fact that the full object data corresponding to the first object is non-empty data may refer to the presence of the full object data corresponding to the first object in the full data. For example, if set index information that matches the set index information corresponding to the first object is found from at least one set index information, it can be said that the full object data corresponding to the first object is non-empty data. When it is determined that the full object data corresponding to the first object is non-empty data, the object incremental data corresponding to the first object can be added to the object full data corresponding to the first object to obtain the object aggregate data corresponding to the first object.

[0096] According to an embodiment of the present disclosure, by determining the object incremental data corresponding to the first object as the object aggregate data corresponding to the first object when it is determined that the full object data corresponding to the first object is empty data, and adding the object incremental data corresponding to the first object to the full object data corresponding to the first object when it is determined that the full object data corresponding to the first object is non-empty data, the object aggregate data corresponding to the first object is obtained, thereby achieving relatively accurate acquisition of the object aggregate data corresponding to the first object.

[0097] According to an embodiment of the present disclosure, when it is determined that the full amount of object data corresponding to the first object is empty data, the following operations may also be included.

[0098] According to the first object identification information corresponding to the first object, a data set corresponding to the first object identification information is created, and the object aggregate data corresponding to the first object is stored in the data set corresponding to the first object identification information.

[0099] According to an embodiment of the present disclosure, when it is determined that the full object data corresponding to a first object is empty data, collection index information corresponding to the first object identification information can be determined based on the first object identification information. Based on the collection index information corresponding to the first object identification information, a data collection corresponding to the collection index information is created. The object aggregate data corresponding to the first object is stored in the data collection corresponding to the collection index information.

[0100] According to an embodiment of the present disclosure, by creating a data set corresponding to the set index information based on the set index information corresponding to the first object identification information when determining that the full object data corresponding to the first object is empty data, the accuracy and completeness of the data set are effectively guaranteed.

[0101] According to an embodiment of the present disclosure, operation S240 may include the following operations.

[0102] Determine first intermediate full data corresponding to the third predetermined time period from other full data and the object aggregate data corresponding to the first object. Obtain full data corresponding to the third predetermined time period based on the first intermediate full data corresponding to the third predetermined time period.

[0103] According to an embodiment of the present disclosure, the first intermediate full data in the third predetermined time period can be determined from other full data and object aggregate data corresponding to the first object. After determining the first intermediate full data, in response to determining that there is no full data that meets the predetermined deletion condition in the first intermediate full data, the first intermediate full data corresponding to the third predetermined time period can be determined as the full data corresponding to the third predetermined time period. In response to determining that there is full data that meets the predetermined deletion condition in the first intermediate full data, the full data that meets the predetermined deletion condition can be deleted from the first intermediate full data to obtain the full data corresponding to the third predetermined time period. The scheduled deletion bar can be used to determine whether the first intermediate full data is the full data to be deleted. The full data to be deleted can refer to the full data that meets the predetermined deletion condition. The full data to be deleted can include at least one of the following: full data of a single object that does not exist in the third predetermined time period, and full data of an object that does not exist in the third predetermined time period.

[0104] According to an embodiment of the present disclosure, obtaining the full data corresponding to the third predetermined time period based on the first intermediate full data corresponding to the third predetermined time period may include the following operations.

[0105] If it is determined that the first intermediate full data corresponding to the second object identification information in the first intermediate full data corresponding to the third predetermined time period is empty data, the first intermediate full data corresponding to the second object identification information is deleted to obtain the second intermediate full data corresponding to the third predetermined time period. The full data corresponding to the third predetermined time period is obtained based on the second intermediate full data corresponding to the third predetermined time period.

[0106] According to an embodiment of the present disclosure, the fact that the first intermediate full data corresponding to the second object identification information is empty data may mean that the first intermediate full data corresponding to the first object identification information does not exist in the first intermediate full data corresponding to the third predetermined time period. For example, if no set index information matching the set index information corresponding to the first object identification information is found in at least one set index information, it may be indicated that the first intermediate full data corresponding to the second object identification information is empty data in the first intermediate full data corresponding to the third predetermined time period.

[0107] According to an embodiment of the present disclosure, if it is determined that the first intermediate full data corresponding to the second object identification information in the first intermediate full data corresponding to the third predetermined time period is empty data, the first intermediate full data corresponding to the second object identification information can be deleted to obtain the second intermediate full data corresponding to the third predetermined time period. For example, the first intermediate full data corresponding to the set index information corresponding to the second object identification information can be deleted to obtain the second intermediate full data corresponding to the third predetermined time period.

[0108] According to an embodiment of the present disclosure, obtaining the full data corresponding to the third predetermined time period based on the second intermediate full data corresponding to the third predetermined time period may include: in response to determining that the second intermediate full data corresponding to the third object is empty data in the second intermediate full data corresponding to the third predetermined time period, deleting the second intermediate full data corresponding to the third object to obtain the full data corresponding to the third predetermined time period. In response to determining that the second intermediate full data corresponding to the third object is empty data in the second intermediate full data corresponding to the third predetermined time period, determining the second intermediate full data corresponding to the third predetermined time period as the full data corresponding to the third predetermined time period.

[0109] According to an embodiment of the present disclosure, by deleting the first intermediate full data corresponding to the second object identification information when it is determined that the first intermediate full data corresponding to the third predetermined time period is empty data, the second intermediate full data corresponding to the third predetermined time period is obtained, thereby saving storage space and effectively ensuring the accuracy and integrity of the full data.

[0110] According to an embodiment of the present disclosure, obtaining the full data corresponding to the third predetermined time period based on the second intermediate full data corresponding to the third predetermined time period may include the following operations.

[0111] In response to determining that the second intermediate full data corresponding to the third object in the second intermediate full data corresponding to the third predetermined time period is empty data, the second intermediate full data corresponding to the third object is deleted to obtain the full data corresponding to the third predetermined time period.

[0112] According to an embodiment of the present disclosure, the fact that the second intermediate full data corresponding to the third object is empty data may mean that the second intermediate full data corresponding to the third object does not exist in the second intermediate full data corresponding to the third predetermined time period. If it is determined that the second intermediate full data corresponding to the third object exists in the second intermediate full data corresponding to the third predetermined time period but is empty data, the second intermediate full data corresponding to the third object may be deleted to obtain the full data corresponding to the third predetermined time period.

[0113] According to the embodiment of the present disclosure, the second object identification information may be the same as or different from the first object identification information. The second object corresponding to the second object identification information may be the same as or different from the first object corresponding to the first object identification information. The third object may be the same as or different from the first object and the second object.

[0114] According to an embodiment of the present disclosure, by deleting the second intermediate full data corresponding to the third object when it is determined that the second intermediate full data corresponding to the third object is empty data in the second intermediate full data corresponding to the third predetermined time period, the full data corresponding to the third predetermined time period is obtained, thereby saving storage space and effectively ensuring the accuracy and integrity of the full data.

[0115] According to an embodiment of the present disclosure, the above data mining method may further include the following operations.

[0116] In the case where it is determined that the first intermediate full data corresponding to the second object identification information does not exist in the first intermediate full data corresponding to the third predetermined time period and is empty data, in response to determining that the first intermediate full data corresponding to the fourth object exists in the first intermediate full data corresponding to the third predetermined time period and is empty data, the first intermediate full data corresponding to the fourth object is deleted to obtain the full data corresponding to the third predetermined time period.

[0117] According to an embodiment of the present disclosure, in response to determining that the first intermediate full data corresponding to the fourth object does not exist in the first intermediate full data corresponding to the third predetermined time period and is empty data, the first intermediate full data corresponding to the fourth object is deleted to obtain the full data corresponding to the third predetermined time period.

[0118] According to an embodiment of the present disclosure, the fourth object may be the same as the first object, the second object, and the third object, or may be different from at least one of the first object, the second object, and the third object.

[0119] For example, the set index information corresponding to the second object identification information is "15". The third object identification information corresponding to the third object is "18912". The set index information corresponding to the third object identification information is "28". If it is determined that the first intermediate full data corresponding to "15" does not exist in the first intermediate full data corresponding to the third predetermined time period, the first intermediate full data corresponding to "15" can be deleted. If it is determined that the first intermediate full data corresponding to "18912" does not exist in the first intermediate full data corresponding to the third predetermined time period, the first intermediate full data corresponding to "18912" can be deleted.

[0120] According to an embodiment of the present disclosure, by deleting the first intermediate full data corresponding to the fourth object when it is determined that the first intermediate full data corresponding to the third predetermined time period is empty data, the full data corresponding to the third predetermined time period is obtained, thereby saving storage space and effectively ensuring the accuracy and completeness of the full data.

[0121] According to an embodiment of the present disclosure, the above data mining method may further include the following operations.

[0122] A third predetermined time period is determined according to the predetermined time condition and the second predetermined time period.

[0123] According to an embodiment of the present disclosure, the predetermined time condition may refer to a condition for determining a third predetermined time period. The second predetermined time period may be a time period formed by a start time and an end time. For example, the predetermined time condition may refer to a condition for determining a predetermined time period after the start time of the second predetermined time period as the start time of the third predetermined time period, and determining the end time of the first predetermined time period as the end time of the third predetermined time period.

[0124] According to an embodiment of the present disclosure, the above data mining method may further include the following operations.

[0125] The full amount of data corresponding to the third predetermined time period is stored in the predetermined storage area.

[0126] According to the embodiments of the present disclosure, the predetermined storage area can be configured according to actual business needs and is not limited here. For example, the predetermined storage area can be a predetermined database. The database type of the predetermined database can be configured according to actual business needs and is not limited here.

[0127] According to an embodiment of the present disclosure, the full amount of data corresponding to the third predetermined time period may be stored in a predetermined storage area, so that the full amount of data corresponding to the third predetermined time period may be subsequently obtained from the predetermined storage area.

[0128] Reference below Figure 3A and Figure 3B , the data mining method according to the embodiment of the present disclosure is further explained in combination with specific embodiments.

[0129] Figure 3A The schematic diagram schematically shows the principle of the data mining method according to the embodiment of the present disclosure.

[0130] like Figure 3A As shown in 300A, the full amount of data 305 corresponding to the second predetermined time period may include M data sets. For example, data set 305_1, data set 305_2, ..., data set 305_m, ..., data set 305_M-1, and data set 305_M. M may be an integer greater than or equal to 1. m∈{1, 2, ..., M-1, M}. Data set 305_m may have set index information associated with data set 305_m.

[0131] Based on incremental data 301 for a first predetermined time period, object incremental data 302 corresponding to a first object is determined. Based on first object identification information 303 corresponding to the first object, set index information 304 corresponding to the first object is determined. Based on set index information 304, a data set 305_M-1 corresponding to set index information 304 is determined from the M data sets. The data included in data set 305_M-1 corresponding to set index information 304 is determined as the full object data 306 corresponding to the first object.

[0132] Based on the object incremental data 302 corresponding to the first object and the object full data 306 corresponding to the first object, object aggregate data 307 corresponding to the first object is obtained. Full data 309 for a third predetermined time period is determined from other full data 308 and the object aggregate data 307 corresponding to the first object. The third predetermined time period includes the first predetermined time period. There is an overlap between the third predetermined time period and the second predetermined time period. Other full data 308 is all data in the full data 305 for the second predetermined time period except for the object full data 306 (i.e., data set 305_M-1). For example, other full data 308 may include data set 305_1, data set 305_2, ..., data set 305_m, ..., data set 305_M.

[0133] Figure 3B The following schematically illustrates an example of a first predetermined time period, a second predetermined time period, and a third predetermined time period according to an embodiment of the present disclosure.

[0134] like Figure 3B As shown in 300B, 310 may represent a first predetermined time period, 311 may represent a second predetermined time period, and 312 may represent a third predetermined time period.

[0135] Figure 4 The flowchart of the multimedia task processing method according to the embodiment of the present disclosure is schematically shown.

[0136] like Figure 4 As shown, the method 400 includes operations S410 to S420.

[0137] In operation S410 , full data of the multimedia task in a fourth predetermined time period is determined.

[0138] In operation S420 , the multimedia task is processed using the full amount of data of the fourth predetermined time period.

[0139] According to an embodiment of the present disclosure, the full amount of data of the fourth predetermined time period may be obtained by using the data mining method according to the embodiment of the present disclosure.

[0140] According to an embodiment of the present disclosure, the data mining method according to the embodiment of the present disclosure can be used to obtain the full amount of data of the multimedia task in the fourth predetermined time period. The fourth predetermined time period can be a predetermined time period that is unrelated to at least one of the third predetermined time period, the second predetermined time period, and the first predetermined time period. Alternatively, the fourth predetermined time period can be a predetermined time period that is related to at least one of the third predetermined time period, the second predetermined time period, and the first predetermined time period. For example, the fourth predetermined time period can be the third predetermined time period. Alternatively, the fourth predetermined time period can be the second predetermined time period.

[0141] According to an embodiment of the present disclosure, after determining the full amount of data for the fourth predetermined time period, the full amount of data for the fourth predetermined time period can be used to process a multimedia task. The multimedia task may include at least one of the following: an image task, an audio task, and a text task. For example, the image task may include at least one of the following: an image recognition task, an image retrieval task, an image segmentation task, an image classification task, and an object detection task. The audio task may include at least one of the following: an audio recognition task, an audio retrieval task, and an audio classification task. The text task may include at least one of the following: a text recognition task, a text retrieval task, a text segmentation task, a text classification task, and a text detection task.

[0142] According to an embodiment of the present disclosure, since the full amount of data of the multimedia task in the fourth predetermined time period is determined using the data mining method described in accordance with the embodiment of the present disclosure, the amount of data mining is reduced, thereby reducing resource consumption and improving data mining efficiency. The full amount of data in the fourth predetermined time period is then used to process the multimedia task, thereby improving the processing efficiency of the multimedia task. Since the amount of data mining is reduced, the amount of data processing by electronic devices such as processors is reduced, thereby improving the processing efficiency of electronic devices such as processors. In addition, the effect of improving the internal performance of electronic devices in accordance with natural laws is obtained, thereby enhancing the core competitiveness of electronic devices.

[0143] According to an embodiment of the present disclosure, the full amount of data for the fourth predetermined time period may be

[0144] Figure 5 An example schematic diagram of a multimedia task processing method according to an embodiment of the present disclosure is schematically shown.

[0145] like Figure 5 As shown in 500, the multimedia task may be an image task. The full data 503 for the image task in the fourth predetermined time period is obtained using the data mining method described in accordance with an embodiment of the present disclosure. The full data for the fourth predetermined time period may include object incremental data 501 for the fifth predetermined time period and full data 502 for the sixth predetermined time period. The fifth predetermined time period is a predetermined time period after the sixth predetermined time period. The fourth predetermined time period includes the fifth predetermined time period. There is overlap between the fourth and sixth predetermined time periods.

[0146] The multimedia task is processed using the full amount of data 503 of the fourth predetermined time period to obtain multimedia task processing information 504 .

[0147] The above are merely exemplary embodiments, but are not limited thereto. Other data mining methods and multimedia task processing methods known in the art may also be included, as long as the amount of data mining can be reduced.

[0148] In the technical solutions disclosed herein, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0149] Figure 6 The block diagram of the data mining device according to the embodiment of the present disclosure is schematically shown.

[0150] like Figure 6 As shown, the data mining device 600 may include a first determination module 610 , a second determination module 620 , a first obtaining module 630 and a third determination module 640 .

[0151] The first determining module 610 is configured to determine object incremental data corresponding to the first object based on incremental data in a first predetermined time period.

[0152] The second determining module 620 is configured to determine the full object data corresponding to the first object from the full data of a second predetermined time period according to the first object identification information corresponding to the first object. The first predetermined time period is a predetermined time period after the second predetermined time period.

[0153] The first obtaining module 630 is configured to obtain object aggregate data corresponding to the first object based on the object incremental data and the object full data corresponding to the first object.

[0154] A third determining module 640 is configured to determine the full data for a third predetermined time period from the other full data and the object aggregate data corresponding to the first object. The third predetermined time period includes the first predetermined time period. The third predetermined time period overlaps with the second predetermined time period. The other full data is at least a portion of the full data for the second predetermined time period excluding the object full data.

[0155] According to an embodiment of the present disclosure, the second determining module 620 may include a first determining submodule, a second determining submodule, and a third determining submodule.

[0156] The first determining submodule is configured to determine the set index information corresponding to the first object according to the first object identification information corresponding to the first object.

[0157] The second determining submodule is configured to determine, based on the set index information, a data set corresponding to the set index information from at least one data set, wherein the at least one data set includes all data of a second predetermined time period.

[0158] The third determining submodule is configured to determine the data included in the data set corresponding to the set index information as the full object data corresponding to the first object.

[0159] According to an embodiment of the present disclosure, the first object identification information includes a plurality of object identifiers.

[0160] According to an embodiment of the present disclosure, the first determining submodule may include a first determining unit and a first obtaining unit.

[0161] The first determining unit is configured to determine at least one target object identifier from a plurality of object identifiers corresponding to the first object.

[0162] The first obtaining unit is configured to obtain set index information corresponding to the first object according to at least one target object identifier.

[0163] According to an embodiment of the present disclosure, the first obtaining module 630 may include a first obtaining sub-module.

[0164] The first obtaining submodule is configured to merge the object incremental data and the object full data corresponding to the first object to obtain the object aggregate data corresponding to the first object.

[0165] According to an embodiment of the present disclosure, the first obtaining submodule may include a second determining unit and a second obtaining unit.

[0166] The second determining unit is configured to determine the object incremental data corresponding to the first object as the object aggregate data corresponding to the first object when it is determined that the object full data corresponding to the first object is empty data.

[0167] The second obtaining unit is used to add the object incremental data corresponding to the first object to the object full data corresponding to the first object to obtain the object aggregate data corresponding to the first object when it is determined that the object full data corresponding to the first object is non-empty data.

[0168] According to an embodiment of the present disclosure, when it is determined that the full amount of object data corresponding to the first object is empty data, the method may further include:

[0169] According to the first object identification information corresponding to the first object, a data set corresponding to the first object identification information is created, and the object aggregate data corresponding to the first object is stored in the data set corresponding to the first object identification information.

[0170] According to an embodiment of the present disclosure, the third determining module 640 may include a fourth determining submodule and a second obtaining submodule.

[0171] The fourth determining submodule is configured to determine first intermediate full data corresponding to a third predetermined time period from other full data and the object aggregate data corresponding to the first object.

[0172] The second obtaining submodule is configured to obtain the full data corresponding to the third predetermined time period based on the first intermediate full data corresponding to the third predetermined time period.

[0173] According to an embodiment of the present disclosure, when it is determined that the first intermediate full data corresponding to the third predetermined time period contains empty data corresponding to the second object identification information, the second obtaining submodule may include a third obtaining unit and a fourth obtaining unit.

[0174] The third obtaining unit is configured to delete the first intermediate full data corresponding to the second object identification information, and obtain the second intermediate full data corresponding to a third predetermined time period.

[0175] The fourth obtaining unit is configured to obtain the full data corresponding to the third predetermined time period based on the second intermediate full data corresponding to the third predetermined time period.

[0176] According to an embodiment of the present disclosure, the fourth obtaining unit may include an obtaining sub-unit.

[0177] The obtaining subunit is used to delete the second intermediate full data corresponding to the third object in response to determining that the second intermediate full data corresponding to the third predetermined time period is empty data, and obtain the full data corresponding to the third predetermined time period.

[0178] According to an embodiment of the present disclosure, the data mining device 600 may further include a second obtaining module.

[0179] The second acquisition module is used to, in response to determining that the first intermediate full data corresponding to the third predetermined time period contains the first intermediate full data corresponding to the fourth object and is empty data, delete the first intermediate full data corresponding to the fourth object and obtain the full data corresponding to the third predetermined time period.

[0180] According to an embodiment of the present disclosure, the data mining device 600 may further include a fourth determining module.

[0181] The fourth determining module is configured to determine a third predetermined time period according to the predetermined time condition and the second predetermined time period.

[0182] According to an embodiment of the present disclosure, the first determining module 610 may include a fifth determining submodule and a third obtaining submodule.

[0183] The fifth determining submodule is configured to determine at least one single object incremental data corresponding to the first object based on the incremental data of the first predetermined time period.

[0184] The third obtaining submodule is configured to merge at least one single object incremental data corresponding to the first object to obtain object incremental data corresponding to the first object.

[0185] According to an embodiment of the present disclosure, the data mining device 600 may further include a storage module.

[0186] The storage module is configured to store the full amount of data corresponding to the third predetermined time period in a predetermined storage area.

[0187] According to an embodiment of the present disclosure, the full amount of data includes the full amount of data of user-generated content.

[0188] Figure 7The block diagram of the multimedia task processing device according to an embodiment of the present disclosure is schematically shown.

[0189] like Figure 7 As shown, the multimedia task processing device 700 may include a fifth determining module 710 and a processing module 720 .

[0190] The fifth determining module 710 is configured to determine the full amount of data of the multimedia task in a fourth predetermined time period.

[0191] The processing module 720 is configured to process the multimedia task using the full amount of data in the fourth predetermined time period.

[0192] According to an embodiment of the present disclosure, the full amount of data of the fourth predetermined time period is obtained by using the data mining device according to an embodiment of the present disclosure.

[0193] According to an embodiment of the present disclosure, the multimedia task may include at least one of the following: an image task, an audio task, and a text task.

[0194] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0195] According to an embodiment of the present disclosure, an electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described above.

[0196] According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the method described above.

[0197] According to an embodiment of the present disclosure, a computer program product includes a computer program, and when the computer program is executed by a processor, the computer program implements the method described above.

[0198] Figure 8 A block diagram of an electronic device suitable for implementing a data mining method and a multimedia task processing method according to an embodiment of the present disclosure is schematically shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0199] like Figure 8 As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0200] Multiple components in the electronic device 800 are connected to the I / O interface 805, including an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the electronic device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0201] The computing unit 801 can be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as the data mining method and the multimedia task processing method. For example, in some embodiments, the data mining method and the multimedia task processing method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the data mining method and the multimedia task processing method described above can be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute the data mining method and the multimedia task processing method in any other appropriate manner (for example, by means of firmware).

[0202] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0203] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data mining device, so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0204] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0205] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0206] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0207] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0208] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0209] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A data mining method, comprising: Determining object incremental data corresponding to the first object based on incremental data of the first predetermined time period; determining, based on the first object identification information corresponding to the first object, the full amount of object data corresponding to the first object from the full amount of data in a second predetermined time period, wherein the first predetermined time period is a predetermined time period after the second predetermined time period; Obtaining object aggregate data corresponding to the first object based on the object incremental data and the object full data corresponding to the first object; and determining full data for a third predetermined time period from other full data and object aggregate data corresponding to the first object, wherein the third predetermined time period includes the first predetermined time period, the third predetermined time period overlaps with the second predetermined time period, and the other full data is at least a portion of the full data for the second predetermined time period excluding the full data for the object; Wherein, the data is multimedia data.

2. The method according to claim 1, wherein The determining, based on the first object identification information corresponding to the first object, the full amount of object data corresponding to the first object from the full amount of data in the second predetermined time period includes: Determining, based on first object identification information corresponding to the first object, set index information corresponding to the first object; Determining, based on the set index information, a data set corresponding to the set index information from at least one data set, wherein the at least one data set includes all data of the second predetermined time period; and The data included in the data set corresponding to the set index information is determined as the full object data corresponding to the first object.

3. The method according to claim 2, wherein: The first object identification information includes a plurality of object identifiers; The determining, based on the first object identification information corresponding to the first object, the set index information corresponding to the first object includes: determining at least one target object identifier from a plurality of object identifiers corresponding to the first object; and According to the at least one target object identifier, set index information corresponding to the first object is obtained.

4. The method according to any one of claims 1 to 3, wherein The obtaining, based on the object incremental data and the object full data corresponding to the first object, object aggregate data corresponding to the first object includes: The object incremental data and the object full data corresponding to the first object are merged to obtain object aggregate data corresponding to the first object.

5. The method according to claim 4, wherein The merging of the object incremental data and the object full data corresponding to the first object to obtain the object aggregate data corresponding to the first object includes: In a case where it is determined that the full object data corresponding to the first object is empty data, determining the object incremental data corresponding to the first object as the object aggregate data corresponding to the first object; and When it is determined that the full object data corresponding to the first object is non-empty data, the object incremental data corresponding to the first object is added to the full object data corresponding to the first object to obtain the object aggregate data corresponding to the first object.

6. The method according to claim 5, wherein: In the case where it is determined that the full amount of object data corresponding to the first object is empty data, the method further includes: Creating a data set corresponding to the first object identification information according to the first object identification information corresponding to the first object; and The object aggregate data corresponding to the first object is stored in a data set corresponding to the first object identification information.

7. The method according to any one of claims 1 to 6, wherein The determining of the full data for a third predetermined time period from the other full data and the object aggregated data corresponding to the first object includes: Determining first intermediate full data corresponding to the third predetermined time period from the other full data and the object aggregate data corresponding to the first object; and The full data corresponding to the third predetermined time period is obtained according to the first intermediate full data corresponding to the third predetermined time period.

8. The method according to claim 7, wherein: The obtaining, based on the first intermediate full data corresponding to the third predetermined time period, full data corresponding to the third predetermined time period includes: In the case where it is determined that the first intermediate full amount of data corresponding to the third predetermined time period contains empty data corresponding to the second object identification information, Deleting the first intermediate full data corresponding to the second object identification information to obtain the second intermediate full data corresponding to the third predetermined time period; and The full data corresponding to the third predetermined time period is obtained according to the second intermediate full data corresponding to the third predetermined time period.

9. The method according to claim 8, wherein The obtaining, based on the second intermediate full data corresponding to the third predetermined time period, full data corresponding to the third predetermined time period includes: In response to determining that the second intermediate full data corresponding to the third object in the second intermediate full data corresponding to the third predetermined time period is empty data, the second intermediate full data corresponding to the third object is deleted to obtain the full data corresponding to the third predetermined time period.

10. The method according to claim 8 or 9, further comprising: In the case where it is determined that the first intermediate full data corresponding to the second object identification information does not exist in the first intermediate full data corresponding to the third predetermined time period and is empty data, in response to determining that the first intermediate full data corresponding to the fourth object exists in the first intermediate full data corresponding to the third predetermined time period and is empty data, the first intermediate full data corresponding to the fourth object is deleted to obtain the full data corresponding to the third predetermined time period.

11. The method according to any one of claims 1 to 10, further comprising: The third predetermined time period is determined according to the predetermined time condition and the second predetermined time period.

12. The method according to any one of claims 1 to 11, wherein The determining, based on the incremental data of the first predetermined time period, the object incremental data corresponding to the first object includes: determining at least one single object incremental data corresponding to the first object based on the incremental data of the first predetermined time period; and At least one single object incremental data corresponding to the first object is merged to obtain object incremental data corresponding to the first object.

13. The method according to any one of claims 1 to 12, further comprising: The full amount of data corresponding to the third predetermined time period is stored in a predetermined storage area.

14. The method according to any one of claims 1 to 13, wherein The full amount of data includes the full amount of data of user-generated content.

15. A multimedia task processing method, comprising: determining full data of the multimedia task in a fourth predetermined time period, wherein the full data of the fourth predetermined time period is obtained using the method according to any one of claims 1 to 14, and the fourth predetermined time period is the third predetermined time period; and The multimedia task is processed using the full amount of data in the fourth predetermined time period.

16. The method according to claim 15, wherein The multimedia task includes at least one of the following: an image task, an audio task, and a text task.

17. A data mining device comprising: A first determining module, configured to determine object incremental data corresponding to a first object based on incremental data in a first predetermined time period; a second determining module, configured to determine, based on first object identification information corresponding to the first object, the full amount of object data corresponding to the first object from the full amount of data in a second predetermined time period, wherein the first predetermined time period is a predetermined time period after the second predetermined time period; a first obtaining module, configured to obtain object aggregate data corresponding to the first object based on object incremental data and object full data corresponding to the first object; and a third determining module, configured to determine full data for a third predetermined time period from other full data and object aggregate data corresponding to the first object, wherein the third predetermined time period includes the first predetermined time period, the third predetermined time period overlaps with the second predetermined time period, and the other full data is at least a portion of the full data for the second predetermined time period excluding the full data for the object; Wherein, the data is multimedia data.

18. A multimedia task processing device, comprising: a fifth determining module, configured to determine full data of the multimedia task in a fourth predetermined time period, wherein the full data of the fourth predetermined time period is obtained using the apparatus according to claim 17, and the fourth predetermined time period is the third predetermined time period; and A processing module is used to process the multimedia task using the full amount of data in the fourth predetermined time period.

19. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 16.

20. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to enable the computer to execute the method according to any one of claims 1 to 16.

21. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method according to any one of claims 1 to 16.

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