Data processing method, apparatus, device, and medium

Abstract

This invention relates to the field of data processing technology, and particularly to a data processing method, apparatus, device, and medium. The method includes: determining at least one dimension based on historical periodic factual business data, and setting a corresponding expected proportion threshold for each dimension; determining factual business sub-data corresponding to each dimension in the current period's factual business data based on the expected proportion threshold; updating the expected threshold for the current period corresponding to the dimension based on the factual business sub-data corresponding to the dimension; and determining a feature value corresponding to the factual business sub-data for a specified user under the dimension based on the expected threshold and the factual business sub-data corresponding to the dimension. The implementation of this invention achieves rolling updates of the expected threshold without user intervention, improving the accuracy of determining corresponding feature values ​​based on the expected threshold, and further improving the accuracy of user profiling.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a data processing method, apparatus, device, and medium. Background Technology

[0002] In existing technologies, the common practice in enterprise-level data analysis data modeling involves technical personnel determining the required data dimensions based on enterprise needs, pre-defining an expected threshold, and then calculating feature values ​​for various dimensions based on a large amount of actual business data according to this expected threshold. User profiles are then created based on these feature values. However, if the feature values ​​do not match expectations during the feature value determination process, or if there are significant fluctuations in the actual business data later on, multiple manual interventions are usually required to adjust the expected threshold and then recalculate the feature values. This process consumes significant technical manpower and computing resources, relies excessively on the professional judgment of technical personnel, has relatively high maintenance costs, and the resulting user profiles are often inaccurate. Summary of the Invention

[0003] To address the technical problems existing in the prior art, the present invention provides a data processing method, the method comprising:

[0004] Based on historical periodic factual business data, at least one corresponding dimension is determined, and a corresponding expected proportion threshold is set for each dimension. The dimension characterizes the occurrence environment of factual business sub-data in the factual business data, and the factual business data includes factual business sub-data generated under multiple dimensions.

[0005] Based on the expected proportion threshold, determine the factual business sub-data corresponding to each of the dimensions in the factual business data of the current period;

[0006] Update the expected threshold for the current period corresponding to the dimension based on the factual business sub-data corresponding to the dimension;

[0007] Based on the expected threshold and the factual business sub-data corresponding to the dimension, determine the feature value corresponding to the factual business sub-data of the specified user under the dimension.

[0008] Furthermore, the method also includes:

[0009] The user profile of the specified user is updated based on the feature value and the corresponding factual business sub-data.

[0010] Furthermore, the user profile includes feature values ​​corresponding to resource transfers; the method also includes:

[0011] Receive a resource transfer request triggered by a user, the resource transfer request including the number of resources to be transferred and the object to be transferred;

[0012] The number of resources transferred is compared with the expected threshold corresponding to the resource transfer feature value in the user profile, and it is determined whether the resource transfer object is in the blacklist.

[0013] When the resource transfer target is not on the blacklist and the number of resource transfers is greater than the expected threshold corresponding to the resource transfer feature value, a transfer instruction is sent to the user.

[0014] Based on the received confirmation transfer instruction from the user, determined by the transfer instruction, the resource transfer quantity is transferred to the resource transfer object.

[0015] Further, updating the expected threshold for the current period corresponding to the dimension based on the factual business sub-data corresponding to the dimension includes:

[0016] The current expected threshold is determined based on the factual business sub-data corresponding to the dimension.

[0017] Determine whether the current expected threshold is the same as the expected threshold of the historical period;

[0018] When the current expected threshold is different from the expected threshold of the historical period, the current expected threshold is used as the expected threshold of the current period.

[0019] Furthermore, the user profile includes historical feature values ​​corresponding to each of the dimensions;

[0020] Updating the user profile of the specified user based on the feature value and the corresponding factual business sub-data includes:

[0021] Determine whether the feature value is the same as the historical feature value corresponding to the dimension.

[0022] When the feature value is different from the historical feature value corresponding to the dimension, the feature value is updated to the current feature value corresponding to the dimension.

[0023] Furthermore, the method also includes:

[0024] The feature values ​​of each dimension of the specified user are input into a pre-built occupation determination model, and the occupation category of the specified user is output.

[0025] The occupational categories are added as tags to the user profile corresponding to the specified user.

[0026] Furthermore, the method also includes freezing or taking offline users who have not updated their user profiles within a preset time.

[0027] On the other hand, the present invention provides a data processing apparatus, the apparatus comprising:

[0028] The expected proportion threshold determination module is used to determine at least one corresponding dimension based on historical periodic factual business data, and set a corresponding expected proportion threshold for each dimension. The dimension represents the occurrence environment of factual business sub-data in the factual business data, and the factual business data includes factual business sub-data generated under multiple dimensions.

[0029] The sub-data determination module is used to determine the factual business sub-data corresponding to each of the dimensions in the factual business data of the current period based on the expected proportion threshold.

[0030] The expected threshold determination module is used to update the expected threshold of the current period corresponding to the dimension based on the factual business sub-data corresponding to the dimension.

[0031] The feature value determination module is used to determine the feature value corresponding to the factual business sub-data of a specified user under the dimension based on the expected threshold and the factual business sub-data corresponding to the dimension.

[0032] On the other hand, the present invention provides an electronic device comprising:

[0033] processor;

[0034] Memory used to store the processor's executable instructions;

[0035] The processor is configured to execute the instructions to implement the data processing method as described above.

[0036] In another aspect, this statement provides a computer-readable storage medium in which, when the instructions in the computer-readable storage medium are executed by a processor of a data processing device / electronic device, the data processing device / electronic device is enabled to perform the data processing as described above.

[0037] The implementation of this application has the following beneficial effects:

[0038] The data processing method, apparatus, equipment, and medium provided in this application determine multiple dimensions required by an enterprise based on historical periodic business data, and configure a corresponding expected proportion threshold for each dimension. The expected proportion threshold can be used to extract a corresponding number of sub-data points of business data, thereby improving the processing efficiency of business data and avoiding excessive memory consumption due to an excessive number of sub-data points. Simultaneously, multiple dimensions can meet the enterprise's needs and improve user satisfaction. Then, upon receiving the business data of the current period, the expected proportion threshold can be used to determine the sub-data points of business data corresponding to each dimension in the current period's business data. The sub-data points corresponding to different dimensions can be the same or different, thus enabling multi-dimensional processing of business data and avoiding inaccurate calculations due to missing sub-data points. The expected threshold for the current period corresponding to each dimension is then updated based on the sub-data points of business data corresponding to that dimension. This rolling update of the expected threshold can be achieved without user intervention, improving the accuracy of determining corresponding feature values ​​based on the expected threshold and further enhancing the accuracy of user profiling. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the present invention, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0040] Figure 1 This is a flowchart illustrating a data processing method provided in an embodiment of this application;

[0041] Figure 2 This is a flowchart illustrating another data processing method provided in an embodiment of this application;

[0042] Figure 3 This is a flowchart illustrating another data processing method provided in an embodiment of this application;

[0043] Figure 4 This is a schematic diagram of the structure of a data processing device provided in an embodiment of the present invention;

[0044] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0047] To better understand this application, the following explanations and clarifications are provided regarding the technical terms used:

[0048] A data warehouse, abbreviated as DW or DWH, is a strategic collection of data that supports decision-making processes at all levels within an enterprise. It is a single data store created for analytical reporting and decision support purposes. It provides guidance for business process improvement, monitoring of time, cost, quality, and control for enterprises that require business intelligence. The inputs to a data warehouse are a variety of data sources, and the final outputs are used for data analysis, data mining, and data reporting within the enterprise.

[0049] A data warehouse can include a temporary storage layer, a data warehouse layer, and a reference layer. The ODS (Original Data Storage) layer is responsible for pasting the source data. This data is isomorphic to the source system's data. Updates are typically divided into full and incremental updates, and simple data cleaning is usually performed during the pasting process. The DW (Data Warehouse) layer breaks down date-related data for more specific categorization, usually by year, month, and day. The ETL scripts from the ODS to the DW layer clean and design the data based on business needs. If there are no specific business needs, the data is processed according to the source system's data structure and future plans. The requirements for this layer are consistency, accuracy, and maximum data integrity. The APP (Application Layer) provides the data needed for reporting and data visualization.

[0050] Understandably, data in a data warehouse can be linked together using data models to establish relationships between different data types, such as the relationship between orders (order number, payer, etc.) and payments (amount, payment time, etc.).

[0051] In order to realize the technical solution of this application and make it easier for more engineering and technical workers to understand and apply this application, the working principle of this application will be further explained in conjunction with specific embodiments.

[0052] The following describes an embodiment of a data processing method according to this application. Figure 1 This is a flowchart illustrating a data processing method provided in an embodiment of this application. This specification provides the operational steps of the method described in the embodiments or flowchart, but based on conventional or non-inventive methods, more or fewer operational steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many steps and does not represent the only possible execution order. Specifically, as... Figure 1 As shown, the entity executing this solution can be the aforementioned data warehouse, and the method can include:

[0053] S102. Determine at least one corresponding dimension based on historical periodic factual business data, and set a corresponding expected proportion threshold for each dimension. The dimension represents the occurrence environment of factual business sub-data in the factual business data, and the factual business data includes factual business sub-data generated under multiple dimensions.

[0054] Specifically, factual business data can be the data required for reports and data dashboards provided by the aforementioned APP layer. Factual business data can include multiple sub-data items, each generated across multiple dimensions. These dimensions can be time, location, transaction, product, user, payment, etc. It can be understood that each sub-data item can contain data related to multiple dimensions, such as time, location, transaction, product, user, and payment. For example, a sub-data item might include data on time, location, user, and transaction. All the sub-data items corresponding to all dimensions can constitute the actual business data.

[0055] Specifically, the period can be set according to user needs. For example, the period can be one day, meaning that the APP layer will collect and generate the corresponding factual business data at the same time every day.

[0056] S104. Based on the expected proportion threshold, determine the factual business sub-data corresponding to each of the dimensions in the factual business data of the current period.

[0057] Specifically, the expected proportion threshold can be used to select factual business sub-data in factual business data. It can be understood that the factual business data provided by the APP layer is sorted according to dimensions. The sorting method can be sorted in order of size. Then, the expected proportion threshold can be used to select the factual business sub-data of the corresponding dimension with the corresponding proportion. For example, if there are 30 factual business sub-data corresponding to the user dimension in the factual business data of the current period, and the expected proportion threshold is 10%, then the 3 factual business sub-data with the highest ranking can be selected.

[0058] Understandably, each dimension selects corresponding factual business sub-data based on the expected proportion threshold.

[0059] S106. Update the expected threshold of the current period corresponding to the dimension based on the factual business sub-data corresponding to the dimension.

[0060] In an optional embodiment, updating the expected threshold for the current period corresponding to the dimension based on the factual business sub-data corresponding to the dimension includes:

[0061] The current expected threshold is determined based on the factual business sub-data corresponding to the dimension.

[0062] Specifically, the factual business sub-data corresponding to a dimension can include multiple data points corresponding to that dimension. In practical applications, the current expected threshold can be the average or minimum value of these multiple data points corresponding to the dimension. For example, when the dimension is a user's high consumption preference, the determined factual business sub-data are shown in the table below:

[0063]

[0064]

[0065] The threshold is determined based on different transaction dates. For example, the expected threshold for November 1, 2021 can be obtained by averaging: (325+125+50) / 3=166.7 or by using the minimum value: 50; the expected threshold for November 2, 2021 can be obtained by averaging: (1000+891+678+654) / 4=805.75 or by using the minimum value: 654.

[0066] It should be noted that the expected threshold for the same user is determined using the same method across different periods.

[0067] Specifically, the current expected threshold can be the expected threshold corresponding to the most recent period. In the embodiments of this specification, the period is one day. Therefore, the expected threshold corresponding to November 2, 2021 is the current expected threshold.

[0068] Determine whether the current expected threshold is the same as the expected threshold of the historical period.

[0069] When the current expected threshold is different from the expected threshold of the historical period, the current expected threshold is used as the expected threshold of the current period.

[0070] Specifically, the expected threshold for the current period is used to measure the factual business data generated by each user.

[0071] The data processing method provided in the embodiments of this specification can determine the expected threshold of the current period for different dimensions based on the factual business data of the corresponding period in different periods. The expected threshold can be updated without manual intervention, which reduces the difficulty of work, improves data processing efficiency, and ensures the accuracy of data processing.

[0072] S108. Based on the expected threshold and the factual business sub-data corresponding to the dimension, determine the feature value corresponding to the factual business sub-data of the specified user under the dimension.

[0073] Specifically, feature values ​​can be scores for users in corresponding dimensions. For example, if the expected threshold for the current period is 654, users can be scored based on this threshold. For instance, on November 1, 2021, user A1's high-consumption preference score is 1 / 2 = 0.5, while user A2's score is 0. When scoring users based on the expected threshold for the current period, this can be determined by the ratio of factual business data exceeding the expected threshold to all factual business data for the corresponding period.

[0074] Specifically, the designated user can be all users in the factual business data, or all users in the factual business sub-data corresponding to each dimension.

[0075] Understandably, the settings for designated users can also be changed according to the needs of the enterprise.

[0076] By analyzing historical business data, multiple dimensions required by the enterprise are identified, and a corresponding expected proportion threshold is configured for each dimension. This expected proportion threshold is used to extract a corresponding number of sub-data points related to business operations, improving processing efficiency and preventing excessive memory consumption due to a large number of sub-data points. Multiple dimensions also meet the enterprise's needs, improving user satisfaction. Subsequently, upon receiving business data from the current period, the expected proportion thresholds are used to determine the corresponding sub-data points for each dimension within that period's data. These sub-data points can be the same or different for different dimensions, enabling multi-dimensional processing of business data and preventing inaccuracies caused by missing sub-data points. The expected threshold for the current period corresponding to each dimension is then updated based on the sub-data points. This rolling update of the expected thresholds can be achieved without user intervention, improving the accuracy of determining corresponding feature values ​​based on the expected thresholds and further enhancing the accuracy of user profiling.

[0077] Based on the above embodiments, in one embodiment of this specification, Figure 2 This is a flowchart illustrating another data processing method provided in an embodiment of this application, such as... Figure 2 As shown, the method further includes:

[0078] S202. Update the user profile of the specified user based on the feature value and the factual business sub-data corresponding to the feature value.

[0079] Specifically, a data warehouse can create corresponding user profiles based on the factual business data generated by different users. These user profiles can be described using feature values ​​corresponding to different dimensions of the user. For example, if user A1's high consumption preference feature value is 0.5, then 0.5 can be added to the corresponding user profile. It can be understood that a user profile can contain feature values ​​from all dimensions corresponding to the factual business data.

[0080] The data processing method provided in the embodiments of this specification can update the feature values ​​obtained from data processing to the corresponding user profile, so as to realize timely updates of the user profile and enable refined services such as personalized recommendations, advertising systems, event marketing, content recommendations, and interest preferences.

[0081] Based on the above embodiments, in one embodiment of this specification, Figure 3 This is a flowchart illustrating another data processing method provided in an embodiment of this application, as shown below. Figure 3 As shown, the user profile includes feature values ​​corresponding to resource transfers; the method further includes:

[0082] S302. Receive a resource transfer request triggered by a user, wherein the resource transfer request includes the number of resources to be transferred and the resource transfer object.

[0083] Specifically, a resource transfer request can be understood as being triggered when a user conducts a transaction. This request can include at least the quantity and object of the resource transfer. In practical applications, the resource transfer request may carry corresponding factual business data.

[0084] S304. Compare the number of resources transferred with the expected threshold corresponding to the resource transfer feature value in the user profile, and determine whether the resource transfer object is in the blacklist.

[0085] S306. When the resource transfer object is not on the blacklist and the number of resource transfers is greater than the expected threshold corresponding to the resource transfer feature value, a transfer instruction is sent to the user.

[0086] S308. Based on the confirmed transfer instruction returned by the user and determined based on the transfer instruction, the resource transfer quantity is transferred to the resource transfer object.

[0087] Specifically, a blacklist can be a user created by a data warehouse to intercept corresponding transactions; that is, a user on the blacklist cannot receive transactions from other users.

[0088] Specifically, each feature value in the user profile can correspond to an expected threshold. Upon receiving a resource transfer request triggered by a user, the resource transfer quantity can be compared with the expected threshold corresponding to the relevant feature value in the user profile. It is then determined whether the resource transfer target is on a blacklist. If the resource transfer target is not on the blacklist, and the resource transfer quantity is greater than the expected threshold corresponding to the resource transfer feature value, a transfer instruction is sent to the user. This transfer instruction is used for user confirmation of whether the resource transfer request was triggered by the user. Only after receiving a confirmation transfer instruction from the user based on the transfer instruction can the resource transfer quantity be transferred to the resource transfer target.

[0089] For example, the feature value corresponding to the resource transfer is 0.5, the expected threshold corresponding to the feature value is 1000, and the resource transfer request triggered by the user is to transfer 2000 resources to user A2. The above resource transfer quantity of 2000 is greater than the expected threshold of 1000, which means that the corresponding operation is different from the user's normal resource transfer. Therefore, the user can be asked to confirm whether the corresponding resources need to be transferred by sending a confirmation transfer instruction. Only after the user confirms can the corresponding resources be transferred to user A2.

[0090] The data processing method provided in the embodiments of this specification can identify when a user is performing an unconventional operation based on feature values ​​of different dimensions, trigger corresponding risk warnings, and specify the corresponding operation only after the user confirms, thus ensuring the user's operating habits and improving user satisfaction.

[0091] In an optional embodiment, the user profile includes historical feature values ​​corresponding to each of the dimensions;

[0092] Updating the user profile of the specified user based on the feature value and the corresponding factual business sub-data includes:

[0093] Determine whether the feature value is the same as the historical feature value corresponding to the dimension.

[0094] When the feature value is different from the historical feature value corresponding to the dimension, the feature value is updated to the current feature value corresponding to the dimension.

[0095] Specifically, historical feature values ​​are determined over historical periods. When user-generated factual business sub-data lacks a corresponding dimension feature value, the feature value can be marked with 0. When user-generated factual business sub-data generates a corresponding dimension feature value, this feature value can be compared with historical feature values. If the feature value differs from the historical feature value, it can be updated to the feature value corresponding to the dimension.

[0096] Based on the above embodiments, in one embodiment of this specification, the method further includes:

[0097] The feature values ​​of each dimension of the specified user are input into a pre-built occupation determination model, and the occupation category of the specified user is output.

[0098] The occupational categories are added as tags to the user profile corresponding to the specified user.

[0099] Specifically, a career determination model can be established, which includes multiple model parameters. These parameters can represent constraints and can be set based on expert experience. Historical feature values, which can include multi-dimensional features, can be used to train the career determination model. These multi-dimensional feature values ​​are used as input to the career determination model, and the corresponding careers are used as output. The model parameters are continuously adjusted until the model meets preset requirements, such as satisfying preset accuracy and the preset number of parameter adjustments. The model training is then complete. Machine learning algorithms such as GBDT (Gradient Boosting Decision Tree) can be referenced for model training.

[0100] By training the model based on historical feature values, a career determination model can be constructed. This model can identify the careers of different users, improve the accuracy of career determination, and thus improve user profiles.

[0101] Based on the above embodiments, in one embodiment of this specification, the method further includes: freezing or taking offline users who have not updated their user profiles within a preset time.

[0102] Specifically, the preset time is not limited in the embodiments of this specification, and can be set according to actual needs, such as one year.

[0103] In practical applications, users who have not updated their user profiles within a preset time represent users who have generated corresponding factual business sub-data over a long period of time. Users who have not updated their user profiles within a preset time can be frozen or taken offline, thereby reducing data redundancy in the data warehouse.

[0104] On the other hand, the present invention provides a data processing apparatus. Figure 4 This is a schematic diagram of the structure of a data processing device provided in an embodiment of the present invention, with reference to... Figure 4 The device may include:

[0105] The expected proportion threshold determination module 501 is used to determine at least one corresponding dimension based on historical periodic factual business data, and set a corresponding expected proportion threshold for each dimension. The dimension represents the occurrence environment of factual business sub-data in the factual business data, and the factual business data includes factual business sub-data generated under multiple dimensions.

[0106] Sub-data determination module 502 is used to determine the fact business sub-data corresponding to each of the dimensions in the fact business data of the current period based on the expected proportion threshold;

[0107] The expected threshold determination module 503 is used to update the expected threshold of the current period corresponding to the dimension based on the factual business sub-data corresponding to the dimension.

[0108] The feature value determination module 504 is used to determine the feature value corresponding to the factual business sub-data of a specified user under the dimension based on the expected threshold and the factual business sub-data corresponding to the dimension.

[0109] It should be noted that the apparatus provided in the above embodiments is only illustrated by the division of the above functional modules when implementing its functions. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0110] on the other hand, Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, such as... Figure 5 As shown, the present invention provides an electronic device for the data processing method described above. The device includes a processor and a memory. The memory stores at least one instruction or at least one program. The at least one instruction or at least one program is loaded and executed by the processor to implement the data processing method described above.

[0111] If the above functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0112] This invention also provides a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set can be executed by a processor of an electronic device to complete the data processing method described above.

[0113] Optionally, in embodiments of the present invention, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0114] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0115] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0116] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, electronic devices, and storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0117] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0118] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The testing method provided in this invention has the same implementation principle and technical effects as the aforementioned system embodiments. For the sake of brevity, any parts not mentioned in the method embodiments can be referred to the corresponding content in the aforementioned system embodiments.

[0119] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0120] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the scope of the technology disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the above claims.

Claims

1. A data processing method, characterized in that, The method includes: Based on historical periodic factual business data, at least one corresponding dimension is determined, and a corresponding expected proportion threshold is set for each dimension. The dimension characterizes the occurrence environment of factual business sub-data in the factual business data. The factual business data includes factual business sub-data generated under multiple dimensions. The expected proportion threshold is used to select factual business sub-data in the factual business data. Based on the expected proportion threshold, determine the factual business sub-data corresponding to each of the dimensions in the factual business data of the current period; The current expected threshold is determined based on the factual business sub-data corresponding to the dimension. Determine whether the current expected threshold is the same as the expected threshold of the historical period; When the current expected threshold is different from the expected threshold of the historical period, the current expected threshold is used as the expected threshold of the current period. Based on the expected threshold and the factual business sub-data corresponding to the dimension, determine the feature value corresponding to the factual business sub-data of the specified user under the dimension.

2. The data processing method according to claim 1, characterized in that, The method further includes: The user profile of the specified user is updated based on the feature value and the corresponding factual business sub-data.

3. The data processing method according to claim 2, characterized in that, The user profile includes feature values ​​corresponding to resource transfers; the method further includes: Receive a resource transfer request triggered by a user, the resource transfer request including the number of resources to be transferred and the object to be transferred; The number of resources transferred is compared with the expected threshold corresponding to the resource transfer feature value in the user profile, and it is determined whether the resource transfer object is in the blacklist. When the resource transfer target is not on the blacklist and the number of resource transfers is greater than the expected threshold corresponding to the resource transfer feature value, a transfer instruction is sent to the user. Based on the received confirmation transfer instruction from the user, determined by the transfer instruction, the resource transfer quantity is transferred to the resource transfer object.

4. The data processing method according to claim 2 or 3, characterized in that, The user profile includes historical feature values ​​corresponding to each dimension; Updating the user profile of the specified user based on the feature value and the corresponding factual business sub-data includes: Determine whether the feature value is the same as the historical feature value corresponding to the dimension. When the feature value is different from the historical feature value corresponding to the dimension, the feature value is updated to the current feature value corresponding to the dimension.

5. The data processing method according to claim 1, characterized in that, The method further includes: The feature values ​​of each dimension of the specified user are input into a pre-built occupation determination model, and the occupation category of the specified user is output. The occupational categories are added as tags to the user profile corresponding to the specified user.

6. The data processing method according to claim 5, characterized in that, The method also includes freezing or taking offline users who have not updated their user profiles within a preset time.

7. A data processing apparatus, characterized in that, The device includes: The expected proportion threshold determination module is used to determine at least one corresponding dimension based on historical periodic factual business data, and set a corresponding expected proportion threshold for each dimension. The dimension represents the occurrence environment of factual business sub-data in the factual business data. The factual business data includes factual business sub-data generated under multiple dimensions. The expected proportion threshold is used to select factual business sub-data in the factual business data. The sub-data determination module is used to determine the factual business sub-data corresponding to each of the dimensions in the factual business data of the current period based on the expected proportion threshold. The expected threshold determination module is used to determine the current expected threshold based on the factual business sub-data corresponding to the dimension; determine whether the current expected threshold is the same as the expected threshold of the historical period; when the current expected threshold is different from the expected threshold of the historical period, the current expected threshold is used as the expected threshold of the current period. The feature value determination module is used to determine the feature value corresponding to the factual business sub-data of a specified user under the dimension based on the expected threshold and the factual business sub-data corresponding to the dimension.

8. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the data processing method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of a data processing device / electronic device, the data processing device / electronic device is enabled to perform the data processing method as described in any one of claims 1 to 6.

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