Data Processing Method, Apparatus, Electronic Device, and Storage Medium

Abstract

The present disclosure relates to a data processing method, apparatus, electronic device, storage medium, and computer program product. The method includes: in response to a request for determining object information for a target object, obtaining an object identifier of the target object; screening out a resource data prediction model corresponding to the object identifier from a pre-constructed resource data prediction model as a resource data prediction model corresponding to the target object; each resource data prediction model is used to represent the association relationship between the corresponding object information and the total resource data, and the object information is used to represent the resource credential information of the corresponding object; determining the total resource data of the target object under different object information through the resource data prediction model corresponding to the target object; and determining the target object information of the target object according to the total resource data of the target object under different object information. By using this method, the accuracy of determining object information can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of Internet technologies, and in particular, to a data processing method, apparatus, electronic device, storage medium, and computer program product. Background Art

[0002] With the development of Internet technologies, in order to obtain object information of an object, relevant data of the object can be sorted and analyzed through a data analysis model.

[0003] In related technologies, when sorting and analyzing data, a fixed data analysis model is generally used to analyze relevant data of an object; however, the object data of different objects is different. If a fixed data analysis model is used for sorting and analysis, the obtained object information is not accurate, resulting in a low accuracy rate for determining object information. Summary of the Invention

[0004] The present disclosure provides a data processing method, apparatus, electronic device, storage medium, and computer program product to at least solve the problem of low accuracy rate for determining object information in related technologies. The technical solution of the present disclosure is as follows:

[0005] According to a first aspect of an embodiment of the present disclosure, a data processing method is provided, including:

[0006] In response to an object information determination request for a target object, obtain an object identifier of the target object;

[0007] From a pre-constructed resource data prediction model, screen out a resource data prediction model corresponding to the object identifier as a resource data prediction model corresponding to the target object; each resource data prediction model is used to represent the association relationship between the corresponding object information and the total resource data, and the object information is used to represent the resource voucher information of the corresponding object;

[0008] Determine the total resource data of the target object under different object information through the resource data prediction model corresponding to the target object;

[0009] Determine the target object information of the target object according to the total resource data of the target object under different object information.

[0010] In an exemplary embodiment, before responding to an object information determination request for a target object and obtaining the object identifier of the target object, it further includes:

[0011] Obtain sample object information and actual total resource data of a sample object;

[0012] Input the sample object information of the sample object into the resource data prediction model to be trained, and obtain the predicted total resource data of the sample object;

[0013] Train the resource data prediction model to be trained according to the difference between the predicted total resource data and the actual total resource data of the sample object, and obtain the resource data prediction model corresponding to the sample object, which is used as the pre-constructed resource data prediction model.

[0014] In an exemplary embodiment, the training of the resource data prediction model to be trained according to the difference between the predicted total resource data and the actual total resource data of the sample object to obtain the resource data prediction model corresponding to the sample object includes:

[0015] Obtain a loss value according to the difference between the predicted total resource data and the actual total resource data of the sample object;

[0016] Adjust the network parameters of the resource data prediction model to be trained according to the loss value, and retrain the resource data prediction model with adjusted network parameters until the training end condition is reached. The trained resource data prediction model that reaches the training end condition is used as the resource data prediction model corresponding to the sample object.

[0017] In an exemplary embodiment, before obtaining the object identifier of the target object in response to an object information determination request for the target object, it further includes:

[0018] Obtain the sample object information and the actual total resource data of the sample object;

[0019] Determine the target association relationship between the sample object information and the actual total resource data of the sample object according to the sample object information and the actual total resource data of the sample object;

[0020] Construct the resource data prediction model corresponding to the sample object according to the target association relationship, which is used as the pre-constructed resource data prediction model.

[0021] In an exemplary embodiment, the sample object information includes first sample object information and second sample object information. The first sample object information is used to represent the first resource data in the resource voucher information of the sample object, and the second sample object information is used to represent the second resource data in the resource voucher information of the sample object; the sample object also corresponds to first associated account data;

[0022] The determination of the target association relationship between the sample object information and the actual total resource data of the sample object according to the sample object information and the actual total resource data of the sample object includes:

[0023] Determine a first association relationship among the first sample object information, the second sample object information, and the first associated account data, and a second association relationship among the first associated account data, the first sample object information, the second sample object information, and the actual total resource data according to the first sample object information, the second sample object information, the first associated account data, and the actual total resource data;

[0024] Obtain an association relationship among the first sample object information, the second sample object information, and the actual total resource data of the sample object according to the first association relationship and the second association relationship, as the target association relationship.

[0025] In an exemplary embodiment, the sample object further corresponds to second associated account data;

[0026] The first association relationship is obtained through the following manner:

[0027] Determine a third association relationship between the first sample object information and the second associated account data, and a fourth association relationship among the second sample object information, the second associated account data, and the first associated account data according to the first sample object information, the second sample object information, the first associated account data, and the second associated account data;

[0028] Obtain the first association relationship according to the third association relationship and the fourth association relationship.

[0029] In an exemplary embodiment, determining the target object information of the target object according to the total resource data of the target object under different object information includes:

[0030] Screen out target total resource data that meets a preset resource condition from the total resource data of the target object under different object information;

[0031] Determine the object information corresponding to the target total resource data as the target object information of the target object.

[0032] According to a second aspect of the embodiments of the present disclosure, a data processing device is provided, including:

[0033] An identifier acquisition unit configured to execute in response to an object information determination request for a target object to acquire an object identifier of the target object;

[0034] A model determination unit configured to execute screening out a resource data prediction model corresponding to the object identifier from pre-constructed resource data prediction models as the resource data prediction model corresponding to the target object; each resource data prediction model is used to represent the association relationship between the corresponding object information and the total resource data, and the object information is used to represent the resource voucher information of the corresponding object.

[0035] A data determination unit configured to execute determining the total resource data of the target object under different object information through the resource data prediction model corresponding to the target object.

[0036] An information determination unit configured to execute determining the target object information of the target object according to the total resource data of the target object under different object information.

[0037] In an exemplary embodiment, the apparatus further includes a model training unit configured to execute obtaining the sample object information and the actual total resource data of the sample object; inputting the sample object information of the sample object into the resource data prediction model to be trained to obtain the predicted total resource data of the sample object; training the resource data prediction model to be trained according to the difference between the predicted total resource data and the actual total resource data of the sample object to obtain the resource data prediction model corresponding to the sample object as the pre-constructed resource data prediction model.

[0038] In an exemplary embodiment, the model training unit is further configured to execute obtaining a loss value according to the difference between the predicted total resource data and the actual total resource data of the sample object; adjusting the network parameters of the resource data prediction model to be trained according to the loss value, and repeatedly training the resource data prediction model with adjusted network parameters until the training end condition is reached, and then using the trained resource data prediction model that reaches the training end condition as the resource data prediction model corresponding to the sample object.

[0039] In an exemplary embodiment, the apparatus further includes a model construction unit configured to execute obtaining the sample object information and the actual total resource data of the sample object; determining the target association relationship between the sample object information and the actual total resource data of the sample object according to the sample object information and the actual total resource data of the sample object; constructing the resource data prediction model corresponding to the sample object according to the target association relationship as the pre-constructed resource data prediction model.

[0040] In an exemplary embodiment, the sample object information includes first sample object information and second sample object information. The first sample object information is used to represent first resource data in the resource credential information of the sample object, and the second sample object information is used to represent second resource data in the resource credential information of the sample object. The sample object also corresponds to first associated account data;

[0041] The model construction unit is further configured to determine a first association relationship between the first sample object information, the second sample object information, and the first associated account data, and a second association relationship between the first associated account data, the first sample object information, the second sample object information, and the actual total resource data according to the first sample object information, the second sample object information, the first associated account data, and the actual total resource data; and obtain an association relationship between the first sample object information, the second sample object information, and the actual total resource data of the sample object as the target association relationship according to the first association relationship and the second association relationship.

[0042] In an exemplary embodiment, the sample object also corresponds to second associated account data;

[0043] The model construction unit is further configured to determine a third association relationship between the first sample object information and the second associated account data, and a fourth association relationship between the second sample object information, the second associated account data, and the first associated account data according to the first sample object information, the second sample object information, the first associated account data, and the second associated account data; and obtain the first association relationship according to the third association relationship and the fourth association relationship.

[0044] In an exemplary embodiment, the information determination unit is further configured to screen out target total resource data that meets a preset resource condition from the total resource data of the target object under different object information; and determine the object information corresponding to the target total resource data as the target object information of the target object.

[0045] According to a third aspect of the embodiments of the present disclosure, an electronic device is provided, including:

[0046] A processor;

[0047] A memory for storing executable instructions of the processor;

[0048] Wherein, the processor is configured to execute the instructions to implement the data processing method as described in any one of the above.

[0049] According to a fourth aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the data processing method as described in any one of the above.

[0050] According to a fifth aspect of the embodiments of the present disclosure, there is provided a computer program product. The computer program product includes instructions that, when executed by a processor of an electronic device, enable the electronic device to execute the data processing method as described in any one of the above.

[0051] The technical solutions provided by the embodiments of the present disclosure at least bring the following beneficial effects:

[0052] By responding to a request for determining object information for a target object, obtaining an object identifier of the target object; screening out a resource data prediction model corresponding to the object identifier from a pre-constructed resource data prediction model as the resource data prediction model corresponding to the target object; each resource data prediction model is used to represent the association relationship between the corresponding object information and the total resource data, and the object information is used to represent the resource voucher information of the corresponding object; determining the total resource data of the target object under different object information through the resource data prediction model corresponding to the target object; and determining the target object information of the target object according to the total resource data of the target object under different object information. In this way, through the resource data prediction model corresponding to the target object, the total resource data of the target object under different object information is obtained, and then the target object information of the target object is obtained, achieving the purpose of obtaining the target object information of the target object through the resource data prediction model corresponding to the target object, which is beneficial to improving the determination accuracy of the object information.

[0053] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure, and do not constitute an improper limitation of the present disclosure.

[0055] Figure 1 is a flowchart of a data processing method shown according to an exemplary embodiment.

[0056] Figure 2 is a flowchart of the training steps of a resource data prediction model shown according to an exemplary embodiment.

[0057] Figure 3 is a flowchart of the construction steps of a resource data prediction model shown according to an exemplary embodiment.

[0058] Figure 4 is a flowchart of another data processing method shown according to an exemplary embodiment.

[0059] Figure 5 is a block diagram of a data processing apparatus shown according to an exemplary embodiment.

[0060] Figure 6 is a block diagram of an electronic device shown according to an exemplary embodiment. Detailed implementation manners

[0061] In order to enable those of ordinary skill in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0062] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described here can be implemented in an order other than those illustrated or described here. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0063] It should also be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in the present disclosure are all information and data authorized by the user or fully authorized by all parties.

[0064] Figure 1 is a flowchart of a data processing method shown according to an exemplary embodiment. As Figure 1 shown, this data processing method is used in a terminal; it can be understood that this method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this exemplary embodiment, this method includes the following steps:

[0065] In step S110, in response to an object information determination request for a target object, obtain the object identifier of the target object.

[0066] Among them, the target object refers to an object for which object information needs to be determined, such as a commodity. The object information determination request refers to a request for determining the object information of the target object, and can be triggered by a terminal account.

[0067] Among them, the object identifier refers to the unique identifier information of the object, such as the object name, object number, etc.

[0068] Specifically, the terminal responds to the object information determination request for the target object, parses the object information determination request, and obtains the object identifier of the target object.

[0069] For example, the terminal account clicks on the target object on the object information acquisition page, triggering an object information determination request for the target object; the terminal obtains the object identifier of the target object according to the object information determination request.

[0070] In step S120, from the pre-constructed resource data prediction model, the resource data prediction model corresponding to the object identifier is screened out as the resource data prediction model corresponding to the target object; each resource data prediction model is used to represent the association relationship between the corresponding object information and the total resource data, and the object information is used to represent the resource voucher information of the corresponding object.

[0071] Among them, the object information refers to the resource voucher information of the corresponding object; the resource voucher information refers to the resource data reduction information of the object. For example, when obtaining object A, a part of the resource data A2 can be reduced from the total resource data A1 corresponding to object A, that is, only the corresponding resource data (A1 - A2) needs to be transferred. The object information includes the first object information and the second object information. The first object information is used to represent the first resource data in the resource voucher information of the object, specifically referring to the acquisition condition of the resource voucher information. The second object information is used to represent the second resource data in the resource voucher information of the object.

[0072] Among them, the total resource data refers to the total resource value obtained by providing the object information of the object. The association relationship between the object information and the total resource data means that different object information corresponds to different total resource data. For example, the total resource data V1 of the target object under the object information D1 is different from the total resource data V2 of the target object under the object information D2.

[0073] Among them, the resource data prediction model is a model used to determine the total resource data of the object under different object information. The resource data prediction models corresponding to different object identifiers are different. For example, the resource data prediction model corresponding to object A is different from the resource data prediction model corresponding to object B. It should be noted that the pre-constructed resource data prediction model includes resource data prediction models corresponding to multiple object identifiers.

[0074] Specifically, the terminal queries the pre-constructed resource data prediction model according to the object identifier of the target object, obtains the resource data prediction model corresponding to the object identifier; and determines the resource data prediction model corresponding to the object identifier as the resource data prediction model corresponding to the target object.

[0075] For example, the pre-constructed resource data prediction model includes resource data prediction models corresponding to 3 objects. Among them, object A corresponds to resource data prediction model M1, object B corresponds to resource data prediction model M2, and object C corresponds to resource data prediction model M3. If the object identifier of the target object is B, it means that the resource data prediction model corresponding to the target object is M2.

[0076] In step S130, through the resource data prediction model corresponding to the target object, the total resource data of the target object under different object information is determined.

[0077] Specifically, the terminal analyzes the target object through the resource data prediction model corresponding to the target object to obtain the total resource data of the target object under different object information.

[0078] In step S140, according to the total resource data of the target object under different object information, the target object information of the target object is determined.

[0079] Among them, the target object information of the target object refers to the final object information of the target object.

[0080] Specifically, the terminal screens out the object information with the largest corresponding total resource data from different object information according to the total resource data of the target object under different object information, and determines this object information as the target object information of the target object.

[0081] In the above data processing method, by responding to the object information determination request for the target object, the object identifier of the target object is obtained. From the pre-constructed resource data prediction model, the resource data prediction model corresponding to the object identifier is screened out as the resource data prediction model corresponding to the target object. Each resource data prediction model is used to represent the association relationship between the corresponding object information and the total resource data, and the object information is used to represent the resource voucher information of the corresponding object. Through the resource data prediction model corresponding to the target object, the total resource data of the target object under different object information is determined. According to the total resource data of the target object under different object information, the target object information of the target object is determined. In this way, through the resource data prediction model corresponding to the target object, the total resource data of the target object under different object information is obtained, and then the target object information of the target object is obtained, achieving the purpose of obtaining the target object information of the target object through the resource data prediction model corresponding to the target object, which is beneficial to improving the determination accuracy of the object information.

[0082] In an exemplary embodiment, such as Figure 2As shown, before obtaining the object identifier of the target object in response to the object information determination request for the target object in step S110, it further includes the training steps of the resource data prediction model, specifically including the following steps:

[0083] In step S210, obtain the sample object information and the actual total resource data of the sample object.

[0084] In step S220, input the sample object information of the sample object into the resource data prediction model to be trained, and obtain the predicted total resource data of the sample object.

[0085] In step S230, train the resource data prediction model to be trained according to the difference between the predicted total resource data and the actual total resource data of the sample object, and obtain the resource data prediction model corresponding to the sample object as the pre-constructed resource data prediction model.

[0086] Among them, the sample object information of the sample object refers to the actual object information of the sample object, such as actual resource voucher information. The actual total resource data refers to the actual total resource data of the sample object under this sample object information, such as actual total resource value. The predicted total resource data refers to the predicted total resource data of the sample object under this sample object information output by the resource data prediction model to be trained, such as predicted total resource value.

[0087] Among them, the resource data prediction model to be trained refers to a neural network model, a deep learning model, etc.

[0088] Specifically, the terminal extracts the sample object information of the sample object and the actual total resource data of the sample object under this sample object information from the historical data of the sample object; inputs the sample object information of the sample object into the resource data prediction model to be trained, and processes this sample object information through the resource data prediction model to be trained to obtain the predicted total resource data of the sample object under this sample object information; obtains a loss value according to the difference between the predicted total resource data and the actual total resource data of the sample object; trains the resource data prediction model to be trained repeatedly according to this loss value to obtain the trained resource data prediction model as the resource data prediction model corresponding to the sample object; finally, determines the resource data prediction model corresponding to the sample object as the pre-constructed resource data prediction model.

[0089] It should be noted that for each sample object, the resource data prediction model corresponding to each sample object can be obtained through the above steps S210 to S230.

[0090] The technical solution provided by the embodiments of the present disclosure trains resource data prediction models corresponding to different sample objects according to the sample object information of the sample object and the actual total resource data of the sample object under the sample object information, which is beneficial to subsequently screening out the resource data prediction model corresponding to the target object from the resource data prediction models corresponding to different sample objects, and outputting the total resource data of the target object under different object information according to the resource data prediction model corresponding to the target object, making full use of the trained resource data prediction model, so that the finally obtained target object information is more accurate, and further improving the determination accuracy of the object information.

[0091] In an exemplary embodiment, in the above step S230, training the resource data prediction model to be trained according to the difference between the predicted total resource data and the actual total resource data of the sample object to obtain the resource data prediction model corresponding to the sample object specifically includes: obtaining a loss value according to the difference between the predicted total resource data and the actual total resource data of the sample object; adjusting the network parameters of the resource data prediction model to be trained according to the loss value, and retraining the resource data prediction model with the adjusted network parameters until the training end condition is reached, and taking the trained resource data prediction model that reaches the training end condition as the resource data prediction model corresponding to the sample object.

[0092] Among them, the training end condition may but is not limited to reaching a preset number of training times, the loss function reaching convergence, etc.

[0093] Specifically, the terminal calculates a loss value according to the difference between the predicted total resource data and the actual total resource data of the sample object in combination with the loss function; when the training end condition is not reached, the network parameters of the resource data prediction model to be trained are adjusted according to the loss value to obtain the resource data prediction model with the adjusted network parameters; the resource data prediction model with the adjusted network parameters is retrained until the training end condition is reached; when the training end condition is reached, the trained resource data prediction model that reaches the training end condition is determined as the resource data prediction model corresponding to the sample object.

[0094] For example, when the loss value is greater than or equal to a preset threshold, or when the preset number of training times is not reached, the terminal adjusts the network parameters of the resource data prediction model to be trained according to the loss value, and obtains a resource data prediction model with adjusted network parameters, so as to reduce the error between the predicted total resource data obtained by the resource data prediction model and the actual total resource data. Repeat the above steps S220 to S230 to repeatedly train the resource data prediction model with adjusted model parameters until the loss value obtained according to the trained resource data prediction model is less than the preset threshold, or the preset number of training times is reached, then use the trained resource data prediction model or the resource data prediction model trained for the preset number of training times as the resource data prediction model corresponding to the sample object. Through the above training method, resource data prediction models corresponding to different sample objects can be obtained.

[0095] The technical solution provided by the embodiments of the present disclosure is to retrain the resource data prediction model with adjusted network parameters until the training end condition is reached, which is beneficial to improving the accuracy of the total resource data of the target object output by the resource data prediction model under different object information subsequently.

[0096] In an exemplary embodiment, as Figure 3 shown, in the above step S110, before obtaining the object identifier of the target object in response to the object information determination request for the target object, it further includes the construction steps of the resource data prediction model, which specifically include the following steps:

[0097] In step S310, obtain the sample object information and the actual total resource data of the sample object.

[0098] In step S320, according to the sample object information and the actual total resource data of the sample object, determine the target association relationship between the sample object information and the actual total resource data of the sample object.

[0099] In step S330, according to the target association relationship, construct a resource data prediction model corresponding to the sample object as the pre-constructed resource data prediction model.

[0100] Among them, the target association relationship between the sample object information and the actual total resource data means that different sample object information corresponds to different actual total resource data.

[0101] Specifically, the terminal extracts the sample object information of the sample object and the actual total resource data of the sample object under the sample object information from the historical data of the sample object; determines the target association relationship between the sample object information of the sample object and the actual total resource data according to the sample object information of the sample object and the actual total resource data of the sample object under the sample object information; determines the model corresponding to the target association relationship as the resource data prediction model corresponding to the sample object according to the target association relationship; and finally, takes the resource data prediction model corresponding to the sample object as the pre-constructed resource data prediction model. Among them, the model corresponding to the association relationship refers to the model that can represent the association relationship.

[0102] It should be noted that for each sample object, the resource data prediction model corresponding to each sample object can be obtained through the above steps S310 to S330. Moreover, the resource data prediction models corresponding to each sample object are different.

[0103] The technical solution provided by the embodiments of the present disclosure determines the target association relationship between the sample object information of the sample object and the actual total resource data according to the sample object information and the actual total resource data of the sample object, and then constructs the resource data prediction model corresponding to the sample object based on the target association relationship, which is conducive to accurately outputting the total resource data of the target object under different object information through the resource data prediction model of the target object subsequently, thereby improving the determination accuracy of the object information.

[0104] In an exemplary embodiment, the sample object information includes first sample object information and second sample object information. The first sample object information is used to represent the first resource data in the resource voucher information of the sample object, and the second sample object information is used to represent the second resource data in the resource voucher information of the sample object; the sample object also corresponds to first associated account data. Then, in the above step S320, determining the target association relationship between the sample object information of the sample object and the actual total resource data according to the sample object information of the sample object and the actual total resource data specifically includes: determining the first association relationship between the first sample object information, the second sample object information, the first associated account data and the actual total resource data, and the second association relationship between the first associated account data, the first sample object information, the second sample object information and the actual total resource data; and obtaining the association relationship between the first sample object information, the second sample object information and the actual total resource data of the sample object as the target association relationship according to the first association relationship and the second association relationship.

[0105] Among them, the first resource data in the resource voucher information of the sample object refers to the acquisition conditions of the resource voucher information. The second resource data in the resource voucher information of the sample object. The first associated account data refers to the number of accounts added after providing the object information.

[0106] Among them, the first association relationship means that different first sample object information and second sample object information correspond to different first associated account data. When the second sample object information remains unchanged, the first associated account data decreases as the first sample object information increases; when the first sample object information remains unchanged, the first associated account data increases as the second sample object information increases. The second association relationship means that different first associated account data, first sample object information, and second sample object information correspond to different actual total resource data.

[0107] Among them, the association relationship between the first sample object information, the second sample object information of the sample object and the actual total resource data means that different first sample object information and second sample object information correspond to different actual total resource data. It should be noted that when the second sample object information remains unchanged, the actual total resource data decreases as the first sample object information increases; when the first sample object information remains unchanged, the actual total resource data increases as the second sample object information increases.

[0108] Specifically, the terminal determines the association relationship between the first sample object information, the second sample object information, and the first associated account data as the first association relationship, and determines the association relationship between the first associated account data, the first sample object information, the second sample object information, and the actual total resource data as the second association relationship according to the first sample object information, the second sample object information, the first associated account data, and the actual total resource data. Further, after obtaining the first association relationship and the second association relationship, the terminal uses the first association relationship to update the first associated account data in the second association relationship, obtains the association relationship between the first sample object information, the second sample object information of the sample object and the actual total resource data, and determines this association relationship as the target association relationship between the sample object information of the sample object and the actual total resource data.

[0109] The technical solution provided by the embodiments of the present disclosure determines the target association relationship among the first sample object information, the second sample object information, and the actual total resource data based on the first association relationship among the first sample object information, the second sample object information, and the first associated account data, and the second association relationship among the first associated account data, the first sample object information, the second sample object information, and the actual total resource data, achieving the purpose of determining the target association relationship among the first sample object information, the second sample object information, and the actual total resource data. At the same time, it is beneficial to subsequently construct a resource data prediction model corresponding to the sample object based on this target association relationship, and then output relatively accurate target object information through the constructed resource data prediction model.

[0110] In an exemplary embodiment, the sample object also corresponds to second associated account data; the first association relationship is obtained in the following manner: according to the first sample object information, the second sample object information, the first associated account data, and the second associated account data, determine the third association relationship between the first sample object information and the second associated account data, and the fourth association relationship among the second sample object information, the second associated account data, and the first associated account data; according to the third association relationship and the fourth association relationship, obtain the first association relationship.

[0111] Among them, the second associated account data refers to the number of accounts that actually obtain the sample object information, including the first associated account data and the number of accounts that can obtain the sample object without the sample object information.

[0112] Among them, for the third association relationship, the second associated account data decreases as the first sample object information increases. For the fourth association relationship, when the second associated account data remains unchanged, that is, when the first sample object information remains unchanged, the first associated account data increases as the second sample object information increases.

[0113] Specifically, the terminal determines the association relationship between the first sample object information and the second associated account data as the third association relationship, and determines the association relationship among the second sample object information, the second associated account data, and the first associated account data as the fourth association relationship according to the first sample object information, the second sample object information, the first associated account data, and the second associated account data. Further, after obtaining the third association relationship and the fourth association relationship, the terminal uses the third association relationship to update the second associated account data in the fourth association relationship to obtain the first association relationship among the first sample object information, the second sample object information, and the first associated account data.

[0114] The technical solution provided by the embodiments of the present disclosure obtains the first association relationship among the first sample object information, the second sample object information, and the first associated account data based on the third association relationship between the first sample object information and the second associated account data, and the fourth association relationship among the second sample object information, the second associated account data, and the first associated account data, achieving the purpose of determining the association relationship among the first sample object information, the second sample object information, and the first associated account data. Meanwhile, it is beneficial to subsequently determine the target association relationship among the first sample object information, the second sample object information, and the actual total resource data according to the first association relationship and the second association relationship.

[0115] In an exemplary embodiment, in step S140, determining the target object information of the target object according to the total resource data of the target object under different object information specifically includes: screening out the target total resource data that meets the preset resource condition from the total resource data of the target object under different object information; determining the object information corresponding to the target total resource data as the target object information of the target object.

[0116] Wherein, the preset resource condition refers to the largest total resource data.

[0117] For example, assume that the total resource data of the target object under object information D1, object information D2, and object information D3 are V1, V2, and V3 respectively, where V2 is the largest, then object information D2 is determined as the target object information of the target object.

[0118] The technical solution provided by the embodiments of the present disclosure directly determines the object information corresponding to the target total resource data that meets the preset resource condition as the target object information of the target object according to the total resource data of the target object under different object information output by the resource data prediction model, without going through a complex analysis process, consuming less time, and being beneficial to improving the determination efficiency of object information.

[0119] Figure 4 is a flowchart of another data processing method shown according to an exemplary embodiment. As Figure 4 shown, this data processing method is used in a terminal and includes the following steps:

[0120] In step S410, obtain the sample object information and the actual total resource data of the sample object.

[0121] In step S420, determine the target association relationship between the sample object information of the sample object and the actual total resource data according to the sample object information and the actual total resource data of the sample object.

[0122] In step S430, according to the target association relationship, a resource data prediction model corresponding to the sample object is constructed as the pre-constructed resource data prediction model.

[0123] In step S440, in response to a request for determining object information for the target object, the object identifier of the target object is obtained.

[0124] In step S450, from the pre-constructed resource data prediction models, the resource data prediction model corresponding to the object identifier is filtered out as the resource data prediction model corresponding to the target object; each resource data prediction model is used to represent the association relationship between the corresponding object information and the total resource data, and the object information is used to represent the resource credential information of the corresponding object.

[0125] In step S460, through the resource data prediction model corresponding to the target object, the total resource data of the target object under different object information is determined.

[0126] In step S470, from the total resource data of the target object under different object information, the target total resource data that meets the preset resource conditions is filtered out; the object information corresponding to the target total resource data is determined as the target object information of the target object.

[0127] It should be noted that the specific limitations of the above steps can be referred to the specific limitations of a data processing method described above, and will not be elaborated here.

[0128] In the above data processing method, through the resource data prediction model corresponding to the target object, the total resource data of the target object under different object information is obtained, and then the target object information of the target object is obtained, achieving the purpose of obtaining the target object information of the target object through the resource data prediction model corresponding to the target object, which is beneficial to improving the determination accuracy of the object information.

[0129] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limitation, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0130] It is understandable that the same / similar parts among the various embodiments of the above methods in this specification can be referred to each other. Each embodiment focuses on the differences from other embodiments. For the relevant parts, refer to the descriptions of other method embodiments.

[0131] Based on the same inventive concept, embodiments of the present disclosure also provide a data processing apparatus for implementing the data processing method involved above.

[0132] Figure 5 is a block diagram of a data processing apparatus shown according to an exemplary embodiment. Refer to Figure 5 , the apparatus includes an identification acquisition unit 510, a model determination unit 520, a data determination unit 530, and an information determination unit 540.

[0133] The identification acquisition unit 510 is configured to execute to obtain the object identifier of the target object in response to an object information determination request for the target object.

[0134] The model determination unit 520 is configured to execute to screen out the resource data prediction model corresponding to the object identifier from the pre-constructed resource data prediction models as the resource data prediction model corresponding to the target object; each resource data prediction model is used to represent the association relationship between the corresponding object information and the total resource data, and the object information is used to represent the resource credential information of the corresponding object.

[0135] The data determination unit 530 is configured to execute to determine the total resource data of the target object under different object information through the resource data prediction model corresponding to the target object.

[0136] The information determination unit 540 is configured to execute to determine the target object information of the target object according to the total resource data of the target object under different object information.

[0137] In an exemplary embodiment, the data processing apparatus further includes a model training unit, which is configured to execute to obtain the sample object information and the actual total resource data of the sample object; input the sample object information of the sample object into the resource data prediction model to be trained to obtain the predicted total resource data of the sample object; and train the resource data prediction model to be trained according to the difference between the predicted total resource data and the actual total resource data of the sample object to obtain the resource data prediction model corresponding to the sample object as the pre-constructed resource data prediction model.

[0138] In an exemplary embodiment, the model training unit is further configured to obtain a loss value according to the difference between the predicted total resource data and the actual total resource data of the sample object; adjust the network parameters of the resource data prediction model to be trained according to the loss value, and retrain the resource data prediction model with the adjusted network parameters until the training end condition is reached, and use the trained resource data prediction model that reaches the training end condition as the resource data prediction model corresponding to the sample object.

[0139] In an exemplary embodiment, the data processing device further includes a model construction unit, which is configured to obtain the sample object information and the actual total resource data of the sample object; determine the target association relationship between the sample object information and the actual total resource data of the sample object according to the sample object information and the actual total resource data of the sample object; construct a resource data prediction model corresponding to the sample object according to the target association relationship as the pre-constructed resource data prediction model.

[0140] In an exemplary embodiment, the sample object information includes first sample object information and second sample object information. The first sample object information is used to represent the first resource data in the resource voucher information of the sample object, and the second sample object information is used to represent the second resource data in the resource voucher information of the sample object; the sample object also corresponds to first associated account data;

[0141] The model construction unit is further configured to determine the first association relationship between the first sample object information, the second sample object information and the first associated account data, and the second association relationship between the first associated account data, the first sample object information, the second sample object information and the actual total resource data according to the first sample object information, the second sample object information, the first associated account data and the actual total resource data; obtain the association relationship between the first sample object information, the second sample object information and the actual total resource data of the sample object according to the first association relationship and the second association relationship as the target association relationship.

[0142] In an exemplary embodiment, the sample object also corresponds to second associated account data;

[0143] The model construction unit is further configured to determine the third association relationship between the first sample object information and the second associated account data, and the fourth association relationship between the second sample object information, the second associated account data and the first associated account data according to the first sample object information, the second sample object information, the first associated account data and the second associated account data; obtain the first association relationship according to the third association relationship and the fourth association relationship.

[0144] In an exemplary embodiment, the information determination unit 540 is further configured to perform filtering out target total resource data that meets a preset resource condition from the total resource data of the target object under different object information; and determining the object information corresponding to the target total resource data as the target object information of the target object.

[0145] Regarding the device in the above embodiment, the specific manners in which each module performs operations have been described in detail in the embodiment related to the method, and will not be elaborated herein.

[0146] Each module in the above data processing device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the computer device in hardware form or be independent of it, or be stored in the memory in the computer device in software form, so as to be called by the processor to execute the operations corresponding to the above respective modules.

[0147] Figure 6 It is a block diagram of an electronic device 600 for performing a data processing method shown according to an exemplary embodiment. For example, the electronic device 600 can be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0148] Referring to Figure 6 , the electronic device 600 may include one or more of the following components: a processing component 602, a memory 604, a power supply component 606, a multimedia component 608, an audio component 610, an input / output (I / O) interface 612, a sensor component 614, and a communication component 616.

[0149] The processing component 602 generally controls the overall operation of the electronic device 600, such as operations associated with display, telephone call, data communication, camera operation, and recording operation. The processing component 602 may include one or more processors 620 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 602 may include one or more modules to facilitate the interaction between the processing component 602 and other components. For example, the processing component 602 may include a multimedia module to facilitate the interaction between the multimedia component 608 and the processing component 602.

[0150] The memory 604 is configured to store various types of data to support the operation of the electronic device 600. Examples of such data include instructions for any application or method operating on the electronic device 600, contact data, phone book data, messages, pictures, videos, etc. The memory 604 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disks, optical disks, or graphene memory.

[0151] The power supply component 606 provides power for various components of the electronic device 600. The power supply component 606 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 600.

[0152] The multimedia component 608 includes a screen that provides an output interface between the electronic device 600 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can not only sense the boundaries of the touch or swipe actions, but also detect the duration and pressure associated with the touch or swipe operation. In some embodiments, the multimedia component 608 includes a front camera and / or a rear camera. When the electronic device 600 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each of the front camera and the rear camera can be a fixed optical lens system or have a focal length and optical zoom capabilities.

[0153] The audio component 610 is configured to output and / or input audio signals. For example, the audio component 610 includes a microphone (MIC) that is configured to receive external audio signals when the electronic device 600 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 604 or transmitted via the communication component 616. In some embodiments, the audio component 610 further includes a speaker for outputting audio signals.

[0154] The I / O interface 612 provides an interface between the processing component 602 and a peripheral interface module, which can be a keyboard, a click wheel, buttons, etc. These buttons can include, but are not limited to: a home button, a volume button, a power-on button, and a lock button.

[0155] The sensor assembly 614 includes one or more sensors for providing status assessments of various aspects for the electronic device 600. For example, the sensor assembly 614 can detect the on / off state of the electronic device 600, the relative positioning of components, such as the display and keypad of the electronic device 600. The sensor assembly 614 can also detect changes in the position of the electronic device 600 or components of the electronic device 600, the presence or absence of user contact with the electronic device 600, the orientation or acceleration / deceleration of the device 600, and changes in the temperature of the electronic device 600. The sensor assembly 614 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 614 can also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 614 can also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0156] The communication component 616 is configured to facilitate communication, either wired or wirelessly, between the electronic device 600 and other devices. The electronic device 600 can access a wireless network based on communication standards, such as WiFi, a carrier network (such as 2G, 3G, 4G, or 5G), or a combination thereof. In an exemplary embodiment, the communication component 616 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 616 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0157] In an exemplary embodiment, the electronic device 600 can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-described methods.

[0158] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as the memory 604 including instructions, which can be executed by the processor 620 of the electronic device 600 to complete the above-described methods. For example, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0159] In an exemplary embodiment, a computer program product is further provided. The computer program product includes instructions that can be executed by the processor 620 of the electronic device 600 to implement the above method.

[0160] It should be noted that the above-mentioned device, electronic device, computer-readable storage medium, computer program product, etc. may also include other implementation manners according to the description of the method embodiments. The specific implementation manners can refer to the description of the relevant method embodiments and will not be elaborated herein one by one.

[0161] Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common knowledge or conventional technical means in the technical field not disclosed by the present disclosure. The specification and embodiments are only to be regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

[0162] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A data processing method, characterized in that, it includes: responding to an object information determination request for a target object, and obtaining the object identifier of the target object; screening out a resource data prediction model corresponding to the object identifier from a pre-constructed resource data prediction model as the resource data prediction model corresponding to the target object; each resource data prediction model is used to represent the association relationship between the corresponding object information and the total resource data, and the object information is used to represent the resource voucher information of the corresponding object; the pre-constructed resource data prediction model is constructed through the target association relationship between the sample object information of the sample object and the actual total resource data; the sample object information includes the first sample object information and the second sample object information, the first sample object information is used to represent the first resource data in the resource voucher information of the sample object, and the second sample object information is used to represent the second resource data in the resource voucher information of the sample object; the target association relationship is obtained according to the first association relationship between the first sample object information, the second sample object information and the first associated account data corresponding to the sample object, and the second association relationship between the first associated account data, the first sample object information, the second sample object information and the actual total resource data; determining the total resource data of the target object under different object information through the resource data prediction model corresponding to the target object; determining the target object information of the target object according to the total resource data of the target object under different object information.

2. The method according to claim 1, characterized in that, before responding to an object information determination request for a target object and obtaining the object identifier of the target object, it further includes: obtaining the sample object information and the actual total resource data of the sample object; inputting the sample object information of the sample object into the resource data prediction model to be trained, and obtaining the predicted total resource data of the sample object; training the resource data prediction model to be trained according to the difference between the predicted total resource data and the actual total resource data of the sample object, and obtaining the resource data prediction model corresponding to the sample object as the pre-constructed resource data prediction model.

3. The method according to claim 2, characterized in that, the training the resource data prediction model to be trained according to the difference between the predicted total resource data and the actual total resource data of the sample object, and obtaining the resource data prediction model corresponding to the sample object includes: obtaining a loss value according to the difference between the predicted total resource data and the actual total resource data of the sample object; adjusting the network parameters of the resource data prediction model to be trained according to the loss value, and retraining the resource data prediction model after the network parameters are adjusted until the training end condition is reached, and taking the trained resource data prediction model that reaches the training end condition as the resource data prediction model corresponding to the sample object.

4. The method according to claim 1, characterized in that, Before obtaining the object identifier of the target object in response to an object information determination request for the target object, it further includes: Obtaining sample object information and actual total resource data of a sample object; Determining a target association relationship between the sample object information and the actual total resource data of the sample object according to the sample object information and the actual total resource data of the sample object; Constructing a resource data prediction model corresponding to the sample object according to the target association relationship as the pre-constructed resource data prediction model.

5. The method according to claim 1, wherein, The sample object also corresponds to second associated account data; The first association relationship is obtained through the following method: Determining a third association relationship between the first sample object information and the second associated account data, and a fourth association relationship between the second sample object information, the second associated account data and the first associated account data according to the first sample object information, the second sample object information, the first associated account data and the second associated account data; Obtaining the first association relationship according to the third association relationship and the fourth association relationship.

6. The method according to any one of claims 1 to 5, wherein, The determining the target object information of the target object according to the total resource data of the target object under different object information includes: Filtering out target total resource data that meets a preset resource condition from the total resource data of the target object under different object information; Determining the object information corresponding to the target total resource data as the target object information of the target object.

7. A data processing device, wherein, It includes: An identifier acquisition unit configured to execute obtaining the object identifier of the target object in response to an object information determination request for the target object; A model determination unit configured to execute filtering out a resource data prediction model corresponding to the object identifier from the pre-constructed resource data prediction models as the resource data prediction model corresponding to the target object; each resource data prediction model is used to represent the association relationship between the corresponding object information and the total resource data, and the object information is used to represent the resource voucher information of the corresponding object; the pre-constructed resource data prediction model is constructed through the target association relationship between the sample object information and the actual total resource data of the sample object; the sample object information includes first sample object information and second sample object information, the first sample object information is used to represent the first resource data in the resource voucher information of the sample object, and the second sample object information is used to represent the second resource data in the resource voucher information of the sample object; the target association relationship is obtained according to the first association relationship between the first sample object information, the second sample object information and the first associated account data corresponding to the sample object, and the second association relationship between the first associated account data, the first sample object information, the second sample object information and the actual total resource data; A data determination unit, configured to determine the total resource data of the target object under different object information by means of a resource data prediction model corresponding to the target object; An information determination unit, configured to determine the target object information of the target object according to the total resource data of the target object under different object information.

8. The apparatus according to claim 7, wherein, the apparatus further includes a model training unit, configured to obtain the sample object information and the actual total resource data of the sample object; input the sample object information of the sample object into the resource data prediction model to be trained, and obtain the predicted total resource data of the sample object; train the resource data prediction model to be trained according to the difference between the predicted total resource data and the actual total resource data of the sample object, and obtain the resource data prediction model corresponding to the sample object as the pre-constructed resource data prediction model.

9. The apparatus according to claim 8, wherein, the model training unit is further configured to obtain a loss value according to the difference between the predicted total resource data and the actual total resource data of the sample object; adjust the network parameters of the resource data prediction model to be trained according to the loss value, and retrain the resource data prediction model with adjusted network parameters until the training end condition is reached, and use the trained resource data prediction model that reaches the training end condition as the resource data prediction model corresponding to the sample object.

10. The apparatus according to claim 7, wherein, the apparatus further includes a model construction unit, configured to obtain the sample object information and the actual total resource data of the sample object; determine the target association relationship between the sample object information and the actual total resource data of the sample object according to the sample object information and the actual total resource data of the sample object; construct the resource data prediction model corresponding to the sample object according to the target association relationship as the pre-constructed resource data prediction model.

11. The apparatus according to claim 7, wherein, the sample object further corresponds to second associated account data; the model construction unit is further configured to determine a third association relationship between the first sample object information and the second associated account data, and a fourth association relationship between the second sample object information, the second associated account data and the first associated account data according to the first sample object information, the second sample object information, the first associated account data and the second associated account data; obtain the first association relationship according to the third association relationship and the fourth association relationship.

12. The apparatus according to any one of claims 7 to 11, wherein, the information determination unit is further configured to screen out the target total resource data that meets the preset resource conditions from the total resource data of the target object under different object information; determine the object information corresponding to the target total resource data as the target object information of the target object.

13. An electronic device, characterized in that, comprising: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to execute the instructions to implement the data processing method according to any one of claims 1 to 6.

14. A computer-readable storage medium, characterized in that, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the data processing method according to any one of claims 1 to 6.

15. A computer program product including instructions, characterized in that, when the instructions are executed by a processor of an electronic device, the electronic device is enabled to execute the data processing method according to any one of claims 1 to 6.

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