Data processing method, apparatus and device

Abstract

Embodiments of the present description provide a data processing method, apparatus and device. The method comprises: acquiring picture credential data corresponding to a target user triggering execution of a target service; acquiring a pretrained service processing model corresponding to the target service; by means of a second module of the pretrained service processing model, determining sub-picture credential data of different areas containing text information in the picture credential data, and respectively performing text feature extraction processing on the sub-picture credential data to obtain text feature information corresponding to the picture credential data; by means of a first module of the pretrained service processing model, performing credential parsing processing on the text feature information to obtain a credential parsing result for the picture credential data; and on the basis of the credential parsing result, determining a service processing result corresponding to the target user triggering execution of the target service.

Description

Data processing method, device and equipment Technical Field

[0001] This document relates to the field of data processing technology, and in particular to a data processing method, device and equipment. Background Art

[0002] With the rapid development of the Internet industry, the types and number of business services provided by network operators to users are increasing. How to parse the business credentials of users when using business services to better provide business services to users (such as quickly and accurately performing identity authentication based on the parsing results of business credentials to protect users' privacy data from being leaked, etc.) has become a focus of network operators.

[0003] Business vouchers can be parsed and processed using preset voucher parsing rules corresponding to the business to be processed. For example, whether the business voucher contains risk keywords that indicate violations can be parsed and processed. However, as the number of business types increases, parsing business vouchers using pre-set voucher parsing rules will result in low accuracy and efficiency in voucher parsing. Therefore, a solution is needed that can improve the accuracy and efficiency of business voucher parsing so as to accurately process business.

[0004] Summary of the Invention

[0005] The purpose of the embodiments of this specification is to provide a solution that can improve the accuracy and efficiency of business credential parsing to accurately perform business processing.

[0006] In order to realize the above technical solution, the embodiments of this specification are implemented as follows.

[0007] In a first aspect, an embodiment of the present specification provides a data processing method, including: obtaining picture voucher data corresponding to a target business triggered by a target user; obtaining a pre-trained business processing model corresponding to the target business, wherein the business processing model is obtained by training a business processing model constructed by a first module and a pre-trained second module using the first picture voucher data corresponding to the target business, the second module is obtained by training a module constructed by a preset deep learning algorithm based on sub-picture voucher data in different areas of the second picture voucher data and text description data corresponding to the second picture voucher data, the first module is used to perform voucher parsing on the output result of the second module; determining, through the second module of the pre-trained business processing model, sub-picture voucher data in different areas containing text information in the picture voucher data, and performing text feature extraction on the sub-picture voucher data respectively to obtain text feature information corresponding to the picture voucher data; performing voucher parsing on the text feature information through the first module of the pre-trained business processing model to obtain a voucher parsing result for the picture voucher data; and determining, based on the voucher parsing result, a business processing result corresponding to the target business triggered by the target user.

[0008] In a second aspect, an embodiment of the present specification provides a data processing device, which includes: a first acquisition module for acquiring picture voucher data corresponding to the target business triggered by the target user; a model acquisition module for acquiring a pre-trained business processing model corresponding to the target business, wherein the business processing model is obtained by training a business processing model constructed by a first module and a pre-trained second module through the first picture voucher data corresponding to the target business, and the second module is obtained by training a module constructed by a preset deep learning algorithm based on sub-picture voucher data in different areas of the second picture voucher data and text description data corresponding to the second picture voucher data, and the first module is used to perform voucher parsing processing on the output result of the second module; a first processing module is used to determine the sub-picture voucher data in different areas containing text information in the picture voucher data through the second module of the pre-trained business processing model, and perform text feature extraction processing on the sub-picture voucher data respectively to obtain text feature information corresponding to the picture voucher data; a second processing module is used to perform voucher parsing processing on the text feature information through the first module of the pre-trained business processing model. Obtain a credential parsing result for the image credential data; and a result determination module, configured to determine a business processing result corresponding to triggering execution of the target business for the target user based on the credential parsing result.

[0009] On the third aspect, an embodiment of the present specification provides a data processing device, the data processing device comprising: a processor; and a memory arranged to store computer-executable instructions, wherein the executable instructions, when executed, cause the processor to: obtain picture voucher data corresponding to the target user triggering the execution of a target business; obtain a pre-trained business processing model corresponding to the target business, the business processing model being obtained by training a business processing model constructed by a first module and a pre-trained second module through first picture voucher data corresponding to the target business, the second module being sub-picture voucher data based on different areas in the second picture voucher data, and text description data corresponding to the second picture voucher data, The module constructed by the preset deep learning algorithm is trained, and the first module is used to perform voucher parsing processing on the output result of the second module; through the second module of the pre-trained business processing model, the sub-picture voucher data of different areas containing text information in the picture voucher data are determined, and text feature extraction processing is performed on the sub-picture voucher data respectively to obtain text feature information corresponding to the picture voucher data; through the first module of the pre-trained business processing model, the text feature information is subjected to voucher parsing processing to obtain a voucher parsing result for the picture voucher data; based on the voucher parsing result, the business processing result corresponding to the target business triggered for the target user is determined.

[0010] In a fourth aspect, an embodiment of the present specification provides a storage medium for storing computer-executable instructions, which implement the following process when executed: obtaining picture voucher data corresponding to the target business triggered by the target user; obtaining a pre-trained business processing model corresponding to the target business, wherein the business processing model is obtained by training a business processing model constructed by a first module and a pre-trained second module through the first picture voucher data corresponding to the target business, and the second module is obtained by training a module constructed by a preset deep learning algorithm based on sub-picture voucher data in different areas of the second picture voucher data and text description data corresponding to the second picture voucher data, and the first module is used to perform voucher parsing processing on the output result of the second module; through the second module of the pre-trained business processing model, sub-picture voucher data in different areas containing text information in the picture voucher data are determined, and text feature extraction processing is performed on the sub-picture voucher data respectively to obtain text feature information corresponding to the picture voucher data; through the first module of the pre-trained business processing model, voucher parsing processing is performed on the text feature information to obtain a voucher parsing result for the picture voucher data; based on the voucher parsing result, determining the business processing result corresponding to the triggering of the target business by the target user. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of this specification or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] FIG1 is a schematic diagram of a data processing system of the present specification;

[0013] FIG2A is a flow chart of an embodiment of a data processing method of this specification;

[0014] FIG2B is a schematic diagram of a processing process of a data processing method of this specification;

[0015] FIG3 is a schematic diagram of a picture voucher data of this specification;

[0016] FIG4 is a schematic diagram of the processing process of another data processing method of this specification;

[0017] FIG5 is a schematic diagram of a training process of a second module of this specification;

[0018] FIG6 is a schematic diagram of a processing process of a business processing model in this specification;

[0019] FIG7 is a schematic structural diagram of a data processing device according to an embodiment of the present specification;

[0020] FIG8 is a schematic structural diagram of a data processing device in this specification. DETAILED DESCRIPTION

[0021] The embodiments of this specification provide a data processing method, apparatus, and device.

[0022] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative work should fall within the scope of protection of this specification.

[0023] The technical solution of this specification can be applied to a data processing system. As shown in FIG1 , the data processing system may include terminal devices and servers, wherein the server may be an independent server or a server cluster composed of multiple servers, and the terminal device may be a device such as a personal computer or a mobile terminal device such as a mobile phone or a tablet computer.

[0024] The data processing system may include n terminal devices and m servers, where n and m are positive integers greater than or equal to 1. The server may be a background server of an application, and the terminal device may be a client device of the application. The application may be an application that can provide users with services such as resource transfer services, video viewing services, and instant messaging services.

[0025] The terminal device can collect the picture voucher data corresponding to the user triggering the execution of a certain service, and send the collected picture voucher data to the server. In this way, the server can obtain the picture voucher data corresponding to the user triggering the execution of the service, and obtain the pre-trained business processing model corresponding to the service, so as to perform text feature extraction processing on the sub-picture voucher data in different areas of the picture voucher data through the second module in the pre-trained business processing model, and obtain the text feature information corresponding to the picture voucher data. Then, the server can perform voucher parsing processing on the text feature information through the first module of the pre-trained business processing model to obtain the voucher parsing result for the picture voucher data. Finally, based on the voucher parsing result, the server can determine the business processing result corresponding to the target user triggering the execution of the target service.

[0026] In addition, the server can also store the picture voucher data collected by the terminal device, so that when the model training cycle is reached, the business processing model can be trained using the stored picture voucher data to obtain a trained business processing model.

[0027] In addition, a central server (such as server 1) can also be provided in the data processing system. The central server can receive historical picture voucher data stored by the terminal device and / or the server to determine the second picture voucher data based on the historical picture voucher data, so as to train the second module in the business processing model to obtain the trained second module. Then, the central server can obtain the first picture voucher data corresponding to the target business in the historical picture voucher data, and train the business processing model (i.e., the business processing model constructed by the first module and the pre-trained second module) through the first picture voucher data to obtain the trained business processing model. In this way, the central server can send the model parameters of the trained business processing model to other servers in the data processing system, and the other servers can update the local business processing model according to the received model parameters to obtain the trained business processing model, and then perform voucher parsing processing on the picture voucher data corresponding to the target business through the trained business processing model, so as to obtain the voucher parsing result corresponding to the picture voucher data, and then determine the business processing result based on the voucher parsing result. This avoids the occurrence of business interruptions due to the need to train the business processing model, and meets the user's business usage needs.

[0028] The data processing method in the following embodiments can be implemented based on the above data processing system structure.

[0029] Example 1

[0030] As shown in Figures 2A and 2B, an embodiment of this specification provides a data processing method, wherein the execution subject of the method can be a server, wherein the server can be an independent server or a server cluster composed of multiple servers. The method can specifically include the following steps S202 to S210.

[0031] In S202 , the image voucher data corresponding to the target user triggering the execution of the target service is obtained.

[0032] Among them, the target business can be any business that may involve data risk issues such as leakage of user privacy data. For example, the target business can be a resource transfer business, an identity authentication business, an account registration business, a resource accounting business, etc. The image credential data can be credential data that can indicate the image type related to the target user triggering the execution of the target business. For example, the image credential data can include credential data of the image type used to indicate the user identity of the target user, credential data of the image type used to indicate the processing process or processing result of the target business, etc. Specifically, taking the target business as the resource transfer business as an example, the image credential data can include the target user's identity authentication image (that is, the image entered by the target user for identity verification, etc.) data, the resource transfer page corresponding to the target user triggering the execution of the resource transfer business (that is, the page containing information such as the resource transfer quantity, resource transfer method, and resource transfer correspondence) data, etc. Taking the target business as the account registration business as an example, the image credential data can include the target user's identity authentication image data, the account registration page corresponding to the target user triggering the execution of the account registration business (that is, the page containing information such as the registered account, registered user name, registered password, verification method, etc.) data, etc.

[0033] In practice, with the rapid development of the Internet industry, the types and number of business services provided by network operators to users are increasing. How to parse the business credentials of users when using business services in order to better provide business services to users (such as quickly and accurately performing identity authentication based on the parsing results of business credentials to protect the user's privacy data from being leaked, etc.) has become the focus of network operators. The business credentials can be parsed and processed by the preset credential parsing rules corresponding to the business to be processed. For example, whether the business credentials contain risk keywords that indicate violations can be parsed and processed. However, as the number of business types gradually increases, parsing the business credentials through pre-set credential parsing rules will result in low accuracy and efficiency in credential parsing. Therefore, a solution is needed that can improve the accuracy and efficiency of business credential parsing so as to accurately process business. To this end, the embodiments of this specification provide a technical solution that can solve the above problems. For details, please refer to the following content.

[0034] Taking the target business as resource transfer business as an example, the target user can trigger the start of the resource transfer business through the resource transfer application installed in the terminal device. That is, when the terminal device detects that the target user triggers the start of the resource transfer business through a resource transfer application, the terminal device can collect the target user's identity authentication picture data (such as the user login page when the target user logs in to the resource transfer application, the certificate picture data entered by the target user that can be used for identity authentication, etc.), as well as the resource transfer page corresponding to the target user triggering the execution of the resource transfer business.

[0035] The terminal device can determine the collected image data as the image voucher data corresponding to the target user triggering the execution of the target service, and send the image voucher data to the server, that is, the server can receive the image voucher data corresponding to the target user triggering the execution of the target service.

[0036] In addition, the above-mentioned method for obtaining image credential data is an optional and feasible determination method. In actual application scenarios, there may be a variety of different acquisition methods, which may vary depending on the actual application scenarios. The embodiments of this specification do not make specific limitations on this.

[0037] In S204, a pre-trained business processing model corresponding to the target business is obtained.

[0038] Among them, the business processing model can be obtained by training the business processing model constructed by the first module and the pre-trained second module through the first picture voucher data corresponding to the target business. The second module can be obtained by training the module constructed by the preset deep learning algorithm based on the sub-picture voucher data of different areas in the second picture voucher data, and the text description data corresponding to the second picture voucher data. The first module can be used to perform voucher parsing processing on the output results of the second module.

[0039] In implementation, the server can select the second picture voucher data from the pre-stored historical picture voucher data based on a preset model update cycle (such as the historical picture voucher data can be determined as the second picture voucher data), and then the server can pre-train the second module based on the second picture voucher data to obtain the trained second module.

[0040] When training the second module, the server can divide the second picture voucher data into regions to obtain multiple sub-picture voucher data. For example, the server can divide the second picture voucher data into regions according to the types of elements contained in the second picture voucher data. Specifically, taking the second picture voucher data as a resource transfer page corresponding to the resource transfer service triggered by the target user as an example, as shown in Figure 3, the second picture voucher data can contain picture type elements, text type elements, video type elements and audio type elements. Based on the differences in the above elements, the server can divide the second picture voucher data into sub-picture voucher data 1 corresponding to region 1, sub-picture voucher data 2 corresponding to region 2, sub-picture voucher data 3 corresponding to region 3, and sub-picture voucher data 4 corresponding to region 4.

[0041] The above-mentioned method for determining the sub-image credential data is an optional and feasible determination method. In actual application scenarios, there may be a variety of different acquisition methods, which may vary depending on the actual application scenarios. The embodiments of this specification do not make specific limitations on this.

[0042] The server can train the second module through the sub-picture voucher data of different areas in the second picture voucher data and the text description data corresponding to the second picture voucher data, so that the sub-picture voucher data of each area in the second picture voucher data can be aligned with the sub-text description data corresponding to the sub-picture voucher data of the area in the text description data, thereby improving the second module's perception of various types of information in the picture voucher data, that is, the alignment of the second picture voucher data and the text description data can be improved at a finer granularity, so that the trained second module can dig out more detailed element content in the picture voucher data as much as possible.

[0043] After obtaining the trained second module, the server may select first image voucher data corresponding to the target business from the historical image data, and train the business processing model using the first image voucher data to obtain a trained business processing model.

[0044] In S206, the second module of the pre-trained business processing model is used to determine the sub-picture voucher data in different areas containing text information in the picture voucher data, and perform text feature extraction processing on the sub-picture voucher data to obtain text feature information corresponding to the picture voucher data.

[0045] In implementation, since the second module is trained based on the sub-picture voucher data in different areas of the second picture voucher data and the text description data corresponding to the second picture voucher data, the trained second module can dig out more detailed element content in the picture voucher data as much as possible. Therefore, the server can determine the sub-picture voucher data in different areas containing text information in the picture voucher data through the second module of the pre-trained business processing model, and perform text feature extraction processing on the sub-picture voucher data respectively to obtain text feature information corresponding to the picture voucher data.

[0046] In S208 , the first module of the pre-trained business processing model is used to perform voucher parsing on the text feature information to obtain a voucher parsing result for the image voucher data.

[0047] Among them, the first module can be a module built based on a preset machine learning algorithm, which is used to perform credential parsing on text feature information. The credential parsing result can include the result obtained by parsing the image credential data based on the business processing requirements corresponding to the target business. For example, taking the target business as resource transfer business as an example, the business processing requirements corresponding to the target business can be risk detection requirements, then the credential parsing result can include the result obtained by performing risk detection processing on the image credential data, or taking the target business as account registration business as an example, the business processing requirements corresponding to the target business can be risk detection requirements and account information extraction requirements, then the credential parsing result can include the result obtained by performing risk detection processing on the image credential data, and the result obtained by performing account information extraction processing on the image credential data.

[0048] During implementation, the server can construct a corresponding first module based on the business processing requirements of the target business. For example, the business processing requirements of the target business may be risk detection requirements. Then, the server can construct the first module based on a preset classification algorithm, that is, the first module can be used for classification processing based on text feature information to obtain the risk classification results of the image voucher data (that is, the voucher parsing results).

[0049] In addition, the target business may have various business processing requirements, and the corresponding first module may be constructed according to the actual business processing requirements of the target business. This embodiment of the specification does not specifically limit this.

[0050] In S210 , based on the credential parsing result, a business processing result corresponding to triggering execution of the target business for the target user is determined.

[0051] In implementation, taking the risk classification result of the image credential data as an example, if it is determined that the image credential data is risky based on the risk classification result, then the server can suspend the execution of the target business and determine the result of the suspension as the business processing result corresponding to the target business triggered for the target user.

[0052] Alternatively, if it is determined based on the risk classification result that the image credential data does not pose a risk, then the server may continue to execute the target service based on the image credential data and obtain a corresponding service processing result.

[0053] An embodiment of the present specification provides a data processing method, which obtains a pre-trained business processing model corresponding to the target business by obtaining picture voucher data corresponding to the target user triggering the execution of the target business. The business processing model is obtained by training a business processing model constructed by a first module and a pre-trained second module through the first picture voucher data corresponding to the target business. The second module is obtained by training a module constructed by a preset deep learning algorithm based on sub-picture voucher data in different areas of the second picture voucher data and text description data corresponding to the second picture voucher data. The first module is used to perform voucher parsing processing on the output result of the second module, and the sub-picture voucher data in different areas containing text information in the picture voucher data are determined through the second module of the pre-trained business processing model, and text feature extraction processing is performed on the sub-picture voucher data respectively to obtain text feature information corresponding to the picture voucher data. The text feature information is subjected to voucher parsing processing by the first module of the pre-trained business processing model to obtain a voucher parsing result for the picture voucher data. Based on the voucher parsing result, the business processing result corresponding to the target business triggered by the target user is determined. Since the second module is trained based on the sub-image voucher data of different areas in the second image voucher data and the text description data corresponding to the second image voucher data, the trained second module can mine more detailed element content in the image voucher data as much as possible. In this way, through the second module of the pre-trained business processing model, the more detailed element content in the image voucher data can be subjected to text feature extraction processing to obtain text feature information corresponding to the image voucher data, so as to improve the accuracy of the subsequent voucher parsing processing by the first module. In addition, after obtaining the trained second module, different training image voucher data can be obtained for different businesses to train the business processing model, that is, the server can use the first image voucher data corresponding to the target business to train the business processing model constructed by the first module and the pre-trained second module, which can improve the training efficiency of the business processing model corresponding to the target business, improve the efficiency and accuracy of the voucher parsing processing of the image voucher data, and thereby improve the efficiency and accuracy of determining the business processing result corresponding to the target business triggered by the target user.

[0054] Example 2

[0055] As shown in Figure 4, an embodiment of this specification provides a data processing method, the execution subject of which can be a server, wherein the server can be an independent server or a server cluster composed of multiple servers. The method can specifically include the following steps S202 to S424.

[0056] In S202 , the image voucher data corresponding to the target user triggering the execution of the target service is obtained.

[0057] In S402 , second picture voucher data and text description data corresponding to the second picture voucher data are obtained.

[0058] Among them, the data volume of the second picture voucher data can be greater than the data volume of the first picture voucher data. For example, the first picture voucher data can be picture voucher data corresponding to the target business, and the second picture voucher data can be picture voucher data corresponding to multiple different businesses. That is, the second module can be pre-trained through a large amount of training sample data to improve the training effect of the second module. The text description data can be data used to describe the content contained in the second picture voucher data. For example, assuming that the second picture voucher data is the resource transfer page shown in Figure 3, the text description data for the second picture voucher data can be "This is a screenshot, which says: Welcome to the resource transfer application, the resource transfer quantity is xx, the resource transfer object is xx, and video ads and voice ads are playing."

[0059] In S404, text recognition processing is performed on the second picture voucher data to obtain a text recognition result, and based on the text recognition result, sub-picture data in different areas containing text information in the second picture voucher data are determined as sub-picture voucher data in the second picture voucher data.

[0060] In implementation, the server may perform text recognition processing on the second image voucher data based on a preset text recognition algorithm to obtain a text recognition result. The server may then perform entity recognition processing, segmentation processing, or classification processing on the text recognition result, and process the result to divide the second image voucher data into multiple sub-image data. Finally, the server may determine the sub-image data in different areas of the second image voucher data containing text information as the sub-image voucher data in the second image voucher data.

[0061] For example, taking the second image voucher data as shown in FIG3 as an example, assuming that the text recognition results for the second image voucher data include "Welcome to the resource transfer application," "Please enter:," "Resource transfer quantity," "Resource transfer object," "Video playback area," and "Audio playback area," the server may determine the sub-image corresponding to each of the above text recognition results as the sub-image voucher data in the second image voucher data. That is, the sub-image voucher data may include sub-image voucher data 1 corresponding to area 1, sub-image voucher data 2 corresponding to area 2, sub-image voucher data 3 corresponding to area 3, and sub-image voucher data 4 corresponding to area 4.

[0062] Alternatively, the server may further classify the text recognition results. The classification results may include Category 1, which includes "Welcome to the resource transfer application," Category 2, which includes "Please enter:," "Resource transfer quantity," "Resource transfer object," and "Video playback area," and Category 3, which includes "Video playback area" and "Audio playback area." The server may obtain the area corresponding to each category in the second image voucher data and determine the sub-image corresponding to the area as the sub-image voucher data in the second image voucher data. That is, the sub-image voucher data may include Sub-image voucher data 1 corresponding to Area 1, Sub-image voucher data 2 corresponding to Area 2, and Sub-image voucher data 3 corresponding to Areas 3 and 4.

[0063] The above-mentioned method for determining the sub-image credential data is an optional and feasible determination method. In actual application scenarios, there may be a variety of different acquisition methods, which may vary depending on the actual application scenarios. The embodiments of this specification do not make specific limitations on this.

[0064] In S406 , the second module performs text feature extraction processing on the sub-picture voucher data in the second picture voucher data to obtain first text features.

[0065] In S408, the third module performs feature extraction processing on the text description data to obtain a second text feature.

[0066] In S410, based on the first text feature and the second text feature, a first training error value is determined, and based on the first training error value, it is determined whether the second module has converged. If the second module has not converged, the second module and the third module are continued to be trained based on the sub-image voucher data in the second image voucher data and the text description data until the second module converges to obtain the trained second module.

[0067] In implementation, taking the model constructed by the second and third modules as the base model, the second module can perform text feature extraction processing through visual encoding, and the third module can perform feature extraction processing through text encoding. As shown in Figure 5, the server can input the sub-image voucher data in the second image voucher data into the second module to obtain the first text feature, and input the text description data into the third module to obtain the second text feature corresponding to the text description data.

[0068] In this way, by performing comparative learning on the second module and the third module based on the first training error value determined by the first text feature and the second text feature, the alignment between the sub-image voucher data and the text description data can be improved, thereby improving the ability of the trained second module to extract text features of finer-grained text feature information in the image voucher data.

[0069] In S412, first image voucher data and a first voucher parsing result corresponding to the first image voucher data are obtained.

[0070] In implementation, the server may obtain, based on the service type of the target service, first image voucher data corresponding to the service type and a first voucher parsing result corresponding to the first image voucher data.

[0071] In S414 , based on the pre-trained second module, text feature extraction processing is performed on the sub-picture voucher data in different regions of the first picture voucher data to obtain third text features.

[0072] In implementation, since the second module is obtained through comparative learning training of the sub-image voucher data and the corresponding text description data, it has the ability to extract text features of more fine-grained text feature information in the image voucher data. Therefore, as shown in Figure 6, the server can input the first image data into the second module, so as to perform text feature extraction processing on the sub-image voucher data in different areas of the first image data through the second module to obtain the first voucher parsing result.

[0073] Alternatively, the server may perform text recognition on the first image voucher data to obtain a text recognition result, and based on the text recognition result, determine the sub-image data in different areas of the first image voucher data containing text information as the sub-image voucher data in the first image voucher data. The server may then input the sub-image voucher data in the first image voucher data into the second module to obtain a first voucher parsing result.

[0074] In S416, based on the first module, the third text feature is subjected to voucher parsing processing to obtain a second voucher parsing result, and a second training error value is determined based on the first voucher parsing result and the second voucher parsing result.

[0075] In implementation, in actual applications, different first modules may be constructed for different businesses. For example, the first module may include a first submodule for performing business information extraction processing and a second submodule for performing image tampering detection. The credential parsing result may include the business information extraction result and the image tampering detection result. Then, the processing method of the above S416 may refer to the following steps 1 to 3:

[0076] Step 1: Based on the first submodule of the first module, business information extraction processing is performed on the third text feature to obtain the first business information extraction result in the second voucher parsing result.

[0077] Among them, the first business information extraction result may include the business information required for processing the target business in the first image voucher data. For example, assuming that the first image voucher data is the image data shown in Figure 3, and the target business is the resource transfer business, then the first business information extraction result may include the resource transfer quantity, resource transfer time and resource transfer object.

[0078] Step 2: Based on the second submodule of the first module, perform image tampering detection processing on the third text feature to obtain a first image tampering detection result in the second credential parsing result.

[0079] In the event that the second submodule determines that the first image voucher data has been tampered with, the first image tampering detection result may include the image data of the tampered area in the first image voucher data. In the event that the second submodule determines that the first image voucher data has not been tampered with, the first image tampering detection result may be that the first image voucher data has not been tampered with. For example, assuming that the first image voucher data is the image data shown in Figure 3, if the image tampering detection processing of the second submodule determines that the first image voucher data has been tampered with and area 2 is the tampered area, then the first image tampering detection result output by the second submodule may be the image data corresponding to area 2.

[0080] In implementation, as shown in Figure 6, the first sub-module can perform business information extraction processing on the third text feature output by the second module to obtain a first business information extraction result, and the second sub-module can perform image tampering detection processing on the third text feature output by the second module to obtain a first image tampering detection result.

[0081] Step three: determine a second training error value based on the first business information extraction result, the first image tampering detection result, and the first credential parsing result.

[0082] In an implementation, the server may determine a first sub-error value based on the first business information extraction result and the business information extraction result included in the first voucher parsing result, and determine a second sub-error value based on the first image tampering detection result and the image tampering detection result in the first voucher parsing result. Finally, the server may determine a second training error value based on the first sub-error value and the second sub-error value.

[0083] In S418, based on the second training error value, it is determined whether the business processing model has converged. If the business processing model has not converged, the business processing model is continued to be trained based on the first image voucher data and the first voucher parsing result until the business processing model converges to obtain a trained business processing model.

[0084] In S204, a pre-trained business processing model corresponding to the target business is obtained.

[0085] In S206, the second module of the pre-trained business processing model is used to determine the sub-picture voucher data in different areas containing text information in the picture voucher data, and perform text feature extraction processing on the sub-picture voucher data to obtain text feature information corresponding to the picture voucher data.

[0086] In S208 , the first module of the pre-trained business processing model is used to perform voucher parsing on the text feature information to obtain a voucher parsing result for the image voucher data.

[0087] In S422 , based on the image tampering detection result in the credential parsing result, a risk detection result for the image credential data is determined.

[0088] In practice, in actual applications, the processing method of the above S422 can be various. An optional implementation method is provided below. For details, please refer to the following steps 1 to 3.

[0089] Step 1: When it is determined based on the image tampering detection result that the image voucher data is tampered image data, a tampered area in the image voucher data is determined based on the image tampering detection result.

[0090] Step 2: Obtain the business information corresponding to the tampered area in the business information extraction result.

[0091] Step three: Based on the tampered area and the corresponding business information, risk detection processing is performed on the image credential data to obtain a risk detection result for the image credential data.

[0092] In implementation, assuming the first image voucher data is the image data shown in Figure 3, if the image tampering detection processing of the second submodule determines that the first image voucher data has been tampered with, and area 2 is the tampered area, then the first image tampering detection result output by the second submodule can be the image data corresponding to area 2. The business information extraction result output by the second submodule based on the image voucher result can include the resource transfer amount, resource transfer time, and resource transfer object.

[0093] The server can obtain the business information corresponding to the tampered area in the business information extraction result, that is, the information matching the business information extraction result in the business information contained in the image data corresponding to area 2, that is, the resource transfer quantity and resource transfer object.

[0094] The server can perform risk detection on the image credential data based on the resource transfer amount and the resource transfer object, and obtain a risk detection result for the image credential data. For example, the server can obtain the resource transfer threshold set by the target user for the resource transfer object and determine the risk detection result based on the resource transfer threshold and the resource transfer amount. If the resource transfer amount is greater than the resource transfer threshold, the risk detection result for the image credential data may be that there is a risk. If the resource transfer amount is not greater than the resource transfer threshold, the risk detection result for the image credential data may be that there is no risk.

[0095] The above-mentioned method for performing risk detection and processing on image credential data is an optional and feasible risk detection and processing method. In actual application scenarios, there can also be a variety of different risk detection and processing methods. Different risk detection and processing methods can be selected according to different actual application scenarios. The embodiments of this specification do not make specific limitations on this.

[0096] In S424 , based on the risk detection result and the business information extraction result in the credential parsing result, a business processing result corresponding to triggering execution of the target business for the target user is determined.

[0097] During implementation, the server may determine that there is no risk in the image credential data based on the risk detection result, and then execute the target business based on the business information extraction result to obtain the business processing result corresponding to the target business triggered for the target user.

[0098] An embodiment of the present specification provides a data processing method, which obtains a pre-trained business processing model corresponding to the target business by obtaining picture voucher data corresponding to the target user triggering the execution of the target business. The business processing model is obtained by training a business processing model constructed by a first module and a pre-trained second module through the first picture voucher data corresponding to the target business. The second module is obtained by training a module constructed by a preset deep learning algorithm based on sub-picture voucher data in different areas of the second picture voucher data and text description data corresponding to the second picture voucher data. The first module is used to perform voucher parsing processing on the output result of the second module, and the sub-picture voucher data in different areas containing text information in the picture voucher data are determined through the second module of the pre-trained business processing model, and text feature extraction processing is performed on the sub-picture voucher data respectively to obtain text feature information corresponding to the picture voucher data. The text feature information is subjected to voucher parsing processing by the first module of the pre-trained business processing model to obtain a voucher parsing result for the picture voucher data. Based on the voucher parsing result, the business processing result corresponding to the target business triggered by the target user is determined. Since the second module is trained based on the sub-image voucher data of different areas in the second image voucher data and the text description data corresponding to the second image voucher data, the trained second module can mine more detailed element content in the image voucher data as much as possible. In this way, through the second module of the pre-trained business processing model, the more detailed element content in the image voucher data can be subjected to text feature extraction processing to obtain text feature information corresponding to the image voucher data, so as to improve the accuracy of the subsequent voucher parsing processing by the first module. In addition, after obtaining the trained second module, different training image voucher data can be obtained for different businesses to train the business processing model, that is, the server can use the first image voucher data corresponding to the target business to train the business processing model constructed by the first module and the pre-trained second module, which can improve the training efficiency of the business processing model corresponding to the target business, improve the efficiency and accuracy of the voucher parsing processing of the image voucher data, and thereby improve the efficiency and accuracy of determining the business processing result corresponding to the target business triggered by the target user.

[0099] Example 3

[0100] The above is the data processing method provided in the embodiment of this specification. Based on the same idea, the embodiment of this specification also provides a data processing device, as shown in FIG7 .

[0101] The data processing device includes: a first acquisition module 701 , a model acquisition module 702 , a first processing module 703 , a second processing module 704 and a result determination module 705 .

[0102] The first acquisition module 701 is used to obtain the image voucher data corresponding to the target user triggering the execution of the target service;

[0103] Model acquisition module 702 is configured to acquire a pre-trained business processing model corresponding to the target business. The business processing model is obtained by training a business processing model constructed by a first module and a pre-trained second module using first image voucher data corresponding to the target business. The second module is obtained by training a module constructed by a preset deep learning algorithm based on sub-image voucher data of different regions in the second image voucher data and text description data corresponding to the second image voucher data. The first module is configured to perform voucher parsing on the output of the second module.

[0104] A first processing module 703 is configured to determine, using the second module of the pre-trained business processing model, sub-picture voucher data in different regions containing text information in the picture voucher data, and perform text feature extraction processing on each of the sub-picture voucher data to obtain text feature information corresponding to the picture voucher data;

[0105] The second processing module 704 is configured to perform voucher parsing on the text feature information using the first module of the pre-trained business processing model to obtain a voucher parsing result for the image voucher data;

[0106] The result determination module 705 is configured to determine, based on the credential parsing result, a business processing result corresponding to triggering execution of the target business for the target user.

[0107] In an embodiment of the present specification, the device also includes: a second acquisition module, used to acquire the second picture voucher data and text description data corresponding to the second picture voucher data; a picture partitioning module, used to perform text recognition processing on the second picture voucher data to obtain a text recognition result, and based on the text recognition result, determine the sub-picture data of different areas containing text information in the second picture voucher data as the sub-picture voucher data in the second picture voucher data; a third processing module, used to perform text feature extraction processing on the sub-picture voucher data in the second picture voucher data through the second module to obtain a first text feature; a fourth processing module, used to perform feature extraction processing on the text description data through the third module to obtain a second text feature; a first training module, used to determine a first training error value based on the first text feature and the second text feature, and determine whether the second module converges based on the first training error value. If the second module does not converge, the second module and the third module continue to be trained based on the sub-picture voucher data in the second picture voucher data and the text description data until the second module converges to obtain a trained second module.

[0108] In an embodiment of the present specification, the device also includes: a third acquisition module, used to obtain the first picture voucher data, and a first voucher parsing result corresponding to the first picture voucher data; a fifth processing module, used to perform text feature extraction processing on the sub-picture voucher data in different areas of the first picture voucher data based on the pre-trained second module to obtain a third text feature; an error determination module, used to perform voucher parsing processing on the third text feature based on the first module to obtain a second voucher parsing result, and determine a second training error value based on the first voucher parsing result and the second voucher parsing result; a second training module, used to determine whether the business processing model has converged based on the second training error value. If the business processing model has not converged, the business processing model is continued to be trained based on the first picture voucher data and the first voucher parsing result until the business processing model converges to obtain a trained business processing model.

[0109] In the embodiment of this specification, the data volume of the second picture voucher data is greater than the data volume of the first picture voucher data.

[0110] In an embodiment of the present specification, the first module includes a first submodule for performing business information extraction processing and a second submodule for performing image tampering detection, the voucher parsing result includes a business information extraction result and an image tampering detection result, and the error determination module is used to: based on the first submodule of the first module, perform business information extraction processing on the third text feature to obtain a first business information extraction result in the second voucher parsing result; based on the second submodule of the first module, perform image tampering detection processing on the third text feature to obtain a first image tampering detection result in the second voucher parsing result; and determine the second training error value based on the first business information extraction result, the first image tampering detection result, and the first voucher parsing result.

[0111] In an embodiment of the present specification, the result determination module 705 is used to: determine the risk detection result for the image credential data based on the image tampering detection result in the credential parsing result; and determine the business processing result corresponding to triggering the execution of the target business for the target user based on the risk detection result and the business information extraction result in the credential parsing result.

[0112] In an embodiment of the present specification, the result determination module 705 is used to: when it is determined that the picture voucher data is tampered picture data based on the picture tampering detection result, determine the tampered area in the picture voucher data based on the picture tampering detection result; obtain the business information corresponding to the tampered area in the business information extraction result; based on the tampered area and the corresponding business information, perform risk detection processing on the picture voucher data to obtain a risk detection result for the picture voucher data.

[0113] In an embodiment of the present specification, the result determination module 705 is used to: when it is determined based on the risk detection result that the image credential data does not pose a risk, execute the target business based on the business information extraction result, and obtain a business processing result corresponding to the target business triggered for the target user.

[0114] An embodiment of the present specification provides a data processing device, which obtains a pre-trained business processing model corresponding to the target business by obtaining picture voucher data corresponding to the target user triggering the execution of the target business. The business processing model is obtained by training a business processing model constructed by a first module and a pre-trained second module through the first picture voucher data corresponding to the target business. The second module is obtained by training a module constructed by a preset deep learning algorithm based on sub-picture voucher data in different areas of the second picture voucher data and text description data corresponding to the second picture voucher data. The first module is used to perform voucher parsing processing on the output result of the second module, and determine the sub-picture voucher data of different areas containing text information in the picture voucher data through the second module of the pre-trained business processing model, and perform text feature extraction processing on the sub-picture voucher data respectively to obtain text feature information corresponding to the picture voucher data. The text feature information is subjected to voucher parsing processing by the first module of the pre-trained business processing model to obtain a voucher parsing result for the picture voucher data. Based on the voucher parsing result, the business processing result corresponding to the target business triggered by the target user is determined. Since the second module is trained based on the sub-image voucher data of different areas in the second image voucher data and the text description data corresponding to the second image voucher data, the trained second module can mine more detailed element content in the image voucher data as much as possible. In this way, through the second module of the pre-trained business processing model, the more detailed element content in the image voucher data can be subjected to text feature extraction processing to obtain text feature information corresponding to the image voucher data, so as to improve the accuracy of the subsequent voucher parsing processing by the first module. In addition, after obtaining the trained second module, different training image voucher data can be obtained for different businesses to train the business processing model, that is, the server can use the first image voucher data corresponding to the target business to train the business processing model constructed by the first module and the pre-trained second module, which can improve the training efficiency of the business processing model corresponding to the target business, improve the efficiency and accuracy of the voucher parsing processing of the image voucher data, and thereby improve the efficiency and accuracy of determining the business processing result corresponding to the target business triggered by the target user.

[0115] Example 4

[0116] Based on the same idea, the embodiment of this specification also provides a data processing device, as shown in FIG8 .

[0117] The data processing device may vary significantly due to different configurations or performance, and may include one or more processors 801 and memory 802. The memory 802 may store one or more applications or data. The memory 802 may be either short-term or persistent storage. The application stored in the memory 802 may include one or more modules (not shown), each of which may include a series of computer-executable instructions for the data processing device. Furthermore, the processor 801 may be configured to communicate with the memory 802 to execute the series of computer-executable instructions in the memory 802 on the data processing device. The data processing device may also include one or more power supplies 803, one or more wired or wireless network interfaces 804, one or more input / output interfaces 805, and one or more keyboards 806.

[0118] Specifically, in this embodiment, the data processing device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the data processing device, and the one or more programs are configured to be executed by one or more processors, including computer-executable instructions for performing the following:

[0119] Obtain the image credential data corresponding to the target user triggering the execution of the target business;

[0120] Obtaining a pre-trained business processing model corresponding to the target business, the business processing model being obtained by training a business processing model constructed by a first module and a pre-trained second module using first image voucher data corresponding to the target business, the second module being obtained by training a module constructed by a preset deep learning algorithm based on sub-image voucher data of different regions in the second image voucher data and text description data corresponding to the second image voucher data, the first module being configured to perform voucher parsing processing on an output result of the second module;

[0121] determining, by the second module of the pre-trained business processing model, sub-picture voucher data in different areas containing text information in the picture voucher data, and performing text feature extraction processing on each of the sub-picture voucher data to obtain text feature information corresponding to the picture voucher data;

[0122] Performing voucher parsing on the text feature information through the first module of the pre-trained business processing model to obtain a voucher parsing result for the image voucher data;

[0123] Based on the credential parsing result, a business processing result corresponding to triggering execution of the target business for the target user is determined.

[0124] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences between the other embodiments. In particular, the data processing device embodiment is generally similar to the method embodiment, so its description is relatively simple. For relevant portions, refer to the description of the method embodiment.

[0125] An embodiment of the present specification provides a data processing device, which obtains a pre-trained business processing model corresponding to the target business by obtaining picture voucher data corresponding to the target user triggering the execution of the target business. The business processing model is obtained by training a business processing model constructed by a first module and a pre-trained second module through the first picture voucher data corresponding to the target business. The second module is obtained by training a module constructed by a preset deep learning algorithm based on sub-picture voucher data in different areas of the second picture voucher data and text description data corresponding to the second picture voucher data. The first module is used to perform voucher parsing processing on the output result of the second module, and determine the sub-picture voucher data of different areas containing text information in the picture voucher data through the second module of the pre-trained business processing model, and perform text feature extraction processing on the sub-picture voucher data respectively to obtain text feature information corresponding to the picture voucher data. The text feature information is subjected to voucher parsing processing by the first module of the pre-trained business processing model to obtain a voucher parsing result for the picture voucher data. Based on the voucher parsing result, the business processing result corresponding to the target business triggered by the target user is determined. Since the second module is trained based on the sub-image voucher data of different areas in the second image voucher data and the text description data corresponding to the second image voucher data, the trained second module can mine more detailed element content in the image voucher data as much as possible. In this way, through the second module of the pre-trained business processing model, the more detailed element content in the image voucher data can be subjected to text feature extraction processing to obtain text feature information corresponding to the image voucher data, so as to improve the accuracy of the subsequent voucher parsing processing by the first module. In addition, after obtaining the trained second module, different training image voucher data can be obtained for different businesses to train the business processing model, that is, the server can use the first image voucher data corresponding to the target business to train the business processing model constructed by the first module and the pre-trained second module, which can improve the training efficiency of the business processing model corresponding to the target business, improve the efficiency and accuracy of the voucher parsing processing of the image voucher data, and thereby improve the efficiency and accuracy of determining the business processing result corresponding to the target business triggered by the target user.

[0126] Example 5

[0127] The embodiments of this specification also provide a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the various processes of the above-mentioned data processing method embodiments are implemented and can achieve the same technical effects. To avoid repetition, the details are not described here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0128] An embodiment of the present specification provides a computer-readable storage medium, which obtains a pre-trained business processing model corresponding to the target business by obtaining picture voucher data corresponding to the target user triggering the execution of the target business. The business processing model is obtained by training a business processing model constructed by a first module and a pre-trained second module through the first picture voucher data corresponding to the target business. The second module is obtained by training a module constructed by a preset deep learning algorithm based on sub-picture voucher data in different areas of the second picture voucher data and text description data corresponding to the second picture voucher data. The first module is used to perform voucher parsing processing on the output result of the second module, and through the second module of the pre-trained business processing model, determine the sub-picture voucher data in different areas containing text information in the picture voucher data, and perform text feature extraction processing on the sub-picture voucher data respectively to obtain text feature information corresponding to the picture voucher data. Through the first module of the pre-trained business processing model, perform voucher parsing processing on the text feature information to obtain a voucher parsing result for the picture voucher data. Based on the voucher parsing result, determine the business processing result corresponding to the target business triggered by the target user. Since the second module is trained based on the sub-image voucher data of different areas in the second image voucher data and the text description data corresponding to the second image voucher data, the trained second module can mine more detailed element content in the image voucher data as much as possible. In this way, through the second module of the pre-trained business processing model, the more detailed element content in the image voucher data can be subjected to text feature extraction processing to obtain text feature information corresponding to the image voucher data, so as to improve the accuracy of the subsequent voucher parsing processing by the first module. In addition, after obtaining the trained second module, different training image voucher data can be obtained for different businesses to train the business processing model, that is, the server can use the first image voucher data corresponding to the target business to train the business processing model constructed by the first module and the pre-trained second module, which can improve the training efficiency of the business processing model corresponding to the target business, improve the efficiency and accuracy of the voucher parsing processing of the image voucher data, and thereby improve the efficiency and accuracy of determining the business processing result corresponding to the target business triggered by the target user.

[0129] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0130] In the 1990s, technological improvements could be clearly distinguished as either hardware improvements (for example, improvements to circuit structures such as diodes, transistors, and switches) or software improvements (improvements to process flows). However, with the advancement of technology, many process flow improvements today can now be considered direct improvements to hardware circuit structures. Designers almost always create the corresponding hardware circuit structure by programming the improved process flow into the hardware circuit. Therefore, it cannot be said that a process flow improvement cannot be implemented using hardware modules. For example, a programmable logic device (PLD), such as a field programmable gate array (FPGA), is an integrated circuit whose logical function is determined by user programming. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to hire a chip manufacturer to design and manufacture a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly done using "logic compiler" software. This is similar to the software compiler used when developing programs. Before compilation, the original code must also be written in a specific programming language, called a hardware description language (HDL). There is not just one HDL, but many, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art will also understand that by simply programming the method flow in one of these hardware description languages ​​and then programming it into an integrated circuit, a hardware circuit that implements the logic method flow can be easily obtained.

[0131] The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that in addition to implementing the controller in a purely computer-readable program code format, the controller can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules that implement the method and structures within the hardware component.

[0132] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0133] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0134] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, one or more embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, one or more embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0135] The embodiments of this specification are described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0136] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0138] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0139] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0140] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0141] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0142] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Thus, one or more embodiments of this specification may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware. Furthermore, one or more embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0143] One or more embodiments of this specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. One or more embodiments of this specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0144] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0145] The foregoing is merely an example of the present invention and is not intended to limit the present invention. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.

Claims

1. A data processing method, comprising: Obtain the image credential data corresponding to the target user triggering the execution of the target business; Obtaining a pre-trained business processing model corresponding to the target business, the business processing model being obtained by training a business processing model constructed by a first module and a pre-trained second module using first image voucher data corresponding to the target business, the second module being obtained by training a module constructed by a preset deep learning algorithm based on sub-image voucher data of different regions in the second image voucher data and text description data corresponding to the second image voucher data, the first module being configured to perform voucher parsing processing on an output result of the second module; determining, by the second module of the pre-trained business processing model, sub-picture voucher data in different areas containing text information in the picture voucher data, and performing text feature extraction processing on each of the sub-picture voucher data to obtain text feature information corresponding to the picture voucher data; Performing voucher parsing on the text feature information through the first module of the pre-trained business processing model to obtain a voucher parsing result for the image voucher data; Based on the credential parsing result, a business processing result corresponding to triggering execution of the target business for the target user is determined.

2. The method according to claim 1, before obtaining the pre-trained business process model corresponding to the target business, further comprising: Acquire the second picture credential data and text description data corresponding to the second picture credential data; performing text recognition processing on the second picture voucher data to obtain a text recognition result, and based on the text recognition result, determining sub-picture data in different areas containing text information in the second picture voucher data as sub-picture voucher data in the second picture voucher data; Performing text feature extraction processing on the sub-picture voucher data in the second picture voucher data by the second module to obtain first text features; Performing feature extraction processing on the text description data by a third module to obtain a second text feature; Based on the first text feature and the second text feature, a first training error value is determined, and based on the first training error value, whether the second module has converged is determined; if the second module has not converged, the second module and the third module are continued to be trained based on the sub-image voucher data in the second image voucher data and the text description data until the second module converges to obtain a trained second module.

3. The method according to claim 2, further comprising: Obtaining the first picture credential data and a first credential parsing result corresponding to the first picture credential data; Based on the pre-trained second module, performing text feature extraction processing on the sub-image voucher data in different regions of the first image voucher data to obtain third text features; Based on the first module, performing voucher parsing on the third text feature to obtain a second voucher parsing result, and determining a second training error value based on the first voucher parsing result and the second voucher parsing result; Based on the second training error value, determine whether the business processing model has converged. If the business processing model has not converged, continue to train the business processing model based on the first image voucher data and the first voucher parsing result until the business processing model converges, thereby obtaining a trained business processing model. The method according to claim 3 , wherein the data size of the second picture voucher data is larger than the data size of the first picture voucher data.

5. The method according to claim 4, wherein the first module includes a first submodule for performing business information extraction processing and a second submodule for performing image tampering detection, the voucher parsing result includes a business information extraction result and an image tampering detection result, and the voucher parsing process is performed on the third text feature based on the first module to obtain a second voucher parsing result, and the second training error value is determined based on the first voucher parsing result and the second voucher parsing result, comprising: Based on the first submodule of the first module, performing business information extraction processing on the third text feature to obtain a first business information extraction result in the second voucher parsing result; Based on the second submodule of the first module, performing image tampering detection processing on the third text feature to obtain a first image tampering detection result in the second credential parsing result; The second training error value is determined based on the first business information extraction result, the first image tampering detection result, and the first credential parsing result.

6. The method according to claim 5, wherein determining the business processing result corresponding to triggering execution of the target business for the target user based on the credential parsing result comprises: Determining a risk detection result for the image credential data based on the image tampering detection result in the credential parsing result; Based on the risk detection result and the business information extraction result in the credential parsing result, a business processing result corresponding to triggering execution of the target business for the target user is determined.

7. The method according to claim 6, wherein determining a risk detection result for the image credential data based on the image tampering detection result in the credential parsing result comprises: In a case where the picture voucher data is determined to be tampered picture data based on the picture tampering detection result, determining a tampered area in the picture voucher data based on the picture tampering detection result; Obtaining the business information corresponding to the tampered area in the business information extraction result; Based on the tampered area and the corresponding business information, risk detection processing is performed on the image credential data to obtain a risk detection result for the image credential data.

8. The method according to claim 7, wherein determining a business processing result corresponding to triggering execution of the target business for the target user based on the risk detection result and the business information extraction result in the credential parsing result comprises: When it is determined based on the risk detection result that the image credential data does not pose a risk, the target service is executed based on the service information extraction result to obtain a service processing result corresponding to the target service triggered for the target user.

9. A data processing device comprising: The first acquisition module is used to obtain the image voucher data corresponding to the target user triggering the execution of the target business; a model acquisition module configured to acquire a pre-trained business processing model corresponding to the target business, the business processing model being obtained by training a business processing model constructed by a first module and a pre-trained second module using first image voucher data corresponding to the target business; the second module being obtained by training a module constructed by a preset deep learning algorithm based on sub-image voucher data of different regions in the second image voucher data and text description data corresponding to the second image voucher data; the first module being configured to perform voucher parsing processing on the output of the second module; a first processing module, configured to determine, using the second module of the pre-trained business processing model, sub-picture voucher data in different regions containing text information in the picture voucher data, and perform text feature extraction processing on each of the sub-picture voucher data to obtain text feature information corresponding to the picture voucher data; A second processing module is configured to perform voucher parsing on the text feature information using the first module of the pre-trained business processing model to obtain a voucher parsing result for the image voucher data; A result determination module is used to determine a business processing result corresponding to triggering execution of the target business for the target user based on the credential parsing result.

10. A data processing device, comprising: processor; as well as a memory arranged to store computer-executable instructions which, when executed, cause the processor to: Obtain the image credential data corresponding to the target user triggering the execution of the target business; Obtain a pre-trained business processing model corresponding to the target business, wherein the business processing model is The first image voucher data corresponding to the target service is obtained by training a service processing model constructed by a first module and a pre-trained second module; the second module is obtained by training a module constructed by a preset deep learning algorithm based on sub-image voucher data of different regions in the second image voucher data and text description data corresponding to the second image voucher data; the first module is used to perform voucher parsing processing on the output result of the second module; determining, by the second module of the pre-trained business processing model, sub-picture voucher data in different areas containing text information in the picture voucher data, and performing text feature extraction processing on each of the sub-picture voucher data to obtain text feature information corresponding to the picture voucher data; Performing voucher parsing on the text feature information through the first module of the pre-trained business processing model to obtain a voucher parsing result for the image voucher data; Based on the credential parsing result, a business processing result corresponding to triggering execution of the target business for the target user is determined.

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