Information processing method and device, equipment and storage medium

Abstract

The invention provides an information processing method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence. In some embodiments of the disclosure, initial field information and proof file information uploaded by an outsourcing user terminal are received; the proof file information is input into a field extraction model, target field information is obtained, the field extraction model is a model obtained by training a pre-training model, and the target field information is automatically extracted through the artificial intelligence technology; automatically comparing the initial field information with the target field information to obtain a comparison result; generating an information auditing result according to the comparison result; the related data of the outsourcing user is automatically audited in combination with the artificial intelligence technology, the auditing efficiency is improved, the labor cost is reduced, and the auditing accuracy is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to an information processing method, device, equipment and storage medium. Background Art

[0002] In outsourcing user management, it is necessary to review the identity information, education information and contract information submitted by outsourcing users. At present, the review of outsourcing user materials usually relies on manual work, which has high labor costs and low review efficiency. In addition, there will be information review errors to a certain extent, resulting in outsourcing audit risks. With the continuous expansion of the scale of outsourcing business, traditional review methods can no longer meet the needs of fast and accurate review.

[0003] At present, manual review is used for outsourced user information, which has low review efficiency, high labor costs and low review accuracy. Summary of the invention

[0004] The present disclosure provides an information processing method, device, equipment and storage medium to at least solve the problems of low existing audit efficiency, high labor cost and low audit accuracy.

[0005] The technical solution of the present disclosure is as follows:

[0006] The present disclosure provides an information processing method, including:

[0007] Receiving initial field information and certification document information uploaded by the outsourced user terminal;

[0008] Inputting the certification document information into a field extraction model to obtain target field information, wherein the field extraction model is a model obtained by training a pre-trained model;

[0009] Comparing the initial field information with the target field information to obtain a comparison result;

[0010] Based on the comparison results, an information review result is generated.

[0011] Optionally, before using the field extraction model, the method includes:

[0012] Collecting sample text material information, sample image material information, first sample field information corresponding to the sample text material information, and second sample field information corresponding to the sample image material information;

[0013] A cross entropy loss function is used to train the pre-trained model according to the sample text material information, the sample image material information, the first sample field information and the second sample field information to obtain the field extraction model.

[0014] Optionally, comparing the initial field information with the target field information to obtain a comparison result includes:

[0015] Calculate the similarity between the initial field information and the target field information using a similarity algorithm;

[0016] When the similarity is greater than or equal to the similarity threshold, the comparison result is determined to be a consistent comparison;

[0017] When the similarity is less than the similarity threshold, the comparison result is determined to be inconsistent.

[0018] Optionally, generating an information review result according to the comparison result includes:

[0019] If the comparison result is consistent, an information verification pass result is generated;

[0020] When the comparison result is inconsistent, an information verification failure result is generated.

[0021] Optionally, after the generation information verification passes the result, the method further includes:

[0022] Sending the information verification result to the outsourcing user terminal so that the outsourcing user terminal can display the information verification result;

[0023] After the generated information verification fails, the method further includes:

[0024] The information verification failure result is sent to the outsourcing user terminal, so that the outsourcing user terminal can display the information verification failure result.

[0025] Optionally, after the generation information verification fails the result, the method further includes:

[0026] The target field information replaces the initial field information.

[0027] The present disclosure also provides an information processing device, including:

[0028] A receiving module, used to receive the initial field information and certification document information uploaded by the outsourcing user terminal;

[0029] An extraction module, used for inputting the certification document information into a field extraction model to obtain target field information, wherein the field extraction model is a model obtained by training a pre-trained model;

[0030] A comparison module, used for comparing the initial field information with the target field information to obtain a comparison result;

[0031] A generation module is used to generate information review results based on the comparison results.

[0032] The present disclosure also provides an electronic device, characterized in that it includes:

[0033] processor;

[0034] a memory for storing instructions executable by the processor;

[0035] The processor is configured to execute the instructions to implement each step in the above method.

[0036] The embodiment of the present disclosure further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, each step in the above method is implemented.

[0037] The embodiment of the present disclosure further provides a computer program product, including a computer program / instruction, which implements each step of the above method when executed by a processor.

[0038] The technical solution provided by the embodiments of the present disclosure brings at least the following beneficial effects:

[0039] In some embodiments of the present disclosure, initial field information and certification document information uploaded by an outsourced user terminal are received; the certification document information is input into a field extraction model to obtain target field information, wherein the field extraction model is a model obtained by training a pre-trained model, and artificial intelligence technology is used to automatically extract the target field information; the initial field information and the target field information are automatically compared to obtain a comparison result; based on the comparison result, an information review result is generated; the present disclosure combines artificial intelligence technology to automatically review relevant information of outsourced users, thereby improving review efficiency, reducing labor costs, and improving review accuracy.

[0040] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0042] Figure 1 A flowchart of an information processing method provided for an exemplary embodiment of the present disclosure;

[0043] Figure 2 A schematic diagram of the structure of an information processing device provided for an exemplary embodiment of the present disclosure;

[0044] Figure 3 A schematic structural diagram of an electronic device provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0045] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0046] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0047] It should be noted that the user information involved in this disclosure includes but is not limited to: user device information and user personal information; the collection, storage, use, processing, transmission, provision and disclosure of user information in this disclosure comply with the relevant laws and regulations and do not violate public order and good morals.

[0048] In outsourcing user management, it is essential to review the identity information, education information, and contract information submitted by outsourcing users. Currently, these reviews mainly rely on manual operations, which are costly and inefficient, and even have the risk of review errors. As the scale of outsourcing business expands, traditional review methods are difficult to meet the needs of fast and accurate review.

[0049] In response to the above technical problems, in some embodiments of the present disclosure, initial field information and certification document information uploaded by an outsourced user terminal are received; the certification document information is input into a field extraction model to obtain target field information, wherein the field extraction model is a model obtained by training a pre-trained model, and artificial intelligence technology is used to automatically extract the target field information; the initial field information is automatically compared with the target field information to obtain a comparison result; based on the comparison result, an information review result is generated; the present disclosure combines artificial intelligence technology to automatically review the relevant information of outsourced users, thereby improving review efficiency, reducing labor costs, and improving review accuracy.

[0050] The technical solutions provided by various embodiments of the present disclosure are described in detail below in conjunction with the accompanying drawings.

[0051] Figure 1The flowchart of an information processing method provided by the exemplary embodiment of the present disclosure is as follows. Figure 1 As shown, the method includes:

[0052] S101: receiving initial field information and certification document information uploaded by an outsourcing user terminal;

[0053] S102: Input the certification document information into a field extraction model to obtain target field information, wherein the field extraction model is a model obtained by training a pre-trained model;

[0054] S103: Compare the initial field information with the target field information to obtain a comparison result;

[0055] S104: Generate information review results based on the comparison results.

[0056] In this embodiment, the execution subject of the above method may be a terminal device or a server.

[0057] Among them, the terminal device includes but is not limited to a mobile station (MS), a mobile terminal, a mobile phone, a handset and a portable equipment, etc. The terminal device can communicate with one or more core networks via a radio access network (RAN). For example, the terminal device can be a mobile phone (or a "cellular" phone), a computer with a wireless communication function, etc. The terminal device can also be a computer with a wireless transceiver function, a virtual reality (VR) terminal device, an AR terminal device, a wireless terminal in industrial control, a wireless terminal in self driving, a wireless terminal in remote medical, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, etc., and the operating system installed on the terminal device includes but is not limited to: IOS, Android, windows, linux, Mac OS and other operating systems. In different networks, the terminal may be called different names, such as user equipment, mobile station, subscriber unit, station, cellular phone, personal digital assistant, wireless modem, wireless communication device, handheld device, laptop computer, cordless phone, wireless local loop station, television, etc. For the convenience of description, it is referred to as terminal equipment in this embodiment.

[0058] In this embodiment, the implementation form of the server is not limited. For example, the server can be a conventional server, a cloud server, a cloud host, a virtual center, or other server devices. The server mainly includes a processor, a hard disk, a memory, a system bus, etc., and a general computer architecture type.

[0059] In this embodiment, the server receives initial field information and certification document information uploaded by the outsourcing user terminal; the certification document information is input into the field extraction model to obtain the target field information, wherein the field extraction model is a model obtained by training a pre-trained model, and the target field information is automatically extracted using artificial intelligence technology; the initial field information is automatically compared with the target field information to obtain a comparison result; based on the comparison result, an information review result is generated; the present disclosure combines artificial intelligence technology to automatically review the relevant information of the outsourcing user, thereby improving the review efficiency, reducing labor costs, and improving the review accuracy.

[0060] It should be noted that an outsourcing user refers to a customer who transfers certain business functions or services to an external company or individual for execution. An outsourcing user terminal is a terminal used by an outsourcing user. Closed domain information extraction means that for a given document image, the target is to extract all structured knowledge that meets the constraints of the fields to be extracted from the document image according to a pre-defined set of fields to be extracted.

[0061] In some embodiments of the present disclosure, the outsourcing user terminal sends the initial field information and the certification document information to the server in response to the data upload operation of the outsourcing user; the server receives the initial field information and the certification document information uploaded by the outsourcing user terminal. It should be noted that the certification document information includes but is not limited to: ID card picture, graduation certificate picture, academic certificate picture and labor contract picture. The initial field information includes but is not limited to: academic qualifications, graduation time, employer signatory, employee signatory, written test score and interview score.

[0062] In some embodiments of the present disclosure, the certification document information is input into a field extraction model to obtain target field information, wherein the field extraction model is a model obtained by training a pre-trained model. Before using the field extraction model, it is necessary to first train the field extraction model. One achievable method is to collect sample text material information, sample image material information, first sample field information corresponding to the sample text material information, and second sample field information corresponding to the sample image material information; use a cross entropy loss function to train the pre-trained model based on the sample text material information, sample image material information, first sample field information, and second sample field information to obtain a field extraction model.

[0063] Specifically, first, use Label Studio to label the fields to be extracted for the sample text data information and / or sample image data information. This method only needs to label a very small number of samples in each category to obtain better results. The labeled data set is divided into a training set and a validation set for training. Among them, the sample text data information includes but is not limited to: sample ID card text, sample graduation certificate text, sample academic certificate text and sample labor contract text. The sample image data information includes but is not limited to: sample ID card pictures, sample graduation certificate pictures, sample academic certificate pictures and sample labor contract pictures. Then, the closed domain information extraction method is adopted, and ERNIE Encoder is selected as the basic encoder, which integrates the text, image and layout information of the document image. By integrating text, image and layout information, the problem of low accuracy of manual review of outsourced materials is solved, and the risk of outsourced audit is effectively prevented; the global pointer mechanism is combined to locate and extract structured information that meets the constraints of the fields to be extracted. Input the labeled data, fine-tune the model based on the pre-trained model, verify the model accuracy on the validation set after each round of training, and save the optimal network model, so as to complete the training on the specific task of extracting information from outsourced user certification materials. The cross entropy loss function is used during training to measure the difference between the model output and the label, and the Adam optimizer is used to update the model parameters to minimize the loss function. By constructing a closed domain modeling method, the parallel extraction of the fields to be extracted is realized, which effectively reduces the time for extracting information from the image of the qualification certificate document. Finally, the generated model is directly used for the information extraction task of document images such as identity information, and the recognition results are compared with the actual results to obtain the actual accuracy for model evaluation. The model is optimized and adjusted according to the evaluation results to improve accuracy and robustness.

[0064] In some embodiments of the present disclosure, the initial field information is compared with the target field information to obtain a comparison result. One achievable method is to use a similarity algorithm to calculate the similarity between the initial field information and the target field information; when the similarity is greater than or equal to the similarity threshold, the comparison result is determined to be a consistent comparison; when the similarity is less than the similarity threshold, the comparison result is determined to be inconsistent. Among them, using a similarity algorithm, the similarity between each initial field information and the target field information corresponding to each initial field information is calculated respectively. When the similarity is greater than or equal to the similarity threshold, the initial field information and the target field information corresponding to the initial field information are the same field, and the comparison result is determined to be consistent. When the similarity is less than the similarity threshold, the initial field information and the target field information corresponding to the initial field information are different fields, and the comparison result is determined to be inconsistent. It should be noted that the embodiment of the present disclosure does not limit the similarity threshold, and it can be adjusted according to actual conditions. The similarity threshold can be 0.9, 0.95, etc.

[0065] In some embodiments of the present disclosure, an information review result is generated based on the comparison result. One achievable manner is that, when the comparison result is a consistent comparison, an information verification pass result is generated; when the comparison result is an inconsistent comparison, an information verification fail result is generated. After the information verification pass result is generated, the information verification pass result is sent to the outsourced user terminal so that the outsourced user terminal can display the information verification pass result. After the information verification fail result is generated, the information verification fail result is sent to the outsourced user terminal so that the outsourced user terminal can display the information verification fail result.

[0066] In some embodiments of the present disclosure, after the information verification fails, the target field information replaces the initial field information. After the information verification fails, the target field information replaces the initial field information to correct the initial field information.

[0067] In the above method embodiment of the present disclosure, initial field information and certification document information uploaded by the outsourcing user terminal are received; the certification document information is input into the field extraction model to obtain the target field information, wherein the field extraction model is a model obtained by training the pre-trained model, and the target field information is automatically extracted using artificial intelligence technology; the initial field information is automatically compared with the target field information to obtain a comparison result; based on the comparison result, an information review result is generated; the present disclosure combines artificial intelligence technology to automatically review the relevant information of the outsourcing user, thereby improving the review efficiency, reducing labor costs, and improving the review accuracy.

[0068] Figure 2 FIG. 2 is a schematic diagram of the structure of an information processing device 20 provided by an exemplary embodiment of the present disclosure. Figure 2 As shown, the information processing device 20 includes: a receiving module 21, an extraction module 22, a comparison module 23 and a generation module 24.

[0069] The receiving module 21 is used to receive the initial field information and certification document information uploaded by the outsourcing user terminal;

[0070] An extraction module 22, used for inputting the certification document information into a field extraction model to obtain target field information, wherein the field extraction model is a model obtained by training a pre-trained model;

[0071] A comparison module 23 is used to compare the initial field information with the target field information to obtain a comparison result;

[0072] The generating module 24 is used to generate the information review result according to the comparison result.

[0073] Optionally, before using the field extraction model, the extraction module 22 may also be used to:

[0074] Collecting sample text material information, sample image material information, first sample field information corresponding to the sample text material information, and second sample field information corresponding to the sample image material information;

[0075] The cross entropy loss function is used to train the pre-trained model according to the sample text data information, the sample image data information, the first sample field information and the second sample field information to obtain the field extraction model.

[0076] Optionally, when the comparison module 23 compares the initial field information with the target field information to obtain the comparison result, it is used to:

[0077] Using a similarity algorithm, calculate the similarity between the initial field information and the target field information;

[0078] When the similarity is greater than or equal to the similarity threshold, the comparison result is determined to be a consistent comparison;

[0079] When the similarity is less than the similarity threshold, the comparison result is determined to be inconsistent.

[0080] Optionally, when generating the information review result according to the comparison result, the generating module 24 is used to:

[0081] If the comparison result is consistent, an information verification pass result is generated;

[0082] If the comparison result is inconsistent, an information verification failure result is generated.

[0083] Optionally, after generating the information verification pass result, the generating module 24 may also be used to:

[0084] Sending the information verification result to the outsourced user terminal so that the outsourced user terminal can display the information verification result;

[0085] After generating the information verification failure result, the method further includes:

[0086] The information verification failure result is sent to the outsourcing user terminal, so that the outsourcing user terminal can display the information verification failure result.

[0087] Optionally, after generating the information verification failure result, the generating module 24 may also be used to:

[0088] Replace the original field information with the target field information.

[0089] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0090] Figure 3 FIG. 1 is a schematic diagram of a structure of an electronic device provided by an exemplary embodiment of the present disclosure. Figure 3 As shown, the electronic device includes: a memory 31 and a processor 32. In addition, the electronic device also includes a power supply component 33 and a communication component 34.

[0091] The memory 31 is used to store computer programs and can be configured to store various other data to support operations on the electronic device. Examples of such data include instructions for any application program or method operating on the electronic device.

[0092] The memory 31 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0093] The communication component 34 is used for data transmission with other devices.

[0094] The processor 32 can execute computer instructions stored in the memory 31 to: receive initial field information and certification document information uploaded by the outsourced user terminal; input the certification document information into the field extraction model to obtain target field information, wherein the field extraction model is a model obtained by training a pre-trained model; compare the initial field information with the target field information to obtain a comparison result; and generate an information review result based on the comparison result.

[0095] Optionally, before using the field extraction model, the processor 32 may also be used to:

[0096] Collecting sample text material information, sample image material information, first sample field information corresponding to the sample text material information, and second sample field information corresponding to the sample image material information;

[0097] The cross entropy loss function is used to train the pre-trained model according to the sample text data information, the sample image data information, the first sample field information and the second sample field information to obtain the field extraction model.

[0098] Optionally, when the processor 32 compares the initial field information with the target field information and obtains the comparison result, it is configured to:

[0099] Using a similarity algorithm, calculate the similarity between the initial field information and the target field information;

[0100] When the similarity is greater than or equal to the similarity threshold, the comparison result is determined to be a consistent comparison;

[0101] When the similarity is less than the similarity threshold, the comparison result is determined to be inconsistent.

[0102] Optionally, when generating the information review result according to the comparison result, the processor 32 is used to:

[0103] If the comparison result is consistent, an information verification pass result is generated;

[0104] If the comparison result is inconsistent, an information verification failure result is generated.

[0105] Optionally, after generating the information verification pass result, the processor 32 may also be used to:

[0106] Sending the information verification result to the outsourced user terminal so that the outsourced user terminal can display the information verification result;

[0107] After generating the information verification failure result, the processor 32 may also be used to:

[0108] The information verification failure result is sent to the outsourcing user terminal, so that the outsourcing user terminal can display the information verification failure result.

[0109] Optionally, after generating the information verification failure result, the processor 32 may also be used to:

[0110] Replace the original field information with the target field information.

[0111] Accordingly, the present disclosure also provides a computer-readable storage medium storing a computer program. When the computer-readable storage medium stores the computer program and the computer program is executed by one or more processors, the one or more processors execute Figure 1 Each step in the method embodiment.

[0112] Accordingly, the present disclosure also provides a computer program product, the computer program product includes a computer program / instruction, the computer program / instruction is executed by a processor Figure 1 Each step in the method embodiment.

[0113] Above Figure 3The communication component in is configured to facilitate wired or wireless communication between the device where the communication component is located and other devices. The device where the communication component is located can access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G / LTE, 5G and other mobile communication networks, or a combination thereof. In an exemplary embodiment, the communication component receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0114] Above Figure 3 The power supply component in the device provides power to various components of the device where the power supply component is located. The power supply component may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device where the power supply component is located.

[0115] The electronic device also includes a display screen and an audio component.

[0116] The display screen includes a screen, and the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.

[0117] The audio component may be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC), and when the device where the audio component is located is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal may be further stored in a memory or sent via a communication component. In some embodiments, the audio component also includes a speaker for outputting an audio signal.

[0118] In the above-mentioned apparatus, device, storage medium and computer program product embodiments of the present disclosure, initial field information and certification document information uploaded by an outsourced user terminal are received; the certification document information is input into a field extraction model to obtain target field information, wherein the field extraction model is a model obtained by training a pre-trained model, and artificial intelligence technology is used to automatically extract the target field information; the initial field information is automatically compared with the target field information to obtain a comparison result; based on the comparison result, an information review result is generated; the present disclosure combines artificial intelligence technology to automatically review relevant information of outsourced users, thereby improving review efficiency, reducing labor costs, and improving review accuracy.

[0119] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. 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 generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0121] These computer program instructions may also be stored in a computer-readable memory capable of directing 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 an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0122] 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, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0123] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0124] The 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. The memory is an example of a computer-readable medium.

[0125] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. 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 disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape 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 temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0126] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0127] The above are only specific embodiments of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An information processing method, characterized in that: include: Receiving initial field information and certification document information uploaded by the outsourced user terminal; Inputting the certification document information into a field extraction model to obtain target field information, wherein the field extraction model is a model obtained by training a pre-trained model; Comparing the initial field information with the target field information to obtain a comparison result; Based on the comparison results, an information review result is generated.

2. The method according to claim 1, characterized in that Before using the field extraction model, the method includes: Collecting sample text material information, sample image material information, first sample field information corresponding to the sample text material information, and second sample field information corresponding to the sample image material information; A cross entropy loss function is used to train the pre-trained model according to the sample text material information, the sample image material information, the first sample field information and the second sample field information to obtain the field extraction model.

3. The method according to claim 1, characterized in that: The comparing the initial field information with the target field information to obtain a comparison result includes: Calculate the similarity between the initial field information and the target field information using a similarity algorithm; When the similarity is greater than or equal to the similarity threshold, the comparison result is determined to be a consistent comparison; When the similarity is less than the similarity threshold, the comparison result is determined to be inconsistent.

4. The method according to claim 1, characterized in that Generating information review results according to the comparison results includes: If the comparison result is consistent, an information verification pass result is generated; When the comparison result is inconsistent, an information verification failure result is generated.

5. The method according to claim 4, characterized in that After the generated information verification passes the result, the method further includes: Sending the information verification result to the outsourcing user terminal so that the outsourcing user terminal can display the information verification result; After the generated information verification fails, the method further includes: The information verification failure result is sent to the outsourcing user terminal, so that the outsourcing user terminal can display the information verification failure result.

6. The method according to claim 4, characterized in that After the generated information verification fails, the method further includes: The target field information replaces the initial field information.

7. An information processing device, characterized in that: include: A receiving module, used to receive the initial field information and certification document information uploaded by the outsourcing user terminal; An extraction module, used for inputting the certification document information into a field extraction model to obtain target field information, wherein the field extraction model is a model obtained by training a pre-trained model; A comparison module, used for comparing the initial field information with the target field information to obtain a comparison result; A generation module is used to generate information review results based on the comparison results.

8. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement each step in the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps in the method according to any one of claims 1 to 6 are implemented.

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