Material verification method and device, electronic equipment and storage medium

Abstract

The invention provides a material verification method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining to-be-proof data based on a preset interaction interface, and transmitting the to-be-proof data to a material collection end, so as to obtain proof material data corresponding to the to-be-proof data; receiving and analyzing proof material data sent by the material collection end, and performing image recognition on a proof material image in the proof material data to generate mechanism recognition data; and according to the mechanism identification data and the obtained exogenous verification data and supervision system data, carrying out comparison verification on the proof material data, and generating a material verification result. Compared with the prior art, the collection efficiency of the proof material can be improved; the time and cost of manual auditing can be further reduced, key information in the proof material can be extracted and analyzed more accurately, and the authenticity and accuracy of the proof material can be ensured.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a material verification method and device, electronic equipment, and storage medium. Background Art

[0002] In the work of customer risk evidence, the traditional operation method mainly relies on manual tedious data processing and material collation. Specifically, the traditional customer risk evidence process usually includes multiple links such as data collection, material preparation, manual review and conclusion recording. Each link requires a lot of time and human resources, especially the manual review link, which requires staff to verify industrial and commercial information, regulatory data and financial institution data one by one, which is a large workload and prone to errors. This method is not only inefficient, but also prone to errors, and cannot meet the strict requirements of modern financial supervision and the needs of efficient operation. In addition, since customer risk evidence data is usually transmitted offline, there is a risk of data leakage and loss, which further increases the complexity and uncertainty of business operations.

[0003] In order to solve these problems, the industry has begun to explore the use of technical means to optimize the customer risk evidence process. However, the existing technical solutions still have many shortcomings. For example, although some systems have achieved automation of some processes, the overall process is still not standardized and systematized, resulting in complex operations and low efficiency. At the same time, the existing technical solutions also have limitations in data comparison and intelligent prompts, and cannot accurately judge the correctness of the evidence conclusion, thereby increasing regulatory risks.

[0004] Therefore, there is an urgent need for an operational method for customer risk evidence that can replace the traditional manual workflow and realize functions such as batch identification, automated verification and intelligent prompts to improve work efficiency, reduce error rates and meet regulatory requirements. Summary of the invention

[0005] The present disclosure provides a material verification method and device, electronic device and storage medium, which are mainly intended to solve the problem of being unable to realize batch identification and automatic verification of risk evidence materials.

[0006] According to a first aspect of the present disclosure, a material verification method is provided, the method being applied to a material verification end, the method comprising:

[0007] Acquire the data to be proved based on a preset interactive interface, and send the data to be proved to a material collection terminal, so as to obtain the evidence material data corresponding to the data to be proved;

[0008] Receiving and parsing the evidence material data sent by the material collection terminal, and performing image recognition on the evidence material images in the evidence material data to generate institution identification data;

[0009] Based on the institution identification data and the acquired external verification data and regulatory system data, the data of the evidence materials are compared and verified to generate material verification results.

[0010] In some embodiments, the method further comprises:

[0011] Obtain the regulatory review data corresponding to the evidence material data in the regulatory system;

[0012] Based on the evidence entry data in the evidence material data and the supervision review data, the evidence entry data is evaluated to generate an evidence evaluation result.

[0013] In some embodiments, the receiving and parsing of the evidence material data sent by the material collection terminal, and performing image recognition on the evidence material image in the evidence material data to generate the institution identification data includes:

[0014] Performing data analysis on the evidence material data to obtain a data identifier corresponding to the evidence material data and an image of the evidence material;

[0015] Based on the data identifier, the evidence material data is classified and sorted in a preset database, wherein the evidence material data includes: the evidence material image and evidence entry data;

[0016] An image recognition algorithm is called to perform image recognition on the image of the evidence material to generate the institution identification data.

[0017] In some embodiments, the comparison and verification of the evidence material data is performed based on the institution identification data and the acquired external verification data and supervision system data to generate a material verification result, including:

[0018] Mapping data fields corresponding to the same data type in the institution identification data, the external source verification data, and the supervision system data to obtain mapping data;

[0019] Perform data association matching based on the mapping data, and compare and verify whether the data corresponding to each data type in the institution identification data, the external source verification data, and the regulatory system data are consistent;

[0020] The comparison verification results of each data type are counted, and data analysis is performed on the obtained statistical data to generate the material verification results.

[0021] In some embodiments, the method further comprises:

[0022] The material verification result is sent to the material collection end so that the material verification result can be displayed on the human-computer interaction interface.

[0023] According to a second aspect of the present disclosure, a material verification method is provided, the method being applied to a material collection end, the method comprising:

[0024] Receive the data to be proved sent by the material verification end;

[0025] Parsing the data to be provided as evidence to establish an evidence collection task;

[0026] Dispatching the evidence collection task to obtain the evidence material data corresponding to the data to be provided;

[0027] The evidence material data is sent to the material verification end so as to compare and verify the evidence material data to generate a material verification result.

[0028] In some embodiments, the dispatching of the evidence collection task to obtain the evidence material data corresponding to the data to be provided includes:

[0029] Dispatching the evidence collection task to the target handling party;

[0030] The evidence materials uploaded by the target handling party through the human-computer interaction interface are received, and evidence material data are generated.

[0031] In some embodiments, the method further comprises:

[0032] The material verification result sent by the material verification terminal is received, and the material verification result is displayed on a human-computer interaction interface.

[0033] According to a third aspect of the present disclosure, there is provided a material verification device, comprising:

[0034] A first acquisition unit, configured to acquire the data to be provided as evidence based on a preset interactive interface, and send the data to be provided as evidence to a material collection terminal, so as to acquire the evidence material data corresponding to the data to be provided as evidence;

[0035] an identification unit, configured to receive and analyze the evidence material data sent by the material collection terminal, and perform image recognition on the evidence material images in the evidence material data to generate institution identification data;

[0036] A generating unit is used to compare and verify the evidence material data according to the institution identification data and the acquired external verification data and supervision system data, and generate a material verification result.

[0037] In some embodiments, the apparatus further comprises:

[0038] A second acquisition unit is used to acquire the supervision review data corresponding to the evidence material data in the supervision system;

[0039] An evaluation unit is used to evaluate the evidence entry data according to the evidence entry data and the supervision review data in the evidence material data to generate an evidence evaluation result.

[0040] In some embodiments, the identification unit comprises:

[0041] An acquisition module, used to perform data analysis on the evidence material data, and acquire a data identifier corresponding to the evidence material data and an image of the evidence material;

[0042] A sorting module, used for classifying and sorting the evidence material data in a preset database based on the data identifier, wherein the evidence material data includes: the evidence material image and evidence entry data;

[0043] The identification module is used to call the image recognition algorithm to perform image recognition on the image of the evidence material and generate the organization identification data.

[0044] In some embodiments, the generating unit comprises:

[0045] A mapping module, used to map data fields corresponding to the same data type in the institution identification data, the external source verification data, and the supervision system data to obtain mapping data;

[0046] A matching module, used to perform data association matching based on the mapping data, and compare and verify whether the data corresponding to each data type in the institution identification data, the external source verification data, and the regulatory system data are consistent;

[0047] The generation module is used to collect statistics on the comparison and verification results of each data type, and perform data analysis on the obtained statistical data to generate the material verification results.

[0048] In some embodiments, the apparatus further comprises:

[0049] The sending unit is used to send the material verification result to the material collection end so as to display the material verification result on the human-computer interaction interface.

[0050] According to a fourth aspect of the present disclosure, there is provided a material verification device, comprising:

[0051] A receiving unit, used for receiving the data to be proved sent by the material verification end;

[0052] A parsing unit, used for parsing the data to be provided as evidence to establish a task for providing evidence and collecting evidence;

[0053] A dispatching unit, used for dispatching the evidence collection task, and obtaining the evidence material data corresponding to the data to be provided;

[0054] The sending unit is used to send the evidence material data to the material verification end so as to compare and verify the evidence material data to generate a material verification result.

[0055] In some embodiments, the dispatch unit includes:

[0056] A dispatching module, used to dispatch the evidence collection task to the target handling party;

[0057] The receiving module is used to receive the evidence materials uploaded by the target handling party through the human-computer interaction interface and generate evidence material data.

[0058] In some embodiments, the apparatus further comprises:

[0059] The display unit is used to receive the material verification result sent by the material verification terminal, and display the material verification result on a human-computer interaction interface.

[0060] According to a fifth aspect of the present disclosure, there is provided an electronic device, including:

[0061] at least one processor; and

[0062] a memory communicatively connected to the at least one processor; wherein,

[0063] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect or the second aspect.

[0064] According to a sixth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the first aspect or the second aspect.

[0065] According to a seventh aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method as described in the first aspect or the second aspect.

[0066] The present disclosure provides a method and device for material verification, an electronic device and a storage medium, which obtains data to be proved based on a preset interactive interface, and sends the data to be proved to a material collection end, so as to obtain the evidence material data corresponding to the data to be proved; receives and analyzes the evidence material data sent by the material collection end, and performs image recognition on the evidence material image in the evidence material data to generate organization identification data; compares and verifies the evidence material data according to the organization identification data and the acquired external verification data and supervision system data, and generates a material verification result. Compared with the related art, the embodiment of the present disclosure simplifies the cumbersome manual operation in the traditional evidence process by obtaining the data to be proved and sending it to the material collection end, and can improve the collection efficiency of evidence materials; the automatic recognition of evidence material data and the generation of organization identification data can further reduce the time and cost of manual review, making the entire evidence process more efficient; the recognition of evidence material data can more accurately extract and analyze the key information in the evidence material, reducing the recognition errors caused by human factors; by verifying the consistency of organization identification data with external verification data and supervision system data, the authenticity and accuracy of the evidence material can be ensured.

[0067] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0069] Figure 1 A schematic diagram of a material verification method provided in an embodiment of the present disclosure;

[0070] Figure 2 A schematic flow chart of another material verification method provided in an embodiment of the present disclosure;

[0071] Figure 3 A schematic flow chart of another material verification method provided in an embodiment of the present disclosure;

[0072] Figure 4 A schematic diagram of the structure of a material verification device provided in an embodiment of the present disclosure;

[0073] Figure 5 A schematic diagram of the structure of another material verification device provided by an embodiment of the present disclosure;

[0074] Figure 6 A schematic diagram of the structure of another material verification device provided by an embodiment of the present disclosure;

[0075] Figure 7 A schematic block diagram of an exemplary electronic device provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0076] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0077] The following describes the material verification method and device, electronic device, and storage medium of the embodiments of the present disclosure with reference to the accompanying drawings.

[0078] Figure 1 A schematic flow chart of a material verification method provided in an embodiment of the present disclosure.

[0079] like Figure 1 As shown, the method comprises the following steps:

[0080] Step 101, obtaining data to be proved based on a preset interactive interface, and sending the data to be proved to a material collection end, so as to obtain evidence material data corresponding to the data to be proved.

[0081] In the embodiments of the present disclosure, the system connects to the data source through a preset interactive interface to obtain the data required for evidence. The data generally refers to evidence or information that is required to further verify or support a claim or event in a specific context. The data may involve specific documents, records, images, videos, or other forms of original materials.

[0082] Once the data to be proved is obtained, the system transmits it to the material collection terminal through the designated communication channel. The material collection terminal is a module or service dedicated to collating and processing relevant data. Its role is to further process the data to be proved so as to equip it with corresponding evidence materials. These evidence materials may include official documents, witness statements, data analysis reports or other types of supporting documents.

[0083] The material collection end automatically or manually selects valid evidence materials associated with the data to be proved according to the data type and requirements, and converts them into evidence materials data that meet the requirements. These evidence materials data will be used for subsequent review, verification or submission to relevant institutions to ensure that the data to be proved can be legally and effectively supported.

[0084] Throughout the entire process, the preset interactive interface is not only responsible for data transmission and interaction, but also ensures the security, integrity and accuracy of data exchange. At the same time, the system logs each step of the operation to trace the source and processing of the data, ensuring the controllability and transparency of the process. Ultimately, the data to be used for evidence can be generated into complete evidence materials through this process, providing necessary support for further decision-making, review or legal proceedings.

[0085] Step 102, receiving and parsing the evidence material data sent by the material collection terminal, and performing image recognition on the evidence material images in the evidence material data to generate organization identification data.

[0086] In an embodiment of the present disclosure, the evidence material data sent by the material collection terminal is received and parsed, including but not limited to text, images, audio, video and other formats. First, the system will perform image recognition processing on the images of the evidence materials. The image recognition process includes image preprocessing, feature extraction, object detection and classification, text recognition and other technologies. By applying deep learning or traditional computer vision algorithms, the image content is analyzed and understood. Ultimately, the system will generate corresponding institutional identification data based on the image content, that is, classify, annotate and store relevant information in the image. These institutional identification data can be further used for subsequent analysis, verification and archiving to ensure the validity and accuracy of the entire material.

[0087] Step 103, based on the institution identification data and the acquired external verification data and supervision system data, the evidence material data is compared and verified to generate a material verification result.

[0088] In the embodiment of the present disclosure, the system will comprehensively compare and verify the evidence material data to be verified based on the institution identification data, external verification data and supervision system data. Specifically, the system first performs preliminary information matching and identification based on the institution identification data extracted from the evidence material image, such as identifying key elements such as text, date, signature, seal, barcode, etc. in the image.

[0089] The system then calls external verification data, which usually comes from third-party databases, public information sources or professional certification agencies, as a comparison standard. These external data may include official records, historical data, identity authentication information, etc. related to the evidence materials, which can be used to verify whether the data in the evidence materials is authentic and valid, whether it complies with the regulations, or whether it is consistent with known information.

[0090] At the same time, regulatory system data will also be used for comparison and verification. Regulatory system data usually contains regulatory records, legal requirements, industry standards and other relevant policy data from relevant regulatory agencies. The role of these data is to ensure that the evidence materials comply with the prescribed laws, regulations or industry standards, thereby further verifying the legality and standardization of the materials.

[0091] By comparing the institution identification data, external verification data and regulatory system data, the system will identify potential errors, forgeries or inconsistencies in the evidence materials. This process can use machine learning, data mining and pattern recognition technologies to automatically compare and analyze, and ultimately generate a comprehensive material verification result.

[0092] The material verification results will include the conclusions of the verification, such as the authenticity, compliance, completeness, and compliance with expected standards of the materials. In addition, the system will generate a detailed comparison report, which will explain the data sources, comparison results, and possible doubts used in the verification process for reference by subsequent auditors or relevant decision makers. Ultimately, this identification result will provide a strong basis for further legal proceedings, review decisions, or compliance assessments.

[0093] The present disclosure provides a method for material verification, which obtains data to be proved based on a preset interactive interface, and sends the data to be proved to a material collection end, so as to obtain the evidence material data corresponding to the data to be proved; receives and analyzes the evidence material data sent by the material collection end, and performs image recognition on the evidence material image in the evidence material data to generate organization identification data; compares and verifies the evidence material data according to the organization identification data and the acquired external verification data and supervision system data to generate a material verification result. Compared with the related art, the embodiment of the present disclosure simplifies the cumbersome manual operation in the traditional evidence process by obtaining the data to be proved and sending it to the material collection end, and can improve the collection efficiency of evidence materials; the automatic recognition of evidence material data and the generation of organization identification data can further reduce the time and cost of manual review, making the entire evidence process more efficient; the recognition of evidence material data can more accurately extract and analyze the key information in the evidence material, reducing the recognition errors caused by human factors; by verifying the consistency of organization identification data with external verification data and supervision system data, the authenticity and accuracy of the evidence materials can be ensured.

[0094] In order to clearly illustrate the embodiments of the present disclosure, this embodiment provides a flow chart of another material verification method.

[0095] like Figure 2 As shown, the method comprises the following steps:

[0096] Step 201, obtaining the data to be proved based on a preset interactive interface, and sending the data to be proved to a material collection end, so as to obtain the evidence material data corresponding to the data to be proved.

[0097] Specifically, in step 201, based on the preset interactive interface of the system, the data to be proved is first obtained from the user end, external system or other data source. The data to be proved may include case-related facts, testimony, index of evidence materials, or other relevant information. These data are usually preliminary, unverified, and require further evidence and verification. The role of the interactive interface is to ensure that the data can be transmitted accurately and timely, and at the same time ensure that the data format meets the preset requirements of the system, so as to achieve seamless connection.

[0098] Next, the system will process and organize the acquired data to be presented according to the preset processes and rules, and send it to the material collection end in a suitable format. The material collection end is a data processing module or server responsible for acquiring, collecting and organizing formal evidence materials from multiple sources, which may include file uploads, scanning devices, database retrieval or other methods. The data to be presented sent to the material collection end will serve as an index or guide to help the material collection end quickly identify and locate the corresponding evidence material data.

[0099] After receiving the data to be produced, the material collection end will start the process of data retrieval and collection. This process may include retrieving actual evidence materials related to the data to be produced, such as contracts, certificates, photos, videos, audio recordings or other documents from multiple external databases, archive systems, document management systems or other resource repositories. The evidence material data may be a digitized file or an electronic copy of a scanned traditional paper document. The material collection end may also involve formatting, standardizing or attaching metadata to these evidence materials to ensure that the collected materials can be delivered to the subsequent review, verification or analysis stage in a complete and standardized manner.

[0100] Finally, the collected evidence material data will be returned to the submitter or relevant personnel through the system as the formal proof material for the data to be verified. This process provides important support for subsequent evidence analysis, legality verification, audit inspection and other links, ensuring the integrity and accuracy of the data chain.

[0101] Step 202: parse the evidence data to obtain the data identifier corresponding to the evidence data and the evidence image.

[0102] Specifically, in step 202, the process of data analysis of the evidence material data includes detailed decoding and processing of the material to be analyzed. First, the system pre-processes the evidence material data, which may include image clarity optimization, format conversion, noise removal and other operations to ensure the accuracy of subsequent analysis. Then, the system uses advanced technologies such as optical character recognition (OCR), image recognition, text extraction and other methods to extract key information from the material, such as text, numbers, dates, barcodes, QR codes, signatures, seals and other data.

[0103] Through data analysis, the system can automatically identify the data identifiers in the evidence material data. Data identifiers are usually unique identifiers directly related to the material itself, which can be material numbers, certificate numbers, document IDs, barcodes, QR codes, etc. They help distinguish different materials and track them effectively. The system not only extracts these identifiers, but also verifies the format and validity of the identifiers to ensure that they meet the preset specifications and standards.

[0104] In addition, the extraction of images of evidence materials is also an important part of data analysis. During this process, the system will extract image files related to data identification from the evidence material data. These images may include scanned documents, photos, charts or other visual information. During the image extraction process, the system can ensure the integrity of image quality, including color, resolution, size and other aspects, ensure the accuracy and clarity of image data, and provide strong support for subsequent review, comparison, archiving and other operations.

[0105] After completing the data analysis, the system will store all the acquired data identifiers and evidence material images in a structured data format for subsequent retrieval, analysis, review or verification. This process not only improves the efficiency of data processing, but also provides reliable data support for verifying the authenticity, integrity and compliance of the materials.

[0106] Step 203: Based on the data identifier, the evidence material data is classified and sorted in a preset database, wherein the evidence material data includes: the evidence material image and evidence entry data.

[0107] Specifically, in step 203, based on the data identifier, the system will classify and sort the relevant evidence materials according to the data identifier of each evidence material, and store them in a preset database. The core of this process is to ensure that all evidence material data can be effectively classified in the database according to a certain logic and structure, so as to facilitate subsequent retrieval, query, review and analysis.

[0108] The system will uniquely identify each piece of evidence based on its data identifier. Data identifiers can include information such as material number, case number, timestamp, document ID, QR code, etc. These identifiers can help the system distinguish and track the source, type and other key information of each material. Using these data identifiers, the system can automatically classify the evidence material data into corresponding categories, such as case type, case number, material type (such as contracts, certificates, photos, recordings, etc.) or evidence time.

[0109] In the classification process, the evidence materials are not limited to static document information, but also include related image data and evidence entry data. The images of the evidence materials may include scanned copies, photographs, signature images, etc. The system will ensure that these images can be matched one-to-one with other data according to the corresponding material identifiers and stored in the database. When storing image data, the system will also automatically compress the images, optimize the clarity, and unify the format for subsequent processing and viewing.

[0110] In addition to image data, evidence entry data is also a very important part of the database. Evidence entry data is generated by the evidence personnel based on the actual evidence materials collected. These data usually include analysis, summary, judgment and evidence conclusion of the materials. The generation of evidence entry data often depends on the professional knowledge and experience of the evidence personnel, and may cover judgments such as the authenticity, completeness, and validity of the materials. For example, the evidence personnel may make conclusions such as "the contract is authentic and valid" and "the signing date complies with relevant legal provisions" based on the contract text and related background information, or judge whether certain materials are relevant to the case.

[0111] The evidence entry data not only contains these conclusions, but may also include the notes, suggestions or other supplementary information of the evidence presenter on the materials. All of these evidence entry data will be stored in the database together with the corresponding evidence material image data and associated through data identification. In this way, the system can combine the evidence material image and evidence entry data in subsequent retrieval and analysis to provide comprehensive and structured information for reviewers, legal personnel or other relevant personnel.

[0112] The classified and organized evidence material data will be stored in a structured database, which may include multiple data tables or document libraries, each of which corresponds to different types of evidence materials or different categories of data. For each record, the database not only contains data identification, evidence material images and evidence entry data, but may also include information such as upload time, file source, file status (such as pending review, reviewed, etc.) and other metadata. The system will also ensure that users can efficiently retrieve relevant materials through indexing and search functions.

[0113] For example, when searching, users can quickly find all materials related to a case or evidence conclusion by using keywords such as case number, material category, and evidence conclusion, and view detailed information for each material, including its image data and evidence entry data. The classification and management mechanism of the database can effectively ensure the integrity, accuracy and convenience of all materials, and support complex query, comparison and review work.

[0114] The classified and organized evidence material data will be used for various subsequent operations and processes, including but not limited to: 1. Case review: Based on the classification of evidence materials and evidence entry data, reviewers can quickly access all evidence related to the case to determine the legality and validity of the case. 2. Evidence analysis: By comparing and analyzing the evidence material images and evidence entry data, the system can help users determine the authenticity and relevance of the materials and provide more accurate analysis reports. 3. Data visualization and report generation: After data organization, the system can generate graphical data reports, including material summaries, evidence chain construction, etc., to help users intuitively understand the overall situation of the case or materials.

[0115] Step 204, calling an image recognition algorithm to perform image recognition on the image of the evidence material to generate the organization identification data.

[0116] Specifically, in step 204, an image recognition algorithm is used to process the image in the evidence material. By inputting the image into a pre-trained image recognition model, the algorithm will analyze and extract feature information in the image, including text, objects, graphics or other relevant identifiable content. Then, the system will generate institutional identification data based on these extracted features, that is, label, classify and encode each element in the image. Ultimately, the generated identification data can be used for further analysis, comparison, storage or other purposes to provide support for subsequent work. This process helps to convert image information into actionable data, thereby improving efficiency and accuracy, which is particularly important when a large amount of evidence materials need to be processed and reviewed.

[0117] Step 205, mapping the data fields corresponding to the same data types in the institution identification data, the external verification data, and the supervision system data to obtain mapping data.

[0118] Specifically in step 205, it is necessary to conduct a detailed analysis of each data field in the institution identification data, external verification data, and regulatory system data to find out the same data types between them. Then, these data fields of the same type are mapped accordingly, that is, the value of each data field in different data sources is matched with the corresponding field to ensure its semantic consistency and compatibility of the data structure. Through this mapping process, a mapping data set can be obtained, which contains field information of the same data type in different data sources, so that these data can be used uniformly and accurately in subsequent analysis, processing or verification processes. In short, the process of mapping data is to connect data from different sources on the same data structure for further data integration and analysis.

[0119] Step 206, performing data association matching based on the mapping data, and comparing and verifying whether the data corresponding to each data type in the institution identification data, the external source verification data, and the regulatory system data are consistent.

[0120] Specifically, in step 206, after the generation of the mapping data is completed, data association matching will be performed based on these mapping data. The core goal of this step is to compare and verify whether the relevant data from different data sources are consistent. Specifically, by analyzing the field information corresponding to each data type in the agency identification data, external verification data, and regulatory system data, they are compared one by one to check whether they meet the predetermined consistency standards. In this process, the system will compare each pair of data of the same type in detail, including multiple levels such as values, formats, and logical relationships, to ensure that there is no conflict or inconsistency in the data of the same field in each data source.

[0121] During the comparison and verification process, if it is found that the field value in a data source is inconsistent with the corresponding field value in other data sources, the system will automatically mark it and prompt possible errors or data anomalies. This verification step is crucial to ensure the accuracy and reliability of data, because it helps to find potential data errors, duplications or data missing problems, and provide more accurate basic data for subsequent data analysis, decision support or report generation. Through data association matching, data from multiple sources can be effectively integrated, data quality can be improved, and a solid guarantee can be provided for further data mining and analysis.

[0122] Step 207 , collect statistics on the comparison and verification results of each data type, and perform data analysis on the obtained statistical data to generate the material verification result.

[0123] Specifically, in step 207, after completing the data association matching and comparing and verifying the consistency of the data, the next step is to count the comparison and verification results of each data type. Specifically, the system will count the comparison results of each data type in different data sources one by one, such as verification consistency, verification inconsistency, missing data or data format errors. For each verification result, the system will calculate the frequency of occurrence, proportion and other statistical indicators, and generate a detailed statistical report. These statistical results will provide key data support for subsequent analysis.

[0124] Next, further data analysis is performed based on the obtained statistical data. This analysis process can help identify potential patterns, anomalies or regularities in the data through a variety of methods, such as data aggregation, trend analysis, anomaly detection, etc. By analyzing these statistical data, the system can make an in-depth assessment of the data consistency, identify which data sources or data types have a greater risk of inconsistency or error, and then provide targeted improvement suggestions or decision-making basis.

[0125] Finally, by integrating and summarizing these statistical analysis results, the system will generate material verification results. These results include an overall overview of whether each data type meets expectations and consistency during the comparative verification process, as well as detailed information on data sources or fields that may have problems. These material verification results can not only help decision makers understand the overall quality and consistency of the data, but also provide strong support for subsequent data cleaning, correction or optimization, ensuring that the final data has high accuracy and reliability in practical applications.

[0126] Step 208, obtaining the supervision review data corresponding to the evidence material data in the supervision system; evaluating the evidence entry data according to the evidence material data and the supervision review data, and generating an evidence evaluation result.

[0127] Specifically, in step 208, the system will extract the regulatory review data related to the evidence material data from the regulatory system. Specifically, the system will identify and obtain the regulatory review data corresponding to each piece of evidence material data, which is usually the result of the review or re-examination by the regulatory department, and may include the verification of the evidence material, review opinions or review conclusions, etc. By extracting these review data, the system can ensure that the correspondence between the evidence material and the regulatory requirements is fully presented.

[0128] Next, based on the evidence entry data and the regulatory review data in the evidence material data, the system will evaluate each item of evidence entry data. The evidence entry data is usually the original material data submitted by the user or institution, while the regulatory review data is the review result or review opinion of the regulator on these data. By comparing these two types of data, the system will comprehensively consider the differences, matching and compliance, and conduct an automated assessment. This evaluation process may include checks on the completeness, accuracy, legality and other aspects of the evidence materials, as well as identification of inconsistencies or errors in the regulatory review data.

[0129] Finally, the system will generate evidence assessment results based on the assessment results. These assessment results will reflect the compliance, accuracy and effectiveness of each piece of evidence during the submission process, helping decision makers understand whether the submitted materials meet regulatory standards and whether further supplementation or revision is required. The evidence assessment results not only provide an in-depth assessment of data quality, but also provide an important basis for the relevant review and approval processes, ensuring that the entire process meets regulatory requirements and can effectively support decision-making.

[0130] Further, as an implementable method of the embodiment of the present disclosure, after obtaining the material verification result, the following operations may be adopted but not limited to: sending the material verification result to the material collection end so as to display the material verification result on the human-computer interaction interface.

[0131] Specifically, after completing the verification process of the material, the system will automatically send these verification results to the designated material collection terminal. This operation is intended to ensure that all relevant verification information can be centrally stored and managed so that it can be displayed in real time on the subsequent human-computer interaction interface. In this way, users can intuitively view the verification results of the material, thereby effectively evaluating the qualification and reliability of the material. In addition, the design of the interface will provide a friendly user experience, allowing users to quickly obtain the required information and support their reference and use in the decision-making process.

[0132] Figure 3 A schematic flow chart of a material verification method provided in an embodiment of the present disclosure.

[0133] like Figure 3 As shown, the method comprises the following steps:

[0134] Step 301, receiving the data to be proved sent by the material verification end.

[0135] Step 302: parse the data to be provided as evidence to establish a task for providing evidence and collecting evidence.

[0136] Specifically, in steps 301 to 302, the system first receives the data to be proved from the material verification end. These data to be proved usually include relevant materials, documents or data fields to be submitted, which contain key information that needs further verification, review or confirmation. These data may come from multiple channels, such as enterprise submission, third-party agency provision or system automatic generation, involving specific business processes, compliance requirements or audit matters. After receiving the data to be proved, the system will process and analyze the data to ensure the integrity and accuracy of the information.

[0137] Next, the system will parse the received data to be used for evidence. The parsing process usually includes the identification of data structure, extraction and classification of data content. For example, the system may need to identify key fields in text, analyze key information in images, or sort records in a data table. During the parsing process, the system will format, standardize or clean the data to be used for evidence according to predefined rules or standards to ensure that the acquired data meets the requirements of subsequent processing. This process may also include data verification, such as confirming whether the data is complete, whether it meets the predetermined format requirements, or whether it contains any abnormal items.

[0138] After the analysis is completed, the system will establish an evidence collection task based on the content of the data to be proved. This task is to ensure that the data to be verified can be collected, reviewed and verified according to a specific process. The evidence collection task will clarify what supporting materials, verification standards or confirmation steps are required for each data, and will also define the execution cycle of the task, relevant responsible persons, review process and other information. By creating such a task, the system can provide clear guidance for subsequent evidence work and ensure that the process of data collection, verification and review proceeds smoothly.

[0139] In short, the goal of this process is to generate an orderly collection task by receiving, parsing and organizing the data to be proved, so that relevant personnel can complete data collection and verification work accurately and efficiently according to the established process, and provide reliable evidence support for subsequent review, evaluation and decision-making.

[0140] Step 303: dispatch the evidence collection task to obtain the evidence material data corresponding to the data to be provided.

[0141] Step 3031, dispatching the evidence collection task to the target handling party.

[0142] Step 3032, receiving the evidence materials uploaded by the target handling party through the human-computer interaction interface, and generating evidence material data.

[0143] Specifically, in step 303, the core task of the system is to assign the evidence collection task obtained from the previous analysis to the relevant responsible person (i.e. the target handling party), and finally obtain the required evidence material data through a series of processes. This is a key step in the entire evidence process, ensuring that all data to be provided can be supplemented and verified in a timely and accurate manner.

[0144] Step 3031: Assign the evidence collection task to the target handling party

[0145] First, the system will assign the evidence collection task to the designated target handler according to the preset task assignment rules. The target handler usually refers to the person or organization that has the ability to process relevant materials, provide evidence, or submit required data. This operation usually relies on the authority and role management mechanism defined in the system to ensure that each task is assigned to the appropriate handler.

[0146] During the dispatch process, the system will provide the target handler with detailed information such as the specific content of the task, the type of evidence required, the time requirement, and the priority of task processing. The dispatch method can be automatic notification, email reminder, task management system or platform push, etc., to ensure that the target handler can receive the task notification in time and start execution.

[0147] In addition, the system can track the progress of tasks by dynamically updating the task status to ensure that the handler completes the task within the specified time, and provide necessary technical support or training to help the target handler complete the task smoothly.

[0148] Step 3032: Receive the evidence materials uploaded by the target handling party through the human-computer interaction interface and generate evidence material data

[0149] After the task is assigned, the target handler will upload the required evidence materials through the system's human-computer interaction interface within the specified time window according to the task requirements. These evidence materials can be various types of files, images, audio or video materials, and the specific form depends on the required evidence content and task requirements.

[0150] The system will provide the target agent with a simple and intuitive upload interface, supporting multiple file formats (such as PDF, images, text files, form data, etc.), and has functions such as file size limit and file verification to ensure that the uploaded materials meet the specified standards and requirements. During the upload process, the system usually performs real-time data verification, such as checking the integrity of the file, whether the format is correct, whether the file is damaged, etc., to ensure that the submitted materials meet the specifications.

[0151] After the handling party successfully uploads the evidence materials, the system will automatically classify, organize and archive these files and data. The system will extract key information from each uploaded file, such as submission date, uploader, material type, etc., and automatically parse and process these data. This includes extracting text from scanned documents through image recognition technology, structuring table data, or understanding document content through text analysis technology.

[0152] After parsing, the system will generate evidence material data, match and associate this data with the original data to be proved, and store it in the database for subsequent verification, review and evaluation.

[0153] The process of generating evidence data can include the following specific steps:

[0154] 1. File storage and management: Save the uploaded materials into the system to ensure the security, integrity and traceability of the data.

[0155] 2. Data structuring and annotation: Structuring document content and converting unstructured data into a format that is easy to analyze (such as extracting information from scanned documents into searchable text).

[0156] 3. Data verification: Automatically verify the uploaded materials to see if they meet the task requirements, such as file size, format, content, etc.

[0157] 4. Data association and marking: Associate the generated evidence material data with the data to be proved, ensure that each material corresponds to the corresponding task to be proved, and mark the upload status, validity, etc.

[0158] Furthermore, as an achievable method of an embodiment of the present disclosure, after uploading the evidence materials, a material verification result sent by the material verification end will be obtained, after which the following operations can be adopted but are not limited to: receiving the material verification result sent by the material verification end, and displaying the material verification result on the human-computer interaction interface.

[0159] Specifically, the system will receive the material verification results sent from the material verification end. This process ensures that all verification information can be transmitted to the control center in a timely manner. The received results will be processed and organized so that they can be clearly displayed on the designed human-computer interaction interface. In this way, users can easily access and view the verification status of the material, which will help them quickly understand whether the material meets the predetermined standards or requirements. In addition, the interface presents information in a friendly format, allowing users to effectively analyze data and take appropriate actions when necessary.

[0160] In summary, the system ensures the smooth progress of the evidence collection process by accurately dispatching evidence collection tasks to the target handling party and receiving uploaded evidence materials through the human-computer interaction interface. Through automated data processing and generation of evidence material data, the entire process is both efficient and orderly, laying a solid foundation for subsequent data review, re-examination, and evaluation. At the same time, the system can also ensure that tasks are completed on time and seamlessly connected to subsequent processes through dynamic task management, real-time notifications, and progress tracking.

[0161] Step 304: Send the evidence material data to the material verification end so as to compare and verify the evidence material data to generate a material verification result.

[0162] Specifically, in step 304, the data of the evidence materials are sent to the designated material verification end system through a secure data transmission channel, which has efficient data reception and processing capabilities. After receiving the evidence materials, the material verification end will use advanced recognition algorithms and technologies to conduct detailed comparative analysis and verification of the data of the materials. This process is designed to ensure the authenticity, accuracy and legality of the evidence materials and generate verified material verification results. This recognition result will provide the necessary data support and basis for subsequent review, evaluation or legal proceedings, thereby ensuring the fairness and transparency of the entire evidence process.

[0163] The present disclosure provides a method for material verification, receiving data to be proved sent by a material verification terminal; parsing the data to be proved to establish a task for evidence collection; dispatching the task for evidence collection to obtain the evidence material data corresponding to the data to be proved; sending the evidence material data to the material verification terminal so as to compare and verify the evidence material data to generate a material verification result. Compared with the related art, the embodiment of the present disclosure simplifies the cumbersome manual operations in the traditional evidence process by obtaining the data to be proved and sending it to the material collection terminal, and can improve the efficiency of collecting evidence materials; the automatic identification of evidence material data and the generation of agency identification data can further reduce the time and cost of manual review, making the entire evidence process more efficient; the identification of evidence material data can more accurately extract and parse the key information in the evidence material, reducing the identification errors caused by human factors; by verifying the consistency of agency identification data with external verification data and regulatory system data, the authenticity and accuracy of the evidence materials can be ensured.

[0164] It should be noted that the embodiments of the present disclosure may include multiple steps. For the convenience of description, these steps are numbered, but these numbers do not limit the execution time slots or execution order between the steps; these steps can be implemented in any order, and the embodiments of the present disclosure do not limit this.

[0165] Corresponding to the above-mentioned material verification method (material verification end), the present invention also provides a material verification device. Since the device embodiment of the present invention corresponds to the above-mentioned method embodiment, the details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment, and will not be repeated in the present invention.

[0166] Figure 4 A schematic diagram of a material verification device provided in an embodiment of the present disclosure is shown in FIG. Figure 4 As shown, including:

[0167] A first acquisition unit 41 is used to acquire the data to be provided as evidence based on a preset interactive interface, and send the data to be provided as evidence to a material collection terminal, so as to acquire the evidence material data corresponding to the data to be provided as evidence;

[0168] The identification unit 42 is used to receive and analyze the evidence material data sent by the material collection terminal, and perform image recognition on the evidence material image in the evidence material data to generate institution identification data;

[0169] The generating unit 43 is used to compare and verify the evidence material data according to the institution identification data and the acquired external verification data and supervision system data, and generate a material verification result.

[0170] The present disclosure provides a device for material verification, which obtains data to be proved based on a preset interactive interface, and sends the data to be proved to a material collection end, so as to obtain the evidence material data corresponding to the data to be proved; receives and analyzes the evidence material data sent by the material collection end, and performs image recognition on the evidence material image in the evidence material data to generate organization identification data; compares and verifies the evidence material data according to the organization identification data and the acquired external verification data and supervision system data, and generates a material verification result. Compared with the related art, the embodiment of the present disclosure simplifies the cumbersome manual operation in the traditional evidence process by obtaining the data to be proved and sending it to the material collection end, and can improve the collection efficiency of evidence materials; the automatic recognition of evidence material data and the generation of organization identification data can further reduce the time and cost of manual review, making the entire evidence process more efficient; the recognition of evidence material data can more accurately extract and analyze the key information in the evidence material, and reduce the recognition errors caused by human factors; by verifying the consistency of organization identification data with external verification data and supervision system data, the authenticity and accuracy of the evidence materials can be ensured.

[0171] Furthermore, in a possible implementation of this embodiment, as Figure 5 As shown, the device also includes:

[0172] The second acquisition unit 44 is used to acquire the supervision review data corresponding to the evidence material data in the supervision system;

[0173] The evaluation unit 45 is used to evaluate the evidence entry data according to the evidence entry data and the supervision review data in the evidence material data, and generate an evidence evaluation result.

[0174] Furthermore, in a possible implementation of this embodiment, as Figure 5 As shown, the identification unit 42 includes:

[0175] The acquisition module 421 is used to perform data analysis on the evidence material data to obtain the data identifier corresponding to the evidence material data and the evidence material image;

[0176] The sorting module 422 is used to classify and sort the evidence material data in a preset database based on the data identifier, wherein the evidence material data includes: the evidence material image and evidence entry data;

[0177] The identification module 423 is used to call the image recognition algorithm to perform image recognition on the image of the evidence material to generate the organization identification data.

[0178] Furthermore, in a possible implementation of this embodiment, as Figure 5 As shown, the generating unit 43 includes:

[0179] A mapping module 431 is used to map data fields corresponding to the same data type in the institution identification data, the external source verification data, and the supervision system data to obtain mapping data;

[0180] A matching module 432 is used to perform data association matching based on the mapping data, and compare and verify whether the data corresponding to each data type in the institution identification data, the external source verification data, and the supervision system data are consistent;

[0181] The generation module 433 is used to collect statistics on the comparison and verification results of each data type, and perform data analysis on the obtained statistical data to generate the material verification results.

[0182] Furthermore, in a possible implementation of this embodiment, as Figure 5 As shown, the device also includes:

[0183] The sending unit 46 is used to send the material verification result to the material collection end so that the material verification result can be displayed on the human-computer interaction interface.

[0184] Corresponding to the above-mentioned material verification method (material collection end), the present invention also proposes a material verification device. Since the device embodiment of the present invention corresponds to the above-mentioned method embodiment, the details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment, and will not be repeated in the present invention.

[0185] Figure 6 A schematic diagram of a material verification device provided in an embodiment of the present disclosure is shown in FIG. Figure 6 As shown, including:

[0186] A receiving unit 51 is used to receive the data to be proved sent by the material verification end;

[0187] A parsing unit 52, used for parsing the data to be provided as evidence to establish a task for providing evidence and collecting evidence;

[0188] A dispatching unit 53, configured to dispatch the evidence collection task and obtain the evidence material data corresponding to the data to be provided;

[0189] The sending unit 54 is used to send the evidence material data to the material verification end, so as to compare and verify the evidence material data to generate a material verification result.

[0190] The present disclosure provides a device for material verification, which receives data to be proved sent by a material verification terminal; analyzes the data to be proved to establish a task for evidence collection; dispatches the task for evidence collection to obtain the evidence material data corresponding to the data to be proved; and sends the evidence material data to the material verification terminal so as to compare and verify the evidence material data to generate a material verification result. Compared with the related art, the embodiment of the present disclosure simplifies the cumbersome manual operations in the traditional evidence process by acquiring the data to be proved and sending it to the material collection terminal, thereby improving the efficiency of collecting evidence materials; the automatic identification of evidence material data and the generation of institution identification data can further reduce the time and cost of manual review, making the entire evidence process more efficient; the identification of evidence material data can more accurately extract and analyze key information in the evidence material, reducing identification errors caused by human factors; by verifying the consistency of institution identification data with external verification data and regulatory system data, the authenticity and accuracy of the evidence materials can be ensured.

[0191] Furthermore, in a possible implementation of this embodiment, as Figure 6 As shown, the dispatching unit 53 includes:

[0192] A dispatching module 531, used for dispatching the evidence collection task to a target handling party;

[0193] The receiving module 532 is used to receive the evidence materials uploaded by the target handling party through the human-computer interaction interface and generate evidence material data.

[0194] Furthermore, in a possible implementation of this embodiment, as Figure 6 As shown, the device also includes:

[0195] The display unit 55 is used to receive the material verification result sent by the material verification terminal, and display the material verification result on a human-computer interaction interface.

[0196] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and the principle is the same, which is not limited in this embodiment.

[0197] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0198] Figure 7 A schematic block diagram of an example electronic device 600 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0199] like Figure 7 As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 602 or a computer program loaded from a storage unit 608 to a RAM (Random Access Memory) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An I / O (Input / Output) interface 605 is also connected to the bus 604.

[0200] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0201] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as a method for material verification. For example, in some embodiments, the method for material verification may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the aforementioned material verification method in any other appropriate manner (for example, by means of firmware).

[0202] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a special purpose or general purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

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

[0204] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or a flash memory, an optical fiber, a CD-ROM (Compact Dis sc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

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

[0206] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0207] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0208] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0209] The various numerical numbers such as first and second involved in the present disclosure are only for the convenience of description and are not used to limit the scope of the embodiments of the present disclosure, but also indicate the order of precedence.

[0210] At least one in the present disclosure may also be described as one or more, and a plurality may be two, three, four or more, which is not limited in the present disclosure. In the embodiments of the present disclosure, for a technical feature, the technical features in the technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", etc., and there is no order of precedence or size between the technical features described by the "first", "second", "third", "A", "B", "C" and "D".

[0211] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0212] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for material verification, characterized in that: The method is applied to a material verification end, and the method comprises: Acquire the data to be proved based on a preset interactive interface, and send the data to be proved to a material collection terminal, so as to obtain the evidence material data corresponding to the data to be proved; Receiving and parsing the evidence material data sent by the material collection terminal, and performing image recognition on the evidence material images in the evidence material data to generate institution identification data; Based on the institution identification data and the acquired external verification data and regulatory system data, the data of the evidence materials are compared and verified to generate material verification results.

2. The method according to claim 1, characterized in that The method further comprises: Obtain the regulatory review data corresponding to the evidence material data in the regulatory system; Based on the evidence entry data in the evidence material data and the supervision review data, the evidence entry data is evaluated to generate an evidence evaluation result.

3. The method according to claim 1, characterized in that The receiving and parsing of the evidence material data sent by the material collection terminal, and performing image recognition on the evidence material images in the evidence material data to generate institution identification data, includes: Performing data analysis on the evidence material data to obtain a data identifier corresponding to the evidence material data and an image of the evidence material; Based on the data identifier, the evidence material data is classified and sorted in a preset database, wherein the evidence material data includes: the evidence material image and evidence entry data; An image recognition algorithm is called to perform image recognition on the image of the evidence material to generate the institution identification data.

4. The method according to claim 1, characterized in that: The method of comparing and verifying the evidence material data based on the institution identification data and the acquired external verification data and regulatory system data to generate a material verification result includes: Mapping data fields corresponding to the same data type in the institution identification data, the external source verification data, and the supervision system data to obtain mapping data; Perform data association matching based on the mapping data, and compare and verify whether the data corresponding to each data type in the institution identification data, the external source verification data, and the regulatory system data are consistent; The comparison verification results of each data type are counted, and data analysis is performed on the obtained statistical data to generate the material verification results.

5. The method according to claim 1, characterized in that The method further comprises: The material verification result is sent to the material collection end so that the material verification result can be displayed on the human-computer interaction interface.

6. A method for material verification, characterized in that: The method is applied to a material collection end, and the method comprises: Receive the data to be proved sent by the material verification end; Parsing the data to be provided as evidence to establish an evidence collection task; Dispatching the evidence collection task to obtain the evidence material data corresponding to the data to be provided; The evidence material data is sent to the material verification end so as to compare and verify the evidence material data to generate a material verification result.

7. The method according to claim 6, characterized in that The step of dispatching the evidence collection task and obtaining the evidence material data corresponding to the data to be provided includes: Dispatching the evidence collection task to the target handling party; The evidence materials uploaded by the target handling party through the human-computer interaction interface are received, and evidence material data are generated.

8. The method according to claim 6, characterized in that The method further comprises: The material verification result sent by the material verification terminal is received, and the material verification result is displayed on a human-computer interaction interface.

9. A device for verifying risk evidence materials, characterized in that: The method is applied to a material verification end, and the device comprises: A first acquisition unit, configured to acquire the data to be provided as evidence based on a preset interactive interface, and send the data to be provided as evidence to a material collection terminal, so as to acquire the evidence material data corresponding to the data to be provided as evidence; an identification unit, configured to receive and analyze the evidence material data sent by the material collection terminal, and perform image recognition on the evidence material images in the evidence material data to generate institution identification data; A generating unit is used to compare and verify the evidence material data according to the institution identification data and the acquired external verification data and supervision system data, and generate a material verification result.

10. A device for verifying risk evidence materials, characterized in that: The method is applied to a material collection end, and the device comprises: A receiving unit, used for receiving the data to be proved sent by the material verification end; A parsing unit, used for parsing the data to be provided as evidence to establish a task for providing evidence and collecting evidence; A dispatching unit, used for dispatching the evidence collection task, and obtaining the evidence material data corresponding to the data to be provided; The sending unit is used to send the evidence material data to the material verification end so as to compare and verify the evidence material data to generate a material verification result.

11. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

12. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-8.

13. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 8.

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