Transaction data management method and device, equipment and storage medium
By receiving data authorization instructions and client query requests in the transaction data management system, generating target query information, and filtering valid data based on time, the problems of cumbersome transaction data query and data leakage are solved, and efficient and secure transaction data management is achieved.
Patent Information
- Application Number
- CN202411778274.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-04
- Publication Date
- 2025-03-28
AI Technical Summary
The current transaction data query process is cumbersome, the query efficiency is low, and it is easy to leak relevant data with high confidentiality requirements, affecting data security.
By receiving data authorization instructions, determine the authorization information and valid time of the transaction data that can be disclosed in the transaction database; receive the transaction data query request from the client, parse and generate target query information; filter the current valid data based on the time and valid time of the query request; generate and feedback the query results to ensure data security.
It improves the comprehensiveness and intelligence of transaction data query, personalizes the public time of transaction data, and enhances the confidentiality and security of data.
Smart Images
Figure CN119720272A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of artificial intelligence technology and can be applied to the field of financial technology. In particular, the present invention relates to transaction data management methods, devices, equipment and storage media. Background Art
[0002] In the open banking model, the client needs to access the user's transaction data to provide personalized services to the user. For example, the client is used to run third-party applications, which requires sending a transaction data query request to the transaction data management terminal (server), and the transaction data management terminal performs data query and feedback based on the transaction data query request.
[0003] However, the current transaction data query process is cumbersome, which is not conducive to improving the query efficiency of the current transaction data. In addition, for the scenario of banking transactions, transaction information with the same substantive meaning may be referred to by many different nouns, which leads to the problem that the data query performed by the transaction data query request is not comprehensive and intelligent enough. Moreover, when the transaction data is fed back to the client, it is easy to bring out related data with high confidentiality requirements, which seriously affects data security. Summary of the invention
[0004] The present invention provides a transaction data management method, device, computer equipment and storage medium to solve the technical problems that current transaction data query is not comprehensive enough and related data with high confidentiality requirements are easily leaked.
[0005] In a first aspect, a transaction data management method is provided, comprising:
[0006] Receive a data authorization instruction, and determine authorization information of at least one publicly available transaction data in a transaction database and validity time information corresponding to each authorization information according to the data authorization instruction;
[0007] Receive a transaction data query request input by a client, and parse the transaction data query request to obtain a query statement;
[0008] Determine an expansion word corresponding to the transaction data query request based on the query statement, and input the expansion word and the query statement into a preset target language model to generate target query information;
[0009] Retrieving target query data from a transaction database according to target query information;
[0010] Filter out currently valid data from publicly available transaction data based on the input time and validity time information of the transaction data query request;
[0011] Generate query feedback information based on the data in the target query data that matches the current valid data, and output the query feedback information to the client.
[0012] In some implementations, determining an expansion word corresponding to a transaction data query request based on a query statement includes:
[0013] Get the keywords in the query statement;
[0014] Select synonyms or near synonyms of the query statement as expansion words corresponding to the transaction data query request.
[0015] In some implementations, inputting the expansion word and the query sentence into a preset target language model to generate target query information includes:
[0016] Obtain the model file and load the preset target language model from the model file;
[0017] Calling the input function of the target language model, and inputting the query sentence and the expansion words into the target language model through the input function;
[0018] Input the query sentence and the expanded word into the feature extractor of the target language model, extract features of the query sentence and the expanded word through the feature extractor, and obtain the feature vector of the query sentence and the feature vector of the expanded word;
[0019] The feature vector of the query statement and the feature vector of the expanded word are concatenated to generate the target feature vector of the current transaction data, the target feature vector is input into the generator of the target language model, and the target feature vector is processed by the generator to obtain the target query information.
[0020] In some implementations, retrieving target query data from a transaction database according to target query information includes:
[0021] Get the preset query conditions and submit the query conditions to the query interface;
[0022] The query operation corresponding to the query condition is executed on the transaction database through the query interface to obtain the target query data.
[0023] In some implementations, the data in the transaction database is encrypted data, and query feedback information is generated based on data in the target query data that matches the current valid data, including:
[0024] Generate a corresponding information feedback template according to the target query data;
[0025] Decrypt the data in the target query data that matches the current valid data to obtain decrypted data, and perform desensitization processing on the decrypted data to obtain displayable data;
[0026] A first display area corresponding to the displayable data on the information feedback template is determined, and the displayable data is set in the first display area to obtain query feedback information.
[0027] In some embodiments, the method further comprises:
[0028] Determine a second display area on the information feedback template corresponding to the data in the target query data that does not match the current valid data, and set the encryption identifier in the second display area.
[0029] In some implementations, decrypted data is anonymized to obtain displayable data, including:
[0030] Comparing the text data corresponding to the decrypted data with the sensitive data to identify the first sensitive information in the decrypted data, hiding the first sensitive information or replacing the first sensitive information with preset characters to obtain desensitized text data;
[0031] Identify the sensitive information area in the image data corresponding to the decrypted data, blur the sensitive information area or cover the sensitive information area with the background color of the sensitive information area to obtain desensitized image data;
[0032] Generate displayable data based on desensitized text data and desensitized image data.
[0033] In a second aspect, a transaction data management device is provided, comprising:
[0034] An authorization receiving module is used to receive a data authorization instruction, and determine authorization information of at least one publicly available transaction data in the transaction database and valid time information corresponding to each authorization information according to the data authorization instruction;
[0035] A query receiving module is used to receive a transaction data query request input by a client, and parse the transaction data query request to obtain a query statement;
[0036] A query expansion module, used to determine the expansion word corresponding to the transaction data query request based on the query statement, and input the expansion word and the query statement into a preset target language model to generate target query information;
[0037] A data query module, used to retrieve target query data from a transaction database according to target query information;
[0038] A data screening module, used to screen out currently valid data from publicly available transaction data according to the input time and validity time information of the transaction data query request;
[0039] The feedback output module is used to generate query feedback information according to the data in the target query data that matches the current valid data, and output the query feedback information to the client.
[0040] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned transaction data management method when executing the computer program.
[0041] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned transaction data management method are implemented.
[0042] The present application provides a transaction data management method, apparatus, computer equipment and storage medium, the method comprising: receiving a data authorization instruction, and determining authorization information of at least one publicly available transaction data in a transaction database and valid time information corresponding to each authorization information according to the data authorization instruction; receiving a transaction data query request input by a client, and parsing the transaction data query request to obtain a query statement; determining an expansion word corresponding to the transaction data query request based on the query statement, and inputting the expansion word and the query statement into a preset target language model to generate target query information; retrieving target query data from the transaction database according to the target query information; filtering out current valid data from publicly available transaction data according to the input time and valid time information of the transaction data query request; generating query feedback information from data in the target query data that matches the current valid data, and outputting the query feedback information to the client. The beneficial effects of the present application lie in two aspects. On the one hand, query operations are performed based on the query statements and expansion words corresponding to the transaction data query request, which effectively improves the comprehensiveness and intelligence of the transaction data query scope; on the other hand, the current valid data is screened out based on the input time and effective time information of the transaction data query request, and then the current valid data is fed back to the client. The corresponding public time can be set for different transaction data in a personalized manner, while avoiding outdated authorization information from authorizing the transaction data to be disclosed, thereby improving the confidentiality and security of transaction information management. BRIEF DESCRIPTION OF THE DRAWINGS
[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.
[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.
[0045] Figure 1 is a schematic diagram of an application environment of a transaction data management method in one embodiment of the present invention;
[0046] Figure 2 A flowchart of a transaction data management method provided by an embodiment of the present invention;
[0047] Figure 3 yes Figure 1 A schematic flow chart of a specific implementation method of step S3;
[0048] Figure 4 yes Figure 1 A schematic flow chart of a specific implementation of step S6;
[0049] Figure 5 is a structural diagram of a transaction data management device in one embodiment of the present invention;
[0050] Figure 6 It is a structural diagram of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION
[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0052] See also Figure 1 , Figure 1 is a schematic diagram of an application environment of a transaction data management method according to an embodiment of the present invention. The transaction data management method provided by the embodiment of the present invention can be applied in Figure 1 The server in the application environment shown is a client that communicates with the server through a network.
[0053] It should be noted that the device running the client is referred to as a client device. The client is used to run a third-party application, for example.
[0054] The client devices may include but are not limited to various personal computers, laptops, smart phones, tablet computers, and portable wearable devices.
[0055] It should be noted that the device running the server is referred to as: server device.
[0056] The server device may be implemented by an independent server or a server cluster composed of multiple servers. The application of the present invention to an independent server is described in detail below through specific embodiments.
[0057] See also Figure 2 , Figure 2 A flowchart of a transaction data management method provided by an embodiment of the present invention includes steps S1 to S6:
[0058] Step S1: receiving a data authorization instruction, and determining authorization information of at least one publicly available transaction data in a transaction database and validity time information corresponding to each authorization information according to the data authorization instruction.
[0059] Exemplarily, the data authorization instruction may be sent by the user operating a preset terminal to the server.
[0060] Specifically, the transaction database is used to store historical transaction data related to users, such as data records corresponding to transaction operations such as user purchase of services, transfer of funds, and payment of outstanding debts.
[0061] Specifically, the transaction database contains multiple transaction data, and each transaction data corresponds to a transaction operation. The data authorization instruction is used to indicate which transaction data in the transaction database is publicly available and the corresponding disclosure time of these data. It should be understood that based on the authorization information, it can be determined that the corresponding transaction data can be made public, and the corresponding transaction data is called publicly available transaction data, and the effective time information refers to the timeliness of the transaction data as publicly available transaction data.
[0062] Step S2: receiving a transaction data query request input by a client, and parsing the transaction data query request to obtain a query statement.
[0063] Specifically, the server executing the method performs extraction processing on the transaction data query request to extract the query statement.
[0064] Step S3: Determine the expansion word corresponding to the transaction data query request based on the query statement, and input the expansion word and the query statement into a preset target language model to generate target query information.
[0065] It should be noted that in the scenario of banking transactions, transaction information with the same substantive meaning may be referred to by many different nouns. Therefore, introducing extended words corresponding to the transaction data before querying can cover more concepts and terms related to transaction behaviors, thereby providing more comprehensive transaction information, which is conducive to bringing more comprehensive and complete transaction data to the client.
[0066] In some implementations, determining an expansion word corresponding to a transaction data query request based on a query statement includes:
[0067] Get the keywords in the query statement;
[0068] Select synonyms or near synonyms of the query statement as expansion words corresponding to the transaction data query request.
[0069] Specifically, obtaining the keywords in the query statement includes: processing the query statement by using a semantic role labeling method and a dependency syntax analysis method to obtain the processed query statement, and obtaining the keywords in the processed query statement.
[0070] Among them, Semantic Role Labeling (SRL) is a natural language processing technology that assigns a specific semantic role to each component in a sentence by analyzing the predicate in the sentence. Semantic role labeling can not only capture the grammatical structure of the sentence, but more importantly, it can reveal the deep semantic information of the sentence. Therefore, through semantic role labeling, the meaning of the sentence can be understood more accurately.
[0071] Among them, dependency syntactic analysis determines the syntactic structure of a sentence by analyzing the dependency relationship between words in the sentence. This analysis technique reveals the dominant and dominated relationships between sentence components, such as subject-predicate relationships, verb-object relationships, etc., and expresses these relationships in the form of a tree structure.
[0072] In the embodiment of the present invention, the synonyms or near-synonyms of the keyword are selected as the expansion words corresponding to the keyword, which improves the richness and depth of the query sentence content and is conducive to mining more valuable information.
[0073] See also Figure 3 , Figure 3 yes Figure 1 A schematic flow chart of a specific implementation of step S3 in FIG.
[0074] like Figure 3 As shown, in some implementations, in step S3, the expansion word and the query sentence are input into a preset target language model to generate target query information, including steps S31 to S34:
[0075] Step S31: Obtain a model file, and load a preset target language model from the model file;
[0076] Step S32: calling the input function of the target language model, and inputting the query sentence and the expansion word into the target language model through the input function;
[0077] Step S33: inputting the query sentence and the expanded word into the feature extractor of the target language model, extracting features of the query sentence and the expanded word through the feature extractor, and obtaining a feature vector of the query sentence and a feature vector of the expanded word;
[0078] Step S34: concatenate the feature vector of the query statement and the feature vector of the expanded word to generate a target feature vector of the current transaction data, input the target feature vector into the generator of the target language model, process the target feature vector through the generator, and obtain the target query information.
[0079] In an embodiment of the present invention, the target feature vector is input into the generator of the target language model, and the target feature vector is processed by the generator to obtain target query information, and the generator displays the target query information in a structured manner, so that the target query information is clearer. Among them, loading the preset target language model does not require repeated training, which greatly saves time and computing resources, because training models often require a large amount of data and computing power, while loading the preset target language model can directly use existing results. It is easy to deploy and expand quickly, and can significantly improve the efficiency of data processing.
[0080] Step S4: Retrieve target query data from the transaction database according to the target query information.
[0081] Specifically, the target query data is retrieved from the transaction database according to the target query information, including: connecting to the target platform, extracting the current transaction data from the transaction database of the target platform, and the target platform includes one of a financial platform, a technology platform, and an insurance platform, or a combination thereof.
[0082] Among them, the current transaction data is the current transaction data.
[0083] Among them, transaction data refers to the numbers and text descriptions in written reports compiled and released regularly by enterprises, which are intended to comprehensively, systematically and accurately reflect their financial status, operating results, cash flow and other key economic information to stakeholders such as investors, creditors, regulators and the public. These data are professionally processed and audited, follow specific accounting principles and reporting standards, and are highly objective and comparable.
[0084] Among them, the financial platform is an online service system based on advanced information technology architecture. The financial platform integrates multiple computer technologies such as data processing, network communication, user interface design, and security encryption. The financial platform can help investors evaluate corporate value and formulate investment strategies by collecting, organizing and displaying the financial report data of listed companies.
[0085] Among them, the science and technology platform is a comprehensive system that integrates a variety of advanced scientific and technological resources and services. The science and technology platform closely connects various scientific and technological innovation achievements, R&D resources, professional services and market demands.
[0086] Among them, the insurance platform is an online service platform that integrates insurance product sales, information consultation, customer service and risk management. The insurance platform uses modern information technologies such as the Internet, big data, and cloud computing to closely connect insurance companies, insurance agents, consumers, and third-party service providers, forming an efficient, convenient, and transparent insurance service ecosystem.
[0087] Among them, on the insurance platform, users can access the current financial report data of the insurance company. The current financial report data can help users evaluate the profitability indicators, risk status and growth potential of the insurance company, and provide a basis for investment decisions. At the same time, it is also convenient for regulatory agencies to supervise and evaluate the financial status of insurance companies to ensure the stability and healthy development of the insurance market.
[0088] As an embodiment, after the target query data is retrieved, the target query data may be preprocessed, and the preprocessing includes one or a combination of format conversion processing, duplicate data processing, and error data processing.
[0089] In some implementations, retrieving target query data from a transaction database according to target query information includes:
[0090] Get the preset query conditions and submit the query conditions to the query interface;
[0091] The query operation corresponding to the query condition is executed on the transaction database through the query interface to obtain the target query data.
[0092] Exemplarily, a query operation corresponding to the query condition is performed on the transaction database through the query interface to obtain the query result of the current transaction data, including:
[0093] Classifying the transaction data according to the transaction behavior type corresponding to each transaction data in the transaction database, where the transaction behavior type includes one of purchasing services, transferring money, and paying debts;
[0094] When the transaction behavior type is purchasing services, a query operation corresponding to the purchased services is performed in the transaction database through the query interface to obtain first associated information, the first associated information including: one or a combination of the purchased items, the transaction amount, and the service provided, and then the target query data is matched from the transaction database according to the first associated information;
[0095] When the transaction behavior type is transfer, a query operation corresponding to the transfer is performed in the transaction database through the query interface to obtain second associated information, the second associated information including: one or a combination of the transferor, the transfer amount, and the transfer time, and then the target query data is matched from the transaction database according to the second associated information;
[0096] When the transaction behavior type is payment of outstanding debts, a query operation corresponding to the payment of outstanding debts is executed in the transaction database through the query interface to obtain third associated information, which includes: one or a combination of the transferor, the payment amount, and the number of payment periods, and then the target query data is matched from the transaction database according to the third associated information.
[0097] In the embodiment of the present invention, a query operation corresponding to the query condition is performed on the transaction database through a query interface to obtain target query data. Since no manual query is required, the query time is reduced, which is conducive to improving the query efficiency of the target query data.
[0098] Step S5: Filter out currently valid data from publicly available transaction data according to the input time and validity time information of the transaction data query request.
[0099] Specifically, the effective time information refers to the timeliness of the transaction data corresponding to the data authorization instruction as publicly available transaction data. Filtering out the current valid data from the publicly available transaction data according to the input time and effective time information of the transaction data query request specifically includes: when the input time matches the effective time information of the publicly available transaction data, determining that the publicly available transaction data is the current valid data;
[0100] It should be understood that by filtering out the current valid data based on the input time and effective time information of the transaction data query request and then feeding back the current valid data to the client, the corresponding public disclosure time can be set for different transaction data in a personalized manner, while avoiding outdated authorization information from authorizing the transaction data to be disclosed, thereby improving the confidentiality and security of transaction information management.
[0101] Step S6: Generate query feedback information based on the data in the target query data that matches the current valid data, and output the query feedback information to the client.
[0102] See also Figure 4 , Figure 4 yes Figure 1 A schematic flow chart of a specific implementation of step S6 in FIG.
[0103] like Figure 4 As shown, in some implementations, the data in the transaction database is encrypted data, and step S6 generates query feedback information according to the data in the target query data that matches the current valid data, including steps S61-S63:
[0104] Step S61: Generate a corresponding information feedback template according to the target query data;
[0105] Step S62: decrypting the data in the target query data that matches the current valid data to obtain decrypted data, and performing desensitization processing on the decrypted data to obtain displayable data;
[0106] Step S63: determining a first display area corresponding to the displayable data on the information feedback template, and setting the displayable data in the first display area to obtain query feedback information.
[0107] Exemplarily, step S61 generates a corresponding information feedback template according to the target query data, including: selecting a BERT model or a GPT model as an initial model, inputting the target query data into the initial model, and obtaining the information feedback template output by the initial model.
[0108] The Chinese name of the BERT model is: Bidirectional Transformer Model, and the English name of the BERT model is: Bidirectional Encoder Representations from Transformers. The BERT model is a bidirectional model that can be trained with contextual information, which has better language understanding and generation capabilities compared to traditional unidirectional models (such as
[0109] Among them, the BERT model not only focuses on the previous context information of the current word, but also considers the subsequent context information, so that it can more accurately capture the overall structure and semantic relationship of the language.
[0110] Among them, the Chinese name of the GPT model is: Generative Pre-trained Transformer model, and the English name of the GPT model is: Generative Pre-trained Transformer.
[0111] Among them, the GPT model has generative capabilities, that is, it can generate new text content that conforms to grammatical rules and semantic logic based on the input text or instructions. This generative ability makes the GPT model have broad application prospects in the field of natural language processing, such as text generation, language translation, dialogue systems, etc.
[0112] After the information feedback template is generated, the data in the target query data that matches the current valid data is decrypted to obtain decrypted data, and the decrypted data is desensitized to obtain displayable data, and the displayable data is set in the first display area.
[0113] It should be noted that in order to ensure the security of data, the data stored in the transaction database is encrypted data. In the process of generating query feedback information, only the data that matches the current valid data is decrypted to avoid decryption of data that does not match the valid data in the transaction database, increasing the possibility of leakage of this data, so as to improve the security of information storage.
[0114] In some embodiments, step S6 further includes step S64:
[0115] Step S64: determining a second display area on the information feedback template corresponding to the data in the target query data that does not match the current valid data, and setting the encrypted identifier in the second display area.
[0116] Specifically, based on the implementation mode of the present application, the query feedback information output to the client includes not only the displayable data in the first display area, but also the encrypted identifier in the second display area, so that when the client views the query feedback information, it can clearly see the displayable part of the queried data, and can also see the confidential, non-displayable part, so as to obtain more detailed query feedback.
[0117] In some implementations, decrypted data is anonymized to obtain displayable data, including:
[0118] Comparing the text data corresponding to the decrypted data with the sensitive data to identify the first sensitive information in the decrypted data, hiding the first sensitive information or replacing the first sensitive information with preset characters to obtain desensitized text data;
[0119] Identify the sensitive information area in the image data corresponding to the decrypted data, blur the sensitive information area or cover the sensitive information area with the background color of the sensitive information area to obtain desensitized image data;
[0120] Generate displayable data based on desensitized text data and desensitized image data.
[0121] It should be noted that the decrypted data includes text data and image data. Specifically, the text information in the decrypted data is compared with the preset sensitive data to determine whether the decrypted data includes the first sensitive information. When the first sensitive information is stored in the decrypted data, the first sensitive information is desensitized, for example, the first sensitive information is hidden, or the first sensitive information is replaced with preset characters to obtain desensitized metadata.
[0122] Similarly, the text information in the image data is extracted and compared with the sensitive data to identify whether the area corresponding to the text information is a sensitive information area. When a sensitive information area exists, the sensitive information area is desensitized, for example, by blurring the sensitive information area or covering the sensitive information area with the background color of the decrypted data.
[0123] Therefore, the implementation method of the present application further reduces the possibility of leakage of highly confidential data in the query feedback information, thereby improving the security of information storage.
[0124] In summary, the present application provides a transaction data management method, apparatus, computer equipment and storage medium, the method comprising: receiving a data authorization instruction, and determining the authorization information of at least one publicly available transaction data in a transaction database and the effective time information corresponding to each authorization information according to the data authorization instruction; receiving a transaction data query request input by a client, and parsing the transaction data query request to obtain a query statement; determining an expansion word corresponding to the transaction data query request based on the query statement, and inputting the expansion word and the query statement into a preset target language model to generate target query information; retrieving target query data from the transaction database according to the target query information; filtering out current valid data from publicly available transaction data according to the input time and effective time information of the transaction data query request; generating query feedback information from data in the target query data that matches the current valid data, and outputting the query feedback information to the client. The beneficial effects of the present application lie in two aspects. On the one hand, query operations are performed based on the query statements and expansion words corresponding to the transaction data query request, which effectively improves the comprehensiveness and intelligence of the transaction data query scope; on the other hand, the current valid data is screened out based on the input time and effective time information of the transaction data query request, and then the current valid data is fed back to the client. The corresponding public time can be set for different transaction data in a personalized manner, while avoiding outdated authorization information from authorizing the transaction data to be disclosed, thereby improving the confidentiality and security of transaction information management.
[0125] See also Figure 5 , Figure 5 It is a structural diagram of a transaction data management device in one embodiment of the present invention.
[0126] like Figure 5 As shown, the transaction data management device 700 includes an extraction module 701, an acquisition module 702, a first determination module 703, an input module 704, a second determination module 705, and a query module 706. The functional modules are described in detail as follows:
[0127] The authorization receiving module 701 is used to receive a data authorization instruction, and determine the authorization information of at least one publicly available transaction data in the transaction database and the validity time information corresponding to each authorization information according to the data authorization instruction;
[0128] The query receiving module 702 is used to receive a transaction data query request input by a client, and parse the transaction data query request to obtain a query statement;
[0129] A query expansion module 703 is used to determine an expansion word corresponding to the transaction data query request based on the query statement, and input the expansion word and the query statement into a preset target language model to generate target query information;
[0130] The data query module 704 is used to retrieve the target query data from the transaction database according to the target query information;
[0131] The data screening module 705 is used to screen out the current valid data from the publicly available transaction data according to the input time and valid time information of the transaction data query request;
[0132] The feedback output module 706 is used to generate query feedback information according to the data in the target query data that matches the current valid data, and output the query feedback information to the client.
[0133] In some implementations, the query expansion module 703 determines the expansion words corresponding to the transaction data query request based on the query statement, including:
[0134] Get the keywords in the query statement;
[0135] Select synonyms or near synonyms of the query statement as expansion words corresponding to the transaction data query request.
[0136] In some implementations, the query expansion module 703 inputs the expansion words and the query statement into a preset target language model to generate target query information, including:
[0137] Obtain the model file and load the preset target language model from the model file;
[0138] Calling the input function of the target language model, and inputting the query sentence and the expansion words into the target language model through the input function;
[0139] Input the query sentence and the expanded word into the feature extractor of the target language model, extract features of the query sentence and the expanded word through the feature extractor, and obtain the feature vector of the query sentence and the feature vector of the expanded word;
[0140] The feature vector of the query statement and the feature vector of the expanded word are concatenated to generate the target feature vector of the current transaction data, the target feature vector is input into the generator of the target language model, and the target feature vector is processed by the generator to obtain the target query information.
[0141] In some implementations, the data query module 704 retrieves target query data from the transaction database according to the target query information, including:
[0142] Get the preset query conditions and submit the query conditions to the query interface;
[0143] The query operation corresponding to the query condition is executed on the transaction database through the query interface to obtain the target query data.
[0144] In some implementations, the data in the transaction database is encrypted data, and the feedback output module 706 generates query feedback information according to the data in the target query data that matches the current valid data, including:
[0145] Generate a corresponding information feedback template according to the target query data;
[0146] Decrypt the data in the target query data that matches the current valid data to obtain decrypted data, and perform desensitization processing on the decrypted data to obtain displayable data;
[0147] A first display area corresponding to the displayable data on the information feedback template is determined, and the displayable data is set in the first display area to obtain query feedback information.
[0148] In some implementations, the feedback output module 706 generates query feedback information based on the data in the target query data that matches the current valid data, and further includes:
[0149] Determine a second display area on the information feedback template corresponding to the data in the target query data that does not match the current valid data, and set the encryption identifier in the second display area.
[0150] In some implementations, the feedback output module 706 performs desensitization processing on the decrypted data to obtain displayable data, including:
[0151] Comparing the text data corresponding to the decrypted data with the sensitive data to identify the first sensitive information in the decrypted data, hiding the first sensitive information or replacing the first sensitive information with preset characters to obtain desensitized text data;
[0152] Identify the sensitive information area in the image data corresponding to the decrypted data, blur the sensitive information area or cover the sensitive information area with the background color of the sensitive information area to obtain desensitized image data;
[0153] Generate displayable data based on desensitized text data and desensitized image data.
[0154] Each module in the transaction data management device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.
[0155] See also Figure 6 , Figure 6 A schematic block diagram of the structure of a computer device provided in an embodiment of the present application.
[0156] like Figure 6As shown, the computer device 800 includes a processor 801 and a memory 802 , and the processor 801 and the memory 802 are connected via a bus 803 , such as an I2C (Inter-integrated Circuit) bus.
[0157] Specifically, the processor 801 is used to provide computing and control capabilities to support the operation of the entire computer device. The processor 801 can be a central processing unit (CPU), and the processor 801 can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.
[0158] Specifically, the memory 802 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.
[0159] Those skilled in the art will understand that Figure 6 The structure shown in the figure is merely a block diagram of a partial structure related to the embodiment of the present application, and does not constitute a limitation on the computer device to which the embodiment of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
[0160] The processor is used to run a computer program stored in a memory, and implement any one of the conflict detection graph construction methods provided in the embodiments of the present application when executing the computer program.
[0161] In one embodiment, the processor 801 is used to run a computer program stored in the memory 802, and implement the following steps when executing the computer program:
[0162] Receive a data authorization instruction, and determine authorization information of at least one publicly available transaction data in a transaction database and validity time information corresponding to each authorization information according to the data authorization instruction;
[0163] Receive a transaction data query request input by a client, and parse the transaction data query request to obtain a query statement;
[0164] Determine an expansion word corresponding to the transaction data query request based on the query statement, and input the expansion word and the query statement into a preset target language model to generate target query information;
[0165] Retrieving target query data from a transaction database according to target query information;
[0166] Filter out currently valid data from publicly available transaction data based on the input time and validity time information of the transaction data query request;
[0167] Generate query feedback information based on the data in the target query data that matches the current valid data, and output the query feedback information to the client.
[0168] In some implementations, the processor 801 determines, based on the query statement, an expansion word corresponding to the transaction data query request, including:
[0169] Get the keywords in the query statement;
[0170] Select synonyms or near synonyms of the query statement as expansion words corresponding to the transaction data query request.
[0171] In some implementations, the processor 801 inputs the expansion word and the query sentence into a preset target language model to generate target query information, including:
[0172] Obtain the model file and load the preset target language model from the model file;
[0173] Calling the input function of the target language model, and inputting the query sentence and the expansion words into the target language model through the input function;
[0174] Input the query sentence and the expanded word into the feature extractor of the target language model, extract features of the query sentence and the expanded word through the feature extractor, and obtain the feature vector of the query sentence and the feature vector of the expanded word;
[0175] The feature vector of the query statement and the feature vector of the expanded word are concatenated to generate the target feature vector of the current transaction data, the target feature vector is input into the generator of the target language model, and the target feature vector is processed by the generator to obtain the target query information.
[0176] In some implementations, the processor 801 retrieves target query data from the transaction database according to the target query information, including:
[0177] Get the preset query conditions and submit the query conditions to the query interface;
[0178] The query operation corresponding to the query condition is executed on the transaction database through the query interface to obtain the target query data.
[0179] In some implementations, the data in the transaction database is encrypted data, and the processor 801 generates query feedback information according to the data in the target query data that matches the current valid data, including:
[0180] Generate a corresponding information feedback template according to the target query data;
[0181] Decrypt the data in the target query data that matches the current valid data to obtain decrypted data, and perform desensitization processing on the decrypted data to obtain displayable data;
[0182] A first display area corresponding to the displayable data on the information feedback template is determined, and the displayable data is set in the first display area to obtain query feedback information.
[0183] In some implementations, the processor 801 retrieves the target query data from the transaction database according to the target query information, further comprising:
[0184] Determine a second display area on the information feedback template corresponding to the data in the target query data that does not match the current valid data, and set the encryption identifier in the second display area.
[0185] In some implementations, the processor 801 performs desensitization processing on the decrypted data to obtain displayable data, including:
[0186] Comparing the text data corresponding to the decrypted data with the sensitive data to identify the first sensitive information in the decrypted data, hiding the first sensitive information or replacing the first sensitive information with preset characters to obtain desensitized text data;
[0187] Identify the sensitive information area in the image data corresponding to the decrypted data, blur the sensitive information area or cover the sensitive information area with the background color of the decrypted data to obtain desensitized image data;
[0188] Generate displayable data based on desensitized text data and desensitized image data.
[0189] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the computer device described above can refer to the corresponding process in the aforementioned animal identification method embodiment, and will not be repeated here.
[0190] The embodiment of the present application also provides a storage medium, which stores a computer program. The computer program can be executed by one or more processors to implement the steps of any conflict detection map construction method provided in the embodiment description of the present application.
[0191] The storage medium may be an internal storage unit of the computer device described in the above embodiment, such as a hard disk or memory of the computer device. The storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device.
[0192] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware embodiment, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.
[0193] It should be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system 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 system. In the absence of further restrictions, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system including the element.
[0194] The serial numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments. The above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.
Claims
1. A transaction data management method, characterized in that: include: Receive a data authorization instruction, and determine authorization information of at least one publicly available transaction data in a transaction database and validity time information corresponding to each authorization information according to the data authorization instruction; Receiving a transaction data query request input by a client, and parsing the transaction data query request to obtain a query statement; Determining an expansion word corresponding to the transaction data query request based on the query statement, and inputting the expansion word and the query statement into a preset target language model to generate target query information; Retrieving target query data from the transaction database according to the target query information; Filtering out currently valid data from the publicly available transaction data according to the input time of the transaction data query request and the validity time information; Generate query feedback information according to the data in the target query data that matches the current valid data, and output the query feedback information to the client.
2. The method according to claim 1, characterized in that The determining, based on the query statement, an expansion word corresponding to the transaction data query request includes: Obtaining keywords in the query statement; A synonym or near synonym of the query statement is selected as an expanded word corresponding to the transaction data query request.
3. The method according to claim 2, characterized in that The step of inputting the expansion word and the query sentence into a preset target language model to generate target query information includes: Obtain a model file, and load a preset target language model from the model file; Calling an input function of the target language model, and inputting the query sentence and the expansion word into the target language model through the input function; Inputting the query sentence and the expanded word into the feature extractor of the target language model, performing feature extraction on the query sentence and the expanded word by the feature extractor, and obtaining a feature vector of the query sentence and a feature vector of the expanded word; The feature vector of the query statement and the feature vector of the expanded word are concatenated to generate a target feature vector of the current transaction data, the target feature vector is input into the generator of the target language model, and the target feature vector is processed by the generator to obtain the target query information.
4. The method according to claim 1, characterized in that: The step of retrieving target query data from the transaction database according to the target query information includes: Obtaining preset query conditions and submitting the query conditions to the query interface; The query operation corresponding to the query condition is performed on the transaction database through the query interface to obtain the target query data.
5. The method according to any one of claims 1 to 4, characterized in that: The data in the transaction database is encrypted data, and the query feedback information is generated according to the data in the target query data that matches the current valid data, including: Generate a corresponding information feedback template according to the target query data; Decrypting the data in the target query data that matches the current valid data to obtain decrypted data, and performing desensitization processing on the decrypted data to obtain displayable data; A first display area corresponding to the displayable data on the information feedback template is determined, and the displayable data is set in the first display area to obtain the query feedback information.
6. The method according to claim 5, characterized in that The method further comprises: Determine a second display area on the information feedback template corresponding to the data in the target query data that does not match the current valid data, and set the encryption identifier in the second display area.
7. The method according to claim 5, characterized in that The desensitizing the decrypted data to obtain displayable data includes: Comparing the text data corresponding to the decrypted data with the sensitive data to identify first sensitive information in the decrypted data, hiding the first sensitive information or replacing the first sensitive information with preset characters to obtain desensitized text data; Identify the sensitive information area in the image data corresponding to the decrypted data, perform blurring processing on the sensitive information area or cover the sensitive information area with the background color of the sensitive information area to obtain desensitized image data; The displayable data is generated according to the desensitized text data and the desensitized image data.
8. A transaction data management device, characterized in that: include: An authorization receiving module, used to receive a data authorization instruction, and determine authorization information of at least one publicly available transaction data in a transaction database and validity time information corresponding to each authorization information according to the data authorization instruction; A query receiving module, used to receive a transaction data query request input by a client, and parse the transaction data query request to obtain a query statement; A query expansion module, configured to determine an expansion word corresponding to the transaction data query request based on the query statement, and input the expansion word and the query statement into a preset target language model to generate target query information; A data query module, used for retrieving target query data from the transaction database according to the target query information; A data screening module, used for screening out currently valid data from the publicly available transaction data according to the input time of the transaction data query request and the validity time information; The feedback output module is used to generate query feedback information according to the data in the target query data that matches the current valid data, and output the query feedback information to the client.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the transaction data management method according to any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the transaction data management method according to any one of claims 1 to 7 are implemented.