Credit data storage method, device, equipment and medium

Abstract

The embodiment of the present invention discloses a credit data storage method and device, equipment and storage medium, the method comprising: obtaining a data storage request, the data storage request carrying the credit data to be stored; storing the credit data in a preset first storage interval; classifying and integrating the credit data in the first storage interval to obtain a metadata index chain of the data storage request; generating a user identifier based on the generation time and file format of the metadata index chain, and storing the user identifier in a preset second storage interval. On the one hand, by classifying and storing the credit data, establishing a metadata index of the first storage interval to obtain a metadata index chain, the efficiency of credit data storage and search is improved; on the other hand, by storing the user identifier generated by the generation time and file format of the metadata index chain in the second storage interval, double encryption of the credit data is achieved, and the security of data storage is improved.

Description

Technical Field

[0001] The present invention relates to the field of data storage technology, and in particular to a credit information data storage method and device, equipment and medium. Background Art

[0002] The data used by the big data credit reporting platform comes from diverse sources. With the rapid development of mobile Internet, credit reporting data needs to be professionally stored due to its wide coverage and complete data in order to facilitate later scheduling and updating.

[0003] Credit data covers all regulatory information from government departments, bank credit information, industry association evaluation information, media evaluation information, enterprise operation and management information, and market feedback information, etc. Its content includes various data such as text and layout files.

[0004] In this case, traditional structured data tables can no longer meet the storage requirements of data diversity and have low storage efficiency. Summary of the invention

[0005] Based on this, it is necessary to propose credit data storage methods, devices, equipment and media to address the above problems in order to achieve efficient data storage.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides a credit information data storage method, the method comprising:

[0007] Obtaining a data storage request, wherein the data storage request carries credit information data to be stored;

[0008] Storing the credit investigation data in a preset first storage interval;

[0009] Classifying and integrating the credit investigation data in the first storage interval to obtain a metadata index chain of the data storage request;

[0010] A user identifier is generated based on the generation time and file format of the metadata index chain, and the user identifier is stored in a preset second storage interval.

[0011] Furthermore, the data storage request also carries the file format of the credit data, and the step of storing the credit data in a preset first storage interval specifically includes:

[0012] Inputting the credit information data and the file format of the credit information data into a preset text recognition model to obtain a text content result output by the text recognition model;

[0013] Storing the text content result in the first storage interval;

[0014] Then, the step of classifying and integrating the credit investigation data in the first storage interval to obtain the metadata index chain of the data storage request specifically includes:

[0015] The text content results are classified and integrated within the first storage interval to obtain a metadata index chain of the data storage request.

[0016] Furthermore, the first storage interval contains a preset number of related credit data models, and the related credit data models represent the types of credit data;

[0017] Then, the text content results are classified and integrated in the first storage interval to obtain the metadata index chain of the data storage request, specifically including:

[0018] Input the text content results into various relevant credit investigation data models in turn to perform data type matching, and determine the classification results of the text content results;

[0019] Integrate according to the classification results to obtain a metadata index chain of the data storage request.

[0020] Furthermore, the step of sequentially inputting the text content results into various relevant credit investigation data models to match data types and determine the classification results of the text content results specifically includes:

[0021] If the text content result successfully matches the first relevant credit data model, the text content result is filled into the first relevant credit data model, and the type of credit data corresponding to the first relevant credit data model is used as the classification result, and the first relevant credit data model is any one of all relevant credit data models;

[0022] If the text content result fails to match all relevant credit data models, a second relevant credit data model is generated based on the text content result, the text content result is filled into the second relevant credit data model, and the credit data type corresponding to the second relevant credit data model is used as the classification result.

[0023] Furthermore, the data storage request also carries the collection time of the credit data, and before storing the credit data in the preset first storage interval, it also includes:

[0024] Obtaining a target collection number of days, where the target collection number of days is the number of days between the collection time of the credit information data and the time when the data storage request is received;

[0025] When the target collection days is not greater than the preset effective collection days, the step of storing the credit investigation data into the preset first storage interval is continued;

[0026] When the target collection fill data is greater than the effective collection days, the credit data will be listed as invalid credit data and deleted.

[0027] Furthermore, the method is applied to an electronic device, and before obtaining the data storage request, it also includes:

[0028] receiving a connection request sent by an application system through a preset network, wherein the application system is used to collect credit data, generate a data storage request from the credit data and send it to the electronic device, and the connection request is used to request to establish a connection with the electronic device;

[0029] After receiving the connection request, generating a detection instruction and sending it to the application system, the detection instruction is used to detect whether the current account of the application system is a target account, and the target account is an account for logging into the electronic device;

[0030] When the current account of the application system is the target account, a connection is established with the application system, and the operation of obtaining the data storage request is continued;

[0031] When the current account of the application system is not the target account, no connection is established with the application system and the connection request is cleared.

[0032] Furthermore, the preset network is a combination of one or more of 3G network, 4G network, 5G network, and WIFI network.

[0033] To achieve the above-mentioned purpose, the second aspect of the present application provides a credit information data storage device, the device comprising: a data acquisition unit, a data identification unit and a data storage unit:

[0034] The data acquisition unit is used to acquire a data storage request, wherein the data storage request carries the credit information data to be stored;

[0035] The data identification unit is used to store the credit data into a preset first storage interval;

[0036] Classifying and integrating the credit investigation data in the first storage interval to obtain a metadata index chain of the data storage request;

[0037] The data storage unit is used to generate a user identifier based on the generation time and file format of the metadata index chain, and store the user identifier in a preset second storage interval.

[0038] To achieve the above-mentioned purpose, the third aspect of the present application provides a computer-readable storage medium storing a computer program, characterized in that when the computer program is executed by a processor, the processor executes the steps of the method described in the first aspect.

[0039] To achieve the above-mentioned purpose, the fourth aspect of the present application provides a computer device, including a memory and a processor, characterized in that the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method described in the first aspect.

[0040] The embodiments of the present invention have the following beneficial effects:

[0041] The present invention proposes a credit data storage method, which includes: obtaining a data storage request, the data storage request carries the credit data to be stored; storing the credit data in a preset first storage interval; classifying and integrating the credit data in the first storage interval to obtain a metadata index chain of the data storage request; generating a user identifier based on the generation time and file format of the metadata index chain, and storing the user identifier in a preset second storage interval. On the one hand, by classifying and storing the credit data in the first storage interval and establishing a metadata index for the first storage interval, the efficiency of credit data storage and search is improved; on the other hand, by storing the user identifier generated by the generation time and file format of the metadata index chain in the second storage interval, double encryption of the credit data is achieved, thereby improving the security of data storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] in:

[0044] Figure 1 A flow chart of a credit information data storage method according to an embodiment of the present invention;

[0045] Figure 2 It is a structural block diagram of a credit information data storage device according to an embodiment of the present invention;

[0046] Figure 3 1 is a diagram showing the internal structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] 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 only 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.

[0048] Due to the diverse sources of credit data, it covers a variety of information, such as regulatory information from all government functional departments: basic corporate identity information, administrative licenses, certification information, administrative rewards and penalties, product quality monitoring information, court judgment information and intellectual property information, etc.; bank credit information: People's Bank of China credit evaluation information, commercial bank credit evaluation information, small loan company credit evaluation information and private lending credit evaluation information, etc.; industry association evaluation information: evaluation information from various industry associations, water, electricity, gas, telecommunications, transportation and other social public utilities evaluation information; media evaluation information; enterprise operation and management information; market feedback information: feedback information from consumers, counterparties, partners, employees, etc.; the content, format and type of these information data are diverse, resulting in the inability of traditional structured data tables to meet the storage requirements of these diverse data.

[0049] Based on this, the embodiment of the present invention proposes a credit data storage method to achieve the effects of efficient storage, secure storage and convenient retrieval of credit data. Figure 1 , Figure 1 1 is a flow chart of a credit information data storage method according to an embodiment of the present invention, which specifically includes:

[0050] Step 110: Obtain a data storage request, which carries the credit data to be stored.

[0051] The data storage request may be generated and issued by an application system. Specifically, the application system collects user-related personal information of the login account as credit data, generates and issues a data storage request based on the credit data. Therefore, the data storage request may carry the credit data to be stored so as to store the user's credit data.

[0052] Step 130: store the credit investigation data into a preset first storage interval.

[0053] After receiving the data storage request, the credit information data in the data storage request is stored in a first storage interval, which is pre-set and is used to classify and store the credit information data.

[0054] Step 150: classify and integrate the credit investigation data in the first storage interval to obtain a metadata index chain of the data storage request.

[0055] The embodiment of the present invention establishes a metadata index for the first storage interval to achieve fast search of the classified stored credit data. Optionally, it can be a keyword index, a letter index, a date index, etc., to search the classified stored credit data through keywords, letters, dates and other information. Specifically, the credit data stored in the first storage interval is classified to obtain a classification result. According to the classification results of the credit data, it is integrated to obtain a metadata index chain for the data storage request. By classifying and storing and establishing an index, fast search of credit data can be achieved, and the management of credit data can be facilitated.

[0056] Step 170: Generate a user identifier based on the generation time and file format of the metadata index chain, and store the user identifier in a preset second storage interval.

[0057] Specifically, relevant parameters of the metadata index chain are obtained, the relevant parameters at least including the generation time and file format of the metadata index chain, the relevant parameters are collected and sorted to generate a user identifier, and the user identifier is stored in a preset second storage interval.

[0058] Among them, the generation time of the metadata index chain is used to record the time interval for generating the metadata index chain, which can be the time interval from the time point of collecting the credit data to the time point of generating the metadata index chain; it can also be the time interval from the time point of receiving the data storage request to the time point of generating the metadata index chain.

[0059] The file formats of metadata index chains are divided into single index files (.IDX) and compound index files (.CDX). The file formats facilitate the quick extraction of metadata index chains of the same file format and the storage of the same type of file formats.

[0060] The embodiment of the present invention classifies and stores the credit data in the first storage interval, and establishes the metadata index of the first storage interval to obtain the integrated metadata index chain of the user data storage request, thereby improving the efficiency of credit data storage and search. In addition, the user identifier is stored in the second storage interval according to the user identifier generated by the metadata index chain, so as to realize the double encryption of the credit data sent by the application system, protect the security of the credit data, and when the credit data is retrieved, it can be quickly searched in the storage interval through the index, so that the credit data can be checked in time, which improves the convenience of the administrator when retrieving information.

[0061] In an embodiment of the present invention, the credit information data storage method is applied to an electronic device, so before obtaining the data storage request in step 110, the method further includes:

[0062] Step 101, receiving a connection request sent by an application system through a preset network, wherein the application system is used to collect credit data, generate a data storage request from the credit data and send it to an electronic device, and the connection request is used to request to establish a connection with the electronic device.

[0063] Step 102, after receiving the connection request, a detection instruction is generated and sent to the application system, the detection instruction is used to detect whether the current account of the application system is the target account, and the target account is the account for logging into the electronic device.

[0064] Step 103, when the current account of the application system is the target account, a connection is established with the application system and the operation of obtaining the data storage request is continued; when the current account of the application system is not the target account, a connection is not established with the application system and the connection request is cleared.

[0065] In an embodiment of the present invention, a data storage request is sent by an application system. Specifically, after the application system logs into an account, it collects the user's relevant personal data in the account as credit data. After collecting the credit data, it generates a data storage request based on the credit data and other parameters, and sends the data storage request to the electronic device. Other parameters may include collection time and file format, etc.

[0066] Before the electronic device receives the data storage request sent by the application system, it should first establish a connection with the application system. Specifically, before the application system needs to send the data storage request, it sends a connection request to the electronic device, and the connection request is used to request to establish a connection with the electronic device.

[0067] The electronic device receives a connection request sent by the application system through a preset network. In a feasible embodiment of the present invention, the preset network can be a combination of one or more of the 3G network, 4G network, 5G network, and WIFI network. The connection between the application system and the electronic device is achieved through a wireless connection, which facilitates the application system to transmit the collected credit data to the electronic device.

[0068] After the electronic device receives the connection request, it generates and sends a detection instruction to the application system, which is used to indicate whether the current account logged in by the detection application system is consistent with the account logged in by the electronic device. For example, if the account logged in by the electronic device is the target account, the detection instruction is used to indicate whether the current account logged in by the detection application system is the target account. After the detection is completed, the application system sends the detection result to the electronic device.

[0069] When the electronic device receives the detection result, it determines whether to establish a connection with the application system based on the detection result. Specifically, when the detection result shows that the current account of the application system is the target account, the electronic device establishes a connection with the application system and receives a data storage request sent by the application system; when the detection result shows that the current account of the application system is not the target account, the electronic device does not establish a connection with the application system and clears the connection request.

[0070] By determining the consistency of the login account when establishing a connection between the electronic device and the application system, the accuracy of data storage can be guaranteed and anomalies in the stored user credit data can be avoided as much as possible.

[0071] In the embodiment of the present invention, the data storage request also carries the file format of the credit data, and step 130, storing the credit data into a preset first storage interval, specifically includes:

[0072] Step 131, input the credit information data and the file format of the credit information data into a preset text recognition model to obtain the text content result output by the text recognition model.

[0073] Step 132: Store the text content result into the first storage interval.

[0074] Due to the diverse sources of credit data, the file formats are diverse. For example, JPEG files among image files are only used to store static images, GIF files can store both static images and simple animations, and Quicktime files can store a variety of different media types. TXT files among text files generally only store simple unformatted ASCII or Unicode text, HTML files can store formatted text, and PDF files can store rich content, illustrated text, etc.

[0075] In order to facilitate data storage and classification, the embodiment of the present invention needs to format the credit data before storing it. Specifically, the credit data and the file format of the credit data are input into a preset file recognition model, and the file recognition model can convert the credit data and the file format of the credit data into a preset standard format, which can be a binary language. The conversion result of the file recognition model is output as a text content result, and the text content result is stored in the first storage interval.

[0076] Based on this, step 150, classifying and integrating the credit data in the first storage interval to obtain a metadata index chain of the data storage request, specifically includes: classifying and integrating the text content results in the first storage interval to obtain a metadata index chain of the data storage request.

[0077] In a feasible embodiment of the present invention, the first storage interval contains a preset number of related credit data models, and the related credit data model represents the type of credit data, that is, one related credit data model corresponds to one or more types of credit data; then step 150, classifying and integrating the text content results in the first storage interval to obtain a metadata index chain of the data storage request, specifically includes:

[0078] Step 151, input the text content results into each relevant credit investigation data model in turn to perform data type matching, and determine the classification results of the text content results.

[0079] Step 152: Integrate the classification results to obtain a metadata index chain for the data storage request.

[0080] All relevant credit data models are identified in the electronic device, and after identification, all relevant credit data models are sorted according to a preset sorting rule, which can be understood as arranging the credit data classification items so that the text content results are sequentially input into each relevant credit data model according to the sorting for matching, and a matching result is obtained, and further a classification result of the text content result is obtained, that is, a classification result of the credit data. By establishing a metadata index based on the relevant credit data model in the first storage interval, an integrated metadata index chain can be obtained according to the classification result.

[0081] Specifically, step 151, the text content results are sequentially input into various relevant credit investigation data models to perform data type matching, and the classification results of the text content results are determined, which specifically includes:

[0082] A. If the text content result successfully matches the first relevant credit data model, the text content result is filled into the first relevant credit data model, and the credit data type corresponding to the first relevant credit data model is used as the classification result. The first relevant credit data model is any one of all relevant credit data models.

[0083] B. If the text content result fails to match all relevant credit data models, a second relevant credit data model is generated based on the text content result, and the text content result is filled into the second relevant credit data model, and the credit data type corresponding to the second relevant credit data model is used as the classification result.

[0084] In the embodiment of the present invention, the text content results are matched with the relevant credit data model in sequence. The text content results are input into the relevant credit data model, and the file format, keyword information and other data are compared. If the comparison results are consistent, it is confirmed that the text content results are successfully matched with the relevant credit data model. The successfully matched relevant credit data model is referred to as the first relevant credit data model. Successful matching indicates that the classification of the text content results should be the credit data type corresponding to the first relevant credit data model. After successful matching, the text content results can be filled into the first relevant credit data model to achieve classified storage.

[0085] If the match fails, the next relevant credit data model is matched; if the match fails with all relevant credit data models, a new relevant credit data model is generated according to the text content result, and the corresponding credit data type is obtained according to the new relevant credit data model, which is referred to as the second relevant credit data model, and the second relevant credit data model is arranged at the end, and the text content result is filled into the second relevant credit data model to achieve classified storage. In addition, in the subsequent credit data storage process, the second relevant credit data model can also be matched with the credit data to be stored to achieve accurate classification of credit data.

[0086] After obtaining the classification results of the text content results, the classification results of the user can be integrated to obtain a metadata index chain.

[0087] In the embodiment of the present invention, the data storage request also carries the collection time of the credit data. In step 130, before storing the credit data in the preset first storage interval, the following steps are also included:

[0088] Step 121, obtaining the target collection days, where the target collection days are the number of days between the collection time of the credit information data and the time when the data storage request is received.

[0089] Step 122, when the target collection days are not greater than the preset effective collection days, continue to execute the step of storing the credit data into the preset first storage interval; when the target collection fill data is greater than the effective collection days, the credit data is listed as invalid credit data and deleted.

[0090] By obtaining the target collection time and determining whether the target collection time is valid, the timeliness of the credit data can be guaranteed, making the stored credit data more convincing.

[0091] In the embodiment of the present invention, a credit information data storage device is also provided. Figure 2 , Figure 2This is a structural block diagram of a credit information data storage device according to an embodiment of the present invention. The device includes: a data acquisition unit 201, a data identification unit 202 and a data storage unit 203.

[0092] The data acquisition unit 201 is used to acquire a data storage request, where the data storage request carries the credit information data to be stored.

[0093] The data identification unit 202 is used to store the credit investigation data into a preset first storage interval.

[0094] The credit investigation data is classified and integrated in the first storage interval to obtain a metadata index chain of the data storage request.

[0095] The data storage unit 203 is used to generate a user identifier based on the generation time and file format of the metadata index chain, and store the user identifier in a preset second storage interval.

[0096] The credit data storage device of the embodiment of the present invention obtains a data storage request, which carries the credit data to be stored; stores the credit data in a preset first storage interval; classifies and integrates the credit data in the first storage interval to obtain a metadata index chain of the data storage request; generates a user identifier based on the generation time and file format of the metadata index chain, and stores the user identifier in a preset second storage interval. On the one hand, by classifying and storing the credit data and establishing a metadata index of the first storage interval to generate a metadata index chain, the efficiency of credit data storage and search is improved; on the other hand, by storing the user identifier generated by the generation time and file format of the metadata index chain in the second storage interval, double encryption of the credit data is achieved, thereby improving the security of data storage.

[0097] Figure 3 FIG. 1 shows an internal structure diagram of a computer device in one embodiment of the present invention. The computer device may be a terminal or a system. Figure 3 As shown, the computer device includes a processor, a memory and a network interface connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor may implement each step in the above method embodiment. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor may implement each step in the above method embodiment. Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution 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.

[0098] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes each step in the above method embodiment.

[0099] In one embodiment, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the processor executes each step in the above method embodiment.

[0100] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0101] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0102] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A credit information data storage method, characterized in that: The method comprises: Obtaining a data storage request, wherein the data storage request carries credit information data to be stored; Storing the credit investigation data in a preset first storage interval and establishing a metadata index for the first storage interval; Classifying and integrating the credit investigation data in the first storage interval to obtain a metadata index chain of the data storage request; Generate a user identifier based on the generation time and file format of the metadata index chain, and store the user identifier in a preset second storage interval; The data storage request also carries the file format of the credit data, and the step of storing the credit data in a preset first storage interval specifically includes: Inputting the credit information data and the file format of the credit information data into a preset text recognition model to obtain a text content result output by the text recognition model; Storing the text content result in the first storage interval; Then, the step of classifying and integrating the credit investigation data in the first storage interval to obtain the metadata index chain of the data storage request specifically includes: Classifying and integrating the text content results in the first storage interval to obtain a metadata index chain of the data storage request; The first storage interval contains a preset number of relevant credit data models, and the relevant credit data models represent the types of credit data; Then, the text content results are classified and integrated in the first storage interval to obtain the metadata index chain of the data storage request, specifically including: Input the text content results into various relevant credit investigation data models in turn to perform data type matching, and determine the classification results of the text content results; Integrate according to the classification results to obtain a metadata index chain of the data storage request; The step of sequentially inputting the text content results into various relevant credit investigation data models to match data types and determine the classification results of the text content results specifically includes: If the text content result successfully matches the first relevant credit data model, the text content result is filled into the first relevant credit data model, and the type of credit data corresponding to the first relevant credit data model is used as the classification result, and the first relevant credit data model is any one of all relevant credit data models; If the text content result fails to match all relevant credit data models, a second relevant credit data model is generated based on the text content result, the text content result is filled into the second relevant credit data model, and the credit data type corresponding to the second relevant credit data model is used as the classification result.

2. The method according to claim 1, characterized in that The data storage request also carries the collection time of the credit data, and before storing the credit data in the preset first storage interval, it also includes: Obtaining a target collection number of days, where the target collection number of days is the number of days between the collection time of the credit information data and the time when the data storage request is received; When the target collection days is not greater than the preset effective collection days, the step of storing the credit investigation data into the preset first storage interval is continued; When the target collection days are greater than the effective collection days, the credit data will be listed as invalid credit data and deleted.

3. The method according to claim 1, characterized in that The method is applied to an electronic device, and before obtaining a data storage request, further includes: receiving a connection request sent by an application system through a preset network, wherein the application system is used to collect credit data, generate a data storage request from the credit data and send it to the electronic device, and the connection request is used to request to establish a connection with the electronic device; After receiving the connection request, generating a detection instruction and sending it to the application system, the detection instruction is used to detect whether the current account of the application system is a target account, and the target account is an account for logging into the electronic device; When the current account of the application system is the target account, a connection is established with the application system, and the operation of obtaining the data storage request is continued; When the current account of the application system is not the target account, no connection is established with the application system and the connection request is cleared.

4. The method according to claim 3, characterized in that The preset network is a combination of one or more of 3G network, 4G network, 5G network, and WIFI network.

5. A credit information data storage device, characterized in that: The device comprises: a data acquisition unit, a data identification unit and a data storage unit: The data acquisition unit is used to acquire a data storage request, wherein the data storage request carries the credit information data to be stored; The data identification unit is used to store the credit data into a preset first storage interval; Classifying and integrating the credit investigation data in the first storage interval to obtain a metadata index chain of the data storage request; The data storage unit is used to generate a user identifier based on the generation time and file format of the metadata index chain, and store the user identifier in a preset second storage interval; Wherein, the data storage request also carries the file format of the credit data, and the data recognition unit is further used to input the credit data and the file format of the credit data into a preset text recognition model to obtain a text content result output by the text recognition model; Storing the text content result in the first storage interval; Classifying and integrating the text content results in the first storage interval to obtain a metadata index chain of the data storage request; Wherein, the first storage interval contains a preset number of relevant credit data models, and the relevant credit data models represent the type of credit data; The data identification unit is further used to input the text content results into various related credit investigation data models in sequence to perform data type matching and determine the classification results of the text content results; Integrate according to the classification results to obtain a metadata index chain of the data storage request; The step of sequentially inputting the text content results into various relevant credit investigation data models to match data types and determine the classification results of the text content results specifically includes: If the text content result successfully matches the first relevant credit data model, the text content result is filled into the first relevant credit data model, and the type of credit data corresponding to the first relevant credit data model is used as the classification result, and the first relevant credit data model is any one of all relevant credit data models; If the text content result fails to match all relevant credit data models, a second relevant credit data model is generated based on the text content result, the text content result is filled into the second relevant credit data model, and the credit data type corresponding to the second relevant credit data model is used as the classification result.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 4.

7. A computer device comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 4.

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