Asset data processing device and processing method, electronic equipment and storage medium

Abstract

The invention discloses an asset data processing device and method, electronic equipment and a storage medium, and particularly relates to the field of data processing. Comprising a data integration and collection module, a data cleaning and processing module, a data storage and management module, a big data analysis and application module and an application expansion and service support module. And the data integration and collection module is used for acquiring transaction flow data of enterprise customers from each commercial bank and integrating the transaction flow data. According to the technical scheme of the invention, the production and operation conditions of small, medium and micro enterprises can be comprehensively known by integrating the transaction flow data of the enterprise customers of each commercial bank and combining a big data analysis technology, and more accurate credit information and risk assessment can be provided for financial institutions. The defect of information asymmetry in a traditional credit mode is overcome, and the credit support efficiency of banks for small, medium and micro enterprises is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and more specifically, to a processing device and method for asset data, an electronic device, and a storage medium. Background Art

[0002] In order to solve the financing problems of small, medium and micro enterprises, as well as problems such as information asymmetry and data fragmentation faced by financial institutions. In the current financial environment, although new technologies such as mobile Internet, big data, and blockchain have made certain progress, small, medium and micro enterprises still face problems such as difficult financing and information asymmetry. These problems have led to low support efficiency of financial institutions for small, medium and micro enterprises and it is difficult to comprehensively understand the true situation of enterprises.

[0003] Existing financing channels mainly rely on bank credit, but there is competition among banks, resulting in scattered and fragmented data among banks, making it difficult to comprehensively evaluate enterprises. In addition, existing financing models often rely on collateral, but small, medium and micro enterprises often have limited asset scales and it is difficult to provide sufficient collateral, thus increasing the financing difficulty. At the same time, traditional credit assessment methods also have problems such as insufficient information acquisition and unclear assessment criteria, making it difficult to accurately assess the credit status of enterprises.

[0004] In order to solve the above problems, a technical solution is provided now. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a processing device and method for asset data, an electronic device, and a storage medium to solve the problems raised in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A processing device for asset data, comprising:

[0008] A data integration and collection module, a data cleaning and processing module, a data storage and management module, a big data analysis and application module, and an application expansion and service support module;

[0009] The data integration and collection module is used to obtain transaction flow data of enterprise customers from each commercial bank and integrate it;

[0010] The data cleaning and processing module is used to clean the collected data and keep the format standardized and unified;

[0011] The data storage and management module is used to store and manage the processed data;

[0012] The big data analysis and application module is used to deeply mine and analyze the income and expenditure flow data of enterprises;

[0013] The application expansion and service support module is used to develop applications and services suitable for different scenarios.

[0014] In a preferred embodiment, the data integration and collection module specifically includes the following:

[0015] Obtain the transaction flow data of enterprise customers from various commercial banks, including fund in and out records, transaction objects, and transaction times;

[0016] Establish a data interface or protocol to ensure that the interface is compatible with the system architectures and data formats of different commercial banks, and select a suitable network protocol according to the requirements of data transmission security and real-time performance;

[0017] Determine the data packaging method according to the characteristics of the data interface and network protocol;

[0018] At the receiving end, unpack the data in the reverse order of the packaging rules, and perform data verification and storage;

[0019] Specify the order of data transmission. First, transmit the basic information of enterprise customers, then transmit the transaction flow data, and finally transmit the data verification information, so that the data can be processed and stored in the correct order at the receiving end.

[0020] In a preferred embodiment, the data cleaning and processing module specifically includes the following:

[0021] Clean the collected data to remove duplicate, incorrect, or incomplete information;

[0022] For the transaction flow data of enterprise customers, determine the combination of transaction serial number, transaction time, and amount as the basis for duplicate checking;

[0023] Generate a hash value for the data content of each transaction record using the hash algorithm. If the hash values of two records are the same, further compare the detailed data to determine whether they are duplicates;

[0024] When it is determined that two or more data are completely duplicate, only retain one of the records and delete the remaining duplicate records;

[0025] Standardize and unify the data format;

[0026] Identify and classify date and time data in different formats, and convert all date and time data into a standard format;

[0027] Analyze the data structure differences from different commercial banks, define a standard data structure model, and map the data from different sources to this standard structure.

[0028] In a preferred embodiment, the data storage and management module specifically includes the following:

[0029] Establish a data storage system, select a master-slave replication architecture, where the master database is responsible for handling all write operations, and the slave database synchronizes data from the master database and is responsible for handling read operations.

[0030] Perform full backups regularly and formulate a storage strategy for full backups;

[0031] On the basis of full backups, perform incremental backups every day. Incremental backups only back up the data that has changed since the last full backup or incremental backup, and determine the data to be backed up by recording the transaction logs or data modification marks of the database;

[0032] Control access to the database based on user identity authentication and authorization mechanisms, and set different access permissions for different data objects.

[0033] In a preferred embodiment, the big data analysis and application module specifically includes the following:

[0034] Calculate and analyze the basic statistical characteristics of the enterprise's income and expenditure transaction data, draw time series graphs, and display the changing trends of the enterprise's income and expenditure over time;

[0035] Extract key features from the enterprise's income and expenditure transaction data as the basis for credit scoring, establish an enterprise credit scoring model, and evaluate and predict the credit status of the enterprise based on the income and expenditure transaction data and other relevant information.

[0036] In a preferred embodiment, the application expansion and service support module specifically includes the following:

[0037] Combine the needs of financial institutions to develop application programs and services applicable to different scenarios, including credit approval, risk management, and inclusive finance.

[0038] Provide customized service support, including data report generation, risk warning, and post-loan management functions.

[0039] A method for processing asset data, which is implemented based on the asset data processing device described in any one of the above, and the method for processing asset data includes:

[0040] Obtain the transaction flow data of enterprise customers from each commercial bank and integrate it;

[0041] Clean the collected data and keep the format standardized and unified;

[0042] Store and manage the processed data;

[0043] Deeply mine and analyze the income and expenditure transaction data of enterprises;

[0044] Develop applications and services suitable for different scenarios.

[0045] An electronic device, the electronic device includes:

[0046] A memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, it implements the processing method of asset data as described above.

[0047] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the processing method of asset data as described above.

[0048] Technical effects and advantages of the processing device and method of asset data, electronic device and storage medium of the present invention:

[0049] 1. By integrating the enterprise customer transaction flow data of each commercial bank and combining big data analysis technology, a comprehensive evaluation and credit analysis of small, medium and micro enterprises are realized. Compared with the traditional credit model, this technical solution can provide a more comprehensive and real enterprise portrait, accurately grasp the production and operation status of enterprises, thereby improving the accuracy and efficiency of credit decision-making;

[0050] 2. Based on the comprehensive analysis of enterprise income and expenditure transaction data, the credit risks of small, medium and micro enterprises can be better identified and evaluated, and potential risk situations can be timely warned. Financial institutions can take corresponding risk control measures according to the credit scores and risk analysis results provided by the platform, reduce the non-performing loan rate, and reduce financial risks and losses;

[0051] 3. By providing accurate credit information and risk assessment, this technical solution can alleviate the financing difficulties of small, medium and micro enterprises, improve the opportunities for small, medium and micro enterprises to obtain financial support. When making loan decisions, financial institutions can more comprehensively understand the real situation of borrowing enterprises and provide them with more suitable financing products and services, promoting the financing development of small, medium and micro enterprises;

[0052] 4. By building a big data credit information platform for enterprise income and expenditure transactions, financial institutions can better serve small, medium and micro enterprises, improve the inclusiveness and coverage of financial services. Financial institutions can customize financial products and services according to the actual situation of enterprises, improve the personalization level of financial services, and enhance the attractiveness and competitiveness of financial services.

[0053] The processing device, equipment and storage medium for asset data proposed by the present invention can comprehensively understand the production and operation status of small, medium and micro enterprises by integrating the transaction flow data of corporate customers of each commercial bank and combining big data analysis technology, providing more accurate credit information and risk assessment for financial institutions. This technical solution makes up for the defect of information asymmetry in the traditional credit model by using a large amount of customer data and transaction data of commercial banks, and improves the efficiency of bank credit support for small, medium and micro enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is the overall architecture diagram of the present invention.

[0055] Figure 2 It is the flowchart of data collection of the present invention.

[0056] Figure 3 It is the flowchart of data processing of the present invention.

[0057] Figure 4 It is the process of data analysis of the present invention Figure 1 .

[0058] Figure 5 It is the process of data analysis of the present invention Figure 2 .

[0059] Figure 6 It is the flowchart of data management of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0061] Embodiment 1

[0062] Referring to Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 and Figure 6 , the present invention proposes a processing device for asset data, including:

[0063] A data integration and collection module, a data cleaning and processing module, a data storage and management module, a big data analysis and application module, and an application expansion and service support module;

[0064] The data integration and collection module is used to obtain and integrate the transaction flow data of corporate customers from each commercial bank;

[0065] The data cleaning and processing module is used to clean the collected data and keep the format standardized and unified;

[0066] The data storage and management module is used to store and manage the processed data;

[0067] The big data analysis and application module is used to deeply mine and analyze the income and expenditure transaction data of enterprises;

[0068] The application expansion and service support module is used to develop application programs and services suitable for different scenarios.

[0069] The data integration and collection module specifically includes the following:

[0070] Obtain the transaction flow data of enterprise customers from various commercial banks, including fund in and out records, transaction objects, and transaction times;

[0071] Establish a data interface or protocol to ensure that the interface is compatible with the system architectures and data formats of different commercial banks, and select a suitable network protocol according to the security and real-time requirements of data transmission;

[0072] Determine the data packaging method according to the characteristics of the data interface and network protocol;

[0073] Unpack the data at the receiving end in the reverse order of the packaging rules, and perform data verification and storage;

[0074] Specify the order of data transmission. First, transmit the basic information of enterprise customers, then transmit the transaction flow data, and finally transmit the data verification information, so that the data can be processed and stored in the correct order at the receiving end.

[0075] It should be noted that collecting information such as the fund in and out records, transaction objects, and transaction times of enterprise customers from various commercial banks can build a panoramic view of the enterprise's fund flow. Comprehensive transaction flow data helps to deeply understand the enterprise's business activities. Whether it is daily business income and expenditure, major investment projects, or the capital turnover situation, it can be clearly presented, providing a solid foundation for subsequent accurate analysis and decision-making. For example, when evaluating an enterprise's solvency, complete expenditure records can reflect its debt repayment situation; rich income data can reflect the enterprise's profitability and the stability of income sources.

[0076] By establishing rigorous data interfaces or protocols, ensure the accuracy of data during transmission. At the data collection source, i.e., each commercial bank, organize and transmit data according to unified data formats and standards. Conduct data verification at the receiving end, which can promptly detect and correct possible errors, omissions, or tampering during data transmission, effectively reducing analysis deviations and decision-making mistakes caused by inaccurate data. For example, when verifying transaction amount data, reasonable value ranges and data type checks can be set to prevent incorrect amount data from entering the analysis process.

[0077] Determine the data packaging method based on the characteristics of the data interface and network protocol, and unpack the data in the reverse order at the receiving end, enabling the data to proceed orderly during transmission and processing. A reasonable packaging method can reduce the number of data transmissions and the amount of data, improving transmission efficiency; while a standardized unpacking process ensures that the data can be accurately restored and recognized by the system. For example, pack a batch of related transaction flow data into a data packet for transmission, and quickly unpack and import it into the database at the receiving end, reducing the time and complexity of data processing.

[0078] Specify the order of first transmitting the basic information of enterprise customers, then the transaction flow data, and finally the data verification information, enabling the receiving end to process and store the data according to the predetermined logical order. This standardized order helps in the classified management and associated integration of data, facilitating subsequent data query, analysis, and report generation. For example, when storing data, first store the basic information of enterprise customers in the corresponding customer information table, then associate and store the transaction flow data with the customer in the transaction flow table, and finally ensure the integrity and consistency of the data based on the verification information, improving the standardization and convenience of data management.

[0079] The data cleaning and processing module specifically includes the following:

[0080] Clean the collected data to remove duplicate, incorrect, or incomplete information;

[0081] For the transaction flow data of enterprise customers, determine the combination of transaction serial number, transaction time, and amount as the basis for duplicate checking;

[0082] Generate a hash value for the data content of each transaction record using the hash algorithm. If the hash values of two records are the same, further compare the detailed data to determine whether they are duplicates;

[0083] When it is determined that two or more pieces of data are completely duplicate, only retain one of the records and delete the remaining duplicate records;

[0084] Standardize and unify the data format;

[0085] Identify and classify date and time data in different formats, and uniformly convert all date and time data into a standard format;

[0086] Analyze the differences in data structures from different commercial banks, define a standard data structure model, and map data from different sources to this standard structure.

[0087] It should be noted that using the combination of transaction serial number, transaction time, and amount as the basis for duplicate checking, and combining with the hash algorithm for efficient duplicate checking, can accurately identify duplicate data. Whether it is due to system failures, data entry errors, or business process problems leading to duplicate records, they can all be effectively screened out. This avoids multiple repeated calculations of the same data during the data analysis process, making the data statistical results more authentic and reliable. For example, when calculating the total enterprise transaction amount, if duplicate data is not cleared, it will lead to an inflated total amount, thus misleading the assessment of the enterprise's capital flow scale and business activity. Through an accurate duplicate checking mechanism, the accuracy of the data quantity is ensured, providing a solid foundation for subsequent analysis based on the data volume.

[0088] Analyzing the differences in data structures from different commercial banks and defining a standard data structure model, and mapping data from various sources to this standard structure, achieves data structure consistency. This enables seamless docking and sharing of data between different systems and analysis modules, improving the generality and compatibility of data processing. For example, when importing enterprise customer transaction data from the bank system into the enterprise's internal financial analysis system, if the data structures are not unified, a large amount of conversion and adaptation work is required, and it may even lead to data loss or errors. The standardized data structure ensures the smooth flow of data, reduces the complexity of data processing and the probability of errors, and improves the efficiency and stability of the entire data processing process.

[0089] The data storage and management module specifically includes the following:

[0090] Establish a data storage system, select a master-slave replication architecture, where the master database is responsible for handling all write operations, and the slave database synchronizes data from the master database and is responsible for handling read operations.

[0091] Perform full backups regularly and formulate a storage strategy for full backups;

[0092] On the basis of full backups, perform incremental backups every day. Incremental backups only back up the data that has changed since the last full backup or incremental backup, and determine the data to be backed up by recording the database transaction log or data modification marks;

[0093] Control access to the database based on user identity authentication and authorization mechanisms, and set different access permissions for different data objects.

[0094] It should be noted that by adopting the master-slave replication architecture and separating read and write operations, the system can handle more requests simultaneously. The master database focuses on write operations to ensure consistent data writing, while the slave database shares the load of read operations. In a high-concurrency scenario, such as when multiple users query the transaction flow data of enterprise customers simultaneously, the slave database can efficiently respond to these read requests, reducing the waiting time of users and improving the overall availability and response speed of the system.

[0095] Performing full backups regularly can completely preserve all data in the data storage system. In the event of catastrophic events such as data corruption, loss, or system failures, full backups can serve as the basis for data recovery. For example, if the database loses data due to hardware failures, the data can be quickly restored to the state at the time of backup by restoring the most recent full backup, minimizing data loss.

[0096] The access control mechanism helps enterprises meet the requirements of data protection regulations and industry norms. In industries such as finance, there are strict regulatory requirements for data access and use. By setting reasonable access permissions, it can be ensured that the database operations of enterprises comply with relevant regulations. At the same time, recording user access operation logs, including information such as access time, access user, accessed data objects, and operation types, enables traceability and auditing in case of data security issues, clarifies responsibilities, and enhances the controllability of enterprises in data security management.

[0097] The big data analysis and application module specifically includes the following:

[0098] Calculate and analyze the basic statistical characteristics of the enterprise's income and expenditure flow data, draw time series graphs, and display the changing trends of the enterprise's income and expenditure over time;

[0099] Extract key features from the enterprise's income and expenditure flow data as the basis for credit scoring, establish an enterprise credit scoring model, and evaluate and predict the credit status of the enterprise based on the income and expenditure flow data and other relevant information.

[0100] It should be noted that by calculating the basic statistical characteristics of the enterprise's income and expenditure flow data, such as mean, median, standard deviation, etc., the central tendency and dispersion degree of the enterprise's income and expenditure can be deeply understood. For example, the mean can reflect the average income and expenditure level of the enterprise, and the standard deviation reflects the fluctuation of income and expenditure. This helps enterprise managers, financial institutions, etc. clearly grasp the stability and risk degree of the enterprise's finances, and can more accurately evaluate risks and returns when formulating investment strategies and credit decisions, avoiding misjudgments caused by incomplete information.

[0101] Extract key features such as average income level, income stability, and expenditure rationality from the income and expenditure transaction data, and construct the basis for credit scoring by combining external information such as the enterprise registration years and industry categories, making the credit scoring model more comprehensive and scientific. These features can reflect the business conditions and credit risks of enterprises from multiple dimensions. Compared with the evaluation based on a single data source, they can more accurately grade the credit status of enterprises, provide a reliable quantitative basis for the credit approval of financial institutions, and reduce the non-performing loan rate.

[0102] The application expansion and service support module specifically includes the following:

[0103] Combined with the needs of financial institutions, develop application programs and services applicable to different scenarios, including credit approval, risk management, and inclusive finance.

[0104] Provide customized service support, including data report generation, risk warning, and post-loan management functions.

[0105] It should be noted that developing specialized application programs for different scenarios such as credit approval, risk management, and inclusive finance can greatly improve the work efficiency and accuracy of financial institutions in these business processes. For example, the credit approval application program can quickly screen out eligible enterprise customers based on the results of big data analysis, automatically evaluate their credit risks and generate approval suggestions, greatly shortening the approval cycle and increasing the speed of capital investment; the risk management application program can monitor the changes in the credit status of enterprises in real time, timely discover potential risk points and issue warnings, effectively reducing the risk losses of financial institutions.

[0106] A method for processing asset data, the method for processing asset data is implemented based on the device for processing asset data as described in any one of the above, and the method for processing asset data includes:

[0107] Obtain the transaction flow data of enterprise customers from each commercial bank and integrate them;

[0108] Clean the collected data and keep the format standardized and unified;

[0109] Store and manage the processed data;

[0110] Deeply mine and analyze the income and expenditure transaction data of enterprises;

[0111] Develop application programs and services applicable to different scenarios.

[0112] An electronic device, the electronic device includes:

[0113] A memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, it implements the method for processing asset data as described above.

[0114] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, it implements the method for processing asset data as described above.

[0115] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0116] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0117] In several embodiments provided by the present application, it should be understood that the disclosed method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in an electrical, mechanical, or other form.

[0118] The unit described as a separation component may or may not be physically separated. The component displayed as a unit may or may not be a physical unit, and it may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0119] In addition, each functional unit in various embodiments of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0120] If the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0121] As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claimed rights.

[0122] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An asset data processing device, characterized in that: include: Data integration and collection module, data cleaning and processing module, data storage and management module, big data analysis and application module, and application expansion and service support module; The data integration and collection module is used to obtain and integrate the transaction flow data of corporate customers from various commercial banks; The data cleaning and processing module is used to clean the collected data and keep the format standardized and unified; The data storage and management module is used to store and manage the processed data; The big data analysis and application module is used to conduct in-depth mining and analysis of the enterprise's revenue and expenditure flow data; The application expansion and service support module is used to develop application programs and services suitable for different scenarios.

2. The asset data processing device according to claim 1, characterized in that: The data integration and collection module specifically includes the following contents: Obtain transaction flow data of corporate customers from various commercial banks, including fund inflow and outflow records, transaction objects and transaction time; Establish data interfaces or protocols to ensure that the interfaces are compatible with the system architectures and data formats of different commercial banks, and select appropriate network protocols based on the security and real-time requirements of data transmission; Determine the data packaging method based on the characteristics of the data interface and network protocol; At the receiving end, the data is unpacked in the reverse order of the packing rules, and the data is verified and stored; The order of data transmission is specified, with the basic information of corporate customers transmitted first, followed by transaction flow data, and finally data verification information. The data can be processed and stored in the correct order at the receiving end.

3. The asset data processing device according to claim 1, characterized in that: The data cleaning and processing module specifically includes the following contents: Clean the collected data to remove duplicate, erroneous or incomplete information; For corporate customers’ transaction data, determine the combination of transaction serial number, transaction time and amount as the basis for duplicate checking; A hash algorithm is used to generate a hash value for the data content of each transaction record. If the hash values ​​of two records are the same, the detailed data is further compared to determine whether they are duplicates; When two or more data are determined to be completely duplicated, only one of the records is retained and the remaining duplicate records are deleted; Standardize and unify the data format; Identify and classify date and time data in different formats, and convert all date and time data into a standard format; Analyze the differences in data structures from different commercial banks, define a standard data structure model, and map data from different sources to the standard structure.

4. The asset data processing device according to claim 1, characterized in that: The data storage and management module specifically includes the following contents: Establish a data storage system and select a master-slave replication architecture. The master database is responsible for processing all write operations, while the slave database synchronizes data from the master database and is responsible for processing read operations. Perform full backups regularly and formulate storage strategies for full backups; On the basis of full backup, incremental backup is performed every day. Incremental backup only backs up the data that has changed since the last full backup or incremental backup. The data to be backed up is determined by recording the database transaction log or data modification mark. Access to the database is controlled based on user authentication and authorization mechanisms, and different access permissions are set for different data objects.

5. The asset data processing device according to claim 1, characterized in that: The big data analysis and application module specifically includes the following contents: Calculate and analyze the basic statistical characteristics of the company's revenue and expenditure flow data, draw a time series chart, and show the trend of the company's revenue and expenditure over time; Extract key features from the company's income and expenditure flow data as the basis for credit scoring, establish an enterprise credit scoring model, and evaluate and predict the company's credit status based on the income and expenditure flow data and other relevant information.

6. The asset data processing device according to claim 1, characterized in that: The application expansion and service support module specifically includes the following contents: In line with the needs of financial institutions, we develop applications and services for different scenarios, including credit approval, risk management and inclusive finance. Provide customized service support, including data report generation, risk warning and post-loan management functions.

7. A method for processing asset data, characterized in that: The asset data processing method is implemented based on the asset data processing device according to any one of claims 1 to 6, and the asset data processing method includes: Obtain and integrate transaction flow data of corporate customers from various commercial banks; Clean the collected data and keep the format standardized and unified; Store and manage processed data; Conduct in-depth mining and analysis of the company's revenue and expenditure flow data; Develop applications and services for different scenarios.

8. An electronic device, characterized in that: The electronic device comprises: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the asset data processing method as claimed in claim 7 when executing the computer program.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for processing asset data as claimed in claim 8 is implemented.

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