Financial lease cross-industry asset management method and system

Abstract

The invention provides a cross-industry asset management method and system for financing and leasing, and is applied to the field of asset data processing. By generating the asset data standard and the description information thereof, it is ensured that integration of the cross-industry data has a clear structure and a unified standard, the method is crucial to subsequent data analysis, risk assessment and decision support, and meanwhile, by analyzing the financing leasing information, the related cross-industry asset data is identified, so that the accuracy of the financing leasing information is improved. According to the method and the system, the assets are integrated into a decision-making process, barriers among industries are broken, more comprehensive asset data are allowed to be used for evaluation, data and insights of multiple fields can be combined through a cross-industry collaborative decision-making mechanism, more valuable decision-making information is generated, and the mechanism not only improves the financial lease approval efficiency, but also improves the financial lease approval efficiency. And manual intervention and decision delay can be effectively reduced.

Description

Technical Field

[0001] The present invention relates to the field of asset data processing, and in particular to a cross-industry asset management method and system for financial leasing. Background Art

[0002] As a flexible financing method, financial leasing is widely used in various industries, providing enterprises with the right to use equipment and assets while reducing their capital expenditures. However, the financial leasing process involves multiple participants, including leasing companies, customers, equipment suppliers, etc., and the entire process is accompanied by various risks.

[0003] In the financial leasing industry, data from all parties are often scattered across different systems and platforms, resulting in information silos and making it impossible to effectively share and integrate data, thus affecting the accuracy and timeliness of risk assessment. Summary of the invention

[0004] The present invention aims to solve the problem of how to achieve effective sharing and integration of cross-industry asset data, improve the accuracy and timeliness of risk assessment, and provide a cross-industry asset management method and system for financial leasing.

[0005] The present invention adopts the following technical means to solve the technical problem:

[0006] The present invention provides a cross-industry asset management method for financial leasing, comprising:

[0007] Based on the pre-input business data requirements, the business data requirements are entered into a preset data lake, and data types corresponding to the business data requirements are generated through the data lake, wherein the data types specifically include text, images, videos, and log files;

[0008] Determine whether the data type is adaptable to the preset data lake platform;

[0009] If it is possible, then according to the preset data lake architecture, the preset hierarchical structure is adopted to organize the data of the business data demand in the data lake, construct the asset data standard corresponding to the data type, generate the description information of the asset data standard, migrate the business data demand from the preset data center to the data lake, and divide the financing lease information of the business data demand, wherein the data lake architecture specifically includes a data storage layer, a data processing layer and a data access layer, the description information specifically includes the source, format and creation time, and the financing lease information specifically includes financing lease contracts, asset evaluation and risk management;

[0010] Determine whether the financial lease information meets the preset integrity;

[0011] If it meets the requirements, the user's cross-industry asset data is identified based on the financial leasing information, the financial leasing information and the cross-industry asset data are input into the pre-trained risk dimension data model, the user's cross-industry collaborative decision-making mechanism is updated, and the financial leasing approval information corresponding to the business data requirements is generated through the cross-industry collaborative decision-making mechanism, wherein the cross-industry asset data specifically includes asset categories, customer groups and market popularity.

[0012] Furthermore, before the step of organizing the business data requirements in the data lake using a preset hierarchical structure according to the preset data lake architecture, the step further includes:

[0013] Based on the source data pre-collected in the data lake, identifying the industry type corresponding to the business data demand, wherein the source data specifically includes structured data, semi-structured data and unstructured data;

[0014] Determine whether the industry type covers the source data;

[0015] If not, then based on the industry type, obtain the data source of the business data requirement, extract the corresponding business data from the data source, convert the business data into the data format preset by the data lake, and regularly synchronize and update the new data of the business data in the data lake within a preset time period, wherein the data source specifically includes production equipment sensor data, inventory management data and supply chain data.

[0016] Furthermore, the step of migrating the business data demand from the preset data center to the data lake and dividing the financing lease information of the business data demand further includes:

[0017] Based on the data fields preset in the financial lease information, identifying key fields in the data fields, wherein the key fields specifically include contract number, lease amount, lease term, customer ID and asset number;

[0018] Determining whether the key field matches a preset unified format;

[0019] If so, based on the key field, a preset identifier is applied to associate the financial leasing information with the user, index information of the financial leasing information in the data lake is established, and the user is granted access rights to the index information.

[0020] Furthermore, after the step of inputting the financial lease information and the cross-industry asset data into the pre-trained risk dimension data model, it also includes:

[0021] Based on the preset risk factors, the corresponding risk levels are divided, and a risk dimension data model is constructed according to the risk levels, wherein the risk factors specifically include asset risk, customer credit risk and market risk, and the risk levels specifically include customer level, contract level, asset level and market level;

[0022] Determining whether the risk dimension data model can generate risk assessment results;

[0023] If not, obtain the error log of the risk dimension data model during runtime, apply the preset outlier filter to identify the extreme values ​​of the cross-industry asset data from the error log, and limit the user to perform risk assessment based on the extreme values, wherein the extreme values ​​specifically include extremely high lease amounts, too low customer credit scores, and excessively long lease terms.

[0024] Furthermore, the step of updating the user's cross-industry collaborative decision-making mechanism and generating the financing lease approval information corresponding to the business data demand through the cross-industry collaborative decision-making mechanism further includes:

[0025] Based on the decision variable information preset by the data center for the business data demand, the decision variable information is synchronized to the preset industry department, and the approval standard value of the user is established through the industry department, wherein the decision variable information specifically includes customer credit score, asset assessment value and contract terms;

[0026] Determine whether the approval standard value reaches the preset review benchmark;

[0027] If so, the user and his / her financial leasing information will be shared to the digital collaboration platform of the industry department according to the execution authority preset by the data center, and the financial leasing approval information of the user will be constructed in real time on the digital collaboration platform.

[0028] Furthermore, the step of determining whether the data type is adaptable to the preset data lake platform also includes:

[0029] Obtain the time field preset by the business data requirement;

[0030] Determine whether the time field matches a timestamp unit preset by the data lake platform;

[0031] If not, the time fields are unified according to the timestamp unit, and the timestamp partitions required by the business data are reordered according to the time window of the timestamp unit.

[0032] Furthermore, the step of entering the business data requirements into a preset data lake based on the pre-input business data requirements and generating data types corresponding to the business data requirements through the data lake further includes:

[0033] Based on the cleaning measures preset by the data center, detecting the items to be cleaned required by the business data;

[0034] Determining whether the item to be cleaned matches a preset washable item;

[0035] If so, the business data requirements are pre-cleaned to generate data to be confirmed, and the data to be confirmed is synchronized to the user's preset terminal. Through the data center, the operation permissions for the business data requirements provided by the user are obtained, wherein the data pre-cleaning specifically includes removing useless fields, processing missing values, and correcting erroneous data.

[0036] The present invention also provides a cross-industry asset management system for financial leasing, comprising:

[0037] A generation module, configured to input the pre-input business data requirements into a preset data lake, and generate data types corresponding to the business data requirements through the data lake, wherein the data types specifically include text, images, videos, and log files;

[0038] A judgment module, used to judge whether the data type is adaptable to a preset data lake platform;

[0039] An execution module, for organizing data of the business data demand in the data lake using a preset hierarchical structure according to a preset data lake architecture, if possible, constructing an asset data standard corresponding to the data type, generating description information of the asset data standard, migrating the business data demand from a preset data center to the data lake, and dividing the financing lease information of the business data demand, wherein the data lake architecture specifically includes a data storage layer, a data processing layer, and a data access layer, the description information specifically includes a source, a format, and a creation time, and the financing lease information specifically includes a financing lease contract, an asset evaluation, and risk management;

[0040] The second judgment module is used to judge whether the financial lease information meets the preset integrity;

[0041] The second execution module is used to identify the user's cross-industry asset data based on the financial leasing information, input the financial leasing information and the cross-industry asset data into the pre-trained risk dimension data model, update the user's cross-industry collaborative decision-making mechanism, and generate the financial leasing approval information corresponding to the business data demand through the cross-industry collaborative decision-making mechanism, wherein the cross-industry asset data specifically includes asset categories, customer groups and market popularity.

[0042] Furthermore, it also includes:

[0043] An identification module, configured to identify the industry type corresponding to the business data demand based on the source data pre-recorded in the data lake, wherein the source data specifically includes structured data, semi-structured data, and unstructured data;

[0044] A third judgment module is used to judge whether the industry type covers the source data;

[0045] The third execution module is used to, if not, obtain the data source of the business data demand according to the industry type, extract the corresponding business data from the data source, convert the business data into a data format preset by the data lake, and regularly synchronize and update the new data of the business data in the data lake within a preset time period, wherein the data source specifically includes production equipment sensor data, inventory management data and supply chain data.

[0046] Furthermore, the execution module also includes:

[0047] An identification unit, configured to identify key fields in the data fields based on the data fields preset in the financial lease information, wherein the key fields specifically include a contract number, a lease amount, a lease term, a customer ID, and an asset number;

[0048] A judging unit, used to judge whether the key field matches a preset unified format;

[0049] and an execution unit, configured to, if yes, associate the financial leasing information with the user by using a preset identifier according to the key field, establish index information of the financial leasing information in the data lake, and grant the user access rights to the index information.

[0050] The present invention provides a cross-industry asset management method and system for financial leasing, which has the following beneficial effects:

[0051] The present invention generates asset data standards and their description information to ensure that the integration of cross-industry data has a clear structure and unified standards, which is crucial for subsequent data analysis, risk assessment and decision support. At the same time, through the analysis of financial leasing information, the cross-industry asset data related to it is identified and integrated into the decision-making process, breaking down the barriers between industries and allowing more comprehensive asset data to be used for evaluation. Moreover, through the cross-industry collaborative decision-making mechanism, data and insights from multiple fields can be combined to generate more valuable decision-making information. This mechanism not only improves the efficiency of financial leasing approval, but also effectively reduces manual intervention and decision-making delays. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1A flow chart of an embodiment of the cross-industry asset management method for financial leasing of the present invention;

[0053] Figure 2 This is a structural block diagram of an embodiment of a cross-industry asset management system for financial leasing of the present invention. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. The implementation of the objectives, functional features and advantages of the present invention will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

[0055] 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.

[0056] Reference Figure 1 , is a cross-industry asset management method for financial leasing in one embodiment of the present invention, comprising:

[0057] S: Based on the pre-input business data requirements, the business data requirements are entered into a preset data lake, and data types corresponding to the business data requirements are generated through the data lake, wherein the data types specifically include text, images, videos, and log files;

[0058] S2: Determine whether the data type is adaptable to the preset data lake platform;

[0059] S3: If yes, then according to the preset data lake architecture, the preset hierarchical structure is used to organize the data of the business data demand in the data lake, construct the asset data standard corresponding to the data type, generate the description information of the asset data standard, migrate the business data demand from the preset data center to the data lake, and divide the financing lease information of the business data demand, wherein the data lake architecture specifically includes a data storage layer, a data processing layer and a data access layer, the description information specifically includes the source, format and creation time, and the financing lease information specifically includes financing lease contracts, asset evaluation and risk management;

[0060] S4: Determine whether the financial lease information meets the preset integrity;

[0061] S5: If it meets the requirements, the user's cross-industry asset data is identified based on the financial leasing information, the financial leasing information and the cross-industry asset data are input into the pre-trained risk dimension data model, the user's cross-industry collaborative decision-making mechanism is updated, and the financial leasing approval information corresponding to the business data requirements is generated through the cross-industry collaborative decision-making mechanism, wherein the cross-industry asset data specifically includes asset categories, customer groups and market popularity.

[0062] In this embodiment, the system enters the business data requirements into a pre-set data lake based on the business data requirements pre-entered into the data center, and generates data types corresponding to the business data requirements through the data lake. The data types specifically include asset text, asset images, asset videos, and asset log files. The system then determines whether these data types can be adapted to the preset data lake platform to execute the corresponding steps. For example, when the system determines that the data type corresponding to the business data requirement cannot be adapted to the preset data lake platform, the system will believe that the data type may lack the necessary tags or metadata, resulting in an inability to effectively index or classify. The system will ensure that the data structure meets the requirements of the data lake platform through steps such as data cleaning, tag supplementation, and metadata addition. At the same time, for files that lack tags, use automated tools or manual intervention to add necessary tags and metadata so that the data lake can effectively identify and process them, and store the data in an external database or cloud platform. Then, through the index or association mechanism, the data lake can access and call the data, which can avoid direct changes to the data lake structure and ensure the integrity and accessibility of the data; for example, when the system determines that the data type corresponding to the business data demand can adapt to the pre-set data lake platform, the system will think that the data type can be effectively indexed or classified in the data lake. The system will organize the business data demand in the data lake according to the pre-set data lake architecture, which specifically includes the data storage layer, the data processing layer and the data access layer, using a pre-set hierarchical structure, build asset data standards corresponding to the data type, generate description information of the asset data standard, and the description information specifically includes the source, format and creation time, migrate these business data demands from the pre-set data center to the data lake, and divide the financial leasing information of the business data demand, which specifically includes financial leasing contracts, asset evaluation and risk management;The system manages business data by adopting the layered architecture of the data lake (data storage layer, data processing layer and data access layer), so that different types of data can be processed and optimized accordingly at each level. The data storage layer can centrally manage the storage and backup of data, the data processing layer is responsible for cleaning and analyzing data, and the data access layer can support the needs of different users to access data. At the same time, it builds asset data standards corresponding to data types, standardizes the structure and attributes of data through unified description information, and ensures that data is consistent in the data lake. This standardization ensures data quality, enables cross-industry data to be stored and analyzed in a unified format, and provides a reliable data foundation for subsequent risk assessment and decision support. In addition, through effective indexing and classification of data types, the data lake can quickly find data that meets specific query conditions, allowing the system to query, classify and filter data more flexibly, especially when cross-industry data integration and analysis are required, it can quickly obtain relevant information and realize data association, greatly improving the efficiency of data access. The business data is divided into specific categories such as financial leasing contracts, asset evaluation and risk management, which facilitates the refined management of financial leasing business data and makes the data more in line with business needs, thereby helping the system to conduct risk analysis, customer evaluation and Asset management; the system then determines whether the financing lease information can meet the pre-set integrity to execute the corresponding steps; for example, when the system determines that the financing lease information required by the business data cannot meet the pre-set integrity, the system will consider that the information is incomplete or inaccurate, and the system will format the data, unify the date, currency and text encoding formats, so that all financing lease information meets the preset format standards, and add metadata such as source and creation time to the incomplete data to ensure the traceability and reliability of the data in subsequent processing, and mark the data that does not meet the integrity requirements so that it can be identified and excluded in the risk assessment process to avoid negative impact on the overall assessment results; for example, when the system determines that the financing lease information required by the business data can meet the pre-set integrity, the system will consider that the information does not have the problem of incomplete data, and the system will identify the user's cross-industry asset data based on the financing lease information. The cross-industry asset data specifically includes asset categories, customer groups and market popularity, and input these financing lease information and cross-industry asset data into the pre-trained risk dimension data model, update the user's cross-industry collaborative decision-making mechanism, and generate financing lease approval information corresponding to the business data requirements through the cross-industry collaborative decision-making mechanism;The system combines financial leasing information with cross-industry asset data, such as asset categories, customer groups, and market popularity, to provide a richer reference for risk assessment. Compared with single-dimensional data analysis, comprehensive multi-dimensional information can help the model identify potential risks more comprehensively, especially in a complex market environment. This multi-dimensional data integration is particularly important. At the same time, by updating the user's cross-industry collaborative decision-making mechanism, the system can collaboratively use information, analysis, and decision-making in multi-department and cross-industry cooperation, provide management with smarter decision-making support, and improve the efficiency and accuracy of financial leasing approval. Based on the intelligent risk model and cross-industry collaborative mechanism, the system can quickly analyze business data needs and generate financial leasing approval information, which can help enterprises quickly respond to market changes and improve the market response speed of financial leasing business by shortening the approval process and response time. ;

[0063] It should be noted that, based on the financial lease information, the user's cross-industry asset data is identified, the financial lease information and the cross-industry asset data are input into the pre-trained risk dimension data model, the user's cross-industry collaborative decision-making mechanism is updated, and the financial lease approval information corresponding to the business data demand is generated through the cross-industry collaborative decision-making mechanism. The specific examples are as follows:

[0064] The scenario is that a large manufacturing company, Company X, applies for financial leasing of a batch of high-value heavy equipment. The specific process is as follows:

[0065] First, the cross-industry asset data is identified. The system reads the financial lease application of Company X and obtains the basic information of the application, including the application amount, lease period, equipment type, etc. At the same time, the system will identify the cross-industry asset data related to Company X through the data lake. These data not only include equipment leasing data in the manufacturing industry, but also include multi-dimensional data such as Company X's financial health, credit score, and market activity in the industry. The system finds that Company X mainly operates in the manufacturing industry and has a certain global business coverage. By reading Company X's financial statement data and third-party credit score, the system understands that Company X's credit score is at an upper-middle level in the industry. At the same time, the system finds that the demand for heavy equipment in the manufacturing industry has increased recently, but the maintenance cost of the equipment is high, and the average profit margin of the industry has fluctuated. This information, as part of the asset data, will be used in subsequent risk assessments.

[0066] Then, the risk dimension data model is input. After obtaining complete financial lease information and cross-industry asset data, the system inputs all relevant data into the pre-trained risk dimension data model for evaluation. The model analyzes multiple risk dimensions, such as the customer's financial health, past lease performance records, industry market risks, etc., and combines external factors such as global market conditions and industry trends. Model analysis shows that Company X's financial situation is relatively stable, its past lease record is good, and its performance is reliable. The industry dimension shows that the current market demand for heavy equipment is high, but prices fluctuate greatly and are significantly affected by the economic cycle. Based on these factors, the model determines that Company X is in the "medium risk" category, but has a high performance potential. At this time, the system may mark the lease as "controllable risk" and recommend the addition of some risk mitigation measures.

[0067] Then, the cross-industry collaborative decision-making mechanism is updated. After the model completes the risk level assessment, the system automatically updates the cross-industry collaborative decision-making mechanism, synchronizes the assessment results to relevant departments (such as finance, risk control, and market analysis, etc.), and integrates cross-industry data to provide reference for these departments so that they can make more comprehensive suggestions for financing decisions based on their own responsibilities. In this case, after analyzing the financial data of Company X, the financial department believes that its qualifications are good, but it needs to pay attention to the risk of equipment price fluctuations, and recommends shortening the lease period or increasing the lease interest rate; the risk control department recommends adding a regular inspection clause to reduce the risk of fluctuations in the maintenance cost of the equipment during the lease period; the market analysis department determines through industry data and market demand that the leasing demand for the equipment has seasonal fluctuations, so it recommends dynamically adjusting the lease amount according to the market conditions during the lease period;

[0068] Finally, the system generates financial leasing approval information. The system integrates the evaluations and suggestions of various departments to generate a financial leasing approval information and incorporates it into business decisions. The approval information will list in detail the approved lease amount, lease period, risk control measures, etc., to ensure that the financial leasing operation is carried out smoothly under the premise of controlling risks.

[0069] Finally, the approval information generated by the system includes the following:

[0070] Approved leasing amount: 80% of the equipment value, i.e., the financing amount in line with the market average;

[0071] Lease period: one year, with flexible renewal terms;

[0072] Lease interest rate: 3% higher than the standard interest rate to cope with the risk of equipment price fluctuations;

[0073] Risk control clauses: including quarterly inspections, equipment maintenance costs during the lease period shall be borne by the lessee, and if market demand fluctuates significantly, the lease contract can be terminated in advance according to actual conditions; this approval information will be directly used for customer communication and contract generation on the business side, helping business personnel to provide customer X with a financial leasing solution that meets its needs and takes risk management into account;

[0074] In summary, through the above process and cross-industry data collaboration, the financing leasing plan generated by the system can comprehensively assess risks, reasonably allocate resources, and ensure the accuracy of business decisions. In the case of Company X mentioned above, the system combines financial, market and risk information to provide a plan with flexibility and risk control clauses, thereby improving the security of the financing leasing business and customer satisfaction.

[0075] In this embodiment, before step S3 of organizing data in the data lake according to the preset data lake architecture using the preset hierarchical structure for the business data requirements, the method further includes:

[0076] S301: Based on the source data pre-recorded in the data lake, identifying the industry type corresponding to the business data demand, wherein the source data specifically includes structured data, semi-structured data and unstructured data;

[0077] S302: Determine whether the industry type covers the source data;

[0078] S303: If not, then according to the industry type, obtain the data source of the business data demand, extract the corresponding business data from the data source, convert the business data into a data format preset by the data lake, and regularly synchronize and update the new data of the business data in the data lake within a preset time period, wherein the data source specifically includes production equipment sensor data, inventory management data, and supply chain data.

[0079] In this embodiment, the system identifies the industry types corresponding to the business data requirements based on the source data pre-collected in the data lake, which specifically includes structured data, semi-structured data, and unstructured data. The system then determines whether these industry types cover the source data to execute the corresponding steps. For example, when the system determines that the industry type corresponding to the business data requirement can cover the source data, the system will believe that a comprehensive data source related to the industry can be found in the data lake. The system will automatically retrieve the source data related to the business requirement from the data lake, classify it as structured, semi-structured or unstructured data, and perform preliminary data integration, including classifying the source data according to the business requirement type. Screening, such as selecting financial data, customer profiles, and market dynamics that match the industry type, and using the hierarchical structure of the data lake to classify the extracted source data into appropriate levels according to data type and content, and store them in the data storage layer, data processing layer, and data access layer to support subsequent analysis and access operations, and input the pre-processed source data into the corresponding analysis model, combined with the specific goals of the business data requirements, analyze the multi-dimensional industry data, including market trend forecasts, customer demand assessments, financial risk analysis, etc., in order to provide reliable data support for business decisions; for example, when the system determines that the industry type corresponding to the business data requirements cannot cover the source data, at this time The system will think that it is impossible to find a comprehensive data source related to the industry in the data lake. The system will obtain the data source of business data needs according to the industry type. The data source specifically includes production equipment sensor data, inventory management data and supply chain data, extract the corresponding business data from these data sources, convert these business data into the data format pre-set in the data lake, and regularly update the new data of the business data in the data lake within the pre-set time period; by extracting data from external data sources such as production equipment sensor data, inventory management data and supply chain data, the system can expand the coverage of the data lake, so that the data lake covers the data needs of more industry types, so that even if there is no direct The system can also flexibly respond to various business needs by connecting to related data, improving the adaptability and scalability of the data lake. At the same time, after the external data is converted into the preset format of the data lake, it is managed in a unified manner with the existing data in the data lake. The standardization of this format allows data from different sources to be accessed and analyzed on a unified platform, ensuring data consistency and ease of call, laying the foundation for cross-industry analysis and subsequent integration, and regularly synchronizing business data to the data lake to keep the data updated and promptly reflect changes in the external environment. This can avoid analysis errors caused by data lags and ensure that risk assessments and business decisions are based on the latest information, thereby improving the accuracy of risk assessments and the timeliness of decision-making.

[0080] In this embodiment, the step S3 of migrating the business data demand from the preset data center to the data lake and dividing the financing lease information of the business data demand further includes:

[0081] S31: Based on the data fields preset in the financial lease information, identifying key fields in the data fields, wherein the key fields specifically include contract number, lease amount, lease term, customer ID and asset number;

[0082] S32: Determine whether the key field matches a preset unified format;

[0083] S33: If so, then according to the key field, a preset identifier is applied to associate the financial leasing information with the user, establish index information of the financial leasing information in the data lake, and grant the user access rights to the index information.

[0084] In this embodiment, the system identifies the key fields in the data fields based on the pre-set data fields of the financial leasing information. The key fields specifically include the contract number, lease amount, lease term, customer ID and asset number. Then the system determines whether these key fields match the pre-set unified format to execute the corresponding steps. For example, when the system determines that the key fields in the data fields cannot match the pre-set unified format, the system will consider that the data format is inconsistent or does not meet the specifications, which may cause the data to be unable to be effectively processed or analyzed. The system will provide detailed error feedback, indicating which key fields do not meet the preset standards, such as non-numeric characters may appear in the contract number field, the lease amount field may contain illegal symbols, and the customer ID field may be empty or formatted incorrectly. At the same time, it records which data fields have been formatted and provides corresponding traceability functions to ensure that the data processing process is transparent and auditable, which is not only helpful for later error troubleshooting, but also ensures that key information is not lost during the processing process. In the subsequent data entry and processing links, the system can automatically detect and prevent any data that does not meet the unified format from entering the system by setting more stringent format verification rules, thereby improving data quality from the source. For example, when the system determines that the key fields in the data fields can match the pre-set unified format, the system will automatically detect and prevent any data that does not meet the unified format from entering the system by setting more stringent format verification rules. The unified format of the system will consider that the data format is consistent and can be effectively processed and analyzed. The system will associate the financial leasing information with the user according to the key fields and the pre-set identifiers, establish the index information of the financial leasing information in the data lake, and grant the user access rights to the index information. By establishing index information according to the key fields, the system can retrieve and manage data more efficiently. The relevant data and user data of each financial leasing information are associated through the index, which greatly improves the speed of data search and access. Especially in the case of large data volume, it can significantly shorten the retrieval time. At the same time, when granting users access rights to index information, the system can control the access rights of the data to ensure that only authorized users can access the relevant data, enhance the security of the data, and realize fine-grained permission control, avoiding the risk of unauthorized access or data leakage. In addition, the construction of data association and index can strengthen the relationship mining between different data, so that the system can extract valuable information from various types of data and optimize the accuracy and timeliness of business decisions. The establishment of data index and user rights makes cross-departmental or cross-industry data sharing and collaboration more convenient, that is, different departments can work together based on a unified data platform and quickly locate the required information through indexes, thereby improving work efficiency.

[0085] In this embodiment, after step S5 of inputting the financing lease information and the cross-industry asset data into the pre-trained risk dimension data model, the method further includes:

[0086] S501: Based on the preset risk factors, the corresponding risk levels are divided, and a risk dimension data model is constructed according to the risk levels, wherein the risk factors specifically include asset risk, customer credit risk and market risk, and the risk levels specifically include customer level, contract level, asset level and market level;

[0087] S502: Determine whether the risk dimension data model can generate a risk assessment result;

[0088] S503: If not, obtain the error log of the risk dimension data model during operation, apply the preset outlier filter to identify the extreme values ​​of the cross-industry asset data from the error log, and limit the user to perform risk assessment based on the extreme values, wherein the extreme values ​​specifically include extremely high lease amounts, too low customer credit scores, and excessively long lease terms.

[0089] In this embodiment, the system divides the corresponding risk levels based on pre-set risk factors. The risk factors specifically include asset risk, customer credit risk and market risk. The risk levels specifically include customer level, contract level, asset level and market level. A risk dimension data model is constructed based on these risk levels, and then the system determines whether the risk dimension data model can generate risk assessment results to execute the corresponding steps; for example, when the system determines that the risk dimension data model can generate risk assessment results, the system will consider that the model has the ability to generate effective and reliable risk assessment results and can accurately assess various risk factors. The system will conduct a comprehensive assessment of the business based on the preset risk factors and corresponding risk levels. These risk factors and levels will be combined into the model to form a comprehensive risk assessment report, which will include the assessment results of different risk dimensions. Once the risk assessment results are generated, the system will promote cross-departmental collaboration, especially the business department, risk control department and financial department. By sharing the risk assessment report, all parties can carry out more targeted work based on the assessment results. For example, the financial department adjusts the financing amount according to the risk assessment results, the risk department adjusts the risk control strategy, and the business department optimizes customer communication. In addition, according to market changes, asset value fluctuations or customer behavior changes, the system can adjust the parameters of the risk dimension data model in real time to ensure that the assessment results are always synchronized with the external environment. When the system determines that the risk dimension data model cannot generate risk assessment results, the system will consider that the user's financial leasing information and cross-industry asset data are abnormal. The system will obtain the error log of the risk dimension data model during operation, and apply the pre-set abnormal value filter to identify the extreme values ​​of the cross-industry asset data from the error log. The extreme values ​​include extremely large lease amounts, too low customer credit scores, and excessively long lease terms. Users are restricted from conducting risk assessments based on different extreme values. By obtaining the error log of the risk dimension data model during operation, the system can quickly locate and identify abnormal points in the data processing process, so that the system can accurately identify which data items or data sets have problems, thereby effectively reducing The system can also detect errors or invalid outputs in the evaluation, and at the same time, the fault tolerance of the system is enhanced by introducing an outlier filtering mechanism in the system. When abnormal data appears, the system can identify and correct it in time to ensure that its operation will not fail due to data problems. This ensures that the system can still stably and efficiently output effective risk assessment results when processing large-scale data, and by setting different restrictions on different extreme values, the system can flexibly adjust risk management strategies. For example, for extremely large lease amounts, the system may automatically set a stricter approval process, and for customers with too low credit scores, it may require additional guarantees or other risk mitigation measures. This flexibility enhances the accuracy and timeliness of risk control.

[0090] It should be noted that the preset outlier filter is applied to identify the extreme values ​​of the cross-industry asset data from the error log, and the user is restricted from performing risk assessment based on the extreme values. The specific examples are as follows:

[0091] Assume that this financial leasing company is conducting a financial leasing risk assessment of cross-industry assets and has collected some customer and asset data from the medical, manufacturing, and energy industries; this data includes asset amounts, lease terms, customer credit scores, etc.; the company uses an advanced risk assessment model that relies on this data to make reasonable decisions; however, the data may contain some extreme values, which, if not processed, may lead to incorrect risk assessment results;

[0092] 1. Medical industry:

[0093] Lease amount: RMB 5 million;

[0094] Lease term: 5 years;

[0095] Customer credit score: 750 (good);

[0096] 2. Manufacturing:

[0097] Lease amount: RMB 20 million;

[0098] Lease term: 8 years;

[0099] Customer credit score: 680 (medium);

[0100] 3. Energy industry:

[0101] Lease amount: RMB 100 million;

[0102] Lease term: 15 years;

[0103] Customer credit score: 450 (low);

[0104] Among these data, the third piece of customer information from the energy industry is obviously problematic; the customer's credit score is 450, far below the minimum credit score requirement (600) stipulated by the financial leasing company, and the lease amount is as high as 100 million yuan, significantly exceeding the amount the company usually handles (usually no more than 50 million yuan); these data items may cause incorrect predictions in the risk assessment model, because low credit scores and large lease amounts may mean high risks;

[0105] When the system is running, an error log will be generated according to the preset rules, recording the following:

[0106] The lease amount exceeded the limit: the lease amount of RMB 100 million was beyond the scope of the company's regular business;

[0107] Credit score is too low: A credit score of 450 is far below the minimum credit score required by the company;

[0108] Therefore, these error messages will be recorded in the system's log files;

[0109] The outlier filtering mechanism is applied. The system first scans the data according to the preset outlier criteria. For the lease amount and credit score, it compares these values ​​with the set range: the lease amount exceeds the upper limit of 50 million yuan, and the customer's credit score is below the minimum standard of 600. Therefore, this energy industry lease data will be marked as "extreme value" by the system;

[0110] Extreme value processing: Once the system identifies an extreme value, it will immediately take the following steps to restrict the data from entering the risk assessment model: In order to avoid incorrectly entering this abnormal data into the risk assessment model, the system will refuse to continue processing this data; the system will prompt the business personnel to indicate that there is a problem with the data and it cannot enter the assessment process; for such problems, the system will suggest that the business personnel contact the customer to correct the credit score, or add more guarantee measures to reduce the risk; for example, the customer can be asked to provide additional financial statements, cash flow proof or ask the customer to provide more assets as collateral to mitigate the risk of low credit scores; and if the lease amount is too high, the system will suggest that the business personnel consider adjusting the lease amount to a reasonable range, or ask the customer to provide more explanations of the source of funds;

[0111] Dynamic risk assessment adjustment: if the business personnel revise the data, the system will update the data in real time and re-enter it into the risk assessment model; the system will recalculate the risk assessment based on the new data (such as the updated credit score or the adjusted lease amount); the system will generate a new risk assessment result for each customer based on different industry types, customer credit and other information; for example, after the revision, the customer's credit score is increased to 600 and the lease amount is reduced to 40 million yuan, then the system will re-evaluate the customer's risk and provide a more reasonable financing plan;

[0112] Continuous monitoring and feedback: The system will continuously monitor the data source to ensure the accuracy of the data. When new outliers occur, the system will automatically restart the outlier detection mechanism and provide feedback to users so that they can take timely measures. If there is new data later (such as customer credit score updates or changes in lease amounts), the system will re-evaluate and update the risk assessment results to ensure that the approval information for financial leasing is based on the latest and revised data.

[0113] In summary, through the above-mentioned outlier filtering mechanism, the system can effectively identify and process extreme values ​​in the data to avoid erroneous risk assessment results; this not only ensures the accuracy of risk assessment, but also can adjust the decision-making process in real time when data anomalies occur, thereby reducing unnecessary risks; for example, if the credit score of a customer in the energy industry remains at 450 and the lease amount is 100 million yuan, and no adjustment is made through the outlier filtering mechanism, the customer may be mistakenly considered to be low-risk, and the final approved financial leasing agreement may lead to a risk of default; if the anomaly is identified in time through the preset filtering mechanism, and measures are taken to correct the data or increase guarantees, the final financing decision will be more reliable, thereby reducing potential losses; through this dynamic and automated outlier detection and correction mechanism, financial leasing companies can maintain efficient risk management and control in cross-industry asset risk assessment, and ensure the rationality and security of the decision-making process.

[0114] In this embodiment, the step S5 of updating the user's cross-industry collaborative decision-making mechanism and generating the financing lease approval information corresponding to the business data demand through the cross-industry collaborative decision-making mechanism further includes:

[0115] S51: Based on the decision variable information preset by the data center for the business data demand, the decision variable information is synchronized to a preset industry department, and the approval standard value of the user is established through the industry department, wherein the decision variable information specifically includes customer credit score, asset evaluation value and contract terms;

[0116] S52: Determine whether the approval standard value reaches a preset review benchmark;

[0117] S53: If so, then according to the preset execution authority of the data center, the user and his / her financial leasing information are shared to the digital collaboration platform of the industry department, and the financial leasing approval information of the user is constructed in real time on the digital collaboration platform.

[0118] In this embodiment, the system pre-sets decision variable information based on the data center's business data needs. The decision variable information specifically includes customer credit scores, asset appraisal values, and contract terms. These decision variable information is synchronized to pre-set industry departments, and approval standard values ​​belonging to users are established through different industry departments. Then the system determines whether the user's approval standard value reaches the pre-set audit benchmark to execute the corresponding steps. For example, when the system determines that the user's approval standard value does not reach the pre-set audit benchmark, the system will believe that the user's financial leasing application has certain risks and cannot meet the company's financing requirements or credit standards. The system will automatically generate an error or warning message, marking that the user's approval standard value does not reach the preset benchmark. The feedback will clearly indicate which decision variable caused the approval standard value to fail to meet the standard. At the same time, a buffer period is set to allow customers to adjust their financial status, provide supplementary information, or modify contract terms within a specified time, thereby increasing the approval standard value. The approval standard is regularly optimized based on historical audit results, market changes, and customer feedback. Different approval benchmarks can be set for different industries and different customer types, making the approval process more flexible and adaptable to market needs. For example, when the system determines that the user's approval standard value reaches the pre-set audit benchmark, the system will automatically generate an error or warning message to mark that the user's approval standard value does not reach the preset benchmark. The system will consider that no risk has been detected in the user's financing lease application. The system will share the user and his financing lease information to the digital collaboration platform of the industry department according to the execution authority pre-set by the data center, and build the user's financing lease approval information in real time on the digital collaboration platform; the system can obtain the user's financing lease information in real time through different industry departments, and work together on the digital platform to avoid information islands and improve the communication efficiency between departments, that is, all relevant departments can view and update information on the same platform to ensure transparency and consistency in the financing lease approval process, thereby reducing communication errors and information loss. At the same time, data judgment is made based on the preset review benchmark to reduce the deviation caused by human factors. The information of users who have been confirmed to meet the conditions is shared on the digital platform, which can ensure that all decisions and approvals are based on the same and unified data to ensure the accuracy of decisions. After sharing the financing lease information in real time and generating the approval information, the digital platform can also collect data feedback to help enterprises monitor the bottlenecks and potential problems of the approval process. The system can further optimize the approval standards and decision-making models based on these feedbacks, and by setting standardized approval benchmarks and data sharing mechanisms, the approval process of all financing lease applications becomes more standardized and consistent, thereby promoting the standardization and continuous optimization of enterprise business processes.

[0119] In this embodiment, the step S2 of determining whether the data type is adaptable to the preset data lake platform also includes:

[0120] S21: Obtaining a preset time field of the business data requirement;

[0121] S22: Determine whether the time field matches the timestamp unit preset by the data lake platform;

[0122] S23: If not, unify the time fields according to the timestamp unit, and reorder the timestamp partitions required by the business data according to the time window of the timestamp unit.

[0123] In this embodiment, the system obtains the time fields pre-set by the business data requirements, and then determines whether these time fields match the timestamp units pre-set by the data lake platform to execute the corresponding steps; for example, when the system determines that the time fields pre-set by the business data requirements can match the timestamp units pre-set by the data lake platform, the system will consider that the time field format in the business data requirements is consistent with the timestamp units of the data lake platform, which means that the system can successfully parse these time data without additional conversion or processing steps. The system will directly enter the time field information in the business data requirements into the data lake platform without additional conversion, ensuring the originality and accuracy of the time data, and at the same time create a time series index based on these time fields. The time series index helps to quickly locate data in a specific time period and improve the efficiency of data query and analysis, especially when time period comparison, trend analysis or anomaly detection is required, which can significantly speed up the processing speed, and establish a synchronization strategy with other data sources based on the matching timestamps to ensure that the business data in the data lake is consistent with the external data source, so that all time-related data can be kept synchronized and updated in multi-system or cross-departmental collaborative work; for example, when the system determines that the time fields pre-set by the business data requirements can match the timestamp units pre-set by the data lake platform, the system will consider that the time field format in the business data requirements is consistent with the timestamp units of the data lake platform, which means that the system can successfully parse these time data without additional conversion or processing steps. The system will directly enter the time field information in the business data requirements into the data lake platform without additional conversion, ensuring the originality and accuracy of the time data, and at the same time create a time series index based on these time fields. The time series index helps to quickly locate data in a specific time period and improve the efficiency of data query and analysis, especially when time period comparison, trend analysis or anomaly detection is required, which can significantly speed up the processing speed, and establish a synchronization strategy with other data sources based on the matching timestamps to ensure that the business data in the data lake is consistent with the external data source, so that all time If the time field cannot match the timestamp unit pre-set by the data lake platform, the system will think that the time field format in the business data requirements is inconsistent with the timestamp unit of the data lake platform and cannot parse these time data. The system will unify the time field according to the timestamp unit and reorder the timestamp partitions of the business data requirements according to the time windows of these timestamp units. By unifying the time field into the timestamp unit of the data lake platform, the system can ensure that the time field in the business data requirements is consistent with other stored data. This unified format allows data across data sources to be directly integrated, improves the overall consistency of the data, and reduces ambiguity in the analysis and query process. At the same time, after the timestamp units are consistent and reordered by time windows, the system can efficiently establish a time partition index, which helps to accelerate data query and retrieval based on time fields, especially in a data lake environment with large amounts of data, which significantly improves data processing and analysis efficiency. The unified timestamp unit makes time series analysis more accurate and avoids errors caused by inconsistent time fields. For example, in time-based analyses such as trend analysis and anomaly detection, the consistency of time fields allows data to correctly reflect the relationship between different time points and enhances the reliability of analysis results.

[0124] In this embodiment, based on the pre-input business data requirements, the business data requirements are entered into a preset data lake, and the step S1 of generating the data type corresponding to the business data requirements through the data lake further includes:

[0125] S11: Based on the cleaning measures preset by the data center, detecting the items to be cleaned required by the business data;

[0126] S12: Determine whether the item to be cleaned matches a preset washable item;

[0127] S3: If so, the business data demand is pre-cleaned to generate data to be confirmed, and the data to be confirmed is synchronized to the user's preset terminal. Through the data center, the operation authority of the business data demand provided by the user is obtained, wherein the data pre-cleaning specifically includes removing useless fields, processing missing values ​​and correcting erroneous data.

[0128] In this embodiment, the system detects the items to be cleaned of the business data requirements based on the cleaning measures pre-set by the data center, and then the system determines whether these items to be cleaned match the pre-set cleanable items to execute the corresponding steps; for example, when the system determines that the items to be cleaned of the business data requirements cannot match the pre-set cleanable items, the system will consider that some data in the business data requirements have a format or content type that does not meet the cleaning standards, and these data may cause errors or omissions during the cleaning process, making it difficult to perform standardized processing. The system will mark the unmatched items to be cleaned and record the specific content and source of these abnormal items for further analysis or manual processing. This can prevent data that does not meet the cleaning standards from entering the subsequent process, thereby improving the overall quality of the data. At the same time, according to the actual business needs, the system can try to dynamically adjust the cleaning rules. For example, if there are slight differences in the formats of the items to be cleaned (such as slightly different date formats), the system can expand or relax the cleaning rules to allow more formats of items to be cleaned to match the requirements of the cleanable items, so as to accommodate more data in the automated cleaning process. For data items that cannot be cleaned automatically, the system can notify the data maintenance personnel to review and confirm whether the data needs to be manually supplemented or converted to ensure data integrity. Especially when it comes to important fields, manual intervention can improve the efficiency of the data cleaning process. Accuracy; for example, when the system determines that the items to be cleaned of the business data requirements can match the pre-set cleanable items, the system will consider that these data can be standardized during the cleaning process, and the system will pre-clean the business data requirements. Data pre-cleaning specifically includes removing useless fields, processing missing values, and correcting erroneous data, generating data to be confirmed for the user to confirm again, synchronizing the data to be confirmed to the user's pre-set terminal, and obtaining the user's operating authority for the business data requirements through the data center; by removing useless fields, processing missing values, and correcting erroneous data, the system can eliminate interference information in the early stage of cleaning, ensure the accuracy and consistency of business data, and make subsequent Analysis or decision-making is based on more reliable data sources, thereby improving the accuracy of data application. At the same time, the pre-cleaned data to be confirmed is synchronized to the user terminal for the user to confirm again. This method not only allows users to grasp the final data quality, but also reduces the risk of misoperation due to inconsistent data formats or data anomalies, ensuring data security and the robustness of business decisions. After synchronizing the data to be confirmed and obtaining the user's operating permissions, the system provides users with the opportunity to directly review and confirm, allowing users to control the final data status. In different links of data processing, users' visibility and controllability of data are improved, which helps to enhance user participation and trust in data quality.

[0129] Reference Figure 2 , is a cross-industry asset management system for financial leasing in one embodiment of the present invention, comprising:

[0130] A generating module 10 is used to input the business data requirements into a preset data lake based on the pre-input business data requirements, and generate data types corresponding to the business data requirements through the data lake, wherein the data types specifically include text, images, videos, and log files;

[0131] A judgment module 20 is used to judge whether the data type is adaptable to a preset data lake platform;

[0132] An execution module 30 is used to organize the business data demand in the data lake according to a preset hierarchical structure according to a preset data lake architecture, construct an asset data standard corresponding to the data type, generate description information of the asset data standard, migrate the business data demand from a preset data center to the data lake, and divide the financing lease information of the business data demand, wherein the data lake architecture specifically includes a data storage layer, a data processing layer, and a data access layer, the description information specifically includes a source, a format, and a creation time, and the financing lease information specifically includes a financing lease contract, an asset evaluation, and risk management;

[0133] The second judgment module 40 is used to judge whether the financial lease information meets the preset integrity;

[0134] The second execution module 50 is used to identify the user's cross-industry asset data based on the financial leasing information, input the financial leasing information and the cross-industry asset data into the pre-trained risk dimension data model, update the user's cross-industry collaborative decision-making mechanism, and generate the financial leasing approval information corresponding to the business data demand through the cross-industry collaborative decision-making mechanism, wherein the cross-industry asset data specifically includes asset categories, customer groups and market popularity.

[0135] In this embodiment, the generation module 10 enters the business data requirements into the pre-set data lake based on the business data requirements pre-input into the data center, and generates data types corresponding to the business data requirements through the data lake. The data types specifically include asset text, asset images, asset videos and asset log files. Then the judgment module 20 judges whether these data types can be adapted to the preset data lake platform to execute the corresponding steps; for example, when the system determines that the data type corresponding to the business data requirement cannot be adapted to the preset data lake platform, the system will believe that the data type may lack the necessary tags or metadata, resulting in ineffective indexing or classification. The system will ensure that the data structure meets the requirements of the data lake platform through steps such as data cleaning, tag supplementation and metadata addition. At the same time, for files that lack tags, use automated tools or manual intervention to add necessary tags and metadata so that the data lake can effectively identify and process them, and store these data in an external database or cloud. On the platform, the data lake is then enabled to access and call the data through an index or association mechanism, which can avoid direct changes to the data lake structure and ensure the integrity and accessibility of the data; for example, when the system determines that the data type corresponding to the business data demand can adapt to the pre-set data lake platform, the execution module 30 will think that the data type can be effectively indexed or classified in the data lake, and the system will organize the business data demand in the data lake according to the pre-set data lake architecture, which specifically includes a data storage layer, a data processing layer, and a data access layer, using a pre-set hierarchical structure, construct asset data standards corresponding to the data type, generate description information of the asset data standard, and the description information specifically includes the source, format, and creation time, migrate these business data demands from the pre-set data center to the data lake, and divide the financing lease information of the business data demand, and the financing lease information specifically includes financing lease contracts, asset evaluation, and risk management;The system manages business data by adopting the layered architecture of the data lake (data storage layer, data processing layer and data access layer), so that different types of data can be processed and optimized accordingly at each level. The data storage layer can centrally manage the storage and backup of data, the data processing layer is responsible for cleaning and analyzing data, and the data access layer can support the needs of different users to access data. At the same time, it builds asset data standards corresponding to data types, standardizes the structure and attributes of data through unified description information, and ensures that data is consistent in the data lake. This standardization ensures data quality, enables cross-industry data to be stored and analyzed in a unified format, and provides a reliable data foundation for subsequent risk assessment and decision support. In addition, through effective indexing and classification of data types, the data lake can quickly find data that meets specific query conditions, so that the system can query, classify and filter data more flexibly, especially when cross-industry data integration and analysis are required, it can quickly obtain relevant information and realize data association, greatly improving the efficiency of data access. The business data is divided into specific categories such as financial leasing contracts, asset evaluation and risk management, which facilitates the refined management of financial leasing business data, making the data more in line with business needs, thereby helping the system to conduct risk analysis, customer evaluation and asset management more accurately. The second judgment module 40 then judges whether the financial leasing information can meet the pre-set integrity, so as to execute the corresponding steps; for example, when the system determines that the financial leasing information required by the business data cannot meet the pre-set integrity, the system will consider that the information is incomplete or inaccurate, and the system will format the data, unify the date, currency and text encoding formats, so that all financial leasing information meets the preset format standards, and add metadata such as source and creation time to the incomplete data to ensure the traceability and reliability of the data in subsequent processing, and mark the data that does not meet the integrity requirements so that it can be identified in the subsequent risk assessment process. Identify and exclude these data to avoid negative impact on the overall evaluation results; for example, when the system determines that the financing lease information required by the business data meets the pre-set integrity, the second execution module 50 will consider that there is no problem of incomplete data in the information, and the system will identify the user's cross-industry asset data based on the financing lease information. The cross-industry asset data specifically includes asset categories, customer groups and market popularity, and input these financing lease information and cross-industry asset data into the pre-trained risk dimension data model, update the user's cross-industry collaborative decision-making mechanism, and generate financing lease approval information corresponding to the business data requirements through the cross-industry collaborative decision-making mechanism;The system combines financial leasing information with cross-industry asset data, such as asset categories, customer groups, and market popularity, to provide a richer reference for risk assessment. Compared with single-dimensional data analysis, comprehensive multi-dimensional information can help the model identify potential risks more comprehensively, especially in a complex market environment. This multi-dimensional data integration is particularly important. At the same time, by updating the user's cross-industry collaborative decision-making mechanism, the system can collaboratively use information, analysis, and decision-making in multi-department and cross-industry cooperation, provide management with smarter decision-making support, and improve the efficiency and accuracy of financial leasing approval. Based on the intelligent risk model and cross-industry collaborative mechanism, the system can quickly analyze business data needs and generate financial leasing approval information, which can help enterprises quickly respond to market changes and improve the market response speed of financial leasing business by shortening the approval process and response time. ;

[0136] In this embodiment, it also includes:

[0137] An identification module, configured to identify the industry type corresponding to the business data demand based on the source data pre-recorded in the data lake, wherein the source data specifically includes structured data, semi-structured data, and unstructured data;

[0138] A third judgment module is used to judge whether the industry type covers the source data;

[0139] The third execution module is used to, if not, obtain the data source of the business data demand according to the industry type, extract the corresponding business data from the data source, convert the business data into a data format preset by the data lake, and regularly synchronize and update the new data of the business data in the data lake within a preset time period, wherein the data source specifically includes production equipment sensor data, inventory management data and supply chain data.

[0140] In this embodiment, the system identifies the industry types corresponding to the business data requirements based on the source data pre-collected in the data lake, which specifically includes structured data, semi-structured data, and unstructured data. The system then determines whether these industry types cover the source data to execute the corresponding steps. For example, when the system determines that the industry type corresponding to the business data requirement can cover the source data, the system will believe that a comprehensive data source related to the industry can be found in the data lake. The system will automatically retrieve the source data related to the business requirement from the data lake, classify it as structured, semi-structured or unstructured data, and perform preliminary data integration, including classifying the source data according to the business requirement type. Screening, such as selecting financial data, customer profiles, and market dynamics that match the industry type, and using the hierarchical structure of the data lake to classify the extracted source data into appropriate levels according to data type and content, and store them in the data storage layer, data processing layer, and data access layer to support subsequent analysis and access operations, and input the pre-processed source data into the corresponding analysis model, combined with the specific goals of the business data requirements, analyze the multi-dimensional industry data, including market trend forecasts, customer demand assessments, financial risk analysis, etc., in order to provide reliable data support for business decisions; for example, when the system determines that the industry type corresponding to the business data requirements cannot cover the source data, at this time The system will think that it is impossible to find a comprehensive data source related to the industry in the data lake. The system will obtain the data source of business data needs according to the industry type. The data source specifically includes production equipment sensor data, inventory management data and supply chain data, extract the corresponding business data from these data sources, convert these business data into the data format pre-set in the data lake, and regularly update the new data of the business data in the data lake within the pre-set time period; by extracting data from external data sources such as production equipment sensor data, inventory management data and supply chain data, the system can expand the coverage of the data lake, so that the data lake covers the data needs of more industry types, so that even if there is no direct The system can also flexibly respond to various business needs by connecting to related data, improving the adaptability and scalability of the data lake. At the same time, after the external data is converted into the preset format of the data lake, it is managed in a unified manner with the existing data in the data lake. The standardization of this format allows data from different sources to be accessed and analyzed on a unified platform, ensuring data consistency and ease of call, laying the foundation for cross-industry analysis and subsequent integration, and regularly synchronizing business data to the data lake to keep the data updated and promptly reflect changes in the external environment. This can avoid analysis errors caused by data lags and ensure that risk assessments and business decisions are based on the latest information, thereby improving the accuracy of risk assessments and the timeliness of decision-making.

[0141] In this embodiment, the execution module further includes:

[0142] An identification unit, configured to identify key fields in the data fields based on the data fields preset in the financial lease information, wherein the key fields specifically include a contract number, a lease amount, a lease term, a customer ID, and an asset number;

[0143] A judging unit, used to judge whether the key field matches a preset unified format;

[0144] and an execution unit, configured to, if yes, associate the financial leasing information with the user by using a preset identifier according to the key field, establish index information of the financial leasing information in the data lake, and grant the user access rights to the index information.

[0145] In this embodiment, the system identifies the key fields in the data fields based on the pre-set data fields of the financial leasing information. The key fields specifically include the contract number, lease amount, lease term, customer ID and asset number. Then the system determines whether these key fields match the pre-set unified format to execute the corresponding steps. For example, when the system determines that the key fields in the data fields cannot match the pre-set unified format, the system will consider that the data format is inconsistent or does not meet the specifications, which may cause the data to be unable to be effectively processed or analyzed. The system will provide detailed error feedback, indicating which key fields do not meet the preset standards, such as non-numeric characters may appear in the contract number field, the lease amount field may contain illegal symbols, and the customer ID field may be empty or formatted incorrectly. At the same time, it records which data fields have been formatted and provides corresponding traceability functions to ensure that the data processing process is transparent and auditable, which is not only helpful for later error troubleshooting, but also ensures that key information is not lost during the processing process. In the subsequent data entry and processing links, the system can automatically detect and prevent any data that does not meet the unified format from entering the system by setting more stringent format verification rules, thereby improving data quality from the source. For example, when the system determines that the key fields in the data fields can match the pre-set unified format, the system will automatically detect and prevent any data that does not meet the unified format from entering the system by setting more stringent format verification rules. The unified format of the system will consider that the data format is consistent and can be effectively processed and analyzed. The system will associate the financial leasing information with the user according to the key fields and the pre-set identifiers, establish the index information of the financial leasing information in the data lake, and grant the user access rights to the index information. By establishing index information according to the key fields, the system can retrieve and manage data more efficiently. The relevant data and user data of each financial leasing information are associated through the index, which greatly improves the speed of data search and access. Especially in the case of large data volume, it can significantly shorten the retrieval time. At the same time, when granting users access rights to index information, the system can control the access rights of the data to ensure that only authorized users can access the relevant data, enhance the security of the data, and realize fine-grained permission control, avoiding the risk of unauthorized access or data leakage. In addition, the construction of data association and index can strengthen the relationship mining between different data, so that the system can extract valuable information from various types of data and optimize the accuracy and timeliness of business decisions. The establishment of data index and user rights makes cross-departmental or cross-industry data sharing and collaboration more convenient, that is, different departments can work together based on a unified data platform and quickly locate the required information through indexes, thereby improving work efficiency.

[0146] In this embodiment, it also includes:

[0147] A classification module is used to classify corresponding risk levels based on preset risk factors, and construct a risk dimension data model according to the risk levels, wherein the risk factors specifically include asset risk, customer credit risk and market risk, and the risk levels specifically include customer level, contract level, asset level and market level;

[0148] A fourth judgment module is used to judge whether the risk dimension data model can generate a risk assessment result;

[0149] The fourth execution module is used to, if not, obtain the error log of the risk dimension data model during operation, apply a preset outlier filter to identify extreme values ​​of the cross-industry asset data from the error log, and limit the user to perform risk assessment based on the extreme values, wherein the extreme values ​​specifically include extremely high lease amounts, too low customer credit scores, and excessively long lease terms.

[0150] In this embodiment, the system divides the corresponding risk levels based on pre-set risk factors. The risk factors specifically include asset risk, customer credit risk and market risk. The risk levels specifically include customer level, contract level, asset level and market level. A risk dimension data model is constructed based on these risk levels, and then the system determines whether the risk dimension data model can generate risk assessment results to execute the corresponding steps; for example, when the system determines that the risk dimension data model can generate risk assessment results, the system will consider that the model has the ability to generate effective and reliable risk assessment results and can accurately assess various risk factors. The system will conduct a comprehensive assessment of the business based on the preset risk factors and corresponding risk levels. These risk factors and levels will be combined into the model to form a comprehensive risk assessment report, which will include the assessment results of different risk dimensions. Once the risk assessment results are generated, the system will promote cross-departmental collaboration, especially the business department, risk control department and financial department. By sharing the risk assessment report, all parties can carry out more targeted work based on the assessment results. For example, the financial department adjusts the financing amount according to the risk assessment results, the risk department adjusts the risk control strategy, and the business department optimizes customer communication. In addition, according to market changes, asset value fluctuations or customer behavior changes, the system can adjust the parameters of the risk dimension data model in real time to ensure that the assessment results are always synchronized with the external environment. When the system determines that the risk dimension data model cannot generate risk assessment results, the system will consider that the user's financial leasing information and cross-industry asset data are abnormal. The system will obtain the error log of the risk dimension data model during operation, and apply the pre-set abnormal value filter to identify the extreme values ​​of the cross-industry asset data from the error log. The extreme values ​​include extremely large lease amounts, too low customer credit scores, and excessively long lease terms. Users are restricted from conducting risk assessments based on different extreme values. By obtaining the error log of the risk dimension data model during operation, the system can quickly locate and identify abnormal points in the data processing process, so that the system can accurately identify which data items or data sets have problems, thereby effectively reducing The system can also detect errors or invalid outputs in the evaluation, and at the same time, the fault tolerance of the system is enhanced by introducing an outlier filtering mechanism in the system. When abnormal data appears, the system can identify and correct it in time to ensure that its operation will not fail due to data problems. This ensures that the system can still stably and efficiently output effective risk assessment results when processing large-scale data, and by setting different restrictions on different extreme values, the system can flexibly adjust risk management strategies. For example, for extremely large lease amounts, the system may automatically set a stricter approval process, and for customers with too low credit scores, it may require additional guarantees or other risk mitigation measures. This flexibility enhances the accuracy and timeliness of risk control.

[0151] In this embodiment, the second execution module further includes:

[0152] A synchronization unit, configured to synchronize the decision variable information preset by the data center for the business data demand to a preset industry department, and establish the approval standard value of the user through the industry department, wherein the decision variable information specifically includes a customer credit score, an asset assessment value, and contract terms;

[0153] A second judgment unit is used to judge whether the approval standard value reaches a preset audit benchmark;

[0154] The second execution unit is used to share the user and his / her financial leasing information to the digital collaboration platform of the industry department according to the execution authority preset by the data center, and to build the financial leasing approval information of the user in real time on the digital collaboration platform.

[0155] In this embodiment, the system pre-sets decision variable information based on the data center's business data needs. The decision variable information specifically includes customer credit scores, asset appraisal values, and contract terms. These decision variable information is synchronized to pre-set industry departments, and approval standard values ​​belonging to users are established through different industry departments. Then the system determines whether the user's approval standard value reaches the pre-set audit benchmark to execute the corresponding steps. For example, when the system determines that the user's approval standard value does not reach the pre-set audit benchmark, the system will believe that the user's financial leasing application has certain risks and cannot meet the company's financing requirements or credit standards. The system will automatically generate an error or warning message, marking that the user's approval standard value does not reach the preset benchmark. The feedback will clearly indicate which decision variable caused the approval standard value to fail to meet the standard. At the same time, a buffer period is set to allow customers to adjust their financial status, provide supplementary information, or modify contract terms within a specified time, thereby increasing the approval standard value. The approval standard is regularly optimized based on historical audit results, market changes, and customer feedback. Different approval benchmarks can be set for different industries and different customer types, making the approval process more flexible and adaptable to market needs. For example, when the system determines that the user's approval standard value reaches the pre-set audit benchmark, the system will automatically generate an error or warning message to mark that the user's approval standard value does not reach the preset benchmark. The system will consider that no risk has been detected in the user's financing lease application. The system will share the user and his financing lease information to the digital collaboration platform of the industry department according to the execution authority pre-set by the data center, and build the user's financing lease approval information in real time on the digital collaboration platform; the system can obtain the user's financing lease information in real time through different industry departments, and work together on the digital platform to avoid information islands and improve the communication efficiency between departments, that is, all relevant departments can view and update information on the same platform to ensure transparency and consistency in the financing lease approval process, thereby reducing communication errors and information loss. At the same time, data judgment is made based on the preset review benchmark to reduce the deviation caused by human factors. The information of users who have been confirmed to meet the conditions is shared on the digital platform, which can ensure that all decisions and approvals are based on the same and unified data to ensure the accuracy of decisions. After sharing the financing lease information in real time and generating the approval information, the digital platform can also collect data feedback to help enterprises monitor the bottlenecks and potential problems of the approval process. The system can further optimize the approval standards and decision-making models based on these feedbacks, and by setting standardized approval benchmarks and data sharing mechanisms, the approval process of all financing lease applications becomes more standardized and consistent, thereby promoting the standardization and continuous optimization of enterprise business processes.

[0156] In this embodiment, the judgment module further includes:

[0157] An acquisition unit, used to acquire a time field preset by the business data requirement;

[0158] A third judgment unit is used to judge whether the time field matches the timestamp unit preset by the data lake platform;

[0159] The third execution unit is used to unify the time field according to the timestamp unit if not, and reorder the timestamp partitions required by the business data according to the time window of the timestamp unit.

[0160] In this embodiment, the system obtains the time fields pre-set by the business data requirements, and then determines whether these time fields match the timestamp units pre-set by the data lake platform to execute the corresponding steps; for example, when the system determines that the time fields pre-set by the business data requirements can match the timestamp units pre-set by the data lake platform, the system will consider that the time field format in the business data requirements is consistent with the timestamp units of the data lake platform, which means that the system can successfully parse these time data without additional conversion or processing steps. The system will directly enter the time field information in the business data requirements into the data lake platform without additional conversion, ensuring the originality and accuracy of the time data, and at the same time create a time series index based on these time fields. The time series index helps to quickly locate data in a specific time period and improve the efficiency of data query and analysis, especially when time period comparison, trend analysis or anomaly detection is required, which can significantly speed up the processing speed, and establish a synchronization strategy with other data sources based on the matching timestamps to ensure that the business data in the data lake is consistent with the external data source, so that all time-related data can be kept synchronized and updated in multi-system or cross-departmental collaborative work; for example, when the system determines that the time fields pre-set by the business data requirements can match the timestamp units pre-set by the data lake platform, the system will consider that the time field format in the business data requirements is consistent with the timestamp units of the data lake platform, which means that the system can successfully parse these time data without additional conversion or processing steps. The system will directly enter the time field information in the business data requirements into the data lake platform without additional conversion, ensuring the originality and accuracy of the time data, and at the same time create a time series index based on these time fields. The time series index helps to quickly locate data in a specific time period and improve the efficiency of data query and analysis, especially when time period comparison, trend analysis or anomaly detection is required, which can significantly speed up the processing speed, and establish a synchronization strategy with other data sources based on the matching timestamps to ensure that the business data in the data lake is consistent with the external data source, so that all time If the time field cannot match the timestamp unit pre-set by the data lake platform, the system will think that the time field format in the business data requirements is inconsistent with the timestamp unit of the data lake platform and cannot parse these time data. The system will unify the time field according to the timestamp unit and reorder the timestamp partitions of the business data requirements according to the time windows of these timestamp units. By unifying the time field into the timestamp unit of the data lake platform, the system can ensure that the time field in the business data requirements is consistent with other stored data. This unified format allows data across data sources to be directly integrated, improves the overall consistency of the data, and reduces ambiguity in the analysis and query process. At the same time, after the timestamp units are consistent and reordered by time windows, the system can efficiently establish a time partition index, which helps to accelerate data query and retrieval based on time fields, especially in a data lake environment with large amounts of data, which significantly improves data processing and analysis efficiency. The unified timestamp unit makes time series analysis more accurate and avoids errors caused by inconsistent time fields. For example, in time-based analyses such as trend analysis and anomaly detection, the consistency of time fields allows data to correctly reflect the relationship between different time points and enhances the reliability of analysis results.

[0161] In this embodiment, the generation module further includes:

[0162] A detection unit, configured to detect the items to be cleaned required by the business data based on the cleaning measures preset by the data center;

[0163] A fourth judgment unit, used to judge whether the item to be cleaned matches a preset washable item;

[0164] The fourth execution unit is used to pre-clean the business data requirements, generate data to be confirmed, synchronize the data to be confirmed to the user's preset terminal, and obtain the operation authority of the business data requirements provided by the user through the data center, wherein the data pre-cleaning specifically includes removing useless fields, processing missing values ​​and correcting erroneous data.

[0165] In this embodiment, the system detects the items to be cleaned of the business data requirements based on the cleaning measures pre-set by the data center, and then the system determines whether these items to be cleaned match the pre-set cleanable items to execute the corresponding steps; for example, when the system determines that the items to be cleaned of the business data requirements cannot match the pre-set cleanable items, the system will consider that some data in the business data requirements have a format or content type that does not meet the cleaning standards, and these data may cause errors or omissions during the cleaning process, making it difficult to perform standardized processing. The system will mark the unmatched items to be cleaned and record the specific content and source of these abnormal items for further analysis or manual processing. This can prevent data that does not meet the cleaning standards from entering the subsequent process, thereby improving the overall quality of the data. At the same time, according to the actual business needs, the system can try to dynamically adjust the cleaning rules. For example, if there are slight differences in the formats of the items to be cleaned (such as slightly different date formats), the system can expand or relax the cleaning rules to allow more formats of items to be cleaned to match the requirements of the cleanable items, so as to accommodate more data in the automated cleaning process. For data items that cannot be cleaned automatically, the system can notify the data maintenance personnel to review and confirm whether the data needs to be manually supplemented or converted to ensure data integrity. Especially when it comes to important fields, manual intervention can improve the efficiency of the data cleaning process. Accuracy; for example, when the system determines that the items to be cleaned of the business data requirements can match the pre-set cleanable items, the system will consider that these data can be standardized during the cleaning process, and the system will pre-clean the business data requirements. Data pre-cleaning specifically includes removing useless fields, processing missing values, and correcting erroneous data, generating data to be confirmed for the user to confirm again, synchronizing the data to be confirmed to the user's pre-set terminal, and obtaining the user's operating authority for the business data requirements through the data center; by removing useless fields, processing missing values, and correcting erroneous data, the system can eliminate interference information in the early stage of cleaning, ensure the accuracy and consistency of business data, and make subsequent Analysis or decision-making is based on more reliable data sources, thereby improving the accuracy of data application. At the same time, the pre-cleaned data to be confirmed is synchronized to the user terminal for the user to confirm again. This method not only allows users to grasp the final data quality, but also reduces the risk of misoperation due to inconsistent data formats or data anomalies, ensuring data security and the robustness of business decisions. After synchronizing the data to be confirmed and obtaining the user's operating permissions, the system provides users with the opportunity to directly review and confirm, allowing users to control the final data status. In different links of data processing, users' visibility and controllability of data are improved, which helps to enhance user participation and trust in data quality.

[0166] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A cross-industry asset management method for financial leasing, characterized in that: The following steps are involved: Based on the pre-input business data requirements, the business data requirements are entered into a preset data lake, and data types corresponding to the business data requirements are generated through the data lake, wherein the data types specifically include text, images, videos, and log files; Determine whether the data type is adaptable to the preset data lake platform; If it is possible, then according to the preset data lake architecture, the preset hierarchical structure is adopted to organize the data of the business data demand in the data lake, construct the asset data standard corresponding to the data type, generate the description information of the asset data standard, migrate the business data demand from the preset data center to the data lake, and divide the financing lease information of the business data demand, wherein the data lake architecture specifically includes a data storage layer, a data processing layer and a data access layer, the description information specifically includes the source, format and creation time, and the financing lease information specifically includes financing lease contracts, asset evaluation and risk management; Determine whether the financial lease information meets the preset integrity; If it meets the requirements, the user's cross-industry asset data is identified based on the financial leasing information, the financial leasing information and the cross-industry asset data are input into the pre-trained risk dimension data model, the user's cross-industry collaborative decision-making mechanism is updated, and the financial leasing approval information corresponding to the business data requirements is generated through the cross-industry collaborative decision-making mechanism, wherein the cross-industry asset data specifically includes asset categories, customer groups and market popularity.

2. The cross-industry asset management method for financial leasing according to claim 1 is characterized in that: Before the step of organizing data in the data lake according to the preset data lake architecture and using a preset hierarchical structure to meet the business data requirements, the step further includes: Based on the source data pre-collected in the data lake, identifying the industry type corresponding to the business data demand, wherein the source data specifically includes structured data, semi-structured data and unstructured data; Determine whether the industry type covers the source data; If not, then based on the industry type, obtain the data source of the business data requirement, extract the corresponding business data from the data source, convert the business data into the data format preset by the data lake, and regularly synchronize and update the new data of the business data in the data lake within a preset time period, wherein the data source specifically includes production equipment sensor data, inventory management data and supply chain data.

3. The cross-industry asset management method for financial leasing according to claim 1 is characterized in that: The step of migrating the business data demand from the preset data center to the data lake and dividing the financing lease information of the business data demand also includes: Based on the data fields preset in the financial lease information, identifying key fields in the data fields, wherein the key fields specifically include contract number, lease amount, lease term, customer ID and asset number; Determining whether the key field matches a preset unified format; If so, based on the key field, a preset identifier is applied to associate the financial leasing information with the user, index information of the financial leasing information in the data lake is established, and the user is granted access rights to the index information.

4. The cross-industry asset management method for financial leasing according to claim 1 is characterized in that: After the step of inputting the financial lease information and the cross-industry asset data into the pre-trained risk dimension data model, the method further includes: Based on the preset risk factors, the corresponding risk levels are divided, and a risk dimension data model is constructed according to the risk levels, wherein the risk factors specifically include asset risk, customer credit risk and market risk, and the risk levels specifically include customer level, contract level, asset level and market level; Determining whether the risk dimension data model can generate risk assessment results; If not, obtain the error log of the risk dimension data model during runtime, apply the preset outlier filter to identify the extreme values ​​of the cross-industry asset data from the error log, and limit the user to perform risk assessment based on the extreme values, wherein the extreme values ​​specifically include extremely high lease amounts, too low customer credit scores, and excessively long lease terms.

5. The cross-industry asset management method for financial leasing according to claim 1 is characterized in that: The step of updating the user's cross-industry collaborative decision-making mechanism and generating the financing lease approval information corresponding to the business data demand through the cross-industry collaborative decision-making mechanism also includes: Based on the decision variable information preset by the data center for the business data demand, the decision variable information is synchronized to the preset industry department, and the approval standard value of the user is established through the industry department, wherein the decision variable information specifically includes customer credit score, asset assessment value and contract terms; Determine whether the approval standard value reaches the preset review benchmark; If so, the user and his / her financial leasing information will be shared to the digital collaboration platform of the industry department according to the execution authority preset by the data center, and the financial leasing approval information of the user will be constructed in real time on the digital collaboration platform.

6. The cross-industry asset management method for financial leasing according to claim 1 is characterized in that: The step of determining whether the data type is adaptable to the preset data lake platform also includes: Obtain the time field preset by the business data requirement; Determine whether the time field matches a timestamp unit preset by the data lake platform; If not, the time fields are unified according to the timestamp unit, and the timestamp partitions required by the business data are reordered according to the time window of the timestamp unit.

7. The cross-industry asset management method for financial leasing according to claim 1 is characterized in that: The step of inputting the business data requirements into a preset data lake based on the pre-input business data requirements and generating data types corresponding to the business data requirements through the data lake further includes: Based on the cleaning measures preset by the data center, detecting the items to be cleaned required by the business data; Determining whether the item to be cleaned matches a preset washable item; If so, the business data requirements are pre-cleaned to generate data to be confirmed, and the data to be confirmed is synchronized to the user's preset terminal. Through the data center, the operation permissions for the business data requirements provided by the user are obtained, wherein the data pre-cleaning specifically includes removing useless fields, processing missing values, and correcting erroneous data.

8. A cross-industry asset management system for financial leasing, characterized in that: include: A generation module, configured to input the pre-input business data requirements into a preset data lake, and generate data types corresponding to the business data requirements through the data lake, wherein the data types specifically include text, images, videos, and log files; A judgment module, used to judge whether the data type is adaptable to a preset data lake platform; An execution module, for organizing data of the business data demand in the data lake using a preset hierarchical structure according to a preset data lake architecture, if possible, constructing an asset data standard corresponding to the data type, generating description information of the asset data standard, migrating the business data demand from a preset data center to the data lake, and dividing the financing lease information of the business data demand, wherein the data lake architecture specifically includes a data storage layer, a data processing layer, and a data access layer, the description information specifically includes a source, a format, and a creation time, and the financing lease information specifically includes a financing lease contract, an asset evaluation, and risk management; The second judgment module is used to judge whether the financial lease information meets the preset integrity; The second execution module is used to identify the user's cross-industry asset data based on the financial leasing information, input the financial leasing information and the cross-industry asset data into the pre-trained risk dimension data model, update the user's cross-industry collaborative decision-making mechanism, and generate the financial leasing approval information corresponding to the business data demand through the cross-industry collaborative decision-making mechanism, wherein the cross-industry asset data specifically includes asset categories, customer groups and market popularity.

9. The cross-industry asset management system for financial leasing according to claim 8, characterized in that: Also includes: An identification module, configured to identify the industry type corresponding to the business data demand based on the source data pre-recorded in the data lake, wherein the source data specifically includes structured data, semi-structured data, and unstructured data; A third judgment module is used to judge whether the industry type covers the source data; The third execution module is used to, if not, obtain the data source of the business data demand according to the industry type, extract the corresponding business data from the data source, convert the business data into a data format preset by the data lake, and regularly synchronize and update the new data of the business data in the data lake within a preset time period, wherein the data source specifically includes production equipment sensor data, inventory management data and supply chain data.

10. The cross-industry asset management system for financial leasing according to claim 8, characterized in that: The execution module also includes: An identification unit, configured to identify key fields in the data fields based on the data fields preset in the financial lease information, wherein the key fields specifically include a contract number, a lease amount, a lease term, a customer ID, and an asset number; A judging unit, used to judge whether the key field matches a preset unified format; and an execution unit, configured to, if yes, associate the financial leasing information with the user by using a preset identifier according to the key field, establish index information of the financial leasing information in the data lake, and grant the user access rights to the index information.

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