Repayment plan generation method and device, electronic equipment and storage medium

Abstract

The invention discloses a repayment plan generation method and device, electronic equipment and a storage medium, and relates to the data processing technology, and the method comprises the steps that a data collection layer obtains user data and public information data of a target user; a calculation engine layer calculates cash flow pressure, total interest expenditure and expansion hedging income according to the user data and the public information data acquired by the data acquisition layer; performing iterative optimization on parameters of a calculation engine layer based on a multi-objective optimization algorithm according to the total interest expenditure, the cash flow pressure and the expansion hedging income until a non-inferior solution set is obtained; and the decision output layer generates repayment suggestions according to the non-inferior solution set output by the calculation engine layer. According to the invention, a repayment mode in which the total interest expenditure is minimum, the cash flow pressure is adaptive to the target user and the unexpanded hedging income is maximum can be obtained, and the reliability and timeliness of a repayment plan of a borrower are improved. Therefore, money can be saved for borrowers, and the sustainability of subsequent repayment can be ensured.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, electronic device, and storage medium for generating repayment plans. Background Technology

[0002] With the continuous development of banking services, personal loans, especially mortgages and auto loans, have increased rapidly. As the economic situation and borrowers' own financial strength change, borrowers will have a need for early repayment.

[0003] Currently, providing early repayment advice to borrowers relies on human experience to help them develop an early repayment plan. However, relying solely on human experience cannot quickly provide borrowers with a comprehensive repayment plan. Therefore, improving the reliability and timeliness of repayment plans has become an urgent issue to be addressed. Summary of the Invention

[0004] This invention provides a repayment plan generation method, apparatus, electronic device, and storage medium to solve the problems of poor reliability and timeliness of current repayment plans.

[0005] According to one aspect of the present invention, a method for generating a repayment plan is provided, comprising:

[0006] The data acquisition layer obtains user data and public information data of the target users;

[0007] The computing engine layer calculates cash flow pressure, total interest expense, and inflation hedging returns based on the user data and public information data collected by the data acquisition layer.

[0008] Based on a multi-objective optimization algorithm, the parameters of the computing engine layer are iteratively optimized according to the total interest expense, the cash flow pressure and the inflation hedging return until a non-dominated solution set is obtained.

[0009] The decision output layer generates repayment suggestions based on the set of non-dominated solutions output by the computation engine layer.

[0010] According to another aspect of the present invention, a repayment plan generation apparatus is provided, comprising:

[0011] The data acquisition module is used to acquire user data and public information data of the target user through the data acquisition layer;

[0012] The calculation module is used to calculate cash flow pressure, total interest expense, and inflation hedging returns based on the user data and public information data collected by the data acquisition layer through the calculation engine layer;

[0013] The optimization module is used to iteratively optimize the parameters of the computing engine layer based on the total interest expense, the cash flow pressure and the inflation hedging return according to the multi-objective optimization algorithm until a non-dominated solution set is obtained.

[0014] The output module is used to generate repayment suggestions through the decision output layer based on the non-dominated solution set output by the computation engine layer.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the repayment plan generation method according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the repayment plan generation method according to any embodiment of the present invention.

[0020] The technical solution of this invention involves a data acquisition layer that obtains user data and public information data of the target user; a calculation engine layer that calculates cash flow pressure, total interest expense, and inflation hedging return based on the user data and public information data collected by the data acquisition layer; an iterative optimization algorithm that optimizes the parameters of the calculation engine layer based on the total interest expense, cash flow pressure, and inflation hedging return until a non-dominated solution set is obtained; and a decision output layer that generates repayment suggestions based on the non-dominated solution set output by the calculation engine layer. Compared to the current practice of relying on manual experience to specify repayment plans, which results in poor reliability and timeliness, the technical solution provided by this invention can obtain the cash flow pressure, total interest expense, and inflation hedging return of the target user based on user data and public information data, and obtain a non-dominated solution set through multi-objective optimization. The repayment suggestions obtained based on the non-dominated solution set can comprehensively consider the three factors of the target user's cash flow pressure, total interest expense, and inflation hedging return, resulting in a repayment method that minimizes total interest expense, matches the cash flow pressure to the target user, and maximizes the inflation hedging return, thereby improving the reliability and timeliness of the borrower's repayment plan. It not only helps borrowers save money, but also ensures the sustainability of subsequent repayments and guarantees financial security.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a repayment plan generation method provided in an embodiment of the present invention;

[0024] Figure 2 This is a flowchart illustrating another repayment plan generation method provided in an embodiment of the present invention;

[0025] Figure 3 This is a flowchart illustrating another repayment plan generation method provided in an embodiment of the present invention;

[0026] Figure 4 This is a schematic diagram of a repayment plan generation device provided in an embodiment of the present invention;

[0027] Figure 5 This is a schematic diagram of another repayment plan generation device provided in an embodiment of the present invention;

[0028] Figure 6 This is a schematic diagram of the structure of an electronic device that implements the repayment plan generation method of this invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] The information collected in this invention embodiment is information and data authorized by the user or fully authorized by all parties. The collection, storage, use, processing, transmission, provision, disclosure, and application of this data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, and necessary confidentiality measures have been taken. This does not violate public order and good morals, and corresponding operation entry points are provided for users to choose to authorize or refuse. Users are provided with corresponding operation entry points to choose to agree to or refuse the automated decision-making results; if the user chooses to refuse, the process enters the expert decision-making process to avoid relevant legal and public opinion risks.

[0032] With the continuous development of banking services, personal loans, especially mortgages and auto loans, have increased rapidly. As the economic situation and borrowers' own financial strength change, borrowers will have a need for early repayment.

[0033] The inventors discovered that currently, when borrowers wish to prepay their loans, they must either repay on time according to the signed contract or apply for early repayment. There are two main methods for early repayment: one is to shorten the loan term while keeping the monthly payment the same; after prepaying part of the principal, the monthly payment remains unchanged, and the system automatically recalculates the repayment period. The other is to reduce the monthly payment while keeping the loan term the same; after prepaying part of the principal, the repayment period remains the same, but the monthly payment amount decreases. When borrowers are unsure which method is more suitable, they consult bank staff. When providing early repayment advice, bank staff rely on their experience to help borrowers develop an early repayment plan. However, relying solely on experience cannot quickly provide borrowers with a comprehensive repayment plan. Therefore, improving the reliability of repayment plans to overcome the aforementioned problems and ensure timeliness is a pressing issue.

[0034] Figure 1This is a flowchart illustrating a repayment plan generation method provided by an embodiment of the present invention. This embodiment is applicable to situations where repayment plans for borrowers are to be optimized, particularly for mortgage repayment plans. The method can be executed by a repayment plan generation device, which can be implemented in hardware and / or software and can be configured in electronic devices such as personal computers and servers. Figure 1 As shown, it includes:

[0035] Step S110: The data acquisition layer acquires the target user's user data and public information data.

[0036] Public information data includes economic data and information data. The data acquisition layer is used for unified access to multi-source heterogeneous data. Multi-source heterogeneous data includes user data, information data from public information data, and economic data from public information data. User data includes the target user's income, assets, and liabilities. The target user is the borrower, and the loan type can be a mortgage or car loan, etc. The information data from public information data can obtain the latest Loan Prime Rate (LPR), relevant information on pre-deposited funds, and tax data by connecting to the corresponding data source interface. The economic data from public information data includes China's Consumer Price Index (CPI), Gross Domestic Product (GDP), and interest rate data.

[0037] Step S120: The calculation engine layer calculates cash flow pressure, total interest expense, and inflation hedging returns based on the user data and public information data collected by the data acquisition layer.

[0038] Optionally, the computation engine layer includes an inflation hedging subnetwork, an information capture subnetwork, and a repayment risk adaptation subnetwork.

[0039] Accordingly, the computing engine layer calculates cash flow pressure, total interest expense, and inflation hedging returns based on the user data and public information data collected by the data acquisition layer, which can be implemented in the following ways:

[0040] The inflation hedging sub-network determines the inflation hedging return based on the economic data in the public information data. The information capture sub-network determines the total interest expense based on the information data, user data, and inflation hedging return in the public information data. The repayment risk matching sub-network determines the cash flow pressure based on the user data.

[0041] The above implementation method can calculate inflation hedging returns, total interest expenses, and cash flow pressure through inflation hedging subnetwork, information capture subnetwork, and repayment risk adaptation subnetwork, respectively, thereby improving the accuracy of the above data calculation and improving the reliability of the repayment plan.

[0042] Step S130: Based on the multi-objective optimization algorithm, iteratively optimize the parameters of the calculation engine layer according to the total interest expense, the cash flow pressure and the inflation hedging return until a non-dominated solution set is obtained.

[0043] Step S140: The decision output layer generates repayment suggestions based on the non-dominated solution set output by the computing engine layer.

[0044] Optionally, the decision output layer generates repayment suggestions based on the non-dominated solution set output by the computation engine layer, including:

[0045] The decision output layer generates a repayment plan, adaptation suggestions, and risk warning reports based on the non-dominated solution set output by the computing engine layer.

[0046] The above implementation method can explain the repayment strategy from multiple dimensions through repayment schedule, adaptation suggestions and risk warning reports, thereby improving readability.

[0047] The repayment plan generation method provided in this invention involves a data acquisition layer that obtains user data and public information data of the target user; a calculation engine layer that calculates cash flow pressure, total interest expense, and inflation hedging return based on the user data and public information data collected by the data acquisition layer; an iterative optimization algorithm that optimizes the parameters of the calculation engine layer based on the total interest expense, cash flow pressure, and inflation hedging return until a non-dominated solution set is obtained; and a decision output layer that generates repayment suggestions based on the non-dominated solution set output by the calculation engine layer. Compared to the current practice of relying on manual experience to specify repayment plans, which results in poor reliability and timeliness, the repayment plan generation method provided in this invention can obtain the cash flow pressure, total interest expense, and inflation hedging return of the target user based on user data and public information data, and obtain a non-dominated solution set through multi-objective optimization. The repayment suggestions obtained based on the non-dominated solution set can comprehensively consider the three factors of the target user's cash flow pressure, total interest expense, and inflation hedging return, resulting in a repayment method that minimizes total interest expense, matches cash flow pressure with the target user, and maximizes inflation hedging return, thereby improving the reliability and timeliness of the borrower's repayment plan. It not only helps borrowers save money, but also ensures the sustainability of subsequent repayments and guarantees financial security.

[0048] Figure 2 This is a flowchart illustrating another repayment plan generation method provided by an embodiment of the present invention. As a further explanation of the above embodiments, the method includes:

[0049] Step S210: The data acquisition layer acquires user data of the target user, information data from public information data, and economic data from public information data.

[0050] Step S220: The inflation hedging sub-network determines the inflation hedging return based on the economic data in the public information data.

[0051] Optionally, the inflation hedging sub-network determines the inflation hedging return based on the economic data in the public information data, which can be implemented as follows:

[0052] The inflation hedging sub-network predicts future inflation trends based on economic data from the public information data; determines the real interest rate corresponding to the target repayment time based on the inflation trend and the contract interest rate; and determines the inflation hedging return based on the real interest rate corresponding to the target repayment time.

[0053] It can integrate historical CPI, Producer Price Index (PPI), and monetary public information data based on Long Short-Term Memory (LSTM) networks to predict inflation trends over the next N years.

[0054] Based on the predicted inflation trend, the projected annual inflation rate for each target repayment period is obtained. The effective interest rate for different target repayment periods is then dynamically adjusted using the following formula.

[0055] Real interest rate = Contract interest rate - Projected annual inflation rate.

[0056] The contract interest rate is the repayment rate stipulated in the loan contract at the time the loan contract was signed.

[0057] The above implementation method can accurately predict future inflation trends, thereby accurately obtaining inflation hedging returns for future target repayment dates and improving accuracy.

[0058] Step S230: The information capture sub-network determines the total interest expense based on the information data, user data, and inflation hedging returns in the public information data.

[0059] Optionally, the information capture sub-network determines the total interest expense based on the information data, user data, and inflation hedging returns in the public information data, which can be implemented as follows:

[0060] The information capture sub-network obtains the deposit change information of the preset amount based on the information data in the public information data; determines the ratio of different types of loan amounts for the target user at the target repayment time based on the deposit change information of the preset amount; and determines the total interest expense of the target user based on the actual interest rate corresponding to the target repayment time and the ratio of the different types of loan amounts.

[0061] By connecting to relevant data source interfaces, the system can automatically obtain information on LPR adjustment announcements and interest rate changes for pre-set loans. Based on this information, text and semantic recognition are used to determine the deposit change information for the pre-set loans. This deposit change information represents the future change value of the pre-set loans. The system automatically calculates the optimal allocation between different loan types, including commercial and non-commercial loans, based on the pre-set deposit ratio. The interest amount for each loan type is calculated by combining the allocation ratios with the future repayment period. Finally, the total interest expense for the target user is obtained by summing the interest amounts for all loan types selected.

[0062] The above implementation method can effectively obtain the deposit change information of preset funds, and based on the deposit change information of preset funds and the user's commercial loan repayment amount, obtain a reasonable ratio between different types of loan funds, and thus accurately obtain the target user's future total interest expenditure.

[0063] Step S240: The repayment risk adaptation sub-network determines the cash flow pressure based on the user data.

[0064] Optionally, the repayment risk adaptation sub-network can determine cash flow pressure based on the user data, which can be implemented as follows:

[0065] The repayment risk matching sub-network determines the income stability information of the target user based on the user data.

[0066] The repayment flexibility range and repayment safety margin are determined based on the income stability information; the cash flow pressure is determined based on the repayment flexibility range and the repayment safety margin.

[0067] An Income Stability Index (ISI) can be constructed based on the target users' occupational attributes to adjust the repayment flexibility range. A stress test sandbox can be used to simulate debt repayment ability under extreme scenarios such as unemployment and illness to determine the repayment safety margin. Combining the target users' repayment flexibility range, repayment safety margin, and cash flow information, cash flow pressure can be determined.

[0068] The above implementation method assesses income stability information based on the target user's occupation, accurately estimates the target user's repayment ability through stress test sandboxes and other methods, thereby obtaining cash flow pressure and improving the accuracy of cash flow pressure calculation.

[0069] Step S250: Based on the multi-objective optimization algorithm, iteratively optimize the parameters of the calculation engine layer according to the total interest expense, the cash flow pressure and the inflation hedging return until a non-dominated solution set is obtained.

[0070] Optionally, a multi-objective optimization algorithm is used to iteratively optimize the parameters of the computation engine layer based on the total interest expense, the cash flow pressure, and the inflation hedging return until a non-dominated solution set is obtained, including:

[0071] Based on the analysis of the Pareto frontier corresponding to the total interest expense, the cash flow pressure, and the inflation hedging return, the network parameters in the inflation hedging subnetwork, the information capture subnetwork, and the repayment risk adaptation subnetwork are optimized according to the analysis results until the Pareto optimal solution is obtained, which serves as the non-dominated solution set of the multi-objective optimization algorithm.

[0072] Pareto frontier analysis involves simultaneously optimizing three objectives: total interest expense, cash flow pressure, and inflation hedging returns, to generate a non-dominated solution set.

[0073] Furthermore, strategy weights can be dynamically adjusted based on users' historical behavior data (such as past early repayment records) to achieve reinforcement learning optimization.

[0074] The above implementation method can accurately calculate the non-dominated solution set through the Pareto front, thereby improving the reliability of the repayment plan.

[0075] Step S260: The decision output layer generates repayment suggestions based on the non-dominated solution set output by the computing engine layer.

[0076] Through continuous iteration of calculations and optimization schemes at the computational engine layer, repayment suggestions are provided to users as a decision-making reference. These suggestions include various formats such as repayment schedules, matching recommendations, and risk warning reports.

[0077] Figure 3 This is a flowchart illustrating another repayment plan generation method provided by an embodiment of the present invention. As a further explanation of the above embodiments, the method includes:

[0078] Step S310: The data acquisition layer acquires user data of the target user, information data from public information data, and economic data from public information data.

[0079] Step S320: The inflation hedging sub-network predicts future inflation trends based on the economic data in the public information data; determines the actual interest rate corresponding to the target repayment time based on the inflation trend and the contract interest rate; and determines the inflation hedging return based on the actual interest rate corresponding to the target repayment time.

[0080] The above implementation method can accurately predict future inflation trends, thereby accurately obtaining inflation hedging returns for future target repayment dates and improving accuracy.

[0081] Step S330: The information capture sub-network obtains the deposit change information of the preset amount based on the information data in the public information data; determines the ratio of different types of loan amounts for the target user at the target repayment time based on the deposit change information of the preset amount; and determines the total interest expense of the target user based on the actual interest rate corresponding to the target repayment time and the ratio of different types of loan amounts.

[0082] The above implementation method can effectively obtain the deposit change information of preset funds, and based on the deposit change information of preset funds and the user's commercial loan repayment amount, obtain a reasonable ratio between different types of loan funds, and thus accurately obtain the target user's future total interest expenditure.

[0083] Step S340: The repayment risk adaptation sub-network determines the income stability information of the target user based on the user data; determines the repayment flexibility range and repayment safety margin based on the income stability information; and determines the cash flow pressure based on the repayment flexibility range and the repayment safety margin.

[0084] The above implementation method assesses income stability information based on the target user's occupation, accurately estimates the target user's repayment ability through stress test sandboxes and other methods, thereby obtaining cash flow pressure and improving the accuracy of cash flow pressure calculation.

[0085] Step S350: Analyze the Pareto frontier corresponding to the total interest expense, the cash flow pressure, and the inflation hedging return. Based on the analysis results, optimize the network parameters in the inflation hedging subnetwork, the information capture subnetwork, and the repayment risk adaptation subnetwork until a Pareto optimal solution is obtained, which serves as the non-dominated solution set for the multi-objective optimization algorithm.

[0086] Step S360: The decision output layer generates a repayment plan table, adaptation suggestions, and risk warning reports based on the non-dominated solution set output by the computing engine layer.

[0087] The above implementation method, through data collection, computing engine, and decision output, can effectively reduce total interest expenses and optimize repayment costs. Matching cash flow pressure with the target users' payment ability reduces repayment risk.

[0088] Figure 4 This invention provides a schematic diagram of a repayment plan generation device. This embodiment is applicable to situations where repayment plans for borrowers are optimized, particularly for mortgage repayment plans. Figure 4 As shown, the device includes: a data acquisition module 41, a calculation module 42, an optimization module 43, and an output module 44.

[0089] The acquisition module 41 is used to acquire user data and public information data of the target user through the data acquisition layer;

[0090] Calculation module 42 is used to calculate cash flow pressure, total interest expense and inflation hedging returns based on the user data and public information data collected by the data acquisition layer through the calculation engine layer;

[0091] Optimization module 43 is used to iteratively optimize the parameters of the computing engine layer based on the total interest expense, the cash flow pressure and the inflation hedging return according to a multi-objective optimization algorithm until a non-dominated solution set is obtained.

[0092] Output module 44 is used to generate repayment suggestions through the decision output layer based on the non-dominated solution set output by the computing engine layer.

[0093] The computing engine layer includes an inflation hedging subnetwork, an information capture subnetwork, and a repayment risk adaptation subnetwork.

[0094] Based on the above embodiments, optionally, such as Figure 5 As shown, the calculation module 42 includes: an inflation hedging submodule 421, an information capture submodule 422, and a risk adaptation submodule 423.

[0095] Inflation hedging submodule 421 is used to determine inflation hedging returns based on economic data in the public information data through the inflation hedging subnetwork.

[0096] Information capture submodule 422 is used to determine total interest expense through information capture subnetwork based on information data, user data and inflation hedging returns in the public information data;

[0097] Risk adaptation submodule 423 is used to determine cash flow pressure based on the user data through the repayment risk adaptation subnetwork.

[0098] Based on the above embodiments, optionally, the inflation hedging submodule 421 is used for:

[0099] The inflation hedging subnetwork predicts future inflation trends based on economic data from the public information data.

[0100] The actual interest rate corresponding to the target repayment period is determined based on the aforementioned inflation trend and contract interest rate.

[0101] Inflation hedging returns are determined based on the actual interest rate corresponding to the target repayment period.

[0102] Based on the above embodiments, optionally, the information capture submodule 422 is used for:

[0103] The information capture subnetwork obtains the deposit change information of the preset funds based on the information data in the public information data;

[0104] The allocation ratio of different types of loan funds for the target user at the target repayment time is determined based on the deposit change information of the preset funds.

[0105] The total interest expense of the target user is determined based on the actual interest rate corresponding to the target repayment period and the ratio between the different types of loan amounts.

[0106] Based on the above embodiments, optionally, the risk adaptation submodule 423 is used for:

[0107] The repayment risk matching sub-network determines the income stability information of the target user based on the user data.

[0108] The repayment flexibility range and repayment safety margin are determined based on the aforementioned income stability information;

[0109] The cash flow pressure is determined based on the repayment flexibility range and the repayment safety margin.

[0110] Based on the above embodiments, optionally, the optimization module 43 is used for:

[0111] Based on the analysis of the Pareto frontier corresponding to the total interest expense, the cash flow pressure, and the inflation hedging return, the network parameters in the inflation hedging subnetwork, the information capture subnetwork, and the repayment risk adaptation subnetwork are optimized according to the analysis results until the Pareto optimal solution is obtained, which serves as the non-dominated solution set of the multi-objective optimization algorithm.

[0112] Based on the above embodiments, optionally, the output module 44 is used for:

[0113] The decision output layer generates a repayment plan, adaptation suggestions, and risk warning reports based on the non-dominated solution set output by the calculation engine layer.

[0114] The repayment plan generation device provided in this embodiment of the invention includes a data acquisition module 41, used to acquire user data and public information data of the target user through a data acquisition layer; a calculation module 42, used to calculate cash flow pressure, total interest expense, and inflation hedging return based on the user data and public information data acquired by the data acquisition layer through a calculation engine layer; an optimization module 43, used to iteratively optimize the parameters of the calculation engine layer based on the total interest expense, cash flow pressure, and inflation hedging return using a multi-objective optimization algorithm until a non-dominated solution set is obtained; and an output module 44, used to generate repayment suggestions based on the non-dominated solution set output by the calculation engine layer through a decision output layer. Compared with the current situation where repayment plans are determined by human experience, resulting in poor reliability and timeliness, the repayment plan generation device provided in this embodiment of the invention can obtain the cash flow pressure, total interest expense, and inflation hedging return of the target user based on user data and public information data, and obtain a non-dominated solution set through multi-objective optimization. Repayment recommendations derived from non-dominated solution sets comprehensively consider three factors: the target user's cash flow pressure, total interest expense, and inflation hedging returns. This results in a repayment method that minimizes total interest expense, aligns with the target user's cash flow pressure, and maximizes inflation hedging returns, thereby improving the reliability and timeliness of the borrower's repayment plan. It not only helps borrowers save money but also ensures the sustainability of subsequent repayments, guaranteeing financial security.

[0115] The repayment plan generation device provided in this embodiment of the invention can execute the repayment plan generation method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0116] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0117] like Figure 6As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0118] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0119] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the repayment plan generation method.

[0120] In some embodiments, the repayment plan generation method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the repayment plan generation method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the repayment plan generation method by any other suitable means (e.g., by means of firmware).

[0121] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0122] Computer programs used to implement the repayment plan generation method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0123] The invention also provides a computer-readable storage medium storing computer instructions for causing a processor to execute a repayment plan generation method, including:

[0124] The data acquisition layer obtains user data and public information data of the target users;

[0125] The computing engine layer calculates cash flow pressure, total interest expense, and inflation hedging returns based on the user data and public information data collected by the data acquisition layer.

[0126] Based on a multi-objective optimization algorithm, the parameters of the computing engine layer are iteratively optimized according to the total interest expense, the cash flow pressure and the inflation hedging return until a non-dominated solution set is obtained.

[0127] The decision output layer generates repayment suggestions based on the set of non-dominated solutions output by the computation engine layer.

[0128] Based on the above embodiments, optionally, the calculation engine layer includes an inflation hedging sub-network, an information capture sub-network, and a repayment risk adaptation sub-network; correspondingly, the calculation engine layer calculates cash flow pressure, total interest expense, and inflation hedging returns based on the user data and public information data collected by the data acquisition layer, including:

[0129] The inflation hedging sub-network determines the inflation hedging return based on the economic data in the aforementioned public information data;

[0130] The information capture sub-network determines the total interest expense based on the information data, user data, and inflation hedging returns in the public information data.

[0131] The repayment risk adaptation sub-network determines cash flow pressure based on the user data.

[0132] Based on the above embodiments, optionally, the inflation hedging sub-network determines the inflation hedging return based on the economic data in the public information data, including:

[0133] The inflation hedging subnetwork predicts future inflation trends based on economic data from the aforementioned public information data.

[0134] The actual interest rate corresponding to the target repayment period is determined based on the aforementioned inflation trend and contract interest rate.

[0135] Inflation hedging returns are determined based on the actual interest rate corresponding to the target repayment period.

[0136] Based on the above embodiments, optionally, the information capture sub-network determines the total interest expense according to the information data, user data, and inflation hedging returns in the public information data, including:

[0137] The information capture sub-network obtains the deposit change information of the preset funds based on the information data in the public information data;

[0138] The allocation ratio of different types of loan funds for the target user at the target repayment time is determined based on the deposit change information of the preset funds.

[0139] The total interest expense of the target user is determined based on the actual interest rate corresponding to the target repayment period and the ratio between the different types of loan amounts.

[0140] Based on the above embodiments, optionally, the repayment risk adaptation sub-network determines cash flow pressure based on the user data, including:

[0141] The repayment risk matching sub-network determines the income stability information of the target user based on the user data.

[0142] The repayment flexibility range and repayment safety margin are determined based on the aforementioned income stability information;

[0143] The cash flow pressure is determined based on the repayment flexibility range and the repayment safety margin.

[0144] Based on the above embodiments, optionally, a multi-objective optimization algorithm is used to iteratively optimize the parameters of the computation engine layer according to the total interest expense, the cash flow pressure, and the inflation hedging return until a non-dominated solution set is obtained, including:

[0145] Based on the analysis of the Pareto frontier corresponding to the total interest expense, the cash flow pressure, and the inflation hedging return, the network parameters in the inflation hedging subnetwork, the information capture subnetwork, and the repayment risk adaptation subnetwork are optimized according to the analysis results until the Pareto optimal solution is obtained, which serves as the non-dominated solution set of the multi-objective optimization algorithm.

[0146] Based on the above embodiments, optionally, the decision output layer generates repayment suggestions based on the non-dominated solution set output by the computing engine layer, including:

[0147] The decision output layer generates a repayment plan, adaptation suggestions, and risk warning reports based on the non-dominated solution set output by the computing engine layer.

[0148] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0149] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0150] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0151] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0152] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0153] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for generating a repayment plan, characterized in that, include: The data acquisition layer obtains user data and public information data of the target users; The computing engine layer calculates cash flow pressure, total interest expense, and inflation hedging returns based on the user data and public information collected by the data acquisition layer. Based on a multi-objective optimization algorithm, the parameters of the computing engine layer are iteratively optimized according to the total interest expense, the cash flow pressure and the inflation hedging return until a non-dominated solution set is obtained. The decision output layer generates repayment suggestions based on the set of non-dominated solutions output by the computation engine layer.

2. The method according to claim 1, characterized in that, The calculation engine layer includes an inflation hedging subnetwork, an information capture subnetwork, and a repayment risk adaptation subnetwork. Accordingly, the calculation engine layer calculates cash flow pressure, total interest expense, and inflation hedging returns based on the user data and public information data collected by the data acquisition layer, including: The inflation hedging sub-network determines the inflation hedging return based on the economic data in the aforementioned public information data; The information capture sub-network determines the total interest expense based on the information data, user data, and inflation hedging returns in the public information data. The repayment risk adaptation sub-network determines cash flow pressure based on the user data.

3. The method according to claim 2, characterized in that, The inflation hedging sub-network determines inflation hedging returns based on economic data from the aforementioned public information data, including: The inflation hedging subnetwork predicts future inflation trends based on economic data from the aforementioned public information data. The actual interest rate corresponding to the target repayment period is determined based on the aforementioned inflation trend and contract interest rate. Inflation hedging returns are determined based on the actual interest rate corresponding to the target repayment period.

4. The method according to claim 3, characterized in that, The information capture subnetwork determines the total interest expense based on information data, user data, and inflation hedging returns from the aforementioned public information data, including: The information capture sub-network obtains the deposit change information of the preset funds based on the information data in the public information data; The allocation ratio of different types of loan funds for the target user at the target repayment time is determined based on the deposit change information of the preset funds. The total interest expense of the target user is determined based on the actual interest rate corresponding to the target repayment period and the ratio between the different types of loan amounts.

5. The method according to claim 4, characterized in that, The repayment risk adaptation sub-network determines cash flow pressure based on the aforementioned user data, including: The repayment risk matching sub-network determines the income stability information of the target user based on the user data. The repayment flexibility range and repayment safety margin are determined based on the aforementioned income stability information; The cash flow pressure is determined based on the repayment flexibility range and the repayment safety margin.

6. The method according to claim 5, characterized in that, Based on a multi-objective optimization algorithm, the parameters of the computation engine layer are iteratively optimized according to the total interest expense, the cash flow pressure, and the inflation hedging return until a non-dominated solution set is obtained, including: Based on the analysis of the Pareto frontier corresponding to the total interest expense, the cash flow pressure, and the inflation hedging return, the network parameters in the inflation hedging subnetwork, the information capture subnetwork, and the repayment risk adaptation subnetwork are optimized according to the analysis results until the Pareto optimal solution is obtained, which serves as the non-dominated solution set of the multi-objective optimization algorithm.

7. The method according to claim 1, characterized in that, The decision output layer generates repayment suggestions based on the non-dominated solution set output by the computation engine layer, including: The decision output layer generates a repayment plan, adaptation suggestions, and risk warning reports based on the non-dominated solution set output by the computing engine layer.

8. A repayment plan generation device, characterized in that, include: The data acquisition module is used to acquire user data and public information data of the target user through the data acquisition layer; The calculation module is used to calculate cash flow pressure, total interest expense, and inflation hedging returns based on the user data and public information data collected by the data acquisition layer through the calculation engine layer; The optimization module is used to iteratively optimize the parameters of the computing engine layer based on the total interest expense, the cash flow pressure and the inflation hedging return according to the multi-objective optimization algorithm until a non-dominated solution set is obtained. The output module is used to generate repayment suggestions through the decision output layer based on the non-dominated solution set output by the computation engine layer.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the repayment plan generation method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the repayment plan generation method according to any one of claims 1-7.

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