Business data processing method and device, computer equipment and storage medium

Abstract

The invention belongs to the technical field of artificial intelligence, and relates to a business data processing method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining overdue data of a target customer; obtaining a contract number from the overdue data, and determining a target operation system matched with the contract number; generating corresponding withholding data based on the client overdue data, and constructing a withholding request based on the withholding data and the client overdue data; sending the withholding request to the target operation system; receiving withholding progress data which is fed back by the target operation system and corresponds to the withholding request, and verifying the withholding progress data based on the verification strategy; if the withholding progress data passes the verification, judging whether the target client meets a withholding stopping condition or not based on the withholding progress data; and if yes, sending a withholding stopping request to the target operation system. The overdue data processing method and device can be applied to overdue data processing scenes in the field of financial science and technology, and the processing efficiency and processing intelligence of the overdue data are effectively improved through the method and device.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and can be applied to the field of fintech, particularly to methods, devices, computer equipment, and storage media for processing business data. Background Art

[0002] In the business scope of financial enterprises such as financial leasing companies and insurance enterprises, the financing business related to charging piles has become an important business growth point in recent years. Such businesses mainly target electric vehicle charging station operators, charging pile manufacturers, and installers, providing financial support to promote the construction and operation of charging piles. However, in the actual business operation process, financial enterprises face the problem of customers' overdue repayments, which not only affects the capital recovery of financial enterprises but also increases business risks.

[0003] Currently, in the processing of overdue data of customers who have overdue repayments, financial enterprises mainly adopt traditional business data reminder methods such as text messages and phone calls. Although these methods can remind customers to pay rent as soon as possible to a certain extent, they have the limitation of low processing efficiency. This business data reminder method requires a large amount of human and time costs. Especially in the case of a large number of customers and frequent overdue situations, it is often difficult to handle, resulting in low business processing efficiency. Summary of the Invention

[0004] The purpose of the embodiments of this application is to propose a method, device, computer equipment, and storage media for processing business data to solve the technical problem of low processing efficiency existing in the existing business data reminder methods.

[0005] In a first aspect, a method for processing business data is provided, including:

[0006] Obtain the overdue data of the target customer;

[0007] Obtain the contract number from the overdue data, and determine the target operation system that matches the contract number;

[0008] Generate corresponding withholding data based on the customer overdue data, and construct a withholding request based on the withholding data and the customer overdue data;

[0009] Send the withholding request to the target operation system;

[0010] Receive the withholding progress data corresponding to the withholding request feedback by the target operation system, and verify the withholding progress data based on a preset verification strategy;

[0011] If the withholding progress data passes the verification, determine whether the target customer meets the condition for stopping withholding based on the withholding progress data;

[0012] If so, send a stop withholding request to the target operating system.

[0013] In a second aspect, a processing device for business data is provided, including:

[0014] A first acquisition module, configured to acquire overdue data of a target customer;

[0015] A first processing module, configured to obtain a contract number from the overdue data and determine a target operating system that matches the contract number;

[0016] A second processing module, configured to generate corresponding withholding data based on the customer overdue data, and construct a withholding request based on the withholding data and the customer overdue data;

[0017] A first sending module, configured to send the withholding request to the target operating system;

[0018] A verification module, configured to receive withholding progress data corresponding to the withholding request feedback by the target operating system, and verify the withholding progress data based on a preset verification policy;

[0019] A judgment module, configured to, if the withholding progress data passes the verification, judge whether the target customer meets the stop withholding condition based on the withholding progress data;

[0020] A second sending module, configured to, if so, send a stop withholding request to the target operating system.

[0021] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned processing method for business data are implemented.

[0022] In a fourth aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned processing method for business data are implemented.

[0023] In the solution implemented by the above-mentioned method, apparatus, computer device, and storage medium for processing business data, overdue data of a target customer can be obtained; then, a contract number can be obtained from the overdue data, and a target operation system matching the contract number can be determined; thereafter, corresponding withholding data can be generated based on the customer overdue data, and a withholding request can be constructed based on the withholding data and the customer overdue data; subsequently, the withholding request can be sent to the target operation system; further, withholding progress data corresponding to the withholding request feedback by the target operation system can be received, and the withholding progress data can be verified based on a preset verification strategy; if the withholding progress data passes the verification, it can be determined whether the target customer meets the condition for stopping withholding based on the withholding progress data; if so, a stop withholding request can be sent to the target operation system. Through the present application, by obtaining a contract number from the overdue data of a target customer, determining a target operation system matching the contract number, then generating corresponding withholding data based on the customer overdue data, constructing a withholding request based on the withholding data and the customer overdue data, then sending the withholding request to the target operation system, and receiving the withholding progress data corresponding to the withholding request feedback by the target operation system, and further verifying the withholding progress data based on the use of a preset verification strategy, if the withholding progress data passes the verification, it can be determined whether the target customer meets the condition for stopping withholding based on the withholding progress data, and if it is detected that the target customer meets the condition for stopping withholding, a stop withholding request can be sent to the target operation system. In this way, through the processing method of the present application, it is possible to automatically, intelligently, and efficiently complete the withholding processing of the overdue data of the target customer, effectively improving the processing efficiency and intelligence of the overdue data, and helping to reduce business risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the solutions in the present application, the following will briefly introduce the drawings required for the description of the embodiments of the present application. Obviously, the following-described drawings are some embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0025] Figure 1 is an exemplary system architecture diagram to which the present application can be applied;

[0026] Figure 2 is a flowchart of an embodiment of the method for processing business data according to the present application;

[0027] Figure 3 is a schematic structural diagram of an embodiment of the apparatus for processing business data according to the present application;

[0028] Figure 4 is a schematic structural diagram of an embodiment of the computer device according to the present application. Detailed implementation manners

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs; the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion. The terms "first", "second", etc. in the specification and claims of this application or the above drawings are used to distinguish different objects and not to describe a specific order.

[0030] Reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0031] In order to enable those skilled in the technical field to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the drawings.

[0032] As Figure 1 shown, the system architecture 100 may include a terminal device 101, a network 102, and a server 103. The terminal device 101 may be a laptop computer 1011, a tablet computer 1012, or a mobile phone 1013. The network 102 is a medium for providing a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0033] The user may use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications may be installed on the terminal device 101, such as a web browser application, a shopping application, a search application, an instant messaging tool, an email client, a social platform software, etc.

[0034] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop 1011, tablet computer 1012, or mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop computer, a desktop computer, and so on.

[0035] The server 103 can be a server that provides various services, such as a background server that supports the pages displayed on the terminal device 101.

[0036] It should be noted that the method for processing service data provided in the embodiments of the present application is generally executed by the server / terminal device. Correspondingly, the device for processing service data is generally arranged in the server / terminal device.

[0037] It should be understood that Figure 1 the numbers of terminal devices, networks, and servers in

[0038] are merely illustrative. According to the implementation requirements, there can be any number of terminal devices, networks, and servers. Figure 2 Continuing to refer to

[0039] shows a flowchart of an embodiment of the method for processing service data according to the present application. According to different requirements, the order of the steps in this flowchart can be changed, and some steps can be omitted. The method for processing service data provided in the embodiments of the present application can be applied to any scenario that requires overdue data processing. Then, the method for processing service data can be applied to the products in these scenarios. For example, the overdue data processing in the financial insurance field. The method for processing service data includes the following steps:

[0040] In this embodiment, the electronic device on which the method for processing service data runs (such as Figure 1The server / terminal device shown can obtain the overdue data of target customers through wired connection or wireless connection. It should be noted that the above wireless connection methods can include, but are not limited to, 3G / 4G / 5G connection, Wi-Fi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods known now or developed in the future. The execution entity of this application can specifically be a financing business system, which can be simply referred to as the system. This system is a system for financial institutions to carry out financial business. This application can be applied to the overdue business processing scenario in the financing business related to charging piles carried out by financial enterprises such as financial leasing companies and banks. The overdue data of target customers can be collected from the financing business system of financial enterprises. The overdue data of target customers can at least include data such as the customer name corresponding to the target customer, contract number, installment number, current due rent amount (i.e., the amount to be withheld), current actual received rent amount, and overdue days. Among them, during the customer financing process, a withholding agreement is signed among the financial enterprise, the customer, and the charging pile operation system to clarify that when the customer fails to pay the rent overdue, the financial enterprise has the right to withhold the due rent through the charging pile operation system. In addition, the financial enterprise has preset overdue data pulling rules, including pulling time, pulling frequency, data fields, etc. Then the financing business system automatically pulls the customer overdue data from the database according to the set rules. And further verify the pulled data to ensure the accuracy and integrity of the data. Subsequently, the verified overdue data is stored in a specified database or data warehouse for use in subsequent steps.

[0041] The charging pile operation system is used to support the operation of the charging station platform for the charging station. After an enterprise purchases charging piles to complete the construction of the charging station, it usually needs to carry out charging service business based on the charging operation platform. After a charging user completes a charging order, the paid charging fee will first go to the charging operation platform. After the enterprise applies for a cash withdrawal on the charging operation platform, the funds are transferred to the enterprise account, providing a natural condition for withholding the rent of financial companies through the charging pile operation system. There are multiple charging pile operation systems in the current market. When financial enterprises carry out financing business related to charging piles, the relevant charging pile assets will also involve multiple charging pile operation systems.

[0042] Step S202: Obtain the contract number from the overdue data and determine the target operation system that matches the contract number.

[0043] In this embodiment, the required contract number can be obtained by extracting information from the above overdue data. Then, based on this contract number, the charging pile operation system corresponding to the overdue contract of the above target customer, that is, the above target operation system, can be matched.

[0044] Step S203: Generate corresponding withholding data based on the customer overdue data, and construct a withholding request based on the withholding data and the customer overdue data.

[0045] In this embodiment, the customer name, contract number, installment number, and amount to be withheld can be extracted from the above customer overdue data, and the obtained customer name, contract number, installment number, and amount to be withheld are integrated to obtain the corresponding withholding data. Among them, for the specific implementation process of constructing the withholding request based on the withholding data and the customer overdue data, this application will further describe the details in subsequent specific embodiments and will not elaborate here too much.

[0046] Step S204: Send the withholding request to the target operation system.

[0047] In this embodiment, after the construction of the withholding request is completed, subsequently, the withholding interface of the above target operation system can be called to send the withholding request to the target operation system.

[0048] Step S205: Receive the withholding progress data corresponding to the withholding request feedback by the target operation system, and verify the withholding progress data based on a preset verification policy.

[0049] In this embodiment, after receiving the withholding request message, the target operation system will verify the information of the withholding request to ensure the accuracy and legality of the information. Then, according to the request information, the corresponding amount is deducted from the charging income of the target customer. And at the same time, log information such as the time of the withholding operation, customer name, contract number, installment number, and withholding amount is recorded. Subsequently, a payment is made to the financial enterprise, that is, the withheld amount is transferred to the financial enterprise account by means of bank transfer, etc. Finally, a withholding feedback message containing the withholding progress data is sent to the financial enterprise, and the withholding progress data may include information such as customer name, contract number, installment number, and withheld amount. Among them, for the specific implementation process of verifying the withholding progress data based on a preset verification policy, this application will further describe the details in subsequent specific embodiments and will not elaborate here too much.

[0050] Step S206: If the withholding progress data passes the verification, determine whether the target customer meets the condition for stopping withholding based on the withholding progress data.

[0051] In this embodiment, for the specific implementation process of determining whether the target customer meets the condition for stopping withholding based on the withholding progress data, this application will further describe the details in subsequent specific embodiments and will not elaborate here too much.

[0052] Step S207: If so, send a stop withholding request to the target operation system.

[0053] In this embodiment, if it is detected that the target customer meets the conditions for stopping the withholding, a request to stop the withholding is sent to the charging pile operation system. Specifically, according to the information in the request to stop the withholding, a stop request message that conforms to the interface specification of the charging pile operation system can be constructed. Then, the stop withholding interface of the charging pile operation system is called through the system to send the stop request message. After receiving the stop request message, the charging pile operation system performs the stop withholding operation and sends a stop confirmation message to the financial enterprise. At the same time, log information such as the sending time, recipient, request content of the stop withholding request, and the receiving time of the stop confirmation is recorded.

[0054] This application first obtains the overdue data of the target customer; then obtains the contract number from the overdue data and determines the target operation system that matches the contract number; then generates corresponding withholding data based on the customer overdue data, and constructs a withholding request based on the withholding data and the customer overdue data; subsequently sends the withholding request to the target operation system; further receives the withholding progress data corresponding to the withholding request feedback by the target operation system, and verifies the withholding progress data based on a preset verification strategy; if the withholding progress data passes the verification, it is determined whether the target customer meets the conditions for stopping the withholding based on the withholding progress data; if so, a request to stop the withholding is sent to the target operation system. This application obtains the contract number from the overdue data of the target customer, determines the target operation system that matches the contract number, then generates corresponding withholding data based on the customer overdue data, constructs a withholding request based on the withholding data and the customer overdue data, then sends the withholding request to the target operation system, and receives the withholding progress data corresponding to the withholding request feedback by the target operation system, and then verifies the withholding progress data based on the use of a preset verification strategy. If the withholding progress data passes the verification, it is determined whether the target customer meets the conditions for stopping the withholding based on the withholding progress data. If it is detected that the target customer meets the conditions for stopping the withholding, a request to stop the withholding is sent to the target operation system. In this way, through the processing method of this application, it is possible to automatically, intelligently and efficiently complete the withholding processing of the overdue data of the target customer, effectively improving the processing efficiency and intelligence of the overdue data, and helping to reduce the business risk.

[0055] In some alternative implementation manners, the constructing the withholding request based on the withholding data and the customer overdue data in step S203 includes the following steps:

[0056] Parse the withholding data to obtain the corresponding customer name, contract number, installment number, and amount to be withheld.

[0057] In this embodiment, information such as the customer name, contract number, installment number, and amount to be withheld required for constructing the withholding request can be extracted as needed from the above-mentioned customer overdue data.

[0058] Obtain the preset interface specification.

[0059] In this embodiment, the above-mentioned interface specification refers to the interface specification corresponding to the above-mentioned target operation system.

[0060] Based on the interface specification, request construction processing is performed on the customer name, the contract number, the installment number, and the amount to be withheld to obtain the corresponding target request.

[0061] In this embodiment, by using the above-mentioned interface specification to perform request construction processing on the obtained customer name, contract number, installment number, and amount to be withheld, request information that conforms to the interface specification corresponding to the above-mentioned target operation system, that is, the above-mentioned target request, is obtained.

[0062] Use the target request as the withholding request.

[0063] In this embodiment, after the construction of the withholding request is completed, subsequently, the withholding request can be sent to the target operation system by calling the withholding interface of the above-mentioned target operation system.

[0064] This application parses the withholding data to obtain the corresponding customer name, contract number, installment number, and amount to be withheld; then obtains the preset interface specification; then performs request construction processing on the customer name, the contract number, the installment number, and the amount to be withheld based on the interface specification to obtain the corresponding target request; subsequently, uses the target request as the withholding request. This application obtains the customer name, contract number, installment number, and amount to be withheld from the customer overdue data, and then performs request construction processing on the customer name, contract number, installment number, and amount to be withheld based on the use of the obtained interface specification, so that an accurate and compliant withholding request can be automatically constructed, ensuring the accuracy and standardization of the generated withholding request.

[0065] In some optional implementation manners of this embodiment, the verification of the withholding progress data based on the preset verification strategy in step S205 includes the following steps:

[0066] Obtain the amount already withheld from the withholding progress data.

[0067] In this embodiment, the required amount already withheld can be obtained by extracting information from the above-mentioned withholding progress data.

[0068] Obtain the amount to be withheld from the customer overdue data.

[0069] In this embodiment, the amount to be withheld can be obtained by extracting information from the above-mentioned customer overdue data.

[0070] Determine whether the withheld amount is the same as the amount to be withheld.

[0071] In this embodiment, the obtained withheld amount and the amount to be withheld can be verified to determine whether the withheld amount and the amount to be withheld are the same data, and the corresponding determination result can be obtained. Among them, the determination result can include that the withheld amount is the same as the amount to be withheld, or the withheld amount is different from the amount to be withheld.

[0072] If the withheld amount is the same as the amount to be withheld, it is determined that the withholding progress data passes the verification.

[0073] In this embodiment, if it is detected that the withheld amount is the same as the amount to be withheld, it indicates that the rent has been confirmed to be recorded, and then it is determined that the withholding progress data passes the verification. In addition, the contract and installment corresponding to the overdue data of the above target customer can be further marked as paid off in the system to complete the rent write-off operation. In addition, the log information such as the time of rent recording, customer name, contract number, installment, and recorded amount is recorded at the same time for subsequent log query.

[0074] If the withheld amount is different from the amount to be withheld, it is determined that the withholding progress data fails the verification.

[0075] In this embodiment, if it is detected that the withheld amount is different from the amount to be withheld, the rent has not been recorded, or there is an abnormality in the amount of rent recorded, and then it is determined that the withholding progress data fails the verification.

[0076] This application obtains the withheld amount from the withholding progress data; and obtains the amount to be withheld from the customer overdue data; then determines whether the withheld amount is the same as the amount to be withheld; if the withheld amount is the same as the amount to be withheld, it is determined that the withholding progress data passes the verification; and if the withheld amount is different from the amount to be withheld, it is determined that the withholding progress data fails the verification. This application obtains the withheld amount from the withholding progress data; and obtains the amount to be withheld from the customer overdue data, then determines whether the withheld amount is the same as the amount to be withheld, and then can efficiently and accurately complete the verification process of the withholding progress data according to the obtained determination result, effectively ensuring the accuracy of the generated verification result.

[0077] In some alternative implementation manners, step S206 includes the following steps:

[0078] Obtain the verified rent records of the target customer and obtain the rent receivable records of the target customer.

[0079] In this embodiment, the verified rent records of the above target customer can be read from the system, and the rent receivable records corresponding to the above target customer can be read.

[0080] Based on the verified rent records and the rent receivable records, calculate the remaining rent amount of the target customer.

[0081] In this embodiment, the remaining rent amount of the target customer can be calculated by subtracting the received rent amount in the above verified rent records from the rent receivable amount in the above rent receivable records.

[0082] Determine whether the remaining rent amount is 0.

[0083] If the remaining rent amount is 0, it is determined that the target customer meets the condition for stopping the withholding.

[0084] In this embodiment, if the calculated remaining rent amount is 0, it indicates that the target customer has paid off the rent, and then it is determined that the target customer meets the condition for stopping the withholding.

[0085] If the remaining rent amount is not 0, it is determined that the withholding progress data does not meet the condition for stopping the withholding.

[0086] In this embodiment, if the calculated remaining rent amount is not 0, it indicates that the target customer has not paid off the rent, and then it is determined that the target customer does not meet the condition for stopping the withholding.

[0087] This application obtains the verified rent records of the target customer and obtains the rent receivable records of the target customer; then based on the verified rent records and the rent receivable records, calculates the remaining rent amount of the target customer; subsequently determines whether the remaining rent amount is 0; if the remaining rent amount is 0, it is determined that the target customer meets the condition for stopping the withholding; and if the remaining rent amount is not 0, it is determined that the withholding progress data does not meet the condition for stopping the withholding. This application obtains the verified rent records of the target customer and obtains the rent receivable records of the target customer, then based on the verified rent records and the rent receivable records, calculates the remaining rent amount of the target customer, then determines whether the remaining rent amount is 0, and then according to the obtained discrimination result, it can realize the intelligent and accurate identification process of whether the target customer meets the condition for stopping the withholding, effectively ensuring the accuracy of the obtained identification result.

[0088] In some alternative implementation manners, after step S201, the above electronic device may further perform the following steps:

[0089] Preprocess the overdue data to obtain corresponding target overdue data.

[0090] In this embodiment, the above preprocessing includes data cleaning and data standardization. Among them, data cleaning includes: removing duplicate data, dealing with missing values (such as through interpolation, mean filling or deleting samples with serious missing values), correcting incorrect data, etc. Data standardization is used to eliminate the influence of different feature dimensions.

[0091] Invoke multiple pre-constructed risk assessment models.

[0092] In this embodiment, for the model construction process of the above risk assessment models, this application will further describe the details in subsequent specific embodiments and will not elaborate too much here.

[0093] Based on each of the risk assessment models, perform risk assessment processing on the target overdue data to obtain corresponding multiple risk assessment results.

[0094] In this embodiment, the above target overdue data can be respectively input into each risk assessment model, so as to perform risk assessment processing on the target overdue data through each risk assessment model and output corresponding multiple risk assessment results.

[0095] Combine the multiple risk assessment results to generate corresponding target risk assessment results.

[0096] In this embodiment, for the specific implementation process of combining the multiple risk assessment results to generate corresponding target risk assessment results, this application will further describe the details in subsequent specific embodiments and will not elaborate too much here.

[0097] Perform output processing on the target risk assessment results.

[0098] In this embodiment, the target customers can be classified according to the obtained target risk assessment results. Specifically, according to the risk scores, the target customers are divided into three levels: low risk, medium risk, and high risk. Exemplarily, customers with a risk score lower than 0.3 are classified as low-risk level; customers with a risk score between 0.3 and 0.7 are classified as medium-risk level; customers with a risk score higher than 0.7 are classified as high-risk level. In addition, a risk assessment report corresponding to the target customer can be further generated according to the target risk assessment results, including the customer's basic information (such as name, contract number, etc.), risk score, risk level, potential risk points (such as a large number of overdue times, long overdue days, etc.), and suggested measures (such as strengthening the collection efforts, providing repayment reminders, etc.). Then, the risk assessment report is output. So that relevant personnel can take corresponding risk management measures by referring to the risk assessment report. For example, for high-risk customers, the collection efforts can be strengthened and a more flexible repayment plan can be provided; for medium-risk customers, a repayment reminder service can be provided and early repayment can be encouraged; for low-risk customers, normal business transactions can be maintained and high-quality services can be continued.

[0099] In this application, the overdue data is preprocessed to obtain the corresponding target overdue data; then multiple pre-constructed risk assessment models are called; then, based on each of the risk assessment models, risk assessment processing is performed on the target overdue data respectively to obtain corresponding multiple risk assessment results; subsequently, the multiple risk assessment results are combined to generate the corresponding target risk assessment result; finally, output processing is performed on the target risk assessment result. After obtaining the overdue data of the target customer, this application preprocesses the overdue data to obtain the corresponding target overdue data, and then automatically and intelligently calls multiple pre-constructed risk assessment models to perform risk assessment processing on the target overdue data respectively to obtain corresponding multiple risk assessment results, combines the multiple risk assessment results to generate the corresponding target risk assessment result, and performs output processing on the target risk assessment result subsequently, which can intelligently and accurately complete the risk assessment processing of the target customer, improve the processing efficiency of the risk assessment, and ensure the accuracy of the generated target risk assessment result.

[0100] In some alternative implementation manners of this embodiment, the combining the multiple risk assessment results to generate the corresponding target risk assessment result includes the following steps:

[0101] Obtain a variety of preset combination strategies.

[0102] In this embodiment, the above combination strategies may at least include a hard voting strategy, a soft voting strategy, and a weighted average strategy.

[0103] Specifically, hard voting means that each base model gives a class label as the prediction result, and the final prediction result is the class that appears most frequently among the prediction results of all base models. The strategy content of the hard voting strategy includes: Collect prediction results: For each base model, use the test set (or validation set) for prediction to obtain the class label of each sample. Store the prediction results of all base models in a two-dimensional array, where the rows represent samples and the columns represent the prediction results of the base models. Calculate the votes: For each sample, count the number of times each class label appears. Select the class label with the most occurrences as the final prediction result for that sample. Output the final prediction: Combine the final prediction results of all samples into a one-dimensional array as the final output of the ensemble model.

[0104] Soft voting means that each base model gives a probability distribution as the prediction result, and the final prediction result is the class with the highest probability among the prediction results of all base models. The strategy content of the soft voting strategy includes: Collect prediction probabilities: For each base model, use the test set (or validation set) for prediction to obtain the probability of each sample belonging to each class. Store the prediction probabilities of all base models in a three-dimensional array, where the first two dimensions represent samples and classes, and the third dimension represents the prediction probabilities of the base models. Calculate the average probability: For each sample and each class, calculate the average of the prediction probabilities of all base models. Determine the final class: For each sample, select the class with the highest average probability as the final prediction result for that sample. Output the final prediction: Combine the final prediction results of all samples into a one-dimensional array as the final output of the ensemble model.

[0105] The strategy content of the weighted average strategy includes: Collect prediction values: For each base model, use the test set (or validation set) for prediction to obtain the prediction value of each sample. Store the prediction values of all base models in a two-dimensional array, where the rows represent samples and the columns represent the prediction values of the base models. Determine the weights: Determine the weights according to the performance of the base model on the validation set (such as the mean squared error MSE). The better the performance of the model, the greater the weight. The weights can be fixed or optimized through methods such as cross-validation. Calculate the weighted average: For each sample, calculate the weighted average of the prediction values of all base models. Output the final prediction: Combine the final prediction values of all samples into a one-dimensional array as the final output of the ensemble model.

[0106] Determine the target combination strategy from all the above combination strategies.

[0107] In this embodiment, the selection of the above target combination strategy is not specifically limited and can be determined according to actual business requirements. For example, the strategy with the highest usage frequency among all combination strategies can be used as the above target combination strategy, or the strategy with the highest evaluation among all combination strategies can be used as the above target combination strategy, and so on.

[0108] Combining and processing all the risk assessment results based on the target combination strategy to obtain corresponding processing results.

[0109] In this embodiment, according to the strategy content corresponding to the selected target combination strategy above, the combination processing of all risk assessment results can be performed to obtain corresponding processing results, which are used as the final target risk assessment results.

[0110] Taking the processing result as the target risk assessment result.

[0111] This application obtains a variety of preset combination strategies; then determines a target combination strategy from all the combination strategies; then combines and processes all the risk assessment results based on the target combination strategy to obtain corresponding processing results; subsequently, taking the processing result as the target risk assessment result. By obtaining a variety of preset combination strategies and then determining a target combination strategy from all the combination strategies, and then combining and processing all the risk assessment results based on the use of the target combination strategy, the data accuracy of the generated target risk assessment results can be effectively improved.

[0112] In some alternative implementation manners of this embodiment, before the step of calling multiple pre-constructed risk assessment models, the above electronic device may further perform the following steps:

[0113] Obtaining pre-collected historical overdue data.

[0114] In this embodiment, the above historical overdue data refers to the overdue data of all customers within the historical time period collected from the system, including information such as customer name, contract number, leased equipment type, lease term, amount of each period's rent, number of overdue days, and whether it has been settled.

[0115] Constructing corresponding sample data based on the historical overdue data.

[0116] In this embodiment, the corresponding sample data can be constructed by preprocessing, feature engineering, and tagging the historical overdue data. Among them, feature engineering includes feature selection: the following features valuable for risk assessment are extracted from the original data: the number of overdue times, the total number of overdue days, the longest overdue day, the average overdue amount, whether there has been an overdue (yes / no), the type of leased equipment (categorical variable), and the lease term (in months). Feature construction: new features are constructed based on the existing features, such as the overdue rate (overdue amount / total rent amount) and the overdue frequency (number of overdue times / total number of periods). At the same time, the type of leased equipment and the lease term are encoded so that the model can process them. Feature scaling: numerical features are scaled to ensure the stability and convergence during model training. In addition, the labeled data can include: whether there is a default, that is, whether the rent has not been paid for more than a certain number of overdue days. In addition, for the above preprocessing process, reference can be made to the process of preprocessing the overdue data to obtain the corresponding target overdue data, which will not be elaborated here.

[0117] Call the preset initial model.

[0118] In this embodiment, the above initial model may include base models corresponding to multiple risk assessment models, such as logistic regression, decision tree, random forest, etc. These base models have different learning mechanisms and feature representation capabilities so that they can complement each other when integrated.

[0119] Obtain the preset adaptive learning rate adjustment strategy.

[0120] In this embodiment, during the training process of the machine learning model, the learning rate is a crucial hyperparameter. It determines the amplitude of parameter update in each iteration of the model. In traditional methods, the learning rate is usually set to a fixed value. The adaptive learning rate adjustment strategy in this embodiment includes optimization algorithms such as Adam and RMSprop, which can dynamically adjust the learning rate according to the performance of the model during the training process. These algorithms automatically adjust the learning rate of each parameter by calculating the first-order moment estimate and second-order moment estimate (or their variants) of the gradient. In this way, the model can converge quickly in the initial stage of training, and in the later stage, it can finely adjust the parameters to avoid overfitting. By using the adaptive learning rate adjustment, it can be ensured that the model updates the parameters at the optimal speed during the training process.

[0121] Based on the adaptive learning rate adjustment strategy, use the sample data to train the initial model to obtain the trained specified risk assessment model.

[0122] In this embodiment, each base model can be trained by applying an adaptive learning rate adjustment strategy. During the training process, the learning rate is dynamically adjusted according to the performance of the model on the validation set to ensure that the model can converge quickly and avoid overfitting. Specifically, the sample data is divided into a training set, a validation set, and a test set. And the data is preprocessed, such as feature scaling, missing value handling, etc. Then for the initial model, an optimizer is set and the training data is passed in. During the training process, metrics such as the loss or accuracy on the validation set are monitored, and the learning rate is dynamically adjusted according to these metrics. Subsequently, a 5-fold cross-validation method is used to evaluate the model. In each cross-validation, the dataset is divided into a training set and a test set, the model is trained with the training set, and the performance of the model is evaluated with the test set. By calculating the area under the AUC-ROC curve, the performance estimates of the model under different folds are obtained. The AUC-ROC curve is selected as the main performance metric because it can handle the problem of imbalanced datasets well. At the same time, metrics such as accuracy, recall, and F1-score are also calculated to comprehensively evaluate the performance of the model. Finally, the model is fine-tuned according to the evaluation results. For example, some valuable features are added, hyperparameters such as the learning rate and the number of iterations are adjusted, and different model structures (such as increasing the depth or number of trees) are tried. Eventually, a model with good performance can be obtained. In addition, after the training is completed, the parameters and structure of each base model are saved for subsequent use.

[0123] Perform a storage process on the specified risk assessment model.

[0124] In this embodiment, there is no specific limitation on the storage method of the above-mentioned specified risk assessment model, which can be determined according to actual storage requirements. For example, local database storage, local disk storage, cloud server storage, blockchain storage, etc. can be used. The above-mentioned specified risk assessment model can refer to any one of the above-mentioned multiple risk assessment models.

[0125] This application obtains pre-collected historical overdue data; then constructs corresponding sample data based on the historical overdue data; then calls a preset initial model; and obtains a preset adaptive learning rate adjustment strategy; subsequently, based on the adaptive learning rate adjustment strategy, uses the sample data to train the initial model to obtain a trained specified risk assessment model; finally, performs a storage process on the specified risk assessment model. This application constructs corresponding sample data based on pre-collected historical overdue data, and then uses the sample data to train the called initial model based on the obtained adaptive learning rate adjustment strategy, so that an efficient and accurate specified risk assessment model that meets the requirements can be constructed, effectively improving the model construction efficiency of the specified risk assessment model and ensuring the model processing effect of the obtained specified risk assessment model.

[0126] In some alternative implementations, the user information obtained has obtained the consent of the user and complies with the provisions of relevant laws and relevant policies.

[0127] In addition, the non-company software tools or components that appear in the embodiments of the present application are only introduced by way of example and do not represent actual use.

[0128] In addition, according to the characteristics of the charging pile financing project that the charging pile assets will generate capital income by themselves and the capital income will first enter the charging pile operation system, the present application stipulates that when the customer's repayment is overdue, rent withholding can be carried out through the charging pile operation system when signing the financing contract. After the customer's rent payment is overdue, by connecting the financing business system and the charging pile operation system, a series of processes for automatic rent withholding are completed in a timely manner to solve the problems that the customer maliciously refuses to pay rent or the customer's cash flow is insufficient resulting in no priority payment of rent.

[0129] When the financial enterprise discovers that the customer's repayment is overdue, it transmits the relevant information on rent withholding to the charging pile operation system, and the charging pile operation system executes the withholding. The charging pile operation system transmits the withholding progress and the information on the withheld payment to the financial enterprise account to the financial enterprise, and the financial enterprise conducts rent write-off after comparing the remittance data fed back by the charging pile operation system with the actual amount credited to the account. At the same time, during the withholding process, once the customer repays the rent by himself / herself and the sum of the rent repaid by himself / herself and the rent that has been withheld and written off is greater than or equal to the current rent, that is, the rent has been fully repaid, the financial enterprise transmits a stop-withholding message to the charging pile operation system, and the charging pile operation system stops the withholding in a timely manner.

[0130] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0131] It should be emphasized that to further ensure the privacy and security of the above overdue data, the above overdue data can also be stored in a node of a blockchain.

[0132] The blockchain referred to in the present application is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm. A blockchain, essentially a decentralized database, is a string of data blocks generated by using cryptographic methods. Each data block contains information on a batch of network transactions, which is used to verify the validity (anti-counterfeiting) of the information and generate the next block. A blockchain can include a blockchain underlying platform, a platform product service layer, and an application service layer, etc.

[0133] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is a theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0134] Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation / interaction systems, and mechatronics. Artificial intelligence software technologies mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0135] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, read-only memory (ROM), or a random access memory (RAM), etc.

[0136] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless specifically stated herein, there is no strict order restriction for the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0137] Further reference Figure 3 to Figure 2 As an implementation of the method shown above, an embodiment of a processing device for service data is provided in the present application. This device embodiment corresponds to the method embodiment shown in Figure 2 and this device can be specifically applied to various electronic devices.

[0138] Such as Figure 3As shown in the figure, the processing device 300 for business data according to this embodiment includes: a first acquisition module 301, a first processing module 302, a second processing module 303, a first sending module 304, a verification module 305, a judgment module 306, and a second sending module 307.

[0139] Among them:

[0140] The first acquisition module 301 is used to acquire the overdue data of the target customer;

[0141] The first processing module 302 is used to obtain the contract number from the overdue data and determine the target operation system that matches the contract number;

[0142] The second processing module 303 is used to generate corresponding withholding data based on the customer overdue data and construct a withholding request based on the withholding data and the customer overdue data;

[0143] The first sending module 304 is used to send the withholding request to the target operation system;

[0144] The verification module 305 is used to receive the withholding progress data corresponding to the withholding request feedback by the target operation system and verify the withholding progress data based on a preset verification strategy;

[0145] The judgment module 306 is used to judge whether the target customer meets the condition for stopping withholding based on the withholding progress data if the withholding progress data passes the verification;

[0146] The second sending module 307 is used to send a stop withholding request to the target operation system if so.

[0147] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the method for processing business data in the foregoing embodiment, and will not be elaborated herein.

[0148] In some optional implementation manners of this embodiment, the second processing module 303 includes:

[0149] An analysis sub-module, used to analyze the withholding data to obtain the corresponding customer name, contract number, installment number, and amount to be withheld;

[0150] A first acquisition sub-module, used to acquire a preset interface specification;

[0151] A construction sub-module, used to perform request construction processing on the customer name, the contract number, the installment number, and the amount to be withheld based on the interface specification to obtain a corresponding target request;

[0152] The first determination sub-module is used to use the target request as the withholding request.

[0153] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the method for processing service data in the foregoing embodiment, and will not be elaborated herein.

[0154] In some alternative implementation manners of this embodiment, the verification module 305 includes:

[0155] The second acquisition sub-module is used to acquire the amount already withheld from the withholding progress data;

[0156] The third acquisition sub-module is used to acquire the amount to be withheld from the customer overdue data;

[0157] The first judgment sub-module is used to judge whether the amount already withheld is the same as the amount to be withheld;

[0158] The first determination sub-module is used to determine that the withholding progress data passes the verification if the amount already withheld is the same as the amount to be withheld;

[0159] The second determination sub-module is used to determine that the withholding progress data fails to pass the verification if the amount already withheld is not the same as the amount to be withheld.

[0160] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the method for processing service data in the foregoing embodiment, and will not be elaborated herein.

[0161] In some alternative implementation manners of this embodiment, the judgment module 306 includes:

[0162] The fourth acquisition sub-module is used to acquire the written-off rent records of the target customer and acquire the receivable rent records of the target customer;

[0163] The calculation sub-module is used to calculate the remaining rent amount of the target customer based on the written-off rent records and the receivable rent records;

[0164] The second judgment sub-module is used to judge whether the remaining rent amount is 0;

[0165] The third determination sub-module is used to determine that the target customer meets the condition for stopping withholding if the remaining rent amount is 0;

[0166] The fourth determination sub-module is used to determine that the withholding progress data does not meet the condition for stopping withholding if the remaining rent amount is not 0.

[0167] In this embodiment, the operations respectively performed by the above-mentioned modules or units correspond one by one to the steps of the method for processing service data in the foregoing embodiment, and will not be elaborated herein.

[0168] In some optional implementation manners of this embodiment, the service data processing apparatus further includes:

[0169] A preprocessing module, configured to preprocess the overdue data to obtain corresponding target overdue data;

[0170] A first calling module, configured to call a plurality of pre-constructed risk assessment models;

[0171] An evaluation module, configured to respectively perform risk assessment processing on the target overdue data based on each of the risk assessment models to obtain corresponding multiple risk assessment results;

[0172] A combining module, configured to perform combining processing on the multiple risk assessment results to generate corresponding target risk assessment results;

[0173] An output module, configured to perform output processing on the target risk assessment results.

[0174] In this embodiment, the operations respectively performed by the above-mentioned modules or units correspond one by one to the steps of the method for processing service data in the foregoing embodiment, and will not be elaborated herein.

[0175] In some optional implementation manners of this embodiment, the combining module includes:

[0176] A fifth obtaining sub-module, configured to obtain a plurality of preset combining strategies;

[0177] A second determining sub-module, configured to determine a target combining strategy from all the combining strategies;

[0178] A combining sub-module, configured to perform combining processing on all the risk assessment results based on the target combining strategy to obtain corresponding processing results;

[0179] A third determining sub-module, configured to use the processing result as the target risk assessment result.

[0180] In this embodiment, the operations respectively performed by the above-mentioned modules or units correspond one by one to the steps of the method for processing service data in the foregoing embodiment, and will not be elaborated herein.

[0181] In some optional implementation manners of this embodiment, the service data processing apparatus further includes:

[0182] A second obtaining module, configured to obtain pre-collected historical overdue data;

[0183] A construction module for constructing corresponding sample data based on the historical overdue data;

[0184] A second calling module for calling a preset initial model;

[0185] A third obtaining module for obtaining a preset adaptive learning rate adjustment strategy;

[0186] A training module for training the initial model with the sample data based on the adaptive learning rate adjustment strategy to obtain a trained specified risk assessment model;

[0187] A storage module for storing and processing the specified risk assessment model.

[0188] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the method for processing business data in the foregoing embodiment, and will not be elaborated herein.

[0189] To solve the above technical problems, an embodiment of the present application further provides a computer device. For details, please refer to Figure 4 , Figure 4 which is the basic structural block diagram of the computer device in this embodiment.

[0190] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are communicatively connected to each other through a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented. Among them, those skilled in the art of the present technology can understand that the computer device here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0191] The computer device can be a desktop computer, a notebook, a palm computer, a cloud server, and other computing devices. The computer device can perform human-computer interaction with the user through a keyboard, a mouse, a remote control, a touchpad, a voice control device, and other means.

[0192] The memory 41 at least includes one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disc, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, FlashCard, etc., equipped on the computer device 4. Of course, the memory 41 may also include both the internal storage unit and the external storage device of the computer device 4. In this embodiment, the memory 41 is generally used to store the operating system installed in the computer device 4 and various application software, such as computer-readable instructions for the processing method of service data. In addition, the memory 41 may also be used to temporarily store various data that have been output or will be output.

[0193] In some embodiments, the processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the computer-readable instructions stored in the memory 41 or process data, such as running the computer-readable instructions for the processing method of the service data.

[0194] The network interface 43 may include a wireless network interface or a wired network interface, and the network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0195] Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects:

[0196] In the embodiments of the present application, the present application obtains a contract number from the overdue data of a target customer, determines a target operation system that matches the contract number, then generates corresponding withholding data based on the customer overdue data, constructs a withholding request based on the withholding data and the customer overdue data, then sends the withholding request to the target operation system, and receives the withholding progress data corresponding to the withholding request feedback by the target operation system. Furthermore, the withholding progress data is verified based on the use of a preset verification strategy. If the withholding progress data passes the verification, it is determined whether the target customer meets the condition for stopping withholding based on the withholding progress data. If it is detected that the target customer meets the condition for stopping withholding, a stop withholding request is sent to the target operation system. Thus, through the processing method of the present application, it is possible to automatically, intelligently and efficiently complete the withholding processing of the overdue data of the target customer, effectively improving the processing efficiency and intelligence of the overdue data, and helping to reduce business risks.

[0197] The present application also provides another implementation manner, that is, to provide a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor, so that the at least one processor executes the steps of the processing method of the service data as described above.

[0198] Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects:

[0199] In the embodiments of the present application, the present application obtains a contract number from the overdue data of a target customer, determines a target operation system that matches the contract number, then generates corresponding withholding data based on the customer overdue data, constructs a withholding request based on the withholding data and the customer overdue data, then sends the withholding request to the target operation system, and receives the withholding progress data corresponding to the withholding request feedback by the target operation system. Furthermore, the withholding progress data is verified based on the use of a preset verification strategy. If the withholding progress data passes the verification, it is determined whether the target customer meets the condition for stopping withholding based on the withholding progress data. If it is detected that the target customer meets the condition for stopping withholding, a stop withholding request is sent to the target operation system. Thus, through the processing method of the present application, it is possible to automatically, intelligently and efficiently complete the withholding processing of the overdue data of the target customer, effectively improving the processing efficiency and intelligence of the overdue data, and helping to reduce business risks.

[0200] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0201] Obviously, the above-described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The accompanying drawings show the preferred embodiments of the present application, but do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing specific embodiments, or perform equivalent replacements on some of the technical features. Any equivalent structure directly or indirectly using the content of the specification and drawings of the present application in other related technical fields is equally within the scope of the patent protection of the present application.

Claims

1. A method for processing service data, characterized in that Including the following steps: Obtain the overdue data of the target customer; Obtain the contract number from the overdue data and determine the target operation system that matches the contract number; Generate corresponding withholding data based on the customer overdue data and construct a withholding request based on the withholding data and the customer overdue data; Send the withholding request to the target operation system; Receive the withholding progress data corresponding to the withholding request feedback by the target operation system and verify the withholding progress data based on a preset verification strategy; If the withholding progress data passes the verification, determine whether the target customer meets the condition for stopping withholding based on the withholding progress data; If so, send a stop withholding request to the target operation system.

2. The method for processing service data according to claim 1, wherein The step of constructing a withholding request based on the withholding data and the customer overdue data specifically includes: Parse the withholding data to obtain the corresponding customer name, contract number, installment number, and amount to be withheld; Obtain the preset interface specification; Perform request construction processing on the customer name, the contract number, the installment number, and the amount to be withheld based on the interface specification to obtain a corresponding target request; Use the target request as the withholding request.

3. The method for processing service data according to claim 1, wherein, The step of verifying the withholding progress data based on a preset verification strategy specifically includes: Obtain the amount already withheld from the withholding progress data; Obtain the amount to be withheld from the customer overdue data; Determine whether the amount already withheld is the same as the amount to be withheld; If the amount already withheld is the same as the amount to be withheld, determine that the withholding progress data passes the verification; If the amount already withheld is not the same as the amount to be withheld, determine that the withholding progress data fails the verification.

4. The method for processing service data according to claim 1, wherein The step of determining whether the target customer meets the condition for stopping withholding based on the withholding progress data specifically includes: Obtain the written-off rent records of the target customer and obtain the receivable rent records of the target customer; Calculate the remaining rent amount of the target customer based on the written-off rent records and the receivable rent records; Determine whether the remaining rent amount is 0; If the remaining rent amount is 0, determine that the target customer meets the condition for stopping withholding; If the remaining rent amount is not 0, determine that the withholding progress data does not meet the condition for stopping withholding.

5. The method for processing service data according to claim 1, wherein After the step of obtaining the overdue data of the target customer, it further includes: Preprocess the overdue data to obtain corresponding target overdue data; Call multiple pre-constructed risk assessment models; Perform risk assessment processing on the target overdue data based on each of the risk assessment models to obtain corresponding multiple risk assessment results; Perform combination processing on the multiple risk assessment results to generate a corresponding target risk assessment result; Perform output processing on the target risk assessment result.

6. The method for processing service data according to claim 5, wherein The step of performing combination processing on the multiple risk assessment results to generate a corresponding target risk assessment result specifically includes: Obtain preset multiple combination strategies; Determine a target combination strategy from all the combination strategies; Combining and processing all the risk assessment results based on the target combination strategy to obtain corresponding processing results; Taking the processing results as the target risk assessment results.

7. The method for processing service data according to claim 6, wherein Before the step of invoking a plurality of pre-constructed risk assessment models, it further includes: Obtaining pre-collected historical overdue data; Constructing corresponding sample data based on the historical overdue data; Invoking a preset initial model; Obtaining a preset adaptive learning rate adjustment strategy; Based on the adaptive learning rate adjustment strategy, training the initial model with the sample data to obtain a trained specified risk assessment model; Performing a storage process on the specified risk assessment model.

8. A processing device for service data, characterized in that, Including: A first acquisition module, configured to acquire overdue data of a target customer; A first processing module, configured to obtain a contract number from the overdue data and determine a target operation system that matches the contract number; A second processing module, configured to generate corresponding withholding data based on the customer overdue data and construct a withholding request based on the withholding data and the customer overdue data; A first sending module, configured to send the withholding request to the target operation system; A verification module, configured to receive withholding progress data corresponding to the withholding request feedback by the target operation system and verify the withholding progress data based on a preset verification strategy; A judgment module, configured to, if the withholding progress data passes the verification, judge whether the target customer meets the condition for stopping withholding based on the withholding progress data; A second sending module, configured to, if so, send a stop withholding request to the target operation system.

9. A computer device, characterized in that, Including a memory and a processor, wherein computer-readable instructions are stored in the memory, and when the processor executes the computer-readable instructions, the steps of the processing method of business data according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that, Computer-readable instructions are stored on the computer-readable storage medium, and when the computer-readable instructions are executed by a processor, the steps of the processing method of business data according to any one of claims 1 to 7 are implemented.

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