Method and device for controlling success rate of batch receipt and payment services, and storage medium

Abstract

The invention discloses a method and device for controlling the success rate of batch receipt and payment services, and a storage medium. The method can be applied to the field of financial science and technology. The method comprises the following steps: acquiring failed transaction data of batch payment and receipt services; a hybrid model is adopted to analyze the failed transaction data, the redoing probability is determined, and the hybrid model is used for representing multi-model collaborative analysis; in response to the condition that the redo probability meets a redo probability threshold, starting a polling process for the batch on-behalf receipt and payment service; and in response to the condition that the polling times corresponding to the current polling in the polling process are smaller than the polling times threshold, generating a re-transaction instruction which is used for controlling the target equipment to re-transact the batch receipt and payment services in the failed transaction state. Through the method and the device, the problem that the success rate of batch receipt and payment services is reduced due to host interface fluctuation or customer behavior characteristics in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a method, device and storage medium for controlling the success rate of batch collection and payment services. Background Art

[0002] At present, the main processing mode for batch collection and payment is that the partner or service agency prepares the batch collection and payment documents in advance. After sending the documents to the bank, the bank pre-processes the data and calls the host or platform related interface to perform accounting processing concurrently. After all the details are processed, the return conclusion will be provided to the sending agency.

[0003] Batch collection and payment can automatically process thousands of transactions, saving the time and labor required to manually process each transaction. Enterprises can arrange batch collection and payment to be carried out during non-working hours, such as at night or on weekends, so that transaction processing can be completed without affecting daily operations, helping enterprises better manage cash flow.

[0004] The existing batch collection and payment mechanism has the following shortcomings: 1. When the core transaction system such as the host is unavailable due to special reasons, a large number of transactions will fail, which will seriously affect the customer experience; 2. There is no analysis based on historical data, which may affect the business success rate. For example, in the batch deduction business, the customer only has sufficient balance during a fixed period of time. When the partner fails to initiate the batch at the appropriate time node, the deduction may fail.

[0005] There is currently no effective solution to the problem that the success rate of batch collection and payment services decreases due to fluctuations in core interfaces such as the host or customer behavior characteristics in related technologies. Summary of the invention

[0006] The main purpose of the present application is to provide a method, device and storage medium for controlling the success rate of batch collection and payment services, so as to solve the problem in the related art that the success rate of batch collection and payment services decreases due to host interface fluctuations or customer behavior characteristics.

[0007] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for controlling the success rate of batch collection and payment services is provided. The method includes: obtaining failed transaction data of batch collection and payment services; analyzing the failed transaction data using a hybrid model to determine the redo probability, wherein the hybrid model is used to represent multi-model collaborative analysis; in response to the redo probability meeting the redo probability threshold, starting a polling process for the batch collection and payment services; in response to the number of polls corresponding to the current poll in the polling process being less than the polling number threshold, generating a re-transaction instruction, wherein the re-transaction instruction is used to control the target device to re-transact the batch collection and payment services in the failed transaction state.

[0008] Optionally, the hybrid model includes a long short-term memory network model and a random forest model, wherein the hybrid model is used to analyze the failed transaction data to determine the redo probability, including: using the long short-term memory network model to analyze the time series characteristics of the failed transaction data to obtain a time series prediction value, wherein the time series characteristics are used to represent the transaction time of the batch collection and payment business in the failed transaction state; using the random forest model to analyze the non-time series characteristics of the failed transaction data to obtain a non-time series prediction value, wherein the non-time series prediction value is used to at least represent the transaction account information, transaction amount, and customer credit score of the batch collection and payment business in the failed transaction state; and fusing the time series prediction value with the non-time series prediction value to obtain the redo probability.

[0009] Optionally, the time series prediction value is fused with the non-time series prediction value to obtain the redo probability, including: determining the weight of the time series prediction value based on the time series characteristics; determining the weight of the non-time series prediction value based on the non-time series characteristics; and performing weighted averaging fusion of the time series prediction value and the non-time series prediction value based on the time series prediction value weight and the non-time series prediction value weight to obtain the redo probability.

[0010] Optionally, the redo probability threshold includes a first redo probability threshold and a second redo probability threshold, the first redo probability threshold is greater than the second redo probability threshold, and in response to the redo probability satisfying the redo probability threshold, a polling process is started for the batch collection and payment business, including: in response to the redo probability satisfying the first redo probability threshold, setting the time interval of the polling process to a first time period; according to the first time period, starting a corresponding polling process for the batch collection and payment business; in response to the redo probability satisfying the second redo probability threshold, setting the time interval of the polling process to a second time period, wherein the first time period is less than the second time period; according to the second time period, starting a corresponding polling process for the batch collection and payment business.

[0011] Optionally, in response to the redo probability satisfying the redo probability threshold, after starting the polling process for the batch collection and payment service, the method further includes: in response to the redo probability not satisfying the redo probability threshold, generating stop transaction information, and sending the stop transaction information to the target node.

[0012] Optionally, obtaining failed transaction data of batch collection and payment services includes: obtaining transaction data of batch collection and payment services, wherein the transaction data includes failed transaction data and normal transaction data, and the normal transaction data is used to represent attribute information of the batch collection and payment services in a normal transaction state during the transaction process; and filtering out failed transaction data from the transaction data.

[0013] Optionally, in response to the polling number corresponding to the current polling in the polling process being less than the polling number threshold, a re-trading instruction is generated, which then includes: in response to the polling number corresponding to the current polling in the polling process exceeding the polling number threshold, pausing the polling process.

[0014] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a device for controlling the success rate of batch collection and payment services is provided. The device includes: an acquisition module, the acquisition module is used to acquire failed transaction data of batch collection and payment services; an analysis module, the analysis module is used to analyze the failed transaction data using a hybrid model to determine the redo probability, wherein the hybrid model is used to represent multi-model collaborative analysis; a startup module, the startup module is used to start a polling process for the batch collection and payment services in response to the redo probability meeting the redo probability threshold; a re-transaction module, the re-transaction module is used to generate a re-transaction instruction in response to the number of polls corresponding to the current polling in the polling process being less than the polling number threshold, wherein the re-transaction instruction is used to control the target device to re-transact the batch collection and payment services in the failed transaction state.

[0015] In order to achieve the above purpose, according to another aspect of the present application, a computer-readable storage medium is provided, the computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the method of the above embodiment.

[0016] In order to achieve the above-mentioned purpose, according to another aspect of the present application, an electronic device is provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the method of the above-mentioned embodiment is executed when the program is running.

[0017] In order to achieve the above objective, according to another aspect of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the method according to the above embodiment is implemented.

[0018] In an embodiment of the present application, after obtaining the failed transaction data of the batch collection and payment business, a hybrid model is used to analyze the failed transaction data to obtain the redo probability of the batch collection and payment business in a failed state. The redo probability is used to characterize the probability that the transaction can be re-performed. In response to the redo probability satisfying the preset redo probability threshold, the system starts a polling process. This process automatically checks the redo conditions of the failed transaction within a preset time interval until the predetermined polling number threshold is reached, thereby achieving the purpose of accurately identifying which failed transactions are worth re-trading. In this way, the system can continuously and strategically generate re-transaction instructions, and control the target device (such as a bank host system) to re-transact the batch collection and payment business that is in a failed state but has a high probability of successful redo, rather than blindly redoing all failed transactions, effectively avoiding waste of resources and achieving a significant improvement in the success rate of batch collection and payment business, thereby solving the technical problem of frequent batch transaction failures caused by host fluctuations, changes in customer funds status or network problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 A hardware structure block diagram of a computer terminal for implementing a method for controlling the success rate of batch collection and payment services is shown;

[0021] Figure 2 It is a flow chart of a method for controlling the success rate of batch collection and payment services provided in an embodiment of the present application;

[0022] Figure 3 It is a schematic diagram of a device for controlling the success rate of batch collection and payment services provided in an embodiment of the present application;

[0023] Figure 4 It is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following explanations:

[0027] It should be noted that the collected information (including but not limited to customer credit score information, transaction amount information, transaction account information, transaction time information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data are in compliance with relevant laws, regulations and standards, necessary confidentiality measures are taken, and public order and good customs are not violated, and corresponding operation entrances are provided for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or institutions to provide users with corresponding operation entrances for users to choose to agree or refuse the results of automated decision-making; if the user chooses to refuse, the expert decision-making process will be entered.

[0028] Example 1

[0029] According to an embodiment of the present application, a method embodiment for controlling the success rate of batch collection and payment services is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for controlling the success rate of batch collection and payment services is shown. Figure 1As shown, the computer terminal 10 (or mobile device) may include one or more (102a, 102b, ..., 102n are used to illustrate) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0031] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0032] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the Z method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above-mentioned Z method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0033] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0034] The display may be, for example, a touch screen type liquid crystal display (LCD) and a touch display (also referred to as a "touch screen" or "touch display screen"). The liquid crystal display may enable a user to interact with a user interface of the mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), and a user may interact with the GUI by finger contacts and / or gestures on a touch-sensitive surface, wherein the human-computer interaction functions here may optionally include the following interactions: creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital videos, playing digital music and / or web browsing, etc. The executable instructions for executing the above human-computer interaction functions are configured / stored in a computer program product or readable storage medium executable by one or more processors.

[0035] Under the above operating environment, this application provides Figure 2 The method shown is for controlling the success rate of batch collection and payment services. Figure 2 It is a flowchart of the method for controlling the success rate of batch collection and payment services according to Example 1 of the present application.

[0036] Step S101, obtaining failed transaction data of batch collection and payment services.

[0037] In step S101, the batch collection and payment service is used to characterize the financial transaction processing method, specifically indicating that the partner or service agency automatically collects money from multiple customers' accounts or pays money to multiple payees, such as subscription fees, bill payments, salary distribution, supplier settlement, social security and provident fund deposits. Batch collection and payment can improve transaction efficiency, reduce manual processing errors, and reduce transaction costs.

[0038] The above-mentioned failed transaction data is used to represent the transaction data of the batch collection and payment services in a failed state, and the transaction data at least includes: transaction time, transaction amount, and transaction account information.

[0039] Step S102: Use a hybrid model to analyze failed transaction data to determine a redo probability, wherein the hybrid model is used to represent multi-model collaborative analysis.

[0040] In step S102, the above-mentioned failed transaction data is used to represent the transaction data of the batch collection and payment service in the failed transaction state, and the failed transaction data at least includes: transaction time, transaction amount, and transaction account information.

[0041] The above hybrid model is used to represent the coordinated use of multiple models to analyze the transaction data of batch collection and payment services in a failed transaction state. The above hybrid model includes a long short-term memory network model, a random forest model, and a weighted average fusion model.

[0042] Step S103: In response to the redo probability satisfying the redo probability threshold, a polling process is initiated for the batch collection and payment service.

[0043] In step S103, the above-mentioned redo probability threshold represents the maximum probability threshold that the batch collection and payment business in the failed transaction state can be traded again. The above-mentioned polling process is used to characterize the number of monitoring and inspections. Specifically, the polling process will regularly check the failed transaction records according to preset time intervals or conditions to verify whether there are new conditions that make it possible to successfully redo the transaction, for example, check whether the customer account balance has been updated, whether the bank system is available, whether the network connection status is stable, etc.

[0044] Step S104, in response to the polling number corresponding to the current polling in the polling process being less than the polling number threshold, a re-transaction instruction is generated, wherein the re-transaction instruction is used to control the target device to re-transact the batch collection and payment services in a failed transaction state.

[0045] In step S104, if the conditions for transaction redo are met, that is, the probability of redo is still higher than the threshold, a re-trading instruction will be generated to try to re-execute the transaction. This usually means that the system will call the bank interface or platform again to submit the transaction request, hoping that the transaction can be successful at a different time point or system state.

[0046] In summary, the method for controlling the success rate of batch collection and payment services provided in Example 1 of the present application obtains the failed transaction data of the batch collection and payment services, and then uses a hybrid model to analyze the failed transaction data to obtain the redo probability of the batch collection and payment services in a failed state. The redo probability is used to characterize the probability that the transaction can be re-performed. In response to the redo probability satisfying the preset redo probability threshold, the system starts a polling process. This process automatically checks the redo conditions of the failed transactions within a preset time interval until the predetermined polling number threshold is reached, thereby achieving the purpose of accurately identifying which failed transactions are worth re-trading. In this way, the system can continuously and strategically generate re-transaction instructions, control the target device (such as a bank host system) to re-transact the batch collection and payment services that are in a failed state but have a high probability of successful redo, instead of blindly redoing all failed transactions, effectively avoiding waste of resources, achieving a significant improvement in the success rate of batch collection and payment services, and solving the technical problem of frequent batch transaction failures caused by host fluctuations, changes in customer funds status or network problems.

[0047] Optionally, in the method for controlling the success rate of batch collection and payment services provided in the first embodiment of the present application, the hybrid model includes a long short-term memory network model and a random forest model, wherein the hybrid model is used to analyze failed transaction data to determine the redo probability, including:

[0048] S1021, using a long short-term memory network model to analyze the time series characteristics of the failed transaction data to obtain a time series prediction value, wherein the time series characteristics are used to represent the transaction time of the batch collection and payment business in a failed transaction state.

[0049] In step S1021, the above-mentioned time series features are used to characterize the characteristics of information such as transaction date, time, historical transaction frequency, etc. The long short-term memory network model (LSTM) can capture the long-term dependencies between these features to make the prediction more accurate, especially when dealing with problems involving time continuity, such as predicting the financial status of an account within a specific time period or predicting at which point in time the host system may be in a relatively stable state.

[0050] The above-mentioned process of obtaining the time series prediction value is specifically as follows: prepare the time series part of the failed transaction data, such as the historical transaction date and time, the change sequence of the transaction amount, etc., and input these time series features into the pre-trained LSTM model. The LSTM model processes the input sequence through its internal gating mechanism, retains important information related to the predicted redo probability, and discards irrelevant or redundant information. The model outputs the time series prediction value, which reflects the probability of successful transaction redo predicted based on the time series characteristics.

[0051] S1022, using a random forest model to analyze non-time series features of failed transaction data to obtain non-time series prediction values, wherein the non-time series prediction values ​​are used to at least represent transaction account information, transaction amount, and customer credit score of batch collection and payment services in failed transaction status.

[0052] In step S1022, the above-mentioned random forest model (RF model) analyzes the non-time series features of the failed transaction data to obtain the non-time series prediction value. The specific process is: extracting the non-time series features in the failed transaction data, such as transaction amount, account type, credit score, etc., and inputting these features into the pre-trained random forest model. Each tree in the model independently predicts the data. The final prediction value is the average of the prediction results of all trees or is determined by a voting mechanism, and the non-time series prediction value is output, which reflects the probability of successful transaction redo based on the account static information and transaction attribute prediction.

[0053] S1023, the time series prediction value and the non-time series prediction value are integrated to obtain the redo probability.

[0054] In step S1023, the time series prediction values ​​output by the LSTM model and the non-time series prediction values ​​output by the RF model are processed by weighted average or other fusion algorithms. The weights are adjusted to reflect the importance the model places on different features. For example, if the transaction amount has a greater impact on the success rate, the weight of the non-time series prediction value can be appropriately increased. The fusion result reflects the comprehensive probability of transaction redoing. This probability value combines the prediction results of time series and non-time series features, providing a basis for deciding whether to redo the transaction.

[0055] Through the above steps, a hybrid model is used to analyze the failed transaction data and determine the redo probability. By combining time series analysis and non-time series feature processing and utilizing the advantages of long short-term memory network (LSTM) and random forest (RF) models, the purpose of more accurately evaluating the possibility of successful redo of failed transactions is achieved, thereby achieving the technical effect of improving the success rate of batch collection and payment services, and further solving the technical problems of high batch transaction failure rate, poor customer experience and reduced business success rate caused by fluctuations in core transaction systems such as the host or changes in customer behavior characteristics.

[0056] Specifically, the adoption of a hybrid model can take into account the time dependency of transactions and other static factors that affect transaction success, such as account information, transaction amount, and customer credit score. The LSTM model is good at capturing long-term dependencies in sequence data, so it can predict the probability of a successful redo of a transaction at a certain point in the future based on the historical transaction time series; while the RF model can effectively analyze non-time series data and identify complex patterns related to the probability of a successful redo of a transaction. By combining the prediction results of these two models, the system can more comprehensively evaluate the redo potential of failed transactions, rather than relying solely on the predictions of a single model.

[0057] Optionally, in the method for controlling the success rate of batch collection and payment services provided in the first embodiment of the present application, the time series prediction value and the non-time series prediction value are merged to obtain the redo probability, including:

[0058] S10231, determine the weight of the time series prediction value based on the time series characteristics.

[0059] In step S10231, the time series prediction value weight is used to characterize the importance of the time series feature in determining the redo probability. The larger the weight value, the greater the contribution of the time series feature to the prediction result.

[0060] S10232, determining the weight of the non-time series prediction value based on the non-time series characteristics.

[0061] In step S10232, the non-time series prediction value weight is used to characterize the importance of the non-time series feature in determining the redo probability. The larger the weight value, the greater the contribution of the non-time series feature to the prediction result.

[0062] S10233, based on the weights of the time series prediction values ​​and the weights of the non-time series prediction values, perform weighted averaging fusion on the time series prediction values ​​and the non-time series prediction values ​​to obtain the redo probability.

[0063] In step S10233, the weights determined in steps S10231 and S10232 are used to perform a weighted average calculation on the time series prediction values ​​and the non-time series prediction values ​​to obtain the redo probability of the batch collection and payment business in a failed transaction state.

[0064] Through the above steps, the time series prediction value and the non-time series prediction value are fused by weighted average to obtain the final transaction redo probability. This fusion method can comprehensively consider the time dynamics and static properties of the transaction and improve the accuracy and reliability of the prediction.

[0065] Optionally, in the method for controlling the success rate of batch collection and payment services provided in the first embodiment of the present application, the redo probability threshold includes a first redo probability threshold and a second redo probability threshold, the first redo probability threshold is greater than the second redo probability threshold, and in response to the redo probability satisfying the redo probability threshold, a polling process is started for the batch collection and payment services, including:

[0066] S1031: In response to the redo probability satisfying a first redo probability threshold, setting a time interval of the polling process to a first period.

[0067] In step S1031, the first redo probability threshold is used to represent a higher setting value. For such transactions, the system needs to respond quickly to capture the best time for redo in the shortest time. Therefore, the system will set a shorter time interval (first period) for polling transactions with high redo probability so as to make redo attempts as soon as possible.

[0068] S1032: According to the first time period, a corresponding polling process is started for the batch collection and payment service.

[0069] In step S1032Z, through high-frequency polling, the system can respond to changes in transaction status more quickly, ensuring that when the conditions for transaction redo are met, timely action can be taken to improve the success rate of redo.

[0070] S1033: In response to the redo probability satisfying a second redo probability threshold, setting a time interval of the polling process to a second period, wherein the first period is smaller than the second period.

[0071] In step S1033, the second redo probability is used to represent a lower set value, and the system adjusts the polling time interval to set it to a longer second period. This strategy can balance the use of system resources, while ensuring that transactions have a chance to be redone, reducing excessive consumption of resources. The selection of the second period should take into account the probability of successful transaction redo and cost-effectiveness, avoid high-frequency polling of all transactions, and thus ensure system efficiency.

[0072] S1034: According to the second time period, a corresponding polling process is started for the batch collection and payment service.

[0073] In step S1034, the system periodically checks whether the redo conditions of the medium potential redo transactions are met at the time interval of the second period. If the redo conditions of the transaction are found to be met during the check, the system will start a redo attempt, otherwise it will continue to wait for the next polling time point. Polling will continue until the transaction is successful, the maximum redo count limit is reached, or the transaction status changes so that it no longer meets the second redo probability threshold. At this time, the system may stop polling or adjust the polling strategy.

[0074] Through the above steps, the system can dynamically adjust the polling time interval according to the redo probability of the transaction, monitor transactions with high redo probability at a high frequency, and monitor transactions with medium redo probability at a lower frequency, thereby achieving effective control of the success rate of batch collection and payment services. This differentiated polling strategy based on the redo probability threshold helps to optimize the transaction processing process, improve the business efficiency of the bank, and reduce unnecessary costs caused by excessive resource consumption.

[0075] Optionally, in the method for controlling the success rate of batch agency collection and payment services provided in the first embodiment of the present application, in response to the redo probability satisfying the redo probability threshold, after starting the polling process for the batch agency collection and payment services, the method further includes:

[0076] S1035 , in response to the redo probability not satisfying the redo probability threshold, generating stop transaction information, and sending the stop transaction information to the target node.

[0077] In step S1035, the above-mentioned target node is used to represent the bank's transaction processing system, payment gateway or front-end system that interacts with customers.

[0078] Through the above steps, the system can automatically adjust the transaction processing strategy according to the dynamic changes in the transaction redo probability. For transactions with a redo probability lower than the threshold, the system will promptly send a "stop transaction message" to the target node to stop unnecessary polling and redo attempts.

[0079] Optionally, in the method for controlling the success rate of batch agency collection and payment services provided in the first embodiment of the present application, obtaining failed transaction data of the batch agency collection and payment services includes:

[0080] S1011, obtaining transaction data of the batch collection and payment service, wherein the transaction data includes failed transaction data and normal transaction data, and the normal transaction data is used to represent attribute information of the batch collection and payment service in a normal transaction state during the transaction process.

[0081] In step S1011, the above acquisition process is that the system acquires all relevant transaction data from sources such as the bank's transaction database, payment gateway logs, customer service records, etc. Through database query, log file reading, etc., the system can extract detailed attribute information of the transaction, including but not limited to: transaction time, transaction amount, information of both parties to the transaction, transaction status (successful or failed), etc.

[0082] S1012, filtering out failed transaction data from the transaction data.

[0083] In step S1012, the above-mentioned failed transaction data is used to characterize the attribute information of the batch collection and payment business in the failed transaction state during the transaction process. For each transaction marked as failed, the system records all its relevant attributes, including the reason for failure, transaction amount, transaction time, number of redo attempts, etc.

[0084] Through the above steps, by obtaining complete transaction data and accurately screening out failed transactions, the system can analyze failure modes based on actual transaction situations, predict redo values, and ultimately optimize the processing flow of batch collection and payment services to improve success rates and customer satisfaction.

[0085] Optionally, in the method for controlling the success rate of batch collection and payment services provided in Example 1 of the present application, in response to the number of polling times corresponding to the current polling in the polling process being less than the polling number threshold, a re-transaction instruction is generated, which then includes: S1041, in response to the number of polling times corresponding to the current polling in the polling process exceeding the polling number threshold, the polling process is suspended.

[0086] The above steps help the system to achieve effective resource management and risk control in the batch collection and payment business. By setting the polling number threshold, the system can try to redo failed transactions within a reasonable time. Once the threshold is reached, the attempt will stop, avoiding invalid or inefficient redo attempts, thereby improving the overall efficiency and robustness of the system. This mechanism ensures that the batch collection and payment business can handle failed transactions more intelligently and balance the relationship between system efficiency and business success rate.

[0087] The method for controlling the success rate of batch collection and payment services provided in the embodiment of the present application obtains the failed transaction data of the batch collection and payment services, and then uses a hybrid model to analyze the failed transaction data to obtain the redo probability of the batch collection and payment services in a failed state. The redo probability is used to characterize the probability that the transaction can be re-transacted. In response to the redo probability meeting the preset redo probability threshold, the system starts a polling process. This process automatically checks the redo conditions of the failed transactions within a preset time interval until the preset polling number threshold is reached, thereby achieving the purpose of accurately identifying which failed transactions are worth re-trading. In this way, the system can continuously and strategically generate re-transaction instructions, control the target device (such as a bank host system) to re-transact the batch collection and payment services that are in a failed state but have a high probability of successful redo, rather than blindly redoing all failed transactions, effectively avoiding waste of resources and achieving a significant improvement in the success rate of the batch collection and payment services, thereby solving the technical problem of frequent batch transaction failures caused by host fluctuations, changes in customer funds status or network problems.

[0088] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0089] Example 2

[0090] The embodiment of the present application also provides a device for controlling the success rate of batch collection and payment services. It should be noted that the device for controlling the success rate of batch collection and payment services in the embodiment of the present application can be used to execute the method for controlling the success rate of batch collection and payment services provided in the embodiment of the present application. The device for controlling the success rate of batch collection and payment services provided in the embodiment of the present application is introduced below.

[0091] According to an embodiment of the present application, a device for implementing the above method for controlling the success rate of batch collection and payment services is also provided. Figure 3 As shown, the device includes: an acquisition module 201, the acquisition module 201 is used to acquire failed transaction data of batch collection and payment services; an analysis module 202, the analysis module 202 is used to analyze the failed transaction data using a hybrid model to determine the redo probability, wherein the hybrid model is used to represent multi-model collaborative analysis; a startup module 203, the startup module 203 is used to start a polling process for the batch collection and payment services in response to the redo probability satisfying the redo probability threshold; a re-transaction module 204, the re-transaction module 204 is used to generate a re-transaction instruction in response to the number of pollings corresponding to the current polling in the polling process being less than the polling number threshold, wherein the re-transaction instruction is used to control the target device to re-transact the batch collection and payment services in a failed transaction state.

[0092] The device for controlling the success rate of batch collection and payment services provided in the embodiment of the present application obtains the failed transaction data of the batch collection and payment services, and then uses a hybrid model to analyze the failed transaction data to obtain the redo probability of the batch collection and payment services in a failed state. The redo probability is used to characterize the probability that the transaction can be re-performed. In response to the redo probability satisfying the preset redo probability threshold, the system starts a polling process. This process automatically checks the redo conditions of the failed transaction within a preset time interval until the preset polling number threshold is reached, thereby achieving the purpose of accurately identifying which failed transactions are worth re-trading. In this way, the system can continuously and strategically generate re-transaction instructions, control the target device (such as a bank host system) to re-transact the batch collection and payment services that are in a failed state but have a high probability of successful redo, rather than blindly redoing all failed transactions, effectively avoiding waste of resources and achieving a significant improvement in the success rate of the batch collection and payment services, thereby solving the technical problem of frequent batch transaction failures caused by host fluctuations, changes in customer funds status or network problems.

[0093] Optionally, in the device for controlling the success rate of batch collection and payment services provided in an embodiment of the present application, the analysis module 202 includes a first analysis module, a second analysis module, and a fusion module. The above-mentioned hybrid model includes a long short-term memory network model and a random forest model. The first analysis module is used to analyze the time series characteristics of failed transaction data using a long short-term memory network model to obtain a time series prediction value, wherein the time series characteristics are used to represent the transaction time of the batch collection and payment services in a failed transaction state. The second analysis module is used to analyze the non-time series characteristics of the failed transaction data using a random forest model to obtain a non-time series prediction value, wherein the non-time series prediction value is used to at least represent the transaction account information, transaction amount, and customer credit score of the batch collection and payment services in a failed transaction state. The fusion module is used to fuse the time series prediction value with the non-time series prediction value to obtain a redo probability.

[0094] Optionally, in the device for controlling the success rate of batch collection and payment services provided in the embodiment of the present application, the fusion module includes: a first determination submodule, a second determination submodule, and a weighted average fusion submodule. The first determination submodule is used to determine the weight of the time series prediction value based on the time series characteristics. The second determination submodule is used to determine the weight of the non-time series prediction value based on the non-time series characteristics. The weighted average fusion submodule is used to perform weighted average fusion of the time series prediction value and the non-time series prediction value based on the time series prediction value weight and the non-time series prediction value weight to obtain the redo probability.

[0095] Optionally, in the device for controlling the success rate of batch collection and payment services provided in an embodiment of the present application, the startup module 203 includes a first setting module, a first startup submodule, a second setting module, and a second startup submodule. The first setting module is used to set the time interval of the polling process to a first time period in response to the redo probability satisfying a first redo probability threshold. The first startup submodule is used to start the corresponding polling process for the batch collection and payment services according to the first time period. The second setting module is used to set the time interval of the polling process to a second time period in response to the redo probability satisfying a second redo probability threshold, wherein the first time period is smaller than the second time period. The second startup submodule is used to start the corresponding polling process for the batch collection and payment services according to the second time period.

[0096] Optionally, the device for controlling the success rate of batch collection and payment services provided in the embodiment of the present application also includes a stop transaction module, which is used to generate stop transaction information in response to the redo probability not meeting the redo probability threshold, and send the stop transaction information to the target node.

[0097] Optionally, in the device for controlling the success rate of batch collection and payment services provided in the embodiment of the present application, the acquisition module 201 includes: a first acquisition submodule and a screening module. The first acquisition submodule is used to acquire transaction data of the batch collection and payment services, wherein the transaction data includes failed transaction data and normal transaction data, and the normal transaction data is used to represent the attribute information of the batch collection and payment services in the normal transaction state during the transaction process. The screening module is used to screen the failed transaction data from the transaction data.

[0098] Optionally, the device for controlling the success rate of batch collection and payment services provided in the embodiment of the present application also includes a pause module, which is used to pause the polling process in response to the number of pollings corresponding to the current polling in the polling process exceeding the polling number threshold.

[0099] It should be noted that the acquisition module 201 and the analysis module 202 correspond to steps S101 to S102 in Example 1, and the examples and application scenarios implemented by the two modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned Example 1. It should be noted that the above-mentioned modules or units may be hardware components or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n), and the above-mentioned modules may also be part of the device and may be run in the computer terminal 10 provided in Example 1.

[0100] Example 3

[0101] An embodiment of the present application may provide an electronic device, Figure 4 is a structural block diagram of an electronic device according to an embodiment of the present application. Figure 4 As shown, the electronic device may include: one or more ( Figure 4 (only one is shown) processor 1002, memory 1004, storage controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0102] Among them, the memory can be used to store software programs and modules, such as program instructions / modules corresponding to the methods and devices in the embodiments of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0103] The processor may call the information and application program stored in the memory through the transmission device to perform the following steps:

[0104] S101, obtaining failed transaction data of batch collection and payment services.

[0105] S102, using a hybrid model to analyze failed transaction data and determine a redo probability, wherein the hybrid model is used to represent multi-model collaborative analysis.

[0106] S103: In response to the redo probability satisfying the redo probability threshold, a polling process is initiated for the batch collection and payment services.

[0107] S104, in response to the polling number corresponding to the current polling in the polling process being less than the polling number threshold, generating a re-transaction instruction, wherein the re-transaction instruction is used to control the target device to re-transact the batch collection and payment services in a failed transaction state.

[0108] The processor may also call the information and application program stored in the memory through the transmission device to perform the following steps:

[0109] S1021, using a long short-term memory network model to analyze the time series characteristics of the failed transaction data to obtain a time series prediction value, wherein the time series characteristics are used to represent the transaction time of the batch collection and payment business in a failed transaction state.

[0110] S1022, using a random forest model to analyze non-time series features of failed transaction data to obtain non-time series prediction values, wherein the non-time series prediction values ​​are used to at least represent transaction account information, transaction amount, and customer credit score of batch collection and payment services in failed transaction status.

[0111] S1023, the time series prediction value and the non-time series prediction value are integrated to obtain the redo probability.

[0112] The processor may also call the information and application program stored in the memory through the transmission device to perform the following steps:

[0113] S10231, determine the weight of the time series prediction value based on the time series characteristics.

[0114] S10232, determining the weight of the non-time series prediction value based on the non-time series characteristics.

[0115] S10233, based on the weights of the time series prediction values ​​and the weights of the non-time series prediction values, perform weighted averaging fusion on the time series prediction values ​​and the non-time series prediction values ​​to obtain the redo probability.

[0116] The processor may also call the information and application program stored in the memory through the transmission device to perform the following steps:

[0117] S1031: In response to the redo probability satisfying a first redo probability threshold, setting a time interval of the polling process to a first period.

[0118] S1032: According to the first time period, a corresponding polling process is started for the batch collection and payment service.

[0119] S1033: In response to the redo probability satisfying a second redo probability threshold, setting a time interval of the polling process to a second period, wherein the first period is smaller than the second period.

[0120] S1034: According to the second time period, a corresponding polling process is started for the batch collection and payment service.

[0121] The processor may also call the information and application program stored in the memory through the transmission device to perform the following steps:

[0122] S1035 , in response to the redo probability not satisfying the redo probability threshold, generating stop transaction information, and sending the stop transaction information to the target node.

[0123] The processor may also call the information and application program stored in the memory through the transmission device to perform the following steps:

[0124] S1041: In response to the polling number corresponding to the current polling in the polling process exceeding the polling number threshold, suspend the polling process.

[0125] By adopting the embodiment of the present application, a scheme for controlling the success rate of batch collection and payment services is provided. After obtaining the failed transaction data of the batch collection and payment services, the failed transaction data is analyzed by a hybrid model to obtain the redo probability of the batch collection and payment services in a failed state. The redo probability is used to characterize the probability that the transaction can be re-transacted. In response to the redo probability meeting the preset redo probability threshold, the system starts a polling process, which automatically checks the redo conditions of the failed transaction within a preset time interval until the preset polling number threshold is reached, thereby achieving the purpose of accurately identifying which failed transactions are worth re-trading. In this way, the system can continuously and strategically generate re-transaction instructions, control the target device (such as the bank host system) to re-transact the batch collection and payment services in a failed state but with a high probability of successful redo, instead of blindly redoing all failed transactions, effectively avoiding the waste of resources, achieving a significant improvement in the success rate of the batch collection and payment services, and thus solving the technical problem of frequent batch transaction failures caused by host fluctuations, changes in customer fund status or network problems.

[0126] It can be understood by those skilled in the art that Figure 4 The structure shown is for illustration only, and the electronic device may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, MID), a PAD, or other terminal devices. Figure 4 The structure of the electronic device is not limited. Figure 4 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 4 Different configurations are shown.

[0127] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0128] Example 4

[0129] The embodiment of the present application further provides a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the method for controlling the success rate of batch collection and payment services provided in the first embodiment.

[0130] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0131] S101, obtaining failed transaction data of batch collection and payment services.

[0132] S102, using a hybrid model to analyze failed transaction data and determine a redo probability, wherein the hybrid model is used to represent multi-model collaborative analysis.

[0133] S103: In response to the redo probability satisfying the redo probability threshold, a polling process is initiated for the batch collection and payment services.

[0134] S104, in response to the polling number corresponding to the current polling in the polling process being less than the polling number threshold, generating a re-transaction instruction, wherein the re-transaction instruction is used to control the target device to re-transact the batch collection and payment services in a failed transaction state.

[0135] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0136] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0137] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing the program steps of the method for controlling the success rate of batch collection and payment services.

[0138] Optionally, in this embodiment, the computer program product may be configured as a computer program for executing the following steps:

[0139] S101, obtaining failed transaction data of batch collection and payment services.

[0140] S102, using a hybrid model to analyze failed transaction data and determine a redo probability, wherein the hybrid model is used to represent multi-model collaborative analysis.

[0141] S103: In response to the redo probability satisfying the redo probability threshold, a polling process is initiated for the batch collection and payment services.

[0142] S104, in response to the polling number corresponding to the current polling in the polling process being less than the polling number threshold, generating a re-transaction instruction, wherein the re-transaction instruction is used to control the target device to re-transact the batch collection and payment services in a failed transaction state.

[0143] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0144] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0145] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0146] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0147] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0148] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0149] The above are only preferred implementations of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for controlling the success rate of batch collection and payment services, characterized in that: include: Obtain failed transaction data for batch collection and payment services; Using a hybrid model to analyze the failed transaction data to determine a redo probability, wherein the hybrid model is used to represent multi-model collaborative analysis; In response to the redo probability satisfying the redo probability threshold, starting a polling process for the batch agency collection and payment service; In response to the polling number corresponding to the current polling in the polling process being less than the polling number threshold, a re-transaction instruction is generated, wherein the re-transaction instruction is used to control the target device to re-transact the batch collection and payment service in a failed transaction state.

2. The method according to claim 1, characterized in that The hybrid model includes a long short-term memory network model and a random forest model, wherein the hybrid model is used to analyze the failed transaction data to determine the redo probability, including: The long short-term memory network model is used to analyze the time series characteristics of the failed transaction data to obtain a time series prediction value, wherein the time series characteristics are used to represent the transaction time of the batch collection and payment services in the failed transaction state; The random forest model is used to analyze the non-time series characteristics of the failed transaction data to obtain a non-time series prediction value, wherein the non-time series prediction value is used to at least represent the transaction account information, transaction amount, and customer credit score of the batch collection and payment service of the failed transaction status; The time series prediction value and the non-time series prediction value are merged to obtain the redo probability.

3. The method according to claim 2, characterized in that The time series prediction value and the non-time series prediction value are merged to obtain the redo probability, including: Based on the time series characteristics, determining the weight of the time series prediction value; Determining a non-time series prediction value weight based on the non-time series feature; Based on the time series prediction value weight and the non-time series prediction value weight, the time series prediction value and the non-time series prediction value are weighted averaged and fused to obtain the redo probability.

4. The method according to claim 1, characterized in that: The redo probability threshold includes a first redo probability threshold and a second redo probability threshold, the first redo probability threshold is greater than the second redo probability threshold, and in response to the redo probability satisfying the redo probability threshold, a polling process is started for the batch collection and payment service, including: In response to the redo probability satisfying the first redo probability threshold, setting the time interval of the polling process to a first time period; According to the first time period, starting the corresponding polling process for the batch collection and payment service; In response to the redo probability satisfying the second redo probability threshold, setting the time interval of the polling process to a second time period, wherein the first time period is smaller than the second time period; According to the second time period, the corresponding polling process is started for the batch collection and payment service.

5. The method according to claim 4, characterized in that In response to the redo probability satisfying the redo probability threshold, after starting a polling process for the batch agency collection and payment service, the method further includes: In response to the redo probability not satisfying the redo probability threshold, a stop transaction message is generated and sent to the target node.

6. The method according to claim 1, characterized in that Obtain failed transaction data for batch collection and payment services, including: Acquire transaction data of the batch collection and payment service, wherein the transaction data includes the failed transaction data and normal transaction data, and the normal transaction data is used to represent attribute information of the batch collection and payment service in a normal transaction state during the transaction process; The failed transaction data is obtained by screening the transaction data.

7. The method according to claim 1, characterized in that In response to the polling number corresponding to the current polling in the polling process being less than the polling number threshold, generating a re-trading instruction, which then includes: In response to the polling number corresponding to the current polling in the polling process exceeding the polling number threshold, the polling process is suspended.

8. A device for controlling the success rate of batch collection and payment services, characterized in that: include: An acquisition module, the acquisition module is used to acquire failed transaction data of batch collection and payment services; An analysis module, the analysis module is used to analyze the failed transaction data using a hybrid model to determine a redo probability, wherein the hybrid model is used to represent multi-model collaborative analysis; A starting module, the starting module is used to start a polling process for the batch collection and payment service in response to the redo probability meeting the redo probability threshold; A re-transaction module, the re-transaction module is used to generate a re-transaction instruction in response to the polling number corresponding to the current polling in the polling process being less than the polling number threshold, wherein the re-transaction instruction is used to control the target device to re-transact the batch collection and payment business in the failed transaction state.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A memory storing an executable program; A processor, configured to run the program, wherein the program executes the method according to any one of claims 1 to 7 when running.

11. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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