WO2020177479A1 - 用于账户处理的方法、系统和计算设备 - Google Patents

用于账户处理的方法、系统和计算设备 Download PDF

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Publication number
WO2020177479A1
WO2020177479A1 PCT/CN2020/070509 CN2020070509W WO2020177479A1 WO 2020177479 A1 WO2020177479 A1 WO 2020177479A1 CN 2020070509 W CN2020070509 W CN 2020070509W WO 2020177479 A1 WO2020177479 A1 WO 2020177479A1
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balance
target account
account
available
level
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French (fr)
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黄治龙
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Alibaba Group Holding Ltd
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Alibaba Group Holding Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/08—Payment architectures
    • G06Q20/10—Payment architectures specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
    • G06Q20/108—Remote banking, e.g. home banking
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/22—Payment schemes or models

Definitions

  • the embodiments of this specification relate to the field of financial data processing, and more specifically, to methods, systems, and computing devices for account processing.
  • the embodiments of this specification provide a method, system and computing device for account processing.
  • the embodiments of this specification provide a method for account processing, wherein the method is executed by a decision system, and the method includes: receiving a status query request for a target account from a business system; determining the target The balance of the account; based on the balance, determine the availability information of the target account, where the availability information is used to indicate whether the target account is currently available; send a status query response to the business system so that the The business system processes the deduction task of the target account based on the status query response, wherein the status query response at least includes the availability information.
  • the embodiments of this specification provide a method for account processing, wherein the method is executed by a metering system, and the method includes: obtaining real-time flow data of a target account from a streaming computing platform; Real-time flow data to calculate the balance of the target account, wherein the balance of the target account is used by the decision system to determine the availability information of the target account, and the availability information is used to indicate whether the target account is currently available , The availability information is used by the business system as a basis for processing the deduction task of the target account.
  • the embodiment of this specification provides a system for account processing, including: a receiving unit, configured to: receive a status query request for a target account from a business system; and a determining unit, configured to: determine the target The balance of the account; based on the balance, determine the availability information of the target account, where the availability information is used to indicate whether the target account is currently available; a sending unit is used to: send the status to the business system Query response, so that the business system processes the deduction task of the target account based on the status query response, wherein the status query response at least includes the availability information.
  • the embodiment of this specification provides a system for account processing, including: an obtaining unit, configured to: obtain real-time flow data of a target account from a streaming computing platform; and a computing unit, configured to: Flow data to calculate the balance of the target account, wherein the balance of the target account is used by the decision system to determine the availability information of the target account, and the availability information is used to indicate whether the target account is currently available, The availability information is used by the business system as a basis for processing the deduction task of the target account.
  • the embodiments of the present specification provide a computing device, including: at least one processor; a memory in communication with the at least one processor, and executable instructions stored thereon, the executable instructions being When the at least one processor is executed, the at least one processor implements the method executed by the decision system.
  • the embodiments of the present specification provide a computing device, including: at least one processor; a memory in communication with the at least one processor, and executable instructions stored thereon, the executable instructions being When the at least one processor is executed, the at least one processor implements the method executed by the metering system.
  • the decision-making system after receiving the status query request of the business system for the target account, the decision-making system can obtain the availability information about whether the target account is available based on the balance of the target account, so that the business system can at least base on the availability information. Handle deduction tasks. In this way, the risk of account overdraft can be greatly reduced.
  • Figure 1 is a schematic diagram of an exemplary architecture for account processing according to one embodiment.
  • Fig. 2A is a schematic flowchart of a method for account processing according to an embodiment.
  • Fig. 2B is a schematic flowchart of a method for account processing according to an embodiment.
  • Fig. 3A is a schematic block diagram of a system for account processing according to an embodiment.
  • Fig. 3B is a schematic block diagram of a system for account processing according to an embodiment.
  • Fig. 4A is a hardware structure diagram of a computing device for account processing according to an embodiment.
  • Fig. 4B is a hardware structure diagram of a computing device for account processing according to an embodiment.
  • hotspot accounts may refer to an account that performs high-frequency deductions and entry operations during the transaction process.
  • massive concurrent updates of hotspot accounts are required every second or even every millisecond.
  • overdrafts The overdraft of hotspot accounts will not only affect business, but even bring regulatory risks.
  • the embodiments of this specification provide a technical solution for account processing.
  • the decision system can determine the balance of the target account after receiving the status query request for the target account from the business system.
  • the target account may be a hotspot account.
  • the target account can also be a non-hot account.
  • the decision system determines whether the target account is currently available based on the balance, and obtains the availability information of the target account. Subsequently, a status query response including at least the availability information is sent to the business system, so that the business system can process the deduction task of the target account based on the status query response.
  • the decision-making system after receiving the status query request for the target account from the business system, the decision-making system obtains the availability information about whether the target account is available based on the balance of the target account, so that the business system can at least base on the availability information. Handle deduction tasks. For example, if the account is available, the business system can perform deduction tasks; if the account is not available, the business system can suspend the deduction tasks, and so on. In this way, the risk of account overdraft can be greatly reduced.
  • Figure 1 is a schematic diagram of an exemplary architecture for account processing according to one embodiment.
  • the data synchronization middleware can obtain various data of the data source, for example, messages related to each account, log records (for example, real-time account flow data), and so on.
  • the data synchronization middleware can synchronize the data of the data source to the streaming computing platform.
  • the streaming computing platform can aggregate the data provided by the data synchronization middleware, and then provide it to the metering system.
  • the metering system can obtain real-time account flow data from the data provided by the streaming computing platform, such as real-time account inflow and outflow data. Then, the metering system can calculate the account balance based on the real-time flow data of the account.
  • the data synchronization middleware, the streaming computing platform and the metering system can interact in real time and continuously, so that the metering system can always get the latest balance of the account.
  • the machine learning platform can build an account prediction model based on the business scenario of the account, and obtain the predicted value of the net outflow rate of the account based on the prediction model.
  • the machine learning platform can build predictive models based on any applicable deep learning frameworks, such as Tensorflow, Caffe, MXNet, and so on.
  • the machine learning platform can provide the predicted value of the account net outflow rate to the metering system, and the metering system will store it.
  • the machine learning platform can update the predicted value of the account net outflow rate in real time based on the prediction model, and continuously provide the updated value to the metering system.
  • the accounting system can manage the information of each account, can create new accounts, can cancel accounts, and so on.
  • the decision-making system can interact with the business system, the measurement system and the accounting system.
  • the business system can send a status query request for the account to the decision system.
  • the business system can send a status query request at a certain frequency.
  • the decision-making system can determine the balance of the account after receiving the status query request.
  • the decision system can obtain the balance of the account from the metering system or the accounting system. Then, the decision system can determine whether the account is currently available with availability information based on the balance of the account. After that, the decision system can provide the business system with a status query response including at least availability information.
  • the business system can process various tasks for the corresponding account according to the status query response, such as account entry and deduction tasks.
  • Fig. 2A is a schematic flowchart of a method for account processing according to an embodiment.
  • the method of Figure 2A can be executed by a decision system.
  • step 202A a status query request for the target account is received from the business system.
  • step 204A the balance of the target account is determined.
  • step 206A based on the balance, the availability information of the target account is determined, where the availability information is used to indicate whether the target account is currently available.
  • a status query response is sent to the business system, so that the business system processes the deduction task of the target account based on the status query response, where the status query response at least includes availability information.
  • the decision-making system after receiving the status query request of the business system for the target account, the decision-making system can obtain the availability information about whether the target account is available based on the balance of the target account, so that the business system can at least base on the availability information. Handle deduction tasks. In this way, the risk of account overdraft can be greatly reduced.
  • the decision system may obtain the balance of the target account from the metering system.
  • the balance of the target account may be calculated by the metering system based on the real-time flow data of the target account.
  • the balance of the target account obtained by the decision-making system is real-time and reliable, so that real-time and reliable account availability information can be provided to the business system, thereby avoiding account overdraft caused by the operation of the business system on the account.
  • step 204A if the decision system fails to obtain the balance from the metering system within a predetermined period of time, the balance is obtained from the accounting system.
  • the predetermined duration may be less than or equal to the maximum delay for the accounting system to update the balance of the target account.
  • the decision-making system fails to obtain the balance of the target account from the metering system in real time due to the delay, the balance of the target account can be obtained from the accounting system.
  • the decision-making system can obtain the balance of the target account from the accounting system through Remote Procedure Call (RPC).
  • RPC Remote Procedure Call
  • the decision system may determine whether the target account is currently available based on the balance.
  • the decision system may determine whether the target account is currently available based on the balance and the upper limit of the balance level and the lower limit of the balance level for the target account.
  • the upper limit of the balance level and the lower limit of the balance level can be set for each account.
  • the upper limit of the balance water level and the lower limit of the balance water level may be preset.
  • the upper limit of the balance water level and the lower limit of the balance water level can be set according to actual business scenarios, combined with requirements and experience.
  • the upper limit of the balance level and the lower limit of the balance level can be dynamically updated through a machine learning model. This approach can better adapt to business changes.
  • the decision-making system can determine that the target account is unavailable. For example, if the balance is lower than the lower limit of the balance level, the target account is in an unavailable state, which can also be understood as entering the "water storage stage".
  • the decision-making system can determine that the target account is available.
  • the decision system can determine that the target account is available. Specifically, if the previous balance of the target account has undergone some deduction operations, resulting in the current balance being between the lower limit of the balance water level and the upper limit of the balance water level, in this case, the target account is in an available state.
  • the decision-making system can determine that the target account is unavailable. Specifically, if the previous balance of the target account is lower than the lower limit of the balance level, that is, the target account was previously in an unavailable state, then the balance rises to between the lower limit of the balance level and the upper limit of the balance level, but the target account does not reach the upper limit of the balance level. The account is still unavailable. Until the balance rises to the upper limit of the balance level, the target account will not switch to an available state.
  • the status query response may also include the balance of the target account.
  • the decision system can send the availability information of the target account and the balance to the business system. In this way, the business system can process the deduction task of the target account based on this information.
  • the status query response may also include the available time of the target account.
  • the decision system can determine the available time of the target account. Before the available time expires, the business system can perform the deduction task of the target account; and when the available time expires, the business system suspends the deduction task of the target account.
  • the available time can provide a time point for the business system to perform the deduction task of the target account.
  • the interaction delay between distributed architectures can be taken into account. For example, if the status query request sent by the business system before the available time fails to be responded in time, the corresponding deduction task can also be performed based on the available time, and when the available time expires, the deduction task is suspended to avoid continuing The operation caused an account overdraft problem.
  • the decision system may obtain the predicted value of the net outflow rate of the target account from the metering system.
  • the predicted value of the account net outflow rate can be obtained through the prediction model of the machine learning platform. Then, the available time is determined based on the last update time of the balance, the predicted value of the balance and the net outflow rate of the account.
  • the available time may be equal to the most recent update time of the balance + the predicted value of the balance/account net outflow rate.
  • AccountNo account number of the account
  • UpperWaterLine the upper limit of the balance water level, the unit can be "yuan”
  • LowerWaterLine the lower limit of the balance water level, the unit It can be "yuan”
  • Status status, which includes available or unavailable
  • AvgLendingRates predicted value of account net outflow rate, the unit can be "yuan/minute”
  • Fig. 2B is a schematic flowchart of a method for account processing according to an embodiment. The method of FIG. 2B can be executed by a metering system.
  • step 202B the real-time flow data of the target account is obtained from the streaming computing platform.
  • step 204B the balance of the target account is calculated based on the real-time flow data.
  • the balance of the target account can be used by the decision system to determine the availability information of the target account, the availability information is used to indicate whether the target account is currently available, and the availability information is used by the business system as a basis for processing the deduction task of the target account.
  • the balance of the target account is obtained based on the real-time flow data of the target account, so that the decision-making system can obtain relatively reliable availability information based on the balance, so that the business system can perform reliable reliability at least based on the availability information. Operation, thereby reducing the overdraft risk of the target account.
  • the metering system may obtain the predicted value of the net outflow rate of the target account obtained through the prediction model from the machine learning platform.
  • the predicted value of the account net outflow rate can be determined by the decision system combining the balance and the latest update time of the balance to determine the available time of the target account.
  • the business system can perform the deduction task of the target account; when the available time expires, the business system suspends the deduction task of the target account.
  • Fig. 3A is a schematic block diagram of a system for account processing according to an embodiment.
  • the system 300A may be the aforementioned decision-making system.
  • the system 300A may include a receiving unit 302A, a determining unit 304A, and a sending unit 306A.
  • the receiving unit 302A may receive a status query request for the target account from the business system.
  • the determining unit 304A can determine the balance of the target account.
  • the determining unit 304A may also determine the availability information of the target account based on the balance, where the availability information is used to indicate whether the target account is currently available.
  • the sending unit 306A may send a status query response to the business system, so that the business system can process the deduction task of the target account based on the status query response, where the status query response at least includes availability information.
  • the decision-making system after receiving the status query request of the business system for the target account, the decision-making system can obtain the availability information about whether the target account is available based on the balance of the target account, so that the business system can at least base on the availability information. Handle deduction tasks. In this way, the risk of account overdraft can be greatly reduced.
  • the determining unit 304A may obtain the balance from the metering system.
  • the balance can be calculated by the metering system based on the real-time flow data of the target account.
  • the determining unit 304A may obtain the balance from the accounting system, where the predetermined period of time is less than or equal to the account system’s balance update of the target account. Maximum delay.
  • the status query response may also include the balance.
  • the determining unit 304A may also determine the available time of the target account.
  • the status query response may also include the available time. Among them, before the available time expires, the business system can perform the deduction task of the target account; when the available time expires, the business system suspends the deduction task of the target account.
  • the determining unit 304A may obtain the predicted value of the net outflow rate of the target account from the metering system, where the predicted value of the net outflow rate of the account is obtained through the prediction model of the machine learning platform.
  • the determining unit 304A may determine the available time based on the most recent update time of the balance, the predicted value of the balance and the net outflow rate of the account.
  • the determining unit 304A may determine whether the target account is currently available based on the balance and the upper limit of the balance level and the lower limit of the balance level for the target account.
  • the determining unit 304A may determine that the target account is unavailable. If the balance is higher than the upper limit of the balance level, the determining unit 304A may determine that the target account is available. If the balance is between the lower limit of the balance water level and the upper limit of the balance water level due to a decline, the determining unit 304A can determine that the target account is available. If the balance is between the lower limit of the balance level and the upper limit of the balance level due to an increase, and the balance before the increase is lower than the lower limit of the balance level, the determining unit 304A can determine that the target account is unavailable.
  • the upper limit of the balance water level and the lower limit of the balance water level may be preset.
  • the upper limit of the balance water level and the lower limit of the balance water level may be dynamically updated through a machine learning model.
  • Each unit of the system 300A can execute the corresponding steps in the method embodiments of FIGS. 1 and 2A. Therefore, for brevity of description, the specific operations and functions of each unit of the device 300A will not be repeated here.
  • Fig. 3B is a schematic block diagram of a system for account processing according to an embodiment.
  • the system 300B may be the aforementioned metering system.
  • the system 300B may include an acquisition unit 302B and a calculation unit 304B.
  • the obtaining unit 302B may obtain the real-time flow data of the target account from the streaming computing platform.
  • the calculation unit 304B may calculate the balance of the target account based on the real-time flow data, where the balance of the target account is used by the decision-making system to determine the availability information of the target account, the availability information is used to indicate whether the target account is currently available, and the availability information is determined by the business The system is used as the basis for processing the deduction task of the target account.
  • the balance of the target account is obtained based on the real-time flow data of the target account, so that the decision-making system can obtain relatively reliable availability information based on the balance, so that the business system can perform reliable reliability at least based on the availability information. Operation, thereby reducing the overdraft risk of the target account.
  • the obtaining unit 302B may also obtain the predicted value of the net outflow rate of the target account obtained through the prediction model from the machine learning platform, where the predicted value of the net outflow rate of the account is combined with the balance and the latest update time of the balance by the decision-making system To determine the available time of the target account.
  • the business system can perform the deduction task of the target account; when the available time expires, the business system suspends the deduction task of the target account.
  • Each unit of the system 300B can execute corresponding steps in the method embodiments of FIGS. 1 and 2B. Therefore, for the sake of brevity of description, the specific operations and functions of each unit of the device 300B will not be repeated here.
  • the aforementioned systems 300A and 300B can be implemented by hardware, software, or a combination of software and hardware.
  • the system 300A or 300B when the system 300A or 300B is implemented by software, it can be formed by reading the corresponding executable instructions in the memory (such as non-volatile memory) into the memory by the processor of the device where it is located and running it.
  • Fig. 4A is a hardware structure diagram of a computing device for account processing according to an embodiment.
  • the computing device 400A may include at least one processor 410A, memory 420A, memory 430A, and communication interface 440A, and at least one processor 410A, memory 420A, memory 430A, and communication interface 440A are connected together via a bus 450A.
  • At least one processor 410A executes at least one executable instruction (that is, the above-mentioned element implemented in the form of software) stored or encoded in the memory 420A.
  • the executable instructions stored in the memory 420A when executed by the at least one processor 410A, enable the computing device to implement the various processes described above in conjunction with FIGS. 1 and 2A.
  • the computing device 400A can be implemented in any suitable form in the art, for example, it includes, but is not limited to, a desktop computer, a laptop computer, a smart phone, a tablet computer, a consumer electronic device, a wearable smart device, and so on.
  • the computing device 400A can implement various functions and operations of the aforementioned decision-making system.
  • Fig. 4B is a hardware structure diagram of a computing device for account processing according to an embodiment.
  • the computing device 400B may include at least one processor 410B, memory 420B, memory 430B, and communication interface 440B, and at least one processor 410B, memory 420B, memory 430B, and communication interface 440B are connected together via a bus 450B.
  • At least one processor 410B executes at least one executable instruction (that is, the above-mentioned element implemented in the form of software) stored or encoded in the memory 420B.
  • the executable instructions stored in the memory 420B when executed by the at least one processor 410B, enable the computing device to implement the various processes described above in conjunction with FIGS. 1 and 2B.
  • the computing device 400B can be implemented in any suitable form in the art, for example, it includes but is not limited to a desktop computer, a laptop computer, a smart phone, a tablet computer, a consumer electronic device, a wearable smart device, and so on.
  • the computing device 400B can implement various functions and operations of the aforementioned metering system.
  • the embodiments of the present specification also provide a machine-readable storage medium.
  • the machine-readable storage medium may store executable instructions, and the executable instructions, when executed by the machine, cause the machine to implement the specific process of the method embodiment described above with reference to FIGS. 1 and 2A.
  • the embodiments of the present specification also provide a machine-readable storage medium.
  • the machine-readable storage medium may store executable instructions, and the executable instructions, when executed by a machine, cause the machine to implement the specific process of the method embodiment described above with reference to FIGS. 1 and 2B.
  • the foregoing machine-readable storage medium may include, but is not limited to, random access memory (RAM), read-only memory (Read-Only Memory, ROM), and electrically erasable programmable read-only memory (Electrically-Erasable).
  • RAM random access memory
  • ROM read-only memory
  • EEPROM Electrically erasable programmable read-only memory
  • SRAM Static Random Access Memory
  • hard disk hard disk
  • flash memory etc.

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Abstract

本说明书的实施例提供了用于账户处理的方法、系统和计算设备。该方法包括：从业务系统接收针对目标账户的状态查询请求；确定目标账户的余额；基于余额，确定目标账户的可用性信息，其中，可用性信息用于指示目标账户当前是否是可用的；向业务系统发送状态查询响应，以便业务系统基于状态查询响应来处理目标账户的扣款任务，其中，状态查询响应至少包括可用性信息。

Description

用于账户处理的方法、系统和计算设备 技术领域
本说明书的实施例涉及金融数据处理领域，并且更具体地，涉及用于账户处理的方法、系统和计算设备。
背景技术
随着电子交易业务的快速增长，热点账户可能每天产生上亿笔的变动明细。在某些场景下，可能每秒甚至每毫秒都需要对某些账户进行海量的并发更新操作。在这种情况下，任何更新操作的延迟等都有可能造成账户透支问题，轻则影响业务，重则带来监管风险。因此，如何有效地防止账户的透支问题成为亟待解决的问题之一。
发明内容
考虑到现有技术的上述问题，本说明书的实施例提供了用于账户处理的方法、系统和计算设备。
一方面，本说明书的实施例提供了一种用于账户处理的方法，其中，所述方法由决策系统执行，所述方法包括：从业务系统接收针对目标账户的状态查询请求；确定所述目标账户的余额；基于所述余额，确定所述目标账户的可用性信息，其中，所述可用性信息用于指示所述目标账户当前是否是可用的；向所述业务系统发送状态查询响应，以便所述业务系统基于所述状态查询响应来处理所述目标账户的扣款任务，其中，所述状态查询响应至少包括所述可用性信息。
另一方面，本说明书的实施例提供了一种用于账户处理的方法，其中，所述方法由计量系统执行，所述方法包括：从流计算平台获取目标账户的实时流水数据；基于所述实时流水数据，计算所述目标账户的余额，其中，所述目标账户的余额由决策系统用于确定所述目标账户的可用性信息，所述可用性信息用于指示所述目标账户当前是否是可用的，所述可用性信息由业务系统用作处理所述目标账户的扣款任务的依据。
另一方面，本说明书的实施例提供了一种用于账户处理的系统，包括：接收单元，用于：从业务系统接收针对目标账户的状态查询请求；确定单元，用于：确定所述目标账户的余额；基于所述余额，确定所述目标账户的可用性信息，其中，所述可用性信息 用于指示所述目标账户当前是否是可用的；发送单元，用于：向所述业务系统发送状态查询响应，以便所述业务系统基于所述状态查询响应来处理所述目标账户的扣款任务，其中，所述状态查询响应至少包括所述可用性信息。
另一方面，本说明书的实施例提供了一种用于账户处理的系统，包括：获取单元，用于：从流计算平台获取目标账户的实时流水数据；计算单元，用于：基于所述实时流水数据，计算所述目标账户的余额，其中，所述目标账户的余额由决策系统用于确定所述目标账户的可用性信息，所述可用性信息用于指示所述目标账户当前是否是可用的，所述可用性信息由业务系统用作处理所述目标账户的扣款任务的依据。
另一方面，本说明书的实施例提供了一种计算设备，包括：至少一个处理器；与所述至少一个处理器进行通信的存储器，其上存储有可执行指令，所述可执行指令在被所述至少一个处理器执行时使得所述至少一个处理器实现上述由决策系统执行的方法。
另一方面，本说明书的实施例提供了一种计算设备，包括：至少一个处理器；与所述至少一个处理器进行通信的存储器，其上存储有可执行指令，所述可执行指令在被所述至少一个处理器执行时使得所述至少一个处理器实现上述由计量系统执行的方法。
可见，在该技术方案中，决策系统在接收到业务系统针对目标账户的状态查询请求之后，可以基于目标账户的余额来得到关于目标账户是否可用的可用性信息，使得业务系统至少可以基于可用性信息来处理扣款任务。这样，能够极大地降低账户透支的风险。
附图说明
通过结合附图对本说明书的实施例的更详细的描述，本说明书的实施例的上述以及其它目的、特征和优势将变得更加明显，其中，在本说明书的实施例中，相同的附图标记通常代表相同的元素。
图1是根据一个实施例的用于账户处理的示例性架构的示意图。
图2A是根据一个实施例的用于账户处理的方法的示意性流程图。
图2B是根据一个实施例的用于账户处理的方法的示意性流程图。
图3A是根据一个实施例的用于账户处理的系统的示意性框图。
图3B是根据一个实施例的用于账户处理的系统的示意性框图。
图4A是根据一个实施例的用于账户处理的计算设备的硬件结构图。
图4B是根据一个实施例的用于账户处理的计算设备的硬件结构图。
具体实施方式
现在将参考各实施例讨论本文描述的主题。应当理解的是，讨论这些实施例仅是为了使得本领域技术人员能够更好地理解并且实现本文描述的主题，并非是对权利要求书中所阐述的保护范围、适用性或者示例的限制。可以在不脱离权利要求书的保护范围的情况下，对所讨论的元素的功能和排列进行改变。各个实施例可以根据需要，省略、替换或者添加各种过程或组件。
随着电子交易业务的急剧增长，很多账户的日记账量已经达到千万级以上。例如，某些账户每天可能产生上亿笔的变动明细。这些账户可以被称为热点账户。也就是说，热点账户可以是指交易过程中进行高频的扣款、入账操作的账户。在某些场景下，每秒甚至每毫秒都需要对热点账户进行海量的并发更新。对于热点账户而言，最主要的问题之一是避免透支。热点账户的透支不仅会影响业务，甚至会带来监管风险。
鉴于此，本说明书的实施例提供了一种用于账户处理的技术方案。在该技术方案中，决策系统可以在从业务系统接收到针对目标账户的状态查询请求之后，确定目标账户的余额。例如，目标账户可以是热点账户。此外，目标账户也可以是非热点账户。
然后，决策系统基于余额来确定目标账户当前是否是可用的，得到目标账户的可用性信息。随后，向业务系统发送至少包括可用性信息的状态查询响应，使得业务系统能够基于状态查询响应来处理目标账户的扣款任务。
可见，在该技术方案中，决策系统在从业务系统接收到针对目标账户的状态查询请求之后，基于目标账户的余额来得到关于目标账户是否可用的可用性信息，使得业务系统至少可以基于可用性信息来处理扣款任务。例如，如果账户是可用的，业务系统可以执行扣款任务；而如果账户是不可用的，业务系统可以暂停扣款任务等等。这样，能够极大地降低账户透支的风险。
下面将结合具体实施例来详细描述上述技术方案。
图1是根据一个实施例的用于账户处理的示例性架构的示意图。
在图1所示的例子中，数据同步中间件可以获取数据源的各种数据，例如，与各个账户相关的消息、日志记录(比如，账户实时流水数据)等等。数据同步中间件可以将数据源的数据同步至流计算平台。
流计算平台可以将由数据同步中间件提供的数据进行汇聚处理，然后提供给计量系统。
计量系统可以从流计算平台提供的数据中获得账户实时流水数据，比如账户实时流入流出数据。然后，计量系统可以根据账户实时流水数据，计算得到账户余额。
可以理解的是，数据同步中间件、流计算平台和计量系统可以实时、持续地交互，使得计量系统始终可以得到账户的最新余额。
机器学习平台可以基于账户的业务场景来构建账户预测模型，基于预测模型得到账户净流出速率预测值。例如，机器学习平台可以基于现有的任何适用的深度学习框架来构建预测模型，比如，Tensorflow、Caffe、MXNet等等。
机器学习平台可以将账户净流出速率预测值提供给计量系统，由计量系统进行存储。此外，机器学习平台可以基于预测模型实时地更新账户净流出速率预测值，并且不断地将经更新的值提供给计量系统。
账务系统可以对各个账户的信息进行管理，可以建立新账户，可以注销账户等等。
决策系统可以与业务系统、计量系统和账务系统进行交互。
业务系统可以向决策系统发送针对账户的状态查询请求，例如，业务系统可以以一定的频率发送状态查询请求。决策系统可以在接收到状态查询请求之后，确定账户的余额。例如，决策系统可以从计量系统或账务系统获得账户的余额。然后，决策系统可以基于账户的余额，确定账户当前是否可用的可用性信息。之后，决策系统可以向业务系统提供至少包括可用性信息的状态查询响应。决策系统的具体功能和操作将在下文中详细描述。
业务系统可以根据状态查询响应，来处理针对相应的账户的各种任务，比如入账、扣款任务等等。
下面将从决策系统的角度详细描述本说明书的技术方案。
图2A是根据一个实施例的用于账户处理的方法的示意性流程图。图2A的方法可以由决策系统执行。
如图2A所示，在步骤202A中，从业务系统接收针对目标账户的状态查询请求。
在步骤204A中，确定目标账户的余额。
在步骤206A中，基于余额，确定目标账户的可用性信息，其中，可用性信息用于 指示目标账户当前是否是可用的。
在步骤208A中，向业务系统发送状态查询响应，以便业务系统基于状态查询响应来处理目标账户的扣款任务，其中，状态查询响应至少包括可用性信息。
可见，在该技术方案中，决策系统在接收到业务系统针对目标账户的状态查询请求之后，可以基于目标账户的余额来得到关于目标账户是否可用的可用性信息，使得业务系统至少可以基于可用性信息来处理扣款任务。这样，能够极大地降低账户透支的风险。
在一个实施例中，在步骤204A中，决策系统可以从计量系统获取目标账户的余额。如上所述，目标账户的余额可以是计量系统基于目标账户的实时流水数据来计算得到的。这样，决策系统得到的目标账户的余额是实时可靠的，从而能够向业务系统提供实时可靠的账户可用性信息，由此避免业务系统对账户的操作导致账户透支。
在一个实施例中，在步骤204A中，如果决策系统在预定时长内未能从计量系统获取到余额，则从账务系统获取余额。该预定时长可以小于或等于账务系统对目标账户进行余额更新的最大延迟。
由于在分布式架构中，各个系统之间可能会存在一些交互延迟。通常这些延迟是很短暂的，但是在某些异常情况下，延迟可能会被放大致使发生账户透支问题。因此，在本实施例中，如果决策系统因为延迟而未能实时从计量系统中获取到目标账户的余额，则可以从账务系统中获得目标账户的余额。例如，决策系统可以通过远程过程调用(Remote Procedure Call，RPC)从账务系统获取目标账户的余额。这样，决策系统得到的目标账户的余额最多是账务系统处的余额更新延迟，从而能够避免目标账户的可用性信息不准确。
在得到目标账户的余额之后，在步骤206A中，决策系统可以基于余额来确定目标账户当前是否是可用的。
例如，决策系统可以基于余额以及针对目标账户的余额水位上限和余额水位下限来确定目标账户当前是否是可用的。可以针对每个账户设置相应的余额水位上限和余额水位下限。通过设置余额水位上限和余额水位下限来决定目标账户的可用性状态，能够避免目标账户的状态频繁切换，来回抖动，造成系统不稳定。
在一个实施例中，余额水位上限和余额水位下限可以是预先设定的。例如，可以根据实际业务场景，结合需求、经验等来设置余额水位上限和余额水位下限。
在一个实施例中，余额水位上限和余额水位下限可以是通过机器学习模型来动态更 新的。这种方式能够更好地适应业务变化。
具体而言，如果余额低于余额水位下限，则决策系统可以确定目标账户是不可用的。例如，如果余额低于余额水位下限，则目标账户处于不可用状态，也可以理解为进入“蓄水阶段”。
如果余额高于余额水位上限，则决策系统可以确定目标账户是可用的。
而对于余额处于余额水位上限和余额水位下限之间，分为两种情况。例如，如果余额是由于下降而处于余额水位下限和余额水位上限之间的，则决策系统可以确定目标账户是可用的。具体地，如果目标账户之前的余额经过一些扣款操作之后，导致当前的余额处于余额水位下限和余额水位上限之间，这种情况下，目标账户处于可用状态。
如果余额是由于上涨而处于余额水位下限和余额水位上限之间的，并且在上涨之前的余额低于余额水位下限，则决策系统可以确定目标账户是不可用的。具体地，如果目标账户之前的余额低于余额水位下限，也就是说目标账户先前处于不可用的状态，那么余额上涨到余额水位下限和余额水位上限之间，但是没有达到余额水位上限时，目标账户仍然处于不可用状态。直到余额上涨到余额水位上限，目标账户才会切换为可用状态。
可见，通过设置余额水位上限和余额水位下限来决定目标账户的可用性状态，能够避免目标账户的状态频繁切换，来回抖动，造成系统不稳定。
在一个实施例中，如果目标账户当前是可用的，状态查询响应还可以包括目标账户的余额。也就是说，如果目标账户当前是可用的，决策系统可以将目标账户的可用性信息和余额一起发送给业务系统。这样，业务系统可以根据这些信息来处理目标账户的扣款任务。
在一个实施例中，如果目标账户当前是可用的，状态查询响应还可以包括目标账户的可用时间。具体地，决策系统可以确定目标账户的可用时间。在可用时间到期之前，业务系统能够执行目标账户的扣款任务；而在可用时间到期时，业务系统暂停目标账户的扣款任务。
可见，可用时间可以为业务系统提供执行目标账户的扣款任务的时间点。这样，能够将分布式架构之间的交互延迟考虑在内。例如，如果业务系统在可用时间之前发出的状态查询请求未能及时得到响应，也可以基于该可用时间来进行相应的扣款任务，而在可用时间到期时，暂停扣款任务，从而避免继续操作导致发生账户透支问题。
在一个实施例中，决策系统可以从计量系统获取目标账户的账户净流出速率预测值。 如上所述，账户净流出速率预测值可以是通过机器学习平台的预测模型得到的。然后，基于余额的最近更新时间、余额和账户净流出速率预测值来确定可用时间。
例如，可用时间可以等于余额的最近更新时间+余额/账户净流出速率预测值。
可见，通过向业务系统提供目标账户的余额、可用性信息和可用时间，能够在一定程度上降低由于分布式架构的交互延迟而带来的账户透支风险。
从上述可知，在一种实现方式中，在决策系统中，可以维护如下的字段：AccountNo(账户的账号)、UpperWaterLine(余额水位上限，单位可以是“元”)、LowerWaterLine(余额水位下限，单位可以是“元”)、Status(状态，其包括可用或不可用)、AvgLendingRates(账户净流出速率预测值，单元可以是“元/分钟”)。
下面从计量系统的角度进行描述。图2B是根据一个实施例的用于账户处理的方法的示意性流程图。图2B的方法可以由计量系统来执行。
如图2B所示，在步骤202B中，从流计算平台获取目标账户的实时流水数据。
在步骤204B中，基于实时流水数据，计算目标账户的余额。
目标账户的余额可以由决策系统用于确定目标账户的可用性信息，可用性信息用于指示目标账户当前是否是可用的，可用性信息由业务系统用作处理目标账户的扣款任务的依据。
可见，在该技术方案中，通过基于目标账户的实时流水数据来得到目标账户的余额，使得决策系统能够基于该余额得到相对可靠的可用性信息，从而使得业务系统能够至少基于可用性信息来进行可靠的操作，从而降低目标账户的透支风险。
在一个实施例中，计量系统可以从机器学习平台获取通过预测模型得到的目标账户的账户净流出速率预测值。账户净流出速率预测值可以由决策系统结合余额和余额的最近更新时间来确定目标账户的可用时间。
其中，在可用时间到期之前，业务系统能够执行目标账户的扣款任务；在可用时间到期时，业务系统暂停目标账户的扣款任务。
可见，通过向业务系统提供执行目标账户的扣款任务的时间点，能够在一定程度上降低由于分布式架构的交互延迟而带来的账户透支风险。
图3A是根据一个实施例的用于账户处理的系统的示意性框图。例如，系统300A可以为上述决策系统。
如图3A所示，系统300A可以包括接收单元302A、确定单元304A和发送单元306A。
接收单元302A可以从业务系统接收针对目标账户的状态查询请求。确定单元304A可以确定目标账户的余额。确定单元304A还可以基于余额，确定目标账户的可用性信息，其中，可用性信息用于指示目标账户当前是否是可用的。发送单元306A可以向业务系统发送状态查询响应，以便业务系统基于状态查询响应来处理目标账户的扣款任务，其中，状态查询响应至少包括可用性信息。
可见，在该技术方案中，决策系统在接收到业务系统针对目标账户的状态查询请求之后，可以基于目标账户的余额来得到关于目标账户是否可用的可用性信息，使得业务系统至少可以基于可用性信息来处理扣款任务。这样，能够极大地降低账户透支的风险。
在一个实施例中，确定单元304A可以从计量系统获取余额。该余额可以是由计量系统基于目标账户的实时流水数据计算得到的。
在另一实施例中，如果在预定时长内未能从计量系统获取到余额，则确定单元304A可以从账务系统获取余额，其中，预定时长小于或等于账务系统对目标账户进行余额更新的最大延迟。
在另一实施例中，如果目标账户当前是可用的，则状态查询响应还可以包括余额。
在另一实施例中，如果目标账户当前是可用的，则确定单元304A还可以确定目标账户的可用时间。状态查询响应还可以包括可用时间。其中，在可用时间到期之前，业务系统能够执行目标账户的扣款任务；在可用时间到期时，业务系统暂停目标账户的扣款任务。
在另一实施例中，确定单元304A可以从计量系统获取目标账户的账户净流出速率预测值，其中，账户净流出速率预测值是通过机器学习平台的预测模型得到的。确定单元304A可以基于余额的最近更新时间、余额和账户净流出速率预测值，确定可用时间。
在另一实施例中，确定单元304A可以基于余额以及针对目标账户的余额水位上限和余额水位下限，确定目标账户当前是否是可用的。
在另一实施例中，如果余额低于余额水位下限，则确定单元304A可以确定目标账户是不可用的。如果余额高于余额水位上限，则确定单元304A可以确定目标账户是可用的。如果余额是由于下降而处于余额水位下限和余额水位上限之间的，则确定单元304A可以确定目标账户是可用的。如果余额是由于上涨而处于余额水位下限和余额水位上限之间的并且在上涨之前的余额低于余额水位下限，则确定单元304A可以确定目 标账户是不可用的。
在另一实施例中，余额水位上限和余额水位下限可以是预先设定的。或者，余额水位上限和余额水位下限可以是通过机器学习模型来动态更新的。
系统300A的各个单元可以执行图1和2A的方法实施例中的相应步骤，因此，为了描述的简洁，装置300A的各个单元的具体操作和功能此处不再赘述。
图3B是根据一个实施例的用于账户处理的系统的示意性框图。例如，系统300B可以是上述计量系统。
如图3B所示，系统300B可以包括获取单元302B和计算单元304B。
获取单元302B可以从流计算平台获取目标账户的实时流水数据。计算单元304B可以基于实时流水数据，计算目标账户的余额，其中，目标账户的余额由决策系统用于确定目标账户的可用性信息，可用性信息用于指示目标账户当前是否是可用的，可用性信息由业务系统用作处理目标账户的扣款任务的依据。
可见，在该技术方案中，通过基于目标账户的实时流水数据来得到目标账户的余额，使得决策系统能够基于该余额得到相对可靠的可用性信息，从而使得业务系统能够至少基于可用性信息来进行可靠的操作，从而降低目标账户的透支风险。
在一个实施例中，获取单元302B还可以从机器学习平台获取通过预测模型得到的目标账户的账户净流出速率预测值，其中，账户净流出速率预测值由决策系统结合余额和余额的最近更新时间来确定目标账户的可用时间。其中，在可用时间到期之前，业务系统能够执行目标账户的扣款任务；在可用时间到期时，业务系统暂停目标账户的扣款任务。
系统300B的各个单元可以执行图1和2B的方法实施例中的相应步骤，因此，为了描述的简洁，装置300B的各个单元的具体操作和功能此处不再赘述。
上述系统300A和300B可以采用硬件实现，也可以采用软件实现，或者可以通过软硬件的组合来实现。例如，系统300A或300B在采用软件实现时，其可以通过其所在设备的处理器将存储器(比如非易失性存储器)中对应的可执行指令读取到内存中运行来形成。
图4A是根据一个实施例的用于账户处理的计算设备的硬件结构图。如图4A所示，计算设备400A可以包括至少一个处理器410A、存储器420A、内存430A和通信接口 440A，并且至少一个处理器410A、存储器420A、内存430A和通信接口440A经由总线450A连接在一起。至少一个处理器410A执行在存储器420A中存储或编码的至少一个可执行指令(即，上述以软件形式实现的元素)。
在一个实施例中，在存储器420A中存储的可执行指令在被至少一个处理器410A执行时，使得计算设备实现以上结合图1和2A描述的各种过程。
计算设备400A可以采用本领域任何适用的形式来实现，例如，其包括但不限于台式计算机、膝上型计算机、智能电话、平板计算机、消费电子设备、可穿戴智能设备等等。
例如，计算设备400A可以实现上述决策系统的各种功能和操作。
图4B是根据一个实施例的用于账户处理的计算设备的硬件结构图。如图4B所示，计算设备400B可以包括至少一个处理器410B、存储器420B、内存430B和通信接口440B，并且至少一个处理器410B、存储器420B、内存430B和通信接口440B经由总线450B连接在一起。至少一个处理器410B执行在存储器420B中存储或编码的至少一个可执行指令(即，上述以软件形式实现的元素)。
在一个实施例中，在存储器420B中存储的可执行指令在被至少一个处理器410B执行时，使得计算设备实现以上结合图1和2B描述的各种过程。
计算设备400B可以采用本领域任何适用的形式来实现，例如，其包括但不限于台式计算机、膝上型计算机、智能电话、平板计算机、消费电子设备、可穿戴智能设备等等。
例如，计算设备400B可以实现上述计量系统的各种功能和操作。
本说明书的实施例还提供了一种机器可读存储介质。该机器可读存储介质可以存储有可执行指令，可执行指令在被机器执行时使得机器实现上面参照图1和2A描述的方法实施例的具体过程。
本说明书的实施例还提供了一种机器可读存储介质。该机器可读存储介质可以存储有可执行指令，可执行指令在被机器执行时使得机器实现上面参照图1和2B描述的方法实施例的具体过程。
例如，上述机器可读存储介质可以包括但不限于随机存取存储器(Random Access Memory，RAM)、只读存储器(Read-Only Memory，ROM)、电可擦除可编程 只读存储器(Electrically-Erasable Programmable Read-Only Memory，EEPROM)、静态随机存取存储器(Static Random Access Memory，SRAM)、硬盘、闪存等等。
应当理解的是，本说明书中的各个实施例均采用递进的方式来描述，各个实施例之间相同或相似的部分相互参见即可，每个实施例重点说明的都是与其它实施例的不同之处。例如，对于上述关于装置的实施例、关于计算设备的实施例以及关于机器可读存储介质的实施例而言，由于它们基本相似于方法实施例，所以描述的比较简单，相关之处参见方法实施例的部分说明即可。
上文对本说明书特定实施例进行了描述。其它实施例在所附权利要求书的范围内。在一些情况下，在权利要求书中记载的动作或步骤可以按照不同于实施例中的顺序来执行并且仍然可以实现期望的结果。另外，在附图中描绘的过程不一定要求示出的特定顺序或者连续顺序才能实现期望的结果。在某些实施方式中，多任务处理和并行处理也是可以的或者可能是有利的。
应当理解的是，对于本领域普通技术人员来说，对本说明书中的实施例进行的各种修改将是显而易见的，并且可以在不脱离权利要求书的保护范围的情况下，将本文所定义的一般性原理应用于其它变型。

Claims (24)

  1. 一种用于账户处理的方法，其中，所述方法由决策系统执行，所述方法包括：
    从业务系统接收针对目标账户的状态查询请求；
    确定所述目标账户的余额；
    基于所述余额，确定所述目标账户的可用性信息，其中，所述可用性信息用于指示所述目标账户当前是否是可用的；
    向所述业务系统发送状态查询响应，以便所述业务系统基于所述状态查询响应来处理所述目标账户的扣款任务，其中，所述状态查询响应至少包括所述可用性信息。
  2. 根据权利要求1所述的方法，其中，所述确定所述目标账户的余额，包括：
    从计量系统获取所述余额，其中，所述余额是由所述计量系统基于所述目标账户的实时流水数据计算得到的。
  3. 根据权利要求2所述的方法，其中，所述确定所述目标账户的余额，包括：
    如果在预定时长内未能从所述计量系统获取到所述余额，则从账务系统获取所述余额，其中，所述预定时长小于或等于所述账务系统对所述目标账户进行余额更新的最大延迟。
  4. 根据权利要求1至3中任一项所述的方法，其中，如果所述目标账户当前是可用的，则所述状态查询响应还包括所述余额。
  5. 根据权利要求1至4中任一项所述的方法，其中，如果所述目标账户当前是可用的，则所述方法还包括：
    确定所述目标账户的可用时间，其中，在所述可用时间到期之前，所述业务系统能够执行所述目标账户的扣款任务；在所述可用时间到期时，所述业务系统暂停所述目标账户的扣款任务；
    其中，所述状态查询响应还包括所述可用时间。
  6. 根据权利要求5所述的方法，其中，所述确定所述目标账户的可用时间，包括：
    从计量系统获取所述目标账户的账户净流出速率预测值，其中，所述账户净流出速率预测值是通过机器学习平台的预测模型得到的；
    基于所述余额的最近更新时间、所述余额和所述账户净流出速率预测值，确定所述可用时间。
  7. 根据权利要求1至6中任一项所述的方法，其中，所述基于所述余额，确定所述目标账户的可用性信息，包括：
    基于所述余额以及针对所述目标账户的余额水位上限和余额水位下限，确定所述目 标账户当前是否是可用的。
  8. 根据权利要求7所述的方法，其中，所述确定所述目标账户当前是否是可用的，包括：
    如果所述余额低于所述余额水位下限，则确定所述目标账户是不可用的；
    如果所述余额高于所述余额水位上限，则确定所述目标账户是可用的；
    如果所述余额是由于下降而处于所述余额水位下限和所述余额水位上限之间的，则确定所述目标账户是可用的；
    如果所述余额是由于上涨而处于所述余额水位下限和所述余额水位上限之间的并且在上涨之前的余额低于所述余额水位下限，则确定所述目标账户是不可用的。
  9. 根据权利要求7或8所述的方法，其中，
    所述余额水位上限和所述余额水位下限是预先设定的；或者
    所述余额水位上限和所述余额水位下限是通过机器学习模型来动态更新的。
  10. 一种用于账户处理的方法，其中，所述方法由计量系统执行，所述方法包括：
    从流计算平台获取目标账户的实时流水数据；
    基于所述实时流水数据，计算所述目标账户的余额，其中，所述目标账户的余额由决策系统用于确定所述目标账户的可用性信息，所述可用性信息用于指示所述目标账户当前是否是可用的，所述可用性信息由业务系统用作处理所述目标账户的扣款任务的依据。
  11. 根据权利要求10所述的方法，还包括：
    从机器学习平台获取通过预测模型得到的所述目标账户的账户净流出速率预测值，其中，所述账户净流出速率预测值由所述决策系统结合所述余额和所述余额的最近更新时间来确定所述目标账户的可用时间；
    其中，在所述可用时间到期之前，所述业务系统能够执行所述目标账户的扣款任务；在所述可用时间到期时，所述业务系统暂停所述目标账户的扣款任务。
  12. 一种用于账户处理的系统，包括：
    接收单元，用于：从业务系统接收针对目标账户的状态查询请求；
    确定单元，用于：确定所述目标账户的余额；基于所述余额，确定所述目标账户的可用性信息，其中，所述可用性信息用于指示所述目标账户当前是否是可用的；
    发送单元，用于：向所述业务系统发送状态查询响应，以便所述业务系统基于所述状态查询响应来处理所述目标账户的扣款任务，其中，所述状态查询响应至少包括所述可用性信息。
  13. 根据权利要求12所述的系统，其中，所述确定单元在确定所述目标账户的余额时，具体用于：
    从计量系统获取所述余额，其中，所述余额是由所述计量系统基于所述目标账户的实时流水数据计算得到的。
  14. 根据权利要求13所述的系统，其中，所述确定单元在确定所述目标账户的余额时，具体用于：
    如果在预定时长内未能从所述计量系统获取到所述余额，则从账务系统获取所述余额，其中，所述预定时长小于或等于所述账务系统对所述目标账户进行余额更新的最大延迟。
  15. 根据权利要求12至14中任一项所述的系统，其中，如果所述目标账户当前是可用的，则所述状态查询响应还包括所述余额。
  16. 根据权利要求12至15中任一项所述的系统，其中，如果所述目标账户当前是可用的，则所述确定单元还用于：
    确定所述目标账户的可用时间，其中，在所述可用时间到期之前，所述业务系统能够执行所述目标账户的扣款任务；在所述可用时间到期时，所述业务系统暂停所述目标账户的扣款任务；
    其中，所述状态查询响应还包括所述可用时间。
  17. 根据权利要求16所述的系统，其中，所述确定单元在确定所述目标账户的可用时间时，具体用于：
    从计量系统获取所述目标账户的账户净流出速率预测值，其中，所述账户净流出速率预测值是通过机器学习平台的预测模型得到的；
    基于所述余额的最近更新时间、所述余额和所述账户净流出速率预测值，确定所述可用时间。
  18. 根据权利要求12至17中任一项所述的系统，其中，所述确定单元在基于所述余额确定所述目标账户的可用性信息时，具体用于：
    基于所述余额以及针对所述目标账户的余额水位上限和余额水位下限，确定所述目标账户当前是否是可用的。
  19. 根据权利要求18所述的系统，其中，所述确定单元在确定所述目标账户当前是否是可用的时，具体用于：
    如果所述余额低于所述余额水位下限，则确定所述目标账户是不可用的；
    如果所述余额高于所述余额水位上限，则确定所述目标账户是可用的；
    如果所述余额是由于下降而处于所述余额水位下限和所述余额水位上限之间的，则确定所述目标账户是可用的；
    如果所述余额是由于上涨而处于所述余额水位下限和所述余额水位上限之间的并且在上涨之前的余额低于所述余额水位下限，则确定所述目标账户是不可用的。
  20. 根据权利要求18或19所述的系统，其中，
    所述余额水位上限和所述余额水位下限是预先设定的；或者
    所述余额水位上限和所述余额水位下限是通过机器学习模型来动态更新的。
  21. 一种用于账户处理的系统，包括：
    获取单元，用于：从流计算平台获取目标账户的实时流水数据；
    计算单元，用于：基于所述实时流水数据，计算所述目标账户的余额，其中，所述目标账户的余额由决策系统用于确定所述目标账户的可用性信息，所述可用性信息用于指示所述目标账户当前是否是可用的，所述可用性信息由业务系统用作处理所述目标账户的扣款任务的依据。
  22. 根据权利要求21所述的系统，其中，所述获取单元还用于：
    从机器学习平台获取通过预测模型得到的所述目标账户的账户净流出速率预测值，其中，所述账户净流出速率预测值由所述决策系统结合所述余额和所述余额的最近更新时间来确定所述目标账户的可用时间；
    其中，在所述可用时间到期之前，所述业务系统能够执行所述目标账户的扣款任务；在所述可用时间到期时，所述业务系统暂停所述目标账户的扣款任务。
  23. 一种计算设备，包括：
    至少一个处理器；
    与所述至少一个处理器进行通信的存储器，其上存储有可执行指令，所述可执行指令在被所述至少一个处理器执行时使得所述至少一个处理器实现根据权利要求1至9中任一项所述的方法。
  24. 一种计算设备，包括：
    至少一个处理器；
    与所述至少一个处理器进行通信的存储器，其上存储有可执行指令，所述可执行指令在被所述至少一个处理器执行时使得所述至少一个处理器实现根据权利要求10或11所述的方法。
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