A periodic charge method and system supporting multi-platform and high performance

Abstract

This invention relates to the field of internet finance payments and provides a high-performance, multi-platform periodic billing method. The method includes: scanning a business database for signed orders requiring billing at the current time point according to a preset time window, generating a set of tasks to be billed; reading the payment channel identifier of each task in the set of tasks to be billed, and converting the business order parameters into a standardized payment message required by that payment channel; pushing the standardized payment message to a distributed message queue and distributing it to multiple parallel billing execution nodes using a load balancing strategy; the billing execution nodes retrieving messages from the message queue and locking the current order status using a distributed lock mechanism to prevent duplicate billing; updating the local order status and the next billing cycle, and recording the billing log; and combining a distributed lock with an automatic reconciliation mechanism to avoid resource conflicts and double-deduction risks, ensuring fund security and eventual data consistency.

Description

Technical Field

[0001] This invention belongs to the field of internet financial payment, and in particular relates to a periodic billing method and system that supports multiple platforms and high performance. Background Technology

[0002] With the booming development of the subscription economy, business models such as video memberships, cloud services, and SaaS software widely adopt periodic automatic billing mechanisms. Currently, payment channels in the market are fragmented, with mainstream channels including Alipay, WeChat Pay, UnionPay, and various overseas payment gateways. The interface protocols, parameter specifications, and interaction processes of each platform differ significantly. Traditional billing systems are typically developed for a single channel or use a simple serial polling method to handle billing requests, resulting in high system coupling and poor scalability.

[0003] When dealing with billing scenarios involving tens of millions of users, existing technologies exhibit significant performance bottlenecks and stability vulnerabilities. Specifically, traditional single-threaded or database-based polling billing methods are prone to database deadlocks during peak daily billing periods, leading to low system throughput and difficulty in handling duplicate billing or callback delays caused by network fluctuations. Furthermore, the lack of a unified routing strategy results in extremely high maintenance costs for multi-platform integration. Therefore, there is an urgent need for a periodic billing method that can shield underlying channel differences, support distributed concurrent processing, and possess high fault tolerance to achieve high-performance cross-platform fund settlement. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-platform, high-performance periodic billing method and system, which aims to solve the problems mentioned in the background art.

[0005] This invention is implemented as follows: On one hand, a multi-platform, high-performance periodic billing method is provided, the method comprising: Based on the preset time window, scan the business database for signed orders that need to be deducted at the current time and generate a set of tasks to be deducted. Read the payment channel identifier of each task in the set of tasks to be charged, call the corresponding payment adapter, and convert the business order parameters into the standardized payment message required by the payment channel; The standardized payment message is pushed to a distributed message queue and distributed to multiple parallel deduction execution nodes through a load balancing strategy; The deduction execution node retrieves messages from the message queue, initiates a deduction request to the corresponding third-party payment platform, and uses a distributed lock mechanism to lock the current order status to prevent duplicate deductions. Receive synchronous or asynchronous deduction results from third-party payment platforms, update local order status and the next deduction cycle, and record deduction logs.

[0006] As a further aspect of the present invention, according to a preset time window, the contracted orders that need to be deducted at the current time are scanned from the business database to generate a set of tasks to be deducted using a sharding scanning mechanism: The pending payment orders are hashed and moduloed according to user ID or order number, and the data is divided into several logical fragments. Start several scanning threads, each of which scans and extracts only the due orders in the corresponding logical shard in parallel, in order to avoid performance blocking caused by full table scans of the database.

[0007] As a further aspect of the present invention, the payment channel identifier of each task in the set of tasks to be charged is read, the corresponding payment adapter is invoked, and the business order parameters are converted into the standardized payment message required by the payment channel. The payment adapter is constructed based on the strategy pattern. It provides a pre-defined, standardized abstract payment interface and implements specific protocol conversion classes for WeChat, Alipay, UnionPay, and custom channels. The corresponding protocol conversion class is dynamically loaded based on the channel ID in the order, and the unified internal order structure is mapped to the API parameters of the specific channel, so as to realize transparent calling of multi-platform interfaces.

[0008] As a further aspect of the present invention, the deduction execution node retrieves a message from the message queue, initiates a deduction request to the corresponding third-party payment platform, and uses a distributed lock mechanism to lock the current order status to prevent duplicate deductions. When a deduction request returns a network timeout or non-business error, it is not immediately marked as a failure. Instead, the task is re-entered into the dead letter queue after the delay time is calculated using the exponential backoff algorithm. When the number of retries reaches a preset threshold, a risk control alarm is triggered and the task is marked as requiring manual intervention.

[0009] As a further aspect of the present invention, receiving synchronous or asynchronous deduction results from a third-party payment platform, updating the local order status and the next deduction cycle, and recording deduction logs specifically also includes: Receive synchronous or asynchronous deduction results from third-party payment platforms, update local order status and the next deduction cycle, and record deduction logs.

[0010] As a further aspect of the present invention, another option is a multi-platform, high-performance periodic billing system, the system comprising: The task scanning module is used to efficiently retrieve due orders from the database according to the sharding strategy, aiming to solve the read performance bottleneck under massive data. The channel adaptation module stores configuration information for each payment channel and provides a unified parameter assembly interface, aiming to isolate the differences between business logic and the underlying payment gateway. The message distribution module is used to serialize the generated billing instructions and push them into a high-throughput message middleware, aiming to achieve traffic peak smoothing and decoupling of the scanning and execution processes; The concurrent execution module is used to deploy parallel consumption and deduction instructions on multiple server nodes, aiming to improve the system's transaction throughput per second through horizontal scaling; The results settlement module is used to handle payment callbacks and abnormal status transitions, aiming to ensure data consistency between the flow of funds and the flow of information.

[0011] As a further aspect of the present invention, the task scanning module specifically includes: The sharding calculation unit is used to dynamically calculate data sharding rules based on the number of cluster nodes. The timed triggering unit is used to precisely trigger scanning tasks through a time wheel algorithm, avoiding the resource idleness of traditional timed tasks.

[0012] As a further aspect of the present invention, the channel adaptation module specifically includes: The routing decision unit is used to dynamically select the optimal billing channel based on rates, channel availability, or user preferences. The parameter mapping unit is used to map standard fields defined internally by the system to external channel messages in XML, JSON, or key-value pair format.

[0013] As a further aspect of the present invention, the concurrent execution module specifically includes: Distributed lock units are used to maintain order-level mutex locks in distributed cache databases or distributed coordination services to prevent the same order from being deducted repeatedly by multiple nodes in extreme network environments. The connection pool management unit is used to reuse network communication protocol connections with third-party payment gateways.

[0014] As a further aspect of the present invention, the result clearing module specifically includes: The callback listener unit is used to provide a unified network hook interface to receive asynchronous notifications from various platforms; The reconciliation correction unit is used to download third-party reconciliation statements during off-peak hours each day, compare them with local transaction records, and detect and automatically correct any discrepancies in funds.

[0015] This invention provides a high-performance, multi-platform periodic billing method and system. Through distributed sharding scanning and policy adaptation, it enables high-concurrency processing of massive orders and flexible access to multiple platforms. Combined with distributed locks and automatic reconciliation mechanisms, it avoids resource conflicts and double-deduction risks, ensuring fund security and eventual data consistency. Attached Figure Description

[0016] Figure 1 This is the main flowchart of a multi-platform, high-performance periodic billing method.

[0017] Figure 2 This is a flowchart of the slice scanning mechanism in a multi-platform, high-performance periodic billing method.

[0018] Figure 3 This is a flowchart illustrating a multi-platform, high-performance periodic billing method that reads the payment channel identifier of each task in the set of tasks to be billed, calls the corresponding payment adapter to convert the business order parameters into a standardized payment message required by the payment channel, and constructs the payment adapter based on a strategy pattern.

[0019] Figure 4 This is the main structure diagram of a multi-platform, high-performance periodic billing system.

[0020] Figure 5 This is a structural block diagram of a task scanning module in a multi-platform, high-performance periodic billing system.

[0021] Figure 6 This is a structural block diagram of a channel adaptation module in a multi-platform, high-performance periodic billing system.

[0022] Figure 7 This is a structural block diagram of a concurrent execution module in a multi-platform, high-performance periodic billing system. Figure 8 This is a structural block diagram of the result settlement module in a multi-platform, high-performance periodic billing system. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0024] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0025] This invention provides a multi-platform, high-performance periodic billing method and system, which solves the technical problems in the background art.

[0026] like Figure 1 The diagram shown is a main flowchart of a multi-platform, high-performance periodic billing method according to an embodiment of the present invention. The multi-platform, high-performance periodic billing method includes: Step S100: Based on the preset time window, scan the business database for signed orders that need to be deducted at the current time point and generate a set of tasks to be deducted; Step S200: Read the payment channel identifier of each task in the set of tasks to be charged, call the corresponding payment adapter, and convert the business order parameters into the standardized payment message required by the payment channel; Step S300: Push the standardized payment message to the distributed message queue and distribute it to multiple parallel deduction execution nodes through a load balancing strategy; Step S400: The deduction execution node retrieves a message from the message queue, initiates a deduction request to the corresponding third-party payment platform, and uses a distributed lock mechanism to lock the current order status to prevent duplicate deductions; Step S500: Receive synchronous or asynchronous deduction results from the third-party payment platform, update the local order status and the next deduction cycle, and record the deduction log; In this implementation, a time-window-based task triggering mechanism is first established to systematically extract data to be processed from the source. Then, an adaptation layer concept is introduced to streamline the chaotic multi-channel payment interfaces into standard messages within the system, effectively shielding the complexity of the underlying heterogeneous system. In the core execution phase, this method does not employ traditional serial calls but combines distributed scheduling steps with concurrent deduction execution steps. It utilizes message queues to smooth traffic peaks and valleys, distributing computational pressure across multiple parallel nodes, thus overcoming the throughput limitations of a single machine in the physical architecture. Finally, it receives synchronous or asynchronous deduction results from third-party payment platforms, updates the local order status and the next deduction cycle, and records deduction logs, constructing a complete status feedback loop. This not only enables pluggable expansion of payment channels but also ensures high availability and eventual data consistency at the architectural level for tens of millions of orders, solving the fundamental problems of "slow, chaotic, and error-prone" traditional deduction systems.

[0027] like Figure 2 As shown, in a preferred embodiment of the present invention, according to a preset time window, the system scans the business database for contracted orders that need to be charged at the current time point, generating a set of tasks to be charged using a sharding scanning mechanism. Step S101: Perform a hash modulo operation on the contracted orders to be charged according to the user ID or order number, and divide the data into several logical fragments; Step S102: Start several scanning threads, each thread only scans and extracts the due orders in the corresponding logical shard in parallel, so as to avoid performance blocking caused by full table scan of the database; In this embodiment, traditional scheduled task designs often lead to severe database lock contention when multiple servers simultaneously scan a large table, potentially crippling the entire business database. This solution employs a sharding scanning mechanism, treating the massive set of pending payment orders as a whole and using hash modulo operations to evenly divide it into several logical shards. Each scanning thread is strictly limited to processing only its own portion of data, eliminating data contention between threads and allowing multiple servers to simultaneously and at full speed read data without interference. This mechanism is designed to maximize the parallel read capabilities of the database cluster, significantly shortening the window period for batch processing tasks. Its technical advantage lies in the fact that even when facing hundreds of millions of signed orders, the system can complete task extraction in a very short time. Furthermore, by dynamically adjusting the number of shards N, the system possesses near-linear horizontal scalability, fundamentally breaking the performance ceiling of single-table scans.

[0028] like Figure 3 As shown, in a preferred embodiment of the present invention, the payment channel identifier of each task in the set of tasks to be charged is read, the corresponding payment adapter is called, and the business order parameters are converted into the standardized payment message required by the payment channel. The payment adapter is constructed based on the strategy pattern: Step S201: Pre-set a standardized abstract payment interface, and implement specific protocol conversion classes for WeChat, Alipay, UnionPay and custom channels respectively; Step S202: Dynamically load the corresponding protocol conversion class based on the channel ID in the order, map the unified internal order structure to the API parameters of the specific channel, and realize transparent calling of multi-platform interfaces; This embodiment introduces the strategy pattern from software engineering, defining an abstract payment interface independent of specific payment channels. During system runtime, the corresponding specific protocol conversion class is dynamically loaded based on the channel ID within the order. This design achieves complete decoupling between business logic and payment channels; adding a new payment channel only requires adding a new strategy class without modifying the core scheduling code. The technical benefits include extremely high system maintainability and scalability, enabling enterprises to quickly respond to market changes and integrate new payment methods, while ensuring the purity and stability of the core transaction system and reducing the risk of system crashes due to interface changes.

[0029] In a preferred embodiment of the present invention, the deduction execution node retrieves a message from the message queue, initiates a deduction request to the corresponding third-party payment platform, and uses a distributed lock mechanism to lock the current order status to prevent duplicate deductions. When a deduction request returns a network timeout or non-business error, it is not immediately marked as a failure. Instead, the task is re-entered into the dead letter queue after the delay time is calculated using the exponential backoff algorithm. When the number of retries reaches a preset threshold, a risk control alarm is triggered and the task is marked as requiring manual intervention. It should be understood that a combined mechanism of exponential backoff algorithm and dead-letter queue is introduced. When a payment request fails due to non-business reasons, the system does not mechanically retry immediately, but calculates a waiting time that increases exponentially with the number of retries, temporarily suspending the task. This design aims to give downstream systems time to recover, achieving flexible retries. Combined with the dead-letter queue and risk control alarms, the system can accurately identify truly persistent faults and introduce manual intervention. The effect of this technology is to significantly improve the payment success rate, reduce false judgments caused by network jitter, and protect third-party payment gateways through intelligent backoff mechanisms, avoiding the risk of IP blocking due to high-frequency invalid retries.

[0030] In a preferred embodiment of the present invention, receiving synchronous or asynchronous deduction results from a third-party payment platform, updating the local order status and the next deduction cycle, and recording deduction logs specifically further include: Receive synchronous or asynchronous deduction results from third-party payment platforms, update local order status and the next deduction cycle, and record deduction logs.

[0031] In this embodiment, an active query command is added, constructing a dual confirmation mechanism. When the system does not receive a clear result within a preset time threshold, it no longer passively waits but instead calls a third-party order query interface. This forces synchronization of the state machines of both parties. The technical effect is to achieve eventual consistency in distributed transactions, ensuring strict synchronization between user fund changes and system order status. This not only avoids user complaints about duplicate deductions due to inconsistent states but also prevents service losses for enterprises due to misjudged payment failures.

[0032] like Figure 4 As shown, in another preferred embodiment of the present invention, a multi-platform, high-performance periodic billing system is provided, the system comprising: The task scanning module 100 is used to efficiently retrieve due orders from the database according to the sharding strategy, aiming to solve the read performance bottleneck under massive data. The channel adaptation module 200 is used to store the configuration information of each payment channel and provide a unified parameter assembly interface, aiming to isolate the differences between business logic and the underlying payment gateway. The message distribution module 300 is used to serialize the generated deduction instructions and push them into a high-throughput message middleware, aiming to achieve traffic peak shaving and valley filling, and decouple the scanning and execution processes. The concurrent execution module 400 is used to deploy parallel consumption and deduction instructions on multiple server nodes, aiming to improve the system's transaction throughput per second through horizontal scaling. The result clearing module 500 is used to handle payment callbacks and abnormal status transitions, aiming to ensure data consistency between the flow of funds and the flow of information.

[0033] In this implementation, the system is divided into five core modules: task scanning, channel adaptation, message distribution, concurrent execution, and result settlement. The task scanning module 100 focuses on efficient I / O reading, the channel adaptation module 200 focuses on protocol standardization, the message distribution module 300 acts as a system buffer, the concurrent execution module 400 is responsible for high-throughput computation, and the result settlement module 500 ensures data closure. By breaking down complex business processes into independent concerns, each module can be deployed and scaled independently. When traffic peaks, the server nodes of the concurrent execution module can be scaled up individually without modifying other parts; when integrating a new bank, only the channel adaptation module needs to be upgraded.

[0034] like Figure 5 As shown, in another preferred embodiment of the present invention, the task scanning module 100 specifically includes: The sharding calculation unit 101 is used to dynamically calculate data sharding rules based on the number of cluster nodes. The timed triggering unit 102 is used to accurately trigger scanning tasks through a time wheel algorithm, avoiding resource idleness in traditional timed tasks.

[0035] This embodiment incorporates a time wheel algorithm. Traditional timed tasks often require frequent database polling to check for expired tasks, resulting in significant CPU resource waste and invalid database connections. The sharded computing unit 101 in this solution, combined with the time wheel algorithm, changes this busy-wait mode. The time wheel algorithm, using a circular array and linked list structure, can manage millions of delayed tasks with extremely low time complexity, triggering execution only when a task actually expires. This optimizes server resource utilization, especially when handling massive amounts of periodic subscriptions, accurately capturing trigger timing. It consumes almost no resources during low-load periods, while ensuring millisecond-level trigger accuracy during task-intensive periods, completely solving the drawbacks of traditional timers in high-concurrency scenarios, such as latency accumulation and resource idleness.

[0036] like Figure 6 As shown, in another preferred embodiment of the present invention, the channel adaptation module 200 specifically includes: The routing decision unit 201 is used to dynamically select the optimal billing channel based on rates, channel availability, or user preferences. The parameter mapping unit 202 is used to map standard fields defined internally by the system to external channel messages in XML, JSON, or key-value pair format.

[0037] In this embodiment, under multi-channel payment scenarios, a single user may support multiple payment methods, and the fees and success rates of different channels change in real time. The routing decision unit 201 uses a dynamic algorithm to intelligently select the optimal channel based on preset weights, maximizing profits. Simultaneously, the parameter mapping unit 202 automatically converts the system's internal unified data structure into the XML, JSON, or key-value pair formats required by external channels. By shielding the diverse external interfaces from intrusion into the internal core business logic, this not only reduces the enterprise's payment channel costs but also significantly improves the system's compatibility, enabling seamless integration with any third-party payment institution using standard or non-standard protocols.

[0038] like Figure 7 As shown, in another preferred embodiment of the present invention, the concurrent execution module 300 specifically includes: Distributed lock unit 301 is used to maintain order-level mutex locks in a distributed cache database or distributed coordination service to prevent the same order from being deducted repeatedly by multiple nodes in extreme network environments. The connection pool management unit 302 is used to reuse the network communication protocol connection with the third-party payment gateway.

[0039] In this embodiment, firstly, the distributed lock unit 301 utilizes a distributed cache database or distributed coordination service to implement cross-node mutual exclusion control, ensuring that only one node can operate on the same order at any given time. Its design purpose is to construct a critical section in a distributed environment, completely eliminating serious double-deduction incidents caused by network latency or retry mechanisms, and ensuring the idempotency of financial transactions. Secondly, the connection pool management unit 302 addresses the overhead of the TCP / IP protocol's three-way handshake by maintaining a long-lived connection pool to reuse network communication protocol connections with third-party gateways. This significantly reduces the network establishment latency for each deduction request, while also reducing the consumption of server file handle resources due to frequent connection establishment or disconnection.

[0040] like Figure 8 As shown, in another preferred embodiment of the present invention, the result clearing module 500 specifically includes: Callback listening unit 501 is used to provide a unified network hook interface to receive asynchronous notifications from various platforms; The reconciliation correction unit 502 is used to download third-party reconciliation statements during daily off-peak hours, compare them with local transaction records, and detect and automatically correct any discrepancies in funds.

[0041] In this embodiment, the callback listening unit 501 receives real-time payment result feedback from external sources via a standardized network hook or network callback interface, ensuring timely updates to transaction status. More importantly, the reconciliation correction unit 502 introduces an end-of-day reconciliation mechanism, automatically downloading third-party fund flow files during off-peak hours and comparing them item by item with local accounts. This identifies and corrects anomalies that may be missed during real-time processing, namely, discrepancies between local and counterparty records (where a record exists locally but not on the other end, or vice versa), ensuring the absolute accuracy of financial data and enabling the system's self-correction capabilities. Through an automated reconciliation process, the workload of manual verification is minimized, not only improving the efficiency of financial settlement but also eliminating financial risks caused by human error at the system level, providing strong technical support for the company's compliant operations.

[0042] In order for the above methods and systems to operate smoothly, the system may include more or fewer components than those described above, or combine certain components, or different components, in addition to the various modules mentioned above. For example, it may include input / output devices, network access devices, buses, processors, and memory.

[0043] The processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (OPGs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the system, connecting various parts via various interfaces and lines.

[0044] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0045] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0046] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A periodic billing method that supports multiple platforms and high performance, characterized in that, The method includes: Based on the preset time window, scan the business database for signed orders that need to be deducted at the current time and generate a set of tasks to be deducted. Read the payment channel identifier of each task in the set of tasks to be charged, call the corresponding payment adapter, and convert the business order parameters into the standardized payment message required by the payment channel; The standardized payment message is pushed to a distributed message queue and distributed to multiple parallel deduction execution nodes through a load balancing strategy; The deduction execution node retrieves messages from the message queue, initiates a deduction request to the corresponding third-party payment platform, and uses a distributed lock mechanism to lock the current order status to prevent duplicate deductions. Receive synchronous or asynchronous deduction results from third-party payment platforms, update local order status and the next deduction cycle, and record deduction logs.

2. The periodic billing method supporting multiple platforms and high performance according to claim 1, characterized in that, Based on a preset time window, the system scans the business database for signed orders that require payment at the current time, generating a set of tasks to be deducted using a sharded scanning mechanism. The pending payment orders are hashed and moduloed according to user ID or order number, and the data is divided into several logical fragments. Start several scanning threads, each of which scans and extracts only the due orders in the corresponding logical shard in parallel, in order to avoid performance blocking caused by full table scans of the database.

3. The multi-platform, high-performance periodic billing method according to claim 1, characterized in that, Read the payment channel identifier of each task in the set of tasks to be charged, call the corresponding payment adapter, and convert the business order parameters into the standardized payment message required by the payment channel. The payment adapter is constructed based on the strategy pattern. It provides a pre-defined, standardized abstract payment interface and implements specific protocol conversion classes for WeChat, Alipay, UnionPay, and custom channels. The corresponding protocol conversion class is dynamically loaded based on the channel ID in the order, and the unified internal order structure is mapped to the API parameters of the specific channel, so as to realize transparent calling of multi-platform interfaces.

4. The multi-platform, high-performance periodic billing method according to claim 1, characterized in that, The deduction execution node retrieves messages from the message queue, initiates a deduction request to the corresponding third-party payment platform, and uses a distributed lock mechanism to lock the current order status to prevent duplicate deductions. This also includes: When a deduction request returns a network timeout or non-business error, it is not immediately marked as a failure. Instead, the task is re-entered into the dead letter queue after the delay time is calculated using the exponential backoff algorithm. When the number of retries reaches a preset threshold, a risk control alarm is triggered and the task is marked as requiring manual intervention.

5. The multi-platform, high-performance periodic billing method according to claim 1, characterized in that, Receiving synchronous or asynchronous deduction results from third-party payment platforms, updating local order status and the next deduction cycle, and recording deduction logs specifically also includes: Receive synchronous or asynchronous deduction results from third-party payment platforms, update local order status and the next deduction cycle, and record deduction logs.

6. A multi-platform, high-performance periodic billing system, characterized in that, The system employs the multi-platform, high-performance periodic billing method as described in any one of claims 1-5, wherein the system comprises: The task scanning module is used to efficiently retrieve due orders from the database according to the sharding strategy, aiming to solve the read performance bottleneck under massive data. The channel adaptation module stores configuration information for each payment channel and provides a unified parameter assembly interface, aiming to isolate the differences between business logic and the underlying payment gateway. The message distribution module is used to serialize the generated billing instructions and push them into a high-throughput message middleware, aiming to achieve traffic peak shaving and valley filling, and decouple the scanning and execution processes. The concurrent execution module is used to deploy parallel consumption and deduction instructions on multiple server nodes, aiming to improve the system's transaction throughput per second through horizontal scaling; The results settlement module is used to handle payment callbacks and abnormal status transitions, aiming to ensure data consistency between the flow of funds and the flow of information.

7. The multi-platform, high-performance periodic billing system according to claim 6, characterized in that, The task scanning module specifically includes: The sharding calculation unit is used to dynamically calculate data sharding rules based on the number of cluster nodes. The timed triggering unit is used to precisely trigger scanning tasks through a time wheel algorithm, avoiding the resource idleness of traditional timed tasks.

8. The multi-platform, high-performance periodic billing system according to claim 6, characterized in that, The channel adaptation module specifically includes: The routing decision unit is used to dynamically select the optimal billing channel based on rates, channel availability, or user preferences. The parameter mapping unit is used to map standard fields defined internally by the system to external channel messages in XML, JSON, or key-value pair format.

9. The multi-platform, high-performance periodic billing system according to claim 6, characterized in that, The concurrent execution module specifically includes: Distributed lock units are used to maintain order-level mutex locks in distributed cache databases or distributed coordination services to prevent the same order from being deducted repeatedly by multiple nodes in extreme network environments. The connection pool management unit is used to reuse network communication protocol connections with third-party payment gateways.

10. The multi-platform, high-performance periodic billing system according to claim 6, characterized in that, The result clearing module specifically includes: The callback listener unit is used to provide a unified network hook interface to receive asynchronous notifications from various platforms; The reconciliation correction unit is used to download third-party reconciliation statements during off-peak hours each day, compare them with local transaction records, and detect and automatically correct any discrepancies in funds.

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