An internet point value automatic reconciliation method and system
By building an automatic points reconciliation system, which collects and processes points transaction data in real time, identifies and automatically handles anomalies, the system solves the problems of long reconciliation cycles and low accuracy in existing technologies, and achieves efficient and accurate points management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Filing Date
- 2026-05-20
- Publication Date
- 2026-07-14
AI Technical Summary
Existing point reconciliation methods suffer from long cycles, lack of real-time monitoring, and the tendency for manual or semi-automated comparisons to result in omissions or misjudgments, making it difficult to guarantee accuracy and efficiency. They also lack the ability to comprehensively judge complex business rules and cannot achieve automatic handling of anomalies.
The rules for the points business are configured using a rule configuration engine. Data is collected in real time and formatted through a flow-through and standardization module. An anomaly detection engine is used for real-time comparison and logical verification. Combined with an automatic handling and execution module, anomalies are automatically handled, forming a closed-loop management system.
It enables real-time detection and automatic processing of points data, improving the accuracy and efficiency of reconciliation, reducing manual intervention, and enhancing the system's adaptability and stability.
Smart Images
Figure CN122387971A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of Internet data processing technology, specifically referring to a method and system for automatic reconciliation of Internet points. Background Technology
[0002] With the rapid development of internet platforms, points systems have been widely applied in e-commerce, finance, government affairs, and membership services, serving as an important means of user incentives and rights management. The generation, consumption, and transfer of points typically involve multiple business systems, with frequent data interactions and complex business rules between them, making the consistency and accuracy of points data a critical issue in system operation.
[0003] In existing technologies, points reconciliation typically employs a timed batch processing method, periodically exporting and comparing data from various systems to identify discrepancies. However, this method has significant shortcomings. On the one hand, the reconciliation cycle is long, making real-time monitoring impossible and leading to delayed detection of anomalies, which can easily escalate the impact. On the other hand, faced with massive amounts of data and complex rules, manual or semi-automated comparison methods are prone to omissions or misjudgments, making it difficult to guarantee the accuracy of the reconciliation results.
[0004] In addition, although some existing systems have introduced real-time monitoring mechanisms, they mostly rely on simple thresholds or trigger conditions to issue alarms. They can only indicate abnormal situations and lack the ability to make comprehensive judgments on complex business rules. They also cannot achieve automatic handling of abnormalities and still need to rely on manual intervention for subsequent processing. Overall efficiency is low and they cannot form a complete closed-loop management. Summary of the Invention
[0005] In view of the above situation and to overcome the defects of the prior art, the present invention provides an automatic reconciliation method and system for Internet points, so as to at least partially solve the above technical problems.
[0006] The technical solution adopted in this invention is as follows: This invention proposes an automatic reconciliation method for internet points, including a rule configuration engine for configuring points business rules, which at least include points issuance rules, points consumption rules, validity period rules, cross-system mutual recognition rules, and user status association rules; a transaction data collection and standardization module for collecting points transaction data from multiple business systems in real time or near real time, and performing field mapping, format standardization, and time alignment on the points transaction data; an anomaly detection engine for concurrently comparing and logically verifying the standardized points transaction data based on the points business rules to identify abnormal data; an automatic handling execution module for determining the anomaly type based on the identified abnormal data and automatically triggering the corresponding handling operation; and a reconciliation report and visualization module for generating reconciliation results, anomaly details, and handling records; wherein, a mapping relationship between anomaly types and handling strategies is established between the anomaly detection engine and the automatic handling execution module to achieve automated closed-loop processing of reconciliation anomalies.
[0007] Furthermore, the rule configuration engine includes a visual configuration unit, which is used to configure, modify, and combine points-based business rules through a graphical interface.
[0008] Furthermore, the flow data acquisition and standardization module adopts a streaming data processing method to continuously acquire and process the integrated flow data in real time.
[0009] Furthermore, the anomaly detection engine is used to identify abnormal data that includes at least the following situations: duplicate issuance of points, inconsistent point records across systems, expired points not being cleared, points of blacklisted users not being frozen, and accounting imbalances caused by data delays between systems.
[0010] Furthermore, the automatic handling execution module includes a strategy matching unit and an interface calling unit. The strategy matching unit is used to match the corresponding handling strategy according to the exception type, and the interface calling unit is used to call the interface of the external business system to execute the handling strategy.
[0011] Furthermore, the handling strategies include at least reversing the points, reissuing points, synchronizing user status, freezing points, and issuing alarm notifications.
[0012] Furthermore, the reconciliation report and visualization module is used to generate real-time reconciliation dashboards and supports the tracing and querying of abnormal data as well as the display of the handling process.
[0013] Furthermore, this invention proposes an automatic reconciliation method for internet points, comprising the following steps: S1, configuring points business rules; S2, collecting points transaction data from multiple business systems and performing standardized processing; S3, performing real-time comparison and logical verification of the standardized points transaction data based on the points business rules to identify abnormal data; S4, determining the abnormality type based on the abnormal data and matching the corresponding handling strategy; S5, automatically executing the corresponding operation according to the handling strategy; S6, generating a reconciliation report and recording the abnormality handling process to achieve closed-loop reconciliation management.
[0014] Furthermore, in step S2, the standardization process includes field mapping, data format unification, and timestamp alignment.
[0015] Furthermore, after step S5, the process also includes: when the automatic execution of the handling operation fails or the abnormal complexity exceeds a preset threshold, the abnormal data is transferred to the manual review process.
[0016] Compared with the prior art, the present invention has the following advantages: By constructing an automatic reconciliation system based on a rule configuration engine, flexible configuration and dynamic adjustment of points business rules are achieved, enabling the system to adapt to different business scenarios and complex rule changes. This avoids the limitations of traditional fixed script methods, thereby significantly improving the system's adaptability and scalability. At the same time, by standardizing the points transaction data from multiple systems, data consistency and comparability are ensured, providing a reliable foundation for subsequent anomaly detection.
[0017] By introducing an anomaly detection engine and an automatic processing module, real-time detection and automatic processing of points data are achieved, forming a closed-loop mechanism of "detection-identification-processing-feedback". This mechanism can not only detect points anomalies in a timely manner, but also automatically perform operations such as reversal, reissue or synchronization, reducing manual intervention, improving processing efficiency, and effectively enhancing the accuracy of reconciliation and the stability of system operation. Attached Figure Description
[0018] Figure 1 This is a flowchart illustrating the automatic reconciliation method for internet points in this invention.
[0019] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation
[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.
[0021] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.
[0022] This embodiment provides an automatic reconciliation system for internet-based points, used for real-time reconciliation, anomaly detection, and automatic handling of points data across multiple business systems. The system includes a rule configuration engine, a data acquisition and standardization module, an anomaly detection engine, an automatic handling execution module, and a reconciliation report and visualization module. These modules are interconnected via a network.
[0023] In this embodiment, the rule configuration engine is used to uniformly configure and manage the points-based service rules. These rules include points distribution rules, points consumption rules, validity period rules, cross-system mutual recognition rules, and user status association rules. The rule configuration engine includes a rule storage unit and a rule parsing unit. The rule storage unit stores rule data in a structured format, and the rule parsing unit converts the rules into executable logical expressions, which are then provided to the anomaly detection engine for invocation.
[0024] In this embodiment, the rules are defined using a structured expression. For example, the points distribution rule can be defined as "order amount multiplied by a preset ratio equals the number of points issued," with the logical expression: order_amount × 0.01 = points_issued. The cross-system consistency rule can be defined as the points value of the same user in different systems should remain consistent. The validity period rule can be defined as the points value being reset to zero when the current time exceeds the validity period. The rule parsing unit converts the above rules into executable functions for subsequent automatic detection.
[0025] The transaction data acquisition and standardization module is used to collect points transaction data from multiple business systems and perform unified processing on the data. These business systems include e-commerce systems, financial systems, and membership systems. Transaction data acquisition methods include API calls and message queue subscriptions. The collected raw data undergoes unified field transformation through a field mapping unit, and the timestamps from different systems are uniformly processed through a time alignment unit, thereby forming standardized points transaction data.
[0026] For example, order data collected in e-commerce systems includes user ID, order ID, order amount, and number of points issued. Points change data collected in financial systems includes user ID, transaction ID, and points change value. By mapping different fields, the data is unified into a unified format such as user ID, business ID, source system ID, points change value, and timestamp. Time alignment is achieved through time offset correction.
[0027] The anomaly detection engine is used to process the standardized integral transaction data in real time. The anomaly detection engine includes a rule matching unit and a concurrent processing unit. The concurrent processing unit uses a hash partitioning method based on user ID to divide the data into shards, and each shard performs rule matching in parallel on different processing threads or computing nodes. The rule matching unit calls the logical expressions provided by the rule configuration engine to perform verification on each integral transaction to identify abnormal data.
[0028] In this embodiment, anomaly detection includes the following specific scenarios: when multiple identical points issuance records appear for the same business number within a short period of time, it is determined to be duplicate points issuance; when the accumulated points value of the same user differs in different systems, it is determined to be cross-system inconsistency; when points have exceeded their validity period but have not been cleared, it is determined to be expired and unprocessed; when a user is on a blacklist but points still fluctuate, it is determined to be an abnormal status; when there is a time delay between different systems causing a short-term imbalance, it is determined to be an abnormal accounting delay.
[0029] The automatic handling execution module is used to process the identified abnormal data. The automatic handling execution module includes a strategy matching unit and an interface calling unit. The strategy matching unit determines the corresponding handling strategy based on the abnormality type from a preset mapping relationship between abnormality types and handling strategies. The interface calling unit calls the corresponding business system interface to execute the operation according to the handling strategy. The handling strategies include points reversal, points reissue, user status synchronization, points freezing, and alarm notification.
[0030] For example, when duplicate points are detected, a reversal operation is automatically generated to deduct the excess points; when inconsistencies are detected across systems, the data in the main system is used as a benchmark to synchronize other systems; when expired points are detected, the points are cleared; when an anomaly is detected that cannot be handled automatically, the abnormal data is marked and transferred to the manual review process.
[0031] The reconciliation report and visualization module is used to generate and display reconciliation results. This module includes a data statistics unit and a display unit. The data statistics unit is used to calculate the total transaction volume, the number of anomalies, and their processing status. The display unit is used to generate a real-time reconciliation dashboard and provides functions for querying anomaly data and tracing the processing process. Each anomaly data item is assigned a unique identifier and its entire process information, from detection, classification, and handling to completion, is recorded.
[0032] like Figure 1 As shown, this embodiment also provides a method for automatically reconciling internet points, including the following steps: Step S1: Configure the points business rules through the rule configuration engine and parse the rules into executable logical expressions; Step S2: Collect points transaction data from multiple business systems through the transaction data acquisition and standardization module, and perform field mapping, data format unification and timestamp alignment on the points transaction data to form standardized data; Step S3: The anomaly detection engine performs real-time comparison and logical verification of the standardized points transaction data based on the points business rules to identify abnormal data. Step S4: Determine the anomaly type based on the identified abnormal data, and match the corresponding handling strategy based on the preset mapping relationship; Step S5: The automatic handling execution module calls the business system interface to perform operations according to the handling strategy to complete the exception handling; Step S6: Generate a reconciliation report and record the anomaly handling process through the reconciliation report and visualization module, thereby achieving closed-loop management of reconciliation.
[0033] In the specific implementation process, taking user ID 1001 as an example, when user 100 completes a transaction with an order amount of 100 in the e-commerce system, they should receive 1 point according to the points distribution rules. However, the system actually distributes 2 points. In step S3, the rule verification finds that the number of points distributed is inconsistent with the rule calculation result, thus identifying it as abnormal data. In step S4, the abnormality type is determined to be a points distribution abnormality. In step S5, a points reversal operation is performed to deduct the 1 point that was over-distributed. In step S6, the processing result is recorded and the reconciliation report is updated to restore the consistency of the accounts.
[0034] In this embodiment, when the automatic processing fails or the abnormal complexity exceeds a preset threshold, the system will transfer the corresponding abnormal data to the manual review process for further processing by humans, and record the processing results for subsequent rule optimization.
[0035] Through the above implementation methods, the present invention realizes real-time collection, rule-based detection and automatic processing of points data, forming a complete automated reconciliation closed loop. This not only improves reconciliation efficiency and accuracy, but also reduces the cost of manual intervention and enhances the system's adaptability to complex business scenarios.
[0036] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.
[0037] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.
Claims
1. An automatic reconciliation system for internet points, characterized in that, include: The rule configuration engine is used to configure the points business rules, which include at least the points distribution rules, points consumption rules, validity period rules, cross-system mutual recognition rules, and user status association rules. The data acquisition and standardization module is used to acquire points flow data from multiple business systems in real time or near real time, and to perform field mapping, format standardization and time alignment processing on the points flow data. An anomaly detection engine is used to perform concurrent comparisons and logical verifications on the standardized points transaction data based on the points business rules in order to identify abnormal data. The automatic handling execution module is used to determine the anomaly type based on the identified abnormal data and automatically trigger the corresponding handling operation. The reconciliation report and visualization module is used to generate reconciliation results, anomaly details, and handling records; The anomaly detection engine establishes a mapping relationship between anomaly types and handling strategies with the automatic handling execution module to achieve automated closed-loop processing of reconciliation anomalies.
2. The Internet points automatic reconciliation system according to claim 1, characterized in that: The rule configuration engine includes a visual configuration unit, which is used to configure, modify, and combine points-based business rules through a graphical interface.
3. The Internet points automatic reconciliation system according to claim 1, characterized in that: The flow data acquisition and standardization module adopts a streaming data processing method to continuously acquire and process integrated flow data in real time.
4. The Internet points automatic reconciliation system according to claim 1, characterized in that: The anomaly detection engine is used to identify abnormal data that includes at least the following situations: duplicate issuance of points, inconsistent point records across systems, expired points not being cleared, points of blacklisted users not being frozen, and accounting imbalances caused by data delays between systems.
5. The Internet points automatic reconciliation system according to claim 1, characterized in that: The automatic handling execution module includes a strategy matching unit and an interface calling unit. The strategy matching unit is used to match the corresponding handling strategy according to the exception type, and the interface calling unit is used to call the interface of an external business system to execute the handling strategy.
6. The Internet points automatic reconciliation system according to claim 5, characterized in that: The handling strategies include at least point reversal, point reissue, user status synchronization, point freezing, and alarm notification.
7. The Internet points automatic reconciliation system according to claim 1, characterized in that: The reconciliation report and visualization module is used to generate real-time reconciliation dashboards and supports the tracing and querying of abnormal data and the display of the handling process.
8. A method for automatically reconciling internet points, characterized in that, Includes the following steps: S1. Configure points-based service rules; S2. Collect points transaction data from multiple business systems and perform standardized processing; S3. Based on the points business rules, perform real-time comparison and logical verification of the standardized points transaction data to identify abnormal data; S4. Determine the anomaly type based on the abnormal data and match the corresponding handling strategy; S5. Automatically execute the corresponding operation according to the aforementioned handling strategy; S6. Generate reconciliation reports and record the handling process for any anomalies to achieve closed-loop management of reconciliation.
9. The method for automatic reconciliation of internet points according to claim 8, characterized in that: In step S2, the standardization process includes field mapping, data format unification, and timestamp alignment.
10. The method for automatic reconciliation of internet points according to claim 8, characterized in that: The process after step S5 includes: when the automatic processing operation fails or the abnormal complexity exceeds a preset threshold, the abnormal data is transferred to the manual review process.