Scalable intelligent rules orchestration method and system based on microservices architecture

Abstract

The application relates to an extensible intelligent rule arrangement method and system based on a micro-service architecture. The method determines a bank collision connection mode according to a business scenario; when the bank collision connection mode is real-time bank collision, a channel request message is received; rule screening is performed on the channel request message, and after processing is completed, a synchronous processing mode is adopted to return a bank collision result; when the bank collision connection mode is batch offline bank collision, batch user data is packed into a specific file and uploaded to a server, a bank collision task queue is triggered to be generated, and is executed through timing task scheduling; after the task is completed, the processing result is uploaded to the server, and the channel side is notified through a callback interface. The method has the advantages of improving user quality and conversion efficiency, and realizing a high return rate of marketing investment.

Description

Technical Field

[0001] This invention relates to the field of Internet technology applications, and in particular to a scalable intelligent rule orchestration method and system based on a microservice architecture. Background Technology

[0002] With customer acquisition costs continuing to rise and customer quality varying widely in the e-commerce industry, improving customer acquisition efficiency and accurately identifying high-value customers has become one of the core issues in platform operation.

[0003] However, in existing technologies, users submitted through cooperative channels are not effectively screened, resulting in large differences in value and unstable user quality. Furthermore, low-quality users consume resources, leading to poor subsequent marketing conversion results and wasted marketing resources on ineffective users. This results in a low return on investment and limited revenue.

[0004] Currently, no effective solution has been proposed to address the issues of inconsistent user quality, low conversion rates, and limited marketing revenue in customer acquisition channels for online financial platforms using related technologies. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by providing a scalable intelligent rule orchestration method and system based on a microservice architecture, in order to solve the technical problems of inconsistent user quality, low conversion rates, and limited marketing revenue in the channel customer acquisition of online financial platforms.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] This invention provides a scalable intelligent rule orchestration method based on a microservice architecture, comprising: determining the credential stuffing connection method according to the business scenario; when the credential stuffing connection method is real-time credential stuffing, receiving channel request messages; performing rule filtering on the channel request messages, and returning the credential stuffing result in a synchronous processing mode after processing; when the credential stuffing connection method is batch offline credential stuffing, packaging batch user data into a specific file and uploading it to the server, triggering the generation of a credential stuffing task queue, executing it through a scheduled task, uploading the processing result to the server after the task is completed, and notifying the channel side through a callback interface.

[0008] Optionally, the process of filtering channel request messages according to rules and returning the credential stuffing result in a synchronous processing mode after processing includes: verifying the address of the received channel request message; if the verification passes, performing parameter preprocessing on the channel request message to obtain the processing result; determining whether the number of credential stuffing attempts by the channel exceeds the preset rate limiting configuration based on the processing result; if the result is yes, returning a failure result; if the result is no, filtering the processing result through preset pre-defined general rules to obtain a filtered result; determining the user group to which the user belongs based on the filtered result, loading the rule strategy set corresponding to the user group, and obtaining the rule execution result; and confirming the rule execution result as the credential stuffing result and returning it to the channel side while uploading it to the data management center.

[0009] Optionally, the verification based on the address of the received channel request message includes: obtaining the address of the channel request message; verifying the address through a preset whitelist; and returning a verification failure result if the verification fails.

[0010] Optionally, the channel request message can be preprocessed with parameters to obtain the following processing results: performing signature verification and decryption operations on the data in the channel request message to verify the integrity and legality of the data source; performing basic format and business-level checks on the data to ensure that the data meets the expected structure and rule requirements; and obtaining the processing result when the data verification is complete.

[0011] Optionally, the processing results can be filtered using preset general rules. The filtered results include: determining whether the user in the processing results belongs to a group that does not meet the basic access conditions; if the user belongs to a group that does not meet the basic access conditions, the filtering result is rejected; if the user does not belong to a group that does not meet the basic access conditions, the filtering result is approved.

[0012] Optionally, determining the rule execution result as a credential stuffing result and returning it to the channel side while uploading it to the data management center includes: asynchronously sending the credential stuffing result to the data management center via Kafka, performing security processing on the credential stuffing result to ensure confidentiality and integrity during transmission and storage, and synchronously responding to the channel side with the credential stuffing result in a standard format.

[0013] Optionally, the rule strategy set corresponding to the user group is loaded, and the rule execution results are obtained by: conducting A / B experiments based on the user group, configuring multiple sets of rule strategies for the user group, and using them as experimental control groups for parallel execution and effect comparison; by comparing the performance of each rule strategy on key indicators, the actual effect of multiple sets of rule strategies is evaluated, and the rule execution results are obtained.

[0014] Optionally, the method further includes: during the process of performing batch offline credential stuffing, if the service is abnormal or unexpectedly interrupted, the task execution status is automatically detected and the interruption situation is actively identified. When it is determined that the task has not been completed normally, the interruption position is calculated, and the task execution is resumed based on the interruption position, and the remaining data is processed from the interruption position.

[0015] This invention provides a scalable intelligent rule orchestration system based on a microservice architecture, comprising: an access layer, an application layer, and a basic service layer. The access layer performs token authentication, URL mapping and routing, IP whitelist verification, request parameter validation, rate limiting, and log recording and auditing. Token authentication is used to authenticate the requester's identity through an integrated token mechanism, determining whether the requester has legitimate access rights. URL mapping and routing is used to route external requests to corresponding internal microservices via URL mapping. IP whitelist verification is used to verify the IP address configured for access control. The whitelist strategy controls access from trusted sources; rate limiting control limits request frequency; logging and auditing provide foundational support for subsequent fault tracing and security compliance; the application layer handles external server access and backend management, including external server access for verifying and parsing channel request messages received from the channel side, and executing corresponding rule judgments and filtering logic through the credential stuffing processing flow; backend management configures channel credential stuffing related information and centrally manages and intelligently orchestrates credential stuffing rules, experimental strategies, and execution processes; the basic service layer executes basic and storage services, including data support, file management, and scheduled task scheduling; data support provides multi-source data required for rule judgment, including risk control information, user activity data, credit records, and accessed third-party data; file management receives and manages batch credential stuffing data uploaded from the channel side through object storage services, supporting file upload, parsing, and storage; scheduled task scheduling handles the scheduling and execution of batch credential stuffing tasks, automatically triggering batch tasks at preset times, and has task status tracking and interruption identification capabilities, automatically taking over and recovering in the event of an abnormal interruption.

[0016] Optionally, the scalable intelligent rule orchestration system based on a microservice architecture also includes: a domain layer for executing interface services and a management backend, wherein the interface service is used to execute credential stuffing rules and process credential stuffing results; and the management backend is used to configure credential stuffing channels, credential stuffing rules, credential stuffing experiments, offline credential stuffing management, and blocking blacklist feedback.

[0017] This invention adopts the above technical solution, determining the credential stuffing connection method based on the business scenario. When the credential stuffing connection method is real-time, it receives channel request messages; performs rule filtering on the channel request messages, and returns the credential stuffing result in a synchronous processing mode after processing. When the credential stuffing connection method is batch offline, it packages batch user data into specific files and uploads them to the server, triggering the generation of a credential stuffing task queue, which is executed through scheduled tasks. After the task is completed, the processing result is uploaded to the server, and the channel side is notified through a callback interface. Compared with existing technologies, this invention has the following technical effects: improving user quality and conversion efficiency, and achieving a high return on marketing investment. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a scalable intelligent rule orchestration method based on a microservice architecture according to Embodiment 1 of the present invention.

[0019] Figure 2 This is a schematic diagram of real-time credential stuffing in a scalable intelligent rule orchestration method based on a microservice architecture according to Embodiment 1 of the present invention;

[0020] Figure 3 This is a schematic diagram of batch offline credential stuffing in a scalable intelligent rule orchestration method based on a microservice architecture according to Embodiment 1 of the present invention;

[0021] Figure 4 This is a schematic diagram of a scalable intelligent rule orchestration system based on a microservice architecture according to Embodiment 2 of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated 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 scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0023] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0024] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0025] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units (elements) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or apparatus. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms “multiple” / “several” used in this application refer to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can indicate: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0026] Example 1

[0027] An illustrative embodiment of the present invention, such as Figure 1 As shown, Figure 1 This is a flowchart illustrating a scalable intelligent rule orchestration method based on a microservice architecture according to Embodiment 1 of the present invention. The scalable intelligent rule orchestration method based on a microservice architecture provided in this application includes:

[0028] Step S102: Determine the credential stuffing integration method based on the business scenario;

[0029] Specifically, the scalable intelligent rule orchestration method based on microservice architecture provided in this application embodiment determines the corresponding credential stuffing connection method based on different business scenarios. The credential stuffing connection methods in this application embodiment include: real-time credential stuffing and batch offline credential stuffing. Real-time credential stuffing is executed in step S104, and batch offline credential stuffing is executed in step S106.

[0030] The real-time credential stuffing in this embodiment is as follows:

[0031] Step S104: When the credential stuffing connection method is real-time credential stuffing, receive the channel request message; perform rule filtering on the channel request message, and return the credential stuffing result in synchronous processing mode after processing is completed;

[0032] Optionally, step S104, which involves filtering the channel request message according to rules and returning the credential stuffing result in a synchronous processing mode after processing, includes: verifying the address of the received channel request message; if the verification passes, performing parameter preprocessing on the channel request message to obtain a processing result; determining whether the number of credential stuffing attempts by the channel exceeds the preset rate limiting configuration based on the processing result; returning a failure result if the determination result is yes; filtering the processing result using preset pre-defined general rules to obtain a filtered result if the determination result is no; determining the user group to which the user belongs based on the filtered result, loading the rule strategy set corresponding to the user group, and obtaining the rule execution result; and returning the rule execution result, which is determined as the credential stuffing result, to the channel side while uploading it to the data management center.

[0033] Specifically, such as Figure 2 As shown, Figure 2 This is a schematic diagram of real-time credential stuffing in a scalable intelligent rule orchestration method based on a microservice architecture according to Embodiment 1 of the present invention. During the real-time credential stuffing process, on the system side, the gateway receives channel request messages sent by the channel side. The gateway verifies whether the IP address of the channel side is in the whitelist. If not, a verification failure is returned to the channel side. If so, the channel request message undergoes parameter preprocessing, which includes signature verification and decryption. If the processing is successful, the processing result is obtained, and based on the processing result, it is determined whether the number of credential stuffing attempts by the channel exceeds the preset rate limiting configuration, and rate limiting is performed. If the processing fails, a processing failure is returned to the channel side.

[0034] When performing traffic limiting judgment, if the judgment result is negative, a failure result is returned; if the judgment result is negative, the processing result is filtered by a preset general rule to obtain a filtered result; wherein, if the filtering of the processing result by the pre-set general rule is successful, the user group to which the user belongs is filtered; if the filtering fails, a filtering failure is returned to the channel side.

[0035] Load the rule strategy set corresponding to the user group and obtain the rule execution result; determine the execution result as the credential stuffing result and return it to the channel side while uploading it to the data management center; A / B testing can also be carried out during the process of loading the rule strategy set corresponding to the user group.

[0036] In addition, in this embodiment of the application, the final rule execution result is converted by parameter name and encrypted, and finally the data is reported through asynchronous execution.

[0037] Optionally, the verification based on the address of the received channel request message includes: obtaining the address of the channel request message; verifying the address through a preset whitelist; and returning a verification failure result if the verification fails.

[0038] Specifically, after the channel side sends a channel request message through a standardized API interface, the system applied by the scalable intelligent rule orchestration method based on microservice architecture in this embodiment performs whitelist verification on the server IP of the source of the channel request message (that is, the address is verified by a preset whitelist in this embodiment) to ensure that only requests from authorized channels can be accessed.

[0039] Optionally, the channel request message can be preprocessed with parameters to obtain the following processing results: performing signature verification and decryption operations on the data in the channel request message to verify the integrity and legality of the data source; performing basic format and business-level checks on the data to ensure that the data meets the expected structure and rule requirements; and obtaining the processing result when the data verification is complete.

[0040] Specifically, after whitelist verification, the request parameters undergo signature verification and decryption to verify the integrity and legitimacy of the data source. After decryption, further basic format and business-level checks are performed on the parameter content to ensure the data conforms to the expected structure and rule requirements, thus providing a secure and reliable input guarantee for subsequent credential stuffing attacks.

[0041] Optionally, the processing results can be filtered using preset general rules. The filtered results include: determining whether the user in the processing results belongs to a group that does not meet the basic access conditions; if the user belongs to a group that does not meet the basic access conditions, the filtering result is rejected; if the user does not belong to a group that does not meet the basic access conditions, the filtering result is approved.

[0042] Specifically, the preset general rule in this application embodiment can be: first determine whether the user belongs to a blacklist, canceled user, or other groups that do not meet the basic access conditions. That is, in this application embodiment, not meeting the basic access conditions means belonging to a blacklist or canceled user.

[0043] Optionally, determining the rule execution result as a credential stuffing result and returning it to the channel side while uploading it to the data management center includes: asynchronously sending the credential stuffing result to the data management center via Kafka, performing security processing on the credential stuffing result to ensure confidentiality and integrity during transmission and storage, and synchronously responding to the channel side with the credential stuffing result in a standard format.

[0044] Specifically, after the rule execution is completed and the judgment result is obtained, the rule execution result is confirmed as the credential stuffing result and sent asynchronously to the data management center via Kafka. This achieves efficient decoupling and real-time integration with the data platform, facilitating subsequent management activities such as data archiving, statistical analysis, and strategy optimization. Simultaneously, the credential stuffing result is encrypted and subjected to other security measures to ensure the confidentiality and integrity of the data during transmission and storage. After post-processing, the result is finally synchronously responded to the channel side in a standard format, ensuring timely, accurate, and secure feedback, thus completely closing the loop of this credential stuffing process.

[0045] Optionally, the rule strategy set corresponding to the user group is loaded, and the rule execution results are obtained by: conducting A / B experiments based on the user group, configuring multiple sets of rule strategies for the user group, and using them as experimental control groups for parallel execution and effect comparison; by comparing the performance of each rule strategy on key indicators, the actual effect of multiple sets of rule strategies is evaluated, and the rule execution results are obtained.

[0046] Specifically, to evaluate the effectiveness of different rule combinations in practical applications, the scalable intelligent rule orchestration method based on a microservice architecture provided in this application supports A / B testing configurations based on user groups. Multiple rule strategies can be flexibly configured for different user groups, serving as experimental control groups for parallel execution and effect comparison. By comparing the performance of each strategy on key indicators such as hit rate and conversion rate, the actual effectiveness of different rule combinations can be comprehensively evaluated, thereby providing data support for strategy adjustment and rule system optimization, and continuously improving the accuracy and business value of rule orchestration.

[0047] The specific steps of batch offline credential stuffing in this application embodiment are as follows:

[0048] Step S106: When the credential stuffing method is batch offline credential stuffing, the batch of user data is packaged into a specific file and uploaded to the server, triggering the generation of the credential stuffing task queue, which is executed through scheduled task. After the task is completed, the processing result is uploaded to the server and the channel side is notified through the callback interface.

[0049] In this embodiment, the batch offline credential stuffing attack is designed for large-scale data processing scenarios. The channel side needs to organize user data according to a specified format and package the batch data into a TXT file (i.e., the specific file in this embodiment) and upload it to the OSS file server. After uploading, a notification is sent to the system using the scalable intelligent rule orchestration method based on microservice architecture provided in this embodiment via an interface. Upon receiving the notification, a corresponding credential stuffing attack task queue is generated and assigned to a scheduled task for execution. After the task is completed, the system organizes the processing results into a file, uploads it to OSS, and notifies the channel side via a callback interface for download.

[0050] Specifically, such as Figure 3 As shown, Figure 3 This is a schematic diagram of batch offline credential stuffing in a scalable intelligent rule orchestration method based on a microservice architecture according to Embodiment 1 of the present invention; the gateway performs IP whitelist verification on the channel request messages sent by the channel side; if the IP whitelist verification fails, a verification failure is returned to the channel side; if the IP whitelist verification passes, parameter preprocessing is performed for signature verification and decryption; if the processing fails, a processing failure is returned to the channel side; if the processing succeeds, the channel request message is saved in the database, and the current state is initialized; wherein, after the channel request message is saved in the database, a credential stuffing task queue is generated, such as... Figure 3 As shown, this embodiment of the application uses the setting of scheduled task 1 and scheduled task 2 as an example for illustration:

[0051] When scheduled task 1 is executed, it checks whether the number of running tasks has exceeded the limit. If the result is yes, the current task execution ends. If the result is no, it queries the task with the most recently initialized state and executes it asynchronously using a thread pool. In this process, the task status is first updated to running, the data file is downloaded to the local machine, and the total data volume of the task is updated. The file data is read in batches, and the amount of data processed is accumulated. It checks whether the data volume has reached the upper limit. If the result is yes, the current task execution ends. If the result is yes, the rule judgment is executed in batches, the task status is updated, and the current task execution ends.

[0052] When scheduled task 2 is executed, it checks if there is a task in progress that has not been updated for more than 1 minute. If the result is no, the current task execution ends. If the result is yes, it uses a thread pool for asynchronous processing, downloads the file to the local machine, resumes processing from the point of interruption based on the recorded data, reads the file data in batches, accumulates the amount of data processed, and checks if the data amount has reached the upper limit. If the result is yes, the current task execution ends. If the result is yes, it executes the rule judgment in batches, updates the task status, and the current task execution ends.

[0053] Optionally, the scalable intelligent rule orchestration method based on microservice architecture provided in this application embodiment further includes: during the execution of batch offline credential stuffing, if the service is abnormal or unexpectedly interrupted, the task execution status is automatically detected and the interruption situation is actively identified. When it is determined that the task has not been completed normally, the interruption position is calculated, and the task execution is resumed based on the interruption position, and the remaining data is processed from the interruption position.

[0054] Specifically, during the execution of batch offline tasks, in the event of service anomalies or unexpected interruptions, the system's built-in scheduled task module will automatically detect the task execution status and proactively identify the interruption. Once it is detected that a task has not completed normally, the system will accurately calculate the interruption point and resume task execution based on this point, continuing to process the remaining data from the interruption point, avoiding duplicate calculations and data omissions. This mechanism effectively ensures the continuity and integrity of batch tasks, significantly improving the system's robustness and fault tolerance in large-scale data processing scenarios.

[0055] The scalable intelligent rule orchestration method based on microservice architecture provided in this application embodiment achieves efficient and flexible channel access capabilities through two modes: real-time credential stuffing and batch offline credential stuffing. It not only ensures real-time requirements but also takes into account the stability and controllability of large-scale data processing.

[0056] This invention adopts the above technical solution, determining the credential stuffing connection method based on the business scenario. When the credential stuffing connection method is real-time, it receives channel request messages; performs rule filtering on the channel request messages, and returns the credential stuffing result in a synchronous processing mode after processing. When the credential stuffing connection method is batch offline, it packages batch user data into specific files and uploads them to the server, triggering the generation of a credential stuffing task queue, which is executed through scheduled tasks. After the task is completed, the processing result is uploaded to the server, and the channel side is notified through a callback interface. Compared with existing technologies, this invention has the following technical effects: improving user quality and conversion efficiency, and achieving a high return on marketing investment.

[0057] Example 2

[0058] An illustrative embodiment of the present invention, such as Figure 4 As shown, Figure 4 This is a schematic diagram of a scalable intelligent rule orchestration system based on a microservice architecture according to Embodiment 2 of the present invention. The scalable intelligent rule orchestration system based on a microservice architecture provided in this application includes:

[0059] The system comprises an access layer, an application layer, and a basic service layer. The access layer performs token authentication, URL mapping and routing, IP whitelist verification, request parameter validation, rate limiting, and logging and auditing. Token authentication verifies the requester's legitimacy by integrating a token mechanism. URL mapping and routing routes external requests to corresponding internal microservices. IP whitelist verification verifies the user's IP address by configuring a whitelist. The whitelist strategy controls access from trusted sources; rate limiting control limits request frequency; logging and auditing provide foundational support for subsequent fault tracing and security compliance; the application layer handles external server access and backend management, including external server access for verifying and parsing channel request messages received from the channel side, and executing corresponding rule judgments and filtering logic through the credential stuffing processing flow; backend management configures channel credential stuffing related information and centrally manages and intelligently orchestrates credential stuffing rules, experimental strategies, and execution processes; the basic service layer executes basic and storage services, including data support, file management, and scheduled task scheduling; data support provides multi-source data required for rule judgment, including risk control information, user activity data, credit records, and accessed third-party data; file management receives and manages batch credential stuffing data uploaded from the channel side through object storage services, supporting file upload, parsing, and storage; scheduled task scheduling handles the scheduling and execution of batch credential stuffing tasks, automatically triggering batch tasks at preset times, and has task status tracking and interruption identification capabilities, automatically taking over and recovering in the event of an abnormal interruption.

[0060] Optionally, the scalable intelligent rule orchestration system based on microservice architecture in this application embodiment further includes: a domain layer, used to execute interface services and a management backend, wherein the interface service is used to execute credential stuffing rules and process credential stuffing results; and the management backend is used to configure credential stuffing channels, credential stuffing rules, credential stuffing experiments, offline credential stuffing management, and blocking blacklist feedback.

[0061] Among them, such as Figure 4 As shown, the access layer includes token authentication, URL mapping, and internal service communication; the application layer includes external service access, backend management, and external interface request permissions; the domain layer includes interface services and a management backend, where the interface services include execution of credential stuffing rules and processing of credential stuffing results; the management backend includes configuration for credential stuffing channels, credential stuffing rules, credential stuffing experiments, offline credential stuffing, and blocking blacklist feedback; the basic service layer... Figure 4The CSS is represented as the infrastructure layer, which includes the basic service layer and the storage layer. The basic services include: OSS file service, CIS user management, TDS institutional service, hover risk control service, LPS credit service, BCS management service, hub traffic service, CRS third-party service, HBase data service, and XXLJob service. The storage layer includes: MySQL, Redis (Remote Dictionary Server), OSS (Object Storage Service), Kafka, and MQ (Message Queue).

[0062] Specifically, the access layer, as the front-end gate of the entire microservice architecture, bears important responsibilities for security protection and traffic control. Its core functions include: token authentication, URL mapping routing, IP whitelist verification, request parameter validation, rate limiting control, and logging and auditing. By integrating a token mechanism for identity authentication, it ensures that requesters have legitimate access rights, effectively preventing unauthorized access and API abuse. Through URL mapping, it accurately routes external requests to the corresponding internal microservice modules, achieving efficient business distribution and decoupling. Simultaneously, the system supports configuring IP whitelist policies, allowing only requests from trusted sources to access the system, further enhancing boundary security. In addition, the gateway supports rate limiting control of request frequency to prevent malicious API spamming and ensure system stability under high-concurrency scenarios. The logging and auditing mechanisms provide fundamental support for subsequent fault tracing and security compliance. By building a unified and reliable gateway entry point, it effectively improves the overall security, maintainability, and scalability of the system, making it one of the key components for ensuring stable service operation.

[0063] The application layer includes: external service access and backend management modules; among which,

[0064] External Service Integration: The channel side initiates user data requests by calling our standardized credential stuffing API. Upon receiving the request, our system first performs basic validation and parsing of the request parameters, then proceeds with the pre-defined credential stuffing processing flow. Based on business rules and user characteristics, the system automatically executes the corresponding rule judgment and filtering logic to complete the user data rule filtering operation. Through this standardized interface, external services can achieve rapid integration and low cost, effectively improving the efficiency of channel cooperation and the system's openness.

[0065] The backend management module, serving as the system's central control center, is primarily used to configure channel-specific credential stuffing information and to centrally manage and intelligently orchestrate credential stuffing rules, experimental strategies, and execution processes. Through a visual configuration interface, operations or strategy personnel can flexibly set access parameters, permission policies, and data format requirements for different channels, achieving unified configuration and management of multi-channel credential stuffing tasks. Simultaneously, the system supports modular definition and combination of rules and strategies, facilitating rapid adjustments to filtering logic based on business needs and improving response efficiency. For credential stuffing experiments, the module supports A / B testing to evaluate the effectiveness of different strategy combinations, thereby continuously optimizing the rule system. Supported by the backend management module, the entire channel credential stuffing process is under full-chain control and flexible strategy adjustment, significantly improving the system's operability, scalability, and strategy intelligence.

[0066] The basic service layer includes key modules such as data support, file management, and scheduled task scheduling, providing comprehensive support for the stable operation of the credential stuffing attack process. The data support module is responsible for providing the necessary multi-source data for rule judgment, including risk control information, user activity data, credit records, and accessed third-party data, ensuring that rule execution has sufficient basis and high accuracy. The file management module receives and manages batch credential stuffing attack data uploaded from the channel side through Object Storage Service (OSS), supporting file uploading, parsing, and storage, ensuring the efficiency and stability of large-scale data processing. The scheduled task module is responsible for scheduling and executing batch credential stuffing attack tasks, automatically triggering batch tasks at preset times, and has task status tracking and interruption identification capabilities. In the event of an abnormal interruption, it can automatically take over and recover, ensuring the continuity of the task process and the reliability of system operation. Overall, these modules work together to provide strong support for the credential stuffing attack engine, further improving the system's data processing capabilities, automation level, and fault tolerance.

[0067] This invention adopts the above technical solution, determining the credential stuffing connection method based on the business scenario. When the credential stuffing connection method is real-time, it receives channel request messages; performs rule filtering on the channel request messages, and returns the credential stuffing result in a synchronous processing mode after processing. When the credential stuffing connection method is batch offline, it packages batch user data into specific files and uploads them to the server, triggering the generation of a credential stuffing task queue, which is executed through scheduled tasks. After the task is completed, the processing result is uploaded to the server, and the channel side is notified through a callback interface. Compared with existing technologies, this invention has the following technical effects: improving user quality and conversion efficiency, and achieving a high return on marketing investment.

[0068] The above description is merely a preferred embodiment of the present invention and does not limit the implementation and protection scope of the present invention. Those skilled in the art should realize that any equivalent substitutions and obvious changes made based on the description and illustrations of the present invention should be included within the protection scope of the present invention.

Claims

1. A scalable intelligent rule orchestration method based on a microservice architecture, characterized in that, include: Determine the credential stuffing integration method based on the business scenario; When the credential stuffing connection method is real-time credential stuffing, receive channel request messages; The request messages from the aforementioned channels are filtered according to rules, and the results of the credential stuffing attack are returned in a synchronous processing mode after processing is completed. When the credential stuffing method is batch offline credential stuffing, a batch of user data is packaged into a specific file and uploaded to the server, triggering the generation of a credential stuffing task queue, which is executed through a scheduled task. After the task is completed, the processing result is uploaded to the server and the channel side is notified through a callback interface. The step of performing rule filtering on the channel request message and returning the credential stuffing result in a synchronous processing mode after processing includes: verifying the address of the received channel request message; if the verification passes, performing parameter preprocessing on the channel request message to obtain a processing result; determining whether the number of credential stuffing attempts on the channel exceeds a preset rate limiting configuration based on the processing result; if the determination result is yes, returning a failure result; if the determination result is no, filtering the processing result using preset pre-defined general rules to obtain a filtered result; determining the user group to which the user belongs based on the filtered result, loading the rule strategy set corresponding to the user group, and obtaining a rule execution result; and returning the rule execution result, which is the credential stuffing result, to the channel side while simultaneously uploading it to the data management center. The method further includes: during the execution of the batch offline credential stuffing, if the service is abnormal or unexpectedly interrupted, the task execution status is automatically detected and the interruption situation is actively identified. When it is determined that the task has not been completed normally, the interruption position is calculated, and the task execution is resumed based on the interruption position, and the remaining data is processed from the interruption position. The step of loading the rule strategy set corresponding to the user group and obtaining the rule execution result includes: conducting A / B experiments based on the user group, configuring multiple sets of rule strategies for the user group, and using them as experimental control groups for parallel execution and effect comparison; evaluating the actual effect of multiple sets of rule strategies by comparing the performance of each rule strategy on key indicators, and obtaining the rule execution result.

2. The scalable intelligent rule orchestration method based on microservice architecture according to claim 1, characterized in that, The verification based on the address of the received channel request message includes: Obtain the address of the channel request message; The address is verified using a preset whitelist; If the verification fails, a verification failure result will be returned.

3. The scalable intelligent rule orchestration method based on microservice architecture according to claim 1 or 2, characterized in that, The parameter preprocessing of the channel request message to obtain the processing result includes: The data in the channel request message is verified by signature verification and decryption to verify the integrity and legality of the source of the data in the channel request message; The data undergoes basic format and business-level validation to ensure it conforms to the expected structure and rule requirements. Once the data verification is complete, the processing result is obtained.

4. The scalable intelligent rule orchestration method based on microservice architecture according to claim 3, characterized in that, The step of filtering the processing results using preset general rules to obtain filtered results includes: The preset general rules are used to determine whether the user in the processing result belongs to a group that does not meet the basic admission criteria; If the user belongs to a group that does not meet the basic admission criteria, the screening result will be "failed". If the user does not belong to the group that does not meet the basic admission criteria, then the screening result is obtained.

5. The scalable intelligent rule orchestration method based on microservice architecture according to claim 4, characterized in that, The step of determining the rule execution result as the credential stuffing result and simultaneously uploading it to the data management center and returning it to the channel side includes: The credential stuffing results are sent asynchronously to the data management center via Kafka. At the same time, the credential stuffing results are processed for security to ensure confidentiality and integrity during transmission and storage. The credential stuffing results are then synchronously sent to the channel side in a standard format.

6. A scalable intelligent rule orchestration system based on a microservice architecture, characterized in that, The scalable intelligent rule orchestration method based on a microservice architecture, applied to any one of claims 1 to 5, includes: The access layer, application layer, and basic service layer are, among which, The access layer is used to perform token authentication, URL mapping and routing, IP whitelist verification, request parameter verification, rate limiting control, and logging and auditing. Token authentication is used to authenticate the requester's identity through an integrated token mechanism, determining that the requester has legitimate access rights. URL mapping and routing is used to route external requests to the corresponding internal microservices through URL mapping. IP whitelist verification is used to control access from trusted sources by configuring IP whitelist policies. Rate limiting control is used to limit the request frequency. Logging and auditing provide basic support for subsequent fault tracing and security compliance. The application layer is used to perform external server access and backend management. The external server access is used to verify and parse the channel request messages received from the channel side, and execute the corresponding rule judgment and filtering logic through the credential stuffing processing flow. The backend management is used to configure channel credential stuffing related information, and to centrally manage and intelligently orchestrate credential stuffing rules, experimental strategies and execution processes. The basic service layer is used to execute basic services and storage services. The basic services include data support, file management, and scheduled task scheduling. Data support provides multi-source data required for rule-based judgment, including risk control information, user activity data, credit records, and accessed third-party data. File management receives and manages batch credential stuffing data uploaded from the channel side through object storage services, supporting file uploading, parsing, and storage. Scheduled task scheduling handles the scheduling and execution of batch credential stuffing tasks, automatically triggering batch tasks at preset times, and also has task status tracking and interruption identification capabilities. In the event of an abnormal interruption, it automatically takes over and recovers. The process of filtering channel request messages according to rules and returning the credential stuffing result in a synchronous processing mode after processing includes: verifying the address of the received channel request message; if the verification passes, performing parameter preprocessing on the channel request message to obtain a processing result; determining whether the number of credential stuffing attempts on the channel exceeds a preset rate limiting configuration based on the processing result; if the determination result is positive, returning a failure result; if the determination result is negative, filtering the processing result using preset pre-defined general rules to obtain a filtered result; determining the user group to which the user belongs based on the filtered result, loading the rule strategy set corresponding to the user group, and obtaining a rule execution result; and returning the rule execution result, which is the credential stuffing result, to the channel side while simultaneously uploading it to the data management center. During the execution of batch offline credential stuffing, if the service is abnormal or unexpectedly interrupted, the task execution status will be automatically detected and the interruption will be actively identified. When it is determined that the task has not been completed normally, the interruption position will be calculated and the task execution will be resumed based on the interruption position, and the remaining data will be processed from the interruption position.

7. The scalable intelligent rule orchestration system based on microservice architecture according to claim 6, characterized in that, The scalable intelligent rule orchestration system based on microservice architecture also includes: The domain layer is used to execute interface services and manage the backend. The interface services are used to execute credential stuffing rules and process credential stuffing results. The management backend is used to configure credential stuffing channels, credential stuffing rules, credential stuffing experiments, offline credential stuffing management, and block blacklist feedback.

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