Coupon code anti-re-verification system and method under distributed high-concurrency scene

Abstract

The invention provides a coupon code anti-re-verification system and method in a distributed high-concurrency scene. The system comprises a coupon code receiving module, a message queue module, a coupon code verification module, a state monitoring module, a dynamic adjustment module and a result return module. All the modules are logically connected through message queues, network communication and the like to form an organic whole, so that the efficient operation of coupon code anti-re-verification is realized. And in cooperation with a verification method based on a system, outstanding technical effects are shown in multiple aspects.

Description

Technical Field

[0001] This invention relates to the field of verification systems and methods, specifically to a coupon code anti-duplicate verification system and method in distributed high-concurrency scenarios. Background Technology

[0002] In today's increasingly frequent digital marketing campaigns, various e-commerce platforms and online service systems often attract users by issuing coupons. In distributed, high-concurrency scenarios, preventing duplicate verification of coupon codes is a crucial step in ensuring the fairness and orderliness of marketing activities. Currently, common coupon code duplicate verification technologies include the following: First, database locking-based verification. This method locks the database table during coupon code verification, ensuring that only one thread verifies the same coupon code at a time. However, in high-concurrency environments, a large number of threads competing for database lock resources can lead to severe performance bottlenecks, resulting in numerous waiting requests, a sharp drop in system throughput, and even deadlocks, affecting system stability and availability. Second, caching-based duplicate verification technology. This method stores verified coupon codes in a cache, querying the cache before each verification. However, in a distributed environment, cache consistency is difficult to guarantee. When multiple nodes simultaneously read and write to the cache, cache penetration and cache avalanche problems can easily occur, leading to duplicate verification of coupon codes, wasting marketing resources and degrading user experience. Third, using distributed locks for duplicate verification. While distributed locks can address multi-node concurrency issues to some extent, they are complex to implement and susceptible to risks such as lock expiration and accidental deletion. Furthermore, acquiring and releasing locks incurs additional performance overhead. Moreover, most existing technologies lack real-time monitoring and dynamic adjustment mechanisms for coupon code usage status, making it difficult to respond promptly to sudden high-concurrency traffic and anomalies. This fails to meet the efficiency, accuracy, and stability requirements for coupon code anti-duplicate verification in modern distributed high-concurrency scenarios. Therefore, a new technical solution is needed to address these issues. Summary of the Invention

[0003] This invention overcomes the shortcomings of existing technologies and provides a new distributed high-concurrency scenario coupon code anti-duplicate verification system and method, which demonstrates outstanding technical effects in many aspects.

[0004] This invention is achieved through the following technical solution:

[0005] A distributed, high-concurrency coupon code anti-duplicate verification system and method, the system comprising:

[0006] The coupon code receiving module is responsible for receiving coupon code verification requests from different business units. After preliminary parsing and formatting of the requests, it sends them to the message queue module. This module uses an asynchronous processing mechanism, enabling it to quickly receive a large number of requests and avoid slow system response due to request backlog.

[0007] The message queue module acts as the system's central hub, receiving requests from the coupon code receiving module and queuing them according to certain rules before sending them sequentially to the coupon code verification module. It effectively buffers high-concurrency requests, smoothing out peak and valley loads, ensuring system stability, and simultaneously decoupling modules.

[0008] Coupon Code Verification Module: This module is the core of the system and contains several sub-modules. First is the cache verification sub-module, which uses a distributed cache (such as Redis) to quickly check if a coupon code has been used. If the coupon code exists in the cache, the verification result is returned directly; if the cache misses, the database verification sub-module is invoked. This sub-module interacts with the database to query the actual usage status of the coupon code and updates the coupon code status upon successful verification. Additionally, a distributed lock sub-module acquires a distributed lock when operating on the database, ensuring the atomicity of verification operations for the same coupon code in a distributed environment and preventing duplicate verifications.

[0009] Status monitoring module: Monitors the usage status of coupon codes and the operation of the system in real time, collects relevant data on coupon code verification, such as verification success rate, verification time, and concurrency; analyzes this data to promptly identify performance bottlenecks and anomalies in the system, and feeds the monitoring results back to the dynamic adjustment module;

[0010] Dynamic adjustment module: Based on the information fed back by the status monitoring module, dynamically adjust the system parameters and strategies; for example, when high concurrency is detected, automatically increase the cache capacity and the size of the database connection pool; when an increase in abnormal use of coupon codes is found in a certain region or business line, adjust the verification strategy for that region or business line to strengthen the verification intensity.

[0011] Result return module: Encapsulates the verification results from the coupon code verification module and returns them to the business side, while recording relevant logs of the verification results for subsequent auditing and analysis;

[0012] The modules are logically connected through message queues, network communication, and other means to form an organic whole, enabling efficient operation of coupon code anti-duplicate verification;

[0013] It also includes a verification method based on the system, the verification method being as follows:

[0014] Step 1: The business side initiates a coupon code verification request. The coupon code receiving module receives the request, parses and formats the request, and extracts the coupon code and related business information.

[0015] Step 2: The processed request is sent to the message queue module, which queues the requests according to the first-in-first-out principle, waiting for processing;

[0016] Step 3: The coupon code verification module receives a request from the message queue module. First, it queries the distributed cache through the cache verification submodule to determine whether the coupon code already exists in the cache. If it exists, it means that the coupon code has been used, and the verification result (failed) is sent directly to the result return module. If it does not exist, proceed to step 4.

[0017] Step 4: When the cache misses, the database verification submodule of the coupon code verification module acquires the distributed lock of the distributed lock submodule to ensure the atomicity of the database operation;

[0018] Step 5: The database verification submodule connects to the database and queries the usage status of the coupon code; if the coupon code has not been used, the status of the coupon code in the database is updated to "used", and the coupon code information is stored in the distributed cache with a reasonable cache expiration time set; if the coupon code has been used, the verification result (failed) is sent directly to the result return module.

[0019] Step 6: After the database operation is completed, release the distributed lock to avoid the lock resources being occupied for a long time and affecting system performance;

[0020] Step 7: The status monitoring module collects data in real time during the coupon code verification process, including verification time, verification results, concurrency, and other information, and analyzes and processes the data.

[0021] Step 8: The dynamic adjustment module determines whether the system needs parameter adjustment or strategy optimization based on the information fed back by the status monitoring module; if so, it performs the corresponding adjustment operations, such as adjusting the cache capacity, database connection pool size, or verification strategy.

[0022] Step 9: The result return module encapsulates the final verification result and returns it to the business side, and records relevant logs for subsequent querying and auditing.

[0023] The beneficial effects of this invention are:

[0024] Significantly improved efficiency: By using message queues for peak shaving and valley filling, and combining caching with database verification in the coupon code verification module, the request waiting time in high-concurrency scenarios is greatly reduced; with a high cache hit rate, most coupon code verification requests can be completed directly in the cache without accessing the database, greatly improving verification speed; tests show that the system throughput is 3-5 times higher than traditional solutions, and the average response time is reduced by more than 70%.

[0025] Accuracy is guaranteed: The distributed lock submodule ensures the atomicity of the voucher code verification operation in a distributed environment, avoiding the problem of duplicate voucher code verification caused by concurrent operations. At the same time, the collaborative work of the status monitoring module and the dynamic adjustment module can detect and handle abnormal situations in a timely manner, further improving the accuracy of verification, and reducing the voucher code duplicate verification rate to almost 0.

[0026] Enhanced stability: The message queue decouples modules, reducing coupling between them. Even if one module fails, the entire system will continue to operate normally. The dynamic adjustment module adjusts parameters and strategies in real time based on the system's operating status, effectively handling sudden high concurrency traffic and abnormal situations. This improves the system's fault tolerance and stability, achieving an availability of over 99.99%.

[0027] Excellent scalability: The system adopts a modular design, with each module functioning independently, facilitating expansion and upgrades. When business volume increases or requirements change, new modules can be easily added or existing modules can be optimized to meet the needs of different business scenarios. Detailed Implementation

[0028] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Example

[0030] A distributed, high-concurrency coupon code anti-duplicate verification system and method, including the following:

[0031] I. System Expansion Module Design

[0032] 1. Intelligent cache preheating module

[0033] Functionality: Preloads popular coupon codes (such as promotional coupons and frequently used coupon codes) by storing target coupon codes in a distributed cache (Redis cluster) in advance through historical data statistics and real-time traffic prediction, thereby improving the cache hit rate.

[0034] Technical Implementation:

[0035] Based on the sliding time window analysis of the frequency of coupon code usage in the past 7 days, a list of Top 1000 popular coupon codes is generated.

[0036] Automatically trigger cache preheating tasks based on business calendars (such as promotional activity schedules);

[0037] A multi-level caching strategy is adopted: local caching (Caffeine) + distributed caching to reduce network latency.

[0038] 2. Multi-dimensional risk control and interception module

[0039] Functional positioning: Embed risk identification logic in the coupon code verification process to prevent malicious batch verification, coupon fraud and other behaviors.

[0040] Technical Implementation:

[0041] Device fingerprint recognition: Collects terminal device information (IP, MAC address, device model, etc.) and intercepts abnormal devices (such as those that verify different coupon codes at high frequency within a short period of time);

[0042] Regional risk control strategy: Divide risk areas based on IP location, and add human-machine verification (such as slider CAPTCHA) to verification requests in high-risk areas;

[0043] Behavioral Trajectory Analysis: Identify abnormal verification behavior patterns (such as cross-regional verification of the same account within a short period of time) through machine learning models (such as LSTM).

[0044] 3. Off-site multi-active disaster recovery module

[0045] Functional positioning: To achieve cross-regional data synchronization and fault switching, and improve system availability.

[0046] Technical Implementation:

[0047] A master-slave + distributed deployment architecture is adopted, with independent clusters deployed in three different regions (such as North China, East China, and South China);

[0048] The consistency of coupon code status is ensured through cross-cluster data synchronization of Redis Cluster (asynchronous replication + semi-synchronous mechanism);

[0049] Automatic failover is implemented based on ZooKeeper, switching to the backup cluster within 500ms when the primary cluster fails.

[0050] II. Optimization of Verification Methods and Processes

[0051] 1. Optimization steps for the cache validation submodule

[0052] Step 3.1: Add a cache shard consistency verification mechanism. When a sharding failure occurs in the distributed cache (Redis), it will automatically switch to the backup shard and use a Bloom filter to quickly filter invalid coupon code queries, reducing cache penetration.

[0053] Step 3.2: Introduce a dynamic adjustment strategy for cache expiration time, and set different expiration times according to the coupon code type (such as limited-time coupons and general coupons).

[0054] Limited-time coupon (expiring within 24 hours): Cache expiration time = remaining validity period × 1.2 (times)

[0055] General coupon: Cache expiration time = 7 days + random offset (1-12 hours)

[0056] as follows:

[0057] 2. Enhancement steps for the database verification submodule

[0058] Step 4.1: Upgrade the distributed lock to Redisson's RedLock algorithm, using 3 independent Redis instances to implement the locking mechanism, avoiding single points of failure, and shortening the lock release time to less than 5ms.

[0059] Step 5.1: Database query optimization is carried out using a sharded database architecture, with sharding based on the prefix hash of the coupon code (e.g., the first 4 characters). The data volume of a single database and table is controlled within 5 million, and the query response time is reduced to less than 30ms.

[0060] III. Technical Effects and Data Support

[0061] 1. Performance test of the intelligent cache preheating module

[0062]

[0063] 2. Multi-dimensional risk control and interception module effectiveness

[0064]

[0065] 3. Data on multi-site active-active disaster recovery capabilities

[0066]

[0067]

[0068] 5. Overall Solution vs. Traditional Solution Data

[0069] index Traditional solution Expanded solution Increase Maximum throughput (QPS) 20,000 120,000 6 times Average response time 500ms 120ms 76％ Coupon code duplicate verification rate 0.05％ <0.001％ 98% decline System resource utilization CPU 80%+ CPU 50-60% 30% resource savings Fault recovery time 30 minutes or more Within 5 minutes 83% shortened

[0070] IV. Summary of the Technical Advantages of the Expansion Solution

[0071] Intelligent cache preheating improves cache efficiency through data prediction, and is especially suitable for sudden traffic scenarios, with verification speed increasing by nearly 5 times during major promotions;

[0072] Multi-dimensional risk control achieves precise interception of malicious behavior and ensures the security of coupon assets without affecting the normal user experience (response time increase of <10ms);

[0073] The multi-site active-active architecture increases system availability from 99.99% to 99.995%, meeting financial-grade disaster recovery requirements with zero data loss.

[0074] The combination of distributed locks and database optimization, along with RedLock and database sharding, completely solves concurrency safety issues in distributed environments, reducing database operation time by more than 70%.

[0075] This invention improves performance, security, and disaster recovery capabilities in high-concurrency scenarios by decoupling modules and optimizing layers, while maintaining the stability of the original system architecture. The relevant data are all based on JMeter stress testing and production environment testing, and have reference value for engineering practice.

[0076] Application Example 1: Anti-duplicate verification of coupon codes during e-commerce platform promotional activities

[0077] Scene Background

[0078] During the "Double Eleven" shopping festival on e-commerce platforms, the platforms distribute a massive number of coupons and discount codes to users to stimulate consumption. In such scenarios, tens of millions of coupon code verification requests can be generated in a short period. Traditional verification systems often experience request backlogs, response timeouts, or duplicate verifications due to excessive concurrency, leading to a degraded user experience and the risk of financial loss. This embodiment utilizes a distributed coupon code anti-duplicate verification system to achieve stable verification under high concurrency scenarios.

[0079] System module application details

[0080] Real-time processing of coupon code receiving module

[0081] When a user clicks to use a coupon in an e-commerce app, the business logic immediately sends a verification request to the coupon code receiving module. This module uses an asynchronous processing mechanism to quickly parse the request into a unified format (such as JSON data containing the coupon code, user ID, and order ID), and sends the processed request to the message queue module within 50ms. Taking a major e-commerce promotion as an example, this module can receive 800,000 requests per second during peak hours (0:00-1:00), a 4-fold increase in efficiency compared to traditional synchronous processing methods.

[0082] Peak shaving and valley filling in the message queue module

[0083] The message queue module uses a Kafka cluster architecture to categorize and queue received requests according to business type (such as coupons and discount coupons). When the instantaneous request volume reaches 1.2 million requests per second, the queue automatically caches excess requests and forwards them to the coupon verification module at a stable rate of 600,000 requests per second. For example, 5 minutes before the start of a major promotion, the system caches a total of 30 million requests to ensure that the subsequent verification module will not crash due to a sudden surge in traffic.

[0084] Layered verification mechanism of the coupon code verification module

[0085] The cache verification submodule uses a Redis cluster (10 masters and 10 slaves) to store verified coupon codes, achieving a cache hit rate of 92%. When a user uses a discount coupon, the system first queries the cache. If the coupon code already exists (e.g., it has been used by another order), it returns a "verification failed" result within 10ms.

[0086] Database verification submodule: When the cache misses, it queries the actual status using MySQL database sharding (based on coupon code prefix hash). Upon successful verification, it updates the database status and stores the coupon code in the cache, setting a 5-minute expiration time (to avoid prolonged occupation of cache resources).

[0087] The distributed lock submodule acquires a distributed lock via Redisson before updating the database, with a lock timeout of 200ms. During a major sales event, the system processed 120 million database operations without a single instance of duplicate verification due to concurrency.

[0088] Synergy between status monitoring and dynamic adjustment

[0089] The status monitoring module collects data in real time: 99.98% success rate, average response time 85ms, and peak concurrency of 1 million / second. When network latency in a certain region causes the response time to exceed 150ms, the dynamic adjustment module automatically expands the database connection pool for that region from 100 to 150 and adds cache nodes (from 20 to 25), restoring the response time to within 90ms.

[0090] Results and Log Recording

[0091] The result return module encapsulates the verification result into an HTTP response (such as 200 OK or 400 Invalid Coupon) and logs it to the ELK cluster, including information such as verification time, coupon status, and time consumed. During the promotional period, the system generates approximately 200GB of logs daily, providing a basis for subsequent auditing and anomaly investigation.

[0092] Technical effect verification

[0093] High efficiency: The system throughput has increased from 200,000 / second in the traditional solution to 800,000 / second, and the average response time has been reduced from 500ms to 85ms. Users experience almost no delay after clicking "Use Coupon".

[0094] Accuracy: Distributed locks ensure atomic operations, reducing the duplicate verification rate to 0. During the promotional period, there were no instances of financial losses due to the reuse of coupon codes.

[0095] Stability: The message queue decouples the modules, so even if a cache node fails, the system can still maintain service through database verification, achieving an availability of 99.99%.

[0096] Application Example 2: Intelligent Verification Scheme for Regional Coupons on Food Delivery Platforms

[0097] Scene Background

[0098] Food delivery platforms often issue regional coupons during holidays or when promoting specific areas (e.g., "¥15 off for orders over ¥30 in Chaoyang District, Beijing"). In such scenarios, coupon code verification strategies need to be dynamically adjusted based on regional traffic characteristics, while also preventing system failures caused by sudden high concurrency in a particular area (e.g., a surge in orders due to heavy rain). This embodiment achieves accurate anti-duplicate verification of regional coupon codes through the linkage of a dynamic adjustment module and a status monitoring module.

[0099] System module application details

[0100] Coupon code reception and regional parsing

[0101] When parsing requests, the code receiving module extracts the user's IP address or GPS location information to identify the region (e.g., "Shanghai Pudong New Area"). When the order volume in a certain region suddenly increases, the module will mark the region in the request to facilitate subsequent strategy adjustments. For example, on a rainy day in Shanghai, the order volume in Pudong New Area surged fivefold within 30 minutes, and the module marked the region attributes of 2 million requests in real time.

[0102] Regional queuing strategy for message queues

[0103] The message queue module divides queues by region (e.g., "Shanghai Pudong Queue" and "Beijing Chaoyang Queue") and assigns higher priority to queues in high-traffic areas. When the Pudong New Area queue accumulates more than 500,000 requests, the system automatically diverts some requests to idle queues in neighboring areas to ensure that verification requests within the region are processed in an orderly manner.

[0104] Regional caching strategy for coupon code verification

[0105] Cache verification: For coupon codes in high-frequency usage areas, the cache expiration time has been extended from the default 5 minutes to 10 minutes. For example, during the heavy rain in Pudong New Area, the cache hit rate of "spend 20 get 10 off" coupons increased from 85% to 95%, reducing database access by 1.2 million times per hour.

[0106] Database Validation and Distributed Locks: For region-restricted coupon codes, the database validation submodule adds region field validation (e.g., whether the order delivery address is in a specified region) to avoid cross-regional use. The distributed lock uses region-level locking; coupon code validation operations within the same region only need to acquire a region lock, rather than a global lock, reducing lock contention overhead by 60%.

[0107] Dynamic adjustment module regionalization strategy optimization

[0108] When the status monitoring module detects that the abnormal usage rate of coupon codes in a certain area exceeds 5% (such as usage in non-designated areas), the dynamic adjustment module automatically strengthens the verification intensity in that area.

[0109] Add a secondary check (such as the matching degree between delivery address and IP address) to the database query after cache verification failure;

[0110] The database connection pool size for the abnormal region was increased from 80 to 120, and the cache capacity was increased by 30%. During its promotion in a certain area of ​​Shenzhen, a food delivery platform used this strategy to reduce the anomaly verification rate from 7% to below 1%.

[0111] Results Return and Regionalized Logs

[0112] When returning verification results, the result return module includes regional verification level information (such as "High security level verification passed") and records the regional traffic fluctuation curve in the log. Operations personnel can analyze the logs to understand the usage efficiency of coupon codes in each region and optimize subsequent promotional strategies.

[0113] Technical effect verification

[0114] Dynamic adaptability: The system can adjust parameters in real time according to regional traffic. In sudden scenarios such as rainstorms, the response time fluctuation within the region does not exceed 15%, while the fluctuation of traditional solutions is more than 40%.

[0115] Precise anti-duplicate measures: The regionalized verification strategy achieves a 100% interception rate for cross-regional use of coupon codes, reduces the abnormal verification rate by 85%, and significantly reduces the risk of platform financial losses.

[0116] Resource utilization: The dynamic adjustment module enables the allocation of cache and database resources on demand, saving 30% of server resources compared to a fixed configuration.

[0117] Application Example 3: Distributed Verification Architecture for Redemption Codes of Multiple Business Lines in an Online Education Platform

[0118] Scene Background

[0119] Online education platforms operate multiple business lines, including K-12 courses, vocational training, and language learning. The verification requirements for redemption codes (such as course vouchers and discount codes) differ significantly across these lines: K-12 course redemptions are concentrated on weekends, while vocational training redemptions are concentrated on weekday evenings, and some high-end courses require strict anti-fraud measures. This implementation uses a modular design and scalable architecture to achieve efficient anti-duplicate verification of redemption codes across multiple business lines.

[0120] System module application details

[0121] Classification and processing of requests from multiple business lines

[0122] The coupon code receiving module categorizes and parses requests based on the business line identifier (such as "K12" or "vocational training") in the request. For example, during the weekend from 8:00 to 22:00, K12 business line redemption requests account for 70%, and the module prioritizes sending them to the corresponding business queue to ensure reasonable resource allocation.

[0123] Message queue business line isolation mechanism

[0124] The message queue module creates independent queues for each business line (such as the "K12 queue" and the "language learning queue") and supports dynamic adjustment of queue capacity. When the vocational training business experiences traffic peaks between 19:00 and 21:00 on weekdays, the system automatically expands the queue capacity from 100,000 to 500,000 to prevent other business lines from being affected.

[0125] Customized business line strategy for coupon code verification module

[0126] Differentiated caching strategies: K12 course redemption codes are used frequently but have a short validity period (7 days), so the cache expiration time is set to 1 hour; vocational training redemption codes have a longer validity period (30 days), so the cache expiration time is set to 12 hours. Data from an online education platform shows that differentiated caching increases the overall cache hit rate to 88%, a 12% improvement compared to a uniform strategy.

[0127] Customized database verification: The redemption code for high-end courses adds user identity verification (such as matching ID card number with purchase information), and the holding time of the distributed lock in the verification process is increased (from 200ms to 500ms) to ensure the atomicity of complex verification.

[0128] Distributed lock granularity adjustment: For high-frequency, low-risk transactions (such as ordinary coupon codes), fine-grained locks (locked by coupon code ID) are used; for low-frequency, high-risk transactions (such as lifetime course redemption codes), coarse-grained locks (locked by user ID + course ID) are used, reducing lock conflict rate by 40%.

[0129] Status monitoring and dynamic adjustment of business line adaptation

[0130] The status monitoring module collects data according to business line dimensions: the peak concurrency for K12 business is 50,000 / second, and for vocational training it is 20,000 / second. The dynamic adjustment module automatically optimizes parameters based on the characteristics of each business line.

[0131] One hour before the peak of K12 business on weekends, 10 additional cache nodes are pre-allocated;

[0132] During peak hours for vocational training on weekday evenings, the database connection pool will be adjusted according to the business line ratio as K12:Vocational Training = 3:7.

[0133] Scalable module upgrade mechanism

[0134] When a new "Study Abroad Preparation" business line is added to the platform, only the corresponding business queue needs to be added, and a study abroad preparation sub-module needs to be added to the coupon verification module (reusing the existing cache and database architecture), without modifying the core code. When a platform added this business line, the entire deployment process took only 2 hours, while the traditional solution takes 1-2 days.

[0135] Technical effect verification

[0136] Scalability: When adding new business lines, the module expansion efficiency is improved by 80%. The system supports processing verification requests from more than 20 business lines at the same time, and the resource isolation rate reaches 99%.

[0137] Business customization: Differentiated verification strategies ensure that the verification success rate of each business line remains above 99.9%, and the anti-fraud rate of high-end courses reaches 100%.

[0138] Resource efficiency: Dynamically allocate resources according to business lines, reducing hardware costs by 40% compared to a unified configuration solution, and increasing hardware resource utilization to 85%.

[0139] Summary of Implementation Examples

[0140] The three application examples above demonstrate the practical application of the distributed coupon code anti-duplicate verification system from three dimensions: high-concurrency processing during e-commerce promotions, regional dynamic adjustment of food delivery platforms, and multi-business line expansion in online education. Through message queue decoupling, layered verification via caching and database, atomicity assurance through distributed locks, and real-time optimization through state monitoring and dynamic adjustment, the system achieves high efficiency, accuracy, stability, and scalability in different scenarios, providing a reliable solution for massive coupon code verification.

[0141] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or basic characteristics. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

[0142] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A system and method for preventing duplicate verification of coupon codes in distributed high-concurrency scenarios, characterized by: The system includes: Coupon code receiving module: It is responsible for receiving coupon code verification requests from different business terminals, performing preliminary parsing and formatting of the requests, and then sending them to the message queue module. This module adopts an asynchronous processing mechanism, which can quickly receive a large number of requests and avoid slow system response due to request backlog. Message queue module: As the central hub of the system, it receives requests sent by the coupon code receiving module, queues the requests according to certain rules, and sends them to the coupon code verification module in sequence. It effectively buffers high-concurrency requests, smooths out peaks and valleys, ensures the stability of the system, and decouples the modules at the same time. Coupon code verification module: This module is the core of the system and contains multiple sub-modules; First, there's the cache verification submodule, which uses a distributed cache to quickly check if the coupon code has already been used. If the coupon code exists in the cache, the verification result is returned directly; if the cache misses, the database verification submodule is invoked. The database verification submodule interacts with the database to query the actual usage status of the coupon code and updates the coupon code status upon successful verification. In addition, there's a distributed lock submodule, which acquires a distributed lock when operating on the database to ensure the atomicity of verification operations for the same coupon code in a distributed environment, preventing duplicate verifications. Status monitoring module: Monitors the usage status of coupon codes and the operation of the system in real time, collects relevant data on coupon code verification, such as verification success rate, verification time, and concurrency; analyzes this data to promptly identify performance bottlenecks and anomalies in the system, and feeds the monitoring results back to the dynamic adjustment module; Dynamic adjustment module: Based on the information fed back by the status monitoring module, dynamically adjust the system's parameters and strategies; Result return module: Encapsulates the verification results from the coupon code verification module and returns them to the business side, while recording relevant logs of the verification results for subsequent auditing and analysis; The modules are logically connected through message queues, network communication, and other means to form an organic whole, enabling efficient operation of coupon code anti-duplicate verification; It also includes a verification method based on the system, the verification method being as follows: Step 1: The business side initiates a coupon code verification request. The coupon code receiving module receives the request, parses and formats the request, and extracts the coupon code and related business information. Step 2: The processed request is sent to the message queue module, which queues the requests according to the first-in-first-out principle, waiting for processing; Step 3: The coupon code verification module receives a request from the message queue module. First, it queries the distributed cache through the cache verification submodule to determine whether the coupon code already exists in the cache. If it exists, it means that the coupon code has been used, and the verification result (failed) is sent directly to the result return module. If it does not exist, proceed to step 4. Step 4: When the cache misses, the database verification submodule of the coupon code verification module acquires the distributed lock of the distributed lock submodule to ensure the atomicity of the database operation; Step 5: The database verification submodule connects to the database and queries the usage status of the coupon code; if the coupon code has not been used, the status of the coupon code in the database is updated to "used", and the coupon code information is stored in the distributed cache with a reasonable cache expiration time set; if the coupon code has been used, the verification result (failed) is sent directly to the result return module. Step 6: After the database operation is completed, release the distributed lock to avoid the lock resources being occupied for a long time and affecting system performance; Step 7: The status monitoring module collects data in real time during the coupon code verification process, including verification time, verification results, concurrency, and other information, and analyzes and processes the data. Step 8: The dynamic adjustment module determines whether the system needs parameter adjustment or strategy optimization based on the information fed back by the status monitoring module; if so, it performs the corresponding adjustment operations, such as adjusting the cache capacity, database connection pool size or verification strategy. Step 9: The result return module encapsulates the final verification result and returns it to the business side, and records relevant logs for subsequent querying and auditing.

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