An electronic coupon security check and cancellation risk control system and method

Abstract

This invention relates to the field of data processing and discloses an electronic card and voucher secure redemption risk control system and method, comprising: a voucher data stream receiving module that collects card and voucher redemption request vectors and parses out card and voucher identifiers, settlement credit codes, and timestamps; a topology graph dynamic processing module that converts the request vectors into an asynchronous time-series graph of voucher status in memory and generates data graph nodes with time-series topology constraints; and a central risk control verification module that retrieves the remaining credit limit, calculates redemption risk control instructions based on timestamps and constraints, and feeds them back to the acquiring settlement node. This invention utilizes network time-series jitter delay to generate risk hedging compensation factors and adaptively calculates the remaining credit limit. Combined with the determination of available credit balance and negative feedback bias, it directly outputs blocking instructions, and solves the problem of concurrent redemption bad debts caused by time differences between remote settlement nodes through the elastic damping of credit limits.

Description

Technical Field

[0001] This invention relates to a secure electronic coupon verification risk control system and method, belonging to the field of data processing technology. Background Technology

[0002] In current distributed commercial transaction supervision networks, acquiring networks typically delegate verification authority to edge settlement nodes, utilizing their locally stored transient voucher states to handle instantaneous high-frequency traffic flow, ensuring immediate response and low-latency data interaction under high-concurrency daily settlement conditions. This architecture employs eventual consistency control logic to ensure high throughput performance. Voucher verification status changes generated by each settlement node are asynchronously broadcast to the central risk control side via communication links. When data packets traverse complex routing topologies, transmission time differences arise due to information transmission obstacles and physical clock drift, causing the distributed cluster to be in a global asynchronous state phase within a specific time window.

[0003] With the expansion of transaction networks and the evolution of large-scale promotional operations, the time difference blind spots in asynchronous and synchronous architectures are prone to inducing high-frequency concurrent replay conflicts, leading to the duplicate access of the same voucher data at different settlement nodes. To prevent such non-real business data entry, existing systems typically deploy strong consistency locking mechanisms or distributed transaction queuing blocking mechanisms at the data layer, resulting in settlement network response stagnation and transaction queue backlog. Traditional solutions focus on stacking the underlying physical architecture to achieve system expansion, but cannot eliminate the time difference blind spots between settlement nodes. The supporting software control methods are also insufficient. For example, Chinese invention patent application CN121094880A discloses a discount card verification method, system, and storage medium, which completes multi-dimensional comparison by constructing personalized behavior pattern thresholds. This technology relies on global steady-state synchronization and does not analyze the topology conflicts caused by network timing jitter. In the scenario of concurrent settlement in different locations, due to the lack of topology dependency analysis of timing activity vertices, it is easy to have duplicate verification of excess vouchers due to the lag of static thresholds, resulting in a mismatch between preset parameters and actual working conditions.

[0004] Therefore, the technical problem to be solved by this invention is how to construct a spatial topology dependency verification mechanism based on a resident memory asynchronous time-series flow graph, and combine it with network timing jitter state to generate risk hedging compensation factors to adaptively adjust the available credit balance. By maintaining the spatial topology dependency verification mechanism and the dynamic negative feedback adjustment of the available credit balance, blocking and write-off control instructions are generated in situ, thereby coordinating the inherent conflict between throughput performance and risk control accuracy. Summary of the Invention

[0005] To address the problems in the background art, the technical solution of the present invention is as follows: An electronic coupon security verification risk control system, comprising:

[0006] The voucher data stream receiving module is used to collect the de-identified digital voucher redemption request vector sent by the acquiring settlement node, and parse out the anonymized voucher unique identifier hash sequence, merchant node settlement credit code and local timestamp;

[0007] The topology graph dynamic processing module communicates with the voucher data stream receiving module through a logical data bus. It is used to map the de-identified digital card redemption request vector to the asynchronous time sequence graph of commercial voucher status in memory, and generate data graph nodes with time sequence topology constraints. The topology graph dynamic processing module is equipped with an automatic data cleaning unit. The automatic data cleaning unit is used to monitor the historical redemption action time increment of the hash sequence of the anonymized card unique identifier. When the historical redemption action time increment crosses a fixed critical point of 30 minutes, it triggers a discretization pruning operation on the corresponding data graph node in the asynchronous time sequence graph of commercial voucher status.

[0008] The central risk control verification module interacts with the topology graph dynamic processing module through a network communication interface. It retrieves the remaining credit limit corresponding to the merchant node's settlement credit code, calculates the write-off risk control judgment instruction based on the local timestamp and the temporal topology constraints of the data graph nodes, and feeds the write-off risk control judgment instruction back to the acquiring settlement node.

[0009] Preferably, the central risk control verification module is equipped with a network environment monitoring unit and an adaptive degradation routing control unit. The network environment monitoring unit is used to obtain the global communication reachability online. When the global communication reachability drops to 85%, the adaptive degradation routing control unit is used to close the read channel to the external database and change the calculation basis of the available credit balance to the locally stored merchant transaction flow space topology density. The central risk control verification module independently completes the credit limit decision on the de-identified digital card redemption request vector based on the merchant transaction flow space topology density.

[0010] Preferably, the voucher data stream receiving module also includes a clock offset dynamic compensation unit; the clock offset dynamic compensation unit is used to collect the clock offset parameter sequence of multiple acquiring settlement nodes, calculate the mean of the clock offset parameter sequence in the time dimension using a sliding time window, and subtract the mean from the local timestamp to complete the clock calibration.

[0011] Preferably, the asynchronous sequence graph of commercial voucher status is carried by a graph data structure resident in memory; the data graph nodes contain anonymized voucher unique identifier hash sequence, merchant node settlement credit code, and directed edge topological constraints indicating the order of reimbursement actions.

[0012] Preferably, the central risk control verification module also has a clearing and settlement information interaction feedback unit; the clearing and settlement information interaction feedback unit is used to send the real-time data status of the write-off risk control judgment instruction and the adjustment value of the remaining credit limit to the acquiring settlement node, and establish a data interaction channel between the acquiring settlement node and the central risk control verification module.

[0013] Preferably, the central risk control verification module has a concurrent tampering verification unit; the concurrent tampering verification unit is used to retrieve the hash sequence of the unique identifier of the anonymized card and compare it with the same hash sequence in the resident state in the asynchronous time sequence diagram of the commercial voucher status. When the same hash sequence is detected to be in the unredeemed state, a rejection redemption instruction is output.

[0014] Preferably, the voucher data stream receiving module also includes a multi-level distribution tracking unit; the multi-level distribution tracking unit is used to parse the multi-level distribution network credit relationship contained in the de-identified digital voucher redemption request vector, and to overlay the multi-level distribution network credit relationship onto the temporal topology constraints of the data graph node in the asynchronous time sequence diagram of the commercial voucher status.

[0015] Preferably, when the automatic data cleaning unit triggers the discretization pruning operation, the automatic data cleaning unit is used to reclaim the memory space occupied by the corresponding data graph node and send a quota release instruction to the central risk control verification module to return the temporarily retrieved remaining credit quota.

[0016] Preferably, the central risk control verification module also includes a transaction frequency fluctuation verification unit; the transaction frequency fluctuation verification unit is used to count the difference between the current reconciliation request frequency and the historical reconciliation request frequency of the acquiring settlement node within a fixed time window of 5 minutes, calculate the reconciliation request frequency change rate, and output an abnormal transaction warning signal when the reconciliation request frequency change rate exceeds the safe fluctuation limit of 1.5.

[0017] A method for secure verification and risk control of electronic coupons, used to run an electronic coupon secure verification and risk control system, includes:

[0018] Step S1: Use the voucher data stream receiving module to collect the de-identified digital card and voucher redemption request vector sent by the acquiring settlement node, and parse out the anonymized card and voucher unique identifier hash sequence, merchant node settlement credit code and local timestamp;

[0019] In step S2, the topology graph dynamic processing module communicates with the voucher data stream receiving module through the logical data bus. The topology graph dynamic processing module maps the de-identified digital card redemption request vector to the asynchronous time sequence graph of commercial voucher status in memory, generating data graph nodes with time sequence topology constraints. The automatic data cleaning unit monitors the historical redemption action time increment of the hash sequence of the anonymized card unique identifier. When the historical redemption action time increment crosses a fixed critical point of 30 minutes, a discretization pruning operation is triggered for the corresponding data graph node in the asynchronous time sequence graph of commercial voucher status.

[0020] In step S3, the central risk control verification module interacts with the topology graph dynamic processing module through the network communication interface. The central risk control verification module retrieves the remaining credit limit corresponding to the merchant node settlement credit code, calculates the write-off risk control judgment instruction based on the local timestamp and the temporal topology constraints of the data graph node, and feeds the write-off risk control judgment instruction back to the acquiring settlement node.

[0021] Compared with the prior art, the beneficial effects of the present invention are:

[0022] 1. In the secure verification risk control of electronic coupons, the voucher data stream receiving module obtains a request containing the merchant node settlement credit code and local timestamp. The topology graph dynamic processing module maps the data transiently to the memory asynchronous time-series flow graph, generating time-series topology activity vertices. The central risk control verification module uses the time-series jitter delay to generate a risk hedging compensation factor, and accordingly performs adaptive scaling on the steady-state remaining credit limit. Using the judgment that the available credit balance is less than the negative feedback bias, it directly outputs the blocking verification control command without relying on the full network synchronization lock, instructing the downstream settlement module to refuse transaction updates. Thus, the elastic damping of the credit limit solves the problem of concurrent verification bad debts caused by the time difference of settlement nodes in different locations.

[0023] 2. The central risk control verification module obtains the global communication reachability by monitoring the network transmission status. When the reachability drops to the preset safety boundary value due to objective network environment disturbances, the control flow operator drives the decision logic to adaptively adjust, forcibly switching from the collaborative verification state to the local limited credit limit adaptive degradation and optimization state. The calculation basis of the available credit balance is changed to the local historical transaction density average. Under the condition of removing the external database read dependency, each settlement node independently implements credit limit adjudication on the write-off request using the spatial distribution topology density of local storage, thereby ensuring that the system still has continuous safety boundary control even under non-ideal network split conditions.

[0024] 3. The topology graph dynamic processing module includes a self-evolving cleaning subunit. During commercial operation, this unit continuously monitors the historical redemption time increment of each coupon identifier. When the increment crosses the parameter critical point of 30 minutes, it automatically triggers discretization pruning for the corresponding historical vertices in the asynchronous time-series flow graph, returns the temporarily occupied merchant credit bias, and reduces the accumulation of data in local memory space by implementing spatial topology convergence on the accumulated transactions. This constrains the operational complexity and implementation cost of the risk control system under large-scale high-concurrency conditions, controls the processing delay caused by continuous data accumulation, and thus stably maintains the redemption judgment response latency within 20ms. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the module architecture of an electronic coupon security verification risk control system according to the present invention;

[0026] Figure 2 This is a logic diagram of the secure verification and risk control method for electronic coupons according to the present invention.

[0027] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0029] like Figure 1 As shown, Figure 1 This is a schematic diagram of the module architecture of an electronic coupon secure verification risk control system according to the present invention. The system specifically includes:

[0030] The voucher data stream receiving module is used to collect the de-identified digital voucher redemption request vector sent by the acquiring settlement node, and parse out the anonymized voucher unique identifier hash sequence, merchant node settlement credit code and local timestamp;

[0031] The topology graph dynamic processing module communicates with the voucher data stream receiving module through a logical data bus. It is used to map the de-identified digital card redemption request vector to the asynchronous time sequence graph of commercial voucher status in memory, and generate data graph nodes with time sequence topology constraints. The topology graph dynamic processing module is equipped with an automatic data cleaning unit. The automatic data cleaning unit is used to monitor the historical redemption action time increment of the hash sequence of the anonymized card unique identifier. When the historical redemption action time increment crosses a fixed critical point of 30 minutes, it triggers a discretization pruning operation on the corresponding data graph node in the asynchronous time sequence graph of commercial voucher status.

[0032] The central risk control verification module interacts with the topology graph dynamic processing module through a network communication interface. It retrieves the remaining credit limit corresponding to the merchant node's settlement credit code, calculates the write-off risk control judgment instruction based on the local timestamp and the temporal topology constraints of the data graph nodes, and feeds the write-off risk control judgment instruction back to the acquiring settlement node.

[0033] Preferably, the central risk control verification module is equipped with a network environment monitoring unit and an adaptive degradation routing control unit. The network environment monitoring unit is used to obtain the global communication reachability online. When the global communication reachability drops to 85%, the adaptive degradation routing control unit is used to close the read channel to the external database and change the calculation basis of the available credit balance to the locally stored merchant transaction flow space topology density. The central risk control verification module independently completes the credit limit decision on the de-identified digital card redemption request vector based on the merchant transaction flow space topology density.

[0034] Preferably, the voucher data stream receiving module also includes a clock offset dynamic compensation unit; the clock offset dynamic compensation unit is used to collect the clock offset parameter sequence of multiple acquiring settlement nodes, calculate the mean of the clock offset parameter sequence in the time dimension using a sliding time window, and subtract the mean from the local timestamp to complete the clock calibration.

[0035] Preferably, the asynchronous sequence graph of commercial voucher status is carried by a graph data structure resident in memory; the data graph nodes contain anonymized voucher unique identifier hash sequence, merchant node settlement credit code, and directed edge topological constraints indicating the order of reimbursement actions.

[0036] Preferably, the central risk control verification module also has a clearing and settlement information interaction feedback unit; the clearing and settlement information interaction feedback unit is used to send the real-time data status of the write-off risk control judgment instruction and the adjustment value of the remaining credit limit to the acquiring settlement node, and establish a data interaction channel between the acquiring settlement node and the central risk control verification module.

[0037] Preferably, the central risk control verification module has a concurrent tampering verification unit; the concurrent tampering verification unit is used to retrieve the hash sequence of the unique identifier of the anonymized card and compare it with the same hash sequence in the resident state in the asynchronous time sequence diagram of the commercial voucher status. When the same hash sequence is detected to be in the unredeemed state, a rejection redemption instruction is output.

[0038] Preferably, the voucher data stream receiving module also includes a multi-level distribution tracking unit; the multi-level distribution tracking unit is used to parse the multi-level distribution network credit relationship contained in the de-identified digital voucher redemption request vector, and to overlay the multi-level distribution network credit relationship onto the temporal topology constraints of the data graph node in the asynchronous time sequence diagram of the commercial voucher status.

[0039] Preferably, when the automatic data cleaning unit triggers the discretization pruning operation, the automatic data cleaning unit is used to reclaim the memory space occupied by the corresponding data graph node and send a quota release instruction to the central risk control verification module to return the temporarily retrieved remaining credit quota.

[0040] Preferably, the central risk control verification module also includes a transaction frequency fluctuation verification unit; the transaction frequency fluctuation verification unit is used to count the difference between the current reconciliation request frequency and the historical reconciliation request frequency of the acquiring settlement node within a fixed time window of 5 minutes, calculate the reconciliation request frequency change rate, and output an abnormal transaction warning signal when the reconciliation request frequency change rate exceeds the safe fluctuation limit of 1.5.

[0041] like Figure 2 As shown, Figure 2 This is an execution logic diagram of an electronic coupon secure verification risk control method according to the present invention, which is used to run the above-mentioned electronic coupon secure verification risk control system. The method specifically includes:

[0042] Step S1: Use the voucher data stream receiving module to collect the de-identified digital card and voucher redemption request vector sent by the acquiring settlement node, and parse out the anonymized card and voucher unique identifier hash sequence, merchant node settlement credit code and local timestamp;

[0043] In step S2, the topology graph dynamic processing module communicates with the voucher data stream receiving module through the logical data bus. The topology graph dynamic processing module maps the de-identified digital card redemption request vector to the asynchronous time sequence graph of commercial voucher status in memory, generating data graph nodes with time sequence topology constraints. The automatic data cleaning unit monitors the historical redemption action time increment of the hash sequence of the anonymized card unique identifier. When the historical redemption action time increment crosses a fixed critical point of 30 minutes, a discretization pruning operation is triggered for the corresponding data graph node in the asynchronous time sequence graph of commercial voucher status.

[0044] In step S3, the central risk control verification module interacts with the topology graph dynamic processing module through the network communication interface. The central risk control verification module retrieves the remaining credit limit corresponding to the merchant node settlement credit code, calculates the write-off risk control judgment instruction based on the local timestamp and the temporal topology constraints of the data graph node, and feeds the write-off risk control judgment instruction back to the acquiring settlement node.

[0045] Example 1: The current distributed commercial electronic card clearing system listens to the settlement interface of each distributed account node and intercepts the transaction event data stream in real time. The system listener has a pre-set circular buffer in physical memory, which is used to store the card and coupon circulation metadata stream. The system captures the card and coupon status change information output by the transaction interface in real time through a direct memory mapping mechanism. Whenever the settlement interface generates a transaction event containing the outgoing account identifier, the incoming account identifier, the card and coupon unique code, and the timestamp, the data acquisition module immediately captures it and writes it into the memory circular queue to construct a ledger of card and coupon circulation status changes arranged in time sequence. The topology convergence index calculation unit periodically polls the register of the physical clearing thread to read the single ledger lock holding latency. Length of the transaction execution buffer queue list According to the dynamic window width calculation formula The system sets the reference sampling window width. Perform nonlinear compression, where, This represents the current dynamic sliding sampling window width. The value is 60s. The physical latency for the current processing core to complete a single-note accounting task. The queue count for events to be processed within the buffer. As a preset proportional adjustment constant, when the system encounters high-frequency concurrent impacts from automated scripts, and The product of the two numbers jumps, driving the... The time resolution of topology sampling was spontaneously reduced from 60s to 200ms.

[0046] In dynamically established sliding sampling window width Internally, the system reads the ledger of card and coupon flow status changes, constructs a directed acyclic graph of card and coupon flow in memory with account identifiers as vertices and directed transfer routes of card and coupon assets as directed edges, extracts the adjacency matrix of the graph, and calculates the feature convergence index. The determination follows the formula. ;in, For characteristic convergence index, This represents the local path density of the target account set within the current sliding window. The central risk control verification module uses indicators to determine the historical benchmark turnover density of coupons for this category. Network timing jitter Generate risk hedging compensation factors Extract round-trip time jitter , The second-order central moment of the round-trip delay between the acquiring settlement node and the central terminal, and the risk factor. Satisfy the formula The relevant parameters are defined as follows: This is a risk hedging compensation factor, with a value range of 0 to 1; This is the risk adjustment operator, with values ​​ranging from 0.01 to 0.05; risk adjustment operator The value range is determined based on the average synchronization pulse period of the distributed system under 10 Gigabit Ethernet conditions; in the actual calibration process, it was found by simulating different packet loss rate environments that when Below 0.01, the system's sensitivity to network timing jitter is too low, failing to produce sufficient credit-based speed reduction during peak concurrency periods, easily leading to bad debt interception failure; when Above 0.05, even minor network fluctuations will lead to a decrease in the risk factor. Severe overshooting can cause frequent false cancellations of credit limits, affecting the continuity of normal business operations. This embodiment selects the median value within this range for deployment, ensuring that the adjustment curve of the credit balance can smoothly fit the data synchronization uncertainty caused by clock drift without increasing the system's computational burden. To account for network timing jitter latency, the central risk control verification module retrieves the merchant's corresponding original remaining credit limit. Calculate available credit balance The value of the voucher in the redemption request vector satisfy Under certain conditions, a blocking instruction is sent to the acquiring settlement node to resolve concurrent write-off bad debts caused by time differences between settlement nodes in different locations, using the credit limit elastic damping.

[0047] When the characteristic convergence index When the value reaches 1.5 and lasts for more than 5 seconds, the adaptive routing control unit activates the risk interception state machine. This control unit takes over the memory addressing bus of the transaction interface module through atomic memory access instructions, modifying the underlying settlement routing table addressing pointers of the abnormal account nodes in situ to the static address of the security suspension queue. Specifically, this takeover and modification is performed at the logical bus level defined by the application framework. The system maintains a routing mapping table in a shared memory area allocated in user space, associating the transaction processing threads of each merchant through logical pointers. The adaptive routing control unit uses the processor's atomic operation instructions to modify the virtual address pointers of the corresponding abnormal merchants in the routing mapping table to the preset static interception logic entry address without suspending the global process. In order to achieve equivalent blocking of the underlying data flow at the software logic level, this invention requests a contiguous shared physical memory area through the kernel-mode driver during the system initialization phase and maps it to the user-mode virtual address space using memory descriptors to establish a logical routing mapping table. When the identification and control system determines that the risk indicator exceeds the preset threshold, the adaptive routing control unit calls a high-level programming language to block... The atomic variable operation interface atomically replaces the function jump pointer in the index entry corresponding to the target merchant with the starting address of the system's safe suspend routine within a single instruction cycle of the processor. Since this image table is located on the logical path of all clearing and settlement threads, and this replacement operation is visible to multiple cores and indivisible, subsequent requests from abnormal accounts can be redirected to a pre-defined suspend queue within microseconds without modifying the hardware physical bus connection. This achieves rapid interception of the underlying data flow. Because this operation occurs within the virtual memory space pre-allocated by the application software, it follows the operating system... The system's memory isolation rules physically block the flow of underlying data through pointer reset at the logical level, without directly interfering with the hardware-level addressing bus. This operation is executed within the processor's hardware clock cycle, stripping the target account of its card transfer authorization and achieving in-situ interception of illegal transactions. When the system's global computing power consumption exceeds the 85% load threshold, the adaptive routing control unit initiates a dimensionality reduction strategy, suspending the decomposition calculation of the global graph adjacency matrix and switching to state machine judgment logic based on the upper limit of the transfer frequency of a single card per unit time. At this time, the system only monitors the asset change frequency of a single account. ,when When the sampling rate exceeds 200 times per second within the sampling window, an early warning is triggered and route interception is implemented to ensure the continuity and security of the clearing business under conditions of limited physical resources.

[0048] Example 2: This example verifies the system's performance in identifying violations under complex coupon transaction environments. The test environment is configured with a dedicated computing node equipped with an 8-core processor and 128GB of memory. This node directly listens to the raw traffic of the distributed transaction gateway via the memory bus. The test sample is selected from the full coupon transaction logs of a certain region over 48 hours, totaling 120 million transaction records. Three types of violation transaction samples, including high-frequency order splitting, cross-merchant wash trading, and abnormal discrete flow, were artificially inserted, accounting for 3% of the total transaction volume. The test sets up a benchmark control group and a sample group of the present invention for performance comparison. The benchmark control group uses a statistical filtering method based on a fixed time window and a static transaction frequency threshold; the sample group of the present invention uses a dynamic sliding sampling window. With characteristic convergence index The decision-making strategy, in the experiment, will be Initialized to 60s Set to 0.015 to monitor the transfer routes of coupon assets in the target account within a unit of time.

[0049] The experimental data are recorded as follows: When the coupon circulation environment is stable, the false alarm rate of the baseline control group is 0.4%, and the feature convergence index is... The average value remained around 0.35. When the test environment simulated high-frequency coupon fraud, the system monitored the single ledger lock holding latency. The buffer queuing list length increased from 10 μs to 80 μs. The dynamic sampling window width calculation formula is increased from 5 to 40. The calculation results show Automatic convergence to 2.88s, in With the reduced dynamic resolution, the sample group of this invention successfully captured 99.2% of the abnormal routing features in this batch of illegal transactions, achieving a feature convergence index. The risk level instantly jumped to 2.1, far exceeding the 1.5 risk interception threshold. The system, through a low-level address pointer rewriting mechanism, blocked further transfer of the target coupon assets within 5ms. Experimental analysis shows that when... Under high load conditions exceeding 500 cycles per second, if maintaining If the value is 60s, it's because the adjacency matrix is ​​too large. The computation latency exceeded 500ms, causing the clearing thread to block. Furthermore, the excessively long window resulted in the smoothing dilution of abnormal routing features, reducing the accuracy of violation interception to 62% in this scenario. Comparative results show that this invention... The dynamic correlation with system load status establishes a physical constraint between computing resources and recognition sensitivity, thus maintaining the deterministic execution of the routing interception logic even under extremely high load. To verify the rationality of the numerical range, an out-of-range control group was established. The circuit breaker threshold is set to 3.0. Under this setting, the system's false negative rate for violations increases to 28%, indicating that the threshold has exceeded the system's effective capture range. Experimental data confirms that when the threshold is locked at around 1.5, the system can effectively distinguish between normal coupon splitting and illegal transaction routes, proving that this parameter setting constitutes an effective working window for the system to identify violations.

[0050] Example 3: The current intelligent identification and control system for card and coupon violations listens to the transaction data stream of each distributed account node through the settlement interface and extracts the outgoing account identifier, incoming account identifier, unique card and coupon code, and timestamp. The data acquisition module parses the transaction records into metadata quadruples that conform to the card and coupon status transition standards and stores them in a pre-built memory circular buffer. This buffer is based on physical memory addressing with the beginning and end connected and is maintained by read and write pointers to ensure that the sequential writing and interception of event records can be achieved under extremely high throughput. The system introduces an account flow topology risk quantification procedure to solve the false alarm rate caused by traditional single threshold judgment. To address the sparsity issue of abnormal transaction features, the system identifies the core parameters affecting accuracy: the connectivity density of inter-account transaction paths and the dispersion of asset transfers per unit time. For these two parameters, the system establishes an adaptive weight evaluation model. This model dynamically adjusts the weight coefficients of each transfer node based on the account's commercial credit rating and historical transaction activity. When coupons undergo discrete cross-regional transfers within a specific time window, the adjacency matrix reconstruction logic assigns a higher risk weight to the path based on the physical attributes of the coupon transfer. This provides a physically enhanced representation of illegal order splitting behavior at the statistical level, and the feature convergence index... This indicator is used to characterize the topological steady state of card circulation; specifically, it is composed of local path density. Compared with the reference flow density The ratio is determined, among which This represents the total frequency of connections for the target account set within the current sliding window. This represents the historical statistical benchmark for this type of coupon, achieved through an exponential smoothing algorithm during daily inactive periods. To be updated, among which, For the updated density, For the observed recent actual density, Using the density value from the previous period, this logic achieves physical alignment between feature indicators and card asset circulation characteristics, effectively avoiding feature value drift induced by normal traffic surges caused by commercial activities.

[0051] The system executes a low-level addressing pointer circuit breaker procedure for identified risky accounts. When the identification and control system determines that the risk indicator exceeds a preset threshold, the risk control engine sends an interrupt signal to the low-level memory controller. The memory controller locates the address of the transaction record in the settlement physical memory and forcibly resets the routing matching pointer of the corresponding account to the safety suspension queue. This action is completed by atomic instructions at the hardware level, changing the physical flow of card asset transfer logic at the low-level addressing layer. This operation bypasses traditional application-layer state synchronization, completing the physical blocking of abnormal flow at the moment the clearing logic is generated, thereby achieving deterministic control after the identification of illegal transactions. The system controls the physical clearing latency, queue link length, and sampling window width. Establishing a non-linear linkage ensures that the data sampling resolution can change synchronously with the computing power margin when concurrency fluctuations are severe. The system compares... Time and The feature extraction performance at that time showed that, At that time, the local path density fluctuations caused by high-frequency knockback behavior are in This is more evident in the metrics, and the peak width is limited to the order of 200ms, confirming the effectiveness of the sliding window width. Dynamically anchoring to the system's physical processing capabilities can effectively improve the accuracy of capturing microsecond-level illegal transaction characteristics and provide physical data support for the consistency of the identification algorithm under different load conditions.

[0052] Example 4: In an industrial-grade high-frequency card and coupon transaction clearing scenario, to ensure the system's stability under extreme concurrent conditions, the system initiates an initial baseline calibration procedure. This procedure aims to eliminate the impact of environmental noise on the convergence index of card and coupon circulation characteristics. To mitigate interference, before the clearing logic is officially activated, the system presets a 600-second idle operation period during which no routing interception is performed. The data acquisition module stores all settlement interface data detected during this period as the system's baseline noise level in the database. This also addresses the frequently fluctuating memory page table lookup latency within the system. The system calculates its average value and standard deviation over a 600s period. When the holding latency of a single ledger lock is monitored in real time in the subsequent production environment satisfy When the logic judgment unit determines that the latency fluctuation is caused by systemic delays due to non-business logic, rather than the illegal circulation of coupons, this benchmark calibration procedure will physically separate the transient latency jitter caused by physical hardware load from the logical latency caused by coupon topology reconstruction, preventing the risk interception state machine from being triggered erroneously.

[0053] To address the parameter drift issue during long-term algorithm operation, the system implements an adaptive risk assessment threshold reconstruction procedure. The system adjusts the risk assessment threshold based on the real-time distribution characteristics of coupon circulation. Dynamic calibration is implemented, and the system periodically calculates the width of the sliding sampling window. Inside The data distribution histogram is used, and the 95th percentile value of the distribution histogram is used as the dynamic risk assessment threshold. The update basis and calculation formula are as follows: ;in, The updated judgment threshold, The historical risk assessment threshold from the previous observation period. Within the current observation period The 95th percentile value, The forgetting factor, set to 0.85, is used to ensure a smooth transition of the decision rules. Through this procedure, the system achieves adaptive following of normal business logic changes, ensuring feature convergence metrics. The effective judgment range is always anchored at the edge of the normal distribution density of coupon circulation, effectively eliminating the risk of underreporting caused by traditional fixed thresholds during business model iteration.

[0054] Example 5: The construction process of the current card and coupon transaction violation identification model in an offline environment is based on the feature vector space containing normal and violation behaviors in historical transaction logs. A random forest classifier is constructed as the core identification engine. The system extracts the outflow rate of funds from the transferring account, the average number of hops for card and coupon transfers per unit time, and the degree centrality of the account node as core features for each transaction. The construction dimension is as follows: Training feature vectors For this feature space, the information gain value of each feature in judging illegal transactions is determined by calculating the interaction entropy of different subsets of data. In this way, features with a contribution rate of less than a preset threshold for identifying illegal paths are automatically eliminated. Secondary features are used to ensure that the feature set at the model input has high recognition sensitivity. During the model training phase, the system adopts the 10-fold cross-validation method, sets the classification tree depth to 15, and the minimum sample size of the leaf node to 10. The set of decision rules output by the training constitutes the dynamic risk control rule base of the system.

[0055] Before deployment, the coupon transaction identification system performs a multi-level baseline calibration procedure to determine the logical judgment threshold for violation detection. The system projects the feature vectors of normal transaction behavior onto the decision space, calculates the shortest Mahalanobis distance between sample points and the logical judgment boundary, and sets the initial detection threshold as a fraction of the sample distribution. The confidence interval boundary is determined within the first 72 hours after the system goes live. Based on real-time captured normal transaction feedback, the system uses incremental gradient descent logic to adjust the judgment weights. Perform online smooth updates; the update logic is based on the weight iteration formula. Drive; among which, For the updated feature weights, The weights from the previous iteration period, To verify the authenticity of the transaction, Predict labels for the model, For the first Standardized values ​​of each feature The learning rate is 0.001. This value is used when a certain account node in the coupon asset transfer topology... When frequent non-directional route deviations are triggered within the time window, the system determines that the node is in an abnormal state and initiates a targeted data collection density doubling procedure. The specific steps are as follows: The system increases the sampling frequency of the settlement interface associated with the account from... Upgraded to This enables high-frequency capture of the underlying routing process of accounts; utilizing the updated feature weights. Perform real-time logical judgment; if the sampling is doubled... If the indicator maintains an upward trend for three consecutive sampling periods, the system sends an interrupt suspension instruction to the underlying memory controller to modify the transaction routing image address of the account to the system's preset static dead loop security zone. This physically blocks the illegal transaction link at the hardware addressing level. This process directly couples the abstract risk control rules with the processor's addressing memory control, ensuring that the identification and control system has closed-loop determinism from feature analysis to physical action execution when facing implicit violations.

[0056] 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 present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A secure electronic coupon verification and risk control system, characterized in that, include: The voucher data stream receiving module is used to collect the de-identified digital voucher redemption request vector sent by the acquiring settlement node, and parse out the anonymized voucher unique identifier hash sequence, merchant node settlement credit code and local timestamp; The topology graph dynamic processing module communicates with the voucher data stream receiving module through a logical data bus. It is used to map the de-identified digital card redemption request vector to the asynchronous time sequence graph of commercial voucher status in memory, and generate data graph nodes with time sequence topology constraints. The topology graph dynamic processing module is equipped with an automatic data cleaning unit. The automatic data cleaning unit is used to monitor the historical redemption action time increment of the hash sequence of the anonymized card unique identifier. When the historical redemption action time increment crosses a fixed critical point of 30 minutes, it triggers a discretization pruning operation on the corresponding data graph node in the asynchronous time sequence graph of commercial voucher status. The central risk control verification module interacts with the topology graph dynamic processing module through a network communication interface. It retrieves the remaining credit limit corresponding to the merchant node's settlement credit code, calculates the write-off risk control judgment instruction based on the local timestamp and the temporal topology constraints of the data graph nodes, and feeds the write-off risk control judgment instruction back to the acquiring settlement node.

2. The electronic coupon security verification risk control system according to claim 1, characterized in that, The central risk control verification module has a network environment monitoring unit and an adaptive degradation routing control unit. The network environment monitoring unit is used to obtain the global communication reachability online. When the global communication reachability drops to 85%, the adaptive degradation routing control unit is used to close the read channel to the external database and change the calculation basis of the available credit balance to the locally stored merchant transaction flow space topology density. The central risk control verification module independently completes the credit limit decision on the de-identified digital card redemption request vector based on the merchant transaction flow space topology density.

3. The electronic coupon security verification risk control system according to claim 1, characterized in that, The voucher data stream receiving module also includes a clock offset dynamic compensation unit. The clock offset dynamic compensation unit is used to collect the clock offset parameter sequence of multiple acquiring settlement nodes, calculate the mean of the clock offset parameter sequence in the time dimension using a sliding time window, and subtract the mean from the local timestamp to complete the clock calibration.

4. The electronic coupon security verification risk control system according to claim 1, characterized in that, The asynchronous sequence graph of commercial voucher status is carried by a graph data structure that resides in memory; the data graph nodes contain anonymized voucher unique identifier hash sequences, merchant node settlement credit codes, and directed edge topological constraints indicating the order of reimbursement actions.

5. The electronic coupon security verification risk control system according to claim 1, characterized in that, The central risk control verification module also has a clearing and settlement information interaction feedback unit. The clearing and settlement information interaction feedback unit is used to send the real-time data status of the write-off risk control judgment instruction and the adjustment value of the remaining credit limit to the acquiring settlement node, and establish a data interaction channel between the acquiring settlement node and the central risk control verification module.

6. The electronic coupon security verification risk control system according to claim 1, characterized in that, The central risk control verification module has a concurrent tampering verification unit. The concurrent tampering verification unit is used to retrieve the unique identifier hash sequence of the anonymized card and compare it with the same hash sequence in the asynchronous time sequence diagram of the commercial voucher status. When the same hash sequence is detected to be in the unredeemed state, a rejection redemption instruction is output.

7. The electronic coupon security verification and risk control system according to claim 1, characterized in that, The voucher data stream receiving module also includes a multi-level distribution tracking unit. The multi-level distribution tracking unit is used to parse the multi-level distribution network credit relationship contained in the de-identified digital voucher redemption request vector, and to overlay the multi-level distribution network credit relationship onto the time-series topology constraints of the data graph nodes in the asynchronous time-series diagram of the commercial voucher status.

8. The electronic coupon security verification risk control system according to claim 1, characterized in that, When the automatic data cleaning unit triggers the discretization pruning operation, the automatic data cleaning unit is used to reclaim the memory space occupied by the corresponding data graph node and send a quota release instruction to the central risk control verification module to return the temporarily retrieved remaining credit quota.

9. The electronic coupon security verification and risk control system according to claim 1, characterized in that, The central risk control verification module also includes a transaction frequency fluctuation verification unit. The transaction frequency fluctuation verification unit is used to count the difference between the current reconciliation request frequency and the historical reconciliation request frequency of the acquiring settlement node within a fixed time window of 5 minutes, calculate the reconciliation request frequency change rate, and output an abnormal transaction warning signal when the reconciliation request frequency change rate exceeds the safe fluctuation limit of 1.

5.

10. A method for secure verification and risk control of electronic coupons, used to run the secure verification and risk control system for electronic coupons as described in claim 1, characterized in that, include: Step S1: Use the voucher data stream receiving module to collect the de-identified digital card and voucher redemption request vector sent by the acquiring settlement node, and parse out the anonymized card and voucher unique identifier hash sequence, merchant node settlement credit code and local timestamp; In step S2, the topology graph dynamic processing module communicates with the voucher data stream receiving module through the logical data bus. The topology graph dynamic processing module maps the de-identified digital card redemption request vector to the asynchronous time sequence graph of commercial voucher status in memory, generating data graph nodes with time sequence topology constraints. The automatic data cleaning unit monitors the historical redemption action time increment of the hash sequence of the anonymized card unique identifier. When the historical redemption action time increment crosses a fixed critical point of 30 minutes, a discretization pruning operation is triggered for the corresponding data graph node in the asynchronous time sequence graph of commercial voucher status. In step S3, the central risk control verification module interacts with the topology graph dynamic processing module through the network communication interface. The central risk control verification module retrieves the remaining credit limit corresponding to the merchant node settlement credit code, calculates the write-off risk control judgment instruction based on the local timestamp and the temporal topology constraints of the data graph node, and feeds the write-off risk control judgment instruction back to the acquiring settlement node.

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