Intelligent interactive risk control method for sports lottery terminal

Abstract

The invention provides an intelligent interactive risk control method for a sports lottery terminal. The method comprises the following steps: acquiring a multi-modal feature; respectively inputting the multi-modal features into a preset diffusion model and a flow matching model for training to obtain an updated noise prediction network and an updated flow matching model; extracting noise prediction intermediate layer features and stream matching intermediate layer features, then executing multi-head attention calculation with spatial bias and time bias based on the intermediate layer features, generating an attention weight with space-time enhancement, and enhancing the stream matching intermediate layer features by using the attention weight to obtain enhanced features; performing risk assessment on the transaction behavior based on the enhanced features to obtain a risk score; and executing a preset risk control strategy according to the risk score. According to the method, accurate, real-time and self-adaptive prevention and control of risks are realized by predicting the future trend of the behavior trajectory and evaluating the deviation degree of the behavior trajectory from the normal mode.

Description

Technical Field

[0001] This invention relates to the field of sports lottery risk control technology, and specifically to an intelligent interactive risk control method for sports lottery terminals. Background Technology

[0002] With the rapid development of the sports lottery industry, the issue of risk control at lottery terminals has become increasingly prominent. User behavior is no longer an isolated event, but a continuous and dynamic data stream containing complex temporal patterns. Traditional risk control methods, whether based on static rules-based "snapshot" judgments or on discretization based on simple machine learning models, have fundamental flaws: 1. Inability to understand the “fluidity” of behavior: They break down continuous behavioral processes into discrete, independent events for analysis, thus losing key dynamic information such as the trend, rhythm, and acceleration of behavioral evolution.

[0003] 2. Slow response to new patterns: Fraudulent methods are constantly evolving, forming new dynamic patterns. Models trained based on rules and old data are poorly adaptable to these "unseen" behavioral evolution paths, leading to high false positives and high false negatives.

[0004] 3. Inability to simultaneously model determinism and randomness: Normal user behavior usually follows a predictable pattern (determinism), while risky behavior often manifests as sudden deviations from this pattern or abnormal random perturbations (randomness). Existing methods struggle to capture both characteristics simultaneously within a unified framework.

[0005] Therefore, this invention urgently requires a new technological paradigm that can view user behavior as a trajectory that evolves continuously over time and accurately model its inherent deterministic patterns and random disturbances, thereby achieving truly dynamic and intelligent risk control. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent interactive risk control method for sports lottery terminals. This method achieves real-time risk assessment and intelligent prevention and control of user behavior on sports lottery terminals by constructing a continuous-time probability flow field of multimodal behavioral characteristics.

[0007] In this invention, a user's multimodal behavior is viewed as a trajectory continuously evolving in a high-dimensional feature space, and a hybrid probabilistic model is constructed that can simultaneously describe the deterministic "flow" (normal pattern) and random "diffusion" (abnormal perturbation) of this trajectory. Based on this model, by predicting the future direction of the behavioral trajectory and assessing its deviation from the normal pattern, precise, real-time, and adaptive risk control is achieved.

[0008] A smart interactive risk control method for sports lottery terminals includes the following steps: Step S1: Data collection and feature extraction. Real-time collection of user transaction behavior, network information, geographic information and sports event information from sports lottery terminals. Encapsulation of the information into a timestamped event stream and feature extraction of the event stream to obtain multimodal features. Step S2: Hybrid model training, where the multimodal features are input into a preset diffusion model and a flow matching model for training, the training including: The diffusion model is used to add noise to the multimodal features to generate pure noise data, and a noise prediction network is constructed to predict the noise, thereby calculating the diffusion loss term. The multimodal features are input into the flow matching model to train its internal velocity field network, thereby calculating the flow matching loss term; The diffusion loss term and the flow matching loss term are weighted and summed to obtain the hybrid optimization loss term. The parameters of the noise prediction network and the flow matching model are updated synchronously by backpropagating the hybrid optimization loss term to obtain the updated noise prediction network and the updated flow matching model. Step S3: Spatiotemporal feature enhancement. Extract noise prediction intermediate layer features and flow matching intermediate layer features from the updated noise prediction network and the updated flow matching model, respectively. Perform multi-head attention calculation with spatial and temporal bias based on the intermediate layer features to generate attention weights with spatiotemporal enhancement. Use the attention weights to enhance the features to obtain the enhanced features. Step S4: Risk assessment, based on the enhanced features, to assess the risk of the transaction and obtain a risk score; Step S5: Strategy execution: Execute the preset risk control strategy based on the risk score.

[0009] In step S2, the multimodal features are noise-added using the diffusion model to generate pure noise data, and a noise prediction network is constructed to predict the noise. Specifically, this includes: The multimodal features are subjected to noise along the time evolution path using stochastic differential equations (SDEs) until the pure noise data is generated. A U-Net-based neural network is used as the noise prediction network, taking the pure noise data as input to predict the added noise or predict the original features.

[0010] In step S3, the multi-head attention calculation with spatial and temporal biases is performed using the following formula: in, These are the query matrix, key matrix, and value matrix, respectively. For vector dimensions, Let be the spatial bias matrix. This is the time bias matrix; The spatial bias matrix The spatial distance between behavior i and behavior j is calculated in the following way. Exceeding the preset space threshold At the same time, attention between the two should be prohibited: in These are the elements in the matrix corresponding to rows i and j; This is an indicator function; its value is 1 when the internal condition is true, and 0 otherwise. For behavior and behavior Spatial distance between them; This formula is used to prevent attention from being drawn to behaviors that are more than a preset spatial distance threshold. The time offset matrix The time difference between behavior i and behavior j is calculated in the following way. Apply decay penalty: in For behavior and behavior The absolute difference between the timestamps of occurrence; This is the time decay constant, and the formula is used to impose a stronger attention penalty on behaviors with larger time differences.

[0011] In step S4, the risk assessment calculates the risk score R(x,t) using the following formula: in In response to the current behavior In time Calculated risk score; first item For the behavioral abnormality item, where, The probability distribution of future actions predicted by the model based on the current action x; These are samples of future behaviors sampled from this probability distribution of future behaviors; For a predefined normal behavior template; This is a function used to measure the difference between the sample and the template; the second term... Let be the distribution divergence term, where The prior distribution of normal behavior learned from massive historical data; To calculate the KL divergence between two probability distributions; These are the weighting coefficients used to balance the behavioral anomaly term and the distribution divergence term.

[0012] In step S5, the risk control strategy includes comparing the risk score with a dynamic risk threshold, wherein the dynamic risk threshold... Adaptive adjustment is performed based on the following formula: in The dynamic risk threshold at time t; A fixed base threshold; The variance of the recent risk score R; The variance of the historical risk score R; The coefficient for adjusting the amplitude controls the maximum range of threshold adjustment; To adjust the sensitivity coefficient, the threshold's response speed to changes in risk variance is controlled; It is a hyperbolic tangent function used to smoothly constrain the adjustment effect of the rate of change of variance within a finite range.

[0013] The method of the present invention further includes a real-time monitoring and feedback learning step: Continuously monitor newly incoming data and use statistical methods such as Maximum Mean Difference (MMD) to detect the drift between the current data distribution and the historical reference distribution; When a significant distribution drift is detected or labeled feedback data is received, an online update of the model parameters is triggered. The parameters of the noise prediction network and the flow matching model are fine-tuned by minimizing an online loss function that combines the new data prediction loss, the feedback data learning loss, and the distribution drift penalty.

[0014] Compared with the prior art, the present invention has the following beneficial effects: I. High-precision risk identification: Through a continuous-time diffusion-flow hybrid model, it can simultaneously and accurately capture the deterministic evolution trend and random disturbance of user behavior, significantly improving the accuracy of identifying complex and unknown risk patterns.

[0015] II. Strong adaptability: The online learning mechanism and distribution drift detection enable the system to quickly adapt to new fraud patterns and changes in data distribution, avoid model failure, and maintain continuous high performance.

[0016] III. Superior Real-Time Performance: Through efficient mathematical models and optimizable network structures, the efficiency of real-time risk assessment is ensured, meeting the response time requirements of high-concurrency sports lottery business systems.

[0017] IV. Strong robustness: Multimodal feature fusion and spatiotemporal attention mechanism effectively utilize diverse information sources, and regularization mechanism improves the model's robustness to noise and data sparsity.

[0018] V. Enhanced interpretability: By analyzing attention weights and probability flow fields, the source of high-risk scores can be traced, providing a basis for decision-making in the analysis and handling of risk events, thus improving the interpretability of the model. Attached Figure Description

[0019] Figure 1 This is a flowchart of the system operation of the present invention.

[0020] Figure 2 This is a flowchart of the training process for the diffusion-flow hybrid model of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] like Figure 1 As shown, Figure 1 This presentation showcases the complete technical architecture of an intelligent risk control system, employing a layered design approach to construct an end-to-end intelligent risk control solution from data acquisition to decision execution. The system's core utilizes a diffusion-flow hybrid model as its modeling engine. The data input layer enables unified acquisition and preprocessing of multi-source heterogeneous data. The core modeling layer employs continuous-time diffusion processes and probabilistic flow field construction techniques to achieve deep modeling of user behavior. The decision execution layer achieves accurate risk identification and tiered processing through a dynamic threshold adjustment mechanism. The learning and optimization layer ensures the system continuously adapts to changes in the business environment through online learning and feedback mechanisms. The entire architecture possesses high scalability and adaptability, capable of handling multi-dimensional risk identification needs in complex business scenarios such as sports lottery.

[0023] like Figure 2 As shown in the flowchart, this process illustrates the training procedure of the diffusion-flow hybrid model. This model combines the stochastic modeling capabilities of the diffusion process with the deterministic transport advantages of the probabilistic flow field. The diffusion process models the stochastic evolution of user behavior using stochastic differential equations (SDEs), training a noise prediction network to learn the data distribution structure. The probabilistic flow field models the flow of probability density using continuity equations, training a velocity field network to learn the data transport dynamics. The two branches are jointly optimized using a hybrid loss function, which includes diffusion loss, flow matching loss, and regularization terms.

[0024] A smart interactive risk control method for sports lottery terminals, the specific implementation process of which is as follows: Step S1: Multimodal behavioral feature extraction and preprocessing To mathematically describe user behavior patterns, it is first necessary to integrate and quantify the raw, heterogeneous user interaction data from sports lottery terminals. The collected raw interaction dataset... It contains a series of events ordered by time: in: Raw, unprocessed datasets of user interactions. The total number of interaction events in the dataset. : No. The timestamp of the next interaction event. Behavioral feature vectors, such as betting amount, frequency, and game type. Contextual feature vectors, such as device IP address, geographical location, current sports event, etc. Metadata feature vectors, such as user account information, device ID, etc.

[0025] To eliminate the influence of time units and facilitate model processing, timestamps are standardized and mapped to... Interval: Next, to form a unified and information-rich feature representation, a learnable linear transformation is used to project and fuse the feature vectors of different modalities into the same high-dimensional feature space, resulting in a fused feature vector for each interaction event. : in: : No. The multimodal fusion feature vector of the interaction event is the basic unit for subsequent modeling. A learnable weight matrix used to project features from different sources onto a matrix of dimension 1. Public spaces. The bias vector is also a learnable parameter of the model.

[0026] Through this step, the user's complete behavioral history is transformed into a sequence of trajectory points in a high-dimensional space. This laid the foundation for subsequent dynamic modeling.

[0027] Step S2: Modeling the continuous-time diffusion process Real-world behavioral patterns are highly random, and anomalous behavior often manifests as drastic, random deviations from conventional patterns. To capture this uncertainty, we introduce a continuous-time diffusion process, which describes the evolution of behavioral characteristics from a clear state to pure noise through a stochastic differential equation (SDE): in: : Feature vector At a very small pseudo-time step The change within. The drift function describes the deterministic trend of data evolution. : Diffusion coefficient, which controls the intensity of random noise. The increment of standard Brownian motion represents a random perturbation.

[0028] We train a neural network (Usually a U-Net structure) to learn the inverse process of this process, that is, from a data point contaminated by noise. The original added noise is predicted in the middle. Its training objective is to minimize the following loss function: in: : Original, clear behavioral feature vectors. Random noise sampled from a standard Gaussian distribution. The diffusion process occurs at any given time. The noise addition formula, It is a coefficient related to noise intensity. Noise prediction network, whose task is to accurately reproduce noise.

[0029] Through this step, the model gains a deep understanding of the various random perturbation patterns present in the data and has the ability to recover structure (denoising) from the chaos.

[0030] Step S3: Probabilistic flow field construction and flow matching optimization Aside from random perturbations, the behavior of normal users typically follows certain inherent, deterministic evolutionary patterns. To accurately characterize this deterministic trend, we construct a probabilistic flow field based on continuous normalized flow. This flow field consists of a velocity field vector. The description determines the probability density. The direction and speed of "flow" in space evolve according to the continuity equation: We use a neural network This is used to approximate the complex velocity field. To train the network efficiently, a flow matching method is employed, the goal of which is to make the network predict velocities... The goal is to approximate as closely as possible the speed along a predetermined path from the real data distribution to a simple prior distribution (such as a Gaussian distribution). Its loss function is defined as: in: Points sampled from the real data distribution and the prior noise distribution, respectively. Defined from arrive A simple linear path. The target velocity on the linear path. This loss function drives... Learn the deterministic laws governing how normal data points should "flow" and evolve.

[0031] Step S4: Design of the diffusion-flow hybrid model To construct a complete model that can understand deterministic patterns and capture random anomalies, we organically combine the aforementioned diffusion and flow matching processes. We then use a unified, weighted hybrid loss function to collaboratively optimize the two neural networks. and ): in: Hyperparameters are used to balance the importance of diffusion loss, flow matching loss, and regularization terms.

[0032] Among them, the regularization term Used to prevent model overfitting and improve generalization ability: in: : Apply L2 regularization (weight decay) to the weights of the two networks. Penalizing the Jacobian matrix of the velocity field with respect to the input encourages a smoother velocity field in space, avoids drastic changes, and enhances model stability.

[0033] Through joint optimization, the model learns to consider both the inherent deterministic trends and potential random perturbations when describing behavioral evolution, thus forming a comprehensive understanding of behavioral dynamics.

[0034] Step S5: Design of Spatiotemporal Attention Mechanism The risk level of an action often depends not on the action itself, but on when, where, and with which other actions it occurs. To imbue our dynamic model with this context-awareness, we embed a multi-head spatiotemporal attention mechanism as the core computational layer into the neural network. and Internal. The calculation formula is: in: The query, key, and value matrix is ​​the core of the standard attention mechanism. Spatial bias matrix. It adds a bias to the attention score based on the geographical or network spatial distance between actions, causing the model to focus on spatially nearby actions. Its elements are defined as: (If the spatial distance between behavior i and j) Exceeding the threshold (Then attention between them is prohibited.) The time bias matrix. It introduces a decay penalty based on the time interval between actions, making the model focus more on actions that are temporally close. Its elements are defined as follows: (The greater the time difference, the greater the penalty, and the lower the weight of attention.) In this way, when analyzing any behavioral point, the model can dynamically and intelligently aggregate relevant spatiotemporal context information from the entire behavioral sequence, thereby making more accurate judgments.

[0035] Step S6: Risk Score Calculation and Dynamic Threshold Adjustment Based on a trained hybrid dynamics model with spatiotemporal awareness, we conduct a prospective risk assessment. Specifically, for the user's current behavioral sequence... We use the model to infer its future evolutionary probability distribution. It then calculates the degree of deviation from the "standard normal behavior pattern" to obtain a quantitative risk score. : Wherein: The first item, behavioral anomaly score, calculates the future behavior predicted by the model. With normal behavior template average difference The second term, distribution divergence: calculates the distribution of predicted future behavior. Compared to the prior distribution of normal behavior learned from massive amounts of data KL divergence between .

[0036] If a user's current behavior suggests a high probability that their future trajectory will deviate from the expected path, their risk score will increase significantly. To address changes in the market environment (such as peak betting periods during holidays or the launch of new events), we do not use fixed thresholds but instead implement a dynamic threshold adjustment strategy. Where: the threshold It will be based on the variance of recent risk scores Relative to historical variance The threshold adapts to changes. When the overall risk fluctuation of the system intensifies, the threshold will be adjusted accordingly to maintain a balance between sensitivity and accuracy.

[0037] Step S7: Real-time monitoring and feedback learning In a dynamic risk-manipulation environment, models must possess the ability to continuously evolve. To this end, we designed an online learning and feedback loop. The system continuously monitors newly incoming data and uses statistical methods such as the maximum mean difference (MMD) to detect the current data distribution. Compared with historical reference distribution Drift between: Once a significant distribution drift is detected, or new human-reviewed results are received (i.e., feedback data with accurate labels), the system triggers an online update of the model parameters. This is achieved through an online loss function that integrates the prediction loss on new data, the learning loss on feedback data, and the distribution drift penalty. Fine-tune the model: in: Learning rate adjusted over time. The loss function is integrated with the online learning objectives. This step ensures that the risk control model can keep pace with the times, quickly adapt to emerging fraud patterns and market changes, and achieve long-term stable and efficient operation.

[0038] The present invention also provides an intelligent interactive risk control device for sports lottery terminals, comprising: Data acquisition module: Used to collect user interaction data from sports lottery terminals in real time through interfaces, including multimodal data streams such as transaction behavior, operation sequence, and device information.

[0039] Feature processing module: performs real-time preprocessing, feature extraction and multimodal fusion on the collected raw data, and constructs a continuous time behavior sequence according to step S1.

[0040] Diffusion Modeling Module: This module models the continuous-time diffusion process in step S2 and includes a noise prediction network. Flow matching module: Implements the probabilistic flow field construction in step S3, and internally includes a velocity field network. .

[0041] Hybrid optimization module: Implements the hybrid objective function in step S4, and synchronously trains the parameters of the diffusion modeling module and the flow matching module through a joint optimizer.

[0042] Attention calculation module: As a core component, it is embedded in the neural network of the diffusion and flow matching module to implement the multi-head spatiotemporal attention mechanism in step S5.

[0043] Risk assessment module: Based on the trained hybrid model, following the logic of step S6, it calculates the risk score of user behavior in real time and adjusts the strategy to output the risk level according to the dynamic threshold.

[0044] Online learning module: Implements online update and feedback learning in step S7, receives external feedback data, calculates online loss, and incrementally updates model parameters.

[0045] Decision execution module: Based on the risk level output by the risk assessment module, trigger corresponding risk control strategies, such as rejecting transactions, sending verification codes, transferring to the manual review queue, or freezing accounts.

[0046] Example: Real-time risk control method for high-frequency abnormal betting behavior in sports lottery terminals This embodiment aims to build an intelligent risk control system capable of identifying and intercepting abnormal behaviors such as high-frequency, small-amount, and dispersed betting using lottery terminals in real time. Such behavioral patterns may be precursors to money laundering, credit card fraud testing, or arbitrage exploiting odds loopholes.

[0047] System environment and configuration: Hardware platform: Deployed on a cloud server cluster, equipped with an NVIDIA A100 GPU for model training and real-time inference, a CPU with a main frequency of 3.0GHz or higher, and no less than 256GB of memory.

[0048] Software stack: The operating system is Ubuntu 20.04. The core algorithms are implemented using Python 3.9 and PyTorch 2.0 frameworks, with GPU acceleration utilizing CUDA 11.8. Data stream processing uses Apache Kafka, and feature storage and querying use PostgreSQL and Redis.

[0049] Dataset: Anonymized historical transaction data from the past 24 months provided by a sports lottery center. The data has undergone rigorous anonymization and includes samples of normal users and confirmed high-risk users.

[0050] Step 1: Multimodal behavioral feature extraction and preprocessing The system consumes data streams from lottery sales terminals in real time via Kafka. For each interaction record, such as (timestamp=1678886400, user_id='U123', transaction_amount=10.0, bet_type='Soccer_WinLose', ip_address='123.45.67.89', device_id='DevABC'), the feature processing module performs the following operations: 1. Feature quantization: One-hot encoding is performed on categorical features such as bet_type. The ip_address is converted into geographic coordinates (latitude and longitude) and IP range values. 2. Derived feature calculation: Based on user history, derived features are calculated, such as "betting frequency in the past hour," "average betting amount," and "the degree of unpopularity of the bet event." 3. Feature fusion: All original and derived features (assuming a total of 256-dimensional vectors) are uniformly mapped to a d=128-dimensional fusion feature space through the linear transformation layer described in step S1, forming a feature vector. .

[0051] Steps 2 and 3: Defining the Model Network Structure In this embodiment, the noise prediction network and velocity field network Both networks employ an improved U-Net architecture. Network structure: This U-Net consists of 4 downsampling layers and 4 upsampling layers. Each layer comprises two residual convolutional blocks. In the deepest layer (bottleneck layer), we embed the multi-head spatiotemporal attention module (8 heads, d_k=16) described in step S5. The output of the downsampling layer is directly passed to the corresponding upsampling layer via skip connections to preserve high-resolution feature information. Activation function: SiLU (Sigmoid-weighted Linear Unit) is used as the primary activation function. Temporal encoding: Pseudo-time step. It is converted into a vector through sinusoidal positional embedding and incorporated into each residual block of the network.

[0052] Step 4: Offline Model Training The model was trained using a prepared 24-month historical dataset. 1. Training configuration: The optimizer was set to AdamW, the learning rate to 1e-4, and the batch size to 256. 2. Loss function weights: Based on experience and cross-validation, the weights of the hybrid loss function were set to... (Diffusion loss is the dominant factor) (Stream matching as an auxiliary regular expression) (Regularization term). 3. Training process: Distributed training is performed on 4 A100 GPUs, for a total of 150 epochs. During training, the model simultaneously learns to recover normal behavioral structures from noise. ), and the evolutionary flow of normal behavioral patterns ( The trained model is saved for online deployment.

[0053] Steps 5 and 6: Demonstration of a specific risk identification scenario Suppose a normal user, User_B, whose behavior pattern is stable: usually between 8 and 10 pm, under an IP address in the Beijing area, they place a medium-sized bet of 50-200 yuan on mainstream football leagues.

[0054] Risk event occurred: At 3 a.m., the account suddenly launched 50 consecutive 2-yuan bets against a niche basketball league in South America from an IP address located in Southeast Asia within 5 minutes.

[0055] System processing flow: 1. Real-time feature generation: The system generates a new sequence of user behavior trajectories in real time. These vectors contain significant anomalies such as "betting time = 3 AM", "IP location = Southeast Asia", "betting frequency = 10 times / minute", "betting amount = 2 yuan", and "event type = niche basketball".

[0056] Context awareness: When these new feature vectors are fed into the model, the built-in spatiotemporal attention module begins to work: Spatial attention: Because the geographical distance between the new IP address and the historical IP address (Beijing) far exceeds the set threshold. km, spatial bias matrix This will cause the model to pay extreme attention to this drastic change in geographical location. Time attention: Due to the extremely short time intervals between 50 bets occurring within 5 minutes, the time bias matrix... This will cause the model to pay close attention to these high-density, short-term behavioral clusters.

[0057] Forward-looking risk assessment: The risk assessment module receives the attention-weighted contextual information and uses a trained model to predict the future trajectory of the behavior. Based on learned knowledge, the model determines that this combination of "early morning + out-of-town IP + high frequency + small amount + niche event" is highly similar to known credit card fraud tests or the behavioral patterns of professional arbitrage groups. Therefore, the model predicts the future behavioral distribution. This will deviate significantly from the prior distribution representing the normal behavior of User_B. This leads to the risk scoring formula. The distribution divergence term in A sharp increase. Simultaneously, the predicted specific behaviors (such as continuing to make such bets) also align with normal behavioral templates. Vastly different, leading to abnormal behavior. It also becomes very high. 4. Decision and Execution: The calculated comprehensive risk score R = 95.8 (assuming a maximum score of 100). At this time, the dynamic threshold module, based on the overall stable state of the system during the current off-peak period, gives an alarm threshold of τ_threshold = 75.0. Since 95.8 > 75.0, the decision execution module immediately triggers a high-risk response: rejecting subsequent betting requests. Temporarily freezing the account's trading permissions. Automatically generating a high-priority alarm ticket and pushing it to the manual review team. The ticket includes key characteristics leading to the high-risk score (login from a different location, high-frequency trading) and an attention analysis chart for the reviewers' reference.

[0058] Step 7: Online Learning and Model Iteration Scenario Continuation: The manual review team verifies within one hour that User_B's account has been compromised. The reviewer marks the event as "confirmed fraud" in the risk control backend. 1. Feedback Learning: This behavioral sequence labeled "fraud" is sent as a high-quality training sample to the online learning module. 2. Model Fine-tuning: The system triggers an online model update. This update is achieved by minimizing the online loss function. Model parameters and The fine-tuning further enhances the model's sensitivity to this specific "high-frequency, low-amount" fraud pattern across different locations. 3. Distribution Drift Monitoring: Simultaneously, the system continuously monitors the overall data distribution. Assuming the World Cup starts next month, user betting frequency and amounts generally increase. The MMD drift detection module will detect significant differences between the current data distribution and historical distributions. When the drift value exceeds a preset threshold, the system will automatically flag it and suggest a larger-scale model retraining to adapt to the new market norm.

[0059] Through the above embodiments, the present invention successfully transforms an abstract mathematical model and method into a powerful, clear, and intelligent risk control system that can solve practical business pain points, demonstrating its great value and feasibility in practical applications.

Claims

1. A smart interactive risk control method for sports lottery terminals, characterized in that, Includes the following steps: Step S1: Collect user transaction behavior, network information, geographic information and sports event information from sports lottery terminals in real time, encapsulate the collected information into a time-stamped event stream, and extract features from the time-stamped event stream to obtain multimodal features; Step S2: Input the multimodal features into the preset diffusion model and flow matching model respectively for training to obtain the updated noise prediction network and the updated flow matching model; Step S3: Extract the intermediate layer features of noise prediction and the intermediate layer features of flow matching from the updated noise prediction network and the updated flow matching model, respectively. Then, perform multi-head attention computation with spatial and temporal biases based on the intermediate layer features to generate attention weights with spatiotemporal enhancement. Use the attention weights to enhance the intermediate layer features of flow matching to obtain the enhanced features. Step S4: Assess the risk of trading behavior based on the enhanced features to obtain a risk score; Step S5: Execute the preset risk control strategy based on the risk score.

2. The intelligent interactive risk control method for sports lottery terminals according to claim 1, characterized in that, In step S2, the multimodal features are input into the preset diffusion model and flow matching model for training, specifically including: The multimodal features are noise-added using a diffusion model to generate pure noise data, and a noise prediction network is constructed to predict the noise. The diffusion loss term is calculated from the output of the noise prediction network. The velocity field network inside the flow matching model is trained by inputting multimodal features, and the flow matching loss term is calculated. The diffusion loss term and the flow matching loss term are weighted and summed to obtain the hybrid optimization loss term. The parameters of the noise prediction network and the flow matching model are updated synchronously by backpropagating the hybrid optimization loss term, thus obtaining the updated noise prediction network and the updated flow matching model.

3. The intelligent interactive risk control method for sports lottery terminals according to claim 1, characterized in that, In step S2, the multimodal features are noise-added using a diffusion model to generate pure noise data, and a noise prediction network is constructed to predict the noise. Specifically, this includes: Stochastic differential equations (SDEs) are used to add noise to multimodal features along the time evolution path until pure noise data is generated; A U-Net-based neural network is used as the noise prediction network. It takes pure noise data as input and predicts the added noise or the original features.

4. The intelligent interactive risk control method for sports lottery terminals according to claim 1, characterized in that, In step S3, the multi-head attention calculation with spatial and temporal biases is performed using the following formula: ； in, These are the query matrix, key matrix, and value matrix, respectively. For vector dimensions, Let be the spatial bias matrix. The time bias matrix, Let be the attention function. For activation functions; The spatial bias matrix The spatial distance between behavior i and behavior j is calculated in the following way. Exceeding the preset space threshold At the same time, attention between the two should be prohibited: ； in These are the elements in the matrix corresponding to rows i and j; This is an indicator function; its value is 1 when the internal condition is true, and 0 otherwise. For behavior and behavior Spatial distance between them; The preset spatial distance threshold; The time offset matrix The time difference between behavior i and behavior j is calculated in the following way. Apply decay penalty: ； in For behavior and behavior The absolute difference between the timestamps of occurrence; is the time decay constant.

5. The intelligent interactive risk control method for sports lottery terminals according to claim 1, characterized in that, In step S4, the risk assessment uses the following formula to calculate the risk score R(x,t): ； in In response to the current behavior In time Calculated risk score; first item For the behavioral abnormality item, where, The probability distribution of future actions predicted by the model based on the current action x; These are samples of future behaviors sampled from this probability distribution of future behaviors; For a predefined normal behavior template; This is a function used to measure the difference between the sample and the template; the second term... Let be the distribution divergence term, where The prior distribution of normal behavior learned from massive historical data; To calculate the KL divergence between two probability distributions; These are the weighting coefficients used to balance the behavioral anomaly term and the distribution divergence term.

6. The intelligent interactive risk control method for sports lottery terminals according to claim 1, characterized in that, In step S5, a preset risk control strategy is executed based on the risk score, specifically including: The risk score is compared to a dynamic risk threshold. Adaptive adjustment is performed based on the following formula: ； in, The dynamic risk threshold at time t; A fixed base threshold; The variance of the recent risk score R; The variance of the historical risk score R; This is a coefficient for adjusting the amplitude; To adjust the sensitivity coefficient; It is the hyperbolic tangent function.

7. The intelligent interactive risk control method for sports lottery terminals according to claim 1, characterized in that, Also includes: Real-time monitoring and feedback learning.

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