A system for tracking and managing insurance card life cycle data

Abstract

The present application relates to the technical field of data life cycle management, and discloses an insurance card coupon life cycle data tracking management system, comprising a data acquisition module, a data processing module, a tracking and monitoring module, a risk assessment module, a report generation module and an optimization management module. Through the life cycle event recording unit and the distributed acquisition unit in the data acquisition module, multi-source card coupon data is collected in real time, and the data format is standardized by using the data preprocessing unit, so that the information missing and distortion problems caused by transmission delay and interface abnormalities can be identified in real time, the accuracy and integrity of the card coupon state data are ensured, the data acquisition error is reduced, the external environmental variables and user behavior data are integrated by using the multi-dimensional analysis unit, the risk model parameters are dynamically updated by using the adaptive learning unit of the optimization management module, the card coupon risk level can be dynamically corrected in real time according to the change factors, and the timeliness and accuracy of risk assessment are ensured.

Description

Technical Field

[0001] This invention relates to the field of data lifecycle management technology, specifically to an insurance card / voucher lifecycle data tracking and management system. Background Technology

[0002] Data lifecycle management is a strategy-based information management approach that uses automated processes to manage the entire lifecycle of data from creation and storage to destruction. Its core objective is to optimize storage efficiency through a tiered storage strategy, placing frequently accessed data on high-performance media and migrating low-frequency data to low-cost devices.

[0003] Currently, because insurance vouchers involve multiple stages and participants in their circulation and use, the data collection points deployed during real-time tracking of voucher lifecycle data cannot identify in real time whether data collection is missing or distorted due to transmission delays or interface anomalies. When key lifecycle events are missed, it can cause significant deviations in subsequent data analysis, making it impossible to guarantee the accuracy of voucher status judgment.

[0004] Therefore, an insurance card / voucher lifecycle data tracking and management system is proposed to solve the above problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an insurance card / voucher lifecycle data tracking and management system, which solves the problem mentioned in the background art of being unable to identify in real time whether data collection is caused by transmission delays and interface anomalies, resulting in information loss and distortion.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an insurance card / voucher lifecycle data tracking and management system, comprising:

[0007] The data acquisition module uses the card and coupon information acquisition unit to collect basic attribute data of insurance cards and coupons, records card and coupon creation, activation, usage and expiration events in real time through the life cycle event recording unit, and outputs standardized collected data through the data preprocessing unit.

[0008] The data processing module receives the standardized collected data, removes outliers through the data cleaning unit, analyzes the coupon usage pattern through the feature extraction unit, and outputs integrated processed data through the data fusion unit.

[0009] The tracking and monitoring module receives the integrated processing data, monitors the card lifecycle status in real time through the status tracking unit, triggers abnormal alarms using the threshold warning unit, and outputs real-time monitoring data through the data update unit.

[0010] The risk assessment module receives the real-time monitoring data, quantifies the risk level of the coupon through the risk indicator calculation unit, outputs the risk assessment results through the multi-dimensional analysis unit, and generates risk control suggestions through the risk feedback unit.

[0011] The report generation module integrates the risk assessment results with real-time monitoring data, customizes the output format through the report template configuration unit, and generates a dynamic lifecycle report using the visualization rendering unit.

[0012] The optimization management module receives the risk assessment results, optimizes the data tracking strategy through the strategy adjustment unit, iteratively improves the risk model through the adaptive learning unit, and outputs system tuning instructions through the parameter optimization unit.

[0013] Preferably, the data acquisition module further includes a distributed acquisition unit, which is used to collect lifecycle data of different insurance card types in parallel through multiple nodes. The card information acquisition unit specifically includes an optical character recognition subunit and an application programming interface (API) interface subunit. The optical character recognition subunit parses paper and electronic card image data, and the API interface interface subunit directly connects to the insurance business system to obtain digital card transaction data.

[0014] Preferably, the feature extraction unit in the data processing module further includes a behavior pattern analysis subunit and a time-series feature construction subunit. The behavior pattern analysis subunit identifies abnormal user behavior based on coupon usage frequency and geographical distribution data. The time-series feature construction subunit encodes coupon events into feature vectors according to time series and classifies coupon lifecycle stages through clustering algorithms.

[0015] Preferably, the status tracking unit in the tracking and monitoring module uses blockchain technology to record card status change logs to ensure that the data is tamper-proof, and the threshold warning unit dynamically adjusts the warning threshold, with the threshold setting being adaptively updated based on historical card expiration data and real-time risk assessment results.

[0016] Preferably, in the risk assessment module, the risk indicator calculation unit calculates the probability of coupon fraud and the life cycle deviation index, and the multidimensional analysis unit integrates geographic information system data and social network analysis data, and detects coupon-related risks through a graph neural network model.

[0017] Preferably, the report generation module further includes an interactive query unit, which allows users to customize query conditions to generate reports in real time. The visualization rendering unit supports 3D charts and heat maps to display the distribution of coupons and risk hotspots, and sends real-time alarm reports to administrators through the mobile terminal push sub-unit.

[0018] Preferably, the strategy adjustment unit in the optimization management module adopts a reinforcement learning algorithm to optimize the tracking strategy with the extension of the coupon life cycle and the reduction of risk as the reward function. The adaptive learning unit periodically retrains the risk model and updates the model parameters using newly added coupon data.

[0019] Preferably, the system further includes a data storage module that connects all modules and provides data persistence services. The data storage module adopts a distributed database architecture, partitions the storage of historical and real-time data for different insurance card types, and reduces storage space usage through a data compression unit.

[0020] Preferably, the data storage module further includes a data encryption unit and an access control unit. The data encryption unit uses homomorphic encryption technology to protect sensitive card information, and the access control unit restricts the scope of data access based on a role-based access control model.

[0021] Preferably, the system integrates an external data interface module to connect a third-party credit scoring system and an insurance regulatory platform. The external data interface module verifies external data through a data verification unit and combines external data with internal card and coupon data using a data fusion unit.

[0022] Compared with the prior art, the present invention provides an insurance card / voucher lifecycle data tracking and management system, which has the following beneficial effects:

[0023] 1. In this invention, when tracking insurance card lifecycle data in real time, the lifecycle event recording unit and distributed acquisition unit in the data acquisition module collect multi-source card data in real time, and the data preprocessing unit standardizes the data format. This enables real-time identification of information loss and distortion caused by transmission delays and interface anomalies, ensuring the accuracy and integrity of card status data and reducing data acquisition errors.

[0024] 2. In this invention, when conducting risk assessment of insurance vouchers, the risk level of the voucher is quantified by the risk indicator calculation unit in the risk assessment module, and external environmental variables and user behavior data are integrated by the multi-dimensional analysis unit. The risk model parameters are dynamically updated by the adaptive learning unit of the optimization management module. This allows for real-time dynamic correction of the voucher risk level based on changing factors, reducing the lag and misjudgment of risk warnings, and ensuring the timeliness and accuracy of risk assessment.

[0025] 3. In this invention, when performing full-cycle data integration and analysis of insurance coupons, the feature extraction unit in the data processing module uses time-series features to construct sub-units to analyze the lifecycle stages of coupons, and utilizes the visualization rendering unit of the report generation module to support cross-channel data association and in-depth mining. This enables multi-regional tiered data detection, reduces coupon data comparison errors, and improves the accuracy and decision-making effectiveness of coupon lifecycle management. Attached Figure Description

[0026] Figure 1 This is a diagram illustrating the architecture of an insurance card / voucher lifecycle data tracking and management system according to the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Please see Figure 1 The insurance card / voucher lifecycle data tracking and management system includes:

[0029] The data acquisition module uses the card and coupon information acquisition unit to collect basic attribute data of insurance cards and coupons, records card and coupon creation, activation, usage and expiration events in real time through the life cycle event recording unit, and outputs standardized collected data through the data preprocessing unit.

[0030] The data processing module receives standardized collected data, removes outliers through the data cleaning unit, analyzes coupon usage patterns through the feature extraction unit, and outputs integrated processed data through the data fusion unit.

[0031] The tracking and monitoring module receives and integrates data, monitors the card lifecycle status in real time through the status tracking unit, triggers abnormal alarms through the threshold warning unit, and outputs real-time monitoring data through the data update unit.

[0032] The risk assessment module receives real-time monitoring data, quantifies the risk level of coupons through the risk indicator calculation unit, outputs the risk assessment results through the multi-dimensional analysis unit, and generates risk control suggestions through the risk feedback unit.

[0033] The report generation module integrates risk assessment results with real-time monitoring data, allows users to customize the output format through the report template configuration unit, and generates dynamic lifecycle reports using the visualization rendering unit.

[0034] The optimization management module receives risk assessment results, optimizes data tracking strategies through the strategy adjustment unit, iteratively improves the risk model through the adaptive learning unit, and outputs system tuning instructions through the parameter optimization unit.

[0035] The data acquisition module also includes a distributed acquisition unit, which is used to collect lifecycle data of different insurance card types in parallel through multiple nodes. The card information acquisition unit specifically includes an optical character recognition subunit and an application programming interface (API) interface subunit. The optical character recognition subunit parses paper and electronic card image data, while the API interface interface subunit directly connects to the insurance business system to obtain digital card transaction data.

[0036] The feature extraction unit in the data processing module further includes a behavior pattern analysis subunit and a time-series feature construction subunit. The behavior pattern analysis subunit identifies abnormal user behavior based on coupon usage frequency and geographic distribution data. The time-series feature construction subunit encodes coupon events into feature vectors according to time series and classifies coupon lifecycle stages through clustering algorithms. The specific implementation is as follows:

[0037] First, this subunit acquires standardized card event sequence data from the data acquisition module and converts discrete events into continuous time series based on time window sliding technology;

[0038] Next, a feature encoding method is used to transform the time series data into numerical feature vectors. By calculating the mean, variance, and peak statistics of the event intervals and introducing a time decay factor to give higher weight to recent events, a multidimensional feature vector is constructed. Its mathematical expression can be represented as a feature vector. Each component Corresponding to a time series feature;

[0039] Then, a clustering algorithm is applied to perform unsupervised learning on these feature vectors, identifying natural groupings in the data by minimizing the intra-class distance objective function:

[0040] ;

[0041] Where j is the objective function, i is the starting index for summation, k is the upper limit of the summation, and x is a data point. Let i be the set of all data points of class i. As cluster center;

[0042] Finally, based on the clustering results, coupons are divided into typical lifecycle stages, and by interacting with the risk assessment module, the stage boundary thresholds are dynamically optimized, thereby achieving a refined classification of coupon behavior throughout its entire lifecycle.

[0043] The status tracking unit in the tracking and monitoring module uses blockchain technology to record card status change logs to ensure that the data is tamper-proof. The threshold warning unit dynamically adjusts the warning threshold, and its threshold setting is adaptively updated based on historical card expiration data and real-time risk assessment results.

[0044] The specific implementation method of the status tracking unit using blockchain technology to record card status change logs is as follows:

[0045] First, when the status of the coupon changes, the data acquisition module sends standardized change event data to the status tracking unit. The unit then calls the built-in smart contract, which predefines the rules for coupon status transition. After verifying that the current change conforms to the business logic, the smart contract packages the current change data, the hash value of the previous status record, and the current timestamp to generate a block candidate data package to be uploaded to the blockchain.

[0046] Next, the system reaches a consensus on the validity of the data packet among network nodes through the selected consensus mechanism. After consensus is reached, the data packet is formally encapsulated into a new block. The key technical step is that the system calculates the cryptographic hash value of all transaction data in the block using the SHA-256 algorithm, represented as follows:

[0047] ;

[0048] Where H is the output hash value, and SHA-256 is the secure hash algorithm -256 bits. For transaction data;

[0049] This hash value is then written together with the hash value of the previous block into the header of the new block. This hash pointer linking method ensures that any tampering with historical block data will cause the hash values ​​of all subsequent blocks to become invalid, thus guaranteeing the log's immutability.

[0050] In terms of data structure, the system typically uses Merkle trees to efficiently organize multiple card status change records within a block. The Merkle tree root hash is stored in the block header, and its calculation formula can be abstractly represented as:

[0051] ;

[0052] Where RootHush is the Merkle root hash, and MerkleHush is the Merkle hash function. Represents the change of transaction in Article j;

[0053] This enables the rapid verification of the existence of a specific transaction in a block with logarithmic time complexity. After the transaction is recorded on the blockchain, the tracking and monitoring module and the risk assessment module can verify the authenticity and completeness of any card / voucher status history in real time through the query interface.

[0054] The risk assessment module's risk indicator calculation unit calculates the probability of coupon fraud and the lifecycle deviation index. The multidimensional analysis unit integrates geographic information system data and social network analysis data, and uses a graph neural network model to detect coupon-related risks. The specific implementation process is as follows:

[0055] First, the graph structure is constructed. The system abstracts each coupon, each user, each sales channel, and each geographical location entity as a "node" in the graph, and the interaction relationships between entities as "edges" in the graph, thus forming a heterogeneous information graph:

[0056] G=(B,E);

[0057] Where G represents a heterogeneous information graph, B is a set of nodes, and E is a set of edges. Each node and edge can be assigned rich feature attributes.

[0058] Secondly, in graph neural network modeling and training, the multidimensional analysis unit uses a graph convolutional network model to perform deep learning on the constructed graph structure. The core of the model is to update the node representation by aggregating neighbor information through a "message passing" mechanism. A typical graph convolutional layer operation can be formally represented as:

[0059] ;

[0060] in, It is the adjacency matrix with self-joins, where K is the original adjacency matrix. It is the identity matrix. yes The degree matrix, This step performs normalization, making the training process more stable. It is the first The feature representation matrix of all nodes in the layer, It is the trainable parameter matrix of this layer. It is a non-linear activation function. Indicates the number of layers in a neural network;

[0061] By stacking multiple layers of such graph convolutional layers, the model can achieve a final representation of each card node. It not only contains its own attributes, but also encodes the topological information and features of its multi-hop neighbors. This representation will then be fed into a classifier to predict the probability that the coupon has associated risks.

[0062] ;

[0063] Where p is a scalar probability value, typically ranging from 0 to 1. For classifier function, This represents the final feature representation of node v;

[0064] Finally, regarding risk identification and feedback, the model uses historically labeled risky coupon data for supervised learning during the training phase to learn to identify high-risk association patterns.

[0065] The report generation module also includes an interactive query unit that allows users to customize query conditions to generate reports in real time. The visualization rendering unit supports 3D charts and heat maps to display the distribution of coupons and risk hotspots, and sends real-time alert reports to administrators through the mobile push sub-unit.

[0066] The strategy adjustment unit in the optimization management module uses a reinforcement learning algorithm to optimize the tracking strategy with the reward function of extending the coupon's lifespan and reducing risk. The specific implementation process is as follows:

[0067] First, the system clearly defines the key elements of reinforcement learning. The agent is the policy adjustment unit itself, and its interaction environment is the operational state of the entire card and coupon management system, including real-time data of all cards and coupons, risk status, and the external market environment. At each decision time t, the agent observes a state. This state is a vector that integrates multi-dimensional information, including but not limited to: the comprehensive risk score of the coupon output by the risk assessment module, the current lifecycle stage of the coupon identified by the sub-units constructed from time-series features, historical usage patterns, network density, and remaining value. Based on this state, the agent moves from the action space... The algorithm selects one action to execute, which includes specific management instructions such as "increase monitoring frequency", "trigger secondary verification", "adjust reimbursement permissions", "send promotional notifications", and "maintain default policy". The core driver of the algorithm is a carefully designed reward function. Its design directly addresses the dual business objectives of "extending the coupon's lifespan" and "reducing risk," and the function is typically constructed as a weighted form:

[0068] ;

[0069] in, It is a lifecycle extension reward. It is the cost of risk. and It's a hyperparameter. For state, Let t be the action space;

[0070] The goal of an intelligent agent is to learn a policy. To maximize the expected value of future cumulative rewards ,in Here, d is the discount factor, d is the index, and L is the expectation. The policy adjustment unit typically employs advanced deep reinforcement learning algorithms, such as deep deterministic policy gradient and proximal policy optimization, to train a neural network parameterized policy function. Initially, the system uses historical card management data and decision records for offline pre-training. After deployment, it switches to online learning mode, transferring the new state transition tuple. Store the data in the experience replay pool and continuously update the policy network parameters;

[0071] Ultimately, a well-trained policy network can adjust its strategy based on the real-time input of the card status. Output the optimal management action These optimization instructions are output to the tracking and monitoring module and the risk assessment module to dynamically adjust the monitoring threshold, verification process and resource allocation, thereby forming an intelligent closed loop of "perception-decision-feedback-optimization".

[0072] The adaptive learning unit periodically retrains the risk model, updating the model parameters with newly added coupon data. The entire implementation process includes the following key stages:

[0073] First, there is the data preparation and validation phase. When the retraining cycle is triggered, the adaptive learning unit will extract a training set from the data storage module that integrates historical baseline data and recently added coupon data. This dataset needs to undergo rigorous validation, including data consistency checks and feature distribution stability detection. This phase ensures the quality of the data used for retraining.

[0074] Secondly, the core model retraining phase begins. This phase doesn't simply involve training from scratch with new data; instead, it employs incremental learning and fine-tuning strategies to improve efficiency and avoid catastrophic forgetting. Specifically, the system loads the risk model currently used in the production environment as the initial state, and then continues training the model using a prepared mixed dataset. The training process typically uses gradient-based optimization algorithms, and its parameter update rules can be expressed as:

[0075] ;

[0076] in, These are the newly obtained model parameters. For the risk model used in the current production environment, It's the learning rate. It is the gradient of the loss function with respect to the model parameters. Represents newly added data. It is historical data sampling;

[0077] Next comes the model validation and evaluation phase, where the newly trained model... It will not be deployed immediately. It needs to be evaluated on a separate validation set that is isolated from the training data timestamp. The evaluation metrics include not only traditional accuracy and AUC, but more importantly, business-oriented metrics. Only when the overall performance of the new model is better than or no worse than the current production model will it be pushed to the next stage.

[0078] Finally, in the security deployment and version management phase, the validated new model will smoothly replace the old model using shadow mode and blue-green deployment strategy. In shadow mode, the new model processes real-time traffic in parallel, but its output does not directly affect the decision and is only used for the final effect comparison. The switch will be made after it is confirmed to be correct.

[0079] The system also includes a data storage module that connects all modules and provides data persistence services. The data storage module adopts a distributed database architecture, partitions the storage of historical and real-time data for different insurance card types, and reduces storage space usage through a data compression unit.

[0080] The data storage module further includes a data encryption unit and an access control unit. The data encryption unit uses homomorphic encryption technology to protect sensitive card information, and the access control unit restricts the scope of data access based on a role-based access control model.

[0081] The system integrates an external data interface module to connect to third-party credit scoring systems and insurance regulatory platforms. The external data interface module verifies external data through a data verification unit and combines external data with internal card and coupon data using a data fusion unit.

[0082] The operation steps of an insurance card / voucher lifecycle data tracking and management system are as follows:

[0083] Step 1: Real-time acquisition and standardization of multi-source heterogeneous data:

[0084] The system's operation begins with the comprehensive collection of coupon data. The data acquisition module, through its distributed acquisition unit, acquires coupon information from multiple nodes in parallel from different sources. The optical character recognition sub-unit under the coupon information acquisition unit is responsible for processing images of paper and electronic coupons, converting them into structured text data. Meanwhile, the application programming interface (API) interface sub-unit directly interconnects with the insurance business system, pulling real-time transaction and status change data of digital coupons. Subsequently, the lifecycle event recording unit marks key moments of coupon creation, activation, use, and expiration. All these raw data are cleaned, formatted, and standardized by the data preprocessing unit, outputting unified standardized collected data, laying the foundation for subsequent processing and solving the problems of information loss and distortion caused by transmission delays and interface differences.

[0085] Step 2: Deep Data Processing and Behavioral Feature Extraction

[0086] After receiving standardized data, the data processing module enters the deep processing stage. The data cleaning unit first uses rules and algorithm models to remove outliers and noisy data to ensure data quality. Then, the feature extraction unit begins its core work: its behavior pattern analysis subunit identifies abnormal user behavior based on the frequency of coupon usage and geographical distribution. Its time series feature construction subunit encodes a series of events of the coupon into multi-dimensional feature vectors according to time series and uses clustering algorithms to automatically classify the life cycle stage of the coupon. Finally, the data fusion unit integrates the feature information from different dimensions into a high-quality integrated processing data.

[0087] Step 3: Real-time status monitoring and dynamic early warning based on blockchain:

[0088] The tracking and monitoring module is responsible for continuously tracking coupons. The status tracking unit uses blockchain technology to encapsulate each coupon status change as a transaction. After verification by a smart contract, its hash value is calculated and stored in an immutable distributed ledger to ensure data authenticity and traceability. The threshold warning unit no longer uses a fixed threshold, but dynamically adjusts the warning line based on historical failure data and real-time risk assessment results. Once the monitoring data reaches the threshold, the system immediately triggers an anomaly alarm through the data update unit and outputs real-time monitoring data, realizing the transformation from passive response to proactive warning.

[0089] Step 4: Intelligent assessment of associated risks using graph neural networks:

[0090] The risk assessment module is the intelligent core of the system. The risk indicator calculation unit first quantifies the fraud probability and lifecycle deviation of individual coupons. Then, the multi-dimensional analysis unit integrates external data from geographic information systems and social network analysis to construct a heterogeneous information graph with coupons, users, and channels as nodes and relationships as edges. The graph neural network model aggregates the neighbor information of nodes in the graph through a message passing mechanism to deeply mine the group risks hidden in the complex network of relationships. Finally, the risk feedback unit generates specific risk control suggestions, realizing a leap from point-like risk identification to network-like risk insight.

[0091] Step 5: Interactive Visual Report Generation and Decision Support

[0092] The report generation module presents the data processing results intuitively, the report template configuration unit allows users to customize the report format according to their needs, the interactive query unit supports users to customize query conditions in real time and dynamically generate analysis results, and the visualization rendering unit uses advanced technologies such as 3D charts and heat maps to display the distribution of coupons and risk hotspots in a three-dimensional and visual way. It also delivers key alarm reports to administrators in a timely manner through the mobile terminal push sub-unit, providing efficient support for management decisions.

[0093] Step 6: Continuous policy optimization and model self-evolution based on reinforcement learning:

[0094] The optimization management module enables the system to self-evolve. The strategy adjustment unit adopts a reinforcement learning algorithm with a reward function that has the dual objectives of "extending the life cycle of coupons" and "reducing risk". This guides the system to learn the optimal tracking strategy autonomously in its interaction with the environment. At the same time, the adaptive learning unit regularly uses newly added coupon data to incrementally train and fine-tune the risk model, and updates the model parameters in a timely manner to ensure that the model performance continues to optimize with business development, forming an intelligent closed loop of "decision-feedback-learning-optimization".

[0095] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0096] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An insurance card / voucher lifecycle data tracking and management system, characterized in that, include: The data acquisition module uses the card and coupon information acquisition unit to collect basic attribute data of insurance cards and coupons, records card and coupon creation, activation, usage and expiration events in real time through the life cycle event recording unit, and outputs standardized collected data through the data preprocessing unit. The data processing module receives the standardized collected data, removes outliers through the data cleaning unit, analyzes the coupon usage pattern through the feature extraction unit, and outputs integrated processed data through the data fusion unit. The tracking and monitoring module receives the integrated processing data, monitors the card lifecycle status in real time through the status tracking unit, triggers abnormal alarms using the threshold warning unit, and outputs real-time monitoring data through the data update unit. The risk assessment module receives the real-time monitoring data, quantifies the risk level of the coupon through the risk indicator calculation unit, outputs the risk assessment results through the multi-dimensional analysis unit, and generates risk control suggestions through the risk feedback unit. The report generation module integrates the risk assessment results with real-time monitoring data, customizes the output format through the report template configuration unit, and generates a dynamic lifecycle report using the visualization rendering unit. The optimization management module receives the risk assessment results, optimizes the data tracking strategy through the strategy adjustment unit, iteratively improves the risk model through the adaptive learning unit, and outputs system tuning instructions through the parameter optimization unit. The strategy adjustment unit in the optimization management module uses a reinforcement learning algorithm to optimize the tracking strategy with the reward function of extending the coupon's lifespan and reducing risk. The specific implementation process is as follows: The agent is the policy adjustment unit itself, and its interaction environment is the operational state of the entire card and coupon management system, including real-time data of all cards and coupons, risk status, and external market environment. At each decision time t, the agent observes a state. This state is a vector that integrates multi-dimensional information, including: the comprehensive risk score of the coupon output by the risk assessment module, the current lifecycle stage of the coupon identified by the sub-units constructed from time-series features, historical usage patterns, correlation network density, and remaining value. Based on this state, the agent moves from the action space... Select one action to execute, including specific management instructions such as "increase monitoring frequency", "trigger two-factor authentication", "adjust revocation permissions", "send activity notification" and "keep default policy". Reward function: ; in, It is a lifecycle extension reward. It is the cost of risk. and It's a hyperparameter. For state, Let t be the action space; The adaptive learning unit periodically retrains the risk model, updating the model parameters using newly added coupon data.

2. The insurance card / voucher lifecycle data tracking and management system according to claim 1, characterized in that: The data acquisition module also includes a distributed acquisition unit, which is used to collect lifecycle data of different insurance card types in parallel through multiple nodes. The card information acquisition unit specifically includes an optical character recognition subunit and an application programming interface (API) interface subunit. The optical character recognition subunit parses paper and electronic card image data, and the API interface interface subunit directly connects to the insurance business system to obtain digital card transaction data.

3. The insurance card / voucher lifecycle data tracking and management system according to claim 1, characterized in that: The feature extraction unit in the data processing module further includes a behavior pattern analysis subunit and a time-series feature construction subunit. The behavior pattern analysis subunit identifies abnormal user behavior based on coupon usage frequency and geographical distribution data. The time-series feature construction subunit encodes coupon events into feature vectors according to time series and classifies coupon lifecycle stages through clustering algorithms.

4. The insurance card / voucher lifecycle data tracking and management system according to claim 1, characterized in that: The status tracking unit in the tracking and monitoring module uses blockchain technology to record card status change logs to ensure that the data is tamper-proof. The threshold warning unit dynamically adjusts the warning threshold, and its threshold setting is adaptively updated based on historical card expiration data and real-time risk assessment results.

5. The insurance card / voucher lifecycle data tracking and management system according to claim 1, characterized in that: The risk assessment module includes a risk indicator calculation unit that calculates the probability of coupon fraud and the lifecycle deviation index. The multidimensional analysis unit integrates geographic information system data and social network analysis data, and uses a graph neural network model to detect coupon-related risks.

6. The insurance card / voucher lifecycle data tracking and management system according to claim 1, characterized in that: The report generation module also includes an interactive query unit, which allows users to customize query conditions to generate reports in real time. The visualization rendering unit supports 3D charts and heat maps to display the distribution of coupons and risk hotspots, and sends real-time alarm reports to administrators through the mobile terminal push sub-unit.

7. The insurance card / voucher lifecycle data tracking and management system according to claim 1, characterized in that: The system also includes a data storage module that connects all modules and provides data persistence services. The data storage module adopts a distributed database architecture, partitions the storage of historical and real-time data for different insurance card types, and reduces storage space usage through a data compression unit.

8. The insurance card / voucher lifecycle data tracking and management system according to claim 7, characterized in that: The data storage module further includes a data encryption unit and an access control unit. The data encryption unit uses homomorphic encryption technology to protect sensitive card information, and the access control unit restricts the scope of data access based on a role-based access control model.

9. The insurance card / voucher lifecycle data tracking and management system according to claim 1, characterized in that: The system integrates an external data interface module, which is used to connect a third-party credit scoring system and an insurance regulatory platform. The external data interface module verifies external data through a data verification unit and combines external data with internal card and coupon data using a data fusion unit.

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