Automated reimbursement process system based on dynamic policy configuration

Abstract

This invention belongs to the field of electronic digital data processing technology. It discloses an automated reconciliation process system based on dynamic strategy configuration, comprising: acquiring reconciliation interaction information, extracting scenario features and deconstructing strategy elements to form a strategy element knowledge base; parsing voucher identifier codes, performing multi-dimensional voucher attribute deconstruction and type profiling to generate intelligent voucher profiles; evaluating rule matching degree and tracing strategy sources to lock in applicable strategy rule sets; performing strategy conflict reconciliation analysis and rule priority sorting to generate a reconciliation execution instruction chain; performing traffic peak prediction and resource scheduling orchestration to form a load balancing strategy table; and performing intelligent channel diversion scheduling to establish a reconciliation process execution network. This invention achieves intelligent automation of the reconciliation process, improves system processing efficiency, and can adapt to complex and ever-changing business scenario requirements.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology, and more specifically, to an automated reimbursement process system based on dynamic strategy configuration. Background Technology

[0002] Voucher verification in e-commerce and retail is a crucial aspect of enterprise marketing and financial management, especially in promotional activities, membership points, and rebate systems, where it holds significant economic value. Traditional verification processes primarily rely on manual review or simple rule matching. However, these methods require substantial professional personnel and are prone to system congestion, erroneous verifications, and fraud vulnerabilities under high-concurrency scenarios, resulting in severe losses of enterprise funds and customer trust. With the development of information technology, automated verification systems based on fixed rule engines and preset processes have emerged. However, these systems generally suffer from insufficient adaptability, complex configuration, and high maintenance costs.

[0003] Dynamic policy configuration, as an intelligent business process management method, has been widely used in enterprise information systems in recent years. Dynamic policies can be adjusted in real time according to changes in the business environment during system operation. Through the decoupling and reorganization of policy components, flexible adaptation of business processes is achieved. Research shows that different industries and enterprises exhibit systematic differences in voucher processing logic in reconciliation scenarios due to variations in business rules, regulatory requirements, and market strategies. Although these differences are subtle, they can lead to branch variations in the reconciliation process, thereby affecting system processing efficiency and accuracy.

[0004] Existing technologies struggle to effectively address the diverse scenarios and dynamic rule requirements in verification processes, particularly during complex promotional activities. Verification rules for different types of vouchers, such as coupons and discount codes, often need to consider multiple factors simultaneously, including timeliness, stacking restrictions, channel differences, and risk control thresholds. These rule relationships are extremely complex, and traditional hard-coding methods are ill-suited for flexible adjustments. During peak business periods, such as holiday promotions, system concurrency surges, reaching 10 to 15 times the normal level. Existing statically configured systems struggle to dynamically adjust resource allocation and processing strategies based on load, leading to prolonged system response times or service interruptions. Frequent adjustments to enterprise marketing strategies require real-time updates to verification rules, but traditional system configuration modifications typically involve a complete development-testing-deployment process, failing to meet the demands of rapid business iteration. Furthermore, existing systems lack adaptive identification mechanisms for abnormal transaction patterns. Especially with the emergence of new fraudulent methods, fixed risk control rules quickly become ineffective, severely impacting the automation efficiency and business continuity of the verification process.

[0005] In view of this, the present invention proposes an automated reimbursement process system based on dynamic strategy configuration to solve the above problems. Summary of the Invention

[0006] To overcome the aforementioned shortcomings of the prior art and to achieve the above objectives, the present invention provides the following technical solution: an automated reimbursement process system based on dynamic strategy configuration, comprising:

[0007] Scene perception module: acquires verification interaction information, extracts scene features and deconstructs strategy elements based on the verification interaction information, and forms a strategy element knowledge base;

[0008] Voucher parsing module: Parses the voucher identification code in the reconciliation request; performs multi-dimensional voucher attribute deconstruction based on the voucher identification code, and performs voucher type profiling to generate a voucher intelligent profile;

[0009] Strategy matching module: Based on the strategy element knowledge base, evaluate the rule matching degree of the intelligent profile of vouchers, trace the source of the strategy, and lock the applicable strategy rule set;

[0010] Strategy Analysis Module: Performs strategy conflict reconciliation analysis on the applicable strategy rule set, and then sorts the rules by priority to generate a chain of revocation execution instructions;

[0011] Resource scheduling module: Based on the reconciliation interaction information, it performs traffic peak prediction and resource scheduling orchestration to form a load balancing strategy table;

[0012] The traffic distribution execution module performs intelligent channel traffic distribution and scheduling based on the load balancing strategy table and the reimbursement execution instruction chain, and establishes a reimbursement process execution network.

[0013] The technical effects and advantages of the automated reimbursement process system based on dynamic strategy configuration of this invention are as follows:

[0014] This invention achieves intelligent identification and classification of different reimbursement scenarios through scene awareness and strategy element deconstruction, thereby enhancing the system's scenario adaptability. Through multi-dimensional voucher attribute deconstruction and voucher type profiling, the accuracy and security of reimbursement verification are improved. By evaluating the rule matching degree between the strategy element knowledge base and the intelligent voucher profile, the system can accurately identify the applicable strategy rule set, achieving dynamic adaptation of business rules. This ensures the correct application of multi-dimensional factor reimbursement rules in complex promotional activities, significantly improving the accuracy of reimbursement processing and avoiding mismatches and erroneous reimbursement problems caused by traditional static rules. Through strategy conflict reconciliation analysis and rule priority sorting, the processing risks caused by the complexity of business rules are significantly reduced. Through traffic peak prediction and resource scheduling orchestration, the system ensures stable and efficient operation even in high-concurrency scenarios such as holiday promotions, effectively solving the problem of response delays or service interruptions in traditional systems during peak business periods. Intelligent channel diversion scheduling based on the load balancing strategy table and the reimbursement execution instruction chain improves the system's ability to cope with sudden traffic surges and significantly enhances processing efficiency. By employing channel health assessment and malicious reimbursement attack detection mechanisms, this invention effectively prevents new fraud methods and improves system security and resilience. Through its innovative dynamic policy configuration design, this invention completely solves the problems of insufficient adaptability, complex configuration, and high maintenance costs associated with traditional reimbursement systems. It can adapt to frequent adjustments in enterprise marketing strategies in real time, achieving real-time updates of reimbursement rules without requiring a complete development-testing-deployment process, significantly reducing system maintenance costs and improving business response speed. Thus, it achieves more intelligent, efficient, and secure automated management of the reimbursement process for diverse reimbursement scenarios and dynamic rule requirements. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the automated reimbursement process system based on dynamic strategy configuration according to the present invention;

[0016] Figure 2 This is a schematic diagram of the automated reimbursement process method based on dynamic strategy configuration of the present invention. Detailed Implementation

[0017] 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. Example 1

[0018] Please see Figure 1 As shown, this embodiment of the automated reimbursement process system based on dynamic policy configuration includes:

[0019] Scene perception module: acquires verification interaction information, extracts scene features and deconstructs strategy elements based on the verification interaction information, and forms a strategy element knowledge base;

[0020] Voucher parsing module: Parses the voucher identification code in the reconciliation request; performs multi-dimensional voucher attribute deconstruction based on the voucher identification code, and performs voucher type profiling to generate a voucher intelligent profile;

[0021] Strategy matching module: Based on the strategy element knowledge base, evaluate the rule matching degree of the intelligent profile of vouchers, trace the source of the strategy, and lock the applicable strategy rule set;

[0022] Strategy Analysis Module: Performs strategy conflict reconciliation analysis on the applicable strategy rule set, and then sorts the rules by priority to generate a chain of revocation execution instructions;

[0023] Resource scheduling module: Based on the reconciliation interaction information, it performs traffic peak prediction and resource scheduling orchestration to form a load balancing strategy table;

[0024] The traffic distribution execution module performs intelligent channel traffic distribution and scheduling based on the load balancing strategy table and the reimbursement execution instruction chain, and establishes a reimbursement process execution network.

[0025] Preferably, the process involves acquiring verification interaction information, extracting scenario features and deconstructing strategy elements based on this information, and forming a strategy element knowledge base, specifically including:

[0026] Obtain verification interaction information; extract scene features from the verification interaction information to generate scene feature fingerprints; parse business rules based on the verification interaction information to generate a business rule spectrum; mine channel associations from the verification interaction information to obtain a channel association network; deconstruct the scene feature fingerprints into strategy elements based on the channel association network and the business rule spectrum to form a strategy element knowledge base.

[0027] Specifically, the first step is to acquire the verification interaction information. Verification interaction information refers to all interactive data generated during the verification process, including user-submitted verification requests, system-returned verification results, timestamps during the verification process, geographical location information, terminal device information, and user operation sequences. This verification interaction information is collected through system logs, API call records, and user behavior tracking, and stored in the verification interaction database. The verification interaction database employs a distributed storage architecture, supporting high-concurrency read and write operations to ensure that verification interaction information can be acquired and processed in real time.

[0028] Scene feature extraction is performed on reimbursement interaction information to generate scene feature fingerprints. Scene feature extraction is the process of extracting key features that characterize the reimbursement scene from reimbursement interaction information. First, spatiotemporal distribution features are extracted from the reimbursement interaction information to obtain the temporal distribution patterns and geospatial distribution characteristics of reimbursement activities. Temporal distribution features include peak reimbursement periods, average reimbursement frequency, and reimbursement time intervals, obtained through statistical analysis of reimbursement timestamp data. Geospatial distribution features include the popularity and regional concentration of reimbursement locations, obtained through cluster analysis of reimbursement geographic location data.

[0029] A voucher usage density analysis is performed on the spatiotemporal distribution characteristic data to generate a voucher density heatmap. Voucher usage density refers to the number of reimbursement activities occurring within a specific area per unit time. By performing two-dimensional statistics on the time and location data of reimbursement activities, the number of reimbursement activities in different geographical areas during different time periods is calculated, forming a voucher density matrix. After smoothing and normalization, the voucher density matrix is ​​converted into a voucher density heatmap, visually displaying the spatiotemporal distribution intensity of reimbursement activities. Simultaneously, terminal device characteristic indicators are identified from the spatiotemporal distribution characteristic data. Terminal device characteristic indicators include device type, operating system version, network connection method, and device identification code. By parsing and classifying the device information in reimbursement requests, a terminal device feature vector is established to describe the distribution and characteristics of different device types in reimbursement activities. Furthermore, user operation sequence characteristics are measured based on the spatiotemporal distribution characteristic data. User operation sequence characteristics refer to the sequence of user actions before and after a reimbursement activity. By recording user click paths, dwell times, and interaction methods in applications or websites, a user operation sequence model is constructed. The user operation sequence model extracts typical patterns and abnormal behaviors of user operations through sequence coding and behavioral pattern recognition.

[0030] Finally, a comprehensive analysis and feature fusion of the voucher density heatmap, terminal device characteristic indicators, and user operation sequence characteristics are performed to generate a scene feature fingerprint. A scene feature fingerprint is a multi-dimensional feature vector that uniquely identifies a specific reimbursement scenario. Feature fusion employs a weighted feature combination method, assigning different weights to features of different dimensions based on their discriminative power and stability to form a unified feature representation. The scene feature fingerprint is stored in a feature fingerprint database, serving as the basis for scene recognition and policy matching.

[0031] Based on the reconciliation interaction information, business rules are parsed to generate a business rule hierarchy. Business rules refer to the various business conditions and restrictions that must be followed during the reconciliation process. Business rule parsing first extracts rule-related keywords and conditional statements from the reconciliation interaction information, and then converts them into structured rule representations using natural language processing technology. The rule representation adopts a "condition-action" format, where the condition describes the context in which the rule applies, and the action describes the operation to be performed when the condition is met. The business rule hierarchy is a hierarchical structure organized according to attributes such as scope of application, priority, and source, forming an inheritance and association diagram between rules.

[0032] Channel correlation mining is performed on reimbursement interaction information to obtain a channel correlation network. Channel correlation mining refers to analyzing the relationships and interaction patterns between different reimbursement channels. First, channel identifiers and channel flow records are extracted from the reimbursement interaction information to construct a channel interaction matrix. The matrix elements represent the interaction frequency and direction between two channels. Then, a correlation rule mining algorithm is used to analyze strong correlation rules between channels, identifying frequently co-occurring channel combinations and channel conversion patterns. Finally, based on the channel interaction matrix and correlation rules, a channel correlation network graph is constructed. In the graph, nodes represent reimbursement channels, and edges represent the correlation strength and flow direction between channels.

[0033] Based on the channel association network and business rule hierarchy, the scenario feature fingerprint is deconstructed into strategy elements to form a strategy element knowledge base. Strategy elements are the basic building blocks of reimbursement strategies, including conditional elements, action elements, constraint elements, and priority elements. The strategy element deconstruction process extracts strategy elements applicable to specific scenarios by analyzing scenario feature fingerprints and combining the business rule hierarchy and channel association network. Specifically, the scenario feature fingerprint is first decomposed into atomic features; then, a mapping relationship is established between rule conditions and atomic features in the business rule hierarchy to identify the rule set relevant to the specific scenario; next, the applicability and variability of rules in different channels are analyzed based on the channel association network; finally, the rules are decomposed into strategy elements, including triggering conditions, execution actions, constraint conditions, and priority settings. The strategy element knowledge base is stored using a graph database, supporting fast querying and dynamic updates, providing a foundation for subsequent strategy matching and configuration.

[0034] Preferably, the voucher identifier code in the reimbursement request is parsed; based on the voucher identifier code, multi-dimensional voucher attributes are deconstructed, and a voucher type profile is created to generate a voucher intelligent profile, specifically including:

[0035] Parse the voucher identifier code in the reimbursement request; deconstruct the voucher attributes in multiple dimensions based on the voucher identifier code to generate a voucher attribute structure diagram; perform timeliness verification analysis on the voucher identifier code to generate a timeliness assessment value; determine the usage permission boundary based on the voucher identifier code; and create a voucher type profile based on the voucher attribute structure diagram, timeliness assessment value, and usage permission boundary to generate a voucher intelligent profile.

[0036] Specifically, the first step is to parse the voucher identification code in the reimbursement request. The voucher identification code is a unique code used to identify the reimbursement voucher, containing information such as voucher type, issuer, scope of use, and validity period. Voucher identification codes typically use specific encoding rules, such as QR codes, barcodes, or alphanumeric codes. The voucher identification code parsing process includes three steps: code format identification, content extraction, and verification. Code format identification determines the encoding method of the voucher identification code, such as EAN-13 or QR code; content extraction parses the information of each field contained in the voucher identification code according to the code format rules; the verification step verifies the validity and integrity of the voucher identification code through check bits or cryptographic methods.

[0037] Based on the voucher identifier code, multi-dimensional voucher attributes are deconstructed to generate a voucher attribute structure diagram. Voucher attributes are various parameters and characteristics describing the voucher's features, including basic attributes, business attributes, and extended attributes. Basic attributes include voucher ID, type, name, and face value; business attributes include applicable scenarios, usage conditions, and reimbursement rules; extended attributes include promotional information and additional services. Voucher attribute deconstruction is the process of extracting these attribute information from the voucher identifier code and constructing an attribute structure diagram based on the relationships between attributes. The attribute structure diagram is represented using a tree structure or a graphical structure, where nodes represent attributes and edges represent the relationships between attributes.

[0038] Next, a timeliness verification analysis is performed on the voucher identification code to generate a timeliness assessment value. The timeliness verification analysis is the process of assessing the current validity status and remaining validity period of the voucher. First, the validity period information, including the start and end times, is extracted from the voucher identification code. Then, it is compared with the current system time to calculate the voucher's timeliness status and remaining validity period. Finally, based on the timeliness status and remaining validity period, the timeliness assessment value is calculated. The timeliness assessment value is a value between 0 and 1, representing the voucher's timeliness status; 0 indicates expired, 1 indicates it is in the middle of its validity period, and the middle value indicates it is close to the validity period boundary. Simultaneously, the usage permission boundaries are determined based on the voucher identification code. The usage permission boundaries define the scope and conditions under which the voucher can be used, including applicable stores, applicable products, and applicable users. The usage permission boundary determination process first extracts permission-related information from the voucher identification code, then queries the permission configuration database to obtain detailed permission rules, and finally generates a permission boundary description. The permission boundary description uses a multi-dimensional vector representation, with each dimension corresponding to a permission type, and the vector value representing the scope and level of the permission.

[0039] Finally, based on the voucher attribute structure diagram, timeliness assessment value, and usage permission boundaries, a voucher type profile is created, generating a voucher intelligent profile. The voucher intelligent profile is a comprehensive description of various characteristics of a voucher and is the foundation for the system's understanding and processing of vouchers. The voucher intelligent profile construction process first converts the voucher attribute structure diagram, timeliness assessment value, and usage permission boundaries into feature vectors; then, according to a predefined profile template, the feature vectors are mapped to profile dimensions; finally, the voucher intelligent profile is generated. The voucher intelligent profile consists of three layers: a basic information layer, a business characteristic layer, and a behavioral characteristic layer. The basic information layer describes the voucher's basic attributes, the business characteristic layer describes the voucher's business rules and usage conditions, and the behavioral characteristic layer describes the voucher's usage patterns and historical performance.

[0040] Preferably, the rule matching degree of the intelligent profile of the voucher is evaluated based on the strategy element knowledge base, and the source of the strategy is traced to identify the applicable strategy rule set, specifically including:

[0041] The rule matching degree of the voucher intelligent profile is evaluated based on the strategy element knowledge base, and the rule matching weight data is marked. Multiple strategy coordination analysis is performed on the rule matching weight data to generate a strategy coordination index. Applicable strategy rules are screened based on the strategy coordination index to obtain candidate strategy rule groups. The candidate strategy rule groups are prioritized and the reimbursement strategy execution sequence chain is extracted. Based on the reimbursement strategy execution sequence chain, the source of the strategy is traced to lock the applicable strategy rule set.

[0042] Specifically, the first step is to evaluate the rule matching degree of the intelligent credential profile based on the strategy element knowledge base, and then assign rule matching weight data. Rule matching degree evaluation is the process of calculating the degree of matching between the intelligent credential profile and each strategy rule in the strategy element knowledge base. The evaluation process uses a feature vector similarity calculation method to match the feature vector of the intelligent credential profile with the rule condition vector. The matching degree calculation considers three cases: exact match, range match, and fuzzy match, each assigned a different weight. An exact match refers to a feature value that is completely identical to the rule condition, and has the highest weight; a range match refers to a feature value that falls within the range defined by the rule condition, and has the second highest weight; a fuzzy match refers to a feature value that partially overlaps with or is similar to the rule condition, and has the lowest weight. The rule matching weight data is a mapping table that records the matching degree score and matching type between the intelligent credential profile and each rule in the knowledge base.

[0043] A multi-strategy coordination analysis is performed on rule matching weight data to generate a strategy coordination index. This analysis evaluates the compatibility and synergistic effects among multiple strategy rules. First, a set of rules with matching degrees exceeding a threshold is selected based on the rule matching weight data. Then, the relationships between rules within the set are analyzed, including complementary, conflicting, and independent relationships. Finally, the coordination index of the rule set is calculated. The strategy coordination index is a comprehensive indicator reflecting the degree of coordination within the rule set and its external adaptability. The calculation of the coordination index considers three aspects: rule compatibility, coverage, and execution efficiency. Compatibility represents the degree of conflict between rules, coverage represents the extent to which the rule set covers the business scenario, and execution efficiency represents the complexity and resource consumption of rule execution.

[0044] Next, applicable policy rules are filtered based on the policy coordination index to obtain a candidate policy rule group. Applicable policy rule filtering is the process of selecting the subset of rules most suitable for the current voucher and scenario from the matching rule set. The filtering process first sorts the rule set according to the policy coordination index; then, it determines the optimal number of rules based on business needs and system resource status; finally, it selects the subset of rules with the highest coordination index as the candidate policy rule group. The candidate policy rule group is a set of mutually coordinated policy rules suitable for the current scenario and will be used for subsequent reimbursement processing.

[0045] Next, the candidate policy rule groups are prioritized, and the reimbursement policy execution order chain is extracted. Prioritization is the process of determining the execution order of rules based on their importance, urgency, and dependencies. First, priority attributes are extracted from the rule metadata, including rule level, effective time, and rule source. Then, a rule dependency graph is constructed based on the dependencies between rules, ensuring that dependencies do not lead to circular dependencies. Finally, the priority attributes and dependencies are combined to generate the reimbursement policy execution order chain. The reimbursement policy execution order chain is an ordered list that defines the execution order and conditions of each rule in the candidate policy rule group.

[0046] Finally, the source of the reimbursement policy is traced based on the execution sequence chain to identify the applicable policy rule set. Policy source tracing is the process of tracking the origin and scope of application of policy rules. First, rule identifiers are extracted from the reimbursement policy execution sequence chain; then, the policy management system is queried to obtain detailed information about the rules, including the rule creator, creation time, modification history, and scope of application; finally, based on the rule's source and scope of application, the validity and authority of the rule are confirmed, and the applicable policy rule set is identified. The applicable policy rule set is the set of valid rules confirmed through source tracing and will be used for subsequent reimbursement process execution.

[0047] Preferably, the applicable policy rule set is subjected to policy conflict reconciliation analysis, and then the rule priority is sorted to generate a revocation execution instruction chain, specifically including:

[0048] The applicable policy rule set is subjected to policy conflict reconciliation analysis to extract multiple conflicting policy points; based on the multiple conflicting policy points, write-off risk is assessed to generate write-off risk warning values; based on the write-off risk warning values, urgency is classified and adaptive scheduling decisions are made to generate an adaptive scheduling scheme; the policy coverage is defined according to the multiple conflicting policy points, and multiple policy boundary intersections are marked; based on the adaptive scheduling scheme, the applicable policy rule set and multiple policy boundary intersections are sorted by rule priority to generate a write-off execution instruction chain.

[0049] Specifically, the first step is to perform a strategy conflict reconciliation analysis on the applicable policy rule set, extracting multiple conflicting policy points. Policy conflict reconciliation analysis is the process of identifying and resolving conflicts and inconsistencies within the rule set. First, the rules in the applicable policy rule set are compared pairwise to check for conflicts between rule conditions and execution actions. Conflict types include direct conflict (opposite actions), indirect conflict (actions and results mutually influence each other), and conditional conflict (overlapping conditions but different actions). Then, the discovered conflicts are classified and marked, forming a list of conflicting policy points. A conflicting policy point is a specific location or combination of conditions in the rule set where a conflict exists; each conflicting policy point contains information such as the conflict type, the rules involved, and the severity of the conflict.

[0050] Based on conflicting policy points, write-off risk assessment is performed to generate a write-off risk warning value. Write-off risk assessment is the process of evaluating the potential business and system risks caused by policy conflicts. First, the impact scope of each conflicting policy point is analyzed, including affected business processes, user groups, and system modules. Then, the potential risks caused by the conflict are assessed, including business interruption risk, data inconsistency risk, and user experience risk. Finally, a comprehensive risk warning value is calculated. The write-off risk warning value is a risk level indicator, representing the comprehensive risk level of the policy conflict, and is divided into three levels: low risk, medium risk, and high risk.

[0051] Next, based on the write-off risk warning value, an urgency level is determined, and an adaptive scheduling decision is made to generate an adaptive scheduling plan. The urgency level determination is the process of determining processing priorities based on the risk warning value and business needs. Urgency is divided into three levels: routine processing, priority processing, and emergency processing. The adaptive scheduling decision is the process of formulating resource allocation and task scheduling strategies based on the urgency level and system resource status. The scheduling decision considers system load, resource availability, and task priority, and uses an adaptive scheduling algorithm to dynamically allocate computing resources and processing time. The adaptive scheduling plan includes task allocation strategies, resource configuration schemes, and scheduling schedules, providing scheduling guidance for conflict resolution and rule enforcement.

[0052] The policy coverage is defined based on conflict policy points, and multiple policy boundary intersections are marked. Defining policy coverage is the process of determining the condition space and business scope to which each rule applies. First, rule conditions are represented as regions in a multi-dimensional condition space; then, the overlap of condition regions from different rules is analyzed to identify region intersections; finally, policy boundary intersections are marked. Policy boundary intersections are the locations where multiple rule condition spaces intersect, representing the boundary conditions where multiple rules apply simultaneously. Marking policy boundary intersections helps to accurately locate rule conflicts and rule application boundaries.

[0053] Based on an adaptive scheduling scheme, the applicable policy rule set and policy boundary intersections are prioritized to generate a reimbursement execution instruction chain. Rule priority prioritization is the process of finalizing the rule execution order based on the scheduling scheme. First, resource allocation and execution timing are determined according to the adaptive scheduling scheme; then, rule priorities are adjusted based on urgency levels and conflict resolution strategies; next, policy boundary intersections are processed to determine rule selection strategies under boundary conditions; finally, a rule execution instruction sequence is generated. The reimbursement execution instruction chain is a sequence of instructions containing complete execution logic, defining the rule execution order, condition judgment logic, and action execution methods, and serves as a direct guide for the automated execution of the reimbursement process.

[0054] Preferably, based on the reconciliation interaction information, traffic peak prediction and resource scheduling are performed to form a load balancing strategy table, specifically including:

[0055] The system extracts multi-time period traffic features from the verification interaction information, extracting traffic distribution parameters for multiple time windows; it then performs peak and trough identification analysis on the traffic distribution parameters for multiple time windows to generate traffic fluctuation features; based on the traffic fluctuation features, it calculates server response time changes and plots response time change trends; it predicts traffic peaks based on response time change trends to obtain traffic peak warning points; and it performs resource scheduling and orchestration based on traffic peak warning points to form a load balancing strategy table.

[0056] Specifically, the first step is to extract multi-time-period traffic features from the reconciliation interaction information, extracting traffic distribution parameters for multiple time windows. Multi-time-period traffic feature extraction is the process of analyzing the traffic variation characteristics of the reconciliation system over different time periods. First, the reconciliation interaction information is sorted by time series and segmented into time windows of different granularities (e.g., minutes, hours, days). Then, traffic statistics parameters within each time window are calculated, including the number of requests, data transmission volume, and concurrent connections. Finally, traffic distribution parameters, such as average traffic, peak traffic, and traffic variance, are extracted. These traffic distribution parameters reflect the temporal distribution characteristics and variation patterns of the system load.

[0057] Then, peak and trough identification analysis is performed on the flow distribution parameters across multiple time windows to generate flow fluctuation characteristics. Peak and trough identification analysis is the process of detecting significant peaks and troughs in the flow sequence. First, smoothing is applied to the flow time series to reduce the impact of random fluctuations; then, peak detection methods are used to identify local maxima (peaks) and local minima (troughs); finally, the distribution characteristics of peaks and troughs are analyzed, including periodicity, amplitude variation, and duration. Flow fluctuation characteristics are a quantitative description of the system's flow change pattern, including parameters such as the temporal regularity, amplitude distribution, and duration of peaks and troughs.

[0058] Next, server response time variations are calculated based on traffic fluctuation characteristics, and a trend chart of these variations is plotted. Server response time variation calculation is a process of predicting system performance changes based on traffic variations. First, a model is established showing the relationship between traffic and response time, illustrating how system response time changes with traffic. Then, traffic fluctuation characteristic data is input into the model to calculate the expected response time under different traffic conditions. Finally, a response time trend chart is plotted, displaying the predicted trend of response time with time and traffic variations. The response time trend chart is used to evaluate system performance under different loads and identify potential performance bottlenecks and service quality degradation points.

[0059] Then, traffic peak prediction is performed on the response time change trend to obtain traffic peak warning points. Traffic peak prediction is the process of predicting future traffic peaks based on historical traffic data and current trends. First, a time series prediction model is built based on historical traffic data, which considers seasonal, trend, and periodic factors. Then, the sliding window method is used to predict traffic for a future period. Finally, traffic peak warning points are identified based on the prediction results. The traffic peak warning point is the predicted time when the traffic will exceed the system's capacity threshold, and includes information such as the warning time, expected traffic, and duration.

[0060] Finally, resource scheduling and orchestration are performed based on traffic peak warning points to form a load balancing strategy table. Resource scheduling and orchestration is the process of formulating system resource allocation and load balancing strategies based on traffic prediction results. First, based on traffic peak warning points, the time periods and resource types requiring additional resources are determined; then, dynamic resource expansion and contraction strategies are formulated, including automatic scaling rules, load balancing configurations, and request routing strategies; finally, a load balancing strategy table is generated. The load balancing strategy table is a time-series configuration table that defines the resource configuration and load balancing strategies the system should adopt at different points in time, including parameters such as the number of server instances, load balancer configuration, queue depth limits, and request timeout settings.

[0061] Preferably, intelligent channel routing and scheduling are performed based on the load balancing strategy table and the reimbursement execution instruction chain to establish a reimbursement process execution network, specifically including:

[0062] Extract real-time verification channel status parameters and verification queue length parameters; evaluate the channel health of the real-time verification channel status parameters to generate a channel health index; perform health fluctuation analysis on the channel health index to obtain a channel status fluctuation map; perform multi-channel queuing trend analysis on the verification queue length parameter to extract a channel congestion index; perform malicious verification attack detection based on the channel status fluctuation map and channel congestion index; when an abnormal verification channel is detected, perform global rate limiting on verification requests to obtain an anomaly protection strategy; perform intelligent channel traffic distribution and scheduling based on the verification execution instruction chain, load balancing strategy table, and anomaly protection strategy to establish a verification process execution network.

[0063] Specifically, the real-time verification channel status parameters and verification queue length parameters are extracted first. The real-time verification channel status parameters are a set of indicators describing the current operating status of the verification processing channel, including channel throughput, processing latency, error rate, and resource utilization. The verification queue length parameters are indicators describing the number of requests waiting to be processed in each verification queue, including the current queue length, average waiting time, and queue growth rate. These parameters are collected in real time by the system monitoring module, providing a real-time snapshot of the verification system's operating status.

[0064] Then, a channel health assessment is performed on the real-time verification channel status parameters to generate a channel health index. Channel health assessment is a process of comprehensively analyzing channel status parameters to evaluate the channel's operational health. First, each status parameter is normalized, converting indicators with different dimensions into a unified scoring standard. Then, weighting coefficients are set according to the importance and impact of the parameters. Finally, a weighted average score is calculated to obtain the channel health index. The channel health index is a comprehensive indicator reflecting the overall operational status of the channel, divided into three levels: healthy, warning, and dangerous.

[0065] Health fluctuation analysis is performed on the channel health index to obtain a channel status fluctuation map. Health fluctuation analysis is the process of studying the characteristics of channel health status changes over time. First, time series data of the health index is constructed; then, the rate of change and fluctuation amplitude of health are calculated; finally, the change patterns and trend characteristics of health are identified. The channel status fluctuation map is a graphical representation of the channel health status change pattern, including information such as health change trend, fluctuation cycle, and outlier markers, used to monitor the stability of channel status and predict potential failure risks. Simultaneously, multi-channel queuing trend analysis is performed on the reimbursement queue length parameter to extract the channel congestion index. Multi-channel queuing trend analysis is the process of studying the queue change characteristics of different reimbursement channels. First, time series data of queue length for each channel are collected; then, the change trend and growth rate of queue length are analyzed; finally, the degree of queue backlog and processing capacity are assessed. The channel congestion index is an indicator of channel load; it is calculated as the ratio of queue length to processing capacity, representing the time required for queue processing. A higher congestion index indicates greater channel congestion.

[0066] Then, malicious repurchase attack detection is performed based on the channel status fluctuation map and channel congestion index. When an abnormal state of the repurchase channel is detected, global rate limiting is applied to repurchase requests, resulting in an anomaly protection strategy. Malicious repurchase attack detection is the process of identifying abnormal repurchase request patterns and suspicious attack behaviors. First, a baseline model of normal repurchase behavior is established; then, the deviations of the current channel status and queue status from the baseline model are compared; finally, the degree and characteristics of the deviation are used to determine whether a malicious attack exists. The anomaly protection strategy is a set of protective measures developed for detected anomalies, including request rate limiting rules, IP blacklists, abnormal request interception, and resource protection strategies.

[0067] Finally, intelligent channel routing and scheduling are performed based on the reimbursement execution instruction chain, load balancing strategy table, and anomaly protection strategy to establish the reimbursement process execution network. Intelligent channel routing and scheduling is the process of rationally allocating reimbursement requests to different processing channels according to system status and policy requirements. Specifically, intelligent channel routing and scheduling first prioritizes the reimbursement execution instruction chain and extracts key reimbursement instructions; identifies available channels based on the load balancing strategy table to form a channel resource pool; classifies the channel resource pool into high-speed channels, standard channels, and backup channels; reserves high-speed channels based on the priority of key reimbursement instructions; sets channel access thresholds based on the anomaly protection strategy and performs request rate limiting control; constructs a dynamic routing table to allocate reimbursement requests to the optimal execution channel according to request type, priority, and channel load status; and monitors the execution status in real time and dynamically adjusts the channel allocation strategy based on execution feedback.

[0068] The reconciliation process execution network is a dynamic request processing network composed of multiple processing channels, routing nodes, and a scheduling controller. The execution network features adaptive adjustment, load balancing, and fault isolation, enabling it to dynamically adjust the processing paths and resource allocation for reconciliation requests based on system status and business needs, ensuring the efficient and stable execution of the reconciliation process.

[0069] This embodiment achieves intelligent identification and classification of different reimbursement scenarios through scene awareness and strategy element deconstruction, thereby enhancing the system's scenario adaptability. Multi-dimensional voucher attribute deconstruction and voucher type profiling improve the accuracy and security of reimbursement verification. Through rule matching evaluation between the strategy element knowledge base and the intelligent voucher profile, the system can accurately identify the applicable strategy rule set, achieving dynamic adaptation of business rules. This ensures the correct application of multi-dimensional reimbursement rules in complex promotional activities, significantly improving the accuracy of reimbursement processing and avoiding mismatches and erroneous reimbursement problems caused by traditional static rules. Strategy conflict reconciliation analysis and rule priority sorting significantly reduce the processing risks caused by the complexity of business rules. Traffic peak prediction and resource scheduling orchestration ensure the system maintains stable and efficient operation even in high-concurrency scenarios such as holiday promotions, effectively solving the problem of response delays or service interruptions in traditional systems during peak business periods. Intelligent channel diversion scheduling based on the load balancing strategy table and the reimbursement execution instruction chain improves the system's ability to handle sudden traffic surges and significantly enhances processing efficiency. By employing channel health assessment and malicious reimbursement attack detection mechanisms, this invention effectively prevents new fraud methods and improves system security and resilience. Through its innovative dynamic policy configuration design, this invention completely solves the problems of insufficient adaptability, complex configuration, and high maintenance costs associated with traditional reimbursement systems. It can adapt to frequent adjustments in enterprise marketing strategies in real time, achieving real-time updates of reimbursement rules without requiring a complete development-testing-deployment process, significantly reducing system maintenance costs and improving business response speed. Thus, it achieves more intelligent, efficient, and secure automated management of the reimbursement process for diverse reimbursement scenarios and dynamic rule requirements. Example 2

[0070] Please see Figure 2 As shown, parts not described in detail in this embodiment are described in Embodiment 1. A method for automating the reimbursement process based on dynamic policy configuration is provided, including:

[0071] Step S1: Obtain the verification interaction information, extract scene features and deconstruct strategy elements based on the verification interaction information, and form a strategy element knowledge base;

[0072] Step S2: Parse the voucher identifier code in the reconciliation request; deconstruct the voucher attributes in multiple dimensions based on the voucher identifier code, and create a voucher type profile to generate a voucher intelligent profile;

[0073] Step S3: Evaluate the rule matching degree of the intelligent profile of the voucher based on the strategy element knowledge base, trace the source of the strategy, and lock the applicable strategy rule set;

[0074] Step S4: Perform policy conflict reconciliation analysis on the applicable policy rule set, and then sort the rules by priority to generate a revocation execution instruction chain;

[0075] Step S5: Based on the verification interaction information, perform traffic peak prediction and resource scheduling orchestration to form a load balancing strategy table;

[0076] Step S6: Perform intelligent channel routing and scheduling based on the load balancing strategy table and the reimbursement execution instruction chain to establish a reimbursement process execution network.

[0077] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0078] It should be noted that, in this document, 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. Unless otherwise specified, 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.

[0079] In the description of this invention, it should be understood that the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0080] In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0081] In the description of this invention, "several" means one or more, and "a large number" means two or more.

[0082] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0083] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0084] Although embodiments of the invention have been shown and described, those skilled in the art will understand 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 claims and their equivalents.

Claims

1. An automated reimbursement process system based on dynamic strategy configuration, characterized in that, include: Scene perception module: acquires verification interaction information, extracts scene features and deconstructs strategy elements based on the verification interaction information, and forms a strategy element knowledge base; Voucher parsing module: Parses the voucher identification code in the reconciliation request; performs multi-dimensional voucher attribute deconstruction based on the voucher identification code, and performs voucher type profiling to generate a voucher intelligent profile; Strategy matching module: Based on the strategy element knowledge base, evaluate the rule matching degree of the intelligent profile of vouchers, trace the source of the strategy, and lock the applicable strategy rule set; Strategy Analysis Module: Performs strategy conflict reconciliation analysis on the applicable strategy rule set, and then sorts the rules by priority to generate a chain of revocation execution instructions; Resource scheduling module: Based on the reconciliation interaction information, it performs traffic peak prediction and resource scheduling orchestration to form a load balancing strategy table; Traffic splitting and execution module: intelligently splits and schedules traffic according to the load balancing strategy table and the reimbursement execution instruction chain, and establishes a reimbursement process execution network; The step of establishing an intelligent channel routing and scheduling system based on the load balancing strategy table and the reimbursement execution instruction chain, and establishing a reimbursement process execution network, includes: Extract real-time verification channel status parameters and verification queue length parameters; evaluate the channel health of the real-time verification channel status parameters to generate a channel health index; perform health fluctuation analysis on the channel health index to obtain a channel status fluctuation map; perform multi-channel queuing trend analysis on the verification queue length parameter to extract a channel congestion index; perform malicious verification attack detection based on the channel status fluctuation map and channel congestion index; when an abnormal verification channel is detected, perform global rate limiting on verification requests to obtain an anomaly protection strategy; perform intelligent channel traffic splitting and scheduling based on the verification execution instruction chain, load balancing strategy table, and anomaly protection strategy to establish a verification process execution network; The specific methods for intelligent channel diversion and scheduling include: Prioritize the rewrite execution instruction chain and extract key rewrite instructions; identify available channels based on the load balancing strategy table to form a channel resource pool; classify the channel resource pool into high-speed channels, standard channels, and backup channels; reserve and configure high-speed channels according to the priority of key rewrite instructions; set channel access thresholds based on anomaly protection strategies and implement request rate limiting control; construct a dynamic routing table to allocate rewrite requests to the best execution channel according to request type, priority, and channel load status; monitor the execution status in real time and dynamically adjust the channel allocation strategy based on execution feedback.

2. The automated reimbursement process system based on dynamic strategy configuration according to claim 1, characterized in that, Obtain verification interaction information, extract scenario features and deconstruct strategy elements based on the verification interaction information, and form a strategy element knowledge base, including: Obtain verification interaction information; extract scene features from the verification interaction information to generate scene feature fingerprints; parse business rules based on the verification interaction information to generate a business rule spectrum; mine channel associations from the verification interaction information to obtain a channel association network; deconstruct the scene feature fingerprints into strategy elements based on the channel association network and the business rule spectrum to form a strategy element knowledge base.

3. The automated reimbursement process system based on dynamic strategy configuration according to claim 2, characterized in that, The specific methods for extracting scene features from the verification interaction information to generate scene feature fingerprints include: The spatiotemporal distribution features of the verification interaction information are extracted, and all spatiotemporal distribution feature data are extracted; the voucher usage density analysis is performed on the spatiotemporal distribution feature data to generate a voucher density heatmap; the terminal device feature indicators of the spatiotemporal distribution feature data are identified; the user operation sequence features are measured based on the spatiotemporal distribution feature data; and scene feature extraction is performed on the voucher density heatmap, terminal device feature indicators, and user operation sequence features to generate a scene feature fingerprint.

4. The automated reimbursement process system based on dynamic strategy configuration according to claim 1, characterized in that, Parse the voucher identifier code in the reimbursement request; deconstruct the voucher attributes based on the voucher identifier code, and create a voucher type profile to generate a voucher intelligent profile, including: Parse the voucher identifier code in the reimbursement request; deconstruct the voucher attributes in multiple dimensions based on the voucher identifier code to generate a voucher attribute structure diagram; perform timeliness verification analysis on the voucher identifier code to generate a timeliness assessment value; determine the usage permission boundary based on the voucher identifier code; and create a voucher type profile based on the voucher attribute structure diagram, timeliness assessment value, and usage permission boundary to generate a voucher intelligent profile.

5. The automated reimbursement process system based on dynamic strategy configuration according to claim 1, characterized in that, The rule matching degree of the intelligent profile of vouchers is evaluated based on the strategy element knowledge base, and the source of the strategy is traced to identify the applicable strategy rule set, including: The rule matching degree of the voucher intelligent profile is evaluated based on the strategy element knowledge base, and the rule matching weight data is marked. Multiple strategy coordination analysis is performed on the rule matching weight data to generate a strategy coordination index. Applicable strategy rules are screened based on the strategy coordination index to obtain candidate strategy rule groups. The candidate strategy rule groups are prioritized and the reimbursement strategy execution sequence chain is extracted. Based on the reimbursement strategy execution sequence chain, the source of the strategy is traced to lock the applicable strategy rule set.

6. The automated reimbursement process system based on dynamic strategy configuration according to claim 1, characterized in that, The applicable policy rule set is subjected to policy conflict reconciliation analysis, followed by rule priority sorting to generate an write-off execution instruction chain, including: The applicable policy rule set is subjected to policy conflict reconciliation analysis to extract multiple conflicting policy points; based on the multiple conflicting policy points, write-off risk is assessed to generate write-off risk warning values; based on the write-off risk warning values, urgency is classified and adaptive scheduling decisions are made to generate an adaptive scheduling scheme; the policy coverage is defined according to the multiple conflicting policy points, and multiple policy boundary intersections are marked; based on the adaptive scheduling scheme, the applicable policy rule set and multiple policy boundary intersections are sorted by rule priority to generate a write-off execution instruction chain.

7. The automated reimbursement process system based on dynamic strategy configuration according to claim 6, characterized in that, The specific methods for performing rule priority sorting include: The system extracts precise policy execution instructions based on an adaptive scheduling scheme and records them synchronously in the audit log. Based on the audit log, it performs authorization evaluation to identify the current execution permission level, obtaining the permission execution level. The permission execution level includes primary, intermediate, and advanced levels. Policy execution instructions are issued based on the permission execution level. The policy execution instructions are decoded to obtain the execution scheduling level. When the execution scheduling level is primary: deep semantic deconstruction is performed on the applicable policy rule set and multiple policy boundary intersections to extract a rule semantic association graph. Rule mutual exclusion detection is performed based on the rule semantic association graph, and mutually exclusive rule sets are marked. The mutually exclusive rule sets are sequentially arranged and reconstructed to obtain the primary execution instruction chain. When the execution scheduling level is intermediate: all rule influence domains of the applicable policy rule set and multiple policy boundary intersections are identified. The rule influence domains are processed for regional isolation orchestration. When the execution scheduling level is advanced: emergency write-off is performed.

8. The automated reimbursement process system based on dynamic strategy configuration according to claim 1, characterized in that, Based on the aforementioned verification interaction information, traffic peak prediction and resource scheduling are performed to form a load balancing strategy table, including: The system extracts multi-time period traffic features from the verification interaction information, extracting traffic distribution parameters for multiple time windows; it then performs peak and trough identification analysis on the traffic distribution parameters for multiple time windows to generate traffic fluctuation features; based on the traffic fluctuation features, it calculates server response time changes and plots response time change trends; it predicts traffic peaks based on response time change trends to obtain traffic peak warning points; and it performs resource scheduling and orchestration based on traffic peak warning points to form a load balancing strategy table.

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