Automatic system for cancel-after-verification process based on dynamic strategy configuration

Abstract

The invention belongs to the technical field of electric digital data processing, and discloses a dynamic strategy configuration-based cancel-after-verification process automation system, which comprises the steps of obtaining cancel-after-verification interaction information, performing scene feature extraction and strategy element deconstruction, and forming a strategy element knowledge base; analyzing the voucher identification code, performing multi-dimensional voucher attribute deconstruction and type portrait description, and generating a voucher intelligent portrait; performing rule matching degree evaluation and strategy source tracing, and locking an applicable strategy rule set; performing strategy conflict harmonic analysis and rule priority ranking to generate a cancel-after-verification execution instruction chain; performing flow wave crest prediction and resource scheduling arrangement to form a load balancing strategy table; performing intelligent channel shunting scheduling, and establishing a cancel-after-verification process execution network; according to the invention, the intelligent automation of the cancel-after-verification process is realized, the system processing efficiency is improved, and the method can adapt to complex and changeable business scene requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and more particularly to a write-off process automation system based on dynamic strategy configuration. Background Art

[0002] Voucher verification in the e-commerce and retail industries is a critical component of marketing and financial management, particularly in promotional activities, membership points, and consumer rebate systems, where it holds significant economic value. Traditional verification processes rely primarily on manual review or simple rule matching. However, these methods require significant professional input and are prone to system blockages, incorrect verifications, and fraud vulnerabilities in high-concurrency scenarios, resulting in significant losses of corporate funds and customer trust. With the advancement of information technology, automated verification systems based on fixed rule engines and pre-set processes have emerged, but 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 during system operation based on changes in the business environment. By decoupling and reorganizing policy components, flexible adaptation of business processes is achieved. Research has shown that in write-off scenarios, different industries and enterprises have systematic differences in voucher processing logic due to differences in business rules, regulatory requirements, and market strategies. Although these differences are subtle, they can lead to branching and variation in the write-off process, which in turn affects system processing efficiency and accuracy.

[0004] Existing technologies struggle to effectively address the diverse scenarios and dynamic rule requirements of write-offs. This is especially true in complex promotions, where the write-off rules for different types of vouchers, such as coupons and discount codes, often need to consider multiple factors, including timeliness, overlapping restrictions, channel differences, and risk control thresholds. These rules are extremely complex, making traditional hard-coding methods difficult to flexibly adjust. During peak business periods, such as holiday promotions, the system's concurrent processing volume surges, reaching 10 to 15 times that of normal days. Existing static configuration systems struggle to dynamically adjust resource allocation and processing strategies based on load, resulting in extended system response times or service interruptions. Frequent adjustments to corporate marketing strategies require real-time updates to write-off rules, but traditional systems typically require a complete development-test-deployment process for configuration changes, 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 fraud methods, fixed risk control rules quickly become ineffective, severely impacting the automation efficiency and business continuity of the write-off process.

[0005] In view of this, the present invention proposes a write-off process automation system based on dynamic policy configuration to solve the above problems. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned objectives, the present invention provides the following technical solution: a write-off process automation system based on dynamic policy configuration, comprising: Scenario Perception Module: This module obtains verification and cancellation interaction information, extracts scenario features and deconstructs policy elements based on the verification and cancellation interaction information, and forms a policy element knowledge base. Voucher parsing module: parses the voucher identification code in the write-off request; deconstructs the multi-dimensional voucher attributes based on the voucher identification code, and profiles the voucher type to generate an intelligent voucher profile; Policy matching module: This module evaluates the rule matching degree of the credential intelligent profile based on the policy element knowledge base, traces the policy source, and locks the applicable policy rule set; Policy analysis module: performs policy conflict reconciliation analysis on applicable policy rule sets, and then prioritizes the rules to generate a verification execution instruction chain; Resource scheduling module: performs traffic peak prediction and resource scheduling according to the verification interaction information to form a load balancing strategy table; Diversion execution module: performs intelligent channel diversion scheduling based on the load balancing strategy table and the write-off execution instruction chain, and establishes a write-off process execution network.

[0007] The technical effects and advantages of the automatic write-off process system based on dynamic policy configuration of the present invention are as follows: Through scenario perception and policy element deconstruction, the present invention achieves intelligent identification and classification of different verification scenarios, thereby enhancing the system's scenario adaptability. Multi-dimensional voucher attribute deconstruction and voucher type profiling improve the accuracy and security of verification. By evaluating the rule matching between the policy element knowledge base and the intelligent voucher profiling, the system can accurately identify the applicable policy rule set, achieve dynamic adaptation of business rules, and ensure the correct application of multi-dimensional verification rules in complex promotional activities. This significantly improves the accuracy of verification processing and avoids the mismatches and incorrect verifications caused by traditional static rules. Through policy conflict reconciliation analysis and rule prioritization, the processing risks associated with the complexity of business rules are significantly reduced. Through traffic peak prediction and resource scheduling, the system ensures stable and efficient operation in high-concurrency scenarios such as holiday promotions, effectively resolving the response delays or service interruptions of traditional systems during peak business periods. Intelligent channel diversion and scheduling based on the load balancing policy table and verification execution instruction chain enhances the system's ability to cope with sudden traffic bursts and significantly improves processing efficiency. Through channel health assessment and malicious write-off attack detection mechanisms, new fraud methods are effectively prevented, improving the system's security and risk resistance. Through the innovative design of dynamic policy configuration, this invention thoroughly solves the problems of traditional write-off systems' lack of adaptability, complex configuration, and high maintenance costs. It can adapt to frequent adjustments to corporate marketing strategies in real time and achieve real-time updates of write-off rules without going through a complete development-testing-deployment process, significantly reducing system maintenance costs and improving business response speed. This enables more intelligent, efficient, and secure automated management of the write-off process, tailored to changing write-off scenarios and dynamic rule requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 Schematic diagram of the write-off process automation system based on dynamic policy configuration of the present invention; Figure 2 This is a schematic diagram of the write-off process automation method based on dynamic policy configuration of the present invention. DETAILED DESCRIPTION

[0009] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. Example 1

[0010] See also Figure 1 As shown, the write-off process automation system based on dynamic policy configuration in this embodiment includes: Scenario Perception Module: This module obtains verification and cancellation interaction information, extracts scenario features and deconstructs policy elements based on the verification and cancellation interaction information, and forms a policy element knowledge base. Voucher parsing module: parses the voucher identification code in the write-off request; deconstructs the multi-dimensional voucher attributes based on the voucher identification code, and profiles the voucher type to generate an intelligent voucher profile; Policy matching module: This module evaluates the rule matching degree of the credential intelligent profile based on the policy element knowledge base, traces the policy source, and locks the applicable policy rule set; Policy analysis module: performs policy conflict reconciliation analysis on applicable policy rule sets, and then prioritizes the rules to generate a verification execution instruction chain; Resource scheduling module: performs traffic peak prediction and resource scheduling according to the verification interaction information to form a load balancing strategy table; Diversion execution module: performs intelligent channel diversion scheduling based on the load balancing strategy table and the write-off execution instruction chain, and establishes a write-off process execution network.

[0011] Preferably, the verification and cancellation interaction information is obtained, and scenario features are extracted and policy elements are deconstructed based on the verification and cancellation interaction information to form a policy element knowledge base, specifically including: Acquire write-off interaction information; extract scenario features from the write-off interaction information to generate a scenario feature fingerprint; parse business rules based on the write-off interaction information to generate a business rule pedigree; perform channel relevance mining on the write-off interaction information to obtain a channel association network; deconstruct the scenario feature fingerprint into strategic elements based on the channel association network and the business rule pedigree to form a strategic element knowledge base.

[0012] Specifically, verification interaction information is first obtained. Verification interaction information refers to all interaction data generated during the verification process, including verification requests submitted by users, verification results returned by the system, verification process timestamps, geographic location information, terminal device information, user operation sequences, and more. Verification interaction information is collected through system logs, API call records, user behavior tracking, and other methods, and stored in the verification interaction database. The verification interaction database uses a distributed storage architecture that supports high-concurrency read and write operations, ensuring that verification interaction information can be obtained and processed in real time.

[0013] Scenario feature extraction is performed on write-off interaction information to generate a scenario feature fingerprint. Scenario feature extraction is the process of extracting key features that characterize the write-off scenario from write-off interaction information. First, the temporal and spatial distribution features of write-off interaction information are extracted to obtain the temporal distribution patterns and geographic spatial distribution characteristics of write-off activities. Temporal distribution features include peak write-off periods, average write-off frequency, and write-off intervals, and are obtained through statistical analysis of write-off timestamp data. Geographic spatial distribution features include the popularity of write-off locations and regional concentration, and are obtained through cluster analysis of write-off geographic location data.

[0014] Voucher usage density analysis is performed on spatiotemporal distribution feature data to generate a voucher density heat map. Voucher usage density refers to the number of redemption activities occurring within a specific area per unit time. By performing two-dimensional statistics on the time and location of redemption activities, the number of redemption 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 heat map, visually displaying the spatiotemporal distribution of redemption activity. Furthermore, terminal device characteristic indicators are identified within the spatiotemporal distribution feature data. These include device type, operating system version, network connection method, and device identification code. By parsing and classifying the device information in redemption requests, a terminal device feature vector is constructed to describe the distribution and characteristics of different device types in redemption activities. Furthermore, user operation sequence characteristics are measured based on the spatiotemporal distribution feature data. User operation sequence characteristics refer to the sequence of user operation behaviors before and after a redemption activity. A user operation sequence model is constructed by recording user click paths, dwell time, and interaction methods within an application or website. The user operation sequence model uses sequence encoding and behavioral pattern recognition to extract typical and abnormal user operation patterns.

[0015] Finally, a comprehensive analysis and feature fusion of the voucher density heat map, terminal device characteristic indicators, and user operation sequence features is performed to generate a scenario feature fingerprint. A scenario feature fingerprint is a multidimensional feature vector that uniquely identifies the feature combination of a specific write-off scenario. Feature fusion uses a weighted feature combination method, assigning different weights to features of different dimensions based on their discriminability and stability to form a unified feature representation. The scenario feature fingerprint is stored in a feature fingerprint library and serves as the basis for scenario identification and policy matching.

[0016] Business rules are parsed based on the write-off interaction information to generate a business rule genealogy. Business rules refer to the various business conditions and restrictions that must be followed during the write-off process. Business rule parsing first extracts rule-related keywords and conditional statements from the write-off interaction information and converts them into a structured rule representation using natural language processing technology. Rules are represented in a "condition-action" format, with the condition describing the context in which the rule applies and the action describing the action to be performed when the condition is met. The business rule genealogy organizes related business rules into a hierarchical structure based on their scope of application, priority, source, and other attributes, forming a diagram of inheritance and association relationships between rules.

[0017] Channel relevance mining is performed on write-off interaction information to obtain a channel relevance network. Channel relevance mining involves analyzing the relationships and interaction patterns between different write-off channels. First, channel identifiers and channel flow records are extracted from the write-off interaction information to construct a channel interaction matrix. Matrix elements represent the frequency and direction of interaction between two channels. Next, an association rule mining algorithm is used to analyze strong association rules between channels, identifying frequently co-occurring channel combinations and channel switching patterns. Finally, based on the channel interaction matrix and association rules, a channel relevance network diagram is constructed. Nodes in the diagram represent write-off channels, and edges represent the strength and direction of associations between channels.

[0018] Based on the channel association network and business rule pedigree, the scenario feature fingerprint is deconstructed into policy elements to form a policy element knowledge base. Policy elements are the basic components of the write-off strategy, including condition elements, action elements, constraint elements, and priority elements. The policy element deconstruction process extracts policy elements applicable to specific scenarios by analyzing the scenario feature fingerprint and combining the business rule pedigree and channel association network. The specific method is to first perform feature decomposition on the scenario feature fingerprint, breaking down the composite features into atomic features; then, a mapping relationship is established between the rule conditions in the business rule pedigree and the atomic features to identify the rule set related to the specific scenario; then, the applicability and changes of the rules in different channels are analyzed based on the channel association network; finally, the rules are decomposed into policy elements, including trigger conditions, execution actions, constraints, and priority settings. The policy element knowledge base is stored in a graph database, supporting fast queries and dynamic updates, providing a foundation for subsequent policy matching and configuration.

[0019] Preferably, the voucher identification code in the cancellation request is parsed; multi-dimensional voucher attribute deconstruction is performed based on the voucher identification code, and a voucher type profile is performed to generate a voucher intelligent profile, specifically including: Parse the voucher identification code in the cancellation request; perform multi-dimensional voucher attribute deconstruction based on the voucher identification code to generate a voucher attribute structure diagram; perform timeliness inspection and analysis on the voucher identification code to generate a timeliness evaluation value; determine the usage permission boundary based on the voucher identification code; perform voucher type portrait based on the voucher attribute structure diagram, timeliness evaluation value and usage permission boundary to generate a voucher intelligent portrait.

[0020] Specifically, the voucher identification code in the verification request is first parsed. The voucher identification code is a code used to uniquely identify the verification voucher, and contains information such as the voucher type, issuer, scope of use, and validity period. The voucher identification code usually adopts specific encoding rules, such as a QR code, barcode, or alphanumeric combination code. The voucher identification code parsing process includes three steps: code recognition, content extraction, and verification. Code recognition determines the encoding method of the voucher identification code, such as EAN-13, QR code, etc.; content extraction parses the information of each field contained in the voucher identification code according to the code rules; the verification step verifies the validity and integrity of the voucher identification code through check bits or cryptographic methods.

[0021] Based on the voucher identification code, multi-dimensional voucher attribute deconstruction is performed to generate a voucher attribute structure diagram. Voucher attributes are the various parameters and characteristics that describe the characteristics of a voucher, 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 verification rules; and extended attributes include promotional information and additional services. Voucher attribute deconstruction is the process of extracting this attribute information from the voucher identification code and constructing an attribute structure diagram based on the relationships between the attributes. The attribute structure diagram is represented using a tree structure or a graph structure, with nodes representing attributes and edges representing the relationships between attributes.

[0022] Next, the credential identification code undergoes a timeliness check and analysis to generate a timeliness assessment value. Timeliness check and analysis evaluates the credential's current validity status and remaining validity period. First, validity period information, including the start and end times, is extracted from the credential identification code. This information is then compared with the current system time to calculate the credential's timeliness status and remaining validity period. Finally, a timeliness assessment value is calculated based on the timeliness status and remaining validity period. The timeliness assessment value is a value between 0 and 1, representing the credential's timeliness status. 0 indicates expired, 1 indicates the credential is in the middle of its validity period, and intermediate values ​​indicate a state close to the end of its validity period. Simultaneously, the usage permission boundary is determined based on the credential identification code. The usage permission boundary defines the scope and conditions under which the credential can be used, including applicable stores, applicable products, and applicable users. The usage permission boundary determination process first extracts permission-related information from the credential identification code, then queries the permission configuration database to obtain detailed permission rules. Finally, a permission boundary description is generated. The permission boundary description is represented as a multidimensional vector, with each dimension corresponding to a permission type, and the vector value representing the scope and level of the permission.

[0023] Finally, based on the credential attribute structure diagram, timeliness evaluation value, and usage permission boundaries, the credential type is profiled to generate a credential intelligent profile. The credential intelligent profile is a comprehensive description of all aspects of the credential and is the basis for the system to understand and process the credential. The credential intelligent profile construction process first uniformly converts the credential attribute structure diagram, timeliness evaluation value, and usage permission boundaries into feature vectors; then, based on the predefined profile template, the feature vectors are mapped to the profile dimensions; finally, the credential intelligent profile is generated. The credential intelligent profile consists of three layers: the basic information layer, the business feature layer, and the behavioral feature layer. The basic information layer describes the basic attributes of the credential, the business feature layer describes the business rules and usage conditions of the credential, and the behavioral feature layer describes the usage pattern and historical performance of the credential.

[0024] Preferably, the rule matching degree of the credential intelligent profile is evaluated based on the policy element knowledge base, and the policy source is traced to lock the applicable policy rule set, including: Based on the policy element knowledge base, the rule matching degree of the credential intelligent portrait is evaluated and the rule matching weight data is marked; multiple policy coordination analysis is performed on the rule matching weight data to generate a policy coordination index; applicable policy rules are screened based on the policy coordination index to obtain candidate policy rule groups; candidate policy rule groups are prioritized and the cancellation policy execution sequence chain is extracted; the policy source is traced based on the cancellation policy execution sequence chain to lock the applicable policy rule set.

[0025] Specifically, first, the rule matching degree of the credential intelligent profile is evaluated based on the policy element knowledge base, and the rule matching weight data is marked. The rule matching degree evaluation is the process of calculating the matching degree between the credential intelligent profile and each policy rule in the policy element knowledge base. The evaluation process adopts the feature vector similarity calculation method to match the feature vector of the credential intelligent profile with the rule condition vector. The matching degree calculation considers three situations: exact matching items, range matching items, and fuzzy matching items, and assigns different weights to each. Exact matching items refer to situations where the feature value is completely consistent with the rule condition, and have the highest weight; range matching items refer to situations where the feature value falls within the range defined by the rule condition, and have the second highest weight; fuzzy matching items refer to situations where the feature value partially overlaps or is similar to the rule condition, and have the lowest weight. The rule matching weight data is a mapping table that records the matching score and matching type between the credential intelligent profile and each rule in the knowledge base.

[0026] A multi-strategy coordination analysis is performed on the rule matching weight data to generate a strategy coordination index. Multi-strategy coordination analysis is the process of evaluating the compatibility and synergy between multiple strategy rules. First, the rule set with a matching degree exceeding the threshold is screened out based on the rule matching weight data; then, the relationship between each rule in the rule set is analyzed, including complementary relationships, conflicting relationships, and independent relationships; finally, the coordination index of the rule set is calculated. The strategy coordination index is a comprehensive indicator that reflects the degree of coordination within the rule set and the degree of external adaptation. The coordination index calculation considers three aspects: compatibility, coverage, and execution efficiency between rules. Compatibility indicates the degree of conflict between rules, coverage indicates the degree of coverage of the rule set for business scenarios, and execution efficiency indicates the complexity and resource consumption of rule execution.

[0027] Next, applicable policy rules are screened based on the policy coordination index to obtain a candidate policy rule group. This screening process involves selecting the subset of rules from the matching rule set that best suits the current credential and scenario. The screening process first sorts the rule set based on the policy coordination index. Then, the optimal number of rules is determined based on business needs and system resources. Finally, the subset of rules with the highest coordination index is selected as the candidate policy rule group. This candidate policy rule group is a set of coordinated and appropriate policy rules for the current scenario and will be used for subsequent verification processing.

[0028] Next, the candidate policy rule groups are prioritized and the policy execution sequence chain is extracted. Prioritization determines the order of rule execution based on their importance, urgency, and dependencies. First, priority attributes, including rule level, effective time, and rule source, are extracted from rule metadata. Then, based on the dependencies between rules, a rule dependency graph is constructed to ensure that dependencies do not result in circular dependencies. Finally, the priority attributes and dependencies are combined to generate the policy execution sequence chain. The policy execution sequence chain is an ordered list that defines the execution order and conditions for each rule in the candidate policy rule group.

[0029] Finally, based on the policy execution chain, the policy source is traced and the applicable policy rule set is locked. Policy source tracing is the process of tracking the source and scope of application of policy rules. First, the rule identifier is extracted from the policy execution chain. Then, the policy management system is queried to obtain detailed information about the rule, including the rule creator, creation time, modification history, and scope of application. Finally, based on the rule source and scope of application, the validity and authority of the rule is confirmed, and the applicable policy rule set is locked. The applicable policy rule set is a valid set of rules that has been traced and confirmed and will be used for subsequent execution of the redemption process.

[0030] Preferably, a policy conflict reconciliation analysis is performed on the applicable policy rule set, and then a rule priority sorting process is performed to generate a cancellation execution instruction chain, specifically including: Perform policy conflict reconciliation analysis on the applicable policy rule set and extract multiple conflicting policy points; perform write-off risk assessment based on multiple conflicting policy points to generate a write-off risk warning value; determine urgency levels based on the write-off risk warning value and make adaptive scheduling decisions to generate an adaptive scheduling plan; define policy coverage based on multiple conflicting policy points and mark multiple policy boundary intersections; perform rule priority sorting on the applicable policy rule set and multiple policy boundary intersections based on the adaptive scheduling plan to generate a write-off execution instruction chain.

[0031] Specifically, policy conflict reconciliation analysis is first performed on the applicable policy rule set to extract 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 within the applicable policy rule set are compared pairwise to check for conflicts between rule conditions and execution actions. Conflict types include direct conflicts (opposite actions), indirect conflicts (actions that affect each other), and conditional conflicts (overlapping conditions but different actions). Discovered conflicts are then categorized and labeled to form a list of conflicting policy points. A conflicting policy point is a specific location or combination of conditions within the rule set where a conflict occurs. Each conflicting policy point contains information such as the conflict type, the rules involved, and the severity of the conflict.

[0032] Based on the conflicting policy points, a write-off risk assessment is performed to generate a write-off risk warning value. Write-off risk assessment is the process of evaluating the business and system risks that may result from policy conflicts. First, the scope of impact of each conflicting policy point is analyzed, including the affected business processes, user groups, and system modules. Next, the potential risks resulting from 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 that represents the overall risk level of the policy conflict and is categorized into three levels: low risk, medium risk, and high risk.

[0033] Next, based on the risk warning value, an urgency classification is determined, and adaptive scheduling decisions are made to generate an adaptive scheduling plan. Urgency classification is the process of determining processing priorities based on risk warning values ​​and business needs. Urgency is categorized into three levels: routine processing, priority processing, and emergency processing. Adaptive scheduling decisions are the process of developing resource allocation and task scheduling strategies based on the urgency level and system resource availability. Scheduling decisions take into account system load, resource availability, and task priority, using an adaptive scheduling algorithm to dynamically allocate computing resources and processing time. The adaptive scheduling plan includes a task allocation strategy, resource allocation plan, and scheduling timetable, providing scheduling guidance for conflict resolution and rule execution.

[0034] Policy coverage is defined based on conflicting policy points, and multiple policy boundary intersections are marked. Policy coverage definition is the process of determining the conditional space and business scope within which each rule applies. First, rule conditions are represented as regions in a multidimensional conditional space. Then, the overlap of conditional regions across different rules is analyzed to identify region intersections. Finally, policy boundary intersections are marked. Policy boundary intersections are where multiple rule condition spaces intersect, representing boundary conditions where multiple rules apply simultaneously. Marking policy boundary intersections helps pinpoint rule conflicts and rule application boundaries.

[0035] Based on the adaptive scheduling scheme, rule prioritization is performed on the applicable policy rule set and policy boundary intersections to generate a write-off execution instruction chain. Rule prioritization is the process of finalizing the rule execution order based on the scheduling scheme. Prioritization first determines resource allocation and execution timing based on the adaptive scheduling scheme; then, rule priorities are adjusted based on urgency levels and conflict resolution strategies; then, policy boundary intersections are processed to determine the rule selection strategy under boundary conditions; finally, a rule execution instruction sequence is generated. The write-off execution instruction chain is an instruction sequence that contains complete execution logic, defining the rule execution order, conditional judgment logic, and action execution method, and directly guides the automated execution of the write-off process.

[0036] Preferably, traffic peak prediction and resource scheduling are performed based on the verification interaction information to form a load balancing strategy table, which specifically includes: The verification interaction information is subjected to multi-period traffic feature extraction to extract traffic distribution parameters of multiple time windows; the traffic distribution parameters of multiple time windows are subjected to peak and trough identification and analysis to generate traffic fluctuation characteristics; the server response time change is measured based on the traffic fluctuation characteristics, and the response time change trend is plotted; the response time change trend is subjected to traffic peak prediction to obtain traffic peak warning points; resource scheduling and orchestration are performed based on the traffic peak warning points to form a load balancing strategy table.

[0037] Specifically, we first extract multi-period traffic features from the verification interaction information and extract traffic distribution parameters for multiple time windows. Multi-period traffic feature extraction analyzes the changing characteristics of verification system traffic within different time periods. First, verification interaction information is sorted by time series and segmented into time windows of varying granularity (such as minutes, hours, and days). Then, traffic statistics are calculated within each time window, including the number of requests, data transfer volume, and number of 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 changing patterns of system load.

[0038] 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 a 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. The flow fluctuation characteristics quantitatively describe the system flow variation pattern, including parameters such as the timing of peaks and troughs, amplitude distribution, and duration.

[0039] Next, based on the traffic fluctuation characteristics, the server response time variation is measured and the response time variation trend is plotted. Server response time variation measurement is the process of predicting system performance changes based on traffic changes. First, a relationship model between traffic and response time is established. This model represents how system response time varies with traffic. Then, the traffic fluctuation characteristic data is input into the model to calculate the expected response time under different traffic conditions. Finally, a response time variation trend chart is plotted, showing the predicted trend of response time over time and traffic. The response time variation trend chart is used to evaluate system performance under different loads and identify possible performance bottlenecks and service quality degradation points.

[0040] Next, traffic peaks are predicted based on the response time trend to determine peak traffic 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 constructed based on historical traffic data, taking into account seasonal, trend, and cyclical factors. Then, a sliding window method is used to predict traffic for a specific period of time. Finally, based on the prediction results, peak traffic warning points are identified. Peak traffic warning points are predicted to exceed the system's capacity threshold and include information such as the warning time, expected traffic volume, and duration.

[0041] Finally, resource scheduling and orchestration are performed based on traffic peak warning points to form a load balancing policy table. Resource scheduling and orchestration is the process of developing system resource allocation and load balancing strategies based on traffic forecast results. First, based on the traffic peak warning points, the time period and resource type requiring additional resources are determined. Then, a dynamic resource expansion and contraction strategy is developed, including automatic scaling rules, load balancing configuration, and request routing strategies. Finally, a load balancing policy table is generated. The load balancing policy table is a time-series configuration table that defines the resource configuration and load balancing strategies that the system should adopt at different time points, including parameters such as the number of server instances, load balancer configuration, queue depth limits, and request timeout settings.

[0042] Preferably, intelligent channel diversion scheduling is performed according to the load balancing strategy table and the write-off execution instruction chain to establish a write-off process execution network, specifically including: Extract real-time cancellation channel status parameters and cancellation queue length parameters; perform channel health assessment on the real-time cancellation 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 cancellation queue length parameters to extract the channel congestion index; perform malicious cancellation attack detection based on the channel status fluctuation map and the channel congestion index; when it is detected that the cancellation channel is in an abnormal state, perform global flow limiting on the cancellation request to obtain an abnormal protection strategy; perform intelligent channel diversion scheduling based on the cancellation execution instruction chain, load balancing strategy table and abnormal protection strategy, and establish a cancellation process execution network.

[0043] Specifically, we first extract real-time verification channel status parameters and verification queue length parameters. These parameters are a set of metrics describing the current operational status of the verification processing channel, including channel throughput, processing latency, error rate, and resource utilization. Verification queue length parameters describe the number of requests awaiting processing 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 operational status.

[0044] Next, a channel health assessment is performed on the real-time verification channel status parameters to generate a channel health index. Channel health assessment is the process of comprehensively analyzing channel status parameters to assess the channel's operational health. First, each status parameter is normalized, converting indicators of different dimensions into a unified scoring standard. Then, weight coefficients are set based on the parameter's importance and impact. Finally, the weighted average score is calculated to generate the channel health index. The channel health index is a comprehensive indicator that reflects the overall operational status of the channel and is categorized into three levels: healthy, warning, and critical.

[0045] Health fluctuation analysis is performed on the channel health index to generate a channel status fluctuation map. Health fluctuation analysis studies the temporal characteristics of channel health. First, time series data for the health index is constructed; then, the rate of change and fluctuation amplitude of the health index are calculated; and finally, the health change patterns and trend characteristics are identified. The channel status fluctuation map is a graphical representation of the channel health status change pattern, containing information such as health change trends, fluctuation cycles, and outlier markers. It is used to monitor channel status stability and predict potential failure risks. Simultaneously, multi-channel queue trend analysis is performed on the redemption queue length parameter to extract the channel congestion index. Multi-channel queue trend analysis studies the changing characteristics of queues in different redemption channels. First, time series data for queue lengths is collected for each channel; then, the changing trends and growth rates of queue lengths are analyzed; and finally, the queue backlog and processing capacity are assessed. The channel congestion index is a measure of channel load. It is calculated as the ratio of queue length to processing capacity and indicates the time required to process the queue. A higher congestion index indicates more congested channels.

[0046] Malicious cancellation attack detection is then performed based on the channel status fluctuation graph and channel congestion index. When an abnormal cancellation channel is detected, global throttling is performed on the cancellation request, resulting in an anomaly protection strategy. Malicious cancellation attack detection is the process of identifying abnormal cancellation request patterns and suspicious attack behaviors. First, a baseline model of normal cancellation behavior is established; then, the current channel status and queue status are compared with the baseline model for deviations; finally, the presence of a malicious attack is determined based on the degree and characteristics of the deviation. Anomaly protection strategies are protective measures developed in response to detected anomalies, including request throttling rules, IP blacklists, abnormal request interception, and resource protection strategies.

[0047] Finally, intelligent channel diversion scheduling is performed based on the write-off execution instruction chain, load balancing policy table, and anomaly protection policy to establish a write-off process execution network. Intelligent channel diversion scheduling is the process of rationally allocating write-off requests to different processing channels based on system status and policy requirements. Specifically, intelligent channel diversion scheduling first prioritizes the write-off execution instruction chain and extracts key write-off instructions; identifies available channels based on the load balancing policy 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 write-off instructions; sets channel access thresholds based on anomaly protection policies and performs request flow control; constructs a dynamic routing table to allocate write-off requests to the best execution channel based on request type, priority, and channel load status; monitors execution status in real time and dynamically adjusts channel allocation policies based on execution feedback.

[0048] The write-off process execution network is a dynamic request processing network consisting of multiple processing channels, routing nodes, and a scheduling controller. Featuring adaptive adjustment, load balancing, and fault isolation, the execution network dynamically adjusts the processing path and resource allocation of write-off requests based on system status and business needs, ensuring efficient and stable execution of the write-off process.

[0049] This embodiment, through scenario perception and policy element deconstruction, enables intelligent identification and classification of different write-off scenarios, thereby enhancing the system's adaptability to these scenarios. Multi-dimensional voucher attribute deconstruction and voucher type profiling improve the accuracy and security of write-off verification. By evaluating the rule matching between the policy element knowledge base and the intelligent voucher profiling, the system accurately identifies the applicable policy rule set, enabling dynamic adaptation of business rules and ensuring the correct application of multi-dimensional write-off rules in complex promotional activities. This significantly improves the accuracy of write-off processing and avoids the mismatches and incorrect write-offs caused by traditional static rules. Policy conflict reconciliation analysis and rule prioritization significantly reduce the processing risks associated with complex business rules. Traffic peak prediction and resource scheduling ensure stable and efficient system operation in high-concurrency scenarios such as holiday promotions, effectively resolving the issues of response delays or service interruptions during peak business periods in traditional systems. Intelligent channel diversion and scheduling based on the load balancing policy table and the write-off execution instruction chain enhances the system's ability to cope with sudden traffic bursts and significantly improves processing efficiency. Through channel health assessment and malicious write-off attack detection mechanisms, new fraud methods are effectively prevented, improving the system's security and risk resistance. Through the innovative design of dynamic policy configuration, this invention thoroughly solves the problems of traditional write-off systems' lack of adaptability, complex configuration, and high maintenance costs. It can adapt to frequent adjustments to corporate marketing strategies in real time and achieve real-time updates of write-off rules without going through a complete development-testing-deployment process, significantly reducing system maintenance costs and improving business response speed. This enables more intelligent, efficient, and secure automated management of the write-off process, tailored to changing write-off scenarios and dynamic rule requirements. Example 2

[0050] See also Figure 2 As shown, for the parts not described in detail in this embodiment, please refer to the description of Example 1. A method for automating the write-off process based on dynamic policy configuration is provided, including: Step S1: Acquire verification interaction information, extract scenario features and deconstruct policy elements based on the verification interaction information, and form a policy element knowledge base; Step S2: Parsing the voucher identification code in the cancellation request; performing multi-dimensional voucher attribute deconstruction based on the voucher identification code, and performing voucher type profiling to generate a voucher intelligent profile; Step S3: Evaluate the rule matching degree of the credential intelligent profile based on the policy element knowledge base, trace the policy source, and lock the applicable policy rule set; Step S4: Perform policy conflict reconciliation analysis on the applicable policy rule set, and then perform rule priority sorting to generate a cancellation execution instruction chain; Step S5: performing traffic peak prediction and resource scheduling according to the verification interaction information to form a load balancing strategy table; Step S6: Perform intelligent channel diversion scheduling according to the load balancing strategy table and the write-off execution instruction chain to establish a write-off process execution network.

[0051] The foregoing 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 will be able to modify the technical solutions described in the foregoing embodiments or to substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

[0052] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0053] In the description of the present invention, it should be understood that the terms "first", "second", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0054] In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0055] In the description of the present invention, “several” means one or more, and “a large number” means two or more.

[0056] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations 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 any one or more embodiments or examples.

[0057] The formulas in this manual are all dimensionless and calculated using numerical values. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field based on actual conditions.

[0058] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. The write-off process automation system based on dynamic policy configuration is characterized by: include: Scenario Perception Module: This module obtains verification and cancellation interaction information, extracts scenario features and deconstructs policy elements based on the verification and cancellation interaction information, and forms a policy element knowledge base. Voucher parsing module: parses the voucher identification code in the write-off request; deconstructs the multi-dimensional voucher attributes based on the voucher identification code, and profiles the voucher type to generate an intelligent voucher profile; Policy matching module: This module evaluates the rule matching degree of the credential intelligent profile based on the policy element knowledge base, traces the policy source, and locks the applicable policy rule set; Policy analysis module: performs policy conflict reconciliation analysis on applicable policy rule sets, and then prioritizes the rules to generate a verification execution instruction chain; Resource scheduling module: performs traffic peak prediction and resource scheduling according to the verification interaction information to form a load balancing strategy table; Diversion execution module: performs intelligent channel diversion scheduling based on the load balancing strategy table and the write-off execution instruction chain, and establishes a write-off process execution network.

2. The write-off process automation system based on dynamic policy configuration according to claim 1 is characterized in that: Obtain verification and cancellation interaction information, extract scenario features and deconstruct policy elements based on the verification and cancellation interaction information, and form a policy element knowledge base, including: Acquire write-off interaction information; extract scenario features from the write-off interaction information to generate a scenario feature fingerprint; parse business rules based on the write-off interaction information to generate a business rule pedigree; perform channel relevance mining on the write-off interaction information to obtain a channel association network; deconstruct the scenario feature fingerprint into strategic elements based on the channel association network and the business rule pedigree to form a strategic element knowledge base.

3. The write-off process automation system based on dynamic policy configuration according to claim 2 is characterized in that: The specific method of extracting scene features from the verification interaction information to generate scene feature fingerprints includes: Perform spatiotemporal distribution feature extraction on the verification interaction information to extract all spatiotemporal distribution feature data; perform voucher usage density analysis on the spatiotemporal distribution feature data to generate a voucher density heat map; identify terminal device feature indicators of the spatiotemporal distribution feature data; measure user operation sequence features based on the spatiotemporal distribution feature data; perform scenario feature extraction on the voucher density heat map, terminal device feature indicators, and user operation sequence features to generate a scenario feature fingerprint.

4. The write-off process automation system based on dynamic policy configuration according to claim 1 is characterized in that: Parse the voucher identification code in the write-off request; perform multi-dimensional voucher attribute deconstruction based on the voucher identification code, and perform voucher type profiling to generate a voucher intelligent profile, including: Parse the voucher identification code in the cancellation request; perform multi-dimensional voucher attribute deconstruction based on the voucher identification code to generate a voucher attribute structure diagram; perform timeliness inspection and analysis on the voucher identification code to generate a timeliness evaluation value; determine the usage permission boundary based on the voucher identification code; perform voucher type portrait based on the voucher attribute structure diagram, timeliness evaluation value and usage permission boundary to generate a voucher intelligent portrait.

5. The write-off process automation system based on dynamic policy configuration according to claim 1 is characterized in that: Evaluate the rule matching degree of the credential intelligent profile based on the policy element knowledge base, trace the policy source, and lock the applicable policy rule set, including: Based on the policy element knowledge base, the rule matching degree of the credential intelligent portrait is evaluated and the rule matching weight data is marked; multiple policy coordination analysis is performed on the rule matching weight data to generate a policy coordination index; applicable policy rules are screened based on the policy coordination index to obtain candidate policy rule groups; candidate policy rule groups are prioritized and the cancellation policy execution sequence chain is extracted; the policy source is traced based on the cancellation policy execution sequence chain to lock the applicable policy rule set.

6. The write-off process automation system based on dynamic policy configuration according to claim 1 is characterized in that: Perform policy conflict reconciliation analysis on the applicable policy rule set, and then prioritize the rules to generate a write-off execution instruction chain, including: Perform policy conflict reconciliation analysis on the applicable policy rule set and extract multiple conflicting policy points; perform write-off risk assessment based on multiple conflicting policy points to generate a write-off risk warning value; determine urgency levels based on the write-off risk warning value and make adaptive scheduling decisions to generate an adaptive scheduling plan; define policy coverage based on multiple conflicting policy points and mark multiple policy boundary intersections; perform rule priority sorting on the applicable policy rule set and multiple policy boundary intersections based on the adaptive scheduling plan to generate a write-off execution instruction chain.

7. The write-off process automation system based on dynamic policy configuration according to claim 6 is characterized in that: The specific method of performing rule priority sorting includes: Extract precise policy execution instructions based on the adaptive scheduling scheme and record them synchronously in the audit log; perform authorization evaluation based on the audit log, identify the current execution authority level, and obtain the authority execution level; the authority execution levels include primary, intermediate, and advanced; issue policy execution instructions based on the authority execution level; decode the policy execution instructions to obtain the execution scheduling level; when the execution scheduling level is primary: perform deep semantic deconstruction of the applicable policy rule set and multiple policy boundary intersections, and extract the rule semantic association graph; perform rule mutual exclusion detection based on the rule semantic association graph, and mark the mutually exclusive rule set; sequentially arrange and reconstruct the mutually exclusive rule set to obtain a primary execution instruction chain; when the execution scheduling level is intermediate: identify all rule impact domains of the applicable policy rule set and multiple policy boundary intersections; perform regional isolation and orchestration processing on the rule impact domain; when the execution scheduling level is advanced: perform emergency cancellation.

8. The write-off process automation system based on dynamic policy configuration according to claim 1 is characterized in that: Traffic peak prediction and resource scheduling are performed based on the verification interaction information to form a load balancing strategy table, including: The verification interaction information is subjected to multi-period traffic feature extraction to extract traffic distribution parameters of multiple time windows; the traffic distribution parameters of multiple time windows are subjected to peak and trough identification and analysis to generate traffic fluctuation characteristics; the server response time change is measured based on the traffic fluctuation characteristics, and the response time change trend is plotted; the response time change trend is subjected to traffic peak prediction to obtain traffic peak warning points; resource scheduling and orchestration are performed based on the traffic peak warning points to form a load balancing strategy table.

9. The write-off process automation system based on dynamic policy configuration according to claim 1 is characterized in that: Based on the load balancing strategy table and the write-off execution instruction chain, intelligent channel diversion and scheduling are carried out to establish a write-off process execution network, including: Extract real-time cancellation channel status parameters and cancellation queue length parameters; perform channel health assessment on the real-time cancellation 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 cancellation queue length parameters to extract the channel congestion index; perform malicious cancellation attack detection based on the channel status fluctuation map and the channel congestion index; when it is detected that the cancellation channel is in an abnormal state, perform global flow limiting on the cancellation request to obtain an abnormal protection strategy; perform intelligent channel diversion scheduling based on the cancellation execution instruction chain, load balancing strategy table and abnormal protection strategy, and establish a cancellation process execution network.

10. The write-off process automation system based on dynamic policy configuration according to claim 9, characterized in that: The specific method of performing intelligent channel diversion scheduling includes: Priority marking is performed on the write-off execution instruction chain to extract key write-off instructions; available channels are identified according to the load balancing policy table to form a channel resource pool; channel resource pools are classified into high-speed channels, standard channels, and backup channels according to their capabilities; high-speed channels are reserved according to the priority of key write-off instructions; channel access thresholds are set based on the anomaly protection policy, and request flow control is performed; a dynamic routing table is constructed to allocate write-off requests to the optimal execution channel according to request type, priority, and channel load status; execution status is monitored in real time, and the channel allocation strategy is dynamically adjusted based on execution feedback.

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