Digital coupon dynamic configuration method in dual-mode operation and related device

Abstract

The embodiment of the invention relates to the field of data processing, and provides a dual-mode operation digital coupon dynamic configuration method and a related device, and the method comprises the steps: obtaining an operator side access data set and a financial institution side access data set in a dual-mode operation state; performing dual-mode operation demand analysis according to the operator side access data set and the financial institution side access data set to obtain a dual-mode operation demand set; performing rule configuration processing according to the dual-mode operation demand set, the operator side access data set and the financial institution side access data set to obtain a digital coupon configuration rule set; generating a reference digital coupon set according to the digital coupon configuration rule set under the condition that a preset digital coupon generation condition is met; according to the reference digital coupon set, the operator side access data set and the financial institution side access data set, digital coupon dynamic matching processing is carried out to obtain a dynamic configuration digital coupon set, and the accuracy of the digital coupon configuration process under dual-mode operation can be improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method and related apparatus for dynamically configuring digital coupons in a dual-mode operation. Background Technology

[0002] Against the backdrop of rapid development in the digital economy, telecom operators and financial institutions are widely using digital coupons to achieve goals such as increasing user activity and promoting business conversion, based on user operation needs. Dual-mode operation (i.e., operator-side and financial institution-side) has become an important industry trend. However, current digital coupon configuration and matching technologies remain at a single-scenario, static, and generalized stage, making it difficult to meet the core requirement of accuracy for dual-mode operation. Therefore, how to improve the accuracy of the digital coupon configuration process under dual-mode operation has become an urgent problem to be solved. Summary of the Invention

[0003] This application provides a method and related apparatus for dynamically configuring digital coupons under dual-mode operation, which can improve the accuracy of the digital coupon configuration process under dual-mode operation.

[0004] The first aspect of this application provides a method for dynamically configuring digital vouchers in a dual-mode operation, the method comprising: Acquire the operator-side access data set and the financial institution-side access data set under dual-mode operation; Based on the access data sets from both the operator and financial institution sides, a dual-mode operation requirement analysis was conducted to obtain a dual-mode operation requirement set. Based on the dual-mode operation requirements set, the operator-side access data set, and the financial institution-side access data set, rule configuration processing is performed to obtain the digital voucher configuration rule set; If the preset digital coupon generation conditions are met, a reference digital coupon set is generated according to the digital coupon configuration rule set; The digital vouchers are dynamically matched based on the reference digital voucher set, the operator-side access data set, and the financial institution-side access data set to obtain a dynamically configured digital voucher set.

[0005] A second aspect of this application provides a dual-mode digital coupon dynamic configuration device, the dual-mode digital coupon dynamic configuration device comprising: The acquisition unit is used to acquire the operator-side access data set and the financial institution-side access data set under dual-mode operation. The first processing unit is used to perform dual-mode operation requirement analysis based on the operator-side access data set and the financial institution-side access data set to obtain a dual-mode operation requirement set. The second processing unit is used to perform rule configuration processing based on the dual-mode operation requirement set, the operator-side access data set, and the financial institution-side access data set to obtain a digital coupon configuration rule set. The third processing unit is used to generate a reference digital coupon set according to the digital coupon configuration rule set, provided that the preset digital coupon generation conditions are met. The fourth processing unit is used to perform dynamic matching processing of digital coupons based on the reference digital coupon set, the operator-side access data set, and the financial institution-side access data set to obtain a dynamically configured digital coupon set.

[0006] A third aspect of this application provides a terminal including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the step instructions as described in the first aspect of this application.

[0007] A fourth aspect of this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of this application.

[0008] A fifth aspect of this application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of this application. The computer program product may be a software installation package.

[0009] Implementing the embodiments of this application has the following beneficial effects: By acquiring the operator-side access data set and the financial institution-side access data set under dual-mode operation, dual-mode operation requirements analysis can be performed based on the operator-side access data set and the financial institution-side access data set to obtain a dual-mode operation requirements set. Then, rule configuration processing can be performed based on the dual-mode operation requirements set, the operator-side access data set, and the financial institution-side access data set to obtain digital coupon configuration rules. Under the condition of meeting preset digital coupon generation conditions, a reference digital coupon set is generated according to the digital coupon configuration rules. Furthermore, dynamic matching processing of digital coupons can be performed based on the reference digital coupon set, the operator-side access data set, and the financial institution-side access data set to obtain a more accurate dynamically configured digital coupon set. This avoids resource mismatch problems and dynamically adjusts the matching results according to the user's real-time status, which helps improve the accuracy of the digital coupon configuration process under dual-mode operation, making the digital coupon configuration more closely aligned with the real-time operation requirements of dual-mode services. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This application provides a schematic diagram of the structure of a dual-mode digital coupon dynamic configuration method according to an embodiment of the present application; Figure 2 This application provides a flowchart illustrating a method for dynamically configuring digital coupons in a dual-mode operation. Figure 3 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application; Figure 4 This application provides a schematic diagram of the structure of a dual-mode digital coupon dynamic configuration device. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0014] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0015] To better understand the dynamic configuration method for digital vouchers in a dual-mode operation provided in this application, a brief introduction to existing digital voucher configuration and matching technologies is given below. Existing technologies often design digital voucher configuration logic based on a single business scenario, failing to fully consider the differentiated operational objectives of operators and financial institutions. Specifically, operators need to focus on user network type identification, points deduction control, and enterprise user stratification, while financial institutions need to prioritize meeting hard requirements such as risk compliance verification, customer asset level matching, and business compliance threshold restrictions. Existing solutions often use a combination of a unified rule framework and general parameters to configure digital vouchers. They neither set dedicated data fields for enterprise user identification and points threshold control on the operator side, nor reserve rigid verification interfaces for risk assessment results and customer compliance status on the financial institution side, resulting in the inaccurate implementation of dual-mode requirements. For example, existing technologies use a single dimension of "user level" to configure dual-mode vouchers, mixing operator "5G user level" and financial institution "platinum customer level." This not only causes operator enterprise vouchers to be mistakenly issued to individual users but also results in financial vouchers being pushed to users who have not passed the risk assessment, leading to a voucher accuracy rate of less than 30%.

[0016] Meanwhile, the matching logic of existing digital coupon technologies is significantly static, relying heavily on historical user data or fixed thresholds to generate matching results. This fails to synchronize with users' dynamic states in real time. Key real-time data, such as changes in user points balances and network status switching on the operator's side, and updates to user compliance status and wealth management holdings on the financial institution's side, are not included in the matching calculation, resulting in severe delays in digital coupon adaptation. For example, a user may have exhausted their points but the system still pushes operator coupons with high point thresholds, or a user may receive financial coupons even after their risk assessment has expired. Such invalid pushes account for over 40%, wasting operational resources and significantly reducing user experience.

[0017] Furthermore, existing technologies lack a precise linkage mechanism between operational needs, rule configuration, and matching results. Long-term core needs (such as operators preventing the abuse of points and managing financial risks) have not been transformed into rigid rules embedded in the configuration process. Real-time dynamic needs (such as operators increasing holiday benefits and financial institutions boosting sales at the end of the month) also require manual adjustment of parameters, which further exacerbates the problem of insufficient accuracy in digital coupon configuration and seriously restricts the improvement of efficiency and effectiveness in dual-mode operation.

[0018] To address the aforementioned issues, this application provides a method for dynamically configuring digital vouchers in a dual-mode operation. This method can accurately adapt to the differentiated needs of operators and financial institutions. By linking the entire chain of needs, rules, and data, it achieves layered configuration of digital vouchers, avoiding the generality bias of single-mode rules. Combined with real-time data, it dynamically adjusts the voucher matching results, ensuring the rigid implementation of long-term core needs (such as points management and compliance verification) while efficiently responding to short-term dynamic needs, thereby improving the accuracy and efficiency of digital vouchers in dual-mode operation.

[0019] Please see Figure 1 , Figure 1 A schematic diagram of a dual-mode digital voucher dynamic allocation system is shown. Figure 1As shown, the dual-mode digital coupon dynamic configuration system can include a data acquisition module, a demand analysis module, a rule configuration module, a digital coupon generation module, and a dynamic matching module. The data acquisition module collects and cleans core data from both the operator side (user information, behavior, real-time data) and the financial institution side (customer attributes, compliance, business data) to provide basic data support for system operation. The demand analysis module breaks down the dual-mode operation requirements, separating the specific requirements for the operator / financial institution side to output structured demand results. The rule configuration module constructs dual-mode-specific rules based on the requirements and data, and through initial screening, basic configuration, core constraint embedding, refinement, and verification, obtains the digital coupon configuration rules. The digital coupon generation module parses the configuration rules and automatically generates a set of reference digital coupons containing types, parameters, and restrictions, thus forming a candidate coupon pool for matching. The dynamic matching module performs feature tagging on coupons and users, calculates multi-dimensional matching degrees, combines threshold filtering, and integrates and outputs a set of dynamically configured digital coupons for both modes.

[0020] Please see Figure 2 , Figure 2 This application provides a flowchart illustrating a method for dynamically configuring digital coupons in a dual-mode operation. For example... Figure 2 As shown, the dynamic configuration method for digital vouchers in dual-mode operation includes: S10: Obtain the operator-side access data set and the financial institution-side access data set under dual-mode operation.

[0021] The dual-mode operation status can refer to a collaborative operation model that simultaneously targets both operator business scenarios (such as user traffic / points operation) and financial institution business scenarios (such as customer wealth management / account opening benefits operation). It is understood that although the business objectives, compliance requirements, and user groups of the two modes may differ under the dual-mode operation status, data processing can be performed separately, but digital coupons can be configured uniformly. For example, operators need to use coupons to increase 5G user activity, and financial institutions need to use coupons to promote new customer wealth management account openings. Both can use a common dynamic configuration method to generate their respective coupons; this application does not impose any restrictions on this.

[0022] The operator-side access data set may include one or more operator access data sets. This operator access data can refer to all raw data related to users and services collected from operator business systems (such as user management systems, points systems, and network management systems). This operator access data can serve as the basis for operator-side demand analysis and tag generation. The operator-side access data set may include basic user data, such as mobile phone number, network type (5G / 4G), affiliated enterprise (government / enterprise user / individual user), and package level; user behavior data, such as login frequency in the past 7 days, points usage records, and historical coupon redemption / use records; and real-time status data, such as current points balance, real-time location, and current network signal strength. This application does not impose any restrictions on this. For example, one piece of operator-side access data in the operator-side access data set could be: mobile phone number 138XXXX1234, network type 5G, affiliated enterprise XX Group, 5 logins in the past 7 days, current points balance 800 points, and real-time location Beijing.

[0023] The financial institution-side access data set may include one or more financial institution-side access data sets. This financial institution-side access data can refer to all raw data related to customers and business collected from financial institution business systems (such as customer relationship management systems, compliance verification systems, and wealth management transaction systems). It is understood that this financial institution-side access data can serve as the basis for financial-side demand analysis and compliance verification. The financial institution-side access data set may include basic customer data, such as customer ID, asset size, customer level (platinum / ordinary), account opening duration, etc.; compliance verification data, such as risk assessment results (pass / fail), bank card binding status (bound / unbound), anti-money laundering screening records, etc.; and real-time business data, such as current wealth management holdings, transaction amounts in the past 30 days, and outstanding credit card bills, etc. This application does not impose any restrictions on this. For example, a single financial institution-side access data set could be: Customer ID 62XXXX89, asset size 600,000 yuan (platinum customer), account opening duration 20 days, risk assessment passed, current wealth management holdings 12,000 yuan.

[0024] Specifically, the data can be connected to the operator's business system and the financial institution's business system through a preset data interface to collect and obtain the aforementioned operator-side access data set and financial institution-side access data set. This application does not impose any restrictions on this.

[0025] S20: Perform dual-mode operation requirement analysis based on the operator-side access data set and the financial institution-side access data set to obtain a dual-mode operation requirement set.

[0026] The dual-mode operation requirements analysis can target the business objectives of operators and financial institutions, combining the characteristics of data accessed from both ends (such as abnormal data, trend data, and user segmentation data) to analyze and extract the problems or goals that need to be solved or achieved through digital coupons. For example, it can analyze abnormal points deduction data on the operator side, such as 80% of users deducting more than 500 points in a single day, to extract the requirement of "preventing points abuse"; it can analyze new customer wealth management conversion rate data on the financial side, such as a 60% conversion rate of users who receive coupons within 30 days of opening an account, to extract the requirement of "improving new customer wealth management conversion".

[0027] In other words, by transforming the collected raw data (i.e., the data sets of operators and financial institutions) into clear operational objectives (i.e., the set of dual-mode operational requirements), such as identifying the long-term stable requirements and short-term sudden requirements of dual modes through data mining, we can further provide direction for subsequent rule configuration.

[0028] The set of dual-mode operation requirements may include one or more dual-mode operation requirements, which may include operator-side requirements and financial institution-side requirements. Specifically, these requirements may include both long-term core requirements and real-time dynamic requirements (or short-term emergency requirements). For example, long-term core requirements of operators may include preventing the abuse of points and compliance in government and enterprise distribution; real-time dynamic requirements of operators may include enhanced 5G user benefits during the Mid-Autumn Festival; long-term core requirements of financial institutions may include risk compliance management and new customer value mining; real-time dynamic requirements of financial institutions may include end-of-month wealth management promotions, etc. This application does not impose any restrictions on these requirements.

[0029] Among these, long-term core needs refer to fundamental needs that exist stably and continuously in dual-mode operation and need to be met over a long period. These needs are usually derived from historical data patterns, business compliance requirements, or core objectives and do not change frequently. For example, the operator's need to prevent the abuse of points is a long-term need to control the risk of points consumption.

[0030] Real-time dynamic demand refers to short-term, sudden, and temporary demands that arise during dual-mode operation and change with business scenarios. These demands are typically based on real-time data trends, holidays, or short-term business objectives and change frequently. For example, a telecom operator's demand for enhanced 5G user benefits during the Mid-Autumn Festival can be understood as the need to increase user activity during this period; similarly, a financial institution's demand to boost wealth management sales at the end of the month is to meet its monthly transaction volume targets.

[0031] S30: Based on the dual-mode operation requirement set, the operator-side access data set, and the financial institution-side access data set, rule configuration processing is performed to obtain the digital coupon configuration rules.

[0032] The rule configuration process refers to breaking down the dual-mode operation requirements into specific, executable rule parameters such as the generation conditions, redemption restrictions, usage thresholds, and validity periods of digital coupons, and ensuring the rules are enforceable through data association and verification. For example, the requirement to prevent points abuse can be broken down into rule parameters such as: a single user's daily points deduction ≤ 500 points, and points coupons can only be redeemed by users with consumption records in the past 30 days. This application does not impose restrictions on this.

[0033] Digital coupon configuration rules refer to a unified set of rules generated through the above-mentioned rule configuration process, guiding the generation and distribution of digital coupons. These rules can include operator-specific rules and financial institution-specific rules, covering all core parameters such as coupon type, face value, redemption conditions, and usage restrictions. For example, digital coupon configuration rules could include configuration rules for operator-issued 5G coupons: face value 30 yuan, validity period 7 days, redemption only for designated enterprise IP segments, and daily point deduction ≤ 500 points; digital coupon configuration rules could also include configuration rules for financial institution new customer wealth management coupons: 50 yuan off for purchases over 1000 yuan, validity period 20 days, redemption only for users with accounts opened ≤ 30 days ago and who have passed the risk assessment, etc. This application does not impose any restrictions on these.

[0034] Specifically, the dual-mode operation requirements can be transformed into executable digital coupon rules through a process of initial screening framework, dedicated configuration, core rule embedding, detailed adjustment and verification to ensure that the configuration rules of digital coupons not only meet the requirements but also adapt to dual-end data. For details, please refer to the detailed description in the following embodiments, which will not be repeated here.

[0035] S40: If the preset digital coupon generation conditions are met, a reference digital coupon set is generated according to the digital coupon configuration rules.

[0036] The preset digital coupon generation conditions refer to the pre-set trigger conditions that initiate the generation of digital coupons. Optionally, the preset digital coupon generation conditions can typically be set based on dimensions such as time nodes, user scale, and business objectives to ensure that digital coupons are generated when needed. For example, it is possible to set up automatic generation of operator enterprise coupons on the 1st of each month (based on a time node), to set up generation of new customer wealth management coupons when the scale of new financial customers reaches 100,000 (based on user scale), or to set up generation of Mid-Autumn Festival exclusive coupons 7 days before Mid-Autumn Festival (based on business objectives), etc. This application does not impose any restrictions on this.

[0037] The reference digital coupon set may include one or more reference digital coupons, which can refer to the full pool of candidate coupons generated according to digital coupon configuration rules. This reference digital coupon set may include all types of coupons from both the operator and financial institution sides. It should be noted that in subsequent steps, coupons suitable for different user types can be filtered from the reference digital coupon set; therefore, this reference digital coupon set can also be understood as a candidate coupon pool, and this application does not impose any restrictions on this. For example, the reference digital coupon set may include: government and enterprise 5G coupons, ordinary 5G points coupons, Mid-Autumn Festival data coupons (operator side); new customer wealth management coupons, platinum customer discount coupons, end-of-month sales promotion coupons (financial institution side), etc.

[0038] S50: Perform dynamic matching processing of digital coupons based on the reference digital coupon set, the operator-side access data set, and the financial institution-side access data set to obtain a dynamically configured digital coupon set.

[0039] The dynamic matching process for digital coupons refers to the process of calculating the suitability (matching degree) between each reference digital coupon and the user based on the user's static tags (such as 5G users, platinum customers) and real-time data (such as current points, compliance status), and then filtering out suitable coupons according to a threshold. It should be noted that the core of this dynamic matching process for digital coupons is real-time adaptation and dynamic adjustment (such as the matching result changing as the user's status changes).

[0040] For example, if User A (a 5G enterprise user with 800 points) has a 100% match (meets the standard) with the "Enterprise 5G Coupon" and an 80% match (meets the standard) with the "Regular 5G Coupon," then these two coupons can be selected as a subset of User A's dynamically configured digital coupons. Optionally, if User A's points drop to 400, the match with "coupons with a point deduction of ≤500 points" still meets the standard, and the matching result remains unchanged; if User A's points drop to 300, the match with "coupons with a point deduction of ≥400 points" does not meet the standard, and in this case, the "coupon with a point deduction of ≥400 points" is excluded.

[0041] The dynamically configured digital coupon set can include one or more dynamically configured digital coupons. These dynamically configured digital coupons refer to precise digital coupons adapted to different user types after dynamic matching processing. This dynamically configured digital coupon set can include a subset of dynamically adapted coupons from the operator side (i.e., the operator dynamically configured digital coupon set mentioned later) and a subset of dynamically adapted coupons from the financial institution side (i.e., the financial institution dynamically configured digital coupon set mentioned later). These are the sets of digital coupons that can be actually pushed to different user types in subsequent steps. For example, the dynamically configured digital coupon set for user type 1 could be: operator: government and enterprise 5G coupons, ordinary 5G points coupons; financial: platinum customer discount coupons; the dynamically configured digital coupon set for user type 2 could be: operator: ordinary 4G points coupons; financial: new customer wealth management coupons, platinum customer discount coupons.

[0042] Specifically, from the aforementioned full set of candidate coupons (i.e., the reference digital coupon set), combined with user tags, real-time status, etc. in the dual-end access data, the matching degree can be calculated to filter out coupons that are suitable for different user types, thereby further forming a precise coupon set in dual modes, realizing dynamic matching of coupons to users. For relevant descriptions, please refer to the detailed description in the embodiments below, which will not be repeated here.

[0043] In this embodiment, the entire process from data collection to dynamic matching forms a closed loop: data-driven demand, demand transformation rules, rule-generated coupons, and coupons accurately matching users. This achieves more precise dual-mode differentiated operation. By collecting data, refining demands, and configuring rules for each mode, it avoids a one-size-fits-all approach to coupon configuration and adapts to different business objectives in both modes. It also achieves greater dynamism and flexibility, with real-time data access supporting dynamic adjustments to demands (such as increased Mid-Autumn Festival benefits or end-of-month financial promotions). Real-time data matching ensures that coupons are compatible with the user's current status (such as changes in points balance or compliance status), solving the problem that traditional fixed rules cannot respond to real-time business changes. This results in a dual improvement in resource utilization and user experience. Multi-round rule verification avoids the generation of invalid coupons, and accurate matching reduces resource waste. At the same time, it allows users to obtain coupons that match their attributes and needs, increasing users' willingness to claim and use coupons.

[0044] In this embodiment, by acquiring the operator-side access data set and the financial institution-side access data set under dual-mode operation, dual-mode operation requirements analysis can be performed based on the operator-side access data set and the financial institution-side access data set to obtain a dual-mode operation requirements set. Then, rule configuration processing can be performed based on the dual-mode operation requirements set, the operator-side access data set, and the financial institution-side access data set to obtain digital coupon configuration rules. Under the condition of meeting preset digital coupon generation conditions, a reference digital coupon set is generated according to the digital coupon configuration rules. Furthermore, dynamic matching processing of digital coupons can be performed based on the reference digital coupon set, the operator-side access data set, and the financial institution-side access data set to obtain a more accurate dynamically configured digital coupon set. This avoids resource mismatch problems and dynamically adjusts the matching results according to the user's real-time status, which helps improve the accuracy of the digital coupon configuration process under dual-mode operation, making the digital coupon configuration more aligned with the real-time operation requirements of dual-mode services.

[0045] In one possible implementation, rule configuration can be achieved through a progressive process: initial framework screening, dedicated configuration, core rule embedding, dynamic refinement, and validation. For example, first, dedicated rule frameworks for operators and financial institutions are separated based on dual-mode operational needs to clarify their respective mandatory fields and core dimensions; then, basic rule parameters are combined with the framework configuration to form an initial rule set for each mode; next, rigid rules corresponding to long-term core needs are embedded to build a stable core rule structure; subsequently, rules for different user types are refined and adjusted according to real-time dynamic needs to enhance flexibility; finally, full-dimensional validation ensures the integrity, compliance, and executability of the rules, integrating them to form a set of digital coupon configuration rules covering both modes. This ensures the stable implementation of long-term core needs while responding to real-time business changes. Specifically, a method for obtaining a digital coupon configuration rule set by performing rule configuration processing based on the dual-mode operational needs set, the operator-side access data set, and the financial institution-side access data set may include: A1. Based on the set of dual-mode operation requirements, perform preliminary rule screening for each mode to obtain a set of mode-specific rule framework data. A2. Based on the pattern-specific rule framework data set, perform pattern-specific rule configuration processing for each pattern to obtain the pattern-specific rule configuration data set; A3. Based on the long-term core requirements subset in the mode-specific rule configuration data set, the operator-side access data set, the financial institution-side access data set, and the dual-mode operation requirement set, perform core rule structure configuration processing to obtain the mode-specific core rule data set. A4. Based on the model-specific core rule data set and the real-time dynamic demand subset in the dual-mode operation demand set, refine the user type rules to obtain the model-specific refined rule data set. A5. Perform rule verification processing on each mode-specific refined rule data in the mode-specific refined rule data set to obtain the digital coupon configuration rule set.

[0046] The initial rule screening process refers to the process of selecting the core dimensions and mandatory fields that must be included in the rule design for each mode (i.e., operators and financial institutions) based on the set of dual-mode operation requirements, eliminating redundant dimensions that are irrelevant to the dual-mode operation requirements, and defining the boundaries for subsequent rule configuration.

[0047] The model-specific rule framework data set may include one or more model-specific rule framework data, which can refer to the basic rule framework output by the model. This model-specific rule framework data may include operator-side rule frameworks and financial institution-side rule frameworks. Each rule framework data can specify the required fields that the model rule must cover, and may not have specific parameter values, only defining the framework; this application does not impose any restrictions on this.

[0048] Specifically, the needs of operators (such as preventing the abuse of points and bulk issuance to government and enterprises) and the needs of financial institutions (such as risk compliance management and new customer conversion) can be separated from the dual-mode operation needs set. Based on the preset needs field mapping table (such as the points deduction ratio field for the needs of points abuse, and the risk assessment result field for the needs of compliance management), the required fields corresponding to the two types of needs can be extracted respectively. The required fields of operators are integrated into the operator rule framework, and the required fields of financial institutions are integrated into the financial rule framework, thus forming the data set of the rule framework specific to the above-mentioned modes.

[0049] Dedicated rule configuration processing refers to the process of filling in specific rule parameter values ​​under the required fields of the mode-specific rule framework to form a preliminary executable sub-mode rule. The mode-specific rule configuration dataset can include one or more mode-specific rule configuration data, which can refer to the initial rule with specific parameters output by the sub-mode. The mode-specific rule configuration data can include operator-side dedicated rule configuration data and financial-side dedicated rule configuration data. Each mode-specific rule configuration data can include explicit parameter values ​​(e.g., for operator enterprise vouchers: the points deduction ratio is ≤10%, and the validity period is 7 days). It should be noted that this mode-specific rule configuration data does not yet incorporate long-term core requirements and real-time dynamic requirements.

[0050] Specifically, for the mandatory fields in the operator's rule framework, combined with common parameters of operator business (such as the validity period of historical coupons is usually 7-15 days, and the points deduction ratio is usually 5%-15%), specific parameters can be configured, such as the user's network type being 5G, the points deduction ratio being ≤10%, and the validity period being 7 days, to form a subset of operator-specific rule configuration data. Similarly, for the mandatory fields in the financial rule framework, combined with common parameters of financial business (such as the minimum spending requirement for wealth management coupons being usually 1000-5000 yuan, and the validity period being usually 15-30 days), specific parameters can be configured, such as the customer level being platinum, the minimum spending requirement being 1000 yuan, and the validity period being 15 days, to form a subset of financial-specific rule configuration data, which can then be further integrated to obtain the set of rule configuration data specific to the above-mentioned models.

[0051] The long-term core requirements subset may include one or more long-term core requirements. These long-term core requirements refer to the long-term, stable, and rigidly implemented requirements within the dual-mode operation requirement set, and are the fundamental requirements that the configured rules must meet. Core rule structure configuration processing refers to embedding the rigid constraints corresponding to the long-term core requirements into the mode-specific rule configuration data to adjust the rule structure (such as adding restrictions or validation logic), thereby ensuring that the configured rules conform to the long-term core requirements.

[0052] The pattern-specific core rule data set may include one or more pattern-specific core rule data sets. These pattern-specific core rule data sets refer to rigid rule data output by the pattern that incorporates long-term core requirements. It can be understood that these pattern-specific core rule data sets incorporate constraints corresponding to long-term requirements, exhibiting strong stability and typically not adjusting to short-term business changes.

[0053] Specifically, a subset of long-term core requirements can be extracted from the dual-mode operation requirement set to verify the feasibility of the requirements by combining dual-end access data (e.g., operator-side data shows an 80% anomaly rate for daily point deductions exceeding 500 points, and financial-side data shows a 30% complaint rate for users who did not pass the assessment when claiming coupons). This allows for the further embedding of rigid constraints into the initial rules, such as adding to the operator rules: single user daily point deduction ≤ 500 points (corresponding to the requirement to prevent point abuse), and adding to the financial rules: only users who pass the risk assessment can claim coupons (corresponding to the requirement for risk compliance management). This can further form a subset of operator-specific core rule data containing rigid constraints and a subset of financial-specific core rule data, which in turn yields the aforementioned model-specific core rule data set.

[0054] The real-time dynamic demand subset can include one or more real-time dynamic demands. These demands can refer to short-term, sudden, and scenario-dependent needs within the dual-mode operation demand set (such as operators' Mid-Autumn Festival 5G benefit enhancements or financial institutions' end-of-month wealth management promotions). These real-time dynamic demands can be refined through rule elaboration to adapt to short-term target needs. User type rule elaboration refers to adjusting the parameters (such as face value and validity period) of core rules based on real-time dynamic demands for different user types (such as operators' 5G enterprise users / general 5G users, financial institutions' new / existing customers, etc.) to make the configured rules more aligned with short-term goals and user segmentation processes.

[0055] The model-specific refined rule data set can include one or more model-specific refined rule data sets. These model-specific refined rule data sets refer to the hierarchical rules output by the model that incorporate real-time dynamic requirements. It is understood that the rule parameters in this model-specific refined rule data set can be set differently according to user type (e.g., a 30 yuan face value for a Mid-Autumn Festival 5G government and enterprise coupon, and a 20 yuan face value for a regular 5G coupon), offering high flexibility and allowing for adjustments based on short-term needs.

[0056] Specifically, a subset of real-time dynamic demands can be extracted from the dual-mode operation demand set (such as operators' Mid-Autumn Festival 5G benefits and financial institutions' end-of-month wealth management promotions). User types can be categorized based on dual-end access data, such as 5G enterprise users and ordinary 5G users on the operator side, and new customers (account opened ≤ 30 days) and old customers (account opened > 30 days) on the financial side. This allows for the refinement of rule parameters for different user types. For example, the face value of the operator's 5G enterprise coupon can be increased from 20 yuan to 30 yuan (Mid-Autumn Festival bonus), while the face value of the ordinary 5G coupon can remain at 20 yuan. The validity period of the financial institution's new customer wealth management coupon can be extended from 15 days to 20 days (end-of-month promotion), while the validity period of the old customer coupon can remain at 15 days. This forms a hierarchical and refined subset of operator-specific detailed rule data and a subset of financial institution-specific detailed rule data, which can then be further integrated to obtain the aforementioned model-specific detailed rule data set.

[0057] Rule validation processing refers to performing multi-dimensional validation on each mode-specific detailed rule data in the mode-specific detailed rule data set. This validation can be performed from dimensions such as completeness (whether required fields are missing), compliance (whether it meets business compliance requirements), and feasibility (whether parameters are within a reasonable range), in order to correct the handling of abnormal rules. The digital coupon configuration rule set is a complete rule set output after rule validation processing, covering both modes and capable of directly generating coupons. It integrates detailed rules from both the operator and financial institution sides, and all rules have passed validation; this application does not impose any restrictions on this.

[0058] Specifically, multi-dimensional verification can be conducted for each detailed rule, such as completeness verification (checking whether the operator rules include enterprise codes and whether the financial rules include account opening duration; if missing, complete them); compliance verification (checking whether the financial rules include compliance verification items and whether the operator rules exceed the points deduction limit; if not, correct them); and feasibility verification (checking whether the voucher face value is lower than the cost price and whether the validity period is too short; if abnormal, adjust them), etc. This application does not impose restrictions on this. For example, if the face value of a financial institution's new customer wealth management voucher is 100 yuan (exceeding the cost limit of 80 yuan), the face value can be corrected to 80 yuan. After all rules have been verified and passed, the detailed rule data from the operator side and the financial institution side can be further integrated to obtain the above-mentioned set of digital voucher configuration rules.

[0059] In this embodiment, a progressive process of initial framework screening, initial configuration, core embedding, dynamic refinement, and verification implementation achieves a balance between accuracy, stability, and flexibility in dual-mode rules. Initial rule screening and core rule embedding ensure that operator-side rules meet long-term needs such as points-based management and government / enterprise compliance, while financial institution-side rules meet compliance requirements such as risk verification and customer segmentation, preventing rules from deviating from core objectives. User type rule refinement allows rules to quickly respond to real-time needs such as Mid-Autumn Festival benefits and end-of-month sales targets, and differentiated parameter configuration based on user type avoids rule waste. Full-dimensional verification proactively corrects issues such as rule omissions and compliance risks, ensuring smooth subsequent digital coupon generation and dynamic matching processes. This lays the foundation for accurate dual-mode digital coupon generation and provides reliable rule guidelines for subsequent dynamic matching, effectively improving the efficiency and quality of digital coupon configuration under dual-mode operation.

[0060] In one possible implementation, the initial rule screening for each mode is performed through a process of requirement decomposition, keyword extraction, field mapping, and framework integration. For example, first, the specific requirements for operators and financial institutions are separated from the dual-mode operation requirements, and the core keywords for each mode are extracted. Then, the keywords are converted into mandatory rule fields through a pre-defined mapping relationship. Finally, the two types of fields are integrated to form a mode-specific rule framework, defining the basic dimensions for subsequent rule configuration. Specifically, a method for performing initial rule screening for each mode based on the dual-mode operation requirement set to obtain a mode-specific rule framework data set may include: B1. Based on the dual-mode operation requirement set, perform operator-side requirement keyword extraction processing to obtain the operator requirement keyword set; B2. Based on the dual-mode operation requirements, extract keywords from the financial institution side to obtain a set of financial institution demand keywords. B3. Based on the preset requirement field mapping table, perform requirement field mapping processing on each operator requirement keyword in the operator requirement keyword set to obtain the operator rule mandatory field set; B4. Based on the preset requirement field mapping table, perform requirement field mapping processing on each financial institution requirement keyword in the financial institution requirement keyword set to obtain the set of required fields for financial institution rules. B5. Based on the set of required fields for operator rules and the set of required fields for financial institution rules, determine the data set of pattern-specific rule framework.

[0061] The operator-side demand keyword extraction process refers to the process of extracting the core terms (such as 5G, points, government and enterprise) that best represent the operator's needs from the operator's business scenario needs (such as 5G user benefits, points management, etc.) in the dual-mode operation demand set. The operator demand keyword set may include one or more operator demand keywords, which can refer to the extracted keywords and / or combinations of keywords that reflect the core needs of the operator, and are the basis for subsequent mapping to operator rule fields (i.e., mandatory fields for operator rules).

[0062] Specifically, based on the operator-side requirements in the dual-mode operation requirement set (such as improving the activity of 5G government and enterprise users, preventing the abuse of points, and issuing government and enterprise vouchers in bulk), core words such as 5G, government and enterprise users, points, and bulk issuance can be extracted using natural language processing tools (such as keyword extraction algorithms) to form and obtain the above-mentioned operator requirement keyword set. For example, the operator requirement keyword set is {5G, government and enterprise users, points, bulk issuance}. This application does not impose any restrictions on this.

[0063] The extraction and processing of financial institution-side demand keywords refers to the process of extracting the core terms (such as new customers, risk assessment, and wealth management) that best represent the financial institution's business scenario needs (such as new customer wealth management conversion and compliance control) from the dual-mode operation demand set. The financial institution demand keyword set may include one or more financial institution demand keywords, which may refer to the extracted keywords and / or combinations of keywords that reflect the core needs of the financial institution, and can be used for subsequent mapping to financial institution rule fields (i.e., mandatory fields for financial institution rules).

[0064] Specifically, based on the financial institution-side needs in the dual-mode operation needs set (such as increasing the new customer wealth management account opening rate, allowing users to receive coupons only after passing the risk assessment, and exclusive benefits for platinum customers), core keywords such as "new customer," "wealth management," "risk assessment," and "platinum customer" can be extracted using a keyword extraction tool to form and obtain the aforementioned set of keywords for financial institution needs. For example, if the set of keywords for financial institution needs is {new customer, wealth management, risk assessment, platinum customer}, this application does not impose any restrictions on this.

[0065] The preset requirement field mapping table can refer to a predefined table of correspondence between "requirement keywords → rule fields". It is understood that this preset requirement field mapping table can clearly define the specific data fields that each requirement keyword needs to be reflected in the rule, such as "5G" mapping to the "user network type field", "points" mapping to the "points deduction ratio field", etc. This application does not impose any restrictions on this.

[0066] Demand field mapping refers to the process of converting demand keywords into specific data fields that must be included in the rule design, based on a preset demand field mapping table. The set of mandatory operator rule fields can include one or more mandatory operator rule fields. These mandatory operator rule fields refer to the combination of fields that must be included in the operator-side rules after mapping, ensuring that the configured rules cover the core needs of the operator.

[0067] Specifically, a preset requirement field mapping table can be called, such as defining "5G → User Network Type Field", "Government and Enterprise Users → Enterprise Code Field", "Points → Points Deduction Ratio Field", and "Batch Issuance → Issuance Quantity Limit Field". Then, each word in the operator's requirement keyword set can be transformed according to the mapping table to obtain the operator's rule required field set. For example, the operator's requirement keyword set {5G, Government and Enterprise Users, Points, Batch Issuance} can be mapped to obtain the operator's rule required field set {User Network Type, Enterprise Code, Points Deduction Ratio, Issuance Quantity Limit}.

[0068] The set of required fields for financial institution rules can include one or more mandatory fields. These mandatory fields refer to the combination of fields that must be included in the financial institution's rules after mapping, ensuring that the configured rules cover the core needs of the financial institution. Specifically, a preset requirement field mapping table (financial side) can be called, such as defining "New Customer → Account Opening Duration Field", "Wealth Management → Wealth Management Holding Amount Field", "Risk Assessment → Risk Assessment Result Field", and "Platinum Customer → Customer Level Field". Then, each word in the set of financial institution requirement keywords can be transformed according to the mapping table to obtain the set of required fields for financial institution rules. For example, mapping the set of financial institution requirement keywords {New Customer, Wealth Management, Risk Assessment, Platinum Customer} will result in the set of required fields for financial institution rules {Account Opening Duration, Wealth Management Holding Amount, Risk Assessment Result, Customer Level}.

[0069] Further integrating the required fields from operators and financial institutions results in a set of rule frameworks for different modes, which constitutes the aforementioned mode-specific rule framework data set. In other words, this mode-specific rule framework data set includes both operator rule frameworks and financial institution rule frameworks; the mode-specific rule framework data only defines the fields that rules must include and does not involve specific parameters.

[0070] In this embodiment, the initial rule screening process accurately extracts dual-mode requirement keywords and maps them to mandatory fields, thus defining clear basic dimensions for subsequent rule configuration. By ensuring that core requirements are inevitably covered by operator-side rules and included in financial institution-side rules, deviations in requirement implementation due to missing rule dimensions can be avoided. By constructing a rule framework based on different modes, the differences between dual-mode businesses are fully reflected (e.g., operators focus on network type and points, while financial institutions focus on compliance and customer segmentation), providing a precise framework for subsequent dedicated rule configuration. This improves the targeting and efficiency of rule configuration from the source and reduces the redundant costs of subsequent rule adjustments.

[0071] In one possible implementation, the core rule structure configuration process can be achieved through a workflow of long-term requirement decomposition, data association mapping, matrix verification iteration, core rule extraction, and rule embedding integration. For example, long-term core requirements can first be decomposed into executable rule elements, and a mapping relationship between requirements and rules can be established using dual-end data. After data verification iteration, dual-mode core rules can be extracted and further embedded into the previously configured mode-specific rule data to form a core rule structure containing long-term rigid constraints. Specifically, a method for configuring the core rule structure based on a subset of long-term core requirements from the mode-specific rule configuration data set, the operator-side access data set, the financial institution-side access data set, and the dual-mode operation requirement set to obtain a mode-specific core rule data set may include: C1. Based on the subset of long-term core requirements in the dual-mode operation requirements, perform rule decomposition processing to obtain a set of decomposed rule elements; C2. Perform data association processing based on the set of disassembly rule elements, the set of operator-side access data, and the set of financial institution-side access data to obtain an initial dual-mode demand rule mapping matrix; C3. Based on the operator-side access data set and the financial institution-side access data set, the initial dual-mode demand rule mapping matrix is ​​verified and iterated to obtain the target dual-mode demand rule mapping matrix. C4. Based on the target dual-mode requirement rule mapping matrix and the long-term core requirement subset, perform requirement decomposition processing to obtain the dual-mode core rule data set; C5. Embedding processing is performed on the dual-mode core rule data set and the mode-specific rule configuration data set to obtain the mode-specific core rule data set.

[0072] As mentioned above, the long-term core requirements subset can be the stable, rigidly implemented requirements within the dual-mode operation requirements set, representing the fundamental requirements that the configured rules must meet. Rule decomposition can be understood as the process of transforming abstract long-term core requirements into concrete, executable rule elements (such as conditions, restrictions, thresholds, etc.).

[0073] The set of decomposed rule elements may include one or more decomposed rule elements. These decomposed rule elements may refer to the specific combination of elements that constitute the long-term core requirement rules after decomposition. For example, if the daily points deduction for a single user is ≤500 points, the rule decomposition process can yield decomposed rule elements such as "single user", "single day", and "500 points". This application does not impose any restrictions on this.

[0074] Specifically, a demand element decomposition tool can be used to break down the long-term core demand subset in the dual-mode operation demand into rule elements. For example, on the operator side, the decomposition could be: points abuse → elements include user dimension (single user), time dimension (single day), and threshold (points deduction ≤ 500 points); on the financial side, the decomposition could be: compliance control → elements include user qualifications (risk assessment results) and restrictions (only available to users). Further integration yields the above-mentioned decomposed rule element set, such as {single user, single day, 500 points, risk assessment results, only available to users}. This application does not impose any restrictions on this.

[0075] Data association processing can be understood as establishing corresponding associations between the rule elements obtained after decomposition and specific fields and data indicators in the operator-side access data and financial institution-side access data (such as associating "500 points" with the "points balance field"), so as to clarify the processing process of the data source of the rule elements.

[0076] The initial dual-mode requirement rule mapping matrix can be understood as a matrix representation of the correspondence between "long-term core requirements → rule elements → data fields". This initial dual-mode requirement rule mapping matrix can include mapping relationships between the operator side and the financial institution side.

[0077] Specifically, based on the data sets accessed by operators and financial institutions, the decomposed rule elements can be associated with data fields. For example, on the operator side: "single user" is associated with "user information table, user ID", "single day" is associated with "points transaction record table, transaction date", and "500 points" is associated with "points transaction record table, single day deduction amount", etc. On the financial side: "risk assessment result" is associated with "risk assessment result table, assessment status", and "only available to users" is associated with "risk assessment result table, assessment status is passed", etc. The above relationships can be further integrated in a matrix form to form and obtain the above initial dual-mode requirement rule mapping matrix.

[0078] Verification iteration can be understood as the process of checking the matching between rule elements and data fields in the initial dual-mode demand rule mapping matrix (such as whether the "500 points" threshold conforms to historical data patterns) by using dual-end access data (i.e., the operator-side access data set and the financial institution-side access data set), so as to further correct mismatched mapping relationships (such as adjusting the threshold to "600 points").

[0079] The target dual-mode requirement rule mapping matrix refers to a requirement rule mapping matrix that, after verification and iteration, matches actual data and is executable. This target dual-mode requirement rule mapping matrix can serve as the basis for extracting core rules. Optionally, operator-side access data can be used to verify the "points deduction threshold," such as analyzing historical data to find that "80% of abnormal users deduct more than 500 points in a single day," thus confirming that the "500 points" threshold is reasonable. Financial-side access data can be used to verify the "compliance conditions," such as statistics showing that "30% of users who failed the assessment complained after receiving coupons," thus confirming the necessity of "only qualified users can receive coupons." Further, the verified mapping relationships are retained, and the mismatched mapping relationships are adjusted (for example, if "500 points" causes 90% of normal users to be restricted, it can be adjusted based on historical data, such as raising it to "600 points" to avoid the problem of users being restricted), thereby obtaining the target dual-mode requirement rule mapping matrix.

[0080] The dual-mode core rule data set can include one or more dual-mode core rule data. This dual-mode core rule data can be understood as a rigid set of rules extracted from the target mapping matrix that covers the long-term core needs of operators and the financial side (e.g., operators: single user's daily points deduction ≤ 500 points; financial institutions: only users who pass the risk assessment can receive coupons), which has stability and is mandatory.

[0081] Specifically, based on the target dual-mode demand rule mapping matrix and combined with a subset of long-term core demands, the mapping relationship can be transformed into specific rules. For example, core rules on the operator side: the daily point deduction amount for a single user is ≤500 points (related to the points transaction record table), and government and enterprise coupons are only available to users with non-empty enterprise codes (related to the user information table and enterprise code), etc.; core rules on the financial side: wealth management coupons are only available to users whose risk assessment result is "passed" (related to the risk assessment result table), and exclusive coupons for platinum customers are only available to users with a "platinum" customer level (related to the customer information table and customer level), etc.; thereby further integrating to form the above-mentioned dual-mode core rule data set.

[0082] Embedding can be understood as integrating the dual-mode core rules (rigid constraints) into the initial mode-specific rule configuration data (basic parameters), adding long-term requirements-related constraints to the basic rules. The mode-specific core rule data set is the complete rule set output by each mode, containing long-term core rules. For example, a carrier enterprise coupon: face value 20 yuan, single user daily points deduction ≤ 500 points, containing both basic parameters and rigid constraints.

[0083] Specifically, dual-mode core rules can be embedded into mode-specific rule configuration data. For example, on the operator side, "single user daily points deduction ≤ 500 points" and "only for users with non-empty enterprise codes" can be embedded in "government and enterprise coupon (face value 20 yuan, validity period 7 days)". On the financial side, "new customer wealth management coupon (50 yuan off for purchases over 1000 yuan, validity period 15 days)" and "only for users who have passed the risk assessment" can be embedded in "account opening duration ≤ 30 days". This forms operator-specific core rule data and financial-specific core rule data, which in turn form the above-mentioned mode-specific core rule data set.

[0084] In this embodiment, long-term core requirements are transformed into rigid rules and embedded into basic configurations through data-driven mapping and verification, achieving rule stability and compliance. Dual-end data verification ensures that core rules (such as points management and compliance verification) conform to actual business rules, avoiding implementation difficulties caused by rules being detached from data. Embedding core rules by mode (operators focus on points and government / enterprise compliance, while finance focuses on risk and customer segmentation) strengthens the differentiated constraints of the two modes and ensures that long-term core requirements are not weakened by short-term adjustments. The resulting set of mode-specific core rule data provides a rigid framework for subsequent rule refinement, fundamentally ensuring that digital voucher configuration does not deviate from the core objectives of dual-mode operation and reducing resource waste or compliance risks caused by rule loopholes.

[0085] In one possible implementation, the dynamic matching process for digital coupons is achieved through a process of bidirectional analysis of coupon features and user features, calculation of matching degree across dimensions, threshold filtering, and integration of dual-mode results. For example, the reference digital coupons and user data from both ends can be first tagged with features. Then, the matching degree between user attributes, behaviors (compliance), real-time data, and coupon types, rules, and parameters can be calculated separately for the operator / financial institution sides. Furthermore, suitable coupons are filtered out by combining preset thresholds, and the dual-mode results are integrated to obtain a dynamically configured digital coupon set. Specifically, a method for dynamically matching digital coupons based on the reference digital coupon set, the operator-side access data set, and the financial institution-side access data set to obtain a dynamically configured digital coupon set may include: D1. Perform digital voucher feature analysis on each reference digital voucher in the reference digital voucher set to obtain a subset of digital voucher type tags and a subset of digital voucher rule tags; D2. Perform user feature analysis on each operator-side access data in the operator-side access data set to obtain a subset of operator user attribute tags and a subset of operator user behavior tags. D3. Calculate the matching degree based on the subset of operator user attribute tags and the subset of digital coupon type tags to obtain the matching subset of operator user attribute tags and coupon type tags; D4. Calculate the matching degree based on the subset of operator user behavior tags and the subset of digital coupon rule tags to obtain the matching subset of operator user behavior tags and coupon rule tags; D5. Calculate the matching degree based on the real-time updated data subset in the operator-side access data set and the digital coupon quantification parameter subset in the reference digital coupon set to obtain the matching subset of operator real-time data and coupon parameters; D6. Determine the operator's dynamically configured digital coupon set based on the operator's preset matching score threshold, the matching set of the operator's user attribute tags and coupon type tags, the matching set of the operator's user behavior tags and coupon rule tags, and the matching set of the operator's real-time data and coupon parameters. D7. Perform user feature analysis on each financial institution-side access data in the financial institution-side access data set to obtain a subset of financial institution user attribute tags and a subset of financial institution user compliance tags. D8. Calculate the matching degree based on the subset of financial institution user attribute tags and the subset of digital coupon type tags to obtain the matching subset of financial institution user attribute tags and coupon type tags; D9. Calculate the matching degree based on the subset of compliance tags for financial institution users and the subset of rule tags for digital coupons to obtain the matching subset of compliance tags for financial institution users and coupon rule tags; D10. Calculate the matching degree based on the subset of real-time business data in the data set accessed by the financial institution and the subset of digital voucher quantification parameters in the reference digital voucher set to obtain the matching subset of financial institution business data and voucher parameters. D11. Based on the financial institution's preset matching score threshold, the matching set of the financial institution's user attribute tags and coupon type tags, the matching set of the financial institution's user compliance tags and coupon rule tags, and the matching set of the financial institution's business data and coupon parameters, determine the financial institution's dynamically configured digital coupon set. D12. Determine the dynamically configured digital voucher set based on the operator's dynamically configured digital voucher set and the financial institution's dynamically configured digital voucher set.

[0086] The digital coupon feature analysis can be understood as the process of extracting the core features (such as type and usage rules) of each reference digital coupon and converting them into tags. The digital coupon type tag subset may include one or more digital coupon type tags, which can reflect the type of the digital coupon, such as operator-5G coupon, operator-government / enterprise coupon, financial-new customer wealth management coupon, etc. The digital coupon rule tag subset may include one or more digital coupon rule tags, which can reflect the tags indicating restrictions on coupon usage, such as limited to 5G users, points deduction ≤ 500 points, limited to users who pass the risk assessment, etc. This application does not impose any restrictions on this.

[0087] User feature analysis can be understood as the process of extracting key user features (such as user attributes and user behaviors) from the operator's access data set and transforming them into tags. The operator user attribute tag subset can include one or more operator user attribute tags, which can reflect static user attributes, such as 5G users, enterprise users, and points ≥ 800. The operator user behavior tag subset can include one or more operator user behavior tags, which can reflect dynamic user behaviors, such as activity in the past 7 days, coupon redemption in the past 30 days, and frequent points usage.

[0088] Matching degree calculation refers to the process of quantifying the degree of fit between various tags using tag similarity algorithms (such as cosine similarity). Specifically, by quantifying the fit between user attribute tags and digital coupon type tags, a matching score can be obtained for the operator's user attribute tags and coupon type tags. For example, the matching degree between user A's "5G user tag, government and enterprise user tag" and the operator's "5G coupon, government and enterprise coupon" can be 100%, meaning the matching score for user A's "5G user tag, government and enterprise user tag" and the operator's "5G coupon, government and enterprise coupon" can be 100 points; the matching degree between user A's "5G user tag, government and enterprise user tag" and the operator's "4G coupon" can be 0%, meaning the matching score for user A's operator's "5G user tag, government and enterprise user tag" and the operator's "4G coupon" can be 0 points. This application does not impose any restrictions on this.

[0089] By quantifying the degree of fit between user behavior tags and digital coupon rule tags, a matching score can be obtained for the operator's user behavior tags and coupon rule tags. For example, if user B's "active in the last 7 days" user behavior tag matches the "coupon limited to active users" coupon rule tag with a 100% match, then user B's matching score for the "active in the last 7 days" user behavior tag and the "coupon limited to active users" coupon rule tag is 100 points. However, user C's "not logged in in the last 30 days" user behavior tag matches the "coupon limited to active users" coupon rule tag with a 0% match, then user C's matching score for the "coupon limited to active users" user behavior tag and the "coupon limited to active users" coupon rule tag is 0 points. This application does not impose any restrictions on this.

[0090] The real-time updated data subset may include one or more real-time updated data, which may refer to the user's real-time status data, such as current points balance, location, etc., and this application does not impose any restrictions on this. The digital voucher quantification parameter subset may include one or more digital voucher quantification parameters, which may refer to specific numerical parameters in the digital voucher, such as the points deduction limit, usage area restrictions, etc., and this application does not impose any restrictions on this.

[0091] By quantifying the degree of compatibility between the operator's real-time updated data and the quantitative parameters of the digital coupon, a matching score can be obtained between the operator's real-time data and the coupon parameters. For example, if user A currently has 400 points and the matching degree between the digital coupon parameter "can be claimed with points ≥ 500 points" is 0%, then the matching score between user A's current points of 400 points and the coupon parameter "can be claimed with points ≥ 500 points" can be 0 points. This application does not impose any restrictions on this.

[0092] Optionally, for each target user, the three types of matching scores from the operator side can be calculated. Based on the matching scores of the operator user attribute tags and coupon type tags, the matching scores of the operator user behavior tags and coupon rule tags, and the matching scores of the operator's real-time data and coupon parameters, the target user's total operator matching score can be determined. The specific formula is as follows: in, It can represent the target user's total carrier matching score; It can represent the matching weight of the matching score between the target user's operator user attribute tag and the coupon type tag; It can represent the matching score between the target user's operator user attribute tags and the coupon type tags; It can represent the matching weight of the matching score between the target user's operator user behavior tags and the coupon rule tags; It can represent the matching score between the target user's operator user behavior tags and coupon rule tags; It can represent the matching weight of the matching score between the target user's real-time operator data and the coupon parameters; It can represent the matching score between the target user's real-time operator data and the coupon parameters.

[0093] The operator's preset matching score threshold can refer to the minimum acceptable matching score (e.g., 80 points) pre-set by the operator to exclude digital coupons below this minimum value. In other words, after calculating the operator's total matching score, it can be further compared with the operator's preset matching score threshold to exclude digital coupons below this threshold. The operator's dynamically configured digital coupon set can include one or more operator dynamically configured digital coupons, which can refer to digital coupons selected from reference digital coupons that have a matching score greater than or equal to the threshold for each user type of the operator.

[0094] For example, the average (or weighted average) of the three types of scores (assuming a matching score of 100 for user A and coupon type, 100 for user behavior and coupon rule, and 100 for real-time data and coupon parameters) can be calculated for user A and digital coupon 1. If the operator's preset matching score threshold is 80, since the average score of 100 is ≥ 80, the corresponding digital coupon 1 will be included in the operator's dynamically configured digital coupon set. If the average score of user A and digital coupon 2 is 70, and 70 < 80, then digital coupon 2 can be excluded. This application does not impose any restrictions on this.

[0095] Furthermore, by performing user feature analysis on the data accessed by each financial institution, subsets of user attribute tags and compliance tags for financial institution users can be obtained. User feature analysis on the financial institution side can be understood as the process of extracting core customer characteristics (such as static user attributes and compliance status) from the data accessed by the financial institution and transforming them into structured tags.

[0096] The subset of financial institution user attribute tags may include one or more financial institution user attribute tags. These tags can refer to tags obtained through user characteristic analysis that reflect the static attributes of financial institution customers. These user attribute tags can be relatively stable tags generated based on basic customer information (such as customer level, account opening duration). The subset of financial institution user compliance tags may include one or more financial institution user compliance tags. These tags can refer to tags obtained through user characteristic analysis that reflect the compliance status of financial institution customers. These compliance tags are generated based on compliance verification data and can be directly associated with the compliance requirements of financial business (such as risk assessment, anti-money laundering status). This application does not impose any restrictions on this.

[0097] Understandably, the matching degree between financial user attribute tags and digital coupon type tags, financial institution user compliance tags and digital coupon rule tags, and real-time business data (such as wealth management holdings) and digital coupon quantitative parameters can be calculated separately to obtain three types of matching scores on the financial institution side. The real-time business data subset can include one or more real-time business data points. This real-time business data can refer to a set of real-time data (such as current wealth management holdings, real-time transaction amounts) extracted from data accessed by the financial institution side, reflecting the customer's current business status, and has dynamic updating characteristics. For example, real-time business data could include data such as current wealth management holdings of 12,000 yuan, credit card spending of 500 yuan in the past hour, and real-time available balance of 8,000 yuan, which can be updated in real time with business operations.

[0098] It should be noted that the matching score between financial institution user attribute tags and digital voucher type tags can be obtained by quantifying the degree of adaptation between them; the matching score between financial institution user compliance tags and digital voucher rule tags can be obtained by quantifying the degree of adaptation between them; and the matching score between financial institution business data and voucher parameters can be obtained by quantifying the degree of adaptation between real-time business data and digital voucher quantitative parameters.

[0099] Optionally, a tag matching algorithm (such as overlapping tag ratio) or a tag similarity algorithm (such as cosine similarity) can be used to calculate the above matching score, and this application does not impose any restrictions on this. For example, if the overlapping tag between the financial institution user attribute tag {new customer, platinum customer} and the digital coupon type tag {finance-new customer coupon} is "new customer", then the matching degree between the financial institution user attribute tag {new customer, platinum customer} and the digital coupon type tag {finance-new customer coupon} is 100% (matching score is 100 points); if the financial institution user attribute tag {new customer, platinum customer} and {finance-existing customer coupon} do not overlap, then the matching degree between the financial institution user attribute tag {new customer, platinum customer} and {finance-existing customer coupon} is 0% (matching score is 0 points).

[0100] Optionally, for each target user, the three types of matching scores from the financial institution side can be calculated. Based on the matching scores of the financial institution user attribute tags and coupon type tags, the matching scores of the financial institution user compliance tags and coupon rule tags, and the matching scores of the financial institution business data and coupon parameters, the total financial institution matching score for each target user can be determined. The specific formula is as follows: in, It can represent the total matching score of the target user's financial institution; It can represent the matching weight of the matching score between the financial institution user attribute tags and the coupon type tags of the target user; It can represent the matching score between the financial institution user attribute tags and the coupon type tags of the target user; It can represent the matching weight of the matching score between the financial institution user compliance label and the coupon rule label of the target user; It can represent the matching score between the financial institution user compliance label and the coupon rule label of the target user; It can represent the matching weight of the matching score between the target user's financial institution business data and the coupon parameters; It can represent the matching score between the target user's financial institution business data and the coupon parameters.

[0101] A financial institution's preset matching score threshold refers to a minimum score standard (e.g., 90 points) that the institution sets in advance based on its business needs to determine whether a digital voucher is a good fit for a customer. Digital vouchers scoring below this threshold are considered unsuitable and will be excluded. In other words, after calculating the financial institution's total matching score, this total score can be compared with the institution's preset matching score threshold to exclude digital vouchers scoring below it.

[0102] Specifically, financial institutions can pre-set a matching score threshold of 90 points based on compliance risk control requirements (due to the strong compliance requirements of financial institutions, this pre-set matching score threshold can be higher than the operator's pre-set matching score threshold). That is, the weighted average of the above three types of scores (i.e., the matching score between financial institution user attribute tags and voucher type tags, the matching score between financial institution user compliance tags and voucher rule tags, and the matching score between financial institution business data and voucher parameters) must be greater than or equal to 90 points to be considered as a match.

[0103] Furthermore, by integrating the results from both modes (i.e., the dynamically configured digital coupon set from operators and the dynamically configured digital coupon set from financial institutions), a dynamically configured digital coupon set can be determined. This dynamically configured digital coupon set can be a precise set of coupons that can be pushed to users after integrating the dynamically configured digital coupon sets from operators and financial institutions.

[0104] In this embodiment, a refined process involving tag-based analysis, multi-dimensional matching, and threshold filtering achieves precise matching between dual-mode coupons and users. Matching dimensions are designed for different modes (operators focus on attributes and behaviors, while finance focuses on compliance and business data), fully aligning with the characteristics of both modes (e.g., strong compliance in finance, high user activity for operators), avoiding cross-mode adaptation bias. Real-time data participation in matching (e.g., user's current points, compliance status) ensures dynamic synchronization between coupons and the user's current state (matching results automatically adjust when state changes), resolving the time lag issue in traditional static matching. Preset threshold filtering effectively filters low-matching coupons, reducing user interference and resource waste. The resulting dynamically configured digital coupon set ensures differentiated accuracy in dual-mode operations and enhances users' willingness to claim and use coupons through real-time updates, providing efficient coupon distribution support for collaborative dual-mode operations.

[0105] For examples consistent with the above embodiments, please refer to... Figure 3 , Figure 3 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application, such as... Figure 3 As shown, it includes a processor, an input device, an output device, and a memory, which are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions. The program includes instructions for performing the following steps. Acquire the operator-side access data set and the financial institution-side access data set under dual-mode operation; Based on the operator-side access data set and the financial institution-side access data set, a dual-mode operation requirement analysis is performed to obtain a dual-mode operation requirement set. Based on the dual-mode operation requirement set, the operator-side access data set, and the financial institution-side access data set, rule configuration processing is performed to obtain the digital coupon configuration rule set; If the preset digital coupon generation conditions are met, a reference digital coupon set is generated according to the digital coupon configuration rule set; Dynamic matching of digital coupons is performed based on the reference digital coupon set, the operator-side access data set, and the financial institution-side access data set to obtain a dynamically configured digital coupon set.

[0106] The above mainly describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the terminal includes the corresponding hardware structure and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0107] This application embodiment can divide the terminal into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0108] For those consistent with the above, please refer to Figure 4 , Figure 4 This application provides a schematic diagram of the structure of a dual-mode digital coupon dynamic configuration device. (See attached diagram.) Figure 4 As shown, the device includes: The acquisition unit 101 is used to acquire the operator-side access data set and the financial institution-side access data set under dual-mode operation. The first processing unit 102 is used to perform dual-mode operation requirement analysis based on the operator-side access data set and the financial institution-side access data set to obtain a dual-mode operation requirement set. The second processing unit 103 is used to perform rule configuration processing based on the dual-mode operation requirement set, the operator-side access data set, and the financial institution-side access data set to obtain a digital coupon configuration rule set. The third processing unit 104 is used to generate a reference digital coupon set according to the digital coupon configuration rule set, provided that the preset digital coupon generation conditions are met. The fourth processing unit 105 is used to perform dynamic matching processing of digital coupons based on the reference digital coupon set, the operator-side access data set, and the financial institution-side access data set to obtain a dynamically configured digital coupon set.

[0109] In one possible implementation, the second processing unit 103 is used to perform rule configuration processing based on the dual-mode operation requirement set, the operator-side access data set, and the financial institution-side access data set to obtain a digital voucher configuration rule set, specifically for: Based on the set of dual-mode operation requirements, a preliminary rule screening process is performed for each mode to obtain a set of mode-specific rule framework data. Based on the pattern-specific rule framework data set, perform pattern-specific rule configuration processing for each pattern to obtain the pattern-specific rule configuration data set; Based on the long-term core requirements subset from the mode-specific rule configuration data set, the operator-side access data set, the financial institution-side access data set, and the dual-mode operation requirement set, the core rule structure configuration processing is performed to obtain the mode-specific core rule data set. Based on the model-specific core rule data set and the real-time dynamic requirement subset in the dual-mode operation requirement set, user type rules are refined to obtain the model-specific refined rule data set. The rule validation process is performed on each mode-specific refined rule data in the mode-specific refined rule data set to obtain the digital coupon configuration rule set.

[0110] In one possible implementation, the second processing unit 103 is used to perform preliminary rule screening for each mode based on the dual-mode operation requirement set, to obtain a mode-specific rule framework data set, specifically for: Based on the dual-mode operation requirement set, operator-side requirement keywords are extracted to obtain the operator requirement keyword set. Based on the dual-mode operation requirements, the financial institution side demand keywords are extracted and processed to obtain the financial institution demand keyword set. Based on the preset requirement field mapping table, each operator requirement keyword in the operator requirement keyword set is processed by requirement field mapping to obtain the set of mandatory fields for operator rules. Based on the preset requirement field mapping table, each financial institution requirement keyword in the financial institution requirement keyword set is processed by requirement field mapping to obtain the set of required fields for financial institution rules. Based on the set of required fields for operator rules and the set of required fields for financial institution rules, determine the pattern-specific rule framework data set.

[0111] In one possible implementation, the second processing unit 103 is configured to perform core rule structure configuration processing based on the mode-specific rule configuration data set, the operator-side access data set, the financial institution-side access data set, and the long-term core demand subset from the dual-mode operation demand set, to obtain a mode-specific core rule data set, specifically used for: Based on the subset of long-term core requirements in the dual-mode operation requirements, the rules are decomposed to obtain a set of decomposed rule elements. Data association processing is performed based on the set of disassembly rule elements, the set of operator-side access data, and the set of financial institution-side access data to obtain an initial dual-mode requirement rule mapping matrix; Based on the operator-side access data set and the financial institution-side access data set, the initial dual-mode demand rule mapping matrix is ​​verified and iterated to obtain the target dual-mode demand rule mapping matrix. Based on the target dual-mode requirement rule mapping matrix and the long-term core requirement subset, the requirement decomposition process is performed to obtain the dual-mode core rule data set. The dual-mode core rule data set and the mode-specific rule configuration data set are embedded to obtain the mode-specific core rule data set.

[0112] In one possible implementation, the fourth processing unit 105 is configured to perform dynamic matching processing of digital vouchers based on the reference digital voucher set, the operator-side access data set, and the financial institution-side access data set to obtain a dynamically configured digital voucher set, specifically for: Perform digital coupon feature analysis on each reference digital coupon in the reference digital coupon set to obtain a subset of digital coupon type tags and a subset of digital coupon rule tags; User feature analysis is performed on each operator-side access data in the operator-side access data set to obtain a subset of operator user attribute tags and a subset of operator user behavior tags. The matching degree is calculated based on the subset of operator user attribute tags and the subset of digital coupon type tags to obtain the matching subset of operator user attribute tags and coupon type tags; The matching degree is calculated based on the subset of operator user behavior tags and the subset of digital coupon rule tags to obtain the matching subset of operator user behavior tags and coupon rule tags; The matching degree is calculated based on the real-time updated data subset in the operator-side access data set and the digital coupon quantification parameter subset in the reference digital coupon set to obtain the matching subset of operator real-time data and coupon parameters; The operator dynamically configured digital coupon set is determined based on the operator's preset matching score threshold, the matching set of the operator's user attribute tags and coupon type tags, the matching set of the operator's user behavior tags and coupon rule tags, and the matching set of the operator's real-time data and coupon parameters. User feature analysis is performed on each financial institution-side access data in the financial institution-side access data set to obtain a subset of financial institution user attribute tags and a subset of financial institution user compliance tags. The matching degree is calculated based on the subset of financial institution user attribute tags and the subset of digital coupon type tags to obtain the matching subset of financial institution user attribute tags and coupon type tags; The matching degree is calculated based on the subset of compliance tags for financial institution users and the subset of rule tags for digital coupons to obtain the matching subset of compliance tags for financial institution users and coupon rule tags. The matching degree is calculated based on the subset of real-time business data in the data set accessed by the financial institution and the subset of digital voucher quantification parameters in the reference digital voucher set, to obtain the matching subset of financial institution business data and voucher parameters; The set of digital coupons that financial institutions can dynamically configure is determined based on the preset matching score threshold of financial institutions, the matching set of user attribute tags and coupon type tags of financial institutions, the matching set of user compliance tags and coupon rule tags of financial institutions, and the matching set of business data and coupon parameters of financial institutions. The dynamically configured digital coupon set is determined based on the operator's dynamically configured digital coupon set and the financial institution's dynamically configured digital coupon set.

[0113] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the dual-mode digital voucher dynamic configuration methods described in the above method embodiments.

[0114] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the dual-mode operation digital voucher dynamic configuration methods described in the above method embodiments.

[0115] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0116] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0117] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0118] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0119] Furthermore, the functional units in the various embodiments of the application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0120] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0121] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc.

[0122] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for dynamically configuring digital coupons in a dual-mode operation, characterized in that, The method for dynamically configuring digital vouchers in the dual-mode operation includes: Acquire the operator-side access data set and the financial institution-side access data set under dual-mode operation; Based on the operator-side access data set and the financial institution-side access data set, a dual-mode operation requirement analysis is performed to obtain a dual-mode operation requirement set. Based on the dual-mode operation requirement set, the operator-side access data set, and the financial institution-side access data set, rule configuration processing is performed to obtain the digital coupon configuration rule set; If the preset digital coupon generation conditions are met, a reference digital coupon set is generated according to the digital coupon configuration rule set; Dynamic matching of digital coupons is performed based on the reference digital coupon set, the operator-side access data set, and the financial institution-side access data set to obtain a dynamically configured digital coupon set.

2. The method for dynamically configuring digital coupons in a dual-mode operation according to claim 1, characterized in that, The rule configuration process, based on the dual-mode operation requirement set, the operator-side access data set, and the financial institution-side access data set, yields a digital voucher configuration rule set, including: Based on the set of dual-mode operation requirements, a preliminary rule screening process is performed for each mode to obtain a set of mode-specific rule framework data. Based on the pattern-specific rule framework data set, perform pattern-specific rule configuration processing for each pattern to obtain the pattern-specific rule configuration data set; Based on the long-term core requirements subset from the mode-specific rule configuration data set, the operator-side access data set, the financial institution-side access data set, and the dual-mode operation requirement set, the core rule structure configuration processing is performed to obtain the mode-specific core rule data set. Based on the model-specific core rule data set and the real-time dynamic requirement subset in the dual-mode operation requirement set, user type rules are refined to obtain the model-specific refined rule data set. The rule validation process is performed on each mode-specific refined rule data in the mode-specific refined rule data set to obtain the digital coupon configuration rule set.

3. The method for dynamically configuring digital coupons in a dual-mode operation according to claim 2, characterized in that, The initial rule screening process for each mode based on the dual-mode operation requirement set yields a mode-specific rule framework data set, including: Based on the dual-mode operation requirement set, operator-side requirement keywords are extracted to obtain the operator requirement keyword set. Based on the dual-mode operation requirements, the financial institution side demand keywords are extracted and processed to obtain the financial institution demand keyword set. Based on the preset requirement field mapping table, each operator requirement keyword in the operator requirement keyword set is processed by requirement field mapping to obtain the set of mandatory fields for operator rules. Based on the preset requirement field mapping table, each financial institution requirement keyword in the financial institution requirement keyword set is processed by requirement field mapping to obtain the set of required fields for financial institution rules. Based on the set of required fields for operator rules and the set of required fields for financial institution rules, determine the pattern-specific rule framework data set.

4. The method for dynamically configuring digital coupons in a dual-mode operation according to claim 2, characterized in that, The core rule structure configuration process is performed based on the long-term core requirement subset from the mode-specific rule configuration data set, the operator-side access data set, the financial institution-side access data set, and the dual-mode operation requirement set to obtain a mode-specific core rule data set, including: Based on the subset of long-term core requirements in the dual-mode operation requirements, the rules are decomposed to obtain a set of decomposed rule elements. Data association processing is performed based on the set of disassembly rule elements, the set of operator-side access data, and the set of financial institution-side access data to obtain an initial dual-mode requirement rule mapping matrix; Based on the operator-side access data set and the financial institution-side access data set, the initial dual-mode demand rule mapping matrix is ​​verified and iterated to obtain the target dual-mode demand rule mapping matrix. Based on the target dual-mode requirement rule mapping matrix and the long-term core requirement subset, the requirement decomposition process is performed to obtain the dual-mode core rule data set. The dual-mode core rule data set and the mode-specific rule configuration data set are embedded to obtain the mode-specific core rule data set.

5. The method for dynamically allocating digital vouchers in a dual-mode operation according to any one of claims 1-4, characterized in that, The step of dynamically matching digital vouchers based on the reference digital voucher set, the operator-side access data set, and the financial institution-side access data set to obtain a dynamically configured digital voucher set includes: Perform digital coupon feature analysis on each reference digital coupon in the reference digital coupon set to obtain a subset of digital coupon type tags and a subset of digital coupon rule tags; User feature analysis is performed on each operator-side access data in the operator-side access data set to obtain a subset of operator user attribute tags and a subset of operator user behavior tags. The matching degree is calculated based on the subset of operator user attribute tags and the subset of digital coupon type tags to obtain the matching subset of operator user attribute tags and coupon type tags; The matching degree is calculated based on the subset of operator user behavior tags and the subset of digital coupon rule tags to obtain the matching subset of operator user behavior tags and coupon rule tags; The matching degree is calculated based on the real-time updated data subset in the operator-side access data set and the digital coupon quantification parameter subset in the reference digital coupon set to obtain the matching subset of operator real-time data and coupon parameters; The operator dynamically configured digital coupon set is determined based on the operator's preset matching score threshold, the matching set of the operator's user attribute tags and coupon type tags, the matching set of the operator's user behavior tags and coupon rule tags, and the matching set of the operator's real-time data and coupon parameters. User feature analysis is performed on each financial institution-side access data in the financial institution-side access data set to obtain a subset of financial institution user attribute tags and a subset of financial institution user compliance tags. The matching degree is calculated based on the subset of financial institution user attribute tags and the subset of digital coupon type tags to obtain the matching subset of financial institution user attribute tags and coupon type tags; The matching degree is calculated based on the subset of compliance tags for financial institution users and the subset of rule tags for digital coupons to obtain the matching subset of compliance tags for financial institution users and rule tags for coupons. The matching degree is calculated based on the subset of real-time business data in the data set accessed by the financial institution and the subset of digital voucher quantification parameters in the reference digital voucher set, to obtain the matching subset of financial institution business data and voucher parameters; The set of digital coupons that financial institutions can dynamically configure is determined based on the preset matching score threshold of financial institutions, the matching set of user attribute tags and coupon type tags of financial institutions, the matching set of user compliance tags and coupon rule tags of financial institutions, and the matching set of business data and coupon parameters of financial institutions. The dynamically configured digital coupon set is determined based on the operator's dynamically configured digital coupon set and the financial institution's dynamically configured digital coupon set.

6. A dual-mode digital coupon dynamic configuration device, characterized in that, The device includes: The acquisition unit is used to acquire the operator-side access data set and the financial institution-side access data set under dual-mode operation. The first processing unit is used to perform dual-mode operation requirement analysis based on the operator-side access data set and the financial institution-side access data set to obtain a dual-mode operation requirement set. The second processing unit is used to perform rule configuration processing based on the dual-mode operation requirement set, the operator-side access data set, and the financial institution-side access data set to obtain a digital coupon configuration rule set. The third processing unit is used to generate a reference digital coupon set according to the digital coupon configuration rule set, provided that the preset digital coupon generation conditions are met. The fourth processing unit is used to perform dynamic matching processing of digital coupons based on the reference digital coupon set, the operator-side access data set, and the financial institution-side access data set to obtain a dynamically configured digital coupon set.

7. The dual-mode digital coupon dynamic configuration device according to claim 6, characterized in that, The second processing unit is used to perform rule configuration processing based on the dual-mode operation requirement set, the operator-side access data set, and the financial institution-side access data set to obtain a digital coupon configuration rule set, specifically for: Based on the set of dual-mode operation requirements, a preliminary rule screening process is performed for each mode to obtain a set of mode-specific rule framework data. Based on the pattern-specific rule framework data set, perform pattern-specific rule configuration processing for each pattern to obtain the pattern-specific rule configuration data set; Based on the long-term core requirements subset from the mode-specific rule configuration data set, the operator-side access data set, the financial institution-side access data set, and the dual-mode operation requirement set, the core rule structure configuration processing is performed to obtain the mode-specific core rule data set. Based on the model-specific core rule data set and the real-time dynamic requirement subset in the dual-mode operation requirement set, user type rules are refined to obtain the model-specific refined rule data set. The rule validation process is performed on each mode-specific refined rule data in the mode-specific refined rule data set to obtain the digital coupon configuration rule set.

8. The dual-mode digital coupon dynamic configuration device according to claim 7, characterized in that, The second processing unit is used to perform initial rule screening for each mode based on the dual-mode operation requirement set, to obtain a mode-specific rule framework data set, specifically used for: Based on the dual-mode operation requirement set, operator-side requirement keywords are extracted to obtain the operator requirement keyword set. Based on the dual-mode operation requirements, the financial institution side demand keywords are extracted and processed to obtain the financial institution demand keyword set. Based on the preset requirement field mapping table, each operator requirement keyword in the operator requirement keyword set is processed by requirement field mapping to obtain the set of mandatory fields for operator rules. Based on the preset requirement field mapping table, each financial institution requirement keyword in the financial institution requirement keyword set is processed by requirement field mapping to obtain the set of required fields for financial institution rules. Based on the set of required fields for operator rules and the set of required fields for financial institution rules, determine the pattern-specific rule framework data set.

9. A terminal, characterized in that, The device includes a processor, an input device, an output device, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute the dual-mode digital voucher dynamic configuration method as described in any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the dual-mode operation digital voucher dynamic configuration method as described in any one of claims 1-5.

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