Credit exchange method and device, electronic equipment and storage medium

Abstract

According to the point exchange method and device, the electronic equipment and the storage medium provided by the embodiment of the invention, the technical scheme establishes an intelligent right point automatic exchange architecture based on the three core parameters of the user level coefficient, the exchange proportion and the right liquidity index by constructing the multi-dimensional right quantification model, so that the user experience is improved. According to the scheme, automatic conversion from fixed-period rights and interests to points is achieved, the validity period of user assets is changed from a rigid period to an elastic period, compared with a traditional fixed-period mode, the scheme essentially reconstructs a value chain of'rights and interests-points', and a bidirectional enhancement loop for improving user stickiness and the platform resource turnover rate is formed while the problem of resource idleness is solved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of computer technology, and particularly relate to a method and device for integral exchange, electronic equipment and storage medium. BACKGROUND

[0002] Currently, the Internet service platform generally adopts a business model of "fixed-term member benefits", and users obtain member use benefits within a specific period through payment or activity rewards. However, under this business model, the actual use behavior of the user does not match the effective period of the benefits, resulting in "sunk costs" of unused benefits. This idle resource not only causes the platform party to be unable to realize the recycling of benefits, but also reduces user stickiness and activity, forming a two-way value loss. SUMMARY

[0003] In view of this, embodiments of the present application provide a method and device for integral exchange, electronic equipment and storage medium, which can effectively alleviate the technical problem of resource idling caused by fixed-term benefits.

[0004] In a first aspect, embodiments of the present application provide a method for integral exchange, the method comprising:

[0005] Collecting user benefit operation data of a user; wherein the user benefit operation data comprises current benefit inventory and historical benefit average consumption data;

[0006] Determining a user level coefficient and an exchange ratio of the user based on the user benefit operation data;

[0007] Determining a benefit liquidity index according to the current benefit inventory and the historical benefit average consumption data;

[0008] Performing integral exchange on the current benefit inventory based on the user level coefficient, the exchange ratio and the benefit liquidity index, to obtain the integral corresponding to the user.

[0009] In one possible implementation, the user benefit operation data further comprises user behavior data, same-type benefit market supply and demand data and benefit inventory data, wherein the benefit inventory data comprises benefit type and historical benefit exchange data;

[0010] Determining a user level coefficient and an exchange ratio of the user based on the user benefit operation data comprises:

[0011] Inputting the user behavior data, the same-type benefit market supply and demand data and the benefit inventory data into a pre-trained user level generation model, and the user level generation model outputs the user level corresponding to the user;

[0012] Determining the user level coefficient based on the user level;

[0013] The user's level, benefit type, and historical benefit redemption data are input into a pre-trained redemption ratio generation model, which then outputs the user's redemption ratio.

[0014] In one possible implementation, determining the user level coefficient based on the user level includes:

[0015] Retrieve the user level coefficient corresponding to the user level from the user level coefficient lookup table; the user level coefficient lookup table stores the correspondence between user levels and user level coefficients in advance.

[0016] In one possible implementation, points are redeemed based on the current equity inventory according to the user level coefficient, redemption ratio, and equity liquidity index to obtain the user's corresponding points, including:

[0017] Check whether the equity liquidity index is within the preset equity liquidity index range;

[0018] If the equity liquidity index is detected to be within the preset equity liquidity index range, the current equity inventory is redeemed for points based on the user level coefficient and the redemption ratio to obtain the user's corresponding points.

[0019] If the equity liquidity index is detected to be outside the preset equity liquidity index range, the exchange ratio is adjusted according to the equity liquidity index to obtain the adjusted exchange ratio.

[0020] Based on the user's level coefficient and the adjusted redemption ratio, the current equity inventory is redeemed with points to obtain the corresponding points for the user.

[0021] In one possible implementation, the exchange ratio is adjusted based on the equity liquidity index to obtain the adjusted exchange ratio, including:

[0022] If the equity liquidity index is lower than the preset equity liquidity index range, the exchange ratio is adjusted by reducing the exchange ratio based on the first preset value to obtain the adjusted exchange ratio.

[0023] If the equity liquidity index is higher than the preset equity liquidity index range, the exchange ratio is adjusted upward based on the second preset value to obtain the adjusted exchange ratio.

[0024] In one possible implementation, the method further includes:

[0025] Determine user profiles based on user behavior data;

[0026] Identify target redeemable items that closely match the user profile from among the various redeemable items included in the redemption pool;

[0027] The points redemption threshold for target redeemable items is determined based on user level;

[0028] The system pushes target redemption items and points redemption thresholds to users to enable points redemption.

[0029] In one possible implementation, the points redemption threshold for the target redeemable item is determined based on the user's level, including:

[0030] Retrieve the redemption threshold lookup table for the target redemption item;

[0031] Retrieve the points redemption threshold corresponding to the user level from the redemption threshold lookup table; the redemption threshold lookup table stores the correspondence between user levels and points redemption thresholds in advance.

[0032] Secondly, embodiments of the present invention provide a points redemption device, the device comprising:

[0033] The data acquisition module is used to collect user rights operation data; among which, user rights operation data includes current rights inventory and historical average rights consumption data;

[0034] The first determination module is used to determine the user's user level coefficient and redemption ratio based on user rights operation data;

[0035] The second determination module is used to determine the equity liquidity index based on the current equity inventory and historical average equity consumption data.

[0036] The points redemption module is used to redeem points based on the user's level coefficient, redemption ratio, and equity liquidity index, based on the current equity inventory, to obtain the user's corresponding points.

[0037] Thirdly, embodiments of the present invention provide an electronic device, comprising: a processor and a memory, wherein the processor is configured to execute a points redemption program stored in the memory to implement the aforementioned points redemption method.

[0038] Fourthly, embodiments of the present invention provide a storage medium, wherein the storage medium stores one or more programs, which can be executed by one or more processors to implement the above-described points redemption method.

[0039] The present invention provides a method, apparatus, electronic device, and storage medium for points redemption. The method includes: collecting user rights operation data; wherein the user rights operation data includes current rights inventory and historical average rights consumption data; determining the user's user level coefficient and redemption ratio based on the user rights operation data; determining the rights liquidity index based on the user rights data and historical average rights consumption data; and redeeming points for the current rights inventory based on the user level coefficient, redemption ratio, and rights liquidity index to obtain the user's corresponding points. This technical solution constructs a multi-dimensional rights quantification model and establishes an intelligent rights points automatic redemption architecture based on three core parameters: user level coefficient, redemption ratio, and rights liquidity index. This achieves the automated conversion of fixed-term rights into points, changing the validity period of user assets from a rigid cycle to a flexible cycle. Compared with the traditional fixed-term model, this solution essentially reconstructs the "rights-points" value chain, solving the problem of idle resources while forming a two-way reinforcing loop that improves user stickiness and platform resource turnover. Attached Figure Description

[0040] Figure 1 A flowchart illustrating an embodiment of a points redemption method provided by this invention;

[0041] Figure 2 A flowchart illustrating another method for redeeming points provided by an embodiment of the present invention;

[0042] Figure 3 A flowchart illustrating another method for redeeming points provided by an embodiment of the present invention;

[0043] Figure 4 A flowchart illustrating another method for redeeming points provided by an embodiment of the present invention;

[0044] Figure 5 A block diagram illustrating an embodiment of a points redemption device provided by this invention;

[0045] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] To facilitate understanding of the embodiments of the present invention, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0048] This invention provides a method for redeeming points. This method is applicable to internet service platforms, including but not limited to video streaming platforms, e-commerce membership systems, knowledge payment platforms, and lifestyle service platforms. The core scenario is the management and redemption of membership benefits, solving the problem of converting unused user benefits. See also... Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of a points redemption method provided by the present invention. Figure 1 The process shown may include the following steps:

[0049] Step 101: Collect user rights operation data;

[0050] In practical applications, user rights operation data can be collected at fixed intervals (such as 1 minute, 3 minutes, etc., which can be adjusted), or user rights operation data can be collected in real time, without any limitation.

[0051] In the user rights operation system, the collected user rights operation data provides an important basis for the subsequent points redemption mechanism. Specifically, this data system includes two core dimensions: first, the current rights inventory, which refers to the remaining valid duration of a user's existing membership rights. For example, if a user purchases an annual membership and has one month of validity remaining, their current rights inventory is recorded as 30 days; second, the historical average rights consumption rate, which reflects the efficiency of a user's past rights usage. It is calculated as the ratio of the total duration of accumulated purchased rights to the actual holding time. Assuming a user has accumulated 365 days of membership rights over three years, their historical average rights consumption rate is 365 / (365×3). These two dimensions of data together constitute the basic elements of the user rights profile.

[0052] Step 102: Determine the user's user level coefficient and redemption ratio based on user rights operation data;

[0053] In the user rights management system, the user level coefficient serves as a core evaluation indicator. Through multi-dimensional user rights operation data analysis, it quantifies the user's value contribution to the platform. The coefficient's value range is positively correlated with the user's value level; a higher coefficient indicates a greater contribution to the platform's ecosystem. The exchange ratio is a dynamic parameter in the rights conversion mechanism. Based on user rights operation data, the exchange ratio between rights and points is adjusted in real time. This ratio is influenced by multiple variables such as market factors, inventory pressure, and user level. A non-linear mapping relationship is established to ensure the fairness and incentive of the exchange system. Simultaneously, this parameter setting must adhere to the basic rules of the platform's economic model, ensuring both the value of user rights and maintaining commercial sustainability. These two core parameters together constitute the basic framework for user rights value assessment and conversion.

[0054] Step 103: Determine the equity liquidity index based on the current equity inventory and historical average equity consumption data;

[0055] The equity liquidity index, a core indicator for user behavior analysis, is a dynamic quantitative value derived by dividing the current equity inventory by the historical average equity consumption data. The index's value directly reflects user activity on the platform; a higher index value indicates more frequent and deeper user interaction with the platform. In business applications, this index serves as a key input parameter in the dynamic adjustment of redemption ratios, enabling internet service platforms to implement differentiated equity management strategies based on user characteristics, thereby optimizing resource allocation efficiency and improving user retention. This indicator system fully embodies the core value of data-driven decision-making in user operations.

[0056] Step 104: Based on the user level coefficient, exchange ratio, and equity liquidity index, redeem the current equity inventory with points to obtain the user's corresponding points.

[0057] Finally, in step 104, points are redeemed. The current equity inventory is converted into points using user level coefficients, redemption ratios, and equity liquidity indices, resulting in personalized points that match user characteristics, thus completing the value conversion loop from equity to points. This process achieves accurate quantification and efficient circulation of equity resources through a data-driven approach.

[0058] This technical solution constructs a multi-dimensional equity quantification model. Based on three core parameters—user level coefficient, exchange ratio, and equity liquidity index—it establishes an intelligent equity points automatic exchange architecture, realizing the automatic conversion of fixed-term equity into points. This transforms the validity period of user assets from a rigid cycle to a flexible cycle. Compared with the traditional fixed-term model, this solution essentially reconstructs the "equity-points" value chain, solving the problem of idle resources while forming a two-way reinforcing loop that improves user stickiness and platform resource turnover.

[0059] In practical applications, the aforementioned user rights operation data also includes user behavior data, market supply and demand data for similar rights, and rights inventory data. Specifically, user behavior data includes, but is not limited to, personal behaviors such as browsing, redemption, and consumption records, recent consumption time, consumption frequency, and consumption amount; market supply and demand data for similar rights refers to the redemption price of similar rights on the partner platform; rights inventory data includes not only the current rights inventory and historical average rights consumption data, but may also include rights types and historical rights redemption data. No limitations are imposed on the aforementioned user rights operation data; any necessary data can be collected for the points redemption in this application based on actual needs.

[0060] Based on the above, in an optional implementation, step 102, which determines the user's user level coefficient and redemption ratio based on user rights operation data, specifically includes the following process:

[0061] Step 201: Input user behavior data, similar rights market supply and demand data, and rights inventory data into the pre-trained user level generation model. The user level generation model outputs the user level corresponding to the user.

[0062] The user rating generation model is an intelligent classification system built using a supervised learning framework. Its training process employs a methodology combining feature engineering and machine learning. The training dataset consists of standardized three-dimensional data: historical user behavior data, historical supply and demand data for similar rights in the market, and historical rights inventory data. These data are labeled by experts to form training samples with user rating labels. In terms of model architecture selection, it supports multiple scalable algorithm frameworks, including deep neural networks and random forests. Cross-validation and hyperparameter tuning ensure the model's generalization ability. The final trained classification model can dynamically adapt to changes in user behavior patterns and the market environment, achieving accurate user rating classification.

[0063] In practical applications, collected user behavior data, supply and demand data for similar rights and interests, and rights and interests inventory data can be used as input data for the model. The user level generation model analyzes the input data and outputs the user level corresponding to the input data. The user level is a comprehensive evaluation system used by internet service platforms to quantify user value contribution and activity level.

[0064] Step 202: Determine the user level coefficient based on the user level;

[0065] The specific implementation process of step 201 above, which determines the user level coefficient based on the user level, is as follows: The user level coefficient is retrieved from the user level coefficient lookup table, which pre-stores the correspondence between user levels and user level coefficients. This user level coefficient lookup table may be a database table, an Excel spreadsheet, a configuration file, or other data structure; no specific limitation is made here. For ease of understanding, it is illustrated in Table 1.

[0066] Table 1

[0067] User level User level coefficient Silver 1.1 Gold 1.3 Diamond 1.5

[0068] It should be noted that the above-described mapping relationship between user levels and user level coefficients is only an example. The specific mapping relationship between user levels and user level coefficients can be set according to actual needs, and is not limited here.

[0069] For example, if the user level determined in step 201 is Gold, then by referring to Table 1, we can see that the corresponding user level coefficient is 1.3.

[0070] Step 203: Input the user level, benefit type, and historical benefit redemption data into the pre-trained redemption ratio generation model, and the redemption ratio generation model outputs the user's redemption ratio.

[0071] The exchange ratio generation model is an intelligent decision-making system built on reinforcement learning algorithms (such as Q-Learning). Its framework design incorporates complete Markov decision process elements: In state space construction, the system aggregates user level, benefit type, and historical benefit exchange data to form a multi-dimensional state representation; the action space is strictly limited to an exchange ratio adjustment range of ±20%, balancing exploration and utilization through an ε-greedy strategy; the reward function adopts a dual-objective optimization design, considering both user-side exchange rate indicators (reflecting user satisfaction) and platform-side benefit consumption efficiency (measuring inventory turnover), updating parameters through TD (Temporal Difference) error backpropagation. During the online inference phase, the model receives user level, benefit type, and historical benefit exchange data collected in step 101, calculates the optimal exchange ratio decision (e.g., 18.5%) through the policy network, and this dynamic adjustment mechanism enables the platform to achieve Pareto optimality between user incentives and resource management.

[0072] In an optional implementation, step 103 above, which involves redeeming points based on the user's level coefficient, exchange ratio, and equity liquidity index to obtain the user's corresponding points, specifically includes the following process:

[0073] Step 301, detect whether the equity liquidity index is within the preset equity liquidity index range;

[0074] The preset equity liquidity index range (usually expressed as [Lmin, Lmax]) is a key operation threshold pre-determined by the Internet service platform through big data modeling. When the equity liquidity index ∈ [Lmin, Lmax], it indicates that core indicators such as platform user activity and equity transfer efficiency are within the normal fluctuation range. At this time, maintain the established exchange ratio strategy (execute Step 302) to ensure operational stability; while when the equity liquidity index exceeds the threshold range (L < Lmin or L > Lmax), trigger the dynamic adjustment of the exchange ratio strategy (execute Step 303), and adapt to the personalized needs of users by adjusting the exchange ratio in real time to achieve precise control of points redemption.

[0075] Step 302, conduct points redemption for the current equity inventory based on the user level coefficient and the exchange ratio to obtain the corresponding points for the user;

[0076] The corresponding points for the above user can be calculated through the following formula:

[0077] Points = Current equity inventory * User level coefficient * Exchange ratio. From the formula, it can be seen that points are obtained through the product operation of the current equity inventory, the user level coefficient, and the exchange ratio. Continuing the previous example, the current equity inventory is thirty days, the user level coefficient is 1.3, and the exchange ratio is 15%, then points = 30 * 1.3 * 0.15 = 5.85.

[0078] Step 303, adjust the exchange ratio according to the equity liquidity index to obtain the adjusted exchange ratio;

[0079] In the case where the equity liquidity index is lower than the preset equity liquidity index range, reduce and adjust the exchange ratio based on the first preset value to obtain the adjusted exchange ratio; specifically, when the equity liquidity index is lower than the lower limit (L < Lmin), it indicates that the equity transfer speed of the platform is too slow, and there may be problems such as insufficient user activity or decreased equity attractiveness. The role of reducing the exchange ratio (based on the first preset value): one is to reduce the points output per unit of equity and inhibit the excessive consumption of the equity inventory, and the other is to indirectly stimulate more frequent active behaviors by increasing the difficulty for users to obtain points.

[0080] The first preset value is a non-negative number less than 1. In one implementation, the first preset value can be set to a fixed value such as 0.1 or 0.5. In another implementation, the first preset value can be determined based on the difference between L and Lmin. Specifically, the difference is calculated based on the equity liquidity index and the minimum equity liquidity index to obtain the difference calculation result. Then, the preset value corresponding to the difference calculation result is retrieved from the first lookup table as the first preset value. The first lookup table stores the correspondence between the query calculation result and the preset value in advance. It should be noted that the larger the difference calculation result, the smaller the corresponding first preset value.

[0081] Regarding the above, the formula for adjusting the exchange rate is: exchange rate * first preset value. For example, if the first preset value is 0.1 and the exchange rate is 15%, then the adjusted exchange rate is 0.1 * 15% = 1.5%.

[0082] When the equity liquidity index exceeds the preset range, the exchange ratio is adjusted upwards based on a second preset value to obtain the adjusted exchange ratio. Specifically, when the equity liquidity index exceeds the upper limit (L>Lmax), it reflects that equity is being over-exchanged, which may lead to points inflation or uncontrolled platform costs. The intention of increasing the exchange ratio (based on the second preset value) is twofold: firstly, to increase the amount of points issued, thus incentivizing users to accelerate the consumption of equity inventory in the short term; and secondly, to maintain the platform's attractiveness by enhancing the perceived value for users.

[0083] The second preset value is a non-negative number greater than 1. In one implementation, the second preset value can be set to a fixed value such as 1.1, 1.5, etc. In another implementation, the second preset value can be determined based on the difference between L and Lmax. Specifically, the difference is calculated based on the equity liquidity index and the maximum equity liquidity index to obtain the difference calculation result. Then, the preset value corresponding to the difference calculation result is retrieved from the second lookup table as the second preset value. The second lookup table stores the correspondence between the query calculation result and the preset value in advance. It should be noted that the larger the difference calculation result, the larger the corresponding second preset value.

[0084] Regarding the above, the formula for adjusting the exchange rate is: exchange rate * second preset value. For example, if the second preset value is 1.1 and the exchange rate is 15%, then the adjusted exchange rate is 1.1 * 15% = 16.5%.

[0085] Step 304: Based on the user level coefficient and the adjusted exchange ratio, the current equity inventory is redeemed with points to obtain the corresponding points for the user.

[0086] The points corresponding to the above users can be calculated using the following formula:

[0087] Points = Current Equity Inventory * User Level Coefficient * Adjusted Redemption Ratio. As the formula shows, points are calculated by multiplying the current equity inventory by the user level coefficient and the adjusted redemption ratio. Continuing the previous example, if the current equity inventory is 30 days, the user level coefficient is 1.3, and the adjusted redemption ratio is 1.5%, then points = 30 * 1.3 * 0.015 = 0.585; if the current equity inventory is 30 days, the user level coefficient is 1.3, and the adjusted redemption ratio is 16.5%, then points = 30 * 1.3 * 0.165 = 6.435.

[0088] This mechanism, while ensuring the platform's risk control objectives, achieves differentiated design through user level coefficients, adjustments to exchange ratios, or changes to exchange ratios, in order to meet personalized rights and benefits allocation.

[0089] To boost user activity and repurchase rates, after implementing points redemption in the above embodiments, users can also redeem goods, creating a closed-loop ecosystem of "benefits-points-goods" to further enhance user stickiness. Figure 1 Based on this, see Figure 4 , Figure 4 A flowchart illustrating another method for redeeming points provided by an embodiment of the present invention. Figure 4 The process shown may include the following steps:

[0090] Step 401: Collect user rights operation data; wherein, user rights operation data includes current rights inventory and historical average rights consumption data;

[0091] Step 402: Determine the user's user level coefficient and redemption ratio based on user rights operation data;

[0092] Step 403: Determine the equity liquidity index based on the current equity inventory and historical average equity consumption data;

[0093] Step 404: Based on the user level coefficient, exchange ratio, and equity liquidity index, redeem the current equity inventory with points to obtain the user's corresponding points;

[0094] The implementation process of steps 401 to 404 above can be found in steps 101 to 104 of the above embodiments, and will not be repeated here.

[0095] Step 405: Determine the user profile based on user behavior data;

[0096] Specifically, user profiles corresponding to user behavior data can be determined based on pre-trained user profile generation models. User profiles specifically describe the user's preferences, spending power, and other personal characteristics.

[0097] The user profile generation model is an intelligent classification system built using a supervised learning framework. Its training process employs a methodology that combines feature engineering and machine learning. The training dataset consists of standardized data, specifically historical user behavior data and training samples labeled with user profile tags after expert annotation. In terms of model architecture selection, it supports multiple scalable algorithm frameworks, including deep neural networks and random forests. Cross-validation and hyperparameter tuning ensure the model's generalization ability. The final trained classification model can dynamically adapt to changes in user behavior patterns and the market environment, achieving accurate segmentation of user profiles.

[0098] Step 406: Identify target redemption items that highly match the user profile from the various redemption items included in the redemption pool;

[0099] The redemption pool is a multi-tiered redemption system (basic layer, cooperation layer, and scarcity layer), supporting the redemption of points for membership extensions, cross-platform benefits, physical goods, etc., to meet the needs of different users.

[0100] Specifically, the basic layer supports points redemption for extended membership periods (e.g., 100 points = 1 day of membership) and e-commerce physical goods redemption (connected to third-party supply chains); the cooperation layer supports cross-platform benefit redemption (e.g., video membership points redemption for music platform membership, food delivery platform coupons), achieving data interoperability through API (Application Programming Interface); and the scarcity layer supports limited-edition product auctions (e.g., celebrity merchandise, offline event tickets), using a Dutch auction algorithm to dynamically determine the transaction price.

[0101] In practical applications, the collaborative filtering algorithm can identify at least one target redemption item with a high degree of matching with the user profile from the various redemption items included in the redemption pool (such as prioritizing movie merchandise redemption for frequent movie viewers, movie cards, movie figurines, etc.). The specific matching process is the same as the existing process and will not be described in detail here.

[0102] Step 407: Determine the points redemption threshold for the target redeemable goods based on the user level;

[0103] To prevent points inflation, different user levels have different points redemption thresholds (e.g., lower user levels have lower minimum redemption points, and vice versa). The specific process for determining the points redemption threshold for a target item is as follows: obtain the redemption threshold lookup table for the target item; look up the points redemption threshold corresponding to the user level from the redemption threshold lookup table; the redemption threshold lookup table pre-stores the correspondence between user levels and points redemption thresholds.

[0104] The redemption thresholds for different target redemption items vary, therefore the corresponding redemption threshold lookup tables also differ. These lookup tables may be database tables, Excel spreadsheets, configuration files, or other data structures; no specific limitation is made here. For ease of understanding, Table 2 illustrates the example of redeeming a membership period extension target redemption item:

[0105] Table 1

[0106] User level Integral exchange threshold Silver 100 points = 1 day member Gold 150 points = 1 day member Diamond 200 points = 1 day member

[0107] It should be noted that the above-described mapping relationship between user levels and points redemption thresholds is only an example. The specific mapping relationship between user levels and points redemption thresholds can be set according to actual needs, and is not limited here.

[0108] Step 408: Push the target redeemable items and points redemption threshold to the user to enable points redemption.

[0109] Specifically, the target redemption items and points redemption thresholds can be pushed to users through multiple channels such as the platform's app, SMS, and WeChat, so that users can see the redemption information in a timely manner and are encouraged to actively redeem items using their existing points. To ensure the stability of the redemption pool, a points validity period (e.g., 1 year) and a maximum redemption limit (no more than 50% of a member's monthly redemption value per user) can be set. This setting effectively balances user experience and platform costs.

[0110] The proposed closed-loop architecture of "rights-points-products" can not only eliminate the feeling of wasted rights, but also greatly improve member satisfaction, forming a two-way reinforcing loop that enhances user stickiness and platform resource turnover.

[0111] See Figure 5 This is a block diagram illustrating an embodiment of a points redemption device provided by an embodiment of the present invention. Figure 5 As shown, the device includes:

[0112] The data acquisition module 501 is used to collect user rights operation data; among which, user rights operation data includes current rights inventory and historical average rights consumption data;

[0113] The first determining module 502 is used to determine the user's user level coefficient and redemption ratio based on user rights operation data;

[0114] The second determining module 503 is used to determine the equity liquidity index based on the current equity inventory and historical average equity consumption data.

[0115] The points redemption module 504 is used to redeem points based on the user's level coefficient, redemption ratio, and equity liquidity index to obtain the user's corresponding points.

[0116] This technical solution constructs a multi-dimensional equity quantification model. Based on three core parameters—user level coefficient, exchange ratio, and equity liquidity index—it establishes an intelligent equity points automatic exchange architecture, realizing the automatic conversion of fixed-term equity into points. This transforms the validity period of user assets from a rigid cycle to a flexible cycle. Compared with the traditional fixed-term model, this solution essentially reconstructs the "equity-points" value chain, solving the problem of idle resources while forming a two-way reinforcing loop that improves user stickiness and platform resource turnover.

[0117] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Figure 6 The illustrated electronic device 1200 includes at least one processor 1201, a memory 1202, at least one network interface 1204, and other user interfaces 1203. The various components in the electronic device 1200 are coupled together via a bus system 1205. It is understood that the bus system 1205 is used to implement communication between these components. In addition to a data bus, the bus system 1205 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 6 The general labeled all buses as Bus System 1205.

[0118] The user interface 1203 may include a display, keyboard, or clicking device (e.g., mouse, trackball, touchpad, or touchscreen).

[0119] It is understood that the memory 1202 in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate Synchronous DRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 1202 described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0120] In some implementations, memory 1202 stores elements, executable units or data structures, or subsets thereof, or extended sets thereof: operating system 12021 and application program 12022.

[0121] The operating system 12021 includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application program 12022 includes various applications, such as a media player and a browser, used to implement various application functions. The program implementing the method of this embodiment can be included in the application program 12022.

[0122] In this embodiment of the invention, the processor 1201 executes the method steps provided in each method embodiment by calling the program or instructions stored in the memory 1202, specifically the program or instructions stored in the application program 12022.

[0123] The methods disclosed in the above embodiments of the present invention can be applied to processor 1201, or implemented by processor 1201. Processor 1201 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 1201 or by instructions in the form of software. The processor 1201 may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software units in the decoding processor. The software units may be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 1202. Processor 1201 reads the information in memory 1202 and completes the steps of the above method in conjunction with its hardware.

[0124] It is understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described herein, or combinations thereof.

[0125] For software implementation, the techniques described herein can be implemented by units that perform the functions described herein. The software code can be stored in memory and executed by a processor. The memory can be implemented in the processor or external to the processor.

[0126] The electronic device provided in this embodiment may be as follows: Figure 6 The electronic device shown can perform the following: Figures 1-4 All steps of the points redemption method, thereby achieving Figures 1-4 For details on the technical effects of the points redemption method shown, please refer to [link / reference]. Figures 1-4 The relevant descriptions are presented concisely and will not be elaborated upon here.

[0127] This invention also provides a storage medium (computer-readable storage medium). This storage medium stores one or more programs. The storage medium may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive; the memory may also include combinations of the above types of memory.

[0128] When one or more programs in the storage medium can be executed by one or more processors to implement the above-mentioned points redemption method.

[0129] The processor is used to execute the points redemption program stored in the memory to implement the steps of the points redemption method.

[0130] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software 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 implementations should not be considered beyond the scope of this invention.

[0131] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0132] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for redeeming points, characterized in that, The method includes: Collect user rights operation data; wherein, the user rights operation data includes current rights inventory and historical average rights consumption data; The user's user level coefficient and redemption ratio are determined based on the user rights operation data. The equity liquidity index is determined based on the current equity inventory and the historical average equity consumption data. Based on the user level coefficient, the exchange ratio, and the equity liquidity index, the current equity inventory is exchanged for points to obtain the user's corresponding points.

2. The method according to claim 1, characterized in that, The user rights operation data also includes user behavior data, market supply and demand data for similar rights, and rights inventory data, wherein the rights inventory data includes rights types and historical rights redemption data; The process of determining the user's user level coefficient and redemption ratio based on the user rights operation data includes: The user behavior data, the supply and demand data of the same type of rights market, and the rights inventory data are input into a pre-trained user level generation model, and the user level generation model outputs the user level corresponding to the user. Determine the user level coefficient based on the user level; The user level, the benefit type, and the historical benefit redemption data are input into a pre-trained redemption ratio generation model, which then outputs the user's redemption ratio.

3. The method according to claim 2, characterized in that, The process of determining the user level coefficient based on the user level includes: Retrieve the user level coefficient corresponding to the user level from the user level coefficient lookup table; wherein, the user level coefficient lookup table stores the correspondence between user levels and user level coefficients in advance.

4. The method according to claim 1, characterized in that, The process of redeeming points based on the user level coefficient, the exchange ratio, and the equity liquidity index to obtain the user's corresponding points includes: Detect whether the equity liquidity index is within the preset equity liquidity index range; If the equity liquidity index is detected to be within the preset equity liquidity index range, the current equity inventory is redeemed for points based on the user level coefficient and the redemption ratio to obtain the user's corresponding points. If the equity liquidity index is detected to be outside the preset equity liquidity index range, the exchange ratio is adjusted according to the equity liquidity index to obtain an adjusted exchange ratio. Based on the user level coefficient and the adjusted exchange ratio, the current equity inventory is exchanged for points to obtain the user's corresponding points.

5. The method according to claim 4, characterized in that, The step of adjusting the exchange ratio according to the equity liquidity index to obtain the adjusted exchange ratio includes: If the equity liquidity index is lower than the preset equity liquidity index range, the exchange ratio is adjusted by reducing the exchange ratio based on the first preset value to obtain the adjusted exchange ratio. If the equity liquidity index is higher than the preset equity liquidity index range, the exchange ratio is adjusted upward based on the second preset value to obtain the adjusted exchange ratio.

6. The method according to claim 2, characterized in that, The method further includes: A user profile is determined based on the user behavior data; From the various redeemable items included in the redemption pool, identify target redeemable items that highly match the user profile; The points redemption threshold for the target redeemable item is determined based on the user level; The target redeemable items and the points redemption threshold are pushed to the user to enable points redemption.

7. The method according to claim 6, characterized in that, The step of determining the points redemption threshold for the target redeemable item based on the user level includes: Obtain the exchange threshold lookup table corresponding to the target exchange item; The user can retrieve the points redemption threshold corresponding to their user level from the redemption threshold lookup table. The redemption threshold lookup table stores the correspondence between user levels and points redemption thresholds in advance.

8. A points redemption device, characterized in that, The device includes: The data acquisition module is used to collect user rights operation data; wherein, the user rights operation data includes the current rights inventory and historical average rights consumption data; The first determining module is used to determine the user's user level coefficient and redemption ratio based on the user rights operation data; The second determining module is used to determine the equity liquidity index based on the current equity inventory and the historical average equity consumption data. The points redemption module is used to redeem points based on the user level coefficient, the redemption ratio, and the equity liquidity index to obtain the user's corresponding points.

9. An electronic device, characterized in that, include: A processor and a memory, the processor being configured to execute a points redemption program stored in the memory to implement the points redemption method according to any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the points redemption method according to any one of claims 1 to 7.

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