Game voucher issuing method based on dynamic integral adjustment

Abstract

The invention discloses a game voucher issuing method based on dynamic integral adjustment, and relates to the technical field of game marketing and virtual right issuing. The prize-winning probability of next participation is subjected to individualized correction by taking the prize-winning probability as a dynamic integral, so that users with different input intensities under the same activity rule obtain differentiated probability gains, and the experience conflict that the users do not win the prize for a long time due to high input is weakened; the combination of the accumulated disappropriation value and the mapping function can realize the winning probability monotonically increased along with investment accumulation, and meanwhile, a bottom guarantee path with flexibility and rigidity is constructed through an upper limit threshold and a deterministic prize issuing mechanism, so that the reward release is not excessively stacked, and the user rights and interests can be ensured under extreme conditions.

Description

Technical Field

[0001] This invention relates to the field of game marketing and virtual rights distribution technology, and in particular to a method for distributing game vouchers based on dynamic points adjustment. Background Technology

[0002] With the development of the mobile internet and online gaming industry, game operators are increasingly demanding the distribution of digital vouchers, virtual items, and other benefits to users. Among existing technologies, chance-based games have become one of the common interactive forms for distributing digital vouchers. For example, random gameplay such as lotteries, roulette wheels, or scratch cards can be integrated into the game client. The server generates the lottery results according to preset probabilities when users participate, and issues vouchers to the corresponding accounts when users win. This type of method is usually used in in-game promotional activities, login rewards, or sharing tasks to improve user activity and retention rates.

[0003] In the existing schemes mentioned above, the reward results are usually calculated by the server according to a uniformly configured probability model; that is, for different users under the same activity rules, the system uses the same lottery probability table, and the reward settlement is based solely on the random number generation, without considering the actual input of the participating individual in this or historical activities, such as the time spent completing the task, the frequency of participation, the amount paid, or other behavioral costs.

[0004] Due to the lack of differentiation in user input, in actual operation, some high-investment users may still not receive the expected amount of voucher rewards for a long time due to the random mechanism after participating and completing complex pre-tasks multiple times. This type of indiscriminate probability model is prone to causing a significant mismatch between the user's actual input and the feedback received, which in turn affects the user's willingness to continue participating in the activity, and the operator also finds it difficult to perceive and adjust the relevant parameters in a timely manner.

[0005] To address these issues, some solutions have introduced a guaranteed minimum or cumulative participation rewards into the lottery system. For example, if a user fails to win a prize for a preset number of consecutive times, the system issues a fixed-value compensatory voucher, or the system gradually increases the probability of winning in subsequent draws as the number of times a user participates increases. However, these solutions still mainly rely on preset fixed rules, and their triggering conditions are only related to discrete indicators such as the number of participations. They do not quantify the actual cost incurred by the user in each interaction and incorporate it into the reward calculation process.

[0006] Specifically, existing guarantee or compensation mechanisms typically have the following shortcomings: On the one hand, the triggering of compensation strategies is highly delayed, often requiring a certain number of unsuccessful attempts to take effect before they become effective, making it impossible to respond promptly to each high-cost participation behavior; on the other hand, compensation rules apply uniformly to all users and cannot be personalized based on the behavioral characteristics and investment intensity of different users, resulting in both the efficiency of voucher distribution and user experience needing improvement.

[0007] Therefore, there is still an urgent need in the existing technology for a method that can dynamically calculate points based on the behavioral costs generated by users in each interaction in opportunistic game interaction scenarios, and adjust the voucher distribution strategy in real time based on the points, so as to improve the user experience and enhance the refined management capability of voucher distribution. Summary of the Invention

[0008] In view of the aforementioned existing problems, the present invention is proposed.

[0009] This invention provides a method for issuing game vouchers based on dynamic points adjustment. This addresses the problem that existing opportunity-based voucher issuance methods often use a uniform probability model, which does not differentiate between users' different investments in tasks such as time spent and resource consumption. Furthermore, the triggering of the minimum reward and compensation rules is delayed, which can easily lead to high-investment users not receiving their expected rewards for a long time and thus generating a negative experience.

[0010] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0011] In a first aspect, embodiments of the present invention provide a method for issuing game vouchers based on dynamic points adjustment, comprising:

[0012] Step S1: Obtain user participation behavior data in chance-based game activities, the behavior data including the behavioral input cost used to characterize the cost of a user's participation in a single instance;

[0013] Step S2: Based on the results of the lottery and the cost of the behavior, update the user's cumulative disappointment value. The cumulative disappointment value is used as a dynamic score to represent the user's historical level of investment. When the user does not receive a voucher, the cumulative disappointment value is accumulated based on the cost of the behavior. When the user receives a voucher, the cumulative disappointment value is reset.

[0014] Step S3: Based on the updated cumulative disappointment value, calculate the winning probability adjustment parameter corresponding to the user's next participation in the chance-based game activity, and correct the preset basic winning probability according to the winning probability adjustment parameter to obtain the user's target winning probability;

[0015] Step S4: In response to the user initiating a new participation request, execute the lottery logic using the target winning probability and generate the lottery result;

[0016] Step S5: When the lottery result indicates a win, issue a game voucher to the user and reset the accumulated disappointment value associated with the user according to preset rules.

[0017] As a preferred embodiment of the game voucher distribution method based on dynamic points adjustment described in this invention, the behavioral input cost is quantified through at least one of the following dimensions:

[0018] The length of time a user spends completing prerequisite tasks to qualify for this participation;

[0019] The difficulty level set for the prerequisite tasks;

[0020] And the amount of specific virtual resources directly consumed by the user during this participation.

[0021] As a preferred embodiment of the game voucher distribution method based on dynamic points adjustment described in this invention, the step of calculating the winning probability adjustment parameter based on the updated cumulative disappointment value includes:

[0022] The incremental value of disappointment for this participation is determined based on the user's behavioral input cost.

[0023] The updated cumulative disappointment value and the disappointment increment value are substituted into a preset mapping function to obtain the winning probability adjustment parameter, wherein the mapping function is configured such that the winning probability adjustment parameter increases as the cumulative disappointment value increases.

[0024] As a preferred embodiment of the game voucher distribution method based on dynamic points adjustment described in this invention, the method further includes: before updating the accumulated disappointment value:

[0025] Obtain the user's performance data in the skill-based game activities conducted during the same period;

[0026] Based on the performance data and the user's historical ability baseline, determine whether the user has any strategically inefficient participation behavior;

[0027] When it is determined that the user has engaged in strategically inefficient participation behavior, the user's account status will be marked as abnormal for this and subsequent preset participation counts or preset time periods.

[0028] As a preferred embodiment of the game voucher distribution method based on dynamic points adjustment described in this invention, the determination of whether a user exhibits strategically inefficient participation behavior includes:

[0029] Calculate the user's historical ability baseline based on the user's performance data in multiple historical skill-based game activities;

[0030] Within a preset time window, the user's performance data in the current skill-based game activity is compared with the historical ability baseline. When the current performance data is consistently lower than the historical ability baseline within the time window and the difference exceeds a preset difference threshold, it is determined that the user has strategically inefficient participation behavior.

[0031] As a preferred embodiment of the game voucher distribution method based on dynamic points adjustment described in this invention, when the user is marked as being in an abnormal state, the winning probability adjustment parameter is attenuated according to a preset suppression strategy within the current and subsequent preset number of participations or preset time periods, or the adjustment of the target winning probability based on the accumulated disappointment value is suspended.

[0032] As a preferred embodiment of the game voucher distribution method based on dynamic points adjustment described in this invention, the method further includes:

[0033] Set an upper limit threshold for the accumulated disappointment value;

[0034] When the accumulated disappointment value reaches or exceeds the upper limit threshold, the lottery result will be controlled to be a deterministic win when the user participates in the next chance-based game activity, and a voucher with a preset face value or within a face value range will be issued to the user. At the same time, the accumulated disappointment value will be reset to the initial value.

[0035] As a preferred embodiment of the game voucher distribution method based on dynamic points adjustment described in this invention, the step of distributing game vouchers to users includes:

[0036] Obtain information on the types and denominations of the vouchers already held by the user;

[0037] Select a coupon type or denomination that complements the current distribution from the pool of alternative coupons to be issued this time, so that the coupons held by the user meet the preset diversity constraints or balance constraints in terms of type or denomination.

[0038] Secondly, embodiments of the present invention provide a game voucher distribution device, including a memory and a processor. The memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the game voucher distribution method based on dynamic points adjustment as described in the first aspect of the present invention.

[0039] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for issuing game vouchers based on dynamic points adjustment as described in the first aspect of the present invention.

[0040] The beneficial effects of this invention are as follows: By introducing a cumulative disappointment value updated based on behavioral investment cost into the opportunistic game interaction chain, this invention uses it as a dynamic score to individually adjust the winning probability of the next participation. This allows users with different investment intensities under the same activity rules to receive differentiated probability gains, thereby mitigating the experiential conflict of high investment but long-term lack of winning. The combination of the cumulative disappointment value and the mapping function can achieve a winning probability that monotonically increases with investment accumulation. At the same time, by constructing an upper limit threshold and a deterministic reward mechanism, a safety net path that is both flexible and rigid is built, ensuring that reward release is neither excessively stacked nor detrimental to user rights in extreme cases. By comparing performance in skill-based games with historical ability baselines, this invention can also identify strategically inefficient participation behaviors and suppress, freeze, or restore probability adjustment parameters within an abnormal window. This improves investment sensitivity while suppressing malicious probability manipulation and abnormal game theory, which is conducive to maintaining the overall prize pool's risk control and profit balance. On the coupon issuance side, this invention combines the types and denominations of coupons already held by users to select complementary coupon types from the candidate coupon pool for distribution. This avoids the long-term concentration of single large-denomination coupons or low-denomination coupons on a small number of users, improves the overall coupon structure utilization efficiency, further enhances the coverage and perceived value of operational activities for users at different consumption levels, and helps to improve user retention and long-term participation. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation on the scope of this application.

[0042] Figure 1 This is a flowchart illustrating the game voucher distribution method based on dynamic points adjustment in the embodiment. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] All terms used in this application (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0045] For example, the terms “first” and “second” used in this application are only used to distinguish and describe similar objects, to differentiate the first object from another object, and are not used to describe a specific order or sequence, nor should they be interpreted as indicating or implying relative importance.

[0046] This application proposes a method for issuing game vouchers based on dynamic points adjustment, combined with... Figure 1 As shown, the method includes:

[0047] Step S1: Obtain user participation behavior data in chance-based game activities. The behavior data includes the behavioral input cost used to characterize the cost of a user's participation in a single instance.

[0048] Step S2: Based on the results of this lottery and the cost of the behavior, update the user's accumulated disappointment value. The accumulated disappointment value is used as a dynamic score to represent the user's historical level of investment. When the user does not receive a voucher, the accumulated disappointment value is added based on the cost of the behavior in this instance. When the user receives a voucher, the accumulated disappointment value is reset.

[0049] Step S3: Based on the updated cumulative disappointment value, calculate the winning probability adjustment parameter corresponding to the user's next participation in the chance-based game activity, and adjust the preset basic winning probability according to the winning probability adjustment parameter to obtain the user's target winning probability;

[0050] Step S4: In response to a new participation request from a user, execute the lottery logic using the target winning probability and generate the lottery results;

[0051] Step S5: When the lottery result indicates a winner, issue a game voucher to the user and reset the accumulated disappointment value associated with the user according to preset rules;

[0052] In this embodiment, to facilitate deployment in a large-scale online game environment, the game server can record status information such as the current accumulated disappointment value, the time of the most recent participation, and the result of the most recent lottery for each user participating in the activity. Each time a client initiates a chance-based game participation request, steps S1 to S5 are completed sequentially. The default initial value of the accumulated disappointment value can be set to 0. When a user participates in the chance-based game activity for the first time or when the activity period is reset, the accumulated disappointment value is set to the initial value to ensure that all users are at a uniform baseline when entering the activity. In the case of winning a prize, preset rules may include directly clearing the accumulated disappointment value to zero, or only retaining a certain percentage of the residual value to reflect historical investment. For example, the residual percentage can be set between 20% and 50%, configured by the operations side based on the activity budget and target experience. For instance, when the server executes reward distribution, it first checks whether the lottery result has already been written. Only if the result has not yet been confirmed will a voucher record be generated and bound for the user, and the voucher identifier, face value, validity period, and usage restrictions will be written simultaneously to avoid duplicate distribution due to client retries. Optionally, if a network anomaly occurs during the lottery process, causing the client to fail to receive the result in a timely manner, the server can persist the lottery result and mark it as pending notification. The result will be sent to the user upon their next login or refresh of the activity page. Simultaneously, the server should prevent the same participation index from triggering cost deductions and lottery logic again. Furthermore, to avoid contention for updating accumulated disappointment values ​​in high-concurrency scenarios, the backend can use an atomic update operation based on the user identifier or an equivalent row-level locking mechanism when updating this field, ensuring that the same user is only counted as a valid participant once on the same participation index.

[0053] In one embodiment, the cost of behavioral input is quantified using at least one of the following dimensions:

[0054] The length of time a user spends completing prerequisite tasks to qualify for this participation;

[0055] The difficulty level set for the prerequisite tasks;

[0056] And the amount of specific virtual resources directly consumed by the user during this participation;

[0057] Specifically, the time consumed by pre-tasks in the cost of behavioral input can be calculated by the difference between the task start timestamp and the task completion timestamp reported by the client. The server records this difference in seconds or milliseconds and truncates it when it exceeds a reasonable range to prevent abnormal data interference. For example, the time consumed by a single task can be limited to the range of 0 seconds to one hour. The difficulty level of pre-tasks can be pre-marked by the operations personnel as several integer levels during the activity configuration phase. Typically, it can be set to levels 1 to 5 or 1 to 10. The larger the number, the more complex or time-consuming the task. The server directly reads the corresponding level as the difficulty factor when calculating the cost of behavioral input. The amount of specific virtual resources consumed can be obtained by deducting the difference between the account balance before and after the activity. For example, the number of coins, stamina points, or lottery tickets are all considered as the actual resource cost paid in this participation. For example, the system can combine the three dimensions mentioned above into a single scalar of behavioral input cost according to preset weights. The corresponding weight configuration is set by the operations backend based on existing activity data and user feedback experience. Generally, the weight of task time consumption and resource consumption can be slightly higher than the weight of difficulty level to better reflect the user's actual time and asset investment. Optionally, when some activities rely on only one dimension, such as participating only by consuming virtual currency, the behavioral input cost can be simplified to the standardized quantitative value of that dimension, while other dimensions take default values ​​or are not included in the calculation. Furthermore, to improve robustness, when a significant deviation between client time and server time is detected or intermediate logs are missing, the server can ignore the abnormal time consumption field and temporarily use only the difficulty level and resource consumption dimensions to calculate the behavioral input cost, and re-enable the full three-dimensional calculation after the logs are restored or time synchronization is normal.

[0058] In one embodiment, calculating the winning probability adjustment parameter based on the updated cumulative disappointment value includes:

[0059] The incremental value of disappointment for this participation is determined based on the user's behavioral input cost.

[0060] Substitute the updated cumulative disappointment value and the disappointment increment value into the preset mapping function to obtain the winning probability adjustment parameter. The mapping function is configured to make the winning probability adjustment parameter increase as the cumulative disappointment value increases.

[0061] The steps for using the mapping function to determine the probability adjustment parameter for winning are as follows:

[0062] Step S31: Read the input and determine the index to participate in this round, which is the first... The next time the "updated cumulative disappointment value" and "current disappointment increment value" are read, it is recorded as follows: and This step does not change the original value; it is only used as input for subsequent mapping.

[0063] Step S32: Calculate the winning probability and adjust the parameters according to the preset mapping. During system deployment, one of the following equivalent implementations is selected as the default mapping function. Calculation for joint input ;

[0064] Option A: Piecewise linear saturation mapping

[0065] Different slopes are used when the input is in different intervals, and upper limit suppression is set:

[0066] ,

[0067] in, Indicates the first The probability adjustment parameter for each winning round (dimensionless). Indicates the index to be used (an integer incrementing from 1). Indicates the first The cumulative disappointment value (dimensionless) is updated upon each participation. Indicates the first The increment of disappointment value (dimensionless) corresponding to each participation. Indicates the segmentation threshold. Represents the linear weighting coefficients. Indicates the high-interval bias term. This indicates adjustment of the upper limit (dimensionless).

[0068] Option B: Exponential Saturation Mapping

[0069] Employ an exponential function that is monotonically increasing and naturally saturates:

[0070] ,

[0071] in, Indicates the first The probability of winning is adjusted for each time. This indicates an adjustment to the upper limit. Represents an exponential function. Indicates to The sensitivity coefficient, Indicates to The sensitivity coefficient, Indicates the bias term. Indicates the first The cumulative disappointment value of each time, Indicates the first The increment value of disappointment;

[0072] right Expanding this scope will help stabilize the growth of the online user base.

[0073] ,

[0074] in, Indicates as of the date The moving average of cumulative disappointment values ​​over time. Represents the slip coefficient (values) ), This represents the expected group average adjustment. This indicates the smallest positive number whose denominator is zero. Represents the natural logarithm;

[0075] For example, in an implementation using exponential saturation mapping, the server maintains a cumulative disappointment value for each user while also maintaining a sliding state in memory corresponding to the participation index. This sliding state records the smoothed result of the cumulative disappointment value up to the current round. Each time, the sliding state from the previous round is overlaid with the current round's cumulative disappointment value using a sliding coefficient, and the updated value is written back to storage to maintain consistency with the sliding mean defined up to the t-th round. The default value for the sliding coefficient can be set between 0.7 and 0.95 to balance sensitivity to recent behavior and memory of historical investment. Operations personnel can adjust the specific value within this range based on the duration of the activity and the frequency of user participation. The expected average adjustment for the group can be determined based on the overall winning rate statistics of historical activities. For example, under the premise that the basic winning probability remains unchanged, the average increase brought by dynamic points can be controlled within 5%-20% of the basic winning probability, thereby ensuring that the activity budget remains controllable within the target period. Furthermore, the relationship between relevant parameters and adjustment upper limits can be calibrated through offline simulation or small-scale gray-scale experiments. During system deployment, several parameter combinations are pre-set, and the impact of different combinations on the winning rate, coupon issuance quantity, and user retention is observed during testing activities. Ultimately, a recommended configuration that meets business and risk control constraints is determined. Optionally, when a user's participation count is insufficient to support stable moving average estimation (e.g., cumulative participation count is less than 3 times), the system can temporarily skip moving average updates and only calculate the winning probability adjustment parameters based on the current cumulative disappointment value. Moving average is then activated again once the participation count reaches the preset lower limit to improve estimation stability.

[0076] Option C: S-shaped (Logistic) mapping

[0077] Enhance sensitivity in the middle section and smooth out both ends:

[0078] ,

[0079] in, Indicates the first The probability of winning is adjusted for each time. This indicates an adjustment to the upper limit. Represents the logistic function. Indicates to The slope coefficient, Indicates to The slope coefficient, Indicates to The inflection point Indicates to The inflection point Indicates the first The cumulative disappointment value of each time, Indicates the first The increment value of the next disappointment, Represents the natural constant;

[0080] Optionally, the base winning probability can be determined by the operations staff during activity configuration based on the prize pool budget and the target number of participants, generally set between 0.1% and 10%. The upper bound of the probability can be set as a several times the base winning probability but not exceeding 50% to avoid budget risks caused by excessively high single winning probabilities. The lower bound of the probability can be set as a certain percentage of the base winning probability, such as 20%-50%, to prevent the target winning probability from being adjusted too low in extreme cases, significantly damaging the user experience. During system implementation, if the absolute value of the calculated winning probability adjustment parameter is too large, causing the winning probability synthesized according to the target formula to exceed the reasonable range of zero to one, the adjustment parameter can be pre-trimmed before synthesis to limit it to a safe range before being superimposed with the base winning probability, thereby ensuring that the target winning probability is always a legal probability value. Furthermore, to facilitate monitoring of the campaign's performance, the server can statistically analyze the actual winning rate after drawing prizes using the target winning probability on a daily or hourly basis and compare it with the theoretical value. When the deviation continues to exceed the preset tolerance, the server will automatically prompt the operations staff to correct the basic winning probability or adjust the upper limit of the parameters to maintain long-term operational stability.

[0081] Step S33, will Combined with the base winning probability and limited to a safe range:

[0082] ,

[0083] in, Indicates the first The target winning probability for each participation. Denotes the upper bound of probability. Denotes the lower bound of the probability. This represents the preset base probability of winning. Indicates the first The probability of winning is adjusted for each time. Indicates participation in the index;

[0084] Step S34, take the result obtained in step S33 As the input probability for this round of lottery logic, the lottery will be executed in step S4 and processed according to the result in step S5, without changing the monotonicity and parameter semantics of this mapping module;

[0085] Specifically, the above implementation methods offer three alternative implementation paths for the mapping function and provide a complete computational link from input to output. The piecewise linear model emphasizes engineering controllability and ease of gray-scale testing, making it suitable for managing the pace of increase with threshold boundaries. The exponential saturation model highlights the properties of smoothness and rapid approach to the upper limit, facilitating online calibration of sensitivity through the moving average, thereby converging the average increase at the group level to the preset target. The S-shaped model provides higher adjustment sensitivity in the middle section, maintaining gentle changes at both low and high input levels to avoid abrupt changes in the user experience. All three models adhere to the monotonicity of the input dimension and incorporate probability correction and upper limit pruning to meet the safety constraints of the probability space.

[0086] Similarly, in actual deployment, operators can choose one of the mapping models as the default implementation based on the user scale and budget of the activity, and only switch or fine-tune parameters through the configuration center when necessary, without modifying the server code. The initial values ​​of various weight coefficients and thresholds in the mapping function can be obtained by analyzing historical activity logs. For example, the typical range of accumulated disappointment values ​​can be divided into several segments, so that most users are in the middle range and a small number of extremely high-investment users are in the high range. Then, low, medium, and high growth slopes can be configured for each range to balance fairness and cost control. In terms of numerical terms, the winning probability adjustment parameter is usually kept within a certain percentage range of the basic winning probability. For example, the upper limit is no more than three times the basic winning probability or half of the total winning probability. The lower limit of the basic winning probability can be set at the level of one in a thousand, and the upper limit can be controlled between 5% and 30% to avoid excessively high or low winning perceptions. Furthermore, when an abnormally high cluster of accumulated disappointment values ​​is detected within a short period of time for a certain group, the system can temporarily tighten the awarding pace by uniformly lowering the upper limit or moderately raising the segment thresholds without changing the form of the mapping function. The original configuration can be reverted once the overall indicators stabilize. Optionally, to reduce parameter maintenance costs, the game server can pre-configure several verified parameter templates, grouped by activity type, target audience, and budget level. When creating a new activity, the closest solution is selected directly from the template group, requiring only minor adjustments to a few key thresholds to complete the configuration.

[0087] In one embodiment, before updating the accumulated disappointment value, the following is also included:

[0088] Acquire user performance data during concurrent skill-based gaming activities;

[0089] Based on performance data and the user's historical ability baseline, determine whether the user has engaged in strategically inefficient participation behavior;

[0090] When a user is determined to have engaged in strategically inefficient participation behavior, the user's account status will be marked as abnormal for this and subsequent preset participation counts or preset time periods.

[0091] Optionally, performance data in skill-based game activities can select appropriate metrics based on the specific game type, such as level completion rate, win / loss results, star rating, average time spent, or overall score, or one or more of these metrics. The server can normalize these metrics to values ​​between zero and one during recording to facilitate comparison with historical performance baselines. Historical performance baselines can be obtained by statistically analyzing a user's average performance over a longer time window, such as selecting the most recent 50 games or task records from the last 7-30 calendar days for calculation. When a user's historical records are insufficient to support a complete window, the initial performance baseline is estimated using all existing historical records as a sample. The default setting for the difference threshold can be a certain percentage of the historical performance baseline, such as a value within the range of 10% to 30%, used to distinguish between normal fluctuations and obviously abnormal behavior. The length of the time window can be set to 10 to 30 games or a corresponding number of days, depending on the game pace and average online time. If performance falls below the threshold multiple times consecutively within this window, a strategic inefficient participation flag is triggered. Furthermore, to reduce the risk of misjudgment, the system can require a minimum number of games with abnormal performance within a time window before truly marking an abnormal state, such as at least five or ten games. This prevents accidental errors caused by short-term poor performance or network fluctuations from being mistaken for deliberate behavior. Similarly, when subsequent monitoring shows that a user's performance has recovered to near or above the historical baseline, and no significant deviations occur within a new observation window, the server can automatically remove the strategically inefficient participation flag, allowing the account to return to normal.

[0092] In one embodiment, determining whether a user exhibits strategically inefficient engagement behavior includes:

[0093] Calculate the user's historical ability baseline based on performance data from multiple historical skill-based game activities;

[0094] Within a preset time window, the user's performance data in the current skill-based game activity is compared with the historical ability baseline. When the current performance data is consistently lower than the historical ability baseline within the time window and the difference exceeds the preset difference threshold, it is determined that the user has strategically inefficient participation behavior.

[0095] In one embodiment, when a user is marked as being in an abnormal state, the winning probability adjustment parameter is attenuated according to a preset suppression strategy within the current and subsequent preset number of participations or preset time periods, or the adjustment of the target winning probability based on the accumulated disappointment value is suspended.

[0096] During the deployment of the preset suppression strategy, when an account is in an abnormal state, the winning probability parameters obtained in the previous stage are adjusted. Attenuation is performed to obtain the suppressed version. And calculate the probability of winning the target prize based on this; the steps include:

[0097] Step S21, when the account is in Once an event is marked as an anomaly, it is suppressed within the subsequent anomaly window:

[0098] ,

[0099] in, Indicates the first The indicator value for whether the window is in an abnormal state (takes 0 or 1). This indicates an indicator function (1 if the condition is true, 0 otherwise). The participation index indicates the point at which the abnormal state begins. Indicates the length of the exception window (in terms of the number of times it was entered). Indicates from the start of the exception to the number The number of times the participation in the count has been suppressed. and Indicates the index (an integer incrementing from 1);

[0100] Step S22, apply suppression mapping within the exception window. Acting on The view outside the window remains unchanged:

[0101] ,

[0102] in, This represents the parameter for adjusting the probability of winning after suppression. Indicates the first The suppression mapping function used this time. This represents the parameter for adjusting the winning probability when the prize is not suppressed.

[0103] Step S23, Three optional suppression strategies

[0104] During deployment, choose one of three options, or assign one option to different groups according to the strategy table;

[0105] Option A: Exponential multiplicative decay and upper / lower bound constraints

[0106] Scaled by exponential decay factor And cropped using a symmetrical threshold:

[0107] ,

[0108] in, This represents the upper limit of the positive clipping (dimensionless). This represents the upper limit of negative clipping (dimensionless). Indicates the first The attenuation coefficient of the second order. This indicates the lower limit of the attenuation coefficient. Represents the base of the exponential decay (take) ), The meaning is the same as the previous formula. and These represent the binary minimum and maximum operators, respectively.

[0109] Option B: Cycle duty cycle suppression (alternating strong suppression and weak suppression cycles)

[0110] With participation cycle and the length of the strong inhibition segment Define the period coefficient:

[0111] ,

[0112] ,

[0113] in, This indicates a weak inhibition coefficient (close to 1). Indicates a strong suppression coefficient (less than) ), Indicates the length of the suppression period. This indicates the length of the strong inhibition segment within each cycle. This represents the modulo operation;

[0114] Option C: Freeze-Linear Recovery Inhibition (including cooling section)

[0115] First freeze several times (with a coefficient of 0), then linearly restore to the upper limit:

[0116] ,

[0117] ,

[0118] in, This indicates that the number of times you can participate is frozen. Indicates the recovery starting point coefficient. This represents the linear recovery increment for each participation. This indicates the upper limit of the coefficient during the recovery phase;

[0119] Step S24, when obtained Then, the probability of winning the target prize in this round is determined and limited to a safe range:

[0120] ,

[0121] in, This indicates the target winning probability when the suppression takes effect. Denotes the upper bound of probability. Denotes the lower bound of the probability. This represents the preset base probability of winning. This represents the parameter for adjusting the winning probability after suppression;

[0122] Specifically, the above implementation method introduces three key elements—abnormal window, participation counting, and suppression mapping—without altering the original method architecture, and provides a decay implementation for adjusting the winning probability parameters. The general principle uses an indicator to distinguish between inside and outside the window, ensuring that suppression only takes effect within a limited range. Exponential multiplicative decay provides a monotonically smooth convergence path, and a symmetrical threshold avoids extreme amplification or reverse surges. Periodic duty cycle suppression reduces predictability in continuous games through alternating strong and weak suppression, balancing risk control strength and user experience fluctuations. The freeze-linear recovery strategy creates a significant suppression effect early on, then recovers at a fixed slope, facilitating linkage with the cooling-off period. Each strategy uses an adjustable decay coefficient as the core control variable, and upper and lower limits ensure the stability of the probability space.

[0123] Furthermore, regarding the parameter configuration of the suppression strategy, the length of the anomaly window can be set within a range of participation counts based on the activity's risk tolerance, such as between ten and fifty participations. Once this length is exceeded, the participation count can stop accumulating and remain at the upper limit to prevent the suppression intensity from increasing indefinitely. The lower limit of the decay coefficient can be set between 0.1 and 0.5, and the upper limit can be no higher than one to ensure that the winning probability adjustment parameter after suppression does not reverse direction or become excessively amplified. The length of the freeze period and the recovery slope can be determined by the operator based on user activity and experience with expected trigger frequencies. For example, a longer freeze period and a slower recovery slope can be used in high-risk activities, while a shorter freeze period and a faster recovery process can be used in ordinary activities to facilitate a faster return to normal. For instance, when an account is marked as an anomaly multiple times, the system can reinitialize the participation count of the anomaly window each time a new anomaly begins, making the suppression strategy independent between different anomaly cycles and preventing the count from old cycles from accumulating in new cycles. Optionally, when the overall risk level of the activity is detected to be decreasing or the activity is nearing its end, the server can moderately relax the suppression strategy by adjusting the configuration, such as increasing the attenuation coefficient or shortening the abnormal window length. However, under any circumstances, the upper and lower limits of the winning probability adjustment parameter shall remain unchanged to ensure that the probability space safety boundary is always effective.

[0124] In one embodiment, the method further includes:

[0125] Set an upper limit threshold for accumulated disappointment values;

[0126] When the accumulated disappointment value reaches or exceeds the upper limit threshold, the lottery result will be controlled to be a guaranteed win when the user participates in the next chance-based game activity, and a voucher with a preset face value or within a face value range will be issued to the user. At the same time, the accumulated disappointment value will be reset to the initial value.

[0127] In one embodiment, the step of issuing game vouchers to users includes:

[0128] Obtain information on the types and denominations of the vouchers held by the user;

[0129] Select coupon types or denominations from the pool of alternative coupons that complement the current distribution to be used as coupons for this issuance, so that the coupons held by users meet the preset diversity constraints or balance constraints in terms of type or denomination.

[0130] In this embodiment, diversity constraints or balance constraints can be achieved by segmenting the distribution of coupon denominations held by the user. The server can divide commonly used denominations into several intervals, such as low denomination intervals, medium denomination intervals, and high denomination intervals, and count the number of coupons held by the user in each interval. When the proportion of a certain denomination interval is too high, coupons from other intervals will be prioritized in subsequent distributions to gradually level out the overall distribution. The distribution of coupon types can be distinguished by purpose or category, such as discount coupons, full reduction coupons, or designated category coupons. The system can also set a target proportion interval for each type. After comparing the proportion of types currently held by the user with the target proportion, coupons that help converge to the target interval will be selected for distribution. For example, when it is detected that the user currently holds a large number of low denomination coupons and almost no medium or high denomination coupons, the probability of medium denomination coupons being selected can be appropriately increased within the budget to increase the user's perceived value; conversely, when the user already holds too many high denomination coupons, low or medium denomination coupons will be selected first. Optionally, when the pool of alternative vouchers cannot fully meet the preset diversity or balance targets at a certain time due to inventory limitations, the server can make a degenerate selection based on the principle of prioritizing the highest or closest voucher distribution. That is, under the premise of meeting the basic budget constraints, priority is given to selecting the voucher type that still has inventory and is closest to the target distribution requirements, and this degenerate selection is recorded in the log for subsequent evaluation of the campaign's performance. Furthermore, to avoid the impact of frequent real-time calculations on performance, the system can periodically pre-calculate recommended voucher issuance strategies offline under different voucher distribution states, and quickly determine the replenishment relationship during real-time issuance using a table lookup method, thereby balancing strategy granularity and operational efficiency.

[0131] This embodiment also provides a game voucher distribution device, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement a game voucher distribution method based on dynamic points adjustment as proposed in the above embodiment.

[0132] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0133] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a game voucher distribution method based on dynamic points adjustment as proposed in the above embodiment. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0135] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of this application and form different embodiments. For example, all the embodiments above can be used in any combination. The information disclosed in this background section is intended only to enhance the understanding of the general background of this application and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art.

Claims

1. A method for issuing game vouchers based on dynamic points adjustment, characterized in that, include: Step S1: Obtain user participation behavior data in chance-based game activities, the behavior data including the behavioral input cost used to characterize the cost of a user's participation in a single instance; Step S2: Based on the results of the lottery and the cost of the behavior, update the user's cumulative disappointment value. The cumulative disappointment value is used as a dynamic score to represent the user's historical level of investment. When the user does not receive a voucher, the cumulative disappointment value is accumulated based on the cost of the behavior. When the user receives a voucher, the cumulative disappointment value is reset. Step S3: Based on the updated cumulative disappointment value, calculate the winning probability adjustment parameter corresponding to the user's next participation in the chance-based game activity, and correct the preset basic winning probability according to the winning probability adjustment parameter to obtain the user's target winning probability; Step S4: In response to the user initiating a new participation request, execute the lottery logic using the target winning probability and generate the lottery result; Step S5: When the lottery result indicates a win, issue a game voucher to the user and reset the accumulated disappointment value associated with the user according to preset rules.

2. The method for issuing game vouchers based on dynamic points adjustment as described in claim 1, characterized in that, The cost of the action is quantified through at least one of the following dimensions: The length of time a user spends completing prerequisite tasks to qualify for this participation; The difficulty level set for the prerequisite tasks; And the amount of specific virtual resources directly consumed by the user during this participation.

3. A method for issuing game vouchers based on dynamic points adjustment as described in claim 1 or 2, characterized in that, The calculation of the winning probability adjustment parameter based on the updated cumulative disappointment value includes: The incremental value of disappointment for this participation is determined based on the user's behavioral input cost. The updated cumulative disappointment value and the disappointment increment value are substituted into a preset mapping function to obtain the winning probability adjustment parameter, wherein the mapping function is configured such that the winning probability adjustment parameter increases as the cumulative disappointment value increases.

4. The method for issuing game vouchers based on dynamic points adjustment as described in claim 1, characterized in that, Before updating the accumulated disappointment value, the following is also included: Obtain the user's performance data in the skill-based game activities conducted during the same period; Based on the performance data and the user's historical ability baseline, determine whether the user has any strategically inefficient participation behavior; When it is determined that the user has engaged in strategically inefficient participation behavior, the user's account status will be marked as abnormal for this and subsequent preset participation counts or preset time periods.

5. The method for issuing game vouchers based on dynamic points adjustment as described in claim 4, characterized in that, Determining whether users exhibit strategically inefficient engagement behavior includes: Calculate the user's historical ability baseline based on the user's performance data in multiple historical skill-based game activities; Within a preset time window, the user's performance data in the current skill-based game activity is compared with the historical ability baseline. When the current performance data is consistently lower than the historical ability baseline within the time window and the difference exceeds a preset difference threshold, it is determined that the user has strategically inefficient participation behavior.

6. A method for issuing game vouchers based on dynamic points adjustment as described in claim 4 or 5, characterized in that, When a user is marked as being in an abnormal state, the winning probability adjustment parameter is attenuated according to a preset suppression strategy within the current and subsequent preset number of participations or preset time periods, or the adjustment of the target winning probability based on the accumulated disappointment value is suspended.

7. The method for issuing game vouchers based on dynamic points adjustment as described in claim 1, characterized in that, The method further includes: Set an upper limit threshold for the accumulated disappointment value; When the accumulated disappointment value reaches or exceeds the upper limit threshold, the lottery result will be controlled to be a deterministic win when the user participates in the next chance-based game activity, and a voucher with a preset face value or within a face value range will be issued to the user. At the same time, the accumulated disappointment value will be reset to the initial value.

8. The method for issuing game vouchers based on dynamic points adjustment as described in claim 1, characterized in that, The steps for issuing game vouchers to users include: Obtain information on the types and denominations of the vouchers already held by the user; Select coupon types or denominations from the pool of alternative coupons that complement the current distribution to be used as coupons for this issuance, so that the coupons held by the user meet the preset diversity constraints or balance constraints in terms of type or denomination.

9. A device for issuing game vouchers, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the game voucher distribution method based on dynamic points adjustment as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the game voucher distribution method based on dynamic points adjustment as described in any one of claims 1 to 8.

Images