A game recharge settlement management method based on big data

Abstract

The application relates to the technical field of big data, and discloses a game recharge settlement management method based on big data, which comprises the following steps: dividing users into different types according to real ages, and recording the game login time periods of users of different types within a specific time period; removing redundant data by calculating the overlap degree of the time periods; calculating the matching degree of the user login mode and different user types by using full permutation, so as to determine the user type of a detected user; setting a recharge threshold of the detected user according to the per capita and single recharge amount of the user type; monitoring the recharge behavior of the detected user in real time, combining the recharges with too short time intervals, judging whether the recharge amount exceeds the threshold, reminding the user and obtaining feedback if the recharge amount exceeds the threshold, and adjusting the threshold according to the feedback result. Meanwhile, if the recharge amount or the real-time cumulative recharge amount of the user exceeds the threshold, the system will issue a recharge reminder, and dynamically adjust the recharge threshold according to the feedback result of the user.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of big data, and particularly to a game recharge settlement management method based on big data. BACKGROUND

[0002] With the popularity of mobile internet, mobile games have become an important way for people to relax and entertain. Game recharge, as an important part of the game industry, its settlement management is directly related to the income and user experience of the game platform. However, the traditional game recharge settlement management method often has many problems, such as unreasonable recharge threshold setting, insufficient monitoring of user recharge behavior, and difficulty in finding recharge anomalies, etc. These problems not only affect the operation efficiency of the game platform, but also bring certain negative impact on the user's game experience. Therefore, developing an intelligent management method that can effectively manage game recharge, improve settlement efficiency, and timely find recharge anomalies is crucial to ensure the healthy development of the game platform and improve user experience.

[0003] Currently, game recharge settlement management mainly faces the following challenges. First, the diversity of user types makes the setting of recharge threshold complex. Different types of users, such as students, office workers, and the elderly, have great differences in consumption ability and consumption habits, and it is difficult to meet the needs of different users by setting a unified recharge threshold, which may lead to limited recharge for some users or excessive consumption for some users. Second, the dynamic and complex nature of user recharge behavior makes real-time monitoring difficult. The time of each login, the frequency of recharge, and the amount of recharge may vary, and traditional static monitoring methods are difficult to adapt to these dynamic changes, making it difficult to find recharge anomalies in time. In addition, traditional methods often ignore the context relationship of user recharge behavior, such as the time interval between two consecutive recharges and the overall amount of user recharge, and only use a single recharge amount as a basis for judgment, which may lead to misjudgment.

[0004] Therefore, the present technical solution proposes a game recharge settlement management method based on big data, which classifies users by analyzing their login time period, recharge amount, and other data, and calculates the overlap of the user's login time period to determine the user's type and set a reasonable recharge threshold according to the user's type. At the same time, this method combines real-time monitoring of user recharge behavior, merging multiple recharge behaviors within a short period of time, and combining time interval and recharge amount information for comprehensive judgment to more accurately find recharge anomalies. This method can effectively solve many problems in current game recharge settlement management and provide strong support for the healthy development of the game platform and the improvement of user experience. SUMMARY

[0005] The application provides a game recharge settlement management method based on big data, which is used for promoting the solution of the problems mentioned in the background art.

[0006] The application provides the following technical scheme: a game recharge settlement management method based on big data, comprising:

[0007] All users who have obtained real ages are divided into multiple user types;

[0008] A time period is set;

[0009] The time period comprises a period start time and a period end time;

[0010] All game login time periods of all users in each user type during the time period are collected respectively, and time period sets are recorded respectively;

[0011] The game login time period is the running time of the user each time the game is opened;

[0012] Each user type corresponds to a time period set;

[0013] A user type is selected as a target type in turn;

[0014] The overlap degree of the time period set corresponding to the target type is calculated by using a first overlap degree formula, and the game login time period with an overlap degree greater than an overlap threshold value is removed and selected, so as to reduce the data amount in the time period set;

[0015] When the time period set corresponding to each user type is removed and selected, the overlap degree of the game login time period of the detected user is calculated by using the first overlap degree formula, and the game login time period with an overlap degree greater than the overlap threshold value is removed;

[0016] The time period set of the detected user after removal is fully arranged, and the overlap degree of each full arrangement result and the time period set corresponding to each user type is calculated in turn by using a second overlap degree formula;

[0017] When the overlap degrees of the time period sets corresponding to all user types are traversed, the user type of the detected user is determined according to the sizes of all overlap degrees, and the average recharge amount and the single recharge amount of the user type are calculated, so as to judge and adjust the recharge threshold value of the detected user;

[0018] The recharge threshold value comprises a single person recharge threshold value and a single recharge threshold value;

[0019] The recharge amount of the detected user in the time period is collected, and the single person recharge threshold value and the single recharge threshold value are set according to the frequency of the recharge of the detected user in the time period;

[0020] The time interval between each time of recharging of the detected user is judged in real time, the recharging amount with a time interval less than a recharging interval threshold is combined, and when the recharging amount of the detected user is greater than a recharging threshold, a recharging reminder is given to the detected user to obtain user feedback, and the recharging threshold is adjusted according to the user feedback result.

[0021] Optionally, the overlap of the target type corresponding time period set is calculated by using a first overlap formula, and a game login time period with an overlap greater than an overlap threshold is removed and selected, so as to reduce the data amount in the time period set, comprising:

[0022] Any game login time period in the target type corresponding time period set is selected as a first time period, and the remaining game login time periods are selected as second time periods, and the overlap of the first time period and each second time period is calculated in turn by using the following first overlap formula, and the specific formula is as follows:

[0023]

[0024] In the above formula, R is the overlap of the first time period and the second time period, max is the maximum value, min is the minimum value, t1 and t2 are the start time and end time of the first time period respectively, t3 and t4 are the start time and end time of the second time period respectively, max(0, min(t2, t4)-max(t1, t3)) is the maximum value of 0 and min(t2, t4)-max(t1, t3), min(t2, t4) is the minimum value of t2 and t4, max(t1, t3) is the maximum value of t1 and t3, max(t2, t4) is the maximum value of t2 and t4, and min(t1, t3) is the minimum value of t1 and t3;

[0025] When R=0, it means that the first time period and the second time period do not overlap;

[0026] When R=1, it means that the first time period and the second time period completely overlap;

[0027] When 0<R<1, it means that the first time period and the second time period partially overlap, and the greater R is, the higher the overlap degree is;

[0028] The overlap threshold is set to judge the overlap degree between the time periods;

[0029] When all the second time periods are traversed, all the second time periods with an overlap greater than or equal to the overlap threshold are removed;

[0030] Any game login time period in all the second time periods is reselected as a new first time period, and the overlap is calculated by using the first overlap formula;

[0031] If all elements in the time period set corresponding to the target type are traversed, any user type is selected as a new target type, and the overlap degree of the time period set corresponding to the target type is calculated;

[0032] When all user types are traversed, the overlap degree is stopped being calculated;

[0033] The time period set corresponding to each user type is obtained respectively, and the time period set corresponding to each user type is numbered from 1 to n in descending order of login frequency;

[0034] The login frequency is the login number of all users in the time period in different game login time periods under the current user type;

[0035] The game login time periods with a number less than or equal to n / 2 in the time period set corresponding to each user type are reserved respectively.

[0036] Optionally, when the time period set corresponding to each user type is removed and selected, the game login time period of the detected user is calculated by using the first overlap degree formula, and the game login time period with an overlap degree greater than an overlap threshold is removed, including:

[0037] All game login time periods of the detected user in the time period are obtained, and the user time period set is recorded;

[0038] Any game login time period in the user time period set is selected as a third time period, all game login time periods in the user time period set except the third time period are selected as fourth time periods, and the overlap degree between the third time period and each fourth time period is calculated in turn by using the first overlap degree formula;

[0039] When all fourth time periods are traversed, all fourth time periods with an overlap degree greater than or equal to the overlap threshold are removed;

[0040] Any game login time period in all fourth time periods is selected as a new third time period, and the overlap degree is calculated by using the first overlap degree formula until all elements in the user time period set are traversed;

[0041] When all elements in the user time period set are traversed, all game login time periods in the user time period set are sorted in descending order of login frequency.

[0042] Optionally, the time period set of the detected user after being removed is fully arranged, and the overlap degree between each full arrangement result and the time period set corresponding to each user type is calculated in turn by using the second overlap degree formula, including:

[0043] All permutations are performed on the user time period set, and each permutation result is taken as a sub time period set respectively;

[0044] S1, randomly select a time period set corresponding to a user type as a comparison set;

[0045] S2, select any sub time period set as a target sub time period set;

[0046] S3, sequentially number the elements in the target sub time period set from 1 to n;

[0047] S4, calculate the overlap degree of the target sub time period set and the comparison set respectively by the following second overlap degree formula, as follows:

[0048]

[0049] In the above formula, S is the overlap degree of the target sub time period set and the comparison set, max is the maximum value, min is the minimum value, t j1 and t j2 are the start time and end time of the jth game login time period in the target sub time period set, t j3 and t j4 are the start time and end time of the jth game login time period in the comparison set, max(0, min(t j2 , t j4 )-max(t j1 , t j3 )) is the maximum value of 0 and min(t j2 , t j4 )-max(t j1 , t j3 ), min(t j2 , t j4 ) is the minimum value of t j2 and t j4 , max(t j1 , t j3 ) is the maximum value of t j1 and t j3 , max(t j2 , t j4 ) is the maximum value of t j2 and t j4 , min(t1, t3) is the minimum value of t1 and t3;

[0050] S5, record S into the overlap degree set;

[0051] S6, reselect any sub time period set as a new target sub time period set, and perform S3 to S6 operations;

[0052] S7, when all the sub time period sets are traversed, then S1 to S7 steps are executed until all the time period sets corresponding to the user types are traversed.

[0053] Optionally, when the overlap degrees of all the time period sets corresponding to the user types are traversed, then the user type of the detected user is determined according to the sizes of all the overlap degrees, and the single-time recharge amount and the per capita recharge amount of the user type are calculated to judge and adjust the recharge threshold of the detected user, comprising:

[0054] Obtaining the maximum value in the overlap degree set, and recording the user type corresponding to the maximum value as a target type;

[0055] Obtaining the per capita recharge amount and the single-time recharge amount of all the users of the target type in the time period;

[0056] The per capita recharge amount = the total sum of the recharge amounts of all the users of the target type in the time period ÷ the number of people who recharge of the target type in the time period;

[0057] The single-time recharge amount = the total sum of the recharge amounts of all the users of the target type in the time period ÷ the total number of times of recharging of all the users of the target type in the time period.

[0058] Optionally, the recharge amounts of the detected user in the time period are collected, and the single-person recharge threshold and the single-time recharge threshold are set according to the frequency of recharging of the detected user in the time period, comprising:

[0059] Setting a recharge frequency threshold for judging the frequency of recharging of the detected user in the time period;

[0060] Seeking the consent of the detected user to obtain the number of times of recharging of the detected user in the time period;

[0061] If the number of times of recharging of the detected user in the time period is less than the recharge frequency threshold, then the single-time recharge amount is taken as the single-time recharge threshold of the detected user, and the per capita recharge amount is taken as the single-person recharge threshold of the detected user;

[0062] If the number of times of recharging of the detected user in the time period is greater than or equal to the recharge frequency threshold, then the total sum of the recharge amounts of the detected user in the time period is taken as the single-person recharge threshold of the detected user, and the maximum value of the amount of one-time recharging of the detected user in the time period is taken as the single-time recharge threshold of the detected user.

[0063] Optionally, the time interval between each time of recharging of the detected user is judged in real time, the recharge amount with a time interval less than a recharge interval threshold is merged, and when the recharge amount of the detected user is greater than a recharge threshold, the detected user is reminded to recharge, the user feedback is obtained, and the recharge threshold is adjusted according to the user feedback result, comprising:

[0064] At the end of the cycle as monitoring time, real-time access to the user login after the behavior of the detected;

[0065] When the detected user makes a recharge, the detected user's recharge amount is obtained, denoted as the first amount, and recorded in the real-time recharge set;

[0066] If the first amount is greater than the single recharge threshold, the detected user is reminded to recharge, and the detected user's feedback is obtained;

[0067] When the detected user feedback is recharge exception, call the staff to handle, otherwise, the first amount is taken as the new single recharge threshold;

[0068] If the first amount is less than or equal to the single recharge threshold, the sum of all recharge amounts in the real-time recharge set is calculated;

[0069] If the sum of all recharge amounts in the real-time recharge set is greater than the single recharge threshold, the detected user is reminded to recharge, and the detected user's feedback is obtained;

[0070] When the detected user feedback is recharge exception, call the staff to handle, otherwise, the starting time and ending time of the first amount recharge are obtained, and are denoted as the first time and the second time respectively;

[0071] The starting time is the time when the detected user makes a recharge, and the ending time is the time when the detected user completes the recharge settlement;

[0072] Set the recharge interval threshold to judge the time interval between adjacent two recharges;

[0073] If the detected user recharges after the ending time of the first amount, the current detected user's recharge amount is obtained, denoted as the second amount;

[0074] The starting time and ending time of the second amount are denoted as the third time and the fourth time respectively;

[0075] The difference between the second time and the third time is calculated, denoted as the interval difference;

[0076] If the interval difference is greater than the recharge interval threshold, the second amount is taken as the new first amount, and recorded in the real-time recharge set;

[0077] If the interval difference is less than or equal to the recharge interval threshold, the first amount is replaced by the sum of the first amount and the second amount, and the sum replaces the latest recorded recharge amount in the real-time recharge set;

[0078] The fourth time is taken as the new second time of the first amount;

[0079] respectively judging the size of the first amount and the single average recharge amount and the size of the sum of all recharge amounts in the real-time recharge set and the single average recharge amount, and updating the single average recharge amount and the single average recharge amount of the detected user;

[0080] If the difference between the second time and the monitoring time is equal to the time length of the time period, the second time is taken as a new monitoring time, and the behavior of the detected user after logging into the game is acquired in real time.

[0081] Optionally, the respective judgment of the size of the first amount and the single average recharge amount and the size of the sum of all recharge amounts in the real-time recharge set and the single average recharge amount, and the updating of the single average recharge amount and the single average recharge amount of the detected user, comprises:

[0082] If the first amount is greater than the single average recharge amount, the detected user is reminded to recharge, and the feedback of the detected user is acquired;

[0083] If the feedback of the detected user is recharge exception, a staff is called to handle, otherwise, the first amount is taken as the new single average recharge amount of the detected user;

[0084] If the first amount is less than or equal to the single average recharge amount, no operation is performed;

[0085] If the sum of all recharge amounts in the real-time recharge set is greater than the single average recharge amount, the detected user is reminded to recharge, and the feedback of the detected user is acquired;

[0086] If the feedback of the detected user is recharge exception, a staff is called to handle, otherwise, the sum of all recharge amounts in the real-time recharge set is taken as the new single average recharge amount of the detected user;

[0087] If the sum of all recharge amounts in the real-time recharge set is less than or equal to the single average recharge amount, no operation is performed.

[0088] The present application has the following beneficial effects:

[0089] 1. The game recharge settlement management method based on big data, a time period is selected from the target user type time period set as a first time period, and the remaining time periods in the set are selected as second time periods. Then, using the first overlap formula, the overlap of the first time period and each second time period is calculated in turn. According to the formula calculation result, the value of overlap R is between 0 and 1, R=0 indicates that there is no overlap between the two time periods, and R=1 indicates complete overlap. If the overlap is greater than or equal to the preset overlap threshold, the second time period is removed. After all overlap calculations for the current first time period are completed, a new first time period is selected from the remaining second time periods, and the above process is repeated until all time periods of the target user type are traversed. After completing the processing of the current user type time period set, the next user type is selected and the above process is repeated. When the time period sets of all user types have undergone overlap calculation and removal, the system will number the time periods in the time period set corresponding to each user type from high to low according to the login frequency. Finally, each user type only retains time periods with a number less than or equal to n / 2; this can effectively remove highly redundant time periods in the user type time period set, reduce data volume, and improve the efficiency of subsequent calculations. The first overlap formula can accurately measure the overlap of two time periods, making the removal process more objective and accurate. Setting the overlap threshold can flexibly control the intensity of time period removal. Sorting by login frequency and retaining high-frequency time periods can ensure that the remaining time periods are the most common login periods for users of that type, are representative, and can retain the main login habits of users and filter less commonly used login times. After the above processing, the data volume for subsequent calculations can be greatly reduced while ensuring the effectiveness and representativeness of the data, providing a more efficient and accurate data basis for subsequent overlap calculations and user type matching.

[0090] 2、The game recharge settlement management method based on big data, through full permutation of the user time period set of the detected user, each permutation result is taken as an independent sub time period set. Next, for each sub time period set, the following operations are performed: a user type corresponding time period set is randomly selected as a comparison set. Then, a sub time period set is selected as a target sub time period set, and the time periods in it are numbered from 1 to n. Subsequently, the second overlap degree formula is used to calculate the overlap degree between the target sub time period set and the comparison set. The calculated overlap degree S is entered into an overlap degree set. This process traverses all sub time period sets to ensure that each permutation is calculated with the current comparison set. When all sub time period sets are calculated, a new user type time period set is selected as a comparison set, and the above process is repeated until all user type time period sets are calculated as comparison sets; this can consider all possible login time period combinations of the detected user through full permutation, thereby more comprehensively evaluating user behavior patterns. The use of the second overlap degree formula can quantify the overlap degree between different time period combinations of the detected user and each user type time period set, thereby finding the most matching user type. By traversing all user type time period sets, the comprehensiveness of the match is ensured, and omissions are avoided. The overlap degree results are entered into the overlap degree set for easy selection and judgment later. This full permutation and overlap degree calculation method can more accurately identify the behavior patterns and real age of the detected user, avoid the possibility of users logging in to the game using false identities, and provide more accurate basis for subsequent recharge threshold setting.

[0091] 3. The game recharge settlement management method based on big data, after completing all overlap degree calculations, the system selects the maximum value from the overlap degree set, and determines the target user type of the detected user as the user type corresponding to the maximum value. Then, the system obtains the per capita recharge amount and single recharge amount of all users under the target user type within a specified time period, and calculates these two values through a formula. Next, the system collects the recharge amount and recharge times of the detected user within the time period, and sets a recharge times threshold. If the recharge times of the detected user within the time period is less than the threshold, the single recharge amount corresponding to the target user type is set as the single recharge threshold of the detected user, and the per capita recharge amount of the target user type is set as the single person recharge threshold of the detected user; if the recharge times of the detected user within the time period is greater than or equal to the threshold, the total recharge amount of the user within the time period is set as the single person recharge threshold, and the maximum single recharge amount of the user within the time period is set as the single recharge threshold; in this way, the type to which the user belongs can be accurately identified according to the login behavior and recharge habit of the user, and a more personalized and reasonable recharge threshold can be set based on the type. The user type is determined by the maximum value of the overlap degree set, ensuring the accuracy of type matching. The per capita recharge amount and single recharge amount are calculated according to the user type, providing a benchmark for setting the threshold. At the same time, the recharge threshold of the user is dynamically set considering the recharge frequency of the user, so as to be more consistent with the actual consumption ability and habit of the user. This method not only effectively prevents the user from over-recharging, but also avoids affecting the game experience of the user due to unreasonable threshold setting.

[0092] 4、The game recharge settlement management method based on big data, by monitoring the user behavior in real time at the end of the time period, when the user makes a recharge, the recharge amount is obtained. If the recharge amount exceeds the single recharge threshold, a recharge reminder is issued, and the threshold is adjusted according to the user's feedback. If the user feedbacks the abnormal recharge, contact the staff to handle. If the recharge amount does not exceed the threshold, the sum of all recharge amounts is calculated, if the sum exceeds the single person recharge threshold, a reminder is also issued, and the threshold is adjusted according to the feedback. More importantly, the method will calculate the time interval between the adjacent two recharges, if the interval is less than the set threshold, the two recharge amounts are combined. Finally, update the single and single person average recharge amount, and continue to cycle; This can identify and respond to abnormal user recharge behavior in a timely manner, such as frequent small amount recharge, excessive recharge, etc. Combining adjacent short interval recharges can effectively identify batch recharge behavior, prevent fraud or errors. The recharge reminder mechanism can timely feedback the user's abnormal recharge and obtain user feedback to make more accurate threshold adjustment to ensure that the recharge strategy meets the user behavior. By updating the single and single person average recharge amount in real time, it can more dynamically adapt to the user's recharge habits, so that the recharge management is more in line with the actual needs of the user. The continuous monitoring and feedback mechanism can effectively prevent malicious behavior and improve user experience. BRIEF DESCRIPTION OF DRAWINGS

[0093] Figure 1 The flowchart of the present application. DETAILED DESCRIPTION

[0094] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0095] Embodiment one, refer to Figure 1 A game recharge settlement management method based on big data, characterized in that it comprises:

[0096] Divide all users who have obtained real age into multiple user types;

[0097] Set a time period;

[0098] The time period includes the starting time and the ending time of the period;

[0099] Collect all users in each user type in the time period when they log in to the game each time, and record the time period set respectively;

[0100] The game login time period is the time when the user opens the game each time;

[0101] Each user type corresponds to a time period set;

[0102] Select a user type as the target type in turn;

[0103] Calculate the overlap degree of the time period set corresponding to the target type by using the first overlap degree formula, and remove and select the game login time period whose overlap degree is greater than the overlap threshold, so as to reduce the data amount in the time period set;

[0104] When the time period set corresponding to each user type is removed and selected, calculate the overlap degree of the game login time period of the detected user by using the first overlap degree formula, and remove the game login time period whose overlap degree is greater than the overlap threshold;

[0105] Make a full permutation of the time period set of the detected user after removal, and calculate the overlap degree between each full permutation result and the time period set corresponding to each user type by using the second overlap degree formula in turn;

[0106] When the overlap degrees of all time period sets corresponding to user types are traversed, determine the user type of the detected user according to the size of all overlap degrees, and calculate the average recharge amount and single recharge amount of the user type, so as to judge and adjust the recharge threshold of the detected user;

[0107] The recharge threshold includes a single person recharge threshold and a single recharge threshold;

[0108] Collect the recharge amount of the detected user within a time period, and set the single person recharge threshold and the single recharge threshold according to the frequency of the detected user's recharge within the time period;

[0109] Real-time judge the time interval between each recharge of the detected user, merge the recharge amount whose time interval is less than the recharge interval threshold, and when the recharge amount of the detected user is greater than the recharge threshold, perform recharge reminding on the detected user to obtain user feedback, and adjust the recharge threshold according to the user feedback result.

[0110] The overlap degree of the time period set corresponding to the target type is calculated by using the first overlap degree formula, and the game login time period whose overlap degree is greater than the overlap threshold is removed and selected, so as to reduce the data amount in the time period set, including:

[0111] Select any game login time period in the time period set corresponding to the target type as a first time period, and the remaining game login time periods as second time periods, and calculate the overlap degree between the first time period and each second time period by using the following first overlap degree formula in turn, as follows:

[0112]

[0113] In the above formula, R is the overlap degree of the first time period and the second time period, max is the maximum value, min is the minimum value, t1 and t2 are the start time and end time of the first time period respectively, t3 and t4 are the start time and end time of the second time period respectively, max(0, min(t2, t4) - max(t1, t3)) is the maximum value of 0 and min(t2, t4) - max(t1, t3), min(t2, t4) is the minimum value of t2 and t4, max(t1, t3) is the maximum value of t1 and t3, max(t2, t4) is the maximum value of t2 and t4, and min(t1, t3) is the minimum value of t1 and t3;

[0114] When R = 0, it means that the first time period and the second time period do not overlap;

[0115] When R = 1, it means that the first time period and the second time period completely overlap;

[0116] When 0 < R < 1, it means that the first time period and the second time period partially overlap, and the larger R is, the higher the overlap degree is;

[0117] The overlap threshold is set to determine the overlap degree between time periods;

[0118] When all the second time periods are traversed, all the second time periods with an overlap degree greater than or equal to the overlap threshold are removed;

[0119] Any game login time period in all the second time periods is selected as a new first time period, and the overlap degree is calculated by the first overlap degree formula;

[0120] If all the elements in the time period set corresponding to the target type are traversed, any user type is selected as a new target type, and the overlap degree of the time period set corresponding to the target type is calculated;

[0121] When all the user types are traversed, the calculation of the overlap degree is stopped;

[0122] The time period set corresponding to each user type is obtained respectively, and the time period set corresponding to each user type is numbered from 1 to n in descending order of login frequency;

[0123] The login frequency is the login number of all users in the time period set corresponding to the current user type in different games in the time period;

[0124] The game login time period in the time period set corresponding to each user type with a number less than or equal to n / 2 is retained respectively.

[0125] The first overlap degree formula is used to calculate the overlap degree between the first time period and each second time period. According to the calculation result of the formula, the value of the overlap degree R is between 0 and 1, R=0 indicates that the two time periods have no overlap, and R=1 indicates complete overlap. If the overlap degree is greater than or equal to the preset overlap threshold, the second time period is removed. After all overlap degree calculations for the current first time period are completed, a new first time period is selected from the remaining second time periods, and the above process is repeated until all time periods of the target user type are traversed. After the processing of the time period set of the current user type is completed, the next user type is selected, and the above process is repeated. When the time period sets of all user types have undergone overlap degree calculation and removal, the system will number the time periods in the time period set corresponding to each user type from high to low according to the login frequency. Finally, each user type only retains time periods with a number less than or equal to n / 2; this can effectively remove highly redundant time periods in the user type time period set, reduce the amount of data, and improve the efficiency of subsequent calculations. The first overlap degree formula can accurately measure the overlap degree of two time periods, making the removal process more objective and accurate. Setting the overlap threshold can flexibly control the intensity of time period removal. Sorting by login frequency and retaining high-frequency time periods can ensure that the retained time periods are the most common login periods for users of that type, are representative, and can retain the main login habits of users, filtering out less commonly used login times. After the above processing, the amount of data for subsequent calculations can be greatly reduced, while ensuring the effectiveness and representativeness of the data, providing a more efficient and accurate data basis for subsequent overlap degree calculations and user type matching.

[0126] When the time period set corresponding to each user type is removed and selected, the first overlap degree formula is used to calculate the overlap degree of the detected user's game login time period, and the game login time period with an overlap degree greater than the overlap threshold is removed, including:

[0127] All game login time periods of the detected user within a time period are obtained and entered into the user time period set;

[0128] Any game login time period in the user time period set is selected as a third time period, all game login time periods in the user time period set except the third time period are selected as fourth time periods, and the first overlap degree formula is used to calculate the overlap degree between the third time period and each fourth time period;

[0129] When all fourth time periods are traversed, all fourth time periods with an overlap degree greater than or equal to the overlap threshold are removed;

[0130] Select any game login time period from all four time periods as the new third time period, and calculate the overlap using the first overlap formula until all elements in the user time period set have been traversed.

[0131] When all elements in the user time period set have been traversed, all game login time periods in the user time period set are sorted from high to low according to login frequency.

[0132] The set of time periods after removing the detected users is permuted, and the overlap between each permutation result and the set of time periods corresponding to each user type is calculated sequentially using the second overlap formula, including:

[0133] Perform full permutations on the user's time period set, and use each permutation result as a sub-time period set;

[0134] S1. Randomly select a set of time periods corresponding to a user type as the comparison set;

[0135] S2. Select any set of sub-time periods as the target set of sub-time periods;

[0136] S3. Number the elements in the target sub-time period set from 1 to n in sequence;

[0137] S4. Calculate the overlap between the target sub-time period set and the comparison set using the following second overlap formula, as follows:

[0138]

[0139] In the above formula, S represents the overlap between the target sub-time period set and the comparison set, max represents the maximum value, min represents the minimum value, and t represents the maximum value. j1 and t j2 Let t be the start and end times of the j-th game login time period in the target sub-time period set, respectively. j3 and t j4 These represent the start and end times of the j-th game login time period in the comparison set, respectively, max(0,min(t) j2 ,t j4 )-max(t j1 ,t j3 )) is 0 and min(t) j2 ,t j4 )-max(t j1 ,t j3 The maximum value in ), min(t) j2 ,t j4 ) for t j2 and t j4 The minimum value in, max(t) j1 ,tj3 ) is the maximum value of t j1 and t j3 , min(t j2 , t j4 ) is the minimum value of t j2 and t j4 ;

[0140] S5, record S into the overlap set;

[0141] S6, reselect any sub time period set as a new target sub time period set, and perform S3 to S6 operations;

[0142] S7, when all sub time period sets are traversed, then perform S1 to S7 steps until all user type corresponding time period sets are traversed.

[0143] By full permutation of the user time period set of the detected user, each permutation result is taken as an independent sub time period set. Next, for each sub time period set, the following operations are performed: a user type corresponding time period set is randomly selected as a comparison set. Then, a sub time period set is selected as a target sub time period set, and the time periods in it are numbered from 1 to n. Subsequently, the second overlap formula is used to calculate the overlap between the target sub time period set and the comparison set. The calculated overlap S is recorded in an overlap set. This process traverses all sub time period sets to ensure that each permutation is calculated with the current comparison set. When all sub time period sets are calculated, a new user type time period set is selected as a comparison set, and the above process is repeated until all user type time period sets are used as comparison sets for calculation; in this way, all possible login time period combinations of the detected user can be considered through full permutation, so as to more comprehensively evaluate the user behavior pattern. The use of the second overlap formula can quantitatively calculate the overlap between different time period combinations of the detected user and each user type time period set, so as to find the most matching user type. By traversing all user type time period sets, the comprehensiveness of matching is ensured, and omission is avoided. The overlap result is recorded in the overlap set, which is convenient for subsequent selection and judgment. This full permutation and overlap calculation method can more accurately identify the behavior pattern and real age of the detected user, avoid the possibility of the user logging in the game using a false identity, and provide a more accurate basis for subsequent recharge threshold setting.

[0144] When the overlap degrees of all user type corresponding time period sets are traversed, the user type of the detected user is determined according to the size of all overlap degrees, and the per capita recharge amount and the single recharge amount of the user type are calculated to judge and adjust the recharge threshold of the detected user, including:

[0145] The maximum value in the overlap degree set is obtained, and the user type corresponding to the maximum value is recorded as a target type;

[0146] The per capita recharge amount and the single recharge amount of all users under the target type in the time period are obtained;

[0147] The per capita recharge amount is the sum of the recharge amount of all users under the target type in the time period divided by the number of users under the target type who recharge in the time period;

[0148] The single recharge amount is the sum of the recharge amount of all users under the target type in the time period divided by the total number of times of recharging of all users under the target type in the time period.

[0149] The recharge amount of the detected user in the time period is collected, and the single recharge threshold and the single recharge threshold are set according to the frequency of the detected user in the time period, including:

[0150] The recharge frequency threshold is set to judge the frequency of the detected user in the time period;

[0151] The detected user is asked to agree to obtain the number of times of recharging of the detected user in the time period;

[0152] If the number of times of recharging of the detected user in the time period is less than the recharge frequency threshold, the single recharge amount is taken as the single recharge threshold of the detected user, and the per capita recharge amount is taken as the single recharge threshold of the detected user;

[0153] If the number of times of recharging of the detected user in the time period is greater than or equal to the recharge frequency threshold, the sum of the recharge amount of the detected user in the time period is taken as the single recharge threshold of the detected user, and the maximum value of the amount of recharging of the detected user in the time period is taken as the single recharge threshold of the detected user.

[0154] After all the overlap calculations are completed, the system selects the maximum value from the set of overlaps and determines the target user type of the detected user as the user type corresponding to the maximum value. Then, the system obtains the per capita recharge amount and single recharge amount of all users under the target user type within a specified time period and calculates these two values through a formula. Next, the system collects the recharge amount and recharge times of the detected user within the time period and sets a recharge times threshold. If the recharge times of the detected user within the time period are less than the threshold, the single recharge amount corresponding to the target user type is set as the single recharge threshold of the detected user, and the per capita recharge amount of the target user type is set as the per capita recharge threshold of the detected user; if the recharge times of the detected user within the time period are greater than or equal to the threshold, the total recharge amount of the user within the time period is set as the per capita recharge threshold, and the maximum single recharge amount of the user within the time period is set as the single recharge threshold; in this way, the user's type can be accurately identified according to the user's login behavior and recharge habits, and a more personalized and reasonable recharge threshold can be set based on the type. The maximum value of the set of overlaps is used to determine the user type, ensuring the accuracy of type matching. The per capita recharge amount and single recharge amount are calculated based on the user type, providing a basis for setting the threshold. At the same time, the user's recharge threshold is dynamically set considering the user's own recharge frequency, which is more in line with the user's actual consumption ability and habits. This method not only effectively prevents the user from over-recharging, but also avoids affecting the user's gaming experience due to unreasonable threshold settings.

[0155] The system determines the time interval between each recharge of the detected user in real time, combines the recharge amounts with time intervals less than the recharge interval threshold, and when the recharge amount of the detected user is greater than the recharge threshold, the system performs a recharge reminder on the detected user, obtains user feedback, and adjusts the recharge threshold based on the user feedback result, including:

[0156] At the end of the period as the monitoring time, the system obtains the behavior of the detected user after logging into the game in real time;

[0157] When the detected user performs a recharge, the system obtains the recharge amount of the detected user, denoted as the first amount, and records it in the real-time recharge set;

[0158] If the first amount is greater than the single recharge threshold, the system performs a recharge reminder on the detected user and obtains the feedback of the detected user;

[0159] When the feedback of the detected user is a recharge anomaly, the system calls a staff member for processing, otherwise, the first amount is set as the new single recharge threshold;

[0160] If the first amount is less than or equal to the single recharge threshold, the system calculates the sum of all recharge amounts in the real-time recharge set;

[0161] If the sum of all recharge amounts in the real-time recharge set is greater than the single recharge threshold, a recharge reminder is given to the detected user, and feedback from the detected user is obtained;

[0162] If the feedback from the detected user is that the recharge is abnormal, a staff member is called to handle the situation, otherwise, the start time and end time of the recharge of the first amount are obtained and recorded as the first time and the second time, respectively;

[0163] The start time is the time when the detected user starts the recharge, and the end time is the time when the detected user completes the recharge settlement;

[0164] A recharge interval threshold is set to determine the time interval between two adjacent recharges;

[0165] If the detected user recharges after the end time of the first amount, the recharge amount of the current detected user is obtained and recorded as the second amount;

[0166] The start time and end time of the second amount are recorded as the third time and the fourth time, respectively;

[0167] The difference between the second time and the third time is calculated and recorded as the interval difference;

[0168] If the interval difference is greater than the recharge interval threshold, the second amount is taken as the new first amount, and the real-time recharge set is updated;

[0169] If the interval difference is less than or equal to the recharge interval threshold, the first amount is replaced by the sum of the first amount and the second amount, and the sum replaces the latest recharge amount in the real-time recharge set;

[0170] The fourth time is taken as the new second time of the first amount;

[0171] The size of the first amount and the single average recharge amount and the sum of all recharge amounts in the real-time recharge set and the single average recharge amount are determined to update the single average recharge amount of the detected user and the single recharge amount;

[0172] If the difference between the second time and the monitoring time is equal to the time length of the time period, the second time is taken as the new monitoring time, and the behavior of the detected user after logging into the game is obtained in real time.

[0173] By monitoring user behavior in real time at the end of the time period, the recharge amount is obtained when the user recharges. If the recharge amount exceeds the single recharge threshold, a recharge reminder is issued, and the threshold is adjusted according to the user's feedback. If the user feedback is abnormal, the staff is contacted for processing. If the recharge amount does not exceed the threshold, the sum of all recharge amounts is calculated, and if the sum exceeds the single person recharge threshold, a reminder is also issued, and the threshold is adjusted according to the feedback. More importantly, the method calculates the time interval between the two adjacent recharges, and if the interval is less than the set threshold, the two recharge amounts are combined. Finally, the single and single person average recharge amounts are updated, and the cycle is continuously looped; this can timely identify and respond to user abnormal behavior, such as frequent small amount recharges, excessive recharges, etc. Combining adjacent short-interval recharges can effectively identify bulk recharge behavior and prevent fraud or errors. The recharge reminder mechanism can timely feedback user recharge abnormalities and obtain user feedback for more accurate threshold adjustment to ensure that the recharge strategy meets user behavior. By updating the single and single person average recharge amounts in real time, the user's recharge habits can be more dynamically adapted, making the recharge management more in line with user actual needs. The continuous monitoring and feedback mechanism can effectively prevent malicious behavior and improve user experience.

[0174] The respective determination of the size of the first amount and the single average recharge amount and the size of the sum of all recharge amounts in the real-time recharge set and the single person average recharge amount is used to update the single average recharge amount and the single person recharge amount of the detected user, including:

[0175] If the first amount is greater than the single average recharge amount, the detected user is reminded of the recharge, and the detected user feedback is obtained;

[0176] When the detected user feedback is abnormal, the staff is called for processing, otherwise, the first amount is taken as the new single average recharge amount of the detected user;

[0177] If the first amount is less than or equal to the single average recharge amount, no operation is performed;

[0178] If the sum of all recharge amounts in the real-time recharge set is greater than the single person average recharge amount, the detected user is reminded of the recharge, and the detected user feedback is obtained;

[0179] When the detected user feedback is abnormal, the staff is called for processing, otherwise, the sum of all recharge amounts in the real-time recharge set is taken as the new single person average recharge amount of the detected user;

[0180] If the sum of all recharge amounts in the real-time recharge set is less than or equal to the single person average recharge amount, no operation is performed.

[0181] It is to be noted that, as used in this document, the term "indicia" is intended to encompass any type of data, information, or other content, whether in the form of text, graphics, images, video, audio, or otherwise. It is also to be noted that, as used in this document, the terms "first" and "second" are merely used to distinguish one entity or operation from another, and do not necessarily imply or suggest any actual relationship or order between the entities or operations. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0182] The above description is merely that of the preferred embodiments of the present application, and it is to be noted that various improvements and modifications can be made by those skilled in the art without departing from the technical principles of the present application, and these improvements and modifications are also to be considered as falling within the scope of the present application.

Claims

1. A game recharge settlement management method based on big data, characterized in that, include: All users whose real ages have been obtained are divided into multiple user types; Set the time period; The time period includes the start time and the end time of the period; Collect the game login time periods for each user under each user type during each login within the time period, and enter the time period sets separately; The game login time period refers to the running time each time a user opens the game; Each user type corresponds to a set of time periods; Select one user type as the target type in turn; The overlap of the target type's corresponding time period set is calculated using the first overlap formula. Game login time periods with an overlap greater than the overlap threshold are removed and selected to reduce the amount of data in the time period set. After removing and selecting the time period set corresponding to each user type, the first overlap formula is used to calculate the overlap of the game login time periods of the detected users, and game login time periods with an overlap greater than the overlap threshold are removed. The time period set after removing the detected users is fully permuted, and the overlap between each permutation result and the time period set corresponding to each user type is calculated sequentially using the second overlap formula; When the overlap of the time period sets corresponding to all user types has been traversed, the user type of the detected user is determined according to the size of all overlaps, and the average recharge amount per user and the single recharge amount of that user type are calculated to judge and adjust the recharge threshold of the detected user. The recharge threshold includes the single-person recharge threshold and the single-recharge threshold; Collect the recharge amount of the monitored users within a time period, and set the single recharge threshold and single recharge threshold according to the recharge frequency of the monitored users within the time period; The system determines the time interval between each recharge by the monitored user in real time, merges recharge amounts with time intervals less than the recharge interval threshold, and reminds the monitored user to recharge when the recharge amount exceeds the recharge threshold, obtains user feedback, and adjusts the recharge threshold based on the user feedback.

2. The game recharge settlement management method based on big data according to claim 1, characterized in that: The step of calculating the overlap of the target type's corresponding time period set using a first overlap formula, and then removing and selecting game login time periods with an overlap greater than the overlap threshold, is used to reduce the amount of data in the time period set. This includes: Select any game login time period from the set of time periods corresponding to the target type as time period one, and the remaining game login time periods as time period two. Then, calculate the overlap between time period one and each time period two using the following first overlap formula, as follows: In the above formula, R is the overlap degree between the first time period and the second time period, max is the maximum value, min is the minimum value, t1 and t2 are the start time and end time of the first time period respectively, t3 and t4 are the start time and end time of the second time period respectively, max(0, min(t2, t4) - max(t1, t3)) is the maximum value between 0 and min(t2, t4) - max(t1, t3), min(t2, t4) is the minimum value between t2 and t4, max(t1, t3) is the maximum value between t1 and t3, max(t2, t4) is the maximum value between t2 and t4, and min(t1, t3) is the minimum value between t1 and t3; When R = 0, it means there is no overlap between the first time period and the second time period; When R = 1, it means the first time period and the second time period completely overlap; When 0 < R < 1, it means the first time period and the second time period partially overlap, and the larger R is, the higher the overlap degree; An overlap threshold is set to judge the overlap degree between time periods; When all the second time periods are traversed, all the second time periods with an overlap degree greater than or equal to the overlap threshold are removed; In all the second time periods, any game login time period is reselected as the new first time period, and the overlap degree is calculated through the first overlap degree formula; If all the elements in the time period set corresponding to the target type are traversed, any user type is reselected as the new target type, and the overlap degree of the time period set corresponding to the target type is calculated; When all the user types are traversed, the calculation of the overlap degree is stopped; The time period sets corresponding to each user type are obtained respectively, and the time period sets corresponding to each user type are numbered from 1 to n in descending order according to the login frequency; The login frequency is the number of logins of all users under the current user type in different game login time periods within the time period; The game login time periods with numbers less than or equal to n / 2 in the time period set corresponding to each user type are retained respectively; 3. The game recharge settlement management method based on big data according to claim 1, characterized in that: When the time period sets corresponding to each user type are removed and selected, the overlap degree of the game login time periods of the detected user is calculated using the first overlap degree formula, and the game login time periods with an overlap degree greater than the overlap threshold are removed, including: Obtain all the game login time periods of the detected user within the time period and enter them into the user time period set; Select any game login time period in the user time period set as the third time period, and use all the game login time periods in the user time period set except the third time period as the fourth time period, and calculate the overlap degree between the third time period and each fourth time period in turn through the first overlap degree formula; When all the fourth time periods are traversed, all the fourth time periods with an overlap degree greater than or equal to the overlap threshold are removed; In all the fourth time periods, any game login time period is reselected as the new third time period, and the overlap degree is calculated through the first overlap degree formula until all the elements in the user time period set are traversed; When all elements in the user time period set have been traversed, all game login time periods in the user time period set are sorted from high to low according to login frequency.

4. The game recharge settlement management method based on big data according to claim 1, characterized in that: The set of time periods after removing the detected users is permuted, and the overlap between each permutation result and the set of time periods corresponding to each user type is calculated sequentially using the second overlap formula, including: Perform full permutations on the user's time period set, and use each permutation result as a sub-time period set; S1. Randomly select a set of time periods corresponding to a user type as the comparison set; S2. Select any set of sub-time periods as the target set of sub-time periods; S3. Number the elements in the target sub-time period set from 1 to n in sequence; S4. Calculate the overlap between the target sub-time period set and the comparison set using the following second overlap formula, as follows: In the above formula, S represents the overlap between the target sub-time period set and the comparison set, max represents the maximum value, min represents the minimum value, and t represents the maximum value. j1 and t j2 Let t be the start and end times of the j-th game login time period in the target sub-time period set, respectively. j3 and t j4 These represent the start and end times of the j-th game login time period in the comparison set, respectively, max(0,min(t) j2 ,t j4 )-max(t j1 ,t j3 )) is 0 and min(t) j2 ,t j4 )-max(t j1 ,t j3 The maximum value in ), min(t) j2 ,t j4 ) for t j2 and t j4 The minimum value in, max(t) j1 ,t j3) For t j1 and t j3 The maximum value in, max(t) j2 ,t j4 ) for t j2 and t j4 The maximum value in t1, min(t1,t3) is the minimum value between t1 and t3; S5. Enter S into the overlap set; S6. Select any sub-time period set as the new target sub-time period set, and execute operations S3 to S6. S7. When all sub-time period sets have been traversed, execute steps S1 to S7 until all time period sets corresponding to all user types have been traversed.

5. The game recharge settlement management method based on big data according to claim 1, characterized in that: When the overlap of all user types across time periods has been traversed, the user type of the detected user is determined based on the magnitude of all overlaps, and the average recharge amount per user and the single recharge amount for that user type are calculated to determine and adjust the recharge threshold for the detected user, including: Get the maximum value in the overlap set, and denote the user type corresponding to the maximum value as the target type; Get the average recharge amount per user and the amount of a single recharge for all users under the target type within a time period; Average recharge amount per user = Total recharge amount of all users under the target type within the time period ÷ Number of users under the target type who recharged within the time period; Single recharge amount = total recharge amount of all users under the target type within the time period ÷ total number of recharges by all users under the target type within the time period.

6. The game recharge settlement management method based on big data according to claim 1, characterized in that: The process of collecting the recharge amount of the monitored users within a time period, and setting single-user recharge thresholds and single-transaction recharge thresholds based on the frequency of recharges by the monitored users within the time period, includes: Set a threshold for the number of recharges to determine the frequency of recharges by the monitored user within a time period; Obtain the consent of the users being tested and obtain the number of times they recharged within the time period; If the number of times a user being monitored makes a recharge within a time period is less than the recharge number threshold, then the single recharge amount will be used as the single recharge threshold for the user being monitored, and the average recharge amount per person will be used as the single recharge threshold for the user being monitored. If the number of times a user being monitored makes a recharge within a time period is greater than or equal to the recharge number threshold, then the total amount of the user being monitored's recharges within the time period will be used as the single recharge threshold for the user being monitored, and the maximum amount of a single recharge made by the user being monitored within the time period will be used as the single recharge threshold for the user being monitored.

7. The game recharge settlement management method based on big data according to claim 1, characterized in that: The process involves real-time determination of the time interval between each recharge by the monitored user, merging recharge amounts with time intervals less than a recharge interval threshold, and when a monitored user's recharge amount exceeds the threshold, issuing a recharge reminder to the monitored user to obtain user feedback, and adjusting the recharge threshold based on the user feedback. This includes: The monitoring time is set at the end of the cycle to capture the behavior of the monitored user after logging into the game in real time. When the monitored user makes a recharge, the recharge amount of the monitored user is obtained, recorded as the first amount, and entered into the real-time recharge set; If the first amount exceeds the single recharge threshold, a recharge reminder will be sent to the detected user, and feedback from the detected user will be obtained. If a user reports an abnormal recharge, staff will be called to handle it; otherwise, the first amount will be used as the new single recharge threshold. If the first amount is less than or equal to the single recharge threshold, then calculate the sum of all recharge amounts in the real-time recharge set; If the total amount of all recharges in the real-time recharge set is greater than the single-person recharge threshold, a recharge reminder will be sent to the detected user, and feedback from the detected user will be obtained. If the user being monitored reports an abnormal recharge, then staff will be called to handle it; otherwise, the start and end times of the first recharge amount will be obtained and recorded as the first time and the second time, respectively. The start time is the time when the monitored user makes a recharge, and the end time is the time when the monitored user completes the recharge settlement. Set a recharge interval threshold to determine the time interval between two adjacent recharges; If the user being monitored makes a recharge after the end time of the first amount, the current recharge amount of the user being monitored is obtained and recorded as the second amount; The start and end times of the second amount are recorded as the third and fourth times, respectively. Calculate the difference between the second and third time points, and record it as the interval difference; If the interval difference is greater than the recharge interval threshold, the second amount will be used as the new first amount and entered into the real-time recharge set. If the interval difference is less than or equal to the recharge interval threshold, the first amount will be replaced with the sum of the first amount and the second amount, and this sum will replace the latest recharge amount entered in the real-time recharge set. The fourth moment is taken as the first amount and the new second moment; The system compares the first amount with the average amount of a single recharge and the sum of all recharge amounts in the real-time recharge set with the average amount of a single recharge, in order to update the average amount of a single recharge and the amount of a single recharge for the detected user. If the difference between the second moment and the monitoring moment is equal to the length of the time period, then the second moment is taken as the new monitoring moment, and the behavior of the monitored user after logging into the game is obtained in real time.

8. The game recharge settlement management method based on big data according to claim 7, characterized in that: The steps of determining the magnitude of the first amount and the average single recharge amount, and the magnitude of the sum of all recharge amounts in the real-time recharge set and the average single recharge amount, are used to update the average single recharge amount and the single recharge amount of the detected user, including: If the first amount is greater than the average amount of a single recharge, a recharge reminder will be sent to the user being monitored, and feedback from the user will be obtained. If the user being monitored reports an abnormal recharge, staff will be called to handle it; otherwise, the first amount will be used as the new average single recharge amount for the user being monitored. If the first amount is less than or equal to the average amount recharged in a single transaction, no action will be taken. If the total amount of all recharges in the real-time recharge set is greater than the average recharge amount per person, a recharge reminder will be sent to the detected user, and feedback from the detected user will be obtained. If the user being monitored reports an abnormal recharge, staff will be called to handle it; otherwise, the sum of all recharge amounts in the real-time recharge set will be used as the new average recharge amount per person for the user being monitored. If the total amount of all recharges in the real-time recharge set is less than or equal to the average recharge amount per person, no action will be taken.

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