Game recharging and settlement management method based on big data

Abstract

The invention relates to the technical field of big data, and discloses a game recharging and settlement management method based on big data, which comprises the following steps: dividing users into different types according to real ages, and recording game login time periods of each type of users in a specific time period; the user type of the detected user is determined by calculating the overlapping degree of the time periods, eliminating redundant data and calculating the matching degree between the user login mode and different user types by using full permutation. And setting a recharging threshold value of the detected user according to the per capita and the single recharging amount of the user type. And monitoring the recharging behavior of the detected user in real time, combining recharging with too short time intervals, judging whether the recharging amount exceeds a threshold value or not, if so, reminding the user and obtaining feedback, and adjusting the threshold value according to a feedback result. Meanwhile, if the recharging amount of the user or the real-time accumulated recharging amount exceeds a threshold value, the system can give out a recharging prompt, and the recharging threshold value is dynamically adjusted according to a feedback result of the user.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a game recharge settlement management method based on big data. Background Art

[0002] With the popularization of mobile Internet, mobile games have become an important way for people to relax and entertain. As an important part of the game industry, game recharge and its settlement management are directly related to the revenue and user experience of the game platform. However, traditional game recharge settlement management methods often have many problems, such as unreasonable recharge threshold settings, insufficient monitoring of user recharge behavior, and difficulty in detecting recharge anomalies. These problems not only affect the operating efficiency of the game platform, but also have a certain negative impact on the user's gaming experience. Therefore, developing an intelligent management method that can effectively manage game recharges, improve settlement efficiency, and promptly detect recharge anomalies is crucial to ensuring the healthy development of the game platform and improving user experience.

[0003] At present, game recharge settlement management mainly faces the following challenges. First, the diversity of user types makes the setting of recharge thresholds complicated. Different types of users, such as students, office workers, and the elderly, have large differences in their consumption capabilities and consumption habits. It is difficult to meet the needs of different users by setting a unified recharge threshold, which may lead to restrictions on recharges for some users or excessive consumption for some users. Secondly, the dynamic and complex nature of user recharge behavior makes real-time monitoring difficult. The time when a user logs into the game each time, the frequency of recharges, the amount of recharges, etc. may vary. Traditional static monitoring methods are difficult to adapt to these dynamic changes, which can easily lead to recharge anomalies that are difficult to be discovered in time. In addition, traditional methods often ignore the contextual relationship of user recharge behavior, such as the time interval between two adjacent recharges and the overall amount of user recharges, and only use a single recharge amount as the basis for judgment, which is prone to misjudgment.

[0004] Therefore, this technical solution proposes a game recharge settlement management method based on big data. This method classifies users by analyzing data such as user login time periods and recharge amounts, and calculates the overlap of user login time periods, thereby determining the type of user and setting a reasonable recharge threshold based on the user type. At the same time, this method monitors user recharge behavior in real time, merges multiple recharge behaviors in a short period of time, and makes a comprehensive judgment based on information such as time intervals and recharge amounts, so as to more accurately detect recharge anomalies. This method can effectively solve many problems existing in the current game recharge settlement management, and provide strong support for the healthy development of game platforms and the improvement of user experience. Summary of the invention

[0005] The present invention provides a game recharge settlement management method based on big data, which is used to promote the solution of the problems mentioned in the above background technology.

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

[0007] Classify all users whose real ages have been obtained into multiple user types;

[0008] Set the time period;

[0009] The time period includes the period start time and the period end time;

[0010] Collect the game login time periods of all users under each user type each time they log into the game within a time period, and enter the time period sets respectively;

[0011] The game login time period is the time that the user runs each time when opening the game;

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

[0013] Select one user type as the target type in turn;

[0014] The overlap of the target type corresponding time period set is calculated by using the first overlap formula, and the game login time periods with an overlap greater than an overlap threshold are eliminated and selected to reduce the amount of data in the time period set;

[0015] When the time period set corresponding to each user type is eliminated and selected, the first overlap formula is used to calculate the overlap of the game login time periods of the detected users, and the game login time periods with overlap greater than the overlap threshold are eliminated;

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

[0017] When the overlap of all user types corresponding to the time period set is traversed, the user type of the detected user is determined according to the size of all overlaps, and the average recharge amount and single recharge amount of the user type are calculated to determine and adjust the recharge threshold of the detected user;

[0018] The recharge threshold includes the single-person recharge threshold and the single-time recharge threshold;

[0019] Collect the recharge amount of the detected user within the time period, and set the single-person recharge threshold and the single-time recharge threshold according to the recharge frequency of the detected user within the time period;

[0020] Judge the time interval between each recharge of the detected user in real time, merge the recharge amounts with a time interval less than the recharge interval threshold, and when the recharge amount of the detected user is greater than the recharge threshold, send a recharge reminder to the detected user to obtain user feedback, and adjust the recharge threshold according to the user feedback result.

[0021] Optionally, calculating the overlap degree of the time period set corresponding to the target type by using the first overlap degree formula, and removing and selecting the game login time periods with an overlap degree greater than the overlap threshold to reduce the amount of data in the time period set, including:

[0022] Select any game login time period in the time period set corresponding to the target type as the first time period, and the remaining game login time periods as the second time periods, and calculate the overlap degree between the first time period and each second time period in turn through the following first overlap degree formula, specifically as follows:

[0023]

[0024] 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;

[0025] When R = 0, it means that there is no overlap between the first time period and the second time period;

[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 larger R is, the higher the overlap degree;

[0028] Set an overlap threshold to judge the overlap degree between time periods;

[0029] When all the second time periods have been traversed, remove all the second time periods with an overlap degree greater than or equal to the overlap threshold;

[0030] Select any game login time period from all the second time periods as the new first time period, and calculate the overlap degree through the first overlap degree formula;

[0031] If all 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 of the time period set corresponding to the target type is calculated;

[0032] When all user types are traversed, stop calculating the overlap;

[0033] Obtain the time period set corresponding to each user type respectively, and number the time period set corresponding to each user type from 1 to n in descending order according to the login frequency;

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

[0035] 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.

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

[0037] Obtain all game login time periods of the detected user within the time period and enter the user time period set;

[0038] Select any game login time period in the user time period set as time period number three, select all game login time periods in the user time period set except time period number three as time period number four, and calculate the overlap between time period number three and each time period number four in turn by using the first overlap formula;

[0039] When all the No. 4 time periods are traversed, all No. 4 time periods with overlap greater than or equal to the overlap threshold are removed;

[0040] Reselect any game login time period from all the four time periods as the new time period number three, and calculate the overlap degree 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 from high to low according to the login frequency.

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

[0043] Perform a full permutation on the user time period set, and use each full permutation result as a sub-time period set;

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

[0045] S2, selecting any sub-time period set as the target sub-time period set;

[0046] S3, numbering the elements in the target sub-time period set from 1 to n in sequence;

[0047] S4. Calculate the overlap of the target sub-time period set and the comparison set respectively by using the following second overlap formula, as follows:

[0048]

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

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

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

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

[0053] Optionally, when the overlap of the time period set corresponding to all user types is traversed, the user type of the detected user is determined according to the size of all overlaps, and the average recharge amount and single recharge amount of the user type are calculated to determine and adjust the recharge threshold of the detected user, including:

[0054] Get the maximum value in the overlap set, and record the user type corresponding to the maximum value as the target type;

[0055] Get the average recharge amount and single recharge amount of all users under the target type within the time period;

[0056] Average recharge amount per person = the sum of recharge amounts of all users of the target type within the time period / the number of people who recharged within the time period of the target type;

[0057] Single recharge amount = total recharge amount of all users under target type within time period ÷ total number of recharges by all users under target type within time period.

[0058] Optionally, the collecting of the recharge amount of the detected user within the time period and setting the single-person recharge threshold and the single-time recharge threshold according to the recharge frequency of the detected user within the time period include:

[0059] Set a recharge frequency threshold to determine the frequency of recharge by the detected user within a time period;

[0060] Solicit the consent of the detected user and obtain the number of recharges made by the detected user within the time period;

[0061] If the number of recharges made by the detected user within the time period is less than the recharge number threshold, the single recharge amount is used as the single recharge threshold of the detected user, and the average recharge amount per person is used as the single recharge threshold of the detected user;

[0062] If the number of recharges made by the detected user within the time period is greater than or equal to the recharge number threshold, the total recharge amount of the detected user within the time period will be used as the single recharge threshold of the detected user, and the maximum amount recharged by the detected user at one time within the time period will be used as the single recharge threshold of the detected user.

[0063] Optionally, the real-time determination of the time interval between each recharge of the detected user, merging the recharge amounts 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, the detected user is reminded to recharge to obtain user feedback, and the recharge threshold is adjusted according to the user feedback result, including:

[0064] The end of the cycle is used as the monitoring moment to obtain the behavior of the detected user after logging into the game in real time;

[0065] When the detected user performs recharge, the recharge amount of the detected user is obtained, recorded as the first amount, and recorded in the real-time recharge set;

[0066] If the first amount is greater than the single recharge threshold, a recharge reminder is given to the detected user, and feedback from the detected user is obtained;

[0067] When the detected user reports that the recharge is abnormal, the staff will be called to handle it. Otherwise, the first amount will be used as the new single recharge threshold;

[0068] If the first amount is less than or equal to the single recharge threshold, then 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-person recharge threshold, the detected user is reminded to recharge and feedback from the detected user is obtained;

[0070] When the detected user reports that the recharge is abnormal, the staff is called to handle it. Otherwise, the start time and the end time of the recharge of the first amount are obtained and recorded as the first time and the second time respectively;

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

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

[0073] 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;

[0074] Record the start time and end time of the second amount as the third time and the fourth time respectively;

[0075] Calculate the difference between the second moment and the third moment, and record it as the interval difference;

[0076] If the interval difference is greater than the recharge interval threshold, the second amount is used 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 recharge amount recorded in the real-time recharge set;

[0078] The fourth moment is regarded as the new second moment of the first amount;

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

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

[0081] Optionally, the determining 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 to update the single average recharge amount and the single recharge amount of the detected user includes:

[0082] If the first amount is greater than the average single recharge amount, a recharge reminder is given to the detected user, and feedback from the detected user is obtained;

[0083] When the detected user reports that the recharge is abnormal, the staff is called to handle it. Otherwise, the first amount is used as the new average single recharge amount of the detected user.

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

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

[0086] If the detected user reports abnormal recharge, the 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 user for the detected user.

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

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

[0089] 1. A game recharge settlement management method based on big data, by selecting a time period from the time period set of the target user type as the first time period, and then the remaining time periods in the set as the second time period. Then, using the first overlap formula, the overlap between the first time period and each second time period is calculated in turn. According to the calculation result of the formula, the value of the overlap R is between 0 and 1, R = 0 means that the two time periods do not overlap, and R = 1 means complete overlap. If the overlap is greater than or equal to the preset overlap threshold, the second time period is eliminated. After completing all overlap calculations for the current first time period, reselect one from the remaining second time periods as the new first time period, and repeat the above process until all time periods of the target user type are traversed. After completing the processing of the current user type time period set, select the next user type and repeat the above process. When the time period sets of all user types have been overlapped and eliminated, 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, only time periods with numbers less than or equal to n / 2 are retained for each user type; this can effectively eliminate 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 formula can accurately measure the degree of overlap between two time periods, making the elimination process more objective and accurate. Setting the overlap threshold can flexibly control the intensity of time period elimination. Sorting by login frequency and retaining high-frequency time periods can ensure that the retained time periods are the most frequently logged-in time periods for users of this type, which is representative, and can retain the user's main login habits and filter out infrequent login times. After the above processing, the amount of data for subsequent calculations can be greatly reduced, while ensuring the validity and representativeness of the data, providing a more efficient and accurate data basis for subsequent overlap calculations and user type matching.

[0090] 2. A game recharge settlement management method based on big data, by fully arranging the user time period set of the detected user, each arrangement result is used as an independent sub-time period set. Next, for each sub-time period set, the following operations are performed: a time period set corresponding to a user type 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 within it are numbered from 1 to n. Subsequently, the overlap between the target sub-time period set and the comparison set is calculated using the second overlap formula. The calculated overlap S will be entered into an overlap set. This process will traverse all sub-time period sets to ensure that each arrangement has been overlapped with the current comparison set. When all sub-time period sets are calculated, a new user type time period set will be reselected as a comparison set, and the above process will be repeated until all user type time period sets have been used as comparison sets for calculation; in this way, all possible login time period combinations of the detected user can be considered by full arrangement, 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 the time period set of all user types, the comprehensiveness of the match is ensured and omissions are avoided. The overlap results are entered into the overlap set to facilitate subsequent selection and judgment. This full arrangement and overlap calculation method can more accurately identify the behavior pattern and real age of the detected user, avoiding the possibility of users using false identities to log in to the game, and at the same time provide a more accurate basis for the subsequent recharge threshold setting.

[0091] 3. This is a game recharge settlement management method based on big data. After completing all overlap calculations, the system will select the maximum value from the overlap set and determine the user type corresponding to the maximum value as the target user type of the detected user. Then, the system will obtain the average recharge amount and single recharge amount of all users under the target user type within a specified time period, and calculate these two values ​​through a formula. Next, the system will collect the recharge amount and recharge times of the detected user within the time period, and set a recharge number threshold. If the number of recharges of the detected user in the time period is less than the threshold, the single recharge amount corresponding to the target user type will be used as the single recharge threshold of the detected user, and the per capita recharge amount of the target user type will be used as the single recharge threshold of the detected user; if the number of recharges of the detected user in the time period is greater than or equal to the threshold, the total recharge amount of the user in the time period will be used as the single recharge threshold, and the maximum amount of the user's single recharge in the time period will be used 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 user type is determined by the maximum value of the overlap set, which ensures the accuracy of type matching. The per capita recharge amount and the single recharge amount are calculated according to the user type, which provides a benchmark for setting the threshold. At the same time, the user's own recharge frequency is considered to dynamically set the user's recharge threshold, so as to be more in line with the user's actual consumption ability and habits. This method can not only effectively prevent users from over-recharging, but also avoid affecting the user's gaming experience due to unreasonable threshold setting.

[0092] 4. A game recharge settlement management method based on big data monitors user behavior in real time at the end of the time period, and obtains the recharge amount 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 reports that the recharge is abnormal, the staff is contacted for processing. If the recharge amount does not exceed the threshold, the sum of all current recharge amounts is calculated. If the sum exceeds the single 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 two adjacent recharges. If the interval is less than the set threshold, the two recharge amounts are merged. Finally, the single and single-person average recharge amounts are updated and continuously cycled; this can timely identify and respond to abnormal user recharge behaviors, such as frequent small recharges, over-recharges, etc. Merging recharges with short adjacent intervals can effectively identify batch recharge behaviors and prevent fraud or erroneous operations. The recharge reminder mechanism can timely feedback user recharge abnormalities and obtain user feedback to make more accurate threshold adjustments to ensure that the recharge strategy is in line with user behavior. By updating the average recharge amount per time and per person in real time, it can more dynamically adapt to the user's recharge habits, so that recharge management is more in line with the actual needs of users. The continuous monitoring and feedback mechanism can effectively prevent malicious behavior and improve user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0094] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0095] Example 1, see Figure 1 A game recharge settlement management method based on big data, characterized by comprising:

[0096] Classify all users whose real ages have been obtained into multiple user types;

[0097] Set the time period;

[0098] The time period includes the period start time and the period end time;

[0099] Collect the game login time periods of all users under each user type each time they log into the game within a time period, and enter the time period sets respectively;

[0100] The game login time period is the time that the user runs each time when opening the game;

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

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

[0103] The overlap of the target type corresponding time period set is calculated by using the first overlap formula, and the game login time periods with an overlap greater than an overlap threshold are eliminated and selected to reduce the amount of data in the time period set;

[0104] When the time period set corresponding to each user type is eliminated and selected, the first overlap formula is used to calculate the overlap of the game login time periods of the detected users, and the game login time periods with overlap greater than the overlap threshold are eliminated;

[0105] The time period set after the detected user is removed is fully arranged, and the overlap between each full arrangement result and the time period set corresponding to each user type is calculated in turn by the second overlap formula;

[0106] When the overlap of all user types corresponding to the time period set is traversed, the user type of the detected user is determined according to the size of all overlaps, and the average recharge amount and single recharge amount of the user type are calculated to determine and adjust the recharge threshold of the detected user;

[0107] The recharge threshold includes the single-person recharge threshold and the single-time recharge threshold;

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

[0109] The time interval between each recharge of the detected user is determined in real time, and the recharge amounts with time intervals less than the recharge interval threshold are merged. When the recharge amount of the detected user is greater than the recharge threshold, the detected user is reminded to recharge to obtain user feedback, and the recharge threshold is adjusted according to the user feedback results.

[0110] The method of calculating the overlap of the target type corresponding time period set by using the first overlap formula, and removing and selecting the game login time period whose overlap is greater than the overlap threshold, so as to reduce the amount of data in the time period set, includes:

[0111] Select any game login time period in the target type corresponding time period set as time period No. 1, and the remaining game login time periods as time periods No. 2. The overlap between time period No. 1 and each time period No. 2 is calculated in turn by the following first overlap formula, as follows:

[0112]

[0113] 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;

[0114] When R = 0, it means there is no overlap between the first time period and the second time period;

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

[0116] 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;

[0117] An overlap threshold is set to judge 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] 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;

[0120] If all 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;

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

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

[0123] 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 cycle;

[0124] The game login time periods with numbers less than or equal to n / 2 in the time period sets corresponding to each user type are retained respectively.

[0125] A time period is selected from the time period set of the target user type as time period No. 1, and the remaining time periods in the set are used as time periods No. 2. Then, the first overlap formula is used to calculate the overlap between time period No. 1 and each time period No. 2 in turn. According to the calculation result of the formula, the value of the overlap R is between 0 and 1, R=0 means that the two time periods do not overlap, and R=1 means that they overlap completely. If the overlap is greater than or equal to the preset overlap threshold, the time period No. 2 is eliminated. After all overlap calculations are completed for the current time period No. 1, a new time period No. 1 is selected from the remaining time periods No. 2, 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 been overlapped and eliminated, 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 numbers less than or equal to n / 2; this can effectively eliminate 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 formula can be used to accurately measure the degree of overlap between two time periods, making the elimination process more objective and accurate. Setting the overlap threshold can flexibly control the intensity of time period elimination. Sorting by login frequency and retaining high-frequency time periods can ensure that the retained time periods are the most frequently logged-in periods for this type of user, which is representative, and can retain the user's main login habits and filter out infrequent login times. After the above processing, the amount of data for subsequent calculations can be greatly reduced, while ensuring the validity and representativeness of the data, providing a more efficient and accurate data basis for subsequent overlap calculations and user type matching.

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

[0127] Obtain all game login time periods of the detected user within the time period and enter the user time period set;

[0128] Select any game login time period in the user time period set as time period number three, select all game login time periods in the user time period set except time period number three as time period number four, and calculate the overlap between time period number three and each time period number four in turn by using the first overlap formula;

[0129] When all the No. 4 time periods are traversed, all No. 4 time periods with overlap greater than or equal to the overlap threshold are removed;

[0130] Reselect any game login time period from all the four time periods as the new time period number three, and calculate the overlap degree using the first overlap degree formula until all elements in the user time period set are traversed;

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

[0132] The method of performing a full arrangement of the time period set after the detected user is removed, and calculating the overlap between each full arrangement result and the time period set corresponding to each user type in turn by using the second overlap formula, includes:

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

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

[0135] S2, selecting any sub-time period set as the target sub-time period set;

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

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

[0138]

[0139] In the above formula, S is the overlap between the target sub-time period set and the comparison set, max is the maximum value, min is the minimum value, and t j1 and t j2 are the start and end times of the jth game login time period in the target sub-time period set, t j3 and t j4 are the start and end times of the jth game login time period in the comparison set, 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 ), min(t j2 ,t j4 ) is t j2 and t j4 The minimum value in the j1 ,tj3 ) is t j1 and t j3 The maximum value in, max(t j2 ,t j4 ) is t j2 and t j4 The maximum value among , min(t1,t3) is the minimum value between t1 and t3;

[0140] S5, enter S into the overlap degree set;

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

[0142] S7. When all sub-time period sets are traversed, steps S1 to S7 are executed until all time period sets corresponding to the user types are traversed.

[0143] By performing a full permutation on the user time period set of the detected user, each permutation result is used as an independent sub-time period set. Next, for each sub-time period set, the following operations are performed: a time period set corresponding to a user type is randomly selected as a comparison set. Then, a sub-time period set is selected as the target sub-time period set, and the time periods within it are numbered from 1 to n. Subsequently, the overlap between the target sub-time period set and the comparison set is calculated using the second overlap formula. The calculated overlap S will be entered into an overlap set. This process will traverse all sub-time period sets to ensure that each permutation has been overlapped with the current comparison set. When all sub-time period sets are calculated, a new user type time period set is selected as the 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 by a full permutation method, thereby more comprehensively evaluating 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 the time period set of all user types, the comprehensiveness of the match is ensured and omissions are avoided. The overlap results are entered into the overlap set to facilitate subsequent selection and judgment. This full arrangement and overlap calculation method can more accurately identify the behavior pattern and real age of the detected user, avoiding the possibility of users using false identities to log in to the game, and at the same time provide a more accurate basis for the subsequent recharge threshold setting.

[0144] When the overlap of all time periods corresponding to the user types is traversed, the user type of the detected user is determined according to the size of all overlaps, and the average recharge amount and single recharge amount of the user type are calculated to determine and adjust the recharge threshold of the detected user, including:

[0145] Get the maximum value in the overlap set, and record the user type corresponding to the maximum value as the target type;

[0146] Get the average recharge amount and single recharge amount of all users under the target type within the time period;

[0147] Average recharge amount per person = the sum of recharge amounts of all users of the target type within the time period / the number of people who recharged within the time period of the target type;

[0148] Single recharge amount = total recharge amount of all users under target type within time period ÷ total number of recharges by all users under target type within time period.

[0149] The collecting of the recharge amount of the detected user within the time period and setting the single-person recharge threshold and the single-time recharge threshold according to the recharge frequency of the detected user within the time period include:

[0150] Set a recharge frequency threshold to determine the frequency of recharge by the detected user within a time period;

[0151] Solicit the consent of the detected user and obtain the number of recharges made by the detected user within the time period;

[0152] If the number of recharges made by the detected user within the time period is less than the recharge number threshold, the single recharge amount is used as the single recharge threshold of the detected user, and the average recharge amount per person is used as the single recharge threshold of the detected user;

[0153] If the number of recharges made by the detected user within the time period is greater than or equal to the recharge number threshold, the total recharge amount of the detected user within the time period will be used as the single recharge threshold of the detected user, and the maximum amount recharged by the detected user at one time within the time period will be used as the single recharge threshold of the detected user.

[0154] After completing all overlap calculations, the system will select the maximum value from the overlap set and determine the user type corresponding to the maximum value as the target user type of the detected user. Then, the system will obtain the average recharge amount and single recharge amount of all users under the target user type in the specified time period, and calculate these two values ​​through the formula. Next, the system will collect the recharge amount and recharge times of the detected user in the time period, and set a recharge times threshold. If the number of recharges of the detected user in the time period is less than the threshold, the single recharge amount corresponding to the target user type will be used as the single recharge threshold of the detected user, and the average recharge amount of the target user type will be used as the single recharge threshold of the detected user; if the number of recharges of the detected user in the time period is greater than or equal to the threshold, the total recharge amount of the user in the time period will be used as the single recharge threshold, and the maximum amount of the user's single recharge in the time period will be used 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 user type is determined by the maximum overlap set value, ensuring the accuracy of type matching. The average recharge amount per person and the single recharge amount are calculated based on the user type, providing a benchmark for setting the threshold. At the same time, the user's own recharge frequency is taken into account to dynamically set the user's recharge threshold, which is more in line with the user's actual consumption ability and habits. This method can not only effectively prevent users from over-recharging, but also avoid affecting the user's gaming experience due to unreasonable threshold settings.

[0155] The real-time determination of the time interval between each recharge of the detected user, merging the recharge amounts 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, the detected user is reminded to recharge, user feedback is obtained, and the recharge threshold is adjusted according to the user feedback result, including:

[0156] The end of the cycle is used as the monitoring moment to obtain the behavior of the detected user after logging into the game in real time;

[0157] When the detected user performs recharge, the recharge amount of the detected user is obtained, recorded as the first amount, and recorded in the real-time recharge set;

[0158] If the first amount is greater than the single recharge threshold, a recharge reminder is given to the detected user, and feedback from the detected user is obtained;

[0159] When the detected user reports that the recharge is abnormal, the staff will be called to handle it. Otherwise, the first amount will be used as the new single recharge threshold;

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

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

[0162] When the detected user reports that the recharge is abnormal, the staff is called to handle it. Otherwise, the start time and the end time of the recharge of the first amount are obtained and recorded as the first time and the second time respectively;

[0163] The starting time is the time when the detected user performs recharge, and the ending time is the time when the detected user completes the recharge settlement;

[0164] Set the recharge interval threshold 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] Record the start time and end time of the second amount as the third time and the fourth time respectively;

[0167] Calculate the difference between the second moment and the third moment, and record it as the interval difference;

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

[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 recorded in the real-time recharge set;

[0170] The fourth moment is regarded as the new second moment of the first amount;

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

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

[0173] By monitoring user behavior in real time at the end of the time period, when the user recharges, 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 reports that the recharge is abnormal, the staff is contacted for processing. If the recharge amount does not exceed the threshold, the sum of all current 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, this method calculates the time interval between two adjacent recharges. If the interval is less than the set threshold, the two recharge amounts are merged. Finally, the single and single person average recharge amounts are updated and continuously cycled; this can timely identify and respond to abnormal user recharge behaviors, such as frequent small recharges, over-recharges, etc. Merging recharges with short adjacent intervals can effectively identify batch recharge behaviors and prevent fraud or erroneous operations. The recharge reminder mechanism can timely feedback user recharge abnormalities and obtain user feedback to make more accurate threshold adjustments to ensure that the recharge strategy is in line with user behavior. By updating the average recharge amount per time and per person in real time, it can more dynamically adapt to the user's recharge habits, so that recharge management is more in line with the actual needs of users. The continuous monitoring and feedback mechanism can effectively prevent malicious behavior and improve user experience.

[0174] The determining of the size of the first amount and the average single recharge amount and the size of the sum of all recharge amounts in the real-time recharge set and the average single recharge amount for each user, to update the average single recharge amount and the single recharge amount for each user, includes:

[0175] If the first amount is greater than the average single recharge amount, a recharge reminder is given to the detected user, and feedback from the detected user is obtained;

[0176] When the detected user reports that the recharge is abnormal, the staff is called to handle it. Otherwise, the first amount is used as the new average single recharge amount of the detected user.

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

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

[0179] If the detected user reports abnormal recharge, the 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 user for the detected user.

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

[0181] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0182] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A game recharge settlement management method based on big data, characterized in that: include: Classify all users whose real ages have been obtained into multiple user types; Set the time period; The time period includes the period start time and the period end time; Collect the game login time periods of all users under each user type each time they log into the game within a time period, and record them into time period sets respectively; The game login time period is the time that the user runs each time when opening the game; Each user type corresponds to a time period set; Select one user type as the target type in turn; The overlap of the target type corresponding time period set is calculated by using the first overlap formula, and the game login time periods with an overlap greater than an overlap threshold are eliminated and selected to reduce the amount of data in the time period set; When the time period set corresponding to each user type is eliminated and selected, the first overlap formula is used to calculate the overlap of the game login time periods of the detected users, and the game login time periods with overlap greater than the overlap threshold are eliminated; The time period set after the detected user is removed is fully arranged, and the overlap between each full arrangement result and the time period set corresponding to each user type is calculated in turn by the second overlap formula; When the overlap of all user types corresponding to the time period set is traversed, the user type of the detected user is determined according to the size of all overlaps, and the average recharge amount and single recharge amount of the user type are calculated to determine and adjust the recharge threshold of the detected user; The recharge threshold includes the single-person recharge threshold and the single-time recharge threshold; Collect the recharge amount of the detected user within the time period, and set the single-person recharge threshold and the single-time recharge threshold according to the recharge frequency of the detected user within the time period; The time interval between each recharge of the detected user is determined in real time, and the recharge amounts with time intervals less than the recharge interval threshold are merged. When the recharge amount of the detected user is greater than the recharge threshold, the detected user is reminded to recharge to obtain user feedback, and the recharge threshold is adjusted according to the user feedback results.

2. A game recharge settlement management method based on big data according to claim 1, characterized in that: The method of calculating the overlap of the target type corresponding time period set by using the first overlap formula, and removing and selecting the game login time period whose overlap is greater than the overlap threshold, so as to reduce the amount of data in the time period set, includes: Select any game login time period in the target type corresponding time period set as time period No. 1, and the remaining game login time periods as time periods No.

2. The overlap between time period No. 1 and each time period No. 2 is calculated in turn by 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 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 cycle; 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. A 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 first overlap degree formula is used to calculate the overlap degree of the game login time periods of the detected user, 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 cycle 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 are traversed, all game login time periods in the user time period set are sorted from high to low according to the login frequency.

4. A game recharge settlement management method based on big data according to claim 1, characterized in that: The method of performing a full arrangement of the time period set after the detected user is removed, and calculating the overlap between each full arrangement result and the time period set corresponding to each user type in turn by using the second overlap formula, includes: Perform a full permutation on the user time period set, and use each full permutation result as a sub-time period set; S1. Randomly select a time period set corresponding to a user type as a comparison set; S2, selecting any sub-time period set as the target sub-time period set; S3, numbering the elements in the target sub-time period set from 1 to n in sequence; S4. Calculate the overlap of the target sub-time period set and the comparison set respectively by using the following second overlap formula, as follows: In the above formula, S is the overlap between the target sub-time period set and the comparison set, max is the maximum value, min is the minimum value, and t j1 and t j2 are the start and end times of the jth game login time period in the target sub-time period set, t j3 and t j4 are the start and end times of the jth game login time period in the comparison set, 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 ), min(t j2 ,t j4 ) is t j2 and t j4 The minimum value in the j1 ,t j3) t j1 and t j3 The maximum value in, max(t j2 ,t j4 ) is t j2 and t j4 The maximum value among , min(t1,t3) is the minimum value between t1 and t3; S5, enter S into the overlap degree set; S6, reselect any sub-time period set as a new target sub-time period set, and perform operations S3 to S6; S7. When all sub-time period sets are traversed, steps S1 to S7 are executed until all time period sets corresponding to the user types are traversed.

5. A game recharge settlement management method based on big data according to claim 1, characterized in that: When the overlap of all time periods corresponding to the user types is traversed, the user type of the detected user is determined according to the size of all overlaps, and the average recharge amount and single recharge amount of the user type are calculated to determine and adjust the recharge threshold of the detected user, including: Get the maximum value in the overlap set, and record the user type corresponding to the maximum value as the target type; Get the average recharge amount and single recharge amount of all users under the target type within the time period; Average recharge amount per person = the sum of recharge amounts of all users of the target type within the time period / the number of people who recharged within the time period of the target type; Single recharge amount = total recharge amount of all users under target type within time period ÷ total number of recharges by all users under target type within time period.

6. A game recharge settlement management method based on big data according to claim 1, characterized in that: The collecting of the recharge amount of the detected user within the time period and setting the single-person recharge threshold and the single-time recharge threshold according to the recharge frequency of the detected user within the time period include: Set a recharge frequency threshold to determine the frequency of recharge by the detected user within a time period; Solicit the consent of the detected user and obtain the number of recharges made by the detected user within the time period; If the number of recharges made by the detected user within the time period is less than the recharge number threshold, the single recharge amount is used as the single recharge threshold of the detected user, and the average recharge amount per person is used as the single recharge threshold of the detected user; If the number of recharges made by the detected user within the time period is greater than or equal to the recharge number threshold, the total recharge amount of the detected user within the time period will be used as the single recharge threshold of the detected user, and the maximum amount recharged by the detected user at one time within the time period will be used as the single recharge threshold of the detected user.

7. A game recharge settlement management method based on big data according to claim 1, characterized in that: The real-time determination of the time interval between each recharge of the detected user, merging the recharge amounts 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, the detected user is reminded to recharge, user feedback is obtained, and the recharge threshold is adjusted according to the user feedback result, including: The end of the cycle is used as the monitoring moment to obtain the behavior of the detected user after logging into the game in real time; When the detected user performs recharge, the recharge amount of the detected user is obtained, recorded as the first amount, and recorded in the real-time recharge set; If the first amount is greater than the single recharge threshold, a recharge reminder is given to the detected user, and feedback from the detected user is obtained; If the detected user reports that the recharge is abnormal, the 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 the sum of all recharge amounts in the real-time recharge set is calculated; If the sum of all recharge amounts in the real-time recharge set is greater than the single-person recharge threshold, a recharge reminder is issued to the detected user, and feedback from the detected user is obtained; When the detected user reports that the recharge is abnormal, the staff is called to handle it. Otherwise, the start time and the end time of the recharge of the first amount are obtained and recorded as the first time and the second time respectively; The starting time is the time when the detected user performs recharge, and the ending time is the time when the detected user completes the recharge settlement; Set the recharge interval threshold to determine the time interval between two adjacent recharges; 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; Record the start time and end time of the second amount as the third time and the fourth time respectively; Calculate the difference between the second moment and the third moment, and record it as the interval difference; If the interval difference is greater than the recharge interval threshold, the second amount is used as the new first amount and recorded in the real-time recharge set; 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 recorded in the real-time recharge set; The fourth moment is taken as the new second moment of the first amount; Determine the size of the first amount and the average single recharge amount and the size of the sum of all recharge amounts in the real-time recharge set and the average single recharge amount, respectively, to update the average single recharge amount and the single recharge amount of the detected user; If the difference between the second moment and the monitoring moment is equal to the length of the time period, the second moment is used as the new monitoring moment to obtain the behavior of the detected user after logging into the game in real time.

8. A game recharge settlement management method based on big data according to claim 7, characterized in that: The determining of the size of the first amount and the average single recharge amount and the size of the sum of all recharge amounts in the real-time recharge set and the average single recharge amount for each user, to update the average single recharge amount and the single recharge amount for each user, includes: If the first amount is greater than the average single recharge amount, a recharge reminder is given to the detected user, and feedback from the detected user is obtained; When the detected user reports that the recharge is abnormal, the staff is called to handle it. Otherwise, the first amount is used as the new average single recharge amount of the detected user. If the first amount is less than or equal to the average single recharge amount, no operation will be performed; If the sum of all recharge amounts in the real-time recharge set is greater than the average recharge amount of a single user, a recharge reminder is given to the detected user, and feedback from the detected user is obtained; If the detected user reports abnormal recharge, the 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 user for the detected user. If the sum of all recharge amounts in the real-time recharge collection is less than or equal to the average recharge amount of a single person, no operation will be performed.

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