Client reward event generation method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a client reward event generation method and device, equipment and a storage medium, and the method comprises the steps: receiving platform login information, determining user data associated with the platform login information, judging whether the user data meets a preset payment activation rule or not, and if yes, determining a recharge amount range according to a historical recharge amount, calculating target recharging time and a second recharging event corresponding to the target recharging time according to the number of days for stopping charging according to the number of days for stopping charging and the first recharging event corresponding to the recharging amount range; analyzing the first recharge event and the second recharge event to determine a reward event type, calculating user activity according to historical login data and a preset cycle period, and determining a payment level of a historical recharge amount; and calculating a target discount proportion according to the user activeness, the number of charging stopping days and the payment level, and generating a target reward event according to the reward event type and the target discount proportion. Corresponding reward events can be generated for different users, so that the recharging conversion rate of the users is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technology, and in particular to a method, apparatus, device and storage medium for generating a client reward event. Background Art

[0002] With the continuous development of computer technology, many platform operators have launched recharge activities to attract more users to recharge, such as game platforms, food delivery platforms, and film and television platforms, so as to increase the platform's revenue. Recharge activities are not just a simple promotion method, but also a way to increase user activity and stickiness, effectively promoting the long-term development of the platform. By providing various discounts and rewards, users are attracted to recharge, increasing consumption within the platform and improving platform revenue. And through recharge activities, users can get more privileges, thereby improving the user experience. In addition, recharge activities can stimulate user consumption, increase purchases within the platform, and thus increase the revenue of platform operators. By providing various recharge activities, platform operators can also establish long-term and stable cooperative relationships with users and improve user loyalty.

[0003] In the related art, for recharge reward activities, a corresponding amount is usually rebated to the user after the user recharges a certain amount, so that the user can use it in the next recharge process. For example, recharge 100 yuan, rebate 10 yuan, recharge 500 yuan, rebate 80 yuan, etc. Or a voucher is drawn by lottery to deduct the corresponding amount during the recharge process. In this process, due to the small rebate amount or the small amount of the voucher drawn, it may also be due to the fact that the user's use of the platform and the use time are inconsistent with the use time of the voucher, resulting in a low recharge conversion rate for the user. Summary of the invention

[0004] The embodiment of the present application provides a client reward event generation method, device, equipment and storage medium, which solves the technical problem of low user recharge conversion rate. By setting reasonable reward information generation rules, corresponding reward events are generated for different users, thereby improving the user's recharge conversion rate.

[0005] In a first aspect, an embodiment of the present application provides a method for generating a client reward event, comprising:

[0006] Receive platform login information sent by the client, determine user data associated with the platform login information, and judge whether the user data meets the preset payment activation rules, wherein the user data includes historical login data, historical recharge amount, and recharge suspension days;

[0007] In the case where the user data satisfies the preset payment activation rule, a recharge amount range is determined according to the historical recharge amount, and a first recharge event corresponding to the recharge amount range is determined, a target recharge time is calculated according to the number of charging suspension days, and a second recharge event corresponding to the target recharge time is determined;

[0008] Performing a recharge preference analysis on the first recharge event and the second recharge event to determine a reward event type, calculating user activity according to the historical login data and a preset cycle, and determining the payment tier to which the historical recharge amount belongs;

[0009] A target discount ratio is calculated according to the user activity, the number of charging suspension days and the payment level, and a target reward event is generated according to the reward event type and the target discount ratio.

[0010] Optionally, the number of the first recharge events is multiple, the number of the second recharge events is one, and the performing recharge preference analysis on the first recharge event and the second recharge event to determine the reward event type includes:

[0011] respectively counting a first historical recharge number of each first recharge event and a second historical recharge number of each second recharge event;

[0012] Calculating a first recharge preference value according to each first historical recharge number and a preset first weight value, and calculating a second recharge preference value according to the second historical recharge number and a preset second weight value;

[0013] The first recharge preference value and the second recharge preference value are compared, and a bonus event type is determined based on the comparison result.

[0014] Optionally, the calculating the target discount ratio according to the user activity, the number of charging suspension days and the payment level includes:

[0015] The user activity is compared with a preset activity threshold, and a first additional discount ratio is determined based on the activity comparison result; the charging suspension days are compared with a preset day threshold, and a second additional discount ratio is determined based on the day comparison result;

[0016] A discount ratio corresponding to the payment level is determined, and a target discount ratio is calculated based on the discount ratio, the first additional discount ratio, and the second additional discount ratio.

[0017] Optionally, determining the first additional discount ratio based on the activity comparison result includes:

[0018] Determine a first discount ratio type based on the activity comparison result, and determine a first additional discount ratio according to the first discount ratio type and a preset discount ratio value;

[0019] Accordingly, the second additional discount ratio is determined based on the day comparison result, including:

[0020] A second discount ratio type is determined based on the day comparison result, and a second additional discount ratio is determined according to the second discount ratio type and the preset discount ratio value.

[0021] Optionally, determining a recharge amount range according to the historical recharge amount, and determining a first recharge event corresponding to the recharge amount range includes:

[0022] Determine each single recharge amount in the historical recharge amount, and determine a recharge amount range according to each single recharge amount;

[0023] According to the recharge amount corresponding to the preset recharge event, a recharge event within the recharge amount range is determined as the first recharge event.

[0024] Optionally, after generating a target reward event according to the reward event type and the target discount ratio, the method further includes:

[0025] Sending the target reward event to the client, and monitoring the triggering of the target reward event in real time;

[0026] Perform payment activity analysis on the triggering situation within the preset cycle period, and adjust the preset payment activation rules in real time based on the analysis results.

[0027] Optionally, after sending the target reward event to the client and monitoring the triggering of the target reward event in real time, the method further includes:

[0028] Record the sending time of the target reward event, determine the continuous reaching time point according to the sending time and the preset continuous reaching rule, and determine the triggering situation before the continuous reaching time point;

[0029] Determine whether to execute the continuous reaching task according to the triggering situation before the continuous reaching time point, and if the target reward event is not triggered before the continuous reaching time point, execute the continuous reaching task at the continuous reaching time point;

[0030] If the target reward event is triggered before the continuous reaching time point, the continuous reaching task will not be performed.

[0031] In a second aspect, an embodiment of the present application provides a client reward event generating device, comprising:

[0032] An information receiving module, used to receive platform login information sent by the client;

[0033] A user data determination module, used to determine user data associated with the platform login information;

[0034] A user data judgment module, used to judge whether the user data meets the preset payment activation rules, the user data includes historical login data, historical recharge amount and recharge suspension days;

[0035] A first recharge event determination module, configured to determine a recharge amount range according to the historical recharge amount when the user data satisfies a preset payment activation rule, and determine a first recharge event corresponding to the recharge amount range;

[0036] A second recharge event determination module, configured to calculate a target recharge time according to the number of days of charging suspension, and determine a second recharge event corresponding to the target recharge time;

[0037] A recharge preference analysis module, configured to perform a recharge preference analysis on the first recharge event and the second recharge event to determine a reward event type;

[0038] A user activity calculation module, used to calculate user activity based on the historical login data and a preset cycle period;

[0039] A payment level determination module, used to determine the payment level to which the historical recharge amount belongs;

[0040] The target reward event generation module is used to calculate the target discount ratio according to the user activity, the number of charging suspension days and the payment level, and generate the target reward event according to the reward event type and the target discount ratio.

[0041] In a third aspect, an embodiment of the present application provides an electronic device, comprising: one or more processors; a storage device configured to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the client reward event generation method described in the first aspect.

[0042] In a fourth aspect, an embodiment of the present application provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to execute the client reward event generation method as described in the first aspect.

[0043] The embodiment of the present application receives the platform login information sent by the client, determines the user data associated with the platform login information, and judges whether the user data meets the preset payment activation rules. The user data includes historical login data, historical recharge amount and recharge suspension days; when the user data meets the preset payment activation rules, the recharge amount range is determined according to the historical recharge amount, and the first recharge event corresponding to the recharge amount range is determined, the target recharge time is calculated according to the recharge suspension days, and the second recharge event corresponding to the target recharge time is determined; the recharge preference analysis of the first recharge event and the second recharge event is performed to determine the reward event type, the user activity is calculated according to the historical login data and the preset cycle period, and the payment level to which the historical recharge amount belongs is determined; the target discount ratio is calculated according to the user activity, the recharge suspension days and the payment level, and the target reward event is generated according to the reward event type and the target discount ratio. It can determine whether the current user is the target user to be activated, and calculate the discount ratio, reward type, etc. that meet the user according to the user data of the target user to be activated, so that the generated target reward event meets the user's recharge habits and consumption level, thereby improving the user's recharge conversion rate and correspondingly improving the user's experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of a method for generating a client reward event provided by an embodiment of the present application;

[0045] Figure 2 is a flow chart of a method for determining a reward event type provided in an embodiment of the present application;

[0046] Figure 3 is a flow chart of a method for calculating a target discount ratio provided in an embodiment of the present application;

[0047] Figure 4 is a flow chart of a paid activity analysis method provided by an embodiment of the present application;

[0048] Figure 5 It is a structural schematic diagram of a client reward event generating device provided in an embodiment of the present application;

[0049] Figure 6 It is a structural diagram of a client reward event generating device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical scheme and advantages of the present application clearer, the specific embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. It is understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for the convenience of description, only the part related to the present application but not all the contents are shown in the accompanying drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow chart describes each operation (or step) as a sequential process, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of each operation can be rearranged. The process can be terminated when its operation is completed, but it can also have additional steps not included in the accompanying drawings. The process can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

[0051] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0052] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0053] The following, in conjunction with the accompanying drawings, describes in detail the client reward event generation method, device, equipment and medium provided in the embodiments of the present application through specific embodiments and their application scenarios.

[0054] The client reward event generation method provided in the embodiment of the present application can be applied to various scenarios of generating platform reward events in which clients obtain more privileges by paying, such as game platforms, food delivery platforms, and film and television platforms, etc. By providing reward events to users, it promotes the consumption behavior of users within the platform and ensures the recharge conversion rate of users. Based on the above application scenarios, it can be understood that the execution subject of this solution can be a server.

[0055] Figure 1is a flow chart of a method for generating a client reward event provided by an embodiment of the present application, such as Figure 1 As shown, including:

[0056] Step S101, receiving platform login information sent by the client, determining user data associated with the platform login information, and judging whether the user data meets the preset payment activation rules, wherein the user data includes historical login data, historical recharge amount and recharge suspension days.

[0057] Among them, the platform login information is the information entered by the user on the client when logging into the corresponding platform, such as the game account and game password entered in the game platform. User data is all the data generated by the user in the process of using the platform, which may include real-time data, historical data, such as user age, user gender, etc. Optionally, the user data includes historical login data, historical recharge amount and recharge suspension days. Among them, the historical login data can be used to indicate the user's login status in the current platform, and may include historical login time, historical login times, etc. The historical recharge amount can indicate the user's historical consumption in the platform, and may include historical total recharge amount and historical word recharge amount. The recharge suspension days indicate the time interval between the current time and the expiration time of the last recharge event. The preset payment activation rules are rules for screening target users, which include various conditions, such as the user's age is greater than or equal to 18 years old.

[0058] In one embodiment, the platform login information sent by the client is received. If the platform login information is the login information of the game platform, the user data associated with the game account in the platform login information is queried in the historical database, such as the game service area, the total historical recharge amount, the number of days of suspension of recharge, etc. The game service area, the total historical recharge amount and the number of days of suspension of recharge are judged according to the preset payment activation rules to determine whether the user corresponding to the user data is the target user to be activated. Exemplarily, the service area in the preset payment activation rules is service area A, the total historical recharge amount is greater than 100 yuan, and the number of days of suspension of recharge exceeds 7 days. It is determined whether the game service area, the total historical recharge amount, and the number of days of suspension of recharge determined above meet the preset payment activation rules. If so, the user corresponding to the user is determined to be the target user to be activated. If not, the user corresponding to the user data is determined to be a non-target user to be activated, and the reward event corresponding to the user is not generated.

[0059] Step S102: When the user data satisfies the preset payment activation rule, a recharge amount range is determined according to the historical recharge amount, and a first recharge event corresponding to the recharge amount range is determined; a target recharge time is calculated according to the number of days of charging suspension, and a second recharge event corresponding to the target recharge time is determined.

[0060] Among them, the recharge amount range is used to indicate the range to which the most recent recharge amount belongs. The first recharge event is a recharge event corresponding to the preset range to which the current total recharge amount belongs, and the first recharge event can be determined based on a pre-set mapping relationship table between recharge events and recharge amounts. The target recharge time can be the time of the most recent recharge operation from the current time, or the expiration time of the most recent recharge event. The second recharge event is a recharge event corresponding to the target recharge time, or a recharge event corresponding to the last recharge operation.

[0061] In one embodiment, when the user data satisfies the preset payment activation rules, it can be determined that the user corresponding to the user data is the target user to be activated, so the most recent recharge amount of the target user to be activated is determined, and the recharge amount range corresponding to the most recent recharge amount is determined. According to the preset mapping relationship table, the recharge event corresponding to the recharge amount range is determined as the first recharge event. The target recharge time is calculated according to the current time and the number of days of charging suspension, and the recharge event corresponding to the target recharge time is determined as the second recharge event. The first recharge event and the second recharge event can be the same recharge event or different recharge events.

[0062] In one embodiment, determining a recharge amount range according to the historical recharge amount, and determining a first recharge event corresponding to the recharge amount range, includes:

[0063] Determine each single recharge amount in the historical recharge amount, and determine a recharge amount range according to each single recharge amount;

[0064] According to the recharge amount corresponding to the preset recharge event, a recharge event within the recharge amount range is determined as the first recharge event.

[0065] Among them, the recharge amount for each time is determined according to the historical recharge amount, and the minimum recharge amount and the maximum recharge amount are determined in each recharge amount, the range consisting of the minimum recharge amount and the maximum recharge amount is determined as the recharge amount range, and the recharge events corresponding to each single recharge amount within the recharge amount range are determined, and the recharge events corresponding to each single recharge amount within the recharge amount range are determined as the first recharge event.

[0066] As described above, by determining the recharge amount range, the user's recharge habits or consumption level can be analyzed, thereby generating reward events that meet the user's needs based on the recharge habits and consumption level, avoiding mismatch between reward events and the user's actual needs, and correspondingly improving the user's recharge conversion rate.

[0067] Step S103: performing a recharge preference analysis on the first recharge event and the second recharge event to determine a reward event type, calculating user activity according to the historical login data and a preset cycle period, and determining the payment level to which the historical recharge amount belongs.

[0068] Among them, user activity is used to indicate the frequency of users logging into the platform within a certain period of time. The payment level is a pre-set range of recharge amounts, such as ≥100 yuan, ≥200 yuan, ≥500 yuan, ≥1000 yuan, etc. In one embodiment, the number of recharges of the first recharge event and the number of recharges of the second recharge event are determined by querying the historical database, and the user's recharge preference is determined according to each recharge number. Exemplarily, the type of recharge event with the most recharges can be determined as a reward event type. The number of historical logins within a preset cycle period is determined based on historical login data, and the number of historical logins is divided by the number of days in the preset cycle period to obtain the user activity within the preset cycle period. Determine the level to which the historical total recharge amount belongs based on the preset payment level.

[0069] Step S104: Calculate a target discount ratio according to the user activity, the number of charging suspension days and the payment level, and generate a target reward event according to the reward event type and the target discount ratio.

[0070] Among them, the target reward event represents an event that can generate discounts or discounts during the consumption process of the platform. Such as vouchers for product A, discount vouchers for product B, etc. In one embodiment, the activity level and the level of days of suspension are pre-set, and different activity levels and different levels of days of suspension correspond to different discount ratios. The corresponding activity level and the discount ratio corresponding to the activity level are determined according to the calculated user activity, and the corresponding level of days of suspension are determined according to the calculated days of suspension, and the discount ratio corresponding to the level of days of suspension. The discount ratio corresponding to the user activity, the discount ratio corresponding to the days of suspension, and the discount ratio corresponding to the level of days of suspension are summed and calculated to obtain the final discount ratio, and the target reward event is generated according to the final discount ratio and the above-determined reward event type. Exemplarily, if the final discount ratio is 60%, the reward event type is product B, such as a movie membership monthly card discount voucher, then the generated target reward event is a movie membership monthly card discount 60% voucher.

[0071] In the above, by receiving the platform login information sent by the client, determining the user data associated with the platform login information, judging whether the user data meets the preset payment activation rules, the user data includes historical login data, historical recharge amount and recharge suspension days; in the case where the user data meets the preset payment activation rules, determining the recharge amount range according to the historical recharge amount, and determining the first recharge event corresponding to the recharge amount range, calculating the target recharge time according to the recharge suspension days, and determining the second recharge event corresponding to the target recharge time; performing recharge preference analysis on the first recharge event and the second recharge event to determine the reward event type, calculating the user activity according to the historical login data and the preset cycle period, and determining the payment level to which the historical recharge amount belongs; calculating the target discount ratio according to the user activity, recharge suspension days and payment level, and generating the target reward event according to the reward event type and the target discount ratio. It can determine whether the current user is the target user to be activated, and calculate the discount ratio, reward type, etc. that meet the user according to the user data of the target user to be activated, so that the generated target reward event meets the user's recharge habits and consumption level, thereby improving the user's recharge conversion rate, and correspondingly improving the user's experience.

[0072] Figure 2 is a flow chart of a method for determining a reward event type provided in an embodiment of the present application. Figure 2 As shown, including:

[0073] Step S201: Count the first historical recharge times of each first recharge event and the second historical recharge times of each second recharge event.

[0074] Optionally, the number of first recharge events is multiple, and the number of second recharge events is one. In one embodiment, the first historical recharge number is the number of recharges corresponding to each first recharge event. The second historical recharge number is the number of recharges corresponding to the second recharge event. In one embodiment, the number of recharges of each first recharge event and the second historical recharge number corresponding to the second recharge event are respectively calculated.

[0075] Step S202: Calculate a first recharge preference value according to each first historical recharge number and a preset first weight value, and calculate a second recharge preference value according to the second historical recharge number and a preset second weight value.

[0076] Among them, the first weight value is used to indicate the influence of each first historical recharge number on determining the user's recharge preference. The second weight value is used to indicate the influence of the second historical recharge number on determining the user's recharge preference. The first recharge preference value is used to indicate the user's preference for the first recharge event, and the second recharge preference value is used to indicate the user's preference for the second recharge event. Exemplarily, if the first recharge events are event A, event B, and event C, respectively, and the second recharge event is event D. The first historical recharge number of event A is 2 times, the first historical recharge number of event B is 1 time, the first historical recharge number of event C is 1 time, and the second historical recharge number of event D is 2 times. The preset first weight value is 40%, and the second weight value is 60%. According to the first historical recharge number and the preset first weight value, the first recharge preference value of event A is calculated to be 0.8, the first recharge preference value of event B is 0.4, and the first recharge preference value of event C is 0.4. The second recharge preference value of event D is 1.2.

[0077] Step S203: compare the first recharge preference value and the second recharge preference value, and determine the reward event type based on the comparison result.

[0078] Exemplarily, the first recharge preference values ​​0.8, 0.4, and 0.4 are compared with the second recharge preference value 1.2, respectively, to determine the recharge event with the largest preference value, and determine the type of the recharge event as a reward event type.

[0079] In one possible embodiment, if the second recharge event is the same as any of the multiple first recharge events, such as the first recharge events are respectively A event, B event, and C event, the second recharge event is A event. The first historical recharge times of A event are 2 times, the first historical recharge times of B event are 1 time, and the first historical recharge times of C event are 1 time. The preset first weight value is 40%, and the second weight value is 60%. Then determine the total number of recharges during time A, and calculate the preference value of A event based on the total number of recharges and the second weight value, that is, 2*0.6=1.2, the first preference value of B event is 0.4, and the first preference value of C event is 0.4. Compare the preference value of A event with the first preference values ​​of 0.4 and 0.4, respectively, determine the recharge event with the largest preference value, and determine the type of the recharge event as a reward event type.

[0080] In the above, by respectively counting the first historical recharge times of each first recharge event and the second historical recharge times of the second recharge event; calculating the first recharge preference value according to each first historical recharge times and the preset first weight value, and calculating the second recharge preference value according to the second historical recharge times and the preset second weight value; comparing the first recharge preference value and the second recharge preference value, and determining the reward event type based on the comparison result. By calculating the first recharge preference value and the second recharge preference value, the user's recharge preference can be accurately analyzed, which facilitates the subsequent generation of reward events that meet the user's needs based on the recharge preference, and further improves the user's recharge conversion rate.

[0081] Figure 3 is a flow chart of a method for calculating a target discount ratio provided in an embodiment of the present application. Figure 3 As shown, including:

[0082] Step S301: compare the user activity with a preset activity threshold, determine a first additional discount ratio based on the activity comparison result, compare the number of days of charging suspension with a preset day threshold, and determine a second additional discount ratio based on the day comparison result.

[0083] Among them, the first additional discount ratio is the discount ratio corresponding to the user's activity, and the second additional discount ratio is the discount ratio corresponding to the number of days of charging suspension. Exemplarily, the user's activity is compared with the preset activity threshold. If it is greater than the activity threshold, the first additional discount ratio is determined to be 10%. If it is less than or equal to the activity threshold, the first additional discount ratio is determined to be 5%. Correspondingly, the number of days of charging suspension is compared with the preset day threshold. If it is greater than the day threshold, the second additional discount ratio is determined to be 10%. If it is less than or equal to the day threshold, the second additional discount ratio is determined to be 5%.

[0084] Step S302: determine a discount ratio corresponding to the payment level, and calculate a target discount ratio based on the discount ratio, the first additional discount ratio, and the second additional discount ratio.

[0085] In one embodiment, the discount ratio corresponding to the payment level is determined according to a preset mapping relationship between the payment level and the discount ratio, and the discount ratio, the first additional discount ratio and the second additional discount ratio are summed to obtain a target discount ratio.

[0086] In the above, by comparing the user activity with the preset activity threshold, determining the first additional discount ratio based on the activity comparison result, comparing the number of days of charging suspension with the preset number of days threshold, and determining the second additional discount ratio based on the number of days comparison result; determining the discount ratio corresponding to the payment level, and calculating the target discount ratio based on the discount ratio, the first additional discount ratio, and the second additional discount ratio. When determining the discount ratio, factors such as the user's activity, the number of days of charging suspension, and the total historical payment amount can be considered at the same time, which improves the calculation accuracy of the target discount ratio and makes the calculation result more able to meet the user's actual recharge needs.

[0087] In one embodiment, determining the first additional discount ratio based on the activity comparison result includes:

[0088] Determine a first discount ratio type based on the activity comparison result, and determine a first additional discount ratio according to the first discount ratio type and a preset discount ratio value;

[0089] Accordingly, the second additional discount ratio is determined based on the day comparison result, including:

[0090] A second discount ratio type is determined based on the day comparison result, and a second additional discount ratio is determined according to the second discount ratio type and the preset discount ratio value.

[0091] Among them, the first discount ratio type is used to indicate the type of the first additional discount ratio, which can be a positive discount and a negative discount. The positive discount is used to indicate the discount ratio increased on the basis of the current discount, and the negative discount is used to indicate the discount ratio reduced on the basis of the current discount. Similarly, the second discount ratio type is used to indicate the type of the second additional discount ratio, and the second discount ratio type can also include a positive discount and a negative discount. The preset amount discount ratio value is a fixed ratio value, such as 5%.

[0092] In one embodiment, the first discount ratio type is determined to be a positive ratio or a negative ratio based on the activity comparison result, wherein if the activity comparison result is greater than the preset activity threshold, the first discount ratio type is determined to be a positive ratio, and if the activity comparison result is less than or equal to the preset activity threshold, the first discount ratio type is determined to be a negative ratio. In the case where the first discount ratio type is a positive ratio, the first additional discount ratio is determined to be +5%, and in the case where the first discount ratio type is a negative ratio, the first additional discount ratio is determined to be -5%. Similarly, if the day comparison result is greater than the preset day threshold, the second discount ratio type is determined to be a positive ratio, and if the day comparison result is less than or equal to the preset day threshold, the second discount ratio type is determined to be a negative ratio. In the case where the second discount ratio type is a positive ratio, the second additional discount ratio is determined to be +5%, and in the case where the second discount ratio type is a negative ratio, the second additional discount ratio is determined to be -5%.

[0093] In the above, by determining the first discount ratio type based on the activity comparison result, determining the first additional discount ratio according to the first discount ratio type and the preset discount ratio value; determining the second discount ratio type based on the day comparison result, and determining the second additional discount ratio according to the second discount ratio type and the preset discount ratio value. The corresponding additional discount ratio can be quickly determined based on the preset discount ratio value and the corresponding discount ratio type, thereby improving the calculation efficiency of the first additional discount ratio and the second additional discount ratio.

[0094] Figure 4 is a flow chart of a paid activity analysis method provided by an embodiment of the present application, such as Figure 4 As shown, including:

[0095] Step S401, sending the target reward event to the client, and monitoring the triggering of the target reward event in real time.

[0096] In one embodiment, the target reward event is sent to the client through a preset access method, wherein the access method is used to indicate a specific sending method for sending the target reward event, such as sending through a pop-up window in the game or through text messages, emails, etc. The specific access method can be determined according to the specific time of sending the target reward event. Exemplarily, if the target reward event is sent during the game time, it is sent in the form of a pop-up window in the game, and if the target reward event is sent after the game is over, it is sent in the form of text messages, emails, etc. And after sending the target reward event to the client, the triggering of the target reward event by the client is monitored in real time. If the client triggers the target reward event, it indicates that the user performs a recharge operation based on the target reward event.

[0097] Step S402: Perform payment activity analysis on the triggering conditions within the preset cycle, and adjust the preset payment activation rules in real time based on the analysis results.

[0098] In one embodiment, the recharge frequency of the user in the preset cycle can be calculated according to the number of times the target reward event is triggered in the preset cycle, so as to analyze the payment activity according to the recharge frequency. The higher the recharge frequency, the more active the user is in paying, and vice versa, the lower the recharge frequency, the less active the user is in paying, and the preset payment activation rules are adjusted in real time according to the analysis results. For example, if the user is not active in paying, the recharge amount in the payment activation rules is reduced.

[0099] In another possible embodiment, the calculation method of the target discount ratio may be adjusted in real time based on the analysis results, or the discount ratio value may be preset. For example, when the user is not active in paying, the preset discount ratio value may be increased accordingly.

[0100] In the above, by sending the target reward event to the client and monitoring the triggering of the target reward event in real time, the triggering situation within the preset cycle is analyzed for payment activity, and the preset payment activation rules are adjusted in real time based on the analysis results. The corresponding reward event generation strategy or rule can be adjusted in time according to the real-time payment activity, thereby further improving the user's recharge conversion rate and avoiding serious user loss.

[0101] In a real-time example, after sending the target reward event to the client and monitoring the triggering of the target reward event in real time, the method further includes:

[0102] Record the sending time of the target reward event, determine the continuous reaching time point according to the sending time and the preset continuous reaching rule, and determine the triggering situation before the continuous reaching time point;

[0103] Determine whether to execute the continuous reaching task according to the triggering situation before the continuous reaching time point, and if the target reward event is not triggered before the continuous reaching time point, execute the continuous reaching task at the continuous reaching time point;

[0104] If the target reward event is triggered before the continuous reaching time point, the continuous reaching task will not be performed.

[0105] Among them, the preset continuous reach rule is used to specify the method for determining the continuous reach time point. The continuous reach time point is one or more time points within the preset cycle period. Exemplarily, if the preset cycle period is 30 days, the preset continuous reach rule may specify that the continuous reach time point is the 7th day and the 15th day after the target reward event is sent. While sending the target reward event, the current sending time can be recorded, and the 7th day and the 15th day after the sending time are determined as the continuous reach time points. Real-time monitoring of whether the target trigger event is triggered within 7 days. If not triggered, the continuous reach task is executed on the 7th day, and continuous monitoring of whether the target reward event is triggered from the 7th day to the 15th day. If it is still not triggered, the continuous reach task is executed on the 15th. If the target reward event is triggered within 7 days, the continuous reach task is not executed.

[0106] In the above, by recording the sending time of the target reward event, the continuous reaching time point is determined according to the sending time and the preset continuous reaching rules, and the triggering situation before the continuous reaching time point is determined; according to the triggering situation before the continuous reaching time point, it is judged whether to execute the continuous reaching task, and if the target reward event is not triggered before the continuous reaching time point, the continuous reaching task is executed at the continuous reaching time point; if the target reward event is triggered before the continuous reaching time point, the continuous reaching task is not executed. It is possible to judge whether to perform the continuous triggering task according to the triggering situation of the target reward event, thereby timely reminding users of the current preferential activities they can enjoy, thereby improving the user's recharge conversion rate and user satisfaction.

[0107] Figure 5 is a schematic diagram of the structure of a client reward event generating device provided in an embodiment of the present application, such as Figure 5 As shown, including:

[0108] The information receiving module 51 is used to receive the platform login information sent by the client;

[0109] A user data determination module 52, used to determine user data associated with the platform login information;

[0110] A user data judgment module 53, used to judge whether the user data meets the preset payment activation rules, the user data includes historical login data, historical recharge amount and recharge suspension days;

[0111] A first recharge event determination module 54 is used to determine a recharge amount range according to the historical recharge amount when the user data meets the preset payment activation rule, and determine a first recharge event corresponding to the recharge amount range;

[0112] A second recharge event determination module 55, configured to calculate a target recharge time according to the number of days of charging suspension, and determine a second recharge event corresponding to the target recharge time;

[0113] A recharge preference analysis module 56, configured to perform a recharge preference analysis on the first recharge event and the second recharge event to determine a reward event type;

[0114] A user activity calculation module 57, used to calculate user activity based on the historical login data and a preset cycle period;

[0115] A payment level determination module 58, configured to determine the payment level to which the historical recharge amount belongs;

[0116] The target reward event generation module 59 is used to calculate the target discount ratio according to the user activity, the number of charging suspension days and the payment level, and generate a target reward event according to the reward event type and the target discount ratio.

[0117] In the above, by receiving the platform login information sent by the client, determining the user data associated with the platform login information, judging whether the user data meets the preset payment activation rules, the user data includes historical login data, historical recharge amount and recharge suspension days; in the case where the user data meets the preset payment activation rules, determining the recharge amount range according to the historical recharge amount, and determining the first recharge event corresponding to the recharge amount range, calculating the target recharge time according to the recharge suspension days, and determining the second recharge event corresponding to the target recharge time; performing recharge preference analysis on the first recharge event and the second recharge event to determine the reward event type, calculating the user activity according to the historical login data and the preset cycle period, and determining the payment level to which the historical recharge amount belongs; calculating the target discount ratio according to the user activity, recharge suspension days and payment level, and generating the target reward event according to the reward event type and the target discount ratio. It can determine whether the current user is the target user to be activated, and calculate the discount ratio, reward type, etc. that meet the user according to the user data of the target user to be activated, so that the generated target reward event meets the user's recharge habits and consumption level, thereby improving the user's recharge conversion rate, and correspondingly improving the user's experience.

[0118] In a possible embodiment, the recharge preference analysis module 56 is specifically used to:

[0119] respectively counting a first historical recharge number of each first recharge event and a second historical recharge number of each second recharge event;

[0120] Calculating a first recharge preference value according to each first historical recharge number and a preset first weight value, and calculating a second recharge preference value according to the second historical recharge number and a preset second weight value;

[0121] The first recharge preference value and the second recharge preference value are compared, and a bonus event type is determined based on the comparison result.

[0122] In a possible embodiment, the target reward event generation module 59 is specifically used to:

[0123] The user activity is compared with a preset activity threshold, and a first additional discount ratio is determined based on the activity comparison result; the charging suspension days are compared with a preset day threshold, and a second additional discount ratio is determined based on the day comparison result;

[0124] A discount ratio corresponding to the payment level is determined, and a target discount ratio is calculated based on the discount ratio, the first additional discount ratio, and the second additional discount ratio.

[0125] In a possible embodiment, the target reward event generation module 59 is further used to:

[0126] Determine a first discount ratio type based on the activity comparison result, and determine a first additional discount ratio according to the first discount ratio type and a preset discount ratio value;

[0127] A second discount ratio type is determined based on the day comparison result, and a second additional discount ratio is determined according to the second discount ratio type and the preset discount ratio value.

[0128] In a possible embodiment, the first recharge event determination module 54 is used to:

[0129] Determine each single recharge amount in the historical recharge amount, and determine a recharge amount range according to each single recharge amount;

[0130] According to the recharge amount corresponding to the preset recharge event, a recharge event within the recharge amount range is determined as the first recharge event.

[0131] In a possible embodiment, the client reward event generating device further includes a sending module, a monitoring module, and a payment activation rule adjustment module, wherein:

[0132] Sending module: used for sending the target reward event to the client;

[0133] The monitoring module is used to: monitor the triggering status of the target reward event in real time;

[0134] The payment activation rule adjustment module is used to perform payment activity analysis on the triggering conditions within the preset cycle period, and adjust the preset payment activation rules in real time based on the analysis results.

[0135] In a possible embodiment, the client reward event generating device further includes a continuous reaching task judging module, and the continuous reaching task judging module is used to:

[0136] Record the sending time of the target reward event, determine the continuous reaching time point according to the sending time and the preset continuous reaching rule, and determine the triggering situation before the continuous reaching time point;

[0137] Determine whether to execute the continuous reaching task according to the triggering situation before the continuous reaching time point, and if the target reward event is not triggered before the continuous reaching time point, execute the continuous reaching task at the continuous reaching time point;

[0138] If the target reward event is triggered before the continuous reaching time point, the continuous reaching task will not be performed.

[0139] The embodiment of the present application also provides an electronic device, and the client reward event generating device can integrate a client reward event generating device provided in the embodiment of the present application. Figure 6 Schematic diagram of a client reward event generation device provided in an embodiment of the present application. Figure 6 The client reward event generation device includes: an input device 63, an output device 64, a memory 62 and one or more processors 61; the memory 62 is used to store one or more programs; when one or more programs are executed by one or more processors 61, the one or more processors 61 implement the client reward event generation method provided in the above embodiment. The input device 63, the output device 64, the memory 62 and the processor 61 can be connected by a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0140] The memory 62, as a computing device readable storage medium, can be used to store software programs, computer executable programs and modules, such as the program instructions / modules corresponding to the client reward event generation method provided in any embodiment of the present application. The memory 62 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application required for a function; the storage data area may store data created according to the use of the device, etc. In addition, the memory 62 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 62 may further include a memory remotely arranged relative to the processor 61, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0141] The input device 63 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 64 may include a display device such as a display screen.

[0142] The processor 61 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 62, that is, implements the above-mentioned client reward event generation method.

[0143] The client reward event generation device, equipment and computer provided above can be used to execute the client reward event generation method provided by any of the above embodiments, and have corresponding functions and beneficial effects.

[0144] The embodiment of the present application also provides a storage medium storing computer executable instructions. When the computer executable instructions are executed by a computer processor, they are used to execute the client reward event generation method provided in the above embodiment. The client reward event generation method includes:

[0145] Acquire text information, and perform sensitive information detection on the text information through a first language model to obtain a plurality of sensitive words, where the first language model is a small local model;

[0146] Determine the text position of each sensitive word in the text information, and determine the context analysis position and the text content corresponding to the context analysis position according to the text position;

[0147] Determine a target sensitive word among the sensitive words by performing context analysis on the text content, and filter the target sensitive words in the text information to obtain security data;

[0148] The security data is reported to a server, and the server performs word segmentation processing on the security data based on a second language model to obtain a plurality of keywords, and generates response information corresponding to the text information according to the plurality of keywords and the part of speech of each keyword.

[0149] Storage medium - any of various types of memory devices or storage devices. The term "storage medium" is intended to include: installation media, such as CD-ROM, floppy disk or tape device; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (such as hard disk or optical storage); registers or other similar types of memory elements, etc. Storage media may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the first computer system in which the program is executed, or may be located in a different second computer system, which is connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations (for example, in different computer systems connected by a network). The storage medium may store program instructions (for example, embodied as a computer program) that can be executed by one or more processors.

[0150] Of course, the storage medium containing computer executable instructions provided in an embodiment of the present application, whose computer executable instructions are not limited to the client reward event generation method as described above, can also execute related operations in the client reward event generation method provided in any embodiment of the present application.

[0151] The client-based reward event generation device, equipment and storage medium provided in the above embodiments can execute the client-based reward event generation method provided in any embodiment of the present application. For technical details not described in detail in the above embodiments, please refer to the client-based reward event generation method provided in any embodiment of the present application.

[0152] The above are only preferred embodiments of the present application and the technical principles used. The present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments and substitutions that can be made by those skilled in the art will not deviate from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the claims.

Claims

1. A method for generating a client reward event, characterized in that: include: Receive platform login information sent by the client, determine user data associated with the platform login information, and judge whether the user data meets the preset payment activation rules, wherein the user data includes historical login data, historical recharge amount, and recharge suspension days; In the case where the user data satisfies the preset payment activation rule, a recharge amount range is determined according to the historical recharge amount, and a first recharge event corresponding to the recharge amount range is determined, a target recharge time is calculated according to the number of charging suspension days, and a second recharge event corresponding to the target recharge time is determined; Performing a recharge preference analysis on the first recharge event and the second recharge event to determine a reward event type, calculating user activity according to the historical login data and a preset cycle, and determining the payment tier to which the historical recharge amount belongs; A target discount ratio is calculated according to the user activity, the number of charging suspension days and the payment level, and a target reward event is generated according to the reward event type and the target discount ratio.

2. The client reward event generation method according to claim 1, characterized in that: The number of the first recharge events is multiple, the number of the second recharge events is one, and the performing recharge preference analysis on the first recharge event and the second recharge event to determine the reward event type includes: respectively counting a first historical recharge number of each first recharge event and a second historical recharge number of each second recharge event; Calculating a first recharge preference value according to each first historical recharge number and a preset first weight value, and calculating a second recharge preference value according to the second historical recharge number and a preset second weight value; The first recharge preference value and the second recharge preference value are compared, and a bonus event type is determined based on the comparison result.

3. The client reward event generation method according to claim 1, characterized in that: The calculating the target discount ratio according to the user activity, the number of charging suspension days and the payment level includes: The user activity is compared with a preset activity threshold, and a first additional discount ratio is determined based on the activity comparison result; the charging suspension days are compared with a preset day threshold, and a second additional discount ratio is determined based on the day comparison result; A discount ratio corresponding to the payment level is determined, and a target discount ratio is calculated based on the discount ratio, the first additional discount ratio, and the second additional discount ratio.

4. The client reward event generation method according to claim 3, characterized in that: The determining of the first additional discount ratio based on the activity comparison result includes: Determine a first discount ratio type based on the activity comparison result, and determine a first additional discount ratio according to the first discount ratio type and a preset discount ratio value; Accordingly, the second additional discount ratio is determined based on the day comparison result, including: A second discount ratio type is determined based on the day comparison result, and a second additional discount ratio is determined according to the second discount ratio type and the preset discount ratio value.

5. The client reward event generation method according to claim 1, characterized in that: The determining of a recharge amount range according to the historical recharge amount, and determining a first recharge event corresponding to the recharge amount range, includes: Determine each single recharge amount in the historical recharge amount, and determine a recharge amount range according to each single recharge amount; According to the recharge amount corresponding to the preset recharge event, a recharge event within the recharge amount range is determined as the first recharge event.

6. The client reward event generation method according to claim 1, characterized in that: After generating the target reward event according to the reward event type and the target discount ratio, the method further includes: Sending the target reward event to the client, and monitoring the triggering of the target reward event in real time; Perform payment activity analysis on the triggering situation within the preset cycle period, and adjust the preset payment activation rules in real time based on the analysis results.

7. The client reward event generation method according to claim 6, characterized in that: After sending the target reward event to the client and monitoring the triggering of the target reward event in real time, the method further includes: Record the sending time of the target reward event, determine the continuous reaching time point according to the sending time and the preset continuous reaching rule, and determine the triggering situation before the continuous reaching time point; Determine whether to execute the continuous reaching task according to the triggering situation before the continuous reaching time point, and if the target reward event is not triggered before the continuous reaching time point, execute the continuous reaching task at the continuous reaching time point; If the target reward event is triggered before the continuous reaching time point, the continuous reaching task will not be performed.

8. A client reward event generating device, characterized in that: include: An information receiving module, used to receive platform login information sent by the client; A user data determination module, used to determine user data associated with the platform login information; A user data judgment module, used to judge whether the user data meets the preset payment activation rules, the user data includes historical login data, historical recharge amount and recharge suspension days; A first recharge event determination module, configured to determine a recharge amount range according to the historical recharge amount when the user data satisfies a preset payment activation rule, and determine a first recharge event corresponding to the recharge amount range; A second recharge event determination module, configured to calculate a target recharge time according to the number of days of charging suspension, and determine a second recharge event corresponding to the target recharge time; A recharge preference analysis module, configured to perform a recharge preference analysis on the first recharge event and the second recharge event to determine a reward event type; A user activity calculation module, used to calculate user activity based on the historical login data and a preset cycle period; A payment level determination module, used to determine the payment level to which the historical recharge amount belongs; The target reward event generation module is used to calculate the target discount ratio according to the user activity, the number of charging suspension days and the payment level, and generate the target reward event according to the reward event type and the target discount ratio.

9. An electronic device, comprising: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, so that the one or more processors implement the client reward event generation method as described in any one of claims 1-7.

10. A storage medium storing computer executable instructions, wherein the computer executable instructions are used to perform the client reward event generation method according to any one of claims 1 to 7 when executed by a computer processor.

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