CN-122022910-A - Multi-mode driving schedule and excitation linkage management method

CN122022910ACN 122022910 ACN122022910 ACN 122022910ACN-122022910-A

Abstract

The invention discloses a multi-mode driving schedule and incentive linkage management method, which relates to the field of behavior driving and incentive mechanisms and comprises the steps of generating daily target combinations through target mapping functions, collecting behavior indexes generated by a user when the modules are applied in real time, carrying out normalization and weighting processing to obtain the completion degree of the user, deciding push actions according to the completion degree and carrying out constraint verification, executing the push actions passing the verification, when the user completes sub-targets, carrying out verification based on a preset anti-cheating mechanism, issuing instant incentive and updating user accounts, generating reports based on the behavior data of the user all the day when the daily is finished, and adjusting dynamic capacity portraits, next-day target generation and push decisions according to the reports. According to the invention, learning, social contact, tasks and transactions are incorporated into a unified driving system, and the user is driven to finish behaviors by a target, tasks and rewarding closed loop, so that the liveness and the completion rate are improved.

Inventors

Zhang Tuoliang
SHAO YALI
YANG JINQIU
ZHANG WENLIANG
ZHANG ZHILIANG
DONG JING

Assignees

北京漂洋过海科技有限责任公司

Dates

Publication Date: 20260512
Application Date: 20260126

Claims (10)

1. A multi-modal driven calendar and incentive linkage management method, comprising: Generating a daily target combination through a target mapping function based on the user capacity image, and transmitting the daily target combination to a client; Acquiring behavior indexes generated by a user when the modules are applied in real time, and carrying out normalization and weighting treatment on each behavior index to obtain the current day score of each module; according to the current day score of each module, calculating to obtain the completion degree of the user ; Comparing the degree of completion And a completion threshold If (if) < If the target is deviated, the target is judged to deviate and remedial pushing is carried out, otherwise, whether opportunistic pushing is carried out is evaluated according to expected benefits and disturbance cost, wherein the pushing time and the pushing mode are decided together by a response model and a context-aware recommendation model; Performing constraint verification on the determined pushing action by utilizing strategy constraint, and executing the pushing action passing the verification, wherein the strategy constraint comprises a single-day pushing upper limit, a minimum time interval and a system global interference budget; When the user completes the sub-objective, checking based on a preset anti-cheating mechanism, issuing instant incentive and updating the user account; at the end of each day, generating a report based on the behavior data of the user throughout the day, and adjusting the dynamic capacity portraits, the next day target generation and push decisions according to the report.
2. The multi-modal driven calendar and incentive linkage management method of claim 1 wherein the objective mapping function is Wherein C represents a user-capability image, A cross-module behavior score vector representing the last day of the user, Ω represents platform policy parameters, Representing daily target combinations.
3. The multi-modal driven calendar and incentive linkage management method of claim 2 wherein the daily goal combinations are generated by a goal mapping function, in particular: estimating an available time budget for a user's current day based on the user capability representation ; Respectively calculating target values of all modules according to the available time budget, the user capacity image and the platform policy parameters, wherein the target values comprise a learning target, a task target and a social target, The learning goal is Wherein, the The time-map coefficients are represented as such, Representing a learning target upper limit; Task targeting , wherein, The degree of task tendency is indicated, Representing a task trend threshold; Social targeting of , wherein, Representing social participation tendency, k representing a social target mapping factor; and combining the calculated target values of the modules into the daily target combination and outputting the daily target combination.
4. The multi-modal driven calendar and incentive linkage management method of claim 1, wherein the normalization and weighting process is performed on each original index to obtain the current day score of each module, specifically: Performing time damage processing on each original index in a historical weighting mode to obtain damage values of each index as , wherein, Representing the decay factor, K representing the historical time window length of participation; Normalizing the indexes subjected to the damage treatment through a normalization function to obtain standard values of the indexes; weighting and summing the standard values of all indexes in the same module according to preset index weights to obtain the current day score of the corresponding module , wherein, The weight of the j-th index in the module m, The standard value of the j-th index is indicated.
5. The multi-modal driven calendar and incentive linkage management method of claim 4 wherein the user's completion is calculated based on the current day score of each module, specifically: Scoring the current day of the user on each module according to preset module importance weights Weighted summation is carried out to obtain the completion degree of the user , wherein, A collection of modules is represented and, 。
6. The multimodal driven calendar and incentive linkage management method of claim 1, wherein the evaluation of whether to enter opportunistic pushing is based on expected revenue and interference costs, in particular: Calculating cost performance based on expected revenue and interference cost , wherein, Representing actions Is used to determine the expected benefit of (1), Representing push actions Is added to the disturbance cost of the (a); When (when) And when the preset income cost threshold value is exceeded, pushing is initiated, otherwise, pushing is not initiated.
7. The multi-modal driven calendar and incentive linkage management method according to claim 1 or 6, wherein the decision pushing timing and mode is specifically: predicting response probabilities of users to push actions in different time periods by using a response model; And selecting the action with highest cost performance from a plurality of candidate occasions by combining the response probability, the current user context and the disturbance cost through a context-aware recommendation model.
8. The multi-modal driven calendar and incentive linkage management method of claim 1 wherein the preset anti-cheating mechanism comprises: checking whether the behavior distribution of the user in each functional module accords with the history mode of the dynamic capability image; comparing evidence from different data sources for key behaviors that need to be verified to verify the authenticity of the behavior; an abnormal user behavior vector is identified based on the distance or anomaly detection method.
9. The multi-modal driven calendar and incentive linkage management method of claim 1 wherein the instant incentive comprises an instant micro-incentive and a continuity incentive, wherein the instant micro-incentive is an instant micro-incentive , wherein, Indicating baseline integral, delta indicating sub-target completion, gamma indicating control margin return factor, continuity reward , wherein, Indicating a baseline prize value for continuous compliance, Representing a prize growth function that increases with consecutive days d of achievement.
10. The multi-modal driven calendar and incentive linkage management method of claim 1 wherein instant incentives are issued, specifically: setting a daily incentive budget R pool ; for the target user set U, an incentive credit is allocated to each user To maximize the desired number of completions And meet the following R pool is less than or equal to the sum of the values, Presentation and rewarding The probability of the user completing the goal.

Description

Multi-mode driving schedule and excitation linkage management method Technical Field The invention relates to the field of behavior driving and excitation mechanisms, in particular to a multi-mode driving schedule and excitation linkage management method. Background Along with the popularization of mobile internet and digital service, various application platforms generally introduce a user target management system, and aim to improve user participation, cultivate use habits and realize the increase of business core indexes by setting daily or staged tasks. However, in the prior art, links such as target setting, progress tracking, message pushing, rewarding and the like are realized as independent functions, and collaborative optimization based on data-driven feedback is lacking. The system cannot continuously learn from the feedback of the user on the historical intervention, so that the user portraits, the target recommendation strategy and the intervention strategy are iteratively optimized, and the long-term self-adaptive capacity and the effect growth potential of the system are limited. In addition, most existing systems adopt a pushing mechanism based on fixed rules (such as timing reminding and progress threshold triggering), but lack of perception of the current state, historical behavior rules and real-time context of the user, and do not consider expected benefits and disturbance cost of pushing actions, so that interference or missing of a good opportunity for promoting target completion may be formed for the user. And the incentive amount is fixed or set in a segmented way, and cannot be dynamically adjusted according to the total incentive budget, the task difficulty, the user value and the marginal utility of the current incentive, so that the yield ratio of the incentive input is not optimal. In view of the foregoing, there is a need for a daily goal management method and system capable of accurately sensing the behavior and state of a user, intelligently deciding the optimal intervention time and mode, and having continuous learning ability, so as to scientifically and efficiently improve the goal achievement rate of the user and the core value of the platform on the premise of ensuring the user experience. Disclosure of Invention The invention provides a multi-mode driving schedule and incentive linkage management method which is used for overcoming at least one technical problem in the prior art. The embodiment of the invention provides a multi-mode driving schedule and incentive linkage management method, which comprises the following steps: Generating a daily target combination through a target mapping function based on the user capacity image, and transmitting the daily target combination to a client; Acquiring behavior indexes generated by a user when the modules are applied in real time, and carrying out normalization and weighting treatment on each behavior index to obtain the current day score of each module; according to the current day score of each module, calculating to obtain the completion degree of the user ; Comparing the degree of completionAnd a completion thresholdIf (if)<If the target is deviated, the target is judged to deviate and remedial pushing is carried out, otherwise, whether opportunistic pushing is carried out is evaluated according to expected benefits and disturbance cost, wherein the pushing time and the pushing mode are decided together by a response model and a context-aware recommendation model; Performing constraint verification on the determined pushing action by utilizing strategy constraint, and executing the pushing action passing the verification, wherein the strategy constraint comprises a single-day pushing upper limit, a minimum time interval and a system global interference budget; When the user completes the sub-objective, checking based on a preset anti-cheating mechanism, issuing instant incentive and updating the user account; at the end of each day, generating a report based on the behavior data of the user throughout the day, and adjusting the dynamic capacity portraits, the next day target generation and push decisions according to the report. Optionally, the target mapping function isWherein C represents a user-capability image,A cross-module behavior score vector representing the last day of the user, Ω represents platform policy parameters,Representing daily target combinations. Optionally, the daily target combination is generated by a target mapping function, specifically: estimating an available time budget for a user's current day based on the user capability representation ; Respectively calculating target values of all modules according to the available time budget, the user capacity image and the platform policy parameters, wherein the target values comprise a learning target, a task target and a social target, The learning goal isWherein, the The time-map coefficients are represented as such,Representing a learning target upp