Forward behavior excitation method and excitation system

Abstract

The invention discloses a forward behavior excitation method and excitation system. The motivation method comprises the following steps: collecting first-class forward behavior data and second-class forward behavior data of a user; settling the first type of forward behavior data as a first virtual resource, and settling the second type of forward behavior data as a second virtual resource; forming a first driving instruction according to the first virtual resource, and forming a second driving instruction according to the second virtual resource; and performing morphological evolution on a set virtual space based on the first driving instruction and the second driving instruction so as to excite a forward behavior of a user. According to the technical scheme, long-term and efficient habit cultivation of the user can be effectively met, and the success rate of habit cultivation is increased.

Description

Technical Field

[0001] This invention relates to the field of behavioral incentive technology, specifically to positive behavior incentive methods and incentive systems. Background Technology

[0002] In behavioral incentives, incentive schemes targeting positive user behavior have been gradually applied to habit formation scenarios. However, in terms of actual application results, existing schemes are difficult to support users' long-term and efficient habit formation needs.

[0003] Specifically, most existing solutions employ a uniform reward design where a single virtual resource corresponds to multiple types of positive behaviors. These designs fail to fully consider the inherent differences in the characteristics of positive behaviors with different attributes, making it impossible to provide precise feedback for each type of positive behavior. Users struggle to clearly perceive the effectiveness of their different behaviors through incentives. On the other hand, a long-term, singular form of incentive can gradually lead to user fatigue, with sensitivity to incentives decreasing over time, even resulting in numbness. This weakens the guiding effect of incentives on positive behaviors, failing to help users maintain positive behaviors in the long term and severely impacting the success rate of habit formation. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a positive behavior incentive method that can effectively meet users' long-term and efficient habit cultivation needs and improve the success rate of habit formation.

[0005] This application provides a positive behavior incentive method, the incentive method comprising:

[0006] Collect user's first type of positive behavior data and second type of positive behavior data;

[0007] The first type of positive behavior data is settled as the first virtual resource, and the second type of positive behavior data is settled as the second virtual resource;

[0008] A first driving instruction is generated based on the first virtual resource, and a second driving instruction is generated based on the second virtual resource;

[0009] Based on the first and second driving instructions, the set virtual space undergoes morphological evolution to incentivize positive user behavior.

[0010] In one aspect, the first type of positive behavior includes focused behavior, and the second type of positive behavior includes sleep behavior;

[0011] The steps of settling the first type of positive behavior data as a first virtual resource and the second type of positive behavior data as a second virtual resource include:

[0012] The duration of the focused behavior is calculated as a first virtual resource according to a first preset algorithm, and the completion degree of the focused behavior is calculated as a first virtual resource.

[0013] The sleep time of the sleep behavior is calculated as a second virtual resource according to a second preset algorithm, and the sleep pattern of the sleep behavior is calculated as a second virtual resource.

[0014] Wherein, the first preset algorithm is: T1×λ1, where T1 is the duration of the focused behavior, T1 is in minutes, and λ1 is a coefficient;

[0015] The second preset algorithm is: (T2-λ2)×20, where T2 is the sleep time in hours and λ2 is a coefficient.

[0016] In one aspect, the steps for collecting users' first and second types of positive behavior data include:

[0017] The duration of users' focused behavior is collected, and the completion rate of focused behavior is determined based on the task progress, task completion quality, and focus level submitted after the focused task is completed.

[0018] The system collects users' sleep time data and compares the deviations between their actual sleep time and planned sleep time, as well as the deviations between their actual wake-up time and planned wake-up time, to calculate sleep patterns.

[0019] In one aspect, the steps for collecting the duration of a user's focused behavior include:

[0020] Set a focus cycle, which includes a focus phase and a rest phase;

[0021] Real-time monitoring of user behavior during the focus phase;

[0022] If the user does not exit focus mode or trigger the application ban during the focus phase, it is determined to be a complete focus cycle, and the duration of the focus phase is included in the duration.

[0023] If the user triggers an interruption operation during the focus phase, the focus cycle is considered to have failed, and the duration of the focus phase is not counted.

[0024] In one aspect, the incentive method further includes:

[0025] Acquire users' historical behavior data and analyze the user behavior results in the historical behavior data;

[0026] The coefficients in the first preset algorithm and the second preset algorithm are dynamically adjusted based on the behavioral results.

[0027] In one aspect, the incentive method further includes:

[0028] If the duration of the focused behavior is greater than a first threshold and the deviation of the sleep pattern is less than a second threshold, then an additional third virtual resource is generated.

[0029] In one aspect, the step of causing a set virtual space to undergo morphological evolution based on the first driving instruction and the second driving instruction includes:

[0030] The first prop in the virtual space is driven to undergo morphological evolution according to the first driving instruction. When the morphological evolution amount corresponding to the first driving instruction is greater than the first limit value, the advanced evolution of the first prop is triggered.

[0031] The second prop in the virtual space is driven to undergo morphological evolution according to the second driving instruction. When the morphological evolution amount corresponding to the second driving instruction is greater than the second limit value, the advanced evolution of the second prop is triggered.

[0032] In one aspect, the incentive method further includes:

[0033] The system automatically generates trend reports at preset intervals and provides adjustment suggestions in these reports based on user behavior trends.

[0034] In one aspect, the incentive method further includes:

[0035] Obtain the goals set by the user for the first type of positive behavior and the second type of positive behavior;

[0036] The target is broken down into sub-targets, each sub-target corresponding to a milestone badge, which is used for display in virtual space.

[0037] Furthermore, to address the aforementioned problems, this application also provides a positive behavior incentive system, the incentive system comprising:

[0038] The data collection module is used to collect users' first-type positive behavior data and second-type positive behavior data;

[0039] The settlement module is used to settle the first type of positive behavior data into a first virtual resource and the second type of positive behavior data into a second virtual resource;

[0040] The conversion module is used to generate a first driving instruction based on the first virtual resource and a second driving instruction based on the second virtual resource;

[0041] The driving module is used to cause the virtual space to undergo morphological evolution based on the first driving instruction and the second driving instruction, so as to encourage positive user behavior.

[0042] The beneficial effects of this invention are as follows: By classifying and collecting two types of positive behavioral data, the two types of data are respectively settled into independent first virtual resources and second virtual resources. Corresponding driving instructions are generated based on the two types of virtual resources, and the first driving instructions and the second driving instructions drive the evolution of the virtual space form. Replacing the single resource corresponding to multiple behaviors with dual-behavioral data classification and settlement and dual-virtual resource classification, the invention achieves accurate matching between behavioral characteristics and resource feedback, solving the problem of insufficient incentive accuracy. In addition, by forming differentiated feedback through the first driving instructions and the second driving instructions, the limitations of a single incentive form are broken, avoiding user fatigue and numbness caused by long-term use. Furthermore, by constructing a process from positive behavior to virtual resources, and then to the evolution of virtual space, and with the two types of behaviors working synergistically in the virtual space, the deep association between behavior and incentive is strengthened, helping users form a long-term habit system and supporting long-term, efficient habit cultivation. Attached Figure Description

[0043] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0044] Figure 1 A flowchart illustrating the steps of the incentive method for positive behavior in this application;

[0045] Figure 2 A schematic diagram of the process steps for obtaining the first and second virtual resources in the incentive method for positive behavior in this application;

[0046] Figure 3 This is a schematic diagram illustrating the data collection process in the incentive method for positive behavior in this application.

[0047] Figure 4 This is a schematic diagram illustrating the process steps for collecting duration data in the incentive method for positive behavior in this application.

[0048] Figure 5 A schematic diagram of the process steps for dynamically adjusting algorithm coefficients in the incentive method for positive behavior in this application;

[0049] Figure 6 A schematic diagram illustrating the phased dynamic evolution of the incentive method for positive behavior in this application;

[0050] Figure 7 This is a schematic diagram illustrating the process steps for breaking down user goals in the incentive method for positive behavior in this application.

[0051] Figure 8 This is a schematic diagram of the functional structure of the incentive system for positive behavior in this application. Detailed Implementation

[0052] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0053] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application should have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0054] like Figure 1 As shown, this application provides a positive behavior incentive method, which includes:

[0055] Step S10 involves collecting data on the user's first and second types of positive behaviors. Categorized data collection is conducted for positive behaviors with different attributes to ensure that the characteristics of each type of positive behavior are accurately captured. This application first clarifies the specific types of the two categories of positive behaviors, and then collects data around the key dimensions of each type of behavior. For example, if the first type of positive behavior is focus behavior, data such as the duration of focus and whether the user maintains stable engagement during the focus period will be collected; if the second type of positive behavior is sleep behavior, data such as the user's actual daily sleep duration and the consistency between actual sleep and wake-up times and planned times will be collected. This categorized data collection method avoids feature ambiguity caused by mixing different behavioral data.

[0056] Step S20: First-category positive behavior data is settled as first-category virtual resources, and second-category positive behavior data is settled as second-category virtual resources. Different types of positive behavior data are transformed into corresponding exclusive virtual resources, achieving precise matching between behavior value and resource feedback. For example, for focus behavior data, the longer the focus duration and the more stable the focus process, the more first-category virtual resources are settled; for sleep behavior data, the longer the sleep duration and the smaller the deviation between the sleep and wake-up times and the plan, the more second-category virtual resources are settled. This categorized settlement method breaks the limitation of a single virtual resource corresponding to multiple types of behavior, allowing users to clearly perceive the resource returns brought by different positive behaviors.

[0057] Step S30: A first driving instruction is generated based on the first virtual resource, and a second driving instruction is generated based on the second virtual resource; the abstract virtual resource is transformed into a specific instruction that can drive changes in the virtual space. Based on the characteristics and quantity of the two types of virtual resources, corresponding driving instructions are generated to ensure that the instructions accurately match the positive behavioral value behind the virtual resource. For example, if the first virtual resource is based on focused behavior settlement, a first driving instruction will be generated based on the quantity of the first virtual resource, specifying the evolution mode and degree of elements related to focused behavior within the virtual space; if the second virtual resource is based on sleep behavior settlement, a second driving instruction will be generated based on the quantity of the second virtual resource, specifying the evolution mode and degree of elements related to sleep behavior within the virtual space.

[0058] Step S40 involves causing the virtual space to evolve in form based on the first and second driving instructions, thereby incentivizing positive user behavior. The dual driving instructions work synergistically within the virtual space, visually presenting the feedback results of positive behavior through morphological evolution, thus strengthening the incentive effect on the user. Based on the first and second driving instructions, the morphological changes of corresponding elements within the virtual space are driven, with the evolution processes of the two types of elements occurring synchronously and complementing each other. For example, the first driving instruction can drive the growth of trees in the virtual space; the trees will gradually grow taller and have more lush foliage as the first driving instruction is executed. The second driving instruction can drive the changes in the garden in the virtual space; the garden will gradually add flowers and buds as the second driving instruction is executed. By observing the dynamic evolution of the virtual space, users can intuitively see the results of their two types of positive behavior, thus becoming more motivated to continue practicing positive behavior.

[0059] In this embodiment, two types of positive behavioral data are collected and classified, and then settled into independent first and second virtual resources. Corresponding driving instructions are generated based on these two types of virtual resources, and the first and second driving instructions drive the evolution of the virtual space morphology. Replacing the single resource with multiple behaviors with dual-behavioral data classification and settlement with dual-virtual resource classification achieves precise matching between behavioral characteristics and resource feedback, solving the problem of insufficient incentive accuracy. Furthermore, differentiated feedback through the first and second driving instructions breaks the limitations of a single incentive form, avoiding user fatigue and numbness from long-term use. Moreover, by constructing a process from positive behavior to virtual resources, and then to the evolution of the virtual space, with the two types of behavior working synergistically in the virtual space, the deep association between behavior and incentive is strengthened, helping users form a long-term habit system and supporting long-term, efficient habit cultivation.

[0060] like Figure 2 As shown, the first category of positive behaviors includes focused behaviors, and the second category of positive behaviors includes sleep behaviors;

[0061] The steps of settling the first type of positive behavior data as the first virtual resource and the second type of positive behavior data as the second virtual resource include:

[0062] Step S210: The duration of the focused behavior is calculated as a first virtual resource according to a first preset algorithm, and the completion rate of the focused behavior is also calculated as a first virtual resource. A dual-dimensional mechanism for calculating the first virtual resource is constructed for focused behavior data to achieve a more comprehensive quantification of the value of focused behavior. Specifically, focused behavior is broken down into two dimensions: duration and completion rate, which are calculated separately and then aggregated into the first virtual resource. On the one hand, the duration of focused behavior is calculated using the first preset algorithm. For example, if λ1 = 1, 100 minutes of focused time can be directly calculated as 100 units of the first virtual resource. On the other hand, supplementary calculation is performed based on the completion rate of focused behavior. For example, if the user completes a focused task with 100% progress and the task quality meets the preset standard, 20 units of the first virtual resource can be calculated; if the task progress is only 50%, only 5 units of the first virtual resource will be calculated. Through the dual calculation of duration and completion rate, the problem of ineffective focus caused by measuring the value of focus solely by duration is avoided, making the calculation of the first virtual resource more in line with the actual effectiveness of focused behavior.

[0063] Step S220: The sleep duration of sleep behavior is calculated as a second virtual resource according to the second preset algorithm, and the sleep pattern of sleep behavior is also calculated as a second virtual resource. For sleep behavior data, a dual-dimensional calculation based on basic duration and regularity is used to convert sleep behavior into second virtual resources, accurately matching the requirements for sleep behavior duration and stable regularity. Regarding duration calculation, the second preset algorithm calculates that, for example, if λ2 = 6, and the user actually sleeps for 7 hours, (7-6) × 20 = 20 units of second virtual resource can be calculated; if the user sleeps for 5 hours, since the basic requirement is not met, the resource corresponding to the basic duration is not calculated temporarily, ensuring that sleep duration meets basic health requirements to obtain resources, guiding users to prioritize sleep duration. Regarding sleep pattern calculation, it is based on the regularity of sleep behavior. For example, if a user's actual sleep and wake-up times deviate from their planned times by less than or equal to 30 minutes for 7 consecutive days, they can be charged 30 units of second virtual resources; if the deviation exceeds 60 minutes, only 5 units of second virtual resources will be charged. By charging based on both duration and regularity, the system avoids focusing solely on sleep duration while ignoring the problem of sleep disorder, allowing the second virtual resources to fully reflect the health of sleep behavior.

[0064] The first preset algorithm is: T1×λ1, where T1 is the duration of focused behavior, T1 is in minutes, and λ1 is a coefficient; the second preset algorithm is: (T2-λ2)×20, where T2 is the sleep time, T2 is in hours, and λ2 is a coefficient.

[0065] like Figure 3As shown, the steps for collecting users' first and second types of positive behavior data include:

[0066] Step S110 involves collecting the duration of the user's focused behavior and determining the completion rate of the focused behavior based on the task progress, task completion quality, and focus input submitted after the focused task is completed. This approach captures both the duration of focused behavior and quantifies the actual effectiveness of focused behavior through completion, avoiding the inability of a single time dimension to reflect the quality of focus. For collecting the duration of focused behavior, the system automatically records the continuous duration from the start of focus mode to its normal end by monitoring the user's device operation status in real time, such as whether the mobile terminal has focus mode enabled or whether the user has exited to an unauthorized application while in focus mode, ensuring the accuracy of the duration data. For determining the completion rate of focused behavior, the system comprehensively judges the results based on the multi-dimensional information submitted by the user after the focused task is completed. For example, after a user completes a focused task of writing a report, they need to submit the actual progress and quality of the report, such as the number of completed chapters / total number of chapters, whether it meets the preset format requirements, and whether the key information is complete. At the same time, combined with the focus input data during the focus process, such as the number of times the application was exited in focus mode and the duration of each exit, the three factors are weighted and a score is calculated. For example, progress accounts for 40%, quality accounts for 40%, and input accounts for 20%. Finally, the score is used as the completion degree of the focus behavior, so as to achieve comprehensive collection of the quantity and quality of focus behavior.

[0067] Step S120 involves collecting the user's sleep time based on their sleep behavior. The deviations between the user's actual sleep time and planned sleep time, as well as the deviations between the actual wake-up time and planned wake-up time, are compared to calculate the sleep pattern. The sleep behavior data collection is designed with duration and regularity in mind, ensuring both the required sleep duration and the goal of stable sleep patterns. For sleep time collection, the system automatically acquires the continuous duration from entering deep sleep to waking up by connecting to device health data interfaces, such as a mobile health kit. Alternatively, it uses monitoring of the user's device during nighttime inactivity and screen-off time to assist in the calculation, ensuring the accuracy of the sleep time data. For sleep pattern calculation, the system uses the user's pre-set sleep time and wake-up time as a benchmark, calculating the daily deviations between the actual sleep time and planned sleep time, and the actual wake-up time and planned wake-up time. A preset algorithm is then used to calculate a daily sleep pattern score, such as averaging two deviations or weighting deviations exceeding 30 minutes. The scores are then statistically analyzed over several days to form the sleep pattern data.

[0068] like Figure 4 As shown, the steps for collecting the duration of a user's first type of positive behavior include:

[0069] Step S111: Set the focus cycle, which includes a focus phase and a rest phase. The default focus cycle duration is configured as follows: 25 minutes for the focus phase and 5 minutes for the rest phase. Users can adjust this according to their own focus ability and task type. For example, complex tasks can be set to a 30-minute focus phase and an 8-minute rest phase, while simple tasks can be set to a 15-minute focus phase and a 3-minute rest phase. By clearly defining the time boundaries between focus and rest, users are guided to form a regular rhythm of focus and rest, and a clear time range is provided for subsequent judgment on the effectiveness of focus behavior, ensuring that the collection of focus behavior data closely matches the user's actual operating scenario.

[0070] Step S112 involves real-time monitoring of the user's actions during the focus phase. This monitoring targets key operations on the user's device, such as mobile phones and computers. Specifically, it includes: whether the user actively exits focus mode after it is activated, whether they open any system-preset prohibited applications, and whether they frequently switch between applications. The monitoring process runs in real-time, recording and marking every action to provide direct evidence for determining whether the focus cycle is complete.

[0071] Step S113: If the user does not exit focus mode or trigger the application ban during the focus phase, it is determined to be a complete focus cycle, and the duration of the focus phase is included in the total duration; ensuring that only the duration of effective focus behavior is included in the calculation. When it is detected that the user maintains focus mode throughout the entire focus phase and does not trigger any application ban or illegal switching operation, the focus cycle is determined to be complete and effective. At this time, the preset duration of the focus phase will be automatically extracted, or the continuous duration of the actual operation of focus mode will be included in the total duration of focus behavior, as one of the data for subsequent settlement of the first virtual resource, ensuring that the focus time data can truly reflect the user's effective investment.

[0072] Step S114: If the user triggers an interruption operation during the focus phase, the focus cycle is deemed a failure, and the duration of the focus phase is not counted. When it is detected that the user triggers an interruption operation during the focus phase, such as actively exiting focus mode, opening a game application, or frequently switching applications causing focus interruption, the focus cycle is deemed a failure. For failed focus cycles, the duration of that phase will not be included in the focus behavior duration, and no focus-related virtual resources will be allocated for that cycle. This rule design of not counting failures effectively constrains the user's distracting behavior during the focus phase, guides the user to maintain focus continuity as much as possible, and ensures that subsequent incentive feedback based on focus duration only applies to genuine and effective focus behavior.

[0073] like Figure 5 As shown, the incentive methods also include:

[0074] Step S50: Acquire the user's historical behavior data and analyze the user behavior results within this data. Through systematic collection and in-depth analysis of the user's historical behavior data, accurately capture changes in user behavior habits and abilities. Automatically summarize two types of positive behavior data from the user's past preset periods: for focus behavior, collect data such as total historical focus time, daily focus time fluctuation range, and number of times focus completion targets were met; for sleep behavior, collect data such as total historical sleep time, changes in sleep pattern deviation, and number of days with target sleep duration. Based on this data collection, further analyze the user behavior results. For example, determine whether the user exhibits a positive behavioral trend such as continuously increasing focus time and gradually decreasing sleep pattern deviation, or whether there are issues such as excessive fluctuations in focus time and frequent failures to meet sleep duration targets, requiring optimization. Through quantitative analysis, clarify the user's current behavioral abilities and habit formation stage, and determine the direction for coefficient adjustment.

[0075] Step S51: Dynamically adjust the coefficients in the first and second preset algorithms based on the behavioral results. Dynamically adjusting the coefficients in the first and second preset algorithms breaks the limitations of fixed algorithms, making virtual resource settlement more aligned with users' actual behavioral capabilities and improving the accuracy and effectiveness of incentives.

[0076] The specific adjustments are as follows: For the coefficient λ1 in the first preset algorithm, if the analysis finds that the user's focus time has been greater than or equal to 150 minutes in the past 7 days and the completion rate is greater than or equal to 80%, it indicates that the focus ability has improved. In this case, λ1 will be increased from the initial value of 1.0 to 1.2, so that the user can obtain more first virtual resources for the same focus time, thus strengthening positive incentives. If the analysis finds that the user's focus time has been less than 60 minutes in the past 5 days and the completion rate is greater than or equal to 50%, it indicates that the current focus ability needs to be improved. In this case, λ1 will be temporarily reduced to 0.8 to lower the threshold for obtaining virtual resources and prevent users from giving up due to difficulty in obtaining incentives.

[0077] Regarding the coefficient λ2 in the second preset algorithm, if analysis reveals that the user's sleep duration over the past 10 days is consistently greater than or equal to 7 hours with a regular deviation of less than or equal to 20 minutes, indicating good sleep habits, then λ2 is increased from its initial value of 6.0 to 6.5 to guide the user towards higher quality sleep. Conversely, if analysis reveals that the user's sleep duration over the past 7 days is consistently less than 6 hours, indicating insufficient sleep, then λ2 is decreased to 5.5, ensuring that the user receives some secondary virtual resources even with slightly lower sleep duration, maintaining motivation. This linkage mechanism between behavioral outcomes and coefficient adjustments ensures that the algorithm always adapts to the user's behavioral state, achieving personalized incentives.

[0078] In one embodiment of this application, the excitation method further includes:

[0079] Step S60: If the duration of focused behavior exceeds a first threshold and the deviation from sleep patterns is less than a second threshold, an additional third virtual resource is generated. The generation of the third virtual resource is triggered by setting dual-behavior judgment criteria, thereby guiding users to simultaneously maintain high-quality focused behavior and regular sleep patterns. The first threshold for focused behavior is the minimum standard that the duration of focused behavior must reach; the second threshold for sleep behavior is the maximum range that the deviation from sleep patterns must be controlled. The user's daily focused behavior duration is compared with the first threshold, and the sleep pattern deviation is compared with the second threshold. When the user's focused behavior duration exceeds the first threshold and the sleep pattern deviation is less than the second threshold, additional incentives are triggered, generating a third virtual resource distinct from the first and second virtual resources. The third virtual resource can be converted into the first or second virtual resource, or it can be formed independently in the virtual space to induce selected props to evolve.

[0080] For example, if the first threshold is set to 120 minutes and the second threshold is set to 30 minutes, when a user's focused behavior lasts for 150 minutes on a given day, and the actual time to fall asleep deviates from the planned time to fall asleep by 20 minutes, and the actual time to wake up deviates from the planned time to wake up by 15 minutes, an additional third virtual resource will be generated, such as 50 units of the third virtual resource. If the user's focused behavior lasts for 130 minutes, but the sleep pattern deviates by 40 minutes, then the dual criteria are not met, and no third virtual resource will be generated.

[0081] like Figure 6 As shown, the steps for causing the set virtual space to undergo morphological evolution based on the first and second driving instructions include:

[0082] Step S410: Drive the first prop in the virtual space to evolve its form according to the first driving instruction. When the amount of form evolution corresponding to the first driving instruction is greater than a first limit value, the advanced evolution of the first prop is triggered. Through the hierarchical logic of basic evolution plus advanced evolution, the changes of the first driving instruction and the first prop are deeply bound together, allowing users to intuitively perceive the value difference caused by focused behavior. First, based on the resource quantification information carried in the first driving instruction, drive the first prop to perform basic form evolution. For example, when 20 units of the first virtual resource are invested, the tree grows new branches and leaves; when 40 units are invested, the tree trunk thickens. At the same time, a first limit is preset, such as the evolution amount corresponding to 80 units of the first virtual resource. When the cumulative evolution amount corresponding to the first driving command exceeds the first limit, the advanced evolution of the first prop is automatically triggered, presenting a special morphological change that is different from the basic evolution. For example, a tree evolves from a lush state to a flowering state, or unlocks exclusive visual effects, such as the leaves showing a gradient color. Through hierarchical evolution, users can clearly see that the more positive behavior they invest, the higher the level of prop evolution, thus strengthening the continuous motivation for focused behavior.

[0083] Step S420: The second prop in the virtual space undergoes morphological evolution based on the second driving command. When the morphological evolution amount corresponding to the second driving command exceeds the second limit value, the advanced evolution of the second prop is triggered. Similarly, based on the evolution parameters in the second driving command, the second prop undergoes basic morphological evolution. For example, investing 15 units of second virtual resources causes one seedling to grow in the garden; investing 30 units causes the seedling to grow into a mature plant. When the cumulative morphological evolution amount corresponding to the second driving command exceeds the second limit value, the advanced evolution of the second prop is triggered, presenting a more scarce morphological change. For example, mature plants in the garden may bloom collectively, or special visual elements such as glow-in-the-dark petals may be generated. The advanced evolved form will be permanently retained in the virtual space. This design provides immediate feedback through basic evolution and sets long-term goals through advanced evolution, guiding users to maintain regular sleep behavior. Simultaneously, it enriches the visual presentation of the virtual space with accumulated behavior, avoiding the sense of stimulation numbness caused by a single evolutionary form.

[0084] In addition, the evolution of virtual space can be divided into multiple stages, such as the primary stage, intermediate stage, and advanced stage.

[0085] In the initial stage, the focus is on incentivizing sleep and deep focus behaviors. The first virtual resource drives the evolution of trees in the virtual space, the second virtual resource drives the evolution of gardens, and the third virtual resource is used to unlock rare forms of trees and gardens, such as flowering trees and exotic flowers.

[0086] In the intermediate stage, when a user accumulates more than or equal to 5,000 units of the first virtual resource and more than or equal to 3,000 units of the second virtual resource, the villa construction virtual reality scene is unlocked, and new villa construction resources are added. These resources are generated by settling data from extended behaviors such as work, study, and games, and are used to unlock villa floor plans and upgrade interior decorations.

[0087] In the advanced stage, when the villa construction is more than 80% complete, the island virtual reality scene is unlocked, and new island development resources are added. These resources are generated by settling social behavior and group collaborative behavior data and are used for island terrain modification and public facility construction.

[0088] Virtual reality scenes at each stage share the same virtual space. Scenes from the previous stage, such as trees and gardens, will become components of subsequent scenes, such as villa courtyards and island landscapes, forming a progressive relationship of behavior accumulation and scene iteration, and strengthening the user's emotional connection to long-term behavior.

[0089] It should be noted that the technical solution of this application can also implement group collaborative incentives. For example, users can create focus / sleep groups, aggregating the virtual resources of all members within the group to drive the evolution of the shared virtual space; as the total focus time of the group gradually increases, exclusive group items, such as a collective fountain, are unlocked in the shared space, and the top three contributors are rewarded with additional collaborative resources. Through group collaborative effects, positive behaviors are further reinforced by utilizing social supervision and peer incentives.

[0090] The incentive method proposed in this application can be integrated into mobile devices to form a standalone app. It possesses multi-level filtering and monitoring capabilities. For example, the basic filtering layer monitors the user's device usage behavior, distinguishing between work apps, study apps, and entertainment apps to determine whether app blocking is triggered during deep focus phases; the intermediate monitoring layer monitors the type of user message notifications, distinguishing between urgent work messages and ordinary social messages, automatically filtering non-urgent messages during deep focus and sleep phases, retaining only urgent messages and reminding them in a low-intrusion manner; the advanced statistics layer, based on the above monitoring data, automatically calculates the duration of each work-rest phase, such as the percentage of meeting time during work hours and the percentage of entertainment time during personal time.

[0091] The technical solution of this application also allows for manual operation, i.e., check-in. It sets up independent check-in entry points for various behaviors such as sleep, deep focus, and meetings. One-click check-in is achieved by combining sensor data with pre-filled calendar schedule information, reducing operational costs. At the same time, a check-in reminder module is added. Reminders are only triggered for important matters such as work and meetings preset by the user. Non-important matters are not reminded by default. The reminder method, whether to enable reminders, and reminder time are all selected by the user. When the user is in a low-interference scenario such as deep focus or sleep, the reminder is automatically delayed to avoid disturbing the user and eliminate the need for the user to force themselves to remember the check-in behavior. In addition, the check-in data can be synchronized to the collection module and cross-validated with sensor and calendar data to ensure accuracy. It is also linked to the incentive system and works in conjunction with message filtering to ensure that the reminder does not conflict with the rules of the current scenario.

[0092] In this application, the monitoring method can rely on three types of hardware: the mobile phone's accelerometer, microphone, and screen operation. It can also rely on other sensors built into the mobile phone or external hardware devices, such as heart rate sensors or smart bracelets, to achieve accurate monitoring of the user's positive behavior.

[0093] The accelerometer is used to assist in the monitoring of sleep and focus behaviors. For sleep behaviors, if the accelerometer detects no movement of the phone for a preset duration during nighttime sleep, it is considered a valid sleep period, and the sleep duration is automatically recorded. If frequent phone movement is detected, the sleep data is corrected based on the user's manual check-in. For focus behaviors, after activating deep focus mode, if the accelerometer detects the phone remaining still for a long time, it indicates the user is in a focused state. If frequent picking up and moving of the phone is detected, a pop-up reminder is displayed to ask if the focus should be interrupted, ensuring the continuity of focus behavior monitoring.

[0094] For the microphone, it is mainly used for scene recognition of meeting behavior and focused behavior. After the meeting mode is activated, the microphone can help monitor ambient sound. If continuous human voice is detected, it can be combined with meeting information synchronized from the calendar schedule to verify the authenticity of the meeting behavior. At the same time, if noisy sounds unrelated to the meeting are detected, it will remind you that the current environment may affect the meeting and ask whether to turn on noise reduction mode. After the deep focus mode is activated, if the microphone detects continuous high-decibel noise, it will push a suggestion that the current ambient noise is high and whether to switch focus time periods to help optimize the quality of focused behavior.

[0095] Screen operation monitoring is used to determine the effectiveness of various behaviors and supplement data. For focused behavior, screen operations are monitored during the focused phase. If entertainment or social apps are detected, the focused period is considered to have failed, and the focused time for that period is not counted. If only work or study apps are detected, the focused period is considered to be effective, and the focused time is accumulated normally. For sleep behavior, if the screen lights up frequently at night, combined with accelerometer data, it is determined that there may be a sleep interruption, and the user is reminded to manually adjust the sleep time the next day. For meeting behavior, after the meeting mode is started, if the screen is not used for a long time, combined with the voice data detected by the microphone, it helps to determine that the meeting is proceeding normally and automatically records the meeting time without requiring the user to manually check in.

[0096] Meanwhile, all hardware monitoring data must be explicitly authorized by the user before collection. Only the behavioral statistics results are retained, and the original hardware data is not stored to ensure that user privacy is not violated. Furthermore, the hardware monitoring data is cross-validated with the user's manual check-in and calendar itinerary data to further improve the accuracy of behavioral data monitoring.

[0097] Therefore, it can be understood that the technical solution of this application extends the calendar function, breaks through the limitation of the calendar only recording the itinerary, refines the work and rest scenarios based on the calendar itinerary and combines data collection and incentive feedback to form a complete closed loop; guides users to actively adjust their work and rest, builds a digital twin environment that matches the user's real work and rest, and realizes the upgrade of the lightweight metaverse carrier.

[0098] In one embodiment of this application, the excitation method further includes:

[0099] Step S70: A trend report is automatically generated according to a preset cycle, and adjustment suggestions are provided in the trend report based on the user's behavioral trends. By building an automated report generation and personalized suggestion output, users can clearly perceive behavioral change trends and be provided with long-term behavioral guidance.

[0100] By setting preset report generation cycles, both short-term feedback and long-term trend observation can be considered. For example, preset cycles include weekly and monthly cycles, which do not require manual triggering by the user and will automatically extract two types of positive behavior data within the corresponding cycle.

[0101] Based on data extraction, the data will be visualized and trend analyzed. For example, line charts will be used to present the trends of indicators such as the weekly changes in average daily focus time, sleep pattern deviations, and monthly fluctuations. Bar charts will be used to compare the proportion of focus time and sleep time differences on each workday, allowing users to intuitively see the upward, downward, or stable trends of their behavior through charts. At the same time, it can also be combined with preset behavioral achievement standards to identify key nodes in the trend, such as a 20% increase in focus time this week compared to last week, and a widening of sleep pattern deviation to 45 minutes in the past 3 days.

[0102] Based on trend analysis results, targeted adjustment suggestions are generated. These suggestions should closely align with users' actual behavioral pain points and areas for improvement. For example, if it is found that users' focus time is significantly lower on Wednesdays than on other weekdays, and historical data shows that users are more focused in the morning, it is recommended to shift core focus tasks on Wednesdays to the morning. If the increased deviation in sleep patterns is detected as stemming from the continued use of social media apps after 11 PM, it is recommended to enable the automatic locking function for social media apps after 11 PM. These adjustment suggestions, along with trend data and visualization charts, are integrated into a trend report, which is pushed to users via the app. Users can also view historical reports to trace behavioral changes, forming a long-term habit optimization loop from trend awareness and suggestion implementation to effect verification.

[0103] like Figure 7 As shown, the incentive methods also include:

[0104] Step S80: Obtain the goals set by the user for the first and second types of positive behaviors; by using the two types of positive behavior goals set by the user, the problem of mismatch between goals and the user's actual capabilities is reduced. At the same time, the goal data set by the user is retained to ensure that the entire process of goals is traceable.

[0105] Step S81 involves breaking down the long-term goal into sub-goals, each corresponding to a milestone badge, which is displayed in the virtual space. By breaking down the long-term goal, the execution threshold is lowered, and the visual badges enhance the user's sense of accomplishment at each stage, addressing the problem that users are prone to giving up due to the remoteness of long-term goals.

[0106] The system can break down a user's overall goal into actionable sub-goals based on time or difficulty. For example, a user's overall goal of achieving 4500 minutes or more of total focus time per month can be broken down into a weekly sub-goal of 1125 minutes or more of total focus time per week. Similarly, a user's overall goal of maintaining a consistent sleep pattern for 30 consecutive days with a deviation of 30 minutes or less can be broken down into a phased sub-goal of maintaining a consistent sleep pattern for 7 consecutive days with a deviation of 30 minutes or less. Each sub-goal corresponds to a unique milestone badge, designed to align with the sub-goal's attributes. For instance, a "Weekly Focus Expert" badge corresponds to the weekly focus sub-goal, and a "Weekly Sleep Pattern Badge" corresponds to the 7-day consistent sleep pattern sub-goal. When a user completes a sub-goal, the corresponding milestone badge is automatically awarded and displayed in the virtual space. For example, a "Weekly Focus Expert" badge might float next to a virtual tree, and a "Weekly Sleep Pattern Badge" icon might be placed in a corner of a virtual garden. This allows users to visually perceive their progress and continuously motivates them to strive towards their long-term goals.

[0107] like Figure 8 As shown, this application also provides a positive behavior incentive system, which includes: a data acquisition module 10, a settlement module 20, a conversion module 30, and a driving module 40.

[0108] The data collection module 10 is used to collect the user's first type of positive behavior data and second type of positive behavior data. In practical applications, for the first type of positive behavior, its duration is collected, and the data is further supplemented by the task progress submitted after the focused task is completed, the task completion quality, and the level of focus. For the second type of positive behavior, its sleep time is collected, and the data is improved by comparing the deviation between the actual sleep time and the planned sleep time, and the deviation between the actual wake-up time and the planned wake-up time.

[0109] The settlement module 20 is used to settle the first type of positive behavior data as the first virtual resource and the second type of positive behavior data as the second virtual resource. The settlement module 20 settles the first type of positive behavior data acquired by the collection module 10 as the first virtual resource and the second type of positive behavior data as the second virtual resource. The settlement process follows a preset algorithm. For example, for focused behavior in the first type of positive behavior, its duration is settled according to the algorithm T1×λ1, and the completion rate of the focused behavior is used to supplement the settlement of the first virtual resource. For sleep behavior in the second type of positive behavior, it is settled according to the algorithm (T2-λ2)×20, and the sleep pattern is used to supplement the settlement of the second virtual resource, ensuring that the settlement of virtual resources matches the value of user behavior.

[0110] The conversion module 30 is used to generate a first driving instruction based on the first virtual resource and a second driving instruction based on the second virtual resource. The conversion module 30 is a bridge connecting virtual resources and virtual space evolution. It performs quantitative analysis on the virtual resources obtained from the settlement and converts information such as the quantity and type of virtual resources into driving instructions with clear execution logic. For example, it determines the evolution parameters of the first item in the virtual space in the first driving instruction based on the total amount of the first virtual resource and determines the evolution parameters of the second item in the virtual space in the second driving instruction based on the total amount of the second virtual resource, providing direct instruction basis for the driving module 40 to promote the evolution of the virtual space form.

[0111] The driving module 40 is used to cause the virtual space to evolve in form based on the first driving instruction and the second driving instruction, thereby encouraging positive user behavior. The driving module 40 is the execution module that realizes the evolution of the virtual space's form and achieves the incentive goal. It mainly uses the first and second driving instructions generated by the conversion module 30 to cause the virtual space to evolve in form, thereby encouraging positive user behavior. In specific execution, the first prop in the virtual space is driven to evolve in form according to the first driving instruction. When the amount of form evolution corresponding to the first driving instruction exceeds a first limit value, the advanced evolution of the first prop is also triggered. Simultaneously, the second prop in the virtual space is driven to evolve in form according to the second driving instruction. When the amount of form evolution corresponding to the second driving instruction exceeds a second limit value, the advanced evolution of the second prop is triggered. Through the dynamic changes of the virtual space, intuitive behavioral feedback is provided to the user, reinforcing the user's positive behavior.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for incentivizing positive behavior, characterized in that, The incentive methods include: Collect user's first type of positive behavior data and second type of positive behavior data; The first type of positive behavior data is settled as the first virtual resource, and the second type of positive behavior data is settled as the second virtual resource; A first driving instruction is generated based on the first virtual resource, and a second driving instruction is generated based on the second virtual resource; Based on the first and second driving instructions, the set virtual space undergoes morphological evolution to incentivize positive user behavior.

2. The excitation method according to claim 1, characterized in that, The first category of positive behaviors includes focused behaviors, and the second category of positive behaviors includes sleep behaviors; The steps of settling the first type of positive behavior data as a first virtual resource and the second type of positive behavior data as a second virtual resource include: The duration of the focused behavior is calculated as a first virtual resource according to a first preset algorithm, and the completion degree of the focused behavior is calculated as a first virtual resource. The sleep time of the sleep behavior is calculated as a second virtual resource according to a second preset algorithm, and the sleep pattern of the sleep behavior is calculated as a second virtual resource. Wherein, the first preset algorithm is: T1×λ1, where T1 is the duration of the focused behavior, T1 is in minutes, and λ1 is a coefficient; The second preset algorithm is: (T2-λ2)×20, where T2 is the sleep time in hours and λ2 is a coefficient.

3. The excitation method according to claim 2, characterized in that, The steps for collecting users' first and second types of positive behavior data include: The duration of users' focused behavior is collected, and the completion rate of focused behavior is determined based on the task progress, task completion quality, and focus level submitted after the focused task is completed. The system collects users' sleep time data and compares the deviations between their actual sleep time and planned sleep time, as well as the deviations between their actual wake-up time and planned wake-up time, to calculate sleep patterns.

4. The excitation method according to claim 3, characterized in that, The steps for collecting the duration of a user's focused behavior include: Set a focus cycle, which includes a focus phase and a rest phase; Real-time monitoring of user behavior during the focus phase; If the user does not exit focus mode or trigger the application ban during the focus phase, it is determined to be a complete focus cycle, and the duration of the focus phase is included in the duration. If the user triggers an interruption operation during the focus phase, the focus cycle is considered to have failed, and the duration of the focus phase is not counted.

5. The excitation method according to claim 2, characterized in that, The incentive method further includes: Acquire users' historical behavior data and analyze the user behavior results in the historical behavior data; The coefficients in the first preset algorithm and the second preset algorithm are dynamically adjusted based on the behavioral results.

6. The excitation method according to claim 2, characterized in that, The incentive method further includes: If the duration of the focused behavior is greater than a first threshold and the deviation of the sleep pattern is less than a second threshold, then an additional third virtual resource is generated.

7. The excitation method according to any one of claims 1 to 6, characterized in that, The steps of causing the set virtual space to undergo morphological evolution based on the first driving instruction and the second driving instruction include: The first prop in the virtual space is driven to undergo morphological evolution according to the first driving instruction. When the morphological evolution amount corresponding to the first driving instruction is greater than the first limit value, the advanced evolution of the first prop is triggered. The second prop in the virtual space is driven to undergo morphological evolution according to the second driving instruction. When the morphological evolution amount corresponding to the second driving instruction is greater than the second limit value, the advanced evolution of the second prop is triggered.

8. The excitation method according to any one of claims 1 to 6, characterized in that, The incentive method further includes: The system automatically generates trend reports at preset intervals and provides adjustment suggestions in these reports based on user behavior trends.

9. The excitation method according to any one of claims 1 to 6, characterized in that, The incentive method further includes: Obtain the goals set by the user for the first type of positive behavior and the second type of positive behavior; The target is broken down into sub-targets, each sub-target corresponding to a milestone badge, which is used for display in virtual space.

10. A positive behavior incentive system, characterized in that, The incentive system includes: The data collection module is used to collect users' first-type positive behavior data and second-type positive behavior data; The settlement module is used to settle the first type of positive behavior data into a first virtual resource and the second type of positive behavior data into a second virtual resource; The conversion module is used to generate a first driving instruction based on the first virtual resource and a second driving instruction based on the second virtual resource; The driving module is used to cause the virtual space to undergo morphological evolution based on the first driving instruction and the second driving instruction, so as to encourage positive user behavior.

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