Game task adjustment method and device for adjusting diet, equipment and medium

Abstract

This application discloses a method, apparatus, device, and medium for adjusting game tasks related to diet regulation, relating to the field of game content update technology and applicable to interactive game systems. The method includes: collecting historical dietary behavior uploaded by users and analyzing it to identify several abnormal feature tags among multiple feature tags in a first theoretical system; determining a first task ratio based on the correlation between the feature tags and the several abnormal feature tags; determining the user's first task score based on multiple dimensions according to the historical dietary behavior; and determining a second task ratio based on the first task score; constructing several game tasks based on the first theoretical system according to the first task ratio and the second task ratio, so that the user can complete operational interactions with the interactive game system according to the game tasks. By implementing this application, the semantic association of the corresponding theoretical system can be added to gamified diet management, ensuring a better user experience.

Description

Technical Field

[0001] This application relates to the field of game content update technology, and in particular to a method, device, equipment and medium for adjusting game tasks by regulating diet. Background Technology

[0002] As an art form that combines internal design elements, task flows, and feedback mechanisms to guide user behavior and states, games have seen their positive user incentive mechanisms (i.e., gamified incentive mechanisms) widely applied in other industries in recent years, especially in the field of diet management. In diet management, research has demonstrated that gamified incentive mechanisms can guide users in self-health management, thereby shaping their own healthy eating habits to a certain extent.

[0003] Some existing dietary management systems utilize gamified incentive mechanisms to enhance users' self-health management capabilities. However, these systems generally suffer from a fundamental problem: a lack of deep semantic connection between the gamified incentive mechanisms and the underlying health theories. The game elements in these systems often merely abstract and quantify user behavior to trigger positive feedback mechanisms. For example, a user might receive a game achievement for recording a healthy diet, and the system would directly add or adjust the corresponding diet recording task. This fails to effectively link user behavior and health theories through gamified incentive mechanisms. Users struggle to perceive any real improvement in their health from the various game tasks provided by the system, leading to a disconnect between the game tasks and the health habit goals. This weakens the effectiveness of gamified incentive mechanisms and gradually diminishes the user experience. Therefore, how to construct a deep semantic connection between gamified incentive mechanisms and health theories to prevent a decline in user experience during dietary health management remains a pressing technical problem that needs to be addressed in current technologies. Summary of the Invention

[0004] This application provides a method, apparatus, device, and medium for adjusting game tasks related to diet regulation, in order to solve the technical problem that the lack of semantic relevance in existing gamified diet management processes leads to a decline in user experience.

[0005] According to a first aspect of the embodiments of this application, a game task adjustment method for regulating diet is provided, applicable to interactive game systems, the game task adjustment method comprising: Collect users' uploaded historical eating behaviors, analyze the historical eating behaviors, identify several abnormal feature tags among multiple feature tags in a preset first theoretical system, and determine the first task ratio related to the multiple feature tags based on the correlation between each feature tag in the first theoretical system and the several abnormal feature tags. Based on the user's historical dietary behavior, a first task score is determined based on multiple dimensions, and a second task ratio related to preset difficulty levels is determined based on the first task score; wherein, the multiple dimensions include the number of days of behavior recording, the number of times the task is completed, the number of times the behavior is matched, the time period of behavior recording, and the number of consecutive recording days; Based on the first task ratio and the second task ratio, and based on the first theoretical system, several game tasks are constructed for the user to complete the operation and interaction with the interactive game system according to the game tasks; wherein, the game tasks are used to generate positive feedback based on the user's record of eating behavior.

[0006] This application collects and analyzes users' uploaded historical dietary behaviors through an interactive game system. It identifies several anomalous feature tags among various feature tags within a first theoretical framework, and then determines the first task ratio based on the correlation between these feature tags. Simultaneously, it determines the first task score of users' historical dietary behaviors based on multiple dimensions, thereby determining the second task ratio. Finally, it combines the first task ratio and the first theoretical framework to construct several game tasks. Analyzing users' historical dietary behaviors using the first theoretical framework yields the corresponding first task ratio, which imbues the user's historical dietary behaviors with the semantics of the relevant theoretical framework. Combining this with the second task ratio to construct game tasks links the added semantics to the game tasks, and consequently, to the user's completion of the game tasks. This avoids the lack of semantic association in existing dietary management systems, which prevents users from perceiving the impact of game tasks on their actual health status, thus preventing a decline in user experience. Furthermore, the second task ratio determined through multiple dimensions ensures that the constructed game tasks better align with positive feedback incentive mechanisms, guaranteeing a positive user experience.

[0007] In some embodiments of this application, the analysis of a user's historical dietary behavior to determine several abnormal feature tags among multiple feature tags in a preset first theoretical system specifically includes: Analyze users' historical dietary behavior to determine the behavioral feature value corresponding to each feature tag in the first theoretical system; Based on the difference between the behavioral feature value of each feature label and the preset feature threshold, several abnormal feature labels are determined among all feature labels.

[0008] This application first analyzes the user's historical eating behavior to determine the behavioral feature value of each feature tag in the first theoretical system. Then, it determines several abnormal feature tags based on the difference from the preset feature threshold. This allows the user's historical eating behavior to be evaluated through the first theoretical system, with the semantics of the corresponding theoretical system attached. Furthermore, it determines abnormal feature tags through the first theoretical system, providing data and theoretical support for subsequent adjustments to game tasks based on abnormal feature tags.

[0009] In some embodiments of this application, determining the first task ratio related to the multiple feature tags based on the correlation relationships of each feature tag in the first theoretical system, combined with the several abnormal feature tags, specifically includes: Based on the correlation between the feature labels in the first theoretical system, determine the positive and negative correlation labels for each abnormal feature label; Based on the positive and negative correlation labels of each abnormal feature label, the weights of the subtasks corresponding to each feature label are adjusted to determine the proportion of the first task related to each feature label.

[0010] This application first determines the positive and negative correlation labels of each abnormal feature label based on the correlation relationship of each feature label in the first theoretical system, and then adjusts the sub-task weights of each feature label to obtain the first task ratio related to each feature label. By determining the positive and negative correlation labels through the correlation relationship of feature labels, and then adjusting the corresponding sub-task weights, the first task ratio can better meet the adjustment needs of each feature label under the first theoretical system, thereby making the first task ratio more consistent with the game task ratio required by the user and ensuring the accuracy of the first task ratio.

[0011] In some embodiments of this application, determining the user's first task score based on the historical dietary behavior across multiple dimensions specifically includes: Based on the historical dietary behavior, the number of days the user's behavior was recorded, the number of times the task was completed, and the number of consecutive recording days in the preset first time period are determined. Based on the historical dietary behavior, the number of times the user's behavior matched and the number of times the instant record was made in the historical dietary behavior are determined. The number of instant records is obtained based on the actual recording time period of the behavior recording period and the corresponding dietary behavior in the historical dietary behavior. The behavior record score, task completion score, and continuous record score are determined based on the ratio of the number of days of behavior record, the number of times the task is completed, and the ratio of the number of consecutive record days to the duration of the first time period, respectively. The behavior matching score and the instant recording score are determined based on the ratios of the number of behavior matchings and the number of instant recordings to the total number of historical dietary behavior records, respectively. The user's first task score is determined by a weighted sum of the behavior record score, the task completion score, the continuous record score, the behavior matching score, and the instant record score.

[0012] This application first determines the number of days of user behavior recording, number of task completions, number of consecutive recording days, number of behavior matchings, and number of instant recordings in the first period based on historical dietary behavior. Then, it determines the behavior recording score, task completion score, consecutive recording score, behavior matching score, and instant recording score, thereby determining the user's first task score. This allows for a comprehensive measurement of the user's completion of game tasks through historical dietary behavior across multiple dimensions, thus ensuring an accurate evaluation of the user's progress in game tasks.

[0013] In some embodiments of this application, the step of constructing several game tasks based on the first theoretical system according to the first task ratio and the second task ratio specifically includes: Based on the behavioral characteristic value of each abnormal feature tag, determine the task priority and task cycle of each abnormal feature tag, and based on the feature description of each abnormal feature tag, determine the task time period requirement and target food of each abnormal feature tag; Based on the second task ratio, determine the task frequency requirement corresponding to each difficulty level of the game task; Based on the first theoretical framework, several game tasks are constructed according to the task priority, task cycle, task time period requirements of each abnormal feature tag, and the task frequency requirements corresponding to the target food and the game tasks of each difficulty level, combined with the first task ratio.

[0014] This application first determines the task priority and task cycle based on the behavioral feature values ​​of each abnormal feature tag. Then, it determines the task time period requirements and target food based on the corresponding feature descriptions. Furthermore, it determines the task frequency requirements corresponding to each difficulty level of the game task based on the second task ratio. Then, based on the first theoretical system and combined with the first task ratio, it comprehensively constructs the game task. By using the behavioral feature values ​​and feature descriptions of the abnormal feature tags with attached semantics, the characteristics of the game task are determined from multiple dimensions. The relationship between task difficulty and task frequency requirements is obtained through the second task ratio. This allows the attached semantics to be associated with the game task, and thus with the user's completion of the game task. This avoids the situation where existing dietary management lacks semantic association, resulting in users not being able to perceive the impact of the game task on their real health status, thereby preventing a decline in user experience.

[0015] In some embodiments of this application, the game task is used to generate positive feedback based on the user's recorded dietary behavior, specifically including: In response to the first dietary behavior recorded by the user, based on several types of food in the first dietary behavior, the corresponding statistical label in all feature labels of the first theoretical system is determined, and the growth characteristic value of the corresponding statistical label is determined based on the consumption amount of each type of food. Based on the current and growth characteristic values ​​of each tag to be counted, the completion rate of each game task is determined, and based on the completion rate of each game task, preset game rewards are issued to the user to trigger positive feedback.

[0016] This application determines the statistical tags in the first theoretical system by identifying several types of food in the user's first dietary behavior, and determines the growth characteristic value of the corresponding statistical tag based on the amount of food consumed. Then, it determines the completion rate of each game task based on the current characteristic value and growth characteristic value of the statistical tag, so as to issue game rewards to the user and trigger positive feedback. By determining the statistical tags through the first theoretical system and promoting and completing game tasks, the semantics of the corresponding theoretical system can be attached to the user's first dietary behavior, and the attached semantics can be associated with the game task, and then with the user's completion of the game task. This avoids the situation where existing diet management lacks semantic association, which makes users unable to perceive the impact of game tasks on their real health status, thereby avoiding a decline in user experience.

[0017] In some embodiments of this application, it further includes: Based on the score of the first task, the push frequency of the plurality of game tasks is determined, and the plurality of game tasks are pushed to the user according to the push frequency.

[0018] This application determines the push frequency of game tasks based on the user's first task score, and pushes game tasks to the user according to the push frequency. It can provide corresponding push feedback to the user based on the first task score reflected by the user's completion of game tasks, so as to enhance the user's enthusiasm.

[0019] According to a second aspect of the embodiments of this application, a game task adjustment device for adjusting diet is provided, which is suitable for interactive game systems. The game task adjustment device includes a first task analysis module, a second task analysis module, and a game task adjustment module. The first task analysis module is used to collect users' uploaded historical eating behaviors, analyze the historical eating behaviors, identify several abnormal feature tags among multiple feature tags in a preset first theoretical system, and determine the first task ratio related to the multiple feature tags based on the correlation between each feature tag in the first theoretical system and the several abnormal feature tags. The second task analysis module is used to determine the user's first task score based on the historical dietary behavior and multiple dimensions, and to determine the proportion of the second task related to preset multiple difficulty levels based on the first task score; wherein, the multiple dimensions include the number of days of behavior recording, the number of times the task was completed, the number of times the behavior was matched, the time period of behavior recording, and the number of consecutive recording days; The game task adjustment module is used to construct several game tasks based on the first task ratio and the second task ratio, and on the first theoretical system, so that the user can complete the operation and interaction with the interactive game system according to the game tasks; wherein, the game tasks are used to generate positive feedback based on the user's record of dietary behavior.

[0020] In some embodiments of this application, the first task analysis module includes a behavior feature determination unit and an anomaly label determination unit; The behavior feature determination unit is used to analyze the user's historical dietary behavior and determine the behavior feature value corresponding to each feature tag of the first theoretical system. The abnormal label determination unit is used to determine several abnormal feature labels among all feature labels based on the difference between the behavioral feature value of each feature label and a preset feature threshold.

[0021] In some embodiments of this application, the first task analysis module includes an association tag determination unit and a task weight adjustment unit; The association label determination unit is used to determine the positive and negative association labels of each abnormal feature label based on the association relationship of each feature label in the first theoretical system. The task weight adjustment unit is used to adjust the subtask weights corresponding to each feature label based on the positive and negative correlation labels of each abnormal feature label, and to determine the proportion of the first task related to each feature label.

[0022] In some embodiments of this application, the second task analysis module includes a behavior record determination unit, a first score calculation unit, a second score calculation unit, and a task score calculation unit; The behavior recording determination unit is used to determine, based on the historical dietary behavior, the number of days of behavior recording, the number of times the task was completed, and the number of consecutive recording days for the user in a preset first time period, and to determine, based on the historical dietary behavior, the number of times the user's behavior matched and the number of times the behavior was recorded instantly in the historical dietary behavior; wherein, the number of times the behavior was recorded instantly is obtained based on the actual recording time period of the behavior recording period and the corresponding dietary behavior in the historical dietary behavior; The first score calculation unit is used to determine the behavior record score, task completion score, and continuous record score based on the ratio of the number of days of behavior record, the number of times the task is completed, and the ratio of the number of consecutive record days to the duration of the first time period, respectively. The second scoring unit is used to determine the behavior matching score and the instant recording score based on the ratios of the number of behavior matchings and the number of instant recordings to the total number of historical dietary behavior records, respectively. The task score calculation unit is used to determine the user's first task score based on the weighted sum of the behavior record score, the task completion score, the continuous record score, the behavior matching score, and the instant record score.

[0023] In some embodiments of this application, the game task adjustment module includes a first requirement determination unit, a second requirement determination unit, and a game task construction unit; The first requirement determination unit is used to determine the task priority and task cycle of each abnormal feature tag based on the behavioral feature value of each abnormal feature tag, and to determine the task time period requirement and target food of each abnormal feature tag based on the feature description of each abnormal feature tag. The second requirement determining unit is used to determine the task frequency requirement corresponding to each difficulty level of the game task based on the second task ratio; The game task construction unit is used to construct several game tasks based on the first theoretical system, according to the task priority, task cycle, task time period requirements of each abnormal feature tag, and the task frequency requirements corresponding to the target food and the game tasks of each difficulty level, combined with the first task ratio.

[0024] In some embodiments of this application, the game task is used to generate positive feedback based on the user's recorded dietary behavior, specifically including: In response to the first dietary behavior recorded by the user, based on several types of food in the first dietary behavior, the corresponding statistical label in all feature labels of the first theoretical system is determined, and the growth characteristic value of the corresponding statistical label is determined based on the consumption amount of each type of food. Based on the current and growth characteristic values ​​of each tag to be counted, the completion rate of each game task is determined, and based on the completion rate of each game task, preset game rewards are issued to the user to trigger positive feedback.

[0025] In some embodiments of this application, a game task push module is also included; the game task push module is used to determine the push frequency of the plurality of game tasks based on the score of the first task, and push the plurality of game tasks to the user according to the push frequency.

[0026] This application collects and analyzes users' uploaded historical dietary behaviors through an interactive game system. It identifies several anomalous feature tags among various feature tags within a first theoretical framework, and then determines the first task ratio based on the correlation between these feature tags. Simultaneously, it determines the first task score of users' historical dietary behaviors based on multiple dimensions, thereby determining the second task ratio. Finally, it combines the first task ratio and the first theoretical framework to construct several game tasks. Analyzing users' historical dietary behaviors using the first theoretical framework yields the corresponding first task ratio, which imbues the user's historical dietary behaviors with the semantics of the relevant theoretical framework. Combining this with the second task ratio to construct game tasks links the added semantics to the game tasks, and consequently, to the user's completion of the game tasks. This avoids the lack of semantic association in existing dietary management systems, which prevents users from perceiving the impact of game tasks on their actual health status, thus preventing a decline in user experience. Furthermore, the second task ratio determined through multiple dimensions ensures that the constructed game tasks better align with positive feedback incentive mechanisms, guaranteeing a positive user experience.

[0027] According to a third aspect of the embodiments of this application, a computer device is provided, comprising: a processor; a memory; and a computer program stored in the memory and configured to be executed by the processor; wherein the processor executes the computer program to implement a game task adjustment method for regulating diet as described in this application.

[0028] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing a plurality of instructions adapted for loading by a processor to execute a game task adjustment method for regulating diet as described in this application. Attached Figure Description

[0029] Figure 1 This is a flowchart illustrating a method for adjusting game tasks to regulate diet, as shown in some embodiments of this application. Figure 2 This is a modular structure diagram of a game task adjustment device for regulating diet, as shown in some embodiments of this application. Detailed Implementation

[0030] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below in conjunction with the accompanying drawings are exemplary and are only used to explain some embodiments of this application, and should not be construed as limiting the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments shown in this application without inventive effort are within the protection scope of this application.

[0031] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, unless otherwise explicitly specified, "a plurality of" or "several" means two or more.

[0032] Some existing dietary management systems utilize gamified incentive mechanisms to enhance users' self-health management capabilities. However, these systems generally suffer from a fundamental problem: a lack of deep semantic connection between the gamified incentive mechanisms and the underlying health theories. The game elements in these systems often merely abstract and quantify user behavior to trigger positive feedback mechanisms. For example, a user might receive a game achievement for recording a healthy diet, and the system would directly add or adjust the corresponding diet recording task. This fails to effectively link user behavior and health theories through gamified incentive mechanisms. Users struggle to perceive any real improvement in their health from the various game tasks provided by the system, leading to a disconnect between the game tasks and the health habit goals. This weakens the effectiveness of gamified incentive mechanisms and gradually diminishes the user experience. Therefore, how to construct a deep semantic connection between gamified incentive mechanisms and health theories to prevent a decline in user experience during dietary health management remains a pressing technical problem that needs to be addressed in current technologies.

[0033] Based on the above technical background, please refer to Figure 1 This application provides a method for adjusting game tasks by regulating diet, applicable to interactive game systems, including steps S101 to S103, each step as follows: Step S101: Collect users' uploaded historical eating behaviors, analyze the historical eating behaviors, identify several abnormal feature tags among the multiple feature tags in the preset first theoretical system, and determine the first task ratio related to the multiple feature tags based on the correlation relationship of each feature tag in the first theoretical system and the several abnormal feature tags.

[0034] In some embodiments of this application, the analysis of a user's historical dietary behavior to determine several abnormal feature tags among multiple feature tags in a preset first theoretical system specifically includes: Analyze users' historical dietary behavior to determine the behavioral feature value corresponding to each feature tag in the first theoretical system; Based on the difference between the behavioral feature value of each feature label and the preset feature threshold, several abnormal feature labels are determined among all feature labels.

[0035] In some embodiments of this application, the first theoretical system may be a traditional Chinese medicine knowledge system, and the corresponding feature tags include metal element (lung meridian), wood element (liver meridian), water element (kidney meridian), fire element (heart meridian) and earth element (spleen meridian).

[0036] Specifically, when analyzing historical dietary behavior, the corresponding behavioral feature value is determined by the elemental energy fluctuation curve updated for each of the user's historical dietary behaviors. Then, the abnormal feature label is determined based on the difference between the behavioral feature value of each feature label and the preset feature threshold (preferably 50): when the difference is positive and greater than 15, it is determined to be an abnormal feature label, and the corresponding abnormality is abnormally high; when the difference is negative and the absolute value is greater than 10, it is determined to be an abnormal feature label, and the corresponding abnormality is abnormally low.

[0037] This application first analyzes the user's historical eating behavior to determine the behavioral feature value of each feature tag in the first theoretical system. Then, it determines several abnormal feature tags based on the difference from the preset feature threshold. This allows the user's historical eating behavior to be evaluated through the first theoretical system, with the semantics of the corresponding theoretical system attached. Furthermore, it determines abnormal feature tags through the first theoretical system, providing data and theoretical support for subsequent adjustments to game tasks based on abnormal feature tags.

[0038] In some embodiments of this application, determining the first task ratio related to the multiple feature tags based on the correlation relationships of each feature tag in the first theoretical system, combined with the several abnormal feature tags, specifically includes: Based on the correlation between the feature labels in the first theoretical system, determine the positive and negative correlation labels for each abnormal feature label; Based on the positive and negative correlation labels of each abnormal feature label, the weights of the subtasks corresponding to each feature label are adjusted to determine the proportion of the first task related to each feature label.

[0039] Specifically, under the first theoretical system, the relationships between the feature labels are as follows: wood overcomes earth, earth overcomes water, water overcomes fire, fire overcomes metal, and metal overcomes wood; water generates wood, wood generates fire, fire generates earth, earth generates metal, and metal generates water. Therefore, the positively associated label of a certain feature label is the corresponding label that can "generate" the feature label, and the negatively associated label is the corresponding label that can "overcome" the feature label.

[0040] More specifically, the weight of the subtask for the abnormal feature label A with an abnormally low value needs to be increased, the weight of the subtask for the positively associated label of the abnormal feature label A also needs to be increased, while the weight of the subtask for the negatively associated label of the abnormal feature label A needs to be decreased. The weights for increasing and decreasing can be set to 20%, that is, when increasing, increase by 20% on the original basis, and when decreasing, decrease by 20% on the original basis, to ensure the significance of the game task adjustment.

[0041] This application first determines the positive and negative correlation labels of each abnormal feature label based on the correlation relationship of each feature label in the first theoretical system, and then adjusts the sub-task weights of each feature label to obtain the first task ratio related to each feature label. By determining the positive and negative correlation labels through the correlation relationship of feature labels, and then adjusting the corresponding sub-task weights, the first task ratio can better meet the adjustment needs of each feature label under the first theoretical system, thereby making the first task ratio more consistent with the game task ratio required by the user and ensuring the accuracy of the first task ratio.

[0042] Step S102: Based on the historical dietary behavior, determine the user's first task score based on multiple dimensions, and determine the second task ratio related to preset multiple difficulty levels based on the first task score; wherein, the multiple dimensions include the number of days of behavior recording, the number of times the task was completed, the number of times the behavior was matched, the time period of behavior recording, and the number of consecutive recording days.

[0043] In some embodiments of this application, determining the user's first task score based on the historical dietary behavior across multiple dimensions specifically includes: Based on the historical dietary behavior, the number of days the user's behavior was recorded, the number of times the task was completed, and the number of consecutive recording days in the preset first time period are determined. Based on the historical dietary behavior, the number of times the user's behavior matched and the number of times the instant record was made in the historical dietary behavior are determined. The number of instant records is obtained based on the actual recording time period of the behavior recording period and the corresponding dietary behavior in the historical dietary behavior. The behavior record score, task completion score, and continuous record score are determined based on the ratio of the number of days of behavior record, the number of times the task is completed, and the ratio of the number of consecutive record days to the duration of the first time period, respectively. The behavior matching score and the instant recording score are determined based on the ratios of the number of behavior matchings and the number of instant recordings to the total number of historical dietary behavior records, respectively. The user's first task score is determined by a weighted sum of the behavior record score, the task completion score, the continuous record score, the behavior matching score, and the instant record score.

[0044] Specifically, in the first task score, the behavior record score is used to calculate the score of the user's diet record, with a weight of 0.25; the task completion score is used to calculate the relationship between the user receiving and completing tasks, with a weight of 0.30; the continuous record score is used to calculate the user's longest continuous record days, with a weight of 0.10; the behavior matching score is used to analyze the degree of matching between the user's actual diet behavior and the game system's recommendations, with a weight of 0.25; and the real-time record score is used to calculate the relationship between the user's diet record time and the actual meal time, with a weight of 0.10.

[0045] Specifically, the difficulty levels include easy, medium, and hard tasks. When determining the proportion of the second task related to the preset difficulty levels based on the first task score, the ratio of easy, medium, and hard tasks is set to 0.2:0.4:0.4 when the first task score is greater than 90 points; 0.4:0.4:0.4 when the first task score is between 75 and 90 points; 0.6:0.3:0.1 when the first task score is between 60 and 75 points; and 100% of the easy task is assigned when the first task score is below 60 points.

[0046] This application first determines the number of days of user behavior recording, number of task completions, number of consecutive recording days, number of behavior matchings, and number of instant recordings in the first period based on historical dietary behavior. Then, it determines the behavior recording score, task completion score, consecutive recording score, behavior matching score, and instant recording score, thereby determining the user's first task score. This allows for a comprehensive measurement of the user's completion of game tasks through historical dietary behavior across multiple dimensions, thus ensuring an accurate evaluation of the user's progress in game tasks.

[0047] Step S103: Based on the first task ratio and the second task ratio, and based on the first theoretical system, construct several game tasks for the user to complete the operation and interaction with the interactive game system according to the game tasks; wherein, the game tasks are used to generate positive feedback based on the user's record of eating behavior.

[0048] In some embodiments of this application, the step of constructing several game tasks based on the first theoretical system according to the first task ratio and the second task ratio specifically includes: Based on the behavioral characteristic value of each abnormal feature tag, determine the task priority and task cycle of each abnormal feature tag, and based on the feature description of each abnormal feature tag, determine the task time period requirement and target food of each abnormal feature tag; Based on the second task ratio, determine the task frequency requirement corresponding to each difficulty level of the game task; Based on the first theoretical framework, several game tasks are constructed according to the task priority, task cycle, task time period requirements of each abnormal feature tag, and the task frequency requirements corresponding to the target food and the game tasks of each difficulty level, combined with the first task ratio.

[0049] Specifically, the task priority is determined by the ratio of the difference between the behavioral feature value of the abnormal feature label and a preset feature threshold to the preset feature threshold. When the ratio is higher than a preset deviation threshold, the corresponding task priority is set to high priority. The task cycle is determined by the ratio of the difference between the behavioral feature value of the abnormal feature label and a preset feature threshold to the preset feature threshold. When the ratio is between 0.2 and 0.4, the coefficient is 1.0; when the ratio is between 0.4 and 0.6, the coefficient is 1.5; and when the ratio is lower than 0.2, the coefficient is 0.7. The task cycle is then the product of the base cycle (7 days) and the coefficient. The task time period is related to the feature description of the corresponding abnormal feature tag. For example, the active time period of spleen function corresponding to the earth element is 9:00-11:00 AM and 11:00-2:00 PM. Therefore, the task time period corresponding to the earth element as an abnormal feature tag is the active time period of that function. The target food is also related to the feature description of the corresponding abnormal feature tag. For example, if the earth element corresponds to the spleen, then all foods that enter the spleen meridian are target foods of the earth element. The task frequency requirement is related to the task difficulty. The frequency coefficient for easy tasks is 1.0 (once a day), the frequency coefficient for medium tasks is 0.7, and the frequency coefficient for difficult tasks is 0.5.

[0050] This application first determines the task priority and task cycle based on the behavioral feature values ​​of each abnormal feature tag. Then, it determines the task time period requirements and target food based on the corresponding feature descriptions. Furthermore, it determines the task frequency requirements corresponding to each difficulty level of the game task based on the second task ratio. Then, based on the first theoretical system and combined with the first task ratio, it comprehensively constructs the game task. By using the behavioral feature values ​​and feature descriptions of the abnormal feature tags with attached semantics, the characteristics of the game task are determined from multiple dimensions. The relationship between task difficulty and task frequency requirements is obtained through the second task ratio. This allows the attached semantics to be associated with the game task, and thus with the user's completion of the game task. This avoids the situation where existing dietary management lacks semantic association, resulting in users not being able to perceive the impact of the game task on their real health status, thereby preventing a decline in user experience.

[0051] In some embodiments of this application, the game task is used to generate positive feedback based on the user's recorded dietary behavior, specifically including: In response to the first dietary behavior recorded by the user, based on several types of food in the first dietary behavior, the corresponding statistical label in all feature labels of the first theoretical system is determined, and the growth characteristic value of the corresponding statistical label is determined based on the consumption amount of each type of food. Based on the current and growth characteristic values ​​of each tag to be counted, the completion rate of each game task is determined, and based on the completion rate of each game task, preset game rewards are issued to the user to trigger positive feedback.

[0052] Specifically, the corresponding statistical label is determined according to the meridian tropism of each food. For example, if a food that enters the spleen meridian corresponds to the earth element, then the statistical label for the food that enters the spleen meridian is the earth element label. When calculating the growth characteristic value of the statistical label, it is set that every 100 grams of food can provide 5 characteristic values ​​for the primary meridian tropism and 2.5 characteristic values ​​for the secondary meridian tropism. Therefore, 150 grams of food can provide 7.5 characteristic values ​​for the primary meridian tropism.

[0053] For example, if a user consumes 150 grams of yam, its primary meridian is the spleen meridian (earth element), and its secondary meridians are the lung meridian (metal element) and kidney meridian (water element). This provides a characteristic value of 7.5 for the earth element and 3.75 for the metal and water elements respectively.

[0054] This application determines the statistical tags in the first theoretical system by identifying several types of food in the user's first dietary behavior, and determines the growth characteristic value of the corresponding statistical tag based on the amount of food consumed. Then, it determines the completion rate of each game task based on the current characteristic value and growth characteristic value of the statistical tag, so as to issue game rewards to the user and trigger positive feedback. By determining the statistical tags through the first theoretical system and promoting and completing game tasks, the semantics of the corresponding theoretical system can be attached to the user's first dietary behavior, and the attached semantics can be associated with the game task, and then with the user's completion of the game task. This avoids the situation where existing diet management lacks semantic association, which makes users unable to perceive the impact of game tasks on their real health status, thereby avoiding a decline in user experience.

[0055] In some embodiments of this application, it further includes: Based on the score of the first task, the push frequency of the plurality of game tasks is determined, and the plurality of game tasks are pushed to the user according to the push frequency.

[0056] Specifically, when the score of the first task exceeds the preset high score threshold, the corresponding push frequency is 3-4 new tasks per week; when the score of the first task is neither greater than the high score threshold nor lower than the preset low score threshold, the corresponding push frequency is 1-2 new tasks per week; when the score of the first task is lower than the low score threshold, a personalized reminder notification is triggered, and the reminder notification content is customized based on the user's historical eating behavior, such as "You have not recorded your diet for 3 consecutive days. Don't give up on the good habit you have maintained for 20 days. Record your lunch today."

[0057] This application determines the push frequency of game tasks based on the user's first task score, and pushes game tasks to the user according to the push frequency. It can provide corresponding push feedback to the user based on the first task score reflected by the user's completion of game tasks, so as to enhance the user's enthusiasm.

[0058] Compared to existing technologies, this application collects and analyzes users' uploaded historical dietary behaviors through an interactive game system. It identifies several anomalous feature tags among various feature tags within a first theoretical framework, and then determines the first task ratio based on the correlation between these feature tags. Simultaneously, it determines the first task score of the user's historical dietary behavior based on multiple dimensions, thereby determining the second task ratio. Finally, it combines the first task ratio with the first theoretical framework to construct several game tasks. Analyzing the user's historical dietary behavior through the first theoretical framework yields the corresponding first task ratio, which imbues the user's historical dietary behavior with the semantics of the relevant theoretical framework. Combining this with the second task ratio to construct game tasks links the added semantics to the game tasks, and consequently, to the user's completion of the game tasks. This avoids the lack of semantic association in existing dietary management systems, which prevents users from perceiving the impact of game tasks on their actual health status, thus preventing a decline in user experience. Furthermore, the second task ratio determined through multiple dimensions makes the constructed game tasks more aligned with a positive feedback incentive mechanism, ensuring a positive user experience.

[0059] For a method corresponding to the one described above, please refer to [link to relevant documentation]. Figure 2 This application provides a game task adjustment device for regulating diet, which is suitable for interactive game systems. The game task adjustment device includes a first task analysis module 210, a second task analysis module 220 and a game task adjustment module 230. The first task analysis module 210 is used to collect historical dietary behaviors uploaded by users, analyze the historical dietary behaviors, identify several abnormal feature tags among multiple feature tags in a preset first theoretical system, and determine the first task ratio related to the multiple feature tags based on the correlation relationship of each feature tag in the first theoretical system and the several abnormal feature tags. The second task analysis module 220 is used to determine the user's first task score based on the historical dietary behavior and multiple dimensions, and to determine the proportion of the second task related to preset multiple difficulty levels based on the first task score; wherein, the multiple dimensions include the number of days of behavior recording, the number of times the task was completed, the number of times the behavior was matched, the time period of behavior recording, and the number of consecutive recording days; The game task adjustment module 230 is used to construct a number of game tasks based on the first task ratio and the second task ratio, and on the first theoretical system, so that the user can complete the operation and interaction with the interactive game system according to the game tasks; wherein, the game tasks are used to generate positive feedback based on the user's record of eating behavior.

[0060] In some embodiments of this application, the first task analysis module 210 includes a behavior feature determination unit and an anomaly label determination unit; The behavior feature determination unit is used to analyze the user's historical dietary behavior and determine the behavior feature value corresponding to each feature tag of the first theoretical system. The abnormal label determination unit is used to determine several abnormal feature labels among all feature labels based on the difference between the behavioral feature value of each feature label and a preset feature threshold.

[0061] In some embodiments of this application, the first task analysis module 210 includes an association tag determination unit and a task weight adjustment unit; The association label determination unit is used to determine the positive and negative association labels of each abnormal feature label based on the association relationship of each feature label in the first theoretical system. The task weight adjustment unit is used to adjust the subtask weights corresponding to each feature label based on the positive and negative correlation labels of each abnormal feature label, and to determine the proportion of the first task related to each feature label.

[0062] In some embodiments of this application, the second task analysis module 220 includes a behavior record determination unit, a first score calculation unit, a second score calculation unit, and a task score calculation unit; The behavior recording determination unit is used to determine, based on the historical dietary behavior, the number of days of behavior recording, the number of times the task was completed, and the number of consecutive recording days for the user in a preset first time period, and to determine, based on the historical dietary behavior, the number of times the user's behavior matched and the number of times the behavior was recorded instantly in the historical dietary behavior; wherein, the number of times the behavior was recorded instantly is obtained based on the actual recording time period of the behavior recording period and the corresponding dietary behavior in the historical dietary behavior; The first score calculation unit is used to determine the behavior record score, task completion score, and continuous record score based on the ratio of the number of days of behavior record, the number of times the task is completed, and the ratio of the number of consecutive record days to the duration of the first time period, respectively. The second scoring unit is used to determine the behavior matching score and the instant recording score based on the ratios of the number of behavior matchings and the number of instant recordings to the total number of historical dietary behavior records, respectively. The task score calculation unit is used to determine the user's first task score based on the weighted sum of the behavior record score, the task completion score, the continuous record score, the behavior matching score, and the instant record score.

[0063] In some embodiments of this application, the game task adjustment module 230 includes a first requirement determination unit, a second requirement determination unit, and a game task construction unit; The first requirement determination unit is used to determine the task priority and task cycle of each abnormal feature tag based on the behavioral feature value of each abnormal feature tag, and to determine the task time period requirement and target food of each abnormal feature tag based on the feature description of each abnormal feature tag. The second requirement determining unit is used to determine the task frequency requirement corresponding to each difficulty level of the game task based on the second task ratio; The game task construction unit is used to construct several game tasks based on the first theoretical system, according to the task priority, task cycle, task time period requirements of each abnormal feature tag, and the task frequency requirements corresponding to the target food and the game tasks of each difficulty level, combined with the first task ratio.

[0064] In some embodiments of this application, the game task is used to generate positive feedback based on the user's recorded dietary behavior, specifically including: In response to the first dietary behavior recorded by the user, based on several types of food in the first dietary behavior, the corresponding statistical label in all feature labels of the first theoretical system is determined, and the growth characteristic value of the corresponding statistical label is determined based on the consumption amount of each type of food. Based on the current and growth characteristic values ​​of each tag to be counted, the completion rate of each game task is determined, and based on the completion rate of each game task, preset game rewards are issued to the user to trigger positive feedback.

[0065] In some embodiments of this application, a game task push module is also included; the game task push module is used to determine the push frequency of the plurality of game tasks based on the score of the first task, and push the plurality of game tasks to the user according to the push frequency.

[0066] This application collects and analyzes users' uploaded historical dietary behaviors through an interactive game system. It identifies several anomalous feature tags among various feature tags within a first theoretical framework, and then determines the first task ratio based on the correlation between these feature tags. Simultaneously, it determines the first task score of users' historical dietary behaviors based on multiple dimensions, thereby determining the second task ratio. Finally, it combines the first task ratio and the first theoretical framework to construct several game tasks. Analyzing users' historical dietary behaviors using the first theoretical framework yields the corresponding first task ratio, which imbues the user's historical dietary behaviors with the semantics of the relevant theoretical framework. Combining this with the second task ratio to construct game tasks links the added semantics to the game tasks, and consequently, to the user's completion of the game tasks. This avoids the lack of semantic association in existing dietary management systems, which prevents users from perceiving the impact of game tasks on their actual health status, thus preventing a decline in user experience. Furthermore, the second task ratio determined through multiple dimensions ensures that the constructed game tasks better align with positive feedback incentive mechanisms, guaranteeing a positive user experience.

[0067] It should be understood that the apparatus provided in this application corresponds to the aforementioned method. The game task adjustment apparatus for adjusting diet provided in this application can implement the game task adjustment method for adjusting diet provided in any embodiment of this application.

[0068] Adaptively, embodiments of this application also provide a computer device and a computer-readable storage medium.

[0069] The computer device includes: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor; The processor, when executing the computer program, implements a game task adjustment method for regulating diet, as described in this application.

[0070] The computer-readable storage medium stores multiple instructions adapted for loading by a processor to execute a game task adjustment method for regulating diet according to this application.

[0071] The above description represents some embodiments of this application, providing a further detailed explanation of the purpose, technical solution, and beneficial effects of this application. It should be understood that the above-described embodiments of this application should not be construed as limiting this application. In particular, any changes, modifications, equivalent substitutions, and variations made by those skilled in the art within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for adjusting game tasks to regulate diet, characterized in that, Applicable to interactive game systems, the game task adjustment method includes: Collect users' uploaded historical eating behaviors, analyze the historical eating behaviors, identify several abnormal feature tags among multiple feature tags in a preset first theoretical system, and determine the first task ratio related to the multiple feature tags based on the correlation between each feature tag in the first theoretical system and the several abnormal feature tags. Based on the user's historical dietary behavior, a first task score is determined based on multiple dimensions, and a second task ratio related to preset difficulty levels is determined based on the first task score; wherein, the multiple dimensions include the number of days of behavior recording, the number of times the task is completed, the number of times the behavior is matched, the time period of behavior recording, and the number of consecutive recording days; Based on the first task ratio and the second task ratio, and based on the first theoretical system, several game tasks are constructed for the user to complete the operation and interaction with the interactive game system according to the game tasks; wherein, the game tasks are used to generate positive feedback based on the user's record of eating behavior.

2. The method for adjusting game tasks to regulate diet according to claim 1, characterized in that, The analysis of users' historical dietary behavior identifies several abnormal feature tags among multiple feature tags in a pre-defined first theoretical system, specifically including: Analyze users' historical dietary behavior to determine the behavioral feature value corresponding to each feature tag in the first theoretical system; Based on the difference between the behavioral feature value of each feature label and the preset feature threshold, several abnormal feature labels are determined among all feature labels.

3. The method for adjusting game tasks to regulate diet according to claim 1, characterized in that, The step of determining the proportion of the first task related to the multiple feature labels based on the correlation relationships of each feature label in the first theoretical system, combined with the several abnormal feature labels, specifically includes: Based on the correlation between the feature labels in the first theoretical system, determine the positive and negative correlation labels for each abnormal feature label; Based on the positive and negative correlation labels of each abnormal feature label, the weights of the subtasks corresponding to each feature label are adjusted to determine the proportion of the first task related to each feature label.

4. The method for adjusting game tasks to regulate diet according to claim 1, characterized in that, The process of determining the user's first task score based on their historical dietary behavior across multiple dimensions specifically includes: Based on the historical dietary behavior, the number of days the user's behavior was recorded, the number of times the task was completed, and the number of consecutive recording days in the preset first time period are determined. Based on the historical dietary behavior, the number of times the user's behavior matched and the number of times the instant record was made in the historical dietary behavior are determined. The number of instant records is obtained based on the actual recording time period of the behavior recording period and the corresponding dietary behavior in the historical dietary behavior. The behavior record score, task completion score, and continuous record score are determined based on the ratio of the number of days of behavior record, the number of times the task is completed, and the ratio of the number of consecutive record days to the duration of the first time period, respectively. The behavior matching score and the instant recording score are determined based on the ratios of the number of behavior matchings and the number of instant recordings to the total number of historical dietary behavior records, respectively. The user's first task score is determined by a weighted sum of the behavior record score, the task completion score, the continuous record score, the behavior matching score, and the instant record score.

5. A method for adjusting game tasks to regulate diet according to claim 1, characterized in that, Based on the first task ratio and the second task ratio, and grounded in the first theoretical framework, several game tasks are constructed, specifically including: Based on the behavioral characteristic value of each abnormal feature tag, determine the task priority and task cycle of each abnormal feature tag, and based on the feature description of each abnormal feature tag, determine the task time period requirement and target food of each abnormal feature tag; Based on the second task ratio, determine the task frequency requirement corresponding to each difficulty level of the game task; Based on the first theoretical framework, several game tasks are constructed according to the task priority, task cycle, task time period requirements of each abnormal feature tag, and the task frequency requirements corresponding to the target food and the game tasks of each difficulty level, combined with the first task ratio.

6. The method for adjusting game tasks to regulate diet according to claim 1, characterized in that, The game tasks are used to generate positive feedback based on the user's recorded dietary behavior, specifically including: In response to the first dietary behavior recorded by the user, based on several types of food in the first dietary behavior, the corresponding statistical label in all feature labels of the first theoretical system is determined, and the growth characteristic value of the corresponding statistical label is determined based on the consumption amount of each type of food. Based on the current and growth characteristic values ​​of each tag to be counted, the completion rate of each game task is determined, and based on the completion rate of each game task, preset game rewards are issued to the user to trigger positive feedback.

7. The method for adjusting game tasks to regulate diet according to claim 1, characterized in that, Also includes: Based on the score of the first task, the push frequency of the plurality of game tasks is determined, and the plurality of game tasks are pushed to the user according to the push frequency.

8. A game task adjustment device for regulating diet, characterized in that, Applicable to interactive game systems, the game task adjustment device includes a first task analysis module, a second task analysis module, and a game task adjustment module; The first task analysis module is used to collect users' uploaded historical eating behaviors, analyze the historical eating behaviors, identify several abnormal feature tags among multiple feature tags in a preset first theoretical system, and determine the first task ratio related to the multiple feature tags based on the correlation between each feature tag in the first theoretical system and the several abnormal feature tags. The second task analysis module is used to determine the user's first task score based on the historical dietary behavior and multiple dimensions, and to determine the proportion of the second task related to preset multiple difficulty levels based on the first task score; wherein, the multiple dimensions include the number of days of behavior recording, the number of times the task was completed, the number of times the behavior was matched, the time period of behavior recording, and the number of consecutive recording days; The game task adjustment module is used to construct several game tasks based on the first task ratio and the second task ratio, and on the first theoretical system, so that the user can complete the operation and interaction with the interactive game system according to the game tasks; wherein, the game tasks are used to generate positive feedback based on the user's record of dietary behavior.

9. A computer device, characterized in that, include: processor; Memory; A computer program stored in the memory and configured to be executed by the processor; When the processor executes the computer program, it implements a game task adjustment method for regulating diet as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a plurality of instructions adapted for loading by a processor to execute a game task adjustment method for regulating diet as described in any one of claims 1 to 7.

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