CN113658467A - Interactive system and method for optimizing user behavior - Google Patents
Interactive system and method for optimizing user behavior Download PDFInfo
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- CN113658467A CN113658467A CN202110920973.7A CN202110920973A CN113658467A CN 113658467 A CN113658467 A CN 113658467A CN 202110920973 A CN202110920973 A CN 202110920973A CN 113658467 A CN113658467 A CN 113658467A
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- G09B5/08—Electrically-operated educational appliances providing for individual presentation of information to a plurality of student stations
- G09B5/14—Electrically-operated educational appliances providing for individual presentation of information to a plurality of student stations with provision for individual teacher-student communication
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Abstract
The invention discloses an interactive system and method for optimizing user behavior, wherein the system comprises: the user mobile service terminal is used for acquiring user preference data; behavior acquisition means for acquiring user behavior data; the data center is used for analyzing the preference data of the user and establishing a user basic model; visual restoration is carried out on the user basic model by using a digital modeling technology and a paper doll system to obtain a visual personality; the system is also used for deepening a user basic model according to the user behavior data to obtain a user digital personality; synchronizing visual personality according to the user digital personality to obtain synchronized visual personality; and the interaction device interacts with the user through the synchronized visual personality. The online virtual personality close to children is simulated through learning user behaviors, the children are guided to conduct positive education, education and learning achievements which can be quickly understood are described for the children, and the children are enabled to actively seek education opportunities from original passive education receiving.
Description
Technical Field
The invention relates to the field of user behavior optimization, in particular to an interactive system and method for optimizing user behavior.
Background
According to the survey, the Chinese parent-child education conditions are further improved and developed nowadays, which are shown in the following concrete steps: the democratic consciousness and scientific consciousness of parents are obviously improved; the spreading of the scientific concepts and methods of family education is more effective and diversified; parents pay more attention to the overall growth of children, particularly the cultivation of healthy personality of children; the learning enthusiasm of parents continues to rise; paying attention to scientific companions; focusing on parent-child reading; increased paternal engagement, and so on.
Meanwhile, the problems of the current parent-child education are mainly as follows: many parents cannot correctly understand the true meaning of 'love', and the fear, greedy and proficiency of adults are unconsciously transmitted to children as 'love'; anxiety and over-education still exist in the infant; the 'tunnel effect' which embodies the family culture inheritance influence is obvious, and parents often have no clear awareness on the 'tunnel effect'. The report suggests that there are problems of education over-education and education dissimilarity in the family education to be alerted.
The education excess is mainly characterized by infusing knowledge too early, too much and too fast; set too high a target for children; the children play time is occupied too long; excessive speech and lecture, etc. Education dissimilarity can be caused by excessive education. Since the paternity conflict and family anxiety caused by excessive education already constitute social problems to a considerable extent, the vast families are required to be deeply saved.
About half of the families place importance on reading in person, which is a good trend, but it is not enough. Parent-child reading is an important way for promoting healthy growth of children, and not only is the process of acquiring knowledge, but also the process of going deep into the emotional world and cultivating healthy personality. This is crucial for children. The report shows that every family has a 'tunnel' which symbolizes the intercourse of the psychological structure of family culture, which greatly affects the relations of couples, relatives and children and the relation with the previous generation, affects the value, life, emotional and work-around patterns of individuals and families, and further becomes an important element affecting family education. Family education needs to be alert, and children cannot teach or be acquainted with the family education.
In the existing parent-child education mechanism, the education mechanism for guiding children by parents still has the defect of being compensated, a communication gap exists between two generations of people objectively, and the dilemma still cannot be got rid of in various current online course education and online parent-child paradises.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an interactive system and method for optimizing user behaviors, and aims to simulate an online virtual personality close to children by learning the user behaviors and guide the positive education of the children.
First aspect
The invention provides an interactive system for optimizing user behavior, which comprises:
the user mobile service terminal is used for carrying out character test interaction with the user to acquire user preference data;
behavior acquisition means for acquiring user behavior data;
the data center is used for carrying out data analysis on the user preference data and establishing a user basic model; carrying out visual restoration on the user basic model by using a digital modeling technology and a paper doll system to obtain a visual personality; the system is also used for deepening a user basic model according to the user behavior data to obtain a user digital personality; synchronizing the visual personality according to the user digital personality to obtain a synchronized visual personality;
and the interaction device is used for interacting with the user through the synchronized visual personality to obtain co-growth cognition.
Preferably, the user base model includes a character parameter module, and the character parameter module includes a plurality of character parameters and a parameter value corresponding to each character parameter.
Preferably, the data center station is further configured with a learning outcome feedback mechanism, configured to obtain the course adapted to the user basic model and push the course to the user mobile service terminal.
Preferably, the user mobile service terminal is further configured to obtain course information that the user wants to actively learn and send the course information to the data center;
and the data center is also provided with a course learning mechanism for controlling the digital personality to actively learn the corresponding course according to the course information and summarizing the knowledge points.
Preferably, the behavior acquisition device is a wearable device.
Second aspect of the invention
The invention provides an interaction method for optimizing user behaviors, which comprises the following steps:
acquiring user preference data;
performing data analysis on the user preference data, and establishing a user basic model; carrying out visual restoration on the user basic model by using a digital modeling technology and a paper doll system to obtain a visual personality;
acquiring user behavior data;
deepening a user basic model according to the user behavior data to obtain a user digital personality; synchronizing the visual personality according to the user digital personality to obtain a synchronized visual personality;
and interacting with the user through the synchronized visual personality to obtain co-growth cognition.
Preferably, the method further comprises the following steps:
and acquiring courses adapted to the user basic model through a learning result feedback mechanism and feeding back the courses to the user.
Preferably, the method further comprises the following steps:
acquiring course information which a user wants to actively learn;
controlling the digital personality to actively learn the corresponding course through a course learning mechanism according to the course information and summarizing the knowledge key points;
guiding the user to learn the corresponding courses through interaction with the user;
the user base model comprises a plurality of personality parameters; and after the user finishes learning the corresponding courses, updating the personality parameters of the user through a personality synchronization mechanism, and forming a visual chart according to the updated personality parameters of the user.
Preferably, the method further comprises the following steps:
obtaining a character parameter with a lower parameter value according to the visual chart;
and pushing corresponding courses and learning methods to the user according to the character parameters with lower parameter values.
Preferably, the method further comprises the following steps:
supporting an automatic learning mechanism through a learning result feedback mechanism, summing parameter values of all character parameters and averaging the parameter values into learning force values;
comparing and classifying the learning force values, and dividing learning ability grades;
and pushing different courses to the user according to different learning ability levels.
The invention has the beneficial effects that:
the online virtual personality close to children is simulated through learning user behaviors, the children are guided to conduct positive education, education and learning achievements which can be quickly understood are described for the children, and the children are enabled to actively seek education opportunities from original passive education receiving.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a schematic structural diagram of an interactive system for optimizing user behavior according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating an interaction method for optimizing user behavior according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
As shown in fig. 1, an embodiment of the present invention provides an interactive system for optimizing user behavior, including:
the user mobile service terminal is used for carrying out character test interaction with the user to acquire user preference data;
behavior acquisition means for acquiring user behavior data;
the data center is used for carrying out data analysis on the user preference data and establishing a user basic model; visual restoration is carried out on the user basic model by using a digital modeling technology and a paper doll system to obtain a visual personality; the system is also used for deepening a user basic model according to the user behavior data to obtain a user digital personality; synchronizing visual personality according to the user digital personality to obtain synchronized visual personality;
and the interaction device interacts with the user through the synchronized visual personality to obtain co-growth cognition.
The user mobile service terminal may be an APP, a web page, or the like. The user preference data includes the appearance of the visualized personality including gender, hairstyle, height, skin tone, age, clothing, and the like. Parents can set a visual image which children like according to the preferences of the children, and the interaction power of the children is improved. The behavior acquisition device may employ wearable devices, including bracelets, helmets, and the like. After the wearable device is worn by a child, the wearable device can directly record the movement, jumping and other behavior data of the child. The digital personality is synchronized by a game engine of a user service mobile terminal or an interactive device to generate a visual personality, the appearance of the personality can be decorated by an engine doll, and the behavior and the logic are determined by judgment values after the engine is communicated with a data center station.
In the embodiment of the invention, firstly, parents set a visual image according to the preference of children; then, the data center perfects the visual image according to the behavior information of the child to obtain an online virtual personality liked by the child; finally, the virtual personality can be used for guiding the interaction and positive education with the children. The embodiment of the invention takes education theory, visual art, device art, digital technology and information technology as supports, compensates the blank self-driven education field in parent-child education, guides children to learn by digital personality which can grow synchronously with the children, and solves the problems of low initiative, unclear target and forced education to limit imagination of the children in learning.
The user basic model comprises a character parameter module, and the character parameter module comprises a plurality of character parameters and parameter value intervals corresponding to the character parameters. The data center is established with complete configuration service, which comprises a character analysis mechanism, and is used for carrying out front-oriented character analysis and prediction aiming at children of different ages, and the children of different ages are adapted to different character analysis contents and subjects. The analysis content is used in the project interaction device at the same time, after the child finishes the test, the analysis content is uploaded to a data center station by a user mobile service terminal, after the data center station finishes data cleaning and classification, adaptive user data is established to form a child basic model, different character parameter values are set in the character part of the model, the parameter interval is 0-100, and the character performance is more obvious when the numerical value is higher. Meanwhile, the data platform continuously synchronizes the behavior data of the children, and finally forms a digital interactive digital personality.
The data center is provided with a user database, and various education course services are arranged in the user database. The data center station is also provided with a learning result feedback mechanism which is used for acquiring courses matched with the user basic model and pushing the courses to the user mobile service terminal. And carrying out course adaptation aiming at the basic model of the child, if the child is biased to outdoor sports and the team cooperation parameter class value is higher in the character analysis, associating the matched child with the corresponding course and game item, preferentially pushing the class of contents at the user service mobile terminal, and configuring corresponding color matching schemes at the user service mobile terminal and the related item activated by the user.
The user mobile service terminal is also used for acquiring course information which the child wants to actively learn and sending the course information to the data center station. The data center station is also provided with a course learning mechanism for controlling the digital personality to actively learn the corresponding course according to the course information and summarizing the knowledge points. While course adaptation is carried out, the data center platform takes a user basic model as a basic data analysis bottom layer and takes an active learning motivation and behavior simulation as supports, so that the independent and simple digital personality is perfected, and in the mechanism, the optimal behavior is always taken as guidance, so that the social correctness of the digital personality is ensured. The initiative learning motivation is initiated by a child or a parent, after the child or the parent carries out the course appointment action on a user service mobile terminal or an interaction device, the child or the parent starts initiative learning of the course and summarizes the key points and skills of the knowledge (the relevant acquired judgment values of the synchronous course) by the digital personality within the waiting time when the child does not start learning, and obtains the corresponding values to represent the knowledge and the skills after the real learning time, wherein the values can be used as threshold values for activating other projects, services and devices to visualize learning results, and meanwhile, the digital personality actively guides the child to carry out the course learning action through the user service mobile terminal or the interaction device to complete self-education drive of the child.
After the children finish learning according to the guidance of the digital personality, the personality parameters of the children are updated by the data center to form a visual chart, namely the six-transformation of the commonly-called ability.
In the whole interaction period, the data center continuously compares data with the digital personality, and makes up the personality parameters with lower parameters according to the personality parameter conditions, such as the development of a more basic and profound rewarding learning method and course interaction; and optimizing the character parameters with higher parameters, such as deducing the examination and challenge interaction of the corresponding subjects to obtain higher return.
In the whole interaction period or at the end of the interaction period, a learning result feedback mechanism supports an automatic learning mechanism, all character parameters are summed and averaged into a learning force value, the learning force value is compared and classified in a data center, a learning ability and grade mechanism is divided, and different courses are recommended according to different learning ability grades. The learning ability value can be increased after the contents corresponding to the difficulty are finished, and the learning ability of each grade provides periodic rewards. And according to the numerical value growth attributes of the children and the visual image, a learning outcome feedback mechanism is triggered, and the outcome feedback is uploaded, visualized or 3D printed.
In the embodiment of the invention, the data center is also provided with a digital personality growth mechanism, a game interaction feedback mechanism and a character testing mechanism so as to realize forward guidance of children.
As shown in fig. 2, an embodiment of the present invention further provides an interaction method for optimizing user behavior, including the following steps:
acquiring user preference data;
performing data analysis on the user preference data, and establishing a user basic model; visual restoration is carried out on the user basic model by using a digital modeling technology and a paper doll system to obtain a visual personality;
acquiring user behavior data;
deepening a user basic model according to user behavior data to obtain a user digital personality; synchronizing visual personality according to the user digital personality to obtain synchronized visual personality;
and interacting with the user through the synchronized visual personality to obtain co-growth cognition.
The user preference data is obtained through a user mobile service terminal, and the user mobile service terminal can be APP, webpage input and the like. The user preference data includes the appearance of the visualized personality including gender, hairstyle, height, skin tone, age, clothing, and the like. Parents can set a visual image which children like according to the preferences of the children, and the interaction power of the children is improved. User's action data passes through the action acquisition device and acquires, and the action acquisition device can be wearing formula equipment, and wearing formula equipment includes bracelet, helmet and so on. After the wearable device is worn by a child, the wearable device can directly record the movement, jumping and other behavior data of the child. The synchronized visual personality interacts with the child through the interaction device. The digital personality is synchronized by a game engine of a user service mobile terminal or an interactive device to generate a visual personality, the appearance of the personality can be decorated by an engine doll, and the behavior and the logic are determined by judgment values after the engine is communicated with a data center.
In the embodiment of the invention, firstly, a visual image is set according to the preference of children; then, the data center perfects the visual image according to the behavior information of the child, and further obtains an online virtual personality liked by the child; finally, the virtual personality can be used for guiding the interaction and positive education with the children.
The embodiment of the invention also comprises the following steps:
the data center is provided with a user database, and various education course services are arranged in the user database. And acquiring courses matched with the user basic model through a learning achievement feedback mechanism and feeding the courses back to the children or parents. And carrying out course adaptation aiming at the basic model of the child, if the child is biased to outdoor sports and the team cooperation parameter class value is higher in the character analysis, associating the matched child with the corresponding course and game item, preferentially pushing the class of contents at the user service mobile terminal, and configuring corresponding color matching schemes at the user service mobile terminal and the related item activated by the user.
Further comprising the steps of:
acquiring course information which a user wants to actively learn;
controlling the digital personality to actively learn the corresponding course through a course learning mechanism according to the course information and summarizing the knowledge key points;
guiding the user to learn the corresponding courses through interaction with the user;
the user base model comprises a plurality of character parameters; and after the user finishes learning of the corresponding courses, updating the personality parameters of the user through a personality synchronization mechanism, and forming a visual chart according to the updated personality parameters of the user.
The data center is established with complete configuration service, which comprises a character analysis mechanism, and is used for carrying out front-oriented character analysis and prediction aiming at children of different ages, and the children of different ages are adapted to different character analysis contents and subjects. The analysis content is used in the project interaction device at the same time, after the child completes the test, the analysis content is uploaded to a data center station by a user mobile service end, after the data center station completes data cleaning and classification, a basic model of the child is formed, different character parameter values are set in the character part of the model, the parameter interval is 0-100, the character performance is more obvious when the numerical value is higher, meanwhile, the data continuously synchronizes the behavior data of the child, and finally a digital interactive digital personality is formed.
The user mobile service terminal is also used for acquiring course information which the child wants to actively learn and sending the course information to the data center station. While course adaptation is carried out, the data center platform takes a user basic model as a basic data analysis bottom layer and takes an active learning motivation and behavior simulation as supports, so that the independent and simple digital personality is perfected, and in the mechanism, the optimal behavior is always taken as guidance, so that the social correctness of the digital personality is ensured. The initiative learning motivation is initiated by a child or a parent, after the child or the parent performs the course appointment action on a user service moving end or an interaction device, the child or the parent starts initiative learning of the course and summarizes the key points and skills (the relevant acquired judgment values of the synchronous course) by the digital personality within the waiting time when the child does not start learning, and obtains the corresponding values to represent the knowledge and the skills after the real learning time, wherein the values can be used as threshold values for activating other projects, services and devices to visualize learning results, and meanwhile, the digital personality actively guides the child to perform the course learning action through the service moving end or the interaction device to complete self-education driving of the child. After the children finish learning according to the guidance of the digital personality, the personality parameters of the children are updated by the data center to form a visual chart, namely the six-transformation of the commonly-called ability.
Further comprising the steps of:
obtaining a character parameter with a lower parameter value according to the visual chart;
and pushing corresponding courses and learning methods to the children according to the character parameters with lower parameter values.
In the whole interaction period, the data is continuously compared with the digital personality in the data, the personality parameters with lower parameters are made up according to the personality parameter conditions, for example, more basic and more rewarded learning methods and course interaction are promoted, and the personality parameters with higher parameters are optimized, for example, assessment and challenge interaction of corresponding subjects is promoted, so that higher return is obtained.
Further comprising the steps of:
supporting an automatic learning mechanism through a learning result feedback mechanism, summing parameter values of all character parameters and averaging the parameter values into learning force values;
comparing and classifying the learning force values, and dividing the learning ability grades;
pushing different courses to children according to different learning ability levels
In the whole interaction period or at the end of the interaction period, a learning result feedback mechanism supports an automatic learning mechanism, all character parameters are summed and averaged into a learning force value, the learning force value is compared and classified in a data center, a learning ability and grade mechanism is divided, and different courses are recommended according to different learning ability grades. The learning ability value can be increased after the contents corresponding to the difficulty are finished, and the learning ability of each grade provides periodic rewards. And according to the numerical value growth attributes of the children and the visual image, a learning outcome feedback mechanism is triggered, and the outcome feedback is uploaded, visualized or 3D printed.
The embodiment of the invention provides an interactive system and method for optimizing user behaviors, which comprises the steps of firstly, setting a visual image according to the preference of children; then, the visual image is perfected according to the behavior information of the child, and an online virtual personality liked by the child is obtained; then, the interaction between the children and the interaction device and the analysis of the data center platform are used for continuously perfecting the virtual personality, and finally, the interaction and positive education between the children and the virtual personality are realized. The embodiment of the invention mainly makes up the communication gap between children and parents in the parent-child education process, simulates an online virtual personality close to children through learning user behaviors, guides the children to conduct positive education, draws out education and learning achievements which can be quickly understood for the children, and changes the original passive education receiving of the children into the active education seeking opportunity.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. An interactive system for optimizing user behavior, comprising:
the user mobile service terminal is used for carrying out character test interaction with the user to acquire user preference data;
behavior acquisition means for acquiring user behavior data;
the data center is used for carrying out data analysis on the user preference data and establishing a user basic model; carrying out visual restoration on the user basic model by using a digital modeling technology and a paper doll system to obtain a visual personality; the system is also used for deepening a user basic model according to the user behavior data to obtain a user digital personality; synchronizing the visual personality according to the user digital personality to obtain a synchronized visual personality;
and the interaction device is used for interacting with the user through the synchronized visual personality to obtain co-growth cognition.
2. The interactive system for optimizing user behavior as claimed in claim 1, wherein the user base model comprises a personality parameter module, and the personality parameter module comprises a plurality of personality parameters and a parameter value corresponding to each personality parameter.
3. The interactive system for optimizing user behavior as claimed in claim 1, wherein the data center is further configured with a learning outcome feedback mechanism for obtaining and pushing the lesson adapted to the user basic model to the user mobile service end.
4. The interactive system for optimizing user behavior as claimed in claim 1, wherein the user mobile service end is further configured to obtain course information that the user wants to actively learn and send the course information to the data center;
and the data center is also provided with a course learning mechanism for controlling the digital personality to actively learn the corresponding course according to the course information and summarizing the knowledge points.
5. The interactive system for optimizing user behavior as claimed in claim 1, wherein the behavior obtaining device is a wearable device.
6. An interactive method for optimizing user behavior, comprising the steps of:
acquiring user preference data;
performing data analysis on the user preference data, and establishing a user basic model; carrying out visual restoration on the user basic model by using a digital modeling technology and a paper doll system to obtain a visual personality;
acquiring user behavior data;
deepening a user basic model according to the user behavior data to obtain a user digital personality; synchronizing the visual personality according to the user digital personality to obtain a synchronized visual personality;
and interacting with the user through the synchronized visual personality to obtain co-growth cognition.
7. The interactive method for optimizing user behavior according to claim 1, further comprising the steps of:
and acquiring courses adapted to the user basic model through a learning result feedback mechanism and feeding back the courses to the user.
8. The interactive method for optimizing user behavior according to claim 1, further comprising the steps of:
acquiring course information which a user wants to actively learn;
controlling the digital personality to actively learn the corresponding course through a course learning mechanism according to the course information and summarizing the knowledge key points;
guiding the user to learn the corresponding courses through interaction with the user;
the user base model comprises a plurality of personality parameters; and after the user finishes learning the corresponding courses, updating the personality parameters of the user through a personality synchronization mechanism, and forming a visual chart according to the updated personality parameters of the user.
9. The interactive method for optimizing user behavior according to claim 8, further comprising the steps of:
obtaining a character parameter with a lower parameter value according to the visual chart;
and pushing corresponding courses and learning methods to the user according to the character parameters with lower parameter values.
10. The interactive method for optimizing user behavior according to claim 9, further comprising the steps of:
supporting an automatic learning mechanism through a learning result feedback mechanism, summing parameter values of all character parameters and averaging the parameter values into learning force values;
comparing and classifying the learning force values, and dividing learning ability grades;
and pushing different courses to the user according to different learning ability levels.
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| CN107807734A (en) * | 2017-09-27 | 2018-03-16 | 北京光年无限科技有限公司 | A kind of interaction output intent and system for intelligent robot |
| CN107894831A (en) * | 2017-10-17 | 2018-04-10 | 北京光年无限科技有限公司 | A kind of interaction output intent and system for intelligent robot |
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| CN108537321A (en) * | 2018-03-20 | 2018-09-14 | 北京智能管家科技有限公司 | A kind of robot teaching's method, apparatus, server and storage medium |
| CN108766070A (en) * | 2018-05-17 | 2018-11-06 | 山东英才学院 | A kind of children's morning teaching system Internet-based |
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| US20200202737A1 (en) * | 2018-12-17 | 2020-06-25 | Appearition Private Limited | Automated system for mapping ordinary 3d media as multiple event sinks to spawn interactive educational material |
| CN109949619A (en) * | 2019-04-19 | 2019-06-28 | 安徽智训机器人技术有限公司 | A kind of household instruction robot system of self study |
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