Data information management system and method based on infant activities
By designing a data information management system based on children's activities, and using a user preference model to analyze children's activity data, personal interest preference vectors are generated. This solves the problem of difficulty in inferring personality traits in existing technologies and achieves the effect of scientifically cultivating children's personality qualities.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 游佳润
- Filing Date
- 2023-12-01
- Publication Date
- 2026-04-17
AI Technical Summary
Existing technologies make it difficult to effectively analyze and utilize children's activity data to infer their personality traits, making it difficult for parents to scientifically cultivate their children's character.
Design a data information management system based on children's activities. Through user management, data management and analysis, generate personal interest and preference vectors, and use user preference models to analyze children's activity data to infer personality traits.
By classifying and analyzing children's activity data, individual interest preference vectors are generated to help parents scientifically cultivate their children's character and qualities.
Smart Images
Figure CN121880465A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of data analysis technology, specifically relating to a data information management system and method based on children's activities. Background Technology
[0002] Early childhood is a crucial period in everyone's development. Psychological qualities and personality traits are formed during this time. Personality is the sum of unique psychological characteristics in a person's stable attitude towards reality and habitual behavioral patterns. Personality is the result of the interaction between heredity and the environment. Heredity is a prerequisite, while the influence of the environment is the primary condition. Children's performance in activities reflects their personality and individual characteristics. Every child has their own unique personality and individual characteristics, which are fully reflected in their activities. For example, some children are introverted, appearing quiet and less inclined to interact with others; some are extroverted, lively, and enjoy playing with others; and some are attentive, observing and thinking carefully. Exploring the personality traits of young children is a long-term task. Analyzing and defining children's personalities helps parents better nurture their children into well-rounded and healthy individuals. Summary of the Invention
[0003] The purpose of this invention is to provide a data information management system and method based on children's activities, so as to solve the problems mentioned in the background art.
[0004] To achieve the above objectives, the present invention provides the following technical solution:
[0005] A data information management system based on early childhood activities includes: a user management unit for managing user login information and performing permission authentication; an information query unit for retrieving data based on user input conditions and displaying it to the user; a report printing unit for generating data information reports that can be downloaded and printed by the user; and a data management unit for managing and refining the data information of early childhood activities.
[0006] Preferably, in the user management unit, users log in through a login window. Based on the automatic restriction settings in the background, the system automatically identifies the login user's information and grants the corresponding system permissions when the username and password are correct. The user management unit manages the user's password and reminds the operator to change the password regularly.
[0007] Preferably, the data management unit is divided into ordinary user operations and administrator operations based on the permission information obtained by the user through the user management unit. Ordinary users can only modify their own personal information, change their passwords, and query information related to themselves and public information. Administrators can add and modify the information of ordinary users, perform statistics and queries on various information, and modify the information representation format and clean up useless records.
[0008] Preferably, the data management unit records children's activities based on user behavior analysis to obtain a corresponding log library, and then analyzes and processes the log library. The process of organizing the log library information during the analysis mainly involves compressing and merging children's activity logs. The compression and statistical process is as follows: First, read each children's activity record one by one, then count the frequency of the same type of activity, select the most recent time as the start time, and continue until all records have been read, and then save the compressed and statistically analyzed logs.
[0009] Preferably, the data management unit constructs a user preference model to analyze the data in the log database. The specific process for generating a personal interest preference vector is as follows: 1) Construct a keyword weight vector, calculate the value of this vector after Δd days, and select the keywords with the highest weights; 2) Repeat step 1) until the keyword permission vector generated each time within a certain long period of time has been calculated, and store the feature keywords selected in each calculation in a set ΔS; 3) Array the keywords in ΔS into sets (<k1, w1>, <k2, w2>, Λ, <k n ,w n >) Form a document-feature keyword matrix D, where the keyword weights use their initial weights; 4) Calculate the document similarity matrix M. sim =D×D T 5) If the similarity between documents exceeds a certain proportion, these documents are considered to belong to the same category. This can then be determined based on M. sim 6) Extract several document categories from the original data; 7) Analyze each document category to determine whether the category represents a child's long-term personal interest; 8) For categories where the information weight is not high, analyze the frequency of occurrence of each document in the category to determine whether it represents a long-term interest; 9) Delete all documents that are determined to belong to the user's personal interest from the original data.
[0010] Preferably, in step 3), k is the feature keyword, and w is the weight of keyword k in the same group. In step 6), the classification information weight is defined as the percentage of a category's document count relative to the total number of documents in the original data. If a document has a very high information weight, it can be considered a child's personal interest, and the classification group can be formed accordingly.
[0011] p=(<k1,w1>,<k2,w2>,Λ,<k n ,w n The set of personal interest preferences is represented by a vector (p is a vector of personal interest preferences). The weights of the same keywords are summed together to represent the final weight of the keyword. Then, a large initial interest intensity value is assigned to p. The number of documents associated with p is counted. A triple <p,ω,q> is constructed and inserted into the set of personal interest preferences p of the child. q represents the number of documents associated with p, and ω represents the intensity of the corresponding interest p.
[0012] Preferably, in step 7), the documents in the category are accumulated by time period. If the frequency of the documents appearing in each time period after the category is generated is flat and uniform, then the category is considered to be a personal interest of the user. An initial interest intensity value slightly greater than the threshold δ (δ is an interest intensity threshold. Interest vectors with an intensity greater than δ will play a role in the analysis, while interest vectors with an intensity less than δ will be ignored) is assigned. The number of associated documents is counted and added to p.
[0013] Preferably, the system is equipped with a database, which uses a MySQL database and is connected to the database through the Hibernate relational framework. The Hibernate method is to wrap something, that thing is JDBC, but the wrapping is not too serious. There are a total of 6 core interfaces of Hibernate. Users can operate on objects with longer durations, simplifying the process. The information query unit and report printing unit are combined with charts to display the required children's movement data information to users, and at the same time, the children's transportation data is converted into data information and output in the form of charts.
[0014] A data information management method based on children's activities includes: S1: The operator imports the relevant data information of children's activities and stores it in the database; S2: The children's activity data is parsed according to user behavior analysis to obtain the corresponding log database; S3: The log database is analyzed through a user preference model to obtain personal interest information; S4: The personality characteristics of children are obtained through the analysis and processing of personal interest information.
[0015] Compared with the prior art, the beneficial effects of the present invention are:
[0016] This invention classifies and analyzes imported data on children's activities, generates individual interest preference vectors from this data, and then analyzes and processes the data using a user preference model to obtain children's interest data. Combined with the analysis of children's activities, this allows for the inference of children's personality traits, thus enabling parents to scientifically cultivate their children's character and qualities based on their individual circumstances. Attached Figure Description
[0017] Figure 1This is a schematic diagram of the system framework of the present invention;
[0018] Figure 2 This is a schematic diagram of the method flow of the present invention. Detailed Implementation
[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0020] Example:
[0021] Please see Figures 1-2 As shown, a data information management system based on children's activities includes: a user management unit for managing user login information and performing permission authentication; an information query unit for retrieving data based on user input conditions and displaying it to the user; a report printing unit for generating data information reports that can be downloaded and printed by the user; and a data management unit for managing and refining the data information of children's activities.
[0022] In the user management unit, users log in through a login window. Based on the automatic restriction settings in the background, the system automatically identifies the login user's information and grants the corresponding system permissions when the username and password are correct. The user management unit manages the user's password and reminds the operator to change the password regularly.
[0023] The data management unit is divided into ordinary user operations and administrator operations based on the permission information obtained by users through the user management unit. Ordinary users can only modify their own personal information, change their passwords, and query information related to themselves and public information. Administrators can add and modify ordinary users' information, perform statistics and queries on various information, modify the information representation format, and clean up useless records.
[0024] The data management unit records children's activities based on user behavior analysis to obtain a corresponding log database. The log database is then analyzed and processed. During the analysis of the log database, the information in the log database is organized, mainly by compressing and merging children's activity logs. The compression and statistical process is as follows: First, the children's activity records are read one by one. Then, the frequency of the same type of activity is counted, and the most recent time is selected as the start time. This process continues until all records have been read. Finally, the compressed and statistically analyzed logs are saved.
[0025] In the data management unit, a user preference model is built to analyze data in the log database. The specific process for generating personal interest preference vectors is as follows: 1) Construct a keyword weight vector, calculate the value of this vector after Δd days, and select the keywords with the highest weights; 2) Repeat step 1) until the keyword weight vector generated each time within a certain long period of time has been calculated, and store the feature keywords selected in each calculation in a set ΔS; 3) Array the keywords in ΔS into (<k1, w1>, <k2, w2>, Λ, <k n ,w n >) Form a document-feature keyword matrix D, where the keyword weights use their initial weights; 4) Calculate the document similarity matrix M. sim =D×D T 5) If the similarity between documents exceeds a certain proportion, these documents are considered to belong to the same category. This can then be determined based on M. sim 6) Extract several document categories from the original data; 7) Analyze each document category to determine whether the category represents a child's long-term personal interest; 8) For categories where the information weight is not high, analyze the frequency of occurrence of each document in the category to determine whether it represents a long-term interest; 9) Delete all documents that are determined to belong to the user's personal interest from the original data.
[0026] The system is equipped with a database, which uses MySQL and connects to the database through the Hibernate relational framework. Hibernate methods wrap something, which is JDBC, but the wrapping is not too heavy. There are a total of 6 core interfaces of Hibernate. Users can operate on objects with longer execution times. It simplifies the process. The information query unit and report printing unit are combined with charts to display the required children's movement data information to users. At the same time, the children's transportation data is converted into data information and output in the form of charts.
[0027] The user preference model can be formally expressed as a triple P = (I, F, ψ), where:
[0028] I = {I (0) ,I (1) ,Λ,I (i) ,Λ,I (n)}
[0029] F = {F (1) ,F (2) ,Λ,F (i) ,Λ,F (n)}
[0030] ψ:I×F→I,(I (i+1) =ψ(I (i) ,F(i+1) ),i=0,1,Λ,n-1)
[0031] Where I represents the set of all states generated by the user preference model during the update process, F represents the set of newly acquired user feedback information, and ψ represents the feedback update function of the user preference model. (0) As the initial state of user preferences, I (i) For user preferences after i updates, F (i) This represents the feedback information obtained after the i-th update.
[0032] User behavior analysis analyzes which items best reflect children's actual interests based on their activity characteristics, thus avoiding useless records in subsequent processes. After obtaining new feedback from children, the individual interest preference vector p needs to be updated based on this feedback. For any unclassified individual interest vector p, the following operations are performed: 1. Calculate the similarity between p and existing document clusters using the cosine formula; 2. If the similarity between p and a document cluster is greater than a specified classification threshold, then p is merged into that document cluster; if there are multiple such document clusters, then p is merged into the cluster with the highest similarity; 3. If there is no document cluster with a similarity greater than the classification threshold, then p is added as a new document cluster to the cluster set; 4. Record the document cluster to which p belongs, and repeat the above operations with another individual interest vector until all individual interest vectors belong to a document cluster.
[0033] A data information management method based on children's activities includes: S1: The operator imports the relevant data information of children's activities and stores it in the database; S2: The children's activity data is parsed according to user behavior analysis to obtain the corresponding log database; S3: The log database is analyzed through a user preference model to obtain personal interest information; S4: The personality characteristics of children are obtained through the analysis and processing of personal interest information.
[0034] As can be seen from the above, this invention classifies and analyzes imported data on children's activities, generates personal interest preference vectors using this data, and then analyzes and processes the data using a user preference model to obtain children's interest data. Combined with the analysis of children's activities, it infers children's personality traits, thereby making it easier for parents to scientifically cultivate their children's personality traits based on their individual circumstances.
[0035] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A data information management system based on children's activities, characterized in that, include: The user management unit manages user login information and performs permission authentication; the information query unit retrieves data based on user input and displays it to the user; the report printing unit generates data information reports that can be downloaded and printed by the user; and the data management unit manages and refines data information related to children's activities.
2. The data information management system based on children's activities according to claim 1, characterized in that: In the user management unit, users log in through a login window. Based on the automatic restriction settings in the background, the system automatically identifies the login user's information and grants the corresponding system permissions when the username and password are correct. The user management unit manages the user's password and reminds the operator to change the password regularly.
3. A data information management system based on early childhood activities according to claim 1, characterized in that: The data management unit is divided into ordinary user operations and administrator operations based on the permission information obtained by the user through the user management unit. Ordinary users can only modify their own personal information, change their passwords, and query information related to themselves and public information. Administrators can add and modify ordinary users' information, perform statistics and queries on various information, modify the information representation format, and clean up useless records.
4. A data information management system based on children's activities according to claim 1, characterized in that: The data management unit records children's activities based on user behavior analysis to obtain a corresponding log database, and then analyzes and processes the log database. The process of organizing the log database information during the analysis mainly involves compressing and merging children's activity logs. The compression and statistical process is as follows: First, read each children's activity record one by one. Then, count the frequency of the same type of activity, select the most recent time as the start time, and continue until all records have been read. Finally, save the compressed and statistically analyzed logs.
5. A data information management system based on early childhood activities according to claim 1 or 4, characterized in that: The data management unit constructs a user preference model to analyze the data in the log database. The specific process for generating personal interest preference vectors is as follows: 1) Construct a keyword weight vector, calculate the value of this vector after Δd days, and select the keywords with the highest weights; 2) Repeat step 1) until the keyword weight vector generated each time within a certain long period of time has been calculated, and store the feature keywords selected in each calculation in a set ΔS; 3) Array the keywords in ΔS into sets (<k1, w1>, <k2, w2>, Λ, <k n ,w n >) Form a document-feature keyword matrix D, where the keyword weights use their initial weights; 4) Calculate the document similarity matrix M. sim =D×D T 5) If the similarity between documents exceeds a certain proportion, these documents are considered to belong to the same category. This can then be determined based on M. sim 6) Extract several document categories from the raw data; 7) Analyze each document category to determine whether the category represents a long-term personal interest of the child; 8) For categories where the information weight is not high, analyze the frequency of occurrence of each document in the category to determine whether it represents a long-term interest. 8) Remove all documents from the original data that were determined to belong to the user's personal interests.
6. A data information management system based on early childhood activities according to claim 1, characterized in that: The system is equipped with a database, which uses MySQL and is connected to the database through the Hibernate relational framework. The Hibernate method wraps something, which is JDBC, but the wrapping is not too heavy. There are a total of 6 core interfaces of Hibernate. Users can operate on objects with longer durations, simplifying the process. The information query unit and report printing unit are combined with charts to display the required children's movement data information to users. At the same time, the children's transportation data is converted into data information and output in chart form.
7. The data information management system based on children's activities according to claim 1 further discloses a data information management method based on children's activities, characterized in that, include: S1: The operator imports relevant data information about children's activities and stores it in the database; S2: Parse the children's activity data based on user behavior analysis to obtain the corresponding log database; S3: Analyze the log database through the user preference model to obtain personal interest information; S4: Analyze and process the personal interest information to obtain the children's personality characteristics.