WO2015176302A1

WO2015176302A1 - System and method for child characteristics identification and potential development and assessment

Info

Publication number: WO2015176302A1
Application number: PCT/CN2014/078248
Authority: WO
Inventors: 施其洲
Original assignee: 施京
Priority date: 2014-05-23
Filing date: 2014-05-23
Publication date: 2015-11-26
Also published as: CN105518683A; CN105518683B

Abstract

A system and method for child characteristics identification and potential development and assessment; the system comprises a child characteristics identification module (100a), a cultivation and training module (100b), a child characteristics database (100c), a talent characteristics database (100d) and a comparison and assessment module (100e); the child characteristics identification module (100a) is used to receive the collected child characteristics individual data via a network, to acquire the sample data or history data in the child characteristics database (100c) by accessing the child characteristics database (100c), and to analyze and compare with the collected individual data; the cultivation and training module (100b) is used to receive child characteristics analysis result data coming from the child characteristics identification module, and to match a corresponding training solution to conduct intensive training of advantageous characteristics and remedial training of weak; and the comparison and assessment module (100e) is used to receive the analysis result data coming from the cultivation and training module, to compare the analysis result data with the talent characteristics data obtained from the talent characteristics database (100d), and to assess to obtain an analysis result of child talent characteristics, and potential development direction and probability.

Description

System and method for early childhood feature recognition and potential development assessment

The present invention generally relates to systems and methods for use in the field of early childhood education, particularly in the early identification and development of potential development of young children. Background technique

The definition of a child usually means that the child is over 1 year old and has passed the infancy. In children, both in physical and neurodevelopment, and in mental and intellectual development, they have different details and developments than infants, and their independent consciousness is increasing. In the early childhood education, the development of children's emotional development, sensory development, thinking mode training, memory training and other aspects have important significance and role for the future development of the individual.

Current early childhood education and characterization are limited to being discovered and identified empirically by experienced educators. However, the number of such personnel is extremely limited compared to the number of young children, and the accumulated experience cannot be quantified and disseminated. As more and more parents pay more attention to early childhood education, how to identify early childhood characteristics and carry out targeted superior feature enhancement and weak feature compensation becomes an urgent problem to be solved.

At present, the US State Early Childhood Education Center represented by California, USA, has developed a number of early childhood characteristics index systems based on years of theoretical research and practice. Although their index grading and indicator systems are different, they can As an important reference for the collection of early childhood characteristics. However, these indicators still have the following problems:

1. The processing of group children's feature sets has not yet been performed.

2. Childhood feature collection is in manual (artificial) acquisition methods, and electronic data acquisition methods have not been used.

3. After collecting the characteristics of individual children, they can evaluate their individual performance, but there is no way to identify their dominant and weak features. The characteristics of multiple bodies are often the result of innate formation. They can be cultivated and trained through the day after tomorrow, including kindergarten courses, games, outdoor activities, and parental guidance. In certain educational environments, the superior characteristics of young children can be strengthened. Prominence will also make up for the shortcomings of its weak features, although the weak features will not change into their strong features. 4. Excellent talents, including politicians, scientists, engineers, entrepreneurs, professionals including doctors, lawyers, athletes, artists including musicians, painters and dancers. Their outstanding characteristics are obvious, but they all stay. In the qualitative description, there is no outstanding talent feature set and feature database.

5. The children's words and deeds feature set and the excellent talent feature set have not been integrated into a feature comparison system, so that the children's words and deeds feature set and the excellent talent feature set cannot be compared in one system by the same standard. "What kind of work is suitable for people who are characterized by words and deeds" has become a consensus and an important law. Individual children (0-5 years old) are detected to be the important basis for the performance of their adulthood characteristics, and are also an important reference for a certain occupation and job. The relationship between these two relationships has not yet been linked, and there is no potential development direction and possibility from the characteristics of young children that indicate their potential for future success.

Big data is a feature of the development of the Internet to the present stage. Under the technological innovation represented by cloud computing, data that was difficult to collect and use is easy to collect and be used, and the data is continuously developed and excavated. Big data will gradually create more value for humans. Compared with traditional data warehouse applications, big data analysis has the characteristics of large data volume and complex query analysis. Therefore, how to use big data instead of relying solely on individual experience to identify the dominant and weak characteristics of young children, strengthen their superior characteristics, and make up for the shortcomings of weak characteristics, and provide parents, kindergarten teachers and kindergarten managers with information on the potential of individual infants. The direction of development and its possibilities have become an important research direction of big data in the early stage of early childhood feature recognition.

Therefore, there is a need for a system and method for using network technology and big data to evaluate early identification and potential development of young children, so as to use big data to identify the dominant and weak features of young children, strengthen their superior characteristics, and make up for the shortcomings of weak features. Provide parents, kindergarten teachers and kindergarten managers with directions and possibilities for the development of young children's individual talent potential. Summary of the invention

An object of the present invention is to provide a child identification and potential development evaluation system, the system comprising a child feature recognition module, a training training module, a child feature database, a talent feature database, and a comparison evaluation module, wherein the child feature recognition module And configured to receive the collected infant child individual data through a network, and obtain sample or historical data therein by accessing the child feature database, compare and analyze the collected individual data, and transmit the analysis result to the training training. The module performs processing; the culture training module is configured to receive data of the child characteristic analysis result from the child feature recognition module, and match the corresponding training program, thereby performing intensive training of the dominant feature and repair training of the vulnerable feature; The child feature database is used for storing and providing child child characteristic data, including characteristic data of the child group and historical data of the child character; the talent feature database is used for storing and providing talent characteristic data, including talent definition data, talent classification data, and specific types. The feature data of the talent, the talent feature group data, and the talent feature history data; the comparison evaluation module is configured to receive the analysis result data from the training training module, and compare the talent feature data obtained from the talent feature database, Therefore, the analysis results of the talent characteristics, potential development direction and probability of the child are evaluated, and the analysis results are transmitted through the network for display.

Preferably, the child feature recognition module receives the child feature individual data collected by the user terminal through the network.

Preferably, the user terminal is selected from at least one of a desktop computer, a laptop computer, a smart phone, a personal digital assistant, a tablet computer, a game machine, and a multifunctional mobile terminal.

Preferably, the network is selected from the group consisting of Zigbee, Wi Fi or WLAN, GPRS, cellular network, GSM network, 3G network, LTE network or CDMA network, Bluetooth, NFC:, infrared, ultrasonic, Wi s s USB, At least one of RFID.

Preferably, the child feature recognition module inputs the collected child feature individual data into the child feature database as historical data of the child feature database.

Preferably, the system provides a third party interface to obtain the child care feature database and the talent profile database from a third party.

Preferably, the child feature recognition module comprises an individual data collection module, a database access and control module, and a comparison analysis module, wherein the individual data collection module is configured to collect the collected individual data of the child, and access the database through the database. And the child care feature data obtained by the control module from the child feature database access is input to the comparison analysis module for calculation and comparison analysis, and the obtained result is output to the training training module.

Preferably, the child individual characteristic data is divided into a first level indicator and a second level indicator, and each of the first level indicators comprises a plurality of second level indicators and is comprehensively calculated from the scores of the second level indicators.

Preferably, different secondary indicators under the same primary indicator have different weights. Preferably, the dominant and weak features of the infant individual are obtained by comparing the population average eigenvalue and the population TOP value with a certain infant individual eigenvalue.

Preferably, the training module includes an individual feature data collection module, an advantage feature training module, a weak feature training module, and a result feedback module, and the individual feature data collection module is configured to collect the child individual sent from the child feature recognition module. Identifying the data, selecting the dominant feature and the weak feature, respectively, and sending to the dominant feature training module and the vulnerable feature training module; the dominant feature training module is configured to generate a recommended training method to strengthen the dominant feature of the child; The weak feature training module is configured to generate a recommended training method to compensate for the weak features of the child; the result feedback module is configured to use the dominant feature and the weak The results of the feature training are evaluated periodically and the results are output to a comparative evaluation module for subsequent evaluation.

Preferably, the comparison evaluation module includes a consistency comparability determination module, an individual talent comparison module, an evaluation module, and a result output module, and the consistency comparability determination module is configured to verify consistency and comparability of the data to be compared, If it is determined that the data inconsistency cannot be compared, the result output module feeds back to the child feature recognition module to re-collect and process the data; the individual talent comparison module is used for the child individual characteristic data and the talented person The talent feature data collected by the feature database is compared between the two; the evaluation module is used to determine the probability of developing the potential development of the child.

Preferably, the individual talent comparison module compares the child individual characteristic data with the talent characteristic data by using an enumeration method or an exhaustive method.

Preferably, the evaluation module determines the probability of developing a child's potential development direction according to the following method: a) determining the number of dominant characteristics of the child; b) determining whether there is a skill feature, if not entering step c, if yes, proceeding to step bl The probability of developing a child's individual potential development direction is set to 50%, entering step c; c) determining the initial probability of the development potential of the talent type; d) weighting the initial value of the talent potential development potential determined by the step.

The system and method for utilizing network technology and big data to evaluate early identification and potential development of young children according to the present invention can use big data to identify the dominant and weak features of the child, strengthen their superior characteristics, and make up for the disadvantages of the weak features. Parents, preschool teachers, and kindergarten managers provide directions and possibilities for the development of young children's potential.

It is to be understood that the foregoing general descriptions DRAWINGS

Further objects, features, and advantages of the present invention will be made apparent by the following description of the embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a schematic block diagram showing a system for early childhood feature recognition and potential development evaluation in accordance with the present invention.

Fig. 2 shows a process in Fig. 2 showing, in a modular manner, data access and interaction between the infant signature recognition and potential development evaluation system 200 and the user terminal 210 in accordance with the present invention.

Figure 3 illustrates the workflow of the toddler feature recognition module in accordance with the present invention.

Figure 4 is a block diagram showing the structure of the training training module according to the present invention in a modular manner. Figure 5 is a block diagram showing the structure of the comparative evaluation module according to the present invention in a modular manner. Figure 6 shows the determination of the potential of the child in the evaluation module. Calculation method of development direction probability Flow chart.

Figure 7 schematically illustrates a user interface presented by a user terminal. detailed description

The objects and functions of the present invention and methods for achieving the objects and functions will be clarified by reference to the exemplary embodiments. However, the invention is not limited to the exemplary embodiments disclosed below; it can be implemented in various forms. The essence of the description is merely to assist those skilled in the relevant art to understand the specific details of the invention.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the figures, the same reference numerals indicate the same or similar components, or the same or similar steps.

The system and method for developing children's feature recognition and talent potential according to the present invention can integrate the data of the child group and the database of talent characteristics based on the data mining method and the optimization training method by comparing methods, clustering, classification, etc. Identify the dominant and weak characteristics of the child's individual, strengthen its superior characteristics, and make up for the shortcomings of the weak features, and give parents, kindergarten teachers and kindergarten managers the development direction and possibility of the individual's individual talent potential. The input of the system is the characteristics of the children's group words and deeds and the characteristics of individual infants, training and library, and the database of talent characteristics. The output is the advantages and disadvantages of the children's individual and the development direction and possibility of their potential. Furthermore, the training and training library of the system can provide teaching aids corresponding to the matching of the child's characteristics, strengthen the training of the child's weak features, and strengthen and improve the dominant features. The system establishes mathematical models by applying probability and mathematical statistics theory, integration theory, classification and classification theory, discovers the connotation of children's individual characteristic data from children's group characteristic data and talent characteristics data, and explores useful information of children's individual growth, providing parents. , early childhood teachers, early childhood education institutions and management parties for important reference. System Overview

Figure 1 schematically illustrates a system for infant signature recognition and potential development assessment in accordance with an embodiment of the present invention. The child feature recognition and potential development evaluation system 100 communicates and exchanges data with at least one user terminal 102 via the network 101. For illustrative purposes, Figure 1 shows, by way of example only, four user terminals 102a, 102b, 102c and 102n. However, it will be appreciated that in other embodiments, system 100 can interface with and communicate with more user terminals 102.

The user terminal 102 is used for collecting information, for example, collecting child characteristic data, processing the data, and transmitting the data to the child feature recognition and potential development evaluation system 100 through the network 101, and then The evaluation results sent back from the infant signature recognition and potential development assessment system 100 are received via the network 101, preferably presented to the user in a graphical manner. Preferably, the user terminal 102 can also perform partial data processing functions, such as sorting, filtering, pre-sorting, etc. the data, or performing the function of data statistics or calculation of the cartridge. The user of the user terminal 102 may be a parent of a child, a guardian, a kindergarten teacher, a user of a feature evaluation institution, or the like.

User terminal 102 can be a desktop computer, laptop computer, smart phone, personal digital assistant (PDA), tablet, gaming machine, multi-function mobile terminal, or any other device that includes computing and data communication capabilities. User terminal 102 can include an interface application, such as a web browser or a custom application (app), for bi-directional communication with web-enabled applications, thereby allowing a user to interact with system 100 in the form of an interface application. A user who installs a system software application implementing the method of the present invention can log in to the server 106 via the network 103 for various interactive functions such as information uploading, downloading, querying, and analyzing. User terminal 102 can receive input from a user and can present an output, and thus user terminal 102 also includes an I/O interface (input/output interface) that can receive one or more inputs and present an output. For example, the input interface can include one or more of a keyboard, a mouse, a joystick, a trackball, a touchpad, a touchscreen, a stylus, and a microphone. In addition, an output can be presented through the output interface to output a user's control operation instructions or feedback information from other users. The output interface includes one or more of a display screen, one or more speakers, and a tactile interface.

The network 101 may be a wired network or a wireless network, for example, including a local area network ("LAN") such as an intranet and a wide area network ("WAN") such as the Internet. Network 101 can be configured to support the transfer of information in a variety of protocol setup formats. Additionally, network 101 can be a public network, a private network, or a combination thereof. Network 101 may also be implemented using any one or more types of physical media, including wired communication paths and wireless communication paths associated with multiple service providers. Wireless communication methods such as Zigbee, WiFi or WLAN, GPRS, cellular network, GSM network, 3G network, LTE network or CDMA network, at least one of Bluetooth, NFC, infrared, ultrasonic, Wireless USB, RFID, and the like.

Preferably, in order to ensure the security of the data interaction, a firewall (not shown) may be established between the network 101 and the child feature recognition and potential development evaluation system 100. When the user accesses the system 100 through the public network or other unsecure network through the user terminal 102, in order to ensure data security, data security can be achieved by a firewall that secures the network between the user terminal 102 and the system 100. The firewall can be implemented by a combination of software and hardware devices. The firewall can set functional modules such as service access rules, verification tools, packet filtering, and application gateways to monitor and filter the system 100 and the user terminal 102. Data flowing between.

As shown in Fig. 1, the infant feature recognition and potential development evaluation system 100 according to the present invention includes a child feature recognition module 100a, a training training module 100b, a child feature database 100c, a talent feature database 100d, and a comparison evaluation module 100e.

The child feature recognition module 100a is configured to receive the child feature individual data collected from the user terminal 102 through the network 101, perform data sorting, aggregation, classification, statistics, calculation, and the like, and obtain the sample or history by accessing the child feature database 100c. The data is compared and analyzed with the collected individual data, for example, the analysis result data of the child's dominant characteristics and the weak features are obtained, and the analysis result is transmitted to the training training module 100b for subsequent processing. The child feature individual data collected by the child feature recognition module 100a may also be input to the child feature database 100c as historical data of the child feature database 100c after processing.

The training module 100b is configured to receive the child character analysis result data sent from the child character recognition module 100a, classify, filter, compare, and find and match the corresponding training program according to the child character analysis result data, thereby performing the dominant feature. Intensive training and remedial training of weak features. The training training module 100b can feed back to the child feature recognition module 100a after training to measure or evaluate the training effect, thereby dynamically adjusting the training program, and re-analyze the child characteristics according to the training result feedback, and transmit the analysis result to the comparative evaluation. The module performs subsequent evaluations.

The child care feature database 100c is for storing and providing child care feature data, including but not limited to data collected by the user terminal 102 and processed by the user terminal 102 or the child feature recognition module 100a. Alternatively, the system according to the present invention may also provide a third party interface to obtain the child care feature data from a third party.

The talent characteristics database 100d is used to store and provide talent characteristics data, including but not limited to talent definition data, talent classification data, characteristic data of specific types of talents, talent characteristics group data, talent characteristics historical data, and the like. The talent profile database 100d may be built into the system 100 in accordance with the present invention or collected from external data. Alternatively, the system according to the present invention may also provide a third party interface to obtain the child care feature data from a third party.

The comparison evaluation module 100e is configured to receive the analysis result data from the training training module 100b, and compare with the talent characteristic data acquired from the talent characteristic database 100d, thereby evaluating the talent characteristics, potential development direction and probability of the child. The result of the analysis is equalized, and the analysis result is transmitted to the user terminal 102 through the network for display. Preferably, with a chart The way to show the results.

Figure 2 illustrates, in a modular manner, the process of data access and interaction between the Infant Feature Recognition and Potential Development Assessment System 200 and the User Terminal 210 in accordance with the present invention. As shown in FIG. 2, 210 interacts with system 200 for session and data.

In system 200, infant identification module 200a includes an individual data collection module 201, a database access and control module 202, and a comparison analysis module 203. The individual data collection module 201 is configured to collect the child individual data collected from the user terminal 210, and input the child child characteristic data accessed through the database access and control module 202 from the child character database 200c to the comparison analysis module 203 for calculation and comparison. Analysis, the results are output to the training module.

The user terminal 210 includes at least a data collection module 211, a data processing module 212, and an analysis report module 213. Other information modules (not shown) for displaying non-interactive information (such as system settings, etc.) may also be included. The data collection module 211 is used to collect individual data of the child, for example, an observation method, a questionnaire method or a portfolio method can be used to collect the individual data of the child. The collected data can be initially processed by data processing module 212 and transmitted to individual data collection module 201 in infant signature module 200a in system 200. The analysis report module 213 in the user terminal 210 will receive the analysis results from the system 200 presented on the user interface.

The infant individual feature data according to the present invention is preferably collected in a multi-level manner, and an exemplary infant individual data feature collection table is given in Table 1 below. Primary indicator

Level 1 indicator Level 2 indicator (weight) Strong (5) Stronger (4) Fair (3) Weak (2) Very weak (1) Score

Richness ( 0. 3 )

Emotional stability (0. 3)

Positive / negative ( 0. 4 )

Extensive (0.33)

Hobbies and Persistence ( 0. 3 )

Curiosity (0. 4)

Willpower independence (0. 2)

Decisiveness (0. 2) Self-control (0.2) Perseverance (0.4) Creativity creativity

Environmental Adaptability (0.3) Self-awareness Self-esteem (0.5) Tolerance (0.2) Analysis (0.3)

Method (0.4) ability

Results ( 0.3 ) Observing the degree of detail ( 0.5 ) Observing judgment

Judgment correctness (0.5) Memory speed (0·4) Memory size (0.4) Memory

Length of memory time (0.2) Intentionality (0.2) Imagination Reality (0.2) Richness (0.6) Reality (0.2) Logic (0.3) Thinking ability

Abstract logical thinking (0.5) skills (0.5) hands-on operation

Purpose ( 0.2 ) ability

Time Mastery (0.3) Like to make friends (0.5) Social interaction

Communication skills (0.3) ability

Friendly (0.2) eloquence (0.6) language ability

Wencai ( 0.4 ) Concentration time ( 0.7 ) Attention

Distribution ability (0.3) Attraction (0. 3)

Leadership, command and coordination

(0. 7)

Like to learn, like to learn

Do things seriously

Do things seriously and professionally

industry

Note: In the table, strong (5) until very weak (1) indicates the degree of score of each indicator.

Table 1 Infant characteristic index system According to an embodiment shown in Table 1, the infant characteristics index can be divided into two levels, one level has 17 characteristics, and the second level index has 42 characteristics. As shown in Table 1, each primary indicator can contain several secondary indicators, and different secondary indicators under the same primary indicator can have different weights. Table 1 gives an exemplary set of weighting factors as a reference. The weight of the primary indicator is determined by the pairwise comparison method. Each secondary indicator is divided into 5 levels, V level (strong) scores 5 points, grade IV (strong) scores 4 points, grade III (general) scores 3 points, and grade II (weak) scores 2 points, level I (very weak) scored 1 point, and the scores of the secondary indicators can be collected by, for example, questionnaires or scoring.

Take the emotional characteristics under the first level indicator as an example to illustrate the meaning of the indicator. Emotion refers to attitudes and experiences caused by satisfying their own needs, such as happiness, happiness, joy, satisfaction, comfort, etc.; negative attitudes and experiences caused by violation of their own wishes, such as anger, sorrow, sadness, hatred, trouble, despair, etc. . Emotion refers to the reflection of a stable and sustained attitude, such as responsibility, obligation, morality, aesthetics and so on. Optimistic, attentive, calm, honest, enthusiastic, emotionally stable, lively and open, exposed. For example, for the emotional world of 3-4 year olds, most of the children are very lively and happy, and they are easily excited. But sometimes they cry and scream, and suddenly they will break and laugh, and they will change. So they are easy to change, easy to change, and easy to expose. Emotions are characterized by richness, stability, and positive/negative.

According to an embodiment of the invention, the indicator value of the secondary indicator is between 1-5. By calculating the index value of the weighted secondary indicator, the indicator value of the primary indicator can be obtained. For example, for the first-level indicator "emotion", the second-level indicator "richness (0.3) has 4 points, "stability (0.3") has 5 points, "positive/negative (0.44), Get 3 points, then "Emotion, the weighted score of the indicator is:

4 0. 3+5 0. 3+3 0, 4=3. 9

The conclusion is that the child's emotional evaluation is: Close to "stronger."

Similarly, in the infant character database according to the present invention, similar multi-levels can also be used. The form of the indicator stores the child's characteristic data.

Figure 3 illustrates the workflow of the toddler feature recognition module in accordance with the present invention. As shown in FIG. 3, in step 301, the child feature recognition module collects and summarizes the child's individual feature data from the user terminal. For example, several secondary indicator data of the above embodiment.

At step 302, conversion processing of the feature data is performed. For example, in an embodiment having two levels of indicator data, conversion processing of the secondary indicator data to the primary indicator data is performed.

In step 303, the selection of the feature data is performed. For the converted feature data, if it is found that there is obvious data abnormality, the result can be directly judged.

In step 304, if it is determined that some of the features are abnormal, the conclusion that the child individual is a supernormal child can be obtained, and the abnormal features are directly used as the dominant features of the child, and then the process proceeds to step 308. Otherwise, proceed to step 305. Referring to the above embodiment of the two-level indicator characteristic data, the data of the extraordinary child can be obtained by directly judging whether several of the indicators and the data of the indicator exceed the value of the value. For example, you can refer to the language ability, attention, thinking ability, imagination, and learning ability in the first level indicators. The choice of these indicators can be defined by themselves and can be selected according to the criteria judged.

In step 305, if no feature abnormality is found, the data of the child feature database is compared and judged.

In step 306, the data of the child feature database is collated and counted. The collation of the database data can take the method of averaging, for example, taking thousands of data for the mean calculation.

In step 307, the database data is compared to the collected infant individual data. The purpose of comparison is to derive the dominant and weak features of individual data. According to one embodiment, for example, the population average feature value and the population TOP value may be compared to an individual feature value to obtain the dominant and weak features of the individual. The dominant characteristics of an individual reflect that this individual child exhibits a "strong" and dominant characteristic over most children of the same age. The characteristic value of this aspect is Τ0Ρ5% or more than 10% of the group. The weakness of an individual reflects that the individual child exhibits a "weak" and inferiority characteristic for most children of the same age. The characteristic value of this aspect is the last 5% of the group.

At step 308, the result is output and the method ends.

Figure 4 shows, in a modular manner, a block diagram of the architecture of a culture training module in accordance with the present invention. As shown in FIG. 4, in the system 400, the training module 400b includes an individual feature data collection module 401, an advantage feature training module 402, a weak feature training module 403, and a result feedback module 404. Cultivating the training module 4 QOb is to analyze and characterize the features, so as to generate specific training methods according to the algorithm, and the preschool teachers and parents can Intensive training to compensate for weak features. In general, if the dominant characteristics cannot be continuously strengthened, it is easy to lose its advantage. If the shortcomings of the weak features are not compensated in time, the weak features tend to be weaker, which is not conducive to the overall development of young children, but the weak features Insufficient and then make up for it will never become a dominant feature of young children.

The individual feature data collection module 4 Q 1 is configured to collect the infant individual identification data sent from the infant feature recognition module, select the dominant feature and the weak feature, and send the superior feature training module 402 and the vulnerable feature training module 403 respectively. The superior feature training module 402 is used to generate a recommended training method to strengthen the superior characteristics of the child, and to maintain and enhance the superior features, so that the original advantages are more prominent, and the method of recommending the training method according to different dominant features can be obtained. To generate training recommendation information, such as training course recommendation, training schedule scheduling, and so on. The Weak Feature Training Module 403 is used to generate recommended training methods to compensate for the weak features of the child and to compensate for the weakness of the vulnerable features. For example, the training recommendation information may be generated in a manner that the training method is recommended according to different weak features.

The recommendations of the training course can be matched based on the superior characteristics, weak features and other basic information of the children. For example, different courses, games, indoor and outdoor activities can be arranged according to the physiological and psychological characteristics of children of different ages. According to one embodiment, the recommended courses, games, and activities may be based on the above multiple primary indicator characteristics, namely, emotion, hobbies, willpower, creativity, self-awareness, problem solving ability, observation and judgment ability, memory, imagination, thinking 17 areas, such as ability, hands-on ability, social interaction ability, language ability, attention, leadership, like to learn, and work hard, are recommended.

The result feedback module 404 is configured to periodically evaluate the results based on the dominant feature and the weak feature training, and output the result to the comparison evaluation module for subsequent evaluation. Regularly observe and measure the dominant and weak characteristics of individual young children, and evaluate the enhancement of the dominant characteristics and the effect of the inadequacy of the weak features. Based on the effects of reinforcement and compensation, we will formulate the next measures to strengthen (compensate) and carry out retraining. The measurement result of the result feedback module 404 can also be re-evaluated by the return value of the child feature recognition module, and then returned to the comparison evaluation module according to the evaluation result to perform a new round of training and formulation adjustment of the dominant feature and the weak feature.

Figure 5 shows, in a modular manner, a block diagram of the architecture of a comparative evaluation module in accordance with the present invention. As shown in FIG. 5, the comparison evaluation module 500e includes a consistency comparability determination module 501, an individual talent comparison module 502, an evaluation module 503, and a result output module 504 in the system 500. The comparison evaluation module 500e is configured to receive the analysis result data from the culture training module, first determine the consistency and comparability, and then obtain the data from the talent feature database l Q Od The extracted talent characteristic data is compared, so that the analysis result of the talent characteristics, potential development direction and probability of the child is evaluated in the evaluation module 503, and the analysis result is transmitted to the user terminal through the network through the result output module 504 for display. Preferably, the results are presented graphically.

The consistency comparability determination module 501 is used to verify the consistency and comparability of the data to be compared in order to provide a consistent and comparable data base for subsequent evaluation modules. Consistency means that the child's character system and the talent system are consistent, that is, the consistency between the child's individual feature data collected by the previous module and the talent feature data collected from the talent feature database, so that both have Reasonable comparability. This consistency is manifested in two aspects. First, the feature set size of the two systems is basically the same, that is, the number of features in the two systems is basically the same, and the second is the same as the feature. If it is determined that the data is inconsistent and cannot be compared, the result output module 504 can feed back to the child feature recognition module to re-collect and process the data.

The individual talent comparison module 502 is used to compare the child's individual feature data with the talent profile data collected from the talent profile database. In the previous infant identification module, the superior characteristics and weak features of the individual children have been identified. After the training module is strengthened, the superior characteristics of the children are more obvious and more prominent. According to the present invention, based on the feature set of the classified talents given in the talent feature database, the "enumeration method" or the "exhaustive method" is used to compare the superior feature set of the individual child with the various talent feature sets. The most characteristic ratio of the two is the development direction of the child's potential, and in the evaluation module 503, the probability ratio (probability) of the potential development direction is evaluated according to the feature matching number ratio.

The individual talent comparison module 502 also classifies the talent feature data obtained from the talent feature database, including the determination of the common definition of the talent, the rational classification of the talent, and the determination of the talent feature data. According to an embodiment of the present invention, talents are divided into seven categories: academic talents, political talents, management talents, engineering talents, professional talents, and cultural and sports talents. Academic talents can be scientists, including natural scientists, social scientists, such as physicists, chemists, mathematicians, philosophers, jurists, linguists, and researchers; politicians can include politicians, party and government practitioners; Talents can include entrepreneurs, senior managers, managers of industrial and commercial enterprises; engineering talents refer to engineers engaged in design, planning, decision-making, etc., and use mature technology and intelligence to transform design, planning and decision-making into physical form. The production of product talents, such as engineers, engineering and technical personnel; professional skills can include doctors, lawyers and talents; cultural and sports talents can include painters, musicians, dancers and other artistic talents and all kinds of sports talents. There are more than 4 similarities or similarities in the characteristics of various types of talents. The similarities between these features can be found through the method of two-two comparison and induction. The purpose is to facilitate the abstraction of more consistent characteristics and finally sort out the characteristics of talents. Sort and sort the data of the talent characteristics database.

Table 2 below shows a table of correspondence between child characteristics and talent characteristics in accordance with one embodiment of the present invention.

Childhood characteristics and talent characteristics correspondence table Child characteristics Professional skills talents

(number) doctor lawyer athlete musician painter dancer emotional stability

Love, Responsibility, Integrity, Emotional Stability, Emotions (1) Emotional Stability, Emotional Stability, Impulse Inspiration

Heart, respect for the heart, emotional stability, rich emotions, sense of responsibility

Interest, love, body and music, good painting skills, strong aesthetic ability Good, a skill is particularly good, the appreciation of music to the beauty, the sensitivity of the tax, the length (2) the sense of rhythm, there are congenital body movements, intelligence, strong force, strong willpower, strong willpower

Willpower

Strong willpower, strong willpower, perseverance, willpower (3)

Self confidence

Creativity (4) Creativity Creativity Creativity Self-awareness

Strong self-control ability, strong self-control ability (5)

Solve problems, music, language, language

Ability (6) coordination observation and judgment

Observing power, strong judgment, strong observation

Ability (7)

Music memory

Memory (8) strong memory, strong memory

Current ability

Rich imagination, rich imagination, rich imagination, imagination (9) imagination

Ability, ability, thinking ability

logical thinking

(10) Good logical thinking

Hands-on operation

Ability (11)

Social skills teamwork interpersonal skills

(12) Strong communication skills

language skills

Language ability

(13)

Attention

Attention, concentration, attention (14)

leadership

Overall planning

(15)

Like to study, work hard, do things

Study hard, cultural cultivation

(16) Serious

Dedication, career

Do things seriously

Strong heart

Dedication (17)

Heart

Table 2 (continued) Table of correspondence between children's characteristics and talent characteristics According to Table 2, the relationship between talent characteristics and child characteristics is illustrated by taking academic talents as an example. Academic talents include natural scientists, social scientists, such as physicists, chemists, mathematicians, philosophers, economists, jurists, linguists, and so on. Their characteristics are as follows. (1) Insight is also a judgment, judgment on scientific issues (such as the choice of evaluation of various experimental programs or product technologies). Corresponding to the characteristics of young children (7).

(2) Memory, master the accuracy and memory speed of basic data and common sense related to research work. Corresponding to the characteristics of young children (8).

(3) Imagination, good at conceiving new hypotheses, models, structure diagrams, etc. through unfettered free imagination on the basis of a small number of scientific facts. Corresponding to the characteristics of young children (9).

(4) Observing power, good at careful observation and finding problems that are generally difficult to find or easy to ignore. Corresponding to the characteristics of young children (7).

(5) Experimental ability, hands-on ability and practical operation ability. Corresponding to the characteristics of young children (11). (6) Innovative ability, including inductive reasoning ability and deductive reasoning ability, can sum up the scientific laws of formal orders from a large number of seemingly unrelated facts and phenomena; can quickly and spontaneously proceed from existing scientific discoveries Innovative and unique scientific predictions. Corresponding to the characteristics of children (4).

(7) Collaboration ability, people who are good at uniting different academic viewpoints work together, are strict with self-discipline, and treat others with generosity. Corresponding to the characteristics of young children (12).

(8) Ability to social interaction, good at communicating with social organizations and individuals, actively seeking social understanding, support and sponsorship in the field of study, and establishing a wide network of interpersonal relationships. Corresponding to the characteristics of young children (12).

In the individual talent comparison module 502, according to the type, score and number of the individual characteristics of the collected child, the corresponding talent characteristics can be transformed by means of table lookup. If a child with a very small number of abnormalities is found, the individual talent comparison module 502 can also directly output the abnormal result to the result output module 504. For example, the anomaly results can be transferred to a more specialized accreditation body for subsequent identification.

In the evaluation module 503, the probability of developing a child's potential development direction is determined, and the calculation method is as shown in the flowchart of FIG. 6:

Step 601, determining the number M of dominant characteristics of the child, wherein M < N, N is the total number of child characteristics. According to one embodiment of the invention, N is the total number of primary indicator features, N=17.

Step 602, determining whether the child has a "long skill" feature, if not entering step 604, if yes, proceeding to step 603, setting an initial probability of developing toward the length of the skill is 50%, such as sports and music, and then entering Step 604;

Step 604, determining an initial probability of the talent type development potential. According to an embodiment of the present invention, assuming that there is a R person type in the talent feature database, r = 1, 2, ..., R. The characteristics of all types of talents are compared with the number of children's dominant characteristics, M, and the individual's individual advantages. The number of coincidences of the feature numbers is Qr, r=l, 2,..., R. The selection of the largest 3 of the R Qr is defined as the corresponding talent type, as the development direction of the child's potential, and the probability of development is calculated. Assuming that Ql, Q2, and Q3 are the largest, second largest, and second largest, respectively, then Ql /M, Q2/M, and Q3/M are the initial values of the potential development probability of the talent type corresponding to Ql, Q2, and Q3, where r= l, 2, R, ie Fr = Qr/M, where r = l, 2, ..., R.

Step 605: Perform a weight correction on the initial value of the probability potential of the talent type development. It is assumed that one type of talent is allowed to have at most one characteristic special item that is most distinct from other talent types. According to the present invention, the special item weight is given W = 0.15. In order to make the probability meet the requirements, after considering the special item weight, we should subtract 0.1 as the correction.

The probability of developing individual talent type potential:

Pl=Fl+Wl-0. 1

P2=F2+W2-0. 1

P3=F3+W3-0. 1

Here, Wl, W2, and W3 can be set to be the same, for example, W1 = W2 = W3 = 0.15, and can be set to be different as needed.

Step 605, outputting the final probability obtained by the calculation.

The results of the comparison evaluation module 50 Oe are finally output to the user terminal via the network for display, preferably in a graphical manner. Fig. 7 schematically shows a user interface displayed by a user terminal. In this interface, the characteristics of the children and the scores of each feature are displayed in a star-rate manner in a percentage probability to show the level of the score and the degree of compensation.

The system and method for developing children's feature recognition and talent potential according to the present invention can integrate the data of the child group and the database of talent characteristics based on the data mining method and the optimization training method by comparing methods, clustering, classification, etc. Identify the dominant and weak characteristics of the child's individual, strengthen its superior characteristics, and make up for the shortcomings of the weak features, and give parents, kindergarten teachers and kindergarten managers the development direction and possibility of the individual's individual talent potential. The input of the system is the characteristics of the children's group words and deeds and the characteristics of individual infants, the training and training database and the talent characteristics database. The output is the advantages and disadvantages of the young children and the development direction and possibility of their potential. Furthermore, the training and training library of the system can provide teaching aids corresponding to the matching of the child's characteristics, strengthen the training of the child's weak features, and strengthen and improve the dominant features. The system establishes mathematical models by applying probability and mathematical statistics theory, integration theory, classification and classification theory, discovers the connotation of children's individual characteristic data from children's group characteristic data and talent characteristics data, and explores useful information of children's individual growth, providing parents. Early childhood teacher, preschool teacher Institutions and management make important references.

Those skilled in the art will appreciate that many modifications and variations are possible in light of the above disclosure. Some portions of the specification describe embodiments of the invention in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to effectively convey their work to those skilled in the art. The operations described functionally, computationally, or logically are understood to be implemented by computer programs or equivalent circuits, microcode, and the like. In addition, it has been proven many times that it is convenient to arrange these operations as modules without loss of generality. The described operations and their associated modules can be implemented in software, firmware, hardware, or any combination thereof.

Any of the steps, operations, or processes described herein may utilize one or more hardware or libraries, which are implemented by an arithmetic program, a computer program product, and a computer readable medium containing computer program code. The computer program code can be executed by a computer processor for performing any or all of the steps, operations or processes described.

Embodiments of the invention may also relate to apparatus for performing the operations herein. The apparatus may be specially constructed for the required purposes, and/or it may comprise a general purpose computing device that is selectively activated or reconfigured by a computer program stored in a computer. Such a computer program can be stored in a non-transitory tangible computer readable storage medium or any type of medium suitable for storing electronic instructions, which can be coupled to a computer system bus. Additionally, any of the computing systems referred to in this specification can include a single processor or can be an architecture involving multiple processors for increased computing power.

Embodiments of the invention may also relate to products produced by the computing processes described herein. Such products may include information derived from a computing process, wherein the information is stored in a non-transitory tangible computer readable storage medium and may include any implementation of a computer program product or other data combination as described herein.

Other embodiments of the invention will be apparent to those skilled in the <RTIgt; The description and the examples are to be considered as illustrative only, and the true scope and spirit of the invention are defined by the claims.

Claims

Claim

A child identification and potential development evaluation system, the system comprising a child identification module, a training module, a child feature database, a talent feature database, and a comparison evaluation module, wherein

The child feature recognition module is configured to receive the collected child feature individual data through a network, and obtain sample or historical data therein by accessing the child feature database, compare and analyze the collected individual data, and transmit the analysis result. Processing to the culture training module;

The culture training module is configured to receive the child's characteristic analysis result data from the child feature recognition module, and match the corresponding training program, thereby performing intensive training of the superior features and compensation training of the weak features;

The child character database is used for storing and providing child character data, including characteristic data of the child group and historical data of the child character;

The talent feature database is used for storing and providing talent feature data, including talent definition data, talent classification data, feature data of specific types of talents, talent feature group data, and talent feature history data;

The comparison evaluation module is configured to receive analysis result data from the training training module, and compare with the talent characteristic data obtained from the talent characteristic database, thereby evaluating the talent characteristics, potential development direction and probability of the child. The results are analyzed and the results of the analysis are transmitted over the network for display.

2. The system of claim 1 wherein said infant feature recognition module receives child profile individual data collected by the user terminal over a network.

3. The system of claim 2, wherein the user terminal is selected from at least one of a desktop computer, a laptop computer, a smart phone, a personal digital assistant, a tablet computer, a gaming machine, and a multi-functional mobile terminal.

4. The system of claim 1, wherein the network is selected from the group consisting of GIS, WiFi or WLAN, GPRS, cellular network, GSM network, 3G network, LTE network or CDMA network, Bluetooth, NFC, infrared, ultrasound At least one of Wi re es s USB and RFID.

The system of claim 1, wherein the child feature recognition module inputs the collected child feature individual data into the child feature database as historical data of the child feature database.

6. The system of claim 1 wherein the system provides a third party interface to obtain the child signature database and the talent profile database from a third party.

7. The system of claim 1 wherein said infant signature module comprises a body data collection module, a database access and control module, and a comparison analysis module, wherein the individual data collection module is configured to collect the collected individual data of the child, and obtain the access from the child feature database through the database access and control module. The child feature data is input to the comparison analysis module for calculation and comparison analysis, and the obtained result is output to the training training module.

8. The system of claim 1, wherein the infant individual feature data is divided into a primary indicator and a secondary indicator, each of the primary indicators comprising a plurality of secondary indicators and scored by the secondary indicators Comprehensive calculation.

9. The system of claim 8 wherein the different second level indicators under the same level one indicator have different weights.

10. The system of claim 1 wherein the dominant and weak characteristics of the infant individual are obtained using a population average eigenvalue and a population TOP value compared to an infant individual eigenvalue.

11. The system of claim 1 wherein said training module comprises an individual feature data collection module, a dominant feature training module, a weak feature training module, and a result feedback module.

The individual feature data collection module is configured to collect the child individual identification data sent by the child feature recognition module, and select the dominant feature and the weak feature to be sent to the dominant feature training module and the weak feature training module respectively;

The superior feature training module is configured to generate a recommended training method to enhance the superior characteristics of the child;

The weak feature training module is configured to generate a recommended training method to compensate for the weak features of the child;

The result feedback module is configured to perform periodic evaluation according to the results of the dominant feature and the weak feature training, and output the result to the comparison evaluation module for subsequent evaluation.

12. The system of claim 1, wherein the comparison evaluation module comprises a consistency comparability determination module, an individual talent comparison module, an evaluation module, and a result output module.

The consistency comparability determining module is configured to verify the consistency and comparability of the data to be compared. If it is determined that the data inconsistency cannot be compared, the result output module feeds back to the child feature recognition module to re-collect the data. And processing; comparing the talent characteristics data collected by the database;

The evaluation module is used to determine the probability of developing a child's potential development direction.

The system according to claim 12, wherein the individual talent comparison module adopts

14. The system of claim 12, wherein the evaluation module determines the probability of developing a child's potential development direction as follows:

a) determine the number of dominant characteristics of the child;

b) judging whether there is a long feature of the technique, if not entering step c, if yes, proceeding to step bl to set the probability of the potential development direction of the infant individual to 50%, and proceed to step c;

c) determine the initial probability of the development potential of the talent type;

d) Perform a weight correction on the initial value of the probability potential of the talent type determined in the step.