System and method for training children to execute functions

Abstract

The invention discloses a system and a method for training children to execute functions. According to the method, a task environment containing an obstacle and a target area is constructed, roles of an instructor and an executor are set, a voice instruction is received and analyzed, the executor is controlled to move, a moving state is detected, time penalty is recorded, and finally a training score is generated based on task completion time, instruction efficiency and time penalty records. The system introduces instruction efficiency as a core evaluation index, encourages the use of an advanced instruction containing a fine quantization parameter, and trains cognitive flexibility through a coordinate system conversion mechanism. The method can be applied to an entity site and a virtual environment, core execution function components such as work memory, cognitive flexibility and suppression control are effectively trained, and the method has the advantages of standardization and quantifiability.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of educational technology and cognitive training, and particularly relates to a system and method for training children's executive function by controlling the movement of an execution body in a task environment through voice instructions. BACKGROUND

[0002] Executive function is a set of essential high-level cognitive processes, including core components such as working memory, cognitive flexibility, and inhibitory control, which have a decisive impact on children's learning ability, social adaptation, and overall development. Traditional educational training tools often separate cognitive training from physical training, lacking training programs that can effectively integrate the complete cognitive closed loop of "thinking-expression-execution". The deficiencies in the prior art include: 1. Programming thinking games (such as Lightbot) focus on pre-planning logic, lacking real-time language expression and dynamic adjustment training; 2. Voice control games mostly use voice as a simple signal input, without involving complex spatial thinking and language coding processes; 3. Physical collaboration games (such as Keep Talking and Don't Touch the Bomb) involve communication, but lack standardized and quantifiable individual ability assessment systems; 4. Existing cognitive training tools cannot effectively simulate and train the important cognitive flexibility skill of direction instruction conversion caused by different observation perspectives. Therefore, there is an urgent need in the field for an innovative solution that can systematically train the "thinking-expression-execution" cognitive closed loop and provide objective and quantifiable evaluation. SUMMARY

[0003] The present application aims to overcome the deficiencies of the prior art, and provides a system and method for effectively training children's executive function, which controls the movement of an executive body in a task environment through voice instructions, and systematically trains core cognitive abilities such as working memory, cognitive flexibility, and inhibition control. The technical solutions of the present application are as follows: In a first aspect, the present application provides a method for training children's executive function, comprising the following steps: providing a task environment, wherein at least one obstacle and a target area are arranged in the task environment; setting a user as an instructor role and setting an executive body as an executor role; wherein the executor cannot completely perceive the global information of the task environment; receiving multiple voice instructions from the instructor; parsing the voice instructions into movement actions of the executive body in the task environment; controlling the movement of the executive body in the task environment according to the parsed movement actions; detecting the state of the executive body in the moving process with the obstacle or the boundary of the task environment, and recording the penalty time based on the detection result; when the executive body reaches the target area, generating a training score, which is calculated based on at least the task completion time and the penalty time record. Preferably, the training score is also calculated based on the efficiency of the instructions issued by the instructor during the task; the efficiency of the instructions is negatively related to the total number of instructions, and is positively related to the proportion or number of advanced instructions contained in the instructions; the advanced instruction refers to an instruction that can drive the executive body to complete a composite movement action. Further preferably, the advanced instruction refers to an instruction containing at least one quantitative parameter such as distance, direction, or angle. Further preferably, in the step of parsing the voice instructions, a coordinate system conversion step is included for mapping the direction instructions based on the instructor's own coordinate system to the movement actions based on the executive body's own coordinate system; when the instructor and the executive body are in a specific relative position in space, the mapping exhibits a mirror image relationship. Further preferably, the task environment is a physical site, and the executive body is a child wearing an eyeshade. Further preferably, the task environment is a virtual grid space displayed on an electronic device, and the executive body is a virtual character. In a second aspect, the present application provides an executive function training system, comprising: a physical task site, wherein obstacles and a target area are arranged; an instruction terminal for the instructor to input voice instructions; an execution terminal arranged on the executive body, for receiving instructions from the instruction terminal and outputting guidance information to guide the movement of the executive body in the physical task site; a processing module for executing the above-mentioned method. In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned method.The beneficial effects of the present application are: 1. By the role division of the instructor and the executor, a complete "thinking-expression-execution" cognitive closed-loop training environment is constructed; 2. The instruction efficiency is introduced as the core evaluation index, and the working memory and cognitive efficiency are effectively trained; 3. Through the coordinate system conversion mechanism, the cognitive flexibility and spatial perspective conversion ability are systematically trained; 4. A standardized and quantifiable evaluation system is provided, which is convenient for tracking the training effect; 5. Compatible with both physical and virtual implementations, widely applicable. BRIEF DESCRIPTION OF DRAWINGS Figure 1 is the system structure diagram of the present application. Figure 2 is the method flowchart of the present application. Figure 3 is the interface schematic diagram of the virtual environment implementation of the present application. Figure 4 is the coordinate system conversion principle schematic diagram of the present application. Figure 5 is the score model and training effect correlation diagram of the present application. Figure 6 is the comparison schematic diagram of the advanced instruction and the basic instruction of the present application. Figure 7 is the component and layout schematic diagram of the physical system of the present application. DETAILED DESCRIPTION The present application will be described in detail below in conjunction with specific embodiments and drawings. Embodiment 1: Virtual environment implementation This embodiment is realized through electronic devices (such as tablets, smartphones). As shown in Figure 3 , the task environment is a 3x6 virtual grid space (110), in which obstacles and target areas are randomly generated. The obstacles include columnar obstacles (114), point obstacles (115) and Lagrange points (116), and the target area is the entry point (109). The scene also sets the Earth icon (113) and the Moon icon (118). The executor is a virtual character (117, such as a spaceship icon). The instructor (101) inputs voice instructions through the device microphone (integrated in the instruction terminal 102). The processing module converts the instructions into text through the voice recognition module (103), and then analyzes the moving actions through the instruction analysis and coordinate system conversion module (104). In the analysis process, the processing module performs coordinate system conversion. As shown in Figure 4 , the coordinate system conversion includes two mapping relationships of the same direction mode (401) and the mirror direction mode (405), which are realized through the corresponding relationship of the instructor coordinate system (402, 406) and the executor coordinate system (403, 407). Referring to Figure 2 , when the system is set to mirror mode (controlled by the program interface switch, the state is displayed in the top status bar 301), the Figure 2In step 206, the coordinate mapping process involves the instruction parsing module (104) mapping the instruction based on the instructor's own coordinate system to the movement action based on the executor's own coordinate system; otherwise, step 207 maintains the same-direction mapping relationship. The control module (106) controls the movement of the virtual character (117) based on the parsing result, and the performance calculation and evaluation module (105) detects in real time whether there is a collision with obstacles (114, 115, 116) or boundaries (corresponding to...). Figure 2 Step 209). When the virtual character reaches the target area (109) (corresponding to...) Figure 2 Step 213), the program automatically calculates the score (corresponding to...). Figure 2 Step 214). The instruction history and feedback area (119) displays the execution status in real time, and the control and status bar (120) displays the time (121), number of instructions (122), and penalty time (123) information. The score calculation formula is: Total score = T0 (time score) + T1 (efficiency score) + n × T2 (penalty time score) Where, T1 efficiency score = total number of instructions × coefficient. The judgment criterion for advanced instructions is whether they contain fine-grained quantitative parameters, such as "stepping forward" (fine-grained action), "turning right forward" (fine-grained direction), "turning right a little" (fine-grained angle), etc. Figure 5 As shown, the scoring model of this invention has a significant training effect, manifested in the trend (503) that the total score (502, the smaller the value, the better the score) continuously improves with the increase of the number of games (501). Figure 6 As shown, in the same task scenario (110), from the starting point (125) to the target (109), the basic instruction path (126) is significantly more tortuous and lengthy than the advanced instruction path (127). This is clearly demonstrated by the path comparison labels (128, 129), verifying the technical advantage of advanced instructions in improving path planning efficiency. Example 2: Physical Environment Implementation This example is implemented using a dedicated hardware system. Figure 7The entity system layout shown, the processing module (130) as a control core, coordinates the collaborative work of the instruction terminal (102), the execution terminal (107) and the sensor network (112), and builds a complete training environment in the entity task field (110). The entity task field (110) is a physical space of 1.5m*3m, which is arranged with real obstacles and target areas. The obstacles include columnar obstacles (114), point obstacles (115) and Lagrange points (116), and the target area is the orbit entry point (109). The instructor (101) inputs voice instructions through the instruction terminal (102, such as a microphone). The executor (108) is a child wearing an eye patch, and also wears an execution terminal (107, such as a smart watch or earphone). The voice recognition module (103) of the processing module (130, such as an embedded device) receives and analyzes voice instructions, and the instruction analysis and coordinate system conversion module (104) completes instruction analysis. The performance calculation and evaluation module (105) calculates the number of instructions. The control module (106) provides tactile or voice feedback to the executor through the execution terminal (107) to guide its movement; at the same time, the executor (108) can also move through the direct voice channel (111) to accept the original voice instructions of the instructor. The sensor network (112, such as a camera) detects the final movement state of the executor, and the performance calculation and evaluation module (105) scores. The processing module (130) detects the position and state of the executor (108) through the sensor network (112), and records the penalty time. When the executor reaches the target area (109), the system automatically calculates and displays the performance. Embodiment 3: Hybrid implementation This embodiment combines virtual and entity elements. The instructor (101) plans the path in the virtual interface (such as Figure 3 The system converts the planning results into voice instructions, which are guided by the execution terminal (107) or directly issued by the instructor, and are executed by the executor (108, a child wearing an eye patch) in the entity environment. The processing module (130) detects the movement state of the executor through the sensor network (112) and provides real-time feedback. This implementation has the advantages of virtual environment planning and the reality of entity environment execution, and provides a more complete "thinking-expression-execution" closed-loop training experience. The above embodiments are only preferred embodiments of the present application, but the protection scope of the present application is not limited thereto, and any changes or replacements within the technical range disclosed by the present application can be easily thought of by those skilled in the art, which should be covered within the protection scope of the present application.

Claims

1. A method of training executive functions in children, characterized in that, The method comprises the following steps: • providing a task environment, in which at least one obstacle and a target area are arranged; • setting a user as an instructor role and setting an executor as an executor role; wherein the executor cannot completely perceive the global information of the task environment; • receiving a plurality of voice instructions from the instructor; • parsing the voice instructions into movement actions of the executor in the task environment; • controlling the executor to move in the task environment according to the parsed movement actions; • detecting the state of the executor in the moving process with the obstacle or the boundary of the task environment, and recording the penalty time based on the detection result; • generating a training score when the executor reaches the target area, the training score being calculated based on at least the task completion time and the penalty time record.

2. The method of claim 1, wherein, The training score is also calculated based on the efficiency of the instructions issued by the instructor during the task; the efficiency of the instructions is negatively related to the total number of instructions and positively related to the proportion or number of high-level instructions contained in the instructions; the high-level instructions refer to instructions that can drive the executor to complete compound movement actions.

3. The method of claim 2, wherein, The high-level instructions refer to instructions that contain at least one quantitative parameter of distance, direction or angle.

4. The method of claim 1, wherein, In the step of parsing the voice instructions, a coordinate system conversion step is included, which is used to map the direction instructions issued based on the instructor's own coordinate system to the movement actions based on the executor's own coordinate system; when the instructor and the executor have a specific relative position in space, the mapping exhibits a mirror image relationship.

5. The method of claim 1, wherein, The task environment is a physical site, and the executor is a child wearing an eye patch.

6. The method of claim 1, wherein, The task environment is a virtual grid space displayed on an electronic device, and the executor is a virtual role.

7. A system for performing functional training, the system comprising: The method comprises: • an entity task site, in which obstacles and target areas are arranged; • an instruction terminal for the instructor to input voice instructions; • an execution terminal arranged on the executor, for receiving instructions from the instruction terminal and outputting guidance information to guide the executor to move in the entity task site; • a processing module for executing the method according to any one of claims 1 to 6.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the method according to any one of claims 1 to 6.

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