Reservation management method for driving training of driving school students

Abstract

The invention discloses an appointment management method for driving training of a driving school student, and relates to the technical field of appointment management, and the method comprises the steps: student registration and system information input are carried out, and student information is obtained; performing identity verification according to the student information; generating a personalized training plan according to the student information to obtain a training plan; performing reservation scheduling according to the training plan to obtain reservation information; performing real-time updating and notification according to the reservation information, and recording training data; performing data analysis and optimization according to the training data to obtain an optimization result; and training quality feedback is carried out according to an optimization result. According to the invention, the flexibility of training is improved, feedback can be carried out in real time, the training process is dynamically adjusted, the training efficiency and the training effect are improved, the learning experience of students is improved, and the individual requirements of different students are met.

Description

Technical Field

[0001] The present invention relates to the technical field of reservation management, and in particular to a reservation management method for driving training for students in a driving school. Background Art

[0002] With the rapid development of intelligent technology, driving schools are gradually introducing information and automation to improve training efficiency and student experience. Traditional driving school training management methods often rely on manual recording of student information and manually scheduling training courses and teaching plans. This approach is not only inefficient but also lacks personalization and flexibility, failing to effectively address the diverse needs of students during training. Therefore, leveraging modern technology to achieve intelligent management of student information, dynamically adjust teaching content, and enhance training safety and accuracy has become a key focus of driving school training management.

[0003] Existing technologies lack flexibility in adaptive learning algorithms and dynamic adjustment mechanisms. Most rely on static rules and linear models, making them incapable of adapting to the dynamic changes exhibited by students at different stages of learning. For example, factors such as a student's learning speed, mastery level, and feedback satisfaction are not static, and traditional algorithms often lack the flexibility and timeliness to adapt to these changes. Many systems still experience lags in feedback adjustments, preventing timely optimization of the student's learning process, impacting the overall effectiveness of training and the student's learning experience. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method for managing reservations for driving training for students in a driving school, so as to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a method for managing driving training reservations for driving school students, comprising the following steps: S1. Student registration and reservation management system and information input to obtain student information; S2. Authentication based on student information; S3. Generate a personalized training plan based on the trainee information to obtain a training plan; S4. Schedule an appointment according to the training plan and obtain appointment information; S5. Provide real-time updates and notifications based on appointment information and record training data; S6. Perform data analysis and optimization based on the training data to obtain optimization results; S7. Provide training quality feedback based on the optimization results.

[0006] To further optimize this technical solution, the steps of student registration and reservation management system and information input in S1 and student information include: Enter your basic personal information to register in the system; The student registration information is stored and verified by the system; Basic personal information includes: name, ID number, contact information, driver's license type, training needs, expected training period, photo, and fingerprint data.

[0007] To further optimize this technical solution, the method for identity authentication in S2 is based on the following biometric recognition modalities: facial feature vector, fingerprint feature vector and behavior trajectory time series calculation.

[0008] To further optimize this technical solution, the step of generating a personalized training plan in S3 includes: The system generates a personalized training plan based on the training needs and expected training period in the trainee information; Analyze students' historical data and the system provides optimization suggestions based on students' training needs.

[0009] To further optimize this technical solution, the step of performing appointment scheduling in S4 includes: The system displays the coaches’ free time and available resources for training courses in real time; Trainees make training appointments according to their own training plans; The system automatically schedules students based on their choices through an optimization algorithm to determine the best appointment time, avoid conflicts and maximize resource utilization.

[0010] Further optimizing this technical solution, the step of performing real-time updating and notification in S5 and recording training data includes: After the appointment is completed, the system will update the appointment information in real time; Based on the appointment information, the system automatically notifies students and coaches via SMS and APP; The system automatically reminds you when the course is approaching; In case of course conflicts and unexpected events, the system will update and notify students and instructors in real time; Record students' training data after each course.

[0011] To further optimize this technical solution, the method for performing data analysis and optimization in S6 includes: Based on the training data, the system automatically analyzes and obtains the analysis results; Based on the analysis results, the system determines the time period, coach and course that are suitable for the students, and automatically adjusts the subsequent training course arrangements for this teaching stage based on the students' course performance to obtain optimized results.

[0012] To further optimize this technical solution, the method for providing training quality feedback in S7 includes: Based on the optimization results, the system automatically generates training quality reports through adaptive learning algorithms and dynamic adjustment mechanisms and provides feedback to trainees and coaches; The system dynamically adjusts the course content and learning intensity for the next stage based on students' feedback, and provides suggestions for coaches' teaching methods to improve teaching quality.

[0013] To further optimize this technical solution, the adaptive learning algorithm and dynamic adjustment mechanism include: Comprehensive scoring formula and adjustment formula: ; ; ; ; in: : The student's learning progress at time point t, between 0 and 1, where 0 indicates that learning has not started and 1 indicates that learning has been completed. It is obtained based on real-time training data; : The student's learning score at time point t, between 0 and 1, indicating the student's mastery of the course, obtained based on real-time training data; : Student feedback score, between 0 and 1, indicating student satisfaction with the course content and teaching methods, obtained based on student feedback; : The comprehensive learning status score of the students at time point t; : Weight coefficient, which is adjusted according to the actual situation. The sum of the three weight coefficients is 1; , , : The amount of course content, teaching method adaptability, and learning intensity of the trainees at time point t are obtained based on the training courses, trainee feedback scores, and training data at time point t; : The amount of course content, teaching method adaptability, and learning intensity will be adjusted dynamically based on the students' learning progress, learning scores, and student feedback scores; : The amount of course content, teaching method adaptability, and learning intensity of the students after adjustment; Dynamic adjustment mechanism: Adjustment based on comprehensive scores: If the student's comprehensive score is below the threshold, the course content, teaching methods and learning intensity of the next stage will be adjusted; Course content adjustment: Based on students' learning progress and learning scores, the system dynamically adjusts the depth and duration of the next stage of course content; Adjustment of teaching methods: Based on students' feedback and scores, the system dynamically adjusts the teaching methods for the next stage and provides teaching suggestions to the coaches; Adjustment of learning intensity: Based on the students’ learning progress and scores, the system dynamically adjusts the learning intensity of the next stage.

[0014] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the reservation management method for driving training for students in a driving school as described in the first aspect of the present invention are implemented.

[0015] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of a method for managing reservations for driving training for students in a driving school as described in the first aspect of the present invention are implemented.

[0016] Compared with the prior art, the present invention provides a method for managing driving training reservations for driving school students, which has the following beneficial effects: By combining the trainees' real-time learning progress, scores and feedback with the adaptive learning algorithm, the training content, teaching methods and learning intensity can be dynamically adjusted. Compared with the static models and linear adjustment methods in the prior art, the algorithm of the present invention has higher flexibility and real-time performance.

[0017] The trainees’ performance at each stage can be fed back to the system in real time. The system will make flexible adjustments based on the trainees’ specific needs to ensure that the trainees can learn in the most suitable way for them, thereby improving training efficiency.

[0018] The adaptive learning algorithm and dynamic adjustment mechanism not only improve the training effect, but also greatly enhance the students' learning experience, so that driving school training can truly teach students in accordance with their aptitude and meet the personalized needs of different students. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1This is a flow chart of a method for managing driving training reservations for driving school students proposed by the present invention; Figure 2 This is a flow chart of the multiple biometric recognition technology of the reservation management method for driving training for driving school students proposed by the present invention. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0022] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive with other embodiments.

[0024] Example 1: Reference Figures 1 and 2 , which is the first embodiment of the present invention, provides a method for managing driving training reservations for driving school students, comprising the following steps: S1. Student registration and reservation management system and information input to obtain student information.

[0025] In this embodiment, the steps of student registration and reservation management system and information input in S1 include: Enter your basic personal information to register in the system; Student registration information is stored in the system and undergoes basic verification, such as format checking and data uniqueness verification, to ensure the integrity and accuracy of student information; The basic personal information includes: name, ID number, contact information, driver's license type, training needs, expected training period, photo, and fingerprint data, from which facial feature vectors, fingerprint feature vectors, and behavior trajectory time series are extracted for step S2.

[0026] S2. Perform identity verification based on student information.

[0027] In this embodiment, the authentication method in S2 uses the following biometric recognition modalities: Facial feature vector: ; Fingerprint feature vector: ; Behavior trajectory time series: ,in ; The authentication method uses a nonlinear modal cross-consistency model for calculation, and the identity consistency function is: ; in: : Nonlinear cross-similarity tensor function between modalities, used to calculate high-order matching relationships between different modalities in the feature space: ; : Outer product operation, constructing the modal cross tensor; : System registration feature reference tensor; : Frobenius norm, used to measure the difference of high-dimensional tensors; This formula refers to the cross-structural consistency between modes; : The behavior trajectory consistency index function measures the consistency between the current behavior trajectory sequence and the historical behavior model, and is defined as: ; : Kullback-Leibler divergence, used to measure the current behavior distribution and historical behavior model Deviation; : probability distribution of the current behavior point; : Reference distribution of behaviors during the registration phase; : Standardized mapping function (Sigmoid function), limiting the final identity consistency score to interval, which is convenient for subsequent setting of threshold judgment; The model formula includes the following steps when used: The system first collects the student's facial image and fingerprint image, and extracts the vector 、 , and calculate and register the reference tensor The cross-structural similarity.

[0028] At the same time, the behavior trajectory sequence is obtained through the dynamic behavior recognition module , build a behavior probability model and compare it with the historical behavior model to calculate the behavior consistency index .

[0029] Finally, the inter-modal similarity function Behavior consistency function The product of is taken as input and the normalized mapping function Output the final identity matching score .

[0030] The system determines whether the verification is successful based on a set threshold (such as 0.85). If it is lower than the threshold, it will prompt that the verification has failed to prevent alternative training or identity forgery.

[0031] S3. Generate a personalized training plan based on the trainee information to obtain the training plan.

[0032] In this embodiment, the steps of generating a personalized training plan include: Based on the trainee's training needs (e.g., whether the trainee has driving experience, whether they prefer automatic or manual transmission training, etc.) and the desired training period, the system generates a personalized training plan. This plan includes the required course content, the duration of each class, the instructor, and the expected end time of the training. By analyzing students' historical data, the system provides optimization suggestions based on students' training needs, such as adjusting the training cycle, increasing or reducing course content, etc.

[0033] In step S3, a coupling model is constructed to dynamically decompose the training tasks so that the task duration is consistent with the time-varying curve of the trainee's ability, ensuring that each task is completed efficiently within the ability support range; Its model is: ; in, : No. The difficulty weight of each training task; : Assigned to The actual training time of each training task; :Task The time when it is scheduled; : total number of tasks; :Indicates the students’ Learning ability per unit time: ; : Feedback score of the previous task completed; Adjust parameters to control capability continuity, short-term feedback responsiveness, and baseline capability, based on empirical settings; During the execution of step S3, basic information of the college and certification records, as well as previous task execution records, are obtained through S1 and S2; Create training modules for each student , confirm the corresponding ; Confirm learning ability based on early feedback data ; Use nonlinear programming tools (L-BFGS method) to minimize the ability-task matching deviation and find ; The task Assign to the corresponding , forming a reasonable and executable study schedule; A training plan is obtained based on the obtained study schedule.

[0034] S4. Make an appointment according to the training plan and obtain the appointment information.

[0035] In this embodiment, the steps of performing appointment scheduling include: The system displays the instructor's free time and available resources for training courses (such as simulators, vehicles, etc.) in real time; Trainees make training appointments according to the training plan; The system automatically schedules based on the student's choice through an optimization algorithm (which not only considers the student's time needs, but also the instructor's free time and the availability of vehicle resources), determines the best appointment time, avoids conflicts and maximizes resource utilization.

[0036] S5. Provide real-time updates and notifications based on appointment information and record training data.

[0037] In this embodiment, the steps of performing real-time updating and notification and recording training data include: After the appointment is completed, the system will update the appointment information in real time; Based on the reservation information, the system automatically notifies students and coaches via SMS and APP, including course schedule, class time, coach information, etc. The system automatically reminds students and instructors when the class is approaching, preventing them from forgetting to attend the class. In case of course conflicts and unexpected events, the system will update and notify students and instructors in real time; Record students' training data after each course.

[0038] S6. Perform data analysis and optimization based on the training data to obtain optimization results.

[0039] In this embodiment, the method for performing data analysis and optimization includes: Based on training data (including student attendance, course completion, and course progress), the system automatically analyzes which time periods or course content are most popular, and which instructors have the best teaching results. The system then generates analysis results (e.g., if a particular instructor's course schedule is too tight, the system will recommend alternative time periods or instructors during appointment scheduling to avoid wasting student and instructor resources). Based on the analysis results, the system determines the time period, coach and course that are suitable for the students, and automatically adjusts the subsequent training course arrangements for this teaching stage based on the students' course performance to obtain optimized results.

[0040] S7. Provide training quality feedback based on the optimization results.

[0041] In this embodiment, the method for providing training quality feedback includes: Based on the optimization results, the system automatically generates training quality reports through adaptive learning algorithms and dynamic adjustment mechanisms and provides feedback to trainees and coaches; Students will receive detailed feedback based on their course progress and personal performance to help them improve their shortcomings. Instructors can use the report to understand their strengths and areas for improvement in the teaching process. The system dynamically adjusts the course content and learning intensity for the next stage based on students' feedback, and provides suggestions for coaches' teaching methods to improve teaching quality.

[0042] Furthermore, the adaptive learning algorithm and dynamic adjustment mechanism include: Comprehensive scoring formula and adjustment formula: ; ; ; ; in: : The student's learning progress at time point t, between 0 and 1, where 0 indicates that learning has not started and 1 indicates that learning has been completed. It is obtained based on real-time training data; : The student's learning score at time point t, between 0 and 1, indicating the student's mastery of the course, obtained based on real-time training data; : Student feedback score, between 0 and 1, indicating student satisfaction with the course content and teaching methods, obtained based on student feedback; : The comprehensive learning status score of the students at time point t; : Weight coefficient, which is adjusted according to the actual situation. The sum of the three weight coefficients is 1; , , : The amount of course content, teaching method adaptability, and learning intensity of the trainees at time point t are obtained based on the training courses, trainee feedback scores, and training data at time point t; : The amount of course content, teaching methods (such as more practical training or theoretical explanations), and the amount of adjustment in learning intensity are dynamically adjusted based on the students' learning progress, learning scores, and student feedback scores; : The amount of course content, teaching method adaptability, and learning intensity of the students after adjustment; Dynamic adjustment mechanism: Adjustment based on comprehensive scores: At the end of each learning cycle, the system will calculate the student's comprehensive learning status score based on the student's learning progress, scores, and feedback. If the student's comprehensive score is below the threshold, the course content, teaching methods, and learning intensity of the next stage will be adjusted; Course content adjustment: Based on the students' learning progress and learning scores, the system dynamically adjusts the depth and duration of the next stage of the course content. For example, if the learning progress of a module lags behind, the system will increase the learning time of the module or repeat related content; Adjustment of teaching methods: Based on students' feedback and ratings, the system dynamically adjusts the teaching methods for the next stage and provides teaching suggestions to the instructor (for example, more simulations or theoretical explanations). If students' feedback ratings are low, it means that some teaching methods may not be suitable for the student's learning style, and the system will adjust the proportion of teaching methods accordingly. Learning intensity adjustment: Based on the student's learning progress and ratings, the system dynamically adjusts the learning intensity for the next stage. If a student's performance is poor, the system will increase the learning intensity to accelerate their mastery of the content. If a student has already mastered certain content, the system will appropriately reduce the learning intensity to avoid overtraining.

[0043] After the training is completed, the system will mark the completion progress of the study hours according to the training results, making it convenient for students to check their study progress in the system.

[0044] Example 2: This embodiment also provides a computer device, which is suitable for a method for managing reservations for driving training for students in a driving school, and includes a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a method for managing reservations for driving training for students in a driving school as proposed in the above embodiment.

[0045] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a reservation management method for driving training for driving school students as proposed in the above embodiment.

[0046] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0047] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0048] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0049] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0050] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0051] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for managing driving training reservations for driving school students, characterized in that: The following steps are involved: S1. Student registration and reservation management system and information input to obtain student information; S2. Authentication based on student information; S3. Generate a personalized training plan based on the trainee information to obtain a training plan; S4. Schedule an appointment according to the training plan and obtain appointment information; S5. Provide real-time updates and notifications based on appointment information and record training data; S6. Perform data analysis and optimization based on the training data to obtain optimization results; S7. Provide training quality feedback based on the optimization results.

2. The method for managing driving training reservations for driving school students according to claim 1, characterized in that: The steps of the student registration and reservation management system and information input in S1 and the student information include: Enter your basic personal information to register in the system; The student registration information is stored and verified by the system; Basic personal information includes: name, ID number, contact information, driver's license type, training needs, expected training period, photo, and fingerprint data.

3. The method for managing driving training reservations for driving school students according to claim 1, characterized in that: The method for identity authentication in S2 is based on the following biometric recognition modalities: facial feature vector, fingerprint feature vector and behavior trajectory time series calculation.

4. The method for managing driving training reservations for driving school students according to claim 1, characterized in that: The steps of generating a personalized training plan in S3 include: The system generates a personalized training plan based on the training needs and expected training period in the trainee information; Analyze students' historical data and the system provides optimization suggestions based on students' training needs.

5. The method for managing driving training reservations for driving school students according to claim 1, characterized in that: The steps of performing appointment scheduling in S4 include: The system displays the coaches’ free time and available resources for training courses in real time; Trainees make training appointments according to the training plan; The system automatically schedules students based on their choices through an optimization algorithm to determine the best appointment time, avoid conflicts and maximize resource utilization.

6. The method for managing driving training reservations for driving school students according to claim 1, characterized in that: The steps of performing real-time updating and notification in S5 and recording training data include: After the appointment is completed, the system will update the appointment information in real time; Based on the appointment information, the system automatically notifies students and coaches via SMS and APP; The system automatically reminds you when the course is approaching; In case of course conflicts and unexpected events, the system will update and notify students and instructors in real time; Record students' training data after each course.

7. The method for managing driving training reservations for driving school students according to claim 1, characterized in that: The method for performing data analysis and optimization in S6 includes: Based on the training data, the system automatically analyzes and obtains the analysis results; Based on the analysis results, the system determines the time period, coach and course that are suitable for the students, and automatically adjusts the subsequent training course arrangements for this teaching stage based on the students' course performance to obtain optimized results.

8. The method for managing driving training reservations for driving school students according to claim 1, characterized in that: The method for providing training quality feedback in S7 includes: Based on the optimization results, the system automatically generates training quality reports through adaptive learning algorithms and dynamic adjustment mechanisms and provides feedback to trainees and coaches; The system dynamically adjusts the course content and learning intensity for the next stage based on students' feedback, and provides suggestions for coaches' teaching methods to improve teaching quality.

9. The method for managing driving training reservations for driving school students according to claim 8, characterized in that: The adaptive learning algorithm and dynamic adjustment mechanism include: Comprehensive scoring formula and adjustment formula: ； ； ； ； in: : The student's learning progress at time point t, between 0 and 1, where 0 indicates that learning has not started and 1 indicates that learning has been completed. It is obtained based on real-time training data; : The student's learning score at time point t, between 0 and 1, indicating the student's mastery of the course, obtained based on real-time training data; : Student feedback score, between 0 and 1, indicating student satisfaction with the course content and teaching methods, obtained based on student feedback; : The comprehensive learning status score of the students at time point t; : Weight coefficient, which is adjusted according to the actual situation. The sum of the three weight coefficients is 1; , , : The amount of course content, teaching method adaptability, and learning intensity of the trainees at time point t are obtained based on the training courses, trainee feedback scores, and training data at time point t; : The amount of course content, teaching method adaptability, and learning intensity will be adjusted dynamically based on the students' learning progress, learning scores, and student feedback scores; : The amount of course content, teaching method adaptability, and learning intensity of the students after adjustment; Dynamic adjustment mechanism: Adjustment based on comprehensive scores: If the student's comprehensive score is below the threshold, the course content, teaching methods and learning intensity of the next stage will be adjusted; Course content adjustment: Based on students' learning progress and learning scores, the system dynamically adjusts the depth and duration of the next stage of course content; Adjustment of teaching methods: Based on students' feedback and scores, the system dynamically adjusts the teaching methods for the next stage and provides teaching suggestions to the coaches; Adjustment of learning intensity: Based on the students’ learning progress and scores, the system dynamically adjusts the learning intensity of the next stage.

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