A method for managing a reservation of a driving test for a student driver

Abstract

The application discloses a kind of driving school student carries out the reservation management method of driving training, it is related to reservation management technical field, steps include: student registration and system information input, obtain student information;According to student information, identity verification is carried out;According to student information, generate individualized training plan, obtain training plan;According to training plan, reservation scheduling is carried out, obtain reservation information;According to reservation information, real-time update and notification are carried out, record training data;According to training data, data analysis and optimization are carried out, obtain optimization result;According to optimization result, training quality feedback is carried out.The application improves the flexibility of training, can carry out feedback in real time, dynamically adjusts training process, improves training efficiency and training effect, improves the learning experience of student, satisfies the individualized needs of different students.

Description

Technical Field

[0001] This invention relates to the field of appointment management technology, specifically to an appointment management method for driving school students conducting driving training. Background Technology

[0002] With the rapid development of intelligent technology, driving school training management has gradually incorporated information technology and automation to improve training efficiency and student experience. Traditional driving school training management methods typically rely on manual recording of student information and manual arrangement of training courses and teaching plans. This method is not only inefficient but also lacks personalization and flexibility, failing to effectively address the diverse needs exhibited by students during training. Therefore, leveraging modern technology to achieve intelligent management of student information, dynamically adjust teaching content, and improve training safety and accuracy has become a crucial direction for current driving school training management.

[0003] Existing adaptive learning algorithms and dynamic adjustment mechanisms lack flexibility: most rely on static rules and linear models, making it difficult to address the dynamic changes exhibited by learners at different learning stages. For example, factors such as learner learning speed, mastery level, and feedback satisfaction are not static, and traditional algorithms often cannot flexibly and promptly adjust to these changes. Many systems still exhibit lag in feedback adjustments, preventing timely optimization of the learner's learning process and impacting the overall training effectiveness and learner experience. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for managing appointments for driving school students' driving training, thereby resolving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] In a first aspect, embodiments of the present invention provide a method for managing appointments for driving school students' driving training, comprising the following steps:

[0007] S1. Student registration and appointment management system and information input, obtain student information;

[0008] S2. Verify identity based on student information;

[0009] S3. Generate a personalized training plan based on the trainee information to obtain the training plan;

[0010] S4. Schedule appointments according to the training plan and obtain appointment information;

[0011] S5. Update and notify in real time based on appointment information, and record training data;

[0012] S6. Conduct data analysis and optimization based on the training data to obtain optimization results;

[0013] S7. Provide training quality feedback based on the optimization results.

[0014] To further optimize this technical solution, the steps and student information input in the student registration and appointment management system in S1 include:

[0015] Enter your basic personal information to register with the system;

[0016] Student registration information is stored in the system and undergoes basic verification.

[0017] Personal basic information includes: name, ID number, contact information, driver's license type, training needs, expected training period, photo, and fingerprint data.

[0018] To further optimize this technical solution, the method for identity verification in S2 is based on the following biometric modalities: facial feature vector, fingerprint feature vector, and behavioral trajectory time series calculation.

[0019] To further optimize this technical solution, the step of generating a personalized training plan in S3 includes:

[0020] Based on the training needs and desired training periods in the trainees' information, the system generates personalized training plans.

[0021] By analyzing trainees' historical data, the system provides optimization suggestions based on trainees' training needs.

[0022] To further optimize this technical solution, the reservation scheduling step in S4 includes:

[0023] The system displays coaches' free time slots and available training course resources in real time;

[0024] Trainees can schedule training sessions according to their own training plans.

[0025] The system automatically schedules appointments based on student choices using an optimization algorithm, determining the optimal time slot to avoid conflicts and maximize resource utilization.

[0026] To further optimize this technical solution, the steps in S5 for real-time updates and notifications, and for recording training data, include:

[0027] Once the reservation is completed, the system updates the reservation information in real time.

[0028] Based on the appointment information, the system automatically notifies students and coaches via SMS and the app;

[0029] The system will automatically remind you when the class is approaching.

[0030] In the event of course conflicts or unexpected events, the system will update and notify students and instructors in real time;

[0031] Record trainees' training data after each course.

[0032] To further optimize this technical solution, the data analysis and optimization methods in S6 include:

[0033] Based on the training data, the system automatically performs analysis and obtains the analysis results;

[0034] Based on the analysis results, the system determines the appropriate time slots, instructors, and courses for trainees, and automatically adjusts the subsequent training course schedule for this teaching phase according to the trainees' course performance, resulting in optimized outcomes.

[0035] To further optimize this technical solution, the method for providing training quality feedback in S7 includes:

[0036] Based on the optimization results, the system automatically generates a training quality report and provides feedback to trainees and coaches through an adaptive learning algorithm and dynamic adjustment mechanism.

[0037] The system dynamically adjusts the course content and learning intensity for the next stage based on student feedback, and provides suggestions for instructors' teaching methods, thereby improving teaching quality.

[0038] To further optimize this technical solution, the adaptive learning algorithm and dynamic adjustment mechanism include:

[0039] Comprehensive scoring formula and adjustment formula:

[0040] ;

[0041] ;

[0042] ;

[0043] ;

[0044] in:

[0045] 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, is obtained based on real-time training data;

[0046] The student's learning score at time point t, ranging from 0 to 1, represents the student's level of mastery of the course, and is obtained based on real-time training data.

[0047] Student feedback ratings, ranging from 0 to 1, indicate student satisfaction with the course content and teaching methods. These ratings are obtained based on student feedback.

[0048] : The student's overall learning status score at time point t;

[0049] Weighting coefficients: Adjust according to the actual situation; the sum of the three weighting coefficients is 1.

[0050] , , The amount of course content, adaptability to teaching methods, and learning intensity of trainees at time point t are obtained based on the training courses, trainee feedback ratings, and training data at time point t.

[0051] The amount of course content, the suitability of teaching methods, and the intensity of learning are adjusted dynamically based on students' learning progress, learning scores, and student feedback scores.

[0052] The adjusted course content, teaching methods, and learning intensity for students;

[0053] Dynamic adjustment mechanism:

[0054] Adjustments based on overall score: If a student's overall score is below the threshold, the course content, teaching methods, and learning intensity for the next stage will be adjusted.

[0055] Course content adjustment: The system dynamically adjusts the depth and duration of the course content for the next stage based on students' learning progress and scores.

[0056] Teaching method adjustment: Based on the feedback and ratings of the trainees, the system dynamically adjusts the teaching methods for the next stage and provides teaching suggestions to the coaches;

[0057] Learning intensity adjustment: The system dynamically adjusts the learning intensity for the next stage based on the student's learning progress and scores.

[0058] In a second aspect, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of a method for managing appointments for driving training for driving school students as described in the first aspect of the present invention.

[0059] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of a method for managing appointments for driving training for driving school students as described in the first aspect of the present invention.

[0060] Compared with the prior art, the present invention provides a method for managing appointments for driving school students' driving training, which has the following beneficial effects:

[0061] By combining adaptive learning algorithms with students' real-time learning progress, scores, and feedback, 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 this invention has higher flexibility and real-time performance.

[0062] The system provides real-time feedback on each stage of a trainee's performance, and it makes flexible adjustments based on the trainee's specific needs to ensure that the trainee can learn in the way that best suits them, thereby improving training efficiency.

[0063] Adaptive learning algorithms and dynamic adjustment mechanisms not only improve training effectiveness but also greatly enhance the learning experience for students, enabling driving school training to truly be tailored to individual needs and meet the personalized requirements of different students. Attached Figure Description

[0064] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 This is a flowchart illustrating a method for managing appointments for driver training for driving school students, as proposed in this invention.

[0066] Figure 2 This is a flowchart illustrating the multi-biometric identification technology used in a driving school student appointment management method proposed in this invention. Detailed Implementation

[0067] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0068] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0069] Secondly, the term "an embodiment" or "embodiment" as used 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 different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0070] Example 1:

[0071] Reference Figures 1-2 This is the first embodiment of the present invention, which provides a method for managing appointments for driving school students' driving training, including the following steps:

[0072] S1. Student registration and appointment management system and information input, obtain student information.

[0073] In this embodiment, the steps of student registration and appointment management system and information input in S1, and the student information, include:

[0074] Enter your basic personal information to register with the system;

[0075] Student registration information is stored in the system and undergoes basic verification, such as format checking and data uniqueness verification, to ensure the completeness and accuracy of student information;

[0076] Personal basic information includes: name, ID number, contact information, driver's license type, training needs, expected training period, photo, and fingerprint data. Facial feature vector, fingerprint feature vector, and behavioral trajectory time series are extracted from these data for step S2.

[0077] S2. Verify identity based on student information.

[0078] In this embodiment, the authentication method in S2 uses the following biometric modalities:

[0079] Facial feature vector: ;

[0080] Fingerprint feature vector: ;

[0081] Behavioral trajectory time series: ,in ;

[0082] The authentication method uses a nonlinear modal cross-consistency model for computation, and the identity consistency function is:

[0083] ;

[0084] in:

[0085] : The intermodal nonlinear cross-similarity tensor function is used to calculate higher-order matching relationships between different modalities in the feature space.

[0086] ;

[0087] : Outer product operation, construct modal cross tensor;

[0088] : System registration feature reference tensor;

[0089] Frobenius norm, used to measure the difference in high-dimensional tensors;

[0090] This formula refers to the consistency of the cross-structure between modes;

[0091] The behavioral trajectory consistency index function measures the degree of consistency between the current behavioral trajectory sequence and the historical behavioral model, and is defined as follows:

[0092] ;

[0093] Kullback-Leibler divergence is used to measure the difference between the current behavior distribution and historical behavior models. Deviation;

[0094] : The probability distribution of the current action point;

[0095] : Behavioral reference distribution during the registration phase;

[0096] The standardized mapping function (Sigmoid function) limits the final identity consistency score to... The range facilitates subsequent threshold setting and determination;

[0097] The model formula includes the following steps when used:

[0098] The system first collects facial and fingerprint images of the trainees, and then extracts vectors from them. , And calculate and register the reference tensor The similarity of the cross structures.

[0099] Simultaneously, the behavior trajectory sequence is obtained through the dynamic behavior recognition module. A behavioral probability model is constructed and compared with historical behavioral models to calculate the behavioral consistency index. .

[0100] Finally, the intermodal similarity function is... Behavioral consistency function The product of the two is used as input, and then processed by the standardized mapping function. Output the final identity matching score .

[0101] The system determines whether the verification is successful based on a set threshold (such as 0.85). If the value is below the threshold, the system will indicate that the verification has failed to prevent alternative training or identity fraud.

[0102] S3. Generate a personalized training plan based on the trainee information to obtain the training plan.

[0103] In this embodiment, the steps for generating a personalized training plan include:

[0104] Based on the training needs in the student's information (such as whether the student has driving experience, whether they choose automatic or manual transmission training, etc.) and the expected training period, the system generates a personalized training plan, which includes the required course content, the duration of each course, the instructor, and the expected training end time.

[0105] By analyzing trainees' historical data, the system provides optimization suggestions based on trainees' training needs, such as adjusting training cycles, adding or reducing course content, etc.

[0106] In step S3, a coupled model is constructed to dynamically decompose the training tasks, so that the task time is consistent with the time-varying curve of the trainee's ability, ensuring that each task is completed efficiently within the range of the trainee's ability.

[0107] Its model is:

[0108] ;

[0109] in, : No. Difficulty weights for each training task;

[0110] Assigned to the first The actual training time for each training task;

[0111] :Task The scheduled time point;

[0112] Total number of tasks;

[0113] : Indicates the time the student is in Learning capacity per unit of time:

[0114] ;

[0115] Feedback score for completing the previous task;

[0116] Adjust the parameters to control capability continuity, short-term feedback responsiveness, and baseline capability separately, based on experience.

[0117] During the execution of step S3, basic information and authentication records of the college, as well as previous task execution records, are obtained through S1 and S2.

[0118] Develop training modules for each student Confirm the corresponding ;

[0119] Confirm learning ability based on preliminary feedback data ;

[0120] The capability-task mismatch is minimized using a nonlinear programming tool (L-BFGS method) to obtain the following results. ;

[0121] The task Assigned to the corresponding To create a reasonable and feasible learning schedule;

[0122] The training plan is derived from the obtained learning schedule.

[0123] S4. Schedule appointments according to the training plan and obtain appointment information.

[0124] In this embodiment, the steps for scheduling include:

[0125] The system displays instructors' free time slots and available resources for training courses (such as simulators, vehicles, etc.) in real time.

[0126] Trainees make training appointments according to the training plan;

[0127] The system automatically schedules students based on their choices using an optimization algorithm that considers not only students' time needs but also instructors' available time and vehicle availability. This process determines the optimal booking time slot, avoids conflicts, and maximizes resource utilization.

[0128] S5. Update and notify users in real time based on appointment information, and record training data.

[0129] In this embodiment, the steps of real-time updates and notifications, and recording training data, include:

[0130] Once the reservation is completed, the system updates the reservation information in real time.

[0131] Based on the reservation information, the system automatically notifies students and coaches via SMS and the app. The notification includes course arrangements, class times, coach information, etc.

[0132] The system automatically sends reminders when the course is approaching to prevent students and instructors from forgetting to attend.

[0133] In the event of course conflicts or unexpected incidents, the system will update and notify students and instructors in real time.

[0134] Record trainees' training data after each course.

[0135] S6. Perform data analysis and optimization based on the training data to obtain the optimization results.

[0136] In this embodiment, the method for data analysis and optimization includes:

[0137] Based on training data (including student attendance, course completion, course progress, etc.), the system automatically performs analysis (including which time slots or course content are most popular, and which coaches have the best teaching effect) and obtains analysis results (such as if a coach's course schedule is too tight, the system will recommend other time slots or coaches when scheduling appointments to avoid wasting student and coach resources).

[0138] Based on the analysis results, the system determines the appropriate time slots, instructors, and courses for trainees, and automatically adjusts the subsequent training course schedule for this teaching phase according to the trainees' course performance, resulting in optimized outcomes.

[0139] S7. Provide training quality feedback based on the optimization results.

[0140] In this embodiment, the method for providing training quality feedback includes:

[0141] Based on the optimization results, the system automatically generates a training quality report and provides feedback to trainees and coaches through an adaptive learning algorithm and dynamic adjustment mechanism.

[0142] Trainees will receive detailed feedback based on their course progress and individual performance, which will help them improve their shortcomings. Coaches, on the other hand, can use the reports to understand their strengths and areas for improvement in the teaching process.

[0143] The system dynamically adjusts the course content and learning intensity for the next stage based on student feedback, and provides suggestions for instructors' teaching methods, thereby improving teaching quality.

[0144] Furthermore, the adaptive learning algorithm and dynamic adjustment mechanism include:

[0145] Comprehensive scoring formula and adjustment formula:

[0146] ;

[0147] ;

[0148] ;

[0149] ;

[0150] in:

[0151] 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, is obtained based on real-time training data;

[0152] The student's learning score at time point t, ranging from 0 to 1, represents the student's level of mastery of the course, and is obtained based on real-time training data.

[0153] Student feedback ratings, ranging from 0 to 1, indicate student satisfaction with the course content and teaching methods. These ratings are obtained based on student feedback.

[0154] : The student's overall learning status score at time point t;

[0155] Weighting coefficients: Adjust according to the actual situation; the sum of the three weighting coefficients is 1.

[0156] , , The amount of course content, adaptability to teaching methods, and learning intensity of trainees at time point t are obtained based on the training courses, trainee feedback ratings, and training data at time point t.

[0157] The amount of course content, teaching methods (such as more hands-on training or theoretical explanation), and learning intensity are adjusted dynamically based on students' learning progress, learning scores, and student feedback scores.

[0158] The adjusted course content, teaching methods, and learning intensity for students;

[0159] Dynamic adjustment mechanism:

[0160] Adjustments based on comprehensive score: 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 lower than the threshold, the course content, teaching methods and learning intensity for the next stage will be adjusted.

[0161] Course content adjustment: Based on students' learning progress and scores, the system dynamically adjusts the depth and duration of the course content for the next stage. For example, if the learning progress of a certain module is lagging behind, the system will increase the learning time of that module or repeat related content.

[0162] Teaching method adjustment: Based on the students' feedback ratings, the system dynamically adjusts the teaching methods for the next stage and provides teaching suggestions to the coaches (e.g., more simulation operations or theoretical explanations). If the students' feedback ratings are low, it means that some teaching methods may not be suitable for the students' learning style, and the system will adjust the proportion of teaching methods accordingly.

[0163] Learning Intensity Adjustment: Based on the student's learning progress and scores, the system dynamically adjusts the learning intensity for the next stage. If the student performs poorly, the system will increase the learning intensity to accelerate their mastery of the content. If the student has already mastered certain content, the system will appropriately reduce the learning intensity to avoid overtraining.

[0164] After the training is completed, the system will mark the progress of the training hours based on the training results, making it convenient for trainees to check their training progress in the system.

[0165] Example 2:

[0166] This embodiment also provides a computer device applicable to a method for managing appointments for driving training for driving school students, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for managing appointments for driving training for driving school students as proposed in the above embodiment.

[0167] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for managing appointments for driving school students' driving training as proposed in the above embodiments.

[0168] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices 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 the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0169] 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 this invention, or the part that contributes to the prior art, or a part 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0170] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing 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 (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0171] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0172] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0173] 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 it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for managing appointments for driving school students' driver training, characterized in that, Includes the following steps: S1. Student registration and appointment management system and information input, obtain student information; S2. Verify identity based on student information; S3. Generate a personalized training plan based on the trainee information to obtain the training plan; S4. Schedule appointments according to the training plan and obtain appointment information; S5. Update and notify in real time based on appointment information, and record training data; S6. Conduct data analysis and optimization based on the training data to obtain optimization results; S7. Provide training quality feedback based on the optimization results; The identity verification is based on the following biometric modalities: facial feature vector, fingerprint feature vector, and behavioral trajectory time series calculation; and is verified through a nonlinear modal cross-consistency model, with the identity consistency function being: ； in: : The intermodal nonlinear cross-similarity tensor function is used to calculate higher-order matching relationships between different modalities in the feature space. ； : Outer product operation, construct modal cross tensor; : System registration feature reference tensor; Frobenius norm, used to measure the difference in high-dimensional tensors; This formula refers to the consistency of the cross-structure between modes; The behavioral trajectory consistency index function measures the degree of consistency between the current behavioral trajectory sequence and the historical behavioral model, and is defined as follows: ； Kullback-Leibler divergence measures the deviation between the current behavior distribution and the historical behavior model. : The probability distribution of the current action point; : Behavioral reference distribution during the registration phase; : Standardized mapping function; The methods for providing feedback on training quality include: Based on the optimization results, the system automatically generates a training quality report and provides feedback to trainees and coaches through an adaptive learning algorithm and dynamic adjustment mechanism. The system dynamically adjusts the course content and learning intensity for the next stage based on student feedback, and provides suggestions for instructors' teaching methods, thereby improving teaching quality.

2. The method for managing appointments for driving school students' driver training according to claim 1, characterized in that, The steps and student information input in the student registration and appointment management system in S1 include: Enter your basic personal information to register with the system; Student registration information is stored in the system and undergoes basic verification. Personal basic 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 appointments for driving school students' driver training according to claim 1, characterized in that, The steps for generating a personalized training plan in S3 include: Based on the training needs and desired training periods in the trainees' information, the system generates personalized training plans. By analyzing trainees' historical data, the system provides optimization suggestions based on trainees' training needs.

4. The method for managing appointments for driving school students' driver training according to claim 1, characterized in that, The steps for scheduling in S4 include: The system displays coaches' free time slots and available training course resources in real time; Trainees make training appointments according to the training plan; The system automatically schedules appointments based on student choices using an optimization algorithm, determining the optimal time slot to avoid conflicts and maximize resource utilization.

5. The method for managing appointments for driving school students' driver training according to claim 1, characterized in that, The steps for real-time updates and notifications, and recording training data in S5, include: Once the reservation is completed, the system updates the reservation information in real time. Based on the appointment information, the system automatically notifies students and coaches via SMS and the app; The system will automatically remind you when the class is approaching. In the event of course conflicts or unexpected events, the system will update and notify students and instructors in real time; Record trainees' training data after each course.

6. The method for managing appointments for driving school students' driver training according to claim 1, characterized in that, The methods for data analysis and optimization in S6 include: Based on the training data, the system automatically performs analysis and obtains the analysis results; Based on the analysis results, the system determines the appropriate time slots, instructors, and courses for trainees, and automatically adjusts the subsequent training course schedule for this teaching phase according to the trainees' course performance, resulting in optimized outcomes.

7. The method for managing appointments for driving school students' driver training according to claim 1, 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, is obtained based on real-time training data; The student's learning score at time point t, ranging from 0 to 1, represents the student's level of mastery of the course, and is obtained based on real-time training data. Student feedback ratings, ranging from 0 to 1, indicate student satisfaction with the course content and teaching methods. These ratings are obtained based on student feedback. : The student's overall learning status score at time point t; Weighting coefficients: Adjust according to the actual situation; the sum of the three weighting coefficients is 1. , , The amount of course content, adaptability to teaching methods, and learning intensity of trainees at time point t are obtained based on the training courses, trainee feedback ratings, and training data at time point t. The amount of course content, the suitability of teaching methods, and the intensity of learning are adjusted dynamically based on students' learning progress, learning scores, and student feedback scores. The adjusted course content, teaching methods, and learning intensity for students; Dynamic adjustment mechanism: Adjustments based on overall score: If a student's overall score is below the threshold, the course content, teaching methods, and learning intensity for the next stage will be adjusted. Course content adjustment: The system dynamically adjusts the depth and duration of the course content for the next stage based on students' learning progress and scores. Teaching method adjustment: Based on the feedback and ratings of the trainees, the system dynamically adjusts the teaching methods for the next stage and provides teaching suggestions to the coaches; Learning intensity adjustment: The system dynamically adjusts the learning intensity for the next stage based on the student's learning progress and scores.

Images