Training and evaluation system and method based on competency of pilot

Abstract

The invention discloses a training and evaluation system and method based on the competency of a pilot, and relates to the technical field of educational training, and the system comprises a learning progress monitoring module which is used for collecting the learning data, including the learning time and the task completion condition, of the pilot in the training process in real time, and carrying out the deep analysis of the data, according to the method, the learning progress and the potential difficulty of the pilots are recognized, and a learning progress report is formed, the individual difference and the potential difficulty of each pilot are recognized by monitoring the learning progress and performance of the pilots in real time, and then the training content, the training method and the training progress are adjusted; the targeted training mode ensures that the training content is always matched with the actual requirements of the pilots, the potential of each pilot can be played to the maximum extent, the overall training effect is improved, in addition, real-time feedback and suggestions provided by the system also help the pilots to correct errors and optimize learning strategies in time, and the training effect is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of education and training, and in particular to a training and evaluation system and method based on pilot competency. Background Art

[0002] In the current field of pilot training, although there are a variety of training methods and assessment means, there are still some obvious shortcomings. Traditional pilot training methods often adopt unified training content and methods, ignoring the individual differences and learning characteristics of pilots, resulting in unsatisfactory training results. First, due to the lack of effective real-time monitoring and data analysis methods, it is difficult for trainers to accurately understand the learning progress and potential difficulties of each pilot. This makes it difficult to teach students in accordance with their aptitude during the training process, and it is impossible to provide targeted guidance and help based on the actual situation of the pilots.

[0003] Secondly, traditional evaluation methods often rely on subjective judgment and experience summary, lacking objectivity and accuracy. This evaluation method is difficult to fully reflect the actual ability and performance of pilots, and cannot provide effective feedback and guidance for trainers. In addition, traditional training methods often lack flexibility and dynamism, and are difficult to adapt to the learning needs and pace of pilots at different stages. This may cause some pilots to become tired of learning or anxious during the learning process, affecting the training effect.

[0004] Therefore, in order to solve the above problems and improve the training effect and competence of pilots (Here refers to nine cores Core competencies and their corresponding indicators: knowledge application ability, procedure application and regulation compliance ability, automatic path management ability ability, manual path management, communication, workload management, leadership and teamwork, situational awareness and Information management, problem solving and decision making skills. , a training and assessment system based on pilot competency needs to be developed. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide a training and evaluation system and method based on pilot competency. It can identify the individual differences and potential difficulties of each pilot by real-time monitoring of the pilot's learning progress and performance, and then adjust the training content, methods and progress. This targeted training method ensures that the training content always matches the actual needs of the pilots, and can maximize the potential of each pilot, thereby improving the overall training effect.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a pilot competency-based training and assessment system, the system comprising:

[0007] The learning progress monitoring module is used to collect the pilots’ learning data in real time during the training process, including learning time and task completion status. Through in-depth analysis of these data, the learning progress and potential difficulties of the pilots can be identified, and a learning progress report can be generated.

[0008] The performance evaluation module sets evaluation indicators according to the pilot's competency standards, collects the pilot's operational data in real time during the training process, and conducts an objective and comprehensive evaluation of the pilot's learning outcomes through quantitative evaluation, accurately identifying the pilot's deficiencies;

[0009] The training program adjustment module comprehensively analyzes the current training program and makes targeted adjustments to the training content, methods and progress according to the individual differences and real-time performance of pilots to meet the personalized needs of pilots;

[0010] The real-time feedback module determines the feedback content that needs to be provided to the pilots based on the results of the learning progress monitoring module and the performance evaluation module, selects the appropriate feedback method, provides feedback to the pilots in a timely manner, and guides the pilots to develop improvement plans based on the feedback content to improve the training effect.

[0011] Furthermore, the training program adjustment module includes a program analysis submodule, a content adjustment submodule, a method optimization submodule and a progress adjustment submodule;

[0012] The program analysis submodule comprehensively analyzes the current training program, including the training content, methods and progress, and uses the data analysis tool Excel to count and analyze the pilot training data. By comparing the pilot evaluation results and learning progress reports, it can be found whether the training content is too theoretical, whether the methods are not flexible enough, and whether the progress arrangement is reasonable. Based on these analysis results, a comprehensive training program analysis report is formed to provide a basis for subsequent content adjustment, method optimization and progress adjustment;

[0013] The content adjustment submodule adjusts the training content according to the individual differences and real-time performance of the pilots, identifies the weak links and aspects that need to be strengthened of each pilot according to the evaluation results and learning progress reports of the pilots, and makes targeted adjustments to the training content based on these individual differences. The adjustment content includes adding exercises and case analysis of related actions for pilots who perform poorly in specific flight actions; providing additional theoretical learning materials and exercises for pilots who do not have a solid grasp of theoretical knowledge; in addition, dynamically adjusting the training content according to the real-time performance of the pilots to ensure that the training content always matches the actual needs of the pilots;

[0014] The method optimization submodule evaluates the currently used training methods, analyzes their performance in stimulating pilots' learning interest and improving training results, and selects appropriate training methods based on pilots' learning characteristics and preferences, as well as the nature and requirements of the training content. The training methods include a combination of lecture-style teaching and group discussion for theoretical knowledge that requires in-depth understanding; and a combination of simulated flight and field operation for skill training that requires practical operation.

[0015] The method optimization submodule uses the principles of pedagogy and psychology to guide practice. For example, cognitive load theory is used to optimize teaching design and information presentation, multiple intelligence theory is used to discover and cultivate pilots' multiple intelligences, and motivation theory is used to stimulate pilots' learning motivation.

[0016] The progress adjustment submodule flexibly adjusts the training progress according to the pilot's real-time performance, determines the gap between the current training progress and the expected goal according to the pilot's learning progress and evaluation results, and formulates a personalized training progress adjustment plan in combination with the individual differences and actual needs of the pilot. The personalized training progress adjustment plan includes appropriately increasing the training content or increasing the training difficulty for pilots with faster learning progress to fully tap their potential; for pilots with slower learning progress, the training time can be appropriately extended or additional coaching and support can be provided to help them keep up with the training rhythm. The training progress is dynamically adjusted according to the pilot's real-time feedback and performance to ensure that the training process always meets the pilot's actual needs and rhythm;

[0017] The progress adjustment submodule requires the application of relevant principles and techniques of time management and project management. For example, a Gantt chart can be used to visually display the training progress and plan, the critical path method can be used to determine the key nodes and milestones in the training process, and earned value management can be used to evaluate the training progress and cost-effectiveness.

[0018] Furthermore, the content adjustment submodule will use a weight distribution formula to quantify the importance and priority of different training contents. This formula can be set according to multiple factors such as the pilot's job requirements, skill shortcomings, and training goals. For example, for novice pilots, the weight of basic flying skills may be higher; while for experienced pilots, the weight of advanced flying skills and emergency handling capabilities may be greater. The weight distribution formula can be expressed as: W i Represents the weight of the i-th item, I i It represents the importance score of the i-th item, which is a subjective or objective value that reflects the importance of the item to pilot training. represents the sum of the importance scores of all content items, where n is the total number of content items.

[0019] Furthermore, the content adjustment submodule uses a learning curve model to predict the pilot's learning progress and effect at different stages. The learning curve usually describes the relationship between learning time and learning effect, and determines whether the pilot needs to increase or decrease learning time on a certain content. Typical models of learning curves include exponential learning curves and power-law learning curves. By collecting the pilot's historical learning data, a learning curve suitable for them can be fitted, and the future learning effect can be predicted based on the curve. The content adjustment submodule adopts a differentiated teaching strategy to meet the learning needs of different pilots. Differentiated teaching emphasizes personalized teaching based on the characteristics and needs of each pilot. It can be achieved through a variety of methods, such as stratified teaching, group cooperation and individual tutoring. For pilots with strong learning ability, higher-level learning materials and challenging tasks can be provided, while for pilots with learning difficulties, more basic knowledge and skills review and guidance are required.

[0020] Furthermore, the real-time feedback module includes a feedback content determination submodule, a feedback mode selection submodule, a feedback provision submodule and an improvement plan guidance submodule;

[0021] The feedback content determination submodule receives output data from the learning progress monitoring module and the performance evaluation module, which include quantitative indicators of the pilot's flight time, number of errors, and accuracy in completing tasks. The submodule processes and analyzes the data through a preset regression analysis and decision tree to identify the pilot's weak links in flying skills, theoretical knowledge, or operating habits. Based on the analysis results, the submodule determines specific feedback content, such as suggesting that the pilot strengthen the practice of a specific flight action or reminding him to pay attention to a common error.

[0022] The feedback mode selection submodule collects pilots' personal preferences and receiving habits, including whether the pilots prefer to receive feedback in text, graphic or voice form, and selects appropriate feedback modes based on this information and in combination with the characteristics and importance of the feedback content. The feedback modes include text reports or graphic presentations for feedback content that requires detailed explanation and improvement suggestions; and voice prompts for emergency feedback that requires immediate attention from the pilots.

[0023] The feedback providing submodule timely and accurately conveys the feedback information to the pilot according to the determined feedback content and the selected feedback method, and implements it through the user interface of the pilot training system, such as displaying text reports, charts or playing voice prompts. In order to ensure the timeliness and effectiveness of the feedback, the submodule can also set a reminder mechanism, such as regularly pushing feedback reports or providing feedback immediately after the pilot completes a specific task;

[0024] After receiving the feedback, the improvement plan guidance submodule will further analyze the pilot's weaknesses and specific aspects that need improvement, and based on the analysis results, provide the pilot with personalized improvement suggestions, such as formulating targeted training plans, recommending relevant learning resources, or providing opportunities to communicate with outstanding pilots. This submodule uses the linear regression of the machine learning algorithm to continuously track and evaluate the pilot's improvement process, so as to adjust the improvement plan in a timely manner and provide further feedback.

[0025] Furthermore, in the data processing and analysis stage, the feedback content determination submodule uses regression analysis to explore the relationship between independent variables (such as flight time, number of practice sessions) and dependent variables (such as performance evaluation scores) to help identify key factors affecting pilot performance. Its general form can be expressed as: Y = β0 + β1X1 + β2X2 + ... + β n X n , Y represents the dependent variable, which is usually the target to be predicted, such as the pilot's performance score, X1, X2, X n is an independent variable (such as flight time, number of errors), β0, β1, and β2 are regression coefficients, which are estimated by the least squares method. The decision tree in the pilot feedback content submodule determines the pilot's skill level or problems based on the pilot's performance indicators. After processing and analyzing the data through regression analysis and decision tree methods, the feedback content determination submodule can identify the pilot's weak links in flying skills, theoretical knowledge or operating habits.

[0026] Furthermore, the improvement plan guidance submodule constructs a model to predict the performance score of the pilot at a certain point in the future. Assuming that the pilot's current performance score, training time, and number of errors are used as features (independent variables), and the future performance score is used as the target variable (dependent variable), the linear regression model can be expressed as: future performance score = β0 + β1 × current performance score + β2 × training time + β3 × number of errors + ..., where (β0, β1, β2, β3, ...) are regression coefficients, which are estimated by minimizing the error (such as mean square error) between the predicted value and the actual value. Once the model training is completed, the pilot's current data can be input to predict his future performance score, and the improvement plan can be adjusted based on the prediction result.

[0027] A pilot competency-based training and assessment approach comprising:

[0028] S1. During the training process, the learning progress monitoring module is used to collect the pilots’ learning data in real time, including learning time, task completion status and test scores. The collected learning data is deeply analyzed to identify the pilots’ learning speed, difficulties and areas of strength, so as to judge their learning progress and status.

[0029] S2. Collect the actual performance data of pilots’ operational norms, reaction speed and decision-making ability during the training process, use the performance evaluation module to quantitatively evaluate the performance of pilots according to evaluation indicators and weights, and obtain specific evaluation results. Based on the evaluation results, accurately identify the shortcomings of pilots and provide a basis for subsequent training and evaluation;

[0030] S3. According to the individual differences and real-time performance of pilots, flexibly adjust the training content, methods and progress to ensure that the training content matches the actual needs of pilots, optimize the training methods, improve the training effect, and ensure the consistency and efficiency of the training process;

[0031] S4. Choose appropriate feedback methods, such as text, charts, and voice, to provide timely feedback to pilots, point out their deficiencies in learning and performance, provide specific suggestions for improvement, guide pilots to develop effective improvement plans based on the feedback content, and clarify improvement goals and measures;

[0032] S5. During the evaluation process, continuously monitor the pilots’ learning progress and performance to ensure the effectiveness of real-time feedback and dynamic adjustments. Based on the pilots’ feedback and actual needs, continuously iterate and optimize the training and evaluation methods to improve the overall effect.

[0033] In addition, the method and the corresponding system should be flexible, and the entire process and system should be able to be increased delete.

[0034] In practice, if a pilot is particularly outstanding or excellent in one of his competencies, In this case, the evaluation can actually be adjusted appropriately, such as using the learning progress monitoring module to collect real-time The pilots’ learning data, including learning time, task completion and test scores, is collected and analyzed in depth. When analyzing the learning speed, difficulties and strengths of pilots to judge their learning progress and status, a pilot The student has a particularly good learning progress and status (excellent, for example, the full score in the system evaluation setting is 100 points, If the average score is above 95), then for him, this test can be reduced or the next item can be focused on. The same is true for content and modules. This can greatly simplify the evaluation process and time, and make full use of historical data.

[0035] Compared with the existing technology, the pilot competency-based training and assessment system and method has the following beneficial effects:

[0036] 1. The present invention monitors the pilot's learning progress and performance in real time, identifies the individual differences and potential difficulties of each pilot, and then adjusts the training content, methods and progress. This targeted training method ensures that the training content always matches the actual needs of the pilot, and can maximize the potential of each pilot, thereby improving the overall training effect. In addition, the real-time feedback and suggestions provided by the system also help pilots correct mistakes in a timely manner, optimize learning strategies, and further improve training effects.

[0037] 2. Through in-depth analysis of pilots' learning data and performance data, the system of the present invention can accurately evaluate the training effect and adjust the training plan accordingly. This dynamic optimization process ensures that the training is always kept in the best state and can promptly respond to various problems that arise during the training process. At the same time, the system can also continuously iterate and optimize the training and evaluation methods according to the pilots' feedback and actual needs to improve the overall effect. This dynamic optimization mechanism ensures the continuous improvement of training quality.

[0038] 3. The system has a certain flexibility and can adjust specific modules.

[0039] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 The flowchart is a pilot competency-based training and assessment system and method. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Example 1

[0044] The system mainly includes learning progress monitoring module, performance evaluation module, training program adjustment module and real-time feedback module.

[0045] The learning progress monitoring module starts working, collecting real-time learning data of pilots during the training process, including learning time, task completion status, etc. By deeply analyzing these data, the system can identify the pilots' learning progress and potential difficulties, and form learning progress reports. These reports provide trainers with an intuitive understanding of the pilots' learning status.

[0046] At the same time, the performance assessment module evaluates the performance of candidates based on pre-defined competency criteria. (Here refers to the nine core Capacity and its corresponding indicators: knowledge application ability, procedure application and regulation compliance ability, automatic path management ability, human Flight path management skills, communication skills, workload management skills, leadership and teamwork skills, situational awareness and information Management skills, problem solving and decision making skills.) , collects the pilot's operating data in real time, and through quantitative evaluation, the system comprehensively assesses the pilot's flying skills, decision-making ability, reaction speed, etc., and accurately identifies the pilot's shortcomings, which provides strong support for subsequent targeted training.

[0047] It should be noted that competence or competency standards are technically a rather vague concept. Parameter selection is a quantitative process. Here we take the knowledge application ability as an example. Other ideas are similar to this:

[0048] First, the system is designed to assess the individual's understanding and The degree of application, so that the ability to apply knowledge can be quantified.

[0049] The quantitative method can be to design test questions or task scenarios with different difficulty levels and For example, you can design test questions about aircraft operation, flight rules, etc., and then score the pilot based on his or her answers. The accuracy rate is used to assess their ability to apply knowledge.

[0050] Next, the training program adjustment module comes into play. This module first uses Excel data analysis tools to conduct a comprehensive analysis of the current training program, including training content, methods and progress. Based on the results of the learning progress monitoring module and the performance evaluation module, for example, the system finds that some pilots have difficulties in specific flight maneuvers, while other pilots do not have a solid grasp of theoretical knowledge.

[0051] The content adjustment submodule begins to adjust the training content based on the individual differences and real-time performance of pilots. For pilots who perform poorly in specific flight maneuvers, the system adds exercises and case analyses of related maneuvers. For pilots who do not have a solid grasp of theoretical knowledge, the system provides additional theoretical learning materials and exercises. At the same time, the system also uses a learning curve model to predict the pilot's learning progress and effects at different stages, and makes timely adjustments to the training content based on the prediction results.

[0052] The method optimization sub-module evaluated the current training methods. The system found that traditional lecture-based teaching was not effective in some aspects, so it began to try to introduce more interactive and practical aspects. For theoretical knowledge that requires in-depth understanding, the system adopted a combination of lecture-style teaching and group discussion; for skill training that requires practical operations, the system adopted a combination of simulated flight and field operations.

[0053] The progress adjustment submodule flexibly adjusts the training progress according to the pilot's real-time performance and learning progress. For pilots with faster learning progress, the system appropriately increases the training content or increases the training difficulty; for pilots with slower learning progress, the system appropriately extends the training time or provides additional guidance and support.

[0054] Finally, the real-time feedback module determines the feedback content that needs to be provided to the pilots based on the results of the learning progress monitoring module and the performance evaluation module. The system selects appropriate feedback methods, such as text, charts, and voice, and provides timely feedback to the pilots, and guides them to develop improvement plans based on the feedback content. The pilots continuously adjust their learning methods and strategies based on the system's feedback and suggestions, and have made significant progress.

[0055] Example 2

[0056] In order to improve the training effect of pilots, an airline decided to adopt an intelligent pilot training and evaluation system. The system integrates a learning progress monitoring module and a performance evaluation module, which can collect and analyze pilots' learning and operation data in real time, thereby providing them with personalized training and feedback.

[0057] First, after the system started running, the pilots began to participate in training courses. The system collected each pilot's learning data in real time through the learning progress monitoring module, including their learning time, task completion status and test scores in each learning module. These data were automatically transmitted to the system's analysis module. After in-depth analysis, the system was able to identify each pilot's learning speed, difficulties encountered and areas of strength.

[0058] For example, the system found that pilot A learned flight theory quickly but performed poorly in simulated flight operations, especially his slow reaction speed in emergency situations, while pilot B performed well in flight operations but had shortcomings in learning theoretical knowledge.

[0059] Next, the system enters the performance evaluation stage. During the training process, pilots need to carry out a series of simulated flight missions to test their operational standardization, reaction speed and decision-making ability. The system collects these actual performance data through the performance evaluation module and conducts quantitative evaluation according to preset evaluation indicators and weights.

[0060] For Pilot A, the system found that his reaction speed in the simulated emergency landing mission was significantly slow, resulting in low quality of mission completion, while Pilot B scored low in the theoretical knowledge test, indicating a lack of in-depth understanding of certain key concepts.

[0061] Based on these evaluation results, the system began to develop personalized training plans for each pilot. For Pilot A, the system added training content for simulated emergency situations and provided targeted operational guidance to improve his reaction speed. For Pilot B, the system strengthened the explanation and practice of key theoretical knowledge to help him better master this knowledge.

[0062] During the training process, the system also provides timely feedback to pilots through various means such as text, charts and voice. For example, for Pilot A's reaction speed problem, the system generated a detailed feedback report, pointing out his shortcomings in the simulated task and providing specific suggestions for improvement. Pilot A can develop his own improvement plan based on this report and focus on these issues in subsequent training.

[0063] Finally, the system will continue to monitor the pilots' learning progress and performance, and continuously optimize training and assessment methods based on their feedback and actual needs. For example, if pilots generally feel that a certain training module is too difficult or too easy, the system can adjust the difficulty setting of the module to ensure the appropriateness of the training content.

[0064] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A pilot competency-based training and assessment system, characterized in that: The system includes: The learning progress monitoring module is used to collect the pilots’ learning data in real time during the training process, including learning time and task completion status. Through in-depth analysis of these data, the learning progress and potential difficulties of the pilots can be identified, and a learning progress report can be generated. The performance evaluation module sets evaluation indicators according to the pilot's competency standards, collects the pilot's operational data in real time during the training process, and conducts an objective and comprehensive evaluation of the pilot's learning outcomes through quantitative evaluation, accurately identifying the pilot's deficiencies; The training program adjustment module comprehensively analyzes the current training program and makes targeted adjustments to the training content, methods and progress according to the individual differences and real-time performance of pilots to meet the personalized needs of pilots; The real-time feedback module determines the feedback content that needs to be provided to the pilots based on the results of the learning progress monitoring module and the performance evaluation module, selects the appropriate feedback method, provides feedback to the pilots in a timely manner, and guides the pilots to develop improvement plans based on the feedback content to improve the training effect.

2. The pilot competency-based training and assessment system according to claim 1, characterized in that: The training program adjustment module includes a program analysis submodule, a content adjustment submodule, a method optimization submodule and a progress adjustment submodule; The program analysis submodule comprehensively analyzes the current training program, including the training content, methods and progress, and uses the data analysis tool Excel to count and analyze the pilot training data. By comparing the pilot evaluation results and learning progress reports, it can be found whether the training content is too theoretical, whether the methods are not flexible enough, and whether the progress arrangement is reasonable. Based on these analysis results, a comprehensive training program analysis report is formed to provide a basis for subsequent content adjustment, method optimization and progress adjustment; The content adjustment submodule adjusts the training content according to the individual differences and real-time performance of the pilots, identifies the weak links and aspects that need to be strengthened of each pilot according to the evaluation results and learning progress reports of the pilots, and makes targeted adjustments to the training content based on these individual differences. The adjustment content includes adding exercises and case analysis of related actions for pilots who perform poorly in specific flight actions; providing additional theoretical learning materials and exercises for pilots who do not have a solid grasp of theoretical knowledge; in addition, dynamically adjusting the training content according to the real-time performance of the pilots to ensure that the training content always matches the actual needs of the pilots; The method optimization submodule evaluates the currently used training methods, analyzes their performance in stimulating pilots' learning interest and improving training results, and selects appropriate training methods based on pilots' learning characteristics and preferences, as well as the nature and requirements of the training content. The training methods include a combination of lecture-style teaching and group discussion for theoretical knowledge that requires in-depth understanding; and a combination of simulated flight and field operation for skill training that requires practical operation. The method optimization submodule uses the principles of pedagogy and psychology to guide practice. For example, cognitive load theory is used to optimize teaching design and information presentation, multiple intelligence theory is used to discover and cultivate pilots' multiple intelligences, and motivation theory is used to stimulate pilots' learning motivation. The progress adjustment submodule flexibly adjusts the training progress according to the pilot's real-time performance, determines the gap between the current training progress and the expected goal according to the pilot's learning progress and evaluation results, and formulates a personalized training progress adjustment plan based on the individual differences and actual needs of the pilot. The personalized training progress adjustment plan includes appropriately increasing the training content or increasing the training difficulty for pilots with faster learning progress to fully tap their potential; For pilots with slow learning progress, the training time can be appropriately extended or additional coaching and support can be provided to help them keep up with the training pace. The training progress can be dynamically adjusted based on the pilots' real-time feedback and performance to ensure that the training process always meets the pilots' actual needs and pace; The progress adjustment submodule requires the application of relevant principles and techniques of time management and project management. For example, a Gantt chart can be used to visually display the training progress and plan, the critical path method can be used to determine the key nodes and milestones in the training process, and earned value management can be used to evaluate the training progress and cost-effectiveness.

3. The pilot competency-based training and assessment system according to claim 2, characterized in that: The content adjustment submodule will use a weight distribution formula to quantify the importance and priority of different training contents. This formula can be set according to multiple factors such as the pilot's job requirements, skill shortcomings, and training goals. For example, for novice pilots, the weight of basic flying skills may be higher; while for experienced pilots, the weight of advanced flying skills and emergency handling capabilities may be greater. The weight distribution formula can be expressed as: W i Represents the weight of the i-th item, I i It represents the importance score of the i-th item, which is a subjective or objective value that reflects the importance of the item to pilot training. represents the sum of the importance scores of all content items, where n is the total number of content items.

4. The pilot competency-based training and assessment system according to claim 2, characterized in that: The content adjustment submodule uses a learning curve model to predict the pilot's learning progress and effect at different stages. The learning curve usually describes the relationship between learning time and learning effect, and determines whether the pilot needs to increase or decrease learning time on a certain content. Typical models of learning curves include exponential learning curves and power-law learning curves. By collecting the pilot's historical learning data, a learning curve suitable for them can be fitted, and the future learning effect can be predicted based on the curve. The content adjustment submodule adopts a differentiated teaching strategy to meet the learning needs of different pilots. Differentiated teaching emphasizes personalized teaching based on the characteristics and needs of each pilot. It can be achieved through a variety of methods, such as stratified teaching, group cooperation and individual tutoring. For pilots with strong learning ability, higher-level learning materials and challenging tasks can be provided, while for pilots with learning difficulties, more basic knowledge and skills review and guidance are required.

5. The pilot competency-based training and assessment system according to claim 1, characterized in that: The real-time feedback module includes a feedback content determination submodule, a feedback mode selection submodule, a feedback provision submodule and an improvement plan guidance submodule; The feedback content determination submodule receives output data from the learning progress monitoring module and the performance evaluation module, which include quantitative indicators of the pilot's flight time, number of errors, and accuracy in completing tasks. The submodule processes and analyzes the data through a preset regression analysis and decision tree to identify the pilot's weak links in flying skills, theoretical knowledge, or operating habits. Based on the analysis results, the submodule determines specific feedback content, such as suggesting that the pilot strengthen the practice of a specific flight action or reminding him to pay attention to a common error. The feedback mode selection submodule collects pilots' personal preferences and receiving habits, including whether the pilots prefer to receive feedback in text, graphic or voice form, and selects appropriate feedback modes based on this information and in combination with the characteristics and importance of the feedback content. The feedback modes include text reports or graphic presentations for feedback content that requires detailed explanation and improvement suggestions; and voice prompts for emergency feedback that requires immediate attention from the pilots. The feedback providing submodule timely and accurately conveys the feedback information to the pilot according to the determined feedback content and the selected feedback method, and implements it through the user interface of the pilot training system, such as displaying text reports, charts or playing voice prompts. In order to ensure the timeliness and effectiveness of the feedback, the submodule can also set a reminder mechanism, such as regularly pushing feedback reports or providing feedback immediately after the pilot completes a specific task; After receiving the feedback, the improvement plan guidance submodule will further analyze the pilot's weaknesses and specific aspects that need improvement, and based on the analysis results, provide the pilot with personalized improvement suggestions, such as formulating targeted training plans, recommending relevant learning resources, or providing opportunities to communicate with outstanding pilots. This submodule uses the linear regression of the machine learning algorithm to continuously track and evaluate the pilot's improvement process, so as to adjust the improvement plan in a timely manner and provide further feedback.

6. The pilot competency-based training and assessment system according to claim 5, characterized in that: In the data processing and analysis stage, the feedback content determination submodule uses regression analysis to explore the relationship between independent variables (such as flight time, number of practice sessions) and dependent variables (such as performance evaluation scores) to help identify key factors affecting pilot performance. Its general form can be expressed as: Y = β0 + β1X1 + β2X2 + ... + β n X n , Y represents the dependent variable, which is usually the target to be predicted, such as the pilot's performance score, X1, X2, X n is an independent variable (such as flight time, number of errors), β0, β1, and β2 are regression coefficients, which are estimated by the least squares method. The decision tree in the pilot feedback content submodule determines the pilot's skill level or problems based on the pilot's performance indicators. After processing and analyzing the data through regression analysis and decision tree methods, the feedback content determination submodule can identify the pilot's weak links in flying skills, theoretical knowledge or operating habits.

7. The pilot competency-based training and assessment system according to claim 5, characterized in that: The improvement plan guidance submodule constructs a model to predict the performance score of the pilot at a certain point in the future. Assuming that the pilot's current performance score, training time, and number of errors are used as features (independent variables), and the future performance score is used as the target variable (dependent variable), the linear regression model can be expressed as: future performance score = β0 + β1 × current performance score + β2 × training time + β3 × number of errors + ..., where (β0, β1, β2, β3, ...) are regression coefficients, which are estimated by minimizing the error (such as mean square error) between the predicted value and the actual value. Once the model training is completed, the pilot's current data can be input to predict his future performance score, and the improvement plan can be adjusted based on the prediction result.

8. A pilot competency-based training and assessment method, characterized in that: The method includes: S1. During the training process, the learning progress monitoring module is used to collect the pilots’ learning data in real time, including learning time, task completion status and test scores. The collected learning data is deeply analyzed to identify the pilots’ learning speed, difficulties and areas of strength, so as to judge their learning progress and status. S2. Collect the actual performance data of pilots’ operational norms, reaction speed and decision-making ability during the training process, use the performance evaluation module to quantitatively evaluate the performance of pilots according to evaluation indicators and weights, and obtain specific evaluation results. Based on the evaluation results, accurately identify the shortcomings of pilots and provide a basis for subsequent training and evaluation; S3. According to the individual differences and real-time performance of pilots, flexibly adjust the training content, methods and progress to ensure that the training content matches the actual needs of pilots, optimize the training methods, improve the training effect, and ensure the consistency and efficiency of the training process; S4. Choose appropriate feedback methods, such as text, charts, and voice, to provide timely feedback to pilots, point out their deficiencies in learning and performance, provide specific suggestions for improvement, guide pilots to develop effective improvement plans based on the feedback content, and clarify improvement goals and measures; S5. During the evaluation process, continuously monitor the pilots’ learning progress and performance to ensure the effectiveness of real-time feedback and dynamic adjustments. Based on the pilots’ feedback and actual needs, continuously iterate and optimize the training and evaluation methods to improve the overall effect.

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