Method for testing physical and mental quality of flight trainee based on artificial high-pressure environment
By acquiring data on the physical and mental qualities of flight trainees under high-pressure environments using multi-source heterogeneous sensors, establishing a database, and conducting psychological and physiological state assessments, the inaccuracy and inefficiency of existing tests are resolved, enabling accurate assessment and selection of flight trainees' physical and mental qualities.
Patent Information
- Application Number
- CN202511698958.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-19
- Publication Date
- 2026-02-27
AI Technical Summary
The existing physical and mental fitness tests for flight trainees lack standardized procedures and are affected by objective human factors and differences in trainees' abilities, resulting in inaccurate test results and poor efficiency.
Multi-source heterogeneous sensors are used to acquire test data on the physical and mental qualities of flight trainees under high-pressure environments. A database is established, and psychological and physiological stress indicators are evaluated through convolutional recognition and physiological state quantification. Weighted calculations are then performed to determine comprehensive operational capabilities and to judge whether the test is passed.
It enables accurate assessment of flight trainees in high-pressure environments, improving selection efficiency and the accuracy of test results.
Smart Images
Figure CN121570178A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of data analysis, in particular to a method for testing the physical and mental qualities of a flight student in a high-pressure environment. BACKGROUND
[0002] The testing of the physical and mental qualities of a flight student in a high-pressure environment is usually carried out by simulating extreme flight conditions, such as high G force, air pressure changes, complex flight operations, etc., to evaluate the student's psychological endurance, emergency response, decision-making ability, and physiological adaptation ability under pressure. Ensure that the flight student can remain calm and effectively respond to unexpected situations in a high-pressure environment, and ensure flight safety.
[0003] The existing testing of the physical and mental qualities of a flight student mainly relies on human monitoring or intervention to help the flight student gradually acquire experience in coping with high-pressure environments, but is affected by human objective factors and the student's own ability gap, so that the existing testing of the physical and mental qualities of a flight student does not have a standardized monitoring and testing scheme, resulting in inaccurate test results and poor test efficiency. SUMMARY
[0004] To solve the above technical problems, a method for testing the physical and mental qualities of a flight student in a high-pressure environment is provided, which solves the problem of the existing testing of the physical and mental qualities of a flight student mainly relying on human monitoring or intervention to help the flight student gradually acquire experience in coping with high-pressure environments, but being affected by human objective factors and the student's own ability gap, so that the existing testing of the physical and mental qualities of a flight student does not have a standardized monitoring and testing scheme, resulting in inaccurate test results and poor test efficiency.
[0005] To achieve the above purposes, the technical scheme adopted by the present application is as follows:
[0006] A method for testing the physical and mental qualities of a flight student in a high-pressure environment, comprising:
[0007] Compared with the prior art, the present application has the following advantages:
[0008] Based on a plurality of heterogeneous sensors, a plurality of rounds of flight student physical and mental quality test data in a high-pressure environment are obtained, and a flight student physical and mental quality test database in a high-pressure environment is established;
[0009] Based on the flight student physical and mental quality test database in a high-pressure environment, convolution recognition is performed on the behavior and action in the flight student physical and mental quality test data in different high-pressure environments, and the psychological stress state index of the flight student in different high-pressure environments is evaluated;
[0010] The physiological stress state index of the flight student under the artificial high-pressure environment is evaluated according to the physiological state comprehensive quantification in the flight student physical and mental quality test database under different artificial high-pressure environments.
[0011] The comprehensive operation capability index of the flight student under the artificial high-pressure environment is calculated based on the psychological stress state index of the flight student under different artificial high-pressure environments and the physiological stress state index of the flight student under different artificial high-pressure environments.
[0012] The standardized operation vector of the flight student under the artificial high-pressure environment is determined based on the standardized operation data of the flight student under the artificial high-pressure environment.
[0013] It is judged whether the comprehensive operation capability index of the flight student under the artificial high-pressure environment is in the range interval of the standardized operation vector of the flight student under the artificial high-pressure environment, if yes, it is determined to pass the test, and if not, it is determined to fail the test.
[0014] Preferably, the psychological stress state index of the flight student under different artificial high-pressure environments is evaluated according to the behavior action convolution recognition in the flight student physical and mental quality test database under different artificial high-pressure environments, and the psychological stress state index of the flight student under different artificial high-pressure environments specifically includes:
[0015] Based on the OpenPose human posture recognition, the human key point tracking mark is performed on the operation video stream in the flight student physical and mental quality test database under the artificial high-pressure environment, and the operation action feature data of the flight student under the artificial high-pressure environment is obtained; the operation action features include: limb action, limb tension, action speed;
[0016] According to the observation target of action fluency, the operation action feature data of the flight student under the artificial high-pressure environment is screened and quantified to obtain the operation action fluency index of each flight student;
[0017] Based on the limb joint feature data in the operation action feature data of the flight student under the artificial high-pressure environment, the operation action limb tension index of each flight student is calculated;
[0018] Based on the operation action fluency index of each flight student and the operation action limb tension index of each flight student, the operation limb action score of each flight student is calculated.
[0019] Based on the CNN convolution neural network, the flight student face key point tracking mark is performed on the operation video stream in the flight student physical and mental quality test database under the artificial high-pressure environment, and the flight student face action feature data under the artificial high-pressure environment is obtained; the face action features include: face muscle action, eye expression action, and mouth expression action.
[0020] Based on the facial action feature data of the flight trainees in the artificial high-pressure environment, the facial action feature change state of each flight trainee is analyzed, and the facial muscle tension index of each flight trainee is evaluated;
[0021] Based on the facial action feature data of the flight trainees in the artificial high-pressure environment, the facial action feature data of the flight trainees in the artificial high-pressure environment is analyzed, and the facial action feature data of the flight trainees in the artificial high-pressure environment is analyzed.
[0022] According to the emotional type intensity of each flight trainee, the emotional index of each flight trainee is calculated according to the positive and negative emotions.
[0023] Based on the facial muscle tension index of each flight trainee and the emotional index of each flight trainee, the operation facial expression action score of each flight trainee is calculated.
[0024] Based on the operation limb action score of each flight trainee and the operation facial expression action score of each flight trainee, the psychological stress state index of the flight trainee in different artificial high-pressure environment is calculated.
[0025] The calculation of the operation limb action score of each flight trainee is as follows:
[0026]
[0027] In the formula, S i is the operation limb action score of the i-th flight trainee, α is the fluency weight, β is the tension weight coefficient, is the operation action fluency index of each flight trainee, is the operation action limb tension index of each flight trainee, Δx ij is the j-th operation action feature data of the i-th flight trainee, Δy ij is the j-th operation action feature data of the i-th flight trainee, T is the total time length of the operation action, n is the total number of flight trainees, and m is the total number of operation action feature data.
[0028] The calculation of the operation facial expression action score of each flight trainee is as follows:
[0029]
[0030] In the formula, V i is the operation facial expression action score of the i-th flight trainee, G i is the facial muscle tension index of the i-th flight trainee, AU ik(t) represents the facial movement feature of the i-th flight trainee at the t-th unit time, AU ik (t-1) represents the k-th facial movement feature of the i-th flight trainee in the (t-1)-th unit of time, P ik Let i be the emotional index of the i-th flight trainee. For the i-th flight student, the k-th... 正 The probability distribution of each positive emotion type For the i-th flight student, the k-th... 负 The probability distribution of negative emotion types is given by γ, which is the positive emotion influence coefficient, δ, which is the negative emotion influence coefficient, w1, which is the weight of the emotion index, and w2, which is the weight of the facial muscle tension index.
[0031] Specifically, the calculation of psychological stress indicators for flight trainees under high-pressure environments for different individuals involves:
[0032] D i =γ1·S i +γ2·V i
[0033] In the formula, D i This refers to the psychological stress index of the i-th flight trainee under high-pressure conditions for different individuals.
[0034] Preferably, the feature is that, based on the database of flight trainees' physical and mental fitness tests under artificial high-pressure environments, the physiological stress state indicators of flight trainees under different artificial high-pressure environments are comprehensively quantified according to the physiological state in the test data. Specifically, the evaluation includes:
[0035] Physiological data of flight trainees under different artificial high-pressure environments were selected by unit time and physiological data of flight trainees under different artificial high-pressure environments were established.
[0036] Based on the physiological time series data of flight trainees under different high-pressure environments, we analyze the changing trends of the physiological time series data of flight trainees and assess the psychological resilience of flight trainees under different high-pressure environments.
[0037] Based on the physiological time series data of flight trainees under different high-pressure environments, we analyzed the changing trends of the physiological time series data of flight trainees and quantified the heart rate load index and load adaptability of flight trainees under different high-pressure environments.
[0038] Based on random events in the test data of flight trainees’ physical and mental qualities under different high-pressure environments, the decision-making actions of each flight trainee are statistically analyzed to assess their decision-making ability under different high-pressure environments.
[0039] The physiological stress state index of the flight trainee under different artificial high-pressure environments is calculated based on the psychological bearing capacity, heart rate load index, G load adaptation capacity of the flight trainee under different high-pressure environments, and the decision-making capacity of each flight trainee.
[0040] The physiological stress state index of the flight trainee under different artificial high-pressure environments is calculated based on the psychological bearing capacity, heart rate load index, G load adaptation capacity of the flight trainee under different high-pressure environments, and the decision-making capacity of each flight trainee.
[0041] PI i = φ1 × PC i + φ2 × HRLI i + φ3 × GAA i + φ4 × DC i
[0042] In the formula, PI i is the physiological stress state index of the i th flight trainee under different artificial high-pressure environments, PC i is the psychological bearing capacity of the i th flight trainee under different high-pressure environments, HRLI i is the heart rate load index of the i th flight trainee under different artificial high-pressure environments, GAA i is the load adaptation capacity of the flight trainee under different high-pressure environments, DC i is the decision-making capacity of the flight trainee under different high-pressure environments, and φ1, φ2, φ3, and φ4 are the weights of the psychological bearing capacity (PC), heart rate load index (HRLI), load adaptation capacity (GAA), and decision-making capacity (DC), respectively.
[0043] Preferably, whether the comprehensive operation capacity index of the flight trainee under the artificial high-pressure environment is within the standardized operation vector range interval of the flight trainee under the artificial high-pressure environment is determined based on the following steps.
[0044] The psychological stress state index of the flight trainee under different artificial high-pressure environments and the physiological stress state index of the flight trainee under different artificial high-pressure environments are normalized, and the comprehensive operation capacity index of the flight trainee under the artificial high-pressure environment is calculated.
[0045] Based on linear mapping, the comprehensive operation capacity index of the flight trainee under the artificial high-pressure environment is converted into a comprehensive operation capacity index vector of the flight trainee under the artificial high-pressure environment.
[0046] The spatial distance between the comprehensive operation capacity index vector of the flight trainee under the artificial high-pressure environment and the standardized operation vector of the flight trainee under the artificial high-pressure environment is calculated, and whether the comprehensive operation capacity index of the flight trainee under the artificial high-pressure environment is within the standardized operation vector range interval of the flight trainee under the artificial high-pressure environment is determined.
[0047] The comprehensive operation capability index of the flight trainee in the artificial high-pressure environment is calculated as follows:
[0048]
[0049] In the formula, C i is the comprehensive operation capability index of the ith flight trainee in the artificial high-pressure environment, min() is a minimum value function, max() is a maximum value function, p is the weight of the psychological stress index, and s is the weight of the physiological stress index;
[0050] The space distance between the comprehensive operation capability index vector of the flight trainee in the artificial high-pressure environment and the standardized operation vector of the flight trainee in the artificial high-pressure environment is calculated as follows:
[0051]
[0052] In the formula, is the space distance between the comprehensive operation capability index vector of the flight trainee in the artificial high-pressure environment and the standardized operation vector of the flight trainee in the artificial high-pressure environment, is the comprehensive operation capability index of the ith flight trainee in the artificial high-pressure environment, is the standardized operation vector of the ith flight trainee in the artificial high-pressure environment, and Q is the total number of artificial high-pressure environments.
[0053] The present application provides a physical and mental quality testing scheme for flight trainees in an artificial high-pressure environment. The physical and mental quality testing data of the flight trainees in the high-pressure environment are obtained based on multiple source heterogeneous sensors, and a corresponding database is established. By analyzing the behavior and physiological state, the psychological and physiological stress state indexes of the trainees are evaluated, and a weighted calculation is performed to determine the comprehensive operation capability index of the flight trainee, and compared with the standardized operation data to determine whether the trainee passes the test. This scheme can accurately evaluate the physical and mental quality of the flight trainee in the high-pressure environment, and improve the selection efficiency of the pilot. BRIEF DESCRIPTION OF DRAWINGS
[0054] Figure 1 It is a physical and mental quality testing method flow chart for flight trainees in an artificial high-pressure environment;
[0055] Figure 2 It is a method flow chart for evaluating the psychological stress state index of flight trainees in different artificial high-pressure environments;
[0056] Figure 3 It is a method flow chart for evaluating the physiological stress state index of flight trainees in different artificial high-pressure environments;
[0057] Figure 4A flow chart of a method for judging whether a comprehensive operation ability index of a flight student in a high-pressure environment is within a standardized operation vector range of a flight student in a high-pressure environment. DETAILED DESCRIPTION
[0058] The following description is provided to disclose the present application so that those skilled in the art can implement the present application. The preferred embodiments in the following description are only examples, and other obvious modifications can be made by those skilled in the art.
[0059] Reference Figure 1 As shown in the figure, a physical and mental quality testing method for a flight student in a high-pressure environment includes:
[0060] Based on a multi-source heterogeneous sensor, physical and mental quality testing data of a flight student in a high-pressure environment is obtained, and a physical and mental quality testing database of a flight student in a high-pressure environment is established;
[0061] Based on the physical and mental quality testing database of a flight student in a high-pressure environment, convolution recognition is performed according to behavior actions in different physical and mental quality testing data of a flight student in a high-pressure environment, and a psychological stress state index of a flight student in a high-pressure environment is evaluated;
[0062] Based on the physical and mental quality testing database of a flight student in a high-pressure environment, physiological state comprehensive quantification is performed according to different physical and mental quality testing data of a flight student in a high-pressure environment, and a physiological stress state index of a flight student in a high-pressure environment is evaluated;
[0063] Based on the psychological stress state index of a flight student in a high-pressure environment and the physiological stress state index of a flight student in a high-pressure environment, a comprehensive operation ability index of a flight student in a high-pressure environment is calculated by weighting;
[0064] Based on standardized operation data of a flight student in a high-pressure environment, a standardized operation vector of a flight student in a high-pressure environment is determined;
[0065] Whether the comprehensive operation ability index of a flight student in a high-pressure environment is within the standardized operation vector range of a flight student in a high-pressure environment is judged, if yes, it is determined to pass the test, if not, it is determined to fail the test.
[0066] The scheme can accurately evaluate the physical and mental quality of flight students in high-pressure environment and improve the selection efficiency of pilots.
[0067] Referring to Figure 2 According to the behavior action in the flight student physical and mental quality test data in different artificial high-pressure environments, the psychological stress state index of the flight student in different artificial high-pressure environments is evaluated, and the psychological stress state index of the flight student in different artificial high-pressure environments is evaluated.
[0068] Based on the OpenPose human posture recognition, the human key point tracking mark is performed on the operation video stream in the flight student physical and mental quality test database in the artificial high-pressure environment, and the operation action feature data of the flight student in the artificial high-pressure environment is obtained; the operation action feature includes: limb action, limb tension, action speed;
[0069] According to the observation target of action fluency, the flight student operation action feature data in the artificial high-pressure environment is screened and quantified, and the operation action fluency index of each flight student is obtained.
[0070] Based on the limb joint feature data in the flight student operation action feature data in the artificial high-pressure environment, the operation action limb tension index of each flight student is calculated.
[0071] Based on the operation action fluency index of each flight student and the operation action limb tension index of each flight student, the operation limb action score of each flight student is calculated.
[0072] Based on the CNN convolutional neural network, the flight student face key point tracking mark is performed on the operation video stream in the flight student physical and mental quality test database in the artificial high-pressure environment, and the flight student face action feature data in the artificial high-pressure environment is obtained; the face action feature includes: face muscle action, eye expression action, mouth expression action;
[0073] Based on the flight student face action feature data in the artificial high-pressure environment, the face action feature change state of each flight student is analyzed, and the face muscle tension index of each flight student is evaluated.
[0074] Based on the facial motion feature data of each person in a high-pressure environment, the emotional probability distribution of the flight student is calculated according to the facial expression label corresponding to the facial motion feature, and the emotional type intensity of each flight student is quantified; the facial expression label includes joy, surprise, fear and sadness;
[0075] According to the emotional type intensity of each flight student, the emotional index of each flight student is calculated by comprehensively calculating the positive and negative emotions;
[0076] Based on the facial muscle tension index of each flight student and the emotional index of each flight student, the operation facial expression action score of each flight student is calculated;
[0077] Based on the operation limb action score of each flight student and the operation facial expression action score of each flight student, the psychological stress state index of the flight student in different human high-pressure environment is calculated;
[0078] The calculation of the operation limb action score of each flight student is specifically:
[0079]
[0080] In the formula, S i is the operation limb action score of the i th flight student, α is the fluency weight, β is the tension weight coefficient, is the operation action fluency index of each flight student, is the operation action limb tension index of each flight student, Δx ij is the j th operation action feature data of the i th flight student, Δy ij is the j th operation action feature data of the i th flight student, T is the total time length of the operation action, n is the total number of flight students, and m is the total number of operation action feature data.
[0081] The calculation of the operation facial expression action score of each flight student is specifically:
[0082]
[0083] In the formula, V i is the operation facial expression action score of the i th flight student, G i is the facial muscle tension index of the i th flight student, AU ik (t) is the k th facial motion feature of the i th flight student in the t th unit time, AU ik (t-1) is the k th facial motion feature of the i th flight student in the t-1 th unit time, P ik is the emotional index of the i th flight student, For the i-th flight student, the k-th... 正 The probability distribution of each positive emotion type For the i-th flight student, the k-th... 负 The probability distribution of negative emotion types is given by γ, which is the positive emotion influence coefficient, δ, which is the negative emotion influence coefficient, w1, which is the weight of the emotion index, and w2, which is the weight of the facial muscle tension index.
[0084] Specifically, the calculation of psychological stress indicators for flight trainees under high-pressure environments for different individuals involves:
[0085] D i =γ1·S i +γ2·V i
[0086] In the formula, D i This refers to the psychological stress index of the i-th flight trainee under high-pressure conditions for different individuals.
[0087] This solution utilizes OpenPose to track key points in flight trainees' body movements, extracting body movement features (such as body tension and movement fluency) to calculate body movement scores. Secondly, it uses CNN to recognize trainees' facial expressions, assessing facial muscle tension and emotion type, thereby quantifying the trainees' emotion index and facial expression / movement scores. Finally, based on the combined body movement and facial expression scores, it calculates the flight trainees' psychological stress level. Through quantitative movement analysis and emotion recognition, it can accurately reflect the psychological stress of flight trainees, providing parameters for subsequent consideration of their mental well-being.
[0088] Reference Figure 3 As shown, based on the database of physical and mental fitness tests for flight trainees under artificially high-pressure environments, and according to the comprehensive quantification of physiological states in the test data of flight trainees under different artificially high-pressure environments, the specific indicators for evaluating the physiological stress state of flight trainees under different artificially high-pressure environments include:
[0089] Physiological data of flight trainees under different artificial high-pressure environments were selected by unit time and physiological data of flight trainees under different artificial high-pressure environments were established.
[0090] Based on the physiological time series data of flight trainees under different high-pressure environments, we analyze the changing trends of the physiological time series data of flight trainees and assess the psychological resilience of flight trainees under different high-pressure environments.
[0091] Based on the physiological time series data of flight trainees under different high-pressure environments, we analyzed the changing trends of the physiological time series data of flight trainees and quantified the heart rate load index and load adaptability of flight trainees under different high-pressure environments.
[0092] Based on random events in the physical and mental quality test data of flight students in different artificial high-pressure environments, the decision actions of each flight student are counted, and the decision-making ability of flight students in different artificial high-pressure environments is analyzed.
[0093] Based on the psychological bearing capacity, heart rate load index, load adaptation ability of flight students in different high-pressure environments, and the decision-making ability of each flight student, the physiological stress state index of flight students in different artificial high-pressure environments is calculated.
[0094] Among them, the calculation of the physiological stress state index of flight students in different artificial high-pressure environments is specifically:
[0095] PI i = φ1 x PC i + φ2 x HRLI i + φ3 x GAA i + φ4 x DC i
[0096] In the formula, PI i is the physiological stress state index of the i-th flight student in different artificial high-pressure environments, PC i is the psychological bearing capacity of the i-th flight student in different high-pressure environments, HRLI i is the heart rate load index of the i-th flight student in different artificial high-pressure environments, GAA i is the load adaptation ability of flight students in different high-pressure environments, DC i is the decision-making ability of flight students in different high-pressure environments, and φ1, φ2, φ3, φ4 are the weights of psychological bearing capacity (PC), heart rate load index (HRLI), load adaptation ability (GAA), and decision-making ability (DC), respectively.
[0097] The scheme establishes a physiological time series data model based on the physical and mental quality test data of flight students in different artificial high-pressure environments, and comprehensively quantitatively evaluates the physiological state of students in high-pressure environments. The scheme uses physiological data (such as heart rate) of flight students to analyze their psychological bearing capacity, heart rate load index, load adaptation ability, and decision-making ability, and thus evaluates the physiological stress state of flight students in different high-pressure situations. Through in-depth analysis of the trend of physiological data changes, the stress bearing capacity and psychological load of students can be accurately quantified, providing a scientific basis for psychological intervention and individualized training programs in high-pressure training, ensuring the physical and mental health of flight students and improving training effectiveness.
[0098] Referring to Figure 4 , whether the comprehensive operation ability index of flight students in artificial high-pressure environments is within the standardized operation vector range interval of flight students in artificial high-pressure environments includes:
[0099] normalizing the psychological stress state index of the flight trainee under the artificial high-pressure environment and the physiological stress state index of the flight trainee under the artificial high-pressure environment, and calculating the comprehensive operation capability index of the flight trainee under the artificial high-pressure environment;
[0100] based on linear mapping, converting the comprehensive operation capability index of the flight trainee under the artificial high-pressure environment into a comprehensive operation capability index vector of the flight trainee under the artificial high-pressure environment;
[0101] calculating the spatial distance between the comprehensive operation capability index vector of the flight trainee under the artificial high-pressure environment and the standardized operation vector of the flight trainee under the artificial high-pressure environment, and determining whether the comprehensive operation capability index of the flight trainee under the artificial high-pressure environment is within the range interval of the standardized operation vector of the flight trainee under the artificial high-pressure environment;
[0102] The calculation of the comprehensive operation capability index of the flight trainee under the artificial high-pressure environment is specifically as follows:
[0103]
[0104] In the formula, C i is the comprehensive operation capability index of the i th flight trainee under the artificial high-pressure environment, min() is the minimum value function, max() is the maximum value function, ρ is the weight of the psychological stress index, and σ is the weight of the physiological stress index.
[0105] The calculation of the spatial distance between the comprehensive operation capability index vector of the flight trainee under the artificial high-pressure environment and the standardized operation vector of the flight trainee under the artificial high-pressure environment is specifically as follows:
[0106]
[0107] In the formula, is the spatial distance between the comprehensive operation capability index vector of the flight trainee under the artificial high-pressure environment and the standardized operation vector of the flight trainee under the artificial high-pressure environment, is the comprehensive operation capability index of the i th flight trainee under the r th artificial high-pressure environment, is the standardized operation vector of the i th flight trainee under the r th artificial high-pressure environment, and Q is the total number of artificial high-pressure environments.
[0108] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only the principles of the present application. Various changes and improvements can be made without departing from the spirit and scope of the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.
Claims
1. A method for testing the physical and mental qualities of flight trainees under artificially high-pressure environments, characterized in that, include: Based on multi-source heterogeneous sensors, we acquired test data on the physical and mental qualities of flight trainees under several rounds of high-pressure environments, and established a database for testing the physical and mental qualities of flight trainees under artificial high-pressure environments. Based on the database of flight trainees' physical and mental fitness tests under artificial high pressure, convolutional recognition is performed on the behavioral actions in the test data of flight trainees under different artificial high pressure environments to evaluate the psychological stress indicators of flight trainees under different artificial high pressure environments. Based on the database of flight trainees' physical and mental fitness tests under artificial high pressure, the physiological stress state indicators of flight trainees under different artificial high pressure environments are evaluated by comprehensively quantifying the physiological state in the test data of flight trainees under different artificial high pressure environments. The comprehensive operational capability index of flight trainees under artificial high pressure is calculated by weighting the psychological stress index and the physiological stress index of flight trainees under different artificial high pressure environments. Based on the standardized operational data of flight trainees under artificial high pressure environment, the standardized operational vector of flight trainees under artificial high pressure environment is determined. The test determines whether a flight trainee's comprehensive operational capability index under artificial high pressure is within the range of standardized operational vectors for flight trainees under artificial high pressure. If yes, the test is passed; otherwise, the test is failed.
2. The method for testing the physical and mental qualities of flight trainees under artificial high-pressure environment according to claim 1, characterized in that, Based on a database of flight trainees' physical and mental fitness tests under artificially high-pressure environments, convolutional recognition is performed on behavioral actions from these tests under different artificially high-pressure environments to assess the psychological stress levels of flight trainees under these conditions. Specific indicators include: Based on OpenPose human posture recognition, the system tracks and marks key human points in the operation video stream of the flight trainee physical and mental fitness test database under artificial high pressure environment, and obtains the operation action feature data of flight trainees under artificial high pressure environment; the operation action features include: limb movement, limb tension, and movement speed. Based on the observation target of motion smoothness, the characteristic data of flight trainees' operation actions under high-pressure human conditions are screened and quantified to determine the smoothness index of each flight trainee's operation actions. Based on the limb joint feature data in the operational action feature data of flight trainees under high pressure environment, calculate the limb tension index of each flight trainee's operational action. Based on the fluency index of each flight trainee's operational movements and the limb tension index of each flight trainee's operational movements, a score for each flight trainee's operational limb movements is calculated. Based on CNN convolutional neural network, the system tracks and marks key facial points of flight trainees in the operation video process of flight trainees under artificial high pressure environment, and obtains facial movement feature data of flight trainees under artificial high pressure environment; the facial movement features include: facial muscle movement, eye expression movement, and mouth expression movement. Based on facial movement feature data of flight trainees under high-pressure conditions, we analyze the changes in facial movement features of each flight trainee and assess the facial muscle tension index of each flight trainee. Based on the facial movement feature data of each flight trainee under high-pressure human conditions, the probability distribution of the flight trainees' emotions is statistically analyzed according to the facial expression tags corresponding to the facial movement features, and the intensity of the emotional type of each flight trainee is quantified; the facial expression tags include: joy, surprise, fear, and sadness. Each flight trainee's emotional index is calculated by comprehensively considering both positive and negative emotions based on the intensity of their emotional type. Based on each flight trainee's facial muscle tension index and emotional index, a score for each flight trainee's facial expression movements is calculated. Based on the scores of each flight trainee's physical movements and facial expressions, the psychological stress index of flight trainees under high-pressure environments was calculated.
3. The method for testing the physical and mental qualities of flight trainees under artificial high-pressure environment according to claim 2, characterized in that, The specific steps for calculating the score of each flight trainee's physical movements are as follows: In the formula, S i For the i-th flight trainee, the body movements are scored, with α being the smoothness weight and β being the tension weight coefficient. To calculate the smoothness index of each flight trainee's operational actions, To calculate the limb tension index of each flight trainee's operational movements, Δx ij Let Δy be the characteristic data of the j-th operational action of the i-th flight trainee. ij Let T be the limb joint feature data in the j-th operation action feature data of the i-th flight trainee, T be the total duration of the operation action, n be the total number of flight trainees, and m be the total number of operation action feature data.
4. The method for testing the physical and mental qualities of flight trainees under artificial high-pressure environment according to claim 3, characterized in that, The specific steps for calculating the facial expression scores of each flight trainee are as follows: In the formula, V i G scores the facial expressions and movements of the i-th flight trainee. i AU represents the facial muscle tension index of the i-th flight trainee. ik (t) represents the facial movement feature of the i-th flight trainee at the t-th unit time, AU ik (t-1) represents the k-th facial movement feature of the i-th flight trainee in the (t-1)-th unit of time, P ik Let i be the emotional index of the i-th flight trainee. For the i-th flight student, the k-th... 正 The probability distribution of each positive emotion type For the i-th flight student, the k-th... 负 The probability distribution of negative emotion types is given by γ, the influence coefficient of positive emotion, δ, the influence coefficient of negative emotion, w1, the weight of the emotion index, and w2, the weight of the facial muscle tension index.
5. The method for testing the physical and mental qualities of flight trainees under artificial high-pressure environment according to claim 4, characterized in that, The specific calculation of the psychological stress state index of flight trainees under high-pressure environments for different individuals is as follows: Dx=γ1·S i +γ2·V i In the formula, D i This refers to the psychological stress index of the i-th flight trainee under high-pressure conditions for different individuals.
6. The method for testing the physical and mental qualities of flight trainees under artificial high-pressure environment according to claim 5, characterized in that, Based on a database of flight trainees' physical and mental fitness tests under artificially high-pressure environments, and according to the comprehensive quantification of physiological states in the test data under different artificially high-pressure environments, the specific indicators for evaluating the physiological stress state of flight trainees under different artificially high-pressure environments include: Physiological data of flight trainees under different artificial high-pressure environments were selected by unit time and physiological data of flight trainees under different artificial high-pressure environments were established. Based on the physiological time series data of flight trainees under different high-pressure environments, we analyze the changing trends of the physiological time series data of flight trainees and assess the psychological resilience of flight trainees under different high-pressure environments. Based on the physiological time series data of flight trainees under different high-pressure environments, we analyzed the changing trends of the physiological time series data of flight trainees and quantified the heart rate load index and load adaptability of flight trainees under different high-pressure environments. Based on random events in the test data of flight trainees’ physical and mental qualities under different high-pressure environments, the decision-making actions of each flight trainee are statistically analyzed to assess their decision-making ability under different high-pressure environments. Based on the psychological endurance, heart rate load index, G-load adaptability, and decision-making ability of flight trainees under different high-pressure environments, physiological stress state indicators of flight trainees under different high-pressure environments are calculated.
7. The method for testing the physical and mental qualities of flight trainees under artificial high-pressure environment according to claim 6, characterized in that, The calculation of physiological stress state indicators for flight trainees under high-pressure environments for different individuals specifically involves: PI i =φ1×PC i +φ2×HRLI i +φ3×GAA i +φ4×DC i In the formula, PI i PC represents the physiological stress state indicators of the i-th flight trainee under high-pressure conditions for different individuals. i To assess the psychological resilience of the i-th flight trainee under different high-pressure environments, HRLI i GAA represents the heart rate stress index of the i-th flight trainee under high-pressure conditions for different individuals. i DC is designed to improve the load adaptability of flight trainees under different high-pressure environments. i The decision-making ability of flight trainees under different high-pressure environments is represented by the following weights: φ1, φ2, φ3, and φ4, which are respectively the weights of psychological endurance (PC), heart rate load index (HRLI), load adaptability (GAA), and decision-making ability (DC).
8. The method for testing the physical and mental qualities of flight trainees under artificial high-pressure environment according to claim 7, characterized in that, Determining whether a flight trainee's comprehensive operational capability indicators under artificial high-pressure conditions fall within the range of standardized operational vectors under artificial high-pressure conditions specifically includes: Normalization was performed on the psychological stress indicators and physiological stress indicators of flight trainees under different artificial high-pressure environments to calculate the comprehensive operational ability indicators of flight trainees under artificial high-pressure environments. Based on linear mapping, the comprehensive operational capability index of flight trainees under artificial high pressure environment is converted into a comprehensive operational capability index vector of flight trainees under artificial high pressure environment. Calculate the spatial distance between the comprehensive operational capability index vector of flight trainees under artificial high pressure environment and the standardized operational vector of flight trainees under artificial high pressure environment, and determine whether the comprehensive operational capability index of flight trainees under artificial high pressure environment is within the range of the standardized operational vector of flight trainees under artificial high pressure environment.
9. A method for testing the physical and mental qualities of flight trainees under artificial high-pressure environments according to claim 8, characterized in that, The comprehensive operational capability index for flight trainees under high-pressure environments, as calculated by the calculator, is specifically as follows: In the formula, C i Let denot be the comprehensive operational capability index of the i-th flight trainee under high-pressure human conditions, min() be the minimum value function, max() be the maximum value function, ρ be the weight of the psychological stress index, and σ be the weight of the physiological stress index.
10. A method for testing the physical and mental qualities of flight trainees under artificial high-pressure environment according to claim 9, characterized in that, The spatial distance between the comprehensive operational capability index vector of flight trainees under artificial high pressure and the standardized operational vector of flight trainees under artificial high pressure is specifically as follows: In the formula, The spatial distance between the comprehensive operational capability index vector of flight trainees under artificial high pressure and the standardized operational vector of flight trainees under artificial high pressure. Let r be the comprehensive operational capability index of the i-th flight trainee under high-pressure environment. Let be the standardized operation vector of the r-th person under high-pressure environment, and Q be the total number of people under high-pressure environment.