Method for evaluating the performance of an automatic landing aid decision system and medium

Abstract

The application discloses an automatic landing auxiliary decision system performance evaluation method and medium, relates to the automatic landing field, and comprises the following steps: acquiring physiological data and flight data of a pilot when the pilot performs an airplane landing task; the pilot performs the airplane landing task by relying on an automatic landing auxiliary decision system; the physiological data and the flight data are preprocessed respectively; based on the preprocessed physiological data, a load level value of the pilot is calculated by using a plurality of time scales of a sliding window and a dynamic entropy weight method; principal component analysis is used to extract features of the preprocessed flight data, and Euclidean distances between the corresponding features and expert flight data are calculated to obtain a landing operation performance level value of the pilot; the load level value and the landing operation performance level value are normalized and added to obtain a performance evaluation score; when the performance evaluation score is greater than or equal to a first threshold value, the performance of the automatic landing auxiliary decision system is excellent. The application effectively evaluates the performance of the automatic landing auxiliary decision system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic landing, in particular to a method for evaluating the effectiveness of an automatic landing auxiliary decision system and a medium. BACKGROUND

[0002] With the progress of science and technology, the automatic landing auxiliary decision system of an aircraft has developed from a simple aid to a highly complex decision support system. These systems, by integrating advanced sensors, precise navigation equipment and intelligent algorithms, can provide pilots with real-time flight data and landing recommendations in complex weather conditions and airport environments. They not only reduce the workload of pilots, but also provide critical support in low visibility or emergency situations, thereby improving the success rate and safety of aircraft landing.

[0003] In order to verify the effectiveness of the automatic landing auxiliary decision system during the landing process of the aircraft, the actual effectiveness is usually evaluated. In the traditional effectiveness evaluation method, the data obtained during the landing process of the aircraft is compared with fixed numerical indicators. This manual correction method is more mechanical, and in the load evaluation aspect, it also relies too much on the subjective method of pilots filling out questionnaires, lacking unified and objective standards. Therefore, the above traditional method cannot effectively evaluate the effectiveness of the automatic landing auxiliary decision system. SUMMARY

[0004] The purpose of the present application is to provide a method for evaluating the effectiveness of an automatic landing auxiliary decision system and a medium, which comprehensively considers the load level and landing operation performance level of the pilot to effectively evaluate the effectiveness of the automatic landing auxiliary decision system.

[0005] To achieve the above purpose, the present application provides the following solutions.

[0006] In a first aspect, the present application provides a method for evaluating the effectiveness of an automatic landing auxiliary decision system, which is as follows.

[0007] Obtain physiological data and flight data of a pilot during the execution of an aircraft landing task; the pilot executes the aircraft landing task relying on an automatic landing auxiliary decision system.

[0008] Preprocess the physiological data and the flight data respectively.

[0009] Based on the preprocessed physiological data, calculate the load level value of the pilot using multiple time scales of sliding window and dynamic entropy weight method.

[0010] Extract the features of the preprocessed flight data using principal component analysis, and calculate the Euclidean distance between the corresponding features and expert flight data to obtain the landing operation performance level value of the pilot.

[0011] adding the normalized load level value and the normalized landing operation performance level value to obtain a performance evaluation score.

[0012] When the performance evaluation score is greater than or equal to a first threshold value, the performance of the automatic landing assistance decision system is excellent; when the performance evaluation score is greater than or equal to a second threshold value and less than the first threshold value, the performance of the automatic landing assistance decision system is good; and when the performance evaluation score is less than the second threshold value, the performance of the automatic landing assistance decision system is poor.

[0013] In a second aspect, the present application also provides a computer readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the performance evaluation method of the automatic landing assistance decision system according to the first aspect.

[0014] According to the embodiments provided in the present application, the following technical effects are disclosed.

[0015] The present application comprehensively evaluates the automatic landing assistance decision system of the aircraft from the load level and the landing operation performance level of the pilot. On the one hand, the physiological data of the pilot during the execution of the landing task of the aircraft can truly reflect the physical state and load level of the pilot, and based on the physiological data, the load level is analyzed in multiple time scales using the dynamic entropy weight method, which has the advantages of objectivity, real-time, continuity and multi-dimension evaluation. On the other hand, based on the flight data, after data processing, the deviation value is obtained by calculating the Euclidean distance after principal component analysis and expert flight data, which effectively reflects the performance and ability of the pilot during the execution of the landing task of the aircraft with the help of the automatic landing assistance decision system. Therefore, the performance of the automatic landing assistance decision system is evaluated from the above two aspects. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0017] Figure 1 The flowchart of the performance evaluation method of the automatic landing assistance decision system in the embodiments of the present application.

[0018] Figure 2 The calculation flowchart of the dynamic entropy weight method in the embodiments of the present application. DETAILED DESCRIPTION

[0019] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0020] The purpose of the present application is to provide an automatic landing auxiliary decision system performance evaluation method and medium, which comprehensively considers the load level of the pilot and the landing operation performance level, and effectively evaluates the performance of the automatic landing auxiliary decision system.

[0021] In order to make the above-mentioned purposes, characteristics and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0022] In an exemplary embodiment, as shown in Figure 1 An automatic landing auxiliary decision system performance evaluation method is provided, which is as follows.

[0023] Step S1: Obtain physiological data and flight data of the pilot when performing the airplane landing task.

[0024] In the embodiment, the pilot mainly relies on the automatic landing auxiliary decision system to perform the airplane landing task, and the load level of the pilot can be evaluated by measuring the changes of the physiological indicators. The heartbeat, muscles and respiration of the pilot when performing the flight task can reflect the load intensity of the pilot, and the load intensity of the pilot can be evaluated by monitoring the changes of the physiological data such as electromyography data, electrocardiogram data, pulse data and respiration data. In addition, the physiological data evaluation method has the advantages of objectivity, real-time, continuity and multi-dimensional evaluation, and the physiological data acquisition device also has the advantages of non-invasiveness, integration and intelligence with the development of science and technology.

[0025] The performance and ability of the pilot when performing the airplane landing task with the help of the automatic landing auxiliary decision system are evaluated by the landing operation performance level, and the flight data such as the airplane attitude angle, airplane height, airplane airspeed, airplane descent speed, airplane go-around frequency, airplane ground contact speed and airplane horizontal deviation during the flight of the pilot are recorded by using a flight data recording program.

[0026] Step S2: Preprocess the physiological data and flight data respectively.

[0027] In this embodiment, the acquired physiological data is preprocessed according to the data characteristics of each channel, such as power frequency filtering and baseline correction for electromyography data, heartbeat recognition and positioning for electrocardiogram and pulse data, and respiratory phase extraction for respiratory data. For the acquired flight data, repeated values and abnormal values are removed, and data normalization is completed.

[0028] Step S3: Based on the preprocessed physiological data, the load level value of the driver is calculated using a sliding window of multiple time scales and a dynamic entropy weight method.

[0029] In this embodiment, step S3 is as follows.

[0030] Step S31: Based on the preprocessed physiological data, physiological features of different frequencies are extracted using sliding windows of 1s, 2s, and 5s, and a physiological feature dataset X{x kij |k=1,2,3;i=1,…,l;j=1,…,n},x kij is the feature data extracted by the i-th sliding window of the k-th sliding window of the j-th physiological feature channel, l is the total number of sliding windows, and n is the total number of physiological feature channels.

[0031] Step S32: Based on the physiological feature dataset, the dynamic entropy weight method is used to calculate the weight of each physiological feature in the load level calculation, and the load level value of the driver is calculated by weighting.

[0032] As shown in Figure 2 , in the process of data processing using the dynamic entropy weight method, the physiological feature dataset is first dynamically normalized to eliminate the dimensional influence between different indicators and ensure the comparability of each indicator in the evaluation process. The dynamic normalization calculation is as follows.

[0033]

[0034] In the formula, X norm is the dynamically normalized data, X is the exact value of the original data at the current time, X min , and X max are the minimum and maximum values in the previous data. After normalization, the original data is converted to the range of [0, 1].

[0035] Then, the time factor is taken into account, and the information entropy is calculated by time scale. As an objective weighting method, the entropy weight method is based on the principle of information theory to calculate the information entropy of each indicator to determine the weight. Information entropy is used to measure the degree of disorder of data. The smaller the value, the greater the variability of the indicator, the more information, and the greater the weight in evaluation. Due to dynamic normalization, the range of early data may be small, and a time correction coefficient The information entropy of early data is reduced, and the information entropy calculation formula is as follows.

[0036]

[0037] In the formula, e kj is the information entropy of the jth physiological characteristic channel under the kth sliding window.

[0038] Then, the difference coefficient matrix is calculated according to the discrete degree of the difference coefficient measurement index. The difference coefficient d kj of the jth physiological characteristic channel under the kth sliding window is calculated as follows.

[0039] d kj = (1-e kj ).

[0040] According to the difference coefficient, the index weight matrix is obtained by normalizing the difference coefficient. The weight w kj of the jth physiological characteristic channel under the kth sliding window is calculated as follows.

[0041]

[0042] Finally, the load level value Score of the driver is calculated by weighting according to the weight value.

[0043]

[0044] In the formula, x k(end)j is the last sliding window feature measurement value of the jth physiological characteristic channel under the kth sliding window.

[0045] Step S4: The features of the preprocessed flight data are extracted by principal component analysis, and the Euclidean distance between the corresponding features and the expert flight data is calculated to obtain the landing operation performance level value of the driver.

[0046] In this embodiment, in order to effectively evaluate the landing operation performance of the driver, it is necessary to compare with the expert flight data. Generally, the expert flight data refers to the data generated by professional pilots with decades of flight experience during the execution of the aircraft landing process. By recording and summarizing these data, the corresponding expert flight data can be obtained.

[0047] Step S5: The load level value and the landing operation performance level value are normalized and added to obtain the efficiency evaluation score.

[0048] In this embodiment, the load level value is calculated by using the physiological evaluation method, and the landing operation performance level value is calculated by using the performance evaluation method. The physiological evaluation method and the performance evaluation method are combined to effectively evaluate the efficiency of the automatic landing auxiliary decision system.

[0049] Step S6: When the performance evaluation score is greater than or equal to the first threshold value, the performance of the automatic landing assistance decision system is excellent; when the performance evaluation score is greater than or equal to the second threshold value and less than the first threshold value, the performance of the automatic landing assistance decision system is good; and when the performance evaluation score is less than the second threshold value, the performance of the automatic landing assistance decision system is poor.

[0050] As a preferred embodiment, the first threshold value is 1.5, and the second threshold value is 1.

[0051] Based on the above analysis, the automatic landing assistance decision system can be adjusted and improved according to the results of the final performance evaluation. When the performance of the automatic landing assistance decision system is excellent, it does not need to be adjusted, and the system can continue to be applied in the flight process; when the performance of the automatic landing assistance decision system is good or poor, the sensors, navigation devices, intelligent algorithms, etc. in the automatic landing assistance decision system need to be further improved and optimized.

[0052] In another exemplary embodiment, a computer-readable storage medium is provided, which stores a computer program that is executed by a processor to implement the steps in the above embodiments.

[0053] In summary, the present application mainly has the following advantages.

[0054] 1) The present application can accurately and dynamically analyze the physiological data and flight data of the pilot, capture key information through feature extraction technology, reduce the misjudgment rate, and improve the reliability of the evaluation. This efficient evaluation capability is crucial for improving the safety and efficiency of the automatic landing assistance decision system, as it can monitor the pilot's load state in real time, provide timely assistance decision support, and ensure the smooth and safe process of the aircraft landing.

[0055] 2) The present application fully considers the scalability of the system in design, allowing for customized adjustments according to different flight tasks and environments to adapt to changing automatic landing needs and technological progress, ensuring that the system remains advanced in the long term.

[0056] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use, and processing of related data need to comply with relevant regulations.

[0057] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments of each method. Any reference to memory, database or other medium used in each embodiment provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0058] The database involved in each embodiment provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in each embodiment provided by the present application can be a general processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0059] All actions of obtaining signals, information or data in the present application are performed in compliance with the corresponding data protection regulations and policies of the country where the device is located, and with the authorization given by the owner of the corresponding device.

[0060] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between each embodiment can be referred to each other.

[0061] The principles and implementation manners of the present application are described herein by using specific examples, and the above examples are only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manners and application ranges will have changes. In conclusion, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for evaluating the effectiveness of an automatic landing assistance decision-making system, characterized in that, The effectiveness evaluation method for the automatic landing assistance decision-making system includes: Acquire physiological and flight data of the pilot during the aircraft landing mission; the pilot relies on the automatic landing assist decision system to perform the aircraft landing mission; The physiological data and the flight data are preprocessed separately. Based on preprocessed physiological data, the driver's load level was calculated using sliding window and dynamic entropy weighting methods at multiple time scales. Principal component analysis is used to extract features from the preprocessed flight data, and the Euclidean distance between the corresponding features and the expert flight data is calculated to obtain the pilot's landing operation performance level value. The load level value and the landing operation performance level value are normalized and then added together to obtain the performance evaluation score; When the performance evaluation score is greater than or equal to the first threshold, the performance of the automatic landing assistance decision system is excellent; when the performance evaluation score is greater than or equal to the second threshold and less than the first threshold, the performance of the automatic landing assistance decision system is good; when the performance evaluation score is less than the second threshold, the performance of the automatic landing assistance decision system is poor.

2. The method for evaluating the effectiveness of the automatic landing assistance decision-making system according to claim 1, characterized in that, The physiological data includes at least electromyography (EMG) data, electrocardiogram (ECG) data, pulse data, and respiratory data.

3. The method for evaluating the effectiveness of the automatic landing assistance decision-making system according to claim 1, characterized in that, The flight data includes at least the aircraft attitude angle, aircraft altitude, aircraft airspeed, aircraft descent speed, and aircraft level deviation.

4. The method for evaluating the effectiveness of the automatic landing assistance decision-making system according to claim 2, characterized in that, The physiological data is preprocessed, specifically including: The electromyography data were subjected to power frequency filtering and baseline correction. Perform heartbeat identification and localization on the electrocardiogram data and the pulse data; The respiratory phase is extracted from the respiratory data.

5. The method for evaluating the effectiveness of the automatic landing assistance decision-making system according to claim 1, characterized in that, The flight data is preprocessed, specifically including: Remove duplicate and outlier values ​​from the flight data and perform data normalization.

6. The method for evaluating the effectiveness of the automatic landing assistance decision-making system according to claim 1, characterized in that, The sliding window has three time scales: 1s, 2s, and 5s.

7. The method for evaluating the effectiveness of the automatic landing assistance decision-making system according to claim 6, characterized in that, Based on preprocessed physiological data, the driver's load level was calculated using sliding window and dynamic entropy weighting methods across multiple time scales, specifically including: Based on the preprocessed physiological data, physiological features of different frequencies were extracted using sliding windows at three time scales of 1s, 2s and 5s to obtain a physiological feature dataset. Based on the aforementioned physiological feature dataset, the weight of each physiological feature in the load level calculation is calculated using the dynamic entropy weight method, and the driver's load level value is obtained through weighted calculation.

8. The method for evaluating the effectiveness of the automatic landing assistance decision-making system according to claim 7, characterized in that, The formula for calculating the load level value is as follows: In the formula, Score is the load level value, n is the total number of physiological characteristic channels, and w kj Let x be the weight of the j-th physiological feature channel in the k-th sliding window. k(end)j This represents the last sliding window feature measurement value of the k-th sliding window in the j-th physiological feature channel.

9. The method for evaluating the effectiveness of the automatic landing assistance decision-making system according to claim 1, characterized in that, The first threshold is 1.5, and the second threshold is 1.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the performance evaluation method for the automatic landing assistance decision system as described in any one of claims 1-9.

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