System for monitoring incapacitation state of crew on basis of contact sensor

Abstract

The present invention belongs to the field of state of health monitoring of cockpit flight crews, and particularly relates to a system for monitoring an incapacitation state of a crew on the basis of a contact sensor. The system comprises a wearable wrist sensor, a wearable chest strap sensor, a seat pressure sensor, a data synchronization module, an intelligent computing platform, a speech collection and recognition module and a state alarm module. By means of a contact multi-sensor integration technique and algorithm, the present invention can implement accurate monitoring and analysis of an electrocardiograph (ECG) signal, a photoplethysmography (PPG) signal, a saturation of peripheral oxygen (SPO2) signal, a skin temperature (SKT) signal and a heart rate (HR) signal of a pilot, thereby realizing real-time monitoring of an incapacitation state of a flight crew, and improving the safety of a single-pilot driving mode.

Description

A unit failure status monitoring system based on contact sensor Technical Field

[0001] The present invention belongs to the field of cockpit flight crew health status monitoring, and in particular relates to a crew disability status monitoring system based on a contact sensor. Background Art

[0002] Large commercial civil airliners require at least two pilots to operate. With the continuous development of the aviation industry, the proportion of aircraft maintenance and fuel costs in airline operating costs is gradually decreasing, while the proportion of crew costs is gradually increasing, which has an increasingly greater impact on the economic profitability of airlines. According to the International Air Transport Association (IATA), air transport volume is expected to double, and the industry will face a pilot shortage. Therefore, to control crew costs and address the pilot shortage, the aviation industry has conducted extensive technical research and future planning for intelligent flight technology for commercial aircraft, including single pilot operation (SPO), autonomous taxiing, takeoff and landing (ATTOL), Airbus's DragonFly project, NASA's "Aviation Strategy Implementation Plan" and EASA's "Artificial Intelligence Roadmap", building a vision for future intelligent flight for commercial aircraft.

[0003] However, if single-pilot flight is to be achieved through advanced airborne automation equipment or air-ground collaborative operation mode, the workload and driving pressure of a single pilot should not be higher than that of the current two-person crew mode. Therefore, how to reasonably and efficiently monitor the pilot's status, including fatigue, behavior, psychology, emotions, workload and disability, will not only help pilots fly safely, but also help promote commercial aircraft into the era of intelligent flight.

[0004] Pilot incapacitation refers to a decrease in physical fitness to a level that could endanger flight safety. Depending on the severity, it can be categorized as either total or partial incapacitation. When a pilot is incapacitated, their flight control abilities will decline to varying degrees, either noticeably or subtly. In severe cases, this can lead to a flight accident. According to the "1% rule," the fatal accident rate due to a single pilot incapacitation should not exceed 1 in 10. -9 hours, for a two-pilot aircraft, the risk of pilot incapacitation is no more than 1 in 10 -6 Therefore, for single-pilot flight modes, a reasonable disability status monitoring method must be adopted to reduce the risk rate caused by pilot incapacitation to the risk level of two-pilot flight. This is a key technology that urgently needs to be solved for future intelligent flight of aircraft.

[0005] Summary of the Invention

[0006] In view of this, the present invention provides a crew disability status monitoring system based on contact sensors, which can realize natural and non-sensory disability status monitoring, comprehensively analyze and identify various physiological signals of pilots, analyze the crew disability status, improve crew flight safety, and lay the foundation for key technologies of intelligent assisted flight of commercial aircraft.

[0007] In order to achieve the above technical objectives, the specific technical solutions adopted by the present invention are:

[0008] A crew disability status monitoring system based on contact sensors includes a wearable wrist sensor, a wearable chest strap sensor, a seat pressure sensor, a data synchronization module, an intelligent computing platform, a voice acquisition and recognition module, and a status alarm module, wherein:

[0009] Wearable wrist sensor, used to collect pilot physiological parameters and send the collected physiological parameter data to the data synchronization module in real time via wireless mode;

[0010] Wearable chest strap sensor, used to collect pilot physiological parameters and send the collected physiological parameter data to the data synchronization module in real time via wireless mode;

[0011] Seat pressure sensor, used to collect pilot body pressure distribution signals and send the collected body pressure distribution signal data to the data synchronization module in real time via wireless mode;

[0012] A data synchronization module is used to receive the pilot's physiological parameter data sent by the wearable wrist sensor and the wearable chest strap sensor, and the human body pressure distribution signal data sent by the seat pressure sensor, and synchronize the data sent by each sensor to achieve synchronous collection of multi-sensor data;

[0013] The intelligent computing platform is used to receive in real time the pilot's physiological parameter data and human body pressure distribution data processed by the data synchronization module, deploy the crew disability status monitoring model, and output the crew disability status to the status alarm module. The intelligent computing platform is interconnected with the voice acquisition and recognition module to receive pilot voice information, which is used for pilots to actively report their own status and for secondary confirmation of the pilot's status when the system detects crew disability.

[0014] Voice collection and recognition module, used to collect active reporting information, identify pilot active reporting information and confirm secondary voice information, and send the recognized information to the intelligent computing platform;

[0015] The status alarm module receives the crew disability status information output by the intelligent computing platform and generates alarm information on the pilot disability status.

[0016] Furthermore, the physiological parameters collected by the wearable wrist sensor include PPG blood volume pulse signal, SPO2 blood oxygen saturation signal, SKT skin temperature signal and HR heart rate signal.

[0017] The physiological parameters collected by the wearable chest strap sensor include ECG electrocardiogram signal, RESP respiratory signal, RPM respiratory rate signal and HR heart rate signal

[0018] Furthermore, the crew disability status includes total disability, partial disability and normal, where disability means that the pilot's physical fitness is reduced to a level that may endanger flight safety.

[0019] Furthermore, the wearable wrist sensor uses a reflective photoelectric detection method to measure the pilot's PPG blood volume pulse signal, SPO2 blood oxygen saturation signal and HR heart rate signal;

[0020] The wearable wrist sensor uses Bluetooth wireless transmission to send data to the data synchronization and fusion module;

[0021] The wearable chest strap sensor uses patch-type measurement electrodes to measure the pilot's ECG signal, RESP signal, RPM signal and HR signal.

[0022] Furthermore, the wearable chest strap sensor uses Bluetooth wireless transmission to send data to the data synchronization and fusion module.

[0023] Furthermore, the intelligent computing platform is used to perform multi-modal data preprocessing, feature extraction, AI disability state recognition model deployment and disability state output.

[0024] Furthermore, the signal outputted in the disabled state is transmitted based on the UDP network protocol.

[0025] Furthermore, the voice collection and recognition module consists of a voice microphone array and artificial intelligence voice recognition software.

[0026] Furthermore, the status warning module is composed of a speaker and a display, and the status warning module reminds the pilot based on auditory and visual means.

[0027] Furthermore, the crew disability status monitoring system of the contact sensor determines whether the pilot is in a disabled state based on the following steps:

[0028] S1: After collecting physiological parameter data, feature index extraction and data normalization are performed, specifically including: heart rate variability analysis based on ECG signals, blood-melting pulse signals, and heart rate signals, including three analysis methods: time domain analysis, frequency domain analysis, and nonlinear analysis, to extract their feature index parameters; time domain analysis and frequency domain analysis based on respiratory signals to extract their feature index parameters; data normalization of the extracted physiological parameter characteristic values;

[0029] S2: Based on the physiological parameter feature value extraction results, blood oxygen saturation parameters, skin temperature parameters, and the simultaneously collected seat pressure distribution data, data annotation is performed to form a data set, which is divided into a training set and a test set;

[0030] S3: Build an AI crew disability state recognition deep learning network, and train and test it based on the training set and test set to obtain a trained AI crew disability state recognition model;

[0031] S4: If the AI ​​crew disability status recognition model determines that the crew is disabled, visual and auditory warning information will be issued in the status warning module, and the pilot secondary status confirmation page will pop up on the display unit. The pilot's voice information and touch feedback information will be received in real time according to the pilot's ability to reconfirm the pilot's status. If the pilot's status is normal, the visual and auditory warning information will be eliminated. If not, the pilot is confirmed to be disabled and the pilot disability information will be sent to the crew and ground operations support.

[0032] Beneficial effects of the present invention:

[0033] This invention uses wearable wrist sensors, wearable chest strap sensors, and seat pressure sensors to collect multimodal physiological data from pilots. Wireless data transmission technology eliminates the interference of numerous cables, ensuring that pilots are not disturbed and enabling natural, non-invasive disability status monitoring. Secondly, by deploying an AI crew disability status monitoring model, this invention can comprehensively analyze and identify various physiological signals from pilots, analyze crew disability status, improve crew flight safety, and lay the foundation for key technologies for intelligent assisted flight on commercial aircraft. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0035] FIG1 is a schematic diagram of the system composition of the present invention;

[0036] FIG2 shows the sensor arrangement and wearing position of the present invention;

[0037] FIG3 shows the unit failure status monitoring and identification process of the present invention. DETAILED DESCRIPTION

[0038] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0039] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0040] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this disclosure, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement an apparatus and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this apparatus and / or practice this method.

[0041] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The illustrations only show components related to the present disclosure and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0042] Additionally, in the following description, specific details are provided to provide a thorough understanding of the examples. However, one skilled in the art will appreciate that the aspects described can be practiced without these specific details.

[0043] In one embodiment of the present invention, a contact sensor-based crew disability status monitoring system is proposed, comprising a wearable wrist sensor, a wearable chest strap sensor, a seat pressure sensor, a data synchronization module, an intelligent computing platform, a voice acquisition and recognition module, and a status alarm module, wherein:

[0044] Wearable wrist sensor, used to collect pilot physiological parameters and send the collected physiological parameter data to the data synchronization module in real time via wireless mode;

[0045] Wearable chest strap sensor, used to collect pilot physiological parameters and send the collected physiological parameter data to the data synchronization module in real time via wireless mode;

[0046] Seat pressure sensor, used to collect pilot body pressure distribution signals and send the collected body pressure distribution signal data to the data synchronization module in real time via wireless mode;

[0047] A data synchronization module is used to receive the pilot's physiological parameter data sent by the wearable wrist sensor and the wearable chest strap sensor, and the human body pressure distribution signal data sent by the seat pressure sensor, and synchronize the data sent by each sensor to achieve synchronous collection of multi-sensor data;

[0048] The intelligent computing platform is used to receive in real time the pilot's physiological parameter data and human body pressure distribution data processed by the data synchronization module, deploy the crew disability status monitoring model, and output the crew disability status to the status alarm module. The intelligent computing platform is interconnected with the voice acquisition and recognition module to receive pilot voice information, which is used for pilots to actively report their own status and for secondary confirmation of the pilot's status when the system detects crew disability.

[0049] Voice collection and recognition module, used to collect active reporting information, identify pilots' active reporting information and confirm secondary voice information, and send the recognized information to the intelligent computing platform;

[0050] The status alarm module receives the crew disability status information output by the intelligent computing platform and generates alarm information on the pilot disability status.

[0051] In one embodiment, the physiological parameters collected by the wearable wrist sensor include PPG blood volume pulse signal, SPO2 blood oxygen saturation signal, SKT skin temperature signal and HR heart rate signal.

[0052] The physiological parameters collected by the wearable chest strap sensor include ECG electrocardiogram signal, RESP respiratory signal, RPM respiratory rate signal and HR heart rate signal

[0053] In one embodiment, the crew disability status includes total disability, partial disability and normal, wherein disability means that the pilot's physical fitness is reduced to a level that may endanger flight safety.

[0054] The wearable wrist sensor uses a reflective photoelectric detection method to measure the pilot's PPG blood volume pulse signal, SPO2 blood oxygen saturation signal and HR heart rate signal.

[0055] In one embodiment, the wearable wrist sensor uses Bluetooth wireless transmission to send data to the data synchronization module and the fusion module.

[0056] In one embodiment, the wearable chest strap sensor uses patch-type measurement electrodes to measure the pilot's ECG signal, RESP respiratory signal, RPM respiratory rate signal, and HR heart rate signal.

[0057] In one embodiment, the wearable chest strap sensor uses Bluetooth wireless transmission to send data to the data synchronization module and the fusion module.

[0058] In one embodiment, the intelligent computing platform is used to perform multimodal data preprocessing, feature extraction, AI disability state recognition model deployment, and disability state output.

[0059] In one embodiment, the signal outputted in the disabled state is transmitted based on the UDP network protocol.

[0060] In one embodiment, the voice collection and recognition module consists of a voice microphone array and artificial intelligence voice recognition software.

[0061] In one embodiment, the status warning module is composed of a speaker and a display, and the status warning module reminds the pilot based on auditory and visual means.

[0062] In one embodiment, the contact sensor crew incapacity monitoring system determines whether the pilot is incapacitated based on the following method:

[0063] S1: After collecting physiological parameter data, feature index extraction and data normalization are performed, specifically including: heart rate variability analysis based on ECG signals, blood-melting pulse signals, and heart rate signals, including three analysis methods: time domain analysis, frequency domain analysis, and nonlinear analysis, to extract their feature index parameters; time domain analysis and frequency domain analysis based on respiratory signals to extract their feature index parameters; data normalization of the extracted physiological parameter characteristic values;

[0064] S2: Based on the physiological parameter feature value extraction results, blood oxygen saturation parameters, skin temperature parameters, and the simultaneously collected seat pressure distribution data, data annotation is performed to form a data set, which is divided into a training set and a test set;

[0065] S3: Build an AI crew disability state recognition deep learning network, and train and test it based on the training set and test set to obtain a trained AI crew disability state recognition model;

[0066] S4: If the AI ​​crew disability status recognition model determines that the crew is disabled, visual and auditory warning information will be issued in the status warning module, and the pilot secondary status confirmation page will pop up on the display unit. The pilot's voice information and touch feedback information will be received in real time according to the pilot's ability to reconfirm the pilot's status. If the pilot's status is normal, the visual and auditory warning information will be eliminated. If not, the pilot is confirmed to be disabled and the pilot disability information will be sent to the crew and ground operations support.

[0067] As shown in Figure 1, an embodiment of the present invention discloses a crew disability status monitoring system based on contact sensors. The system includes a wearable wrist sensor, a wearable chest strap sensor, a seat pressure sensor, a data synchronization module, an intelligent computing platform, a voice acquisition and recognition module, and a status alarm module.

[0068] in:

[0069] As shown in FIG2 , the wearable wrist sensor is used to collect the pilot's physiological parameters, including PPG blood volume pulse signal, SPO2 blood oxygen saturation signal, SKT skin temperature signal, and HR heart rate signal, and transmit the collected physiological parameter data wirelessly in real time to the data synchronization module;

[0070] As shown in FIG2 , a wearable chest strap sensor is used to collect pilot physiological parameters including ECG, RESP, and HR signals, and to wirelessly transmit the collected physiological parameter data to a data synchronization module in real time.

[0071] As shown in FIG2 , the seat pressure sensor is used to collect the pilot's body pressure distribution signal and send the collected body pressure distribution signal data to the data synchronization module in real time via wireless mode;

[0072] The data synchronization module is used to receive the pilot's physiological parameter data sent by the wearable wrist sensor and the wearable chest strap sensor, and the human body pressure distribution signal data sent by the seat pressure sensor. It synchronizes the data sent by each sensor and then performs fusion processing to achieve synchronous collection and multi-source fusion of multi-sensor data;

[0073] The intelligent computing platform is used to receive in real time the pilot's physiological parameter data and human body pressure distribution data processed by the data synchronization module, deploy the crew disability status monitoring model, and output the crew disability status to the status alarm module. The intelligent computing platform is also interconnected with the voice acquisition and recognition module to receive pilot voice information for secondary confirmation of the pilot's status.

[0074] Voice collection and recognition module, used to collect and recognize pilots' proactive reporting information and secondary confirmation voice information, and send the recognized information to the intelligent computing platform;

[0075] The status alarm module receives the crew disability status information output by the intelligent computing platform and generates alarm information on the pilot disability status.

[0076] In some embodiments, the crew disability status includes three types: total disability, partial disability, and normal, where disability means that the pilot's physical fitness is reduced to a level that may endanger flight safety.

[0077] In some embodiments, the wearable wrist sensor uses a reflective photoelectric detection method to measure the pilot's PPG blood volume pulse signal, SKT skin temperature signal, SPO2 blood oxygen saturation signal, and HR heart rate signal.

[0078] In some embodiments, the wearable wrist sensor uses Bluetooth wireless transmission to send data to the data synchronization module and the fusion module.

[0079] In some embodiments, the wearable chest strap sensor uses patch-type measurement electrodes to measure the pilot's ECG signal, RESP respiratory signal, and HR heart rate signal.

[0080] In some embodiments, the wearable chest strap sensor uses Bluetooth wireless transmission to send data to the data synchronization module and the fusion module.

[0081] In some embodiments, the seat pressure sensor is a flexible pressure sensor that can be arranged on the seat cushion and backrest to collect the pilot's body pressure distribution signal.

[0082] In some embodiments, the data synchronization module can synchronize multi-source data according to the timestamp of the sampled data, and send the synchronized data to the intelligent computing platform.

[0083] In some embodiments, the intelligent computing platform can perform multiple functions such as multi-modal data preprocessing, feature extraction, AI disability state recognition model deployment, and disability state output.

[0084] In some embodiments, the AI ​​disability status recognition model deployment function should meet real-time operation requirements.

[0085] In some embodiments, the disability status output function uses the UDP network protocol to send the unit disability status data to the outside in a broadcast form.

[0086] In some embodiments, the voice collection and recognition module consists of a voice microphone array and artificial intelligence voice recognition software.

[0087] In some embodiments, the status warning module is composed of a touch display unit that can sound and can remind the pilot of the disabled status through auditory and visual means.

[0088] In some embodiments, the touch display unit can perform pilot interaction functions. When the pilot has partial interaction capabilities, the pilot can confirm his or her status again by clicking the corresponding button on the display unit.

[0089] In some embodiments, as shown in FIG3 , the specific process of the system determining whether the pilot is in an incapacitated state is as follows:

[0090] S1: First, after collecting physiological parameter data, feature index extraction and data normalization are performed, specifically including: performing heart rate variability (HRV) analysis based on ECG signals, blood-melting pulse signals and heart rate signals, including three analysis methods: time domain analysis, frequency domain analysis and nonlinear analysis, to extract their feature index parameters; performing time domain analysis and frequency domain analysis based on respiratory signals (RESP) to extract their feature index parameters; and performing data normalization on the extracted physiological parameter characteristic values.

[0091] S2: Based on the physiological parameter feature value extraction results, blood oxygen saturation parameters, skin temperature parameters, and synchronously collected seat pressure distribution data, data annotation is performed to form a data set, which is divided into a training set and a test set.

[0092] S3: Construct a three-category (total disability, partial disability, normal) AI crew disability status recognition deep learning network, and train and test it based on the training set and test set to obtain a trained AI crew disability status recognition model.

[0093] S4: If the AI ​​crew disability status recognition model determines that the crew is disabled, it should issue visual and auditory warning information on the status warning module, and pop up the pilot secondary status confirmation page ("Confirm Disability" button, "Normal Status" button) on the display unit. According to the pilot's ability, the pilot's voice information and touch feedback information are received in real time to confirm the pilot's status for the second time. If the pilot's status is normal, the visual and auditory warning information is eliminated. Otherwise, the pilot is confirmed to be disabled and the pilot disability information is sent to the crew and ground operation support.

[0094] The above description is merely a specific embodiment of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this disclosure should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A unit failure status monitoring system based on contact sensors, characterized in that: It includes a wearable wrist sensor, a wearable chest strap sensor, a seat pressure sensor, a data synchronization module, an intelligent computing platform, a voice acquisition and recognition module, and a status alarm module, among which: Wearable wrist sensor, used to collect pilot's physiological parameters and send the collected physiological parameter data to the data synchronization module in real time via wireless mode; Wearable chest strap sensor, used to collect pilot's physiological parameters and send the collected physiological parameter data to the data synchronization module in real time via wireless mode; Seat pressure sensor, used to collect pilot's human body pressure distribution signal, and send the collected human body pressure distribution signal data to the data synchronization module in real time via wireless mode; A data synchronization module is used to receive the pilot's physiological parameter data sent by the wearable wrist sensor and the wearable chest strap sensor and the human body pressure distribution signal data sent by the seat pressure sensor, and synchronize the data sent by each sensor to achieve synchronous collection of multi-sensor data; The intelligent computing platform is used to receive the pilot's physiological parameter data and human body pressure distribution data after synchronization processing by the data synchronization module in real time, and deploy the crew disability status monitoring model to output the crew disability status to the status alarm module; the intelligent computing platform is interconnected with the voice acquisition and recognition module to receive the pilot's voice information for the pilot to actively report his own status and to re-confirm the flight when the system detects crew disability Member status; Voice collection and recognition module, used to collect active report information, identify pilots' active report information and confirmation of secondary voice information, and send the recognized information to the intelligent computing platform; The status warning module receives the crew disability status information output by the intelligent computing platform and generates warning information on the pilot disability status.

2. The unit failure status monitoring system based on contact sensor according to claim 1 is characterized in that: The physiological parameters collected by the wearable wrist sensor include PPG blood volume pulse signal, SPO2 blood oxygen saturation signal, SKT skin temperature signal and HR heart rate signal; The physiological parameters collected by the wearable chest strap sensor include ECG electrocardiogram signal, RESP respiratory signal, RPM respiratory rate signal and HR heart rate signal.

3. The unit failure status monitoring system based on contact sensor according to claim 1 is characterized in that: The crew disability status includes total disability, partial disability and normal, among which disability means that the pilot's physical fitness is reduced to a level that may endanger flight safety.

4. The unit failure status monitoring system based on contact sensor according to claim 2 is characterized in that: The wearable wrist sensor uses a reflective photoelectric detection method to measure the pilot's PPG blood volume pulse signal, SPO2 blood oxygen saturation signal and HR heart rate signal; The wearable wrist sensor uses Bluetooth wireless transmission to send data to the data synchronization module and the fusion The module sends data; The wearable chest strap sensor uses patch-type measurement electrodes to measure the pilot's ECG electrocardiogram signal, RESP respiratory signal, RPM respiratory rate signal and HR heart rate signal.

5. The unit failure status monitoring system based on contact sensor according to claim 3 is characterized in that: The wearable chest strap sensor uses Bluetooth wireless transmission to send data to the data synchronization module and the fusion module.

6. The unit failure status monitoring system based on contact sensor according to claim 5 is characterized in that: The intelligent computing platform is used to perform multi-mode data preprocessing, feature extraction, AI disability state recognition model deployment and disability state output.

7. The unit failure state monitoring system based on contact sensor according to claim 6 is characterized in that: The signal outputted in the disabled state is transmitted based on the UDP network protocol.

8. The unit failure state monitoring system based on contact sensor according to claim 7 is characterized in that: The voice collection and recognition module consists of a voice microphone array and artificial intelligence voice recognition software.

9. The unit failure status monitoring system based on contact sensors according to claim 8, characterized in that: The status warning module is composed of a speaker and a display, and the status warning module reminds the pilot based on auditory and visual means.

10. The unit failure state monitoring system based on contact sensor according to claim 9, characterized in that: The crew incapacity status monitoring system of the contact sensor determines whether the pilot is in an incapacitated state based on the following steps: S1: After collecting the physiological parameter data, feature index extraction and data normalization are performed, including: heart rate variability analysis based on ECG signals, blood melting pulse signals and heart rate signals, including three analysis methods: time domain analysis, frequency domain analysis and nonlinear analysis, and extracting feature index parameters; time domain analysis and frequency domain analysis based on respiratory signals and extracting feature index parameters; data normalization of the extracted physiological parameter feature values; S2: Based on the physiological parameter feature value extraction results, blood oxygen saturation parameters, skin temperature parameters, and seat pressure distribution data collected simultaneously, data annotation is performed to form a data set, and the data set is divided into a training set and a test set; S3: Build an AI crew disability state recognition deep learning network, and perform training and testing based on the training set and test set to obtain a trained AI crew disability state recognition model; S4: If the AI ​​crew disability status recognition model determines that the crew is disabled, visual and auditory warning information will be issued in the status warning module, and the pilot secondary status confirmation page will pop up on the display unit. The pilot's voice information and touch feedback information will be received in real time according to the pilot's ability, and the pilot's status will be confirmed for the second time. If the pilot's status is normal, the visual and auditory warning information will be eliminated. If not, the pilot is confirmed to be disabled, and the pilot disability information will be sent to the crew and ground operation support.

Images