Pilot situational awareness assessment method and device based on physiological data

Abstract

The present application provides a method and device for assessing the pilot's situational awareness state based on physiological data, which is applied to the field of artificial intelligence technology. The method includes: obtaining the pilot's physiological data within a preset time period in real time, the physiological data including electrocardiogram (ECG) data and eye movement data; determining an ECG characteristic index based on the ECG data, and determining an eye movement characteristic index based on the eye movement data; inputting the ECG characteristic index and the eye movement characteristic index into a state assessment model as physiological characteristic indicators, and obtaining the pilot's situational awareness state output by the state assessment model; wherein the state assessment model is trained based on physiological characteristic indicator samples and situational awareness state samples corresponding to pilot samples. The method implements online assessment of the situational awareness state through the state assessment model based on the physiological characteristic indicators corresponding to the physiological data acquired in real time. The entire process effectively avoids human intervention, so that the accuracy of the final determined situational awareness state is high.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a method and device for evaluating a pilot's situational awareness based on physiological data. Background Art

[0002] A pilot's situational awareness (SA) refers to their perception of various environmental factors, their understanding of their significance, and their predictions of their subsequent states within a specific time and space. Real-time assessment of a pilot's SA is crucial for reflecting and understanding their understanding of the various factors and conditions that affect their operations within a specific time period and context. This is crucial for reducing flight accidents caused by errors in SA.

[0003] Traditional methods for assessing pilots' situational awareness typically include subjective evaluation, memory probes (frozen / real-time), and task performance measurements. Subjective evaluation and memory probes are direct methods for assessing situational awareness, while task performance measures are indirect. However, the presence of human input in all these methods, along with task performance measurements, can lead to human error, resulting in low accuracy in the final assessment results (i.e., the pilot's situational awareness). Summary of the Invention

[0004] The present application provides a method and apparatus for assessing a pilot's situational awareness state based on physiological data. Since physiological data is closely correlated with the situational awareness state, after the electronic device acquires the physiological characteristic indicators corresponding to the physiological data in real time, the physiological characteristic indicators can be used as input to a state assessment model. This state assessment model enables online assessment of the pilot's situational awareness state. The entire process effectively avoids human intervention, resulting in a higher degree of accuracy in the ultimately determined situational awareness state, thereby effectively ensuring the pilot's flight safety during flight.

[0005] The present application provides a method for assessing a pilot's situational awareness state based on physiological data, comprising: acquiring the pilot's physiological data in real time over a preset period of time, the physiological data including electrocardiogram (ECG) data and eye movement data; determining an ECG characteristic index based on the ECG data, and determining an eye movement characteristic index based on the eye movement data; and inputting the ECG characteristic index and the eye movement characteristic index into a state assessment model as physiological characteristic indicators to obtain the pilot's situational awareness state as output by the state assessment model; wherein the state assessment model is trained based on physiological characteristic index samples and situational awareness state samples corresponding to pilot samples.

[0006] According to a method for assessing a pilot's situational awareness state based on physiological data provided by an embodiment of the present application, the state assessment model is trained based on the following steps: determining multiple groups of data sample sets based on the physiological characteristic indicator samples and the situational awareness state samples, each group of data sample sets including a training set and a test set, and each group of data sample sets is different; for each group of data sample sets, updating the model parameters in a first state assessment model based on the training set in the data sample set to obtain a second state assessment model; determining the model accuracy of the second state assessment model based on the test set in the data sample set; and determining the second state assessment model with the highest model accuracy among the multiple second state assessment models as the state assessment model.

[0007] According to an embodiment of the present application, a method for assessing a pilot's situational awareness state based on physiological data is provided. The method updates model parameters in a first state assessment model based on a training set in a data sample set to obtain a second state assessment model. The method includes: inputting physiological characteristic indicator samples of the training set in the data sample set into first state assessment models corresponding to multiple model parameters, respectively, to obtain predicted situational awareness states of the pilot samples output by multiple first state assessment models; determining the similarity between each predicted situational awareness state and the situational awareness state samples of the training set in the data sample set; and determining the second state assessment model based on the model parameters of the first state assessment model corresponding to the maximum similarity among the multiple similarities.

[0008] According to a method for assessing pilot situational awareness based on physiological data provided by an embodiment of the present application, the electrocardiogram data includes: electrocardiogram waveform data; the electrocardiogram characteristic indicators include: average heart rate, average RR interval and normalized target frequency band power; determining the electrocardiogram characteristic indicators based on the electrocardiogram data includes: determining the electrocardiogram characteristic indicators based on the electrocardiogram waveform data; determining the electrocardiogram characteristic indicators based on the electrocardiogram data; determining the electrocardiogram characteristic indicators based on the electrocardiogram waveform ... RR interval and ECG sampling frequency, determine the average heart rate, is an integer greater than or equal to 2, and each RR interval is used to characterize the time interval between two adjacent R waves in the electrocardiogram waveform data; RR intervals, and determine the average RR interval; and determine the standardized target frequency band power according to the target frequency band power of the RR interval sequence in the electrocardiogram waveform data.

[0009] According to a method for assessing pilot situational awareness based on physiological data provided by an embodiment of the present application, the method according to the electrocardiogram waveform data The method further comprises: determining the average heart rate using a first formula, wherein the first formula is: ; represents said average heart rate; represents the ECG sampling frequency; Indicates the The first of the RR intervals RR intervals; RR intervals, and determining the average RR interval, including: using a second formula to determine the average RR interval; wherein the second formula is: ; represents the mean RR interval.

[0010] According to a method for assessing a pilot's situational awareness state based on physiological data provided by an embodiment of the present application, the target frequency band power includes a first frequency band power and a second frequency band power greater than the first frequency band power; the standardized target frequency band power includes a standardized first frequency band power and a standardized second frequency band power; and determining the standardized target frequency band power based on the target frequency band power of the RR interval sequence in the electrocardiogram waveform data includes: determining the standardized first frequency band power using a third formula; and determining the standardized second frequency band power using a fourth formula; wherein the third formula is: ; The fourth formula is: ; represents the normalized first frequency band power; represents the normalized second frequency band power; represents the power of the first frequency band; represents the second frequency band power; Indicates the power of the first frequency band With the second frequency band power sum; Indicates the third frequency band power that is less than the first frequency band power.

[0011] According to an embodiment of the present application, a method for assessing a pilot's situational awareness based on physiological data is provided, wherein the eye movement data includes: A focus point, Scan points and The pupil diameter of the effective sampling points, 、 and are integers greater than or equal to 2; the eye movement characteristic indicators include: fixation rate, scan rate, pupil diameter and average fixation time; the eye movement characteristic indicators are determined based on the eye movement data, including: The fixation point and the preset duration are used to determine the fixation rate; scanning points and the preset duration, determine the scanning rate; according to the The pupil diameter of the effective sampling points is determined; according to the The average gaze duration is determined by calculating the gaze duration of each gaze point in the gaze points.

[0012] The present application also provides a device for assessing a pilot's situational awareness based on physiological data, comprising:

[0013] A data acquisition module is used to obtain the pilot's physiological data in real time within a preset time period, wherein the physiological data includes electrocardiogram data and eye movement data;

[0014] a data interface module, configured to determine an electrocardiogram characteristic index based on the electrocardiogram data, and to determine an eye movement characteristic index based on the eye movement data;

[0015] An online evaluation module is configured to input the electrocardiogram characteristic index and the eye movement characteristic index as physiological characteristic indicators into a state evaluation model, and obtain the pilot's situational awareness state output by the state evaluation model; wherein the state evaluation model is trained based on physiological characteristic indicator samples and situational awareness state samples corresponding to pilot samples.

[0016] The present application also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for assessing the pilot's situational awareness based on physiological data as described above is implemented.

[0017] The present application also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for assessing the pilot's situational awareness based on physiological data as described above is implemented.

[0018] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for assessing the pilot's situational awareness based on physiological data.

[0019] The present invention provides a method and apparatus for assessing a pilot's situational awareness state based on physiological data. The method involves acquiring physiological data from a pilot over a preset period of time in real time, including electrocardiogram (ECG) data and eye movement data. The method then determines an ECG characteristic index based on the ECG data and an eye movement characteristic index based on the eye movement data. The ECG characteristic index and the eye movement characteristic index are then input into a state assessment model as physiological characteristic indicators, thereby outputting the pilot's situational awareness state as output by the state assessment model. The state assessment model is trained based on physiological characteristic index samples and situational awareness state samples corresponding to pilot samples. In this method, because physiological data is closely correlated with situational awareness state, the electronic device, after acquiring the physiological characteristic index corresponding to the physiological data in real time, can use the physiological characteristic index as input to the state assessment model. This state assessment model then performs online assessment of the pilot's situational awareness state. This process effectively eliminates human intervention, resulting in a highly accurate final determination of the situational awareness state, thereby effectively ensuring pilot safety during flight. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1 1 is a flow chart of a method for assessing pilot situational awareness based on physiological data provided by an embodiment of the present application;

[0022] Figure 2 1 is a schematic diagram of a scenario for dynamically evaluating a pilot's situational awareness state provided by an embodiment of the present application;

[0023] Figure 3 1 is a schematic diagram of the structure of a pilot situational awareness assessment system based on physiological data provided by an embodiment of the present application;

[0024] Figure 4 is a schematic diagram of a pilot information collection interface provided in an embodiment of the present application;

[0025] Figure 5 is a schematic diagram of a parameter setting window provided in an embodiment of the present application;

[0026] Figure 6 Schematic diagram of the main interface of online evaluation provided by the embodiment of the present application;

[0027] Figure 7is a schematic diagram of the output result interface provided in an embodiment of the present application;

[0028] Figure 8 is a schematic diagram of an output report interface provided in an embodiment of the present application;

[0029] Figure 9 1 is a schematic diagram of the structure of a pilot situational awareness status assessment device based on physiological data provided by an embodiment of the present application;

[0030] Figure 10 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0032] It should be noted that the pilot situational awareness status assessment method based on physiological data provided in the embodiments of the present application is a physiological measurement method. Compared with traditional pilot situational awareness status assessment methods, namely subjective evaluation methods, memory probe measurement methods, and task performance measurement methods, this physiological measurement method has better objectivity and real-time performance as well as limited task intrusion, and is therefore considered to be an effective means for achieving online assessment of pilot operating status (i.e., situational awareness status).

[0033] The following uses an electronic device as an example to describe in detail the pilot situational awareness assessment method based on physiological data provided by the embodiment of the present application:

[0034] Figure 1 FIG. 1 is a flow chart of a pilot situational awareness assessment method based on physiological data provided in an embodiment of the present application. Figure 1 As shown, the method includes the following steps 101 to 104.

[0035] Step 101: Acquire the pilot's physiological data in real time within a preset time period, where the physiological data includes electrocardiogram data and eye movement data.

[0036] Among them, pilots refer to professionals who operate aircraft (such as airplanes, helicopters, etc.) to perform flight missions and must have corresponding flight qualifications and skills.

[0037] The preset duration is a pre-set time interval used to limit the duration of data collection. For example, the preset duration is 30 seconds (s).

[0038] Physiological data refers to physiological signals collected by physiological sensors to reflect the physiological state of the human body.

[0039] ECG data refers to the heart's electrical activity signals recorded by ECG devices (such as electrocardiogram equipment) and is used to assess heart function and rhythm.

[0040] Eye movement data refers to the eye movement trajectories and gaze characteristics recorded by an eye movement device (such as an eye tracker), which is used to reflect visual attention and cognitive state. It should be noted that due to the asynchronous and non-real-time nature of eye movement feature calculation, the eye tracker's ECG sampling frequency is 60Hz, which means that 60 eye movement data points are collected per second.

[0041] It should be noted that there is no limit to the timing of the electronic device acquiring the electrocardiogram data and eye movement data.

[0042] Optionally, before step 101, the method may further include: the electronic device collecting the pilot's personal information.

[0043] Optionally, the above personal information includes at least: name, age, height, occupation, number, gender, vision, education level, experimental conditions, experimental date, number of experiments, experimental site and experimental duration and other recorded information.

[0044] Step 102: Determine an ECG characteristic index based on the ECG data, and determine an eye movement characteristic index based on the eye movement data.

[0045] Among them, ECG characteristic indicators refer to quantitative parameters extracted by analyzing cardiac electrical activity signals.

[0046] Eye movement feature indicators refer to quantitative parameters extracted through eye movement data analysis.

[0047] Because the calculation of eye movement data is somewhat non-real-time, the electronic device calculates and converts the indicators of the eye movement data at preset time intervals, that is, determines the eye movement characteristic indicators based on the eye movement data. At the same time, the electronic device also calculates and converts the indicators of the heart movement data within the preset time interval to obtain the electrocardiogram characteristic indicators.

[0048] It should be noted that there is no limit to the timing of the electronic device determining the electrocardiogram characteristic index and the eye movement characteristic index.

[0049] The following is a detailed explanation of how electronic devices determine ECG characteristic indicators based on ECG data:

[0050] In some embodiments, the ECG data may include: ECG waveform data; and the ECG characteristic indicators may include: mean heart rate (MHR), mean RR interval (RR interval), and normalized target frequency band power.

[0051] The ECG waveform data refers to the record of the heart's electrical activity on the body surface, which is a continuous waveform formed after being collected and amplified by electrodes. For example, the ECG waveform data may be an electrocardiogram.

[0052] The average heart rate refers to the number of heartbeats per unit time. For example, if the unit time is per minute, the unit of the average heart rate is beats per minute.

[0053] The average RR interval refers to the average value of all RR intervals within a preset time length, and the unit of the average RR interval is milliseconds (ms).

[0054] The normalized target frequency band power refers to the target frequency band spectrum of the RR interval sequence in the ECG waveform data.

[0055] In some embodiments, the electronic device determines the ECG characteristic index based on the ECG data, which may include: the electronic device determines the ECG characteristic index based on the ECG waveform data. RR interval and ECG sampling frequency to determine the average heart rate, is an integer greater than or equal to 2, and each RR interval is used to represent the time interval between two adjacent R waves in the electrocardiogram waveform data; the electronic device is based on RR intervals are used to determine the average RR interval; and the electronic device determines the standardized target frequency band power according to the target frequency band power of the RR interval sequence in the electrocardiogram waveform data.

[0056] in, The RR interval is the core waveform in the aforementioned ECG waveform data. It's important to note that each R wave represents a single ventricular depolarization, and the RR interval represents a complete cardiac cycle—the time interval from one ventricular contraction to the next. The length of this RR interval is closely related to the heart rhythm and is an important indicator for determining whether the heart rate and rhythm are normal.

[0057] The RR interval sequence is a collection of RR intervals on the electrocardiogram over a period of time (i.e., a preset duration) and is used to assess cardiac rhythm and function.

[0058] When determining ECG characteristic indicators, electronic equipment can first determine the ECG waveform data. RR intervals. Then, the electronic device The RR interval and the ECG sampling frequency are calculated to obtain the average heart rate. The RR intervals are averaged to obtain the average RR interval. Finally, the electronic device normalizes the target frequency band power of the RR interval sequence in the ECG waveform data to obtain the normalized target frequency band power. These ECG characteristic indicators can be used as input data for subsequent state assessment models.

[0059] It should be noted that the electronic device is not limited to the timing of determining the average heart rate, the average RR interval, and the standardized target frequency band power.

[0060] In some embodiments, the electronic device performs the following operations based on the ECG waveform data: The average heart rate is determined by using an RR interval and an electrocardiogram sampling frequency, which may include: the electronic device uses a first formula to determine the average heart rate.

[0061] Among them, the first formula is: ; Indicates average heart rate; Indicates the ECG sampling frequency; express The first of the RR intervals RR interval.

[0062] For example, when the unit time is per minute, The value of is 60. At this time, the first formula above is: .

[0063] In some embodiments, the electronic device RR intervals and determining the average RR interval may include: the electronic device using a second formula to determine the average RR interval.

[0064] Among them, the second formula is: ; represents the mean RR interval.

[0065] In some embodiments, the target frequency band power may include a first frequency band power and a second frequency band power greater than the first frequency band power; and the normalized target frequency band power may include a normalized first frequency band power and a normalized second frequency band power.

[0066] The first frequency band power is a low frequency (LF) band power. Exemplarily, the value range of the first frequency band power is 0.04 Hz to 0.15 Hz.

[0067] The second frequency band power is a high frequency (HF) band power. Exemplarily, the value range of the second frequency band power is 0.15 Hz to 0.4 Hz.

[0068] Normalized first frequency band power is a normalized low-frequency power (Normalized LF Power), which is used to reflect the low-frequency spectrum of the RR interval sequence.

[0069] The normalized second frequency band power is a normalized high frequency power (Normalized HF Power), which is used to reflect the high frequency spectrum of the RR interval sequence.

[0070] In some embodiments, the electronic device determines the standardized target frequency band power based on the target frequency band power of the RR interval sequence in the electrocardiogram waveform data, which may include: the electronic device uses the third formula to determine the standardized first frequency band power; and uses the fourth formula to determine the standardized second frequency band power.

[0071] Among them, the third formula is: ;

[0072] The fourth formula is: ;

[0073] represents the normalized first frequency band power; represents the normalized second frequency band power; Indicates the power of the first frequency band; Indicates the power of the second frequency band; Indicates the power of the first frequency band With the second band power The sum of is a total power; It represents a third frequency band power that is smaller than the first frequency band power. The third frequency band power is an extremely low frequency power. It should be noted that the maximum value in the value range of the third frequency band power is smaller than the minimum value of the first frequency band power.

[0074] It should be noted that the timing for the electronic device to determine the normalized first frequency band power and the normalized second frequency band power is not limited.

[0075] Optionally, the electronic device determines the ECG characteristic index based on the ECG data, which may include: the electronic device determines the initial ECG characteristic index based on the ECG data; and the electronic device performs normalization and preprocessing operations on the initial ECG characteristic index to obtain the ECG characteristic index.

[0076] Optionally, the normalization process may include: removing null values ​​and / or outliers, etc.

[0077] Optionally, the preprocessing operation may include: standardization processing, etc.

[0078] After the electronic device analyzes the ECG data and extracts quantitative parameters to obtain initial ECG characteristic indicators, in order to facilitate the unified processing of subsequent data, the electronic device can further perform normalization and preprocessing operations on the initial ECG characteristic indicators to obtain ECG characteristic indicators.

[0079] The following is a detailed explanation of how electronic devices determine eye movement characteristic indicators based on eye movement data:

[0080] In some embodiments, eye movement data may include: A focus point, Scan points and The pupil diameter of the effective sampling points, 、 and All are integers greater than or equal to 2; eye movement feature indicators may include: fixation rate (FR), saccade rate (SR), pupil diameter (PD) and mean fixation duration (MFD).

[0081] Among them, the fixation point refers to a short stay point where the pilot's eyes remain relatively still (without obvious movement) at a certain position, usually lasting for a first preset duration (such as 100-300ms), which is used to reflect attention to a specific visual target or information processing.

[0082] A saccade point refers to a track point where the eyeball moves rapidly between fixation points, usually lasting for a second preset duration (such as 20-50ms) and at a speed of up to 300-700 degrees / second (° / s), and is used to switch visual focus targets.

[0083] The pupil diameter of a valid sampling point refers to the pupil diameter value corresponding to a sampling point that is determined to be valid (such as no blinking, occlusion, or signal loss) during the eye movement data collection process. The unit of the pupil diameter of the valid sampling point is millimeter (mm).

[0084] The fixation rate refers to the number of fixations per unit time.

[0085] The scanning rate refers to the number of scanning points per unit time.

[0086] Pupil diameter is The average pupil diameter of the valid sampling points. The unit of the pupil diameter is (mm).

[0087] The average fixation duration refers to the ratio of the total fixation duration of the eye movement to the number of fixation points, and the unit of the average fixation duration is ms.

[0088] In some embodiments, the electronic device determines the eye movement characteristic index based on the eye movement data, which may include: the electronic device determines the eye movement characteristic index based on the eye movement data; The electronic device determines the gaze rate based on the gaze point and preset duration. The electronic device determines the scanning rate according to the scanning point and the preset time. The pupil diameter is determined by the effective sampling point; the electronic device is based on The fixation duration of each fixation point among the fixation points is used to determine the average fixation duration.

[0089] It should be noted that there is no limit to the timing of the electronic device determining the gaze rate, scan rate, pupil diameter and average gaze duration.

[0090] Optionally, the electronic device is configured to The method of determining the gaze rate by using a gaze point and a preset duration may include: the electronic device adopts the fifth formula to determine the gaze rate.

[0091] Among them, the fifth formula is: ;

[0092] represents the fixation rate; Indicates the preset duration.

[0093] Optionally, the electronic device is configured to The scanning rate is determined by using a scanning point and a preset time, which may include: the electronic device uses a sixth formula to determine the scanning rate.

[0094] Among them, the sixth formula is: ;

[0095] represents the scanning rate.

[0096] Optionally, the electronic device is configured to The pupil diameter is determined by the electronic device using the seventh formula to determine the pupil diameter.

[0097] Among them, the seventh formula is: ;

[0098] represents pupil diameter; express The pupil diameter of the effective sampling point The pupil diameter of the effective sampling points.

[0099] Optionally, the electronic device is configured to The average gaze duration is determined by calculating the gaze duration of each of the gaze points, which may include: the electronic device adopts the eighth formula to determine the average gaze duration.

[0100] Among them, the eighth formula is: ;

[0101] represents the average fixation duration; express The first of the fixation points The duration of fixation on a fixation point.

[0102] Optionally, the electronic device determines the eye movement feature index based on the eye movement data, which may include: the electronic device determines the initial eye movement feature index based on the eye movement data; the electronic device performs normalization and preprocessing operations on the initial eye movement feature index to obtain the eye movement feature index.

[0103] After the electronic device analyzes the eye movement data and extracts quantitative parameters to obtain the initial eye movement feature index, in order to facilitate the unified processing of subsequent data, the electronic device can further normalize and preprocess the initial eye movement feature index to obtain the eye movement feature index.

[0104] Step 103: Input the electrocardiogram characteristic index and the eye movement characteristic index as physiological characteristic indexes into the state assessment model to obtain the pilot's situational awareness state output by the state assessment model.

[0105] Among them, the state assessment model is trained based on the physiological characteristic indicator samples and situational awareness state samples corresponding to the pilot samples.

[0106] It should be noted that the above-mentioned state assessment model can be three "one-against-all" (OAA) classifiers, namely the first classifier, the second classifier, and the third classifier. Among them, the first classifier is used to distinguish high situational awareness states; the second classifier is used to distinguish medium situational awareness states; and the third classifier is used to distinguish low situational awareness states.

[0107] Based on this, after obtaining the electrocardiographic characteristic index and the eye movement characteristic index, the electronic device can input these two characteristic indexes as physiological characteristic indexes into the three classifiers, so that the three classifiers can calculate the physiological characteristic index respectively.

[0108] Specifically, during the process of analyzing and calculating the physiological characteristic index by the first classifier, the vector corresponding to the first classifier is taken as the positive set +1, and the vectors corresponding to the second and third classifiers are taken as the negative set -1. During the process of analyzing and calculating the physiological characteristic index by the second classifier, the vector corresponding to the second classifier is taken as the positive set +1, and the vectors corresponding to the first and third classifiers are taken as the negative set -1. During the process of analyzing and calculating the physiological characteristic index by the third classifier, the vector corresponding to the third classifier is taken as the positive set +1, and the vectors corresponding to the first and second classifiers are taken as the negative set -1. In this way, each classifier will output a prediction result, that is, the first classifier outputs the first prediction result F(1), the second classifier outputs the second prediction result F(2), and the third classifier outputs the third prediction result F(3).

[0109] Then, the electronic device uses the maximum value of the above three prediction results as the pilot's situational awareness state output by the state assessment model, namely max{F(1), F2(2), F(3)}.

[0110] It should be noted that if the situational awareness state is F(1), then the situational awareness state is a high situational awareness state; if the situational awareness state is F(2), then the situational awareness state is a medium situational awareness state; if the situational awareness state is F(3), then the situational awareness state is a low situational awareness state, thereby realizing online real-time evaluation of different situational awareness state levels.

[0111] It should be noted that during the pilot's flight, the electronic device can obtain the pilot's physiological data within the current preset time in real time, and determine the corresponding physiological characteristic indicators, and then use the state assessment model to output the pilot's situational awareness state corresponding to the current preset time; then, the electronic device obtains the physiological data within a new preset time in real time, and determines the corresponding physiological characteristic indicators, and then uses the above-mentioned state assessment model to output the pilot's situational awareness state corresponding to the new preset time. In this way, the electronic device can determine the pilot's situational awareness state corresponding to each preset time during the flight process, and by following the changes in the pilot's physiological state, realize dynamic assessment of the pilot's situational awareness state during the flight.

[0112] For example, Figure 2 Schematic diagram of a scenario for dynamically evaluating a pilot's situational awareness state provided by an embodiment of the present application. Figure 2 As shown, each preset time length is 30 seconds, based on which the electronic equipment can determine the pilot's situational awareness state corresponding to each 30 seconds.

[0113] It should be noted that the executing entity during state assessment model training can be an electronic device or other device. If it is other device, after the state assessment model is trained on the other device, the state assessment model can be transmitted to the electronic device for subsequent use.

[0114] The following uses electronic equipment as an example to explain the training of the state assessment model in detail:

[0115] In some embodiments, the state assessment model is trained based on the following steps: the electronic device determines multiple groups of data sample sets based on physiological characteristic indicator samples and situational awareness state samples, each group of data sample sets can include a training set and a test set, and each group of data sample sets is different; for each group of data sample sets, the electronic device updates the model parameters in the first state assessment model based on the training set in the data sample set to obtain a second state assessment model; determines the model accuracy of the second state assessment model based on the test set in the data sample set; the electronic device determines the second state assessment model with the highest model accuracy among the multiple second state assessment models as the state assessment model.

[0116] During the training of the state assessment model, the electronic device may first obtain electrocardiogram (ECG) data samples, eye movement data samples, and situational awareness state samples corresponding to the pilot sample, and determine ECG feature index samples corresponding to the ECG data samples and eye movement feature index samples corresponding to the eye movement data samples. The electronic device then uses the ECG feature index samples and eye movement feature index samples as physiological feature index samples, and uses the situational awareness state samples as labels to determine multiple data sample sets.

[0117] In the process of determining multiple groups of data set samples, taking three groups of data set samples as an example, the first group of data set samples includes a training set consisting of 90% physiological characteristic indicator samples and 90% situational awareness state samples, and a test set consisting of 10% physiological characteristic indicator samples and 10% situational awareness state samples; the second group of data set samples includes a training set consisting of 80% physiological characteristic indicator samples and 80% situational awareness state samples, and a test set consisting of 20% physiological characteristic indicator samples and 20% situational awareness state samples; the first group of data set samples includes a training set consisting of 70% physiological characteristic indicator samples and 70% situational awareness state samples, and a test set consisting of 30% physiological characteristic indicator samples and 30% situational awareness state samples.

[0118] Because the processing process for each set of data samples is identical, the electronic device can update the model parameters of the first state assessment model based on the training set in the set of data samples to obtain a second state assessment model. The electronic device can then determine the model accuracy of the second state assessment model based on the test set in the set of data samples. In this way, the electronic device can obtain the model accuracy of multiple second state assessment models.

[0119] Finally, the electronic device determines the second state assessment model with the highest model accuracy among the multiple second state assessment models as the state assessment model.

[0120] It's important to note that using multiple different data sets (each consisting of a training set and a test set) for model training and evaluation exposes the model to a wider range of data. These diverse data sets encompass different variations and distributions in physiological indicators and situational awareness states, helping the model learn more general and essential patterns, rather than simply adapting to the characteristics of a specific dataset. This ensures that the resulting state assessment model has greater generalizability, enabling subsequent determination of a pilot's situational awareness state with greater accuracy.

[0121] In some embodiments, the electronic device updates the model parameters in the first state assessment model based on the training set in the data sample set to obtain the second state assessment model, which may include: the electronic device inputs the physiological characteristic indicator samples of the training set in the data sample set into the first state assessment models corresponding to multiple model parameters, to obtain the predicted situational awareness states of the pilot samples respectively output by the multiple first state assessment models; the electronic device determines the similarity between each predicted situational awareness state and the situational awareness state samples of the training set in the data sample set; the electronic device determines the second state assessment model based on the model parameters of the first state assessment model corresponding to the maximum similarity among the multiple similarities.

[0122] It should be noted that the first state assessment model corresponds to multiple model parameters. The above process is to determine the optimal model parameter from these multiple model parameters, and use the first state assessment model corresponding to the optimal model parameter as the second state assessment model.

[0123] Specifically, for a first state assessment model corresponding to a set of data set samples and a model parameter, the electronic device can input the physiological characteristic indicator samples of the training set in this set of data samples into the first state assessment model to obtain the predicted situational awareness state output by the first state assessment model. In this way, for the first state assessment model corresponding to a set of data set samples and multiple model parameters respectively, the electronic device can finally determine the predicted situational awareness states output by multiple first state assessment models respectively.

[0124] Then, the electronic device respectively determines the similarities between the above-mentioned multiple predicted situational awareness states and the situational awareness state samples of the training set in the group of data set samples, and determines the maximum similarity from these multiple similarities, and then determines the model parameters of the first state evaluation model corresponding to the maximum similarity as the optimal model parameters, and determines the first state evaluation model corresponding to the optimal model parameters as the second state evaluation model.

[0125] In summary, the entire process involves inputting physiological characteristic indicator samples from the training set into multiple first-state assessment models with different model parameters to obtain multiple predicted state of situational awareness. The similarity between these predicted state of situational awareness and the actual state of situational awareness samples is then calculated, providing an intuitive measure of how closely each model's predictions match the actual situation. Determining the second-state assessment model based on the maximum similarity is equivalent to precisely selecting the model from multiple first-state assessment models that most accurately reflects the pilot's true state of situational awareness. This avoids subjective judgment and blind selection, and improves the accuracy of model selection.

[0126] Optionally, after step 103, the method may further include at least one of the following implementations:

[0127] Implementation method 1: The electronic device visually outputs a status assessment result in a preset form, where the status assessment result is a status assessment diagram of the pilot's situational awareness status changing over time.

[0128] Optionally, the preset format may include: data list format and / or image format, etc.

[0129] Optionally, the image format may include: Portable Network Graphics (PNG) or Plain Text (TXT) file formats.

[0130] This allows the pilot to intuitively determine the pilot's state of situational awareness during flight.

[0131] Implementation method 2: Electronic equipment outputs the pilot’s personal information.

[0132] This allows the pilot to intuitively determine whether the pilot's personal information is incorrect.

[0133] It should be noted that, whether it is the output of status assessment results or the output of personal information, a path can be specified to export the data in a preset image format.

[0134] In an embodiment of the present application, physiological data of a pilot is acquired in real time over a preset period of time, including electrocardiogram (ECG) data and eye movement data. ECG characteristic indicators are determined based on the ECG data, and eye movement characteristic indicators are determined based on the eye movement data. The ECG characteristic indicators and eye movement characteristic indicators are input into a state assessment model as physiological characteristic indicators, resulting in the pilot's situational awareness state being output by the state assessment model. The state assessment model is trained based on physiological characteristic indicator samples and situational awareness state samples corresponding to pilot samples. In this method, because physiological data and situational awareness state are closely correlated, the electronic device, after acquiring the physiological characteristic indicators corresponding to the physiological data in real time, can use these physiological characteristic indicators as input to the state assessment model. This state assessment model then performs an online assessment of the pilot's situational awareness state. This process effectively eliminates human intervention, resulting in a highly accurate final determination of the situational awareness state, thereby effectively ensuring the pilot's flight safety.

[0135] To better understand the pilot situational awareness assessment method based on physiological data provided by the embodiments of the present application, the pilot situational awareness assessment system based on physiological data is described in detail below:

[0136] Figure 3 This is a schematic diagram of the structure of the pilot situational awareness status assessment system based on physiological data provided by the embodiment of the present application. Figure 3 As shown, the system may include: a data acquisition module, a data interface module, an online evaluation module and an output report module.

[0137] Regarding the data acquisition module, specifically, the data acquisition module may include: a personal information acquisition module, an electrocardiogram module, and an eye movement module.

[0138] Among them, the personal information collection module is used to collect the pilot's personal information and input it through the user interaction interface.

[0139] The ECG module is used to collect ECG data read in real time by wearing an ECG device, such as the spatial position coordinates of eye movements, blink marks, gaze marks, and saccade marks.

[0140] The eye movement module is used to collect eye movement data read in real time by wearing an eye movement device, such as heart rate and electrocardiogram waveform data.

[0141] Regarding the data interface module, the data interface module is used to receive data transmitted by the data acquisition module and convert the data for indicator calculation. Specifically, the data interface module may include: a data transceiver interface module, a data storage module, and a data conversion module.

[0142] The data transceiver interface module is used to read ECG data and eye movement data. Exemplarily, the data transceiver interface module reads ECG data at a frequency of 1 Hz and reads eye movement data at a frequency of 60 Hz.

[0143] The data storage module is used to store the real-time collected ECG data and eye movement data in the memory space.

[0144] The data conversion module is used to determine the electrocardiogram characteristic index based on the electrocardiogram data, and to determine the eye movement characteristic index based on the eye movement data.

[0145] It should be noted that the UDP port number and host address are first set in the ECG module and the eye movement module, respectively. Then, based on a program development platform (such as a C++ platform), UDP data communication is established between the data transceiver interface module and the ECG and eye movement modules through the data transceiver interface to enable subsequent transmission of ECG and eye movement data.

[0146] The online assessment module is configured to receive data transmitted by the data interface module and implement online assessment of the pilot's situational awareness. Specifically, the online assessment module may include an electrocardiogram feature preprocessing module, an eye movement feature preprocessing module, and an online assessment classifier module.

[0147] Among them, the ECG feature preprocessing module is used to determine the initial ECG feature index based on the ECG data, and then perform normalization and preprocessing operations on the initial ECG feature index to obtain the ECG feature index.

[0148] The eye movement feature preprocessing module is used to determine the initial eye movement feature index based on the eye movement data, and then perform normalization and preprocessing operations on the initial eye movement feature index to obtain the eye movement feature index.

[0149] The online evaluation classifier module is used to input the electrocardiogram characteristic index and the eye movement characteristic index as physiological characteristic indexes into the state evaluation model, and obtain the pilot's situational awareness state output by the state evaluation model.

[0150] The output report module is used to output the pilot's situational awareness status and also to output the pilot's personal information.

[0151] It should be noted that Figure 3 The system shown does not rely on cumbersome questionnaires and post-analysis evaluations. It can measure and evaluate the pilot's situational awareness level in real time during flight simply by collecting the pilot's electrocardiogram (ECG) data and eye movement data during flight. The system is simple to use and provides an optimized user interface and usage process. It is suitable for online monitoring and status control of the pilot's situational awareness status during flight, which helps to better complete flight tests.

[0152] Optionally, the user interaction interface of the above system may include: a pilot information collection interface, an online evaluation interface and an output evaluation report interface.

[0153] For the pilot information collection interface, for example, Figure 4 This is a schematic diagram of the pilot information collection interface provided by the embodiment of the present application. Figure 4 The pilot can enter personal information into the pilot information collection interface by selecting a drop-down menu or a text box style. In other words, the pilot information collection interface is used to collect the pilot's personal information.

[0154] For the online evaluation interface, the online evaluation interface is the main interface for online evaluation observation. Optionally, the online evaluation interface may include: a parameter setting window and an online evaluation main interface. For example, Figure 5 is a schematic diagram of a parameter setting window provided in an embodiment of the present application; Figure 6 Schematic diagram of the online evaluation main interface provided in an embodiment of the present application.

[0155] Combine Figure 5 The pilot can click the "Parameter Settings" button in the online evaluation interface to enter the parameter settings window. The parameter settings window includes parameters such as device address, UDP port, Transmission Control Protocol (TCP) port, port selection, and selection mode.

[0156] Combine Figure 6 In the parameter setting window, the pilot enters the device address (e.g., Internet Protocol (IP) address) of the computer hosting the system in the "Device Address" field. The pilot then enters the port numbers of the ECG and eye movement modules in the "Port Selection" field to establish a connection. The pilot then selects the number of ECG and eye movement characteristic indicators to be read during the online assessment of the system, based on actual needs. Finally, the pilot clicks the "OK" button, completing the parameter settings. The pilot then clicks the "Run" button to begin the online assessment of their situational awareness. The graphical area displays the pilot's situational awareness status over time, with the graph updating the status of each cell every 30 seconds. Figure 6 In the figure, different colors represent different situational awareness states. Specifically, green represents a "high situational awareness state", yellow represents a "medium situational awareness state", and red represents a "low situational awareness state".

[0157] Regarding the output evaluation report interface, optionally, the output evaluation report interface may include: an output result interface and an output report interface. For example, Figure 7 is a schematic diagram of the output result interface provided in an embodiment of the present application; Figure 8 This is a schematic diagram of the output report interface provided in an embodiment of the present application.

[0158] Combine Figure 7 ,The output result interface is used to output personal information and visualized situational awareness status.

[0159] Combine Figure 8 The output report interface can be used to output the assessment result data (i.e., the situational awareness status) and visualization graphics (i.e., the situational awareness status after visualization) according to the specified path through the "output report" function.

[0160] The following describes a pilot situational awareness status assessment device based on physiological data provided in an embodiment of the present application. The pilot situational awareness status assessment device based on physiological data described below and the pilot situational awareness status assessment method based on physiological data described above can refer to each other.

[0161] Figure 9 Schematic diagram of the structure of the pilot situational awareness status assessment device based on physiological data provided by the embodiment of the present application. Figure 9 As shown, the device includes: a data acquisition module 901, a data interface module 902 and an online evaluation module 903.

[0162] Data acquisition module 901, used to obtain the pilot's physiological data in real time within a preset time period, the physiological data including electrocardiogram data and eye movement data;

[0163] The data interface module 902 is configured to determine an ECG characteristic index based on the ECG data and an eye movement characteristic index based on the eye movement data;

[0164] Online evaluation module 903 is configured to input the ECG characteristic index and the eye movement characteristic index as physiological characteristic indicators into a state evaluation model, thereby obtaining the pilot's situational awareness state as output by the state evaluation model; wherein the state evaluation model is trained based on physiological characteristic indicator samples and situational awareness state samples corresponding to pilot samples.

[0165] Optionally, the state assessment model is trained based on the following steps: determining multiple groups of data sample sets based on the physiological characteristic indicator samples and the situational awareness state samples, each group of data sample sets includes a training set and a test set, and each group of data sample sets is different; for each group of data sample sets, updating the model parameters in the first state assessment model according to the training set in the data sample set to obtain a second state assessment model; determining the model accuracy of the second state assessment model according to the test set in the data sample set; and determining the second state assessment model with the highest model accuracy among the multiple second state assessment models as the state assessment model.

[0166] Optionally, the physiological characteristic indicator samples of the training set in the data sample set are input into first state assessment models corresponding to multiple model parameters, respectively, to obtain predicted situational awareness states of the pilot samples output by the multiple first state assessment models; the similarity between each predicted situational awareness state and the situational awareness state samples of the training set in the data sample set is determined; and the second state assessment model is determined based on the model parameters of the first state assessment model corresponding to the maximum similarity among the multiple similarities.

[0167] Optionally, the ECG data includes: ECG waveform data; the ECG characteristic indicators include: average heart rate, average RR interval and standardized target frequency band power; the data interface module 902 is specifically used to RR interval and ECG sampling frequency to determine the average heart rate, is an integer greater than or equal to 2, and each RR interval is used to characterize the time interval between two adjacent R waves in the ECG waveform data; RR intervals, and determine the average RR interval; and determine the standardized target frequency band power according to the target frequency band power of the RR interval sequence in the electrocardiogram waveform data.

[0168] Optionally, the data interface module 902 is specifically configured to determine the average heart rate using a first formula; wherein the first formula is: ; Indicates the average heart rate; Indicates the ECG sampling frequency; Indicates that The first of the RR intervals RR interval.

[0169] Optionally, the data interface module 902 is specifically configured to determine the average RR interval using a second formula; wherein the second formula is: ; Represents the average RR interval.

[0170] Optionally, the target frequency band power includes a first frequency band power and a second frequency band power greater than the first frequency band power; the normalized target frequency band power includes a normalized first frequency band power and a normalized second frequency band power; the data interface module 902 is specifically configured to use a third formula to determine the normalized first frequency band power; and use a fourth formula to determine the normalized second frequency band power; wherein the third formula is: ; The fourth formula is: ; represents the normalized first frequency band power; represents the normalized second frequency band power; represents the power of the first frequency band; represents the power of the second frequency band; Indicates the power of the first frequency band With the second frequency band power sum; Indicates a third frequency band power that is less than the first frequency band power.

[0171] Optionally, the eye movement data includes: A focus point, Scan points and The pupil diameter of the effective sampling points, 、 and are integers greater than or equal to 2; the eye movement characteristic indicators include: fixation rate, scan rate, pupil diameter and average fixation time; the data interface module 902 is specifically used to The fixation point and the preset duration are used to determine the fixation rate; The scanning point and the preset time length are used to determine the scanning rate; The pupil diameter of the effective sampling points is determined; according to the The fixation duration of each fixation point in the fixation points is used to determine the average fixation duration.

[0172] Figure 10 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Figure 10As shown, the electronic device may include: a processor 1010, a communications interface 1020, a memory 1030, and a communications bus 1040, wherein the processor 1010, the communications interface 1020, and the memory 1030 communicate with each other via the communications bus 1040. The processor 1010 may invoke logic instructions in the memory 1030 to execute a method for assessing a pilot's situational awareness state based on physiological data. The method includes: acquiring physiological data of the pilot over a preset period of time in real time, wherein the physiological data includes electrocardiogram (ECG) data and eye movement data; determining an ECG characteristic index based on the ECG data, and determining an eye movement characteristic index based on the eye movement data; and inputting the ECG characteristic index and the eye movement characteristic index as physiological characteristic indexes into a state assessment model to obtain the pilot's situational awareness state as output by the state assessment model. The state assessment model is trained based on physiological characteristic index samples and situational awareness state samples corresponding to pilot samples.

[0173] In addition, the logical instructions in the above-mentioned memory 1030 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the existing technology, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0174] On the other hand, the present application also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the pilot situational awareness state assessment method based on physiological data provided by the above-mentioned methods, the method including: acquiring the pilot's physiological data within a preset time period in real time, the physiological data including electrocardiogram (ECG) data and eye movement data; determining an ECG characteristic index based on the ECG data, and determining an eye movement characteristic index based on the eye movement data; inputting the ECG characteristic index and the eye movement characteristic index as physiological characteristic indicators into a state assessment model to obtain the pilot's situational awareness state output by the state assessment model; wherein the state assessment model is trained based on physiological characteristic index samples and situational awareness state samples corresponding to pilot samples.

[0175] On the other hand, the present application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the pilot situational awareness state assessment method based on physiological data provided by the above-mentioned methods, the method comprising: acquiring physiological data of the pilot within a preset time period in real time, the physiological data comprising electrocardiogram (ECG) data and eye movement data; determining an ECG characteristic index based on the ECG data, and determining an eye movement characteristic index based on the eye movement data; inputting the ECG characteristic index and the eye movement characteristic index as physiological characteristic indexes into a state assessment model to obtain the pilot's situational awareness state output by the state assessment model; wherein the state assessment model is trained based on physiological characteristic index samples and situational awareness state samples corresponding to pilot samples.

[0176] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0177] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0178] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for assessing pilot situational awareness based on physiological data, characterized in that: include: Acquire the pilot's physiological data in real time within a preset time period, the physiological data including electrocardiogram data and eye movement data; Determining an electrocardiogram characteristic index based on the electrocardiogram data, and determining an eye movement characteristic index based on the eye movement data; Inputting the electrocardiogram characteristic index and the eye movement characteristic index as physiological characteristic indexes into a state assessment model, the state assessment model comprising a first classifier, a second classifier, and a third classifier, the first classifier being used to distinguish a high situational awareness state, the second classifier being used to distinguish a medium situational awareness state, and the third classifier being used to distinguish a low situational awareness state; Analyzing the physiological characteristic index by the first classifier, obtaining the vector corresponding to the first classifier as a positive set +1, the vectors corresponding to the second classifier and the third classifier as a negative set -1, and outputting a first prediction result; Analyzing the physiological characteristic index by the second classifier, obtaining the vector corresponding to the second classifier as a positive set +1, and the vectors corresponding to the first classifier and the third classifier as a negative set -1, and outputting a second prediction result; Analyzing the physiological characteristic index by the third classifier, obtaining the vector corresponding to the third classifier as a positive set +1, and the vectors corresponding to the first classifier and the second classifier as a negative set -1, and outputting a third prediction result; determining a maximum value among the first prediction result, the second prediction result, and the third prediction result as the pilot's situational awareness state output by the state assessment model; The state assessment model is trained based on the following steps: Determine multiple data sample sets based on the physiological characteristic indicator samples and situational awareness state samples corresponding to the pilot samples, each data sample set includes a training set and a test set, and each data sample set is different; For each of the data sample sets, updating the model parameters in the first state assessment model according to the training set in the data sample set to obtain a second state assessment model; and determining the model accuracy of the second state assessment model according to the test set in the data sample set; The second state estimation model with the highest model accuracy among the multiple second state estimation models is determined as the state estimation model.

2. The method for assessing pilot situational awareness based on physiological data according to claim 1, characterized in that: The updating of the model parameters in the first state assessment model according to the training set in the data sample set to obtain the second state assessment model includes: Inputting the physiological characteristic indicator samples of the training set in the data sample set into the first state assessment models corresponding to the plurality of model parameters, respectively, to obtain the predicted situational awareness states of the pilot samples outputted by the plurality of first state assessment models; determining a similarity between each predicted situational awareness state and a situational awareness state sample of the training set in the data sample set; The second state assessment model is determined according to the model parameters of the first state assessment model corresponding to the maximum similarity among the multiple similarities.

3. The method for assessing pilot situational awareness based on physiological data according to claim 1 or 2, characterized in that: The ECG data includes: ECG waveform data; the ECG characteristic indicators include: average heart rate, average RR interval and standardized target frequency band power; Determining an ECG characteristic index based on the ECG data includes: According to the electrocardiogram waveform data RR interval and ECG sampling frequency, determine the average heart rate, is an integer greater than or equal to 2, and each RR interval is used to represent the time interval between two adjacent R waves in the electrocardiogram waveform data; According to the RR intervals, and determining the average RR interval; The standardized target frequency band power is determined according to the target frequency band power of the RR interval sequence in the electrocardiogram waveform data.

4. The method for assessing pilot situational awareness based on physiological data according to claim 3, characterized in that: The electrocardiogram waveform data The RR interval and the ECG sampling frequency are used to determine the average heart rate, including: Using a first formula, determining the average heart rate; Among them, the first formula is: ; represents said average heart rate; represents the ECG sampling frequency; Indicates the The first of the RR intervals RR interval; According to the RR intervals, and determining the average RR interval, comprising: Using the second formula, determining the mean RR interval; Wherein, the second formula is: ; represents the mean RR interval.

5. The method for assessing pilot situational awareness based on physiological data according to claim 3, characterized in that: The target frequency band power includes a first frequency band power and a second frequency band power greater than the first frequency band power; the normalized target frequency band power includes a normalized first frequency band power and a normalized second frequency band power; The determining the standardized target frequency band power according to the target frequency band power of the RR interval sequence in the electrocardiogram waveform data includes: Using the third formula to determine the normalized first frequency band power; and using the fourth formula to determine the normalized second frequency band power; Wherein, the third formula is: ; The fourth formula is: ; represents the normalized first frequency band power; represents the normalized second frequency band power; represents the power of the first frequency band; represents the second frequency band power; Indicates the power of the first frequency band With the second frequency band power sum; Indicates the third frequency band power that is less than the first frequency band power.

6. The method for assessing pilot situational awareness based on physiological data according to claim 1 or 2, characterized in that: The eye movement data includes: A focus point, Scan points and The pupil diameter of the effective sampling points, 、 and are all integers greater than or equal to 2; the eye movement characteristic indicators include: fixation rate, saccade rate, pupil diameter and average fixation duration; Determining an eye movement characteristic index based on the eye movement data includes: According to the The fixation point and the preset duration are used to determine the fixation rate; According to the The scanning point and the preset duration are used to determine the scanning rate; According to the The pupil diameter of each effective sampling point is determined; According to the The average gaze duration is determined by calculating the gaze duration of each gaze point in the gaze points.

7. A device for assessing pilot situational awareness based on physiological data, characterized in that: include: A data acquisition module is used to obtain the pilot's physiological data in real time within a preset time period, wherein the physiological data includes electrocardiogram data and eye movement data; a data interface module, configured to determine an electrocardiogram characteristic index based on the electrocardiogram data, and to determine an eye movement characteristic index based on the eye movement data; An online evaluation module is used to input the electrocardiogram characteristic index and the eye movement characteristic index as physiological characteristic indicators into a state evaluation model, wherein the state evaluation model includes a first classifier, a second classifier and a third classifier, wherein the first classifier is used to distinguish a high situational awareness state, the second classifier is used to distinguish a medium situational awareness state, and the third classifier is used to distinguish a low situational awareness state; the first classifier is used to analyze the physiological characteristic indicators, and the vector corresponding to the first classifier is obtained as a positive set +1, and the vectors corresponding to the second classifier and the third classifier are obtained as a negative set -1, and a first prediction result is output; the second classifier is used to analyze the physiological characteristic indicators, and the vector corresponding to the second classifier is obtained as a positive set +1, and the vectors corresponding to the first classifier and the third classifier are obtained as a negative set -1, and a second prediction result is output; the third classifier is used to analyze the physiological characteristic indicators, and the third classifier is obtained. The corresponding vectors of the three classifiers are taken as a positive set +1, the vectors corresponding to the first classifier and the second classifier are taken as a negative set -1, and a third prediction result is output; the maximum value among the first prediction result, the second prediction result and the third prediction result is determined as the pilot's situational awareness state output by the state assessment model; wherein, the state assessment model is trained based on the following steps: according to the physiological characteristic indicator samples and situational awareness state samples corresponding to the pilot samples, multiple groups of data sample sets are determined, each group of data sample sets includes a training set and a test set, and each group of data sample sets is different; for each group of data sample sets, according to the training set in the data sample set, the model parameters in the first state assessment model are updated to obtain a second state assessment model; according to the test set in the data sample set, the model accuracy of the second state assessment model is determined; the second state assessment model with the highest model accuracy among multiple second state assessment models is determined as the state assessment model.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for assessing the pilot's situational awareness based on physiological data as claimed in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for assessing the pilot's situational awareness based on physiological data as claimed in any one of claims 1 to 6 is implemented.

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