Man-machine interaction system and method based on eye movement tracking

Abstract

The invention discloses a man-machine interaction system and method based on eye movement tracking, and belongs to the technical field of man-machine interaction. According to the method, the eye movement data of the simulation personnel are collected in the specific simulation environment and analyzed, and the attention degrees of the simulation personnel on different areas in the simulation environment can be known by analyzing the distribution of the fixation points in the analysis process, so that the simulation personnel can be more accurately simulated. Different attention degrees reflect the importance of the area to the operation and cognition of the simulation personnel, and then the analysis of the glancing trajectory can reveal the visual search mode of the simulation personnel, which is crucial to the evaluation of the information acquisition efficiency of the simulation personnel in the complex simulation environment, and based on the cognition, the analysis of the glancing trajectory is very important. A researcher starts to explore how to combine an eye movement tracking technology with a simulation training system, and the man-machine interaction efficiency is improved.

Description

Technical Field

[0001] The present invention relates to a human-computer interaction system and method based on eye tracking, belonging to the technical field of human-computer interaction. Background Art

[0002] Eye tracking EyeTribe is a technology that can track human eye movements and understand their movement trajectory. Eye tracking is a technology that senses subtle changes in the eyes. Depending on the direction we observe, the eyes will produce corresponding features. By comparing these features, a set of references can be formed for eye changes, thereby realizing control functions for eye changes. This is the so-called eye tracking technology.

[0003] According to publication number CN107656613A, a human-computer interaction system based on eye tracking and a working method thereof are disclosed, wherein the system includes a processor, which is respectively connected to an AR / VR head display device and a video acquisition device; an eye tracking sensor and an angular motion sensor are provided on the AR / VR head display device, and the eye tracking sensor and the angular motion sensor are respectively used to capture eye activity information and collect the current motion state of the AR / VR head display device in real time and transmit both to the processor; the video acquisition device is used to collect scene images within the field of vision of the eyes and transmit them to the processor.

[0004] The above patent improves the functional visibility of the human-computer interaction system based on eye tracking, allowing users to easily discover and use it. In this way, visibility naturally guides people to complete tasks correctly.

[0005] However, some existing eye tracking interactive systems, when used, focus on eye tracking analysis in a simulated environment. In many simulation training systems, the collected data is simply identified. How to accurately evaluate the behavior and operation effects of simulated personnel has become a key issue in improving training quality. Secondly, in traditional simulation training evaluation, it mainly relies on some direct operation feedback data, such as operation completion time, accuracy of operation results and other relatively macro indicators. However, these methods often cannot go deep into the visual information processing process of simulated personnel, which is an important part of human cognition and operation behavior. Summary of the invention

[0006] In view of the deficiencies of the prior art, the present invention provides a human-computer interaction system and method based on eye tracking, which solves the problem of not being able to deeply understand the visual information processing of simulated personnel and further not being able to accurately evaluate the behavioral performance and operation of simulated personnel.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A human-computer interaction system based on eye tracking, comprising: An eye movement data collection module is used to collect the eye movement data of the simulated personnel in the simulated environment, and transmit the collected eye movement data to the gaze point distribution recognition module and the eye movement saccade recognition module; A gaze point distribution recognition module is used to analyze the gaze point distribution of the simulated personnel according to the eye movement data transmitted by the eye movement data acquisition module, obtain key areas and non-key areas, and calculate the average duration of the gaze points corresponding to the key areas, further analyze the different attention levels of the key areas, and generate attention information, and transmit the attention information to the recognition information output module; An eye movement scanning recognition module is used to perform scanning analysis based on the acquired eye movement data, analyze the scanning trajectory of the simulated person, and divide the scanning area to obtain segmented areas, and classify the scanning speeds of the segmented areas to obtain pre-surpassing areas and slow-marking areas, respectively calculate the scanning distances corresponding to the scanning points in the pre-surpassing areas and the slow-marking areas, and screen the scanning points to obtain effective scanning points, and calculate the adjustment scanning speed of the corresponding area based on the effective scanning points, then adjust the scanning speed to generate scanning adjustment information, and transmit the scanning adjustment information to the recognition information output module; The effect evaluation and analysis module is used to perform corresponding simulation effect evaluation based on the acquired scanning adjustment information and attention information, and generate evaluation identification information at the same time, and transmit the evaluation identification information to the identification information output module.

[0008] An identification information output module is used to transmit the obtained evaluation identification information to the corresponding simulator terminal.

[0009] As a further solution of the present invention: the specific method of obtaining the key area and the non-key area by the gaze point distribution recognition module is: The eye movement data of the simulated person is obtained, and the corresponding gaze point position and duration of the simulated person are visualized. Cluster analysis is performed based on the colors of different areas and the number of gaze points. The specific cluster analysis method is: the areas with brighter colors and longer stay times are marked as key areas, and the areas with darker colors and shorter stay times are marked as non-key areas.

[0010] As a further solution of the present invention: the specific method of generating the attention information by the gaze point distribution recognition module is: The key areas are labeled as i, and i=1, 2, ..., j, where j represents the number of key areas. At the same time, the fixation points in the key areas are obtained, and the duration corresponding to the fixation points is obtained. Then, the average duration of all fixation points in the key areas is calculated and recorded as Ti. Similarly, the average duration of all key areas is calculated. At the same time, the key areas are classified according to the calculated average duration of the key areas, the average duration of all key areas is averaged to obtain the average standard value, and then the average duration of the key areas is compared with the average standard value; The key areas whose average duration is greater than the mean standard value are classified as complexity areas, and the key areas whose average duration is less than the mean standard value are classified as simplicity areas. At the same time, attention information is generated and transmitted to the effect evaluation and analysis module.

[0011] As a further solution of the present invention: the specific method of the eye movement scanning recognition module to divide the scanning area into segmented areas is: Then, the corresponding scanning area is divided according to the scanning trajectory of the simulated person, and the scanning area is divided into segmented areas using the turning point of the scanning trajectory as the dividing point. At the same time, the segmented areas are labeled as a, and a=1, 2, ..., b, where b represents the number of segmented areas.

[0012] As a further solution of the present invention: the specific method in which the eye movement scanning recognition module classifies the pre-surpassing area and the slow marking area based on the scanning speed of the segmented area is: The scanning trajectory corresponding to the segmented area a is obtained, and the scanning speed corresponding to the scanning trajectory is obtained, and the standard value corresponding to the segmented area a is obtained, and then the scanning speed of the segmented area is compared with the standard value; If the scanning speed of the segmented area is greater than the standard value, the corresponding segmented area is marked as a pre-surpassing area. Otherwise, if the scanning speed of the segmented area is less than the standard value, the corresponding segmented area is marked as a slow-marked area, and the pre-surpassing area and the slow-marked area are analyzed and processed.

[0013] As a further solution of the present invention: the specific method in which the eye movement scanning recognition module analyzes and processes the pre-surpassing area and the slow marking area is: Taking the pre-surpass area as an example for analysis, all the scanning points in the pre-surpass area are obtained and recorded as o, and o=1, 2, ..., p, where p represents the number of scanning points. Then, the corresponding two-dimensional plane coordinate system is established with the center point of the pre-surpass area as the origin, and the coordinates of the starting point and the ending point of the scanning point o are obtained and recorded as (x o1 ,y o1 ) and (x o2 ,y o2 ), and according to the Euclidean distance formula Calculate the scanning distance corresponding to the scanning point o; In this way, all the scanning point distances are calculated, and the standard quantity of the pre-surpassing area is obtained at the same time. The scanning points in the pre-surpassing area are classified into valid scanning points and invalid scanning points according to the standard quantity. The starting point coordinates and the ending point coordinates corresponding to the scanning point o are compared with the standard quantity respectively. If any set of coordinates in the scanning point o does not meet the standard quantity, the corresponding scanning point is classified as an invalid scanning point. On the contrary, if both sets of coordinates of the scanning point o meet the standard quantity, the corresponding scanning point is classified as a valid scanning point. In this way, the slow marking area is analyzed; The standard scanning distance corresponding to the pre-surpassing area is calculated according to the effective scanning point, and the specific calculation method is: , where u is the number of effective scanning points, and the standard scanning distance dz is calculated by the ratio of the scanning distance in the standard value to obtain the adjustment ratio. At the same time, the scanning speed of the pre-surpassing area is adjusted based on the adjustment ratio, and the corresponding scanning adjustment information is generated.

[0014] As a further solution of the present invention: the specific method of generating the evaluation identification information by the effect evaluation analysis module is: The same simulation operation is performed according to the obtained saccadic adjustment information and attention information, and the simulation operation is evaluated and analyzed at the same time. The simulation operation is compared and analyzed with the standard evaluation information, and evaluation identification information is generated at the same time.

[0015] As a further solution of the present invention: a human-computer interaction method based on eye tracking, the method specifically comprises the following steps: S101: collecting eye movement data of a simulated person in a simulated environment, and performing subsequent gaze point distribution recognition and eye movement saccade recognition; S102, visually displaying the fixation point position and duration according to the eye movement data, and classifying the simulation area into key areas and non-key areas, and classifying the key areas into complex areas and simple areas according to the average duration data, and generating attention information; S103, analyzing the scanning trajectory of the simulated person, dividing the scanning area into segmented areas, and classifying the segmented areas based on the scanning speed to obtain pre-overtaking areas and slow-marking areas; S104, respectively calculating the scanning distances corresponding to the scanning points in the pre-surpassing area and the slow-marking area, and screening the scanning points to obtain effective scanning points, and calculating the adjusted scanning speed of the corresponding area according to the effective scanning points, and then adjusting the scanning speed to generate scanning adjustment information; S105 , evaluating the simulation effect according to the attention information and the scanning adjustment information and generating evaluation identification information, and transmitting the evaluation identification information to the corresponding simulator terminal.

[0016] The present invention provides a human-computer interaction system and method based on eye tracking. Compared with the prior art, it has the following beneficial effects: The present invention collects the eye movement data of the simulated personnel in a specific simulation environment and analyzes the eye movement data. During the analysis, by analyzing the distribution of gaze points, the simulated personnel's attention to different areas in the simulation environment can be understood. Different attention levels reflect the importance of the area to the simulated personnel's operation and cognition. Secondly, the analysis of the scanning trajectory can reveal the simulated personnel's visual search pattern, which is crucial for evaluating their information acquisition efficiency in complex simulation environments. Based on these understandings, researchers began to explore how to combine eye tracking technology with simulation training systems to build a more accurate and effective human-computer interaction evaluation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a block diagram of the system principle of the present invention; Figure 2 This is a schematic diagram of the classification of scanning points of the present invention; Figure 3 The figure is a flowchart of the method of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example 1 See also Figure 1 This embodiment provides a human-computer interaction system based on eye tracking, including an eye movement data acquisition module, a gaze point distribution recognition module, an eye movement scanning recognition module, an effect evaluation and analysis module and a recognition information output module, and the eye movement data acquisition module, the eye movement scanning recognition module, the effect evaluation and analysis module and the recognition information output module are unidirectionally electrically connected, and the system is mainly used in a simulation training system.

[0020] The eye movement data acquisition module is used to collect the eye movement data of the simulated personnel in the simulated environment, and transmit the collected eye movement data to the gaze point distribution recognition module and the eye movement scanning recognition module.

[0021] The gaze point distribution recognition module is used to analyze the gaze point distribution of the simulated personnel based on the acquired eye movement data, and the specific analysis method is as follows: The eye movement data of the simulated person is obtained through an eye tracker, and the specific eye tracker used here is a video-based eye tracker. On the surface of the eyeball, the cornea reflects light. By analyzing the position changes of these reflected lights in the video, the movement of the eyeball can be calculated, and the corresponding gaze point position and duration of the simulated person are visualized, and cluster analysis is performed according to the color and number of gaze points in different areas. The specific cluster analysis method is: the brighter the color (such as red), the denser the gaze points and the longer the stay time, and the corresponding area is marked as a key area; the darker the color (such as blue), the fewer the gaze points and the shorter the stay time, and the corresponding area is marked as a non-key area; Then, secondary analysis is performed based on the classified key areas and non-key areas. The secondary analysis mainly calculates the average duration of the fixation points corresponding to the key areas, further analyzes the different attention levels of the key areas, and generates attention information. The specific processing and analysis methods are as follows: Then, the marked key areas are labeled as i, where i=1, 2, ..., j, where j represents the number of key areas. At the same time, the fixation points in the key areas are obtained, and the duration corresponding to the fixation points is obtained. Then, the average duration of all fixation points in the key areas is calculated and recorded as Ti. Similarly, the average duration of all key areas is calculated. At the same time, the key areas will be classified according to the calculated average duration of the key areas, and the specific classification method is: the average duration of all key areas is calculated to obtain a standard average value. For example, there are three key areas. The average duration of all gaze points in the first key area is 350 milliseconds after calculation, the corresponding average duration of the second key area is 420 milliseconds, and the corresponding average duration of the third key area is 380 milliseconds. Finally, the three values ​​are summed up and the average is calculated to get a standard average value of about 383 milliseconds. Then the average duration of the key area is compared with the standard average value, and the key areas with an average duration greater than the standard average value are classified as complexity areas, and the greater here includes the case of being equal to the standard average value. The key areas with an average duration less than the standard average value are classified as simplicity areas, and the attention information is further generated, and the attention information is transmitted to the effect evaluation and analysis module.

[0022] Example 2 As embodiment 2 of the present invention, the difference from embodiment 1 is that:

[0023] The eye movement scanning recognition module is used to perform scanning analysis based on the acquired eye movement data, and the specific scanning analysis method is: Based on the acquired eye movement data, the scanning sequence and path of the simulated person are connected and drawn to obtain a scanning trajectory, and the visual search mode of the simulated person is analyzed according to the obtained scanning trajectory, and visual search information is generated; Then, the corresponding scanning area is divided according to the scanning trajectory of the simulated person, and the specific division method is: the scanning area is divided into segmented areas by taking the turning points of the scanning trajectory as the dividing points. For example, in a scanning area in the simulation environment, there are three turning points in the scanning trajectory of the simulated person. The scanning area is further divided into four segmented areas by taking the turning points as the segmentation points. At the same time, the segmented areas are labeled as a, and a=1, 2, ..., b, where b represents the number of segmented areas. Then, the scanning trajectory corresponding to the segmented area a is obtained, and the scanning speed corresponding to the scanning trajectory is obtained. The specific scanning speed is mainly obtained by dividing the scanning distance by the scanning time, and the standard value corresponding to the segmented area a is obtained. The standard value here represents the corresponding simulation standard in the simulation environment. At the same time, the standard value is set by the operator. Then, the scanning speed of the segmented area is compared with the standard value. In a specific example, for example, in a simulated driving environment, the driver's scanning trajectory has three turning points when observing the road ahead, the left and right rearview mirrors, and the instrument panel, thereby dividing the scanning area of ​​the driving field of view into four segmented areas, labeled 1, 2, 3 and 4 respectively. For the segmented area labeled 1 (for example, the area for observing the road ahead), by calculating the distance and time the driver scans in this area, it is found that the scanning speed is 5 centimeters per second. Based on driving safety and experience, the operator sets the standard value of the normal scanning speed of this area to 4 centimeters per second. Then the scanning speed of the segmented area can be compared and analyzed with the standard value.

[0024] If the scanning speed of the segmented area is greater than the standard value, the corresponding segmented area is marked as a pre-surpassing area. On the contrary, if the scanning speed of the segmented area is less than the standard value, the corresponding segmented area is marked as a slow-marking area, and the case where the scanning speed is equal to the standard value is not included here, and the pre-surpassing area and the slow-marking area are analyzed and processed; Take the pre-surpass area as an example for analysis, obtain all the scanning points in the pre-surpass area and record them as o, and o=1, 2, ..., p, where p represents the number of scanning points, and here take one group of pre-surpass areas as an example for analysis, then establish the corresponding two-dimensional plane coordinate system with the center point of the pre-surpass area as the origin, and obtain the coordinates of the starting point and the ending point of the scanning point o as (x o1 ,y o1 ) and (x o2 ,y o2 ), and according to the Euclidean distance formula Calculate the scanning distance corresponding to the scanning point o; In this way, all the scanning point distances are calculated, and the standard quantity in the pre-surpassing area is obtained at the same time. The standard quantity here means the specified range in the pre-surpassing area, and the scanning points in the pre-surpassing area are classified according to the standard quantity to obtain valid scanning points and invalid scanning points. The specific classification method is: compare the starting point coordinates and the ending point coordinates corresponding to the scanning point o with the standard quantity respectively. If there is any set of coordinates in the scanning point o that does not meet the standard quantity, specifically expressed as not being within the specified range, the corresponding scanning point is classified as an invalid scanning point. On the contrary, if both sets of coordinates of the scanning point o meet the standard quantity, the corresponding scanning point is classified as a valid scanning point. In a specific example, for example, in a visual experiment area represented by a plane coordinate system, the x-coordinate range of the pre-super region is -20-60, and the y-coordinate range is -20-60, as shown in the attached figure. Figure 2 As shown, the starting point coordinates of one of the scanning points are (-60, 10) and the ending point coordinates are (-8, -30). Therefore, since the x coordinate of the starting point is not within the standard range, the corresponding scanning point is further classified as an invalid scanning point.

[0025] At the same time, the standard scanning distance corresponding to the pre-surpassing area is calculated according to the effective scanning point, and the specific calculation method is: , where u is the number of valid scanning points, and the standard scanning distance dz is calculated by ratio with the scanning distance in the standard value to obtain the adjustment ratio, and the adjustment ratio here is obtained by dividing the standard scanning distance dz by the scanning distance, and the scanning speed of the pre-surpassing area is adjusted based on the adjustment ratio, and the corresponding scanning adjustment information is generated; Similarly, the same analysis is performed on the slow-marked area to obtain the saccade adjustment information, and the saccade adjustment information is transmitted to the effect evaluation and analysis module.

[0026] The effect evaluation and analysis module is used to perform the corresponding simulation effect evaluation according to the acquired scanning adjustment information and attention information, and generate the evaluation identification information at the same time, and transmit the evaluation identification information to the identification information output module. The specific method of generating the evaluation identification information is: The same simulation operation is performed based on the obtained scanning adjustment information and attention information, and the simulation operation is evaluated and analyzed at the same time. The evaluation method here can be to perform operation scoring processing, and compare and analyze the simulation operation with standard evaluation information, and generate evaluation identification information at the same time. Specifically, the standard evaluation information here is represented by the standard operation corresponding to different situations or areas in the simulation environment. The simulation operation and the standard operation are evaluated and scored, and finally an overall judgment result is generated.

[0027] An identification information output module is used to transmit the obtained evaluation identification information to the corresponding simulator terminal.

[0028] Example 3 As the third embodiment of the present invention, the focus is on combining the implementation processes of the first embodiment and the second embodiment.

[0029] Example 4 See also Figure 3 , a human-computer interaction method based on eye tracking, the method specifically comprises the following steps: In step S101, the eye movement data of the simulated person in the simulated environment is collected, and subsequent gaze point distribution recognition and eye movement saccade recognition are performed.

[0030] In step S102, the gaze point position and duration are visualized based on the eye movement data, and the simulation area is classified into key areas and non-key areas. At the same time, the key areas are classified into complexity areas and simplicity areas based on the average duration data, and attention information is generated. The processing method here is the same as the analysis method of the gaze point identification module in Example 1.

[0031] In step S103, the simulated person's scanning trajectory is analyzed, and the scanning area is divided into segmented areas, and the segmented areas are classified based on the scanning speed to obtain pre-surpassing areas and slow-marking areas. The analysis method here is similar to the processing method of the eye movement scanning recognition module in the second embodiment.

[0032] In step S104, the scanning distances corresponding to the scanning points in the pre-surpassing area and the slow-marking area are calculated respectively, and the scanning points are screened to obtain effective scanning points, and the adjustment scanning speed of the corresponding area is calculated according to the effective scanning points, and then the scanning speed is adjusted to generate the scanning adjustment information. The analysis method here is the same as the processing method of the eye movement scanning recognition module in the second embodiment.

[0033] In step S105, simulation effect evaluation is performed according to the attention information and the scanning adjustment information to generate evaluation identification information, and the evaluation identification information is transmitted to the corresponding simulator terminal.

[0034] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0035] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A human-computer interaction system based on eye tracking, characterized in that: include: A gaze point distribution recognition module is used to analyze the gaze point distribution of the simulated personnel according to the eye movement data transmitted by the eye movement data acquisition module, obtain key areas and non-key areas, and calculate the average duration of the gaze points corresponding to the key areas, further analyze the different attention levels of the key areas, and generate attention information, and transmit the attention information to the recognition information output module; An eye movement scanning recognition module is used to perform scanning analysis based on the acquired eye movement data, analyze the scanning trajectory of the simulated person, and divide the scanning area to obtain segmented areas, and classify the scanning speeds of the segmented areas to obtain pre-surpassing areas and slow-marking areas, respectively calculate the scanning distances corresponding to the scanning points in the pre-surpassing areas and the slow-marking areas, and screen the scanning points to obtain effective scanning points, and calculate the adjustment scanning speed of the corresponding area based on the effective scanning points, then adjust the scanning speed to generate scanning adjustment information, and transmit the scanning adjustment information to the recognition information output module; The effect evaluation and analysis module is used to perform corresponding simulation effect evaluation based on the acquired scanning adjustment information and attention information, and generate evaluation identification information at the same time, and transmit the evaluation identification information to the identification information output module.

2. The human-computer interaction system based on eye tracking according to claim 1, characterized in that: It also includes an eye movement data acquisition module and a recognition information output module; An eye movement data collection module is used to collect the eye movement data of the simulated personnel in the simulated environment, and transmit the collected eye movement data to the gaze point distribution recognition module and the eye movement saccade recognition module; An identification information output module is used to transmit the obtained evaluation identification information to the corresponding simulator terminal.

3. The human-computer interaction system based on eye tracking according to claim 1, characterized in that: The specific method of obtaining the key area and the non-key area by the gaze point distribution recognition module is as follows: The eye movement data of the simulated person is obtained, and the corresponding gaze point position and duration of the simulated person are visualized. Cluster analysis is performed based on the colors of different areas and the number of gaze points. The specific cluster analysis method is: the areas with brighter colors and longer stay times are marked as key areas, and the areas with darker colors and shorter stay times are marked as non-key areas.

4. The human-computer interaction system based on eye tracking according to claim 1, characterized in that: The specific method of generating the attention information by the gaze point distribution recognition module is as follows: The key areas are labeled as i, and i=1, 2, ..., j, where j represents the number of key areas. At the same time, the fixation points in the key areas are obtained, and the duration corresponding to the fixation points is obtained. Then, the average duration of all fixation points in the key areas is calculated and recorded as Ti. Similarly, the average duration of all key areas is calculated. At the same time, the key areas are classified according to the calculated average duration of the key areas, the average duration of all key areas is averaged to obtain the average standard value, and then the average duration of the key areas is compared with the average standard value; The key areas whose average duration is greater than the mean standard value are classified as complexity areas, and the key areas whose average duration is less than the mean standard value are classified as simplicity areas. At the same time, attention information is generated and transmitted to the effect evaluation and analysis module.

5. The human-computer interaction system based on eye tracking according to claim 1, characterized in that: The specific method of dividing the scanning area into segmented areas by the eye movement scanning recognition module is as follows: Then, the corresponding scanning area is divided according to the scanning trajectory of the simulated person, and the scanning area is divided into segmented areas using the turning point of the scanning trajectory as the dividing point. At the same time, the segmented areas are labeled as a, and a=1, 2, ..., b, where b represents the number of segmented areas.

6. The human-computer interaction system based on eye tracking according to claim 1, characterized in that: The specific method of the eye movement scanning recognition module to classify the pre-surpassing area and the slow marking area based on the scanning speed of the segmented area is: The scanning trajectory corresponding to the segmented area a is obtained, and the scanning speed corresponding to the scanning trajectory is obtained, and the standard value corresponding to the segmented area a is obtained, and then the scanning speed of the segmented area is compared with the standard value; If the scanning speed of the segmented area is greater than the standard value, the corresponding segmented area is marked as a pre-surpassing area. Otherwise, if the scanning speed of the segmented area is less than the standard value, the corresponding segmented area is marked as a slow-marked area, and the pre-surpassing area and the slow-marked area are analyzed and processed.

7. The human-computer interaction system based on eye tracking according to claim 6, characterized in that: The specific method in which the eye movement scanning recognition module analyzes and processes the pre-surpassing area and the slow marking area is: Taking the pre-surpass area as an example for analysis, all the scanning points in the pre-surpass area are obtained and recorded as o, and o=1, 2, ..., p, where p represents the number of scanning points. Then, the corresponding two-dimensional plane coordinate system is established with the center point of the pre-surpass area as the origin, and the coordinates of the starting point and the ending point of the scanning point o are obtained and recorded as (x o1 ,y o1 ) and (x o2 ,y o2 ), and according to the Euclidean distance formula Calculate the scanning distance corresponding to the scanning point o; In this way, all the distances of the scanning points are calculated, and the standard quantity of the pre-surpassing area is obtained at the same time. The scanning points in the pre-surpassing area are classified into valid scanning points and invalid scanning points according to the standard quantity. The starting point coordinates and the ending point coordinates corresponding to the scanning point o are compared with the standard quantity respectively. If any set of coordinates in the scanning point o does not meet the standard quantity, the corresponding scanning point is classified as an invalid scanning point. On the contrary, if both sets of coordinates of the scanning point o meet the standard quantity, the corresponding scanning point is classified as a valid scanning point. In this way, the slow marking area is analyzed; The standard scanning distance corresponding to the pre-surpassing area is calculated according to the effective scanning point, and the specific calculation method is: , where u is the number of effective scanning points, and the standard scanning distance dz is calculated by the ratio of the scanning distance in the standard value to obtain the adjustment ratio. At the same time, the scanning speed of the pre-surpassing area is adjusted based on the adjustment ratio, and the corresponding scanning adjustment information is generated.

8. The human-computer interaction system based on eye tracking according to claim 1, characterized in that: The specific method for the effect evaluation analysis module to generate evaluation identification information is as follows: The same simulation operation is performed according to the obtained saccadic adjustment information and attention information, and the simulation operation is evaluated and analyzed at the same time. The simulation operation is compared and analyzed with the standard evaluation information, and evaluation identification information is generated at the same time.

9. A human-computer interaction method based on eye tracking, used to execute the human-computer interaction system according to any one of claims 1 to 8, characterized in that: The method specifically comprises the following steps: S101: collecting eye movement data of a simulated person in a simulated environment, and performing subsequent gaze point distribution recognition and eye movement saccade recognition; S102, visually displaying the fixation point position and duration according to the eye movement data, and classifying the simulation area into key areas and non-key areas, and classifying the key areas into complex areas and simple areas according to the average duration data, and generating attention information; S103, analyzing the scanning trajectory of the simulated person, dividing the scanning area into segmented areas, and classifying the segmented areas based on the scanning speed to obtain pre-overtaking areas and slow-marking areas; S104, respectively calculating the scanning distances corresponding to the scanning points in the pre-surpassing area and the slow-marking area, and screening the scanning points to obtain effective scanning points, and calculating the adjusted scanning speed of the corresponding area according to the effective scanning points, and then adjusting the scanning speed to generate scanning adjustment information; S105 , evaluating the simulation effect according to the attention information and the scanning adjustment information and generating evaluation identification information, and transmitting the evaluation identification information to the corresponding simulator terminal.

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