A meteorological science popularization display method and system based on immersive technology

Abstract

The present invention discloses a meteorological science popularization display method and system based on immersive technology, which relates to the field of image processing technology. It includes obtaining screen image data and human eye attention data, preprocessing the screen image data and human eye attention data, evaluating the preprocessed screen image data and human eye attention data to obtain an external light entering device evaluation value, and judging the external light penetration device according to the result of obtaining the external light entering device evaluation value. By obtaining screen image data and evaluating the image color difference degree, brightness difference degree and contrast difference degree, the present invention is used to judge whether there is external light penetrating into the head-mounted device, affecting the user's immersive experience. By reducing environmental light interference and adjusting the display mode, the user's immersion and visual comfort are enhanced. At the same time, a realistic three-dimensional virtual environment is provided to improve the attractiveness and effect of meteorological science popularization education.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and particularly to a meteorological science popularization display method and system based on immersive technology. Background Technique

[0002] The application of immersive technology in meteorological science popularization display refers to using technical means such as virtual reality (VR) to create a three-dimensional virtual environment that simulates real meteorological phenomena, allowing users to "be on the scene" to experience and learn meteorological knowledge through a head-mounted display or other interactive devices. This display method can provide an intuitive visual experience of meteorological changes, such as simulating weather phenomena like hurricanes, lightning, rain, and snow, and at the same time combines interactive elements to enable users to more deeply understand the working principle of the meteorological system and the scientific principle of weather changes, thereby enhancing the attractiveness and effectiveness of science popularization education.

[0003] Immersive technology can significantly enhance people's interest in meteorology by providing a realistic three-dimensional virtual environment and a multi-sensory interactive experience. It enables the public to intuitively feel meteorological phenomena in a safe and controllable simulated environment. This novel and interactive science popularization display method promotes a deeper understanding and interest in meteorological science.

[0004] For example, a patent for invention with the publication number CN205987196U discloses a naked-eye 3D virtual reality display system, which includes using a VR sensing unit to collect the motion information of a gamer, issuing a trigger or control instruction to a three-dimensional rendering unit, triggering or controlling the three-dimensional virtual scene of the three-dimensional rendering unit to obtain a rendered image, and sending it to a VR display unit, so that the gamer can have an immersive virtual reality experience; at the same time, using an actual stereo camera array and combining with a green screen keying technology to place the gamer himself into a digital virtual scene, and finally presenting the gamer and the virtual scene in a naked-eye 3D display manner, enabling bystanders to observe the scene that the gamer is experiencing from a third-person perspective without the help of any auxiliary devices; the bystander can also artificially adjust the viewing position and angle of the third-person perspective, so as to view the situation occurring in the virtual scene according to his own interests, and can also conduct an all-round observation and analysis of the entire virtual scene.

[0005] For example, the immersive virtual reality display system disclosed in the invention patent with the publication number of CN118394218A includes performing full-time domain joint analysis on the gesture action images of the user object at multiple predetermined time points within a predetermined detection time period through an intelligent algorithm to understand the full-time domain interaction behavior pattern of the user object, rather than being limited to the gesture information at a single moment, and using this full-time domain interaction behavior pattern information to judge the true interaction intention of the user object, and then controlling the behavior of the digital human in the immersive virtual reality display system based on big data technology, so as to realize mapping the interaction intention of the user object to the behavior of the digital human, and improve the interactivity and realism of the system.

[0006] When ambient light penetrates into the gap between the head-mounted virtual reality device and the user's face, it will weaken the clarity and stereo depth of the image, affecting the user's feeling of immersion in the virtual environment. Wearing the head-mounted virtual reality device for a long time will also cause discomfort to the user's eyes, thus affecting the user's degree of engagement in the virtual reality scene and the satisfaction of meteorological science popularization display. Summary of the Invention

[0007] Technical Problems to be Solved

[0008] Aiming at the deficiencies of the prior art, the present invention provides a method and system for meteorological science popularization display based on immersive technology, which solves the problem that external light penetrates into the head-mounted device and affects the user's immersive experience.

[0009] Technical Solutions

[0010] To achieve the above objectives, the present invention is realized through the following technical solutions: A method and system for meteorological science popularization display based on immersive technology, including the following specific steps: Step 1: Obtain screen image data and human eye attention data, preprocess the screen image data and human eye attention data, and evaluate the preprocessed screen image data and human eye attention data to obtain an evaluation value of external light entering the device; Step 2: Judge whether there is external light penetrating the device according to the result of the obtained evaluation value of external light entering the device; Step 3: If it is judged that there is external light penetrating the device, tighten the immersive device to fit the face, and return to Step 1 to continue obtaining screen image data and human eye attention data; if it is judged that there is no external light penetrating the device, execute Step 4; Step 4: Obtain human eye state data, preprocess the human eye state data, and evaluate the preprocessed human eye state data to obtain an evaluation value of human eye discomfort; Step 5: Judge whether there is human eye discomfort according to the result of the obtained evaluation value of human eye discomfort; Step 6: If it is judged that there is human eye discomfort, adjust the screen, and return to Step 4 to continue obtaining human eye state data until it is judged that there is no human eye discomfort; if it is judged that there is no human eye discomfort, perform meteorological science popularization display.

[0011] Further, in step one, set a detection time period; perform filtering and denoising processing on the screen image data and the human eye attention data; the screen image data includes pixel points, the BGR color space, the Lab value of the pixel points, the pixel point brightness value, and the standard deviation of the pixel points; the human eye attention data includes the position of the eyeball contour and the position of the fixation point; comprehensively analyze the screen image data to obtain the image color difference degree, the image brightness difference degree, and the image contrast difference degree; perform normalization processing on the image color difference degree, the image brightness difference degree, and the image contrast difference degree; evaluate the screen image anomaly coefficient based on the normalized image color difference degree, image brightness difference degree, and image contrast difference degree; comprehensively analyze the human eye attention data to obtain the saccade degree, the fixation time at the fixation point position, and the number of changes in the position of the eyeball fixation point within the detection time period; perform normalization processing on the saccade degree, the fixation time at the fixation point position, and the number of changes in the position of the eyeball fixation point within the detection time period; obtain the human eye inattentiveness coefficient based on the normalized saccade degree, fixation time at the fixation point position, and number of changes in the position of the eyeball fixation point within the detection time period; the method for obtaining the external light entering the device evaluation value based on the evaluation of the screen image anomaly coefficient and the human eye inattentiveness coefficient is as follows: Among them, WG represents the external light entering the device evaluation value, PY represents the screen image anomaly coefficient, and RB represents the human eye inattentiveness coefficient.

[0012] Further, the specific steps for obtaining the image color difference degree, image brightness difference degree, and image contrast difference degree through comprehensive analysis of screen image data are as follows: Using an edge detection algorithm, obtain the edges of pixel points and segment them, divide local regions, and calculate the area of the local regions; Using a color difference detection algorithm, convert the image from the BGR color space to the Lab color space; Obtain the average Lab value of each local region through the Lab values of the pixel points in each local region, and calculate the difference between the Lab values of the pixel points in each local region and the average Lab value of that local region; Sum the absolute values of the difference calculations to obtain the color difference degree of the image local regions, and sum the color difference degrees of each image local region to obtain the image color difference degree; Convert the image from the Lab color space to a grayscale image; Sum the brightness values of the pixel points in the local regions; Divide the sum of the brightness values of the pixel points in the local region by the area of that local region to obtain the average brightness value of that local region; Sum and average the average brightness values of each local region to obtain the average image brightness value; Calculate the difference between the average brightness value of each local region and the average image brightness value to obtain the brightness difference value; Sum the absolute values of the brightness difference values to obtain the image brightness difference degree; Obtain the contrast of the pixel points through the ratio of the standard deviation of the pixel points in each local region to the average brightness value; Sum and average the contrasts of the pixel points in each local region to obtain the average contrast of that local region; Sum and average the average contrasts of each local region to obtain the average image contrast; Calculate the difference between the average contrast of each local region and the average image contrast; Sum the absolute values of the difference calculations to obtain the image contrast difference degree.

[0013] Further, the specific steps for comprehensively analyzing the saccade degree, fixation time at the fixation point position, and the number of changes in the eye fixation point position through human eye attention data are as follows: In the initial stage of the detection period, the position of the eye contour is obtained through an edge detection algorithm; during the detection period, the distance of the movement of the eye contour position, the number of movements of the eye contour position, and the direction of the movement of the eye contour position are obtained through an edge movement tracking algorithm; the saccade amplitude is obtained by calculating the absolute value of the difference in the distance of the movement of the eye contour position; the saccade frequency is obtained by dividing the number of movements of the eye contour position by the detection period; the saccade angle is obtained by calculating the absolute value of the angular difference in the direction of the movement of the eye contour position; the saccade degree is obtained according to the product of the saccade amplitude, saccade frequency, and saccade angle; the movement of the eye contour position is tracked through the edge movement tracking algorithm, and the timing starts when the eye contour position is stationary at the fixation point position and stops when the eye contour position moves, to obtain the fixation time for a single fixation point position; during the detection period, the sum of the fixation times for a single fixation point position is calculated and then averaged to obtain the fixation time at the fixation point position; S1: A fixation threshold is set according to the fixation time at the fixation point position. During the detection period, when the fixation time of the eye at the fixation point position is greater than or equal to the fixation threshold, it is regarded that the eye is fixated on the fixation point position; S2: A saccade threshold is set according to the saccade degree. During the detection period, when the eye contour position moves each time and the position after the movement is different from the previous position, it is regarded that the eye has looked at other fixation point positions; the number of changes in the eye fixation point position during the detection period is obtained according to S1 and S2.

[0014] Further, in step two, an external light penetration device threshold is set and compared with the external light entering device evaluation value; when the external light entering device evaluation value is greater than or equal to the external light penetration device threshold, it is determined that there is an external light penetration device; when the external light entering device evaluation value is less than the external light penetration device threshold, it is determined that there is no external light penetration device.

[0015] Further, in step four, the human eye state data is filtered and denoised; the human eye state data includes human eye images, the number of human eye image frames, eyelid contours, the area of the eyeball, and the area of the eyelid contour; the pupil constriction degree and the eye closure degree are comprehensively analyzed through the filtered and denoised human eye state data; the pupil constriction degree and the eye closure degree are normalized; the human eye state abnormality coefficient is obtained according to the normalized pupil constriction degree and eye closure degree; the method for obtaining the human eye discomfort evaluation value according to the human eye state abnormality coefficient and the human eye inattentiveness coefficient is as follows: Wherein, RP represents the human eye discomfort evaluation value, RB represents the human eye inattentiveness coefficient, and ZY represents the human eye state abnormality coefficient.

[0016] Further, the specific steps for comprehensively analyzing the pupil constriction degree and the degree of eye closure from the processed human eye state data through filtering and noise reduction are as follows: Calculate the pupil area of each frame of the human eye image by using the edge detection algorithm and the edge movement tracking algorithm; Sum and average the pupil areas of each frame of the human eye image according to the number of frames of the human eye image to obtain the average pupil area within the detection time period; Calculate the difference between the pupil area of each frame of the human eye image and the average pupil area within the detection time period.

[0017] Using statistical principles, set a pupil area threshold based on the average pupil area. When the result of the difference calculation is less than the pupil area threshold, screen the pupil areas of each frame of the human eye image; Select the pupil areas of all eligible frames of the human eye image and sum the absolute values of their differences from the average pupil area to obtain the pupil constriction degree; Within the detection time period, use the edge detection algorithm and the edge movement tracking algorithm to obtain the movement trajectory of the eyelid contour; When the area of the eyelid contour increases until it stops and the area of the eyeball decreases until it stops, set a closed-eye threshold. When the area of the eyelid contour decreases until it stops and the area of the eyeball increases until it stops, set an open-eye threshold; Start timing when the area of the eyelid contour increases until it stops and the area of the eyeball decreases until it stops, and stop timing until the area of the eyelid contour decreases and the area of the eyeball increases to obtain the duration of eye closure; Sum and average the durations of multiple eye closures within the detection time period to obtain the duration of a single eye closure; Using statistical principles, count the number of human eye images that meet the closed-eye threshold within the detection time period to obtain the number of eye closures within the detection time period; When the area of the eyelid contour and the area of the eyeball are between the closed-eye threshold and the open-eye threshold, it indicates squinting; Sum and average the areas of the eyelid contours when squinting for each frame of the human eye image to obtain the squinting range; Obtain the degree of eye closure based on the product of the duration of a single eye closure, the number of eye closures within the detection time period, and the squinting range.

[0018] Further, in step five, set a human eye discomfort threshold and compare it with the human eye discomfort evaluation value; When the human eye discomfort evaluation value is greater than or equal to the human eye discomfort threshold, it is determined that there is human eye discomfort; When the human eye discomfort evaluation value is less than the human eye discomfort threshold, it is determined that there is no human eye discomfort.

[0019] Further, the specific steps for adjusting the screen are as follows: Reduce the screen brightness and contrast and improve the clarity of the screen image until it is determined that there is no human eye discomfort.

[0020] Further, there are a data acquisition module, a data analysis module, a data execution module, and an immersive module. The data acquisition module is used to acquire screen image data, human eye attention data, and human eye state data and perform preprocessing, and send the preprocessed data to the data analysis module. The data analysis module is used to receive the data sent by the data acquisition module, analyze and judge the external light penetration device and human eye discomfort, and send the judgment result to the data execution module. The data execution module is used to receive the judgment result of the data analysis module and execute the judgment result of the external light penetration device and human eye discomfort. The immersive module includes an immersive device and a screen, and the data execution module adjusts the immersive device and the screen according to the judgment result of the external light penetration device and human eye discomfort.

[0021] Beneficial effects

[0022] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0023] 1. By acquiring screen image data and evaluating the image color difference degree, brightness difference degree, and contrast difference degree, it is used to judge whether there is external light penetrating into the head-mounted device and affecting the user's immersive experience.

[0024] 2. By acquiring human eye attention data and evaluating the saccade degree, fixation time on the fixation point position, and the number of changes in the position of the eye fixation point, it is used to judge whether the user is distracted by external light.

[0025] 3. By acquiring human eye state data and evaluating the pupil constriction degree and eye closure degree, it is used to judge whether the user feels eye discomfort due to long-term use of the device.

[0026] 4. By reducing environmental light interference and adjusting the display mode, it enhances the user's immersion and visual comfort, and at the same time provides a realistic three-dimensional virtual environment, improving the attractiveness and effect of meteorological science popularization education.

[0027] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. Description of the drawings

[0028] Figure 1 For the present invention: a flowchart of a meteorological science popularization display method based on immersive technology.

[0029] Figure 2 For the present invention: a line graph of image difference degree and anomaly coefficient.

[0030] Figure 3 For the present invention: a structural diagram of a meteorological science popularization display system based on immersive technology. Detailed implementation manners

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0032] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0033] As Figure 1 shown, the embodiments of the present invention provide a meteorological popular science display method based on immersive technology, including the following specific steps:

[0034] Step 1: Set an image capture device in the immersive device to obtain screen image data and human eye attention data, preprocess the screen image data and human eye attention data, and evaluate the preprocessed screen image data and human eye attention data to obtain an evaluation value of external light entering the device.

[0035] Set a detection time period;

[0036] Performing filtering and denoising processing on the screen image data and human eye attention data helps reduce the amount of data, computational complexity, and resource consumption in subsequent processing;

[0037] By obtaining screen image data and evaluating the color difference degree, brightness difference degree, and contrast difference degree of the image, it is used to determine whether external light penetrates into the immersive device and affects the user's immersive experience;

[0038] By obtaining human eye attention data and evaluating the saccade degree, fixation time at the fixation point position, and the number of changes in the position of the eye fixation point, it is used to determine whether the user is distracted by external light;

[0039] The screen image data includes pixel points, BGR color space, Lab values of pixel points, pixel point brightness values, and standard deviations of pixel points;

[0040] The human eye attention data includes the position of the eye contour and the position of the fixation point;

[0041] The color difference degree, brightness difference degree, and contrast difference degree of the image are obtained through comprehensive analysis of the screen image data;

[0042] Normalizing the color difference degree, brightness difference degree, and contrast difference degree of the image helps to improve the calculation efficiency;

[0043] The method for obtaining the screen image anomaly coefficient is as follows according to the evaluation of the normalized color difference degree, brightness difference degree, and contrast difference degree of the image:

[0044]

[0045] Among them, PY represents the screen image anomaly coefficient, SC represents the color difference degree of the image, the external light penetration device interferes with the image color, LD represents the brightness difference degree of the image, the external light penetration device interferes with the image brightness, DB represents the contrast difference degree of the image, and the external light penetration device interferes with the image contrast.

[0046] Table 1 Relationship table of the color difference degree, brightness difference degree, and contrast difference degree of the image to the approximate value of the screen image anomaly coefficient

[0047]

[0048] As shown in Table 1, when the color difference degree of the image is 26, the brightness difference degree of the image is 82, and the contrast difference degree of the image is 12, the approximate value of the screen image anomaly coefficient is 14.63; when the color difference degree of the image is 33, the brightness difference degree of the image is 127, and the contrast difference degree of the image is 22, the approximate value of the screen image anomaly coefficient is 22.69; when the color difference degree of the image is 41, the brightness difference degree of the image is 198, and the contrast difference degree of the image is 43, the approximate value of the screen image anomaly coefficient is 34.98; in order to reduce the screen image anomaly coefficient, external light penetration into the device should be avoided.

[0049] As Figure 2 shown, it is a line graph of the image difference degree and the anomaly coefficient. Using an edge detection algorithm, such as the Sobel algorithm, the edges of the pixel points are detected and segmented, local regions are divided, and the area of the local regions is calculated;

[0050] The Sobel algorithm uses two 3x3 convolution kernels to filter the image in the x - direction and y - direction respectively, and then combines the results of these two directions to determine the edge strength and direction.

[0051] The specific steps to obtain the color difference degree of the image are as follows:

[0052] Using a color difference detection algorithm, such as the CIE color difference detection algorithm, the image is converted from the BGR color space to the Lab color space;

[0053] The average Lab value of each local area is obtained from the Lab values of the pixel points in each local area, and the difference between the Lab value of each pixel point in the local area and the average Lab value of the local area is calculated;

[0054] The sum of the absolute values of the difference calculations is obtained as the color difference degree of the local area of the image, and the sum of the color difference degrees of each local area of the image is obtained as the color difference degree of the image;

[0055] The CIE color difference detection algorithm is a standardized method for quantifying and evaluating the visual difference between two colors. It determines the perceived color difference between colors by calculating the differences in the three components of L* (brightness), a* (red-green axis), and b* (yellow-blue axis) of the color in the Lab color space.

[0056] The specific steps to obtain the image brightness difference degree are as follows:

[0057] Converting the image from the Lab color space to a grayscale image helps improve the calculation efficiency;

[0058] The brightness values of the pixel points in the local area are summed, and the brightness values range from 0 (black) to 255 (white);

[0059] The result of summing the brightness values of the pixel points in the local area is divided by the area of the local area to obtain the average brightness value of the local area;

[0060] The average brightness values of each local area are summed and then averaged to obtain the average brightness value of the image;

[0061] The difference between the average brightness value of each local area and the average brightness value of the image is calculated to obtain the brightness difference value;

[0062] The sum of the absolute values of the brightness difference values is obtained as the image brightness difference degree.

[0063] The specific steps to obtain the image contrast difference degree are as follows:

[0064] The contrast of the pixel points is obtained by the ratio of the standard deviation of the pixel points in each local area to the average brightness value. The larger the standard deviation, the greater the change in the image brightness and the higher the contrast;

[0065] The contrasts of the pixel points in each local area are summed and then averaged to obtain the average contrast of the local area;

[0066] The average contrasts of each local area are summed and then averaged to obtain the average contrast of the image;

[0067] The difference between the average contrast of each local area and the average contrast of the image is calculated;

[0068] Sum the absolute values of the difference calculations to obtain the image contrast difference degree.

[0069] Through comprehensive analysis of human eye attention data, obtain the saccade degree, the fixation time at the fixation point position, and the number of changes in the position of the eye fixation point within the detection time period;

[0070] Normalize the saccade degree, the fixation time at the fixation point position, and the number of changes in the position of the eye fixation point within the detection time period, which helps to improve the calculation efficiency;

[0071] The method for obtaining the human eye inattentiveness coefficient based on the normalized saccade degree, the fixation time at the fixation point position, and the number of changes in the position of the eye fixation point within the detection time period is as follows:

[0072]

[0073] Among them, RB represents the human eye inattentiveness coefficient, YT represents the saccade degree, ZS represents the fixation time at the fixation point position, and ZC represents the number of changes in the position of the eye fixation point within the detection time period.

[0074] The specific steps and methods for obtaining the saccade degree are as follows:

[0075] At the initial stage of the detection time period, detect the position of the eye contour through an edge detection algorithm;

[0076] Within the detection time period, obtain the distance of the movement of the eye contour position, the number of movements of the eye contour position, and the direction of the movement of the eye contour position through an edge movement tracking algorithm;

[0077] Obtain the saccade amplitude by calculating the absolute value of the difference in the distance of the movement of the eye contour position;

[0078] Obtain the saccade frequency by the ratio of the number of movements of the eye contour position to the detection time period;

[0079] Obtain the saccade angle by the absolute value of the angular difference in the direction of the movement of the eye contour position;

[0080] Obtain the saccade degree according to the product of the saccade amplitude, the saccade frequency, and the saccade angle.

[0081] The edge movement tracking algorithm, such as the edge walking algorithm, starts from a point on the detected edge, moves along the edge pixels, and tracks the entire edge path point by point according to the predefined search direction or the change in edge intensity. When the edge path reaches the end or encounters a point where the edge intensity no longer meets the conditions, the algorithm stops, thereby realizing the complete tracking of the continuous edge in the image.

[0082] The specific steps for obtaining the fixation time at the fixation point position are as follows:

[0083] Track the movement of the eye contour position through the edge movement tracking algorithm. Start timing when the eye contour position is stationary relative to the fixation point position, and stop timing when the eye contour position moves, to obtain the fixation time for a single fixation point position.

[0084] During the detection time period, calculate the sum of the fixation times for a single fixation point position and then take the average to obtain the fixation time for the fixation point position.

[0085] The specific steps to obtain the number of changes in the eye fixation point position during the detection time period are as follows:

[0086] S1: Set a fixation threshold based on the fixation time for the fixation point position. During the detection time period, if the fixation time of the eye on the fixation point position is greater than or equal to the fixation threshold, it is regarded as the eye fixating on the fixation point position.

[0087] S2: Set a saccade threshold based on the saccade degree. During the detection time period, when the eye contour position moves each time and the position after the move is different from the previous position, it is regarded as the eye looking at another fixation point position.

[0088] Obtain the number of changes in the eye fixation point position during the detection time period according to S1 and S2.

[0089] The method for obtaining the external light entering the device evaluation value based on the evaluation of the screen image abnormality coefficient and the eye inattention coefficient is as follows:

[0090]

[0091] Among them, WG represents the external light entering the device evaluation value, PY represents the screen image abnormality coefficient, the external light entering the device causes the image to be unclear, RB represents the eye inattention coefficient, and the external light entering the device will interfere with people's attention.

[0092] Step 2: Judge the external light penetration device according to the result of obtaining the external light entering the device evaluation value.

[0093] Set the external light penetration device threshold and compare it with the external light entering the device evaluation value;

[0094] The external light penetration device threshold obtains the external light penetration device experimental data through the screen image data and the eye attention data;

[0095] Store the external light penetration device experimental data in the external light penetration device database and call it.

[0096] When the external light entering the device evaluation value is greater than or equal to the external light penetration device threshold, it is judged that there is an external light penetration device;

[0097] When the evaluation value of the external light entering the device is less than the external light penetration device threshold, it is determined that there is no external light penetration device.

[0098] Step 3: If it is determined that there is an external light penetration device, tighten the immersive device to fit the face. Stop adjusting when the immersive device touches the face on all sides to avoid leaving gaps for external light to penetrate into the device, and return to Step 1 to continue obtaining screen image data and human eye attention data; if it is determined that there is no external light penetration device, execute Step 4.

[0099] Step 4: Obtain human eye state data through an image capture device, preprocess the human eye state data, and evaluate the preprocessed human eye state data to obtain a human eye discomfort evaluation value.

[0100] Performing filtering and denoising processing on the human eye state data helps reduce the amount of data in subsequent processing, reduce computational complexity, and resource consumption;

[0101] By obtaining human eye state data and evaluating the degree of pupil constriction and the degree of eye closure, it is used to determine whether the user feels eye discomfort due to long-term use of the device.

[0102] The human eye state data includes the pupil area, the area of the eyeball, and the area of the eyelid contour;

[0103] The degree of pupil constriction and the degree of eye closure are obtained through comprehensive analysis of the human eye state data after filtering and denoising processing;

[0104] Performing normalization processing on the degree of pupil constriction and the degree of eye closure helps improve the calculation efficiency;

[0105] The method for obtaining the human eye state abnormality coefficient based on the normalized degree of pupil constriction and the degree of eye closure is as follows:

[0106]

[0107] Among them, ZY represents the human eye state abnormality coefficient, TS represents the degree of pupil constriction, light stimulation will cause the pupil to constrict, BC represents the degree of eye closure, eye discomfort will cause the eyes to close, reducing the use of the eyes.

[0108] The specific steps for obtaining the degree of pupil constriction are as follows:

[0109] Use the edge detection algorithm and the edge motion tracking algorithm to calculate the pupil area of each frame of the human eye image;

[0110] Sum and average the pupil areas of each frame of the human eye image according to the number of frames of the human eye image to obtain the average pupil area during the detection time period;

[0111] Sum and average the pupil areas of each frame of the human eye image to obtain the average pupil area during the detection time period;

[0112] Calculate the difference between the pupil area of each frame of the human eye image and the average pupil area within the detection time period;

[0113] Using statistical principles, set a pupil area threshold based on the average pupil area. When the result of the difference calculation is less than the pupil area threshold, screen the pupil area of each frame of the human eye image;

[0114] Select the pupil areas of all frames of the human eye image that meet the conditions, and sum the absolute values of their differences from the average pupil area to obtain the degree of pupil constriction.

[0115] The specific steps to obtain the degree of eye closure are as follows:

[0116] Within the detection time period, use the edge detection algorithm and the edge movement tracking algorithm to obtain the movement trajectory of the eyelid contour;

[0117] Within 0.3 seconds to 1 second, when the area of the eyelid contour increases until it stops and the area of the eyeball decreases until it stops, set a closed-eye threshold. When the area of the eyelid contour decreases until it stops and the area of the eyeball increases until it stops, set an open-eye threshold. 0.3 seconds to 1 second is the normal blinking cycle of humans;

[0118] When within 0.3 seconds to 1 second, the area of the eyelid contour increases until it stops and the area of the eyeball decreases until it stops, start timing until the area of the eyelid contour decreases and the area of the eyeball increases, then stop timing to obtain the duration of eye closure;

[0119] Sum and average the durations of eye closure multiple times within the detection time period to obtain the duration of a single eye closure;

[0120] Using statistical principles, count the number of human eye images that meet the closed-eye threshold within the detection time period to obtain the number of eye closures within the detection time period;

[0121] When the area of the eyelid contour and the area of the eyeball are between the closed-eye threshold and the open-eye threshold, it indicates squinting;

[0122] Sum and average the areas of the eyelid contour when squinting for each frame of the human eye image to obtain the squinting range;

[0123] Obtain the degree of eye closure based on the product of the duration of a single eye closure, the number of eye closures within the detection time period, and the squinting range;

[0124] The method for obtaining the human eye discomfort evaluation value based on the human eye state abnormality coefficient and the human eye inattentiveness coefficient is as follows:

[0125]

[0126] Among them, RP represents the human eye discomfort evaluation value, RB represents the human eye inattentiveness coefficient, and ZY represents the human eye state abnormality coefficient.

[0127] Step Five: Make a judgment on human eye discomfort according to the result of obtaining the human eye discomfort evaluation value.

[0128] Set the human eye discomfort threshold and compare it with the human eye discomfort evaluation value;

[0129] The human eye discomfort threshold obtains the human eye discomfort experimental data through the human eye state data;

[0130] Store the human eye discomfort experimental data in the human eye discomfort database and call it.

[0131] When the human eye discomfort evaluation value is greater than or equal to the human eye discomfort threshold, it is judged that there is human eye discomfort;

[0132] When the human eye discomfort evaluation value is less than the human eye discomfort threshold, it is judged that there is no human eye discomfort.

[0133] Step Six: If it is judged that there is human eye discomfort, adjust the screen until it is judged that there is no human eye discomfort, reduce the use pressure of the eyes, and return to Step Four to continue obtaining the human eye state data; if it is judged that there is no human eye discomfort, conduct meteorological science popularization display.

[0134] Such as Figure 3 shown: A meteorological science popularization display system based on immersive technology, including: a data acquisition module, a data analysis module, a data execution module, and an immersive module;

[0135] The data acquisition module is used to acquire screen image data, human eye attention data, and human eye state data and preprocess them, and send the preprocessed data to the data analysis module;

[0136] The data analysis module is used to receive the data sent by the data acquisition module, analyze and judge the external light penetration device and human eye discomfort, and send the judgment result to the data execution module;

[0137] The data execution module is used to receive the judgment result of the data analysis module and execute the judgment result of the external light penetration device and human eye discomfort;

[0138] The immersive module includes immersive devices and a screen, and the data execution module adjusts the immersive devices and the screen according to the judgment result of the external light penetration device and human eye discomfort.

[0139] By reducing environmental light interference and adjusting the display method, enhancing the user's immersion and visual comfort, and at the same time providing a realistic three-dimensional virtual environment, improving the attractiveness and effect of meteorological science popularization education.

[0140] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A meteorological science popularization display method based on immersive technology, characterized in that: It includes the following specific steps: Step 1: Obtain screen image data and human eye attention data, preprocess the screen image data and human eye attention data, and evaluate the preprocessed screen image data and human eye attention data to obtain an external light entering device evaluation value; Step 2: Judge the external light penetration device according to the result of obtaining the external light entering device evaluation value; Step 3: If it is judged that there is an external light penetration device, tighten the immersive device to fit the face, and return to Step 1 to continue obtaining the screen image data and human eye attention data; If it is judged that there is no external light penetration device, execute Step 4; Step 4: Obtain human eye state data, preprocess the human eye state data, and evaluate the preprocessed human eye state data to obtain a human eye discomfort evaluation value; Step 5: Judge the human eye discomfort according to the result of obtaining the human eye discomfort evaluation value; Step 6: If it is judged that there is human eye discomfort, adjust the screen, and return to Step 4 to continue obtaining the human eye state data until it is judged that there is no human eye discomfort; If it is judged that there is no human eye discomfort, conduct meteorological science popularization display; In Step 1, set the detection time period; Perform filtering and denoising processing on the screen image data and human eye attention data; The screen image data includes pixel points, BGR color space, Lab values of pixel points, pixel point brightness values, and standard deviations of pixel points; The human eye attention data includes the position of the eyeball contour and the position of the fixation point; Comprehensively analyze the screen image data to obtain the image color difference degree, image brightness difference degree, and image contrast difference degree; Normalize the image color difference degree, image brightness difference degree, and image contrast difference degree; Obtain the screen image anomaly coefficient according to the evaluation of the normalized image color difference degree, image brightness difference degree, and image contrast difference degree; Comprehensively analyze the human eye attention data to obtain the saccade degree, fixation time at the fixation point position, and the number of changes in the position of the eyeball fixation point within the detection time period; Normalize the saccade degree, fixation time at the fixation point position, and the number of changes in the position of the eyeball fixation point within the detection time period; Obtain the human eye inattentiveness coefficient according to the normalized saccade degree, fixation time at the fixation point position, and the number of changes in the position of the eyeball fixation point within the detection time period; The method for obtaining the external light entering device evaluation value according to the evaluation of the screen image anomaly coefficient and the human eye inattentiveness coefficient is as follows: Among them, WG represents the external light entering device evaluation value, PY represents the screen image anomaly coefficient, and RB represents the human eye inattentiveness coefficient.

2. The method for meteorological science popularization display based on immersive technology according to claim 1, wherein: The specific steps for comprehensively analyzing the screen image data to obtain the image color difference degree, image brightness difference degree, and image contrast difference degree are as follows: Use the edge detection algorithm to obtain the edges of pixel points and segment them, divide the local areas, and calculate the areas of the local areas; Use the color difference detection algorithm to convert the image from the BGR color space to the Lab color space; Obtain the average Lab value of each local area through the Lab values of the pixel points in each local area, and calculate the difference between the Lab values of the pixel points in each local area and the average Lab value of the local area; Sum the absolute values of the difference calculations to obtain the color difference degree of the local image region, and sum the color difference degrees of each local image region to obtain the color difference degree of the image; Convert the image from the Lab color space to a grayscale image; Sum the brightness values of the pixel points in the local region; Divide the result of summing the brightness values of the pixel points in the local region by the area of the local region to obtain the average brightness value of the local region; Sum and then average the average brightness values of each local region to obtain the average brightness value of the image; Calculate the difference between the average brightness value of each local region and the average brightness value of the image to obtain the brightness difference value; Sum the absolute values of the brightness difference values to obtain the brightness difference degree of the image; Obtain the contrast of the pixel points by the ratio of the standard deviation of the pixel points in each local region to the average brightness value; Sum and then average the contrasts of the pixel points in each local region to obtain the average contrast of the local region; Sum and then average the average contrasts of each local region to obtain the average contrast of the image; Calculate the difference between the average contrast of each local region and the average contrast of the image; Sum the absolute values of the difference calculation results to obtain the contrast difference degree of the image.

3. The method for meteorological science popularization display based on immersive technology according to claim 1, wherein: The specific steps for comprehensively analyzing the saccade degree, fixation time on the fixation point position, and the number of changes in the position of the eye fixation point within the detection time period through the human eye attention data are as follows: At the initial stage of the detection time period, obtain the position of the eye contour through the edge detection algorithm; During the detection time period, obtain the distance of movement of the eye contour position, the number of movements of the eye contour position, and the direction of movement of the eye contour position through the edge movement tracking algorithm; Obtain the saccade amplitude by calculating the absolute value of the difference in the distance of movement of the eye contour position; Obtain the saccade frequency by the ratio of the number of movements of the eye contour position to the detection time period; Obtain the saccade angle by the absolute value of the angular difference in the direction of movement of the eye contour position; Obtain the saccade degree according to the product of the saccade amplitude, saccade frequency, and saccade angle; Track the movement of the eye contour position through the edge movement tracking algorithm. Start timing when the eye contour position is stationary with respect to the fixation point position, and stop timing when the eye contour position moves, to obtain the fixation time for a single fixation point position; During the detection time period, calculate the sum of the fixation times for a single fixation point position and then average to obtain the fixation time for the fixation point position; S1: Set a fixation threshold according to the fixation time on the fixation point position. During the detection time period, when the fixation time of the eye on the fixation point position is greater than or equal to the fixation threshold, it is regarded as the eye fixating on the fixation point position; S2: Set a saccade threshold according to the saccade degree. During the detection time period, when the eye contour position moves each time and the position after the movement is different from the previous position, it is regarded as the eye looking at other fixation point positions; Obtain the number of changes in the eye fixation point position within the detection time period according to S1 and S2.

4. A meteorological science popularization display method based on immersive technology according to claim 1, characterized in that: In step two, set the external light penetration device threshold and compare it with the external light entering the device evaluation value; When the evaluation value of external light entering the device is greater than or equal to the external light penetration device threshold, it is determined that there is external light penetration into the device; When the evaluation value of external light entering the device is less than the external light penetration device threshold, it is determined that there is no external light penetration into the device.

5. The method for meteorological science popularization display based on immersive technology according to claim 1, characterized in that: In step four, the human eye state data is filtered and denoised; The human eye state data includes human eye images, the number of human eye image frames, eyelid contours, the area of the eyeball, and the area of the eyelid contour; The pupil constriction degree and the degree of eye closure are obtained through comprehensive analysis of the human eye state data after filtering and denoising; Normalization processing is performed on the pupil constriction degree and the degree of eye closure; The human eye state abnormality coefficient is obtained according to the pupil constriction degree and the degree of eye closure after normalization processing; The method for obtaining the human eye discomfort evaluation value according to the human eye state abnormality coefficient and the human eye inattentiveness coefficient is as follows: Among them, RP represents the human eye discomfort evaluation value, RB represents the human eye inattentiveness coefficient, and ZY represents the human eye state abnormality coefficient.

6. The method for meteorological popular science display based on immersive technology according to claim 5, wherein: The specific steps for obtaining the pupil constriction degree and the degree of eye closure through comprehensive analysis of the human eye state data after filtering and denoising are as follows: The pupil area of each frame of human eye image is calculated by using the edge detection algorithm and the edge motion tracking algorithm for the human eye image; The pupil areas of each frame of human eye image are summed and averaged according to the number of human eye image frames to obtain the average pupil area during the detection time period; The difference between the pupil area of each frame of human eye image and the average pupil area during the detection time period is calculated; Using statistical principles, a pupil area threshold is set according to the average pupil area. When the result of the difference calculation is less than the pupil area threshold, the pupil areas of each frame of human eye image are screened; Select the pupil areas of all eligible frames of human eye images, and sum the absolute values of their differences from the average pupil area to obtain the pupil constriction degree; During the detection time period, the movement trajectory of the eyelid contour is obtained by using the edge detection algorithm and the edge motion tracking algorithm; When the area of the eyelid contour increases to a stop and the area of the eyeball decreases to a stop, a closing eye threshold is set. When the area of the eyelid contour decreases to a stop and the area of the eyeball increases to a stop, an opening eye threshold is set; Start timing when the area of the eyelid contour increases to a stop and the area of the eyeball decreases to a stop, and stop timing until the area of the eyelid contour decreases and the area of the eyeball increases to obtain the closing eye duration; The closing eye durations for multiple times during the detection time period are summed and averaged to obtain the single closing eye duration; Using statistical principles, the number of human eye images that meet the closing eye threshold during the detection time period is counted to obtain the number of closing eyes during the detection time period; When the area of the eyelid contour and the area of the eyeball are between the closing eye threshold and the opening eye threshold, it indicates squinting; The areas of the eyelid contours when squinting for each frame of human eye image are summed and averaged to obtain the squinting range; The degree of eye closure is obtained according to the product of the single closing eye duration, the number of closing eyes during the detection time period, and the squinting range.

7. The method for meteorological science popularization display based on immersive technology according to claim 1, characterized in that: In step five, set the human eye discomfort threshold and compare it with the human eye discomfort evaluation value; When the human eye discomfort evaluation value is greater than or equal to the human eye discomfort threshold, it is determined that there is human eye discomfort; When the human eye discomfort evaluation value is less than the human eye discomfort threshold, it is determined that there is no human eye discomfort.

8. A method for meteorological popular science display based on immersive technology according to claim 1, characterized in that: The specific steps for adjusting the screen are as follows: Reduce the screen brightness and contrast, and improve the clarity of the screen image until it is determined that there is no human eye discomfort.

9. A meteorological science popularization display system based on immersive technology, for the meteorological science popularization display method based on immersive technology according to any one of claims 1-8, characterized in that, It includes: A data acquisition module, a data analysis module, a data execution module, and an immersive module; The data acquisition module is used to acquire screen image data, human eye attention data, and human eye state data and preprocess them, and send the preprocessed data to the data analysis module; The data analysis module is used to receive the data sent by the data acquisition module, analyze and judge the external light penetration device and human eye discomfort, and send the judgment result to the data execution module; The data execution module is used to receive the judgment result of the data analysis module and execute the judgment result of the external light penetration device and human eye discomfort; The immersive module includes an immersive device and a screen, and the data execution module adjusts the immersive device and the screen according to the judgment result of the external light penetration device and human eye discomfort.

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