Non-contact intraocular pressure dynamic monitoring system

Abstract

The invention discloses a non-contact intraocular pressure dynamic monitoring system, and relates to the field of visual monitoring, and the system comprises a camera module which is used for collecting an eye image of a user in real time, and determining an intraocular pressure monitoring point based on the eye image of the user; the monitoring module is used for acquiring an intraocular pressure monitoring point position and acquiring intraocular pressure characterization information of a user at the intraocular pressure monitoring point position; the refreshing module is used for monitoring the running state of the monitoring module and refreshing system running after the monitoring module finishes running; according to the method, a subcutaneous blood flow rate monitoring technology is combined, a user eye image is collected and analyzed, a proper user intraocular pressure characterization monitoring point position is selected, and the subcutaneous blood flow rate is monitored at the monitoring point position, so that the dynamic risk of the user intraocular pressure is analyzed on the basis of a blood flow rate monitoring result, and brand-new non-contact intraocular pressure dynamic monitoring is realized; therefore, diagnosis and rehabilitation management of various eye diseases related to intraocular pressure are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of vision monitoring, and in particular to a non-contact dynamic intraocular pressure monitoring system. Background Art

[0002] Dynamic intraocular pressure monitoring is a key technology for continuously tracking changes in intraocular pressure. Using specialized equipment, it can measure intraocular pressure regularly over an extended period, breaking the limitations of traditional single-shot measurements. This continuous data allows doctors to accurately understand intraocular pressure fluctuations and promptly identify abnormalities, providing strong support for early diagnosis and treatment of eye diseases such as glaucoma.

[0003] The invention patent application with application number 202210705764.5 discloses a non-contact continuous dynamic intraocular pressure monitoring system, including: a data acquisition module, configured to: acquire an eye pulse wave signal; a feature extraction module, configured to: extract time domain features and frequency domain features based on the acquired eye pulse wave signal; an intraocular pressure calculation module, configured to: obtain the intraocular pressure corresponding to the current eye pulse wave signal based on at least the time domain features and frequency domain features and a pre-trained intraocular pressure model, and continuously monitor the intraocular pressure online without contact; the intraocular pressure model is obtained by machine learning or multivariate linear regression method. This application aims to solve the problem that "intraocular pressure measurement can be divided into two types: contact and non-contact, Goldmann applanation Intraocular pressure measurement (Goldmann Applanation Tonometer, GAT) is currently the gold standard for clinical intraocular pressure measurement. However, when the measuring head contacts and presses the cornea during intraocular pressure measurement, there are risks such as corneal damage and infection. Currently, single-shot tonometer is used in clinical practice to measure intraocular pressure, which requires multiple measurements on patients at intervals. During the measurement process, professional doctors need to continuously operate professional equipment, and sometimes patients need to be anesthetized, making the measurement process cumbersome, complex, time-consuming and labor-intensive. In particular, intermittent measurements at night are almost impossible to achieve. As a result, clinicians can only obtain a small amount of discrete intraocular pressure data, making it impossible to provide accurate treatment for glaucoma patients.

[0004] However, there has been no innovation in new non-contact dynamic intraocular pressure monitoring technology for a long time;

[0005] Therefore, a new non-contact dynamic intraocular pressure monitoring system was proposed. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the prior art, the present invention provides a non-contact dynamic intraocular pressure monitoring system, which can effectively solve the problems of the prior art.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] The present invention discloses a non-contact dynamic intraocular pressure monitoring system, comprising:

[0009] The camera module is used to collect user eye images in real time and determine the intraocular pressure monitoring points based on the user eye images; the monitoring module is used to obtain the intraocular pressure monitoring points and collect the user's intraocular pressure characterization information at the intraocular pressure monitoring points; the refresh module is used to monitor the operating status of the monitoring module and refresh the system operation after the monitoring module ends; the storage module is used to receive the user's intraocular pressure characterization information collected by the monitoring module and store the user's intraocular pressure characterization information; the analysis module is used to traverse the user's intraocular pressure characterization information stored in the storage module and analyze the user's intraocular pressure risk trend based on the user's intraocular pressure characterization information; the output module is used to receive the user's intraocular pressure risk trend analysis results in the analysis module and output the analysis results

[0010] Furthermore, during the stage of collecting user eye image data by the camera module, the user's head is positioned by a chin rest, and a designated video is provided for the user to watch, limiting the user's attention within the designated video playback time threshold;

[0011] During the playback of the designated video, the camera module continuously collects a plurality of user eye images in real time, identifies valid eye images from the plurality of collected user eye images, and identifies the best valid eye image from the valid eye images;

[0012] After the best effective eye image is identified, the best effective eye image is further subjected to noise reduction processing, and the best effective eye image after noise reduction processing is output as a grayscale image, and a contour image is extracted from the grayscale image;

[0013] The outline representing the palpebral fissure is extracted from the outline image, the center point of the outline representing the palpebral fissure is identified, and the center point and the corresponding point in the user's eye image are used as tracking points to capture the intraocular pressure monitoring point in real time.

[0014] Furthermore, the capture logic of the intraocular pressure monitoring point is:

[0015] Edit the major axis length and minor axis length, draw an elliptical closed-loop path in the user's eye image in real time with the tracking point as the center and combining the major axis and minor axis, further edit the number of IOP monitoring points, and capture IOP monitoring points according to the edited number of IOP monitoring points on the elliptical closed-loop path;

[0016] Among them, when editing the long axis length and the short axis length, it is subject to the following: the endpoints of the long axis and the short axis are both located at the midpoint of the shortest path between the outline of the palpebral fissure and the edge of the user's eye image; when editing the number of intraocular pressure monitoring points, it is subject to the following: the higher the intraocular pressure monitoring accuracy requirement, the more intraocular pressure monitoring points there are, and vice versa, the fewer the number of intraocular pressure monitoring points, and the spacing between adjacent intraocular pressure monitoring points is equal.

[0017] Furthermore, the logic for obtaining the optimal effective eye image is:

[0018] Converting each captured user eye image into a grayscale image, extracting a contour image from each grayscale image, identifying whether each contour image contains a contour representing a palpebral fissure, recording each user eye image corresponding to a contour representing a palpebral fissure as a valid eye image, further identifying the quality of each valid eye image, and recording the valid eye image with the best image quality as the best valid eye image;

[0019] The quality identification formula of the effective eye image is:

[0020] ;

[0021] Where: is the quality performance value of the effective eye image; is the weight coefficient; is the Laplace gradient energy value of the effective eye image; is the preset maximum Laplace gradient energy value; is the noise estimate of the effective eye image; is the weighted sum value of the color saturation of the effective eye image; The weighted sum value for the preset maximum color saturation;

[0022] Among them, the weight coefficient The sum is 1, and they are all positive numbers. The larger the value, the higher the quality of the effective eye image. The best effective eye image is the effective eye image with the highest quality.

[0023] Furthermore, the 、 、 The calculation formula is:

[0024] ;

[0025] Where: 、 is the width and height of the effective eye image; is the result of the effective eye image processed by the Laplace operator; is the total amount of image blocks into which the effective eye image is segmented; That is equal to value; is the variance of the (i, j) position of the effective eye image after segmentation processing; is the weight function related to brightness; is the saturation of the pixel at position (x, y) in the effective eye image;

[0026] When the effective eye image is segmented, each segmented image block has the same size and aspect ratio. After the segmentation process is performed on the I and J effective eye images, the number of horizontal image blocks and the number of vertical image blocks are the same.

[0027] Furthermore, the brightness-related weight function for:

[0028] ;

[0029] Where: is the brightness value of the pixel (x, y) in the effective eye image in the HSV color space; is the mean brightness of the effective eye image; is the standard deviation of the effective eye image.

[0030] Furthermore, the monitoring module is integrated with any device having the function of sensing blood flow velocity in subcutaneous blood vessels, including a laser Doppler blood flowmeter. During the operation phase of the monitoring module, the subcutaneous blood flow velocity is collected at each intraocular pressure monitoring point, i.e., information representing the user's intraocular pressure;

[0031] After the monitoring module is refreshed and runs at least three times based on the control of the refresh module, the analysis module runs synchronously. After each monitoring module runs, the storage module stores the user's intraocular pressure characterization information monitored by the monitoring module.

[0032] Furthermore, the analysis logic of the user's intraocular pressure risk trend in the analysis module is expressed as:

[0033] ;

[0034] Where: It is the user's intraocular pressure risk trend performance value; The total number of times the user's intraocular pressure characterization information is stored in the storage module based on system refresh operation; is the total number of intraocular pressure monitoring points in the user's intraocular pressure characterization information stored for the vth time; The blood flow velocity of the subcutaneous blood vessels collected at the i-th intraocular pressure monitoring point; is the weight;

[0035] in, The value of is 1, and it obeys: the closer the weight corresponding to the intraocular pressure monitoring point is to the tracking point, the larger its value is, and vice versa, the smaller its value is, and the weight values are all positive numbers. Based on the above formula and the update of the user's intraocular pressure representation information in the storage module, we have .

[0036] Furthermore, the output module is connected to the mobile computer device held by the system end user through a wireless network, and the analysis result of the analysis module is sent to the mobile computer device, and the system end user reads the result on the mobile computer device;

[0037] The output module simultaneously determines whether there is a risk in the user's intraocular pressure while outputting the analysis results in the analysis module. The determination logic is: , it means that the user's intraocular pressure is not at risk, otherwise, it means that the user's intraocular pressure is at risk;

[0038] in, Indicates the user's intraocular pressure risk trend performance value obtained from the latest three analyses by the analysis module.

[0039] Furthermore, the camera module is interactively connected to the monitoring module via a wireless network, the monitoring module is interactively connected to the refresh module, the storage module and the analysis module via a wireless network, and the analysis module is interactively connected to the output module via a wireless network.

[0040] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0041] The present invention provides a non-contact dynamic intraocular pressure monitoring system. During operation, the system combines subcutaneous blood flow velocity monitoring technology, collects and analyzes user eye images, selects appropriate user intraocular pressure characterization monitoring points, monitors subcutaneous blood flow velocity at the monitoring points, and analyzes the user's intraocular pressure dynamic risk based on the blood flow velocity monitoring results, realizing a new non-contact dynamic intraocular pressure monitoring system to assist in the diagnosis and rehabilitation management of various eye diseases related to intraocular pressure. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0043] Figure 1 This is a structural diagram of a non-contact dynamic intraocular pressure monitoring system. DETAILED DESCRIPTION

[0044] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings 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 making creative efforts are within the scope of protection of the present invention.

[0045] The present invention will be further described below with reference to the embodiments.

[0046] Example:

[0047] A non-contact intraocular pressure dynamic monitoring system of this embodiment, such as Figure 1 Shown, including:

[0048] A camera module is used to capture real-time images of the user's eyes and determine the intraocular pressure monitoring points based on the images;

[0049] When the camera module collects user eye image data, the user's head is positioned using a chin rest, and a designated video is provided for the user to watch, limiting the user's attention to the specified video playback time threshold;

[0050] During the specified video playback process, the camera module continuously collects several user eye images in real time, identifies valid eye images among the several collected user eye images, and identifies the best valid eye image among the valid eye images;

[0051] After the best effective eye image is identified, the best effective eye image is further subjected to noise reduction processing, and the best effective eye image after noise reduction processing is output as a grayscale image, and a contour image is extracted from the grayscale image;

[0052] Extracting the outline representing the palpebral fissure from the outline image, identifying the center point of the outline representing the palpebral fissure, and using the center point and the corresponding point in the user's eye image as tracking points to perform real-time capture of the intraocular pressure monitoring point;

[0053] The capture logic of the intraocular pressure monitoring point is:

[0054] Edit the major axis length and minor axis length, draw an elliptical closed-loop path in the user's eye image in real time with the tracking point as the center and combining the major axis and minor axis, further edit the number of IOP monitoring points, and capture IOP monitoring points according to the edited number of IOP monitoring points on the elliptical closed-loop path;

[0055] When editing the major axis length and minor axis length, the following requirements apply: the endpoints of the major axis and minor axis are located at the midpoint of the shortest path between the outline of the palpebral fissure and the edge of the user's eye image; when editing the number of intraocular pressure monitoring points, the following requirements apply: the higher the IOP monitoring accuracy requirement, the more IOP monitoring points are required; conversely, the fewer IOP monitoring points are required, and the spacing between adjacent IOP monitoring points is equal.

[0056] The logic for obtaining the best effective eye image is:

[0057] Converting each captured user eye image into a grayscale image, extracting a contour image from each grayscale image, identifying whether each contour image contains a contour representing a palpebral fissure, recording each user eye image corresponding to a contour representing a palpebral fissure as a valid eye image, further identifying the quality of each valid eye image, and recording the valid eye image with the best image quality as the best valid eye image;

[0058] The quality identification formula of effective eye images is:

[0059] ;

[0060] Where: is the quality performance value of the effective eye image; is the weight coefficient; is the Laplace gradient energy value of the effective eye image; is the preset maximum Laplace gradient energy value; is the noise estimate of the effective eye image; is the weighted sum value of the color saturation of the effective eye image; The weighted sum value for the preset maximum color saturation;

[0061] Among them, the weight coefficient The sum is 1, and they are all positive numbers. The larger the value, the higher the quality of the effective eye image. The best effective eye image is the one with the highest quality.

[0062] 、 、 The calculation formula is:

[0063] ;

[0064] Where: 、 is the width and height of the effective eye image; is the result of the effective eye image processed by the Laplace operator; is the total amount of image blocks into which the effective eye image is segmented; That is equal to value; is the variance of the (i, j) position of the effective eye image after segmentation processing; is the weight function related to brightness; is the saturation of the pixel at position (x, y) in the effective eye image;

[0065] Wherein, when the effective eye image is segmented, the image blocks obtained by each segmentation are equal in size and have the same aspect ratio; after the segmentation process is performed on the I and J effective eye images, the number of horizontal image blocks and the number of vertical image blocks;

[0066] Brightness-related weight function for:

[0067] ;

[0068] Where: is the brightness value of the pixel (x, y) in the effective eye image in the HSV color space; is the mean brightness of the effective eye image; is the standard deviation of the effective eye image;

[0069] Through the above logic formula, the quality of the effective eye image is identified with a new image quality identification logic, providing an accurate and specified optimal effective eye image for the subsequent module operation of the system in this embodiment.

[0070] A monitoring module is used to obtain intraocular pressure monitoring points and collect user intraocular pressure characterization information at the intraocular pressure monitoring points;

[0071] The monitoring module is integrated with any device capable of sensing blood flow velocity in subcutaneous blood vessels, including a laser Doppler blood flowmeter. During the operation phase of the monitoring module, the subcutaneous blood flow velocity is collected at each intraocular pressure monitoring point, i.e., the user's intraocular pressure representative information;

[0072] After the monitoring module is refreshed and runs at least three times based on the control of the refresh module, the analysis module runs synchronously, and the storage module stores the user's intraocular pressure characterization information monitored by the monitoring module after each monitoring module runs;

[0073] Refresh module, used to monitor the running status of the monitoring module and refresh the system operation after the monitoring module is finished running;

[0074] A storage module is used to receive the user's intraocular pressure characterization information collected by the monitoring module and store the user's intraocular pressure characterization information;

[0075] An analysis module, configured to traverse the user's intraocular pressure characterization information stored in the storage module, and analyze the user's intraocular pressure risk trend based on the user's intraocular pressure characterization information;

[0076] The analysis logic of the user's intraocular pressure risk trend in the analysis module is expressed as follows:

[0077] ;

[0078] Where: It is the user's intraocular pressure risk trend performance value; The total number of times the user's intraocular pressure characterization information is stored in the storage module based on system refresh operation; is the total number of intraocular pressure monitoring points in the user's intraocular pressure characterization information stored for the vth time; The blood flow velocity of the subcutaneous blood vessels collected at the i-th intraocular pressure monitoring point; is the weight;

[0079] in, The value of is 1, and it obeys: the closer the weight corresponding to the intraocular pressure monitoring point is to the tracking point, the larger its value is, and vice versa, the smaller its value is, and the weight values are all positive numbers. Based on the above formula and the update of the user's intraocular pressure representation information in the storage module, we have ;

[0080] The above logical formula is used to calculate the user's intraocular pressure risk trend, thereby determining whether there is a risk in the dynamic change of the user's intraocular pressure.

[0081] The output module is used to receive the user's intraocular pressure risk trend analysis results from the analysis module and output the analysis results;

[0082] The output module is connected to the mobile computer device held by the system end user through a wireless network, and the analysis results of the analysis module are sent to the mobile computer device, and the system end user reads the results on the mobile computer device;

[0083] While the output module analyzes the results in the output analysis module, it also determines whether there is a risk in the user's intraocular pressure. The judgment logic is as follows: , it means that the user's intraocular pressure is not at risk, otherwise, it means that the user's intraocular pressure is at risk;

[0084] in, Indicates the user's intraocular pressure risk trend performance value obtained from the latest three analyses by the analysis module;

[0085] The camera module is interactively connected to the monitoring module via a wireless network. The monitoring module is interactively connected to the refresh module, the storage module and the analysis module via a wireless network. The analysis module is interactively connected to the output module via a wireless network.

[0086] In this embodiment, the camera module runs in real time to collect user eye images, determines the intraocular pressure monitoring points based on the user eye images, the monitoring module runs in the post-position to obtain the intraocular pressure monitoring points, collects the user's intraocular pressure characterization information at the intraocular pressure monitoring points, and the refresh module synchronously monitors the running status of the monitoring module. After the monitoring module runs, the refresh system runs, the storage module receives the user's intraocular pressure characterization information collected by the monitoring module in real time, stores the user's intraocular pressure characterization information, and then the analysis module traverses the user's intraocular pressure characterization information stored in the storage module, analyzes the user's intraocular pressure risk trend based on the user's intraocular pressure characterization information, and finally receives the user's intraocular pressure risk trend analysis results in the analysis module through the output module, and outputs the analysis results.

[0087] Through the operation of the system in the above embodiment, a new dynamic intraocular pressure monitoring technology is provided, which aims to dynamically monitor the intraocular pressure changes and trend prediction of users using this system, thereby realizing effective and convenient dynamic intraocular pressure monitoring and management.

[0088] In summary, in the above embodiment, the system combines subcutaneous blood flow velocity monitoring technology during operation, collects and analyzes user eye images, selects appropriate user intraocular pressure characterization monitoring points, monitors subcutaneous blood flow velocity at the monitoring points, and analyzes the user's intraocular pressure dynamic risk based on the blood flow velocity monitoring results, realizing a new non-contact intraocular pressure dynamic monitoring to assist in the diagnosis and rehabilitation management of various eye diseases related to intraocular pressure.

[0089] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A non-contact intraocular pressure dynamic monitoring system, characterized in that: include: A camera module is used to capture real-time images of the user's eyes and determine the intraocular pressure monitoring points based on the images; A monitoring module is used to obtain intraocular pressure monitoring points and collect user intraocular pressure characterization information at the intraocular pressure monitoring points; Refresh module, used to monitor the running status of the monitoring module and refresh the system operation after the monitoring module is finished running; A storage module is used to receive the user's intraocular pressure characterization information collected by the monitoring module and store the user's intraocular pressure characterization information; An analysis module, configured to traverse the user's intraocular pressure characterization information stored in the storage module, and analyze the user's intraocular pressure risk trend based on the user's intraocular pressure characterization information; The output module is used to receive the user's intraocular pressure risk trend analysis results from the analysis module and output the analysis results.

2. The non-contact intraocular pressure dynamic monitoring system according to claim 1, characterized in that: During the stage of collecting user eye image data, the user's head is positioned by the chin support, and a designated video is provided for the user to watch, limiting the user's attention within the designated video playback time threshold; During the playback of the designated video, the camera module continuously collects a plurality of user eye images in real time, identifies valid eye images from the plurality of collected user eye images, and identifies the best valid eye image from the valid eye images; After the best effective eye image is identified, the best effective eye image is further subjected to noise reduction processing, and the best effective eye image after noise reduction processing is output as a grayscale image, and a contour image is extracted from the grayscale image; The outline representing the palpebral fissure is extracted from the outline image, the center point of the outline representing the palpebral fissure is identified, and the center point and the corresponding point in the user's eye image are used as tracking points to capture the intraocular pressure monitoring point in real time.

3. The non-contact intraocular pressure dynamic monitoring system according to claim 2, characterized in that: The capture logic of the intraocular pressure monitoring point is: Edit the major axis length and minor axis length, draw an elliptical closed-loop path in the user's eye image in real time with the tracking point as the center and combining the major axis and minor axis, further edit the number of IOP monitoring points, and capture IOP monitoring points according to the edited number of IOP monitoring points on the elliptical closed-loop path; Among them, when editing the long axis length and the short axis length, it is subject to the following: the endpoints of the long axis and the short axis are both located at the midpoint of the shortest path between the outline of the palpebral fissure and the edge of the user's eye image; when editing the number of intraocular pressure monitoring points, it is subject to the following: the higher the intraocular pressure monitoring accuracy requirement, the more intraocular pressure monitoring points there are, and vice versa, the fewer the number of intraocular pressure monitoring points, and the spacing between adjacent intraocular pressure monitoring points is equal.

4. The non-contact intraocular pressure dynamic monitoring system according to claim 2, characterized in that: The logic for obtaining the optimal effective eye image is: Converting each captured user eye image into a grayscale image, extracting a contour image from each grayscale image, identifying whether each contour image contains a contour representing a palpebral fissure, recording each user eye image corresponding to a contour representing a palpebral fissure as a valid eye image, further identifying the quality of each valid eye image, and recording the valid eye image with the best image quality as the best valid eye image; The quality identification formula of the effective eye image is: ； Where: is the quality performance value of the effective eye image; is the weight coefficient; is the Laplace gradient energy value of the effective eye image; is the preset maximum Laplace gradient energy value; is the noise estimate of the effective eye image; is the weighted sum value of the color saturation of the effective eye image; The weighted sum value for the preset maximum color saturation; Among them, the weight coefficient The sum is 1, and they are all positive numbers. The larger the value, the higher the quality of the effective eye image. The best effective eye image is the effective eye image with the highest quality.

5. The non-contact dynamic intraocular pressure monitoring system according to claim 4, characterized in that: described 、 、 The calculation formula is: ； Where: 、 is the width and height of the effective eye image; is the result of the effective eye image processed by the Laplace operator; is the total amount of image blocks into which the effective eye image is segmented; That is equal to value; is the variance of the (i, j) position of the effective eye image after segmentation processing; is the weight function related to brightness; is the saturation of the pixel at position (x, y) in the effective eye image; When the effective eye image is segmented, each segmented image block has the same size and aspect ratio. After the segmentation process is performed on the I and J effective eye images, the number of horizontal image blocks and the number of vertical image blocks are the same.

6. The non-contact dynamic intraocular pressure monitoring system according to claim 5, characterized in that: The brightness-related weight function for: ； Where: is the brightness value of the pixel (x, y) in the effective eye image in the HSV color space; is the mean brightness of the effective eye image; is the standard deviation of the effective eye image.

7. The non-contact dynamic intraocular pressure monitoring system according to claim 1, characterized in that: The monitoring module is integrated with any device capable of sensing blood flow velocity in subcutaneous blood vessels, including a laser Doppler blood flowmeter. During operation, the monitoring module collects subcutaneous blood flow velocity at each intraocular pressure monitoring point, i.e., information representing the user's intraocular pressure; After the monitoring module is refreshed and runs at least three times based on the control of the refresh module, the analysis module runs synchronously. After each monitoring module runs, the storage module stores the user's intraocular pressure characterization information monitored by the monitoring module.

8. The non-contact dynamic intraocular pressure monitoring system according to claim 1, characterized in that: The analysis logic of the user's intraocular pressure risk trend in the analysis module is expressed as follows: ； Where: It is the user's intraocular pressure risk trend performance value; The total number of times the user's intraocular pressure characterization information is stored in the storage module based on system refresh operation; is the total number of intraocular pressure monitoring points in the user's intraocular pressure characterization information stored for the vth time; The blood flow velocity of the subcutaneous blood vessels collected at the i-th intraocular pressure monitoring point; is the weight; in, The value of is 1, and it obeys: the closer the weight corresponding to the intraocular pressure monitoring point is to the tracking point, the larger its value is, and vice versa, the smaller its value is, and the weight values are all positive numbers. Based on the above formula and the update of the user's intraocular pressure representation information in the storage module, we have .

9. The non-contact dynamic intraocular pressure monitoring system according to claim 8, characterized in that: The output module is connected to the mobile computer device held by the system end user through a wireless network, and the analysis result of the analysis module is sent to the mobile computer device, and the system end user reads the result on the mobile computer device; The output module simultaneously determines whether there is a risk in the user's intraocular pressure while outputting the analysis results in the analysis module. The determination logic is: , it means that the user's intraocular pressure is not at risk, otherwise, it means that the user's intraocular pressure is at risk; in, Indicates the user's intraocular pressure risk trend performance value obtained from the latest three analyses by the analysis module.

10. The non-contact dynamic intraocular pressure monitoring system according to claim 1, characterized in that: The camera module is interactively connected to the monitoring module via a wireless network, the monitoring module is interactively connected to the refresh module, the storage module and the analysis module via a wireless network, and the analysis module is interactively connected to the output module via a wireless network.

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