User abnormity prompting method and system based on nystagmus monitoring

Abstract

The invention provides a user abnormity prompting method and system based on nystagmus monitoring, and the method comprises the steps: collecting eyeball movement information of a user in real time through nystagmus monitoring equipment, and generating an eyeball movement track of the user based on the eyeball movement information of the user; in the eyeball movement tracks of the user, screening each regular eyeball movement track through a regular track analysis strategy, and based on each regular eyeball movement track, identifying the nystagmus degree of each nystagmus type corresponding to the user through a nystagmus judgment strategy; and based on the nystagmus degrees of the nystagmus types and the interval time between the regular eyeball movement tracks, predicting the abnormal probability of the user and the abnormal time period of the user through an abnormal analysis network, and based on the abnormal probability of the user and the abnormal time period of the user, monitoring the nystagmus of the user through the nystagmus monitoring device. And carrying out abnormity prevention prompt operation on the user. By adopting the scheme, the prediction accuracy of the abnormal condition of the user can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of nystagmus monitoring, and in particular to a method and system for user abnormality prompting based on nystagmus monitoring. Background Art

[0002] Nystagmus (NY) is an involuntary, rhythmic, back-and-forth swinging eye movement. The direction is divided into horizontal, vertical, rotational, etc., with the horizontal type being the most common. The direction of the nystagmus is usually indicated by the fast phase direction, and the fast phase is the compensatory movement to restore the gaze position. It is referred to as nystagmus. Frequent nystagmus or intermittent nystagmus may cause users to experience dizziness, fainting, shock and other abnormal conditions. Therefore, improving the real-time monitoring of nystagmus is the key to improving the prediction of abnormal conditions of users.

[0003] The traditional way to predict nystagmus is to go to a professional medical institution for testing, and then have the medical institution's experts analyze the approximate time when the user may experience an abnormal condition. However, this method cannot predict the user's nystagmus in real time, resulting in a low accuracy in predicting the user's abnormal condition. Summary of the invention

[0004] The main purpose of the present invention is to provide a method and system for user abnormality prompting based on nystagmus monitoring, aiming to solve the problem of low accuracy in predicting user abnormal conditions due to the inability to predict the user's nystagmus in real time in the prior art.

[0005] To achieve the above object, the present invention provides a method for user abnormality prompting based on nystagmus monitoring, the method comprising:

[0006] Collecting the user's eye movement information in real time through an eye nystagmus monitoring device, and generating the user's eye movement trajectory based on the user's eye movement information;

[0007] In the eye movement trajectory of the user, each regular eye movement trajectory is screened by a regular trajectory analysis strategy, and based on each regular eye movement trajectory, a nystagmus degree of each nystagmus type corresponding to the user is identified by a nystagmus determination strategy;

[0008] Based on the nystagmus degree of each nystagmus type and the interval time between each regular eye movement trajectory, the abnormal probability of the user and the abnormal time period of the user are predicted through the abnormal analysis network, and based on the abnormal probability of the user and the abnormal time period of the user, the nystagmus monitoring device is used to provide the user with an abnormal prevention prompt operation.

[0009] Optionally, generating the eye movement trajectory of the user based on the eye movement information of the user includes:

[0010] Splitting the user's eye movement information into eye image information at each moment, and identifying the position information of the pupil center point in each eye image information;

[0011] Connecting the position information of the pupil center in the eyeball image information at adjacent moments to obtain the pupil movement information of the user between the moments, and constructing the three-dimensional eyeball coordinate system of the user based on the eyeball range of the user;

[0012] The pupil movement information between each of the moments is projected into the three-dimensional coordinate system of the eyeball to obtain the eyeball movement trajectory of the user.

[0013] Optionally, in the eye movement trajectory of the user, screening each regular eye movement trajectory by using a regular trajectory analysis strategy includes:

[0014] Dividing the eye movement trajectory of the user into trajectory groups of each moment number according to each moment number, and identifying the overlapping movement trajectory between the sub-eye movement trajectories of each two trajectory groups of the moment number for each moment number;

[0015] Based on the coincident motion trajectories between the sub-eyeball motion trajectories of each two trajectory groups, the trajectory coincidence degree between each two trajectory groups is calculated by the coincidence degree algorithm, and based on the pupil motion information between each two trajectory groups at each moment, the trajectory deviation value of each two trajectory groups is calculated by the trajectory deviation algorithm;

[0016] Based on the trajectory overlap between every two trajectory groups and the trajectory deviation value between every two trajectory groups, similar trajectory groups of similar motion trajectories are screened between the trajectory groups, and the sub-eye movement trajectories corresponding to the similar trajectory groups are used as regular eye movement trajectories.

[0017] Optionally, the step of identifying the nystagmus degree of each nystagmus type corresponding to the user through a nystagmus determination strategy based on each of the regular eye movement trajectories includes:

[0018] Based on each moment corresponding to each regular eye movement trajectory corresponding to each moment number, performing trajectory deduplication processing on each regular eye movement trajectory to obtain each target regular eye movement trajectory, and for each similar movement trajectory, based on each target regular eye movement trajectory corresponding to the similar movement trajectory, identifying the trajectory feature corresponding to the similar movement trajectory;

[0019] Based on each trajectory feature corresponding to the similar motion trajectory, identifying the trajectory type of the similar motion trajectory, and based on each target regular eye movement trajectory corresponding to the similar motion trajectory, identifying the trajectory range of the similar motion trajectory;

[0020] In the nystagmus database, the nystagmus type corresponding to each trajectory type is queried, and based on the trajectory range of similar motion trajectories of each trajectory type corresponding to each nystagmus type, the nystagmus degree of each nystagmus type is identified through the nystagmus degree evaluation strategy of the nystagmus type.

[0021] Optionally, the predicting of the abnormal probability of the user and the abnormal time period of the user through an abnormal analysis network based on the nystagmus degree of each nystagmus type and the interval time between each regular eye movement trajectory includes:

[0022] For each similar motion trajectory, based on the interval time range between each target regular eye movement trajectory of the similar motion trajectory, identifying the interval time between each target regular eye movement trajectory;

[0023] Calculating the nystagmus frequency of the nystagmus type corresponding to the similar motion trajectory based on the interval time between each of the target regular eye movement trajectories, and identifying the nystagmus duration of the nystagmus type corresponding to the similar motion trajectory based on the time range included in all the target regular eye movement trajectories corresponding to the similar motion trajectory;

[0024] Based on the nystagmus degree of each nystagmus type, the average nystagmus frequency of each nystagmus type, and the average nystagmus duration of each nystagmus type, the abnormal probability of the user and the abnormal time period of the user are predicted through the abnormal analysis network.

[0025] Optionally, the performing an abnormal prevention prompt operation to the user through the nystagmus monitoring device based on the abnormal probability of the user and the abnormal time period of the user includes:

[0026] Acquire the prompt template information of the nystagmus monitoring device and the prompt advance time of the nystagmus monitoring device;

[0027] In the prompt database, query the target prompt information corresponding to the abnormal probability of the user, and fill the target prompt information into the prompt template information to obtain the current prompt information of the user;

[0028] Determine a prompt time point of the current prompt information based on the abnormal time period of the user and the prompt advance time, and generate a prompt instruction of the nystagmus monitoring device based on the prompt time point and the current prompt information;

[0029] The prompt instruction is sent to the nystagmus monitoring device, and the nystagmus monitoring device is controlled to transmit current prompt information to the user at the prompt time point.

[0030] In addition, to achieve the above-mentioned purpose, the present invention also provides a user abnormality prompt system based on nystagmus monitoring, and the user abnormality prompt system based on nystagmus monitoring includes:

[0031] A collection module, used to collect the user's eye movement information in real time through a nystagmus monitoring device, and generate the user's eye movement trajectory based on the user's eye movement information;

[0032] an identification module, configured to screen each regular eye movement trajectory in the eye movement trajectory of the user through a regular trajectory analysis strategy, and identify the nystagmus degree of each nystagmus type corresponding to the user through a nystagmus determination strategy based on each regular eye movement trajectory;

[0033] The prevention module is used to predict the abnormal probability of the user and the abnormal time period of the user based on the nystagmus degree of each nystagmus type and the interval time between each regular eye movement trajectory through an abnormal analysis network, and based on the abnormal probability of the user and the abnormal time period of the user, perform abnormal prevention prompt operations to the user through the nystagmus monitoring device.

[0034] Optionally, the acquisition module is specifically used to:

[0035] Splitting the user's eye movement information into eye image information at each moment, and identifying the position information of the pupil center point in each eye image information;

[0036] Connecting the position information of the pupil center in the eyeball image information at adjacent moments to obtain the pupil movement information of the user between the moments, and constructing the three-dimensional eyeball coordinate system of the user based on the eyeball range of the user;

[0037] The pupil movement information between each of the moments is projected into the three-dimensional coordinate system of the eyeball to obtain the eyeball movement trajectory of the user.

[0038] Optionally, the identification module is specifically used to:

[0039] Dividing the eye movement trajectory of the user into trajectory groups of each moment number according to each moment number, and identifying the overlapping movement trajectory between the sub-eye movement trajectories of each two trajectory groups of the moment number for each moment number;

[0040] Based on the coincident motion trajectories between the sub-eyeball motion trajectories of each two trajectory groups, the trajectory coincidence degree between each two trajectory groups is calculated by the coincidence degree algorithm, and based on the pupil motion information between each two trajectory groups at each moment, the trajectory deviation value of each two trajectory groups is calculated by the trajectory deviation algorithm;

[0041] Based on the trajectory overlap between every two trajectory groups and the trajectory deviation value between every two trajectory groups, similar trajectory groups of similar motion trajectories are screened between the trajectory groups, and the sub-eye movement trajectories corresponding to the similar trajectory groups are used as regular eye movement trajectories.

[0042] Optionally, the identification module is specifically used to:

[0043] Based on each moment corresponding to each regular eye movement trajectory corresponding to each moment number, performing trajectory deduplication processing on each regular eye movement trajectory to obtain each target regular eye movement trajectory, and for each similar movement trajectory, based on each target regular eye movement trajectory corresponding to the similar movement trajectory, identifying the trajectory feature corresponding to the similar movement trajectory;

[0044] Based on each trajectory feature corresponding to the similar motion trajectory, identifying the trajectory type of the similar motion trajectory, and based on each target regular eye movement trajectory corresponding to the similar motion trajectory, identifying the trajectory range of the similar motion trajectory;

[0045] In the nystagmus database, the nystagmus type corresponding to each trajectory type is queried, and based on the trajectory range of similar motion trajectories of each trajectory type corresponding to each nystagmus type, the nystagmus degree of each nystagmus type is identified through the nystagmus degree evaluation strategy of the nystagmus type.

[0046] Optionally, the prevention module is specifically used to:

[0047] For each similar motion trajectory, based on the interval time range between each target regular eye movement trajectory of the similar motion trajectory, identifying the interval time between each target regular eye movement trajectory;

[0048] Calculating the nystagmus frequency of the nystagmus type corresponding to the similar motion trajectory based on the interval time between each of the target regular eye movement trajectories, and identifying the nystagmus duration of the nystagmus type corresponding to the similar motion trajectory based on the time range included in all the target regular eye movement trajectories corresponding to the similar motion trajectory;

[0049] Based on the nystagmus degree of each nystagmus type, the average nystagmus frequency of each nystagmus type, and the average nystagmus duration of each nystagmus type, the abnormal probability of the user and the abnormal time period of the user are predicted through the abnormal analysis network.

[0050] Optionally, the prevention module is specifically used to:

[0051] Acquire the prompt template information of the nystagmus monitoring device and the prompt advance time of the nystagmus monitoring device;

[0052] In the prompt database, query the target prompt information corresponding to the abnormal probability of the user, and fill the target prompt information into the prompt template information to obtain the current prompt information of the user;

[0053] Determine a prompt time point of the current prompt information based on the abnormal time period of the user and the prompt advance time, and generate a prompt instruction of the nystagmus monitoring device based on the prompt time point and the current prompt information;

[0054] The prompt instruction is sent to the nystagmus monitoring device, and the nystagmus monitoring device is controlled to transmit current prompt information to the user at the prompt time point.

[0055] In a third aspect, the present application provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the methods in the first aspect are implemented.

[0056] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0057] In a fifth aspect, the present application provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0058] The present invention provides a user abnormality prompt method and system based on nystagmus monitoring, the method comprising: collecting user's eye movement information in real time through a nystagmus monitoring device, and generating the user's eye movement trajectory based on the user's eye movement information; in the user's eye movement trajectory, screening each regular eye movement trajectory through a regular trajectory analysis strategy, and identifying the nystagmus degree of each nystagmus type corresponding to the user through a nystagmus determination strategy based on each regular eye movement trajectory; based on the nystagmus degree of each nystagmus type and the interval time between each regular eye movement trajectory, predicting the user's abnormal probability and the user's abnormal time period through an abnormal analysis network, and based on the user's abnormal probability and the user's abnormal time period, performing an abnormal prevention prompt operation to the user through the nystagmus monitoring device. This solution uses nystagmus detection equipment to collect the user's eye movement information in real time, thereby analyzing the regular eye movement estimates in the user's eye movement trajectory, and then determines the nystagmus level of the nystagmus type that the user may currently be in, so as to analyze the user's abnormal probability and abnormal time period, thereby avoiding the problem of low timeliness and inability to monitor in real time in medical institutions for nystagmus monitoring. At the same time, based on the nystagmus level of the nystagmus type monitored in real time, the user's abnormal probability and abnormal time period are predicted, which can effectively avoid the user's additional damage to the human body caused by the abnormality caused by nystagmus, and can give the user time to prepare and prevent in advance, thereby greatly reducing the degree of additional damage caused by nystagmus to the user. In addition, this solution collects the user's eye movement data in real time, and analyzes the eye movement trajectory corresponding to the eye movement data from multiple angles, thereby predicting the user's abnormal situation, thereby improving the accuracy of predicting the user's abnormal situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 It is a device structure diagram of a nystagmus detection device provided in an embodiment of the present invention.

[0061] Figure 2 is a flow chart of a method for prompting a user abnormality based on nystagmus monitoring provided by an embodiment of the present invention;

[0062] Figure 3 is a structural schematic diagram of a user abnormality prompt system based on nystagmus monitoring provided by an embodiment of the present invention;

[0063] Figure 4An internal structure diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The user abnormality prompt method based on nystagmus monitoring provided by the embodiment of the present invention is applied to the user abnormality prompt system based on nystagmus monitoring. Unless otherwise defined, all technical and scientific terms used in this article have the same meaning as those generally understood by technicians in the technical field of this application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned figures are used to distinguish different objects, not to describe a specific order.

[0065] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0066] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0067] The user abnormality prompt method based on nystagmus monitoring provided in the embodiment of the present application can be applied to the application environment of nystagmus monitoring equipment. Among them, the nystagmus detection equipment includes a bracket unit, an eyeball image acquisition unit, an analysis control unit, and a prevention prompt unit. The eyeball image acquisition unit, the analysis control unit, and the prevention prompt unit are arranged on the bracket unit, and the eyeball image acquisition unit and the prevention prompt unit are respectively connected to the analysis control unit. The analysis and control module may be a terminal for control and analysis, and the terminal includes a computing chip and an instruction transceiver interface, wherein the terminal collects the user's eye movement information in real time, thereby analyzing the regular eye movement estimation in the user's eye movement trajectory, and then determines the degree of nystagmus of the type of nystagmus that the user may currently be in, so as to analyze the user's abnormal probability and abnormal time period, thereby avoiding the problem of low timeliness and inability to monitor in real time in medical institutions for nystagmus monitoring. At the same time, based on the degree of nystagmus of the type of nystagmus monitored in real time, the user's abnormal probability and abnormal time period are predicted, which can effectively avoid the user's additional damage to the human body caused by the abnormality caused by nystagmus, and can give the user time to prepare and prevent in advance, thereby greatly reducing the degree of additional damage caused by nystagmus to the user. In addition, this solution collects the user's eye movement data in real time, and analyzes the eye movement trajectory corresponding to the eye movement data from multiple angles, thereby predicting the user's abnormal situation, thereby improving the accuracy of predicting the user's abnormal situation.

[0068] In one embodiment, Figure 2 As shown, a method for user abnormal prompting based on nystagmus monitoring is provided, and the method is applied to a terminal of an analysis control module as an example for explanation, and includes the following steps:

[0069] Step S201 : collecting the user's eye movement information in real time through a nystagmus monitoring device, and generating the user's eye movement trajectory based on the user's eye movement information.

[0070] In this embodiment, the terminal collects the user's eye image information in real time through the eye image acquisition unit of the nystagmus detection device, and uses the eye image information collected at each moment as the user's eye movement information. Among them, the eye image acquisition unit can be a single-frame camera device, and the single-frame camera device can collect images of the user's eyeball part according to each frame. Then, the terminal constructs the user's eye movement trajectory based on the user's eye movement information. Among them, the eye movement trajectory is an eye movement trajectory established in the eyeball three-dimensional coordinate system with the user's eye center as the origin, the eyeball surface as the plane, and the time axis as the horizontal axis, wherein the unit of the time axis is moment, and the unit of the eyeball surface is. That is, the eye movement trajectory includes the actual movement trajectory corresponding to the movement distance, and includes the time characteristics of the actual movement trajectory. The specific generation process will be described in detail later.

[0071] Step S202, in the eye movement trajectory of the user, each regular eye movement trajectory is screened through a regular trajectory analysis strategy, and based on each regular eye movement trajectory, the nystagmus degree of each nystagmus type corresponding to the user is identified through a nystagmus determination strategy.

[0072] In this embodiment, the terminal uses a regular trajectory analysis strategy to screen the regular eye movement trajectories in the user's eye movement trajectory, and based on the regular eye movement trajectories, identifies the nystagmus degree of each nystagmus type corresponding to the user through a nystagmus determination strategy. Among them, the regular trajectory analysis strategy is to divide the pupil movement information between the position information of the pupil center point in the eye image information at each moment into each trajectory group corresponding to different moment numbers according to the incremental grouping operation of the moment number corresponding to different frame numbers, and then identify each similar trajectory group of similar motion trajectories corresponding to each moment number. The specific identification process will be described in detail later. The nystagmus determination strategy is to analyze the regular eye movement trajectory of each similar trajectory group of each similar motion trajectory, so as to identify the nystagmus type corresponding to each similar motion trajectory, and the determination strategy of the nystagmus degree. The specific determination process will be described in detail later.

[0073] Step S203, based on the nystagmus degree of each nystagmus type and the interval time between each regular eye movement trajectory, the user's abnormal probability and the user's abnormal time period are predicted through the abnormal analysis network, and based on the user's abnormal probability and the user's abnormal time period, the user is prompted to prevent abnormalities through the nystagmus monitoring device.

[0074] In this embodiment, the terminal predicts the user's abnormal probability and abnormal time period based on the degree of nystagmus of each nystagmus type and the interval time between each regular eye movement trajectory through the abnormal analysis network, and based on the user's abnormal probability and the user's abnormal time period, performs abnormal prevention prompt operations to the user through the nystagmus monitoring device. Among them, the user's abnormal probability is the probability that the user may have an abnormal situation in the short future, and the abnormal time period is the time period when the user will have an abnormal situation in the short future. The abnormal situation includes, but is not limited to, physical abnormalities caused by nystagmus, such as fainting, vertigo, dizziness, and coma. The specific prediction process will be described in detail later.

[0075] Based on the above scheme, by collecting the user's eye movement information in real time, the regular eye movement estimation in the user's eye movement trajectory is analyzed, and then the nystagmus level of the nystagmus type that the user may currently be in is determined to analyze the user's abnormal probability and abnormal time period, thereby avoiding the low timeliness and inability to monitor in real time of nystagmus monitoring in medical institutions. At the same time, based on the nystagmus level of the nystagmus type monitored in real time, the user's abnormal probability and abnormal time period are predicted, which can effectively avoid the user's additional damage to the human body caused by the abnormality caused by nystagmus, and can give the user time to prepare and prevent in advance, thereby greatly reducing the degree of additional damage caused by nystagmus to the user. In addition, this scheme collects the user's eye movement data in real time, and analyzes the eye movement trajectory corresponding to the eye movement data from multiple angles, thereby predicting the user's abnormal situation, thereby improving the accuracy of predicting the user's abnormal situation.

[0076] Optionally, based on the user's eye movement information, the user's eye movement trajectory is generated, including: splitting the user's eye movement information into eye image information at each moment, and identifying the position information of the pupil center point in each eye image information; connecting the position information of the pupil center point in the eye image information at adjacent moments to obtain the user's pupil movement information between each moment, and constructing the user's eye three-dimensional coordinate system based on the user's eye range; projecting the pupil movement information between each moment into the eye three-dimensional coordinate system to obtain the user's eye movement trajectory.

[0077] In this embodiment, the terminal splits the user's eye movement information into eye image information at each moment, and identifies the position information of the pupil center in each eye image information. The method of identifying the position information of the pupil center at the center of each eye image is to mark the position of the pupil center in the eye image information through an image recognition network, and identify the position information corresponding to the position of the pupil center in the two-dimensional coordinate system corresponding to the eye image information. The image recognition network is a convolutional neural network.

[0078] Then, the terminal connects the position information of the pupil center in the eyeball image information of adjacent moments to obtain the pupil movement information of the user between each moment, and constructs the user's eyeball three-dimensional coordinate system based on the user's eyeball range. After that, the terminal projects the pupil movement information between each moment into the eyeball three-dimensional coordinate system to obtain the user's eyeball movement trajectory. Among them, the trajectory between each moment in the eyeball movement trajectory is the pupil movement information containing the time feature. That is, the pupil movement information is a two-dimensional movement trajectory, while the eyeball movement trajectory is a three-dimensional movement trajectory.

[0079] Based on the above scheme, by identifying the change information of the pupil center point position at different times, the eye movement trajectory of the user is constructed, thereby improving the practicality and accuracy of the constructed eye movement trajectory.

[0080] Optionally, in the user's eye movement trajectory, regular eye movement trajectories are screened through a regular trajectory analysis strategy, including: dividing the user's eye movement trajectory into trajectory groups of each moment number according to each moment number, and identifying, for each moment number, the overlapping motion trajectory between the sub-eye movement trajectories of each two trajectory groups of the moment number; based on the overlapping motion trajectory between the sub-eye movement trajectories of each two trajectory groups, calculating the trajectory overlap between each two trajectory groups through an overlap algorithm, and based on the pupil movement information between each moment of each two trajectory groups, calculating the trajectory deviation value of each two trajectory groups through a trajectory deviation algorithm; based on the trajectory overlap between each two trajectory groups and the trajectory deviation value between each two trajectory groups, screening each similar trajectory group of similar motion trajectories between each trajectory group, and taking the sub-eye movement trajectory corresponding to each similar trajectory group as each regular eye movement trajectory.

[0081] In this embodiment, the terminal divides the eye movement trajectory of the user into each trajectory group of each moment number according to each moment number, and for each moment number, identifies the overlapping motion trajectory between the sub-eye movement trajectories of each two trajectory groups of the moment number. Among them, each trajectory group of each moment number, for example, with five frames as the moment number, each sub-eye movement trajectory of five adjacent frames is each trajectory group of the moment number of 5 frames, wherein the sub-eye movement trajectory of every five frames screened from the starting moment of each eye image information is the sub-eye movement trajectory of five adjacent frames. The number of moments can be, but is not limited to, 2 frames, 5 frames, 10 frames, 15 frames, etc.

[0082] Then, the terminal calculates the trajectory overlap between each two trajectory groups through the overlap algorithm based on the overlapped motion trajectories between the sub-eyeball motion trajectories of each two trajectory groups, and calculates the trajectory deviation value of each two trajectory groups through the trajectory deviation algorithm based on the pupil motion information between each moment of each two trajectory groups. Among them, the trajectory overlap is the ratio between the length of the overlapped trajectory between the two sub-motion trajectories and the length of each sub-eyeball motion trajectory after setting the starting moments of the two sub-eyeball motion trajectories to the same moment, that is, the trajectory overlap between the two trajectory groups. The trajectory deviation value of the two trajectory groups is the average value of the deviation values ​​between the pupil motion information between each moment of each two trajectory groups.

[0083] Then, the terminal selects each similar trajectory group of similar motion trajectories between each trajectory group based on the trajectory overlap between each two trajectory groups and the trajectory deviation value between each two trajectory groups, and uses the sub-eye movement trajectories corresponding to each similar trajectory group as each regular eye movement trajectory. Among them, each similar trajectory group of similar motion trajectories is a trajectory group in which the trajectory overlap between each similar trajectory group is greater than the overlap threshold value preset in the terminal, and the trajectory deviation value is lower than the deviation value threshold value preset in the terminal. That is, the same time number can include multiple similar trajectory groups of similar motion trajectories.

[0084] Based on the above solution, the comprehensiveness and accuracy of the recognition of various regular eye movement trajectories are improved through frame division and group analysis.

[0085] Optionally, based on each regular eye movement trajectory, a nystagmus determination strategy is used to identify the nystagmus degree of each nystagmus type corresponding to the user, including: based on each moment corresponding to each regular eye movement trajectory corresponding to each moment number, performing trajectory deduplication processing on each regular eye movement trajectory to obtain each target regular eye movement trajectory, and for each similar movement trajectory, based on each target regular eye movement trajectory corresponding to the similar movement trajectory, identifying the trajectory feature corresponding to the similar movement trajectory; based on each trajectory feature corresponding to the similar movement trajectory, identifying the trajectory type of the similar movement trajectory, and based on each target regular eye movement trajectory corresponding to the similar movement trajectory, identifying the trajectory range of the similar movement trajectory; in the nystagmus database, querying the nystagmus type corresponding to each trajectory type, and based on the trajectory range of the similar movement trajectory of each trajectory type corresponding to each nystagmus type, identifying the nystagmus degree of each nystagmus type through a nystagmus degree evaluation strategy for the nystagmus type.

[0086] In this embodiment, the terminal performs trajectory deduplication processing on each regular eye movement trajectory based on each moment corresponding to each regular eye movement trajectory corresponding to each moment number, obtains each target regular eye movement trajectory, and for each similar movement trajectory, identifies the trajectory features corresponding to the similar movement trajectory based on each target regular eye movement trajectory corresponding to the similar movement trajectory. Among them, the deduplication processing method is that in each regular eye movement trajectory, if two regular eye movement trajectories belong to an inclusion relationship, that is, trajectory A completely includes trajectory B, and trajectory B is completely included in trajectory A, then the terminal removes trajectory B and only retains trajectory A, so after deduplication, the number of target regular eye movement trajectories corresponding to each similar movement trajectory may remain unchanged or may decrease. Among them, the method of identifying the trajectory features corresponding to the movement trajectory is to project each regular eye movement trajectory onto the eyeball plane to obtain the trajectory distribution information corresponding to each regular eye movement trajectory, and then, the terminal identifies the trajectory shape features corresponding to each trajectory distribution information through an image feature recognition network, wherein the image feature recognition network is a convolutional neural network based on a self-attention mechanism.

[0087] Then, the terminal identifies the trajectory type of the similar motion trajectory based on the trajectory features corresponding to the similar motion trajectory, and identifies the trajectory range of the similar motion trajectory based on the regular eye movement trajectories of each target corresponding to the similar motion trajectory. Among them, the trajectory database includes trajectory types corresponding to different trajectory features, and each trajectory type corresponds to one or more trajectory features. The terminal identifies the trajectory type of the similar motion trajectory in the trajectory database based on the trajectory features corresponding to the similar motion trajectory. When all the trajectory features of the similar motion trajectory belong to the trajectory type, the terminal determines that the trajectory type is the trajectory type of the similar motion trajectory. The trajectory type can be, but is not limited to, a horizontal straight line trajectory type, an elliptical trajectory type, a circular trajectory type, a vertical straight line trajectory type, a horizontal curve trajectory type, a vertical curve trajectory type, and the like.

[0088] Then, in the nystagmus database, the terminal queries the nystagmus type corresponding to each trajectory type, and based on the trajectory range of similar motion trajectories of each trajectory type corresponding to each nystagmus type, identifies the nystagmus degree of each nystagmus type through the nystagmus degree evaluation strategy of the nystagmus type. Among them, the nystagmus types corresponding to different trajectory types are stored in the nystagmus database, each nystagmus type can correspond to one or more trajectory types, and each trajectory type can only correspond to one nystagmus type, and the nystagmus types include horizontal type, vertical type, and rotation type. Among them, the trajectory types corresponding to the horizontal type include horizontal straight line trajectory type and horizontal curve trajectory type; the trajectory types corresponding to the vertical type include vertical curve trajectory type and vertical straight line trajectory type; the trajectory types corresponding to the rotation include elliptical trajectory type and circular trajectory type. Each trajectory range, for example, the straight line length of the horizontal straight line trajectory type, the trajectory diameter length of the circular trajectory type, the two-focus distance length of the elliptical trajectory type, etc. The nystagmus degree evaluation strategy for each nystagmus type includes nystagmus degree values ​​corresponding to different standard trajectory ranges, and the terminal determines the sub-nystagmus degree of the nystagmus type corresponding to each similar motion trajectory based on the standard trajectory range to which the trajectory range of each similar motion trajectory belongs. Then, the terminal calculates an average value of the sub-nystagmus levels of each nystagmus type as the nystagmus level of each nystagmus type.

[0089] Based on the above scheme, by screening the regular eye movement trajectories, the problem of similar movement estimation affecting the accuracy of data calculation is avoided. By identifying the type of regular eye movement trajectories of each target and analyzing the degree of nystagmus, the accuracy and comprehensiveness of recognition are improved.

[0090] Optionally, based on the nystagmus degree of each nystagmus type and the interval time between each regular eye movement trajectory, the abnormal probability of the user and the abnormal time period of the user are predicted through an abnormal analysis network, including: for each similar movement trajectory, based on the interval time range between each target regular eye movement trajectory of the similar movement trajectory, the interval time between each target regular eye movement trajectory is identified; based on the interval time between each target regular eye movement trajectory, the nystagmus frequency of the nystagmus type corresponding to the similar movement trajectory is calculated, and based on the time range included in all target regular eye movement trajectories corresponding to the similar movement trajectory, the nystagmus duration of the nystagmus type corresponding to the similar movement trajectory is identified; based on the nystagmus degree of each nystagmus type, the average nystagmus frequency of each nystagmus type, and the average nystagmus duration of each nystagmus type, the abnormal probability of the user and the abnormal time period of the user are predicted through an abnormal analysis network.

[0091] In this embodiment, the terminal identifies the interval time between each target regular eye movement track based on the interval time range between each target regular eye movement track of the similar motion track for each similar motion track. Then, the terminal calculates the nystagmus frequency of the nystagmus type corresponding to the similar motion track based on the interval time between each target regular eye movement track. The nystagmus frequency is the number of target regular eye movement tracks within a unit time interval, and the unit time interval is a preset time interval with the terminal, for example, 60 frames, 120 frames, 180 frames, 240 frames, etc.

[0092] Then, the terminal identifies the nystagmus duration of the nystagmus type corresponding to the similar motion trajectory based on the time range contained in all target regular eye movement trajectories corresponding to the similar motion trajectory. The nystagmus duration is the average duration of the time group corresponding to each target regular eye movement trajectory corresponding to the similar motion trajectory. Finally, the terminal calculates the average nystagmus duration of each nystagmus type and the average nystagmus frequency of each nystagmus type to obtain the average nystagmus frequency of each nystagmus type and the average nystagmus duration of each nystagmus type.

[0093] Finally, the terminal predicts the user's abnormal probability and abnormal time period based on the nystagmus degree of each nystagmus type, the average nystagmus frequency of each nystagmus type, and the average nystagmus duration of each nystagmus type through an abnormal analysis network. The abnormal analysis network is an artificial neural network based on a reinforcement learning neural network.

[0094] Based on the above solution, by identifying the nystagmus degree of each nystagmus type of the user, the average nystagmus frequency of each nystagmus type, and the average nystagmus duration of each nystagmus type, and then analyzing the abnormal probability of the user and the abnormal time period of the user, the accuracy and comprehensiveness of the analysis are improved.

[0095] Optionally, based on the user's abnormal probability and the user's abnormal time period, an abnormal prevention prompt operation is performed to the user through the nystagmus monitoring device, including: obtaining prompt template information of the nystagmus monitoring device and the prompt advance time of the nystagmus monitoring device; querying the target prompt information corresponding to the user's abnormal probability in the prompt database, and filling the target prompt information into the prompt template information to obtain the user's current prompt information; determining the prompt time point of the current prompt information based on the user's abnormal time period and the prompt advance time, and generating a prompt instruction for the nystagmus monitoring device based on the prompt time point and the current prompt information; sending the prompt instruction to the nystagmus monitoring device, and controlling the nystagmus monitoring device to transmit the current prompt information to the user at the prompt time point.

[0096] In this embodiment, the terminal obtains the prompt template information of the nystagmus monitoring device and the prompt advance time of the nystagmus monitoring device. Among them, the prompt template information is the template content of the prompt information, and the template content includes the filling position of the prompt information corresponding to different abnormal probabilities, and other template prompt information. Then, the terminal queries the target prompt information corresponding to the abnormal probability of the user in the prompt database, and fills the target prompt information into the prompt template information to obtain the current prompt information of the user. Among them, the prompt database includes the target prompt information corresponding to each abnormal probability range, and the terminal identifies the abnormal probability range to which the abnormal probability of the user belongs, thereby determining the target prompt information corresponding to the abnormal probability of the user. The prompt information may include text information, vibration number information, vibration frequency information, buzzing number information, buzzing frequency information, etc.

[0097] Then, the terminal determines the prompt time point of the current prompt information based on the abnormal time period of the user and the advance time of the prompt, and generates a prompt instruction of the nystagmus monitoring device based on the prompt time point and the current prompt information. Finally, the terminal sends the prompt instruction to the prevention prompt unit of the nystagmus monitoring device, and controls the prevention prompt unit of the nystagmus monitoring device to transmit the current prompt information to the user at the prompt time point.

[0098] Based on the above solution, by generating different current prompt information based on different abnormal probabilities, the comprehensiveness of the prompt information and the accuracy of the prompt are improved.

[0099] In one embodiment, Figure 1 As shown, a nystagmus monitoring device is provided, the device includes a support unit, an eyeball image acquisition unit, an analysis control unit, and a prevention prompt unit, wherein:

[0100] The eyeball image acquisition unit, the analysis control unit, and the precaution prompt unit are arranged on the bracket unit, and the eyeball image acquisition unit and the precaution prompt unit are respectively connected to the analysis control unit; wherein, the bracket unit can be a glasses bracket.

[0101] The support unit is arranged outside the user's eye, and is used to set the eyeball image acquisition unit just in front of the outside of the eyeball, and set the prevention prompt unit at the user's ear contour. That is, the eyeball image acquisition unit is arranged just above the frame of the eye support and facing the user's eyeball, and the prevention prompt unit is arranged at the temple of the eye support.

[0102] The eye image acquisition unit is used to collect the user's eye movement information in real time and send the eye movement information to the analysis control unit. The eye image acquisition unit can be a single-frame camera device arranged on the bracket unit.

[0103] The analysis control unit is used to generate the user's eye movement trajectory based on the user's eye movement information when receiving the eye movement information; in the user's eye movement trajectory, through the regular trajectory analysis strategy, screen each regular eye movement trajectory, and based on each regular eye movement trajectory, identify the nystagmus degree of each nystagmus type corresponding to the user through the nystagmus determination strategy; based on the nystagmus degree of each nystagmus type and the interval time between each regular eye movement trajectory, predict the user's abnormal probability and the user's abnormal time period through the abnormal analysis network, and generate a prompt instruction based on the user's abnormal probability and the user's abnormal time period; send the prompt instruction to the prevention prompt unit. In addition to the computing chip and the instruction transceiver interface, the analysis control unit also includes an information transceiver unit. When it involves neural network, image recognition, image feature extraction and other operations, the analysis control unit sends the data involved in the above operation and the identification information corresponding to the operation mode to be operated to the remote server corresponding to the device, so that the remote server performs the operation, and after the remote server completes the operation, the operation result is fed back to the analysis control unit, thereby avoiding the problem of low processing efficiency of the analysis control unit and improving the efficiency of real-time monitoring and real-time warning.

[0104] The prevention prompt unit includes a timing module and a prompt module. After receiving the prompt instruction, the timing module detects the current time point; when the current time point is the prompt time point, the current prompt information is transmitted to the prompt module, and the current prompt information is transmitted to the user through the prompt module. The timing module includes a timer and an information transmission device. The prompt module includes a small speaker for transmitting the promotion information of the text content; a buzzer for executing the buzzing number information and buzzing frequency information in the current prompt information; and a vibrator for executing the vibration number information and vibration frequency information in the current prompt information.

[0105] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0106] Based on the same inventive concept, the embodiment of the present application also provides a user abnormality prompt system based on nystagmus monitoring for implementing the user abnormality prompt method based on nystagmus monitoring involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the user abnormality prompt system based on nystagmus monitoring provided below can refer to the limitations of the user abnormality prompt method based on nystagmus monitoring above, and will not be repeated here.

[0107] Further references Figure 3 , as a response to the above Figure 1 The present application provides an embodiment of a user abnormality prompt system 300 based on nystagmus monitoring, and the user abnormality prompt system based on nystagmus monitoring includes an acquisition module 310, an identification module 320 and a prevention module 330, wherein:

[0108] The acquisition module 310 is used to acquire the user's eye movement information in real time through the nystagmus monitoring device, and generate the user's eye movement trajectory based on the user's eye movement information;

[0109] The identification module 320 is used to screen each regular eye movement trajectory in the eye movement trajectory of the user through a regular trajectory analysis strategy, and identify the nystagmus degree of each nystagmus type corresponding to the user through a nystagmus determination strategy based on each regular eye movement trajectory;

[0110] The prevention module 330 is used to predict the abnormal probability of the user and the abnormal time period of the user based on the nystagmus degree of each nystagmus type and the interval time between each regular eye movement trajectory through an abnormal analysis network, and based on the abnormal probability of the user and the abnormal time period of the user, provide an abnormal prevention prompt operation to the user through the nystagmus monitoring device.

[0111] Optionally, the acquisition module 310 is specifically used for:

[0112] Splitting the user's eye movement information into eye image information at each moment, and identifying the position information of the pupil center point in each eye image information;

[0113] Connecting the position information of the pupil center in the eyeball image information at adjacent moments to obtain the pupil movement information of the user between the moments, and constructing the three-dimensional eyeball coordinate system of the user based on the eyeball range of the user;

[0114] The pupil movement information between each of the moments is projected into the three-dimensional coordinate system of the eyeball to obtain the eyeball movement trajectory of the user.

[0115] Optionally, the identification module 320 is specifically configured to:

[0116] Dividing the eye movement trajectory of the user into trajectory groups of each moment number according to each moment number, and identifying the overlapping movement trajectory between the sub-eye movement trajectories of each two trajectory groups of the moment number for each moment number;

[0117] Based on the coincident motion trajectories between the sub-eyeball motion trajectories of each two trajectory groups, the trajectory coincidence degree between each two trajectory groups is calculated by the coincidence degree algorithm, and based on the pupil motion information between each two trajectory groups at each moment, the trajectory deviation value of each two trajectory groups is calculated by the trajectory deviation algorithm;

[0118] Based on the trajectory overlap between every two trajectory groups and the trajectory deviation value between every two trajectory groups, similar trajectory groups of similar motion trajectories are screened between the trajectory groups, and the sub-eye movement trajectories corresponding to the similar trajectory groups are used as regular eye movement trajectories.

[0119] Optionally, the identification module 320 is specifically configured to:

[0120] Based on each moment corresponding to each regular eye movement trajectory corresponding to each moment number, performing trajectory deduplication processing on each regular eye movement trajectory to obtain each target regular eye movement trajectory, and for each similar movement trajectory, based on each target regular eye movement trajectory corresponding to the similar movement trajectory, identifying the trajectory feature corresponding to the similar movement trajectory;

[0121] Based on each trajectory feature corresponding to the similar motion trajectory, identifying the trajectory type of the similar motion trajectory, and based on each target regular eye movement trajectory corresponding to the similar motion trajectory, identifying the trajectory range of the similar motion trajectory;

[0122] In the nystagmus database, the nystagmus type corresponding to each trajectory type is queried, and based on the trajectory range of similar motion trajectories of each trajectory type corresponding to each nystagmus type, the nystagmus degree of each nystagmus type is identified through the nystagmus degree evaluation strategy of the nystagmus type.

[0123] Optionally, the prevention module 330 is specifically used to:

[0124] For each similar motion trajectory, based on the interval time range between each target regular eye movement trajectory of the similar motion trajectory, identifying the interval time between each target regular eye movement trajectory;

[0125] Calculating the nystagmus frequency of the nystagmus type corresponding to the similar motion trajectory based on the interval time between each of the target regular eye movement trajectories, and identifying the nystagmus duration of the nystagmus type corresponding to the similar motion trajectory based on the time range included in all the target regular eye movement trajectories corresponding to the similar motion trajectory;

[0126] Based on the nystagmus degree of each nystagmus type, the average nystagmus frequency of each nystagmus type, and the average nystagmus duration of each nystagmus type, the abnormal probability of the user and the abnormal time period of the user are predicted through the abnormal analysis network.

[0127] Optionally, the prevention module 330 is specifically used to:

[0128] Acquire the prompt template information of the nystagmus monitoring device and the prompt advance time of the nystagmus monitoring device;

[0129] In the prompt database, query the target prompt information corresponding to the abnormal probability of the user, and fill the target prompt information into the prompt template information to obtain the current prompt information of the user;

[0130] Determine a prompt time point of the current prompt information based on the abnormal time period of the user and the prompt advance time, and generate a prompt instruction of the nystagmus monitoring device based on the prompt time point and the current prompt information;

[0131] The prompt instruction is sent to the nystagmus monitoring device, and the nystagmus monitoring device is controlled to transmit current prompt information to the user at the prompt time point.

[0132] Each module in the above-mentioned user abnormality prompt system based on nystagmus monitoring can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0133] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input system connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a user abnormality prompt method based on nystagmus monitoring is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input system of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse, etc.

[0134] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0135] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of any one of the methods in the first aspect are implemented.

[0136] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0137] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0138] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0139] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0140] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0141] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A user abnormal prompt method based on nystagmus monitoring, characterized in that: The method comprises: Collecting the user's eye movement information in real time through an eye nystagmus monitoring device, and generating the user's eye movement trajectory based on the user's eye movement information; In the eye movement trajectory of the user, each regular eye movement trajectory is screened by a regular trajectory analysis strategy, and based on each regular eye movement trajectory, a nystagmus degree of each nystagmus type corresponding to the user is identified by a nystagmus determination strategy; Based on the nystagmus degree of each nystagmus type and the interval time between each regular eye movement trajectory, the abnormal probability of the user and the abnormal time period of the user are predicted through the abnormal analysis network, and based on the abnormal probability of the user and the abnormal time period of the user, the nystagmus monitoring device is used to provide the user with an abnormal prevention prompt operation.

2. The method according to claim 1, characterized in that The step of generating the eye movement trajectory of the user based on the eye movement information of the user includes: Splitting the user's eye movement information into eye image information at each moment, and identifying the position information of the pupil center in each eye image information; Connecting the position information of the pupil center in the eyeball image information at adjacent moments to obtain the pupil movement information of the user between the moments, and constructing the three-dimensional eyeball coordinate system of the user based on the eyeball range of the user; The pupil movement information between each of the moments is projected into the three-dimensional coordinate system of the eyeball to obtain the eyeball movement trajectory of the user.

3. The method according to claim 2, characterized in that In the eye movement trajectory of the user, screening each regular eye movement trajectory by using a regular trajectory analysis strategy includes: Dividing the eye movement trajectory of the user into trajectory groups of each moment number according to each moment number, and identifying the overlapping movement trajectory between the sub-eye movement trajectories of each two trajectory groups of the moment number for each moment number; Based on the coincident motion trajectories between the sub-eyeball motion trajectories of each two trajectory groups, the trajectory coincidence degree between each two trajectory groups is calculated by the coincidence degree algorithm, and based on the pupil motion information between each two trajectory groups at each moment, the trajectory deviation value of each two trajectory groups is calculated by the trajectory deviation algorithm; Based on the trajectory overlap between every two trajectory groups and the trajectory deviation value between every two trajectory groups, similar trajectory groups of similar motion trajectories are screened between the trajectory groups, and the sub-eye movement trajectories corresponding to the similar trajectory groups are used as regular eye movement trajectories.

4. The method according to claim 3, characterized in that The step of identifying the nystagmus degree of each nystagmus type corresponding to the user based on each of the regular eye movement trajectories through a nystagmus determination strategy includes: Based on each moment corresponding to each regular eye movement trajectory corresponding to each moment number, performing trajectory deduplication processing on each regular eye movement trajectory to obtain each target regular eye movement trajectory, and for each similar movement trajectory, based on each target regular eye movement trajectory corresponding to the similar movement trajectory, identifying the trajectory feature corresponding to the similar movement trajectory; Based on each trajectory feature corresponding to the similar motion trajectory, identifying the trajectory type of the similar motion trajectory, and based on each target regular eye movement trajectory corresponding to the similar motion trajectory, identifying the trajectory range of the similar motion trajectory; In the nystagmus database, the nystagmus type corresponding to each trajectory type is queried, and based on the trajectory range of similar motion trajectories of each trajectory type corresponding to each nystagmus type, the nystagmus degree of each nystagmus type is identified through the nystagmus degree evaluation strategy of the nystagmus type.

5. The method according to claim 4, characterized in that The method of predicting the abnormal probability of the user and the abnormal time period of the user based on the nystagmus degree of each nystagmus type and the interval time between each regular eye movement trajectory through an abnormal analysis network includes: For each similar motion trajectory, based on the interval time range between each target regular eye movement trajectory of the similar motion trajectory, identifying the interval time between each target regular eye movement trajectory; Calculating the nystagmus frequency of the nystagmus type corresponding to the similar motion trajectory based on the interval time between each of the target regular eye movement trajectories, and identifying the nystagmus duration of the nystagmus type corresponding to the similar motion trajectory based on the time range included in all the target regular eye movement trajectories corresponding to the similar motion trajectory; Based on the nystagmus degree of each nystagmus type, the average nystagmus frequency of each nystagmus type, and the average nystagmus duration of each nystagmus type, the abnormal probability of the user and the abnormal time period of the user are predicted through the abnormal analysis network.

6. The method according to claim 5, characterized in that The operation of providing an abnormal prevention prompt to the user through the nystagmus monitoring device based on the abnormal probability of the user and the abnormal time period of the user includes: Acquire the prompt template information of the nystagmus monitoring device and the prompt advance time of the nystagmus monitoring device; In the prompt database, query the target prompt information corresponding to the abnormal probability of the user, and fill the target prompt information into the prompt template information to obtain the current prompt information of the user; Determine a prompt time point of the current prompt information based on the abnormal time period of the user and the prompt advance time, and generate a prompt instruction of the nystagmus monitoring device based on the prompt time point and the current prompt information; The prompt instruction is sent to the nystagmus monitoring device, and the nystagmus monitoring device is controlled to transmit current prompt information to the user at the prompt time point.

7. A nystagmus monitoring device, characterized in that: The device comprises a support unit, an eyeball image acquisition unit, an analysis control unit, and a prevention prompt unit, wherein: The eyeball image acquisition unit, the analysis control unit, and the prevention prompt unit are arranged on the bracket unit, and the eyeball image acquisition unit and the prevention prompt unit are connected to the analysis control unit respectively; The support unit is arranged outside the eye of the user, and is used to arrange the eyeball image acquisition unit just in front of the outside of the eyeball, and arrange the prevention prompt unit at the ear contour of the user; The eyeball image acquisition unit is used to acquire the user's eyeball movement information in real time and send the eyeball movement information to the analysis control unit; The analysis control unit is used to generate the eye movement trajectory of the user based on the eye movement information of the user when the eye movement information is received; in the eye movement trajectory of the user, screen each regular eye movement trajectory through a regular trajectory analysis strategy, and identify the nystagmus degree of each nystagmus type corresponding to the user through a nystagmus determination strategy based on each regular eye movement trajectory; based on the nystagmus degree of each nystagmus type and the interval time between each regular eye movement trajectory, predict the abnormal probability of the user and the abnormal time period of the user through an abnormal analysis network, and generate a prompt instruction based on the abnormal probability of the user and the abnormal time period of the user; and send the prompt instruction to the prevention prompt unit; The prevention prompt unit includes a timing module and a prompt module, and after receiving the prompt instruction, the timing module is used to detect the current time point; when the current time point is the prompt time point, the current prompt information is transmitted to the prompt module, and the current prompt information is transmitted to the user through the prompt module.

8. A user abnormality prompt system based on nystagmus monitoring, characterized in that: The system comprises: A collection module, used to collect the user's eye movement information in real time through a nystagmus monitoring device, and generate the user's eye movement trajectory based on the user's eye movement information; an identification module, configured to screen each regular eye movement trajectory in the eye movement trajectory of the user through a regular trajectory analysis strategy, and identify the nystagmus degree of each nystagmus type corresponding to the user through a nystagmus determination strategy based on each regular eye movement trajectory; The prevention module is used to predict the abnormal probability of the user and the abnormal time period of the user based on the nystagmus degree of each nystagmus type and the interval time between each regular eye movement trajectory through an abnormal analysis network, and based on the abnormal probability of the user and the abnormal time period of the user, perform abnormal prevention prompt operations to the user through the nystagmus monitoring device.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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