A frame posture monitoring method and system for preventing acquired astigmatism prevention

Abstract

This application provides a method and system for monitoring eyeglass frame posture to prevent acquired astigmatism, relating to the field of automatic monitoring technology. The method includes real-time acquisition of three-dimensional posture data and bilateral interocular distance data of adolescents wearing eyeglass frames, extracting multi-dimensional risk characteristic parameters from these data; inputting these parameters into a preset astigmatism risk quantification assessment model to calculate a real-time astigmatism risk value, and comparing it with a preset threshold to classify it into low, medium, and high risk levels; when the risk value meets the corresponding intervention conditions, the system locks the spatial direction of the deviation that caused the risk, thereby triggering a directional vibration of the corresponding tactile feedback unit on the eyeglass frame, guiding the wearer to adjust their posture in the opposite direction. This method achieves real-time quantitative early warning and precise directional correction of acquired astigmatism-causing behaviors, preventing the continuous accumulation of poor posture without conscious awareness until irreversible corneal deformation occurs.

Description

Technical Field

[0001] This application relates to the field of automatic monitoring technology, and in particular to a method and system for monitoring the posture of eyeglass frames for the prevention of acquired astigmatism. Background Technology

[0002] Acquired astigmatism is mainly caused by poor visual behavior and eye habits (such as prolonged head tilting and squinting, reading while lying on one's side, etc.), leading to uneven pressure on the eyeball from the extraocular muscles, which in turn causes irreversible deformation of the cornea and refractive errors. For adolescents, whose eyes are in a critical period of rapid development, maintaining the normal symmetry of the cornea is crucial. Once long-term poor eye posture leads to substantial plastic changes in the cornea, it will not only cause persistent blurred vision and eye strain, but also permanent vision damage that cannot be reversed naturally. Therefore, timely intervention in adolescents' daily eye habits is a decisive step in preventing the occurrence and worsening of acquired astigmatism.

[0003] Existing acquired astigmatism prevention and monitoring technologies typically rely on regular medical checkups and passive vision correction. Currently, the industry generally requires or recommends that adolescents in their visual development period undergo an ophthalmological checkup every six months, measuring their astigmatism using clinical medical equipment such as corneal topography. Once astigmatism is diagnosed, customized astigmatic lenses are prescribed for optical compensation. Furthermore, some existing smart glasses or eye care devices for myopia prevention on the market typically use only a single infrared sensor to roughly monitor forward head-down reading distance, issuing voice reminders when the user is too close to the book.

[0004] However, current technologies can only provide delayed compensation and are insufficient for real-time risk quantification and prevention of acquired astigmatism based on its pathogenic characteristics. Specifically, current technologies can only prescribe astigmatism lenses after a child is diagnosed with astigmatism. Even eye care devices cannot provide real-time information on the accumulated astigmatism risk from a child's current eye habits; they can only offer rough warnings and cannot provide real-time intervention. This results in adolescents' poor eye habits accumulating unnoticed until irreversible corneal deformation occurs, missing the optimal window for intervention to prevent astigmatism formation in its early stages. Summary of the Invention

[0005] This application provides a method and system for monitoring eyeglass frame posture to prevent acquired astigmatism, which is used to monitor and intervene in poor eye posture of the wearer in real time to prevent acquired astigmatism.

[0006] In a first aspect, this application provides a method for monitoring eyeglass frame posture in the prevention of acquired astigmatism in adolescents. The method includes: acquiring three-dimensional posture data and bilateral interocular distance data of the target subject when wearing eyeglass frames in real time, wherein the bilateral interocular distance data includes left interocular distance data and right interocular distance data; and extracting multi-dimensional risk feature parameters that affect the progression of astigmatism based on the three-dimensional posture data and the bilateral interocular distance data, wherein the multi-dimensional risk feature parameters include at least the difference in horizontal tilt of the eyeglass frame, the deviation of the forward tilt angle, and the exposure to poor eye posture extracted based on the three-dimensional posture data, as well as the difference in asymmetry between the left and right interocular distances extracted based on the bilateral interocular distance data. The multi-dimensional risk characteristic parameters are input into a preset astigmatism risk quantification assessment model to calculate the real-time astigmatism risk value at the current moment. The real-time astigmatism risk value is compared with a preset risk level judgment threshold to obtain the target risk level. If the real-time astigmatism risk value meets the intervention triggering conditions matching the target risk level, the main cause of the current risk state and the deviation spatial direction corresponding to the main cause in real time are determined. Based on the deviation spatial direction, the partitioned tactile feedback unit located at a predetermined position on the frame is triggered to perform directional feedback to guide the target object to recover its posture in the opposite direction to the deviation spatial direction.

[0007] By adopting the above technical solution, the simultaneous acquisition of three-dimensional posture data and bilateral interocular distance data allows the monitoring dimensions to cover both the spatial posture of the eyeglass frame and the optical fit status. The resulting multi-dimensional risk feature parameters can characterize the accumulation of astigmatic pathogenic factors from multiple perspectives, such as tilt, forward tilt, and exposure duration. By inputting the multi-dimensional risk feature parameters into the astigmatic risk quantification assessment model for comprehensive calculation, the astigmatic risk is transformed from a qualitative judgment into a quantifiable real-time numerical output. When the risk value triggers the intervention condition, the spatial direction of the deviation is further locked, and the partitioned tactile feedback unit is driven to perform directional feedback, so that the intervention signal is accurately pointed to the side of the deviation source. This guides the target object to actively restore the correct posture in the opposite direction, realizing real-time quantitative early warning and precise directional correction of acquired astigmatic pathogenic behavior, and preventing the poor posture from accumulating unnoticed until irreversible corneal deformation occurs.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, inertial measurement units are provided at first preset positions on the left and right sides of the eyeglass frame, and distance measuring units are respectively provided at second preset positions on the inner sides of the left and right nose pads of the eyeglass frame. The step of acquiring the three-dimensional attitude data and bilateral interocular distance data of the target object when wearing the eyeglass frame in real time specifically includes: acquiring the three-axis acceleration signal and the three-axis angular velocity signal output by the inertial measurement unit; performing attitude calculation after filtering the three-axis acceleration signal and the three-axis angular velocity signal, and performing spatial reference fusion with the preset three-dimensional relative coordinate system on the left and right sides to obtain the three-dimensional attitude data including at least the pitch angle, roll angle and yaw angle of the current eyeglass frame; synchronously controlling the distance measuring unit to emit detection signals and receive echo signals along the preset corneal direction; and calculating the corresponding left interocular distance and right interocular distance based on the time difference between the detection signal and the echo signal.

[0009] By adopting the above technical solution, inertial measurement units are symmetrically arranged on the left and right sides of the frame. After filtering and attitude calculation, the three-axis acceleration signal and the three-axis angular velocity signal can be fused with the spatial reference of the two-sided three-dimensional relative coordinate system. This eliminates the system deviation caused by the inconsistent tightness of the single-sided sensor and obtains high-precision pitch angle, roll angle and yaw angle data. At the same time, a ranging unit is set on the inside of the nose pad to emit detection signals along the corneal direction. Based on the signal round-trip time difference, the corresponding left and right eye distances can be accurately calculated, realizing independent synchronous measurement of the eye distances of both sides. This provides accurate raw data support for the subsequent extraction of multi-dimensional risk feature parameters.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the step of extracting multi-dimensional risk characteristic parameters affecting the progression of astigmatism based on the three-dimensional posture data and the bilateral inter-eye distance data specifically includes: calculating the absolute value of the deviation between the roll angle and the preset horizontal reference angle to obtain the horizontal tilt difference of the frame; calculating the difference between the pitch angle and the preset standard adaptation forward tilt angle to obtain the forward tilt angle deviation; determining whether at least one of the pitch angle, the roll angle, and the yaw angle exceeds the corresponding preset fatigue posture threshold; if it exceeds, calculating the cumulative duration of exceeding the preset fatigue posture threshold to obtain the exposure amount of poor eye posture; and calculating the relative difference between the left inter-eye distance and the right inter-eye distance to obtain the left and right inter-eye distance asymmetry difference.

[0011] By adopting the above technical solutions, the absolute value of the deviation of the roll angle relative to the horizontal reference angle is used to characterize the difference in horizontal tilt of the frame, directly quantifying the impact of left-right height asymmetry of the frame on the uniformity of corneal force. The difference between the pitch angle and the standard fitting forward tilt angle is used to characterize the amount of forward tilt deviation, reflecting the optical axis offset state caused by excessive or insufficient forward tilt of the frame. By judging whether the three posture angles exceed the fatigue posture threshold and calculating the cumulative duration, the instantaneous deviation is converted into the exposure of poor eye posture with a time-cumulative effect, which is consistent with the pathological characteristics of acquired astigmatism caused by the gradual accumulation of long-term poor posture. The relative difference in the distance between the left and right glasses is used to characterize the difference in the distance between the glasses and the eye, capturing the risk factor of inconsistent optical correction conditions between the two eyes due to frame misalignment. This allows the four characteristic parameters to fully cover the key causes of astigmatism from three levels: spatial angle, time dimension, and optical adaptation.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, if the real-time astigmatism risk value meets the intervention triggering condition for matching the target risk level, the step of determining the main cause leading to the current risk state and the deviation spatial direction corresponding to the main cause in real time specifically includes: extracting the risk contribution weight scores corresponding to each of the multi-dimensional risk feature parameters when calculating the real-time astigmatism risk value; locking the feature parameter corresponding to the largest risk contribution weight score as the main cause; parsing the current real-time spatial vector data of the main cause and extracting the attitude deviation features constituting the main cause; and determining the deviation spatial direction leading to the current astigmatism risk based on the attitude deviation features.

[0013] By adopting the above technical solution, after the intervention condition is triggered, the risk contribution weight scores corresponding to various multi-dimensional risk characteristic parameters are first extracted, so that the contribution of each pathogenic factor to the current risk value is independently quantified. The characteristic parameter corresponding to the maximum weight score is locked as the main cause, avoiding information overload and directional confusion caused by intervening in all deviation factors at the same time. The real-time spatial vector data of the main cause is further analyzed to extract the posture deviation feature, transforming the abstract numerical deviation into spatial information with a clear physical direction. Finally, this orientation is mapped to a three-dimensional physical direction as the deviation spatial direction, so that the subsequent tactile feedback can accurately point to the single deviation source with the greatest pathogenic contribution. This achieves a step-by-step convergence from multi-factor risk assessment to single main cause direction positioning, ensuring that the directionality of the intervention command is clear and the correction efficiency is maximized.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, vibration units are respectively provided at the front and rear ends of the left temple and the right temple of the frame to jointly constitute the partitioned tactile feedback unit. The step of triggering the partitioned tactile feedback unit located at a predetermined position on the frame to perform directional feedback according to the deviation spatial direction specifically includes: determining one or more target vibration units that match the deviation spatial direction according to the physical placement of each vibration unit on the frame; acquiring a feedback vibration mode that matches the target risk level; controlling the target vibration unit to output a warning vibration of the corresponding mode according to the feedback vibration mode, so as to form a local stimulus in the deviation direction and guide the head and face of the target object to move in the opposite direction of no vibration signal output until the reacquired three-dimensional posture data recovers to the preset normal threshold range.

[0015] By adopting the above technical solution, vibration units are set at the front and rear ends of the left and right temples of the eyeglass frame to form a partitioned tactile feedback unit. The corresponding target vibration unit can be accurately matched with the physical layout of each vibration unit based on the spatial direction of the deviation, so as to generate local directional vibration stimulation only in the deviation direction. Combined with the target risk level matching differentiated feedback vibration modes, the deviation of different degrees of severity can obtain warning signals of corresponding intensity. Since the vibration only generates local stimulation on the deviation side, the target object naturally moves its head and face in the opposite direction of no vibration signal under the drive of approach and avoidance response until the three-dimensional posture data recovers to the normal threshold range and the vibration automatically stops, forming a closed-loop posture correction mechanism based on spatial directional stimulation.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, the step of determining one or more target vibration units that match the deviation spatial direction specifically includes: determining the attitude deviation type corresponding to the deviation spatial direction, wherein the attitude deviation type includes one or more of left-side elevation, right-side elevation, left-side skew, right-side skew, forward roll, or backward roll; obtaining a pre-configured correspondence correction relationship between each attitude deviation type and the physical deployment position; querying the corresponding correction relationship based on the current attitude deviation type, matching and obtaining one or more target physical deployment positions, and determining the vibration unit at the target physical deployment position as the target vibration unit.

[0017] By adopting the above technical solution, by first determining the attitude deviation type corresponding to the deviation spatial direction, and then querying and matching based on the pre-configured attitude deviation type and the corresponding correction relationship between the physical deployment position, the target physical deployment position that matches the current deviation can be quickly and accurately located, thereby accurately determining the target vibration unit to be activated, so that the vibration feedback is highly consistent with the attitude deviation direction, the intervention direction is clearer, and invalid vibration and false triggering are avoided.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of acquiring the three-dimensional posture data and bilateral interocular distance data of the target object wearing the glasses in real time, the method further includes: in standby mode, controlling the ranging unit to acquire distance data between the target object and the target object at a preset low acquisition wake-up frequency; determining whether the distance data falls within a preset normal wearing distance range; if it falls within the normal wearing distance range, determining that the target object is in an effective wearing state, and generating a system wake-up command to trigger subsequent data acquisition; if it does not fall within the normal wearing distance range, determining that the target object is not in an effective wearing state, and maintaining the standby mode to block subsequent data acquisition.

[0019] By adopting the above technical solution, the ranging unit acquires distance data with a low acquisition and wake-up frequency in standby mode, thereby reducing the system's standby power consumption. By determining whether the distance data falls within the normal wearing distance range, the system quickly identifies whether the target object is in an effective wearing state. A wake-up command is generated only when the object is being worn effectively to trigger subsequent data acquisition; otherwise, a low-power standby mode is maintained. This solution achieves intelligent wake-up and sleep control of the system, avoiding redundant data acquisition in ineffective wearing states, reducing the overall energy consumption of the device, and ensuring that monitoring and intervention are performed only in actual wearing scenarios, thus improving the system's energy efficiency and practicality.

[0020] In a second aspect, this application provides a glasses frame posture monitoring system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the glasses frame posture monitoring system to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, this application provides a computer-readable storage medium including instructions that, when executed on a frame attitude monitoring system, cause the frame attitude monitoring system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a computer program product, including a computer program that, when run on a glasses frame attitude monitoring system, causes the glasses frame attitude monitoring system to perform the method described in the first aspect and any possible implementation thereof. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the structure of a glasses frame in the glasses frame attitude monitoring system of this application embodiment; Figure 2This is a schematic diagram of an application scenario of the frame posture monitoring method for the prevention of acquired astigmatism in adolescents, as described in the embodiments of this application; Figure 3 This is a flowchart illustrating a method for monitoring eyeglass posture in the prevention of acquired astigmatism in adolescents, as described in this application. Figure 4 This is a schematic diagram of the physical device structure of a frame posture monitoring system in an embodiment of this application.

[0024] Explanation of reference numerals in the attached figures: 1. Three-dimensional attitude monitoring unit 1; 2. Three-dimensional attitude monitoring unit 2; 3. Dual-side mirror-to-eye distance monitoring unit; 4. Main control chip; 5. Battery. Detailed Implementation

[0025] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0026] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0027] For ease of understanding, the frame and related structures provided in this embodiment are described below. Please refer to... Figure 1 This is a schematic diagram of the structure of a glasses frame in the glasses frame attitude monitoring system of this application embodiment.

[0028] exist Figure 1 In the frame, a three-dimensional attitude monitoring unit 1 and a three-dimensional attitude monitoring unit 2 are respectively installed at the pile head positions on the left and right sides. The three-dimensional attitude monitoring unit 1 and the three-dimensional attitude monitoring unit 2 can each be equipped with an inertial measurement unit. The inertial measurement unit can be symmetrically distributed at the first preset position on the left and right sides of the frame to collect three-axis acceleration signals and three-axis angular velocity signals. After attitude calculation, three-dimensional attitude data including pitch angle, roll angle and yaw angle are obtained.

[0029] The inner sides of the left and right nose pads of the eyeglass frame are equipped with dual-lens interocular distance monitoring units 3. These units include a distance measuring unit on the left and a unit on the right side of the frame. Each unit emits a detection signal towards the corresponding left and right corneas and receives the echo signal. This allows for real-time measurement of the interocular distance from the inner surface of the left lens to the apex of the left cornea, and from the inner surface of the right lens to the apex of the right cornea, to obtain dual-lens interocular distance data. A main control chip 4 is integrated inside one temple of the frame. This chip includes an eye posture recognition unit and a wearing timer unit. The eye posture recognition unit works in conjunction with the three-dimensional posture monitoring unit 2 to identify poor eye postures such as looking down while reading or writing, lying on one side while using the eyes, and prolonged unilateral head tilting, and to calculate the cumulative duration. The wearing timer unit starts timing when a valid wearing state is detected and calculates the duration of risk exposure in different time periods. A battery 5 is also installed inside the temple of the frame to power each unit.

[0030] In addition, the left temple and the front and rear ends of the right temple of the frame are respectively equipped with vibration units, which together form a zoned tactile feedback unit, used to output directional warning vibrations at the corresponding positions according to the spatial direction of the deviation, so as to guide the target object to restore its posture.

[0031] In this application, the main control chip 4 can communicate with the three-dimensional attitude monitoring unit 1, the three-dimensional attitude monitoring unit 2, the dual-side mirror eye distance monitoring unit 3 and the vibration unit, and is also electrically connected to the battery 5 to receive and process data throughout the monitoring process.

[0032] The following is combined Figure 1 The image shows a frame structure to describe the scenario in which the method provided in this embodiment is applied. Please refer to [link / reference]. Figure 2 This is a schematic diagram of an application scenario for a method of monitoring the posture of glasses for the prevention of acquired astigmatism in adolescents, as described in this application.

[0033] exist Figure 2 The target subject was a teenager wearing the glasses, who was sitting at a table engaging in close-range visual activities. Inertial measurement units on both sides of the frame collected real-time triaxial acceleration and angular velocity signals, which were then used to calculate three-dimensional attitude data, including pitch, roll, and yaw angles. Simultaneously, a ranging unit located inside the nose pads acquired bilateral inter-lens distance data from the inner surfaces of the lenses to the corneal apex. This three-dimensional attitude data and bilateral inter-lens distance data were input into an astigmatism risk quantification assessment model. This model comprehensively calculated the difference in horizontal tilt of the frame, the deviation in forward tilt angle, the exposure to poor visual posture, and the difference in inter-lens distance asymmetry, obtaining a real-time astigmatism risk value and determining the corresponding risk level.

[0034] Figure 2The scenario shown depicts a teenager exhibiting an undesirable posture with their head tilted to the left. After the system detects that the real-time astigmatism risk value meets the intervention triggering conditions, it determines that the deviation spatial direction is to the left. Subsequently, it triggers the zoned tactile feedback unit located at the corresponding position on the left temple of the glasses to perform directional vibration in a specific area. After the teenager perceives the local vibration stimulus on the left, they naturally move to the right, which is the opposite direction to the deviation spatial direction, driven by the approach-avoidance response, to restore their posture until the three-dimensional posture data returns to the preset normal threshold range, at which point the vibration stops. This completes a real-time closed-loop intervention process from risk detection to posture correction.

[0035] The following describes the process of the method provided in this implementation. Please refer to [link / reference]. Figure 3 This is a flowchart illustrating a method for monitoring the posture of eyeglasses for the prevention of acquired astigmatism in adolescents, as described in this application.

[0036] S101. Real-time acquisition of three-dimensional posture data and bilateral inter-eye distance data of the target object when wearing glasses; The target audience refers to adolescents in the visual development stage who wear the frames described in this application; the frames refer to lightweight eyeglass frames for teenagers that integrate an inertial measurement unit, a ranging unit, a zoned tactile feedback unit, and a main control unit. Three-dimensional attitude data refers to the set of parameters describing the current spatial attitude of the frames obtained after attitude calculation, including at least pitch angle, roll angle, and yaw angle, used to characterize the tilt, rotation, and yaw states of the frames in three-dimensional space. The bilateral interpupillary distance data includes the left interpupillary distance data and the right interpupillary distance data.

[0037] Once the eyeglass frame posture monitoring system confirms that the target object is in a valid wearing state and generates a system wake-up command, the system enters the formal multi-source data synchronous acquisition phase. The eyeglass frame posture monitoring system of this application may include, for example: Figure 1 The overall hardware structure of the eyeglass frame is shown.

[0038] Inertial measurement units (IMUs) are installed at first preset positions on both the left and right sides of the eyeglass frame (there are first preset positions on both sides), and ranging units are installed at second preset positions on the inner sides of the left and right nose pads of the eyeglass frame (there are second preset positions on both sides). The more specific process is as follows: For inertial data acquisition, the system synchronously drives the first IMU located on the left side of the eyeglass frame and the second IMU located on the right side of the eyeglass frame at a preset sampling frequency. The two IMUs continuously output triaxial acceleration and triaxial angular velocity signals in their respective local coordinate systems. After receiving the raw signals, the system first applies digital low-pass filtering with a preset cutoff frequency to the triaxial acceleration and triaxial angular velocity signals to filter out high-frequency mechanical vibration noise generated by the target object's daily activities and the electronic noise of the sensors themselves, retaining the low-frequency effective signal components that reflect the true attitude changes of the eyeglass frame.

[0039] After filtering, the system independently performs attitude calculations on the filtered signals output by the left and right inertial measurement units. The system uses a complementary filtering algorithm to fuse the absolute tilt angle reference provided by the acceleration signal with the dynamic rotation increment provided by the angular velocity signal, thereby obtaining the attitude angles in the left local coordinate system and the right local coordinate system, respectively. Since the two inertial measurement units (IMUs) are installed at different physical locations on the frame, their local coordinate systems have spatial offsets and directional differences. The system further combines the preset three-dimensional relative coordinate systems on the left and right sides to perform spatial reference fusion. That is, by using the pre-calibrated relative position vector and rotation matrix between the left and right IMUs, the attitude data calculated independently on both sides are uniformly transformed into a global reference coordinate system with the geometric center of the frame as the origin. The system then fuses the data through weighted averaging or optimal estimation methods to obtain a unified result representing the spatial attitude of the entire frame. The final output includes at least the current frame pitch angle, roll angle, and yaw angle. The pitch angle reflects the degree of forward and backward tilt of the frame around the horizontal axis, the roll angle reflects the degree of left and right tilt of the frame around the front and rear axis, and the yaw angle reflects the degree of left and right deflection of the frame around the vertical axis.

[0040] For bilateral interocular distance data acquisition, it is performed synchronously with inertial data acquisition. The system controls the left ranging unit located inside the left nose pad of the frame and the right ranging unit located inside the right nose pad of the frame to emit detection signals along a preset corneal direction. The detection signal propagates along a pre-calibrated optical path direction toward the corresponding corneal apex, and is reflected upon reaching the corneal surface. The ranging unit receives the echo signal. Based on the time difference between the detection signal emission time and the echo signal reception time, the system calculates the left interocular distance data from the inner surface of the left lens to the left corneal apex and the right interocular distance data from the inner surface of the right lens to the right corneal apex, respectively, using the speed of light constant. The system merges the left and right eye distance data into a complete set of bilateral eye distance data. The inertial data acquisition and eye distance data acquisition are synchronized in time. The system uses a unified clock reference to timestamp-align the two types of data, ensuring strict temporal consistency between the 3D attitude data and the bilateral eye distance data obtained at the same sampling moment. This provides a complete, spatiotemporally aligned original dataset for subsequent extraction of multi-dimensional risk feature parameters. This step, through the redundant configuration of the bilateral inertial measurement units and the spatial reference fusion mechanism, significantly improves the accuracy and robustness of attitude calculation compared to a single-sensor solution. Simultaneously, the synchronized operation of the bilateral ranging units enables independent and accurate measurement of the left and right eye distances, providing a high-precision, multi-dimensional, and spatiotemporally synchronized data foundation for subsequent astigmatism risk quantification assessment.

[0041] In some embodiments, during the real-time acquisition of 3D posture data and bilateral interocular distance data of the target object wearing the glasses, there may be situations where the glasses are not actually worn on the face but are placed in a resting state, such as on a table, in a storage box, or hanging on clothing. If the system continues to operate all sensing units at full power for data acquisition and posture calculation in this case, it will not only generate invalid false data that interferes with the accuracy of subsequent astigmatism risk assessment, but also cause unnecessary power consumption, shortening the glasses' battery life. In this situation, the following steps can be performed: After the eyeglass frame attitude monitoring system is powered on and initialized, it enters standby mode by default. In this mode, the inertial measurement unit is in a dormant state and does not acquire data, the zoned tactile feedback unit is powered off and does not output any vibration, and only the ranging unit remains operational, intermittently working at a preset low acquisition wake-up frequency. The system periodically controls at least one of the left or right ranging units to emit a detection signal along a preset corneal direction according to the preset low acquisition wake-up frequency, and receives the echo signal to obtain distance data between the unit and the target object.

[0042] After acquiring the distance data, the system compares it with a preset range of normal wearing distances. The lower limit of this normal wearing distance range is the minimum physical distance from the inner surface of the lens to the corneal apex when the frame is worn close to the face, and the upper limit is the maximum lens-to-eye distance that may occur due to facial movement during normal wear. Distance data exceeding the upper limit of this range indicates that there is no facial tissue within the normal wearing distance in front of the ranging unit.

[0043] If the distance data falls within the normal wearing distance range, the system determines that there is a corneal surface within the normal wearing distance in front of the ranging unit, confirming that the target object is in an effective wearing state. It then generates a system wake-up command, which triggers the inertial measurement unit to switch from sleep mode to full-power operation mode and start outputting three-axis acceleration and three-axis angular velocity signals. At the same time, it increases the sampling frequency of the ranging unit from the preset low acquisition wake-up frequency to the high sampling frequency of the formal data acquisition stage, and activates the processor's attitude calculation and risk assessment calculation module, thereby triggering the subsequent complete data acquisition process.

[0044] If the distance data does not fall within the normal wearing distance range, i.e., the distance data is less than the lower limit of the normal wearing distance range or greater than its upper limit, the system determines that the current frame is not being worn normally on the face by the target object, and may be placed on a table, stored in an eyeglass case, or in other non-wearing scenarios. At this time, the system maintains the standby mode and continues to disable the operation of the inertial measurement unit and the partitioned haptic feedback unit. Only the ranging unit continues to perform periodic wearing status detection at a preset low acquisition wake-up frequency until the next time the distance data is detected to fall within the normal wearing distance range before performing the wake-up operation.

[0045] The above embodiments achieve intelligent on-demand startup of the eyeglass frame posture monitoring system by setting a low-power wearing status detection mechanism based on the ranging unit before formal data acquisition. On the one hand, this avoids the interference of invalid data acquisition in non-wearing state on the accuracy of subsequent astigmatism risk assessment. On the other hand, it significantly reduces the power consumption of the system in non-working state, extends the battery life of the eyeglass frame, and ensures that the system only starts the complete monitoring and intervention process when the target object is actually wearing the eyeglass frame.

[0046] S102. Based on the three-dimensional attitude data and the bilateral inter-eye distance data, extract multi-dimensional risk characteristic parameters that affect the progression of astigmatism. The multi-dimensional risk characteristic parameters include at least the frame horizontal tilt difference, forward tilt deviation, and exposure to poor eye posture extracted from the three-dimensional posture data, as well as the left and right lens distance asymmetry difference extracted from the bilateral lens-eye distance data. The frame horizontal tilt difference refers to the absolute value of the deviation between the roll angle and the preset horizontal reference angle, used to characterize the degree of height asymmetry between the left and right sides of the frame. The forward tilt deviation refers to the difference between the pitch angle and the preset standard fitting forward tilt angle, used to characterize the extent to which the forward tilt of the frame deviates from the normal fitting state. The exposure to poor eye posture refers to the cumulative duration of the target object being outside the preset fatigue posture threshold, used to characterize the degree of time accumulation of poor eye behavior. The left and right lens distance asymmetry difference refers to the distance difference obtained by subtracting the left and right lens distances, which is mainly used to reflect the degree of inconsistency between the distances of the left and right eyes to the corresponding lenses when the glasses are worn crookedly.

[0047] Specifically, after the system completes step S101 to obtain three-dimensional posture data and bilateral interocular distance data, the system immediately performs feature extraction calculations on the raw data. First, the system calculates the absolute value of the deviation between the roll angle and the preset horizontal reference angle to obtain the horizontal tilt difference of the frame. This value reflects the degree of height difference between the left and right frames. When the target object has a habit of tilting their head while wearing the glasses, the roll angle deviates from the horizontal reference angle, causing one side of the frame to be higher than the other. This results in an asymmetrical deviation in the optical path of the eyes through the lenses, which will exert uneven mechanical force on the cornea over a long period of time.

[0048] Secondly, the system calculates the difference between the pitch angle and the preset standard adaptation tilt angle to obtain the tilt angle deviation. The preset standard adaptation tilt angle is the reference range of the tilt angle of the lens relative to the vertical plane of the face when the frame is worn normally. When the pitch angle deviates from this reference range, the alignment relationship between the optical center of the lens and the pupil is shifted, which affects the retinal imaging quality and may induce compensatory eye movements.

[0049] Next, the system determines whether at least one of the pitch, roll, and yaw angles exceeds the corresponding preset fatigue posture threshold. If it does, the system calculates the cumulative duration of the excess time to obtain the exposure amount of poor eye posture. This exposure amount covers the cumulative time of various poor eye postures such as looking down while reading or writing, lying on one side while using the eyes, and prolonged unilateral head tilting. Finally, the system calculates the relative difference between the left and right eye distance data to obtain the asymmetry difference in eye distance between the left and right lenses. When the frame is worn crookedly or when one side of the glasses is forward and the other side is backward, a difference in eye distance occurs between the left and right lenses, resulting in inconsistent optical correction effects received by both eyes. Long-term presence of this difference will exacerbate the asymmetric development of corneal curvature.

[0050] This step extracts four quantitative feature parameters directly related to the progression of acquired astigmatism from the raw sensor data, transforming continuous sensor signals into risk indicators with clear clinical significance. This provides structured feature data for the input of subsequent astigmatism risk quantitative assessment models, solving the problem that existing technologies cannot establish a quantitative correlation between wearing behavior data and astigmatism risk.

[0051] S103. Input the corresponding multi-dimensional risk characteristic parameters into the preset astigmatism risk quantification assessment model to calculate the real-time astigmatism risk value at the current moment. Among them, the astigmatism risk quantification assessment model refers to a pre-constructed and calibrated mathematical operation model used to map multi-dimensional risk characteristic parameters into a single quantitative risk value; the real-time astigmatism risk value refers to the comprehensive risk score output by the model at the current moment, which is used to characterize the risk level of acquired astigmatism progression in the current wearing state of the target object. The value range can be from 0 to 100 points, or it can be set according to the actual situation.

[0052] Specifically, after the system completes step S102 to obtain multi-dimensional risk characteristic parameters, the system inputs the differences in horizontal tilt of the frame, the differences in asymmetry between the left and right eye distances, the forward tilt deviation, and the exposure to poor eye posture into a preset astigmatism risk quantification assessment model. This astigmatism risk quantification assessment model can calculate the corresponding individual risk scores based on each multi-dimensional risk characteristic parameter, and then perform a weighted summation based on pre-calibrated risk weight coefficients to obtain the real-time astigmatism risk value. The calculation process of this model can be described as follows: First, each risk characteristic parameter is compared with its corresponding preset scoring rules to obtain a risk sub-score for each parameter. For the difference in horizontal tilt of the frame, the risk sub-score is the lowest when the difference is less than the first preset threshold, the middle when the difference is between the first and second preset thresholds, and the highest when the difference is greater than the second preset threshold. For the difference in asymmetry between the left and right eye distances, the risk sub-score is the second lowest when the difference is less than the third preset threshold, the middle when the difference is between the third and fourth preset thresholds, and the highest when the difference is greater than the fourth preset threshold. For the forward tilt angle... For deviation, the risk sub-score is as follows: when the pitch angle is within the preset standard adaptation tilt angle range, the risk sub-score is the third lowest; when the pitch angle deviates from this range but does not exceed the fifth preset threshold, the risk sub-score is the third medium; and when the pitch angle deviates from the fifth preset threshold, the risk sub-score is the third highest. For exposure to poor eye posture, the risk sub-score is as follows: when the proportion of high-risk posture duration on a single day is less than the sixth preset threshold, the risk sub-score is the fourth lowest; when this proportion is between the sixth and seventh preset thresholds, the risk sub-score is the fourth medium; and when this proportion is greater than the seventh preset threshold, the risk sub-score is the fourth highest.

[0053] The specific preset thresholds, such as the first to the fifth preset thresholds, are different angle, distance, and duration thresholds set in advance based on clinical research data on adolescent optometry, corneal morphological development patterns, and statistical results of a large number of poor eye postures. Each threshold is independent of the others and is adapted to the physical meaning and risk distribution patterns of the corresponding risk characteristic parameters, which can objectively reflect the induction intensity of different degrees of deviation on the progression of acquired astigmatism. The scores matched with each preset threshold, such as the first low score, the first medium score to the fourth high score, are standardized grade scores corresponding to the corresponding preset thresholds. The low, medium, and high scores are in a gradient increasing relationship. In practice, they can also be divided according to the physiological development characteristics of adolescents of different ages and the needs of clinical application scenarios. The preset thresholds and score ranges can be adaptively adjusted and personalized. This application does not impose any restrictions.

[0054] Then, the model enters the weighted summation unit, multiplying each risk component score by its corresponding preset weight coefficient and summing the results to obtain the real-time astigmatism risk value at the current moment. Each preset weight coefficient is pre-calibrated based on clinical research data, reflecting the relative contribution of each risk factor to the progression of acquired astigmatism. This step, through rule matching and weighted calculation within the model, transforms multi-dimensional risk characteristic parameters into a single real-time astigmatism risk value through standardized scoring and weighted fusion calculation. This achieves real-time quantitative assessment of the risk of acquired astigmatism progression, solving the problem that existing technologies cannot perform real-time quantitative calculation of astigmatism risk.

[0055] S104. Compare the real-time astigmatism risk value with the preset risk level judgment threshold to obtain the target risk level; Among them, the risk level determination threshold refers to the pre-set numerical boundary used to divide different risk levels, which is used to discretize the continuous real-time astigmatism risk value into a finite number of risk level categories; the target risk level refers to the risk level category into which the real-time astigmatism risk value falls after comparison, which is used to characterize the severity level of the current astigmatism progression risk, and can include low risk level, medium risk level and high risk level.

[0056] Specifically, after the system completes step S103 to calculate the real-time astigmatism risk value, it compares this value with a preset risk level threshold to determine the target risk level at the current moment. The risk level threshold includes at least a first threshold and a second threshold, where the first threshold is less than the second threshold. When the real-time astigmatism risk value is less than the first threshold, the system determines the current target risk level as low risk, indicating that the target's current wearing condition is good and there are no significant high-risk factors for astigmatism progression. When the real-time astigmatism risk value is greater than or equal to the first threshold and less than the second threshold, the system determines the current target risk level as medium risk, indicating that the target's current wearing condition has a potential risk of astigmatism progression, requiring attention but not yet reaching the level requiring immediate mandatory intervention. When the real-time astigmatism risk value is greater than or equal to the second threshold, the system determines the current target risk level as high risk, indicating that the target's current wearing condition has significant high-risk factors for astigmatism progression, requiring immediate intervention and correction. This risk level classification provides a decision-making basis for subsequent differentiated intervention strategies, with different risk levels corresponding to different intensities and methods of intervention responses. This step maps continuous risk values ​​to discrete risk levels, enabling a graded assessment of the severity of astigmatism risk. This provides a graded decision-making basis for subsequent precise matching of intervention strategies and solves the problem that existing technologies cannot provide graded early warnings for astigmatism risk.

[0057] It should be noted that the intervention process corresponding to different risk levels can be adjusted according to the actual situation and needs, and is not limited here. For example, for younger children whose eye development is more sensitive, the first and second thresholds can be appropriately lowered so that the system can trigger an alert at a lower risk value, achieving stricter early protection. At the same time, the intervention triggering method can also be flexibly switched. For medium-risk levels, options include local recording only, push notifications only through the mini-program, or a single light vibration alert. For high-risk levels, continuous vibration and dual alerts on the parent's end can be maintained, thus achieving a balance between the effectiveness of intervention and wearing comfort, making the risk classification and intervention strategy more in line with the individualized prevention and control needs of different adolescents.

[0058] S105. If the real-time astigmatism risk value meets the intervention triggering condition matching the target risk level, then determine the main cause of the current risk state and the spatial deviation direction corresponding to the main cause in real time. Among them, the intervention triggering condition refers to the preset condition rules that match each target risk level and are used to determine whether to initiate an intervention action. Different risk levels correspond to different intervention triggering conditions. The deviation spatial direction refers to the physical three-dimensional direction of the frame deflection or asymmetry indicated by the risk characteristic parameter that contributes the most to the real-time astigmatism risk value under the current risk state, which is used to determine the spatial direction of subsequent orientation feedback.

[0059] Specifically, after the system completes step S104 to obtain the target risk level, it further determines whether the current real-time astigmatism risk value meets the intervention triggering conditions matched by the target risk level. For low-risk levels, the system does not trigger any intervention action, but only records the current data; for medium-risk levels, the intervention triggering condition is that the duration of a single medium-risk state reaches a preset first duration threshold, that is, the target object must maintain the medium-risk state for a preset duration before intervention is triggered to avoid false triggering due to instantaneous attitude fluctuations; for high-risk levels, the intervention triggering condition is that the real-time astigmatism risk value is triggered immediately after reaching the second threshold, without waiting for the duration to accumulate. When it is determined that the intervention triggering conditions are met, the system enters the process of determining the deviation spatial direction, which is as follows: The eyeglass frame posture monitoring system first retrieves four multi-dimensional risk characteristic parameters used to calculate the real-time astigmatism risk value: the difference in horizontal tilt of the frame, the deviation in forward tilt angle, the exposure to poor eye posture, and the difference in asymmetry between the left and right eye distances. It then extracts the risk contribution weight score corresponding to each parameter. This score is obtained by multiplying the corresponding sub-score by a preset weight coefficient in the model, objectively reflecting the actual driving effect of each parameter on the total risk value. The system performs a horizontal comparison of the four risk contribution weight scores, identifying the characteristic parameter with the largest value as the primary cause, thereby eliminating secondary deviations and ensuring that interventions focus on the core pathogenic factors. After identifying the primary cause, the system further reads the real-time spatial vector data corresponding to this parameter. If the primary cause is the difference in horizontal tilt or forward tilt angle of the frame, it analyzes the angle vector in the three-dimensional coordinate system to extract posture deviation features such as tilting to the left, tilting to the right, excessive forward tilt, or insufficient backward tilt. If the primary cause is the asymmetry in the distance between the left and right lenses, it analyzes the distance difference vector to extract posture deviation features such as an excessive distance between the left and right lenses. Subsequently, the system converts these posture deviation features into physical three-dimensional directions corresponding to the actual wearing position of the frame according to a pre-established spatial mapping relationship, and finally determines this direction as the deviation spatial direction causing the current astigmatism risk.

[0060] This step enables precise positioning from complex, multi-factor risk states to a single, clear deviation direction, avoiding interference and inefficiency caused by indiscriminate alerts. It allows subsequent intervention actions to directly target the root cause of the deviation, significantly improving the targeting and real-time nature of posture correction. This fundamentally solves the problem that traditional techniques cannot locate the core source of astigmatism risk and have unclear intervention directions.

[0061] S106. Based on the deviation spatial direction, trigger the partitioned tactile feedback unit located at the predetermined position of the frame to perform directional feedback, so as to guide the target object to restore its posture in the opposite direction to the deviation spatial direction.

[0062] Among them, the zoned tactile feedback unit refers to a collection of multiple vibration units respectively set at the front and rear ends of the left temple and right temple of the frame. Each vibration unit constitutes an independent feedback execution anchor point, which is used to output local tactile stimulation at a specified position. Directional feedback means that the system selectively activates a specific vibration unit corresponding to the same side of the physical position in the deviation spatial direction to output vibration, rather than all vibration units working at the same time.

[0063] Specifically, the system determines one or more target vibration units that match the deviation spatial direction based on the physical placement of each vibration unit on the frame. More specifically, the system first determines the posture deviation type corresponding to the deviation spatial direction. Since specific posture deviation features (such as tilting to the left, tilting to the right, excessive forward tilting, etc.) have already been extracted in step S105, the system categorizes these posture deviation features according to their offset orientation and maps them to standardized posture deviation types. These posture deviation types include, but are not limited to, one or more of the following: left side high, right side high, left side tilt, right side tilt, forward tilt, or backward tilt. The determination criterion for the posture deviation type is that the corresponding posture parameters exceed a preset corresponding deviation threshold compared to the normal wearing posture. For example, if the attitude deviation feature extracted in S105 is "left side of the frame tilts up and right side sinks down", indicating that the deviation of the roll angle of this feature from the horizontal reference angle exceeds the preset corresponding deviation threshold, then the system classifies it as the attitude deviation type of "left side too high"; if the extracted feature is "the overall forward tilt angle of the frame is too large", then it is classified as "forward tilt"; if both the left side tilting up and the forward tilt are present, then it is determined to be the superposition of the two attitude deviation types of "left side too high" and "forward tilt".

[0064] Then, the system obtains the pre-configured correspondence between each type of posture deviation and its physical placement. This correspondence is a mapping table pre-established and stored during system initialization. Its establishment is based on the principle that to correct a posture deviation in a certain direction, vibration stimulation should be applied to the same side where the deviation occurs, prompting the wearer to instinctively adjust in the opposite direction (this adjustment logic can be explained to the user in advance in the product instruction manual). Therefore, the mapping table clearly records the physical placement of the vibration unit that should be activated for each type of posture deviation. For example, "left side higher" corresponds to "left temple front and / or rear," "forward tilt" corresponds to "left and right temple front," and "right side tilt" corresponds to "right temple rear," etc.

[0065] Subsequently, the system queries the corresponding correction relationship based on the current attitude deviation type, matches and obtains one or more target physical placement locations, and identifies the vibration unit at the target physical placement location as the target vibration unit. For example, when the attitude deviation type is "left side higher", the system queries the mapping table and finds the corresponding target physical placement location to be the left temple, and then identifies the vibration unit on the left temple as the target vibration unit; when the attitude deviation type is "forward tilt", the system queries and finds the corresponding target physical placement location to be the front end of both temples, and identifies the vibration units at both front ends as target vibration units.

[0066] Next, the system acquires a feedback vibration pattern matching the target risk level: a short, single vibration for medium risk and a continuous vibration for high risk. Finally, the system controls the target vibration unit to output a warning vibration corresponding to the feedback vibration pattern, creating a localized stimulus in the deviation direction. Upon sensing this localized vibration stimulus, the target subject naturally moves its head and face in the opposite direction (where no vibration signal is output) based on its approach-avoidance response, thus adjusting its posture in the opposite direction to the deviation. During the intervention, the system continuously reacquires 3D posture data. When the reacquired 3D posture data returns to the preset normal threshold range, the system stops vibration output, completing a full closed-loop intervention. If the 3D posture data fails to return to the preset normal threshold range within the preset maximum intervention duration, the system pushes a risk warning notification to the linked parent's terminal.

[0067] This step achieves precise correction and guidance of the target object's improper wearing posture through partitioned directional tactile feedback based on the spatial direction of the deviation. It forms a complete real-time closed-loop intervention mechanism from risk detection to posture correction, solving the problem that existing technologies can only issue general reminders without directionality and cannot guide the target object to perform precise posture restoration.

[0068] In this embodiment, by simultaneously collecting three-dimensional posture data and bilateral lens-to-eye distance data and extracting multi-dimensional risk features, and achieving real-time risk scoring through an astigmatism risk quantification assessment model, and then locking the spatial direction of deviation according to the risk level and triggering zoned directional tactile feedback, it is possible to conduct full-process, real-time, and accurate quantitative monitoring and closed-loop intervention of adolescents' poor eye use and wearing posture. This effectively solves the problems of existing technologies being unable to prevent acquired astigmatism at the source, unable to quantify risks, having inaccurate interventions, delayed correction, and being unable to coordinate the prevention and control of myopia and astigmatism. Thus, it achieves the technical effects of pre-warning, real-time correction, full-cycle management, and integrated coordinated prevention and control of myopia and astigmatism in adolescents.

[0069] The following describes the eyeglass frame posture monitoring system in this application from a hardware processing perspective. Please refer to [link / reference needed]. Figure 4 This is a schematic diagram of the physical device structure of the eyeglass frame posture monitoring system in an embodiment of this application.

[0070] It should be noted that, Figure 4 The structure of the eyeglass frame attitude monitoring system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0071] like Figure 4As shown, the eyeglass frame attitude monitoring system includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 402 or programs loaded from storage section 408 into Random Access Memory (RAM) 403, such as performing the methods described in the above embodiments. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An Input / Output (I / O) interface 405 is also connected to the bus 404.

[0072] The following components are connected to I / O interface 405: input section 406 including audio input devices, push-button switches, etc.; output section 407 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 408 including a hard disk, etc.; and communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 409 performs communication processing via a network such as the Internet. Drive 410 is also connected to I / O interface 405 as needed. Removable media 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 410 as needed so that computer programs read from them can be installed into storage section 408 as needed.

[0073] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs the various functions defined in this application.

[0074] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0075] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0076] Specifically, the eyeglass frame posture monitoring system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the eyeglass frame posture monitoring method for the prevention of acquired astigmatism in adolescents provided in the above embodiment.

[0077] In another aspect, this application also provides a computer-readable storage medium, which may be included in the frame posture monitoring system described in the above embodiments; or it may exist independently and not assembled into the frame posture monitoring system. The storage medium carries one or more computer programs, which, when executed by a processor of the frame posture monitoring system, cause the frame posture monitoring system to implement the frame posture monitoring method for the prevention of acquired astigmatism in adolescents provided in the above embodiments.

[0078] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0079] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0080] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for monitoring the posture of eyeglass frames to prevent acquired astigmatism, characterized in that, The method includes: Real-time acquisition of three-dimensional posture data of the eyeglasses and bilateral interocular distance data of the target object when wearing eyeglasses, wherein the bilateral interocular distance data includes left interocular distance data and right interocular distance data; Based on the three-dimensional posture data and the bilateral lens-to-eye distance data, multi-dimensional risk feature parameters affecting the progression of astigmatism are extracted. The multi-dimensional risk feature parameters include at least the difference in horizontal tilt of the frame, the deviation of the forward tilt angle of the frame, and the exposure of poor eye posture extracted based on the three-dimensional posture data, as well as the difference in asymmetry between the left and right lens-to-eye distances extracted based on the bilateral lens-to-eye distance data. The multi-dimensional risk characteristic parameters are input into a preset astigmatism risk quantification assessment model to calculate the real-time astigmatism risk value at the current moment. The real-time astigmatism risk value is compared with a preset risk level determination threshold to obtain the target risk level; If the real-time astigmatism risk value meets the intervention triggering condition matching the target risk level, then the main cause of the current risk state and the deviation spatial direction corresponding to the main cause in real time are determined. Based on the deviation spatial direction, the partitioned tactile feedback unit located at a predetermined position on the frame is triggered to perform directional feedback, so as to guide the target object to restore its posture in the opposite direction to the deviation spatial direction.

2. The method according to claim 1, characterized in that, An inertial measurement unit is installed at a first preset position on both sides of the eyeglass frame, and a distance measuring unit is installed at a second preset position on the inner side of the left and right nose pads of the eyeglass frame. The step of acquiring the three-dimensional posture data and bilateral interpupillary distance data of the target object wearing the eyeglass frame in real time specifically includes: The three-axis acceleration signal and three-axis angular velocity signal output by the inertial measurement unit are collected and analyzed to obtain the three-dimensional attitude data, which includes at least the pitch angle, roll angle and yaw angle of the current frame. The ranging unit is synchronously controlled to emit detection signals and receive echo signals along a preset corneal direction; The corresponding left and right eye distances are calculated based on the time difference between the detection signal and the echo signal.

3. The method according to claim 2, characterized in that, The step of extracting multi-dimensional risk feature parameters affecting astigmatism progression based on the three-dimensional pose data and the bilateral interocular distance data specifically includes: Calculate the absolute value of the deviation between the roll angle and the preset horizontal reference angle to obtain the horizontal tilt difference of the frame; Calculate the difference between the pitch angle and the preset standard adaptive tilt angle to obtain the tilt angle deviation; Determine whether at least one of the pitch angle, roll angle and yaw angle exceeds the corresponding preset fatigue attitude threshold; If the threshold is exceeded, the cumulative duration of the duration exceeding the preset fatigue posture threshold is counted to obtain the exposure amount of poor eye posture. The relative difference between the left and right eye distances is calculated to obtain the asymmetry difference between the left and right eye distances.

4. The method according to claim 1, characterized in that, If the real-time astigmatism risk value meets the intervention triggering condition matching the target risk level, then the steps of determining the main cause leading to the current risk state and the spatial deviation direction corresponding to the main cause in real time specifically include: Extract the risk contribution weight score corresponding to each of the multi-dimensional risk feature parameters when calculating the real-time astigmatism risk value; The feature parameter corresponding to the highest risk contribution weight score is identified as the primary cause. Analyze the current real-time spatial vector data of the main cause and extract the attitude deviation features that constitute the main cause; The spatial direction of the deviation that leads to the current astigmatism risk is determined based on the aforementioned attitude skew characteristics.

5. The method according to claim 4, characterized in that, The left temple and the front and rear ends of the right temple of the eyeglass frame are respectively provided with vibration units to jointly constitute the partitioned tactile feedback unit. The step of triggering the partitioned tactile feedback unit located at a predetermined position on the eyeglass frame to perform directional feedback according to the deviation spatial direction specifically includes: Based on the physical arrangement of each vibration unit on the frame, determine one or more target vibration units that match the deviation spatial direction; Obtain a feedback vibration pattern that matches the target risk level; According to the feedback vibration mode, the target vibration unit is controlled to output a warning vibration of the corresponding mode to form a local stimulus in the deviation direction, guiding the head and face of the target object to move in the opposite direction of no vibration signal output until the reacquired three-dimensional posture data is restored to the preset normal threshold range.

6. The method according to claim 5, characterized in that, The step of determining one or more target vibration elements that match the deviation spatial direction specifically includes: Determine the attitude deviation type corresponding to the deviation spatial direction. The attitude deviation type includes one or more of the following: left side high, right side high, left side skew, right side skew, forward roll, or backward roll. Obtain the pre-configured corresponding correction relationship between each of the attitude deviation types and the physical deployment location; Based on the current attitude deviation type, the corresponding correction relationship is queried, and one or more target physical deployment locations are matched to obtain them. The vibration unit at the target physical deployment location is then identified as the target vibration unit.

7. The method according to claim 2, characterized in that, Before the step of acquiring the three-dimensional pose data and bilateral interocular distance data of the target object wearing the glasses in real time, the method further includes: In standby mode, the ranging unit is controlled to acquire distance data between itself and the target object at a preset low acquisition wake-up frequency. Determine whether the distance data falls within a preset normal wearing distance range; If the distance falls within the normal wearing distance range, the target object is determined to be in an effective wearing state, and a system wake-up command is generated to trigger subsequent data collection. If the distance does not fall within the normal wearing distance range, it is determined that the device is not in an effective wearing state, and the standby mode is maintained to block subsequent data collection.

8. A frame posture monitoring system, characterized in that, The eyeglass frame posture monitoring system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the eyeglass frame posture monitoring system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the eyeglasses attitude monitoring system, the eyeglasses attitude monitoring system performs the method as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is run on the eyeglasses posture monitoring system, the eyeglasses posture monitoring system performs the method as described in any one of claims 1-7.

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