A method and device for controlling zoom glasses based on artificial intelligence

Abstract

The present invention belongs to the technical field of zoom glasses and provides a method and device for controlling zoom glasses based on artificial intelligence. The method includes: when the current time reaches the training period in the preset work and rest information, controlling the rangefinder on the zoom glasses to measure obstacles in a preset range in front and obtain a set of first distance data as feedback; analyzing the user's head posture based on the first distance data measured by the rangefinder, and determining that the zoom glasses are target zoom glasses when it is judged that the head posture meets the preset posture; obtaining dynamic training data corresponding to the user associated with each target zoom glasses, and sending the dynamic training data to the corresponding target zoom glasses respectively, so that the target zoom glasses start the training mode, that is, dynamically adjusting the focal length of the glasses according to the dynamic training data. The present invention can at least realize remote, overall and rapid switching of the zoom mode of the zoom glasses.

Description

Technical Field

[0001] The present invention relates to the technical field of zoom glasses, and in particular to an artificial intelligence-based zoom glasses control method and device. Background Art

[0002] In the field of eyewear, as people's demand for vision correction and visual experience continues to increase, traditional single-focal and bifocal lenses are no longer able to meet these increasingly diverse needs. Against this backdrop, varifocal glasses have emerged as a significant innovation in eyewear technology.

[0003] like Figure 1 As shown, the fundamental principle of varifocal glasses lies in their use of advanced optical technology and adaptive lens materials. These lenses automatically adjust their power based on changes in the wearer's eye focus, achieving continuous changes in focal length. Specifically, varifocal glasses typically consist of two sets of free-form surface lenses, which are driven by a motor and can move back and forth. When both lenses have negative powers, they combine to produce a high negative power, suitable for near vision needs; when one is positive and the other negative, the powers cancel out, suitable for long-distance vision needs. Through this design, the refractive power of varifocal glasses can vary over a continuous range, from +3.00D to -3.00D, or even higher, to meet visual needs at different distances.

[0004] Varifocal glasses can be configured in normal mode and training mode. In normal mode, the zoom range of the varifocal glasses will be relatively small to support users' daily use, such as reading; in dynamic mode, the zoom range of the varifocal glasses will be relatively large, which can provide more intensive training for the user's eyes to improve hyperopia and myopia.

[0005] However, current varifocal glasses require manual user interaction (e.g., button or voice control), making them inconvenient. Furthermore, in classrooms, where varifocal glasses are frequently worn, it's difficult to quickly and uniformly switch the modes of the glasses across multiple users. Therefore, improving the convenience of mode switching in classrooms, where varifocal glasses are frequently worn, is a pressing technical issue. Summary of the Invention

[0006] In this regard, the present invention provides an artificial intelligence-based zoom glasses control method, device, electronic device, computer storage medium and computer program product to solve at least one of the above technical problems.

[0007] The present invention provides an artificial intelligence-based control method for zoom glasses, which is applied to a remote management terminal and includes the following method steps: when the current time reaches a training period in preset work and rest information, controlling a rangefinder on the zoom glasses to measure obstacles in a preset range ahead and obtaining a set of first distance data as feedback; analyzing the user's head posture based on the first distance data measured by the rangefinder, and determining that the zoom glasses are target zoom glasses when it is determined that the head posture meets the preset posture; obtaining dynamic training data corresponding to the user associated with each of the target zoom glasses, and sending the dynamic training data to the corresponding target zoom glasses, so that the target zoom glasses start a training mode, that is, dynamically adjusting the focal length of the glasses according to the dynamic training data.

[0008] As an example, controlling the rangefinder on the varifocal glasses to measure obstacles in a preset range in front includes: obtaining another set of second distance data measured by the rangefinder in a normal mode, and calculating the width of a material that the user is looking at based on the second distance data; wherein the width refers to the dimension of the material from an end close to the user's body to an end away from the user's body; determining the preset range based on the width, and controlling the rangefinder on the varifocal glasses to measure obstacles in the preset range in front; wherein the preset range is positively correlated with the width.

[0009] As an example, the analysis of the first distance data measured by the rangefinder to obtain the user's head posture includes: dividing the preset range into a lower area and an upper area, and separating the third distance data corresponding to the lower area and the fourth distance data corresponding to the upper area from the first distance data; determining the size data of the material based on the third distance data, and calculating the equivalent distance based on the fourth distance data, the equivalent distance being the average value or mode value of each distance in the fourth distance data; and evaluating the user's head downward tilt angle based on the size data and the equivalent distance, and the head downward tilt angle is the head posture.

[0010] As an example, the evaluation of the user's head downward tilt angle based on the dimensional data and the equivalent distance includes: retrieving the user's upper body size and the surface height of tables and chairs to form basic data; inputting the basic data, the dimensional data and the equivalent distance into a Transformer-based evaluation model, and the evaluation model outputs the evaluated user's head downward tilt angle.

[0011] As an example, the evaluation model includes a convolutional network, a first encoder, a second encoder and a decoder, the output end of the convolutional network is connected to the input end of the first encoder and the second encoder respectively, and the output end of the first encoder and the second encoder is connected to the decoder respectively.

[0012] The present invention also provides an artificial intelligence-based zoom glasses control device, which is applied to a remote management end, and the device includes an acquisition and sending module and a determination module; the acquisition and sending module is used to control the rangefinder on the zoom glasses to measure obstacles in a preset range in front when the current time reaches the training period in the preset work and rest information, and obtain a set of first distance data as feedback; the determination module is used to analyze the user's head posture based on the first distance data measured by the rangefinder, and when it is judged that the head posture meets the preset posture, determine that the zoom glasses are target zoom glasses; the acquisition and sending module is also used to obtain dynamic training data corresponding to the user associated with each of the target zoom glasses, and send the dynamic training data to the corresponding target zoom glasses respectively, so that the target zoom glasses start the training mode, that is, dynamically adjust the focal length of the glasses according to the dynamic training data.

[0013] As an example, the acquisition and sending module is specifically used to: acquire another set of second distance data measured by the rangefinder in normal mode, and calculate the width of the material the user is looking at based on the second distance data; wherein the width refers to the dimension of the material from the end close to the user's body to the end away from the user's body; determine the preset range based on the width, and control the rangefinder on the zoom glasses to measure obstacles in the preset range in front; wherein the preset range is positively correlated with the width.

[0014] As an example, the determination module is specifically used to: divide the preset range into a lower area and an upper area, and separate from the first distance data the third distance data corresponding to the lower area and the fourth distance data corresponding to the upper area; determine the size data of the material based on the third distance data, and calculate the equivalent distance based on the fourth distance data, and the equivalent distance is the average value or mode value of each distance in the fourth distance data; evaluate the user's head downward tilt angle based on the size data and the equivalent distance, and the head downward tilt angle is the head posture.

[0015] As an example, the determination module is specifically used to: retrieve the user's upper body size and the surface height of the table and chair to form basic data; input the basic data, the size data and the equivalent distance into the Transformer-based evaluation model, and the evaluation model outputs the evaluated user's head downward tilt angle.

[0016] As an example, the evaluation model includes a convolutional network, a first encoder, a second encoder and a decoder, the output end of the convolutional network is connected to the input end of the first encoder and the second encoder respectively, and the output end of the first encoder and the second encoder is connected to the decoder respectively.

[0017] The present invention also provides an electronic device, characterized in that the electronic device includes: at least one processor, a memory, and a computer program stored in the memory and capable of running on the at least one processor, wherein when the computer program is executed by the processor, the method described in any one of the foregoing items is implemented.

[0018] The present invention also provides a computer storage medium storing a computer program that can be executed by a processor to implement any of the methods described above.

[0019] The present invention also provides a computer program product, which includes a computer program that can be executed by a processor to implement any of the methods described above.

[0020] The beneficial effects of the present invention are as follows: On the one hand, the present invention's solution enables centralized control of the zoom modes of multiple users' zoom glasses in scenarios such as classrooms from a remote management terminal, eliminating the need for users to manually adjust the zoom mode, significantly improving ease of use. On the other hand, the present invention also first analyzes whether the user's head posture is suitable for activating the training mode. Only when the head posture is suitable for activating the training mode is personalized dynamic training data sent to the zoom glasses for execution. This avoids large-scale zoom training when the user is lowering their head, thereby improving the effectiveness of zoom training. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 This is a schematic diagram of the basic zoom principle of zoom glasses.

[0023] Figure 2 This is a flow chart of an artificial intelligence-based zoom glasses control method disclosed in an embodiment of the present invention.

[0024] Figure 3 This is a structural diagram of an artificial intelligence-based zoom glasses control device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following describes the implementation of the present invention using specific embodiments. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. Obviously, the embodiments described are only a portion of the present invention, not all of it. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0026] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0027] like Figure 1 As shown, an embodiment of the present invention discloses an artificial intelligence-based method for controlling zoom glasses, which is applied to a remote management terminal and includes the following method steps: when the current time reaches the training period in the preset work and rest information, controlling the rangefinder on the zoom glasses to measure obstacles in a preset range in front and obtaining a set of first distance data as feedback; analyzing the user's head posture based on the first distance data measured by the rangefinder, and determining that the zoom glasses are target zoom glasses when it is determined that the head posture meets the preset posture; obtaining dynamic training data corresponding to the user associated with each of the target zoom glasses, and sending the dynamic training data to the corresponding target zoom glasses respectively, so that the target zoom glasses start the training mode, that is, dynamically adjusting the focal length of the glasses according to the dynamic training data.

[0028] The solution of the present invention is applicable to scenarios such as classrooms and eyeglass rehabilitation / correction centers where multiple users wear varifocal glasses. In these scenarios, users can engage in normal teaching / self-study activities, while in between these activities, there are training periods (such as rest periods or specially designated eye training periods), for which specific preset work and rest information is compiled. During these teaching / self-study periods, the remote management terminal sends a control command to each varifocal pair of glasses in the scene to switch their zoom mode to normal mode. When the current time reaches the training period specified in the preset work and rest information, the remote management terminal sends a control command to each varifocal pair of glasses in the scene to switch their zoom mode from normal mode to training mode.

[0029] Furthermore, before switching the zoom mode from regular mode to training mode, it's necessary to further determine whether the user is ready for training, specifically whether their head posture has reached a preset position, such as an upright, forward-facing position. In training mode, the user should stop lowering their head when reading or studying and instead raise their head forward. This provides a wider field of view and keeps obstacles (such as textbooks) at a greater distance. This allows for more intense zooming to train the eyes.

[0030] Specifically, when the current time reaches the training period specified in the preset work and rest information, the rangefinder on the varifocal glasses is controlled to measure obstacles within a preset range ahead. The rangefinder then transmits a set of first distance data obtained from the measurement to the remote management terminal. The remote management terminal analyzes the user's head posture based on this set of first distance data. If the head posture is determined to match the preset posture, the corresponding varifocal glasses are designated as the target varifocal glasses. Next, the dynamic training data corresponding to each target varifocal glasses is retrieved from a database and sent to the target varifocal glasses for execution, thereby initiating training mode, thereby achieving personalized training for the user's eyes.

[0031] Therefore, the solution of the present invention enables centralized control of the zoom modes of multiple users' varifocal glasses in scenarios such as classrooms from a remote management terminal, eliminating the need for users to manually adjust the zoom mode, significantly improving ease of use. Furthermore, the present invention first analyzes whether the user's head posture is suitable for initiating training mode. Only when this is the case is personalized dynamic training data sent to the varifocal glasses for execution. This avoids large-scale zoom training when the user is lowering their head, thereby improving the effectiveness of zoom training.

[0032] As an example, controlling the rangefinder on the varifocal glasses to measure obstacles in a preset range in front includes: obtaining another set of second distance data measured by the rangefinder in a normal mode, and calculating the width of a material that the user is looking at based on the second distance data; wherein the width refers to the dimension of the material from an end close to the user's body to an end away from the user's body; determining the preset range based on the width, and controlling the rangefinder on the varifocal glasses to measure obstacles in the preset range in front; wherein the preset range is positively correlated with the width.

[0033] In this embodiment, in normal mode, the user lowers their head to gaze at study materials, such as textbooks, placed on the desk. In training mode, the user switches from a lowered head posture to a raised, level-looking posture to accommodate the greater change in focal length of the varifocal glasses. However, the user's switch from a lowered head posture to a raised head posture does not necessarily conform to the pre-set posture. For example, even if the user raises their head somewhat, the height may not be sufficient, and their eyes may still be looking downward (i.e., "peeking") at the study materials on the desk. Furthermore, the wider the study materials, the easier it is for the user to achieve a better gaze by tilting the materials.

[0034] Based on the above practical situation, the present invention first analyzes the width of the learning material that the user is looking at in normal mode, and then determines the size of the preset range within which the rangefinder needs to scan for obstacle distances based on this width. The wider the learning material, the greater the probability that the user's head posture conforms to the preset posture, but that tilting the learning material will achieve a better focusing effect. In this case, the preset range is set to a larger value, which is conducive to scanning tilted learning materials. If the learning material is scanned, there is a high probability that the user is not looking straight ahead, that is, the conditions for switching to training mode are not met. Conversely, the smaller the probability that the user's head posture conforms to the preset posture, but that tilting the learning material will achieve a better focusing effect, the preset range is set to a smaller value.

[0035] In addition, in the normal mode of the zoom glasses, the rangefinder will periodically measure the distance information of obstacles in the area directly in front of the zoom glasses, which is the other set of second distance data mentioned above. Based on the second distance data, the outline of the learning material that the user is looking at can be determined, so that its width can be evaluated.

[0036] As an example, the analysis of the first distance data measured by the rangefinder to obtain the user's head posture includes: dividing the preset range into a lower area and an upper area, and separating the third distance data corresponding to the lower area and the fourth distance data corresponding to the upper area from the first distance data; determining the size data of the material based on the third distance data, and calculating the equivalent distance based on the fourth distance data, the equivalent distance being the average value or mode value of each distance in the fourth distance data; and evaluating the user's head downward tilt angle based on the size data and the equivalent distance, and the head downward tilt angle is the head posture.

[0037] In this embodiment, after the user switches from looking down to looking up, the rangefinder is controlled to detect the distance of obstacles within a preset range in front of them. The preset range can be divided into a lower area and an upper area, and the first distance data is correspondingly divided into third distance data and fourth distance data. The third distance data is used to analyze whether the rangefinder scanned the learning material that the user was previously looking at, and the fourth distance data is used to analyze whether the obstacle in front is generally "far away". For example, if there is a wall within 30cm in front of a user, high-intensity zoom training is not suitable at this time. However, if there is a podium 5m in front of the user, high-intensity zoom training is suitable at this time.

[0038] Based on the third and fourth distance data, the dimensions of the material placed on the desktop (e.g., width, area, etc.) and the equivalent distance of the scene in front can be determined (the equivalent distance is the average or mode value of the distances in the fourth distance data). Based on this, the user's current head downward tilt angle can be assessed (a greater downward tilt angle relative to the vertical direction corresponds to a greater downward bow), thereby obtaining the user's head posture.

[0039] As an example, the evaluation of the user's head downward tilt angle based on the dimensional data and the equivalent distance includes: retrieving the user's upper body size and the surface height of tables and chairs to form basic data; inputting the basic data, the dimensional data and the equivalent distance into a Transformer-based evaluation model, and the evaluation model outputs the evaluated user's head downward tilt angle.

[0040] In this embodiment, the present invention constructs an evaluation model based on Transformer. This evaluation model can comprehensively analyze the dimensional data and equivalent distance analyzed from the first distance data measured by the rangefinder to assess the user's current head tilt angle. Furthermore, because the user's upper body size and the surface height of the desk and chair (i.e., the height of the tabletop and chair seat) also directly affect the size of the learning materials scanned in the lower area, the present invention also inputs this data into the evaluation model, thereby improving the accuracy of the estimated user's head tilt angle.

[0041] As an example, the evaluation model includes a convolutional network, a first encoder, a second encoder and a decoder, the output end of the convolutional network is connected to the input end of the first encoder and the second encoder respectively, and the output end of the first encoder and the second encoder is connected to the decoder respectively.

[0042] In this embodiment, the evaluation model of the present invention integrates a convolutional network, which extracts feature data from the input data. The feature data are then encoded separately by a dual encoder, and the analysis results of the dual encoders are subjected to, for example, weighted fusion, and then decoded by a decoder to obtain the downward tilt angle of the user's head being evaluated.

[0043] As an example, the remote management terminal is further used to send separate control parameters to the varifocal glasses, and the separate control parameters are used to achieve independent training of the left and right eyes.

[0044] In this embodiment, the left and right eyes of a myopic user will have different vision such as myopia, hyperopia or astigmatism (for example, the degree of myopia of the left and right eyes is different), but the left and right eyes of the existing zoom glasses are controlled by one motor, and the left and right eyes cannot be trained independently. In response to this technical problem, the present invention sets a remote management terminal to formulate corresponding zoom control parameters according to the myopia conditions of the left and right eyes of a specific myopic user, and integrates the two zoom control parameters into a single control parameter, and sends it to the corresponding zoom glasses. The zoom glasses can realize independent and personalized training of the left and right eyes according to the two zoom control parameters, wherein the zoom glasses are correspondingly arranged with two left and right motors. The solution of the present invention is suitable for training in different scenarios, such as reading, watching TV, and looking into the distance, and precise training can be performed in these scenarios.

[0045] It should be noted that when the user's reading posture is not correct or is too close to the book during training, the zoom glasses can also detect these bad behaviors based on their own sensors (such as the aforementioned rangefinder, but can also be a gyroscope, etc.) and then issue a sound reminder.

[0046] like Figure 3 As shown, the present invention also provides an artificial intelligence-based zoom glasses control device, which is applied to a remote management terminal, and the device includes an acquisition and sending module and a determination module; the acquisition and sending module is used to control the rangefinder on the zoom glasses to measure obstacles in a preset range in front when the current time reaches the training period in the preset work and rest information, and obtain a set of first distance data as feedback; the determination module is used to analyze the user's head posture based on the first distance data measured by the rangefinder, and when it is judged that the head posture meets the preset posture, determine that the zoom glasses are target zoom glasses; the acquisition and sending module is also used to obtain dynamic training data corresponding to the user associated with each of the target zoom glasses, and send the dynamic training data to the corresponding target zoom glasses respectively, so that the target zoom glasses start the training mode, that is, dynamically adjust the focal length of the glasses according to the dynamic training data.

[0047] As an example, the acquisition and sending module is specifically used to: acquire another set of second distance data measured by the rangefinder in normal mode, and calculate the width of the material the user is looking at based on the second distance data; wherein the width refers to the dimension of the material from the end close to the user's body to the end away from the user's body; determine the preset range based on the width, and control the rangefinder on the zoom glasses to measure obstacles in the preset range in front; wherein the preset range is positively correlated with the width.

[0048] As an example, the determination module is specifically used to: divide the preset range into a lower area and an upper area, and separate from the first distance data the third distance data corresponding to the lower area and the fourth distance data corresponding to the upper area; determine the size data of the material based on the third distance data, and calculate the equivalent distance based on the fourth distance data, and the equivalent distance is the average value or mode value of each distance in the fourth distance data; evaluate the user's head downward tilt angle based on the size data and the equivalent distance, and the head downward tilt angle is the head posture.

[0049] As an example, the determination module is specifically used to: retrieve the user's upper body size and the surface height of the table and chair to form basic data; input the basic data, the size data and the equivalent distance into the Transformer-based evaluation model, and the evaluation model outputs the evaluated user's head downward tilt angle.

[0050] As an example, the evaluation model includes a convolutional network, a first encoder, a second encoder and a decoder, the output end of the convolutional network is connected to the input end of the first encoder and the second encoder respectively, and the output end of the first encoder and the second encoder is connected to the decoder respectively.

[0051] The present invention also discloses an electronic device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the computer program implements the method steps described in any one of the above items when executed by the processor.

[0052] The present invention also discloses a computer storage medium storing a computer program that can be executed by a processor to implement any of the methods described above.

[0053] The present invention also discloses a computer program product, which includes a computer program that can be executed by a processor to implement any of the methods described above.

[0054] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any other combination. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0055] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0056] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0057] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0058] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. An artificial intelligence-based method for controlling zoom glasses, applied to a remote management terminal, characterized by: The method comprises the following steps: when the current time reaches the training period in the preset work and rest information, controlling the rangefinder on the zoom glasses to measure obstacles in a preset range ahead and obtaining a set of first distance data as feedback; deriving a head posture of the user based on the first distance data measured by the rangefinder, and determining the varifocal glasses as target varifocal glasses when it is determined that the head posture meets a preset posture; Acquiring dynamic training data corresponding to a user associated with each of the target varifocal glasses, and sending the dynamic training data to the corresponding target varifocal glasses, so that the target varifocal glasses start a training mode, that is, dynamically adjusting the focal length of the glasses according to the dynamic training data; Controlling the rangefinder on the varifocal glasses to measure obstacles within a preset range ahead includes: obtaining another set of second distance data measured by the rangefinder in a normal mode, and calculating the width of a material being looked at by the user based on the second distance data; wherein the width refers to the dimension of the material from an end closer to the user's body to an end farther from the user's body; determining the preset range based on the width, and controlling the rangefinder on the varifocal glasses to measure obstacles within the preset range ahead; wherein the preset range is positively correlated with the width. Specifically, the wider the material, the greater the probability that the user's head posture conforms to the preset posture but that a better focusing effect is achieved by tilting the material. In this case, the preset range is set larger to facilitate scanning the tilted material. If the learning material is scanned, it indicates that there is a high probability that the user is not looking straight ahead, i.e., the condition for switching to the training mode is not met. Conversely, the smaller the probability that the user's head posture conforms to the preset posture but that a better focusing effect is achieved by tilting the learning material, the preset range is set smaller. Analyzing and deriving the user's head posture based on the first distance data measured by the rangefinder includes: dividing the preset range into a lower area and an upper area, and separating and deriving third distance data corresponding to the lower area and fourth distance data corresponding to the upper area from the first distance data; determining material size data based on the third distance data, and calculating an equivalent distance based on the fourth distance data, wherein the equivalent distance is an average value or a mode value of each distance in the fourth distance data; and evaluating and deriving a downward tilt angle of the user's head based on the size data and the equivalent distance, wherein the downward tilt angle of the head is the head posture; The method of evaluating the user's head downward tilt angle based on the dimension data and the equivalent distance includes: retrieving the user's upper body dimensions and the surface height of tables and chairs to form basic data; inputting the basic data, the dimension data and the equivalent distance into a Transformer-based evaluation model, and the evaluation model outputting the evaluated user's head downward tilt angle.

2. The artificial intelligence-based zoom glasses control method according to claim 1, characterized in that: The evaluation model includes a convolutional network, a first encoder, a second encoder and a decoder. The output end of the convolutional network is connected to the input end of the first encoder and the second encoder respectively, and the output end of the first encoder and the second encoder is connected to the decoder respectively.

3. An artificial intelligence-based zoom glasses control device, used in a remote management terminal, characterized by: The device includes an acquisition and sending module and a determination module; the acquisition and sending module is used to control the rangefinder on the zoom glasses to measure obstacles in a preset range ahead when the current time reaches the training period in the preset work and rest information, and obtain a set of first distance data as feedback; The determining module is configured to analyze the user's head posture based on the first distance data measured by the rangefinder, and determine that the varifocal glasses are target varifocal glasses when the head posture is determined to conform to a preset posture. The acquiring and sending module is further configured to acquire dynamic training data corresponding to the user associated with each target varifocal glasses, and send the dynamic training data to the corresponding target varifocal glasses, so that the target varifocal glasses enter a training mode, i.e., dynamically adjust the focal length of the glasses according to the dynamic training data. The acquisition and transmission module is specifically configured to: acquire another set of second distance data measured by the rangefinder in a normal mode, and calculate the width of the material being gazed at by the user based on the second distance data; wherein the width refers to the dimension of the material from an end close to the user's body to an end away from the user's body; determine the preset range based on the width, and control the rangefinder on the zoom glasses to perform obstacle measurement within the preset range in front; wherein the preset range is positively correlated with the width, and specifically: the wider the material, the greater the probability that the user's head posture conforms to the preset posture but a better gaze effect is achieved by tilting the material, and in this case, the preset range is set to be larger to facilitate scanning the tilted material. If the learning material is scanned, it indicates that there is a high probability that the user is not looking straight ahead at this time, that is, the condition for switching to the training mode is not met; conversely, the lower the probability that the user's head posture conforms to the preset posture but a better gaze effect is achieved by tilting the learning material, and in this case, the preset range is set to be smaller; The determination module is specifically configured to: divide the preset range into a lower area and an upper area, and separate from the first distance data, obtain third distance data corresponding to the lower area and fourth distance data corresponding to the upper area; determine material size data based on the third distance data, and calculate an equivalent distance based on the fourth distance data, where the equivalent distance is the average or mode value of each distance in the fourth distance data; and evaluate and obtain a downward tilt angle of the user's head based on the size data and the equivalent distance, where the downward tilt angle is the head posture; The determination module is specifically used to: retrieve the user's upper body size and the surface height of the table and chair to form basic data; input the basic data, the size data and the equivalent distance into the Transformer-based evaluation model, and the evaluation model outputs the user's head downward tilt angle evaluated by it.

4. The artificial intelligence-based zoom glasses control device according to claim 3, characterized in that: The evaluation model includes a convolutional network, a first encoder, a second encoder and a decoder. The output end of the convolutional network is connected to the input end of the first encoder and the second encoder respectively, and the output end of the first encoder and the second encoder is connected to the decoder respectively.

5. An electronic device, characterized in that: The electronic device comprises: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the computer program implements the method according to claim 1 or 2 when executed by the processor.

6. A computer storage medium, characterized in that: The computer storage medium stores a computer program that can be executed by a processor to implement the method according to claim 1 or 2.

7. A computer program product, characterized in that: The computer program product comprises a computer program executable by a processor to implement the method according to claim 1 or 2 .

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