A smart control method and system for a meibomian gland massager based on AI recognition

Abstract

This invention relates to the technical field of meibomian gland massage, and in particular to an intelligent control method and system for a meibomian gland massager based on AI recognition. The method includes the following steps: acquiring three-dimensional point cloud data of the patient's meibomian gland surface; extracting feature points and constructing their three-dimensional coordinates based on the three-dimensional point cloud data; generating the massage motion trajectory of the massager using an adaptive path planning algorithm based on the three-dimensional coordinates; acquiring image data of the patient's meibomian gland; retrieving massager parameter data from a preset feature database based on the image data; inputting the massager parameter data into a preset control model; outputting the massage time and massage light intensity; and controlling the massager to massage the patient's meibomian glands based on the massage time and massage light intensity. This application has the effect of improving the massage effect on the patient's meibomian glands.

Description

Technical Field

[0001] This invention relates to the technical field of meibomian gland massage, and in particular to an intelligent control method and system for a meibomian gland massager based on AI recognition. Background Technology

[0002] Retinal surgeries (such as vitrectomy and retinal detachment repair) are common and complex ophthalmic procedures. However, postoperatively, patients often experience meibomian gland dysfunction (MGD) and dry eye due to ocular trauma, prolonged exposure of the ocular surface, and the use of anti-inflammatory drugs, severely impacting postoperative recovery quality and visual experience. The meibomian glands are the primary secretory source of the tear film lipid layer on the ocular surface, and their morphology (such as atrophy or blockage) and secretory function directly determine the degree of dry eye. Therefore, accurate postoperative assessment and targeted intervention of the meibomian gland status are crucial.

[0003] To address these issues, healthcare professionals use meibomian gland massagers to massage patients' eyes. However, most existing massagers suffer from insufficient recognition accuracy and inaccurate massage parameter control, making it difficult to meet the massage needs of the meibomian glands. Therefore, there is room for improvement. Summary of the Invention

[0004] To improve the massage effect on patients' meibomian glands, this application provides an intelligent control method and system for a meibomian gland massager based on AI recognition.

[0005] The above-mentioned objective of this application is achieved through the following technical solution:

[0006] A smart control method for a meibomian gland massager based on AI recognition, the method comprising the following steps:

[0007] Acquire three-dimensional point cloud data of the patient's meibomian gland surface, extract feature points based on the three-dimensional point cloud data of the patient's meibomian gland surface and construct its three-dimensional coordinates;

[0008] Based on the three-dimensional coordinates, an adaptive path planning algorithm is used to generate the massage motion trajectory of the massager;

[0009] Acquire patient meibomian gland image data, and retrieve massager parameter data from a preset feature database based on the patient meibomian gland image data;

[0010] The massager parameter data is input into a preset control model, and the massage time and massage light intensity are output. Based on the massage time and massage light intensity, the massager is controlled to massage the display marks on the patient's meibomian glands.

[0011] By employing the aforementioned technical solution, during the massage of the patient's meibomian glands and the displayed markers, high-precision imaging equipment is used to acquire real-time three-dimensional point cloud data of the patient's meibomian gland surface. Feature extraction and identification of key features such as surface inflection points and boundary contours are performed on the three-dimensional point cloud data, and a three-dimensional coordinate system of the patient's meibomian gland surface is constructed. This effectively solves the feature recognition error problem caused by surface reflection in traditional positioning, laying a precise spatial benchmark for subsequent path planning. This not only ensures that the massager can accurately identify and locate every minute position on the patient's meibomian glands, but also lays a solid foundation for achieving high-precision massage motion trajectory planning, improving the accuracy and consistency of the massage path. By utilizing the three-dimensional coordinates of the patient's meibomian gland surface and combining them with an adaptive path planning algorithm, the complexity and diversity of the patient's meibomian gland surface are fully considered. The movement path can be flexibly adjusted according to the actual situation, thereby effectively improving massage efficiency while ensuring massage quality. By acquiring the patient's meibomian gland image data and retrieving the massager parameter data from the preset characteristic database, the massage time and massage light intensity can be precisely controlled. The different requirements of different patients' meibomian glands for massage parameters are considered, ensuring uniform distribution and good solidification of the massager during the massage process, thereby improving the massage effect and achieving high-precision, high-efficiency, and high-quality massage of the patient's meibomian glands.

[0012] In a preferred embodiment, this application can be further configured as follows: after inputting the massager parameter data into a preset control model, outputting the massage time and massage light intensity, and controlling the massager to massage the display marks on the patient's meibomian glands according to the massage time and massage light intensity, the AI-based intelligent control method for meibomian gland massagers further includes:

[0013] Acquire pressure data of the patient's meibomian gland contact surface, and construct a pressure field deviation matrix based on the patient's meibomian gland contact surface pressure data;

[0014] Based on the pressure field deviation matrix, a PID control signal is generated. In response to the PID control signal, the compression parameters are adjusted until the pressure data of the patient's meibomian gland contact surface meets the preset pressure threshold.

[0015] By adopting the above technical solution, during the massage process based on the adjusted massage time and light intensity, pressure data of the contact surface of the patient's meibomian glands is acquired through a pressure sensor array. Based on the pressure data, a pressure field deviation is constructed to accurately identify uneven pressure distribution during the massage process. Based on the pressure deviation matrix, a PID control signal is generated. By dynamically adjusting the pressure parameters, such as the pressure of the massager and the duration of action, the massage process can be precisely controlled until the pressure data of the patient's meibomian gland contact surface stably meets the preset pressure threshold. This ensures the uniformity and firmness of the massage, further improving the massage quality and reducing poor massage effects caused by uneven pressure.

[0016] In a preferred embodiment, this application can be further configured as follows: acquiring three-dimensional point cloud data of the patient's meibomian gland surface, extracting feature points based on the three-dimensional point cloud data of the patient's meibomian gland surface, and constructing its three-dimensional coordinates specifically includes:

[0017] Obtain an image of the patient's meibomian gland surface, and extract edge information and feature point information based on the image of the patient's meibomian gland surface;

[0018] The edge information and feature point information are converted into three-dimensional spatial coordinates to form the three-dimensional coordinates of the patient's meibomian gland surface.

[0019] By adopting the above technical solution, by acquiring images of the patient's meibomian gland surface, and using advanced image processing technology to extract edge information and feature point information, the contour details and key geometric features of the patient's meibomian gland surface are captured, providing an accurate information foundation for subsequent three-dimensional coordinate construction. Based on the extracted edge information and feature point information, it is mapped into three-dimensional space to form the three-dimensional coordinates of the patient's meibomian gland surface, providing solid data support for subsequent massage device path planning and precise massage, and significantly improving the accuracy and efficiency of patient meibomian gland display and identification massage.

[0020] In a preferred embodiment, this application can be further configured such that: the generation of the massage motion trajectory of the massager using an adaptive path planning algorithm specifically includes:

[0021] Based on the three-dimensional coordinates, a set of effective massage area boundary points is selected, and a surface is fitted to the set of effective massage area boundary points to generate an initial path;

[0022] Meibomian gland point set information is obtained based on the effective massage area boundary point set, and the meibomian gland region is obtained based on the meibomian gland point set information.

[0023] The initial path is optimized based on the meibomian gland region to eliminate redundant points on the initial path, thus forming the massage motion trajectory of the massager.

[0024] By adopting the above technical solution, the effective massage area boundary point set is screened based on three-dimensional coordinates, and surface fitting is performed to generate an initial path. This ensures that the massager can move along a path that conforms to the shape of the patient's meibomian gland surface, improving the accuracy of the massage and the flatness of the massage surface. By acquiring the meibomian gland point set information and converting it into the meibomian gland region, potential obstacles in the massage process, such as protrusions, depressions, or other irregular shapes, can be fully identified and avoided, thereby preventing collisions and damage to the massager. The initial path is first optimized based on the meibomian gland region, eliminating redundant points on the path to form the final massager movement trajectory. This ensures the continuity and smoothness of the massage process, continuously massaging the meibomian gland region, greatly improving massage efficiency and reducing unnecessary pauses and adjustments.

[0025] In a preferred embodiment, this application can be further configured such that: the step of generating the massage motion trajectory of the massager using an adaptive path planning algorithm further includes:

[0026] Based on the three-dimensional coordinates, a set of effective massage area boundary points is selected, and a surface is fitted to the set of effective massage area boundary points to generate an initial path;

[0027] Obtain stiffness data of the patient's meibomian glands, obtain the elastic modulus based on the stiffness data of the patient's meibomian glands, and set the minimum radius of curvature according to the elastic modulus;

[0028] The initial path is optimized a second time based on the minimum radius of curvature, and the speed of the end effector of the massager is adjusted to generate a massage motion trajectory.

[0029] By adopting the above technical solution, the stiffness of the patient's meibomian glands has a certain influence on the movement of the massager during the generation of the massager's motion trajectory. Therefore, by obtaining the stiffness data of the patient's meibomian glands and calculating the elastic modulus accordingly, the physical properties of the meibomian glands themselves are taken into account, providing a scientific basis for path planning. By setting the minimum radius of curvature based on the elastic modulus, stress concentration and potential damage to the patient's meibomian glands caused by excessively rapid changes in the massager's motion path during the massage process are effectively avoided, thus protecting the safety of the patient's meibomian glands. The initial path is further optimized based on the minimum radius of curvature, and the end speed of the massager is adjusted to ensure the stability and continuity of the massage process, further improving massage efficiency and ensuring massage quality.

[0030] In a preferred embodiment, this application can be further configured such that: inputting the massager parameter data into a preset massager control model and outputting massage time and massage light intensity specifically includes:

[0031] The duration of the massage light is obtained based on the parameter data of the massager, and the massage time is calculated based on the duration of the massage light.

[0032] The massage environment temperature is obtained, massage light intensity compensation data is obtained based on the massage environment temperature, and the massage light intensity is adjusted according to the massage light intensity compensation data.

[0033] By adopting the above technical solution, the duration of massage light is obtained based on the massager parameter data, and the massage time is calculated accordingly. This ensures precise control of the massager's supply, avoiding poor massage effects caused by improper massage time. Considering the impact of ambient temperature on the massager's performance, the ambient temperature is obtained, and massage light intensity compensation data is acquired accordingly. This allows the massage light intensity to be intelligently adjusted according to the actual environment, ensuring the stability and consistency of massage quality. Timely adjustment of massage light intensity based on the massage light intensity compensation data further improves massage efficiency, ensuring uniform distribution and good adhesion of the massager on the patient's meibomian glands. Precise control of massage time and massage light intensity is achieved, significantly improving massage quality. Through the coordinated control of dynamic compensation of massager parameters and multi-source environmental sensing, high-quality meibomian gland massage is realized.

[0034] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions:

[0035] An AI-based intelligent control system for a meibomian gland massager, comprising:

[0036] The coordinate construction module is used to acquire three-dimensional point cloud data of the patient's meibomian gland surface, extract feature points based on the three-dimensional point cloud data of the patient's meibomian gland surface and construct its three-dimensional coordinates.

[0037] The trajectory generation module is used to generate the massage motion trajectory of the massager based on the three-dimensional coordinates using an adaptive path planning algorithm.

[0038] The massage parameter acquisition module is used to acquire the patient's meibomian gland image data and retrieve the massager parameter data from a preset feature database based on the patient's meibomian gland image data.

[0039] The massage parameter control module is used to input the massager parameter data into a preset control model, output the massage time and massage light intensity, and control the massager to massage the patient's meibomian glands according to the massage time and massage light intensity.

[0040] By employing the aforementioned technical solution, during the massage of the patient's meibomian glands and the displayed markers, high-precision imaging equipment is used to acquire real-time three-dimensional point cloud data of the patient's meibomian gland surface. Feature extraction and identification of key features such as surface inflection points and boundary contours are performed on the three-dimensional point cloud data, and a three-dimensional coordinate system of the patient's meibomian gland surface is constructed. This effectively solves the feature recognition error problem caused by surface reflection in traditional positioning, laying a precise spatial benchmark for subsequent path planning. This not only ensures that the massager can accurately identify and locate every minute position on the patient's meibomian glands, but also lays a solid foundation for achieving high-precision massage motion trajectory planning, improving the accuracy and consistency of the massage path. By utilizing the three-dimensional coordinates of the patient's meibomian gland surface and combining them with an adaptive path planning algorithm, the complexity and diversity of the patient's meibomian gland surface are fully considered. The movement path can be flexibly adjusted according to the actual situation, thereby effectively improving massage efficiency while ensuring massage quality. By acquiring the patient's meibomian gland image data and retrieving the massager parameter data from the preset characteristic database, the massage time and massage light intensity can be precisely controlled. The different requirements of different patients' meibomian glands for massage parameters are considered, ensuring uniform distribution and good solidification of the massager during the massage process, thereby improving the massage effect and achieving high-precision, high-efficiency, and high-quality massage of the patient's meibomian glands.

[0041] Thirdly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0042] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described AI-based intelligent control method for a meibomian gland massager.

[0043] Fourthly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0044] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described AI-based intelligent control method for a meibomian gland massager.

[0045] In summary, this application includes at least one of the following beneficial technical effects:

[0046] 1. By using high-precision imaging equipment, real-time three-dimensional point cloud data of the patient's meibomian gland surface is acquired. Key features such as curved inflection points and boundary contours are extracted and identified from the three-dimensional point cloud data, and three-dimensional coordinates of the patient's meibomian gland surface are constructed. This effectively solves the feature recognition error problem caused by surface reflection in traditional positioning, laying a precise spatial benchmark for subsequent path planning. This not only ensures that the massager can accurately identify and locate every minute position on the patient's meibomian gland, but also lays a solid foundation for high-precision massage motion trajectory planning, improving the accuracy and consistency of the massage path. By using the three-dimensional coordinates of the patient's meibomian gland surface and combining them with an adaptive path planning algorithm, the complexity and diversity of the patient's meibomian gland surface are fully considered. The motion path can be flexibly adjusted according to the actual situation, thereby effectively improving massage efficiency while ensuring massage quality. By acquiring the patient's meibomian gland image data and retrieving the massager parameter data from the preset characteristic database, precise control of massage time and massage light intensity is achieved. The different requirements of different patients' meibomian glands for massage parameters are considered, ensuring uniform distribution and good solidification of the massager during the massage process, thereby improving the massage effect and achieving high-precision, high-efficiency, and high-quality massage of the patient's meibomian gland.

[0047] 2. During the massage process, based on the adjusted massage time and light intensity, pressure data of the patient's meibomian gland contact surface is acquired through a pressure sensor array. Based on the pressure data, a pressure field deviation is constructed to accurately identify uneven pressure distribution during the massage process. Based on the pressure deviation matrix, a PID control signal is generated. By dynamically adjusting the pressure parameters, such as the pressure of the massager and the duration of action, the massage process is precisely controlled until the pressure data of the patient's meibomian gland contact surface stably meets the preset pressure threshold. This ensures the uniformity and firmness of the massage, further improving the massage quality and reducing poor massage effects caused by uneven pressure.

[0048] 3. Based on three-dimensional coordinates, the effective massage area boundary point set is selected and surface fitting is performed to generate an initial path. This ensures that the massager can move along a path that conforms to the shape of the patient's meibomian gland surface, improving the accuracy of the massage and the flatness of the massage surface. By acquiring meibomian gland point set information and converting it into meibomian gland region, potential obstacles in the massage process, such as protrusions, depressions, or other irregular shapes, can be fully identified and avoided, thereby preventing collisions and damage to the massager. The initial path is first optimized based on the meibomian gland region, eliminating redundant points on the path to form the final massager movement trajectory. This ensures the continuity and smoothness of the massage process, and the massager will always be performed in the meibomian gland region, greatly improving massage efficiency and reducing unnecessary pauses and adjustments.

[0049] 4. By obtaining the duration of massage light based on the massager's parameter data and calculating the massage time accordingly, precise control of the massager's supply is ensured, avoiding poor massage effects caused by improper massage time. Considering the impact of ambient temperature on the massager's performance, the ambient temperature is obtained, and massage light intensity compensation data is acquired accordingly. This allows the massage light intensity to be intelligently adjusted according to the actual environment, thus ensuring the stability and consistency of massage quality. Timely adjustment of massage light intensity based on the massage light intensity compensation data further improves massage efficiency, ensuring uniform distribution and good adhesion of the massager on the patient's meibomian glands. Precise control of massage time and massage light intensity is achieved, significantly improving massage quality. Through the coordinated control of dynamic compensation of massager parameters and multi-source environmental sensing, high-quality meibomian gland massage is realized. Attached Figure Description

[0050] Figure 1 This is a flowchart of an AI-based intelligent control method for a meibomian gland massager according to one embodiment of this application;

[0051] Figure 2 This is another implementation flowchart of the intelligent control method for a meibomian gland massager based on AI recognition in one embodiment of this application;

[0052] Figure 3 This is a flowchart illustrating the implementation of step S10 in an AI-based intelligent control method for a meibomian gland massager according to an embodiment of this application.

[0053] Figure 4 This is a flowchart illustrating the implementation of step S20 in an AI-based intelligent control method for a meibomian gland massager according to an embodiment of this application.

[0054] Figure 5 This is another implementation flowchart of step S20 in the AI-based intelligent control method for a meibomian gland massager in one embodiment of this application;

[0055] Figure 6 This is a flowchart illustrating the implementation of step S40 in an AI-based intelligent control method for a meibomian gland massager according to an embodiment of this application.

[0056] Figure 7 This is a block diagram of the intelligent control system for a meibomian gland massager based on AI recognition in one embodiment of this application;

[0057] Figure 8 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0058] The present application will be further described in detail below with reference to the accompanying drawings.

[0059] In one embodiment, such as Figure 1 As shown, this application discloses an intelligent control method for a meibomian gland massager based on AI recognition, which specifically includes the following steps:

[0060] S10: Obtain three-dimensional point cloud data of the patient's meibomian gland surface, extract feature points based on the three-dimensional point cloud data of the patient's meibomian gland surface, and construct its three-dimensional coordinates.

[0061] Specifically, during the automated massage of the patient's meibomian glands and the displayed markers, high-precision imaging equipment, such as a binocular vision camera, is used to acquire real-time three-dimensional point cloud data of the patient's meibomian gland surface at a resolution of 0.01mm. Feature extraction and identification of key features such as surface inflection points and boundary contours are performed on the three-dimensional point cloud data, and three-dimensional coordinates of the patient's meibomian gland surface are constructed. This effectively solves the feature recognition error problem caused by surface reflection in traditional positioning, and lays a precise spatial benchmark for subsequent path planning. This not only ensures that the massager can accurately identify and locate every minute position on the patient's meibomian glands, but also lays a solid foundation for achieving high-precision massage motion trajectory planning.

[0062] S20: Based on the three-dimensional coordinates, an adaptive path planning algorithm is used to generate the massage motion trajectory of the massager.

[0063] Specifically, by utilizing the three-dimensional coordinates of the patient's meibomian gland surface and combining them with an adaptive path planning algorithm, taking into account the complexity and diversity of the patient's meibomian gland surface, the motion path can be flexibly adjusted according to the actual situation to generate the optimal motion trajectory of the massager.

[0064] S30: Acquire patient meibomian gland image data, and retrieve massager parameter data from a preset feature database based on the patient meibomian gland image data.

[0065] Specifically, patient meibomian gland image data refers to the basic properties of the patient's meibomian glands, such as surface tension and stiffness. Using the obtained patient meibomian gland image data, massager parameter data is matched through a characteristic database, taking into account the different requirements of different patients' meibomian glands for massage parameters, so as to facilitate the dynamic control of massage time and parameters.

[0066] S40: Input the massager parameter data into the preset control model, output the massage time and massage light intensity, and control the massager to massage the display marks on the patient's meibomian glands according to the massage time and massage light intensity.

[0067] Specifically, the obtained massager parameter data is input into a preset control model. The control model calculates the massage time and massage light intensity based on the massager parameter data, ensuring the uniformity of the massager during the massage process and thus improving the massage effect.

[0068] In this embodiment, during the massage of the patient's meibomian glands and the displayed markers, high-precision imaging equipment is used to acquire real-time three-dimensional point cloud data of the patient's meibomian gland surface. Feature extraction is performed on the three-dimensional point cloud data to identify key features such as surface inflection points and boundary contours, and a three-dimensional coordinate system of the patient's meibomian gland surface is constructed. This effectively solves the feature recognition error problem caused by surface reflection in traditional positioning, laying a precise spatial benchmark for subsequent path planning. This not only ensures that the massager can accurately identify and locate every minute position on the patient's meibomian glands, but also lays a solid foundation for achieving high-precision massage motion trajectory planning, improving the accuracy and consistency of the massage path. Using the three-dimensional coordinates of the patient's meibomian gland surface, combined with an adaptive path planning algorithm, the complexity and diversity of the meibomian gland surface are fully considered. This allows for flexible adjustment of the movement path based on actual conditions, effectively improving massage efficiency while ensuring massage quality. By acquiring image data of the patient's meibomian glands and retrieving massager parameter data from a pre-set characteristic database, precise control of massage time and light intensity is achieved. The different requirements of different patients' meibomian glands for massage parameters are considered, ensuring uniform distribution and good fixation of the massager during the massage process, thereby improving the massage effect and achieving high-precision, high-efficiency, and high-quality massage of the patient's meibomian glands.

[0069] In one embodiment, such as Figure 2 As shown, after step S40, the AI-based intelligent control method for meibomian gland massagers further includes:

[0070] S50: Obtain the pressure data of the patient's meibomian gland contact surface, and construct a pressure field deviation matrix based on the pressure data of the patient's meibomian gland contact surface.

[0071] Specifically, during the massage process, based on the adjusted massage time and light intensity, pressure data of the contact surface of the patient's meibomian glands is acquired through a pressure sensor array. Based on this pressure data, a pressure field deviation is constructed to accurately identify uneven pressure distribution during the massage process.

[0072] σ is the standard deviation of pressure, and μ is the average pressure.

[0073] S60: Based on the pressure field deviation matrix, generate a PID control signal, and in response to the PID control signal, adjust the compression parameters until the pressure data of the patient's meibomian gland contact surface meets the preset pressure threshold.

[0074] Specifically, when U < 0.9, a PID control signal is generated. By dynamically adjusting the pressure parameters, such as the pressure of the massager and the duration of action, the massage process is precisely controlled until the pressure data of the patient's meibomian gland contact surface stably meets the preset pressure threshold. This ensures the uniformity and firmness of the massage, reduces the poor massage effect caused by uneven pressure, and enhances the controllability and stability of the massage process.

[0075] In one embodiment, such as Figure 3 As shown, in step S10, the three-dimensional point cloud data of the patient's meibomian gland surface is acquired, and feature points are extracted and their three-dimensional coordinates are constructed based on the three-dimensional point cloud data of the patient's meibomian gland surface. Specifically, this includes:

[0076] S11: Obtain an image of the patient's meibomian gland surface, and extract edge information and feature point information based on the image of the patient's meibomian gland surface.

[0077] Specifically, a binocular vision camera was used to acquire images of the patient's meibomian gland surface at a resolution of 0.01 mm. Infrared structured light was used to compensate for reflection interference, and the SIFT feature point extraction algorithm was used to identify the edge and positioning hole of the patient's meibomian gland, capturing the contour details and key geometric features of the patient's meibomian gland surface, providing an accurate information basis for subsequent three-dimensional coordinate construction.

[0078] S12: Convert the edge information and feature point information into three-dimensional spatial coordinates to form the three-dimensional coordinates of the patient's meibomian gland surface.

[0079] Specifically, based on the extracted edge information and feature point information, it is mapped into three-dimensional space to form the three-dimensional coordinates of the patient's meibomian gland surface, providing solid data support for subsequent massage device path planning and precise massage.

[0080] In one embodiment, such as Figure 4 As shown, in step S20, the adaptive path planning algorithm is used to generate the massage motion trajectory of the massager, which specifically includes:

[0081] S21: Based on the three-dimensional coordinates, select the effective massage area boundary point set, perform surface fitting on the effective massage area boundary point set, and generate an initial path.

[0082] Specifically, the process of selecting the effective massage area boundary point set based on three-dimensional coordinates uses a spatial curvature continuity detection algorithm to remove abnormal points caused by surface defects or assembly errors, and generates an initial path through surface fitting to ensure the matching degree between the trajectory geometry and the actual contour of the patient's meibomian glands.

[0083] S22: Obtain meibomian gland point set information based on the effective massage area boundary point set, and obtain the meibomian gland region based on the meibomian gland point set information.

[0084] Specifically, by acquiring meibomian gland point set information and converting it into meibomian gland regions, it is possible to comprehensively identify and avoid potential obstacles during the massage process, such as protrusions, depressions, or other irregular shapes, thereby preventing collisions and damage to the massager.

[0085] S23: The initial path is optimized based on the meibomian gland region to eliminate redundant points on the initial path and form the massage motion trajectory of the massager.

[0086] Specifically, the initial path is optimized based on the meibomian gland region, eliminating redundant points along the path to form the final massager movement trajectory. This ensures the continuity and smoothness of the massage process, greatly improves massage efficiency, and reduces unnecessary pauses and adjustments.

[0087] In one embodiment, such as Figure 5 As shown, in step S20, which involves generating the massage motion trajectory of the massager using an adaptive path planning algorithm, the following steps are also included:

[0088] S24: Based on the three-dimensional coordinates, select the effective massage area boundary point set, perform surface fitting on the effective massage area boundary point set, and generate an initial path.

[0089] S25: Obtain stiffness data of the patient's meibomian glands, obtain the elastic modulus based on the stiffness data of the patient's meibomian glands, and set the minimum radius of curvature according to the elastic modulus.

[0090] Specifically, by dynamically acquiring the stiffness data of the patient's meibomian glands, and accurately measuring the elastic modulus by combining the three-point bending test or ultrasonic elastic wave detection technology, a quantitative relationship model between the material's bending stiffness and the minimum allowable radius of curvature is established. Based on the elastic modulus, the minimum radius of curvature is set, which effectively avoids stress concentration and potential damage to the patient's meibomian glands caused by the massager's movement path changing too drastically during the massage process, thus protecting the patient's meibomian gland safety.

[0091] S26: Based on the minimum radius of curvature, perform a second optimization on the initial path, adjust the speed of the end effector of the massager, and generate a massage motion trajectory.

[0092] Specifically, in the second optimization stage of the path based on the minimum radius of curvature constraint, an adaptive speed planning algorithm is adopted to automatically reduce the movement speed of the end of the massager in areas where the radius of curvature is less than the threshold (such as at acute angles), ensuring the smoothness and continuity of the massage process, further improving massage efficiency, and guaranteeing massage quality.

[0093] In one embodiment, such as Figure 6As shown, in step S40, the massager parameter data is input into a preset massager control model, and the massage time and massage light intensity are output, specifically including:

[0094] S41: Obtain the duration of the massage light based on the parameter data of the massager, and calculate the massage time based on the duration of the massage light.

[0095] Specifically, by obtaining the duration of the massage light based on the massager's parameter data and calculating the massage time accordingly, the precise control of the massager's supply is ensured, avoiding poor massage effects caused by improper massage time. The influence of ambient temperature on the massager's performance is also taken into account.

[0096] S42: Obtain the massage environment temperature, obtain massage light intensity compensation data based on the massage environment temperature, and adjust the massage light intensity according to the massage light intensity compensation data.

[0097] Specifically, considering the impact of ambient temperature on the performance of the massage device, the ambient temperature is obtained and massage light intensity compensation data is acquired accordingly. This allows the massage light intensity to be intelligently adjusted according to the actual environment, thereby ensuring the stability and consistency of massage quality. The massage light intensity is adjusted in a timely manner based on the massage light intensity compensation data, further improving massage efficiency and ensuring the massage effect on the patient's meibomian glands.

[0098] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0099] In one embodiment, an AI-based intelligent control system for a meibomian gland massager is provided, which corresponds one-to-one with the AI-based intelligent control method for a meibomian gland massager described in the above embodiments. For example... Figure 7 As shown, the AI-based meibomian gland massager intelligent control system includes a coordinate construction module, a trajectory generation module, a massage parameter acquisition module, and a massage parameter adjustment module. Detailed descriptions of each functional module are as follows:

[0100] The coordinate construction module is used to acquire three-dimensional point cloud data of the patient's meibomian gland surface, extract feature points based on the three-dimensional point cloud data of the patient's meibomian gland surface and construct its three-dimensional coordinates.

[0101] The trajectory generation module is used to generate the massage motion trajectory of the massager based on the three-dimensional coordinates using an adaptive path planning algorithm.

[0102] The massage parameter acquisition module is used to acquire the patient's meibomian gland image data and retrieve the massager parameter data from a preset feature database based on the patient's meibomian gland image data.

[0103] The massage parameter control module is used to input the massager parameter data into a preset control model, output the massage time and massage light intensity, and control the massager to massage the display marks on the patient's meibomian glands according to the massage time and massage light intensity.

[0104] Preferred options also include:

[0105] The massage pressure monitoring module is used to acquire the pressure data of the patient's meibomian gland contact surface and to construct a pressure field deviation matrix based on the pressure data of the patient's meibomian gland contact surface.

[0106] The compression control module is used to generate a PID control signal based on the pressure field deviation matrix, and adjust the compression parameters in response to the PID control signal until the pressure data of the patient's meibomian gland contact surface meets the preset pressure threshold.

[0107] Specific limitations regarding the AI-based intelligent control system for meibomian gland massagers can be found in the above description of the intelligent control method for AI-based meibomian gland massagers, and will not be repeated here. Each module in the aforementioned AI-based intelligent control system for meibomian gland massagers can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the electronic device, or stored in the memory of the electronic device as software, so that the processor can call and execute the corresponding operations of each module.

[0108] In one embodiment, an electronic device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown, the electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores patient meibomian gland image data and massager parameters. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements an AI-based intelligent control method for the meibomian gland massager.

[0109] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0110] Acquire three-dimensional point cloud data of the patient's meibomian gland surface, extract feature points based on the three-dimensional point cloud data of the patient's meibomian gland surface and construct its three-dimensional coordinates;

[0111] Based on the three-dimensional coordinates, an adaptive path planning algorithm is used to generate the massage motion trajectory of the massager;

[0112] Acquire patient meibomian gland image data, and retrieve massager parameter data from a preset feature database based on the patient meibomian gland image data;

[0113] The massager parameter data is input into a preset control model, and the massage time and massage light intensity are output. Based on the massage time and massage light intensity, the massager is controlled to massage the display marks on the patient's meibomian glands.

[0114] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0115] Acquire three-dimensional point cloud data of the patient's meibomian gland surface, extract feature points based on the three-dimensional point cloud data of the patient's meibomian gland surface and construct its three-dimensional coordinates;

[0116] Based on the three-dimensional coordinates, an adaptive path planning algorithm is used to generate the massage motion trajectory of the massager;

[0117] Acquire patient meibomian gland image data, and retrieve massager parameter data from a preset feature database based on the patient meibomian gland image data;

[0118] The massager parameter data is input into a preset control model, and the massage time and massage light intensity are output. Based on the massage time and massage light intensity, the massager is controlled to massage the display marks on the patient's meibomian glands.

[0119] 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. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0120] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0121] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. 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 spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An intelligent control system for a meibomian gland massager based on AI recognition, characterized in that, The AI-based meibomian gland massager intelligent control system includes: The coordinate construction module is used to acquire three-dimensional point cloud data of the patient's meibomian gland surface, extract feature points based on the three-dimensional point cloud data of the patient's meibomian gland surface, and construct its three-dimensional coordinates, specifically including: Obtain an image of the patient's meibomian gland surface, and extract edge information and feature point information based on the image of the patient's meibomian gland surface; The edge information and feature point information are converted into three-dimensional spatial coordinates to form the three-dimensional coordinates of the patient's meibomian gland surface. The trajectory generation module is used to generate the massage motion trajectory of the massager based on the three-dimensional coordinates using an adaptive path planning algorithm, specifically including: Based on the three-dimensional coordinates, a set of effective massage area boundary points is selected, and a surface is fitted to the set of effective massage area boundary points to generate an initial path; Meibomian gland point set information is obtained based on the effective massage area boundary point set, and the meibomian gland region is obtained based on the meibomian gland point set information. The initial path is first optimized based on the meibomian gland region to eliminate redundant points on the initial path and form the massage motion trajectory of the massager. Obtain stiffness data of the patient's meibomian glands, obtain the elastic modulus based on the stiffness data of the patient's meibomian glands, and set the minimum radius of curvature according to the elastic modulus; The initial path is optimized a second time based on the minimum radius of curvature, and the speed of the end effector of the massager is adjusted. The massage parameter acquisition module is used to acquire the patient's meibomian gland image data and retrieve the massager parameter data from a preset feature database based on the patient's meibomian gland image data. The massage parameter control module is used to input the massager parameter data into a preset control model, output the massage time and massage light intensity, and control the massager to massage the patient's meibomian glands according to the massage time and massage light intensity.

2. The intelligent control system for the meibomian gland massager based on AI recognition according to claim 1, characterized in that, Also includes: The pressure monitoring module is used to acquire pressure data of the patient's meibomian gland contact surface and construct a pressure field deviation matrix based on the pressure data of the patient's meibomian gland contact surface. The massage control module is used to generate a PID control signal based on the pressure field deviation matrix, and adjust the pressure parameters in response to the PID control signal until the pressure data of the patient's meibomian gland contact surface meets the preset pressure threshold.

3. The intelligent control system for the meibomian gland massager based on AI recognition according to claim 1, characterized in that, The step of inputting the massager parameter data into a preset control model and outputting the massage time and massage light intensity specifically includes: The duration of the massage light is obtained based on the parameter data of the massager, and the massage time is calculated based on the duration of the massage light. The massage environment temperature is obtained, massage light intensity compensation data is obtained based on the massage environment temperature, and the massage light intensity is adjusted according to the massage light intensity compensation data.

4. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements an AI-based intelligent control method for a meibomian gland massager, which includes the following control steps: Obtain three-dimensional point cloud data of the patient's meibomian gland surface, extract feature points from the three-dimensional point cloud data of the patient's meibomian gland surface and construct its three-dimensional coordinates, specifically including: Obtain an image of the patient's meibomian gland surface, and extract edge information and feature point information based on the image of the patient's meibomian gland surface; The edge information and feature point information are converted into three-dimensional spatial coordinates to form the three-dimensional coordinates of the patient's meibomian gland surface. Based on the aforementioned three-dimensional coordinates, an adaptive path planning algorithm is used to generate the massage motion trajectory of the massager, specifically including: Based on the three-dimensional coordinates, a set of effective massage area boundary points is selected, and a surface is fitted to the set of effective massage area boundary points to generate an initial path; Meibomian gland point set information is obtained based on the effective massage area boundary point set, and the meibomian gland region is obtained based on the meibomian gland point set information. The initial path is first optimized based on the meibomian gland region to eliminate redundant points on the initial path and form the massage motion trajectory of the massager. Obtain stiffness data of the patient's meibomian glands, obtain the elastic modulus based on the stiffness data of the patient's meibomian glands, and set the minimum radius of curvature according to the elastic modulus; The initial path is optimized a second time based on the minimum radius of curvature, and the speed of the end effector of the massager is adjusted. Acquire patient meibomian gland image data, and retrieve massager parameter data from a preset feature database based on the patient meibomian gland image data; The massager parameter data is input into a preset control model, and the massage time and massage light intensity are output. Based on the massage time and massage light intensity, the massager is controlled to massage the patient's meibomian glands.

5. An electronic device according to claim 4, characterized in that, After inputting the massager parameter data into a preset control model, outputting the massage time and massage light intensity, and controlling the massager to massage the patient's meibomian glands according to the massage time and massage light intensity, the AI-based intelligent control method for meibomian gland massagers further includes: Acquire pressure data of the patient's meibomian gland contact surface, and construct a pressure field deviation matrix based on the patient's meibomian gland contact surface pressure data; Based on the pressure field deviation matrix, a PID control signal is generated. In response to the PID control signal, the compression parameters are adjusted until the pressure data of the patient's meibomian gland contact surface meets the preset pressure threshold.

6. An electronic device according to claim 4, characterized in that, The step of inputting the massager parameter data into a preset control model and outputting the massage time and massage light intensity specifically includes: The duration of the massage light is obtained based on the parameter data of the massager, and the massage time is calculated based on the duration of the massage light. The massage environment temperature is obtained, massage light intensity compensation data is obtained based on the massage environment temperature, and the massage light intensity is adjusted according to the massage light intensity compensation data.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements an AI-based intelligent control method for a meibomian gland massager, which includes the following control steps: Obtain three-dimensional point cloud data of the patient's meibomian gland surface, extract feature points from the three-dimensional point cloud data of the patient's meibomian gland surface and construct its three-dimensional coordinates, specifically including: Obtain an image of the patient's meibomian gland surface, and extract edge information and feature point information based on the image of the patient's meibomian gland surface; The edge information and feature point information are converted into three-dimensional spatial coordinates to form the three-dimensional coordinates of the patient's meibomian gland surface. Based on the aforementioned three-dimensional coordinates, an adaptive path planning algorithm is used to generate the massage motion trajectory of the massager, specifically including: Based on the three-dimensional coordinates, a set of effective massage area boundary points is selected, and a surface is fitted to the set of effective massage area boundary points to generate an initial path; Meibomian gland point set information is obtained based on the effective massage area boundary point set, and the meibomian gland region is obtained based on the meibomian gland point set information. The initial path is first optimized based on the meibomian gland region to eliminate redundant points on the initial path and form the massage motion trajectory of the massager. Obtain stiffness data of the patient's meibomian glands, obtain the elastic modulus based on the stiffness data of the patient's meibomian glands, and set the minimum radius of curvature according to the elastic modulus; The initial path is optimized a second time based on the minimum radius of curvature, and the speed of the end effector of the massager is adjusted. Acquire patient meibomian gland image data, and retrieve massager parameter data from a preset feature database based on the patient meibomian gland image data; The massager parameter data is input into a preset control model, and the massage time and massage light intensity are output. Based on the massage time and massage light intensity, the massager is controlled to massage the patient's meibomian glands.

8. A computer-readable storage medium according to claim 7, characterized in that, After inputting the massager parameter data into a preset control model, outputting the massage time and massage light intensity, and controlling the massager to massage the patient's meibomian glands according to the massage time and massage light intensity, the AI-based intelligent control method for meibomian gland massagers further includes: Acquire pressure data of the patient's meibomian gland contact surface, and construct a pressure field deviation matrix based on the patient's meibomian gland contact surface pressure data; Based on the pressure field deviation matrix, a PID control signal is generated. In response to the PID control signal, the compression parameters are adjusted until the pressure data of the patient's meibomian gland contact surface meets the preset pressure threshold.

9. A computer-readable storage medium according to claim 7, characterized in that, The step of inputting the massager parameter data into a preset control model and outputting the massage time and massage light intensity specifically includes: The duration of the massage light is obtained based on the parameter data of the massager, and the massage time is calculated based on the duration of the massage light. The massage environment temperature is obtained, massage light intensity compensation data is obtained based on the massage environment temperature, and the massage light intensity is adjusted according to the massage light intensity compensation data.

Images