Medical lighting apparatus, operating table, treatment chair, lighting controller, lighting control method, and lighting control device

Abstract

A medical lighting apparatus, an operating table, a treatment chair, a lighting controller (800), a lighting control method, and a lighting control device, relating to the technical field of medical devices. The lighting control device comprises: an image collection device (101) configured to collect a real-time image of a target treatment area of a patient; a controller (102) configured to determine a target irradiation pose on the basis of the real-time image, generate a control signal of a mechanical arm (103) on the basis of the target irradiation pose, and send the control signal to the mechanical arm (103); and the mechanical arm (103) connected to a lighting device and configured to adjust the pose of the lighting device (24) on the basis of the control signal of the mechanical arm (103), such that the light irradiation direction of the lighting device (24) matches the position of the target treatment area.

Description

Medical lighting device, operating table, treatment chair, and lighting controller, method and equipment

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is based on the application with CN application number 202311152946.5 and application date September 7, 2023, and claims its priority. The disclosed content of the CN application is hereby introduced as a whole into this application. Technical Field

[0003] The present disclosure relates to the technical field of medical equipment, and in particular to a medical lighting device, an operating table, a treatment chair, and a lighting controller, method, and equipment. Background Art

[0004] A shadowless lamp is a specialized device that adjusts the angle of the lamp or polished reflective surface to create a circular illumination pattern. This eliminates shadows or blind spots caused by uneven structures in the illuminated area, creating a uniformly bright image and providing illumination for the treatment area. The lamp is typically mounted above the treatment chair. Before treatment, the lamp is adjusted to the appropriate angle and then fixed in place.

[0005] In current dental treatments, for example, patients lie on a dental instrument while the doctor adjusts the light of a shadowless lamp into their mouth to view the affected area. Because the patient's position may change during treatment, the fixed light cannot accurately illuminate the affected area, requiring the doctor to make real-time adjustments.

[0006] Summary of the Invention

[0007] One object of the present disclosure is to improve the automation level and operation efficiency of equipment.

[0008] According to one aspect of some embodiments of the present disclosure, a lighting control device is proposed, including: an image acquisition device, configured to acquire real-time images in the direction of a target treatment area of ​​a patient; a controller, configured to determine a target irradiation posture based on the real-time image, generate a robotic arm control signal based on the target irradiation posture, and send the signal to the robotic arm; a robotic arm, connected to the lighting device, configured to adjust the posture of the lighting device according to the robotic arm control signal so that the light irradiation direction of the lighting device matches the position of the target treatment area.

[0009] In some embodiments, the image acquisition device includes at least one of a binocular vision sensor or a three-dimensional image acquisition device.

[0010] In some embodiments, the lighting device is fixed to the free end of the robotic arm, and the other end of the robotic arm is a fixed end.

[0011] In some embodiments, the controller is configured to: extract predetermined feature points based on a real-time image of the direction of the patient's target treatment area; determine the three-dimensional coordinates of the predetermined feature points, and determine the three-dimensional point cloud data within a predetermined range of the patient's target treatment area, and update the three-dimensional model; and plan the path of the robotic arm based on the three-dimensional model to generate a robotic arm control signal.

[0012] In some embodiments, the controller is further configured to: obtain the patient's oral and maxillofacial image data; mark the position of the oral and maxillofacial area using a trained machine learning model based on the oral and maxillofacial model data, and obtain the data of the marked model; obtain information of the target treatment area, and determine the position information of the target treatment area in the marked model based on the information of the target treatment area and the data of the marked model, wherein the controller determines the updated position information of the target treatment area in the updated three-dimensional model based on the position information of the target treatment area in the marked model and the updated three-dimensional model, and determines the target irradiation posture based on the updated position information.

[0013] In some embodiments, the target treatment area information includes an identification of a target tooth position.

[0014] In some embodiments, the controller is further configured to: determine the position of the target tool based on the real-time image of the direction of the target treatment area of ​​the patient; and determine the target treatment area based on the position of the target tool.

[0015] In some embodiments, the image acquisition device is further configured to: acquire an environmental image and send it to the controller; the controller is further configured to: generate an environmental model based on the environmental image and determine the initial posture of the robotic arm.

[0016] In some embodiments, the controller is further configured to: determine the position of the obstacle based on the environmental model; plan the motion path of the robotic arm based on the obstacle position and the target illumination posture and update the motion path planning results in real time, wherein the motion path avoids obstacles; and the controller generates a robotic arm control signal based on the motion path planning results.

[0017] In some embodiments, the device also includes: a distance sensor attached to the robotic arm, configured to send a warning message to the controller when an object is detected within a predetermined range; the controller is also configured to control the robotic arm to pause movement when it receives the warning message from the distance sensor.

[0018] In some embodiments, the controller is further configured to send a control signal to the lighting device to adjust at least one of the light source or brightness of the lighting device.

[0019] According to one aspect of some embodiments of the present disclosure, a medical lighting device is proposed, comprising: any one of the lighting control devices described above; and a lighting device configured to adjust its posture and emit light under the control of the lighting control device.

[0020] In some embodiments, the lighting device includes a shadowless lamp.

[0021] In some embodiments, the shadowless lamp includes at least one of an LED light source, a laser light source, an ultraviolet light source, or a near-infrared light source.

[0022] In some embodiments, the lighting control device meets at least one of the following requirements: the laser light source includes at least one of the following: a laser with a wavelength in the range of 405±20nm; or a laser with a wavelength in the range of 390-1600nm and a power in the range of 10 -3 -10 -1 W range, the energy is 10 -2 -10 2 J / cm 2 Laser within the range; ultraviolet wavelength is within the range of 280~320nm, and the irradiation intensity is including 15μW / cm 2 The wavelength of near-infrared light is in the range of 780-2526nm; or the wavelength of visible blue light is in the range of 400-500nm and the intensity is in the range of 750-2000mw / cm 2 Within range

[0023] According to one aspect of some embodiments of the present disclosure, an operating table is provided, comprising: a patient support mechanism configured to support a patient; and any one of the medical lighting devices mentioned above, configured to emit light to a target treatment area of ​​the patient.

[0024] According to one aspect of some embodiments of the present disclosure, a dental treatment chair is provided, comprising: a dental chair configured to support a patient; and any one of the medical lighting devices mentioned above, configured to emit light to a target treatment area of ​​the patient's oral and maxillofacial area.

[0025] According to one aspect of some embodiments of the present disclosure, a lighting control method is proposed, including: acquiring a real-time image of the direction of a target treatment area of ​​a patient; determining a target irradiation posture based on the real-time image, generating a robotic arm control signal based on the target irradiation posture, and sending the signal to the robotic arm so as to adjust the posture of the lighting device according to the robotic arm control signal so that the light irradiation direction of the lighting device matches the position of the target treatment area.

[0026] In some embodiments, determining the target irradiation posture based on the real-time image, and generating a robotic arm control signal based on the target irradiation posture include: extracting predetermined feature points based on the real-time image in the direction of the patient's target treatment area; determining the three-dimensional coordinates of the predetermined feature points, and determining the three-dimensional point cloud data within a predetermined range of the patient's target treatment area, and updating the three-dimensional model; and planning the path of the robotic arm based on the updated three-dimensional model to generate a robotic arm control signal.

[0027] In some embodiments, the method also includes: obtaining the patient's oral and maxillofacial model data; labeling the oral and maxillofacial area position based on the trained machine learning model according to the oral and maxillofacial model data, and obtaining the data of the labeled model; obtaining information of the target treatment area, and determining the position information of the target treatment area in the labeled model according to the information of the target treatment area and the data of the labeled model; determining the target irradiation posture according to the real-time image includes: determining the updated position information of the target treatment area in the updated three-dimensional model according to the updated three-dimensional model and the position information of the target treatment area in the labeled model; determining the target irradiation posture according to the updated position information.

[0028] In some embodiments, determining the target irradiation posture based on the real-time image, and generating a robotic arm control signal based on the target irradiation posture also includes: determining the position of the target tool based on the real-time image of the direction of the patient's target treatment area; and determining the target treatment area based on the position of the target tool.

[0029] In some embodiments, the method further includes: acquiring an environmental image captured by an image acquisition device; generating an environmental model based on the environmental image, and determining an initial posture of the robotic arm, wherein the image acquisition device includes at least one of a binocular vision sensor or a three-dimensional image acquisition device; generating a robotic arm control signal based on the target illumination posture includes: determining the target posture of the robotic arm based on the target illumination posture and the current posture of the robotic arm, and generating a robotic arm control signal based on the target posture of the robotic arm.

[0030] In some embodiments, the method also includes determining the obstacle position based on the environmental model; generating a robotic arm control signal based on the target illumination posture includes: planning a motion path for the robotic arm based on the obstacle position and the target illumination posture, wherein the motion path avoids the obstacle position; and generating a robotic arm control signal based on the motion path planning result.

[0031] In some embodiments, the method further includes: controlling the robotic arm to suspend movement upon receiving a warning message from a distance sensor, wherein the distance sensor is attached to the robotic arm and sends a warning message to the controller upon detecting an object within a predetermined range.

[0032] In some embodiments, the method further includes: the controller sending a control signal to the lighting device to adjust at least one of the light source or brightness of the lighting device.

[0033] In some embodiments, the method further includes: obtaining oral and maxillofacial model sample data, wherein the oral and maxillofacial model sample data includes annotations of the positions of various areas of the oral and maxillofacial region; and training a machine learning model using the oral and maxillofacial model sample data until the training is completed.

[0034] In some embodiments, the method further includes: acquiring an image of the target tool and treatment position information on the target tool to identify the target tool based on the real-time image, and determining a target treatment area based on the treatment position information on the target tool.

[0035] According to one aspect of some embodiments of the present disclosure, a lighting controller is provided, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute any one of the above lighting control methods based on instructions stored in the memory.

[0036] According to one aspect of some embodiments of the present disclosure, a non-transitory computer-readable storage medium is provided, on which computer program instructions are stored. When the instructions are executed by a processor, the steps of any one of the above lighting control methods are implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings described herein are used to provide further understanding of the present disclosure and constitute a part of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute improper limitations on the present disclosure.

[0038] FIG1 is a schematic diagram of some embodiments of a lighting control device according to the present disclosure.

[0039] FIG2 is a schematic diagram of some embodiments of the medical lighting device disclosed herein.

[0040] FIG3 is a schematic diagram of some embodiments of the operating table of the present disclosure.

[0041] FIG. 4 is a schematic diagram of some embodiments of the dental chair of the present disclosure.

[0042] FIG5 is a flow chart of some embodiments of the lighting control method disclosed herein.

[0043] FIG6 is a flow chart of other embodiments of the lighting control method disclosed herein.

[0044] FIG. 7 is a schematic diagram of some embodiments of a lighting controller according to the present disclosure.

[0045] FIG8 is a schematic diagram of other embodiments of the lighting controller disclosed herein. DETAILED DESCRIPTION

[0046] The technical solution of the present disclosure is further described in detail below through the accompanying drawings and embodiments.

[0047] The inventors found that when using the methods in the related art, the doctor needs to adjust the shadowless lamp multiple times during the treatment process, which prolongs the operation time and the patient's pain and reduces the efficiency of the operation.

[0048] To address the above issues, the present disclosure proposes a medical lighting device, an operating table, a treatment chair, and a lighting controller, method, and equipment, so that the lighting equipment can adaptively adjust the irradiation position, reduce the manual work required by doctors, and improve operational efficiency.

[0049] A schematic diagram of some embodiments of the lighting control device disclosed herein is shown in FIG1 .

[0050] The image acquisition device 101 is capable of acquiring real-time images in the direction of the patient's target treatment area. In some embodiments, the image acquisition device 101 is a device capable of acquiring three-dimensional information, such as a binocular vision sensor, and the real-time image includes a two-dimensional image obtained by photographing a corresponding area from two positions (angles), or a three-dimensional image obtained based on two-dimensional image processing. The image acquisition device can also be a three-dimensional image acquisition device capable of acquiring two-dimensional images and depth information, and the real-time image includes a two-dimensional image and depth information of at least some pixel positions in the image. In some embodiments, a binocular vision sensor and a three-dimensional image acquisition device can also be used in combination. In some embodiments, the image acquisition device includes at least one of a monocular vision sensor, a binocular vision sensor, a multi-eye vision sensor, and a three-dimensional image acquisition device.

[0051] In some embodiments, the installation position of the image acquisition device 101 is equivalent to the position of the lighting device (such as a shadowless lamp), thereby improving the accuracy of adjusting the position of the lighting device.

[0052] The image acquisition device 101 is connected to the controller 102 via a wired or wireless signal connection. The controller 102 obtains the real-time image captured by the image acquisition device 101, determines the target irradiation position based on the real-time image, generates a robotic arm control signal based on the target irradiation position, and transmits it to the robotic arm 103. In some embodiments, the target irradiation position is a position determined based on the position of the target treatment area so that the light emitted by the lighting device can illuminate the target treatment area, specifically including the position and posture (orientation) of the lighting device. In some embodiments, the robotic arm control signal can be generated by a robot control system in related art.

[0053] In some embodiments, the controller 102 may include a display device to display the images captured by the image acquisition device 101 and other information that the doctor needs to view through the display device for the doctor to view, thereby further improving operational efficiency.

[0054] Robotic arm 103 is connected to a lighting device. In some embodiments, the lighting device is affixed to the free end of the robotic arm, while the other end of the robotic arm is fixed. The fixed end is fixed to a predetermined location as needed, such as equipment related to the operating table, a wall, or a base. Robotic arm 103 can adjust its shape and end angle based on robotic arm control signals, thereby adjusting the position of the lighting device connected to the end of the robotic arm to align the illumination direction of the lighting device with the location of the target treatment area.

[0055] Such lighting control equipment can identify and track the position changes of the patient's target treatment area, control the robotic arm to adjust the position of the lighting mechanism, so that the light irradiation of the lighting equipment is automatically adjusted as the position of the target treatment area changes, thereby improving the degree of automation of the equipment, eliminating the need for manual adjustment of the equipment, and improving operational efficiency.

[0056] In some embodiments, the robotic arm can avoid obstacles, such as objects placed around it, as well as patients and medical staff, during the process of adjusting its position to ensure safety during surgery.

[0057] In some embodiments, in addition to collecting real-time images of the patient's target treatment area, the image acquisition device 101 can also collect information about the surrounding environment. In some embodiments, the image acquisition device 101 can adjust its own angle at a predetermined period to collect information about the surrounding environment. The controller 102 determines the position and size of surrounding obstacles based on the environmental information, such as by calculating the distance of the object based on the binocular visual image by calculating the parallax between the two perspectives, and determining the position and size of the obstacle based on the distance information and image information. In some embodiments, the image captured by the camera can also be analyzed and processed by image processing technology to detect and identify obstacles in the environment, such as detecting objects on the ground through depth images and distinguishing them from obstacles, thereby improving the efficiency of building environmental maps.

[0058] In some embodiments, after determining the position and size of the obstacle or obtaining the environment map, the controller 102 can plan the movement path of the robotic arm according to a predetermined algorithm (such as a machine learning algorithm). In some embodiments, the predetermined algorithm can be implemented based on artificial intelligence technology, and the network model is trained by learning the characteristics of different obstacles in the environment, thereby realizing the path planning for obstacle avoidance. In some embodiments, the predetermined algorithm can be an intelligent detection method based on a deep learning network, which trains an intelligent reasoning model based on a convolutional neural network based on a constructed optimal path data set (such as obtained by intelligent methods such as the A* algorithm, the Dijkstra algorithm, and the costmap cost map) to predict a high-quality obstacle avoidance path.

[0059] In some embodiments, after the controller 102 generates the motion path of the robotic arm 103, it sends a motion control signal to the robotic arm to control the robotic arm to move according to the motion path. In some embodiments, the controller can control the speed and direction of the robotic arm in real time. In some embodiments, during the motion process, the controller can recalculate the path based on changes in the surrounding environment (such as updates to the position of obstacles) or changes in the patient's position to adjust the motion state in real time, thereby improving the timeliness and safety of posture adjustment.

[0060] In some embodiments, the lighting control device may further include one or more distance sensors located on the robotic arm that can detect the distance to an obstacle. When the distance sensor detects an obstacle within a predetermined range, it sends a signal to the controller, which then controls the robotic arm to stop immediately, thereby improving obstacle avoidance response speed and further enhancing intraoperative safety.

[0061] In some embodiments, the following description takes dental treatment as an example. When the target irradiation area is to be changed, processing can be performed based on the data of the corresponding position.

[0062] In some embodiments, the controller can extract predetermined feature points based on a real-time image of the patient's target treatment area. In some embodiments, the predetermined feature points can include easily identifiable locations inside or outside the oral cavity, such as tooth cusps, lips, or predetermined locations on the gums. Multiple predetermined feature points can be used to improve the accuracy of locating the target treatment area. Furthermore, the controller determines the three-dimensional coordinates of the predetermined feature points and determines three-dimensional point cloud data within a predetermined range of the patient's target treatment area to update the three-dimensional model. In some embodiments, the three-dimensional model can be generated based on the patient's oral and maxillofacial model data and then updated in real time during operation, thereby avoiding delays caused by creating a new model during operation and improving model accuracy and data processing efficiency. In some embodiments, the three-dimensional point cloud data can be a dense point cloud, which improves model accuracy. In some embodiments, key points on the image are extracted using the ORB (Oriented Fast and Rotated BRIEF) algorithm or the FAST (Features from Accelerated Segment Test) algorithm. Key points and descriptors are calculated, and feature matching is performed on key frames of two adjacent captured images using the feature points to determine the three-dimensional coordinates corresponding to the matching feature points and construct a dense point cloud. The robot arm path is planned based on the three-dimensional model and the robot arm control signal is generated.

[0063] Such lighting control equipment can update the three-dimensional model through feature extraction based on the collected real-time images, improve the accuracy of lighting tracking, and ensure the lighting effect on the target treatment area.

[0064] In some embodiments, the controller 102 pre-acquires the patient's oral and maxillofacial model data, and labels the oral and maxillofacial region positions based on the trained machine learning model according to the oral and maxillofacial model data, and obtains the data of the labeled model. In some embodiments, the controller 102 can obtain the patient's oral and maxillofacial model data in advance based on the patient's previous diagnosis and treatment process during the surgical preparation process. In some embodiments, the trained machine learning model (such as a deep learning model) is generated based on the oral and maxillofacial model sample data, wherein the oral and maxillofacial model sample data includes an oral and maxillofacial model image and annotations of the positions of various oral and maxillofacial regions. In some embodiments, the region position may include the gum position, the tooth position, and in some embodiments, the position of each tooth position.

[0065] Furthermore, the controller 102 obtains information about the target treatment area and determines the position information of the target treatment area in the annotated model based on the information about the target treatment area and the data of the annotated model. In some embodiments, the information about the target treatment area can be input by a doctor (e.g., providing an identifier of the target tooth position) or annotated in the image. The controller obtains information about the target treatment area and further determines the position information of the target treatment area in the annotated model. In some embodiments, the controller 102 can be connected to an input device to obtain input or information annotated in the image.

[0066] During subsequent use, the controller 102 determines the updated position information of the target treatment area in the updated three-dimensional model based on the position information of the target treatment area in the marked model and the updated three-dimensional model, and determines the target irradiation posture based on the updated position information.

[0067] Such lighting control equipment can determine the position of the target treatment area in the model based on pre-processed data and update it in real time during use, thereby improving the accuracy of locking the position of the target treatment area and ensuring the lighting effect on the target treatment area.

[0068] In some embodiments, the controller 102 can identify the tool used for treatment or auxiliary treatment in the real-time image based on the information of the tool used for treatment or auxiliary treatment acquired in advance. In some embodiments, the tool can be a medical instrument commonly used in surgery, such as a mouth mirror. In some embodiments, the tool information includes image information and annotations of the tool. In some embodiments, the image information can be three-dimensional image information, or two-dimensional image information taken from multiple angles to improve the accuracy of image recognition. In some embodiments, the annotation of the tool can include the location of the area where the tool contacts or corresponds to the location where the patient is treated, such as the needle tip area of ​​a probe, or the mirror area of ​​a mouth mirror, etc.

[0069] In some embodiments, controller 102 identifies the identified tool as a target tool and determines a target treatment area based on the target tool's position. It should be noted that the target treatment area may simply be an area that the doctor needs to observe and does not necessarily require targeted treatment. Such a lighting control device can enable the lighting device to track changes in the tool's position and adjust the lighting position to ensure the lighting condition at the target tool location.

[0070] In some embodiments, controller 102 uses the location of the tool in contact with or corresponding to the area being treated on the patient as the target treatment area, determines the target illumination position of the lighting device, and then generates a robotic arm control signal to control the robotic arm to adjust the position of the lighting device. Such a lighting control device can enable the lighting device to track changes in the tool's position to adjust the illumination position, ensuring the illumination state corresponding to the target tool position. This facilitates the doctor's observation, modification, or addition of the treatment area, improves the device's automation level and ease of operation, and reduces data processing.

[0071] In some embodiments, the controller 102 determines the relative position of the area where the tool contacts or corresponds to the position where the patient is being treated in the annotated model, and then determines the target treatment area corresponding to the relative position in the annotated model. For example, the tooth or gum area that the doctor needs to operate on is determined based on the relative position, and the tooth or gum area is used as the target treatment area. In some embodiments, the annotated model includes multiple divided treatment areas, and the treatment area associated with the relative position can be determined as the target treatment area through a pre-trained prediction algorithm; the treatment area closest to the relative position can be determined as the target treatment area by calculating the Euclidean distance. Such a lighting control device can infer that the doctor needs to observe, modify or add treatment areas by tracking changes in the tool position, thereby improving the intelligence, automation and ease of operation of the device.

[0072] In some embodiments, the image acquisition device 101 can also acquire environmental images and send them to the controller 102. The controller 102 can also generate an environmental model based on the environmental image and determine the initial position of the robotic arm. In the process of generating the robotic arm control signal, the controller 102 adjusts the posture of the robotic arm based on the initial position of the robotic arm, and gradually updates the current posture of the robotic arm. The updated posture of the robotic arm serves as the data basis for subsequent adjustments. The controller 102 plans the motion trajectory of the robotic arm based on the posture of the robotic arm and the environmental model, and then generates the corresponding robotic arm control signal. In some embodiments, considering that the position of the image acquisition device whose installation position is equivalent to that of the lighting device is limited, resulting in difficulty in acquiring environmental images, environmental images can be acquired by deploying cameras, video cameras, etc. in all directions of the space where the lighting control device is located.

[0073] This lighting control device can pre-collect environmental images to determine the initial position of the robotic arm, providing a data basis for the subsequent generation of control signals and improving the accuracy of robotic arm control. Furthermore, the environmental model can provide reference information for the robotic arm's motion path, determining a path that can achieve obstacle avoidance and generating control signals to prevent hazards and improve safety.

[0074] FIG2 is a schematic diagram of some embodiments of the medical lighting device disclosed herein.

[0075] The lighting control device is any one of the above-mentioned ones. In some embodiments, the lighting control device includes an image acquisition device 21 , a controller 22 and a robotic arm 23 .

[0076] The medical lighting device also includes a lighting device 24 that can adjust its position and emit light under the control of the lighting control device. In some embodiments, the lighting control device can receive feedback signals sent by the lighting device 24 to monitor the working status of the lighting device in real time and improve the reliability of the adjustment.

[0077] In some embodiments, the position of the lighting device 24 is comparable to that of the image acquisition device 21, thereby reducing the difficulty of image processing and improving the accuracy of the lighting position. In some embodiments, the lighting device 24 can be a shadowless lamp, thereby improving the clarity of the doctor's field of view.

[0078] In such a medical lighting device, the lighting equipment can automatically adjust the light irradiation as the position of the target treatment area changes under the control of the lighting control device, thereby improving the degree of automation of the equipment, eliminating the need for manual adjustment of the equipment, and improving the doctor's operating efficiency. In some embodiments, the light source in the shadowless lamp includes one or more of an LED lamp, a laser, an ultraviolet light source, or a near-infrared light source. In some embodiments, the light source of the shadowless lamp includes a light source of a specified wavelength, which can display plaque or specific microorganisms, physiological special tissues such as nerves and blood vessels, or pathological tissues such as granulation tissue. The specific wavelength range can be set and adjusted according to the display effect under different wavelengths of light in related technical research. For example, a specific light source can be used to display malignant tumor cells. The use of such a light source can assist doctors in completing the cleaning within the range affected by the cancer more accurately. For example, the range of necrotic bone can be displayed, which can assist doctors in more accurately removing dead bone and retaining healthy bone to the greatest extent.

[0079] In some embodiments, LED light is used to illuminate the surgical area in clinical scenarios. In some embodiments, laser light sources are used to detect dental plaque, bacteria, and pathological cells in the surgical area, promote tissue healing, and provide anti-inflammatory and pain relief. Furthermore, they can be used to detect color marks on the surface of tooth crowns (the marks left by articulating paper placed between opposing teeth after occlusion).

[0080] Such medical lighting devices can provide a variety of light sources. While providing lighting functions, they also provide other additional functions such as plaque irradiation and highlighting (different colors highlight different areas), promoting tissue healing, anti-inflammatory and analgesic, etc., which can be easily applied to different scenarios, improve the auxiliary ability of medical lighting equipment for treatment, and enhance user experience.

[0081] In some embodiments, a medical lighting device includes a lighting controller capable of adjusting the lighting device's light source, such as switching between LEDs, lasers, ultraviolet (UV), or near-infrared (NIR) sources. In some embodiments, the laser wavelength can be switched. In some embodiments, the lighting device controller can be integrated into a lighting control device. In some embodiments, the lighting control device controller performs the functions of the lighting device controller. Such a medical lighting device allows doctors to easily adjust the lighting status according to their needs, improving service flexibility.

[0082] In some embodiments, the light source within the shadowless lamp includes a laser with a wavelength in the range of (405±20) nm. Such a laser can detect dental plaque on teeth in the form of autofluorescence imaging. Because healthy teeth do not have a plaque film, there is no red autofluorescence. However, teeth that have not been cleaned for a long time have a formed plaque film, which exhibits red autofluorescence under 405 nm light, making the dental plaque clearly visible and easier to identify.

[0083] In some embodiments, the image acquisition device in the lighting control device can take a picture of the image acquisition area. For example, it can capture an image of the tooth surface under the condition of laser irradiation to generate fluorescence on the teeth, thereby obtaining image information of dental plaque. Combined with the constructed plaque recognition and segmentation method based on the deep learning network model, intelligent detection and extraction of plaque can be achieved, facilitating recording and subsequent use. In some embodiments, the image acquisition device in the lighting control device can include an optical interference filter to capture the image after filtering through the optical interference filter, such as taking a picture of the teeth to extract optical information of the healthy tooth portion. In some embodiments, the controller can process the image, such as through image enhancement and image segmentation, to achieve a quantitative plaque index (i.e., the ratio of the plaque area to the total tooth area), providing bacterial prompts to the operator, thereby providing more comprehensive information to the doctor, improving the success rate of the surgery, and optimizing postoperative recovery.

[0084] In some embodiments, the light source in the shadowless lamp includes a low-energy laser, such as a laser with a wavelength in the range of 390-1600 nm and a power of 10 -3 -10 -1 W range, the energy is 10 -2 -10 2 J / cm 2 In some embodiments, the medical lighting device can use the laser to illuminate the surgical area during the suturing phase after surgery to promote the healing of soft tissue and bone tissue, as well as to provide anti-inflammatory and analgesic effects.

[0085] In some embodiments, the light source in the shadowless lamp includes ultraviolet light with a wavelength in the range of 280 to 320 nm and an irradiance of about 15 μW / cm2 Using such a light source can improve osteoporosis and promote the synthesis of vitamin D.

[0086] In some embodiments, the light source in the shadowless lamp includes near-infrared light with a wavelength in the range of 780 to 2526 nm. Using such a light source can facilitate the visualization of the location of tumor cells and the analysis and diagnosis of tumor tissue.

[0087] In some embodiments, the light source in the shadowless lamp includes visible blue light with a wavelength in the range of 400-500 nm and a light intensity in the range of 750-2000 mw / cm 2 The use of such a light source is helpful in light curing during the bonding process of dental crowns.

[0088] In some embodiments, during the operation, medical staff can perform control operations on the medical lighting device as needed, such as sending control information to the controller through the human-computer interaction interface, and the controller controls the switching of one or more of the light source type, wavelength or light intensity in the shadowless lamp, so that the irradiation of the shadowless lamp meets the doctor's needs.

[0089] A schematic diagram of some embodiments of the operating table disclosed herein is shown in FIG3 .

[0090] The patient support mechanism 31 is a structure capable of supporting a patient. In some embodiments, the patient support mechanism 31 includes a table and a base. In some embodiments, the table includes five movable sections: a headboard, a backboard, a lumbar board, a seat board, and a leg board.

[0091] The medical lighting device is any one of the above-mentioned ones. In some embodiments, the lighting control device includes an image acquisition device 21 , a controller 22 , a robotic arm 23 and a lighting device 24 , wherein the light of the lighting device 24 is projected onto the patient support mechanism 31 .

[0092] In some embodiments, the patient support mechanism 31 and the medical lighting device can be connected. In some embodiments, this connection can be fixed to reduce the difficulty of position adjustment; in some embodiments, this connection can be movable to increase the adjustable range, making the operating table suitable for patients with different lesions. In some embodiments, the two parts are independent and work together to improve the flexibility of the device.

[0093] In some embodiments, the patient support mechanism 31 further includes a column, and the fixed end of the robotic arm 23 in the medical lighting device is connected to the column.

[0094] The lighting equipment of such an operating table can automatically adjust the light irradiation as the position of the target treatment area changes under the control of the lighting control device, thereby improving the degree of automation of the equipment, eliminating the need for manual adjustment of the equipment, and improving the doctor's operating efficiency.

[0095] A schematic diagram of some embodiments of the dental chair of the present disclosure is shown in FIG4 .

[0096] The dental chair 41 can support the patient. In some embodiments, the dental chair 41 can be an electric dental chair with a base plate fixed to the ground. The base plate is connected to the upper portion of the dental chair via a bracket. The movement of the dental chair is controlled by a control switch. By operating the control switch button, the dental chair can be raised, lowered, tilted, and positioned, and reset according to treatment needs.

[0097] The medical lighting device is any one of the above-mentioned ones. In some embodiments, the lighting control device includes an image acquisition device 21 , a controller 22 , a robotic arm 23 and a lighting device 24 , wherein the light of the lighting device 24 is projected onto the dental chair 41 .

[0098] In some embodiments, the dental chair 41 and the medical lighting device may be connected. In some embodiments, the connection may be fixed to simplify position adjustment; in some embodiments, the connection may be movable to enhance flexibility. In some embodiments, the dental chair 41 and the medical lighting device may be separate components that work together to enhance the flexibility of the device.

[0099] The lighting equipment of such a dental treatment chair can automatically adjust the light irradiation as the position of the target treatment area changes under the control of the lighting control device, thereby improving the automation level of the equipment, eliminating the need for manual adjustment of the equipment, and improving the doctor's operating efficiency.

[0100] FIG5 is a flowchart of some embodiments of the lighting control method disclosed herein.

[0101] In step S52, a real-time image of the patient's target treatment area is obtained. In some embodiments, a real-time image of the target treatment area is obtained by an image acquisition device. In some embodiments, the image acquisition device 101 is a device capable of acquiring three-dimensional information, such as a binocular vision sensor, and the real-time image includes a two-dimensional image taken from two positions, or a three-dimensional image obtained based on two-dimensional image processing. The image acquisition device can also be a three-dimensional image acquisition device capable of acquiring two-dimensional images and depth information, and the real-time image includes a two-dimensional image and depth information of at least part of the pixel positions in the image. In some embodiments, a binocular vision sensor and a three-dimensional image acquisition device can also be used in combination. In some embodiments, the controller acquires the real-time image through a data interface between the controller and the image acquisition device.

[0102] In step S54, a target irradiation position is determined based on the real-time image, and a robotic arm control signal is generated based on the target irradiation position and transmitted to the robotic arm. The robotic arm control signal adjusts the position of the lighting device so that the direction of light irradiation from the lighting device matches the location of the target treatment area. In some embodiments, the target irradiation position is a position of the lighting device determined based on the location of the target treatment area so that light emitted by the lighting device can illuminate the target treatment area, specifically including the position and posture of the lighting device.

[0103] In some embodiments, information about surrounding obstacles can be referenced during the motion planning of the robotic arm. In some embodiments, obstacles include objects placed around, as well as patients and medical staff. In some embodiments, the position and size of obstacles can be determined based on real-time environmental information collected, such as image information collected by an image acquisition device. In some embodiments, the images captured by the camera can also be analyzed and processed using image processing technology to detect and identify obstacles in the environment, such as using depth images to detect objects on the ground and distinguish them from obstacles, thereby improving the efficiency of building environmental maps.

[0104] In some embodiments, after determining the position and size of the obstacle or obtaining the environment map, the movement path of the robotic arm is planned according to a predetermined algorithm (such as a machine / deep learning algorithm). In some embodiments, the predetermined algorithm can be implemented based on artificial intelligence technology, and the network model is trained by learning the characteristics of different obstacles in the environment, thereby realizing path planning for obstacle avoidance paths. In some embodiments, the predetermined algorithm can be generated based on deep learning algorithm training, and based on the constructed optimal path data set (such as A* algorithm, Dijkstra algorithm, costmap cost map and other intelligent methods), an intelligent reasoning model based on a convolutional neural network is trained to predict a high-quality obstacle avoidance path.

[0105] In some embodiments, after the motion path of the robotic arm is generated, a motion control signal is sent to the robotic arm to control the robotic arm to move according to the motion path. In some embodiments, the speed and direction of the robotic arm's movement are controlled in real time. In some embodiments, during the movement process, the path is recalculated based on changes in the surrounding environment (such as updates to the position of obstacles) or changes in the patient's position to adjust the motion state in real time, improving the timeliness and safety of posture adjustment.

[0106] In some embodiments, the lighting control device may further include one or more distance sensors located on the robotic arm that can detect the distance to an obstacle. When the distance sensor detects an obstacle within a predetermined distance range, it sends a signal to the controller, which then controls the robotic arm to urgently stop its movement, thereby improving obstacle avoidance response speed and further enhancing intraoperative safety.

[0107] Through this method, the lighting control device can identify and track the position changes of the patient's target treatment area, control the robotic arm to adjust the position of the lighting mechanism, so that the light irradiation of the lighting device is automatically adjusted as the position of the target treatment area changes, thereby improving the degree of automation of the device, eliminating the need for manual adjustment of the device, and improving operational efficiency.

[0108] 6 shows a flowchart of other embodiments of the lighting control method disclosed herein. In some embodiments, the following description uses dental treatment as an example. When the target treatment area is other parts, processing can be performed based on the data of the corresponding position.

[0109] In step 611, the patient's oral and maxillofacial model data is obtained. In some embodiments, the oral and maxillofacial model data is obtained during surgical preparation based on the patient's previous diagnosis and treatment procedures. In some embodiments, the oral and maxillofacial model data can be obtained through oral scanning.

[0110] In step 612, the oral and maxillofacial region locations are annotated based on the trained machine learning model, and the annotated model data is obtained. In some embodiments, the trained machine learning model is generated based on sample data of the oral and maxillofacial model. In some embodiments, the annotated model includes the location of the gums and teeth, and in some embodiments, the location of each tooth is also annotated.

[0111] In step 613, information about the target treatment area is obtained, and the location of the target treatment area in the annotated model is determined based on the target treatment area information and the annotated model data. In some embodiments, the target treatment area information can be input by a physician (e.g., by providing an identifier of the target tooth position) or annotated in the image. The target treatment area information is then matched with the annotations in the annotated model to determine the location of the target treatment area in the annotated model.

[0112] In some embodiments, steps 611 - 612 may be performed before the surgery begins, thereby saving processing time during the surgery.

[0113] In some embodiments, step 613 can be performed at the beginning of the operation, thereby improving the matching degree between the initially determined three-dimensional model and the subsequently updated three-dimensional model, reducing the amount of data that needs to be modified, and improving processing efficiency; in addition, it can also facilitate doctors to mark the target treatment area in real time, improving flexibility.

[0114] In some embodiments, an image capture device captures an image of the environment, generates an environmental model based on the image, and determines the initial position of the robotic arm, providing a data basis for the subsequent generation of control signals. Furthermore, the environmental model can also serve as the data basis for obstacle avoidance during robotic arm control, preventing dangerous situations.

[0115] In step 621, a real-time image in the direction of the target treatment area of ​​the patient is acquired.

[0116] In step 641, predetermined feature points are extracted based on the real-time image of the patient's target treatment area. In some embodiments, the predetermined feature points may include easily identifiable locations inside or outside the oral cavity, such as tooth tips, lips, and predetermined locations on the gums. Multiple predetermined feature points are used to improve the accuracy of locating the target treatment area.

[0117] In step 642 , the three-dimensional coordinates of the predetermined feature points are determined, and the three-dimensional point cloud data within the predetermined range of the patient's target treatment area is determined, and the three-dimensional model is updated.

[0118] In some embodiments, the three-dimensional model may be the labeled model determined in step 613 above, and then updated in real time during operation, thereby avoiding the delay caused by creating a new model during operation and improving model accuracy and data processing efficiency.

[0119] In step 643, the updated position information of the target treatment area in the updated three-dimensional model is determined based on the updated three-dimensional model and the position information of the target treatment area in the marked model. In some embodiments, the position information may be spatial coordinates.

[0120] In step 644, the target illumination posture is determined based on the updated position information. In some embodiments, the target illumination posture may include position information and posture information, such as the coordinates of the illumination device and the illumination azimuth of the illumination device.

[0121] In step 645, the target posture of the manipulator is determined based on the target illumination posture and the current posture of the manipulator, and a manipulator control signal is generated based on the target posture of the manipulator. In some embodiments, the initial posture of the manipulator determined in step 613 can be used as a basis, and the posture information of the manipulator is updated in real time as the manipulator is adjusted to obtain the current posture of the manipulator. Based on the relative difference between the current posture and the target posture, the adjustment parameters for the manipulator are determined, and the manipulator control signal is generated. In some embodiments, the manipulator control signal can be generated using a robot control system in related technologies. In some embodiments, the manipulator path can be planned based on the established environment map using some intelligent search algorithms (such as A* algorithm, Dijkstra algorithm, costmap cost map, etc.). In some embodiments, based on the current posture of the manipulator, DWA (Dynamic Window Approach) is used to calculate and determine the trajectory and motion strategy of the manipulator, thereby achieving obstacle avoidance while controlling the movement of the manipulator.

[0122] Through the method in the embodiment shown above, data preparation can be performed before the operation begins, reducing the amount of data that needs to be processed later and improving the adequacy of surgical preparation; during the operation, a control signal for the robotic arm can be generated, so that the robotic arm drives the lighting equipment to adjust the lighting, thereby improving the degree of automation of the equipment, eliminating the need for manual adjustment of the equipment, and improving the doctor's operating efficiency.

[0123] In some embodiments, the doctor can add or update the information of the target treatment area at any time according to the situation during the operation, such as inputting through an input device or marking the added or modified surgical site in the image, and performing the operation starting from step 613 above.

[0124] This method can facilitate the response to emergencies during surgery, allowing light tracking for temporarily added target treatment areas, further improving the doctor's operating efficiency.

[0125] In some embodiments, the doctor can add, modify or delete the target treatment area at any time by moving the treatment or auxiliary treatment tools. The target treatment area here may only be the area that the doctor needs to observe, and does not necessarily require targeted treatment operations. In some embodiments, the controller can identify the tools used for treatment or auxiliary treatment in the real-time image based on the information of the tools used for treatment or auxiliary treatment that has been acquired in advance. In some embodiments, the tools may be medical instruments commonly used in surgery, such as a mouth mirror. In some embodiments, the information of the tool includes image information and annotations of the tool. In some embodiments, the image information may be three-dimensional image information, or two-dimensional image information taken from multiple angles to improve the accuracy of image recognition. In some embodiments, the annotation of the tool may include the location of the area where the tool contacts or corresponds to the position where the patient is treated, such as the needle tip area of ​​a probe, or the mirror area of ​​a mouth mirror, etc.

[0126] In some embodiments, the controller uses the identified tool as the target tool and determines the target treatment area based on the position of the target tool. This method enables the lighting device to track changes in the tool position and adjust the lighting position to ensure the lighting state at the corresponding position of the target tool.

[0127] In some embodiments, the controller uses the location of the tool in contact with or corresponding to the area being treated on the patient as the target treatment area, determines the target illumination position of the lighting device, and then generates a control signal for the robotic arm to control the robotic arm to adjust the position of the lighting device. This method enables the lighting device to track changes in the tool's position to adjust the illumination position. The lighting device ensures the illumination state corresponding to the target tool position, making it easier for the doctor to observe, modify, or add treatment areas, improving the device's automation and ease of operation, and reducing data processing requirements.

[0128] In some embodiments, the controller determines the relative position of the area where the tool contacts or corresponds to the position where the patient is being treated in the annotated model, and then determines the target treatment area corresponding to the relative position in the annotated model. For example, the tooth or gum area that the doctor needs to operate on is determined based on the relative position, and the tooth or gum area is used as the target treatment area. In some embodiments, the annotated model includes multiple divided treatment areas, and the treatment area associated with the relative position can be determined as the target treatment area through a pre-trained prediction algorithm; the treatment area closest to the relative position can be determined as the target treatment area by calculating the Euclidean distance. Through such a method, it is possible to infer that the doctor needs to observe, modify or add treatment areas by tracking changes in the tool position, thereby improving the intelligence, automation and ease of operation of the equipment.

[0129] In some embodiments, the lighting control method of the present disclosure further includes obtaining oral and maxillofacial model sample data by collecting historical data. In some embodiments, the oral and maxillofacial model sample data includes identification of oral and maxillofacial region locations. In some embodiments, a normalized, high-information image dataset can be constructed, and sample data can be generated based on the normalized dataset. In some embodiments, based on the constructed normalized image dataset and initial sample data, a machine learning network model (such as a CNN (Convolutional Neural Network), a GAN (Generative Adversarial Network), or a few-shot generative model) constructed to expand sample data is used to generate new sample data, thereby preparing sufficient sample data for subsequent training and reducing the burden of manual labeling. A machine learning network model (such as a CNN or GAN) is established, and a machine / deep learning network model is trained using the oral and maxillofacial model sample data until training is complete, so that the trained machine learning network model has the ability to identify different regions on the oral and maxillofacial model. In step 612 above, the trained model is used to process the patient's oral and maxillofacial model data to obtain labeled model data. In some embodiments, after the machine learning model is trained on a server, it can be sent to each controller for use. In some embodiments, whether the training is completed can be determined based on the processing effect of the machine learning model. For example, when the accuracy is greater than or equal to a predetermined ratio and the training loss function curve tends to a stable state, the training is determined to be completed. Otherwise, the training continues to avoid underfitting or overfitting and improve the accuracy of the machine learning model.

[0130] Through this method, regional division can be performed based on the user's oral and maxillofacial model data, thereby improving the accuracy of subsequent target treatment area positioning and further improving the degree of automation.

[0131] In some embodiments, based on the user's (e.g., doctor's) operation, control information is sent to the lighting device to switch the light source or illumination intensity of the lighting device. This method can facilitate the doctor to adjust the lighting state according to needs, improving the flexible service capability.

[0132] In some embodiments, during the operation, the controller controls the LED light to provide sufficient lighting for the operation. In some embodiments, when it is necessary to illuminate dental plaque, the shadowless lamp is controlled to emit a laser with a wavelength range of (405±20) nm, so that the dental plaque can be clearly displayed and easy to identify. In some embodiments, during the suturing stage after the operation, the shadowless lamp is controlled to emit a wavelength range of 390-1600 nm and a power of 10 -3 -10 -1W range, the energy is 10 -2 -10 2 J / cm 2 Laser within the range can promote the healing of soft tissue and bone tissue, and play an anti-inflammatory and analgesic role.

[0133] A structural diagram of an embodiment of the lighting controller disclosed in the present invention is shown in Figure 7. In some embodiments, the lighting controller serves as a controller in the lighting control device, is connected to the image acquisition device and the robotic arm signal, obtains the image acquired by the image acquisition device, and sends the generated robotic arm control signal to the robotic arm, wherein the free end of the robotic arm is connected to the lighting device. The lighting controller includes a memory 701 and a processor 702. Among them: the memory 701 can be a disk, a flash memory or any other non-volatile storage medium. The memory is used to store the instructions in the corresponding embodiment of the lighting control method above. The processor 702 is coupled to the memory 701 and can be implemented as one or more integrated circuits, such as a microprocessor or a microcontroller. The processor 702 is used to execute the instructions stored in the memory, which can automatically adjust the light irradiation of the lighting device as the position of the target treatment area changes, thereby improving the degree of automation of the device, eliminating the need for manual adjustment of the device, and improving operational efficiency.

[0134] In one embodiment, as shown in FIG8 , a lighting controller 800 includes a memory 801 and a processor 802. The processor 802 is coupled to the memory 801 via a BUS 803. The lighting controller 800 can also be connected to an external storage device 805 via a storage interface 804 to access external data, and can also be connected to a network or another computer system (not shown) via a network interface 806. A detailed description is omitted here.

[0135] In this embodiment, by storing data instructions in a memory and then processing the instructions through a processor, the light irradiation of the lighting device can be automatically adjusted as the position of the target treatment area changes, thereby improving the automation level of the device, eliminating the need for manual adjustment of the device, and improving operational efficiency.

[0136] In another embodiment, a computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the steps of the method in the corresponding embodiment of the lighting control method. Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, apparatus, or computer program products. Therefore, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product implemented on one or more computer-usable, non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0137] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram and the combination of the processes and / or boxes in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate a device for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0138] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0139] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0140] The present disclosure has been described in detail so far. To avoid obscuring the concept of the present disclosure, some details known in the art have not been described. Based on the above description, those skilled in the art can fully understand how to implement the technical solutions disclosed herein.

[0141] The methods and apparatus of the present disclosure may be implemented in many ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present disclosure may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the methods according to the present disclosure. Therefore, the present disclosure also covers recording media that store programs for executing the methods according to the present disclosure.

[0142] It should be noted that the terms "first", "second", etc. in the specification, claims, and drawings of the present disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way are interchangeable where appropriate so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or apparatus.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure and not to limit it. Although the present disclosure has been described in detail with reference to the preferred embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present disclosure can still be modified or some technical features can be replaced by equivalents without departing from the spirit of the technical solutions of the present disclosure, which should all be included in the scope of the technical solutions requested for protection in the present disclosure.

Claims

1. A lighting control device, comprising: An image acquisition device configured to acquire real-time images in the direction of a target treatment area of ​​the patient; A controller is configured to determine a target illumination posture according to the real-time image, generate a robot arm control signal according to the target illumination posture, and send the signal to the robot arm; and The robot arm is connected to the lighting device and is configured to adjust the posture of the lighting device according to the robot arm control signal so that the light irradiation direction of the lighting device matches the position of the target treatment area.

2. The lighting control device according to claim 1, wherein: The image acquisition device includes at least one of a binocular vision sensor or a three-dimensional image acquisition device.

3. The lighting control device according to claim 1, wherein: The lighting device is fixed to the free end of the mechanical arm, and the opposite end of the free end of the mechanical arm is a fixed end.

4. The lighting control device according to claim 1, wherein: The controller is configured to: Extracting predetermined feature points based on a real-time image of the patient's target treatment area direction; Determine the three-dimensional coordinates of the predetermined feature points, determine the three-dimensional point cloud data within a predetermined range of the patient's target treatment area, and update the three-dimensional model; and The path of the robot arm is planned according to the three-dimensional model to generate a robot arm control signal.

5. The lighting control device according to claim 4, wherein: The controller is also configured to: Obtaining the patient's oral and maxillofacial model data; According to the oral and maxillofacial model data, the oral and maxillofacial region positions are annotated based on the trained machine learning model to obtain the annotated model data; Acquire information of a target treatment area, and determine position information of the target treatment area in the annotated model based on the information of the target treatment area and data of the annotated model, wherein the controller determines updated position information of the target treatment area in the updated three-dimensional model based on the position information of the target treatment area in the annotated model and the updated three-dimensional model, and determines the target irradiation posture based on the updated position information.

6. The lighting control device according to claim 5, wherein: The information of the target treatment area includes the identification of the target tooth position.

7. The lighting control device according to claim 4, wherein: The controller is also configured to: determining a location of a target tool based on a real-time image of the patient's target treatment area orientation; and The target treatment area is determined according to the position of the target tool.

8. The lighting control device according to claim 1, wherein: The image acquisition device is further configured to: acquire an environmental image and send it to the controller; The controller is also configured to generate an environment model according to the environment image and determine an initial position and posture of the robotic arm.

9. The lighting control device according to claim 8, wherein: The controller is also configured to: determining the location of obstacles according to the environment model; According to the obstacle position and the target illumination posture, the robot arm performs motion path planning and updates the motion path planning result in real time, wherein the motion path avoids the obstacle position; and The controller generates a robot arm control signal according to the motion path planning result.

10. The lighting control device according to claim 1, further comprising: a distance sensor, attached to the robotic arm, configured to send a warning message to a controller when an object is detected within a predetermined range; The controller is also configured to control the robot arm to stop moving when receiving warning information from the distance sensor.

11. The lighting control device according to claim 1, wherein: The controller is further configured to send a control signal to the lighting device to adjust at least one of a light source or brightness of the lighting device.

12. A medical lighting device, comprising: The lighting control device according to any one of claims 1 to 11; and The lighting device is configured to adjust its position and emit light under the control of the lighting control device.

13. The medical lighting device according to claim 12, wherein: The lighting device includes a shadowless lamp.

14. The medical lighting device according to claim 13, wherein: The shadowless lamp includes at least one of an LED light source, a laser light source, an ultraviolet light source or a near-infrared light source.

15. The medical lighting device according to claim 14, wherein: The lighting control device meets at least one of the following requirements: The laser light source includes at least one of the following: a laser with a wavelength range of 405±20nm; or a laser with a wavelength range of 390-1600nm and a power of 10 -3 -10 -1 W range, energy is 10 -2 -10 2 J / cm 2 Lasers within range; The ultraviolet wavelength is in the range of 280-320nm, and the irradiation intensity is 15μW / cm 2 The predetermined intensity interval; The wavelength of the near infrared light is in the range of 780 to 2526 nm; or The wavelength of the visible blue light is in the range of 400-500nm, and the light intensity is in the range of 750-2000mw / cm 2 within the range.

16. An operating table, comprising: a patient support mechanism configured to support a patient; and The medical lighting device according to any one of claims 12 to 15 is configured to emit light to a target treatment area of ​​a patient.

17. A dental treatment chair, comprising: a dental chair, configured to support the patient; and The medical lighting device according to any one of claims 12 to 15 is configured to emit light to a target treatment area of ​​the patient's oral and maxillofacial region.

18. A lighting control method, comprising: Acquire real-time images of the patient's target treatment area direction; and The target irradiation posture is determined according to the real-time image, and a robotic arm control signal is generated according to the target irradiation posture and sent to the robotic arm, so as to adjust the posture of the lighting device according to the robotic arm control signal so that the light irradiation direction of the lighting device matches the position of the target treatment area.

19. The lighting control method according to claim 18, wherein: Determining the target illumination posture according to the real-time image, and generating a robot arm control signal according to the target illumination posture comprises: Extracting predetermined feature points based on a real-time image of the patient's target treatment area direction; Determine the three-dimensional coordinates of the predetermined feature points, determine the three-dimensional point cloud data within a predetermined range of the patient's target treatment area, and update the three-dimensional model; and The path of the robot arm is planned according to the updated three-dimensional model to generate a robot arm control signal.

20. The lighting control method according to claim 19, further comprising: Obtaining oral and maxillofacial model data of the patient; According to the oral and maxillofacial model data, the oral and maxillofacial region positions are annotated based on the trained machine learning model to obtain the annotated model data; and Acquire information of a target treatment area, and determine position information of the target treatment area in the annotated model according to the information of the target treatment area and data of the annotated model; Determining the target illumination posture according to the real-time image comprises: Determining updated position information of the target treatment area in the updated three-dimensional model according to the updated three-dimensional model and the position information of the target treatment area in the marked model; and The target illumination posture is determined according to the updated position information.

21. The lighting control method according to claim 19, wherein: Determining the target illumination posture according to the real-time image, and generating a robot arm control signal according to the target illumination posture also includes: determining a location of a target tool based on a real-time image of the patient's target treatment area orientation; and The target treatment area is determined according to the position of the target tool.

22. The lighting control method according to claim 18, further comprising: Acquire an environment image acquired by an image acquisition device; and Generate an environment model according to the environment image, and determine the initial position and posture of the robot arm, wherein the image acquisition device includes at least one of a binocular vision sensor or a three-dimensional image acquisition device; Generating a robot arm control signal according to the target illumination posture comprises: determining a target posture of the robot arm according to the target illumination posture and the current posture of the robot arm, and generating the robot arm control signal according to the target posture of the robot arm.

23. The lighting control method according to claim 22, further comprising determining the location of an obstacle according to the environment model; The generating of the robot arm control signal according to the target illumination posture comprises: performing motion path planning for the robot arm according to the obstacle position and the target illumination posture and updating the motion path planning result in real time, wherein the motion path avoids the obstacle position; and generating the robot arm control signal according to the motion path planning result.

24. The lighting control method according to claim 18, further comprising: When receiving the warning information from the distance sensor, the robot arm is controlled to suspend movement, wherein the distance sensor is attached to the robot arm and sends a warning information to the controller when detecting an object within a predetermined range.

25. The lighting control method according to claim 18, further comprising: A control signal is sent to the lighting device to adjust at least one of a light source or brightness of the lighting device.

26. The lighting control method according to claim 20, further comprising at least one of the following: Get the oral and maxillofacial model sample data, where: The oral and maxillofacial model sample data includes oral and maxillofacial Identification of facial area locations, training a machine learning model using the oral and maxillofacial model sample data until training is complete; or An image of a target tool and treatment position information on the target tool are acquired so as to identify the target tool according to the real-time image, and determine a target treatment area according to the treatment position information on the target tool.

27. A lighting controller, comprising: Memory; and a processor coupled to the memory, the processor being configured to execute the method according to any one of claims 18 to 26 based on instructions stored in the memory.

28. A non-transitory computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the steps of the method according to any one of claims 18 to 26.

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