A coffee table lighting angle adjustment control method and system

Abstract

This invention relates to the technical field of lighting angle adjustment, specifically to a method and system for controlling the lighting angle of a coffee table. The method includes the following steps: acquiring optical reflection characteristic data of the desktop area, three-dimensional geometric data of objects, and user observation point information, and constructing a spatial model for lighting evaluation; when a change in the desktop environment is detected, including objects entering an unstable state, evaluating whether the lighting state reaches a preset threshold based on the current light source projection angle, including a glare threshold, an illuminance uniformity threshold, and a secondary reflection avoidance threshold; if any threshold is reached, recalculating and adjusting the current light source projection angle; if no threshold is reached, maintaining the current light source projection angle. This effectively solves problems such as glare, uneven illuminance, and secondary reflection, improving user visual comfort.

Description

Technical Field

[0001] This invention relates to the technical field of lighting angle adjustment, and specifically to a method and system for adjusting the lighting angle of a coffee table. Background Technology

[0002] In modern living spaces, the design and application of lighting equipment increasingly emphasize user comfort and functionality. However, traditional fixed lighting or equipment that only provides simple brightness adjustment often struggles to adapt to complex and ever-changing real-world usage scenarios. Especially when performing tasks requiring fine visual perception, such as reading and drawing, changes in the material and shape of desktop objects, as well as the user's position, can lead to unsatisfactory lighting effects, or even glare, shadows, or reflections, severely impacting visual comfort and work efficiency. Therefore, achieving intelligent adjustment of lighting sources to dynamically adapt to changes in the desktop environment has become a significant challenge in the current technological field. Summary of the Invention

[0003] The purpose of this invention is to address the aforementioned shortcomings by proposing a method and system for adjusting and controlling the lighting angle of a coffee table.

[0004] The present invention adopts the following technical solution:

[0005] A method for adjusting the lighting angle of a coffee table includes the following steps: acquiring optical reflection characteristic data of the desktop area, three-dimensional geometric data of the object, and user observation point information, and constructing a spatial model for lighting evaluation; when a change in the desktop environment is detected, including the object entering an unstable state, evaluating whether the lighting state touches a preset judgment threshold based on the current light source projection angle, the judgment thresholds including a glare threshold, an illuminance uniformity threshold, and a secondary reflection avoidance threshold; if any judgment threshold is touched, initiating the recalculation and adjustment of the current light source projection angle; if no judgment threshold is touched, maintaining the current light source projection angle.

[0006] By acquiring the optical reflection characteristics data of the desktop area, the three-dimensional geometric data of the objects, and the user's observation point information, and constructing a spatial model for lighting evaluation, the desktop environment can be fully perceived. When a change in the desktop environment is detected, the lighting state is evaluated based on the current light source projection angle to determine whether it has reached a preset threshold. Based on the evaluation result, recalculation and adjustment or maintenance of the current angle is initiated, thereby realizing intelligent and dynamic control of the coffee table lighting angle, effectively solving problems such as glare, uneven illuminance, and secondary reflection, and improving the user's visual comfort.

[0007] Based on the above, this application further proposes that when a change in the desktop environment is detected, the step of assessing whether the lighting state has reached a preset judgment threshold based on the current light source projection angle includes: identifying the start of the unstable state of the object; during the unstable state, adjusting the current light source projection angle to a preset transition lighting angle; continuously monitoring the physical state of the object and determining whether the object has entered a stable state; when the object enters a stable state, assessing whether the lighting state has reached a preset judgment threshold based on the transition lighting angle.

[0008] Based on this, this application also proposes a step for continuously monitoring the physical state of an item and determining whether the item has entered a stable state, including: continuously acquiring the item's three-dimensional geometric data and surface optical property data; calculating the changes in the item's three-dimensional geometric data and surface optical property data between consecutive time frames; analyzing the trend of the changes within a preset time window; and determining that the item has entered a stable state when the fluctuation amplitude of the trend is continuously lower than a preset trend stabilization threshold and the absolute value of the changes is continuously lower than a preset static threshold.

[0009] Based on this, this application also proposes that the calculation steps for the transition lighting angle include: obtaining the ambient light intensity of the desktop area; obtaining the user's preset visual comfort preference; determining the brightness and diffusion characteristics of the transition angle based on the ambient light intensity and visual comfort preference of the desktop area; and calculating the transition lighting angle based on the brightness and diffusion characteristics of the transition angle.

[0010] Based on the above, this application further proposes that the steps for initiating the recalculation and adjustment of the current light source projection angle include: obtaining the current light source projection angle and recalculating the target projection angle based on a spatial model and a judgment threshold; if the difference between the target projection angle and the current light source projection angle exceeds a preset angle threshold, or if there are obstacles identified by the spatial model in the table area, generating several intermediate projection angles; wherein, the several intermediate projection angles are distributed between the current light source projection angle and the target projection angle, forming an adjustment path; for each of the several intermediate projection angles, predicting the illuminance distribution, spot position, and shadow area generated by each intermediate projection angle in the table area to form a prediction result; based on the prediction result, identifying any risk situation that occurs on the adjustment path: instantaneous glare, instantaneous drastic shadow change, and instantaneous secondary reflection spot entering the no-projection area; selecting an adjustment path, the adjustment path consisting of part or all of the several intermediate projection angles, ensuring that the frequency or intensity of any risk situation during the adjustment process is lower than a preset safety threshold; controlling the mechanical adjustment mechanism of the light source to adjust the light source step by step to the target projection angle along the selected adjustment path through several intermediate projection angles.

[0011] Based on this, this application also proposes that the steps for selecting an adjustment path include: constructing a comprehensive cost function, which is used to comprehensively measure the risks of instantaneous glare, sudden changes in instantaneous shadows, and instantaneous secondary reflection spots entering the no-projection zone; and selecting an adjustment path from several intermediate projection angles based on the comprehensive cost function and a preset path optimization algorithm.

[0012] Based on this, this application also proposes that the steps for constructing the comprehensive cost function include: obtaining user preference data for different types of visual interference; adjusting the weights of the risks of instantaneous glare, instantaneous shadow changes, and instantaneous secondary reflection spots entering the no-throw zone in the comprehensive cost function based on the user preference data for different types of visual interference; and constructing the comprehensive cost function according to the adjusted weights.

[0013] Based on this, this application also proposes a step for predicting the illuminance distribution, spot position, and shadow area generated by each intermediate projection angle on the tabletop area for each of several intermediate projection angles, in order to form a prediction result. This step includes: acquiring three-dimensional geometric data of the tabletop area and surface optical property data of the object; simulating the propagation, reflection, and absorption of light in the tabletop area using ray tracing or optical simulation technology based on the three-dimensional geometric data of the tabletop area, the surface optical property data of the object, and the intermediate projection angle; and calculating the illuminance distribution, spot formation position and range, and shadow boundary and depth of the tabletop area based on the simulation results, in order to form a prediction result.

[0014] Based on this, this application also proposes a method for obtaining three-dimensional geometric data of a desktop area and surface optical property data of an object, including: obtaining depth information of the desktop area; constructing three-dimensional geometric data of the desktop area based on the depth information of the desktop area; obtaining reflectance spectral information of the object; and resolving the reflectance spectral information of the object to obtain surface optical property data of the object.

[0015] This application also provides a coffee table lighting angle adjustment control system, applied to the aforementioned coffee table lighting angle adjustment control method. The system includes: a processing module for acquiring optical reflection characteristic data of the desktop area, three-dimensional geometric data of objects, and user observation point information, and constructing a spatial model for lighting evaluation; and an evaluation and adjustment module that, when detecting changes in the desktop environment, including objects entering an unstable state, evaluates whether the lighting state reaches a preset judgment threshold based on the current light source projection angle. The judgment thresholds include a glare threshold, an illuminance uniformity threshold, and a secondary reflection avoidance threshold. If any judgment threshold is reached, the current light source projection angle is recalculated and adjusted; if no judgment threshold is reached, the current light source projection angle is maintained.

[0016] The above solution provides a system for implementing the above-mentioned coffee table lighting angle adjustment control method. Through the collaborative work of the processing module and the evaluation and adjustment module, it can effectively execute the various steps in the method, providing a hardware and software implementation carrier for intelligent lighting control, and has good practicality.

[0017] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0018] Figure 1 This is a flowchart of a method for adjusting and controlling the lighting angle of a coffee table according to the present invention;

[0019] Figure 2 This is a schematic diagram of the structure of a coffee table lighting angle adjustment control system according to the present invention. Detailed Implementation

[0020] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0021] This embodiment provides a method and system for adjusting the lighting angle of a coffee table, combined with... Figure 1 and Figure 2 As shown.

[0022] refer to Figure 1 A method for adjusting the lighting angle of a coffee table is disclosed. The method includes the following steps: acquiring optical reflection characteristic data of the desktop area, three-dimensional geometric data of the object, and user observation point information, and constructing a spatial model for lighting evaluation; when a change in the desktop environment is detected, including the object entering an unstable state, evaluating whether the lighting state touches a preset judgment threshold based on the current light source projection angle, the judgment thresholds including a glare threshold, an illuminance uniformity threshold, and a secondary reflection avoidance threshold; if any judgment threshold is touched, initiating the recalculation and adjustment of the current light source projection angle; if no judgment threshold is touched, maintaining the current light source projection angle.

[0023] The optical reflectivity data of the desktop area refers to the reflectivity characteristics of the desktop surface to incident light, including the proportion of specular and diffuse reflection, and the reflectance spectrum. This data can be acquired using optical sensors, spectrometers, or high dynamic range (HDR) imaging devices. For example, scanning the desktop surface to obtain the reflectivity distribution at different wavelengths provides the physical basis for light propagation and reflection in the desktop area, enabling accurate simulation of lighting effects. The three-dimensional geometric data of objects refers to the shape, size, and spatial location information of objects placed within the desktop area. This data can be acquired using 3D sensing technologies such as depth cameras, structured light scanners, or LiDAR. For example, using Kinect or RealSense cameras to acquire point cloud data and reconstruct the 3D model of the object is crucial for determining the object's impact on light occlusion, reflection, and shadow formation, and is an important component in constructing the spatial model. The user's viewpoint information refers to the position and orientation of the user's eyes in space. This data can be acquired using head-tracking sensors, eye trackers, or pre-set user posture models. For example, recognizing facial features and estimating eye position using a camera is essential for determining potential glare paths and assessing visual comfort; it is a key reference point for determining whether glare occurs. The spatial model used for lighting assessment refers to a digital environment model that integrates optical reflection characteristics data of the desktop area, 3D geometric data of objects, and user observation point information. It can be constructed using a 3D rendering engine, ray tracing algorithm, or radiosity algorithm. For example, a virtual scene can be created using platforms such as Unity or Unreal Engine and the aforementioned data imported. Its main purpose is to simulate and predict the illuminance distribution, light spot positions, shadow areas, and potential glare on the desktop area under different light source projection angles, providing a simulation environment for lighting status assessment. An object entering an unstable state refers to a change in the position, posture, or shape of an object on the desktop, leading to an alteration in its interaction with light. This can be monitored using visual recognition algorithms, inertial measurement units (IMUs), or pressure sensors. For example, when a user picks up or moves a water glass or turns pages in a book, the system detects a significant change in the object's position or shape. This primarily triggers a reassessment of the lighting status, ensuring the lighting system can dynamically adapt to real-time changes in the desktop environment. Judgment thresholds refer to the critical values ​​used to measure whether the lighting status meets preset standards, including glare thresholds, illuminance uniformity thresholds, and secondary reflection avoidance thresholds.These thresholds can be set based on the physiological characteristics of human vision, lighting standards, or user preferences. For example, the glare threshold can be defined based on the Uniform Glare Ratio (UGR) or brightness contrast ratio; the illuminance uniformity threshold can be set as the ratio of the minimum illuminance to the average illuminance in the desktop area; and the secondary reflection avoidance threshold can be defined as a specific area (such as the user's line of sight) where high-brightness reflective spots are not allowed. Their main purpose is to provide objective evaluation standards to determine whether the current lighting condition has any adverse effects, thereby deciding whether angle adjustment is necessary. Recalculating and adjusting the current light source projection angle refers to the system recalculating a better light source projection angle based on the spatial model and the judgment threshold when the lighting condition does not meet the judgment threshold. The system then controls the light source's mechanical mechanism to adjust this angle. This can be achieved using optimization algorithms, machine learning models, or preset adjustment strategies. For example, iterative optimization can find a projection angle that minimizes glare and maximizes uniformity. This is mainly to dynamically optimize the lighting effect, solve problems in the current lighting condition, and improve the user experience.

[0024] This application's solution achieves intelligent perception, evaluation, and dynamic adjustment of desktop lighting status by establishing a comprehensive digital environment model. First, the system acquires optical reflectivity data of the desktop area, describing the light reflection behavior of the desktop materials; simultaneously, it acquires three-dimensional geometric data of desktop objects, defining their shape and spatial position; and it obtains user observation point information, determining the precise location of the user's eyes. This multi-dimensional data is integrated to construct a spatial model for lighting evaluation. This spatial model is the cornerstone of the entire method, accurately simulating the propagation, reflection, and absorption of light in the desktop environment, thereby predicting visual effects under different lighting conditions. Subsequently, the system continuously monitors changes in the desktop environment, especially when objects enter an unstable state, such as when objects are moved, picked up, or their shape changes. Once such a change is detected, the system evaluates the current lighting status based on the projection angle of the current light source, using the previously constructed spatial model. The evaluation process checks whether the lighting status reaches preset judgment thresholds, including glare thresholds, illuminance uniformity thresholds, and secondary reflection avoidance thresholds. If the evaluation results show that the lighting condition has reached any of the judgment thresholds, indicating a problem with the current lighting, the system will initiate a recalculation and adjustment process for the current light source projection angle. This means that the system will calculate a new, better target projection angle based on the spatial model and judgment thresholds, and control the mechanical adjustment mechanism of the light source to adjust it to eliminate or mitigate undesirable lighting effects. Conversely, if the lighting condition has not reached any judgment thresholds, indicating that the current lighting effect is good, the system will maintain the current light source projection angle, avoiding unnecessary adjustments, thereby ensuring the stability and energy efficiency of the system. This iterative monitoring, evaluation, and adjustment mechanism enables the lighting system to dynamically and adaptively respond to real-time changes in the desktop environment, ensuring that it provides lighting effects that meet user needs and visual comfort under any circumstances.

[0025] In some preferred embodiments, to acquire optical reflectance data of the desktop area, a multispectral imaging sensor can be deployed. This sensor captures reflectance images of the desktop surface at different wavelengths, and its optical reflectance characteristics are analyzed using image processing algorithms. The three-dimensional geometric data of the object can be acquired in real time using a structured light scanner integrated into the edge of the coffee table. This scanner emits light in a specific pattern and captures its deformation, thereby reconstructing an accurate three-dimensional model of the object on the desktop. User observation point information can be acquired by a small camera that tracks the user's head and eye positions in real time using facial recognition and pose estimation algorithms. This data is transmitted to an embedded processor that runs physically based rendering-based optical simulation software to build a spatial model for lighting evaluation. When the user turns a page or moves a water glass, the structured light scanner detects significant changes in the object's three-dimensional geometric data, and the system determines that the object has entered an unstable state. At this time, the embedded processor simulates light propagation in the spatial model based on the current projection angle of the light source and calculates the illuminance distribution, potential glare areas, and secondary reflection spots in the desktop area. These calculation results are compared with preset judgment thresholds. For example, the glare threshold can be set to ensure that the brightness of a specific area does not exceed a certain candela per square meter; the illuminance uniformity threshold can be set to ensure that the ratio of minimum to maximum illuminance in the desktop area is not less than 0.7; and the secondary reflection avoidance threshold can be set to ensure that no light spots with brightness more than 5 times the background brightness are allowed within the user's line of sight. If the simulation results show that the brightness of a certain area of ​​the desktop exceeds the glare threshold, or the illuminance uniformity is lower than the preset value, or a high-brightness secondary reflection spot enters the user's line of sight, the system will initiate a recalculation of the light source projection angle. The embedded processor will run an optimization algorithm that aims to minimize glare, maximize illuminance uniformity, and avoid secondary reflections, iteratively searching for an optimal light source projection angle in the spatial model. Once the target projection angle is found, the processor will send instructions to the micro stepper motor inside the coffee table to control the mechanical adjustment mechanism of the light source to precisely adjust the light source to the new projection angle. If the evaluation results show that the current lighting state meets all the judgment thresholds, the system will not make any adjustments and will maintain the existing angle.

[0026] This application further proposes a step for assessing whether the lighting state has reached a preset threshold based on the current light source projection angle when a change in the desktop environment is detected. This step includes: identifying the start of the unstable state of the object; adjusting the current light source projection angle to a preset transition lighting angle during the unstable state; continuously monitoring the physical state of the object and determining whether the object has entered a stable state; and assessing whether the lighting state has reached a preset threshold based on the transition lighting angle when the object enters a stable state.

[0027] The identification of the initiation of an object's unstable state refers to the system's determination, through sensor data or image analysis, whether the object on the table begins to change in position, posture, or shape. Specifically, motion sensors, visual recognition algorithms, or weight sensors can be used to detect the movement or manipulation of the object. The goal is to accurately capture the initial moment of the object's state change in order to initiate the subsequent intelligent lighting adjustment process. The unstable state period refers to the entire time from the identification of the object's unstable state to its entry into a stable state, during which the object's position, posture, or shape may continuously change. The preset transition lighting angle refers to a temporary light source projection angle preset or dynamically calculated to reduce visual interference when the object is in an unstable state. Specifically, this can be an empirical value, a compromise angle calculated based on ambient light intensity and user comfort preferences, or a general angle that provides basic lighting during object movement. Its purpose is to provide illumination during object adjustment. A relatively comfortable and minimally disturbed lighting environment is required. Continuous monitoring of the physical state of objects refers to the system's uninterrupted acquisition and analysis of physical parameters such as the object's position, orientation, shape, or surface characteristics. Specifically, data can be collected in real time using devices such as depth cameras, LiDAR, or multispectral sensors. The purpose is to provide continuous data support for determining whether an object has entered a stable state. Determining whether an object has entered a stable state refers to judging whether the object has stopped moving or changing and has maintained a relatively fixed position and orientation based on the continuously monitored physical state data. Specifically, this can be judged based on the amount of change or fluctuation of the object's physical state data over a period of time. The purpose is to determine when to switch from the transitional lighting stage to the precise lighting assessment and adjustment stage. A stable state refers to the object ceasing significant physical movement or shape changes within the tabletop area and maintaining a relatively fixed position and orientation. The purpose is to provide a reliable and unchanging physical basis for subsequent precise lighting assessments.

[0028] This application's solution optimizes the evaluation and adjustment process of lighting angles by introducing an intelligent processing mechanism for unstable states of desktop objects. When the system detects a change in the desktop environment that involves an object entering an unstable state, such as when a user is moving or adjusting an object, the system does not immediately perform a precise lighting state evaluation and angle recalculation. Instead, it first identifies the beginning of the object's unstable state, which serves as a signal to trigger the subsequent smooth adjustment process. Once an unstable state is identified, the system immediately adjusts the current light source projection angle to a preset transition lighting angle. This transition lighting angle aims to provide a relatively soft and less disruptive lighting environment during object adjustment, avoiding visual discomfort to the user caused by frequent changes in light during object movement. During this unstable state, the system continuously monitors the physical state of the object, such as changes in its position, posture, or shape, to determine in real time whether the object has stopped moving and entered a stable state. Only when the object is determined to have entered a stable state will the system re-evaluate whether the lighting state has reached preset judgment thresholds based on the current transition lighting angle, including glare threshold, illuminance uniformity threshold, and secondary reflection avoidance threshold.

[0029] In some preferred embodiments, when the coffee table lighting system detects a new item being placed on the tabletop or an existing item being moved using its built-in visual sensors or millimeter-wave radar, the system initiates the identification of the item's unstable state. For example, the system can analyze changes in the item's outline or center of mass in consecutive frames of images. If it detects a sustained displacement or deformation of the item within a short period, it identifies this as the beginning of an unstable state. Once this beginning is identified, the system immediately controls the mechanical adjustment mechanism of the light source to adjust the current projection angle to a preset transition lighting angle. For example, it can be adjusted to a general angle with a wide diffusion range and moderate brightness to ensure that the tabletop area still has basic lighting during item movement, while avoiding strong glare or shadows. During this transition lighting period, the system continuously monitors the physical state of the item using visual sensors or an inertial measurement unit. For example, by analyzing the item's position, orientation, and velocity data over a continuous time period, it determines whether the item has stopped moving. When the system detects that the item's position and orientation remain unchanged within a preset time window, and its velocity is close to zero, the system determines that the item has entered a stable state. Once the object reaches a stable state, the system will reassess the lighting status of the desktop area based on the current transition lighting angle. This includes analyzing whether there is glare, whether the illuminance is uniform, and whether there are secondary reflections, in order to determine whether it is necessary to further recalculate and adjust the light source projection angle, thereby providing the user with the best lighting effect.

[0030] This application further proposes a step for continuously monitoring the physical state of an item and determining whether the item has entered a stable state, including: continuously acquiring the item's three-dimensional geometric data and surface optical property data; calculating the changes in the item's three-dimensional geometric data and surface optical property data between consecutive time frames; analyzing the trend of the changes within a preset time window; and determining that the item has entered a stable state when the fluctuation amplitude of the trend is continuously lower than a preset trend stabilization threshold and the absolute value of the changes is continuously lower than a preset static threshold.

[0031] Three-dimensional geometric data refers to the shape, size, position, and orientation information of an object in three-dimensional space, such as point cloud data acquired through depth sensors, mesh models, or stereo information reconstructed from multi-view images. Its purpose is to provide a precise description of the object's physical existence and is the basis for determining whether the object has moved or deformed. Surface optical property data refers to the properties of an object's surface in relation to light, such as reflectivity, absorptivity, scattering characteristics, color information, and texture details. This data can be acquired through spectrometers, color sensors, or high dynamic range cameras. Its purpose is to provide a description of the object's visual appearance under illumination and is the basis for determining whether the object's surface state has changed. The amount of change refers to the numerical difference or degree of variation between the object's three-dimensional geometric data and surface optical property data between consecutive time frames. For example, the amount of change in three-dimensional geometric data can be reflected in the displacement, rotation, or deformation of the object's position, orientation, or shape, while the amount of change in surface optical property data can be reflected in changes in color, brightness, or reflectivity. Its purpose is to quantify the dynamic behavior of the object over a short period of time. A preset time window refers to a continuous time interval used to analyze the trend of changes in the object's data. The length of this time window can be configured according to the definition of the stable state of the object in the actual application scenario and the requirements of the system response speed. Its purpose is to provide a sufficiently long time range to distinguish between instantaneous fluctuations and a true stable state. The fluctuation amplitude of the trend refers to the degree of fluctuation in the amount of change in the object's three-dimensional geometric data and surface optical properties over time within the preset time window. For example, it can be characterized by calculating the standard deviation, variance, or difference between the maximum and minimum values ​​of the change sequence. Its purpose is to reflect the smoothness of the object's motion or state change. The trend stability threshold is a preset value used to determine whether the fluctuation amplitude of the trend has reached a stable state. When the fluctuation amplitude of the trend is consistently below this threshold, it indicates that the object's state change is becoming smoother. Its purpose is to set a tolerance range to filter out minor fluctuations or noise that do not affect the stability determination. The stationary threshold is a preset value used to determine whether the absolute value of the object's change has reached a stationary state. When the absolute value of the change is consistently below this threshold, it indicates that the object has physically stopped moving or its surface optical properties have stopped changing significantly. Its purpose is to set a tolerance range to confirm that the object is numerically stationary.

[0032] This application's solution achieves accurate determination of an object's stable state by comprehensively analyzing its three-dimensional geometric data and surface optical properties. Specifically, the system continuously acquires the object's three-dimensional geometric data and surface optical properties, which comprehensively describe the object's physical location, shape, and its response characteristics to light. Subsequently, the system calculates the changes in these data across consecutive time frames, thereby quantifying the object's motion or state change over a short period. Relying solely on the magnitude of instantaneous changes to determine a stable state is insufficient, as the object may experience brief periods of stillness or fluctuation. Therefore, this solution further analyzes the trends of these changes within a preset time window. By examining fluctuations over a period, it can more accurately identify whether the object has truly stabilized. Finally, when the amplitude of the trend fluctuation remains consistently below a preset trend stabilization threshold, and the absolute value of the change remains consistently below a preset stillness threshold, the system determines that the object has entered a stable state. This means that the object not only exhibits instantaneous stillness, but this stillness maintains a high degree of stability over a period of time, effectively filtering out brief periods of stillness or fluctuation, thus avoiding misjudgment. Therefore, this solution is closely integrated with the aforementioned lighting angle adjustment control method, providing a more robust and accurate judgment basis, especially in scenarios where changes in the desktop environment are detected and it is necessary to determine whether an object has entered a stable state. During periods when an object is in an unstable state, the system adjusts the light source projection angle to a transitional lighting angle to maintain basic lighting needs. When the object stops moving or its state changes, the system's precise judgment avoids immediately triggering unnecessary recalculation and adjustment of the lighting angle due to brief periods of stillness, thus reducing frequent adjustments to the light source. Only when the object is confirmed to have truly entered a stable state will the system assess whether the lighting state has reached a preset judgment threshold based on the transitional lighting angle and decide whether to initiate final lighting optimization. This mechanism ensures the timeliness and accuracy of lighting adjustment, avoids frequent changes in illumination due to misjudgment, and significantly improves the consistency and comfort of the user experience.

[0033] In some preferred embodiments, the process of continuously monitoring the physical state of an object and determining whether it has entered a stable state can be implemented as follows: First, the system can utilize a vision sensor module integrating a depth camera and a high-resolution RGB camera to continuously acquire the object's 3D geometric data and surface optical property data. For example, the depth camera can capture 30 frames of point cloud data per second to construct the object's 3D geometric model, while the RGB camera simultaneously captures color images of the object's surface to extract its optical properties such as color, brightness, and texture. Specifically, to calculate the changes in the object's 3D geometric data and surface optical property data between consecutive time frames, the following methods can be used: For 3D geometric data, the system can register point cloud data from two consecutive frames, for example, using the Iterative Closest Point (ICP) algorithm to calculate the average or maximum distance between the registered point clouds, which serves as the geometric change. For surface optical property data, the pixel values ​​of the object's region in two consecutive RGB images can be compared, for example, calculating the average of the color or brightness differences of corresponding pixels, which serves as the optical property change. These changes can be vectors or scalars, representing the dynamics of the object in terms of spatial position, pose, and surface visual features. Furthermore, to analyze the trend of changes in the quantities within a preset time window, the system can maintain a sliding window buffer to store the changes in the most recent N frames. For example, N can be set to 30 frames, corresponding to 1 second of data. Within this time window, trend analysis can be performed on the geometric changes and optical property changes separately. For example, the moving average and standard deviation of each change sequence can be calculated. The moving average reflects the overall trend of the changes, while the standard deviation reflects the fluctuation range. Finally, when the fluctuation range of the change trend is consistently lower than a preset trend stabilization threshold, and the absolute value of the changes is consistently lower than a preset static threshold, the system determines that the item has entered a stable state. For example, if the standard deviation of the geometric changes is lower than a preset geometric trend stabilization threshold for five consecutive time windows, and the absolute value of its moving average is also consistently lower than a preset geometric static threshold; simultaneously, if the standard deviation of the optical property changes is lower than a preset optical trend stabilization threshold for five consecutive time windows, and the absolute value of its moving average is also consistently lower than a preset optical static threshold, then the system can determine that the item has entered a stable state. This multi-dimensional, trend-based judgment method can effectively avoid misjudgments caused by instantaneous shaking or changes in lighting, ensuring the accuracy of judging the stable state of an object.

[0034] This application further proposes the following steps for calculating the transition lighting angle: obtaining the ambient light intensity of the desktop area; obtaining the user's preset visual comfort preference; determining the brightness and diffusion characteristics of the transition angle based on the ambient light intensity and visual comfort preference of the desktop area; and calculating the transition lighting angle based on the brightness and diffusion characteristics of the transition angle.

[0035] Among them, the ambient light intensity of the desktop area refers to the overall light level currently received by the desktop area from external light sources (such as natural light, indoor ambient light, etc.), which can be measured in real time using a light sensor or illuminance meter. Its purpose is to provide a basic reference for determining the brightness of the transition lighting. The user's preset visual comfort preference refers to the user's personalized preferences for light intensity, color temperature, softness, etc., which can be obtained through user interface input, preset configuration files, or by learning user history behavior data. Its purpose is to make the transition lighting more in line with the user's personalized needs. The brightness and diffusion characteristics of the transition angle refer to the brightness and diffusion characteristics of the object in unstable conditions. During the transition period, the intensity and diffusion of the light emitted by the light source can be determined by looking up tables, rule-based algorithms, or machine learning models based on the ambient light intensity and visual comfort preferences. The purpose is to ensure that the transition lighting provides sufficient visibility while avoiding glare or excessive darkness. The transition lighting angle refers to the specific projection direction that the light source should adjust to during the unstable state of the object. It can be determined by optical simulation calculations or preset mapping relationships based on the brightness and diffusion characteristics of the determined transition angle, combined with the optical model of the light source, the geometry and reflection characteristics of the tabletop object. The purpose is to guide the mechanical adjustment mechanism of the light source to make precise adjustments.

[0036] This application's solution acquires the ambient light intensity of the desktop area, providing real-time environmental background information for subsequent lighting adjustments and ensuring the coordination between the lighting scheme and the actual environment. Simultaneously, by acquiring the user's preset visual comfort preferences, it integrates the user's personalized needs into the lighting decision-making process, making the lighting experience more human-centered. Based on this input information, the system intelligently determines the brightness and diffusion characteristics of the transition angle. This considers not only objective environmental conditions but also the user's subjective experience, thus avoiding problems such as excessively bright or dim lighting, or overly concentrated or diffused light. Finally, based on these determined brightness and diffusion characteristics, the specific transition lighting angle is calculated, guiding precise adjustments to the light source.

[0037] In some preferred embodiments, this application is implemented as follows: First, to obtain the ambient light intensity of the desktop area, a high-precision ambient light sensor can be deployed, which can measure the illuminance value of the desktop area in real time and convert the analog signal into a digital signal, which is then transmitted to the control unit for processing. Second, to obtain the user's preset visual comfort preferences, the system can provide a user interface that allows the user to select preset lighting modes (e.g., "reading mode," "leisure mode," "focus mode"), or customize brightness preferences (e.g., "bright," "moderate," "dim") and diffusion preferences (e.g., "soft," "clear") through controls such as sliders. These preference settings can be stored in the device's non-volatile memory and read when needed. Then, based on the obtained ambient light intensity of the desktop area and the user's preset visual comfort preferences, the control unit can consult a pre-established lookup table or execute an algorithm based on fuzzy logic. For example, if the ambient light intensity is low and the user prefers "bright," the system can determine that the brightness of the transition angle should be at a higher level; if the user prefers "soft," the system determines that the diffusion characteristics should be high. The lookup table or algorithm maps ambient light intensity values ​​and visual comfort preferences to specific brightness values ​​(e.g., in lumens or candela) and diffuse coefficients (e.g., values ​​between 0 and 1, representing the degree of light diffusion). Finally, based on the determined brightness and diffuse characteristics of the transition angle, the control unit can calculate the transition lighting angle using a built-in optical model and inverse geometry calculations. This optical model includes the geometry of the light source, its luminous characteristics, and its relative position to the desktop area. Through inverse calculations, the system can determine the specific projection angle the light source needs to be adjusted to produce the desired brightness and diffuse characteristics on the desktop area. For example, if the goal is to achieve specific brightness and softness, the system can calculate how many degrees the light source needs to be tilted and whether additional diffusers or beam-shaping lenses need to be activated or adjusted to achieve the desired diffuse effect. The calculated angle values ​​are then sent to the light source's mechanical adjustment mechanism to perform the actual physical adjustments.

[0038] This application further proposes a step for initiating the recalculation and adjustment of the current light source projection angle, including: obtaining the current light source projection angle and recalculating the target projection angle based on a spatial model and a judgment threshold; if the difference between the target projection angle and the current light source projection angle exceeds a preset angle threshold, or if there are obstacles identified by the spatial model in the table area, generating several intermediate projection angles; wherein, the several intermediate projection angles are distributed between the current light source projection angle and the target projection angle, forming an adjustment path; for each of the several intermediate projection angles, predicting the illuminance distribution, spot position, and shadow area generated by each intermediate projection angle in the table area to form a prediction result; based on the prediction result, identifying any risk situation that occurs on the adjustment path: instantaneous glare, instantaneous dramatic shadow changes, and instantaneous secondary reflection spot entering the no-projection area; selecting an adjustment path, the adjustment path consisting of some or all of the several intermediate projection angles, ensuring that the frequency or intensity of any risk situation during the adjustment process is lower than a preset safety threshold; controlling the mechanical adjustment mechanism of the light source to adjust the light source step by step to the target projection angle along the selected adjustment path through several intermediate projection angles.

[0039] The spatial model refers to a three-dimensional representation of the desktop environment used for lighting assessment. It can include the geometry of the desktop area, the three-dimensional geometric data of objects, and user observation point information, aiming to provide an accurate basis for simulating light propagation and reflection. The judgment threshold refers to a preset standard used to assess whether the lighting condition needs adjustment. It can include glare thresholds, illuminance uniformity thresholds, and secondary reflection avoidance thresholds, aiming to define the boundaries between comfortable and functional lighting. The angle threshold refers to a preset value used to determine whether the difference between the current light source projection angle and the target projection angle needs fine-tuning. It can be an empirical value or a value determined based on the human eye's sensitivity to angle changes, aiming to... To avoid unnecessary path planning and optimize system response efficiency, several intermediate projection angles refer to multiple discrete or continuous angle points generated between the current light source projection angle and the target projection angle. These can be generated based on linear interpolation, curve interpolation, or specific optimization algorithms, aiming to decompose large angle adjustments into a series of small, controllable steps. The adjustment path refers to a sequence of light source angle adjustments composed of some or all of these intermediate projection angles. This can be a straight path, a curved path, or a path composed of discrete points, aiming to guide the light source smoothly from the current angle to the target angle. The prediction result refers to the prediction of the projection angles generated on the desktop area for each intermediate projection angle. The simulated lighting effect dataset, which may include illuminance distribution maps, light spot location coordinates, light spot size, and information on the boundaries and depth of shadow areas, aims to provide data support for risk identification. Among these, transient glare refers to the phenomenon where light directly or indirectly enters the user's line of sight during light source adjustment, causing visual discomfort or decreased vision. It can manifest as excessive brightness, excessive contrast, or uneven light distribution. Its purpose is to identify and avoid situations that negatively impact the user's visual comfort. Transient shadow abrupt changes refer to the phenomenon where the boundaries, shape, or depth of shadows in a desktop area change rapidly and significantly during light source adjustment, causing visual disturbance. This can manifest as shadows suddenly appearing, disappearing, or... Rapid movement aims to identify and avoid situations that interfere with the user's visual perception. Specifically, the instantaneous secondary reflection spot entering the no-light zone refers to the phenomenon where, during light adjustment, a bright spot formed by light reflected from objects on the table enters the user's observation point or a key area of ​​the table. This can manifest as a spot appearing within the user's line of sight or in an important work area. The purpose is to identify and avoid situations that interfere with the user's visual experience. The safety threshold refers to the acceptable upper limit for the frequency or intensity of any risky situation during the adjustment process. It can be set based on ergonomic data, user preferences, or industry standards, with the aim of ensuring the safety and comfort of the light source adjustment process.The mechanical adjustment mechanism refers to the physical device used to drive the light source to change its projection angle. It can include a stepper motor, servo motor, gear transmission system, or linkage mechanism, and its purpose is to achieve precise control of the light source's projection angle.

[0040] This application's solution effectively avoids the instantaneous visual interference that can occur with traditional direct adjustments by refining the adjustment process of the light source projection angle. When the system assesses that the lighting state has reached a preset threshold based on changes in the desktop environment, requiring recalculation and adjustment of the light source projection angle, the system first obtains the current light source projection angle and, based on a pre-built spatial model and the reached threshold, precisely recalculates the target projection angle that meets the lighting requirements of the current desktop environment. This recalculation process ensures that the adjusted lighting effect meets the user's requirements for glare, illuminance uniformity, and avoidance of secondary reflections. Furthermore, to avoid the visual impact that direct adjustments may cause, the system determines whether the difference between the target projection angle and the current light source projection angle exceeds a preset angle threshold, or whether there are obstacles identified by the spatial model in the desktop area. If either condition is met, the system intelligently generates several intermediate projection angles. These intermediate projection angles are distributed between the current angle and the target angle, thus forming a smooth adjustment path. This strategy of generating intermediate angles breaks down what might have been a sudden, large-angle adjustment into a series of small, gradual adjustments, which is crucial for reducing the risk of instantaneous glare and dramatic shadow changes. Meanwhile, obstacle identification ensures that the light source is not blocked during adjustment, thus maintaining the continuity and effectiveness of lighting. Furthermore, to ensure the safety of the adjustment path, the system predicts each intermediate projection angle along the path. Specifically, it predicts the illuminance distribution, spot position, and shadow area generated by each intermediate projection angle on the tabletop area, thus generating detailed prediction results. This predictive capability is one of the core innovations of this solution, enabling the system to identify potential risks before actual adjustment occurs. Based on these predictions, the system can identify any risk situation that may occur on the adjustment path, including instantaneous glare, sudden changes in shadow, and instantaneous secondary reflections entering the no-projection zone. This predictive mechanism allows the system to proactively avoid adverse visual effects, rather than reacting passively. Subsequently, the system intelligently selects an adjustment path based on the identified risk situations. This path can consist of some or all of several intermediate projection angles, and the selection principle is to ensure that the frequency or intensity of any risk situation is below a preset safety threshold throughout the adjustment process. This means the system prioritizes paths that minimize visual interference, even if it might require traversing more intermediate angles or taking more complex routes. This risk-based path selection mechanism significantly improves the safety and comfort of the lighting adjustment process. Ultimately, the system controls the light source's mechanical adjustment mechanism, guiding it along the selected adjustment path through several intermediate projection angles to smoothly adjust the light source to the target projection angle.By using this step-by-step, path-optimized adjustment method, the movement of the light source becomes smooth and controllable, effectively avoiding problems such as instantaneous glare, dramatic shadow changes, and secondary reflection spots that may result from rapid adjustments.

[0041] In some preferred embodiments, when the coffee table lighting system detects a change in the desktop environment, such as a user placing a highly reflective laptop on the coffee table, and the system assesses that the current lighting state has reached the glare threshold due to the laptop's presence, the system initiates a recalculation and adjustment process for the light source projection angle. First, the system obtains the current light source projection angle using a built-in angle sensor, for example, 30 degrees. Simultaneously, based on a pre-built desktop space model, which may include the laptop's three-dimensional geometric data and surface optical reflection characteristics, as well as the user's observation point information, the system recalculates a new target projection angle, for example, 45 degrees, to eliminate glare from the laptop surface. Next, the system determines whether the difference (15 degrees) between the current angle of 30 degrees and the target angle of 45 degrees exceeds a preset angle threshold, for example, 5 degrees. Since 15 degrees is greater than 5 degrees, the system generates several intermediate projection angles. These intermediate angles can be generated using linear interpolation. For example, an intermediate angle can be generated every 2 degrees between 30 and 45 degrees, forming a series of angle points: 32, 34, 36, 38, 40, 42, and 44 degrees, thus creating multiple potential adjustment paths. Subsequently, for each of these generated intermediate projection angles, the system utilizes its internal optical simulation module to simulate the propagation, reflection, and absorption of light on the desktop area using ray tracing technology, based on the 3D geometric data of the desktop area and the surface optical properties of the object. For example, for the intermediate angle of 38 degrees, the system simulates the illuminance distribution, spot position, and shadow area produced on the laptop surface, generating prediction results. Based on these predictions, the system identifies potential risks on each potential adjustment path. For instance, on a certain path, the system might predict that when the light source is adjusted to 36 degrees, there will be momentary glare; or at 40 degrees, the shadow at the edge of the laptop will change drastically; or at 42 degrees, the spot formed by the secondary reflection of light from the water glass might enter the no-projection zone where the user's observation point is located. Next, the system selects an adjustment path. For example, the system can construct a comprehensive cost function, which adjusts the weights of the risks of instantaneous glare, sudden changes in instantaneous shadows, and instantaneous secondary reflections entering the no-throw zone based on the user's preference data for different types of visual interference. Then, the system uses a path optimization algorithm, such as the A* algorithm or Dijkstra's algorithm, to select the path with the lowest comprehensive cost from all potential adjustment paths, ensuring that the frequency or intensity of all risk situations on this path is below a preset safety threshold. For example, the selected path may avoid angles that might pose risks, such as 36 degrees, 40 degrees, and 42 degrees, or choose the path with the lowest risk intensity.Finally, the system controls the mechanical adjustment mechanism of the light source, which can be a gear transmission system driven by a stepper motor. This mechanism moves the light source step-by-step along a selected adjustment path, passing through several intermediate projection angles, such as 30 degrees to 32 degrees to 34 degrees to 38 degrees to 44 degrees to 45 degrees, smoothly adjusting the light source to the target projection angle. This step-by-step adjustment ensures a smooth and visually undisturbed movement of the light source, guaranteeing a comfortable experience for the user throughout the entire lighting adjustment process.

[0042] This application further proposes a step for selecting an adjustment path, including: constructing a comprehensive cost function, which is used to comprehensively measure the risks of instantaneous glare, sudden changes in instantaneous shadows, and instantaneous secondary reflection spots entering the no-projection zone; and selecting an adjustment path from several intermediate projection angles based on the comprehensive cost function and a preset path optimization algorithm.

[0043] The comprehensive cost function refers to a mathematical model used to quantify and assess various visual interference risks that may occur during light source adjustment. Specifically, it normalizes the risks of instantaneous glare, sudden changes in instantaneous shadows, and instantaneous secondary reflections entering the no-projection zone, and then performs a comprehensive calculation through weighted summation or nonlinear combination. Its purpose is to unify different types of risks to a comparable scale, providing a quantitative basis for subsequent path selection. The preset path optimization algorithm refers to the calculation method for finding the optimal path under given constraints. Specifically, it can employ dynamic programming algorithms, A* search algorithms, genetic algorithms, or particle swarm optimization algorithms, etc. Its purpose is to efficiently select the adjustment path that minimizes the comprehensive cost function value from several intermediate projection angles.

[0044] This application's solution constructs a comprehensive cost function to quantify and integrate the risks of transient glare, sudden changes in shadow, and transient secondary reflections entering the no-projection zone along the adjustment path. Specifically, after generating several intermediate projection angles and predicting their illuminance distribution, spot position, and shadow area on the tabletop, and identifying any risk situation along the adjustment path, the system no longer simply judges whether each risk is below a safety threshold. Instead, it uses these identified risks as inputs for a unified numerical evaluation through the comprehensive cost function. This comprehensive cost function can transform risks of different natures, such as the intensity of glare, the degree of shadow change, and the extent to which secondary reflections enter the no-projection zone, into a single, comparable value. Subsequently, based on this comprehensive cost function, the system uses a preset path optimization algorithm to evaluate all possible adjustment paths. The goal of the path optimization algorithm is to find a path that minimizes the comprehensive cost function value, meaning that the path achieves a balance among various risk factors overall, rather than merely meeting the threshold requirements of a single risk. This method allows the system to select an adjustment path that performs optimally in terms of visual comfort from multiple conflicting risk factors.

[0045] In some preferred embodiments, the specific process of selecting an adjustment path can be as follows. First, when constructing the comprehensive cost function, it can be defined as: Cost = W_glare * R_glare + W_shadow * R_shadow + W_reflection * R_reflection. Wherein, R_glare represents the quantified value of the instantaneous glare risk, which can be calculated based on the predicted glare intensity, duration, or the relative position of the user's observation point and the light spot; R_shadow represents the quantified value of the instantaneous shadow change risk, which can be calculated based on the predicted shadow boundary movement speed, shadow area change rate, or shadow depth change; R_reflection represents the quantified value of the risk of the instantaneous secondary reflected light spot entering the no-throw zone, which can be calculated based on the area, brightness, or duration of the light spot entering the no-throw zone. W_glare, W_shadow, and W_reflection represent the weights of the risks of instantaneous glare, sudden changes in instantaneous shadow, and instantaneous secondary reflections entering the no-projection zone in the comprehensive cost function, respectively. These weights can be adjusted based on the user's preference data for different types of visual interference. For example, if the user is sensitive to glare, the weight of W_glare can be increased. Next, based on the constructed comprehensive cost function, a preset path optimization algorithm is used to select an adjustment path from several intermediate projection angles. For example, the A* search algorithm can be used, treating each intermediate projection angle as a node in a graph, assigning a corresponding comprehensive cost function value to the edge connecting adjacent intermediate projection angles. The A* algorithm efficiently searches for a path with the minimum total cost by evaluating the actual cost from the starting node to the current node and the estimated cost from the current node to the target node. Alternatively, a genetic algorithm can be used, encoding each possible adjustment path as an individual, and iteratively optimizing the individuals in the population through simulated natural selection, crossover, and mutation operations, ultimately converging to a path with the minimum comprehensive cost function. Through these specific implementation methods, the system can effectively weigh multiple risks and select the preferred adjustment path.

[0046] This application further proposes the following steps for constructing a comprehensive cost function: obtaining user preference data for different types of visual interference; adjusting the weights of the risks of instantaneous glare, instantaneous shadow changes, and instantaneous secondary reflection spots entering the no-throw zone in the comprehensive cost function based on the user preference data for different types of visual interference; and constructing the comprehensive cost function according to the adjusted weights.

[0047] The user preference data for different types of visual disturbances refers to information reflecting the user's sensitivity or importance to visual disturbances such as glare, dramatic shadow changes, and secondary reflected light spots. This data can be obtained through user questionnaires, user behavior analysis, biofeedback data, or machine learning model inference, with the aim of providing personalized optimization basis for the lighting system. Adjusting the weights of the risks of instantaneous glare, dramatic shadow changes, and instantaneous secondary reflected light spots entering the no-light zone in the comprehensive cost function refers to dynamically changing the proportion of each risk factor in the comprehensive cost function based on the obtained user preference data. This can be achieved using linear mapping, nonlinear function mapping, or rule-based expert systems, with the aim of making the comprehensive cost function more accurately reflect the user's avoidance priority for specific visual disturbances.

[0048] This application's solution achieves personalized construction of the comprehensive cost function by incorporating user preference data for different types of visual interference. Specifically, the system first acquires information on the user's sensitivity or level of importance to visual interferences such as instantaneous glare, sudden changes in shadow, and instantaneous secondary reflections entering the no-projection zone. Based on this personalized preference data, the system can dynamically adjust the weight of each visual risk in the comprehensive cost function. For example, if the user shows high sensitivity to glare, the weight of glare risk in the comprehensive cost function will be increased accordingly, thus giving glare avoidance a higher priority during path optimization. Conversely, if the user becomes concerned about sudden changes in shadow, the weight of sudden changes in shadow will be increased. This dynamic weight adjustment mechanism makes the final constructed comprehensive cost function no longer a fixed, general model, but a customized evaluation tool that accurately reflects the visual needs of specific users. Based on this, when the system needs to select an adjustment path, it combines the personalized comprehensive cost function constructed in the aforementioned solution and uses a preset path optimization algorithm to select an adjustment path from several intermediate projection angles.

[0049] In some preferred embodiments, to obtain user preference data for different types of visual interference, the system can provide a user interface containing multiple adjustable sliders or selection boxes, corresponding to three types of visual interference: transient glare, transient shadow changes, and transient secondary reflections entering the no-projection zone. Users can express their level of importance for each type of interference by dragging the sliders or selecting options based on their visual sensitivity. For example, the slider value range can be set from 0 to 10, where 10 represents extreme avoidance of that type of interference. Based on the user's input preference data, the system can employ a linear mapping algorithm to adjust the weights of each risk in the comprehensive cost function. Specifically, for each type of visual interference, its weight can be proportionally allocated according to the user's input preference values. For example, if the user's preference value for glare is 8, for sudden shadow changes is 5, and for secondary reflections is 3, the system can calculate the corresponding weight coefficients based on these values, ensuring that the sum of all weights is 1. Based on the adjusted weights, the system can construct a comprehensive cost function. This function can be expressed as a weighted sum of each risk value and its corresponding adjusted weight. For example, the comprehensive cost function C can be expressed as: C = W_glare * R_glare + W_shadow * R_shadow + W_spotlight * R_spotlight, where W represents the adjusted weights, and R represents the risk values ​​of instantaneous glare, sudden changes in instantaneous shadows, and instantaneous secondary reflections entering the no-projection zone. In this way, when a user has a higher preference value for a certain visual disturbance, that disturbance has a correspondingly higher weight in the comprehensive cost function. Therefore, in the subsequent path optimization algorithm, the system will prioritize the adjustment path that minimizes this type of risk.

[0050] This application further proposes a method for predicting the illuminance distribution, spot position, and shadow area generated by each intermediate projection angle on a tabletop area to form a prediction result. The method includes: acquiring three-dimensional geometric data of the tabletop area and surface optical property data of the object; simulating the propagation, reflection, and absorption of light on the tabletop area using ray tracing or optical simulation technology based on the three-dimensional geometric data of the tabletop area, the surface optical property data of the object, and the intermediate projection angle; and calculating the illuminance distribution, spot formation position and range, and shadow boundary and depth of the tabletop area based on the simulation results to form a prediction result.

[0051] Three-dimensional geometric data refers to the shape, size, and position information of the tabletop area and objects on it in three-dimensional space. It can be represented in the form of point cloud data, mesh models, CAD models, or voxel data, and its purpose is to provide spatial environment information for simulating light propagation. Surface optical property data refers to the physical properties describing the interaction between the object's surface and light, such as reflectivity, absorptivity, transmittance, scattering characteristics, and the ratio of specular reflection to diffuse reflection. It can be represented in the form of bidirectional reflectance distribution function (BRDF), bidirectional transmittance distribution function (BTDF), or spectral reflectance curves, and its purpose is to simulate the behavior of light on the object's surface. Ray tracing is a computer graphics and optical simulation technique that simulates the process of light emanating from a light source, propagating through a scene, interacting with object surfaces (e.g., reflection, refraction, absorption), and finally reaching the observation point. It can be implemented using algorithms such as Monte Carlo ray tracing, path tracing, or bidirectional path tracing, and its purpose is to calculate the propagation path and energy distribution of light in the environment. Optical simulation technology refers to a comprehensive technique that uses numerical methods or physical models to simulate the propagation of light in a medium and its interaction with matter. It can be implemented using methods such as the finite element method, the finite difference time-domain method, or the radiometric method. Its purpose is to evaluate lighting effects, including optical phenomena such as diffuse reflection and scattering. Illuminance distribution refers to the luminous flux received per unit area at various points within a desktop region, i.e., the spatial distribution of light intensity. Its purpose is to quantify the uniformity and brightness level of lighting in the desktop region. The location and extent of light spots refer to specific locations on the desktop region where light is concentrated and brighter than surrounding areas, along with the size of the space they occupy. Its purpose is to identify potential glare sources or areas of concentrated light. The boundary and depth of shadows refer to dark areas within the desktop region formed by objects blocking light. The boundary is the dividing line between the shadowed and unshadowed areas, and the depth refers to the degree of darkness or reduction in light intensity within the shadowed area. Its purpose is to assess the impact of shadows on visual comfort and the ability to discern details of objects.

[0052] This application's solution provides an environmental foundation for subsequent lighting simulation by acquiring three-dimensional geometric data of the desktop area and surface optical property data of the objects. The three-dimensional geometric data of the desktop area describes the shape and spatial structure of the desktop and objects, while the surface optical property data reflects the object's ability to reflect, absorb, and transmit light, allowing subsequent light propagation simulations to be built upon a physical model. Based on this environmental data and each intermediate projection angle, ray tracing or optical simulation techniques are used to simulate the propagation, reflection, and absorption of light in the desktop area. This simulation method can calculate the path of light in the environment, intensity changes, and interactions with object surfaces, thus overcoming the limitations of basic prediction methods that cannot fully consider the propagation, reflection, and absorption of light in the environment. Finally, based on these simulation results, the illuminance distribution of the desktop area, the location and extent of light spots, and the boundaries and depth of shadows are calculated to form prediction results. This calculation can quantify light intensity, identify areas of concentrated light, and assess the impact of shadows, thereby providing predictions of lighting parameters.

[0053] This predictive capability is integrated with the overall process of recalculating and adjusting the light source projection angle in this application. After generating several intermediate projection angles, this scheme can provide predicted results for illuminance distribution, spot position, and shadow area for each intermediate projection angle. These predicted results serve as the basis for identifying potential risks such as transient glare, sudden changes in shadow, and transient secondary reflections entering restricted areas along the adjustment path. It is precisely because of this prediction that the system can assess the potential risks at each intermediate angle and select an adjustment path where the frequency or intensity of risks during adjustment is lower than a preset safety threshold, ensuring the accuracy and effectiveness of the lighting angle adjustment and avoiding the problem of actual lighting effects not meeting expectations due to prediction deviations.

[0054] In some preferred embodiments, this application is implemented as follows: First, to acquire the three-dimensional geometric data of the tabletop area and the surface optical property data of the objects, a structured light sensor or lidar system integrated inside the coffee table can be used to scan the tabletop area, thereby obtaining the depth information of the tabletop and the objects on it. This depth information can be converted into point cloud data by a processing unit, and then a three-dimensional mesh model of the tabletop area can be constructed as the three-dimensional geometric data. Simultaneously, a multispectral imaging module can be used to perform a spectral scan of the surface of the objects on the tabletop, acquiring the reflectance spectral information of the objects at different wavelengths. After analysis, this spectral information can yield the surface optical property data of the objects, such as their diffuse reflectance coefficient and specular reflectance coefficient in the visible light range.

[0055] Next, based on the acquired 3D geometric data, surface optical property data, and intermediate projection angle, a graphics processing unit (GPU) can be used to execute a ray tracing algorithm. This algorithm simulates light rays emitted from a light source, tracing their reflection and absorption processes on the surfaces of objects within a 3D mesh model on a desktop area. For example, when light shines on a surface with specular reflection properties, the algorithm calculates the direction and intensity of the reflected light according to Fresnel's equations; when light shines on a diffuse reflection surface, it simulates uniform scattering of light in all directions.

[0056] Finally, based on the ray tracing simulation results, the processing unit can discretize the desktop area, dividing it into several tiny grids. For each grid, by accumulating the light energy reaching that grid, the illuminance value of that grid can be calculated, thus forming an illuminance distribution map of the entire desktop area. Simultaneously, by analyzing areas of concentrated light energy, the location and extent of light spots can be identified, such as continuous areas with brightness exceeding a certain threshold. For shadowed areas, their boundaries can be determined by judging whether a specific area is blocked by an object and unable to receive direct light, and the depth of the shadow can be quantified by calculating the illuminance difference between this area and the surrounding non-shadowed areas. These calculation results collectively constitute the predicted lighting effect on the desktop area for each intermediate projection angle.

[0057] This application further proposes steps for obtaining three-dimensional geometric data of a desktop area and surface optical property data of an object, including: obtaining depth information of the desktop area; constructing three-dimensional geometric data of the desktop area based on the depth information of the desktop area; obtaining reflectance spectral information of the object; and resolving the reflectance spectral information of the object to obtain surface optical property data of the object.

[0058] The depth information of the desktop area refers to the spatial distance data of each point within the desktop area obtained from a specific perspective. This can be achieved using technologies such as structured light sensors, time-of-flight (ToF) sensors, or stereo vision systems. Its purpose is to provide the basic data for constructing the three-dimensional spatial structure of the desktop area. The three-dimensional geometric data of the desktop area refers to the data set that digitally describes the spatial shape and size of the desktop and the objects on it. This can be represented in the form of point clouds, triangular mesh models, or voxel models. Its purpose is to accurately reproduce the physical layout of the desktop environment. The reflectance spectral information of the objects refers to the continuous or discrete measurement data of the reflectance of the object's surface to different wavelengths of light. This can be obtained using a spectrometer or multispectral imaging equipment. Its purpose is to comprehensively capture the color and reflectance characteristics of the objects. The surface optical property data of the objects refers to the physical parameters that describe the way the object's surface interacts with light. This can include diffuse reflection coefficient, specular reflection coefficient, roughness, or refractive index. Its purpose is to provide accurate physical model parameters for ray tracing or optical simulation.

[0059] This application's solution systematically acquires depth information of the desktop area and reflectance spectral information of objects, thereby efficiently and accurately constructing the key data required for illumination prediction. Specifically, firstly, by acquiring the depth information of the desktop area, the spatial layout and shape of the desktop and its objects can be quickly and automatically captured, and then the three-dimensional geometric data of the desktop area can be constructed based on this depth data. This method avoids the tediousness and potential errors of traditional manual modeling, significantly improving the efficiency and accuracy of data acquisition. Secondly, by acquiring the reflectance spectral information of objects, the reflectance characteristics of the object's surface to different wavelengths of light can be comprehensively recorded. After analysis, this spectral information can accurately obtain surface optical property data such as the object's color, gloss, and reflectivity. Compared to measurements of a single wavelength or a limited band, the reflectance spectrum provides a richer and more accurate optical description, enabling subsequent light simulation to more realistically reflect the interaction between light and objects. It is precisely because of this efficient and accurate data acquisition mechanism that subsequent steps in predicting the illuminance distribution, spot position, and shadow area generated by each intermediate projection angle on the desktop area can obtain high-quality input data. This ensures the accuracy of ray tracing or optical simulation technology in simulating the propagation, reflection, and absorption of light, thus making the prediction results more reliable.

[0060] In some preferred embodiments, acquiring the three-dimensional geometric data of the tabletop area and the surface optical property data of the objects can be specifically implemented as follows. First, to acquire the depth information of the tabletop area, a structured light camera integrated into the coffee table lighting system can be used. This camera projects light of a known pattern onto the tabletop area and captures images of the deformation on the tabletop and object surfaces. By analyzing these deformation patterns, the precise depth value of each visible point within the tabletop area can be calculated, forming a high-density depth map. Next, based on this depth map, the three-dimensional geometric data of the tabletop area can be constructed. For example, the depth map can be converted into three-dimensional point cloud data, and then, through point cloud processing algorithms such as Poisson reconstruction or triangulation, a triangular mesh model of the tabletop area and the objects on it can be generated. This model can accurately represent the shape, size, and spatial position of the objects. Simultaneously, to acquire the reflectance spectral information of the objects, a miniature spectral sensor can be used. This sensor, during environmental monitoring by the coffee table lighting system, emits broadband light towards the objects on the tabletop and receives the light reflected back from the object surfaces. The sensor decomposes the received light into spectral data of different wavelengths, forming the reflectance spectral curve of the objects. Finally, based on this reflectance spectral information, the surface optical property data of the objects can be obtained. For example, a predefined bidirectional reflectance distribution function (BRDF) model can be fitted to spectral data to extract parameters such as diffuse reflectance, specular reflectance, and roughness of an object. These parameters accurately describe the absorption, scattering, and reflection characteristics of light on the object's surface, providing precise physical input for subsequent optical simulations.

[0061] refer to Figure 2 This application further proposes a coffee table lighting angle adjustment control system, applied to a coffee table lighting angle adjustment control method. The system includes: a processing module, used to acquire optical reflection characteristic data of the desktop area, three-dimensional geometric data of objects, and user observation point information, and construct a spatial model for lighting evaluation; an evaluation and adjustment module, which, when detecting changes in the desktop environment, including objects entering an unstable state, evaluates whether the lighting state touches a preset judgment threshold based on the current light source projection angle. The judgment thresholds include a glare threshold, an illuminance uniformity threshold, and a secondary reflection avoidance threshold; if any judgment threshold is touched, the current light source projection angle is recalculated and adjusted; if no judgment threshold is touched, the current light source projection angle is maintained.

[0062] The processing module refers to the computing unit responsible for data acquisition, preprocessing, and model building. It can be implemented using integrated circuits, microcontrollers, dedicated processors, or computing units containing these components. Its purpose is to provide basic data and environmental representation for subsequent lighting evaluation and angle adjustment. The evaluation and adjustment module refers to the control unit responsible for evaluating the lighting status based on real-time data and triggering or executing light source angle adjustments based on the evaluation results. It can be implemented using software programs running specific algorithms, hardware logic circuits, or a combination of both. Its purpose is to ensure that the lighting effect meets preset standards and dynamically optimize the light source projection angle when necessary. The spatial model refers to the model of the desktop area. The system provides a 3D representation of the lighting environment, including its internal components, light sources, and user observation positions. This representation can be achieved using digital structures built from point cloud data, mesh models, voxel data, or parametric geometric models. Its purpose is to simulate light propagation, reflection, and absorption, predict lighting effects, and serve as a basis for lighting evaluation and angle recalculation. The judgment threshold is a preset standard used to measure whether the lighting state meets the requirements of comfort, uniformity, and safety. It can be implemented using numerical ranges, Boolean conditions, or critical values ​​based on specific optical metrics. Its purpose is to serve as a decision-making basis for triggering the recalculation and adjustment of the light source angle, ensuring lighting quality and user experience.

[0063] The processing module first undertakes the fundamental tasks of data acquisition and model building in the method. It is responsible for collecting optical reflection characteristics data of the desktop area, three-dimensional geometric data of objects, and user observation point information, and constructing an accurate spatial model for lighting evaluation based on this multi-source data. This spatial model is the digital foundation for all subsequent lighting analysis and decision-making, enabling the system to simulate the behavior of light in complex desktop environments. Building upon this, the evaluation and adjustment module, as the core decision-making and execution unit of the system, closely connects to and implements the dynamic evaluation and adjustment logic in the method. When this module detects changes in the desktop environment, especially when objects enter an unstable state, it performs a real-time evaluation of the lighting status based on the current light source projection angle. The evaluation process compares the lighting status against preset judgment thresholds, which cover multiple dimensions such as glare, illuminance uniformity, and secondary reflection avoidance, ensuring the comfort, quality, and safety of the lighting. If the evaluation results show that the lighting status has reached any judgment threshold, indicating a potential lighting problem, the evaluation and adjustment module immediately initiates a recalculation and adjustment process of the current light source projection angle to optimize the lighting effect. Conversely, if the lighting status does not reach any judgment threshold, the system will maintain the current projection angle of the light source, avoiding unnecessary frequent adjustments and thus ensuring the continuity and stability of the lighting. This systematic implementation allows the originally abstract methods and steps to be implemented at the physical level. The processing module provides the evaluation and adjustment modules with accurate environmental perception and simulation capabilities, while the evaluation and adjustment modules translate these perceptions into intelligent lighting decisions and actions. The two work closely together to build a closed-loop control system that can dynamically respond to changes in the desktop environment and automatically optimize the lighting angle. This effectively solves the problem that it is difficult to achieve intelligent lighting adjustment in practical applications based solely on methodological limitations, and improves the system's automation level and user experience.

[0064] In some preferred embodiments, this application is implemented as follows: A coffee table lighting angle adjustment control system can be integrated inside a smart coffee table. The processing module can be an embedded main control unit, such as a high-performance microprocessor, which can be connected to a depth camera to acquire real-time 3D geometric data of objects on the tabletop, and simultaneously connected to a multispectral sensor to collect optical reflection characteristic data of the object surfaces. User observation point information can be acquired through a small eye-tracking sensor or a preset user position sensor. The processing module internally runs 3D reconstruction algorithms and optical characteristic analysis algorithms, capable of integrating the acquired data and constructing an accurate desktop space model. This model can be a digital twin containing geometric shape, material properties, and lighting environment information. The evaluation and adjustment module can be a software service running on the processing module or a separate control chip. When the depth camera or user interaction detects a change in the desktop environment, such as the placement of new objects or the movement of existing objects, causing the objects to enter an unstable state, the evaluation and adjustment module will immediately activate. Based on the current light source projection angle, it uses the spatial model constructed by the processing module to perform light simulation, predicting glare, illuminance uniformity, and secondary reflection under the current lighting conditions. For example, the glare threshold can be set to a specific brightness value, the illuminance uniformity threshold can be set to the upper limit of the standard deviation of illuminance in the desktop area, and the secondary reflection avoidance threshold can be set to prevent reflected light spots from entering the user's line of sight or specific sensitive areas. If the simulation results show that any of the judgment thresholds are triggered, such as when glare is predicted, the evaluation and adjustment module will immediately initiate a recalculation of the light source projection angle and generate an optimized target projection angle. Subsequently, it will send control commands to the light source mechanical adjustment mechanism inside the coffee table, such as a universal joint or linear guide driven by a stepper motor, to adjust the light source to the new target angle step by step or smoothly. If the evaluation results show that none of the judgment thresholds are triggered, the system will maintain the current light source projection angle to maintain the stability and continuity of the lighting.

[0065] The above-disclosed content is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A method for adjusting and controlling the lighting angle of a coffee table, characterized in that, The method includes the following steps: Acquire optical reflection characteristics data of the desktop area, three-dimensional geometric data of objects, and user observation point information, and construct a spatial model for lighting evaluation; When changes are detected in the desktop environment, such as items entering an unstable state, the system assesses whether the lighting state has reached a preset threshold based on the current light source projection angle. The thresholds include glare threshold, illuminance uniformity threshold, and secondary reflection avoidance threshold. If any threshold is reached, the current projection angle of the light source is recalculated and adjusted; if no threshold is reached, the current projection angle of the light source is maintained. The steps for initiating the recalculation and adjustment of the current light source projection angle include: Obtain the current projection angle of the light source, and recalculate the target projection angle based on the spatial model and the judgment threshold; If the difference between the target projection angle and the current light source projection angle exceeds a preset angle threshold, or if there are obstacles in the desktop area identified by the spatial model, several intermediate projection angles are generated; among them, several intermediate projection angles are distributed between the current light source projection angle and the target projection angle, forming an adjustment path; For each of several intermediate projection angles, predict the illuminance distribution, spot position, and shadow area generated by each intermediate projection angle on the tabletop area to form a prediction result; Based on the prediction results, identify any risk situation that occurs on the adjustment path: instantaneous glare, instantaneous dramatic changes in shadow, and instantaneous secondary reflection spot entering the no-throw zone; Select an adjustment path, which consists of some or all of several intermediate projection angles, to ensure that the frequency or intensity of any risk situation during the adjustment process is lower than the preset safety threshold. The mechanical adjustment mechanism that controls the light source adjusts the light source step by step to the target projection angle along the selected adjustment path through several intermediate projection angles.

2. The method for adjusting and controlling the lighting angle of a coffee table as described in claim 1, characterized in that, When a change in the desktop environment is detected, the steps for assessing whether the lighting status has reached a preset threshold based on the current light source projection angle include: Identify the initiation of an unstable state of an item; During periods of instability, the current light source projection angle is adjusted to a preset transition lighting angle; Continuously monitor the physical state of the items to determine whether they have entered a stable state; When the item reaches a stable state, the lighting state is evaluated based on the transition lighting angle to determine whether it has reached a preset threshold.

3. The method for adjusting and controlling the lighting angle of a coffee table as described in claim 2, characterized in that, The steps for continuously monitoring the physical state of an item and determining whether it has entered a stable state include: Continuously acquire the three-dimensional geometric data and surface optical property data of the object; Calculate the changes in the three-dimensional geometric data and surface optical properties of an object between consecutive time frames; Analyze the trend of change in the amount of change within a preset time window; When the fluctuation range of the trend is consistently lower than the preset trend stabilization threshold, and the absolute value of the change is consistently lower than the preset static threshold, the item is determined to have entered a stable state.

4. The method for adjusting and controlling the lighting angle of a coffee table as described in claim 2, characterized in that, The steps for calculating the transition lighting angle include: Get the ambient light intensity of the desktop area; Obtain the user's preset visual comfort preferences; The brightness and diffusion characteristics of the transition angle are determined based on the ambient light intensity and visual comfort preferences of the desktop area. The transition lighting angle is calculated based on the brightness and diffusion characteristics of the transition angle.

5. The method for adjusting and controlling the lighting angle of a coffee table as described in claim 1, characterized in that, The steps to select an adjustment path include: A comprehensive cost function is constructed to comprehensively measure the risks of instantaneous glare, sudden changes in instantaneous shadows, and instantaneous secondary reflected light spots entering the no-throw zone; Based on the comprehensive cost function, a preset path optimization algorithm is used to select an adjustment path from several intermediate projection angles.

6. The method for adjusting and controlling the lighting angle of a coffee table as described in claim 5, characterized in that, The steps to construct the comprehensive cost function include: Obtain user preference data for different types of visual disturbances; Based on user preference data for different types of visual interference, the weights of the risks of instantaneous glare, sudden changes in instantaneous shadows, and instantaneous secondary reflected light spots entering the no-throw zone in the comprehensive cost function are adjusted; Based on the adjusted weights, construct the comprehensive cost function.

7. The method for adjusting and controlling the lighting angle of a coffee table as described in claim 1, characterized in that, For each of a number of intermediate projection angles, the steps to predict the illuminance distribution, spot position, and shadow area produced by each intermediate projection angle on the tabletop area to form the prediction result include: Acquire 3D geometric data of the desktop area and surface optical property data of the object; Based on the three-dimensional geometric data of the desktop area, the surface optical properties of the object, and the intermediate projection angle, ray tracing or optical simulation technology is used to simulate the propagation, reflection, and absorption of light in the desktop area. Based on the simulation results, the illuminance distribution, the location and range of light spots, and the boundary and depth of shadows in the desktop area are calculated to form prediction results.

8. The method for adjusting and controlling the lighting angle of a coffee table as described in claim 7, characterized in that, The steps for acquiring the 3D geometric data of the desktop area and the surface optical property data of the object include: Obtain depth information of the desktop area; Construct three-dimensional geometric data of the desktop area based on the depth information of the desktop area; Obtain the reflectance spectrum information of the item; The surface optical properties of an object are obtained by analyzing its reflectance spectrum information.

9. A coffee table lighting angle adjustment control system, applied to the coffee table lighting angle adjustment control method described in claim 1, characterized in that, The system includes: The processing module is used to acquire optical reflection characteristics data of the desktop area, three-dimensional geometric data of objects, and user observation point information, and to construct a spatial model for lighting evaluation. The evaluation and adjustment module detects changes in the desktop environment, including items entering an unstable state. It assesses whether the lighting state has reached preset thresholds based on the current light source projection angle. These thresholds include glare threshold, illuminance uniformity threshold, and secondary reflection avoidance threshold. If any threshold is reached, the current light source projection angle is recalculated and adjusted. If no threshold is reached, the current light source projection angle is maintained.

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