Illumination adjusting method and system, lighting equipment, table and readable storage medium

Abstract

The invention discloses an illumination adjustment method and system, illumination equipment, a table and a readable storage medium, and the method comprises the steps: collecting perception data which comprises a task type, a user posture, an environment parameter, an illumination parameter in a target region and an equipment operation state of electronic equipment used by a user during work, the illumination parameters comprise illumination and color temperature, and the task type is a current work activity type identified based on the use of electronic equipment by a user; based on the task type, the equipment operation state, the user posture, the environment parameters and the illumination parameters, environment preference models corresponding to the user in different task scenes are constructed, and the task scenes are determined based on the equipment operation state and the task type; and according to the environment preference model, in combination with the task type, the equipment operation state, the user posture and the environment parameters, generating an adjustment instruction for controlling the lighting equipment so as to adjust the illumination and the color temperature irradiated to the target area. The user experience can be improved.

Description

Technical Field

[0001] This invention relates to the field of lighting control technology, and in particular to a lighting adjustment method, system, lighting equipment, table, and readable storage medium. Background Technology

[0002] Users' environmental needs vary significantly depending on the time period and the tasks they perform. However, current equipment adjustments in learning environments generally rely on manual operation, which not only easily disrupts workflows but also makes it difficult to accurately match real-time requirements.

[0003] Among related technologies, environmental control technologies mainly fall into three categories, but they still have significant limitations: single-device timed control type, such as smart lights with a "cool light in the morning, warm light in the afternoon" timed mode, can only mechanically switch at fixed times, which cannot be associated with the user's current task type and ignores individual differences; manual scene memory type requires users to pre-save scene parameters such as "code mode" and "creative mode" and actively trigger them when switching (such as pressing the remote control), which can easily interrupt the state of concentration, and the parameters are fixed and cannot be automatically updated with habits; simple sensor linkage type can only respond to changes in physical quantities such as light to realize brightness adjustment or on / off control, with limited adjustment dimensions. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0005] Therefore, one objective of this invention is to propose a lighting adjustment method that can automatically optimize lighting parameters based on dynamic changes in user habits. This method not only achieves fully automated and seamless intelligent adjustment but also expands the adjustment dimensions. Furthermore, it possesses strong adaptability, high personalization, and real-time response capabilities, effectively meeting the immediate needs of users in different work scenarios and thereby improving the user experience.

[0006] Therefore, a second objective of the present invention is to provide a light regulation system.

[0007] Therefore, a third objective of the present invention is to provide a lighting device.

[0008] Therefore, the fourth objective of this invention is to provide a table.

[0009] Therefore, a fifth objective of the present invention is to provide a computer-readable storage medium.

[0010] To achieve the above objectives, a first aspect of the present invention discloses a lighting adjustment method for a lighting device. The method includes: collecting sensing data, the sensing data including task type, user posture, environmental parameters, lighting parameters on a target area, and the device operating status of an electronic device used by the user, wherein the lighting parameters include illuminance and color temperature, and the task type is the current work activity type identified by the user using the electronic device; constructing an environmental preference model for the user under different task scenarios based on the task type, the device operating status, the user posture, the environmental parameters, and the lighting parameters, wherein the task scenario is determined based on the device operating status and the task type; and generating an adjustment command for controlling the lighting device to adjust the illuminance and color temperature irradiated on the target area based on the environmental preference model and in combination with the task type, the device operating status, the user posture, and the environmental parameters.

[0011] According to the lighting adjustment method of this invention, by collecting multi-dimensional sensing data such as task type, user posture, environmental parameters, lighting parameters on the target area, and the device operating status of the electronic devices used by the user, a user environmental preference model corresponding to different task scenarios is constructed. Based on this environmental preference model, combined with task type, device operating status, user posture, and environmental parameters, an adjustment command for controlling the lighting device is generated to adjust the illuminance and color temperature illuminating the target area. This allows for automatic optimization of lighting parameters according to dynamic changes in user habits, achieving not only fully automated and seamless intelligent adjustment but also expanding the adjustment dimensions. Furthermore, it possesses strong adaptability, high personalization, and real-time response capabilities, effectively adapting to the immediate needs of users in different work scenarios, thereby improving the user experience.

[0012] In addition, the illumination adjustment method according to the above embodiments of the present invention may also have the following additional technical features: In some embodiments, when constructing an environmental preference model for the user under different task scenarios based on the task type, the device operating state, the user posture, the environmental parameters, and the illumination parameters, the method includes: acquiring historical perception data, which includes historical task types, historical device operating states, historical user postures, historical environmental parameters, and historical illumination parameters; establishing a mapping relationship between historical task scenarios and the historical illumination parameters, the historical user postures, and the historical environmental parameters based on the historical perception data and a preset learning algorithm, to determine the optimal range of illumination parameters under the corresponding task scenario and the individual tolerance threshold of the user for the historical illumination parameters, wherein the historical task scenario is determined based on the historical task type and the historical device operating state; and storing the optimal range of illumination parameters and the individual tolerance threshold in association to obtain the environmental preference model.

[0013] In some embodiments, the illumination adjustment method further includes: acquiring user feedback information on the illumination parameters; updating the optimal range of illumination parameters corresponding to illuminance and color temperature irradiated on the target area and the individual tolerance threshold according to the feedback information at preset time intervals; updating the environmental preference model based on the updated optimal range of illumination parameters and the updated individual tolerance threshold; and outputting a warning message to characterize the abnormal illumination parameters when at least one parameter of illuminance and color temperature on the target area is detected to exceed its corresponding individual tolerance threshold.

[0014] In some embodiments, obtaining feedback information from the user regarding the current illumination parameters includes: monitoring the user's adjustment operation on the illumination parameters, determining the adjusted illumination parameters, and generating first feedback information based on the illumination parameters and the adjusted illumination parameters; and / or obtaining the user's comfort evaluation information regarding the illumination parameters, and generating second feedback information based on the comfort evaluation information.

[0015] In some embodiments, when generating adjustment instructions for controlling the lighting device to adjust the illuminance and color temperature irradiated onto a target area based on the environmental preference model and in combination with the task type, the device operating state, the user posture, and the environmental parameters, the process includes: determining the current task scenario based on the current task type and the current operating state; determining initial target values ​​for the illuminance and the color temperature within the optimal range of illumination parameters corresponding to the current task scenario in the environmental preference model; if any initial target value exceeds its individual tolerance threshold corresponding to the current task scenario in the environmental preference model, then constraining the initial target value to a range not exceeding the individual tolerance threshold; combining all constrained initial target values ​​to form the target illumination parameters; generating the adjustment instructions based on the target illumination parameters; and executing the adjustment instructions according to a preset adjustment sequence to adjust the illuminance and color temperature irradiated onto the target area.

[0016] In some embodiments, when executing the adjustment command according to a preset adjustment sequence, the method further includes: obtaining the illumination flicker depth on the target area; if the fluctuation amplitude of the flicker depth exceeds its corresponding preset safety threshold, pausing the execution of the adjustment command and outputting an adjustment interruption prompt.

[0017] To achieve the above objectives, a second aspect of the present invention discloses a lighting adjustment system for a lighting device. The system includes: a sensing module for collecting sensing data, the sensing data including task type, user posture, environmental parameters, lighting parameters on a target area, and the device operating status of an electronic device used by the user, wherein the lighting parameters include illuminance and color temperature, and the task type is the current work activity type identified by the user using the electronic device; a learning module for constructing an environmental preference model corresponding to the user in different task scenarios based on the task type, the device operating status, the user posture, the environmental parameters, and the lighting parameters, wherein the task scenario is determined based on the device operating status and the task type; and an adjustment module for generating adjustment commands for controlling the lighting device, based on the environmental preference model and in combination with the task type, the device operating status, the user posture, and the environmental parameters, to adjust the illuminance and color temperature irradiated on the target area.

[0018] According to the lighting adjustment system of this invention, the sensing module collects task type, user posture, environmental parameters, lighting parameters on the target area, and the operating status of the electronic devices used by the user. The learning module uses this multi-dimensional sensing data as input to construct an environmental preference model corresponding to the user in different task scenarios. Then, based on this environmental preference model, the adjustment module generates adjustment commands to control the lighting equipment in combination with task type, device operating status, user posture, and environmental parameters to adjust the illuminance and color temperature illuminating the target area. This allows for automatic optimization of lighting parameters according to dynamic changes in user habits, achieving not only fully automated and seamless intelligent adjustment but also expanding the adjustment dimensions. It also possesses strong adaptability, high personalization, and real-time response capabilities, effectively adapting to the user's immediate needs in different work scenarios, thereby improving the user experience.

[0019] To achieve the above objectives, a third aspect of the present invention discloses a lighting device, comprising: the light adjustment system described in the second aspect of the present invention.

[0020] According to the lighting device of the present invention, by collecting multi-dimensional sensing data such as task type, user posture, environmental parameters, illumination parameters on the target area, and the device operating status of the electronic devices used by the user, a user environmental preference model corresponding to different task scenarios is constructed. Based on this environmental preference model, combined with task type, device operating status, user posture, and environmental parameters, adjustment commands for controlling the lighting device are generated to adjust the illuminance and color temperature illuminating the target area. This allows for automatic optimization of illumination parameters according to dynamic changes in user habits, achieving not only fully automated and seamless intelligent adjustment but also expanding the adjustment dimensions. Furthermore, it possesses strong adaptability, high personalization, and real-time response capabilities, effectively adapting to the user's immediate needs in different work scenarios, thereby improving the user experience.

[0021] To achieve the above objectives, an embodiment of the fourth aspect of the present invention discloses a table equipped with the lighting device described in the embodiment of the third aspect of the present invention, for implementing the light adjustment method as described in any one of the first aspects of the present invention.

[0022] According to the embodiments of the present invention, the table collects multi-dimensional sensing data such as task type, user posture, environmental parameters, illumination parameters on the target area, and the operating status of the electronic devices used by the user. This data is used as input to construct an environmental preference model for the user in different task scenarios. Based on this model, and combined with task type, device operating status, user posture, and environmental parameters, adjustment commands for controlling the lighting equipment are generated to adjust the illuminance and color temperature illuminating the target area. This allows for automatic optimization of illumination parameters based on dynamic changes in user habits. It not only achieves fully automated and seamless intelligent adjustment but also expands the adjustment dimensions. Furthermore, it possesses strong adaptability, high personalization, and real-time response capabilities, effectively meeting the immediate needs of users in different work scenarios, thereby improving the user experience.

[0023] To achieve the above objectives, a fifth aspect of the present invention discloses a computer-readable storage medium storing a light adjustment program, which, when executed by a processor, implements the light adjustment method as described in any embodiment of the first aspect of the present invention.

[0024] According to embodiments of the present invention, when a computer-readable storage medium storing a lighting adjustment program thereon is executed by a processor, it collects multi-dimensional sensing data such as task type, user posture, environmental parameters, lighting parameters on the target area, and the device operating status of the electronic device used by the user, and constructs an environmental preference model corresponding to the user in different task scenarios. Based on this environmental preference model, and combined with the task type, device operating status, user posture, and environmental parameters, it generates adjustment instructions to control the lighting equipment to adjust the illuminance and color temperature illuminating the target area. This allows for automatic optimization of lighting parameters according to dynamic changes in user habits, achieving not only fully automated and seamless intelligent adjustment, but also expanding the adjustment dimensions. Furthermore, it possesses strong adaptability, high personalization, and real-time response capabilities, effectively adapting to the user's immediate needs in different work scenarios, thereby improving the user experience.

[0025] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0026] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart of a light adjustment method according to an embodiment of the present invention; Figure 2 This is a structural block diagram of a light adjustment system according to another embodiment of the present invention; Figure 3 This is a structural block diagram of a lighting device according to an embodiment of the present invention. Detailed Implementation

[0027] The embodiments of the present invention are described in detail below. The embodiments described with reference to the accompanying drawings are exemplary. The embodiments of the present invention are described in detail below.

[0028] In a specific embodiment, the sensing module includes, for example, two spectral sensors, a desktop camera, a seat pressure sensor, an environmental sensor, and a device status monitor. One spectral sensor is installed, for example, at the front left of the desktop (e.g., at 0°) to detect ambient background brightness; the other is installed at the light source side (e.g., at 60°) to detect direct light. The two sensors are approximately 15 cm apart. This multi-angle deployment enables comprehensive perception of the lighting status of the target area, collaboratively collecting multi-dimensional parameters such as illuminance, color temperature, flicker depth, color rendering index, anti-glare index, and complete spectral distribution illuminating the desktop.

[0029] In routine task scenarios (such as coding and document reading), the spectral sensor operates in low-frequency mode, sampling once every 2 seconds, with each sampling lasting 100ms. When a task scenario change is detected (e.g., the user switches from coding to creative drawing), the system automatically triggers a high-frequency sampling mode, continuously sampling at a frequency of 100ms / sample for 5 seconds to quickly capture changes in lighting requirements. A desktop camera is used to identify the task type, such as determining whether the current operation is a code editor interface or a drawing tablet through image analysis. A seat pressure sensor detects the user's posture, distinguishing between sitting and standing. An environmental sensor collects ambient temperature and humidity data. The device status monitor obtains the real-time operating status of the user's electronic devices through the operating system API (Application Programming Interface), including currently active software (such as code editors and image processing software) and document types, providing key inputs for task scenario recognition and environmental preference modeling. The aforementioned task scenarios (such as "routine task scenarios" and "creative task scenarios") have been pre-classified and stored in the system, providing structured context support for task recognition and environmental preference modeling.

[0030] Lighting equipment, such as intelligent dimming lights, can be installed in the user's work area, such as on a desk, supporting continuous adjustment of color temperature from 2700K to 6500K and stepless dimming of brightness from 0 to 100%. At the same time, an electric anti-glare baffle can be added to the desk, which can dynamically adjust the shading angle (0°–45°) according to the measured glare index, effectively suppressing direct glare and improving visual comfort.

[0031] Specifically, in terms of hardware communication, the spectral sensors communicate with the adjustment module via an I²C (Inter-Integrated Circuit) bus at a rate of 100 kHz to ensure stable and reliable data transmission. The calibration coefficients of each sensor are pre-stored in the adjustment module's non-volatile memory and automatically loaded upon power-up, ensuring the accuracy and consistency of illumination parameter measurements. The adjustment module establishes a wireless connection with the smart gateway via an integrated Bluetooth module, uploading the sensed data to the gateway at a preset frequency (e.g., once every 2 seconds), while ensuring that the delay in receiving adjustment commands from the gateway does not exceed 0.5 seconds, meeting the timeliness requirements of adaptive adjustment.

[0032] In terms of power management, the system adopts a dual-voltage domain power supply architecture: a 3.3V power network supplies power to the regulation module, storage unit, and Bluetooth module, with an average current of 60.1 mA (power consumption of approximately 0.20 W); a 1.8V power network is dedicated to two spectral sensors, with an average current of 100 mA (power consumption of approximately 0.18 W), resulting in a total average power consumption of only 0.38 W. Furthermore, the system supports a low-power operating mode: during non-detection periods (such as within a 1900 ms sleep cycle), the operating current of each spectral sensor can be reduced to less than or equal to 10 μA, and the regulation module enters a deep sleep state (e.g., current ≤ 5 mA). In this mode, relying solely on a 2000mAh battery, the system can operate continuously for approximately 103.6 hours (approximately 4.3 days) in home applications, effectively supporting long-term, maintenance-free environmental sensing and adaptive light adjustment, fully aligning with the technical goals of this invention for intelligence, personalization, and energy efficiency optimization.

[0033] The following is for reference. Figure 1 A method for adjusting illumination according to an embodiment of the present invention is described.

[0034] Figure 1 This is a flowchart of a light adjustment method according to an embodiment of the present invention. Figure 1 As shown, the method includes at least steps S1-S3.

[0035] Step S1: Collect perception data. The perception data includes task type, user posture, environmental parameters, illumination parameters on the target area, and the device operating status of the electronic device used by the user. The illumination parameters include illuminance and color temperature, and the task type is the current work activity type identified based on the user's use of the electronic device.

[0036] In this embodiment, the task type is not manually set by the user, but is automatically identified by analyzing the user's behavior when operating the electronic device. For example, a desktop camera can capture screen interface features in real time (such as line numbers and syntax highlighting in a code editor or brush panels in drawing software), or a device status monitor can call the operating system API to obtain the applications running in the foreground (e.g., detecting "code editor" indicates a programming task, detecting "image processing software" indicates a creative drawing task), thereby achieving seamless and dynamic identification of the task type. User posture refers to the user's current body posture (such as sitting or standing), which can be detected, for example, by a seat pressure sensor to determine the height of the work surface. The system considers factors such as line of sight, which in turn affects lighting coverage requirements; environmental parameters, including ambient temperature and humidity, are collected by temperature and humidity sensors deployed near the work area, reflecting the potential impact of the external physical environment on comfort; illumination parameters on the target area refer to the actual optical indicators received by the user's main work surface (such as the desk surface), such as illuminance (i.e., the brightness of the work surface) and color temperature (i.e., the warmth or coolness of the light), which are measured in real time by spectral sensors placed at different angles on the desktop to ensure that they reflect the actual illumination conditions perceived by the human eye; device operating status is obtained through the system interface from real-time information of the user's electronic devices, including currently active software, window focus, document type, etc., which not only assists in task recognition but also reflects the intensity of user interaction and cognitive load. All of these data collectively constitute a complete, dynamic, and context-aware input system, laying a solid foundation for building a personalized environmental preference model.

[0037] Step S2: Based on task type, device operating status, user posture, environmental parameters, and lighting parameters, construct an environmental preference model for the user in different task scenarios. The task scenario is determined based on the device operating status and task type.

[0038] In this embodiment, task type, device operating status, user posture, environmental parameters, and illumination parameters on the target area are used as input features. Through data association and analysis, a mapping relationship of user environmental preferences under different task scenarios is established. The task scenario is not defined solely by time or a single action, but is jointly determined by the task type and device operating status. For example, when the task type is "programming" and the device operating status shows the foreground application as a "code editor," it constitutes a "code writing" task scenario; when the task type is "graphic creation" and the device operating status detects that "image processing software" is in an active window, it constitutes an "image editing" task scenario. For each task scenario, the system analyzes the correlation between the user's posture (e.g., sitting / standing), environmental parameters, and the actual or satisfactory illumination parameters (illuminance, color temperature) in that task scenario through historical perception data accumulation or online learning. This abstracts the user's preferred illumination configuration pattern for that task scenario and stores it in a structured manner as an environmental preference model. Among them, the environmental preference model is essentially a multi-dimensional condition-response mapping that can quickly recommend or generate lighting parameter combinations that meet user habits and comfort needs when a new task scenario is identified, thus achieving intelligent adjustment from "passive response" to "active adaptation".

[0039] Step S3: Based on the environmental preference model and combined with the task type, device operating status, user posture and environmental parameters, generate adjustment instructions for controlling the lighting equipment to adjust the illuminance and color temperature illuminating the target area.

[0040] In this embodiment, the system first determines the user's task scenario based on the currently identified task type (e.g., programming, drawing, or reading) and the device's operating status (e.g., the foreground application is a code editor or image processing software). Then, it invokes the lighting preference configuration corresponding to the task scenario from the environmental preference model. This configuration includes the preferred range or typical values ​​of illuminance and color temperature statistically derived from historical perception data. Based on this, the system further integrates the current user posture (e.g., lower line of sight when sitting, raised work surface when standing) and environmental parameters to dynamically fine-tune the preference parameters. For example, it appropriately increases illuminance to maintain visual contrast when the ambient light is strong, or selects a slightly lower color temperature to alleviate visual fatigue when the user is in a prolonged focused sitting posture. Ultimately, the system transforms the target illuminance and target color temperature values ​​calculated from the above multi-dimensional factors into specific control signals, such as the PWM (Pulse Width Modulation) duty cycle and the LED (Light Emitting Diode) channel drive current ratio, to generate adjustment commands for controlling lighting equipment. This enables real-time, adaptive, and seamless optimization of the light environment illuminating the target area, ensuring that the lighting conditions always meet the user's current task requirements and physiological comfort.

[0041] Therefore, embodiments of the present invention, by collecting task type, user posture, environmental parameters, illumination parameters on the target area, and the device operating status of the electronic devices used by the user, and using this multi-dimensional perception data as input, construct an environmental preference model corresponding to the user in different task scenarios. Then, based on this environmental preference model, combined with task type, device operating status, user posture, and environmental parameters, adjustment commands for controlling lighting equipment are generated to adjust the illuminance and color temperature illuminating the target area. This allows for automatic optimization of illumination parameters according to dynamic changes in user habits, achieving not only fully automated and seamless intelligent adjustment but also expanding the adjustment dimensions. It also possesses strong adaptability, high personalization, and real-time response capabilities, effectively adapting to the user's immediate needs in different work scenarios, thereby improving the user experience.

[0042] In one embodiment of the present invention, when constructing an environmental preference model for a user under different task scenarios based on task type, device operating status, user posture, environmental parameters, and illumination parameters, the method includes: acquiring historical perception data, which includes historical task type, historical device operating status, historical user posture, historical environmental parameters, and historical illumination parameters; establishing a mapping relationship between historical task scenarios and historical illumination parameters, historical user posture, and historical environmental parameters based on the historical perception data and through a preset learning algorithm, so as to determine the optimal range of illumination parameters under the corresponding task scenario and the individual tolerance threshold of the user for historical illumination parameters, wherein the historical task scenario is determined based on historical task type and historical device operating status; and storing the optimal range of illumination parameters and the individual tolerance threshold in association to obtain the environmental preference model.

[0043] In this embodiment, the system continuously collects and stores historical perception data over a period of time. This data covers multiple dimensions, including historical task types (such as programming, drawing, reading, etc.), historical device operating status (such as foreground applications and window activity status), historical user postures (such as sitting or standing postures), historical environmental parameters (such as ambient temperature and humidity), and historical lighting parameters. The system then combines historical task types with historical device operating status to define specific historical task scenarios. For example, "task type is graphic creation" and "device operating status shows image processing software in an active window" constitutes an "image editing" task scenario. Subsequently, based on these structured historical task scenarios and their corresponding multidimensional contextual data, the system employs a pre-defined learning algorithm (such as the random forest algorithm) to establish a mapping relationship between historical task scenarios and historical lighting parameters, historical user postures, and historical environmental parameters. Through this mapping relationship, the system can statistically analyze or infer the range of illuminance and color temperature distributions that users consistently use in each type of task scenario, thereby determining the optimal range of lighting parameters for that scenario (i.e., the range of illuminance and color temperature that users most frequently accept or find most comfortable). Simultaneously, combining user adjustment behaviors or comfort feedback under abnormal lighting conditions, the system further identifies the user's individual tolerance threshold for various lighting parameters (e.g., users tend to lower the illuminance when it exceeds 800 lux, or experience discomfort when the color temperature is above 5500 K). This individual tolerance threshold represents the upper limit of the user's physiological or psychological tolerance to light stimulation. Finally, the system structurally associates the optimal range of lighting parameters corresponding to each task scenario with its corresponding individual tolerance threshold and stores it in a database or model file, forming a complete environmental preference model. This environmental preference model not only reflects users' ideal lighting preferences in different task scenarios, but also embeds safety boundaries, providing a decision-making basis that combines personalization and comfort for subsequent real-time adjustments.

[0044] In one embodiment of the present invention, the illumination adjustment method further includes: acquiring user feedback information on illumination parameters; updating the optimal range and individual tolerance threshold of illumination parameters corresponding to illuminance and color temperature on the target area according to the feedback information at preset time intervals; updating the environmental preference model based on the updated optimal range and updated individual tolerance threshold of illumination parameters; and outputting a warning message to characterize the abnormal illumination parameters when at least one parameter of illuminance and color temperature on the target area is detected to exceed its corresponding individual tolerance threshold.

[0045] In this embodiment, the system proactively acquires user feedback on current lighting parameters. This feedback can originate from user comfort feedback (e.g., adjusting brightness or color temperature via an app, clicking "too bright" or "too cold" rating buttons), or from user manual adjustments such as frequent manual light adjustments or multiple switching of lighting modes within a short period. This feedback reflects the user's actual perception of the illuminance and color temperature illuminating the target area (e.g., a desk surface). Subsequently, the system analyzes the collected feedback at preset time intervals (e.g., every 24 hours) and dynamically updates the optimal ranges and individual tolerance thresholds for illuminance and color temperature accordingly. For example, if a user repeatedly lowers the illuminance to a certain range, the system identifies that range as a new preference center, thereby shrinking or shifting the original optimal range; if a user frequently expresses discomfort above a specific color temperature value, their individual tolerance threshold is lowered accordingly. After completing the parameter updates, the system synchronously refreshes the environmental preference model based on the updated optimal ranges and individual tolerance thresholds for lighting parameters, ensuring it continuously adapts to evolving user habits.

[0046] In addition, the system is equipped with a status display panel to intuitively present the real-time status of key lighting parameters in the current work environment to the user. This panel continuously displays the measured lighting parameters illuminating the target area (such as a desk surface), including core indicators such as illuminance (e.g., "480 lux"), color temperature (e.g., "4200 K"), and color rendering index (e.g., "Ra93"), allowing users to monitor the current lighting environment quality at any time. More importantly, this status display panel integrates an intelligent warning function: the system compares the monitored illuminance and color temperature in real time in the background to see if they exceed the user's individual tolerance threshold for the current task scenario. Once either illuminance or color temperature exceeds its corresponding individual tolerance threshold—for example, if a user's tolerance limit for high illuminance is 500 lux, and the current desktop illuminance is 580 lux; or if a user's tolerance limit for cool white light is 5000 K, and the current light source output is 5800 K—the status display panel will immediately trigger an abnormality warning mechanism. It will prominently display a message indicating the abnormal lighting parameter on the interface (e.g., red text, flashing icons, or pop-ups), such as "Illuminance too high, may cause visual fatigue" or "Color temperature exceeds the comfortable range, warmer adjustment recommended," to warn the user that the current lighting conditions may cause visual fatigue or discomfort. Furthermore, if the system simultaneously detects other safety-related abnormalities, such as flicker depth greater than 15%, it can also trigger corresponding prompts, such as "Flicker too high, switch light source recommended." This visual feedback helps users promptly identify and avoid potential discomfort or health risks, achieving a transparent, explainable, and user-controllable intelligent lighting adjustment experience.

[0047] In one embodiment of the present invention, when obtaining user feedback information on current illumination parameters, the method includes: monitoring the user's adjustment operation on illumination parameters, determining the adjusted illumination parameters, and generating first feedback information based on the illumination parameters and the adjusted illumination parameters; and / or, obtaining user comfort evaluation information on illumination parameters, and generating second feedback information based on the comfort evaluation information.

[0048] In this embodiment, the system monitors user adjustments to the lighting equipment. For example, if a user manually lowers the illuminance or warms the color temperature due to discomfort, or actively switches to another light source when flicker depth is detected to be excessive, the system automatically records the lighting parameters before adjustment (e.g., illuminance 500 lux, color temperature 6500 K) and the lighting parameters after adjustment (e.g., illuminance 350 lux, color temperature 4000 K). The system generates a first feedback message based on the difference between the two, expressing the user's dissatisfaction with the original lighting conditions and preference for the new parameters. This first feedback message can be captured by the system and used to update the environmental preference model without additional user input. On the other hand, the system also supports obtaining the user's comfort evaluation information of the current lighting environment. For example, it provides a "comfort rating" function through a mobile app, allowing users to give the current lighting environment a subjective score of 1 to 5 stars. Users can also submit structured evaluations related to specific optical indicators (such as a high color rendering index greater than 90 but poor anti-glare effect). Based on this, the system generates a second feedback message, which not only reflects overall satisfaction but can also be refined to specific dimensions (such as "severe glare" or "color distortion"), thereby triggering targeted optimization suggestions (such as "it is recommended to use a light shield to improve the anti-glare effect"). The above two feedback mechanisms can be used alone or in combination. The first feedback message captures real behavioral preferences, avoiding subjective bias; the second feedback message provides clear semantic guidance, enhancing the interpretability of the environmental preference model. Together, they constitute a multi-layered, highly robust user feedback system, providing a rich and reliable data foundation for the dynamic evolution of the environmental preference model.

[0049] In one embodiment of the present invention, when generating adjustment instructions for controlling lighting equipment to adjust the illuminance and color temperature irradiated on a target area based on an environmental preference model and in combination with task type, device operating status, user posture, and environmental parameters, the process includes: determining the current task scenario based on the current task type and current operating status; determining initial target values ​​for illuminance and color temperature within the optimal range of illumination parameters corresponding to the current task scenario in the environmental preference model; if any initial target value exceeds its individual tolerance threshold corresponding to the current task scenario in the environmental preference model, then constraining the initial target value to a range not exceeding the individual tolerance threshold; combining all constrained initial target values ​​to form target illumination parameters; generating adjustment instructions based on the target illumination parameters; and executing the adjustment instructions according to a preset adjustment sequence to adjust the illuminance and color temperature irradiated on the target area.

[0050] In this embodiment, when generating adjustment commands for controlling lighting equipment, the system executes a refined, safety-first parameter decision-making process to ensure that the output lighting conditions meet user preferences without exceeding their physiological comfort boundaries. Specifically, the system first determines the user's current task scenario based on the real-time identified current task type (e.g., "programming" or "image editing") and the current device operating status (e.g., the active foreground application is a "code editor" or "image processing software"). Subsequently, it retrieves the optimal range of lighting parameters corresponding to the task scenario (i.e., the user's historical preferred illuminance and color temperature range in this scenario) from the constructed environmental preference model, and selects an initial target value (e.g., illuminance of 450 lux and color temperature of 4200 K) within their respective optimal ranges for both illuminance and color temperature.

[0051] However, to prevent discomfort caused by environmental preference model recommendations or sudden environmental changes, the system further compares each initial target value with its corresponding individual tolerance threshold in the task scenario (e.g., the upper limit of illuminance tolerance is 500 lux, and the upper limit of color temperature tolerance is 5500 K). If any initial target value exceeds its corresponding individual tolerance threshold (e.g., the initial color temperature is set to 5800 K, but the user's tolerance limit is only 5200 K), the system automatically constrains the parameter to a range not exceeding the threshold (e.g., the color temperature is forcibly limited to 5200 K), thereby embedding a personalized safety protection mechanism while retaining preference guidance. After the constraints are completed, the system combines all the adjusted parameters, namely the constrained target illuminance value and the target color temperature value, to form the final target illumination parameters. Then, based on the target illumination parameters, specific adjustment instructions are generated. Finally, the system executes the adjustment instructions according to the preset adjustment sequence (for example, using a gradual transition method to smoothly adjust to the target value within 3 seconds to avoid visual interference caused by sudden changes in the light environment), accurately controlling the actual illuminance and color temperature of the target area (such as the work surface of a desk) illuminated by the user, thereby taking into account personalization, comfort, and a smooth user experience.

[0052] Building upon this foundation, the system introduces a collaborative adjustment engine to optimize the timing of multi-device actions: First, upon receiving the target illumination parameters, it prioritizes calculating the required baffle angle based on the current anti-glare index and completes the precise positioning of the baffle within a preset time (e.g., 1 second) to quickly suppress direct glare. Subsequently, it simultaneously initiates the adjustment of the color temperature and brightness of the intelligent dimming lights, for example, using a smooth 3-second transition to the target value to avoid visual interference caused by sudden changes in light intensity or color temperature. Finally, after the illumination stabilizes, the system can also link with external devices (such as electric height-adjustable desks and ambient temperature controllers) to adapt to the user's posture and thermal comfort needs. This entire adjustment process not only ensures visual health and operational smoothness but also significantly improves the overall response quality and user experience of the adaptive environment system through a phased and orderly collaborative execution strategy.

[0053] In one embodiment of the present invention, when executing the adjustment command according to the preset adjustment sequence, the method further includes: obtaining the illumination flicker depth on the target area; if the fluctuation amplitude of the flicker depth exceeds its corresponding preset safety threshold, pausing the execution of the adjustment command and outputting an adjustment interruption prompt.

[0054] In this embodiment, to ensure the safety and visual comfort of the illumination adjustment process, the system continuously acquires the illumination flicker depth data of the target area by means of a spectral sensor deployed near the target area (such as the surface of the user's desk) during the process of executing the adjustment command according to the preset adjustment sequence. The illumination flicker depth reflects the amplitude of the light source brightness fluctuation over time and is an important factor affecting visual fatigue and health.

[0055] Specifically, during the execution of adjustment commands (such as gradually increasing illuminance or switching color temperature), the system analyzes the fluctuation range of flicker depth (i.e., the range or standard deviation of flicker depth change within a short period of time) in real time and compares it with a preset safety threshold (e.g., 15%). Once the system detects that the fluctuation range of flicker depth exceeds the preset safety threshold (e.g., due to unstable drive circuit or excessively low PWM dimming frequency causing a sudden increase in flicker depth to more than 20%), the system will immediately pause the currently executing adjustment command to prevent further changes in lighting conditions in an unsafe lighting environment, which could exacerbate user discomfort. At the same time, the system will output an adjustment interruption prompt, which can be presented through a status display panel, mobile software push, voice broadcast, or the lighting device's own indicator light. The content may include messages such as "Flicker abnormality, adjustment has been paused, please check the stability of the light source" or "High-risk flicker detected, it is recommended to switch to DC constant current mode," thereby providing real-time protection for proactively maintaining healthy lighting and improving the user experience.

[0056] This invention expands upon the basic functions in three main directions: functional expansion, technological upgrades, and scenario adaptation. Regarding functional expansion, the system introduces a healthy lighting closed-loop mechanism. By integrating eye fatigue sensors such as an infrared eye tracker, it monitors the user's blink frequency in real time. When the detected blink frequency increases by 30% compared to the preset blink frequency, a comfort adjustment strategy is automatically triggered, reducing illuminance by 20% and increasing color temperature by 500K to alleviate visual fatigue. Simultaneously, the system supports intelligent light source adaptation, recognizing the type of external light source (such as LEDs, fluorescent lamps, etc.) and dynamically loading the corresponding spectral calibration coefficients to ensure the accuracy of lighting parameter measurement and control.

[0057] In terms of technological upgrades, the system integrates AI (Artificial Intelligence) lighting optimization capabilities and introduces a lightweight deep learning model. Based on user eye status, task duration, and historical preference data, it predicts the optimal combination of illuminance and color temperature with a prediction accuracy of greater than or equal to 95%. In addition, the lighting equipment can also integrate a wireless charging module to support real-time charging of the lighting terminal or sensing module battery, achieving near-unlimited battery life.

[0058] In terms of scenario adaptation, the system provides customized strategies for different application environments: In classroom scenarios, it supports multi-user collaborative adjustment. For example, when the teacher switches to "lecture mode" (illuminance 500 lux, high color rendering index), the student simultaneously enters "note-taking mode" (illuminance 400 lux, anti-glare baffle enabled). In medical office scenarios, for tasks requiring color sensitivity, such as handling medical records and images, the system automatically optimizes the light source's color rendering index to be greater than or equal to 95, ensuring accurate and professional color recognition. This enhances the system's intelligence, health care capabilities, and cross-scenario adaptability.

[0059] According to the lighting adjustment method of this invention, by collecting multi-dimensional sensing data such as task type, user posture, environmental parameters, lighting parameters on the target area, and the device operating status of the electronic devices used by the user, a user environmental preference model corresponding to different task scenarios is constructed. Based on this environmental preference model, combined with task type, device operating status, user posture, and environmental parameters, an adjustment command for controlling the lighting device is generated to adjust the illuminance and color temperature illuminating the target area. This allows for automatic optimization of lighting parameters according to dynamic changes in user habits, achieving not only fully automated and seamless intelligent adjustment but also expanding the adjustment dimensions. Furthermore, it possesses strong adaptability, high personalization, and real-time response capabilities, effectively adapting to the immediate needs of users in different work scenarios, thereby improving the user experience.

[0060] A further embodiment of the present invention discloses a light regulation system.

[0061] like Figure 2 As shown, the illumination adjustment system 100 includes: a sensing module 110, a learning module 120, and an adjustment module 130.

[0062] The sensing module 110 is used to collect sensing data, including task type, user posture, environmental parameters, illumination parameters on the target area, and the operating status of the electronic devices used by the user. The illumination parameters include illuminance and color temperature, and the task type is the current work activity type identified based on the user's use of the electronic devices. The learning module 120 is used to construct an environmental preference model for the user under different task scenarios based on the task type, device operating status, user posture, environmental parameters, and illumination parameters. The task scenario is determined based on the device operating status and task type. The adjustment module 130 is used to generate adjustment commands for controlling the lighting equipment according to the environmental preference model and in combination with the task type, device operating status, user posture, and environmental parameters, so as to adjust the illuminance and color temperature irradiated on the target area.

[0063] In one embodiment of the present invention, when the learning module 120 constructs an environmental preference model for a user under different task scenarios based on task type, device operating status, user posture, environmental parameters, and illumination parameters, the following steps are taken: acquiring historical perception data, which includes historical task type, historical device operating status, historical user posture, historical environmental parameters, and historical illumination parameters; establishing a mapping relationship between historical task scenarios and historical illumination parameters, historical user posture, and historical environmental parameters based on the historical perception data and through a preset learning algorithm, so as to determine the optimal range of illumination parameters under the corresponding task scenario and the individual tolerance threshold of the user for historical illumination parameters, wherein the historical task scenario is determined based on historical task type and historical device operating status; and storing the optimal range of illumination parameters and the individual tolerance threshold in association to obtain the environmental preference model.

[0064] In one embodiment of the present invention, the sensing module 110 is further configured to: acquire user feedback information on illumination parameters; update the optimal range and individual tolerance threshold of illumination parameters corresponding to illuminance and color temperature on the target area according to the feedback information at preset time intervals; update the environmental preference model based on the updated optimal range and updated individual tolerance threshold of illumination parameters; and output a warning message to characterize the abnormal illumination parameters when at least one parameter of illuminance and color temperature on the target area is detected to exceed its corresponding individual tolerance threshold.

[0065] In one embodiment of the present invention, when the sensing module 110 obtains user feedback information on the current illumination parameters, it includes: monitoring the user's adjustment operation on the illumination parameters and determining the adjusted illumination parameters, generating first feedback information based on the illumination parameters and the adjusted illumination parameters; and / or, obtaining user comfort evaluation information on the illumination parameters, and generating second feedback information based on the comfort evaluation information.

[0066] In one embodiment of the present invention, when the adjustment module 130 generates an adjustment command for controlling the lighting equipment to adjust the illuminance and color temperature irradiated on the target area based on the environmental preference model and in combination with the task type, device operating status, user posture, and environmental parameters, the following steps are included: determining the current task scenario based on the current task type and the current operating status; determining initial target values ​​for illuminance and color temperature within the optimal range of illumination parameters corresponding to the current task scenario in the environmental preference model; if any initial target value exceeds its individual tolerance threshold corresponding to the current task scenario in the environmental preference model, then constraining the initial target value to a range not exceeding the individual tolerance threshold; combining all constrained initial target values ​​to form target illumination parameters; generating adjustment commands based on the target illumination parameters; and executing the adjustment commands according to a preset adjustment sequence to adjust the illuminance and color temperature irradiated on the target area.

[0067] In one embodiment of the present invention, when the adjustment module 130 executes the adjustment command according to the preset adjustment sequence, it further includes: obtaining the light flicker depth on the target area; if the fluctuation amplitude of the flicker depth exceeds its corresponding preset safety threshold, then suspending the execution of the adjustment command and outputting an adjustment interruption prompt.

[0068] It should be noted that the specific implementation method of the illumination adjustment is similar to that of the illumination adjustment method described in the first aspect embodiment of the present invention. Therefore, for a detailed exemplary description of the illumination adjustment system 100, please refer to the aforementioned description of the illumination adjustment method. To reduce redundancy, it will not be repeated here.

[0069] According to the lighting adjustment system 100 of this embodiment, the sensing module 110 collects task type, user posture, environmental parameters, lighting parameters on the target area, and the device operating status of the electronic devices used by the user. The learning module 120 uses these multi-dimensional sensing data as input to construct an environmental preference model corresponding to the user in different task scenarios. Then, the adjustment module 130 generates adjustment commands to control the lighting equipment based on the environmental preference model, combined with task type, device operating status, user posture, and environmental parameters, so as to adjust the illuminance and color temperature irradiated on the target area. This allows the system to automatically optimize lighting parameters according to the dynamic changes in user habits. It not only achieves fully automated and seamless intelligent adjustment, but also expands the adjustment dimensions. At the same time, it has strong adaptability, high personalization, and real-time response capabilities, which can effectively adapt to the user's immediate needs in different work scenarios, thereby improving the user experience.

[0070] A further embodiment of the present invention also discloses a lighting device.

[0071] In some embodiments, such as Figure 3 As shown, the lighting device 200 includes the light adjustment system 100 described in the above embodiments of the present invention.

[0072] It should be noted that the specific implementation method of the illumination adjustment is similar to that of any of the above embodiments of the illumination adjustment method or system of the present invention. Therefore, for a detailed exemplary description of the illumination adjustment process, please refer to the relevant description of the illumination adjustment method or system described above. To reduce redundancy, it will not be repeated here.

[0073] According to the lighting device 200 of this invention, by collecting multi-dimensional sensing data such as task type, user posture, environmental parameters, illumination parameters on the target area, and the device operating status of the electronic devices used by the user, a user environmental preference model corresponding to different task scenarios is constructed. Based on this environmental preference model, combined with task type, device operating status, user posture, and environmental parameters, adjustment commands for controlling the lighting device are generated to adjust the illuminance and color temperature illuminating the target area. This allows for automatic optimization of illumination parameters according to dynamic changes in user habits, achieving not only fully automated and seamless intelligent adjustment but also expanding the adjustment dimensions. Furthermore, it possesses strong adaptability, high personalization, and real-time response capabilities, effectively adapting to the immediate needs of users in different work scenarios, thereby improving the user experience.

[0074] A further embodiment of the present invention discloses a table equipped with the lighting device described in the above embodiments of the present invention, for implementing the light adjustment method described in any one of the above embodiments of the present invention.

[0075] It should be noted that the specific implementation method of the illumination adjustment is similar to that of any of the above embodiments of the illumination adjustment method or system of the present invention. Therefore, for a detailed exemplary description of the illumination adjustment process, please refer to the relevant description of the illumination adjustment method or system described above. To reduce redundancy, it will not be repeated here.

[0076] According to embodiments of the present invention, the table collects task type, user posture, environmental parameters, illumination parameters on the target area, and the operating status of the electronic devices used by the user. Using this multi-dimensional sensory data as input, it constructs an environmental preference model corresponding to the user in different task scenarios. Based on this environmental preference model, and combining task type, device operating status, user posture, and environmental parameters, it generates adjustment commands to control the lighting equipment, adjusting the illuminance and color temperature illuminating the target area. This allows for automatic optimization of illumination parameters based on dynamic changes in user habits. It not only achieves fully automated and seamless intelligent adjustment but also expands the adjustment dimensions. Furthermore, it possesses strong adaptability, high personalization, and real-time response capabilities, effectively meeting the immediate needs of users in different work scenarios, thereby enhancing the user experience.

[0077] A further embodiment of the present invention discloses a computer-readable storage medium storing a light adjustment program, which, when executed by a processor, implements the light adjustment method as described in the above embodiments of the present invention.

[0078] According to embodiments of the present invention, when a computer-readable storage medium storing a lighting adjustment program thereon is executed by a processor, it collects task type, user posture, environmental parameters, lighting parameters on the target area, and the device operating status of the electronic device used by the user. Using this multi-dimensional sensory data as input, it constructs an environmental preference model corresponding to the user in different task scenarios. Based on this environmental preference model, and combining the task type, device operating status, user posture, and environmental parameters, it generates adjustment commands to control the lighting equipment, thereby adjusting the illuminance and color temperature illuminating the target area. This allows for automatic optimization of lighting parameters based on dynamic changes in user habits. It not only achieves fully automated and seamless intelligent adjustment but also expands the adjustment dimensions. Furthermore, it possesses strong adaptability, high personalization, and real-time response capabilities, effectively adapting to the user's immediate needs in different work scenarios, thereby improving the user experience.

[0079] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example.

[0080] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for adjusting illumination, characterized in that, For use in lighting devices, the method includes: Collect sensing data, which includes task type, user posture, environmental parameters, illumination parameters on the target area, and the device operating status of the electronic device used by the user. The illumination parameters include illuminance and color temperature, and the task type is the current work activity type identified based on the user's use of the electronic device. Based on the task type, the device operating status, the user posture, the environmental parameters, and the illumination parameters, an environmental preference model for the user under different task scenarios is constructed, wherein the task scenario is determined based on the device operating status and the task type; Based on the environmental preference model, and in combination with the task type, the device operating status, the user posture, and the environmental parameters, adjustment instructions are generated to control the lighting device, so as to adjust the illuminance and color temperature illuminating the target area.

2. The illumination adjustment method according to claim 1, characterized in that, When constructing an environmental preference model for the user in different task scenarios based on the task type, device operating status, user posture, environmental parameters, and illumination parameters, the process includes: Acquire historical sensing data, which includes historical task types, historical device operating status, historical user posture, historical environmental parameters, and historical illumination parameters; Based on the historical perception data, a mapping relationship between historical task scenarios and historical lighting parameters, historical user postures, and historical environmental parameters is established through a preset learning algorithm to determine the optimal range of lighting parameters under the corresponding task scenario, as well as the individual tolerance threshold of the user for the historical lighting parameters. The historical task scenario is determined based on the historical task type and the historical device operating status. The optimal range of the illumination parameters and the individual tolerance threshold are associated and stored to obtain the environmental preference model.

3. The illumination adjustment method according to claim 2, characterized in that, Also includes: Obtain user feedback on the lighting parameters; According to the preset time interval, based on the feedback information, the optimal range of the light parameters corresponding to the illuminance and color temperature irradiated on the target area and the individual tolerance threshold are updated. Based on the updated optimal range of illumination parameters and the updated individual tolerance threshold, the environmental preference model is updated, and when at least one parameter of illuminance and color temperature in the target area is detected to exceed its corresponding individual tolerance threshold, an early warning message is output to characterize the abnormal illumination parameters.

4. The illumination adjustment method according to claim 3, characterized in that, When obtaining feedback information from the user regarding the current illumination parameters, the process includes: Monitor the user's adjustment of the illumination parameters, determine the adjusted illumination parameters, and generate first feedback information based on the illumination parameters and the adjusted illumination parameters; and / or, Obtain the user's comfort evaluation information regarding the lighting parameters, and generate second feedback information based on the comfort evaluation information.

5. The illumination adjustment method according to claim 2, characterized in that, When generating adjustment instructions for controlling the lighting equipment to adjust the illuminance and color temperature of the target area based on the environmental preference model, and in conjunction with the task type, the device operating status, the user posture, and the environmental parameters, the process includes: Determine the current task scenario based on the current task type and current running status; For the illuminance and the color temperature, initial target values ​​are determined within the optimal range of lighting parameters corresponding to the current task scene in the environmental preference model, respectively. If any initial target value exceeds the individual tolerance threshold corresponding to the current task scenario in the environmental preference model, then the initial target value is constrained to a range not exceeding the individual tolerance threshold. The target illumination parameters are formed by combining all the constrained initial target values. The adjustment command is generated based on the target illumination parameters; The adjustment instructions are executed according to a preset adjustment sequence to adjust the illuminance and color temperature irradiated onto the target area.

6. The illumination adjustment method according to claim 5, characterized in that, When executing the adjustment command according to the preset adjustment sequence, it also includes: Obtain the illumination flicker depth on the target area; If the fluctuation amplitude of the strobe depth exceeds its corresponding preset safety threshold, the execution of the adjustment command will be suspended and an adjustment interruption prompt will be output.

7. A light regulation system, characterized in that, For lighting equipment, the system includes: The sensing module is used to collect sensing data, which includes task type, user posture, environmental parameters, illumination parameters on the target area, and the device operating status of the electronic device used by the user. The illumination parameters include illuminance and color temperature, and the task type is the current work activity type identified based on the user's use of the electronic device. The learning module is used to construct an environmental preference model for the user in different task scenarios based on the task type, the device operating status, the user posture, the environmental parameters, and the illumination parameters, wherein the task scenario is determined based on the device operating status and the task type. The adjustment module is used to generate adjustment commands for controlling the lighting equipment based on the environmental preference model, combined with the task type, the device operating status, the user posture, and the environmental parameters, so as to adjust the illuminance and color temperature irradiated on the target area.

8. A lighting device, characterized in that, include: The illumination adjustment system as described in claim 7.

9. A table, characterized in that, The lighting device of claim 8 is installed to implement the light adjustment method as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a light adjustment program, which, when executed by a processor, implements the light adjustment method as described in any one of claims 1-6.

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