Office illumination optical system with double light emitting paths and light quality control method

Abstract

The invention discloses an office lighting optical system with double light-emitting paths and a light quality control method, and relates to the technical field of lighting, the system comprises a double-channel module which provides central direct light and peripheral reflected light, and parameters of the double-channel module are independently adjustable; the in-situ acquisition module acquires brightness distribution information of a typical observation area; the dynamic execution mechanism module adjusts the light shielding angle of the lamp; the core processing and control unit executes a rhythm-comfort level fusion optimization algorithm, takes a target illumination parameter generated by a human body physiological rhythm as a guide, takes a constraint that a glare value is controlled to be lower than a preset threshold value, and collaboratively optimizes light source brightness, color temperature and shading angle parameters according to priorities; the control method comprises the steps of target parameter generation, glare value in-situ calculation, multivariable optimization decision and instruction execution and feedback learning, visual comfort is guaranteed, meanwhile, the system fits the physiological rhythm of the human body, the system has the self-optimization capacity, the light quality is stable and reliable, and the system is suitable for office lighting scenes.

Description

Technical Field

[0001] This invention relates to the field of lighting technology, specifically to an office lighting optical system with dual light-emitting paths and a method for controlling light quality. Background Technology

[0002] With the continuous advancement of lighting technology, modern office lighting has evolved from simply meeting the needs of visual tasks to focusing on intelligent design that prioritizes human health, comfort, and energy efficiency. Among these developments, healthy lighting that simulates the dynamic changes of natural light to regulate the body's physiological rhythms, and technologies that use optical and electronic means to suppress uncomfortable glare and improve visual comfort, have become two important research and application directions.

[0003] In existing technologies, the two directions mentioned above are usually studied and implemented independently. For example, some existing technologies control the color temperature and brightness of lamps through preset time programs to simulate the changes in natural light throughout the day. These solutions can automatically adjust the spectrum according to a clock, but their light output is open-loop, failing to consider glare problems that may arise in actual lighting environments. While increasing color temperature and brightness to achieve a refreshing effect, it may actually lead to a uniform increase in glare, creating new visual discomfort. Other existing technologies aim to solve glare problems by acquiring environmental images to identify areas of strong light or the location of the human eye, and accordingly dimming local light sources. However, these methods prioritize glare prevention above all else; their control logic is essentially brightness suppression to eliminate glare, inevitably deviating from or even disrupting the established rhythmic lighting plan, and failing to guarantee a continuous and stable output of a healthy lighting environment.

[0004] This reveals a flaw in current smart lighting technology: the two core functions of dynamic rhythmic lighting and real-time anti-glare control are disconnected at the system level. Either the rhythmic lighting program ignores the actual glare risks it causes, or the anti-glare intervention crudely disrupts the continuity and scientific basis of the rhythmic lighting. This prevents users from simultaneously obtaining a high-quality lighting environment that provides both all-day support for healthy rhythms and all-day visual comfort. The problem lies in the lack of a closed-loop control system capable of sensing the quality of the lighting environment in real time and dynamically and collaboratively adjusting the rhythmic lighting output strategy accordingly, thereby optimally achieving the goal of healthy rhythmic lighting while ensuring visual comfort—a crucial constraint.

[0005] In summary, existing technologies cannot effectively resolve the conflict between dynamic rhythmic lighting and real-time glare control. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide an office lighting optical system and light quality control method with dual light output paths. By independently controlling the dual light paths, collecting brightness in situ and calculating glare in real time, and combining a multivariate collaborative optimization algorithm, it can dynamically meet the needs of human physiological rhythms while ensuring visual comfort, realize the integrated control of healthy lighting and anti-glare, and improve the overall quality of the office lighting environment.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In one aspect, a dual-light-emission path office lighting optical system, the system comprising:

[0008] The system comprises a dual-channel independently controllable light source module, an in-situ visual information acquisition module, a dynamic actuator module, and a core processing and control unit.

[0009] The dual-channel independent controllable light source module includes a first light source channel and a second light source channel, which are used to provide lighting outputs that are independent of each other and have different optical paths.

[0010] The in-situ visual information acquisition module is located inside the light-emitting surface of the lamp and is used to acquire scene brightness distribution information facing the typical observer area.

[0011] The dynamic actuator module includes a motor drive mechanism for adjusting the physical shading angle of the lamp;

[0012] The core processing and control unit is configured to execute a rhythm-comfort fusion optimization algorithm.

[0013] Furthermore, the dual-channel independently controllable light source module specifically includes: a first optical path for providing central direct light and a second optical path for providing peripheral reflected light;

[0014] The first optical path includes a first LED array and a first optical device that works in conjunction with it, the first optical device being configured to allow light emitted from the first LED array to be emitted directly after transmission or a single reflection;

[0015] The second optical path includes a second LED array and a second optical device that works in conjunction with it. The second optical device includes a light guide lens and a reflective surface. The light emitted from the second LED array is guided by the light guide lens, diffusely reflected by the reflective surface, and then emitted through the light-emitting surface.

[0016] The driving circuits of the first LED array and the second LED array are independent of each other, and the color temperature range of the first LED array is different from that of the second LED array.

[0017] Furthermore, the in-situ visual information acquisition module includes multiple distributed wide dynamic range brightness sensors;

[0018] Each of the brightness sensors has a fixed field of view and is oriented to cover one or more preset typical viewing positions from below the luminaire to the side;

[0019] The brightness sensor is configured in non-imaging mode, and its output signal is the distribution data of spatial brightness within the field of view coverage area.

[0020] Furthermore, the rhythm-comfort fusion optimization algorithm includes the following steps:

[0021] S1. Generate a target lighting parameter curve that dynamically changes over time based on a preset human physiological rhythm model. The target lighting parameters include at least the target color temperature and the target illuminance.

[0022] S2. Receive the brightness distribution information collected in real time by the in-situ visual information acquisition module, and calculate the actual UGR value under the current lighting scene in real time based on the unified glare value (UGR) calculation model.

[0023] S3. Using the target lighting parameter curve as the optimization target and maintaining the actual UGR value below a preset threshold as the constraint, perform multi-variable collaborative optimization calculations on the color temperature and brightness parameters of the first light source channel and the second light source channel, as well as the shading angle parameter of the dynamic actuator module, to generate collaborative control commands.

[0024] S4. Output the collaborative control command to the dual-channel independent controllable light source module and the dynamic actuator module to drive them to perform corresponding actions so that the actual output lighting parameters of the system can approach the target lighting parameters while satisfying the UGR constraints.

[0025] Among them, the spatial brightness distribution data is used as a direct input to the Unified Glare Ratio (UGR) calculation model;

[0026] The multivariate collaborative optimization solution in step S3 is achieved by solving the following optimization problem:

[0027]

[0028]

[0029] in, To optimize the variable vector, and These represent the brightness and correlated color temperature of the first light source channel, respectively. and These represent the brightness and correlated color temperature of the second light source channel, respectively. Indicates the adjustment amount of the physical shading angle. This is the actual output vector of lighting parameters from the system, which should include at least the actual average illuminance and average correlated color temperature of the work surface. Represented by vector To optimize the variables, a minimization solution is performed. Let be the objective function, and let represent the actual output vector of lighting parameters of the system. With time Changing target illumination parameter vector The square of the difference in Euclidean norm between them For step S1, generated over time Changing target lighting parameter vector, This represents the constraints that the optimization process must satisfy. To optimize variables The unified glare value calculation function is used as the independent variable, and its calculation depends on the real-time brightness distribution information and luminaire geometric parameters obtained in step S2. The preset glare threshold value is less than 19.

[0030] The core processing and control unit also includes a data recording and learning module, which is used to record historical operation data in a time series.

[0031] Furthermore, the optimization problem is solved using an iterative optimization algorithm with a constraint handling mechanism;

[0032] When the algorithm iteration process detects At that time, a hierarchical coordinated adjustment strategy is initiated:

[0033] First, adjust the brightness of the second light source channel. Related color temperature And adjust the physical shading angle accordingly. ;

[0034] Only when the above adjustments are insufficient to satisfy the constraints should the brightness of the first light source channel be adjusted. Make the minimum necessary adjustments;

[0035] The hierarchical collaborative adjustment strategy is encoded as a correction rule for the gradient direction or search direction in the iterative optimization algorithm.

[0036] Furthermore, the motor drive mechanism in the dynamic actuator module is specifically connected to an adjustable light-shielding blade located at the edge of the lamp's optical cavity;

[0037] The motor drive mechanism is used to receive instructions from the core processing and control unit and drive the light-shielding blades to rotate precisely within a preset angle range, thereby continuously changing the light-shielding angle of the lamp in a specific direction.

[0038] The change in the shading angle, as one of the key variables, is incorporated into the collaborative optimization process of the rhythm-comfort fusion optimization algorithm.

[0039] Furthermore, the nominal correlated color temperature of the first LED array is set in the range of 4000K to 6500K to provide main task lighting that conforms to the rhythm objectives;

[0040] The nominal correlated color temperature of the second LED array is set in the range of 2700K to 4500K, and its color temperature can be continuously or steppedly adjusted by the driving current.

[0041] The second light source channel is configured to serve as an adjustable unit for adjusting ambient light distribution and reducing brightness contrast to help suppress glare during the execution of the fusion optimization algorithm.

[0042] Furthermore, the historical operational data includes at least: target lighting parameters and optimized actual control command vectors. The calculated actual UGR value and environmental labeling information;

[0043] The data recording and learning module is used to train a prediction model based on historical data. The prediction model is used to estimate the risk of UGR constraint conflict that may occur at the same time or under similar conditions in the future, and accordingly apply a preventive adjustment bias in advance in the optimization solution of step S3.

[0044] Furthermore, the human physiological rhythm model is an algorithmic model that generates dynamic target curves based on day time, seasonal information, and optional user preference inputs;

[0045] In the target lighting parameter curve, the target color temperature and target illuminance are both continuous functions of daytime or piecewise continuous functions, pointing to higher color temperature and higher illuminance in the morning of weekdays, and to lower color temperature and lower illuminance in the evening.

[0046] On the other hand, a light quality control method for a dual-light-emission path office lighting optical system, applicable to the aforementioned dual-light-emission path office lighting optical system, comprises the following specific steps:

[0047] Step 1: Target illumination parameter generation: Based on the pre-stored or received human physiological rhythm model and current time information, dynamically generate the target illumination parameter set for the current moment of the system. The target illumination parameter set includes at least the target-related color temperature and the target illuminance.

[0048] Step 2: In-situ calculation of scene glare value: Obtain multi-viewpoint brightness data in real time from the in-situ visual information acquisition module set inside the light-emitting surface of the lamp. Based on the brightness data and the geometric position parameters of the lamp, and according to the calculation model of unified glare value (UGR), calculate the actual UGR value at the preset observation position in the current lighting scene in real time.

[0049] Step 3: Multivariate Collaborative Optimization Decision: Taking the target lighting parameter set as the optimization objective and maintaining the actual UGR value below a preset threshold as a hard constraint, the brightness and color temperature of the first light source channel, the brightness and color temperature of the second light source channel, and the adjustment amount of the physical shading angle are collaboratively optimized using a rhythm-comfort fusion optimization algorithm. The optimization calculation follows a preset adjustment priority strategy, that is, the parameters of the second light source channel and the physical shading angle are adjusted first, and the brightness of the first light source channel is adjusted only when the above adjustments cannot meet the constraints.

[0050] Step 4, Instruction Execution and Feedback Learning: Based on the optimization results generated in Step 3, corresponding drive control instructions are generated and sent to the dual-channel independent controllable light source module and the dynamic actuator module respectively, driving them to perform corresponding brightness, color temperature and shading angle adjustment actions. At the same time, the process data of this optimization event is recorded to update the historical dataset and train the prediction model to optimize the adjustment strategy for similar scenarios in the future.

[0051] On the other hand, a lighting system comprising the aforementioned dual-light-out-path office lighting optical system;

[0052] The data collected by the in-situ visual information acquisition module of each lamp in the lighting system can be uploaded to a centralized or distributed core processing and control unit.

[0053] The centralized or distributed core processing and control unit can perform unified collaborative optimization control on the dual-channel independent controllable light source modules and dynamic actuator modules of multiple lamps in the same physical space, so as to achieve global rhythm adaptation and glare consistency control of the entire spatial light environment.

[0054] Compared with existing technologies, this dual-light-path office lighting optical system and light quality control method have the following advantages:

[0055] I. This invention provides independent and adjustable lighting output by setting up a dual-channel independent controllable light source module. It is combined with an in-situ visual information acquisition module to capture the scene brightness distribution in real time. The physical shading angle of the lamp is adjusted by a dynamic actuator module. Then, the core processing and control unit runs a rhythm-comfort fusion optimization algorithm to achieve multi-variable collaborative control. This avoids the situation where the rhythmic lighting program ignores the risk of glare and prevents the problem of anti-glare intervention rudely disrupting the continuity of rhythmic lighting. Under the premise of ensuring visual comfort, the lighting output continuously conforms to the human physiological rhythm.

[0056] Second, this invention records historical operating data and trains a prediction model through a data recording and learning module, which can predict the risk of glare constraint conflict at the same time or under similar conditions in the future, and apply preventive adjustment bias in advance, so that the system has self-optimization capability. At the same time, the linkage adjustment design of dual light source channels and shading angle gives the lighting system higher adjustment flexibility and accuracy, which can adapt to the usage needs of different office scenarios and effectively improve the stability and reliability of lighting quality.

[0057] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0059] Figure 1 This is a system composition block diagram of the present invention;

[0060] Figure 2 This is a schematic diagram of the dual-light-path illumination principle of the present invention;

[0061] Figure 3 This is a flowchart of the control method of the present invention. Detailed Implementation

[0062] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0063] Example 1

[0064] like Figures 1 to 3As shown, this embodiment discloses an office lighting optical system with dual light output paths. The system provides differentiated lighting output through dual-channel independent controllable light source modules, and combines an in-situ visual information acquisition module to perceive the light environment in real time. The dynamic actuator module adjusts the shading angle, and the core processing and control unit achieves multi-variable collaborative control through a rhythm-comfort fusion optimization algorithm. Ultimately, under the premise of satisfying glare constraints, the lighting output conforms to the human physiological rhythm.

[0065] In this embodiment, the dual-light-output path office lighting optical system includes a dual-channel independently controllable light source module, an in-situ visual information acquisition module, a dynamic actuator module, and a core processing and control unit. These modules cooperate to form a closed-loop control system. The dual-channel independently controllable light source module is the core of the lighting output, the in-situ visual information acquisition module is the sensing input unit, the dynamic actuator module is the mechanical adjustment unit, and the core processing and control unit is the decision-making control core. These four components work together to achieve the integrated optimization of rhythmic lighting and glare control.

[0066] In this embodiment, the specific implementation and function of each module are as follows:

[0067] The dual-channel independent controllable light source module is used to provide lighting outputs that are independent of each other and have different optical paths. Specifically, it includes a first optical path and a second optical path. The driving circuits of the two are independent of each other, and the brightness and color temperature parameters can be adjusted separately.

[0068] Specifically, the first optical path provides central direct light, serving as the core of the main task lighting. This path includes a first LED array and a first optical device. The first LED array uses high color rendering index LED chips with a nominal correlated color temperature set in the range of 4000K to 6500K. Light in this color temperature range is cool white, providing clear and bright lighting effects that meet the physiological rhythm needs of the human body during the morning, helping to improve work focus and efficiency. The first optical device is configured to allow the light emitted by the first LED array to be emitted directly after transmission or a single reflection. In some optional embodiments, the first optical device employs a condenser lens group. This lens group, through optical design, forms a specific light distribution curve for the light emitted from the first LED array, ensuring that the light can be accurately projected onto the office work surface to meet the lighting needs of main tasks such as document reading and writing.

[0069] The second optical path provides peripheral reflected light as an ambient light adjustment unit. This path includes a second LED array and second optical components, including a light guide lens and a reflective surface. The second LED array also uses high color rendering index LED chips, with a nominal correlated color temperature set in the range of 2700K to 4500K. The color temperature can be continuously or incrementally adjusted via the driving current. The light in this color temperature range is warm white, which can effectively adjust the ambient light distribution and reduce brightness contrast. The light guide lens is made of transparent optical material with microstructures on its surface to uniformly guide the light emitted by the second LED array, allowing the light to be smoothly projected onto the reflective surface. The reflective surface is made of diffuse reflection material with a frosted surface, which can uniformly diffuse the light guided by the light guide lens before it is emitted through the light-emitting surface of the lamp, forming soft peripheral ambient light and helping to suppress glare.

[0070] The driving circuits for the first and second LED arrays are independent of each other, each equipped with its own constant current driving module and color temperature adjustment module. The constant current driving module is used to adjust the operating current of the LED array, thereby achieving continuous brightness adjustment; the color temperature adjustment module adjusts the current ratio of LED chips with different color temperatures to achieve continuous or stepped adjustment of the relevant color temperature, ensuring that the parameter adjustments of the two light source channels do not interfere with each other, and providing a hardware foundation for multi-variable collaborative optimization.

[0071] The in-situ visual information acquisition module is located inside the light-emitting surface of the lamp and is used to collect scene brightness distribution information facing the typical observer area, providing data support for UGR calculation and optimization control.

[0072] Specifically, the in-situ visual information acquisition module includes multiple distributed wide dynamic range (WDR) brightness sensors. These WDR sensors offer high brightness detection range and accuracy, adapting to brightness variations in different times and areas within an office environment, accurately acquiring scene brightness data from low to high brightness. Each brightness sensor has a fixed field of view and is oriented inside the light-emitting surface of the luminaire using a bracket. The installation position is optimized to ensure that the field of view of multiple sensors covers one or more preset typical observation positions from below to the side of the luminaire. These typical observation positions are determined based on common sitting postures and observation angles of people in office environments, comprehensively capturing the brightness distribution within the observer's field of view.

[0073] The brightness sensor is configured for non-imaging operation, outputting data on the spatial brightness distribution within the field of view's coverage area. In non-imaging mode, the brightness sensor does not need to output image information, but only data such as the average brightness or brightness distribution curve within the field of view, reducing the complexity of data transmission and processing and ensuring real-time performance. The output brightness distribution data is transmitted to the core processing and control unit via data transmission lines, serving as direct input to the Unified Glare Ratio (UGR) calculation model.

[0074] The dynamic actuator module includes a motor drive mechanism for adjusting the physical shading angle of the luminaire, which suppresses glare by changing the shading angle and provides a mechanical adjustment dimension for optimizing lighting parameters.

[0075] Specifically, the motor drive mechanism uses a stepper motor, which features high control precision, fast response speed, and stable operation, enabling precise adjustment of the shading angle. The motor drive mechanism is specifically connected to adjustable shading blades located at the edge of the lamp's optical cavity. These adjustable shading blades are made of lightweight shading material, and their shape is adapted to the edge of the lamp's optical cavity to ensure effective blocking of light from specific directions.

[0076] The motor drive mechanism receives instructions from the core processing and control unit, driving the light-shielding blades to rotate precisely within a preset angle range, thereby continuously changing the light-shielding angle of the lamp in a specific direction. The change in the light-shielding angle, as one of the key variables, is incorporated into the collaborative optimization process of the rhythm-comfort fusion optimization algorithm. By adjusting the light-shielding angle, light can be directly blocked from entering the observer's eyes, reducing glare intensity and forming a synergistic anti-glare effect with the adjustment of light source parameters.

[0077] The core processing and control unit is the control core of the system. It is configured to execute the rhythm-comfort fusion optimization algorithm and includes a data recording and learning module for dynamic optimization control of lighting parameters and recording and learning of historical data.

[0078] The core processing and control unit employs an embedded processor, which features high processing speed, low power consumption, and high integration, meeting the requirements for real-time optimization calculations and multi-module collaborative control. The core processing and control unit establishes a communication connection with the dual-channel independent controllable light source module, the in-situ visual information acquisition module, and the dynamic actuator module via a data interface, enabling data reception and command transmission.

[0079] In this embodiment, the rhythm-comfort fusion optimization algorithm is a core function of the core processing and control unit. It is used to realize multi-variable collaborative optimization calculation and generate collaborative control commands. Specifically, it includes the following steps:

[0080] Step S1: Target illumination parameter curve generation:

[0081] Based on a pre-defined human physiological rhythm model, a target lighting parameter curve that dynamically changes over time is generated. The target lighting parameters include at least the target color temperature and the target illuminance.

[0082] Specifically, the human physiological rhythm model is an algorithmic model that generates dynamic target curves based on daytime and seasonal information, as well as optional user preference inputs. This model is based on the theory of human circadian rhythms, refers to the body's melatonin secretion patterns and visual physiological characteristics, and is designed to meet the usage needs of office scenarios. During the morning hours on weekdays, the body is in a state of wakefulness and requires higher color temperature and illuminance to promote brain activity and improve work efficiency; in the evening hours, the body needs to gradually relax and prepare for rest, so the target color temperature and target illuminance are set to lower values ​​to reduce the inhibition of melatonin secretion.

[0083] In the target lighting parameter curve, both the target color temperature and target illuminance are continuous functions or piecewise continuous functions of the daytime. The continuous function form enables a smooth transition of lighting parameters, avoiding visual discomfort caused by abrupt parameter changes; the piecewise continuous function form allows for segmented optimization based on the different time-of-day needs of the office environment, balancing practicality and comfort. User preferences can be preset through the luminaire's control interface to fine-tune the target lighting parameter curve to suit different users' habits.

[0084] Step S2: Real-time calculation of actual UGR values:

[0085] It receives brightness distribution information collected in real time by the in-situ visual information acquisition module, and calculates the actual UGR value under the current lighting scene in real time based on the unified glare value (UGR) calculation model.

[0086] Specifically, the Unified Glare Ratio (UGR) calculation model references internationally recognized glare evaluation standards for office lighting. Its calculation process is based on parameters such as the brightness of each light source at the observer's eye, the solid angle of the light source, and the angle between the light source and the line of sight. The brightness distribution information collected by the in-situ visual information acquisition module consists of spatial brightness data within the field of view of each sensor. This data is directly used as input to the UGR calculation model. Combined with the geometric parameters of the luminaire, the actual UGR value at the preset observation position under the current lighting scene can be calculated.

[0087] The UGR value is an important indicator for measuring the degree of glare in a lighting scene. The smaller the UGR value, the lower the degree of glare and the higher the visual comfort. In this embodiment, by solving the actual UGR value in real time, the glare situation of the current lighting environment can be grasped in a timely manner, providing a constraint basis for subsequent optimization control.

[0088] Step S3: Multivariate collaborative optimization solution:

[0089] Using the target lighting parameter curve as the optimization objective and maintaining the actual UGR value below a preset threshold as the constraint, multivariate collaborative optimization calculations are performed on the color temperature and brightness parameters of the first and second light source channels, as well as the shading angle parameters of the dynamic actuator module, to generate collaborative control commands.

[0090] Multivariable collaborative optimization is achieved by solving the following optimization problem:

[0091]

[0092]

[0093] Among them, the optimization variable vector :

[0094] This indicates the brightness of the first light source channel, i.e., the luminous brightness of the first LED array. Its adjustment range is determined by the rated power of the first LED array and the adjustment capability of the driving circuit, enabling continuous adjustment from low brightness to rated brightness.

[0095] This indicates the correlated color temperature of the first light source channel, with a value range of 4000K to 6500K. Continuous or step-by-step adjustment can be achieved through the color temperature adjustment module of the first light source channel.

[0096] This indicates the brightness of the second light source channel, i.e., the luminous brightness of the second LED array. Its adjustment range is determined by the rated power of the second LED array and the adjustment capability of the driving circuit, enabling continuous adjustment from low brightness to rated brightness.

[0097] This indicates the correlated color temperature of the second light source channel, which ranges from 2700K to 4500K. Continuous or step-by-step adjustment can be achieved through the color temperature adjustment module of the second light source channel.

[0098] This indicates the physical shading angle adjustment, which is the change in the rotation angle of the shading blades. Its value range is a preset angle range, and it is precisely adjusted through a motor drive mechanism.

[0099] The actual output vector of lighting parameters of the system :

[0100] It's about optimizing variable vectors. The function is determined by the mapping relationship between the system's optical characteristics and lighting effects, and includes at least the actual average illuminance and average correlated color temperature of the work surface. Actual average illuminance refers to the average illuminance in a specific area of ​​the work surface, reflecting the sufficiency of lighting; average correlated color temperature refers to the average correlated color temperature of the light received by the work surface, reflecting the warm or cool characteristics of the light. This mapping relationship can be established through prior experimental calibration or optical simulation to ensure an accurate description of the correspondence between the optimization variables and actual lighting parameters.

[0101] objective function :

[0102] The objective function represents minimizing the actual output lighting parameter vector of the system. With time Changing target illumination parameter vector The square of the Euclidean norm difference between the actual and target lighting parameters. The square of the Euclidean norm difference is used to quantify the deviation between the actual and target lighting parameters. The minimum value of the objective function corresponds to the case where the deviation is minimized, that is, the actual output of the system is closest to the target lighting parameter curve.

[0103] Constraints :

[0104] To optimize variables The unified glare value calculation function is used for the independent variable. Its calculation depends on the real-time brightness distribution information and luminaire geometric parameters obtained in step S2. It is used to describe the change law of the actual UGR value when the optimization variable changes. The preset glare threshold is less than 19. This threshold is set with reference to relevant standards for office lighting comfort to ensure that the glare level of the lighting scene is within an acceptable range for the human body and to guarantee visual comfort.

[0105] The optimization problem is solved using an iterative optimization algorithm with constraint handling mechanisms. In this embodiment, gradient descent is selected. Gradient descent has the advantages of low computational cost, fast convergence speed, and ease of implementation, which can meet the requirements of real-time optimization control. The core idea of ​​the iterative optimization algorithm is to continuously adjust the optimization variable vector. The value of is chosen to gradually decrease the objective function while ensuring that the constraints are met.

[0106] When the algorithm iteration process detects At this time, a hierarchical collaborative adjustment strategy is initiated. This strategy is encoded as a correction rule for the gradient direction or search direction in the iterative optimization algorithm, as follows:

[0107] First, adjust the brightness of the second light source channel. Related color temperature And adjust the physical shading angle accordingly. The second light source channel provides peripheral reflected light, and its parameter adjustments have minimal impact on the main task lighting. Simultaneously, by adjusting the shading angle in conjunction with the light source, glare intensity can be quickly reduced. For example, when an excessive UGR value is detected, the shading angle can be appropriately reduced. At the same time, adjust The light is adjusted to a warmer color temperature to reduce glare. Then, the shading angle is increased by a motor-driven mechanism to block some direct light from entering the observer's eyes, thereby quickly reducing the UGR value below the threshold.

[0108] Only when the above adjustments are insufficient to satisfy the constraints should the brightness of the first light source channel be adjusted. Make only the minimum necessary adjustments. The first light source channel provides the central direct light, which is the core of the main task lighting. Its brightness adjustment will directly affect the illuminance and rhythmic lighting effect of the work surface. Therefore, make only the minimum adjustment when other adjustment methods are ineffective, so as to minimize the deviation from the target lighting parameter curve.

[0109] Step S4, Output of cooperative control command:

[0110] The coordinated control command is output to the dual-channel independent controllable light source module and the dynamic actuator module to drive them to perform corresponding actions, so that the actual output lighting parameters of the system can approach the target lighting parameters while meeting the UGR constraints.

[0111] Specifically, the core processing and control unit generates corresponding drive control commands based on the optimization calculation results of step S3. These commands include brightness and color temperature adjustment commands for the first light source channel, brightness and color temperature adjustment commands for the second light source channel, and shading angle adjustment commands for the dynamic actuator module. These commands are sent to the corresponding modules via data transmission lines. After receiving the commands, the dual-channel independently controllable light source module adjusts the operating current and color temperature ratio of the corresponding LED array through the drive circuit to achieve precise adjustment of brightness and color temperature. After receiving the commands, the dynamic actuator module drives the shading blades to rotate to the target angle through the motor drive mechanism to achieve precise adjustment of the shading angle.

[0112] Through the coordinated action of each module, the system can adjust the lighting output parameters and shading angle in real time to ensure that the actual UGR value is always lower than the preset threshold, while making the actual lighting parameters as close as possible to the target lighting parameter curve, thus achieving a balance between rhythmic lighting and visual comfort.

[0113] In this embodiment, the data recording and learning module is implemented as follows:

[0114] The data recording and learning module of the core processing and control unit is used to record historical operating data in time series and train a predictive model based on the historical data to provide a basis for preventive adjustment for future optimized control.

[0115] Specifically, historical operational data should include at least the actual control command vector after optimizing the target lighting parameters. The calculated actual UGR value and environmental identification information. The target lighting parameters are the time-varying parameter curve data generated in step S1; the actual control command vector. The final value obtained after optimization in step S3; the actual UGR value is the real-time value calculated in step S2; environmental identification information is used to characterize the current environmental conditions, such as seasonal weather conditions. These data are stored in a time-series manner in the storage module of the core processing and control unit. The storage module uses non-volatile memory to ensure that the data is not lost due to power outages.

[0116] The data recording and learning module trains a prediction model based on historical data. This model employs machine learning algorithms, such as linear regression or decision tree algorithms. These algorithms are simple to implement and have strong generalization capabilities, enabling them to mine the correlation between target lighting parameters, environmental identifier information, and UGR constraint conflict risk based on historical data. The prediction model is used to estimate the UGR constraint conflict risk that may occur at the same time or under similar conditions in the future; that is, to predict the possibility of UGR values ​​exceeding the limit when the system outputs according to the target lighting parameter curve at a certain future time or under similar environmental conditions.

[0117] When the prediction model anticipates the risk of UGR constraint conflict, a preventative adjustment bias is applied in advance during the optimization solution in step S3, that is, the optimization variable vector is appropriately adjusted. The initial search range or gradient direction allows the optimization solution to avoid potential UGR overshooting issues in advance, further improving the stability and foresight of the system control.

[0118] In this embodiment, the overall workflow of the dual-light-output path office lighting optical system is as follows:

[0119] After the system starts, the rhythm-comfort fusion optimization algorithm of the core processing and control unit begins to run. First, based on the human physiological rhythm model and the current time information, it generates the target lighting parameter curve for the current moment. At the same time, the in-situ visual information acquisition module collects scene brightness distribution information in real time and transmits it to the core processing and control unit. Based on the collected brightness distribution information and luminaire geometric parameters, the core processing and control unit calculates the current actual UGR value in real time through the UGR calculation model. Then, with the target lighting parameter curve as the optimization target and the actual UGR value being lower than a preset threshold as the constraint, it generates a collaborative control command through multi-variable collaborative optimization. The collaborative control command is sent to the dual-channel independent controllable light source module and the dynamic actuator module to drive them to perform corresponding adjustment actions. At the same time, the data recording and learning module records historical operating data in time series and trains a prediction model based on historical data to provide preventive adjustment bias for subsequent optimization calculations.

[0120] The system operates continuously through the above process to achieve real-time dynamic adjustment of lighting parameters, ensuring that users can obtain a lighting environment that combines rhythmic health and visual comfort in office settings.

[0121] This embodiment of the dual-light-emission path office lighting optical system provides independently adjustable central direct light and peripheral reflected light through dual-channel independently controllable light source modules. Combined with real-time sensing from the in-situ visual information acquisition module and shading angle adjustment from the dynamic actuator module, a rhythm-comfort fusion optimization algorithm achieves multi-variable collaborative control. This allows the system to better align with human physiological rhythms while meeting visual comfort requirements. Compared to existing technologies, this system solves the disconnect between dynamic rhythmic lighting and real-time glare control, reducing conflicts between the two and improving the stability and reliability of lighting quality. It provides a more suitable lighting environment for office workers, contributing to improved work efficiency and mental comfort. Furthermore, the data recording and learning module enables the system to self-optimize, continuously improving control accuracy and adaptability over time.

[0122] Example 2

[0123] like Figures 1 to 3 As shown, this embodiment is based on the dual-light-output optical system for office lighting disclosed in Embodiment 1, and further elaborates on the specific implementation process of its light quality control method, aiming to clearly explain the actual operation process, key implementation methods and optimized alternatives of the control method.

[0124] Specifically, the light quality control method of this embodiment is applicable to the office lighting optical system in Embodiment 1. This method achieves synergistic optimization of rhythmic lighting and glare control through a four-step core process. The following details the specific implementation methods, implementation principles, and alternative solutions for each step in conjunction with actual application scenarios:

[0125] Step 1: Target illumination parameter generation:

[0126] The core of target lighting parameter generation is to dynamically output the target-related color temperature and target illuminance that meet human health needs based on the human physiological rhythm model and current time information.

[0127] Specifically, the human physiological rhythm model is pre-stored in the storage module of the core processing and control unit. The core data of this model comes from the biological research results of human circadian rhythms and is optimized in combination with the usage characteristics of office scenarios. The model has a built-in mapping relationship between daytime and target lighting parameters, where the target color temperature and target illuminance are both continuous functions of daytime. The example mapping pattern is as follows: from 8:00 AM to 12:00 PM on weekdays, the target color temperature linearly increases from 4500K to 6500K, and the target illuminance linearly increases from 500lx to 750lx; from 12:00 PM to 2:00 PM, the target color temperature remains at 5500K, and the target illuminance remains at 600lx; from 2:00 PM to 6:00 PM, the target color temperature linearly decreases from 5500K to 3000K, and the target illuminance linearly decreases from 600lx to 300lx.

[0128] Seasonal information is acquired through the clock module built into the core processing and control unit. The clock module can automatically record the current month and then match the preset seasonal parameters. There are slight differences in the target parameter curves for different seasons. For example, the target color temperature increase rate in the morning in winter is slightly slower than in summer, in order to adapt to the physiological rhythm characteristics of the human body in winter.

[0129] User preference input can be implemented in two optional ways: In some optional implementations, physical adjustment buttons are provided on the surface of the lamp housing, allowing users to manually fine-tune the offset of the target color temperature and illuminance within a range of ±10%. The adjusted parameters are recorded by the system and superimposed on the default target parameter curve. In other optional implementations, the core processing and control unit supports wireless communication, allowing users to input preference settings via a mobile app. The app interface provides color temperature and illuminance sliders, which users can slide to adjust the parameters. The adjustment commands are transmitted to the core processing and control unit via Bluetooth or Wi-Fi.

[0130] The core processing and control unit reads the current time information from the clock module in real time, combines seasonal parameters and user preference settings, calls the human physiological rhythm model, dynamically generates the target related color temperature and target illuminance at the current moment, forms a target lighting parameter set, and stores the parameter set in a temporary cache area to provide a basis for subsequent optimization decisions.

[0131] Step 2: In-situ calculation of scene glare value:

[0132] The core of in-situ glare value calculation is to acquire multi-viewpoint brightness data through the in-situ visual information acquisition module, combine it with the geometric parameters of the luminaire, and solve the actual UGR value in real time to provide data support for glare constraint control.

[0133] Specifically, the in-situ visual information acquisition module has four brightness sensors, which are installed in a distributed manner and fixed at the four corners inside the light-emitting surface of the lamp. The field of view of each brightness sensor is set to 80 degrees. By adjusting the angle of the bracket, the fields of view of the four sensors complement each other and jointly cover the typical observation position from the vertical area below the lamp to a 45-degree angle to the side. This observation position covers the main line of sight of people in a seated position in an office setting.

[0134] The brightness sensor is a wide dynamic range model, with a brightness detection range of 0.1 lx to 20000 lx and a detection accuracy of ±1%. It can accurately capture brightness changes under different lighting conditions in office scenarios, such as bright sunlight on sunny days and low light on cloudy days. The sensor is configured to operate in non-imaging mode, acquiring brightness distribution data within its field of view every 100 milliseconds. The data is transmitted to the core processing and control unit in digital signal form via the I2C communication protocol at a transmission rate of 100 kbps, ensuring the real-time performance and stability of data transmission.

[0135] After receiving the brightness data, the core processing and control unit calculates the UGR value by combining it with pre-stored luminaire geometric parameters. These parameters include the luminaire's light-emitting surface dimensions and installation height, which are pre-entered into the system during luminaire production. Users can fine-tune these parameters via physical buttons or an app based on the actual installation. The calculation process strictly follows the international standard calculation model for Unified Glare Ratio (UGR). Parameters such as the brightness values ​​of each region, the solid angle of the light source, and the angle between the light source and the line of sight from the brightness distribution data are substituted into the model. Numerical calculations are then used to determine the actual UGR value at the preset observation position in the current lighting scene. The calculation results are updated to the system cache in real time for subsequent constraint judgments.

[0136] In some alternative implementations, the brightness sensor can be replaced with a higher precision model, improving the detection accuracy to ±0.5%, while increasing the number of sensors to 5. An additional sensor is added at the center of the inner side of the light-emitting surface of the lamp to further improve the comprehensiveness of brightness distribution data acquisition. The data transmission method can also be replaced with the SPI communication protocol, increasing the transmission rate to 1Mbps, which is suitable for scenarios with higher real-time requirements.

[0137] Step 3: Multivariate Collaborative Optimization Decision Making

[0138] The core of multivariate collaborative optimization decision-making is to take the target lighting parameter set as the optimization objective, take the actual UGR value being lower than the preset threshold as the constraint, and perform collaborative optimization of multiple variables according to the adjustment priority strategy to generate control commands.

[0139] Specifically, the preset UGR threshold is set to 18, which is lower than the 19 specified in the international office lighting comfort standard, thus providing higher visual comfort. The core processing and control unit first reads the target lighting parameter set and the actual UGR value from the temporary buffer, and determines whether the actual UGR value exceeds 18. If it does not exceed 18, it optimizes based on approximating the target lighting parameters; if it exceeds 18, it initiates a hierarchical collaborative adjustment strategy.

[0140] The specific implementation process of the priority adjustment strategy is as follows: First, the brightness and correlated color temperature of the second light source channel are adjusted first, and the physical shading angle is adjusted accordingly. The example adjustment process is as follows: When the actual UGR value is detected to be 20, exceeding the preset threshold, the core processing and control unit first sends an adjustment command to the second light source channel, reducing its brightness by 25% and adjusting the correlated color temperature from 4000K to 3200K. Simultaneously, it sends a command to the dynamic actuator module to drive the shading blades to rotate 12 degrees, increasing the physical shading angle. After adjustment, the system waits 100 milliseconds, collects brightness data again, and calculates the UGR value. If the UGR value drops to 17.5 at this time, meeting the constraint, the adjustment stops, and the current parameters are used as the optimization result. If the UGR value is still 18.5, not meeting the constraint, the brightness of the second light source channel is further reduced by 15%, and the shading blades are rotated another 8 degrees. The UGR value is detected again until the constraint is met.

[0141] Only when the UGR value remains above the preset threshold after implementing the above adjustments will the brightness of the first light source channel be adjusted to the minimum necessary level. The example adjustment process is as follows: After adjusting the second light source channel and the shading angle, the UGR value is still 18.2. At this point, the core processing and control unit sends a command to the first light source channel to reduce its brightness by 8%, while keeping the correlated color temperature unchanged. The UGR value is then detected again, and it has now dropped to 17.8, meeting the constraints and completing the optimization. During the adjustment process, the brightness adjustment range of the first light source channel is strictly controlled within the minimum range, with a maximum adjustment range not exceeding 20%, ensuring that the basic lighting requirements of the main task are not affected.

[0142] During the optimization decision-making process, the core processing and control unit determines the optimal adjustment amount of each parameter through iterative calculations. The number of iterations is set to 5, and the adjustment instructions are corrected based on the adjustment effect of the previous iteration to ensure the accuracy of the optimization results. In some optional implementations, the adjustment priority strategy can be customized according to user needs. Users can choose through the APP whether to prioritize adjusting the shading angle or whether to limit the adjustment range of the first light source channel, thereby enhancing the adaptability of the system.

[0143] Step 4: Instruction Execution and Feedback Learning

[0144] The core of instruction execution and feedback learning is to send the control instructions generated by the optimization decision to the corresponding module, drive it to perform adjustment actions, record process data, update historical datasets and train prediction models to optimize future adjustment strategies.

[0145] Specifically, the core processing and control unit generates drive control commands based on the optimization decision results. Among them, the brightness adjustment command for the light source channel is output in the form of a PWM signal with a frequency of 20kHz and a duty cycle range of 0-100%. By changing the duty cycle, the working current of the LED array is adjusted to achieve continuous brightness adjustment. The color temperature adjustment command is output in the form of a digital signal. By adjusting the current ratio of LED chips with different color temperatures, the relevant color temperature is adjusted. The shading angle adjustment command is output in the form of a pulse signal. After receiving the pulse signal, the stepper motor rotates according to a preset step distance, driving the shading blades to adjust to the target angle. The step distance accuracy is 0.1 degrees, ensuring the precision of the shading angle adjustment.

[0146] After receiving the command, the dual-channel independently controllable light source module and the dynamic actuator module complete the adjustment action within 500 milliseconds and notify the core processing and control unit of the adjustment completion via feedback signals. Upon receiving the feedback signals, the core processing and control unit records the process data for this optimization event. This process data includes the actual control parameters after target lighting parameter optimization, the actual UGR value, environmental identification information, and the adjustment timestamp. The environmental identification information includes the current seasonal weather conditions, which are obtained through manual user input or from an external weather sensor; the adjustment timestamp is accurate to the second to ensure the temporal correlation of historical data.

[0147] Historical data is stored in the system's flash memory with a capacity of 16GB, capable of storing more than one year's worth of operational data. The data is categorized by time series for easy retrieval. The data recording and learning module reorganizes the historical data every 7 days, using a linear regression algorithm to train a predictive model and uncover the correlation between target lighting parameters, environmental identifiers, and the risk of UGR constraint conflicts. For example, the model analysis of historical data reveals that on sunny summer mornings between 10:00 and 11:00 AM, when the target color temperature is 6000K and the target illuminance is 700lx, the risk of UGR exceeding the limit is relatively high. Based on this, in subsequent optimization calculations for the same scenario, the initial color temperature of the second light source channel is lowered by 300K and the initial shading angle is increased by 5 degrees to achieve preventative adjustments and reduce the probability of UGR exceeding the limit.

[0148] In some alternative implementations, the data storage medium can be replaced with an SD card, expanding the storage capacity to 32GB to meet the needs of storing historical data for a longer period of time; the training algorithm of the prediction model can be replaced with a decision tree algorithm to improve the model's adaptability to complex scenarios; and the feedback signal transmission method for adjustment actions can be replaced with wireless communication, which is suitable for scenarios of multi-lighting control in large office spaces.

[0149] This embodiment details a method for controlling the light quality of a dual-path office lighting optical system through specific operational procedures, component selection, and alternative solutions, ensuring the method's reproducibility and practicality. This method achieves deep integration of rhythmic lighting and glare control by dynamically generating target lighting parameters, monitoring glare values ​​in real time, and prioritizing and coordinating the adjustment of multiple variables. Combined with a feedback learning optimization strategy, it effectively solves the problem of the separation between the two in existing technologies. Furthermore, through various optional implementation methods, the system's adaptability and flexibility are enhanced, ensuring that it can provide users with a lighting environment that combines healthy rhythms and visual comfort under different office scenarios and usage requirements.

[0150] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A dual-light-emission path office lighting optical system, characterized in that, The system consists of: The system comprises a dual-channel independently controllable light source module, an in-situ visual information acquisition module, a dynamic actuator module, and a core processing and control unit. The dual-channel independent controllable light source module includes a first light source channel and a second light source channel, which are used to provide lighting outputs that are independent of each other and have different optical paths. The in-situ visual information acquisition module is located inside the light-emitting surface of the lamp and is used to acquire scene brightness distribution information facing the typical observer area. The dynamic actuator module includes a motor drive mechanism for adjusting the physical shading angle of the lamp; The core processing and control unit is configured to execute a rhythm-comfort fusion optimization algorithm.

2. The office lighting optical system with dual light-emitting paths according to claim 1, characterized in that, The dual-channel independent controllable light source module specifically includes: a first optical path for providing central direct light and a second optical path for providing peripheral reflected light; The first optical path includes a first LED array and a first optical device that works in conjunction with it, the first optical device being configured to allow light emitted from the first LED array to be emitted directly after transmission or a single reflection; The second optical path includes a second LED array and a second optical device that works in conjunction with it. The second optical device includes a light guide lens and a reflective surface. The light emitted from the second LED array is guided by the light guide lens, diffusely reflected by the reflective surface, and then emitted through the light-emitting surface. The driving circuits of the first LED array and the second LED array are independent of each other, and the color temperature range of the first LED array is different from that of the second LED array.

3. The office lighting optical system with dual light-emitting paths according to claim 1, characterized in that, The in-situ visual information acquisition module includes multiple distributed wide dynamic range brightness sensors. Each of the brightness sensors has a fixed field of view and is oriented to cover one or more preset typical viewing positions from below the luminaire to the side; The brightness sensor is configured in non-imaging mode, and its output signal is the distribution data of spatial brightness within the field of view coverage area.

4. The office lighting optical system with dual light-emitting paths according to claim 1, characterized in that, The rhythm-comfort fusion optimization algorithm includes the following steps: S1. Generate a target lighting parameter curve that dynamically changes over time based on a preset human physiological rhythm model. The target lighting parameters include at least the target color temperature and the target illuminance. S2. Receive the brightness distribution information collected in real time by the in-situ visual information acquisition module, and calculate the actual UGR value under the current lighting scene in real time based on the unified glare value (UGR) calculation model. S3. Using the target lighting parameter curve as the optimization target and maintaining the actual UGR value below a preset threshold as the constraint, perform multi-variable collaborative optimization calculations on the color temperature and brightness parameters of the first light source channel and the second light source channel, as well as the shading angle parameter of the dynamic actuator module, to generate collaborative control commands. S4. Output the collaborative control command to the dual-channel independent controllable light source module and the dynamic actuator module to drive them to perform corresponding actions so that the actual output lighting parameters of the system can approach the target lighting parameters while satisfying the UGR constraints. The multivariate collaborative optimization solution in step S3 is achieved by solving the following optimization problem: in, To optimize the variable vector, and These represent the brightness and correlated color temperature of the first light source channel, respectively. and These represent the brightness and correlated color temperature of the second light source channel, respectively. Indicates the adjustment amount of the physical shading angle. This is the actual output vector of lighting parameters from the system, which should include at least the actual average illuminance and average correlated color temperature of the work surface. Represented by vector To optimize the variables, a minimization solution is performed. Let be the objective function, and let represent the actual output vector of lighting parameters of the system. With time Changing target illumination parameter vector The square of the difference in Euclidean norm between them For step S1, generated over time Changing target lighting parameter vector, This represents the constraints that the optimization process must satisfy. To optimize variables The unified glare value calculation function is used as the independent variable, and its calculation depends on the real-time brightness distribution information and luminaire geometric parameters obtained in step S2. The preset glare threshold value is less than 19. The core processing and control unit also includes a data recording and learning module, which is used to record historical operation data in a time series.

5. The office lighting optical system with dual light-emitting paths according to claim 4, characterized in that, The optimization problem is solved using an iterative optimization algorithm with a constraint handling mechanism; When the algorithm iteration process detects At that time, a hierarchical coordinated adjustment strategy is initiated: First, adjust the brightness of the second light source channel. Related color temperature And adjust the physical shading angle accordingly. ; Only when the above adjustments are insufficient to satisfy the constraints should the brightness of the first light source channel be adjusted. Make the minimum necessary adjustments; The hierarchical collaborative adjustment strategy is encoded as a correction rule for the gradient direction or search direction in the iterative optimization algorithm.

6. The office lighting optical system with dual light-emitting paths according to claim 1, characterized in that, The motor drive mechanism in the dynamic actuator module is specifically connected to the adjustable light-shielding blades located at the edge of the optical cavity of the lamp. The motor drive mechanism is used to receive instructions from the core processing and control unit and drive the light-shielding blades to rotate precisely within a preset angle range, thereby continuously changing the light-shielding angle of the lamp in a specific direction. The change in the shading angle, as one of the key variables, is incorporated into the collaborative optimization process of the rhythm-comfort fusion optimization algorithm.

7. The office lighting optical system with dual light-emitting paths according to claim 2, characterized in that, The nominal correlated color temperature of the first LED array is set in the range of 4000K to 6500K to provide main task lighting that conforms to rhythm objectives; The nominal correlated color temperature of the second LED array is set in the range of 2700K to 4500K, and its color temperature can be continuously or steppedly adjusted by the driving current. The second light source channel is configured to serve as an adjustable unit for adjusting ambient light distribution and reducing brightness contrast to help suppress glare during the execution of the fusion optimization algorithm.

8. The office lighting optical system with dual light-emitting paths according to claim 4, characterized in that, The historical operational data includes at least: target lighting parameters and optimized actual control command vectors. The calculated actual UGR value and environmental labeling information; The data recording and learning module is used to train a prediction model based on historical data. The prediction model is used to estimate the risk of UGR constraint conflict that may occur at the same time or under similar conditions in the future, and accordingly apply a preventive adjustment bias in advance in the optimization solution of step S3.

9. The office lighting optical system with dual light-emitting paths according to claim 4, characterized in that, The human physiological rhythm model is an algorithm model that generates dynamic target curves based on daily time, seasonal information, and optional user preference inputs. In the target lighting parameter curve, the target color temperature and target illuminance are both continuous functions of daytime or piecewise continuous functions, pointing to higher color temperature and higher illuminance in the morning of weekdays, and to lower color temperature and lower illuminance in the evening.

10. A method for controlling the light quality of a dual-emission optical path office lighting system, applicable to the dual-emission optical path office lighting system as described in any one of claims 1-9, characterized in that, The specific steps of this method are as follows: Step 1: Target illumination parameter generation: Based on the pre-stored or received human physiological rhythm model and current time information, dynamically generate the target illumination parameter set for the current moment of the system. The target illumination parameter set includes at least the target-related color temperature and the target illuminance. Step 2: In-situ calculation of scene glare value: Obtain multi-viewpoint brightness data in real time from the in-situ visual information acquisition module set inside the light-emitting surface of the lamp. Based on the brightness data and the geometric position parameters of the lamp, and according to the calculation model of unified glare value (UGR), calculate the actual UGR value at the preset observation position in the current lighting scene in real time. Step 3: Multivariate Collaborative Optimization Decision: Taking the target lighting parameter set as the optimization objective and maintaining the actual UGR value below a preset threshold as a hard constraint, the brightness and color temperature of the first light source channel, the brightness and color temperature of the second light source channel, and the adjustment amount of the physical shading angle are collaboratively optimized using a rhythm-comfort fusion optimization algorithm. The optimization calculation follows a preset adjustment priority strategy, that is, the parameters of the second light source channel and the physical shading angle are adjusted first, and the brightness of the first light source channel is adjusted only when the above adjustments cannot meet the constraints. Step 4, Instruction Execution and Feedback Learning: Based on the optimization results generated in Step 3, corresponding drive control instructions are generated and sent to the dual-channel independent controllable light source module and the dynamic actuator module respectively, driving them to perform corresponding brightness, color temperature and shading angle adjustment actions. At the same time, the process data of this optimization event is recorded to update the historical dataset and train the prediction model to optimize the adjustment strategy for similar scenarios in the future.

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