A brightness AI self-adaptive adjusting method and system for a lighting system

Abstract

The present application relates to the field of intelligent lighting, and particularly relates to a brightness AI self-adaptive adjustment method and system for a lighting system, real-time acquisition of light environment parameters of a lighting area; based on spatial distribution characteristics and reflected light source intensity, determination of interference parameters of a reflected light source on a target area, generation of a compensation trigger signal when the interference parameters exceed a preset interference threshold; in response to the compensation trigger signal, acquisition of environmental data of the lighting area; calculation of reflected interference compensation amount according to the environmental data, the environmental data including mobile object reflection characteristics, high reflection object position and environmental light fluctuation trend; generation of a lighting adjustment strategy according to the reflected interference compensation amount, the strategy containing cooperative control parameters of lighting units of the target area; dynamic adjustment of brightness output of the lighting units of the target area based on the lighting adjustment strategy. Dynamic calculation of the reflected interference compensation amount, and then generation of a targeted lighting adjustment strategy, to realize cooperative control of the lighting units.

Description

Technical Field

[0001] The present application relates to the field of intelligent lighting technology, and in particular to a brightness adaptive adjustment method and system for a lighting system using AI. Background Art

[0002] Adaptive brightness control in lighting systems is an intelligent lighting technology designed to automatically adjust the brightness of artificial light sources based on ambient lighting conditions to improve energy efficiency, comfort, and visual quality in the work environment. This system automatically adjusts the intensity of indoor lighting based on changes in external or internal light sources, eliminating manual intervention and ensuring optimal lighting under varying ambient light conditions.

[0003] Existing intelligent lighting systems typically rely on simple light intensity sensors to detect light intensity, but these sensors cannot effectively distinguish between direct and reflected light sources. This means that when highly reflective objects (such as glass windows, mirrored surfaces, or smooth floors) are present, the reflected light is detected by the sensor and mistakenly interpreted as effective illuminance, causing the system to incorrectly adjust brightness. Although reflected light is often less intense than direct light sources, it still affects sensor readings. Without distinguishing the spatial characteristics and spectrum of these light sources, the system may treat reflected light as a direct light source, resulting in inaccurate brightness adjustment. Specifically, areas that should be darker may be mistakenly identified as insufficiently lit, causing the system to mistakenly increase lighting intensity, resulting in an overbright effect. Alternatively, areas that do not require increased lighting may be mistakenly identified as overbright, resulting in reduced lighting. Furthermore, when the illumination from multiple light sources overlaps in certain areas, existing calculation methods struggle to accurately estimate the contribution of each light source. For example, when both natural and artificial light are present, some areas may appear overexposed or underexposed. Simple superposition calculation methods can result in excessively high (overexposed) light intensity in some areas and insufficient (underexposed) light intensity in others.

[0004] Therefore, the existing technology has defects and needs to be improved. Summary of the Invention

[0005] In order to solve one or several problems in the prior art, the main purpose of this application is to provide a brightness AI adaptive adjustment method and system for a lighting system.

[0006] To achieve the above-mentioned object of the invention, the present application proposes a brightness adaptive adjustment method for a lighting system using AI, the method comprising:

[0007] Acquire the light environment parameters of the lighting area in real time, including direct light source intensity, reflected light source intensity and spatial distribution characteristics;

[0008] determining an interference parameter of the reflected light source on the target area based on the spatial distribution characteristics and the intensity of the reflected light source, and generating a compensation trigger signal when the interference parameter exceeds a preset interference threshold;

[0009] acquiring environmental data of the lighting area in response to the compensation trigger signal;

[0010] Calculating a reflection interference compensation amount based on the environmental data, wherein the environmental data includes reflection characteristics of moving objects, positions of highly reflective objects, and ambient light fluctuation trends;

[0011] generating a lighting adjustment strategy based on the reflection interference compensation amount, wherein the strategy includes coordinated control parameters for lighting units in the target area, for offsetting interference from the reflected light source and maintaining effective illumination in the target area;

[0012] The brightness output of the lighting unit in the target area is dynamically adjusted based on the lighting adjustment strategy.

[0013] The present application also provides an AI adaptive brightness adjustment system for a lighting system, including:

[0014] A first acquisition module is used to acquire light environment parameters of the lighting area in real time, wherein the light environment parameters include direct light source intensity, reflected light source intensity and spatial distribution characteristics;

[0015] a determination module, configured to determine an interference parameter of the reflected light source on the target area based on the spatial distribution characteristics and the intensity of the reflected light source, and to generate a compensation trigger signal when the interference parameter exceeds a preset interference threshold;

[0016] a second acquisition module, configured to acquire environmental data of the lighting area in response to the compensation trigger signal;

[0017] a calculation module, configured to calculate a reflection interference compensation amount based on the environmental data, wherein the environmental data includes reflection characteristics of moving objects, positions of highly reflective objects, and ambient light fluctuation trends;

[0018] a generation module, configured to generate a lighting adjustment strategy based on the reflection interference compensation amount, wherein the strategy includes coordinated control parameters for lighting units in the target area, so as to offset the interference of the reflected light source and maintain the effective illumination of the target area;

[0019] An adjustment module is used to dynamically adjust the brightness output of the lighting unit in the target area based on the lighting adjustment strategy.

[0020] The present application also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0021] The present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0022] The AI ​​adaptive brightness adjustment method and system for a lighting system in an embodiment of the present application can actively trigger a compensation mechanism by acquiring the intensity and spatial distribution characteristics of the direct light source and the reflected light source in the lighting area in real time, accurately identifying the degree of interference of the reflected light source on the target area. Combined with the reflection characteristics of moving objects, the position of highly reflective objects and the fluctuation trend of ambient light in the environmental data, the reflection interference compensation amount is dynamically calculated, and then a targeted lighting adjustment strategy is generated to achieve coordinated control of the lighting units. This method effectively offsets the interference caused by the reflected light source and ensures that the effective illumination of the target area is stable and uniform. Overall, the present invention improves the intelligent adaptability and adjustment accuracy of the lighting system, optimizes the lighting effect, and enhances the user's visual comfort. It is particularly suitable for intelligent lighting applications in complex reflective environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 1 is a flow chart of a brightness adaptive adjustment method for a lighting system according to an embodiment of the present application;

[0024] Figure 2 1 is a flow chart of a brightness adaptive adjustment method for a lighting system according to an embodiment of the present application;

[0025] Figure 3 This is a schematic block diagram of the structure of an AI adaptive brightness adjustment system for a lighting system according to an embodiment of the present application;

[0026] Figure 4 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present application.

[0027] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0029] Reference Figure 1 In an embodiment of the present application, a brightness AI adaptive adjustment method for a lighting system is provided, the method comprising:

[0030] S1. Acquire light environment parameters of the lighting area in real time, wherein the light environment parameters include direct light source intensity, reflected light source intensity, and spatial distribution characteristics;

[0031] S2. determining an interference parameter of the reflected light source on the target area based on the spatial distribution characteristics and the intensity of the reflected light source, and generating a compensation trigger signal when the interference parameter exceeds a preset interference threshold;

[0032] S3. acquiring environmental data of the lighting area in response to the compensation trigger signal;

[0033] S4. Calculating a reflection interference compensation amount based on the environmental data, where the environmental data includes reflection characteristics of moving objects, positions of highly reflective objects, and ambient light fluctuation trends;

[0034] S5. Generating a lighting adjustment strategy based on the reflection interference compensation amount, wherein the strategy includes coordinated control parameters for lighting units in the target area, for offsetting interference from the reflected light source and maintaining effective illumination in the target area;

[0035] S6. Dynamically adjust the brightness output of the lighting unit in the target area based on the lighting adjustment strategy.

[0036] As described in steps S1-S3 above, the lighting environment is first monitored in real time using sensors and other means to obtain information on different light sources within the area, including direct light sources (such as lamps and sunlight) and reflected light sources (such as light reflected from glass and the ground). Furthermore, spatial distribution characteristics are obtained. Spatial distribution characteristics refer to the spatial distribution of light sources and the distribution patterns of different light sources within the area, which helps distinguish different light sources. By comprehensively collecting light environment parameters, the system can obtain the actual lighting conditions of different light sources, including the intensity and distribution of direct and reflected light sources, as well as their impact on the target area. This information provides basic data for subsequent calculations and reflection interference compensation. Based on the light environment parameters, especially the spatial distribution characteristics and the intensity of the reflected light source, the degree of interference of the reflected light source on the target area is calculated. The interference parameter of the reflected light source is a quantitative indicator of the impact of the reflected light source on the regional illumination. It is generally determined based on the intensity of the reflected light, the nature of the reflecting object, and the complexity of the reflection path. When the interference parameter exceeds a preset threshold, it indicates that the reflected light source has a significant impact on the lighting effect, and the system needs to make compensation adjustments. The potential interference of the reflected light source on the illumination of the target area is identified in advance and triggered by the set threshold. If the interference is too large, the system will activate the compensation mechanism to prevent the lighting system from incorrectly adjusting the brightness due to the reflected light source. After the system recognizes that the interference of the reflected light source exceeds the standard and generates a compensation signal, the system will further obtain environmental data related to the reflected light source. Environmental data includes the reflection characteristics of moving objects (for example, changes in reflection caused by people moving in the area), the location of highly reflective objects (such as mirrors or glass), and the trend of ambient light fluctuations (such as changes in lighting during the day and at night). Accurately identify dynamic changes that affect illumination, especially factors related to object reflection and environmental changes. This data acquisition enables the system to make timely adjustments when the environment changes, ensuring that the brightness adjustment is more accurate and adapted to the actual scene.

[0037] As described in steps S4-S6 above, the system uses the acquired environmental data to calculate a compensation for reflection interference using a model or algorithm. This compensation takes into account variations in the reflective characteristics of objects, the spatial position of the reflecting object, and fluctuations in ambient light. These factors can affect light intensity, especially as reflected light sources can fluctuate due to environmental changes. Therefore, the system needs to dynamically adjust the compensation to balance the effects of reflected light. By calculating the compensation for reflection interference, the system can achieve more precise brightness adjustment. Reflected light sources can experience significant fluctuations during certain periods of time or under the influence of certain objects. Calculating the compensation can effectively reduce these fluctuations, thereby avoiding inaccurate brightness adjustment. Based on the compensation for reflection interference, a specific lighting adjustment strategy is generated. This strategy coordinates the control of multiple lighting units (such as lamps or LEDs) to adjust their brightness, direction, or other parameters, effectively offsetting the impact of reflected light sources on regional illumination. By dynamically adjusting the output of multiple lighting units, the illumination of the target area remains within the expected range. By collaboratively controlling parameters such as the brightness of the lighting units, the system can accurately compensate for interference caused by reflected light sources, avoiding incorrect brightness adjustments due to false detection of reflected light, and ensuring uniform and stable lighting effects. Based on the lighting adjustment strategy generated in the previous step, the system dynamically adjusts the brightness output of lighting units (such as lamps) in the target area. This adjustment is based on the interference of reflected light sources and the compensation amount calculated based on environmental data. This dynamic change process can adapt to different lighting environments. This achieves intelligent brightness adjustment to cope with different lighting environments and changes in reflected light sources. The system automatically adjusts the brightness of lighting units to ensure that the effective illumination of the target area is always maintained at the required level, avoiding overexposure or underlighting, and improving the overall lighting experience.

[0038] As described above, by acquiring the intensity and spatial distribution characteristics of the direct light source and reflected light source in the lighting area in real time, the degree of interference of the reflected light source on the target area can be accurately identified, and the compensation mechanism can be actively triggered. Combined with the reflection characteristics of moving objects, the position of highly reflective objects, and the fluctuation trend of ambient light in the environmental data, the reflection interference compensation amount is dynamically calculated, and then a targeted lighting adjustment strategy is generated to achieve coordinated control of the lighting units. This method effectively offsets the interference caused by the reflected light source and ensures that the effective illumination of the target area is stable and uniform. Overall, the present invention improves the intelligent adaptability and adjustment accuracy of the lighting system, optimizes the lighting effect, and enhances the user's visual comfort. It is particularly suitable for intelligent lighting applications in complex reflective environments.

[0039] Reference Figure 2 In one embodiment, the step of generating a lighting adjustment strategy according to the reflection interference compensation amount includes:

[0040] S51. Obtain the environmental data, and calculate the spatial weight distribution coefficient of each lighting unit based on the relative spatial relationship between the position of the highly reflective object and the target area, wherein the lighting unit closest to the highly reflective object is assigned the highest suppression weight;

[0041] S52, generating a compensation intensity correction coefficient that is positively correlated with the dynamic reflection interference intensity in combination with the real-time change rate of the reflection characteristic of the moving object;

[0042] S53. Setting a dynamic response rate threshold for brightness adjustment based on the ambient light fluctuation trend, wherein the rate threshold is negatively correlated with the light fluctuation frequency;

[0043] S54. Multi-dimensionally fuse the spatial weight distribution coefficient, compensation intensity correction coefficient, and dynamic response rate threshold to generate a collaborative control parameter set including a light intensity modulation gradient, a regional priority sequence, and a phase delay parameter, wherein the phase delay parameter is used to match the adaptation time constant of the human eye to brightness mutations.

[0044] As described in the above steps, in a lighting system, reflections from highly reflective objects can cause uneven light intensity across the target area. To address this issue, the distribution of lighting units needs to be adjusted, taking into account the relative position of each lighting unit to the highly reflective object. Since the reflection effect of highly reflective objects is most pronounced near them, lighting units near these objects should be assigned higher suppression weights to reduce over-illumination of the target area. By properly assigning weights to each lighting unit, the lighting system can be intelligently adjusted to minimize interference from highly reflective objects, ensuring more uniform illumination of the target area and avoiding light pollution or excessive reflections. In dynamic environments, the reflective characteristics of moving objects vary with factors such as their position and speed. To more accurately address reflected light interference, these reflective characteristics must be tracked in real time. By analyzing the rate of change of the moving object's reflective characteristics, the trend of the reflected interference intensity can be determined, and a corresponding compensation intensity correction factor can be generated. This compensation factor dynamically adjusts the lighting intensity to account for the changing reflection interference. Automatically adjusting the compensation strategy to address changes in reflection interference in dynamic environments ensures consistent and stable illumination of the target area, minimizing uneven illumination or over-brightness caused by varying reflections. Changes in the ambient light fluctuation trend reflect the volatility of surrounding lighting conditions. To ensure that the lighting system's response is not too drastic, causing visual discomfort or over-adjustment, a dynamic response rate threshold is set. This threshold is negatively correlated with the frequency of light fluctuations. This means that the higher the frequency of light fluctuations, the lower the response rate threshold, and the slower the system's response. This prevents the system from overreacting to high-frequency fluctuations and causing unnecessary brightness adjustments. By properly setting the response rate threshold, the lighting system can smoothly adjust brightness, avoiding frequent changes due to short-term ambient light fluctuations, reducing visual disturbances and enhancing user comfort. The calculated multiple parameters (spatial weight distribution coefficient, compensation intensity correction factor, and dynamic response rate threshold) are multi-dimensionally integrated to form a coordinated control parameter set. This parameter set includes: light intensity modulation gradient: controls the amplitude of lighting adjustment; regional priority sequence: determines priority adjustment areas, ensuring that areas most in need of adjustment receive compensation first; and phase delay parameter: Taking into account the human eye's adaptation time to brightness changes, a phase delay parameter is set to smooth the brightness adjustment process and avoid sudden brightness changes that may cause user discomfort. This multi-dimensional, integrated collaborative control strategy comprehensively considers all factors of the lighting environment, enabling more intelligent and smooth lighting adjustment. The phase delay parameter is particularly critical, enabling the system to mimic the human eye's ability to adapt to brightness changes, avoiding overly abrupt brightness changes. This makes lighting system adjustment more natural, comfortable, and consistent with human perception.

[0045] In one embodiment, the step of calculating the reflection interference compensation amount according to the environmental data includes:

[0046] Based on the geometric topological relationship between the high-reflective object position and the target area, a reflection suppression factor of each high-reflective object to the target area is calculated, wherein the reflection suppression factor is positively correlated with the object reflectivity and the square of the distance;

[0047] In combination with the real-time change rate of the reflection characteristics of the moving object, a dynamic compensation gain coefficient is generated, and the gain coefficient exponentially increases with the increase of the change rate of the surface reflectivity of the moving object;

[0048] According to the spectral characteristics of the fluctuation trend of the ambient light, the main frequency of the light intensity fluctuation is extracted and a time sequence smoothing coefficient is calculated, which is used to suppress the instantaneous interference of high-frequency fluctuation on the compensation amount;

[0049] The reflection suppression factor, the dynamic compensation gain coefficient and the time sequence smoothing coefficient are weighted and fused to generate a reflection interference compensation amount of the target area, wherein the fusion weight is dynamically adjusted according to the spatial coverage density of the lighting unit.

[0050] As mentioned above, the reflection suppression factor is a quantitative metric used to describe the illumination interference of highly reflective objects on the target area. According to the principles of physical optics, the reflectivity of an object (i.e., the ability of its surface to reflect light) and the distance between the object and the target area have a significant impact on the intensity of the reflection. Specifically, the higher the reflectivity and the closer the distance, the stronger the reflection interference. The amount of reflection interference is generally inversely proportional to the square of the distance between the object and the target area, while the reflectivity is directly proportional to the interference intensity. Based on the geometric topological relationship (i.e., the spatial positional relationship between the object and the target area), the degree of interference of each highly reflective object on the target area can be accurately calculated. By introducing the reflection suppression factor, the system can accurately quantify the reflection interference based on the object's reflective properties and position, thereby avoiding overcompensation in areas far from highly reflective objects and ensuring appropriate compensation for areas close to highly reflective objects. This improves compensation accuracy and reduces over- or undercompensation. The reflective properties of a moving object, particularly its reflectivity, change as it moves. The rate of change in reflectivity affects the intensity of the reflected light, which in turn affects the illumination distribution in the target area. To accurately compensate for reflective interference caused by dynamic objects, a dynamic compensation gain coefficient is generated to quantify the impact of reflectivity changes. This gain coefficient increases exponentially, meaning that the faster the surface reflectivity changes, the faster the compensation gain increases. This is because rapidly changing reflectivity means the interference from reflected light also changes rapidly, requiring a more sensitive compensation mechanism. This gain coefficient can adapt to the changes in moving objects in real time, dynamically adjusting the lighting compensation. By setting an exponential increase, the system can effectively cope with rapidly changing reflectivity and avoid uneven or unstable lighting caused by rapid changes. Ambient light fluctuations are often accompanied by frequency fluctuations. In particular, high-frequency fluctuations in light intensity can lead to transient instability in the compensation calculation. If these high-frequency fluctuations are directly used to adjust the compensation amount, the lighting system may react too drastically, causing visual discomfort. By analyzing the spectral characteristics of ambient light fluctuations, the main fluctuation frequencies can be identified. A temporal smoothing coefficient is then calculated to suppress the transient interference of high-frequency fluctuations. The temporal smoothing coefficient helps smooth out rapidly changing light intensity fluctuations, reducing their impact on the compensation amount, thereby preventing overreaction of the compensation strategy. The temporal smoothing coefficient can effectively reduce the interference of high-frequency fluctuations in light intensity on the compensation calculation, ensuring the stability of the compensation amount. In this way, excessive adjustments to the lighting system due to short-term fluctuations in the light environment can be avoided, ensuring the long-term stability and comfort of the lighting system. The final compensation amount is obtained by weighted fusion of different factors (reflection suppression factor, dynamic compensation gain coefficient, and temporal smoothing coefficient). The weight of each factor will be adjusted according to different situations. For example, if a lighting unit has a high spatial coverage density, then its compensation demand may be greater than other areas, so it needs to be assigned a higher weight.This weighted fusion strategy comprehensively considers physical reflective properties, dynamic object interference, and ambient noise, ensuring more accurate and dynamically adjusted compensation. By weightedly integrating multiple compensation factors, the system can provide more precise compensation under varying environmental conditions. This not only addresses interference from static reflective sources, but also dynamically adjusts lighting to account for varying reflections from moving objects and mitigates the effects of ambient noise. Weighted adjustment also ensures accurate compensation in each area based on spatial coverage density.

[0051] In one embodiment, before the step of acquiring the environmental data of the lighting area in response to the compensation trigger signal, the method further includes:

[0052] Obtaining frequency spectrum characteristics of the direct light source intensity and the reflected light source intensity;

[0053] Analyzing the difference between the spectral characteristics of the direct light source intensity and the reflected light source intensity based on the spectral characteristics of the two;

[0054] According to the analysis results, determine the overlapping area of ​​natural light source and artificial light source;

[0055] Calculating the light intensity proportion weights of various light sources in the superimposed area, and when the proportion of artificial light sources exceeds a preset dominant threshold, reducing the interference threshold by a first adjustment coefficient;

[0056] When the proportion of natural light sources exceeds a preset dominant threshold, the interference threshold is increased by a second adjustment coefficient;

[0057] The effectiveness of the compensation trigger signal is re-determined based on the adjusted interference threshold. If the interference parameters still exceed the standard in the area dominated by artificial light sources, the local compensation mechanism will be triggered first.

[0058] As mentioned above, direct light intensity refers to the intensity variation of light sources (such as LED lights and sunlight) that directly illuminate the target area. Reflected light intensity refers to the intensity of light reflected or scattered onto the target area (such as sunlight reflection and indoor light reflection). Different light sources (natural light and artificial light) have different spectral characteristics. Natural light (such as sunlight) typically has a relatively smooth and broadband spectral distribution, while artificial light sources (such as LED light) may exhibit a more concentrated spectrum. By analyzing these spectral characteristics, the characteristics of the two light sources can be distinguished, providing a basis for subsequent light source identification and interference threshold adjustment. By obtaining the spectral characteristics of the light sources, the system can distinguish between direct and reflected light sources and perform analysis based on their different spectral distributions. This provides a reliable data foundation for subsequent light source analysis and interference threshold adjustment, effectively addressing mixed light source situations and preventing misjudgment. The spectral characteristics of direct and reflected light sources typically differ significantly. Direct light sources may exhibit relatively concentrated frequency peaks (especially artificial light sources), while reflected light sources may exhibit a more dispersed or broad spectrum. By comparing the differences in the spectral characteristics of the two, it is possible to determine which portions of the light intensity are from direct illumination and which are from reflection. For example, the intensity of reflected light sources may be closely related to environmental changes (such as weather and time of day), while direct light sources may be more stable. Analyzing spectral differences can help the system accurately distinguish between direct and reflected light sources. Natural light sources (such as sunlight) and artificial light sources (such as LED lights) typically have different spectral characteristics. By analyzing the spectral differences between the two, the system can determine the overlapping areas of light sources—that is, the areas where natural and artificial light sources influence each other. This determination is based on spectral differences. The identified overlapping areas are key components of mixed lighting scenes, affected by both light sources and therefore require specific interference threshold adjustment. By accurately determining the overlapping areas of natural and artificial light sources, the system can optimize for these specific areas and avoid misjudgments caused by interference from mixed light sources. This step helps identify key areas in complex lighting environments and provides a basis for adjusting the interference threshold. Within the overlapping areas, the ratio of light intensity between artificial and natural light sources affects the final lighting effect. Calculating the intensity weights of each light source allows for a more accurate assessment of the impact of each light source on the lighting environment. This weight calculation is based on the difference in light source intensity. When the proportion of artificial light sources is higher, it means that the artificial light sources have a greater impact on the area. Conversely, it means that natural light sources are dominant. By calculating the light source proportion weight, the system can dynamically adjust the compensation strategy within the light source superposition area to ensure that the compensation is more in line with the actual lighting conditions. This calculation helps to optimize the threshold setting and avoid misjudgment due to changes in the light source ratio. When the proportion of artificial light sources in the superposition area exceeds the preset threshold, it means that the artificial light sources have a greater impact on the lighting in the area. At this time, the interference threshold needs to be lowered to better identify and suppress the reflection interference caused by artificial light sources.This adjustment strategy is designed to increase the system's sensitivity to artificial light sources, enabling timely activation of the compensation mechanism and preventing excessive reflective interference from artificial light sources in the target area. Lowering the interference threshold allows the system to respond more sensitively in areas dominated by artificial light sources, prioritizing interference caused by artificial light sources. This helps the system perform interference compensation promptly and accurately, improving the accuracy of the compensation effect. Conversely, when the proportion of natural light sources exceeds the threshold, indicating that natural light sources (such as sunlight) dominate the illumination of the area. In this case, the system needs to increase the interference threshold to reduce overreaction to changes in natural light sources and avoid misinterpreting reflection interference. Raising the interference threshold is intended to increase the system's tolerance for changes in natural light sources, preventing frequent triggering of the compensation mechanism due to changes in ambient light. After adjusting the interference threshold, the system needs to reassess the validity of the compensation trigger signal. This reassessment ensures that the compensation mechanism can still trigger accurately under the new lighting conditions. If the adjusted interference threshold still fails to effectively eliminate interference in areas dominated by artificial light sources, the system will prioritize the activation of the local compensation mechanism. The local compensation mechanism provides refined compensation within the areas dominated by artificial light sources, effectively eliminating interference caused by artificial light sources and ensuring the lighting quality of the target area.

[0059] In one embodiment, after the step of calculating the light intensity weights of various light sources in the overlapping area, the method further includes:

[0060] When both the natural light source ratio and the artificial light source ratio do not exceed the preset dominant threshold, the original interference threshold is maintained.

[0061] As mentioned above, when calculating the overlay area, the first step is to calculate the intensity contribution of natural and artificial light sources. This step aims to accurately assess lighting conditions and determine which light source contributes most to the area's illumination. By quantifying the contribution of each light source, the relative strengths of natural and artificial light can be further identified. By calculating the light source contribution, we can clearly understand the contributions of natural and artificial light to the final lighting effect. This contribution calculation helps understand the lighting characteristics of the environment and formulate appropriate lighting adjustment strategies. A "dominant threshold" is set to distinguish between "dominant" and "non-dominant" light sources. If the contribution of both natural and artificial light does not exceed this threshold, the contributions of both light sources are relatively close, with no single light source clearly dominating. The threshold is set to determine when the influence of both light sources is insufficient, reaching a state of equilibrium. This avoids misjudgment when the contribution of natural and artificial light sources is close. Failure to set an appropriate dominant threshold can lead to unnecessary adjustments, impacting system stability and efficiency. When the contribution of both natural and artificial light sources does not exceed the dominant threshold, the original interference threshold remains unchanged. This prevents miscompensation in this "equilibrium" state, as the system cannot determine whether there is a significant change in the light source or the need for adjustment. Incorrectly performing compensation in this situation can lead to unnecessary fluctuations in lighting, impacting lighting stability and comfort. Define processing rules for non-dominant lighting areas and prevent erroneous triggering of compensation in intermediate states (where neither natural light nor artificial light is dominant). By ensuring that adjustments are made only when one light source is clearly dominant, we avoid unreasonable system reactions when the contributions of natural and artificial light are close, thus helping to improve overall system efficiency and accuracy. In practical applications, such a setting can prevent misjudgments of the light source balance, avoid excessive intervention, and ensure the accuracy and stability of lighting adjustments.

[0062] In one embodiment, the step of determining the overlapping area of ​​the natural light source and the artificial light source includes:

[0063] Based on the difference in spectral characteristics between the direct light source intensity and the reflected light source intensity, identifying sub-regions having a continuous spectrum energy ratio higher than a preset natural light threshold and a discrete spectrum peak intensity higher than a preset artificial light threshold;

[0064] Based on the recognition results, the sub-regions that meet the following conditions are marked as overlapping regions.

[0065] As mentioned above, there are certain differences in the spectral characteristics of natural and artificial light sources. Natural light typically has a relatively continuous spectral distribution, especially within the visible light range; whereas artificial light sources (such as incandescent lamps and fluorescent lamps) may have more distinct discrete spectral peaks. This step classifies light sources and delineates regions by comparing the spectral characteristics of direct light source intensity (light directly hitting an object) and reflected light source intensity (light reflected on a surface). Direct light source intensity: This represents light directly hitting a surface, whether from natural or artificial light sources, and is characterized by a strong, continuous spectrum. Reflected light source intensity: This reflected light is typically weaker, and its spectral characteristics are affected by the surface material. While this spectrum may be closer to that of natural light sources, it still exhibits some differences. By analyzing these spectral differences, the characteristics of natural and artificial light can be distinguished, allowing sub-regions with different light source characteristics to be identified. Two threshold criteria are combined: The natural light threshold: When the continuous spectral energy fraction (i.e., the proportion of continuous light energy in the spectrum to the total energy) of a sub-region exceeds the set natural light threshold, it indicates that the primary source of light in that region is natural light. Artificial light threshold: When the discrete spectral peak intensity of a sub-area (i.e., the light intensity that appears as a discrete peak in the spectrum) is higher than the set artificial light threshold, it means that the light source in this area is mainly artificial light. By setting two thresholds, sub-areas with both natural light and artificial light can be accurately identified. If a sub-area meets both conditions (i.e., it has both high continuous spectrum energy and strong discrete spectral peak intensity), the sub-area is determined to be an overlapping area of ​​natural light and artificial light. These areas are calibrated as overlapping areas, that is, in these areas, the characteristics of the light source come from both natural light and artificial light. Through this calibration, the lighting system can perform specific light intensity calculations, adjustments, and optimizations for these areas. These sub-areas are clearly identified as overlapping areas, allowing the system to perform subsequent light source proportion calculations and adjustments based on this identification. For example, the proportion calculation can perform spatial distribution analysis based on these calibrated overlapping areas, so as to more accurately understand and adjust the specific contributions of natural light and artificial light.

[0066] In one embodiment, after the step of dynamically adjusting the brightness output parameters of the target area lighting units based on the lighting adjustment strategy, the method further includes:

[0067] Acquiring the intensity of the reflected light source in real time;

[0068] When the intensity of the reflected light source suddenly increases within a preset time and exceeds a preset sudden increase threshold, a temporary compensation mode is activated, the interference threshold is temporarily lowered to an emergency threshold, and pre-attenuation control is implemented on adjacent lighting units in the sudden increase area.

[0069] As mentioned above, the intensity of the reflected light source is monitored in real-time. The intensity of the reflected light source can be disturbed by external factors such as car lights, street lights, etc., especially around glass walls or other reflective surfaces. In order to identify the source of disturbance in time, the system needs to continuously monitor the intensity of the reflected light source through sensors or optical detection devices. By obtaining the intensity of the reflected light source in real time, sudden changes in the intensity of the reflected light source can be identified in real time. The intensity of the reflected light source can be affected by external disturbances, such as sudden car light irradiation. This step sets a sudden increase threshold, that is, when the intensity of the reflected light source suddenly increases within a predetermined time range and exceeds the threshold, the system determines that it is a reflection interference event, and then starts the temporary compensation mode. The goal of the temporary compensation mode is to reduce the impact of reflection interference by quickly adjusting the output parameters of the lighting units. When there is an abnormal light intensity change, the system can quickly respond and start the temporary compensation mechanism to avoid the impact of reflection interference on the lighting effect of the target area. This response is more rapid than traditional compensation methods, reducing light fluctuations caused by delays. After the temporary compensation mode is started, the system temporarily lowers the interference threshold, that is, reduces the tolerance for changes in the intensity of the reflected light source, thereby increasing the sensitivity of the interference response. In this way, even small changes in the intensity of the reflected light source can be captured in time and compensated. This measure is mainly to deal with sudden strong reflected light sources, so that the system can respond more quickly. By lowering the interference threshold, the system can more sensitively identify changes in the reflected light source in a short period of time, ensuring a quick response to sudden interference and avoiding response delays caused by high thresholds. When detecting a sudden increase in the reflected light source, the system not only adjusts the target area, but also performs pre-attenuation control on the lighting units adjacent to the sudden increase area. This is because the sudden increase in the reflected light source can affect the surrounding area, especially the lighting units in the adjacent area. Therefore, by performing pre-attenuation control on adjacent units in advance, the interference of the reflected light source can be prevented from further spreading, reducing the overall impact. Pre-attenuation control can reduce the lighting intensity of adjacent areas in advance to avoid the influence of the reflected light source spreading to a larger range, ensuring the stability of the lighting in the target area. By attenuating in advance, not only can the interference of the sudden light source be reduced, but unnecessary lighting adjustments to other areas can also be avoided.

[0070] It is worth mentioning that the existing system cannot distinguish the spatial / spectral characteristics of reflected light and direct light, resulting in two typical error adjustment modes: 1. Negative interference scenario: high reflective objects (such as mirrors, glass) reflect effective lighting to non-target areas, while sensors reduce brightness due to receiving reflected light. The actual illuminance of the target area is insufficient (such as the actual illuminance of the office desk is lower than the standard due to wall reflection). 2. Positive interference scenario: outdoor reflected light (such as building glass curtain wall reflecting sunlight) invades indoor sensors, and the system mistakenly judges that the indoor light is sufficient and turns off the light compensation, resulting in insufficient illuminance in the actual working area. It is necessary to distinguish the spatial source difference between outdoor reflected light and indoor effective lighting.

[0071] In one embodiment, after the step of dynamically adjusting the brightness output parameters of the target area lighting units based on the lighting adjustment strategy, the method further includes:

[0072] Acquiring the light environment parameters;

[0073] Analyzing the spatial distribution direction vectors of the direct light source and the reflected light source according to the light environment parameters;

[0074] Determine the angle between the direction vector of the direct light source and the direction vector of the reflected light source based on the spatial distribution direction vectors of the direct light source and the reflected light source;

[0075] When the angle value is greater than the preset intrusion determination threshold, the reflected light source is determined to be outdoor intrusion reflected light.

[0076] According to the angle analysis of the spatial distribution direction, the conditions for outdoor reflected light intrusion are met;

[0077] Based on the determination result of the outdoor reflected light intrusion, an anti-interference compensation mode is activated to implement illumination enhancement control on the area opposite to the incident direction of the reflected light source;

[0078] The duration of the reflected light source is recorded, and when the duration exceeds a preset time threshold, a physical shading linkage signal is generated.

[0079] As mentioned above, the spatial distribution of light sources can be captured using direction-sensitive elements (such as fisheye lenses combined with optical flow methods). This provides raw data support for tracing reflected light sources, addressing the inability of traditional single-light intensity sensors to locate the light source's direction. Based on LiDAR point clouds or binocular vision technology, the 3D propagation paths of direct light sources (such as lamps) and reflected light sources can be reconstructed, converting abstract light intensity signals into concrete spatial vectors. This enables the first digital modeling of lighting paths. Angle threshold detection can be set between 60° and 90° for intrusion detection (experimentally verified, the angle of reflected light indoors is typically less than 45°). Cosine similarity is used to quantify light direction differences. This allows for precise distinction between external building reflections (at large angles) and indoor mirror reflections (at small angles), preventing the misinterpretation of sunset light as lamp light. In areas with inverted reflected light, a "vector compensation" algorithm is activated to dynamically calculate the intensity and direction of the compensated light based on the incident angle. This overcomes the limitations of traditional uniform fill lighting. For example, when sunlight is reflected from the ground outside a window, the system prioritizes enhancing the backlighting of workstations rather than adjusting the overall brightness. Physical shading linkage can establish a communication protocol between the duration of reflected light and the building management system (BMS). When the threshold is exceeded, the shading device will be triggered to form a double-layer defense of "photoelectric compensation → physical blocking", solving the energy consumption problem of long-term strong reflection (such as continuous reflection of glass curtain walls).

[0080] Reference Figure 3, an embodiment of the present application further provides an AI adaptive brightness adjustment system for a lighting system, comprising:

[0081] The first acquisition module 1 is used to obtain the light environment parameters of the lighting area in real time, wherein the light environment parameters include the direct light source intensity, the reflected light source intensity and the spatial distribution characteristics;

[0082] Determination module 2, for determining an interference parameter of the reflected light source on the target area based on the spatial distribution characteristics and the intensity of the reflected light source, and generating a compensation trigger signal when the interference parameter exceeds a preset interference threshold;

[0083] A second acquisition module 3 is configured to acquire environmental data of the lighting area in response to the compensation trigger signal;

[0084] Calculation module 4, used to calculate the reflection interference compensation amount based on the environmental data, the environmental data including the reflection characteristics of the moving object, the position of the highly reflective object and the ambient light fluctuation trend;

[0085] A generating module 5 is configured to generate a lighting adjustment strategy based on the reflection interference compensation amount, wherein the strategy includes coordinated control parameters for lighting units in the target area, so as to offset the interference of the reflected light source and maintain the effective illumination of the target area;

[0086] The adjustment module 6 is configured to dynamically adjust the brightness output of the lighting unit in the target area based on the lighting adjustment strategy.

[0087] As described above, it can be understood that the various components of the brightness AI adaptive adjustment system for the lighting system proposed in this application can realize the functions of any of the brightness AI adaptive adjustment methods for the lighting system described above, and the specific structure will not be repeated.

[0088] Reference Figure 4 In the embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 4 As shown. The computer device includes a processor, memory, network interface and database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data such as monitoring data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements a brightness AI adaptive adjustment method for a lighting system.

[0089] The above-mentioned processor executes the above-mentioned AI adaptive brightness adjustment method for the lighting system, including: acquiring the light environment parameters of the lighting area in real time, the light environment parameters including the direct light source intensity, the reflected light source intensity and the spatial distribution characteristics; based on the spatial distribution characteristics and the reflected light source intensity, determining the interference parameters of the reflected light source on the target area, and generating a compensation trigger signal when the interference parameter exceeds a preset interference threshold; in response to the compensation trigger signal, acquiring the environmental data of the lighting area; calculating the reflection interference compensation amount based on the environmental data, the environmental data including the reflection characteristics of moving objects, the position of highly reflective objects and the ambient light fluctuation trend; generating a lighting adjustment strategy based on the reflection interference compensation amount, the strategy including collaborative control parameters for the lighting unit in the target area, which is used to offset the interference of the reflected light source and maintain the effective illumination of the target area; dynamically adjusting the brightness output of the lighting unit in the target area based on the lighting adjustment strategy.

[0090] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an AI adaptive brightness adjustment method for a lighting system, comprising the following steps: acquiring light environment parameters of a lighting area in real time, the light environment parameters including direct light source intensity, reflected light source intensity, and spatial distribution characteristics; determining interference parameters of the reflected light source on a target area based on the spatial distribution characteristics and the reflected light source intensity, and generating a compensation trigger signal when the interference parameter exceeds a preset interference threshold; acquiring environmental data of the lighting area in response to the compensation trigger signal; calculating a reflection interference compensation amount based on the environmental data, the reflection interference compensation amount including reflection characteristics of moving objects, positions of highly reflective objects, and ambient light fluctuation trends; generating a lighting adjustment strategy based on the reflection interference compensation amount, the strategy including collaborative control parameters for lighting units in the target area, for offsetting interference from reflected light sources and maintaining effective illumination in the target area; and dynamically adjusting the brightness output of lighting units in the target area based on the lighting adjustment strategy.

[0091] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, databases, or other media in this application and in examples provided herein, unless specifically stated otherwise, can include non-volatile and / or volatile memory. Non-volatile memory can include, for example, read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include, for example, random access memory (RAM), or external cache memory. As an illustration and not a limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus DRAM (RDRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0092] It should be noted that the terms "comprising", "including", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, a device, an article or a method that comprises a list of elements does not include only those elements recited, but can also include other elements not expressly listed or inherent to such process, device, article or method. Without further limitation, an element defined by the phrase "comprising a" does not exclude the presence of additional identical elements in the process, device, article or method that includes the element.

[0093] The above description is only the preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields, based on the content of the specification and drawings, is also included in the patent protection scope of the present application.

Claims

1. A brightness AI adaptive adjustment method for a lighting system, characterized in that: The method comprises: Real-time acquisition of light environment parameters of the lighting area, including direct light source intensity, reflected light source intensity, and spatial distribution characteristics. Spatial distribution characteristics refer to the spatial distribution of light sources and the distribution patterns of different light sources in the area. determining an interference parameter of the reflected light source on the target area based on the spatial distribution characteristics and the intensity of the reflected light source, and generating a compensation trigger signal when the interference parameter exceeds a preset interference threshold; acquiring environmental data of the lighting area in response to the compensation trigger signal; Calculating a reflection interference compensation amount based on the environmental data, wherein the environmental data includes reflection characteristics of moving objects, positions of highly reflective objects, and ambient light fluctuation trends; generating a lighting adjustment strategy based on the reflection interference compensation amount, wherein the strategy includes coordinated control parameters for lighting units in the target area, for offsetting interference from the reflected light source and maintaining effective illumination in the target area; Dynamically adjusting the brightness output of the lighting unit in the target area based on the lighting adjustment strategy; The step of generating a lighting adjustment strategy according to the reflection interference compensation amount includes: Obtaining the environmental data, and calculating a spatial weight distribution coefficient of each lighting unit based on a relative spatial relationship between the position of the highly reflective object and the target area, wherein the lighting unit closest to the highly reflective object is assigned the highest suppression weight; In combination with the real-time change rate of the reflection characteristics of the moving object, a compensation intensity correction coefficient positively correlated with the dynamic reflection interference intensity is generated; Based on the ambient light fluctuation trend, setting a dynamic response rate threshold for brightness adjustment, wherein the rate threshold is negatively correlated with the light fluctuation frequency; The spatial weight distribution coefficient, compensation intensity correction coefficient and dynamic response rate threshold are multi-dimensionally fused to generate a collaborative control parameter set including a light intensity modulation gradient, a regional priority sequence and a phase delay parameter, wherein the phase delay parameter is used to match the adaptation time constant of the human eye to brightness mutations.

2. The AI ​​adaptive brightness adjustment method for a lighting system according to claim 1, characterized in that: The step of calculating the reflection interference compensation amount according to the environmental data comprises: Based on the geometric topological relationship between the position of the highly reflective object and the target area, calculating the reflection suppression factor of each highly reflective object on the target area, wherein the reflection suppression factor is positively correlated with the reflectivity of the object and the square of the distance; generating a dynamic compensation gain coefficient in combination with the real-time change rate of the reflectivity characteristic of the moving object, wherein the gain coefficient increases exponentially with the increase in the rate of change of the reflectivity of the surface of the moving object; According to the spectrum characteristics of the ambient light fluctuation trend, the main frequency of the light intensity fluctuation is extracted and a time series smoothing coefficient is calculated, wherein the time series smoothing coefficient is used to suppress the instantaneous interference of high-frequency fluctuations on the compensation amount; The reflection suppression factor, the dynamic compensation gain coefficient and the time series smoothing coefficient are weightedly fused to generate a reflection interference compensation amount for the target area, wherein the fusion weight is dynamically adjusted according to the spatial coverage density of the lighting unit.

3. The AI ​​adaptive brightness adjustment method for a lighting system according to claim 1, characterized in that: Before the step of acquiring the environmental data of the lighting area in response to the compensation trigger signal, the method further includes: Obtaining frequency spectrum characteristics of the direct light source intensity and the reflected light source intensity; Analyzing the difference between the spectral characteristics of the direct light source intensity and the reflected light source intensity based on the spectral characteristics of the two; According to the analysis results, determine the overlapping area of ​​natural light source and artificial light source; Calculating the light intensity proportion weights of various light sources in the superimposed area, and when the proportion of artificial light sources exceeds a preset dominant threshold, reducing the interference threshold by a first adjustment coefficient; When the proportion of natural light sources exceeds a preset dominant threshold, the interference threshold is increased by a second adjustment coefficient; The effectiveness of the compensation trigger signal is re-determined based on the adjusted interference threshold. If the interference parameters still exceed the standard in the area dominated by artificial light sources, the local compensation mechanism will be triggered first.

4. The AI ​​adaptive brightness adjustment method for a lighting system according to claim 3, characterized in that: After the step of calculating the light intensity weights of various light sources in the superimposed area, the method further includes: When both the natural light source ratio and the artificial light source ratio do not exceed the preset dominant threshold, the original interference threshold is maintained.

5. The AI ​​adaptive brightness adjustment method for a lighting system according to claim 3, characterized in that: The step of determining the overlapping area of ​​the natural light source and the artificial light source includes: Based on the difference in spectral characteristics between the direct light source intensity and the reflected light source intensity, identifying sub-regions having a continuous spectrum energy ratio higher than a preset natural light threshold and a discrete spectrum peak intensity higher than a preset artificial light threshold; Based on the recognition results, the sub-regions that meet the following conditions are marked as overlapping regions.

6. The AI ​​adaptive brightness adjustment method for a lighting system according to claim 1, characterized in that: After the step of dynamically adjusting the brightness output parameters of the target area lighting units based on the lighting adjustment strategy, the method further includes: Acquiring the light environment parameters; Analyzing the spatial distribution direction vectors of the direct light source and the reflected light source according to the light environment parameters; Determine the angle between the direction vector of the direct light source and the direction vector of the reflected light source based on the spatial distribution direction vectors of the direct light source and the reflected light source; Analyze whether the outdoor reflected light intrusion condition is met according to the angle value; When the angle value is greater than a preset intrusion determination threshold, the reflected light source is determined to be outdoor intrusion reflected light; Based on the determination result of the outdoor reflected light intrusion, an anti-interference compensation mode is activated to implement illumination enhancement control on the area opposite to the incident direction of the reflected light source; The duration of the reflected light source is recorded, and when the duration exceeds a preset time threshold, a physical shading linkage signal is generated.

7. A brightness AI adaptive adjustment system for a lighting system, used in the method according to any one of claims 1 to 6, characterized in that: include: A first acquisition module is used to acquire light environment parameters of the lighting area in real time. The light environment parameters include direct light source intensity, reflected light source intensity, and spatial distribution characteristics. The spatial distribution characteristics refer to the spatial distribution of light sources and the distribution patterns of different light sources in the area. a determination module, configured to determine an interference parameter of the reflected light source on the target area based on the spatial distribution characteristics and the intensity of the reflected light source, and to generate a compensation trigger signal when the interference parameter exceeds a preset interference threshold; a second acquisition module, configured to acquire environmental data of the lighting area in response to the compensation trigger signal; a calculation module, configured to calculate a reflection interference compensation amount based on the environmental data, wherein the environmental data includes reflection characteristics of moving objects, positions of highly reflective objects, and ambient light fluctuation trends; a generation module, configured to generate a lighting adjustment strategy based on the reflection interference compensation amount, wherein the strategy includes coordinated control parameters for lighting units in the target area, so as to offset the interference of the reflected light source and maintain the effective illumination of the target area; An adjustment module is used to dynamically adjust the brightness output of the lighting unit in the target area based on the lighting adjustment strategy.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images