A training method based on lighting optimization model

Abstract

The present invention relates to the field of optical engineering technology, and in particular to a method for training a lighting optimization model. The method comprises the following steps: obtaining exhibition hall lighting data, and extracting exhibition hall lighting features and exhibit area lighting features based on the exhibition hall lighting data, thereby obtaining exhibition hall lighting data and exhibit area lighting data; performing light source position optimization analysis based on the exhibition hall lighting data, thereby obtaining exhibition hall light source position optimization data; obtaining exhibit area standard lighting data; performing color temperature anomaly analysis on the exhibit area lighting data based on the exhibit area standard lighting data, thereby obtaining exhibit area color temperature anomaly data; performing exhibit color distortion analysis based on the exhibit area color temperature anomaly data, thereby obtaining exhibit color distortion data; performing color temperature dynamic compensation based on the exhibit color distortion data, thereby obtaining exhibit area color temperature dynamic compensation data. The present invention improves lighting quality and energy efficiency based on optical engineering technology.

Description

Technical Field

[0001] The present invention relates to the field of optical engineering technology, and in particular to a training method based on an illumination optimization model. Background Art

[0002] Traditional lighting optimization model training methods often rely on extensive computing resources, particularly for lighting simulation, color temperature adjustment, and light source placement optimization. Especially in large-scale exhibition environments, the lighting system optimization process requires processing a large amount of input data, including light source characteristics, ambient lighting conditions, and the spatial layout of the display area. Processing and analyzing this data often requires significant computing power, increasing computational costs and equipment investment, and lengthening training time. Traditional methods rely on historical or static data to train lighting models. However, this data often fails to reflect real-time changes in the environment, such as external lighting variations and the impact of visitor activity on lighting. Since the lighting optimization process cannot adapt immediately to environmental changes, the trained models lack adaptability and practicality. Furthermore, if the exhibition hall's lighting system and display area are subject to dynamic changes, traditional methods suffer from poor real-time performance, resulting in delayed optimization results and limiting the efficiency of practical applications. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a lighting optimization model training method to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a lighting optimization model training method is provided, comprising the following steps:

[0005] Step S1: Acquire exhibition hall lighting data, and extract exhibition hall lighting features and exhibit area lighting features based on the exhibition hall lighting data, thereby obtaining exhibition hall lighting data and exhibit area lighting data; perform light source position optimization analysis based on the exhibition hall lighting data, thereby obtaining exhibition hall light source position optimization data;

[0006] Step S2: Acquire standard lighting data of the exhibition area; perform color temperature anomaly analysis on the lighting data of the exhibition area based on the standard lighting data of the exhibition area, thereby obtaining color temperature anomaly data of the exhibition area;

[0007] Step S3: performing an exhibit color distortion analysis based on the exhibit area color temperature abnormality data to obtain exhibit color distortion data; performing color temperature dynamic compensation based on the exhibit color distortion data to obtain exhibit area color temperature dynamic compensation data;

[0008] Step S4: Obtaining a lighting optimization model; training the lighting optimization model based on the exhibition hall light source position optimization data and the exhibit area color temperature dynamic compensation data, thereby obtaining a lighting optimization training model;

[0009] Step S5: Perform lighting energy consumption evaluation based on the lighting optimization training model to obtain lighting energy consumption data; perform energy efficiency optimization based on the lighting energy consumption to obtain lighting energy efficiency optimization data, and upload the data to the lighting optimization training model to perform the lighting energy efficiency optimization task.

[0010] By collecting and analyzing exhibition hall lighting data, the present invention accurately extracts the lighting characteristics of the exhibition hall and exhibit area, and then optimizes the position of light sources, ensuring that the exhibition hall's light source layout is more reasonable and meets display requirements. This optimizes the light source distribution and layout, and improves lighting quality. This step can reduce unnecessary energy consumption while improving the visitor's viewing experience. By acquiring standard lighting data for the exhibit area and performing color temperature anomaly analysis, color temperature anomalies in the exhibit area can be quickly identified and corrected, ensuring more accurate and natural color rendering of exhibits and reducing inconsistent display effects caused by color temperature fluctuations. Color temperature adjustment in the exhibit area provides more accurate color reproduction for the display, effectively improving the quality of the exhibits and avoiding visual distortion caused by unstable color temperature. Furthermore, by performing dynamic color temperature compensation, color temperature changes in the exhibit area can be adjusted in real time during the exhibition, ensuring that color rendering remains optimal, optimizing the color reproduction of the exhibits, and enhancing the display effect. This dynamic compensation mechanism can respond to real-time data, avoiding the shortcomings of traditional methods that cannot cope with real-time changes. By inputting the exhibition hall light source position optimization data and the exhibit area color temperature dynamic compensation data into the lighting optimization model for training, a more accurate and efficient lighting optimization training model can be obtained. The model can be intelligently adjusted according to actual needs to optimize the lighting solution. The lighting energy consumption assessment and energy efficiency optimization functions not only help evaluate the energy consumption status of the lighting system, but also further reduce energy consumption costs through lighting energy efficiency optimization, while improving the energy efficiency of the lighting system and ensuring more efficient system operation. This process can continuously optimize and adjust by uploading data to the training model to form an adaptive and intelligent lighting optimization system, which significantly improves the energy-saving effect and operating efficiency of the lighting system, and improves the system's adaptability to dynamically changing environments. These technical advantages ensure that the present invention can respond to environmental changes in real time in actual applications, provide efficient optimization solutions, and ultimately achieve the goals of energy saving, improving display effects, and improving resource utilization efficiency.

[0011] Optionally, step S1 specifically includes:

[0012] Step S11: Acquire exhibition hall lighting data, and extract exhibition hall lighting features and exhibit area lighting features based on the exhibition hall lighting data, thereby obtaining exhibition hall lighting data and exhibit area lighting data;

[0013] Step S12: performing glare optimization analysis based on the exhibition hall lighting data, thereby obtaining exhibition hall lighting glare optimization data;

[0014] Step S13: performing illumination uniformity optimization analysis based on the exhibition hall lighting data, thereby obtaining exhibition hall illumination uniformity optimization data;

[0015] Step S14: integrating the exhibition hall lighting position optimization features according to the exhibition hall lighting glare optimization data and the exhibition hall lighting uniformity optimization data, thereby obtaining the exhibition hall light source position optimization data.

[0016] By acquiring and analyzing exhibition hall lighting data, this method provides a deep understanding of the lighting characteristics of exhibition halls and exhibit areas, laying the foundation for subsequent optimization work. Acquiring exhibition hall lighting data allows for more accurate lighting characteristics, enabling more effective lighting optimization. Analysis of exhibition hall lighting data enables glare optimization, effectively reducing glare caused by excessive light or improper angles, providing visitors with a more comfortable viewing experience and preventing light interference that affects the display. Furthermore, exhibition hall lighting uniformity optimization analysis effectively adjusts the lighting distribution within the exhibition hall, ensuring uniform lighting throughout the entire exhibition area, avoiding issues such as overly bright or dark areas, and enhancing the display quality of exhibits and the audience's visual experience. Optimizing exhibition hall lighting uniformity also improves energy efficiency and avoids unnecessary energy waste. Through glare optimization and lighting uniformity optimization analysis, the exhibition hall's lighting environment is comprehensively improved, resulting in more vivid exhibit displays and a more comfortable audience experience. By integrating these two optimization data sets, the exhibition hall's light source positions are optimized, ensuring a more reasonable and scientific light source placement. This approach can improve the accuracy of light source layout, avoid over-concentration or over-dispersion of light sources, optimize the direction and intensity of exhibit lighting, enhance the overall efficiency of the lighting system, and address the complexity of exhibition space layouts. Compared to traditional lighting optimization methods, this method can process more dynamic data in real time and adapt to changes in different exhibition environments, significantly improving the adaptability and optimization of the lighting system, while avoiding the shortcomings of traditional methods that rely too heavily on static or historical data.

[0017] Optionally, step S12 is specifically as follows:

[0018] Step S121: performing light source luminescence simulation according to the exhibition hall lighting data, thereby obtaining light source luminescence simulation data;

[0019] Step S122: extracting emission angle features from the light source emission simulation data to obtain light source emission angle data;

[0020] Step S123: obtaining audience location data;

[0021] Step S124: performing glare evaluation on the audience position data according to the light source emission angle data, thereby obtaining exhibition hall lighting glare data;

[0022] Step S125: performing light source angle adjustment on the exhibition hall lighting glare data, thereby obtaining light source angle adjustment data;

[0023] Step S126: performing installation height adjustment on the exhibition hall lighting glare data, thereby obtaining installation height adjustment data;

[0024] Step S127: performing exhibition hall lighting glare optimization integration according to the light source angle adjustment data and the installation height adjustment data, thereby obtaining exhibition hall lighting glare optimization data.

[0025] By extracting the characteristics of the light source's emission angle, the present invention can more accurately analyze the angular characteristics of the light source's emission, thereby providing data support for optimizing the lighting effect. This process can provide designers with a more scientific reference for light source layout, thereby achieving the most optimized light source distribution. Furthermore, by obtaining audience position data and combining it with light source emission angle data to perform glare assessment, it is possible to effectively identify glare areas that affect the audience's visual experience during the exhibition. By obtaining exhibition hall lighting glare data, targeted adjustments can be made to ensure that the lighting in the exhibition environment does not cause discomfort to the audience. To address the glare problem, further adjustments to the light source's emission angle and installation height can effectively reduce glare and improve the lighting effect. This process ensures accurate light illumination by adjusting the light source angle and installation height in real time, while avoiding the problem of excessive light concentration, thereby improving the display effect of exhibits and optimizing the audience's viewing experience. By integrating the adjustment of the light source angle and installation height, glare optimization of the exhibition hall lighting is ultimately achieved. Compared with traditional methods, the optimization method provided by the present invention has stronger adaptability and can be adjusted in real time according to the audience's location and the actual lighting environment of the exhibition hall, ensuring the real-time and accuracy of the lighting effect, and avoiding the limitations brought about by the traditional method's over-reliance on historical data or static data.

[0026] Optionally, step S124 is specifically as follows:

[0027] Calculate the relative angle of the audience position data based on the light source emission angle data, thereby obtaining the light source emission-audience position angle data;

[0028] Obtaining glare standard threshold data;

[0029] Extract light source intensity features based on exhibition hall lighting data to obtain light source intensity data;

[0030] Calculating the glare intensity based on the light source intensity data and the light source emission-audience position angle data, thereby obtaining the glare intensity data;

[0031] The exhibition hall lighting glare is evaluated on the glare intensity data according to the glare standard threshold data, thereby obtaining the exhibition hall lighting glare data.

[0032] This invention accurately calculates the relative angle between the light source emission angle and the viewer's position, obtaining light source emission-viewer position angle data. This provides reliable data support for further optimizing glare in exhibition hall lighting. By introducing standard glare threshold data, a scientific benchmark can be effectively established for glare assessment, ensuring that the lighting system design meets the requirements for optimal exhibition viewing. Extracting light source intensity characteristics enables quantification and analysis of light source luminous intensity, providing more detailed light source parameters for subsequent lighting optimization. Furthermore, by combining light source intensity data with light source emission-viewer position angle data to calculate glare intensity, a comprehensive assessment of the glare intensity generated by the light source for the viewer can be achieved, further guiding the optimization of lighting layout and reducing visual discomfort. Combining glare intensity with standard threshold data for exhibition hall lighting glare assessment accurately determines which areas experience excessive glare, enabling precise adjustments to ensure that lighting in each exhibition area meets the requirements for comfortable viewing. This series of steps not only makes the lighting optimization process more refined and intelligent, but also enables real-time adaptation to changes in the exhibition hall environment, avoiding the problem of traditional methods that cannot quickly respond to real-time data.

[0033] Optionally, step S13 is specifically as follows:

[0034] Step S131: Drawing a lighting distribution area map based on the exhibition hall lighting data, thereby obtaining the exhibition hall lighting distribution area map;

[0035] Step S132: performing minimum lighting area identification and maximum lighting area identification on the exhibition hall lighting distribution area map, thereby obtaining minimum lighting area data and maximum lighting area data of the exhibition hall;

[0036] Step S133: Calculating the illumination intensity based on the minimum illumination area data of the exhibition hall, thereby obtaining the illumination intensity data of the minimum illumination area of the exhibition hall;

[0037] Step S134: Calculating the illumination intensity based on the data of the maximum illumination area of the exhibition hall, thereby obtaining the illumination intensity data of the maximum illumination area of the exhibition hall;

[0038] Step S135: Calculating the average illumination intensity according to the exhibition hall lighting distribution area map, thereby obtaining the average illumination intensity of the exhibition hall;

[0039] Step S136: Identifying the light intensity data of the minimum lighting area and the light intensity data of the maximum lighting area according to the average illumination of the exhibition hall, thereby obtaining the data of the too dark lighting area and the data of the too bright lighting area;

[0040] Step S137: increasing the light source density of the dark area data to obtain light source density increase data;

[0041] Step S138: Optimizing the reflectivity of the exhibition space material in the over-bright area data, thereby obtaining the exhibition space material data;

[0042] Step S139: performing exhibition hall lighting uniformity optimization and integration according to the light source density increase data and the exhibition space material data, thereby obtaining exhibition hall lighting uniformity optimization data.

[0043] By identifying minimum and maximum illumination areas, this method can precisely locate areas with low or high illumination intensity, thereby providing target areas for lighting optimization. Calculating illumination intensity based on the minimum and maximum illumination area data quantifies the actual illumination intensity in each area, further providing a scientific basis for adjusting lighting balance. Calculating the average illumination level of the exhibition hall provides a global indicator of the overall lighting effect, helping to assess whether the lighting system is balanced. By identifying areas with excessively dark and bright illumination, areas requiring optimization can be carefully identified, providing guidance for subsequent adjustments. Optimizing areas with excessively dark illumination by increasing light source density can effectively improve the illumination intensity in these areas, enhancing overall lighting quality. Addressing areas with excessively bright illumination by optimizing the reflectivity of the exhibition space's materials can better balance illumination and avoid energy waste. Ultimately, combining increased light source density with optimized exhibition space materials allows for uniform illumination optimization throughout the exhibition hall, improving the lighting effect and ensuring that the illumination intensity in each area meets design requirements, thereby enhancing the exhibition experience and energy efficiency. This series of steps not only addresses the inability of traditional methods to adjust lighting effects in real time, but also effectively improves the adaptability and optimization efficiency of the lighting system.

[0044] Optionally, step S2 is specifically:

[0045] Step S21: Acquire standard lighting data of the exhibition area;

[0046] Step S22: Calculating the color temperature based on the lighting data of the exhibit area, thereby obtaining the color temperature data of the exhibit area;

[0047] Step S23: performing spatial abnormal color temperature analysis on the color temperature data of the exhibit area according to the standard lighting data of the exhibit area, thereby obtaining spatial abnormal color temperature data;

[0048] Step S24: performing temporal abnormal color temperature analysis on the color temperature data of the exhibit area according to the standard lighting data of the exhibit area, thereby obtaining temporal abnormal color temperature data;

[0049] Step S25: integrating the color temperature anomaly of the exhibit area according to the spatial anomaly color temperature data and the temporal anomaly color temperature data, thereby obtaining the color temperature anomaly data of the exhibit area.

[0050] By acquiring standard lighting data for the exhibition area, the present invention provides accurate baseline data for subsequent color temperature analysis, helping to ensure that the lighting effects in the exhibition area meet predetermined standards. By performing color temperature calculations on the exhibition area lighting data, the actual color temperature data for the exhibition area can be obtained, providing a quantitative basis for further optimization. By combining the exhibition area standard lighting data with spatial and temporal abnormal color temperature analysis of the exhibition area color temperature data, it is possible to comprehensively identify color temperature anomalies occurring in different spatial locations and time periods. This can accurately reveal the unevenness of the lighting effect and provide important information for locating areas requiring adjustment. By integrating spatial and temporal abnormal color temperature data, the final color temperature anomaly data for the exhibition area is obtained, effectively summarizing various color temperature issues and providing more targeted guidance for subsequent adjustments. This not only improves the accuracy and efficiency of color temperature adjustment, but also enables the lighting system to better adapt to the dynamic changes in the exhibition environment, ensuring that each exhibition area has optimal lighting effects under different temporal and spatial conditions, thereby improving the quality of exhibit display and enhancing the viewing experience.

[0051] Optionally, step S23 is specifically as follows:

[0052] Step S231: Generate a reference color temperature based on the standard lighting data of the exhibit area, thereby obtaining the reference color temperature data of the exhibit area;

[0053] Step S232: constructing a spatial distribution map of the color temperature data of the exhibit area, thereby obtaining a color temperature spatial distribution map of the exhibit area;

[0054] Step S233: performing color temperature deviation calculation on the color temperature spatial distribution diagram of the exhibit area according to the benchmark color temperature data of the exhibit area, thereby obtaining color temperature deviation data of the exhibit area;

[0055] Step S234: performing color temperature deviation area statistics based on the color temperature deviation data of the exhibit area, thereby obtaining high color temperature area data and low color temperature area data;

[0056] Step S235: performing an exhibit material spectrum analysis on the high color temperature area data, thereby obtaining the exhibit material spectrum data in the high color temperature area;

[0057] Step S236: evaluating the color reproduction of exhibits in the low color temperature area data, thereby obtaining the color reproduction data of exhibits in the low color temperature area;

[0058] Step S237: Merging the spatially abnormal color temperatures based on the material spectrum data of the exhibits in the high color temperature area and the color restoration data of the exhibits in the low color temperature area, thereby obtaining spatially abnormal color temperature data.

[0059] The present invention generates reference color temperature data based on the standard lighting data of the exhibition area, which can provide a clear reference standard for the lighting adjustment of the exhibition area, thereby ensuring that the color temperature meets expectations. Constructing a spatial distribution map of the color temperature data of the exhibition area helps to visualize the lighting effect in the display area, making the spatial distribution of the color temperature clear at a glance, and facilitating the discovery of lighting uniformity problems. Deviation calculation of the color temperature spatial distribution map based on the reference color temperature data of the exhibition area can quantify the difference between the actual color temperature and the ideal color temperature, providing accurate data for subsequent adjustments. By performing regional statistics on the color temperature deviation data, the lighting problem is further refined, and high color temperature and low color temperature areas are identified to provide a specific direction for the adjustment work. Spectral analysis of exhibit materials in high color temperature areas helps to evaluate the reflective properties of exhibit materials in these areas, ensuring that the display effect of exhibits under high color temperature conditions is optimized. The evaluation of the color reproduction of exhibits in low color temperature areas can ensure the color performance and display effect of exhibits in low color temperature environments, and avoid the negative impact of color temperature deviation on the visual effect of the exhibits themselves. By combining and analyzing the spectral data of exhibit materials in high color temperature areas and the color reproduction data of exhibits in low color temperature areas, we can effectively identify and integrate abnormal color temperature problems in the space, optimize the lighting effects of the exhibit area, and improve the exhibition quality and viewing experience.

[0060] Optionally, step S235 is specifically as follows:

[0061] Performing light source type identification on the high color temperature area data to obtain light source type data;

[0062] Extracting spectral features according to the light source type data to obtain light source type spectral data;

[0063] Obtain exhibit material data;

[0064] Collect reflectivity based on exhibit material data to obtain the reflectivity of the exhibit material;

[0065] Perform exhibition area light source identification on the high color temperature area data to obtain exhibition area light source data, and perform light source illumination simulation based on the exhibition area light source data and light source type spectrum data to obtain exhibition area light source illumination data;

[0066] Analyze the light reflected from the exhibit materials based on the light source data of the exhibition area and the reflectivity of the exhibit materials to obtain the exhibit material reflection data;

[0067] Perform reflection band statistics on the reflection data of the exhibit materials to obtain the reflection band data of the exhibit materials;

[0068] Interactive analysis of light source color temperature is performed based on the reflected band data to obtain spectral data of exhibit materials in high color temperature areas.

[0069] By performing light source type identification on high color temperature area data, the present invention can accurately identify the type of light source used in the exhibition area, providing data support for subsequent lighting analysis and optimization. Spectral feature extraction based on light source type data can provide a deep understanding of the impact of different light sources on the lighting environment and provide a theoretical basis for precise color temperature adjustment. Acquiring exhibit material data facilitates detailed analysis of the visual effects of exhibits, ensuring a correct understanding of the material's performance under different lighting conditions. By collecting reflectivity on exhibit material data, the reflectivity characteristics of various materials under different lighting conditions can be determined, providing a basis for the coordination of light source illumination and exhibit display. Performing exhibition area light source identification on high color temperature areas can identify the specific light source configuration in the exhibition area. By combining light source type spectral data with light source illumination simulation, reliable data support is provided for optimizing lighting distribution and color temperature in the exhibition environment. Combining exhibition area light source illumination data with exhibit material reflectivity to analyze exhibit material reflected light can effectively predict light reflection effects, thereby ensuring the realistic presentation of exhibits under lighting conditions. By performing reflection band statistics on exhibit material reflection data, the specific bands reflected by the material can be obtained, providing more dimensional data support for optimizing exhibit display effects. Through interactive analysis of light source color temperature, we can obtain spectral data of exhibit materials in high color temperature areas, thereby ensuring that the display effect and visual quality of exhibit materials in high color temperature areas are maximized, ensuring that the overall effect of the exhibition is optimal.

[0070] Optionally, step S3 specifically includes:

[0071] Step S31: converting the exhibit color model according to the color temperature anomaly data of the exhibit area to obtain the exhibit color model;

[0072] Step S32: obtaining reference color data;

[0073] Step S33: performing color difference calculation on the exhibit color model according to the reference color data, thereby obtaining color difference data;

[0074] Step S34: evaluating the color distortion of the exhibit based on the color difference data, thereby obtaining the color distortion data of the exhibit;

[0075] Step S35: Perform color temperature dynamic compensation according to the exhibit color distortion data, thereby obtaining exhibit area color temperature dynamic compensation data.

[0076] The present invention converts the color model of the exhibit according to the color temperature anomaly data of the exhibit area, and can map the color performance of the exhibit from one environmental condition to another, thereby ensuring the consistency of the color of the exhibit under different lighting conditions. Acquiring baseline color data helps to establish a standard reference, so that the subsequent color difference calculation has a clear comparison benchmark, thereby more accurately evaluating the color changes of the exhibits in different lighting environments. By performing color difference calculations on the exhibit color model, the deviation of the exhibit color under different lighting conditions can be quantified to ensure that any color difference that does not meet the standard can be identified and adjusted in a timely manner. Performing an exhibit color distortion assessment based on color difference data helps to accurately identify color distortion caused by uneven lighting or other factors, and then provide adjustment solutions to enhance the visual effect of the exhibit. By performing color temperature dynamic compensation on the exhibit color distortion data, the color temperature of the exhibit area can be adjusted in real time to compensate for the visual deviation caused by color distortion, ensuring that the exhibition display always maintains the best color presentation effect under different conditions, and improving the overall visual consistency and viewing experience of the exhibition.

[0077] Optionally, step S35 is specifically as follows:

[0078] Step S351: performing color cast identification based on the exhibit's color distortion data to obtain color cast data;

[0079] Step S352: performing statistics on the color cast data to obtain warm color data and cool color data;

[0080] Step S353: adding red light ratio compensation according to the warm color data, thereby obtaining red light ratio compensation data;

[0081] Step S354: adding blue light ratio compensation according to the cool color data, thereby obtaining blue light ratio compensation data;

[0082] Step S355 : performing dynamic color temperature compensation integration of the exhibit according to the red light ratio compensation data and the blue light ratio compensation data, thereby obtaining dynamic color temperature compensation data of the exhibit area.

[0083] The present invention can accurately detect color deviations caused by abnormal color temperature by performing color cast identification on the color distortion data of the exhibits, thereby accurately identifying the color deviations that occur during the display of the exhibits and improving the color stability of the exhibition. Statistics on the color cast data help to identify the warm or cool areas that appear in the exhibits, which provides the necessary basis for subsequent compensation. By adding red light ratio compensation to the warm color data, the red light component of the light source can be adjusted to eliminate the influence of the warm color, so that the color of the exhibits tends to standard white light and avoids yellowish or reddish color temperature distortion. Adding blue light ratio compensation to the cool color data can enhance the blue light component and adjust the cool color exhibit areas to make the exhibit colors more consistent with the desired visual effect. By integrating the red and blue light ratio compensation data, dynamic compensation of the exhibit color temperature can be effectively performed to ensure the stability of the color temperature and the accuracy of the exhibit color during the display process, thereby improving the audience's visual experience and ensuring the real-time and accuracy of the color temperature adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0085] Figure 1 This is a schematic diagram of the steps of the lighting optimization model training method of the present invention;

[0086] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0087] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0088] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0089] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work are within the scope of protection of the present invention.

[0090] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0091] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0092] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for training a lighting optimization model, the method comprising the following steps:

[0093] Step S1: Acquire exhibition hall lighting data, and extract exhibition hall lighting features and exhibit area lighting features based on the exhibition hall lighting data, thereby obtaining exhibition hall lighting data and exhibit area lighting data; perform light source position optimization analysis based on the exhibition hall lighting data, thereby obtaining exhibition hall light source position optimization data;

[0094] In this embodiment, exhibition hall lighting data is obtained, including but not limited to parameters such as light source type, light intensity, illumination angle, brightness, and illuminance. Light intensity for light sources such as LEDs, halogen lamps, or incandescent lamps is typically measured using a light meter in lux, while illumination angle is obtained from the illumination angle information of the light source equipment. This data is then used to extract exhibition hall lighting characteristics, specifically including the location and distribution of the light sources, as well as the spatial variation of light intensity. Extracting lighting characteristics for the exhibit area involves measuring the light intensity distribution within the exhibit area, the distance between the light source and the exhibits, and illumination uniformity. By analyzing these parameters, exhibition hall and exhibit area lighting data can be obtained. When optimizing light source placement based on the exhibition hall lighting data, computer-aided design (CAD) software is used to simulate the lighting effects under different light source positions. Light intensity distribution models (such as the Inverse Square Law) are used to assess whether light sources at different locations can evenly illuminate the exhibition hall. The light source placement is then optimized based on this data. Optimized exhibition hall light source placement data is obtained through the above steps.

[0095] Step S2: Acquire standard lighting data of the exhibition area; perform color temperature anomaly analysis on the lighting data of the exhibition area based on the standard lighting data of the exhibition area, thereby obtaining color temperature anomaly data of the exhibition area;

[0096] In this embodiment, standard lighting data for the exhibition area is obtained, including standard parameters such as ideal light intensity, color temperature, and lighting uniformity. Standard lighting data is generally determined based on the design requirements of the exhibition area, with a target light intensity of 500 lux and a color temperature requirement of 3500K (white light). Next, the actual lighting data of the exhibition area is measured using a light meter and a color temperature meter. The actual lighting data is obtained and compared with the standard lighting data. If there is a difference between the two, a color temperature anomaly analysis is performed. This analysis is performed by calculating the difference between the actual measured color temperature and the standard lighting color temperature. Color temperature deviation (unit: Kelvin) is typically used as an indicator. A deviation exceeding 10K is considered a color temperature anomaly, thereby obtaining color temperature anomaly data for the exhibition area.

[0097] Step S3: performing an exhibit color distortion analysis based on the exhibit area color temperature abnormality data to obtain exhibit color distortion data; performing color temperature dynamic compensation based on the exhibit color distortion data to obtain exhibit area color temperature dynamic compensation data;

[0098] In this embodiment, reflectance data of the exhibit material is obtained. This reflectance data can be measured using a spectrophotometer and is expressed in percentages. For color temperature anomalies, the color changes of the exhibit under different color temperature conditions are compared, and the degree of color change is calculated using a color difference formula (such as CIE76 or CIEDE2000). If the color difference value exceeds a set threshold (e.g., 2.0), the exhibit is determined to have color distortion. Dynamic color temperature compensation is performed based on the exhibit's color distortion data. The required color temperature deviation is first determined, and then the color temperature of the light source is adjusted according to the compensation algorithm to restore the exhibit's original color. The amount of color temperature change to be compensated can be calculated based on experimental results and the color temperature deviation, typically within a ±300K adjustment range.

[0099] Step S4: Obtaining a lighting optimization model; training the lighting optimization model based on the exhibition hall light source position optimization data and the exhibit area color temperature dynamic compensation data, thereby obtaining a lighting optimization training model;

[0100] In this embodiment, a lighting optimization model is obtained. This model is constructed based on historical data, physical lighting principles, and machine learning algorithms. Historical data includes light source type, light intensity, lighting uniformity in the exhibit area, and historical energy efficiency data. Physical lighting principles describe light source characteristics such as illumination range, reflectivity, refractive index, and color temperature, and these principles serve as the model's underlying theoretical framework. Next, an appropriate machine learning algorithm, such as a support vector machine (SVM) or decision tree (DT), is selected and trained based on the input data. The input data includes exhibition hall light source position optimization data (e.g., illuminance distribution and uniformity of light sources at different positions) and dynamic color temperature compensation data for the exhibit area (e.g., light source color temperature adjustment amount and deviation). Each input feature, such as light source type, position, illuminance, and color temperature, must be obtained through data collection and on-site measurement. Light source type is typically obtained through equipment calibration, illuminance is measured in real time using a light meter, and color temperature is obtained using a color thermometer. Cross-validation is used to train the model to ensure robustness and accuracy under different lighting conditions. Cross-validation reduces model overfitting by dividing the training set into multiple subsets and rotating them as validation sets. During training, model parameters (such as the penalty parameter C of the support vector machine, the kernel function type, and the tree depth of the decision tree) are adjusted and refined using optimization methods such as grid search or random search. Ultimately, an optimized lighting optimization training model is obtained. Model optimization steps include loss function minimization and error adjustment to ensure that the model can handle dynamically changing lighting environments and complex lighting conditions.

[0101] Step S5: Perform lighting energy consumption evaluation based on the lighting optimization training model to obtain lighting energy consumption data; perform energy efficiency optimization based on the lighting energy consumption to obtain lighting energy efficiency optimization data, and upload the data to the lighting optimization training model to perform the lighting energy efficiency optimization task.

[0102] In this embodiment, the training model is input based on actual light source data, which includes the power of each light source (in watts W), the operating time (in hours h), and the size of the lighting area (in square meters m). 2 ). The power of each light source is obtained through the specifications of the equipment or on-site measurements, which is usually clearly marked in the technical manual of the lamp, or measured using an electric meter. The operating time of the light source can be obtained through a real-time monitoring system, which usually records the switching time of each lamp. The size of the lighting area is obtained based on the floor plan of the exhibition area and actual measurement data. After entering this data, the training model will calculate the energy consumption estimate of each light source under the specified conditions, in kilowatt-hours (kWh). The energy consumption calculation formula is:

[0103]

[0104] Where P is the light source power (W) and t is the operating time (h). After obtaining the energy consumption data, energy efficiency optimization is performed. The energy efficiency optimization step uses optimization algorithms such as linear programming, particle swarm optimization or genetic algorithm, etc. The goal is to reduce energy consumption by adjusting the working state of the light source. By controlling the on and off state of the light source (for example, turning off unnecessary light sources) and adjusting the brightness (for example, automatically adjusting the brightness of the light source to adapt to the actual needs of the exhibition area), energy consumption can be minimized while maintaining the lighting effect. During the adjustment process, specific energy efficiency targets can be set, such as ensuring that the lighting intensity is within the standard range while reducing energy consumption to a specific level (for example, a 10% reduction). After the optimization is completed, energy efficiency optimization data is obtained, including the adjusted light source configuration, energy consumption data, etc., and these data are uploaded to the lighting optimization training model for the next round of optimization tasks. The uploaded data is used for real-time updating of the model, so that it can adapt to different lighting environment changes after each optimization, and ultimately ensure the optimal energy efficiency of each lighting cycle.

[0105] Optionally, step S1 specifically includes:

[0106] Step S11: Acquire exhibition hall lighting data, and extract exhibition hall lighting features and exhibit area lighting features based on the exhibition hall lighting data, thereby obtaining exhibition hall lighting data and exhibit area lighting data;

[0107] In this embodiment, data of all light sources in the exhibition hall are collected, including parameters such as the power, color temperature, and illuminance of each light source. The power of the light source is obtained by on-site measurement or by checking the equipment specification sheet, and the unit is watt (W). The illuminance data is obtained by measuring at multiple points in the exhibition hall using a illuminance meter, and the unit is lux (lx). The exhibition hall lighting data includes two parts: exhibition hall lighting and exhibit area lighting. The exhibition hall lighting data includes the distribution, power, and illuminance distribution of light sources in the entire exhibition hall. The exhibit area lighting data includes the light source configuration and illuminance level of each exhibit area, ensuring that sufficient and uniform lighting can be provided for each exhibit area. For the illuminance data of each area, it is necessary to ensure that a fixed measurement time period is followed during collection to eliminate the influence of human factors and ensure the accuracy of the data.

[0108] Step S12: performing glare optimization analysis based on the exhibition hall lighting data, thereby obtaining exhibition hall lighting glare optimization data;

[0109] In this embodiment, the illuminance, position and light source type (such as LED, fluorescent lamp, etc.) of each light source in the exhibition hall are obtained. The illuminance distribution of each light source is analyzed by using the glare evaluation formula to ensure that there are no areas of excessive direct illumination that cause visual discomfort. In the standard glare evaluation method, a unified glare rating index (UGR) is used for analysis. When calculating the UGR value, the UGR value of each area is calculated based on parameters such as the power of the light source, the illuminance distribution, and the reflectivity of the illuminated area. Generally, the UGR value should be controlled below 19. For areas exceeding 19, the design is optimized by adjusting the power and position of the light source or using appropriate shading devices to reduce the impact of glare. The range and degree of adjustment need to be clearly specified to ensure that the final light source distribution does not produce unnecessary glare.

[0110] Step S13: performing illumination uniformity optimization analysis based on the exhibition hall lighting data, thereby obtaining exhibition hall illumination uniformity optimization data;

[0111] In this embodiment, the illumination data of each point in the exhibition hall is used to analyze the illumination uniformity of each area of the exhibition hall. The illumination uniformity is evaluated by calculating the unevenness of the illumination distribution (usually refers to the ratio of the maximum to the minimum light intensity). According to the illumination data, a standard uniformity threshold is set (such as the ratio of the maximum illumination to the minimum illumination does not exceed 1.5). If this standard is exceeded, the light source layout is adjusted. At this time, the positions of different light sources need to be optimized according to the illumination distribution map to avoid overly concentrated lighting areas. The adjustment of the light source position is based on the illumination data, and the angle and distance of each light source are gradually adjusted to ensure that the illumination distribution in the entire exhibition hall is more uniform and there is no obvious difference in the illumination of each area.

[0112] Step S14: integrating the exhibition hall lighting position optimization features according to the exhibition hall lighting glare optimization data and the exhibition hall lighting uniformity optimization data, thereby obtaining the exhibition hall light source position optimization data.

[0113] In this embodiment, the two data sets are integrated using weighting coefficients. For example, a standard of 40% for glare optimization and 60% for illumination uniformity optimization is set. After integration, an optimization algorithm (such as a genetic algorithm or particle swarm optimization algorithm) is used to optimize the exhibition hall's light source positions. This algorithm calculates the optimal light source positions based on each light source's illumination data, glare optimization results, and illumination uniformity analysis. Based on this, it outputs the final optimized light source position data for the exhibition hall. This data includes the new position parameters for each light source and the corresponding optimization measures, ensuring that the optimized light source layout meets illumination uniformity requirements while avoiding strong glare.

[0114] Optionally, step S12 is specifically as follows:

[0115] Step S121: performing light source luminescence simulation according to the exhibition hall lighting data, thereby obtaining light source luminescence simulation data;

[0116] In this embodiment, the physical parameters of all light sources in the exhibition hall are obtained, including the power, type (such as LED or fluorescent lamp), color temperature, illuminance, etc. of the light source. Using the specifications and measurement data of the light source, luminous simulation is performed through optical simulation software (such as Radiance or LightTools). When simulating the light source, the lighting data is input into the simulation software to simulate the luminous characteristics of the light source and calculate the light distribution of each light source in the exhibition hall. During the simulation process, modeling is performed based on the structural layout and reflectivity data of the exhibition hall to ensure that the simulation results are close to the actual lighting effect. In this process, the actual lighting coverage of each light source is obtained by setting parameters such as the ambient light reflectivity and the radiation intensity of the light source. The simulation data includes the luminous intensity distribution and illumination changes of each light source.

[0117] Step S122: extracting emission angle features from the light source emission simulation data to obtain light source emission angle data;

[0118] In this embodiment, the light source emission angle information is extracted from the light source luminescence simulation data. Ray tracing technology is used to analyze the angular distribution of light emitted by each light source in space. The emission angle data is usually based on the half-viewing angle and full-viewing angle of the light source to determine the directional range of the light emitted by the light source. This process uses simulation software to subdivide the emitted light of each light source and calculate the emission angle range of the light source. It is usually segmented according to the beam angle of the light source. Common angle ranges are 30°, 60°, 90°, etc. During the extraction process, a threshold is set to determine the effective emission range of the light source, such as only considering the effective light source radiation within the range of ±45°. The emission angle data finally obtained includes the emission angle range and light intensity distribution of each light source.

[0119] Step S123: obtaining audience location data;

[0120] In this embodiment, the location of visitors within the exhibition hall is recorded. A laser rangefinder or handheld device is used to accurately measure the exhibition hall's dimensions and obtain the spatial coordinates of each area within the hall. Thermal imaging sensors or infrared sensors are used to monitor the real-time location of visitors in the main corridors and exhibit areas of the exhibition hall. Visitor location data will include the spatial coordinates (e.g., X, Y, and Z coordinates) of each visitor, and the area in which the visitor is located will be marked. This data is updated in real time via a wireless positioning system (e.g., RFID), ensuring that each visitor's location is always recorded. The acquired data will be used for subsequent analysis to evaluate the lighting experience of visitors at different locations within the exhibition.

[0121] Step S124: performing glare evaluation on the audience position data according to the light source emission angle data, thereby obtaining exhibition hall lighting glare data;

[0122] In this embodiment, the angle between each viewer's line of sight in the exhibition hall and each light source is calculated by combining the light source's emission angle data with the viewer's position data. Based on this angle, it is assessed whether each viewer's line of sight is directly aligned with the light source; if so, glare is caused. This is determined by calculating the relative angle between each light source's emission angle and the viewer's position (typically the angle between the light source's illumination direction and the viewer's line of sight), and using a glare assessment formula (such as the Uncertainty Gradient (UGR) value or maximum brightness value) to determine glare. A standard is set for glare assessment; for example, a UGR value greater than 19 is considered to indicate a significant glare problem at that location. Based on the relationship between each viewer's position and the light source, a specific glare data table is generated, recording whether each viewer is affected by glare and the extent of the impact.

[0123] Step S125: performing light source angle adjustment on the exhibition hall lighting glare data, thereby obtaining light source angle adjustment data;

[0124] In this embodiment, based on the glare data, areas or audiences affected by glare are identified. For these areas, the emission angle of the light source is adjusted to prevent direct light from illuminating the audience's eyes. For example, if the emission angle of a light source is too large, resulting in an overly wide illumination area, the illumination range can be reduced by adjusting the tilt angle or rotation angle of the light source to reduce the glare effect. During adjustment, a mechanical adjustment device or automatic dimming device is used to change the angle of the light source. The angle adjustment range is typically 0° to 45°, and is adjusted according to the type and brightness of the light source to achieve the effect of lighting optimization. The adjusted light source angle data will be used in subsequent optimization processes.

[0125] Step S126: performing installation height adjustment on the exhibition hall lighting glare data, thereby obtaining installation height adjustment data;

[0126] In this embodiment, the relationship between the installation height of the light source in the exhibition hall and the impact of glare on the audience is analyzed based on the glare data. If some light sources are positioned too low or too high, light will directly hit the audience's eyes, causing discomfort. In order to reduce glare, the installation height of the light source needs to be adjusted based on the relationship between the installation height of the light source and its illumination angle. During adjustment, the position of the light source is raised or lowered to ensure that the illumination angle of the light source does not produce excessive direct illumination of the audience. For example, the installation height of the light source is increased to between 2.5 meters and 3.5 meters to reduce the direct illumination of the light source on low areas. The adjusted light source installation height data will be used in the optimization process to ensure that the light source configuration adapts to the actual environment of the exhibition hall.

[0127] Step S127: performing exhibition hall lighting glare optimization integration according to the light source angle adjustment data and the installation height adjustment data, thereby obtaining exhibition hall lighting glare optimization data.

[0128] In this embodiment, an optimization algorithm comprehensively considers the angle and installation height of light sources, performing global optimization. During this integration process, a multi-objective optimization method (such as a genetic algorithm or simulated annealing algorithm) is used to calculate the optimal combination of light source angle and installation height, ensuring that glare is reduced while achieving lighting uniformity and aesthetics. The angle and installation height adjustment data for each light source is used to generate the final light source configuration. The final output of the exhibition hall lighting glare optimization data includes the adjustment angle and installation height of each light source, as well as the optimized lighting distribution, ensuring maximum lighting effect within the exhibition hall.

[0129] Optionally, step S124 is specifically as follows:

[0130] Calculate the relative angle of the audience position data based on the light source emission angle data, thereby obtaining the light source emission-audience position angle data;

[0131] In this embodiment, the emission angle data of the light source is obtained by simulating the light emission of the light source, which is usually the angle of the light beam radiated by the light source, defined as the angle between the light and the center line of the light source. The audience position data is usually obtained through a wireless sensor (such as RFID) or a laser positioning system, which records the spatial coordinates of each audience member in the exhibition hall (such as X, Y, Z coordinates). Based on the coordinates of each audience member and the coordinates of the light source, the relative position vector between the audience and the light source is calculated. Further, by calculating the angle between the light source emission angle and the audience position, the light source emission-audience position angle data is obtained. This process uses the vector calculation formula:

[0132]

[0133] Where A and B are the vectors of the light source emission direction and the viewer position, respectively. The obtained light source emission-viewer position angle data is used for subsequent glare intensity calculations.

[0134] Obtaining glare standard threshold data;

[0135] In this embodiment, the glare standard threshold data is obtained by consulting international standards (such as CIE 117 or the UGR standard). Standard thresholds are typically set based on factors such as the environment, light source type, and viewer perspective. A common setting standard is that a UGR value greater than 19 is considered to produce significant glare in the direction of sight. To ensure accurate implementation, a standard suitable for the exhibition hall environment is selected. The appropriate threshold range is determined through environmental assessment and exhibition hall layout design. The specific threshold is adjusted based on parameters such as the exhibition hall's light source type, installation method, and reflectivity. The threshold data can be calculated based on historical data or industry experience. In this step, the UGR value (Unified Glare Rating) is used as the standard, and the UGR value threshold is set to 19 as the dividing line between acceptable and unacceptable glare intensity.

[0136] Extract light source intensity features based on exhibition hall lighting data to obtain light source intensity data;

[0137] In this embodiment, light source intensity feature extraction obtains preliminary light intensity information based on the light source specifications and input power. Then, a photometer or illuminometer is used to measure the actual illuminance at multiple locations within the exhibition hall, obtaining actual illuminance data at each location. Based on the spatial distribution of light, optical simulation software (such as Radiance or Photopia) can be used to predict the light source's intensity. Light source intensity data is calculated based on the measured illuminance data and the light source's actual power characteristics, with the unit commonly used being lux (lx). Important parameters in this process include light source power, color temperature, and luminous flux, and accurate calibration of the measurement instruments is crucial. This light source intensity data provides the basis for subsequent glare intensity calculations.

[0138] Calculating the glare intensity based on the light source intensity data and the light source emission-audience position angle data, thereby obtaining the glare intensity data;

[0139] In this embodiment, the distance between each viewer and the light source is calculated and combined with the light intensity data of the light source to obtain the illuminance value at that location. Then, based on the viewer's relative angle data, the illuminance intensity value is adjusted using the angle between the light source's emission angle and the viewer's position to simulate the formation of glare. The glare intensity calculation uses a glare evaluation formula, such as:

[0140]

[0141] Where I is the light intensity of the light source, θ is the angle between the light source's emission angle and the viewer's position, and d is the distance between the viewer and the light source. The results calculated using this formula are then adjusted based on the actual exhibition hall environment to ultimately determine the glare intensity data for each location. Values are generally expressed in lumens (lm) or lux (lx). These data serve as the basis for subsequent glare assessments.

[0142] The exhibition hall lighting glare is evaluated on the glare intensity data according to the glare standard threshold data, thereby obtaining the exhibition hall lighting glare data.

[0143] In this embodiment, each audience position within the evaluation range is defined. If the glare intensity at that position exceeds a set glare standard threshold (e.g., an Uncertainty Gradient (UGR) value of 19), that position is considered to have inappropriate glare. For all audience positions, glare intensity data is individually compared against the standard threshold for screening. Based on the evaluation results, a glare data report for the exhibition hall lighting is generated, recording the glare status (pass or fail) and intensity value for each position. The data from all positions is aggregated to provide an overall glare assessment of the exhibition hall lighting. During the evaluation process, pre-set glare standards, such as the Uncertainty Gradient (UGR) and glare intensity thresholds, are used to ensure that all affected areas are detected and adjusted in real time.

[0144] Optionally, step S13 is specifically as follows:

[0145] Step S131: Drawing a lighting distribution area map based on the exhibition hall lighting data, thereby obtaining the exhibition hall lighting distribution area map;

[0146] In this embodiment, a lighting data model of the exhibition hall is established by obtaining information such as the light intensity, installation location, and emission angle of each light source. The illumination range of each light source is determined by simulation or measured data, and a lighting distribution area diagram is drawn in combination with the specific layout information of the exhibition hall (such as room area, wall reflectivity, obstacle location, etc.). This diagram reflects the illuminance distribution at each location in the exhibition hall. The lighting distribution is calculated using an illuminance meter or optical simulation tools (such as Radiance, DIALux). The illuminance value of each point in the diagram is obtained through measurement or simulation, and the unit is lux (lx). The lighting distribution area diagram shows the light intensity of each area in the exhibition hall through color gradients or numerical values. This diagram serves as the basis for subsequent analysis.

[0147] Step S132: performing minimum lighting area identification and maximum lighting area identification on the exhibition hall lighting distribution area map, thereby obtaining minimum lighting area data and maximum lighting area data of the exhibition hall;

[0148] In this embodiment, the illumination data is divided into regions by setting a predetermined illumination threshold (e.g., 300 lx). First, based on the illumination distribution map, areas with illumination below the threshold are identified and calibrated as minimum illumination areas. Areas with illumination below this threshold are analyzed to obtain the spatial extent and corresponding coordinate data of the minimum illumination area. Second, areas with illumination above another predetermined threshold (e.g., 1500 lx) are identified and calibrated as maximum illumination areas. Areas with illumination above this threshold are analyzed to obtain the spatial extent and coordinate data of the maximum illumination area. Finally, the minimum and maximum illumination area data for the exhibition hall are output, recording their respective locations, areas, and corresponding illumination values.

[0149] Step S133: Calculating the illumination intensity based on the minimum illumination area data of the exhibition hall, thereby obtaining the illumination intensity data of the minimum illumination area of the exhibition hall;

[0150] In this embodiment, the overall illumination data for the area is obtained by taking a weighted average of the illumination values at each point in the area. The weighted average is calculated based on the actual illumination value (in lux) at each point, taking into account factors such as the actual location and area of the point. Then, taking into account the location and intensity of the light source in the area, photometric formulas (e.g., the inverse square law) are used to calculate the illumination intensity at each location in the minimum illumination area. The formula is as follows:

[0151]

[0152] Where I is the light intensity of the light source, and d is the distance from the light source to the measurement point. This calculation yields the light intensity data for the exhibition hall's minimum illuminated area, measured in lux.

[0153] Step S134: Calculating the illumination intensity based on the data of the maximum illumination area of the exhibition hall, thereby obtaining the illumination intensity data of the maximum illumination area of the exhibition hall;

[0154] In this embodiment, the illumination intensity at each point within the area is calculated using the illuminance value and the light intensity of the light source. Based on the illumination data at each point, combined with the actual position and intensity of the light source, the intensity calculation is performed using the same inverse square law formula. Given that the maximum illuminated area is typically large and may be illuminated by multiple light sources, the illumination at each point is cumulatively calculated. Ultimately, the overall illumination intensity data for the maximum illuminated area is calculated.

[0155] Step S135: Calculating the average illumination intensity according to the exhibition hall lighting distribution area map, thereby obtaining the average illumination intensity of the exhibition hall;

[0156] In this embodiment, the illuminance data for each area point is obtained from the exhibition hall lighting distribution area map. The illuminance data is obtained by measurement or simulation at different locations in the exhibition hall, and the illuminance value at each location is usually measured in lux (lx). Next, the illuminance values of all area points are accumulated to obtain the total illuminance. The total area of the exhibition hall is calculated, which usually includes the total area of all areas in the exhibition hall. After obtaining the total area, the total illuminance is divided by the total area to obtain the average illumination of the exhibition hall. This value represents the overall lighting level in the exhibition hall and is a key data used as a benchmark in subsequent analysis to help identify areas with too dark or too bright lighting.

[0157] Step S136: Identifying the light intensity data of the minimum lighting area and the light intensity data of the maximum lighting area according to the average illumination of the exhibition hall, thereby obtaining the data of the too dark lighting area and the data of the too bright lighting area;

[0158] In this embodiment, the average illumination level is used as a benchmark, and the illumination intensity data for the minimum illumination area is compared with this benchmark. If the illumination intensity of certain areas falls below a certain threshold (e.g., below 70%) of the benchmark illumination level, they are determined to be areas of excessively dark illumination. For the illumination intensity data for the maximum illumination area, if the illumination intensity of certain areas exceeds a certain threshold (e.g., exceeding 130%) of the benchmark illumination level, they are determined to be areas of excessively bright illumination. In this way, excessively dark and excessively bright areas are identified, ultimately obtaining data for areas of excessively dark and excessively bright illumination, and recording information such as their location, range, and illumination level.

[0159] Step S137: increasing the light source density of the dark area data to obtain light source density increase data;

[0160] In this embodiment, the area of the darkened area and its illumination data are analyzed to determine the degree of insufficient illumination in the area. Then, based on the area of the area and the number of existing light sources, the number of light sources that need to be added is calculated. The light source density increment calculation formula is as follows:

[0161]

[0162] The illuminance difference is the difference between the required illuminance and the actual illuminance in the area, and the area is the area of the area where the lighting is too dark. Based on the calculated light source density increment, the light source positions are reasonably distributed, and the number and layout of light sources are optimized to obtain the light source density increase data.

[0163] Step S138: Optimizing the reflectivity of the exhibition space material in the over-bright area data, thereby obtaining the exhibition space material data;

[0164] In this example, the reflectivity of materials such as walls, floors, and ceilings in various areas of the exhibition space is assessed. Reflectivity data is obtained through field measurements or reference standards. Materials with high reflectivity, such as paint and flooring, are optimized to reduce reflectivity, thereby reducing the intensity of overly bright areas. This optimization process includes selecting low-reflectivity coatings or materials, adjusting the placement of exhibits, and altering the angle of the light source. Ultimately, the optimized reflectivity data for the exhibition space's materials provides the foundation for improving lighting uniformity.

[0165] Step S139: performing exhibition hall lighting uniformity optimization and integration according to the light source density increase data and the exhibition space material data, thereby obtaining exhibition hall lighting uniformity optimization data.

[0166] In this example, the light density increase data is combined with adjustments to the number and position of light sources to ensure uniform illumination across all areas of the exhibition hall. Next, based on the exhibition space material data, material optimization is performed on areas with high reflectivity within the exhibition hall to reduce the impact of light reflections. Finally, by comprehensively considering the optimization of light source layout and density, as well as the exhibition space material, illumination uniformity optimization is integrated by adjusting light source intensity, angle, and the materials used in the exhibition space. Ultimately, the optimized illumination uniformity data for the exhibition hall is obtained, ensuring that the desired illumination uniformity is achieved within the exhibition hall.

[0167] Optionally, step S2 is specifically:

[0168] Step S21: Acquire standard lighting data of the exhibition area;

[0169] In this embodiment, standard lighting data is collected for each lighting point. This data is typically determined by the designated lighting scheme and includes information such as brightness, color temperature, and light source type. Standard lighting data should be measured or simulated based on the actual needs of the exhibit area. To ensure data accuracy, a illuminance meter and color temperature meter are used to measure the light intensity and color temperature at each lighting point, respectively. Standard lighting data also includes information such as the technical parameters of the lighting equipment, rated output power, and the spectral distribution of the light source. All data should be calibrated into standard units, such as lux (lx) and Kelvin (K), and comprehensively recorded in conjunction with the specific layout of the exhibit area.

[0170] Step S22: Calculating the color temperature based on the lighting data of the exhibit area, thereby obtaining the color temperature data of the exhibit area;

[0171] In this embodiment, color temperature data is obtained for each lighting point. A color thermometer is used to measure each lighting point and obtain the corresponding color temperature value. Color temperature is calculated based on the spectral characteristics of the light source. The relationship between the dominant wavelength of the spectrum and color temperature is analyzed to determine the color temperature value for each lighting point. Standard lighting data includes illuminance data and corresponding color temperature data for each point. This data serves as the basis for further correction and optimization of the color temperature using the color temperature calculation formula. During the calculation process, the overall color temperature distribution of the exhibit area is calculated based on the specific standard lighting data and the color temperature ranges of different light sources.

[0172] Step S23: performing spatial abnormal color temperature analysis on the color temperature data of the exhibit area according to the standard lighting data of the exhibit area, thereby obtaining spatial abnormal color temperature data;

[0173] In this embodiment, the exhibit area is divided into multiple measurement points based on its layout. Color temperature data from each point is collected and compared with standard lighting data. If the color temperature data for a particular area deviates from the standard range, it is considered to have a spatial color temperature anomaly. In specific implementations, a standard color temperature range is set, such as between 3000K and 5000K. If the color temperature of a point falls outside this range, it is considered an anomaly. By comparing the color temperature data from all measurement points, areas with color temperature anomalies are recorded and marked, ensuring that all spatial anomalies within the exhibit area are identified.

[0174] Step S24: performing temporal abnormal color temperature analysis on the color temperature data of the exhibit area according to the standard lighting data of the exhibit area, thereby obtaining temporal abnormal color temperature data;

[0175] In this embodiment, color temperature data from each lighting point in the exhibit area at different time points is collected for time series analysis. Color temperature data for each lighting point is collected and recorded at preset time intervals. This data is then compared with standard lighting data to examine color temperature variations within a specific time period. If the color temperature during a specific time period exceeds the normal range (for example, exceeding the maximum or minimum value in the standard lighting data), a color temperature anomaly is determined for that period. The analysis involves setting a time threshold and performing the comparison to identify time periods exceeding the threshold, thereby identifying temporal color temperature anomalies.

[0176] Step S25: integrating the color temperature anomaly of the exhibit area according to the spatial anomaly color temperature data and the temporal anomaly color temperature data, thereby obtaining the color temperature anomaly data of the exhibit area.

[0177] In this embodiment, the spatial anomaly color temperature data obtained from the spatial anomaly color temperature analysis and the temporal anomaly color temperature analysis are compared and integrated with the temporal anomaly color temperature data. The spatial anomaly color temperature data is used to identify all areas within the exhibit area where the color temperature deviates from the standard range. These spatial anomaly areas are identified by comparing them with the set color temperature standard range. For example, assuming the standard color temperature range is 3000K to 5000K, if the color temperature values of certain areas exceed this range (e.g., exceeding 5000K or falling below 3000K), these areas are considered to have spatial anomalies. Temporal analysis is then performed on these spatial anomaly areas to examine the color temperature variations within these areas over different time periods. For example, if the color temperature of a spatial area is too high or too low within a specific time period, the color temperature anomaly in this area can be considered to have temporal variation characteristics. In this process, the temporal anomaly color temperature data is combined with the spatial anomaly data to identify areas that exhibit both spatial and temporal anomalies within a specific time period. These areas are further classified by setting color temperature thresholds. Assuming the temporal anomaly threshold is set at 5% (allowing color temperature fluctuations to not exceed 5%), if the color temperature of a spatially abnormal area fluctuates beyond this threshold within a certain time period, the color temperature anomaly in that area is considered to have not only spatial deviations but also temporal fluctuations. This data is then classified and integrated to ultimately identify areas where color temperature anomalies significantly deviate from the standard range both spatially and temporally. For example, if the color temperature value of a certain area exceeds 5000K and exhibits deviations exceeding the threshold at different time points, this area is considered to have a severe color temperature anomaly and will be included as part of the overall color temperature anomaly data for the exhibit area, providing a basis for subsequent optimization.

[0178] Optionally, step S23 is specifically as follows:

[0179] Step S231: Generate a reference color temperature based on the standard lighting data of the exhibit area, thereby obtaining the reference color temperature data of the exhibit area;

[0180] In this embodiment, it is necessary to obtain standard lighting data for the exhibition area. Standard lighting data generally includes the color temperature values of each lighting point in the exhibition area. These color temperature values can be obtained through light source parameters, lighting design plans, or specifications of lighting equipment. The color temperature of standard lighting data is generally set to a fixed value range, such as 3000K to 5000K. Next, the standard color temperature values of each lighting point in the exhibition area are evenly distributed to generate reference color temperature data. With this data, a reference color temperature value can be generated for each lighting point, and a color temperature reference model can be formed for the entire exhibition area. This generation process requires accurately calculating the reference color temperature value for each area based on the area of the exhibition area and the layout of the light source to ensure that the lighting of the entire exhibition area meets the predetermined standards.

[0181] Step S232: constructing a spatial distribution map of the color temperature data of the exhibit area, thereby obtaining a color temperature spatial distribution map of the exhibit area;

[0182] In this embodiment, it is necessary to collect color temperature data for each location within the exhibit area. This data can be collected in real time by sensors or estimated using a lighting design model. Based on this data, the color temperature value of each location is marked on a floor plan of the exhibit area, and different colors or grayscale values are used to represent different color temperature levels. The color temperature value of each area is visualized using a spatial distribution map. The color temperature distribution map within the exhibit area shows the spatial variation of color temperature, indicating which areas have higher or lower color temperature values. During the construction process, it is necessary to ensure that the color temperature data for each area is accurately collected and reasonably mapped to the spatial distribution map so that the color temperature information at each location can accurately reflect the lighting environment within the exhibit area.

[0183] Step S233: performing color temperature deviation calculation on the color temperature spatial distribution diagram of the exhibit area according to the benchmark color temperature data of the exhibit area, thereby obtaining color temperature deviation data of the exhibit area;

[0184] In this embodiment, the color temperature values at each location within the exhibit area are compared with a baseline color temperature value. For each location, the difference between the actual color temperature and the baseline color temperature is calculated to obtain color temperature deviation data. This deviation can be calculated using a simple subtraction operation: subtracting the baseline color temperature from the actual color temperature to obtain the color temperature deviation. This calculation allows the color temperature deviation of each location within the entire exhibit area to be determined and the distribution of color temperature deviations to be analyzed. This deviation data provides a basis for subsequent identification of abnormal color temperature areas, laying the foundation for detecting areas of high or low color temperature within the exhibit area.

[0185] Step S234: performing color temperature deviation area statistics based on the color temperature deviation data of the exhibit area, thereby obtaining high color temperature area data and low color temperature area data;

[0186] In this embodiment, the color temperature deviation of the entire exhibit area needs to be classified based on the color temperature deviation data. The specific method is to divide the color temperature deviation values of all areas according to a set threshold. For example, if the threshold is set to ±10K, if the color temperature deviation of a certain area exceeds ±10K, it will be marked as a high color temperature or low color temperature area. Through this process, all areas with large deviations in the exhibit area can be counted and classified as high color temperature areas and low color temperature areas respectively. For accurate statistics, weighted processing can also be performed based on the actual area of the area to ensure that large areas of deviation are fully considered and further guide the lighting optimization of the exhibit area.

[0187] Step S235: performing an exhibit material spectrum analysis on the high color temperature area data, thereby obtaining the exhibit material spectrum data in the high color temperature area;

[0188] In this embodiment, it is necessary to determine the spectral characteristics of the exhibit material. The material of the exhibit usually affects its ability to reflect and absorb illumination light, and thus affects its color temperature perception. In order to perform spectral analysis, it is necessary to obtain spectral data of the exhibit, which can be obtained through a spectrometer or other optical measurement tools. By analyzing the spectral characteristics of the exhibit material, it is possible to determine the color changes of the exhibit under different lighting conditions, especially the changes in the high color temperature area. When performing spectral analysis, it is necessary to pay attention to the reflectivity and absorptivity of the material. These parameters can be obtained through actual measurement or reference to standard data. The analysis process can generate spectral data of the exhibit material in the high color temperature area with the help of spectral analysis instruments and software.

[0189] Step S236: evaluating the color reproduction of exhibits in the low color temperature area data, thereby obtaining the color reproduction data of exhibits in the low color temperature area;

[0190] In this embodiment, it is necessary to evaluate the color reproduction of the exhibits under low color temperature lighting. The color reproduction of an exhibit refers to the difference between its color and the real color under different lighting conditions. In order to evaluate the color reproduction, it is necessary to use a colorimeter or color measurement tool to measure the color of the exhibits in the low color temperature area. Usually, the evaluation process uses a standard color card or real color as a benchmark to calculate the color difference value of the exhibits under low color temperature lighting. The color difference value can be calculated using the ΔE value (color difference value standard) to obtain quantitative data on the color reproduction by comparing the difference between the color of the exhibits under low color temperature and the reference color card. During the evaluation process, it is necessary to record the color difference of each exhibit in a low color temperature environment and form the color reproduction data of the exhibits in the low color temperature area.

[0191] Step S237: Merging the spatially abnormal color temperatures based on the material spectrum data of the exhibits in the high color temperature area and the color restoration data of the exhibits in the low color temperature area, thereby obtaining spatially abnormal color temperature data.

[0192] In this embodiment, it is necessary to combine the spectral data of the exhibit material in the high color temperature area and the color reproduction data of the exhibit in the low color temperature area. The merging process analyzes the intersection of the high color temperature area and the low color temperature area to identify which areas show significant abnormalities in color temperature. The abnormalities in these areas are not only changes in color temperature, but are also closely related to the spectral characteristics and color reproduction of the exhibit materials. In the merging process, set thresholds are used, such as the spectral difference of more than 20% in the high color temperature area and the color reproduction difference of more than 15% in the low color temperature area as the basis for merging. According to these standards, areas with significant color temperature anomalies are merged to form spatial abnormal color temperature data.

[0193] Optionally, step S235 is specifically as follows:

[0194] Performing light source type identification on the high color temperature area data to obtain light source type data;

[0195] In this embodiment, it is necessary to collect spectral data of the light source in the high color temperature region through a spectrometer or other equipment to obtain the wavelength distribution of the light source. This data can be obtained by directly measuring the radiation spectrum of the light source in the high color temperature region, or by inferring it based on the specification manual of the light source. The measured spectral data will be compared with common light source types (such as LED, fluorescent lamp, incandescent lamp, etc.) in the light source type database. The comparison process uses the minimum mean square error method (MSE) or a similarity algorithm (such as cosine similarity) to determine the degree of match between the spectral data and the light source type. Based on the matching results, the light source type in the high color temperature region is identified, and light source type data is generated. This data includes the type and spectral characteristics of the light source.

[0196] Extracting spectral features according to the light source type data to obtain light source type spectral data;

[0197] In this embodiment, based on the light source type data, standard spectral data associated with each light source type is selected. This data can be obtained from product manuals or spectral databases provided by the light source manufacturer. For example, for LED light sources, their spectral characteristics are generally concentrated in the blue and green bands, while fluorescent lamps have a strong ultraviolet spectral distribution. Depending on the type of light source, a spectral analysis instrument such as a spectrophotometer is used to measure the radiation intensity of that type of light source at different wavelengths. From this measurement data, the characteristic wavelength and corresponding radiation intensity of each light source type are extracted. This extracted spectral characteristic data will be used for subsequent light source illumination simulation and material reflection analysis.

[0198] Obtain exhibit material data;

[0199] In this embodiment, a materials analyzer (such as an FTIR spectrometer or X-ray fluorescence analyzer) is used to analyze the exhibit's surface to obtain data on its chemical composition and physical structure. Based on this data, the exhibit's material type (e.g., metal, plastic, wood, etc.) is determined. Next, a spectrometer or other instrument is used to measure the exhibit's material reflectivity and transmittance at different wavelengths. Typically, wavelengths in the ultraviolet, visible, and near-infrared regions are selected to analyze the exhibit's material's ability to reflect and absorb light at these wavelengths. Reflectivity data can be obtained directly or estimated based on standard spectral data. The resulting material data will provide the foundation for subsequent reflectivity analysis.

[0200] Collect reflectivity based on exhibit material data to obtain the reflectivity of the exhibit material;

[0201] In this embodiment, a spectroscopic reflectometer is used to illuminate the exhibit surface and record reflectance spectral data. Specifically, a standard light source (such as the D65 standard light source) is selected, and a detector is used to capture the light reflected from the exhibit material surface. This process is typically performed within the ultraviolet to near-infrared wavelength range, with reflectivity measured at multiple wavelengths. Reflectivity data collection requires multi-angle measurements of different exhibit surfaces to ensure representativeness and accuracy. The collected data forms a reflectivity curve for the exhibit material, displaying the reflectivity at different wavelengths. This data provides a detailed understanding of the optical properties of the exhibit material and provides a basis for subsequent light source illumination simulations.

[0202] Perform exhibition area light source identification on the high color temperature area data to obtain exhibition area light source data, and perform light source illumination simulation based on the exhibition area light source data and light source type spectrum data to obtain exhibition area light source illumination data;

[0203] In this embodiment, multiple light sensors are deployed within the high color temperature area to record light source data at different locations. These sensors measure the radiant intensity and spectral information of the light source in real time and transmit it to the central processing system. The system uses spectral analysis and compares it with a database of light source types to identify the specific light source types within the exhibit area. This data provides information on the types of light sources in different areas of the exhibit area. Light source type data can include different types of light sources, such as LEDs and fluorescent lamps. This light source data is used to further simulate light source illumination. The geometric model of the exhibit area is input into illumination simulation software (such as optical simulation software). The software then uses the light source type data for the high color temperature area and combines it with its spectral characteristics to simulate the illumination of the exhibit area. This simulation uses the light source's radiant intensity data, wavelength distribution data, and material reflectance data for the exhibit area as input. During the simulation, the distribution of the light source radiation at different locations is calculated and a light source illumination map is generated. In this way, light source illumination data for the entire exhibit area can be obtained, with detailed records of the light intensity and spectral distribution for each area.

[0204] Analyze the light reflected from the exhibit materials based on the light source data of the exhibition area and the reflectivity of the exhibit materials to obtain the exhibit material reflection data;

[0205] In this embodiment, the intensity of light reflected from the exhibit surface in different wavelength bands is calculated based on the light source illumination map and the exhibit's reflectivity data. This analysis is achieved through ray tracing technology, which tracks the interaction between light emitted by the light source and the exhibit surface and calculates the intensity and direction of the reflected light. For each wavelength band, the distribution and intensity of the reflected light are calculated to generate data on the light reflected from the exhibit material.

[0206] Perform reflection band statistics on the reflection data of the exhibit materials to obtain the reflection band data of the exhibit materials;

[0207] In this embodiment, based on the reflected light analysis results, multiple key wavelength bands (such as the ultraviolet, visible, and near-infrared regions) are selected and the reflected light intensity in each wavelength band is calculated. By statistically analyzing the reflected light in these wavelength bands, the reflectance band data for the exhibit material is obtained. This data can be presented as the distribution of reflected light intensity in different wavelength bands, and the reflectance performance of different exhibit materials in each wavelength band can be compared. This data provides a more detailed understanding of the reflective characteristics of exhibits for different light sources.

[0208] Interactive analysis of light source color temperature is performed based on the reflected band data to obtain spectral data of exhibit materials in high color temperature areas.

[0209] In this embodiment, the reflection band data is matched with the spectral characteristics of different light sources to calculate the reflectance characteristics of the exhibit material under different light sources. This analysis requires an interactive analysis of each light source's color temperature, radiant intensity, and wavelength distribution with the reflection band data. Through calculation, the color temperature reflection characteristics of the exhibit material under different light sources are determined, ultimately obtaining spectral data for the exhibit material in the high color temperature region.

[0210] Optionally, step S3 specifically includes:

[0211] Step S31: converting the exhibit color model according to the color temperature anomaly data of the exhibit area to obtain the exhibit color model;

[0212] In this embodiment, spectral data from the exhibit area is measured using a precise spectral instrument (such as a spectrophotometer). During the measurement process, the instrument's wavelength range covers the ultraviolet, visible, and near-infrared bands to obtain comprehensive color temperature data. By measuring the color temperature of the light source in the exhibit area, the specific color temperature value (e.g., 5000K, 6000K, etc.) is obtained, and the deviation from the standard color temperature value is recorded. Using this abnormal color temperature data, a color conversion formula (such as from XYZ color space to RGB color space) is used to map the light source's color temperature to the actual color of the exhibit. At this point, combined with a standard color temperature model, the abnormal color temperature data from the high color temperature light source is converted into the exhibit's color model. During this conversion process, it is necessary to ensure that standard color temperature compensation coefficients and color space conversion formulas are used for accurate color adjustment to generate a standardized color model for the exhibit. Ultimately, the exhibit's color model data is obtained for subsequent color difference calculation and distortion assessment.

[0213] Step S32: obtaining reference color data;

[0214] In this embodiment, a standard color light source (such as D65 or type A light source) is selected as the reference light source, and the spectrum is collected using reflectance data under standard lighting conditions. Spectroscopic instruments (such as colorimeters and spectrophotometers) are used to measure the color data presented by standard reference objects (such as white standard plates or color cards) under the illumination of standard light sources. The measured data will cover the reflectance spectrum of the exhibit and its color coordinates. The obtained reference color data includes color parameters such as color temperature value, RGB value, and Lab value, which have a fixed reference standard. To ensure the accuracy of the reference color data, the equipment must be calibrated regularly and the test environment must be free of any pollution or interference. The collection of reference color data needs to be completed under ideal color temperature conditions, and the color data under each standard light source must be recorded. This data will then be used to compare the color difference of the actual exhibit color model to ensure that subsequent color difference calculations and distortion assessments have a clear reference reference.

[0215] Step S33: performing color difference calculation on the exhibit color model according to the reference color data, thereby obtaining color difference data;

[0216] In this embodiment, the color coordinate values (such as RGB or Lab values) of the reference color data and the exhibit color model data need to be compared. These color coordinate data are obtained using a colorimeter or colorimeter. Next, the color difference between the reference color and the exhibit color is calculated using the CIEDE2000 formula to determine the differences between the various parameters in the formula (such as hue, brightness, and saturation). During the color difference calculation process, the measured RGB or Lab values are processed and standardized to ensure that the color difference calculation objectively reflects the actual degree of color deviation. When calculating color difference, the standard thresholds set are: ΔE ≤ 1 for no perceptible color difference, 1 < ΔE ≤ 2 for slight color difference, and ΔE > 2 for significant color difference. These values help determine the degree of color distortion in the exhibit and provide a basis for subsequent compensation.

[0217] Step S34: evaluating the color distortion of the exhibit based on the color difference data, thereby obtaining the color distortion data of the exhibit;

[0218] In this embodiment, distortion analysis is performed based on the color difference data using a color difference evaluation standard (such as the ΔE value). In this process, the color difference value needs to be compared with a predetermined distortion standard. The specific operation includes setting a color difference threshold. Generally, when the ΔE value exceeds 3, the color distortion is considered significant. For each exhibit, the degree of color deviation is judged based on the color difference calculation results and classified. At this time, a standard distortion evaluation model is used to analyze the color stability and consistency of the exhibits under different light source color temperatures. If the color difference value exceeds the set threshold, it is considered that color distortion exists and is corrected through further color adjustment or compensation methods. The distortion evaluation can also be presented through a visual chart, in which the color difference data of each exhibit is displayed in different color levels to help identify the most serious color distortion areas. The goal of this step is to quantify the color distortion of the exhibits and provide basic data for subsequent color temperature dynamic compensation.

[0219] Step S35: Perform color temperature dynamic compensation according to the exhibit color distortion data, thereby obtaining exhibit area color temperature dynamic compensation data.

[0220] In this embodiment, a color temperature compensation algorithm is used, taking the color difference data evaluated in the previous step as input. This color difference data represents the deviation between the exhibit's color and the reference color. Next, based on the color temperature compensation model, the color temperature of the light source is dynamically adjusted to reduce the color difference. If the color difference of the exhibit exceeds the standard due to the light source's excessively high color temperature, the color distortion of the exhibit can be reduced by lowering the light source's color temperature. During the compensation process, the exhibit's reflectivity, color temperature compensation coefficient, and compensation strategy (e.g., based on a linear or nonlinear relationship between color temperature and color difference) need to be input to ensure that the compensation result achieves the desired color correction effect. After compensation is completed, the exhibit's color temperature data needs to be measured again to ensure that the color temperature has been adjusted to the ideal range and the color difference value has been reduced to an acceptable range (e.g., a ΔE value of less than 2). Finally, by adjusting the thresholds and standards in the color temperature compensation algorithm, the color temperature dynamic compensation strategy is optimized to adapt to the color correction requirements of different exhibits and environmental conditions.

[0221] Optionally, step S35 is specifically as follows:

[0222] Step S351: performing color cast identification based on the exhibit's color distortion data to obtain color cast data;

[0223] In this embodiment, the color difference value is obtained by calculating the difference between the exhibit's color and a reference color, typically using CIEDE2000 or other standard color difference formulas. After the color difference value is calculated, it is broken down according to its components to identify the type of color cast (e.g., reddish, blueish, yellowish, etc.). The exhibit's position in the color spectrum is determined by analyzing the RGB components of the color difference data or the L, a, and b components of the Lab color model. If the exhibit's color distortion is primarily in the red channel (e.g., R>G>B), it is identified as a reddish cast; if the distortion is in the blue channel (e.g., B>G>R), it is identified as a bluish cast. During this process, a color cast threshold (e.g., a ΔE value greater than 2 indicates significant color cast) is set as the standard for color cast identification. If the color difference is within this threshold, it is determined to be non-significant. Ultimately, through calculation and determination, the exhibit's color cast data is obtained, which is used for subsequent color ratio compensation processing.

[0224] Step S352: performing statistics on the color cast data to obtain warm color data and cool color data;

[0225] In this embodiment, the color cast data obtained in step S351 is used to categorize different color cast types (e.g., warm or cool). To this end, the number and degree of exhibits exhibiting warm (e.g., high R values and low G and B values) and cool (e.g., high B values and low R and G values) casts are statistically analyzed based on the relevant components of the RGB or Lab color model. During the statistical process, a standard color cast threshold is set; a ΔE value greater than 2 is typically considered significant, and further subdivided into warm and cool casts. The statistical results can be presented in a table or graphical format, recording the number of occurrences of each color cast, the range of color cast values, and other information. During the statistical process, each color cast type is processed using the following criteria: warm casts are generally defined as color differences primarily manifesting in the red light channel, while cool casts are defined as color differences primarily manifesting in the blue light channel. The data for each color cast type is weighted and aggregated based on parameters such as the number of exhibits and severity, ultimately yielding warm and cool cast data. These statistical data are used for subsequent light ratio adjustment.

[0226] Step S353: adding red light ratio compensation according to the warm color data, thereby obtaining red light ratio compensation data;

[0227] In this embodiment, data on exhibits with a warmer color cast is extracted based on the aforementioned color cast identification and statistical results. The color difference data for these exhibits indicates a higher red light component (i.e., the red channel value is higher than the green and blue channels). During the compensation process, a compensation target is first set: increasing the red light component to balance the warmer color cast. Specifically, the gain coefficient of the red channel is adjusted to increase the red light ratio. This gain coefficient is calculated by analyzing the color difference data and is typically based on a color difference correction model (e.g., a coefficient of 1.2 indicates a 20% increase in red light gain). During the adjustment process, the adjusted red light ratio is measured in real time using standard color calibration equipment (e.g., a colorimeter) to ensure that the red light gain does not exceed a set threshold (e.g., the red light ratio is no greater than 50%). Ultimately, red light ratio compensation data is obtained, which is used in the subsequent dynamic compensation process for color temperature reduction.

[0228] Step S354: adding blue light ratio compensation according to the cool color data, thereby obtaining blue light ratio compensation data;

[0229] In this embodiment, data for exhibits with a cooler color cast is extracted based on color cast identification and statistical data. Based on this data, the color difference data for exhibits with a cooler color cast indicates a higher blue component (i.e., the blue channel has a higher value than the red and green channels). At the beginning of the compensation process, a compensation target is set: increasing the blue light component to balance the cooler color cast. At this point, the gain coefficient of the blue channel is adjusted. This is typically calculated by calculating the color difference. The gain coefficient is calculated as a compensation factor. For example, a setting of 1.1 indicates a 10% increase in blue light gain. During operation, a standard colorimeter is used to measure the adjusted blue light ratio to ensure that the blue light gain coefficient does not exceed the set upper limit (e.g., the blue light ratio is no greater than 60%). During the compensation process, the gain coefficient can be dynamically adjusted based on the color difference value to ensure that increasing the blue light component does not affect the balance of other color components. Through these operations, blue light ratio compensation data is ultimately obtained, providing the necessary compensation information for subsequent integration steps.

[0230] Step S355 : performing dynamic color temperature compensation integration of the exhibit according to the red light ratio compensation data and the blue light ratio compensation data, thereby obtaining dynamic color temperature compensation data of the exhibit area.

[0231] In this embodiment, red and blue light compensation priorities are set. Typically, the red-to-blue light compensation ratio requires a trade-off based on the specific color cast of the exhibit. The red and blue light compensation coefficients are weighted and combined according to a specific rule to form a new dynamic compensation coefficient. In practice, the compensation coefficients can be derived using a weighted average method. The weighted ratio of the red and blue light compensation coefficients is as follows: If the red light compensation coefficient is 1.2 and the blue light compensation coefficient is 1.1, then based on the actual color cast, a weighted ratio of, for example, 60% (red compensation) and 40% (blue compensation) can be set. This combined compensation coefficient is then applied to the exhibit's color temperature correction process, restoring color balance by adjusting the red and blue light ratio of the light source. During implementation, the compensation effect is monitored in real time using a color thermometer and colorimeter to ensure that the exhibit's color after color temperature adjustment approaches the standard color temperature (e.g., 5000K). By adjusting and optimizing the compensation coefficients, dynamic color temperature compensation data for the exhibit area is obtained, ensuring color restoration of the exhibit.

[0232] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0233] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A lighting optimization model training method, characterized in that: The following steps are involved: Step S1: Acquire exhibition hall lighting data, and extract exhibition hall lighting features and exhibit area lighting features based on the exhibition hall lighting data, thereby obtaining exhibition hall lighting data and exhibit area lighting data; perform light source position optimization analysis based on the exhibition hall lighting data, thereby obtaining exhibition hall light source position optimization data; Step S2: Acquire standard lighting data of the exhibition area; perform color temperature anomaly analysis on the lighting data of the exhibition area based on the standard lighting data of the exhibition area, thereby obtaining color temperature anomaly data of the exhibition area. Step S1 is specifically as follows: Step S11: Acquire exhibition hall lighting data, and extract exhibition hall lighting features and exhibit area lighting features based on the exhibition hall lighting data, thereby obtaining exhibition hall lighting data and exhibit area lighting data; Step S12: performing glare optimization analysis based on the exhibition hall lighting data, thereby obtaining exhibition hall lighting glare optimization data; Step S13: performing illumination uniformity optimization analysis based on the exhibition hall lighting data, thereby obtaining exhibition hall illumination uniformity optimization data; Step S14: integrating the exhibition hall lighting position optimization features based on the exhibition hall lighting glare optimization data and the exhibition hall lighting uniformity optimization data, thereby obtaining the exhibition hall light source position optimization data; Step S3: performing an exhibit color distortion analysis based on the exhibit area color temperature abnormality data to obtain exhibit color distortion data; performing color temperature dynamic compensation based on the exhibit color distortion data to obtain exhibit area color temperature dynamic compensation data; Step S4: Obtaining a lighting optimization model; training the lighting optimization model based on the exhibition hall light source position optimization data and the exhibit area color temperature dynamic compensation data, thereby obtaining a lighting optimization training model; Step S5: performing lighting energy consumption evaluation based on the lighting optimization training model to obtain lighting energy consumption data; Energy efficiency optimization is performed based on lighting energy consumption to obtain lighting energy efficiency optimization data, which is then uploaded to the lighting optimization training model to perform lighting energy efficiency optimization tasks.

2. The lighting optimization model training method according to claim 1, characterized in that: Step S12 is specifically as follows: Step S121: performing light source luminescence simulation according to the exhibition hall lighting data, thereby obtaining light source luminescence simulation data; Step S122: extracting emission angle features from the light source emission simulation data to obtain light source emission angle data; Step S123: obtaining audience location data; Step S124: performing glare evaluation on the audience position data according to the light source emission angle data, thereby obtaining exhibition hall lighting glare data; Step S125: performing light source angle adjustment on the exhibition hall lighting glare data, thereby obtaining light source angle adjustment data; Step S126: performing installation height adjustment on the exhibition hall lighting glare data, thereby obtaining installation height adjustment data; Step S127: performing exhibition hall lighting glare optimization integration according to the light source angle adjustment data and the installation height adjustment data, thereby obtaining exhibition hall lighting glare optimization data.

3. The lighting optimization model training method according to claim 2, characterized in that: Step S124 is specifically as follows: Calculate the relative angle of the audience position data based on the light source emission angle data, thereby obtaining the light source emission-audience position angle data; Obtaining glare standard threshold data; Extract light source intensity features based on exhibition hall lighting data to obtain light source intensity data; Calculating the glare intensity based on the light source intensity data and the light source emission-audience position angle data to obtain the glare intensity data; The exhibition hall lighting glare is evaluated on the glare intensity data according to the glare standard threshold data, thereby obtaining the exhibition hall lighting glare data.

4. The lighting optimization model training method according to claim 1, characterized in that: Step S13 is specifically as follows: Step S131: Drawing a lighting distribution area map based on the exhibition hall lighting data, thereby obtaining the exhibition hall lighting distribution area map; Step S132: performing minimum lighting area identification and maximum lighting area identification on the exhibition hall lighting distribution area map, thereby obtaining minimum lighting area data and maximum lighting area data of the exhibition hall; Step S133: Calculating the illumination intensity based on the minimum illumination area data of the exhibition hall, thereby obtaining the illumination intensity data of the minimum illumination area of the exhibition hall; Step S134: Calculating the illumination intensity based on the data of the maximum illumination area of the exhibition hall, thereby obtaining the illumination intensity data of the maximum illumination area of the exhibition hall; Step S135: Calculating the average illumination intensity based on the exhibition hall lighting distribution area map to obtain the average illumination intensity of the exhibition hall; Step S136: Identifying the light intensity data of the minimum lighting area and the light intensity data of the maximum lighting area according to the average illumination of the exhibition hall, thereby obtaining the data of the too dark lighting area and the data of the too bright lighting area; Step S137: increasing the light source density of the dark area data to obtain light source density increase data; Step S138: Optimizing the reflectivity of the exhibition space material in the over-bright area data, thereby obtaining the exhibition space material data; Step S139: performing exhibition hall lighting uniformity optimization and integration according to the light source density increase data and the exhibition space material data, thereby obtaining exhibition hall lighting uniformity optimization data.

5. The lighting optimization model training method according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: Acquire standard lighting data of the exhibition area; Step S22: Calculating the color temperature based on the lighting data of the exhibit area, thereby obtaining the color temperature data of the exhibit area; Step S23: performing spatial abnormal color temperature analysis on the color temperature data of the exhibit area according to the standard lighting data of the exhibit area, thereby obtaining spatial abnormal color temperature data; Step S24: performing temporal abnormal color temperature analysis on the color temperature data of the exhibit area according to the standard lighting data of the exhibit area, thereby obtaining temporal abnormal color temperature data; Step S25: integrating the color temperature anomaly of the exhibit area according to the spatial anomaly color temperature data and the temporal anomaly color temperature data, thereby obtaining the color temperature anomaly data of the exhibit area.

6. The lighting optimization model training method according to claim 5, characterized in that: Step S23 is specifically as follows: Step S231: Generate a reference color temperature based on the standard lighting data of the exhibit area, thereby obtaining the reference color temperature data of the exhibit area; Step S232: constructing a spatial distribution map of the color temperature data of the exhibit area, thereby obtaining a color temperature spatial distribution map of the exhibit area; Step S233: performing color temperature deviation calculation on the color temperature spatial distribution map of the exhibit area according to the benchmark color temperature data of the exhibit area, thereby obtaining color temperature deviation data of the exhibit area; Step S234: performing color temperature deviation area statistics based on the color temperature deviation data of the exhibit area, thereby obtaining high color temperature area data and low color temperature area data; Step S235: performing an exhibit material spectrum analysis on the high color temperature area data, thereby obtaining the exhibit material spectrum data in the high color temperature area; Step S236: evaluating the color reproduction degree of exhibits in the low color temperature area data, thereby obtaining the color reproduction degree data of exhibits in the low color temperature area; Step S237: Merging the spatial abnormal color temperatures according to the material spectrum data of the exhibits in the high color temperature area and the color restoration data of the exhibits in the low color temperature area, thereby obtaining spatial abnormal color temperature data.

7. The lighting optimization model training method according to claim 6, characterized in that: Step S235 is specifically as follows: Performing light source type identification on the high color temperature area data to obtain light source type data; Extracting spectral features according to the light source type data to obtain light source type spectral data; Obtain exhibit material data; Collect reflectivity based on the exhibit material data to obtain the exhibit material reflectivity; Perform exhibition area light source identification on the high color temperature area data to obtain exhibition area light source data, and perform light source illumination simulation based on the exhibition area light source data and light source type spectrum data to obtain exhibition area light source illumination data; Analyze the light reflected from the exhibit materials based on the light source data of the exhibition area and the reflectivity of the exhibit materials to obtain the exhibit material reflection data; Perform reflection band statistics on the reflection data of the exhibit materials to obtain the reflection band data of the exhibit materials; Interactive analysis of light source color temperature is performed based on the reflected band data to obtain spectral data of exhibit materials in high color temperature areas.

8. The lighting optimization model training method according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: converting the exhibit color model according to the color temperature anomaly data of the exhibit area to obtain the exhibit color model; Step S32: obtaining reference color data; Step S33: performing color difference calculation on the exhibit color model according to the reference color data, thereby obtaining color difference data; Step S34: evaluating the color distortion of the exhibit based on the color difference data, thereby obtaining the color distortion data of the exhibit; Step S35: Perform color temperature dynamic compensation according to the exhibit color distortion data, thereby obtaining exhibit area color temperature dynamic compensation data.

9. The lighting optimization model training method according to claim 8, characterized in that: Step S35 is specifically as follows: Step S351: performing color cast identification based on the exhibit's color distortion data to obtain color cast data; Step S352: performing statistics on the color cast data to obtain warm color data and cool color data; Step S353: adding red light ratio compensation according to the warm color data, thereby obtaining red light ratio compensation data; Step S354: adding blue light ratio compensation according to the cool color data, thereby obtaining blue light ratio compensation data; Step S355 : performing dynamic color temperature compensation integration of the exhibit according to the red light ratio compensation data and the blue light ratio compensation data, thereby obtaining dynamic color temperature compensation data of the exhibit area.

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