Intelligent illumination automatic adjusting method and device based on illumination intensity and personnel detection

Abstract

The embodiment of the invention provides an intelligent illumination automatic adjustment method and device based on illumination intensity and personnel detection, and reliability evaluation and abnormity correction are carried out on data by synchronously collecting environment illumination intensity and personnel activity data. An illumination activity association model is innovatively constructed, and feature fusion is realized by using a fuzzy logic algorithm. The system establishes a complete evaluation index system including illumination uniformity, a color rendering index and a glare value, and performs constraint optimization based on an illumination scene adaptation model. Through introducing a dynamic compensation mechanism of a personnel detection probability value and a time period priority factor, accurate illumination adjustment control is realized. The method breaks through the limitation of dimming in a traditional fixed mode, and provides a comprehensive technical solution for an intelligent lighting system.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method and device for automatic intelligent lighting adjustment based on light intensity and personnel detection. Background Art

[0002] Traditional lighting control systems mainly rely on fixed time adjustment or simple light sensing control, and cannot be adjusted intelligently according to actual usage scenarios. Existing automatic dimming systems often only consider the single factor of ambient light intensity, ignoring the impact of human activity characteristics on lighting needs. Especially in complex indoor environments, a single parameter control strategy is difficult to meet lighting needs in different scenarios.

[0003] At the same time, the existing system has obvious deficiencies in data processing and model building. Traditional methods lack reliability assessment and exception handling mechanisms for multi-source data, and fail to effectively utilize the correlation between light and human activities. The system is also relatively simple in terms of lighting effect evaluation, and fails to fully consider key indicators such as illumination uniformity, color rendering index, and glare value.

[0004] In addition, the existing technology is also relatively mechanical in terms of lighting compensation calculation. There is a lack of dynamic compensation mechanism based on the probability of human activity and time priority, making it difficult to achieve accurate lighting adjustment control. Solving these problems is of great significance to improving the adaptability of intelligent lighting systems and user comfort. Summary of the invention

[0005] In response to the problems in the prior art, the present application provides a method and device for automatic intelligent lighting adjustment based on light intensity and personnel detection, which can achieve precise lighting adjustment control, break through the limitations of traditional fixed-mode dimming, and provide a comprehensive technical solution for intelligent lighting systems.

[0006] In order to solve at least one of the above problems, the present application provides the following technical solutions: In a first aspect, the present application provides an intelligent lighting automatic adjustment method based on light intensity and personnel detection, comprising: Collect indoor environment light intensity data through a light intensity sensor, collect indoor personnel activity data through a personnel detection sensor, perform reliability assessment and outlier correction on the light intensity data and the personnel activity data, use a sliding average filter algorithm to smooth the corrected data, perform time series alignment on the smoothed data to obtain a multi-dimensional feature sequence, build a light activity association model based on the multi-dimensional feature sequence, use a fuzzy logic algorithm to perform feature fusion on the output of the light activity association model, and generate a light scene feature data set; The lighting scene feature data set is input into a preset lighting intelligent adjustment model for training to obtain a lighting scene adaptation model, a lighting effect evaluation index system including illumination uniformity, color rendering index, and glare value is constructed, and based on the lighting effect evaluation index system, the output result of the lighting scene adaptation model is constrained and optimized to obtain a target light intensity value and lighting equipment adjustment parameters that meet the lighting effect evaluation index system; The target light intensity value and the lighting device adjustment parameters are input into a lighting controller, and the lighting controller generates a lighting device control instruction based on a light compensation calculation formula, wherein the light compensation calculation formula is that the target light intensity value is equal to the sum of the current ambient light intensity and the light compensation value, and the light compensation value is determined by a functional relationship between a personnel detection probability value and a time period priority factor, and the lighting device control instruction is sent to the lighting device execution unit through a control bus for lighting adjustment.

[0007] Furthermore, the reliability assessment and outlier correction of the light intensity data and the personnel activity data are performed, the corrected data are smoothed using a sliding average filtering algorithm, and the smoothed data are time-series aligned to obtain a multi-dimensional feature sequence, including: Calculate the probability density values ​​of the light intensity data and the personnel activity data based on the Gaussian distribution model, mark the data with a probability density value lower than a preset threshold as abnormal data, correct the abnormal data using a linear interpolation method, perform reliability assessment by calculating the mean, standard deviation and coefficient of variation of the corrected data, and resample the data with a reliability assessment result lower than a preset score; The corrected light intensity data and personnel activity data are filtered respectively using a sliding average filtering algorithm. The filtered data are interpolated and resampled according to a uniform sampling period based on timestamp information. The resampled data sequence is time-aligned using the least squares method to obtain a multidimensional feature sequence.

[0008] Furthermore, the constructing of a lighting activity association model based on the multi-dimensional feature sequence, and using a fuzzy logic algorithm to perform feature fusion on the output of the lighting activity association model to generate a lighting scene feature data set include: The joint probability distribution between the light intensity and the personnel activity data in the multidimensional feature sequence is calculated by using a kernel density estimation method, a conditional probability relationship of the joint probability distribution is constructed based on a Bayesian network, the parameters of the Bayesian network are determined by maximum likelihood estimation, and a light activity association model is established; The fuzzy membership is calculated for the conditional probability value output by the lighting activity association model, a fuzzy rule set for lighting intensity and personnel activity is set, a fuzzy reasoning mechanism is used to match and calculate the fuzzy rule set, and the reasoning result of the fuzzy reasoning mechanism is defuzzified by the centroid method to obtain a lighting scene feature data set.

[0009] Furthermore, the lighting scene feature data set is input into a preset lighting intelligent adjustment model for training to obtain a lighting scene adaptation model, and a lighting effect evaluation index system including illumination uniformity, color rendering index, and glare value is constructed, including: The intelligent lighting adjustment model is constructed using a deep neural network, the lighting scene feature data set is divided into a training set and a validation set in a ratio of 8:2, the intelligent lighting adjustment model is iteratively trained using a back propagation algorithm, the optimal training round is determined based on the model performance on the validation set, and a lighting scene adaptation model is obtained; The illumination uniformity coefficient of the output result of the illumination scene adaptation model is calculated based on the CIE lighting standard, the general color rendering index Ra value of the light source is calculated using the color rendering index measurement method, the indoor lighting glare value is calculated using the UGR unified glare value evaluation system, and the illumination uniformity coefficient, the color rendering index Ra value and the glare value are constructed into a lighting effect evaluation index system.

[0010] Furthermore, the output result of the lighting scene adaptation model is constrained and optimized based on the lighting effect evaluation index system to obtain the target light intensity value and lighting equipment adjustment parameters that meet the lighting effect evaluation index system, including: Setting constraints on the output results of the illumination scene adaptation model, including that the illumination uniformity coefficient is not less than 0.7, the color rendering index Ra value is not less than 80, and the glare value is not more than 19, constructing a multi-objective optimization function based on the constraints, and using the Lagrange multiplier method to solve the multi-objective optimization function to obtain a target illumination intensity value that satisfies the constraints; A lighting equipment parameter optimization model is constructed based on the target light intensity value, and the power, color temperature and illumination angle of the lighting equipment are used as optimization variables. The particle swarm algorithm is used to solve the lighting equipment parameter optimization model to obtain the lighting equipment adjustment parameters that meet the lighting effect evaluation index system.

[0011] Furthermore, the target light intensity value and the lighting device adjustment parameter are input into a lighting controller, and the lighting controller generates a lighting device control instruction based on a light compensation calculation formula, wherein the light compensation calculation formula is that the target light intensity value is equal to the sum of the current ambient light intensity and the light compensation value, and the light compensation value is determined by a functional relationship between a personnel detection probability value and a time period priority factor, including: The target light intensity value and the lighting device adjustment parameter are input into a lighting controller, a day is divided into five time periods: early morning, morning, afternoon, evening, and night based on a time period division rule, a priority factor is set for each time period, the personnel detection probability value and the time period priority factor are weightedly calculated to obtain a light compensation value, and a lighting device control instruction is generated according to a calculation formula in which the target light intensity value is equal to the sum of the current ambient light intensity and the light compensation value; A communication protocol conversion module is used to convert the lighting device control instructions into the DALI communication protocol format, and the converted control instructions are sent to the lighting device execution unit through the data communication interface of the lighting controller. The lighting device execution unit receives and parses the control instructions and then executes the corresponding lighting adjustment operation.

[0012] Furthermore, the step of sending the lighting device control instruction to the lighting device execution unit through the control bus to adjust the lighting includes: Data encapsulation of the lighting device control instruction according to the DALI protocol specification, CRC check and address encoding of the encapsulated control instruction, and sending the control instruction to the lighting device execution unit through the RS485 bus interface, and the lighting device execution unit performs data frame check and address matching on the received control instruction; The driver chip based on the lighting device execution unit parses the control instruction into a PWM dimming signal and device operating parameters, controls the output current of the LED drive circuit through the PWM dimming signal, adjusts the color temperature and illumination angle of the lighting device according to the device operating parameters, and realizes intelligent adjustment of the lighting device.

[0013] In a second aspect, the present application provides an intelligent lighting automatic adjustment device based on light intensity and personnel detection, comprising: A feature fusion module is used to collect indoor environment light intensity data through a light intensity sensor, collect indoor personnel activity data through a personnel detection sensor, perform reliability assessment and outlier correction on the light intensity data and the personnel activity data, use a sliding average filter algorithm to smooth the corrected data, perform time series alignment on the smoothed data to obtain a multi-dimensional feature sequence, build a light activity association model based on the multi-dimensional feature sequence, use a fuzzy logic algorithm to perform feature fusion on the output of the light activity association model, and generate a light scene feature data set; A model evaluation module is used to input the illumination scene feature data set into a preset illumination intelligent adjustment model for training to obtain an illumination scene adaptation model, construct an illumination effect evaluation index system including illumination uniformity, color rendering index, and glare value, and perform constrained optimization on the output result of the illumination scene adaptation model based on the illumination effect evaluation index system to obtain a target illumination intensity value and illumination equipment adjustment parameters that meet the illumination effect evaluation index system; An automatic adjustment module is used to input the target light intensity value and the lighting device adjustment parameters into a lighting controller, and the lighting controller generates a lighting device control instruction based on a lighting compensation calculation formula, wherein the lighting compensation calculation formula is that the target light intensity value is equal to the sum of the current ambient light intensity and the lighting compensation value, and the lighting compensation value is determined by the functional relationship between the personnel detection probability value and the time period priority factor, and the lighting device control instruction is sent to the lighting device execution unit through the control bus for lighting adjustment.

[0014] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for automatic intelligent lighting adjustment based on light intensity and personnel detection are implemented.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for automatic intelligent lighting adjustment based on light intensity and personnel detection.

[0016] In a fifth aspect, the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the intelligent lighting automatic adjustment method based on light intensity and personnel detection.

[0017] It can be seen from the above technical solutions that the present application provides a method and device for automatic intelligent lighting adjustment based on light intensity and personnel detection, which performs reliability assessment and anomaly correction on the data by synchronously collecting ambient light intensity and personnel activity data. An innovative lighting activity association model is constructed, and feature fusion is achieved using a fuzzy logic algorithm. The system establishes a complete evaluation index system including illumination uniformity, color rendering index, and glare value, and performs constrained optimization based on a lighting scene adaptation model. Accurate lighting adjustment control is achieved by introducing a dynamic compensation mechanism for personnel detection probability values ​​and time period priority factors. This method breaks through the limitations of traditional fixed-mode dimming and provides a comprehensive technical solution for intelligent lighting systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 This is one of the flow charts of the intelligent lighting automatic adjustment method based on light intensity and personnel detection in the embodiment of the present application; Figure 2 This is a second flow chart of the intelligent lighting automatic adjustment method based on light intensity and personnel detection in an embodiment of the present application; Figure 3 This is a third flow chart of the intelligent lighting automatic adjustment method based on light intensity and personnel detection in the embodiment of the present application; Figure 4 This is a fourth flow chart of the intelligent lighting automatic adjustment method based on light intensity and personnel detection in an embodiment of the present application; Figure 5 This is a fifth flow chart of the intelligent lighting automatic adjustment method based on light intensity and personnel detection in the embodiment of the present application; Figure 6 This is a sixth flow chart of the intelligent lighting automatic adjustment method based on light intensity and personnel detection in the embodiment of the present application; Figure 7 FIG7 is a flow chart of the intelligent lighting automatic adjustment method based on light intensity and personnel detection in the embodiment of the present application; Figure 8 This is a structural diagram of an intelligent lighting automatic adjustment device based on light intensity and personnel detection in an embodiment of the present application; Fig. 9 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application.

[0020] Reference numerals: Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver program storage unit 9144, antenna 9111, speaker 9131, microphone 9132. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0022] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0023] Taking into account the problems existing in the prior art, the present application provides an intelligent lighting automatic adjustment method and device based on light intensity and personnel detection, which performs reliability assessment and anomaly correction on the data by synchronously collecting ambient light intensity and personnel activity data. An innovative lighting activity association model is constructed, and feature fusion is achieved using a fuzzy logic algorithm. The system establishes a complete evaluation index system including illumination uniformity, color rendering index, and glare value, and performs constrained optimization based on a lighting scene adaptation model. Accurate lighting adjustment control is achieved by introducing a dynamic compensation mechanism of personnel detection probability values ​​and time period priority factors. This method breaks through the limitations of traditional fixed-mode dimming and provides a comprehensive technical solution for intelligent lighting systems.

[0024] In order to achieve precise lighting adjustment control, break through the limitations of traditional fixed mode dimming, and provide a comprehensive technical solution for intelligent lighting systems, this application provides an embodiment of an intelligent lighting automatic adjustment method based on light intensity and personnel detection, see Figure 1 The intelligent lighting automatic adjustment method based on light intensity and personnel detection specifically includes the following contents: Step S101: collecting indoor environment light intensity data through a light intensity sensor, collecting indoor personnel activity data through a personnel detection sensor, performing reliability assessment and outlier correction on the light intensity data and the personnel activity data, smoothing the corrected data using a sliding average filter algorithm, performing time series alignment on the smoothed data to obtain a multidimensional feature sequence, constructing a light activity association model based on the multidimensional feature sequence, and performing feature fusion on the output of the light activity association model using a fuzzy logic algorithm to generate a light scene feature data set; Optionally, this embodiment first constructs a high-density sensor network layout solution in the smart office scenario. The light intensity sensor adopts a layered and partitioned layout strategy, with a measurement unit set up for every 20 square meters in the office area. Each unit contains three light sensor nodes at different heights (0.2m, 0.8m, and 1.6m) to achieve three-dimensional monitoring of light intensity on different work surfaces. The personnel detection sensor adopts a dual verification mechanism, integrating the thermal imaging sensor and the millimeter-wave radar sensor for deployment. Four detection units are set up in each office area to improve the recognition accuracy of personnel location and activity status through multi-sensor data fusion.

[0025] In the data collection process, the light intensity sensor continuously samples at a frequency of 1Hz, with a measurement range of 0-100000lux and a resolution of 1lux, ensuring that the slight changes in natural light can be accurately captured. The personnel detection sensor scans at a frequency of 2Hz to generate an activity data stream containing multi-dimensional features such as position coordinates, movement speed, and thermal maps. Considering the data transmission reliability requirements in the office environment, a redundant transmission solution combining wired RS485 bus and wireless Zigbee network is adopted to ensure the real-time and reliability of data collection.

[0026] Data reliability assessment uses a multi-level anomaly detection mechanism. First, based on historical data, a Gaussian probability distribution model is established for each sensor node at different time periods to calculate the probability density value of real-time data. For light intensity data, the system considers factors such as weather conditions and time periods to establish a dynamic threshold judgment mechanism. For example, under cloudy weather conditions, the system will adjust the tolerance range of light intensity fluctuations accordingly. For personnel activity data, an activity pattern library is established in combination with the functional division and time characteristics of the office area to identify abnormal personnel gathering or movement behaviors.

[0027] The abnormal data correction process uses a context-aware intelligent interpolation algorithm. The system first analyzes the data change characteristics within 10 time windows around the abnormal data points and extracts local trends and periodic characteristics. Then, based on the physical characteristics of light and the laws of personnel activities, the optimal interpolation strategy is selected. For example, for anomalies caused by sudden changes in natural light, exponential smoothing interpolation is used; for missing personnel activity data, it is supplemented based on trajectory prediction. The corrected data is evaluated for reliability through variance analysis and autocorrelation test to ensure that the data quality meets the modeling requirements.

[0028] Data smoothing innovatively introduces an adaptive sliding average filter algorithm. The algorithm dynamically adjusts the filter window size according to the rate of change of the data, and the window range fluctuates between 3-15 data points. When rapid changes in light intensity are detected, such as sudden changes in natural light caused by moving clouds, the system automatically narrows the filter window to 3-5 points to maintain a rapid response to light changes. During periods of relatively stable light, the filter window is expanded to 10-15 points to provide smoother data output.

[0029] The timing alignment process uses a multi-step precise alignment scheme. First, the data from different sensors are uniformly interpolated to a standard sampling period of 100ms based on the timestamp information. Then, the system delay between different sensors is identified and compensated through cross-correlation analysis of the sliding time window. Finally, the constrained least squares alignment optimization is used to ensure that the light intensity data and the personnel activity data correspond accurately in the time dimension.

[0030] In the light-activity association modeling link, this embodiment develops a probabilistic graph model based on kernel density estimation. First, the joint probability density of light intensity and personnel activity data is calculated using the Gaussian kernel function, and the kernel bandwidth is optimized and selected by the cross-validation method. Then, a three-layer Bayesian network structure is constructed, including an environment layer, an activity layer, and a scene layer, which is used to describe the conditional dependency relationship between variables. The model can accurately depict how to infer the current scene type based on changes in the lighting environment and the activity status of personnel.

[0031] The feature fusion process uses an improved fuzzy logic algorithm. An expert knowledge base containing 25 fuzzy rules is designed, covering the combination of scene features such as different lighting conditions, personnel density, and activity frequency. Fuzzy reasoning uses the Mamdani reasoning mechanism, and the membership function is optimized according to the actual scene characteristics. For example, in response to the lighting requirements of the working area, fuzzy language variables such as "comfort" and "concentration" are designed to achieve intelligent mapping from quantitative parameters to qualitative evaluation.

[0032] Through the implementation of this technical solution, this embodiment not only realizes the accurate perception of the lighting status of the office environment and the activities of personnel, but more importantly, establishes an intelligent association mechanism between scene characteristics and lighting needs. This solution has shown excellent environmental adaptability and scene recognition capabilities in practical applications, providing an accurate decision-making basis for subsequent intelligent lighting control. Especially in the dynamic light environment adjustment of office spaces, this solution can accurately identify the lighting demand characteristics of different areas and different time periods, providing strong support for achieving a balance between energy saving and comfort.

[0033] Step S102: inputting the illumination scene feature data set into a preset illumination intelligent adjustment model for training to obtain an illumination scene adaptation model, constructing an illumination effect evaluation index system including illumination uniformity, color rendering index, and glare value, and performing constrained optimization on the output result of the illumination scene adaptation model based on the illumination effect evaluation index system to obtain a target illumination intensity value and illumination equipment adjustment parameters that meet the illumination effect evaluation index system; Optionally, this embodiment constructs a deep learning architecture intelligent lighting adjustment model based on the lighting scene feature data set. The model adopts an encoder-decoder structure, in which the encoder includes a three-layer convolutional neural network to extract the spatiotemporal dependencies of lighting scene features; the decoder uses a bidirectional LSTM network to achieve serialized prediction of lighting adjustment strategies. The input features of the model include multi-dimensional features such as light intensity time series data, personnel activity density, and spatial location information, and the output includes target light intensity and dimming parameters of each lighting device.

[0034] During the model training process, this embodiment adopts a transfer learning strategy, first using large-scale office scene data for pre-training, and then fine-tuning for specific application scenarios. The training data is divided into training set, validation set, and test set in a ratio of 8:1:1. In order to improve the generalization ability of the model, data enhancement technology is introduced during the training process, including random cropping of time series features, noise injection, and other methods, so that the model can adapt to scene changes under different lighting environments.

[0035] For the evaluation of lighting effects, this embodiment establishes a multi-dimensional evaluation index system. The illumination uniformity index is measured by calculating the ratio of the standard deviation of illumination at multiple measuring points on the work surface to the average value. Taking into account the differences in lighting requirements in different functional areas, differentiated uniformity thresholds are set. The color rendering index evaluation adopts the CIERa index standard, combined with the spectral characteristics of LED light sources, to ensure that the color rendering index meets the color reproduction requirements of the office environment. The glare value evaluation is based on the UGR (unified glare value) calculation method, taking into account multiple factors such as the observer's position and background brightness, and quantitatively evaluates the impact of glare on visual comfort by establishing a visual comfort model.

[0036] In the model output optimization stage, this embodiment innovatively proposes an optimization algorithm based on multi-objective constraints. First, the optimization objective function is established, and the three indicators of illumination uniformity, color rendering index and glare value are weighted and combined. The weight coefficient is dynamically adjusted through expert experience and user feedback data to balance the lighting needs in different scenarios. The optimization process adopts an improved particle swarm algorithm, and the position of the particles represents the dimming parameters of each lighting device. The optimal solution that meets the constraints is found through iterative optimization.

[0037] In order to deal with the deviation between the model output and the actual lighting effect, this embodiment designs a closed-loop feedback mechanism. The actual lighting effect index is calculated through the real-time collected lighting data, and compared with the model prediction result to generate an error signal. The error signal is used to fine-tune the model parameters online through the back propagation algorithm to achieve adaptive optimization of the model.

[0038] In actual applications, when changes in the distribution of people in an office area are detected, the lighting scene adaptation model can respond quickly and generate updated lighting parameters based on the new scene features. For example, when a certain area enters a centralized office state, the model will automatically adjust the lighting parameters of the area to improve the uniformity of illumination, reduce the glare value, and create a comfortable office environment for employees. At the same time, in areas with sufficient natural light, the model will appropriately reduce the output of artificial lighting to achieve a balance between energy saving and comfort.

[0039] Through the implementation of this technical solution, this embodiment effectively solves the problems of delayed response to scene changes and unstable lighting effects of traditional lighting control systems. This solution can intelligently adjust lighting parameters according to real-time scene characteristics to ensure that the lighting effect always meets the needs of the office environment, while achieving energy saving and consumption reduction of the lighting system. Especially in open office areas with dense personnel, this solution shows excellent scene adaptability and lighting effect stability, significantly improving the comfort and work efficiency of the office environment.

[0040] Step S103: input the target light intensity value and the lighting device adjustment parameters into the lighting controller, and the lighting controller generates a lighting device control instruction based on a lighting compensation calculation formula, wherein the lighting compensation calculation formula is that the target light intensity value is equal to the sum of the current ambient light intensity and the lighting compensation value, and the lighting compensation value is determined by the functional relationship between the personnel detection probability value and the time period priority factor, and the lighting device control instruction is sent to the lighting device execution unit through the control bus for lighting adjustment.

[0041] Optionally, after obtaining the target light intensity value and the lighting equipment adjustment parameters, this embodiment designs a precise light compensation control mechanism. The lighting controller first dynamically calculates the required light compensation value based on the real-time collected ambient light data, combined with the personnel detection status and time characteristics. In order to adapt to the dynamic change characteristics of natural light in the office environment, this embodiment adopts a regional and multi-level compensation strategy to ensure that the lighting effect of each functional area is always maintained in the best state.

[0042] For the calculation of illumination compensation, this embodiment innovatively introduces the joint influence mechanism of the probability value of personnel detection and the priority factor of time period. The probability value of personnel detection is calculated by fusing the data of multiple sensor nodes, reflecting the credibility of the activity status of personnel in a specific area; the priority factor of time period is divided according to the time characteristics of the office space, for example, setting the highest priority for working days from 9:00 to 11:30 and setting the lower priority for the lunch break from 12:00 to 13:30. These two factors are combined through a nonlinear mapping function to generate the final illumination compensation value.

[0043] This embodiment designs an adaptive illumination compensation calculation formula, the core idea of ​​which is to achieve precise fill light control through dynamic weights. When human activity is detected in a certain area, the compensation value calculation will give priority to the influence of the probability value of human detection to ensure that the lighting effect meets actual needs. For example, in the process of gradually increasing natural light in the morning, if stable human activity is detected in a certain area, the lighting controller will calculate the required compensation light based on the difference between the target light intensity value and the current ambient light intensity, and accurately implement it through the dimming parameters of the LED lamps.

[0044] In the control instruction generation link, this embodiment adopts a hierarchical dimming strategy. First, the calculated light compensation value is converted into the dimming parameters of each lighting device. Taking into account the differences in light efficiency characteristics of different types of LED lamps, a nonlinear dimming mapping relationship is established. In order to avoid frequent brightness fluctuations causing interference to office workers, a dimming smooth transition mechanism is set up, and a gradual dimming effect is achieved through an interpolation algorithm.

[0045] In the process of issuing commands, this embodiment implements a reliable communication mechanism based on the DALI bus protocol. Control commands are queued according to priority, and high-priority dimming commands (such as the need for fill light when a person suddenly enters a dark area) can interrupt the low-priority commands being executed. At the same time, a command execution confirmation mechanism is set up to verify the execution status of each control command through feedback signals to ensure the reliability of lighting adjustment.

[0046] In order to handle compensation control in complex lighting environments, this embodiment also designs a scene prediction compensation mechanism. By analyzing the lighting change rules and personnel activity patterns in historical data, a short-term lighting change prediction model is established. When an upcoming lighting change (such as clouds blocking the sun) is predicted, the compensation parameters are adjusted in advance to achieve a smoother lighting transition effect.

[0047] Through the implementation of this technical solution, this embodiment effectively solves the problems of delayed response and inaccurate fill light in traditional lighting control systems. In an office environment, this solution can accurately calculate the required fill light amount in real time according to the activity status and time characteristics of personnel, ensuring that the working area always maintains a suitable lighting environment. Especially in the case of drastic changes in natural light, this solution shows excellent compensation control effects, significantly improving the lighting comfort and energy saving effects of the office environment.

[0048] From the above description, it can be seen that the intelligent lighting automatic adjustment method based on light intensity and personnel detection provided in the embodiment of the present application can perform reliability assessment and anomaly correction on the data by synchronously collecting ambient light intensity and personnel activity data. An innovative lighting activity association model is constructed, and feature fusion is achieved using a fuzzy logic algorithm. The system establishes a complete evaluation index system including illumination uniformity, color rendering index and glare value, and performs constrained optimization based on the lighting scene adaptation model. By introducing a dynamic compensation mechanism of personnel detection probability value and time period priority factor, accurate lighting adjustment control is achieved. This method breaks through the limitations of traditional fixed-mode dimming and provides a comprehensive technical solution for intelligent lighting systems.

[0049] In one embodiment of the intelligent lighting automatic adjustment method based on light intensity and personnel detection of the present application, see Figure 2 , and can also include the following: Step S201: Calculate the probability density values ​​of the light intensity data and the personnel activity data based on the Gaussian distribution model, mark the data with a probability density value lower than a preset threshold as abnormal data, correct the abnormal data using a linear interpolation method, perform reliability assessment by calculating the mean, standard deviation and coefficient of variation of the corrected data, and resample the data with a reliability assessment result lower than a preset score; Step S202: Use a sliding average filtering algorithm to filter the corrected light intensity data and personnel activity data respectively, interpolate and resample the filtered data according to a unified sampling period based on timestamp information, and use the least squares method to time-align the resampled data sequence to obtain a multi-dimensional feature sequence.

[0050] Optionally, this embodiment designs a complete data preprocessing solution for the outliers and noise that may exist in the original light intensity data and personnel activity data. First, based on the statistical characteristics of the data, a Gaussian distribution model is used to detect anomalies in the data. By calculating the probability density value of each data point and comparing it with the empirical threshold, abnormal data points that deviate from the normal distribution are identified.

[0051] In terms of abnormal data processing, this embodiment uses a context-based linear interpolation method for correction. For data points marked as abnormal, the normal data points before and after are used as the benchmark, and the time continuity characteristics of the data are considered to calculate a reasonable replacement value through linear interpolation. For example, when the light intensity value at a certain time point is abnormally high, by analyzing the light change trend at the nearby time points and combining the actual light characteristics of the office space, a correction value that conforms to the scene rules is generated.

[0052] To ensure the reliability of data correction, this embodiment designs a multi-dimensional evaluation mechanism. By calculating the statistical characteristics of the corrected data, including the mean, standard deviation, and coefficient of variation, the central tendency and dispersion of the data are comprehensively evaluated. When the evaluation score is lower than the preset standard, the data resampling mechanism is started. During the resampling process, data collection is preferentially performed in time periods with higher confidence levels to ensure that the acquired data can accurately reflect the characteristics of the scene.

[0053] In the data smoothing process, this embodiment adopts an improved sliding average filtering algorithm. In order to adapt to the characteristics of different types of data, different filter window sizes are set for light intensity data and personnel activity data. Since light intensity data is affected by natural light changes, a larger filter window is selected to retain the slowly changing trend; a smaller filter window is used for personnel activity data to retain the mutation characteristics.

[0054] This embodiment innovatively designs a data resampling mechanism based on timestamps. Since the original sampling periods of light intensity and personnel activity data may be inconsistent, they need to be unified to the same time scale. By analyzing the time distribution characteristics of the data, selecting a suitable resampling period, and using a piecewise linear interpolation method to generate an equally spaced data sequence. This processing method not only maintains the temporal continuity of the data, but also ensures the correspondence between features of different dimensions.

[0055] In order to achieve accurate alignment of multi-dimensional feature sequences, this embodiment adopts a time series alignment algorithm based on the least squares method. First, the feature correspondence within the time window is established, and then the optimal alignment parameters are calculated by minimizing the sum of squares of time deviations. This method can effectively deal with the time delay and drift problems in the data acquisition process and ensure the accurate correspondence of different dimensional features on the time axis.

[0056] Through the implementation of this technical solution, this embodiment effectively solves the problems of outlier interference, data discontinuity, inconsistent sampling, etc. in the original data. In the application of intelligent lighting in office places, this solution can provide a high-quality data foundation and provide reliable support for subsequent scene feature analysis and lighting control strategy optimization. Especially in office areas with complex environmental conditions and frequent personnel activities, this solution shows excellent data processing capabilities and significantly improves the stability and reliability of the lighting control system.

[0057] In one embodiment of the intelligent lighting automatic adjustment method based on light intensity and personnel detection of the present application, see Figure 3 , and can also include the following: Step S301: using a kernel density estimation method to calculate the joint probability distribution between light intensity and personnel activity data in the multidimensional feature sequence, constructing a conditional probability relationship of the joint probability distribution based on a Bayesian network, determining the Bayesian network parameters by maximum likelihood estimation, and establishing a light-activity association model; Step S302: perform fuzzy membership calculation on the conditional probability value output by the lighting activity association model, set a fuzzy rule set for lighting intensity and personnel activities, use a fuzzy reasoning mechanism to match and calculate the fuzzy rule set, and defuzzify the reasoning results of the fuzzy reasoning mechanism by the centroid method to obtain a lighting scene feature data set.

[0058] Optionally, this embodiment first designs a joint distribution calculation method based on kernel density estimation for the analysis of light intensity and personnel activity data. Specifically, a Gaussian kernel function is selected as the kernel function, and the kernel bandwidth is optimized and determined by the cross-validation method. For the light intensity x and personnel activity data y at any time point t, calculate their joint probability density value. In practical applications, for example, at 9 am in office area A, the light intensity is observed to be 500 lux, the personnel density is 0.3 people / square meter, and the activity frequency is 20 times / hour. The joint probability of the occurrence of this set of data is obtained by kernel density estimation, thereby quantifying the typicality of the scene characteristics.

[0059] In order to accurately characterize the conditional probability relationship between data, this embodiment constructs a three-layer Bayesian network structure. The first layer is the environmental factor node, including time period, weather conditions, etc.; the second layer is the light intensity node, including natural light intensity, artificial lighting intensity, etc.; the third layer is the personnel activity node, including personnel density, activity frequency, activity duration, etc. The edges between the nodes reflect the conditional dependency between the variables. For example, the personnel activity frequency node has the light intensity node as its parent node, indicating the probability dependency of the activity frequency being affected by the lighting conditions.

[0060] This embodiment determines the parameters of the Bayesian network by the maximum likelihood estimation method. First, the conditional probability table of each node is calculated based on historical data, for example, the probability of observing "high frequency of human activities" in the "9:00-11:00 am" time period given the "sufficient natural light" condition. To improve the accuracy of parameter estimation, the expectation maximization algorithm is used to process the missing values ​​that may exist in the data, and the optimal parameter estimation value is obtained through multiple rounds of iterative optimization.

[0061] For the conditional probability value output by the Bayesian network, this embodiment designs an adaptive fuzzy membership function. The light intensity is divided into five fuzzy subsets according to the visual perception characteristics of the human body: {very dark, dark, moderate, bright, very bright}, and the corresponding membership function adopts a Gaussian function; the activity characteristics of personnel are divided into three fuzzy subsets: {low-frequency activity, medium-frequency activity, high-frequency activity}, and the membership function adopts a trapezoidal function. The function parameters are determined by analyzing the light-activity correspondence in the historical data.

[0062] This embodiment establishes a fuzzy rule set containing 25 rules, covering the light-activity association patterns in different scenes. For example, rule R1: "If the natural light intensity is dim and the frequency of human activities is high, the scene characteristics tend to need supplementary lighting"; rule R2: "If the natural light intensity is moderate and the frequency of human activities is low, the scene characteristics tend to maintain the status quo." The weight coefficient of the rule is determined by analyzing the triggering frequency and accuracy of the rule in the historical data.

[0063] In the fuzzy reasoning process, this embodiment adopts the Mamdani reasoning mechanism. First, fuzzification is performed to convert the input conditional probability value into a fuzzy set; then reasoning is performed based on fuzzy rules, and the activation strength of the rule is calculated using the minimum-maximum synthesis method; finally, the reasoning results of multiple rules are weighted and synthesized. For example, when the natural light intensity is detected to be 450 lux at a certain moment and the frequency of human activities is 15 times / hour, the corresponding scene feature tendency is obtained through fuzzy reasoning.

[0064] This embodiment innovatively improves the centroid defuzzification algorithm. The traditional centroid method may produce deviations in the case of multiple peaks. The improved algorithm introduces local weight factors to weight the centroid values ​​of different regions. Specifically, the output fuzzy set is first divided into several sub-intervals, the local centroid and weight coefficient of each sub-interval are calculated, and then the final defuzzification result is obtained by weighted average.

[0065] Through the implementation of this technical solution, this embodiment realizes accurate scene feature recognition in office spaces. Traditional methods often ignore the dynamic correlation between lighting conditions and human activities, resulting in scene recognition results that do not meet actual needs. This embodiment, through the organic combination of probability statistics and fuzzy logic, can not only accurately capture the statistical laws between data, but also simulate the decision-making thinking of human experts, thereby realizing intelligent recognition of complex scene features. Especially in open office areas with changeable lighting conditions and frequent human activities, this solution shows excellent adaptability and robustness.

[0066] Another significant advantage of this embodiment is that it has scene prediction capabilities. By analyzing the light-activity patterns in historical data and combining the probability distribution characteristics of the current time period, it is possible to predict the scene change trend in the future. This prediction capability provides a decision-making basis for active lighting control and effectively improves the response speed and service quality of the lighting system. For example, when a trend of increasing frequency of human activity is detected, the system can adjust the lighting parameters in advance to avoid the lag of traditional passive control.

[0067] In one embodiment of the intelligent lighting automatic adjustment method based on light intensity and personnel detection of the present application, see Figure 4 , and can also include the following: Step S401: using a deep neural network to construct the lighting intelligent adjustment model, dividing the lighting scene feature data set into a training set and a validation set in a ratio of 8:2, using a back propagation algorithm to iteratively train the lighting intelligent adjustment model, determining the optimal training round based on the model performance on the validation set, and obtaining a lighting scene adaptation model; Step S402: Calculate the illumination uniformity coefficient of the output result of the illumination scene adaptation model based on the CIE lighting standard, calculate the general color rendering index Ra value of the light source using the color rendering index measurement method, calculate the indoor lighting glare value using the UGR unified glare value evaluation system, and construct the illumination uniformity coefficient, the color rendering index Ra value and the glare value into a lighting effect evaluation index system.

[0068] Optionally, this embodiment designs a deep learning-based intelligent lighting adjustment solution, which realizes adaptive adjustment of lighting parameters for different scenes by constructing a multi-layer neural network. The neural network adopts a five-layer structure, including an input layer, three hidden layers and an output layer. The input layer receives feature parameters in the lighting scene feature data set, including light intensity distribution, personnel activity characteristics, time characteristics, etc.; the output layer generates corresponding lighting parameter adjustment strategies, including the switch status of lighting equipment, dimming level, etc.

[0069] The hidden layer uses an improved ReLU activation function, and introduces the LeakyReLU mechanism to avoid the "neuron death" problem that may occur in traditional ReLU. A residual connection module is also added to the network structure to effectively alleviate the gradient vanishing problem during deep network training. A Dropout layer is set after each hidden layer to suppress model overfitting and improve generalization ability.

[0070] In this embodiment, the collected illumination scene feature data set is divided into a training set and a validation set in a ratio of 8:2. In the data preprocessing stage, the feature data is standardized to eliminate the influence of different feature dimension differences. The Mini-batch gradient descent method is used for model training, the batch size is set to 32, the learning rate adopts an adaptive adjustment strategy, and the initial value is set to 0.001.

[0071] During the model training process, an improved back propagation algorithm is used. The loss function uses a combination of mean square error and cross entropy, which not only considers the accuracy of continuous value prediction, but also pays attention to the effect of classification decision. In order to improve the training efficiency, momentum terms and learning rate decay mechanisms are introduced. By evaluating the model performance on the validation set, the early stopping strategy is used to determine the optimal training rounds, effectively avoiding the overfitting problem.

[0072] Based on the trained illumination scene adaptation model, this embodiment establishes a complete lighting effect evaluation system. First, the illumination uniformity coefficient is calculated according to the CIE illumination standard. Multiple illumination detection points are set in the office area, and the illumination uniformity is evaluated by the ratio of the minimum illumination value to the average illumination value. For example, in an open office area, the illumination uniformity coefficient on the work surface needs to meet the requirement of not less than 0.7.

[0073] This embodiment innovatively applies the color rendering index measurement method to the lighting effect evaluation. The general color rendering index Ra value is calculated by measuring the color rendering performance of the light source for 8 standard color samples. In practical applications, differentiated color rendering index requirements are set for different office scenes. For example, the Ra value of the high-precision visual work area needs to be higher than 85, and the Ra value of the ordinary office area needs to be higher than 80.

[0074] In terms of glare control, this embodiment adopts the UGR unified glare value evaluation system. The glare value of indoor lighting is calculated by analyzing factors such as the brightness distribution of the light source, the background brightness, and the observer's position. During the calculation process, the changes in viewing angles at multiple observation positions in the office are taken into account, and the glare value under the most unfavorable conditions is selected as the evaluation index.

[0075] Through actual application verification, the intelligent lighting adjustment solution of this embodiment significantly improves the lighting quality of the office environment. The deep learning model can accurately identify the lighting demand characteristics of different scenes and adaptively adjust the lighting parameters to keep the lighting effect at the best state. The complete lighting effect evaluation system provides a reliable feedback mechanism for lighting control to ensure that the adjustment results meet the standard requirements.

[0076] This embodiment is particularly suitable for the lighting control needs of modern intelligent office buildings. Through the dynamic learning ability of the lighting scene adaptation model, the lighting strategy can be continuously optimized to adapt to various changes in the office environment. The establishment of the evaluation index system provides a scientific basis for the quantitative evaluation of lighting effects and effectively improves the accuracy and reliability of lighting control.

[0077] In one embodiment of the intelligent lighting automatic adjustment method based on light intensity and personnel detection of the present application, see Figure 5 , and can also include the following: Step S501: setting constraint conditions for the output result of the illumination scene adaptation model, including that the illumination uniformity coefficient is not less than 0.7, the color rendering index Ra value is not less than 80, and the glare value is not more than 19, constructing a multi-objective optimization function based on the constraint conditions, and using the Lagrange multiplier method to solve the multi-objective optimization function to obtain a target illumination intensity value that satisfies the constraint conditions; Step S502: construct a lighting equipment parameter optimization model based on the target light intensity value, take the power, color temperature and illumination angle of the lighting equipment as optimization variables, and use the particle swarm algorithm to solve the lighting equipment parameter optimization model to obtain the lighting equipment adjustment parameters that meet the lighting effect evaluation index system.

[0078] Optionally, this embodiment designs an intelligent lighting control solution based on multi-objective optimization, which realizes precise adjustment of lighting parameters through strict constraints and optimization algorithms. First, constraints that meet international lighting standards are set for the output results of the lighting scene adaptation model: the illumination uniformity coefficient must be no less than 0.7 to ensure the uniformity of lighting distribution in the office area; the color rendering index Ra value must be no less than 80 to ensure the true restoration of the color of objects in the office environment; the glare value must not exceed 19 to avoid visual discomfort caused by improper lighting.

[0079] Based on the constraints, this embodiment constructs a multi-objective optimization function. This function comprehensively considers the two main goals of lighting effect and energy efficiency: on the one hand, it pursues the best visual comfort, and on the other hand, it reduces energy consumption. The weight coefficient is introduced into the optimization function to dynamically adjust the priority of each goal according to the requirements of different office scenes. For example, in areas that require high-precision visual work, the weight of lighting effect is increased; in ordinary office areas, the weight of energy-saving goals is appropriately increased.

[0080] This embodiment uses an improved Lagrange multiplier method to solve multi-objective optimization problems. The traditional Lagrange method may fall into a local optimum when dealing with non-convex optimization problems. The improved algorithm improves the stability and global convergence of the solution by introducing penalty terms and adaptive step size adjustment mechanisms. Specifically, the original constrained optimization problem is transformed into an unconstrained problem, and the optimal solution is gradually approached by iteratively updating the Lagrange multiplier and penalty factor.

[0081] In the solution process, this embodiment takes into account the dynamic characteristics of the lighting requirements of the office environment. By monitoring the changing trend of the natural light intensity in real time, the parameter weights in the optimization objective function are dynamically adjusted. For example, when there is sufficient natural light, the target intensity value of artificial lighting is reduced; when there is insufficient natural light, the compensation intensity of artificial lighting is increased accordingly. This dynamic optimization mechanism ensures that the lighting effect is always kept in the best state.

[0082] Based on the target light intensity value obtained through optimization, this embodiment further constructs a lighting equipment parameter optimization model. The model uses the power, color temperature, irradiation angle, etc. of the lighting equipment as optimization variables, and establishes a mapping relationship between these parameters and the lighting effect. In particular, considering the nonlinear dimming characteristics of LED lighting equipment, a correction function is introduced into the model to ensure the accuracy of parameter adjustment.

[0083] In the process of optimizing lighting equipment parameters, this embodiment uses an improved particle swarm algorithm for solving. The traditional particle swarm algorithm is prone to premature convergence problems. The improved algorithm improves the search efficiency and solution quality through dynamic inertia weights and adaptive learning factors. The particle positions in the algorithm represent the parameter combinations of the lighting equipment. By evaluating the lighting effects produced by these parameter combinations, the positions and speeds of the particles are continuously updated, and the optimal parameter configuration is finally found.

[0084] To improve the optimization effect, this embodiment adds a local search strategy to the particle swarm algorithm. When the particle swarm enters the predefined potential optimal area, the local search mechanism is triggered to further optimize the parameter value through refined search. This hybrid optimization strategy not only ensures the global search capability, but also improves the local optimization accuracy.

[0085] In practical applications, the optimization scheme of this embodiment shows excellent adaptability. For example, in an open office area, when it is detected that the illumination uniformity of a local area is insufficient, the optimization algorithm can quickly adjust the parameters of the relevant lighting equipment and achieve balanced illumination distribution by changing the power distribution and illumination angle. At the same time, through the intelligent adjustment of color temperature, a high color rendering index is maintained to ensure the visual comfort of the office environment.

[0086] The innovation of this embodiment is also reflected in the collaborative optimization of lighting parameters. By establishing a coupling relationship model between lighting equipment parameters, the mutual influence of parameter adjustment is considered in the optimization process. For example, the adjustment of power will affect the actual performance of color temperature, and the change of illumination angle will affect the uniformity of illumination. Through collaborative optimization, it is ensured that the adjustment results of various parameters can produce the best comprehensive effect.

[0087] Through the implementation of this technical solution, this embodiment realizes intelligent and precise control of office environment lighting. The method of multi-objective optimization and parameter coordinated adjustment not only ensures that the lighting effect meets the standard requirements, but also improves energy efficiency. Especially in a complex open office environment, this solution can quickly respond to scene changes and maintain the stability and comfort of the lighting effect.

[0088] In one embodiment of the intelligent lighting automatic adjustment method based on light intensity and personnel detection of the present application, see Figure 6 , and can also include the following: Step S601: input the target light intensity value and the lighting device adjustment parameter into the lighting controller, divide a day into five time periods of early morning, morning, afternoon, evening and night based on the time period division rule, set a priority factor for each time period, perform weighted calculation on the personnel detection probability value and the time period priority factor to obtain a light compensation value, and generate a lighting device control instruction according to a calculation formula in which the target light intensity value is equal to the sum of the current ambient light intensity and the light compensation value; Step S602: Use a communication protocol conversion module to convert the lighting device control instruction into a DALI communication protocol format, and send the converted control instruction to the lighting device execution unit through the data communication interface of the lighting controller. The lighting device execution unit receives and parses the control instruction and then executes the corresponding lighting adjustment operation.

[0089] Optionally, this embodiment designs an intelligent lighting control execution scheme, which realizes accurate adjustment of lighting parameters and reliable execution of control instructions through time period division and personnel activity feature analysis. First, the optimized target light intensity value and lighting equipment adjustment parameters are input into the lighting controller to establish a lighting compensation mechanism based on time characteristics.

[0090] This embodiment innovatively proposes a time period division method based on physiological rhythms, dividing a day into five key time periods: early morning (5:00-8:00), morning (8:00-12:00), afternoon (12:00-17:00), evening (17:00-20:00) and night (20:00-5:00). This division fully considers the physiological rhythm of the human body and the law of natural light changes, and provides differentiated support for lighting needs in different time periods.

[0091] In terms of time period priority setting, this embodiment allocates different priority factors to each time period according to the characteristics of human visual needs and work efficiency rules. For example, in the morning period, people are more focused on their work, so a larger priority factor is set to ensure sufficient lighting support; in the evening period, considering the gradual weakening of natural light, the priority factor is appropriately increased to compensate for insufficient lighting; in the night period, the priority factor is reduced to avoid excessive lighting interfering with the human physiological rhythm.

[0092] This embodiment innovatively weights and fuses the probability of personnel detection with the time period priority factor when calculating illumination compensation. The probability of personnel detection reflects the real-time status of space use. The deep learning algorithm extracts the activity features of personnel from the video surveillance data and calculates the probability of personnel presence in the current area. For example, in a conference room scenario, when multiple people are detected to be continuously present, the probability of personnel detection is increased, and the intensity of illumination compensation is increased accordingly.

[0093] The calculation of the illumination compensation value adopts an adaptive weight method, and the final compensation value is obtained according to the product of the probability value of personnel detection and the time period priority factor, combined with the preset reference compensation coefficient. This calculation method ensures the flexibility and rationality of lighting adjustment. In practical applications, this embodiment achieves a dynamic balance of lighting intensity through a calculation formula in which the target illumination intensity value is equal to the sum of the current ambient illumination intensity and the illumination compensation value.

[0094] In the control instruction generation stage, this embodiment establishes a complete instruction mapping system, which converts the optimized lighting equipment parameters (power, color temperature, irradiation angle, etc.) into a standardized control instruction format, and adds a timestamp and priority mark to ensure the timing and reliability of instruction execution.

[0095] In order to achieve precise control of lighting equipment, this embodiment uses a communication protocol conversion module to convert control instructions into the DALI (Digital Addressable Lighting Interface) communication protocol format. As an internationally used intelligent lighting control protocol, the DALI protocol supports two-way communication and multi-level dimming, and can meet the lighting control needs in complex scenarios.

[0096] During the protocol conversion process, this embodiment takes into account the integrity and reliability of the instructions. By adding a check bit and a response mechanism, the accuracy of the instruction transmission is ensured. At the same time, according to the characteristics of the DALI protocol, the control parameters are mapped to the corresponding function codes and address codes to achieve accurate device addressing and parameter setting.

[0097] In the data communication interface design of the lighting controller, this embodiment adopts multiplexing technology to support parallel control of multiple lighting circuits. By establishing a priority queue, it is ensured that high-priority instructions can be executed in time while maintaining the responsiveness to low-priority instructions. For example, the lighting adjustment instruction in an emergency will receive the highest priority to ensure a quick response.

[0098] The lighting equipment execution unit adopts a modular design, including a command parsing module, a parameter verification module and an execution control module. The command parsing module is responsible for identifying the control instructions in the DALI protocol format and extracting relevant parameters; the parameter verification module ensures that the parsed parameters are within the range supported by the device; and the execution control module is responsible for converting the verified parameters into specific hardware control signals.

[0099] Through the implementation of this technical solution, this embodiment realizes intelligent and precise control of office environment lighting. The dynamic compensation mechanism based on time characteristics and personnel activity characteristics ensures that the lighting effect always meets the use requirements. The standardized communication protocol and reliable control mechanism ensure the accurate execution of control instructions. Especially in complex office environments, this solution can quickly respond to scene changes and maintain the stability and comfort of lighting effects.

[0100] In one embodiment of the intelligent lighting automatic adjustment method based on light intensity and personnel detection of the present application, see Figure 7 , and can also include the following: Step S701: encapsulating the lighting device control instruction according to the DALI protocol specification, performing CRC check and address encoding on the encapsulated control instruction, sending the control instruction to the lighting device execution unit through the RS485 bus interface, and the lighting device execution unit performing data frame check and address matching on the received control instruction; Step S702: Based on the driver chip of the lighting device execution unit, the control instruction is parsed into a PWM dimming signal and device operating parameters, the output current of the LED driving circuit is controlled by the PWM dimming signal, and the color temperature and illumination angle of the lighting device are adjusted according to the device operating parameters to realize intelligent adjustment of the lighting device.

[0101] Optionally, this embodiment proposes a lighting control instruction execution scheme based on the DALI protocol, which ensures the reliable transmission and accurate execution of control instructions through a strict data encapsulation and verification mechanism. In the data encapsulation link, the control instruction is encapsulated into a standard data frame format according to the DALI protocol specification, including a start bit, an address bit, a command bit, a data bit, and an end bit, thereby realizing the structured organization of the control information.

[0102] This embodiment pays special attention to the integrity protection of control instructions during the data encapsulation process. The encapsulated data frame is checked and calculated through the CRC cyclic redundancy check algorithm, and a check code is generated and attached to the end of the data frame. This check mechanism can effectively detect data errors during transmission and ensure the accuracy of control instructions. For example, when the lighting equipment in a conference room needs to be adjusted, the control instruction can only be sent after the CRC check to avoid abnormal lighting effects caused by erroneous instructions.

[0103] In terms of address coding, this embodiment adopts a hierarchical coding strategy to encode the physical location information and functional attributes of the lighting equipment into the address field. Specifically, the address coding includes three levels: area identification, device type, and device serial number, so that the control system can accurately locate each lighting device. For example, in a large open office area, this coding method can achieve independent control of different areas and different types of lighting equipment.

[0104] This embodiment selects RS485 bus as the data transmission medium, considering its reliability and anti-interference ability in the field of industrial control. By adopting the differential signal transmission method, the influence of electromagnetic interference on the control signal is effectively reduced. In the bus topology design, the daisy chain connection method is adopted, which not only ensures the reliable transmission of the signal, but also facilitates the expansion and maintenance of the system.

[0105] After receiving the control command, the lighting device execution unit first performs data frame verification to verify the integrity of the frame format and the correctness of the checksum. Then it performs address matching. Only when the address in the command matches the device's own address will the subsequent control operation be executed. This double verification mechanism effectively prevents the execution of erroneous commands.

[0106] This embodiment uses intelligent parsing technology in the design of the driver chip, which can accurately convert the control instructions in the DALI protocol format into PWM dimming signals and device operating parameters. The driver chip has a built-in microprocessor, which uses a table lookup algorithm to map instructions to control signals, ensuring control accuracy. For example, when the lighting brightness needs to be adjusted, the brightness value in the control instruction will be converted into the corresponding PWM duty cycle.

[0107] In terms of LED drive circuit control, this embodiment adopts a high-precision constant current source design to accurately control the output current through a PWM dimming signal. The drive circuit adopts a Buck-Boost topology structure, which can stably adjust the output current over a wide range and achieve linear adjustment of the lighting brightness. At the same time, a temperature compensation circuit is used to ensure stable output characteristics under different temperature conditions.

[0108] This embodiment adopts a dual color temperature LED combination design in color temperature adjustment, and realizes continuous color temperature adjustment by adjusting the relative brightness of cold color temperature and warm color temperature LED. The control algorithm takes into account the human eye's perception characteristics of color temperature changes and adopts a nonlinear mapping relationship to make color temperature adjustment more in line with visual experience. For example, in the morning, a bright and refreshing lighting environment can be created by increasing the color temperature.

[0109] In terms of adjusting the illumination angle, this embodiment designs an electric focusing mechanism, which controls the position of the reflector or lens group through a stepper motor to achieve precise adjustment of the illumination angle. The control algorithm adopts a position feedback design to ensure the accuracy and repeatability of the angle adjustment. For example, in a projection demonstration scene, the range of the illumination area can be adjusted as needed.

[0110] Through the implementation of this technical solution, this embodiment achieves high reliability and high precision in lighting equipment control. The data transmission mechanism based on the DALI protocol ensures the reliable transmission of control instructions; the design of intelligent driver chips and precision driver circuits ensures the accurate execution of control instructions; the implementation of multi-parameter coordinated adjustment meets the lighting needs in different scenarios. Especially in complex office environments, this solution can achieve precise control and intelligent adjustment of lighting effects, improving the user experience and energy efficiency of the lighting system.

[0111] This solution has shown excellent adaptability in practical applications. For example, in conference room scenarios that require frequent adjustments, reliable transmission and rapid response of control commands ensure timely adjustment of lighting effects; in office areas that require stable lighting, precise parameter control ensures continuous comfort of the lighting environment. Through this intelligent control method, efficient operation and energy saving of the lighting system are achieved.

[0112] In order to achieve precise lighting adjustment control, break through the limitations of traditional fixed mode dimming, and provide a comprehensive technical solution for intelligent lighting systems, the present application provides an embodiment of an intelligent lighting automatic adjustment device based on light intensity and personnel detection for implementing all or part of the contents of the intelligent lighting automatic adjustment method based on light intensity and personnel detection, see Figure 8 The intelligent lighting automatic adjustment device based on light intensity and personnel detection specifically includes the following contents: A feature fusion module 10 is used to collect indoor environment light intensity data through a light intensity sensor, collect indoor personnel activity data through a personnel detection sensor, perform reliability assessment and outlier correction on the light intensity data and the personnel activity data, use a sliding average filter algorithm to smooth the corrected data, perform time series alignment on the smoothed data to obtain a multi-dimensional feature sequence, build a light activity association model based on the multi-dimensional feature sequence, use a fuzzy logic algorithm to perform feature fusion on the output of the light activity association model, and generate a light scene feature data set; A model evaluation module 20 is used to input the illumination scene feature data set into a preset illumination intelligent adjustment model for training to obtain an illumination scene adaptation model, construct an illumination effect evaluation index system including illumination uniformity, color rendering index, and glare value, and perform constraint optimization on the output result of the illumination scene adaptation model based on the illumination effect evaluation index system to obtain a target illumination intensity value and illumination equipment adjustment parameters that meet the illumination effect evaluation index system; The automatic adjustment module 30 is used to input the target light intensity value and the lighting device adjustment parameters into the lighting controller, and the lighting controller generates a lighting device control instruction based on the lighting compensation calculation formula, wherein the lighting compensation calculation formula is that the target light intensity value is equal to the sum of the current ambient light intensity and the lighting compensation value, and the lighting compensation value is determined by the functional relationship between the personnel detection probability value and the time period priority factor. The lighting device control instruction is sent to the lighting device execution unit through the control bus for lighting adjustment.

[0113] From the above description, it can be seen that the intelligent lighting automatic adjustment device based on light intensity and personnel detection provided in the embodiment of the present application can perform reliability assessment and anomaly correction on the data by synchronously collecting ambient light intensity and personnel activity data. An innovative lighting activity association model is constructed, and feature fusion is achieved using a fuzzy logic algorithm. The system has established a complete evaluation index system including illumination uniformity, color rendering index and glare value, and performs constraint optimization based on the lighting scene adaptation model. By introducing a dynamic compensation mechanism of personnel detection probability value and time period priority factor, accurate lighting adjustment control is achieved. This method breaks through the limitations of traditional fixed-mode dimming and provides a comprehensive technical solution for intelligent lighting systems.

[0114] From the hardware level, in order to achieve precise lighting adjustment control, break through the limitations of traditional fixed mode dimming, and provide a comprehensive technical solution for the intelligent lighting system, the present application provides an embodiment of an electronic device for implementing all or part of the contents of the intelligent lighting automatic adjustment method based on light intensity and personnel detection, and the electronic device specifically includes the following contents: Processor, memory, communication interface and bus; wherein the processor, memory and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between the intelligent lighting automatic adjustment device based on light intensity and personnel detection and the core business system, user terminal and related database and other related equipment; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., but the present embodiment is not limited thereto. In the present embodiment, the logic controller can be implemented with reference to the embodiment of the intelligent lighting automatic adjustment method based on light intensity and personnel detection and the embodiment of the intelligent lighting automatic adjustment device based on light intensity and personnel detection in the embodiment, and the contents thereof are incorporated herein, and the repeated parts are not repeated.

[0115] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0116] In practical applications, part of the intelligent lighting automatic adjustment method based on light intensity and personnel detection can be executed on the electronic device side as described above, or all operations can be completed in the client device. The specific selection can be based on the processing capability of the client device and the limitations of the user's usage scenario. This application does not limit this. If all operations are completed in the client device, the client device may also include a processor.

[0117] The client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0118] Fig. 9 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Fig. 9 As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Fig. 9is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0119] In one embodiment, the function of the intelligent lighting automatic adjustment method based on light intensity and personnel detection can be integrated into the central processor 9100. The central processor 9100 can be configured to perform the following control: Step S101: collecting indoor environment light intensity data through a light intensity sensor, collecting indoor personnel activity data through a personnel detection sensor, performing reliability assessment and outlier correction on the light intensity data and the personnel activity data, smoothing the corrected data using a sliding average filter algorithm, performing time series alignment on the smoothed data to obtain a multidimensional feature sequence, constructing a light activity association model based on the multidimensional feature sequence, and performing feature fusion on the output of the light activity association model using a fuzzy logic algorithm to generate a light scene feature data set; Step S102: inputting the illumination scene feature data set into a preset illumination intelligent adjustment model for training to obtain an illumination scene adaptation model, constructing an illumination effect evaluation index system including illumination uniformity, color rendering index, and glare value, and performing constrained optimization on the output result of the illumination scene adaptation model based on the illumination effect evaluation index system to obtain a target illumination intensity value and illumination equipment adjustment parameters that meet the illumination effect evaluation index system; Step S103: input the target light intensity value and the lighting device adjustment parameters into the lighting controller, and the lighting controller generates a lighting device control instruction based on a lighting compensation calculation formula, wherein the lighting compensation calculation formula is that the target light intensity value is equal to the sum of the current ambient light intensity and the lighting compensation value, and the lighting compensation value is determined by the functional relationship between the personnel detection probability value and the time period priority factor, and the lighting device control instruction is sent to the lighting device execution unit through the control bus for lighting adjustment.

[0120] From the above description, it can be seen that the electronic device provided in the embodiment of the present application performs reliability assessment and anomaly correction on the data by synchronously collecting ambient light intensity and personnel activity data. An innovative lighting activity association model is constructed, and feature fusion is achieved using a fuzzy logic algorithm. The system establishes a complete evaluation index system including illumination uniformity, color rendering index and glare value, and performs constraint optimization based on the lighting scene adaptation model. By introducing a dynamic compensation mechanism for personnel detection probability values ​​and time period priority factors, accurate lighting adjustment control is achieved. This method breaks through the limitations of traditional fixed-mode dimming and provides a comprehensive technical solution for intelligent lighting systems.

[0121] In another embodiment, the intelligent lighting automatic adjustment device based on light intensity and personnel detection can be configured separately from the central processing unit 9100. For example, the intelligent lighting automatic adjustment device based on light intensity and personnel detection can be configured as a chip connected to the central processing unit 9100, and the function of the intelligent lighting automatic adjustment method based on light intensity and personnel detection can be realized through the control of the central processing unit.

[0122] like Fig. 9 As shown, the electronic device 9600 may also include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Fig. 9 In addition, the electronic device 9600 may also include Fig. 9 For components not shown, reference may be made to the prior art.

[0123] like Fig. 9 As shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0124] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0125] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0126] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.

[0127] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0128] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0129] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0130] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the intelligent lighting automatic adjustment method based on light intensity and personnel detection in the above embodiments, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps of the intelligent lighting automatic adjustment method based on light intensity and personnel detection in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented: Step S101: collecting indoor environment light intensity data through a light intensity sensor, collecting indoor personnel activity data through a personnel detection sensor, performing reliability assessment and outlier correction on the light intensity data and the personnel activity data, smoothing the corrected data using a sliding average filter algorithm, performing time series alignment on the smoothed data to obtain a multidimensional feature sequence, constructing a light activity association model based on the multidimensional feature sequence, and performing feature fusion on the output of the light activity association model using a fuzzy logic algorithm to generate a light scene feature data set; Step S102: inputting the illumination scene feature data set into a preset illumination intelligent adjustment model for training to obtain an illumination scene adaptation model, constructing an illumination effect evaluation index system including illumination uniformity, color rendering index, and glare value, and performing constrained optimization on the output result of the illumination scene adaptation model based on the illumination effect evaluation index system to obtain a target illumination intensity value and illumination equipment adjustment parameters that meet the illumination effect evaluation index system; Step S103: input the target light intensity value and the lighting device adjustment parameters into the lighting controller, and the lighting controller generates a lighting device control instruction based on a lighting compensation calculation formula, wherein the lighting compensation calculation formula is that the target light intensity value is equal to the sum of the current ambient light intensity and the lighting compensation value, and the lighting compensation value is determined by the functional relationship between the personnel detection probability value and the time period priority factor, and the lighting device control instruction is sent to the lighting device execution unit through the control bus for lighting adjustment.

[0131] From the above description, it can be seen that the computer-readable storage medium provided in the embodiment of the present application performs reliability assessment and anomaly correction on the data by synchronously collecting ambient light intensity and personnel activity data. An innovative lighting activity association model is constructed, and feature fusion is achieved using a fuzzy logic algorithm. The system establishes a complete evaluation index system including illumination uniformity, color rendering index, and glare value, and performs constrained optimization based on a lighting scene adaptation model. By introducing a dynamic compensation mechanism for personnel detection probability values ​​and time period priority factors, precise lighting adjustment control is achieved. This method breaks through the limitations of traditional fixed-mode dimming and provides a comprehensive technical solution for intelligent lighting systems.

[0132] The embodiments of the present application also provide a computer program product capable of implementing all the steps of the intelligent lighting automatic adjustment method based on light intensity and personnel detection in the above embodiments, where the execution subject is a server or a client. When the computer program / instruction is executed by a processor, the steps of the intelligent lighting automatic adjustment method based on light intensity and personnel detection are implemented. For example, the computer program / instruction implements the following steps: Step S101: collecting indoor environment light intensity data through a light intensity sensor, collecting indoor personnel activity data through a personnel detection sensor, performing reliability assessment and outlier correction on the light intensity data and the personnel activity data, smoothing the corrected data using a sliding average filter algorithm, performing time series alignment on the smoothed data to obtain a multidimensional feature sequence, constructing a light activity association model based on the multidimensional feature sequence, and performing feature fusion on the output of the light activity association model using a fuzzy logic algorithm to generate a light scene feature data set; Step S102: inputting the illumination scene feature data set into a preset illumination intelligent adjustment model for training to obtain an illumination scene adaptation model, constructing an illumination effect evaluation index system including illumination uniformity, color rendering index, and glare value, and performing constrained optimization on the output result of the illumination scene adaptation model based on the illumination effect evaluation index system to obtain a target illumination intensity value and illumination equipment adjustment parameters that meet the illumination effect evaluation index system; Step S103: input the target light intensity value and the lighting device adjustment parameters into the lighting controller, and the lighting controller generates a lighting device control instruction based on a lighting compensation calculation formula, wherein the lighting compensation calculation formula is that the target light intensity value is equal to the sum of the current ambient light intensity and the lighting compensation value, and the lighting compensation value is determined by the functional relationship between the personnel detection probability value and the time period priority factor, and the lighting device control instruction is sent to the lighting device execution unit through the control bus for lighting adjustment.

[0133] From the above description, it can be seen that the computer program product provided in the embodiment of the present application performs reliability assessment and anomaly correction on the data by synchronously collecting ambient light intensity and personnel activity data. An innovative lighting activity association model is constructed, and feature fusion is achieved using a fuzzy logic algorithm. The system establishes a complete evaluation index system including illumination uniformity, color rendering index, and glare value, and performs constrained optimization based on a lighting scene adaptation model. By introducing a dynamic compensation mechanism for personnel detection probability values ​​and time period priority factors, accurate lighting adjustment control is achieved. This method breaks through the limitations of traditional fixed-mode dimming and provides a comprehensive technical solution for intelligent lighting systems.

[0134] It should be understood by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0135] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0136] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0138] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. An intelligent lighting automatic adjustment method based on light intensity and personnel detection, characterized in that: The method comprises: Collect indoor environment light intensity data through a light intensity sensor, collect indoor personnel activity data through a personnel detection sensor, perform reliability assessment and outlier correction on the light intensity data and the personnel activity data, use a sliding average filter algorithm to smooth the corrected data, perform time series alignment on the smoothed data to obtain a multi-dimensional feature sequence, build a light activity association model based on the multi-dimensional feature sequence, use a fuzzy logic algorithm to perform feature fusion on the output of the light activity association model, and generate a light scene feature data set; The lighting scene feature data set is input into a preset lighting intelligent adjustment model for training to obtain a lighting scene adaptation model, a lighting effect evaluation index system including illumination uniformity, color rendering index, and glare value is constructed, and based on the lighting effect evaluation index system, the output result of the lighting scene adaptation model is constrained and optimized to obtain a target light intensity value and lighting equipment adjustment parameters that meet the lighting effect evaluation index system; The target light intensity value and the lighting device adjustment parameters are input into a lighting controller, and the lighting controller generates a lighting device control instruction based on a light compensation calculation formula, wherein the light compensation calculation formula is that the target light intensity value is equal to the sum of the current ambient light intensity and the light compensation value, and the light compensation value is determined by a functional relationship between a personnel detection probability value and a time period priority factor, and the lighting device control instruction is sent to the lighting device execution unit through a control bus for lighting adjustment.

2. The intelligent lighting automatic adjustment method based on light intensity and personnel detection according to claim 1 is characterized in that: The reliability assessment and outlier correction of the light intensity data and the personnel activity data are performed, the corrected data are smoothed using a sliding average filtering algorithm, and the smoothed data are time-series aligned to obtain a multi-dimensional feature sequence, including: Calculate the probability density values ​​of the light intensity data and the personnel activity data based on the Gaussian distribution model, mark the data with a probability density value lower than a preset threshold as abnormal data, correct the abnormal data using a linear interpolation method, perform reliability assessment by calculating the mean, standard deviation and coefficient of variation of the corrected data, and resample the data with a reliability assessment result lower than a preset score; The corrected light intensity data and personnel activity data are filtered respectively using a sliding average filtering algorithm. The filtered data are interpolated and resampled according to a uniform sampling period based on timestamp information. The resampled data sequence is time-aligned using the least squares method to obtain a multidimensional feature sequence.

3. The intelligent lighting automatic adjustment method based on light intensity and personnel detection according to claim 1 is characterized in that: The step of constructing a lighting activity association model based on the multi-dimensional feature sequence, and using a fuzzy logic algorithm to perform feature fusion on the output of the lighting activity association model to generate a lighting scene feature data set includes: The joint probability distribution between the light intensity and the personnel activity data in the multidimensional feature sequence is calculated by using a kernel density estimation method, a conditional probability relationship of the joint probability distribution is constructed based on a Bayesian network, the parameters of the Bayesian network are determined by maximum likelihood estimation, and a light activity association model is established; The fuzzy membership is calculated for the conditional probability value output by the lighting activity association model, a fuzzy rule set for lighting intensity and personnel activity is set, a fuzzy reasoning mechanism is used to match and calculate the fuzzy rule set, and the reasoning result of the fuzzy reasoning mechanism is defuzzified by the centroid method to obtain a lighting scene feature data set.

4. The intelligent lighting automatic adjustment method based on light intensity and personnel detection according to claim 1 is characterized in that: The lighting scene feature data set is input into a preset lighting intelligent adjustment model for training to obtain a lighting scene adaptation model, and a lighting effect evaluation index system including illumination uniformity, color rendering index, and glare value is constructed, including: The intelligent lighting adjustment model is constructed using a deep neural network, the lighting scene feature data set is divided into a training set and a validation set in a ratio of 8:2, the intelligent lighting adjustment model is iteratively trained using a back propagation algorithm, the optimal training round is determined based on the model performance on the validation set, and a lighting scene adaptation model is obtained; The illumination uniformity coefficient of the output result of the illumination scene adaptation model is calculated based on the CIE lighting standard, the general color rendering index Ra value of the light source is calculated using the color rendering index measurement method, the indoor lighting glare value is calculated using the UGR unified glare value evaluation system, and the illumination uniformity coefficient, the color rendering index Ra value and the glare value are constructed into a lighting effect evaluation index system.

5. The intelligent lighting automatic adjustment method based on light intensity and personnel detection according to claim 1 is characterized in that: The constrained optimization of the output result of the lighting scene adaptation model based on the lighting effect evaluation index system to obtain the target light intensity value and lighting equipment adjustment parameters that meet the lighting effect evaluation index system includes: Setting constraints on the output results of the illumination scene adaptation model, including that the illumination uniformity coefficient is not less than 0.7, the color rendering index Ra value is not less than 80, and the glare value is not more than 19, constructing a multi-objective optimization function based on the constraints, and using the Lagrange multiplier method to solve the multi-objective optimization function to obtain a target illumination intensity value that satisfies the constraints; A lighting equipment parameter optimization model is constructed based on the target light intensity value, and the power, color temperature and illumination angle of the lighting equipment are used as optimization variables. The particle swarm algorithm is used to solve the lighting equipment parameter optimization model to obtain the lighting equipment adjustment parameters that meet the lighting effect evaluation index system.

6. The intelligent lighting automatic adjustment method based on light intensity and personnel detection according to claim 1 is characterized in that: The target light intensity value and the lighting device adjustment parameter are input into a lighting controller, and the lighting controller generates a lighting device control instruction based on a light compensation calculation formula, wherein the light compensation calculation formula is that the target light intensity value is equal to the sum of the current ambient light intensity and the light compensation value, and the light compensation value is determined by a functional relationship between a personnel detection probability value and a time period priority factor, including: The target light intensity value and the lighting device adjustment parameter are input into a lighting controller, a day is divided into five time periods: early morning, morning, afternoon, evening, and night based on a time period division rule, a priority factor is set for each time period, the personnel detection probability value and the time period priority factor are weightedly calculated to obtain a light compensation value, and a lighting device control instruction is generated according to a calculation formula in which the target light intensity value is equal to the sum of the current ambient light intensity and the light compensation value; A communication protocol conversion module is used to convert the lighting device control instructions into the DALI communication protocol format, and the converted control instructions are sent to the lighting device execution unit through the data communication interface of the lighting controller. The lighting device execution unit receives and parses the control instructions and then executes the corresponding lighting adjustment operation.

7. The intelligent lighting automatic adjustment method based on light intensity and personnel detection according to claim 1 is characterized in that: The step of sending the lighting device control instruction to the lighting device execution unit through the control bus to adjust the lighting includes: Data encapsulation of the lighting device control instruction according to the DALI protocol specification, CRC check and address encoding of the encapsulated control instruction, and sending the control instruction to the lighting device execution unit through the RS485 bus interface, and the lighting device execution unit performs data frame check and address matching on the received control instruction; The driver chip based on the lighting device execution unit parses the control instruction into a PWM dimming signal and device operating parameters, controls the output current of the LED drive circuit through the PWM dimming signal, adjusts the color temperature and illumination angle of the lighting device according to the device operating parameters, and realizes intelligent adjustment of the lighting device.

8. An intelligent lighting automatic adjustment device based on light intensity and personnel detection, characterized in that: The device comprises: A feature fusion module is used to collect indoor environment light intensity data through a light intensity sensor, collect indoor personnel activity data through a personnel detection sensor, perform reliability assessment and outlier correction on the light intensity data and the personnel activity data, use a sliding average filter algorithm to smooth the corrected data, perform time series alignment on the smoothed data to obtain a multi-dimensional feature sequence, build a light activity association model based on the multi-dimensional feature sequence, use a fuzzy logic algorithm to perform feature fusion on the output of the light activity association model, and generate a light scene feature data set; A model evaluation module is used to input the illumination scene feature data set into a preset illumination intelligent adjustment model for training to obtain an illumination scene adaptation model, construct an illumination effect evaluation index system including illumination uniformity, color rendering index, and glare value, and perform constrained optimization on the output result of the illumination scene adaptation model based on the illumination effect evaluation index system to obtain a target illumination intensity value and illumination equipment adjustment parameters that meet the illumination effect evaluation index system; An automatic adjustment module is used to input the target light intensity value and the lighting device adjustment parameters into a lighting controller, and the lighting controller generates a lighting device control instruction based on a lighting compensation calculation formula, wherein the lighting compensation calculation formula is that the target light intensity value is equal to the sum of the current ambient light intensity and the lighting compensation value, and the lighting compensation value is determined by the functional relationship between the personnel detection probability value and the time period priority factor, and the lighting device control instruction is sent to the lighting device execution unit through the control bus for lighting adjustment.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the intelligent lighting automatic adjustment method based on light intensity and personnel detection according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent lighting automatic adjustment method based on light intensity and personnel detection as described in any one of claims 1 to 7 are implemented.

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