An intelligent building floodlighting control method and system

Abstract

The present invention relates to an intelligent building floodlighting control method. This method conducts intelligent management of building lighting by constructing a three-dimensional model of the building and combining real-time environmental data. Specifically, a three-dimensional model is established based on the basic building data, and preliminary rendering is performed using the real-time environmental data to generate a natural vision three-dimensional model; by integrating the data of floodlighting devices, lighting rendering is carried out to create a three-dimensional model of the lighting effect; the model is scored according to the preset lighting requirements to generate satisfaction data. By recording and analyzing historical environment, equipment status, and satisfaction data, an adjustment data set is constructed, and a machine learning algorithm is used to generate an adjustment strategy to adapt to environmental changes; in addition, user feedback is collected, the lighting requirements are updated, and corrective adjustments are made according to demand differences to improve user satisfaction. This method realizes the automatic and intelligent control of the lighting system, improving the adaptability of the lighting effect and the user satisfaction.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent lighting, and in particular, to an intelligent building floodlighting control method and system. Background Art

[0002] With the economic development of cities and the increasing improvement of urban construction, the floodlighting of building complexes has gradually become an important part of urban lighting and one of the essential elements in the construction of urban buildings. In order to achieve a unified and coordinated aesthetic effect between building elements and floodlighting, the floodlighting control system plays an indispensable role in its implementation process.

[0003] The existing floodlighting for building complexes generally uses pre-set lighting strategies to control the startup of lighting devices at regular intervals for illuminating the outer walls or surrounding environments of the building complexes.

[0004] For the above-mentioned existing technologies, there are the following technical problems: The pre-set lighting strategies are generally set manually based on experience, and it is difficult to finely adjust the lighting parameters, that is, the pre-set lighting strategies do not match the actual usage environment, thereby reducing the overall lighting effect of the floodlighting. Therefore, improvement is needed. Summary of the Invention

[0005] In order to optimize the effect of building floodlighting, the present application provides an intelligent building floodlighting control method and system.

[0006] In the first aspect, the above-mentioned invention object of the present application is achieved through the following technical solutions:

[0007] An intelligent building floodlighting control method, the method includes the steps of:

[0008] Obtain building basic data and construct a building basic three-dimensional model based on the building basic data;

[0009] Obtain real-time environmental data and perform preliminary rendering on the building basic three-dimensional model according to the real-time environmental data to generate a building natural vision three-dimensional model, and the building natural vision three-dimensional model is used to display the natural lighting conditions corresponding to the building in the real-time environment;

[0010] Obtain the distribution data of all floodlighting devices and the real-time status data of each floodlighting device, and perform lighting rendering on the building natural vision three-dimensional model to generate a lighting effect three-dimensional model;

[0011] Score the lighting effect three-dimensional model based on pre-set lighting requirements to generate first lighting satisfaction data;

[0012] Record the historical environmental data, the status data of the lighting equipment, and the corresponding first lighting satisfaction data based on a preset common timeline, and associate the environmental data, the status data of the lighting equipment, and the corresponding first lighting satisfaction data to construct a preliminary adjustment data set;

[0013] A preset preliminary adjustment model analyzes the preliminary adjustment data set based on a self-learning machine algorithm to generate a first adjustment strategy according to the environmental data. The first adjustment strategy is used to enable the lighting equipment to adjust the status data according to environmental changes so that the floodlighting meets the preset lighting requirements. The first adjustment strategy includes first lighting angle adjustment data, first brightness adjustment data, first color temperature adjustment data, and corresponding energy consumption data;

[0014] Obtain the feedback data of the user and construct a user feedback data set based on the feedback data. Among them, the users include the users of the building, the display objects of the floodlighting, and the residents near the building;

[0015] A preset user feedback analysis model analyzes the user feedback data set based on a machine self-learning algorithm to generate demand adjustment data, and the demand adjustment data is used to update the preset lighting requirements;

[0016] A preset correction adjustment model analyzes the updated lighting requirements and the preset lighting requirements, and generates demand difference data. The correction adjustment model generates a second adjustment strategy based on the demand difference data, and the second adjustment strategy is used to perform correction adjustment control on the lighting equipment to improve the user satisfaction corresponding to the building floodlighting.

[0017] By adopting the above technical solutions, by collecting and analyzing user feedback in real time, the floodlighting settings can be adjusted in a timely manner, better meeting the user needs and improving the user satisfaction. Combining the user feedback data, the floodlighting system can be refined managed, the lighting effect optimized, the aesthetics and functionality of the building improved. Automatically adjusting according to user feedback and environmental changes enhances the adaptability and flexibility of the system. The constructed user feedback data set provides rich data support for building management, contributing to making more scientific and accurate decisions.

[0018] In a preferred example of the present application, it can be further configured as follows: in the step of obtaining the feedback data of the user and constructing a user feedback data set based on the feedback data, the following steps are included:

[0019] Obtain the limb behavior data and facial emotion data of the user when passing by the building as the feedback data of the user;

[0020] Obtain the survey data of the user as the feedback data of the user, where the survey data includes the user's satisfaction survey data on floodlighting in different time periods, the user's satisfaction survey data on floodlighting in different festivals, and the user's satisfaction survey data on floodlighting in different scenarios;

[0021] Construct a user feedback data set based on the limb behavior data, facial emotion data, and survey data.

[0022] By adopting the above technical solution, by collecting and analyzing user feedback in real time, the floodlighting settings can be adjusted in a timely manner, better meeting the user's needs and improving user satisfaction. Combining the user feedback data, the floodlighting system can be refined and managed, the lighting effect can be optimized, and the aesthetics and functionality of the building can be enhanced. Automatically adjusted according to user feedback and environmental changes, the adaptability and flexibility of the system are enhanced. The constructed user feedback data set provides rich data support for building management, helping to make more scientific and accurate decisions.

[0023] In a preferred example of the present application, it can be further configured as follows: in the step where the preset user feedback analysis model analyzes the user feedback data set based on the machine self-learning algorithm to generate demand adjustment data, and the demand adjustment data is used to update the preset lighting demand, the steps include:

[0024] Analyze the limb behavior data of the user based on the preset limb analysis model to generate the first feedback score data;

[0025] Analyze the facial emotion data of the user based on the preset emotion analysis model to generate the second feedback score data;

[0026] Analyze the first feedback score data and the second feedback score data based on the preset comprehensive scoring model to generate the first user feedback data.

[0027] By adopting the above technical solution, by analyzing the limb behavior and facial emotion of the user in real time, the system can more accurately understand the user's needs and preferences, so as to provide a lighting effect that better meets the user's expectations. The demand adjustment data helps the lighting system allocate resources more effectively.

[0028] In a preferred example of the present application, it can be further configured as follows: in the step where the preset user feedback analysis model analyzes the user feedback data set based on the machine self-learning algorithm to generate demand adjustment data, and the demand adjustment data is used to update the preset lighting demand, it further includes the following steps:

[0029] Classify and analyze the survey data of the user, and construct a corresponding survey data subset according to the classification result;

[0030] Analyze different sub - datasets of survey data based on a preset scoring criterion to generate second - user feedback data;

[0031] The user feedback analysis model analyzes the first - user feedback data and the second - user feedback data to generate demand adjustment data, and the demand adjustment data is used to adjust the floodlighting equipment to improve the user's feedback satisfaction.

[0032] By adopting the above - mentioned technical solution, it can be automatically adjusted according to the user's feedback and environmental changes, improving its adaptability in different scenarios and time periods. The user feedback dataset provides rich data support for building management, helping to make more scientific and accurate decisions, such as adjusting the lighting strategy to improve energy efficiency and user satisfaction.

[0033] In a preferred example of the present application, it can be further configured as follows: After the step that the preset user feedback analysis model analyzes the user feedback dataset based on a machine self - learning algorithm to generate demand adjustment data, and the demand adjustment data is used to update the preset lighting requirements, the following steps are included:

[0034] Obtain weather forecast information, and generate corresponding predicted environmental data for a future time period based on the weather forecast information;

[0035] Generate a corresponding preset lighting strategy for a future time period based on the predicted environmental data and the updated lighting requirements, and the preset lighting strategy is used to set the control parameters of the lighting equipment in advance, thereby improving the timeliness of control.

[0036] By adopting the above - mentioned technical solution, by predicting environmental changes and adjusting the lighting strategy in advance, energy waste can be reduced. Especially when the weather changes greatly, the lighting intensity can be adjusted in time to achieve energy conservation. The system can automatically adjust the lighting according to environmental changes, providing a more comfortable visual experience for users, especially in scenarios where the indoor and outdoor light changes greatly, reducing the frequent manual adjustment of lighting equipment caused by environmental changes, reducing maintenance costs and human resource consumption, using machine - learning algorithms to process complex environmental data, and generating highly adaptable lighting strategies, improving the intelligent level and adaptive ability of the system.

[0037] In a preferred example of the present application, it can be further configured as follows: The lighting requirements include a lighting brightness threshold, a wall color temperature threshold, and a pattern clarity.

[0038] In a preferred example of the present application, it can be further configured as follows: The self - learning machine algorithm includes a neural network algorithm and a random forest algorithm.

[0039] In a second aspect, the above - mentioned invention object of the present application is achieved through the following technical solutions:

[0040] An intelligent building floodlighting control device, the device comprising:

[0041] In a third aspect, the above object of the present application is achieved by the following technical solutions:

[0042] 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 computer program, the steps of the above-mentioned intelligent building floodlighting control method are implemented.

[0043] In a fourth aspect, the above object of the present application is achieved by the following technical solutions:

[0044] A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of the above-mentioned intelligent building floodlighting control method are implemented.

[0045] In summary, the present application includes at least one of the following beneficial technical effects:

[0046] 1. By collecting and analyzing user feedback in real time, the floodlighting settings can be adjusted in a timely manner to better meet user needs, improve user satisfaction. Combining user feedback data, the floodlighting system can be refined and managed, optimizing the lighting effect, enhancing the aesthetics and functionality of the building. Automatically adjusting according to user feedback and environmental changes enhances the adaptability and flexibility of the system. The constructed user feedback dataset provides rich data support for building management, helping to make more scientific and accurate decisions;

[0047] 2. By collecting and analyzing user feedback in real time, the floodlighting settings can be adjusted in a timely manner to better meet user needs, improve user satisfaction. Combining user feedback data, the floodlighting system can be refined and managed, optimizing the lighting effect, enhancing the aesthetics and functionality of the building. Automatically adjusting according to user feedback and environmental changes enhances the adaptability and flexibility of the system. The constructed user feedback dataset provides rich data support for building management, helping to make more scientific and accurate decisions;

[0048] 3. By analyzing the body behavior and facial emotions of users in real time, the system can more accurately understand the needs and preferences of users, thereby providing lighting effects that better meet user expectations. The demand adjustment data helps the lighting system allocate resources more effectively. Automatically adjusting according to user feedback and environmental changes improves its adaptability in different scenarios and time periods. The user feedback dataset provides rich data support for building management, helping to make more scientific and accurate decisions, such as adjusting lighting strategies to improve energy efficiency and user satisfaction;

[0049] 4. By predicting environmental changes and adjusting the lighting strategy in advance, energy waste can be reduced. Especially when the weather changes significantly, the lighting intensity can be adjusted in a timely manner to achieve energy conservation. The system can automatically adjust the lighting according to environmental changes, providing a more comfortable visual experience for users. Especially in scenarios where the indoor and outdoor light changes greatly, it reduces the frequent manual adjustment of lighting equipment caused by environmental changes, lowering the maintenance cost and human resource consumption. Machine learning algorithms are used to process complex environmental data and generate highly adaptable lighting strategies, improving the intelligence level and adaptive ability of the system. Brief Description of the Drawings

[0050] Figure 1 is a flowchart of the steps of an intelligent building floodlighting control method according to an embodiment of the present application;

[0051] Figure 2 is a schematic block diagram of an intelligent building floodlighting control device according to an embodiment of the present application;

[0052] Figure 3 is a schematic diagram of an electronic device according to an embodiment of the present application.

[0053] Reference Numerals in the Drawings:

[0054] 1. Building basic 3D model construction unit; 2. Natural vision 3D model generation unit; 3. Lighting effect 3D model generation unit; 4. First lighting satisfaction data generation unit; 5. Preliminary adjustment dataset construction unit; 6. First adjustment strategy generation unit; 7. User feedback dataset construction unit; 8. Demand adjustment data generation unit; 9. Second adjustment strategy generation unit. Detailed Description of the Embodiment

[0055] The present application will be further described in detail below with reference to the accompanying drawings.

[0056] In an embodiment, as Figure 1 shown, the present application discloses an intelligent building floodlighting control method, which specifically includes the following steps:

[0057] S10: Obtain building basic data and construct a building basic 3D model according to the building basic data;

[0058] Specifically, use a high-precision laser scanner to scan the building comprehensively or obtain the building's design and construction drawings to obtain basic data such as the size, shape, and structure of the building. Input these data into professional 3D modeling software to construct the building's basic 3D model, which includes an accurate representation of all visible parts of the building's exterior walls, windows, balconies, etc.

[0059] S20: Obtain real-time environmental data and perform preliminary rendering on the building's basic 3D model based on the real-time environmental data to generate a building's natural vision 3D model, which is used to display the corresponding natural lighting conditions of the building in the real-time environment;

[0060] Specifically, environmental data including light intensity, weather conditions, time, etc. are obtained in real time through weather stations and light sensors installed around the building. Using these data, the building's basic 3D model is preliminarily rendered through light simulation software to simulate the appearance of the building under natural lighting. In this application, HDRI (High Dynamic Range Imaging) technology is used to simulate complex lighting environments to ensure the authenticity of the rendering effect.

[0061] S30: Obtain the distribution data of all floodlighting devices and the real-time status data of each floodlighting device, and perform lighting rendering on the building's natural vision 3D model to generate a 3D model of the lighting effect;

[0062] Specifically, the distribution data and real-time status data of all floodlighting devices are collected through the building management system. Using professional lighting design software, lighting rendering is performed on the building's natural vision 3D model according to these data to simulate the appearance of the building after the floodlighting is turned on. It should be noted that parameters such as the beam angle, color temperature, and brightness of the lighting devices are considered to ensure that the rendering effect is consistent with the actual situation.

[0063] S40: Score the 3D model of the lighting effect based on preset lighting requirements to generate first lighting satisfaction data;

[0064] Among them, according to the preset lighting requirements, in the embodiments of this application, the lighting requirements include lighting brightness thresholds, wall color temperature thresholds, and pattern clarity. An automated scoring system is used to score the 3D model of the lighting effect to generate first lighting satisfaction data, thereby quantifying the lighting effect for subsequent data analysis. The scoring system should include multiple evaluation indicators that can comprehensively reflect the quality of the lighting effect.

[0065] S50: Record historical environmental data, the status data of lighting devices, and the corresponding first lighting satisfaction data based on a preset common timeline, and associate the environmental data, the status data of lighting devices, and the corresponding first lighting satisfaction data to construct a preliminary adjustment dataset;

[0066] Specifically, historical environmental data, lighting device status data, and first lighting satisfaction data are recorded through a preset common timeline. Using a database management system, these data are associated to construct a preliminary adjustment dataset and ensure the integrity and consistency of the data for subsequent analysis and adjustment.

[0067] S60: The pre-set preliminary adjustment model analyzes the preliminary adjustment data set based on the self-learning machine algorithm to generate a first adjustment strategy according to the environmental data;

[0068] wherein the first adjustment strategy is used to enable the lighting device to adjust the status data according to the environmental changes so that the floodlighting meets the pre-set lighting requirements, and the first adjustment strategy includes first lighting angle adjustment data, first brightness adjustment data, first color temperature adjustment data and corresponding energy consumption data;

[0069] In the embodiment of the present application, the neural network algorithm and the random forest algorithm are used to analyze the preliminary adjustment data set to generate a first adjustment strategy. After the adjustment of the first adjustment strategy, the control parameters of the floodlighting are changed according to the actual environmental changes, improving the adaptability of the floodlighting, thereby optimizing the effect of the building floodlighting.

[0070] S70: Obtain the feedback data of the user and construct a user feedback data set based on the feedback data, wherein the user includes the users of the building, the display objects of the floodlighting and the residents near the building;

[0071] S80: The pre-set user feedback analysis model analyzes the user feedback data set based on the machine self-learning algorithm to generate demand adjustment data, and the demand adjustment data is used to update the pre-set lighting requirements;

[0072] Specifically, the user feedback analysis model is used to analyze the user feedback data set based on the self-learning algorithm to generate demand adjustment data for updating the pre-set lighting requirements. The analysis model can identify and understand the subjective descriptions of users and convert them into specific lighting requirement adjustments.

[0073] S90: The pre-set correction adjustment model analyzes the updated lighting requirements and the pre-set lighting requirements and generates demand difference data, and the correction adjustment model generates a second adjustment strategy based on the demand difference data;

[0074] wherein the second adjustment strategy is used to perform correction adjustment control on the lighting device to improve the user satisfaction corresponding to the building floodlighting;

[0075] Furthermore, the correction adjustment model compares and analyzes the updated lighting requirements and the pre-set lighting requirements to generate demand difference data, and generates a second adjustment strategy based on this data for performing correction adjustment control on the lighting device. And the correction adjustment model should be able to respond to the demand changes in real time, quickly generate adjustment strategies, optimize and adjust the lighting device according to the second adjustment strategy, so as to meet the needs of users and optimize the effect of the building floodlighting.

[0076] In summary, through intelligent control, the lighting equipment is automatically adjusted according to environmental changes and user needs, enabling refined control of the lighting equipment. User feedback is collected in real time and adjusted to improve user satisfaction with building floodlighting, reduce manual intervention, achieve automated management of building floodlighting, and lower maintenance costs. It can adapt to different environmental conditions and user needs, with good flexibility and adaptability, thereby optimizing the effect of building floodlighting. The adjustment strategies are all generated based on data analysis, improving the scientificity and accuracy of decision-making.

[0077] In step S70: obtaining the feedback data of users and constructing a user feedback data set based on the feedback data, the following steps are included:

[0078] S71: Obtaining the body behavior data and facial emotion data of users when passing by the building as the feedback data of users;

[0079] Specifically, in the embodiment of the present application, high-definition cameras and sensors installed around the building are used to capture the body behaviors of users when passing by, such as gait, gestures (raising the mobile phone to take pictures of the building exterior wall), etc. At the same time, facial recognition technology and emotion recognition algorithms are used to analyze the facial expressions of users to obtain emotion data. These data can be transmitted to the central processing system in real time through wireless networks. In this step, it is ensured that the resolution of the camera and the sensitivity of the sensor are sufficient to capture subtle body movements and expression changes, and deep learning algorithms, such as convolutional neural networks (CNNs), are used to improve the accuracy of facial emotion recognition.

[0080] S72: Obtaining the survey data of users as the feedback data of users, where the survey data includes the survey data of users' satisfaction with floodlighting at different time periods, the survey data of users' satisfaction with floodlighting on different festivals, and the survey data of users' satisfaction with floodlighting in different scenarios;

[0081] Specifically, an online questionnaire survey is designed and distributed through various channels such as the building management APP, social media, and email. The content of the questionnaire includes the satisfaction survey of floodlighting at different time periods, festivals, and scenarios. Ensure that the questionnaire design is targeted and comprehensive, covering the needs of different user groups.

[0082] In the embodiment of the present application, logical jump and input verification technologies are adopted to ensure that users can complete the questionnaire smoothly and ensure the integrity and validity of the data. Statistical analysis software is used to sort out and analyze the collected survey data.

[0083] S73: Constructing a user feedback data set based on the body behavior data, facial emotion data, and survey data;

[0084] Specifically, integrate the collected limb behavior data, facial emotion data, and survey data to construct a dataset that comprehensively reflects user feedback. Use a database management system to store and index the data for subsequent analysis and application. Design a reasonable data model to ensure the consistency and scalability of the dataset. Adopt data cleaning and preprocessing techniques to remove invalid and redundant data and improve data quality.

[0085] In summary, by collecting and analyzing user feedback in real time, the floodlighting settings can be adjusted in a timely manner to better meet user needs and improve user satisfaction. Combining user feedback data, the floodlighting system can be refined and managed, the lighting effect optimized, and the aesthetics and functionality of the building enhanced. Automatically adjusting according to user feedback and environmental changes enhances the adaptability and flexibility of the system. The constructed user feedback dataset provides rich data support for building management, contributing to more scientific and accurate decision-making.

[0086] In step S80: The pre-set user feedback analysis model analyzes the user feedback dataset based on a machine learning algorithm to generate requirement adjustment data, and the requirement adjustment data is used to update the pre-set lighting requirements. The steps include the following:

[0087] S81: Analyze the limb behavior data of the user based on the pre-set limb analysis model to generate first feedback score data;

[0088] Specifically, use a deep learning algorithm, such as a convolutional neural network (CNN), to analyze the captured user limb behavior video. Train the model to recognize different limb movements and match these movements with pre-set limb behavior patterns to evaluate the user's emotional state and behavioral intention. For example, a rapid pace may indicate the user's sense of urgency, while a slow pace may indicate relaxation or fatigue.

[0089] In the embodiment of the present application, ensure that the algorithm can process high-frame-rate videos to accurately capture rapid limb movements. Use data augmentation techniques, such as random cropping and scaling of video frames, to improve the generalization ability of the model.

[0090] S82: Analyze the facial emotion data of the user based on the pre-set emotion analysis model to generate second feedback score data;

[0091] Specifically, analyze the user's facial expressions through face key point detection and expression recognition techniques. Use pre-trained deep learning models, such as ResNet or VGG, to recognize the seven basic emotions of facial expressions: happiness, sadness, anger, surprise, fear, disgust, and contempt. The model will assign a confidence score to each detected expression.

[0092] What is needed is to train the model to adapt to different lighting conditions and facial orientations to improve the accuracy of recognition. Using transfer learning techniques, the model is pre-trained on a large-scale face dataset and then fine-tuned for specific applications.

[0093] S83: Analyze the first feedback score data and the second feedback score data based on a pre-set comprehensive scoring model to generate first user feedback data;

[0094] Specifically, combining the scoring data of limb behavior and facial emotion, use weighted average or other statistical methods to calculate the comprehensive satisfaction score of the user. This comprehensive scoring model will consider the importance of different feedback data and assign different weights. Ensure that the scoring model can dynamically adjust the weights to meet the needs of different user groups and different scenarios. Use machine learning algorithms to analyze historical data to optimize the weight allocation, thereby generating the first user feedback data.

[0095] S84: Classify and analyze the survey data of users, and construct corresponding sub-datasets of survey data according to the classification results;

[0096] Specifically, classify the collected survey data according to dimensions such as time period, festival, and scenario. For example, separate the lighting satisfaction survey data at night from the data during the day, or classify the satisfaction data of specific festivals separately.

[0097] Use data mining techniques, such as clustering analysis, to identify patterns and trends in the survey data. This helps to construct more targeted sub-datasets for subsequent analysis.

[0098] S85: Analyze different sub-datasets of survey data based on a pre-set scoring criterion to generate second user feedback data;

[0099] Specifically, apply predefined scoring criteria to each sub-dataset, and these criteria may include satisfaction levels, emotion intensity levels, etc. Use statistical analysis methods, such as analysis of variance (ANOVA), to evaluate the differences in user feedback in different sub-datasets.

[0100] In the embodiments of the present application, ensure that the scoring criteria are comparable and can be effectively compared between different sub-datasets. Adopt appropriate statistical tests to determine the significant differences in user feedback.

[0101] S86: The user feedback analysis model analyzes the first user feedback data and the second user feedback data to generate requirement adjustment data, and the requirement adjustment data is used to adjust the floodlighting device to improve the feedback satisfaction of the user;

[0102] Specifically, the first user feedback data and the second user feedback data are input into the user feedback analysis model. This model comprehensively considers an individual's limb behavior, facial emotions, and survey feedback to generate demand adjustment data.

[0103] In the embodiments of the present application, advanced data analysis techniques such as decision trees or random forests are used to process complex data sets and identify key factors affecting user satisfaction. These factors will guide the adjustment strategy of the flood lighting device.

[0104] In summary, by analyzing the limb behavior and facial emotions of users in real time, the system can more accurately understand the needs and preferences of users, thereby providing lighting effects that better meet user expectations. The demand adjustment data helps the lighting system allocate resources more effectively. For example, increasing the lighting intensity or adjusting the color temperature in areas with low user satisfaction, automatically adjusting according to user feedback and environmental changes to improve its adaptability in different scenarios and time periods. The user feedback data set provides rich data support for building management, helping to make more scientific and accurate decisions, such as adjusting the lighting strategy to improve energy efficiency and user satisfaction.

[0105] After step S80: The pre-set user feedback analysis model analyzes the user feedback data set based on the machine self-learning algorithm to generate demand adjustment data, and the demand adjustment data is used to update the pre-set lighting demand, the following steps are included:

[0106] S801: Obtain weather forecast information and generate corresponding predicted environmental data for the future time period based on the weather forecast information;

[0107] Specifically, by accessing the weather API service, such as the free API provided by the China Meteorological Administration, real-time and predicted weather forecast information is obtained. The data obtained using the API includes temperature, humidity, wind speed, wind direction, precipitation, etc. Using these data as inputs, corresponding predicted environmental data for the future time period is generated through a data processing algorithm, ensuring the stability of the API service and the accuracy of the data, and using an appropriate data caching and updating mechanism to ensure the real-time and effectiveness of the environmental data.

[0108] S802: Generate a corresponding pre-set lighting strategy for the future time period based on the predicted environmental data and the updated lighting demand, and the pre-set lighting strategy is used to set the control parameters of the lighting device in advance, thereby improving the timeliness of control;

[0109] Specifically, by using machine learning algorithms and combining predicted environmental data with updated lighting requirements, lighting strategies suitable for different environmental conditions are formulated. For example, when overcast or rainy weather is predicted, the system can automatically adjust the lighting brightness to maintain the uniformity of indoor and outdoor light. In the algorithm design, the influence of different environmental parameters on lighting requirements is considered, such as adjusting the brightness and color temperature according to the cloud cover and precipitation probability in weather forecasts. At the same time, ensure that the algorithm can process real-time updated environmental data and quickly generate corresponding lighting strategies.

[0110] In summary, by predicting environmental changes and adjusting lighting strategies in advance, energy waste can be reduced. Especially when the weather changes greatly, the lighting intensity can be adjusted in a timely manner to achieve energy conservation. The system can automatically adjust lighting according to environmental changes, providing a more comfortable visual experience for users, especially in scenarios where the indoor and outdoor light changes greatly, reducing the frequent manual adjustment of lighting equipment caused by environmental changes, and reducing maintenance costs and human resource consumption. Using machine learning algorithms to process complex environmental data and generate highly adaptable lighting strategies, improving the intelligence level and adaptive ability of the system.

[0111] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0112] In one embodiment, an intelligent building floodlighting control device is provided. The intelligent building floodlighting control device corresponds one-to-one with the intelligent building floodlighting control method in the above embodiment. As Figure 2 shown, the intelligent building floodlighting control device includes a building basic three-dimensional model construction unit 1, which is used to obtain building basic data and construct a building basic three-dimensional model according to the building basic data;

[0113] A natural vision three-dimensional model generation unit 2, which is used to obtain real-time environmental data and perform preliminary rendering on the building basic three-dimensional model according to the real-time environmental data to generate a building natural vision three-dimensional model. The building natural vision three-dimensional model is used to display the natural lighting conditions corresponding to the building in the real-time environment;

[0114] A lighting effect three-dimensional model generation unit 3, which is used to obtain the distribution data of all floodlighting devices and the real-time status data of each floodlighting device, and perform lighting rendering on the building natural vision three-dimensional model to generate a lighting effect three-dimensional model;

[0115] A first lighting satisfaction data generation unit 4, which is used to score the lighting effect three-dimensional model based on preset lighting requirements to generate first lighting satisfaction data;

[0116] The preliminary adjustment dataset construction unit 5 is configured to record historical environmental data, status data of lighting devices, and corresponding first lighting satisfaction data based on a preset common timeline, and associate the environmental data, status data of lighting devices, and corresponding first lighting satisfaction data to construct a preliminary adjustment dataset;

[0117] The first adjustment strategy generation unit 6 is configured to preset a preliminary adjustment model to analyze the preliminary adjustment dataset based on a self-learning machine algorithm, and generate a first adjustment strategy according to the environmental data. The first adjustment strategy is used to enable the lighting device to adjust the status data according to environmental changes, so that the floodlighting meets the preset lighting requirements. The first adjustment strategy includes first lighting angle adjustment data, first brightness adjustment data, first color temperature adjustment data, and corresponding energy consumption data;

[0118] The user feedback dataset construction unit 7 is configured to obtain user feedback data and construct a user feedback dataset based on the feedback data. The users include users of the building, display objects of the floodlighting, and residents near the building;

[0119] The demand adjustment data generation unit 8 is configured to preset a user feedback analysis model to analyze the user feedback dataset based on a machine self-learning algorithm, and generate demand adjustment data. The demand adjustment data is used to update the preset lighting requirements;

[0120] The second adjustment strategy generation unit 9 is configured to preset a correction adjustment model to analyze the updated lighting requirements and the preset lighting requirements, and generate demand difference data. The correction adjustment model generates a second adjustment strategy based on the demand difference data. The second adjustment strategy is used to perform correction adjustment control on the lighting device to improve the user satisfaction corresponding to the floodlighting of the building.

[0121] For the specific limitations of the intelligent building floodlighting control device, reference can be made to the limitations of the intelligent building floodlighting control method in the above text, which will not be elaborated here. Each module in the above intelligent building floodlighting control device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the electronic device in hardware form or independent of it, or stored in the memory in the electronic device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0122] In one embodiment, an electronic device is provided. The electronic device can be a server, and its internal structure diagram can be as Figure 3As shown in the figure. The electronic device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store the database. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements an intelligent building floodlighting control method.

[0123] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0124] Obtain building basic data and construct a building basic three-dimensional model according to the building basic data;

[0125] Obtain real-time environmental data and perform preliminary rendering on the building basic three-dimensional model according to the real-time environmental data to generate a building natural vision three-dimensional model, where the building natural vision three-dimensional model is used to display the corresponding natural lighting conditions of the building in the real-time environment;

[0126] Obtain the distribution data of all floodlighting devices and the real-time status data of each floodlighting device, and perform lighting rendering on the building natural vision three-dimensional model to generate a lighting effect three-dimensional model;

[0127] Score the lighting effect three-dimensional model based on preset lighting requirements to generate first lighting satisfaction data;

[0128] Record historical environmental data, the status data of lighting devices, and the corresponding first lighting satisfaction data based on a preset common time axis, and associate the environmental data, the status data of lighting devices, and the corresponding first lighting satisfaction data to construct a preliminary adjustment data set;

[0129] A preset preliminary adjustment model analyzes the preliminary adjustment data set based on a self-learning machine algorithm to generate a first adjustment strategy according to the environmental data. The first adjustment strategy is used to make the lighting device adjust the status data according to environmental changes so that the floodlighting meets the preset lighting requirements, where the first adjustment strategy includes first lighting angle adjustment data, first brightness adjustment data, first color temperature adjustment data, and corresponding energy consumption data;

[0130] Obtain the feedback data of the user, and construct a user feedback data set based on the feedback data, where the user includes the users of the building, the display objects of the floodlighting, and the residents near the building;

[0131] The pre-set user feedback analysis model analyzes the user feedback data set based on the machine self-learning algorithm to generate demand adjustment data, and the demand adjustment data is used to update the pre-set lighting demand;

[0132] The pre-set correction adjustment model analyzes the updated lighting demand and the pre-set lighting demand, and generates demand difference data. The correction adjustment model generates a second adjustment strategy based on the demand difference data, and the second adjustment strategy is used to perform correction adjustment control on the lighting equipment to improve the user satisfaction corresponding to the building floodlighting.

[0133] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0134] Obtain the building basic data, and construct a building basic three-dimensional model according to the building basic data;

[0135] Obtain the real-time environment data and perform preliminary rendering on the building basic three-dimensional model according to the real-time environment data to generate a building natural vision three-dimensional model, and the building natural vision three-dimensional model is used to display the natural lighting situation corresponding to the building in the real-time environment;

[0136] Obtain the distribution data of all floodlighting devices and the real-time status data of each floodlighting device, and perform lighting rendering on the building natural vision three-dimensional model to generate a lighting effect three-dimensional model;

[0137] Score the lighting effect three-dimensional model based on the pre-set lighting demand to generate the first lighting satisfaction data;

[0138] Record the historical environment data, the status data of the lighting devices, and the corresponding first lighting satisfaction data based on the pre-set common time axis, and associate the environment data, the status data of the lighting devices, and the corresponding first lighting satisfaction data to construct a preliminary adjustment data set;

[0139] The pre-set preliminary adjustment model analyzes the preliminary adjustment data set based on the self-learning machine algorithm to generate a first adjustment strategy according to the environment data. The first adjustment strategy is used to make the lighting device adjust the status data according to the environmental change so that the floodlighting meets the pre-set lighting demand, where the first adjustment strategy includes the first lighting angle adjustment data, the first brightness adjustment data, the first color temperature adjustment data, and the corresponding energy consumption data;

[0140] Obtain the feedback data of users, and construct a user feedback data set based on the feedback data, where the users include the users of the building, the display objects of the floodlighting, and the residents near the building;

[0141] The pre-set user feedback analysis model analyzes the user feedback data set based on the machine self-learning algorithm to generate demand adjustment data, and the demand adjustment data is used to update the pre-set lighting demand;

[0142] The pre-set correction adjustment model analyzes the updated lighting demand and the pre-set lighting demand, and generates demand difference data. The correction adjustment model generates a second adjustment strategy based on the demand difference data, and the second adjustment strategy is used to perform correction adjustment control on the lighting equipment to improve the user satisfaction corresponding to the building floodlighting.

[0143] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to the memory, storage, database or other media used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. The non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. The volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0144] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0145] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. An intelligent building floodlighting control method, characterized in that: The method comprises the steps of: acquiring building basic data, and constructing a building basic three-dimensional model according to the building basic data; Acquire real-time environmental data and preliminarily render the basic three-dimensional model of the building according to the real-time environmental data to generate a natural visual three-dimensional model of the building, wherein the natural visual three-dimensional model of the building is used to display the natural lighting conditions corresponding to the building in the real-time environment; Obtaining the distribution data of all floodlighting devices and the real-time status data of each floodlighting device, and performing lighting rendering on the natural visual three-dimensional model of the building to generate a lighting effect three-dimensional model; Scoring the lighting effect three-dimensional model based on preset lighting requirements to generate first lighting satisfaction data; Recording historical environmental data, status data of lighting equipment and the corresponding first lighting satisfaction data based on a preset common timeline, and associating the environmental data, status data of lighting equipment and the corresponding first lighting satisfaction data to construct a preliminary adjustment data set; The preset preliminary adjustment model analyzes the preliminary adjustment data set based on the self-learning machine algorithm to generate a first adjustment strategy according to the environmental data, wherein the first adjustment strategy is used to enable the lighting device to adjust the state data according to the environmental changes so that the floodlighting meets the preset lighting requirements, wherein the first adjustment strategy includes first lighting angle adjustment data, first brightness adjustment data, first color temperature adjustment data and corresponding energy consumption data; Acquire user feedback data, and construct a user feedback data set based on the feedback data, wherein the users include building users, floodlit display objects, and residents near the building; The preset user feedback analysis model analyzes the user feedback data set based on a machine self-learning algorithm to generate demand adjustment data, wherein the demand adjustment data is used to update the preset lighting demand; The preset correction adjustment model analyzes the updated lighting demand and the preset lighting demand and generates demand difference data. The correction adjustment model generates a second adjustment strategy based on the demand difference data. The second adjustment strategy is used to perform correction adjustment control on the lighting equipment to improve user satisfaction corresponding to the building floodlighting. The step of obtaining user feedback data and constructing a user feedback data set based on the feedback data includes the following steps: Obtaining the user's body behavior data and facial emotion data when passing through the building as user feedback data; Obtaining user survey data as user feedback data, wherein the survey data includes user satisfaction survey data on floodlighting in different time periods, user satisfaction survey data on floodlighting in different festivals, and user satisfaction survey data on floodlighting in different scenes; Constructing a user feedback data set based on the body behavior data, facial emotion data, and survey data; Specifically, the step of analyzing the user feedback data set based on the machine self-learning algorithm in the preset user feedback analysis model to generate demand adjustment data, wherein the demand adjustment data is used to update the preset lighting demand, includes the following steps: Analyze the user's body behavior data based on a preset body analysis model to generate first feedback score data; Analyzing the user's facial emotion data based on a preset emotion analysis model to generate second feedback score data; Analyzing the first feedback scoring data and the second feedback scoring data based on a preset comprehensive scoring model to generate first user feedback data; Classify and analyze the user's survey data, and construct corresponding survey data sub-datasets based on the classification results; Analyzing different survey data sub-data sets based on preset scoring criteria to generate second user feedback data; The user feedback analysis model analyzes the first user feedback data and the second user feedback data to generate demand adjustment data, and the demand adjustment data is used to adjust the floodlighting device to improve the user's feedback satisfaction.

2. The intelligent building floodlighting control method according to claim 1 is characterized in that: After the step of analyzing the user feedback data set based on the machine self-learning algorithm by the preset user feedback analysis model to generate demand adjustment data, and the demand adjustment data is used to update the preset lighting demand, the following steps are included: Acquire weather forecast information, and generate predicted environmental data corresponding to a future time period based on the weather forecast information; A preset lighting strategy corresponding to a future time period is generated based on the predicted environmental data and the updated lighting demand. The preset lighting strategy is used to set control parameters of the lighting equipment in advance, thereby improving the timeliness of the control.

3. The intelligent building floodlighting control method according to claim 1 is characterized in that: The lighting requirements include a lighting brightness threshold, a wall color temperature threshold, and a pattern clarity.

4. The intelligent building floodlighting control method according to claim 1 is characterized in that: The self-learning machine algorithm includes a neural network algorithm and a random forest algorithm.

5. An intelligent building floodlighting control device, applied to an intelligent building floodlighting control method according to any one of claims 1 to 4, characterized in that: The device comprises: a building foundation three-dimensional model construction unit (1), which is used to obtain building foundation data and construct a building foundation three-dimensional model according to the building foundation data; A natural visual three-dimensional model generation unit (2) is used to obtain real-time environmental data and preliminarily render the basic three-dimensional model of the building according to the real-time environmental data to generate a natural visual three-dimensional model of the building, wherein the natural visual three-dimensional model of the building is used to display the natural lighting conditions corresponding to the building in the real-time environment; A lighting effect three-dimensional model generating unit (3) is used to obtain the distribution data of all floodlighting devices and the real-time status data of each floodlighting device, and perform lighting rendering on the natural visual three-dimensional model of the building to generate a lighting effect three-dimensional model; A first lighting satisfaction data generating unit (4) is used to score the lighting effect three-dimensional model based on a preset lighting requirement to generate first lighting satisfaction data; A preliminary adjustment data set construction unit (5) is used to record historical environmental data, lighting equipment status data and the corresponding first lighting satisfaction data based on a preset common time axis, and associate the environmental data, lighting equipment status data and the corresponding first lighting satisfaction data to construct a preliminary adjustment data set; A first adjustment strategy generating unit (6) is used for pre-setting a preliminary adjustment model to analyze the preliminary adjustment data set based on a self-learning machine algorithm to generate a first adjustment strategy according to the environmental data, wherein the first adjustment strategy is used to enable the lighting device to adjust the state data according to environmental changes so that the floodlighting meets the pre-set lighting requirements, wherein the first adjustment strategy includes first lighting angle adjustment data, first brightness adjustment data, first color temperature adjustment data and corresponding energy consumption data; A user feedback data set construction unit (7), used for obtaining user feedback data and constructing a user feedback data set based on the feedback data, wherein the users include building users, floodlit display objects and residents near the building; A demand adjustment data generating unit (8), configured to be pre-set with a user feedback analysis model to analyze the user feedback data set based on a machine self-learning algorithm to generate demand adjustment data, wherein the demand adjustment data is used to update the pre-set lighting demand; A second adjustment strategy generating unit (9) is used to pre-set a correction adjustment model to analyze the updated lighting demand and the pre-set lighting demand and generate demand difference data, wherein the correction adjustment model generates a second adjustment strategy based on the demand difference data, and the second adjustment strategy is used to perform correction adjustment control on the lighting equipment to improve user satisfaction corresponding to the building floodlighting.

6. 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 computer program, the steps of the intelligent building floodlighting control method as described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of an intelligent building floodlighting control method as claimed in any one of claims 1 to 4 are implemented.

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