Intelligent building floodlighting control method and system

Abstract

The invention relates to an intelligent building floodlight illumination control method, which comprises the following steps of: performing intelligent management on building illumination by constructing a three-dimensional model of a building and combining real-time environment data, specifically, establishing the three-dimensional model based on building basic data, and performing preliminary rendering by utilizing the real-time environment data to generate a natural vision three-dimensional model; illumination rendering is carried out by integrating data of floodlight illumination equipment, and an illumination effect three-dimensional model is created; and scoring the model according to a preset illumination demand, and generating satisfaction data. Establishing an adjustment data set by recording and analyzing historical environment, equipment state and satisfaction data, and generating an adjustment strategy by using a machine learning algorithm to adapt to environment change; in addition, user feedback is collected, lighting requirements are updated, and correction and adjustment are carried out according to the requirement difference, so that the user satisfaction is improved. According to the method, the automatic and intelligent control of the lighting system is realized, and the adaptability of the lighting effect and the satisfaction degree of the user are improved.

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 improvement of urban construction, floodlighting of building complexes has gradually become an important part of urban lighting and one of the necessary elements of urban building construction. In order to make architectural elements and floodlighting form a unified and coordinated aesthetic effect, the floodlighting control system plays an indispensable role in its implementation.

[0003] Existing floodlighting for building complexes generally uses a preset lighting strategy to control the start-up of lighting equipment at a timed interval to illuminate the exterior walls or surrounding environment of the building complex.

[0004] With respect to the above-mentioned prior art, the following technical problems exist: the preset lighting strategies are generally manually set based on experience, and it is difficult to finely adjust the lighting parameters, that is, the preset lighting strategies are inconsistent with the actual usage environment, thereby reducing the overall lighting effect of the floodlighting, and therefore need to be improved. 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 objective of the present application is achieved through the following technical solutions:

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

[0008] Acquire basic building data, and construct a basic building three-dimensional model according to the basic building data;

[0009] 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;

[0010] 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;

[0011] Scoring the lighting effect three-dimensional model based on preset lighting requirements to generate first lighting satisfaction data;

[0012] 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;

[0013] 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;

[0014] 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;

[0015] 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;

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

[0017] By adopting the above technical solutions and collecting and analyzing user feedback in real time, the floodlighting settings can be adjusted in time to better meet user needs and improve user satisfaction. Combined with user feedback data, the floodlighting system can be managed in a refined manner, the lighting effect can be optimized, and the aesthetics and functionality of the building can be improved. Automatic adjustments can be made based on user feedback and environmental changes, enhancing the adaptability and flexibility of the system. The constructed user feedback data set provides rich data support for building management, which helps to make more scientific and accurate decisions.

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

[0019] Obtaining the user's body behavior data and facial emotion data when passing through the building as user feedback data;

[0020] 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;

[0021] A user feedback data set is constructed based on the body behavior data, facial emotion data and survey data.

[0022] By adopting the above technical solutions and collecting and analyzing user feedback in real time, the floodlighting settings can be adjusted in time to better meet user needs and improve user satisfaction. Combined with user feedback data, the floodlighting system can be managed in a refined manner, the lighting effect can be optimized, and the aesthetics and functionality of the building can be improved. Automatic adjustments can be made based on user feedback and environmental changes, enhancing the adaptability and flexibility of the system. The constructed user feedback data set provides rich data support for building management, which helps to make more scientific and accurate decisions.

[0023] In a preferred example, the present application can be further configured as follows: in a step where 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 demand, the steps include:

[0024] Analyzing the user's body behavior data based on a preset body analysis model to generate first feedback score data;

[0025] Analyzing the user's facial emotion data based on a preset emotion analysis model to generate second feedback score data;

[0026] The first feedback scoring data and the second feedback scoring data are analyzed based on a preset comprehensive scoring model to generate first user feedback data.

[0027] By adopting the above technical solutions, through real-time analysis of the user's body behavior and facial emotions, the system can more accurately understand the user's needs and preferences, thereby providing lighting effects that are more in line with the user's expectations. The demand adjustment data helps the lighting system allocate resources more efficiently.

[0028] In a preferred example, the present application may be further configured as follows: in 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, the following steps are also included:

[0029] Classify and analyze the user's survey data, and construct corresponding survey data sub-datasets based on the classification results;

[0030] Analyzing different survey data sub-data sets based on preset scoring criteria 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 device to improve the user's feedback satisfaction.

[0032] By adopting the above technical solutions, automatic adjustments are made based on user feedback and environmental changes to improve its adaptability in different scenarios and time periods. The user feedback dataset provides rich data support for building management, which helps to make more scientific and accurate decisions, such as adjusting lighting strategies to improve energy efficiency and user satisfaction.

[0033] In a preferred example, the present application may be further configured as follows: after a step in which 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 demand, the following steps are included:

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

[0035] 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.

[0036] By adopting the above technical solutions, energy waste can be reduced by predicting environmental changes and adjusting lighting strategies in advance. In particular, when the weather changes significantly, the lighting intensity can be adjusted in time to achieve energy saving. The system can automatically adjust the lighting according to environmental changes to provide users with a more comfortable visual experience, especially in scenes with large changes in indoor and outdoor light. Frequent manual adjustments of lighting equipment caused by environmental changes can be reduced, maintenance costs and human resource consumption can be reduced, and machine learning algorithms can be used to process complex environmental data and generate highly adaptable lighting strategies to improve the intelligence level and adaptive capabilities of the system.

[0037] In a preferred example, the present application 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, the present application can be further configured as follows: the self-learning machine algorithm includes a neural network algorithm and a random forest algorithm.

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

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

[0041] On the third aspect, the above-mentioned purpose of the present application is achieved through the following technical solutions:

[0042] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned intelligent building floodlighting control method are implemented.

[0043] Fourthly, the above-mentioned purpose of the present application is achieved through the following technical solutions:

[0044] A computer-readable storage medium stores a computer program, and 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 time to better meet user needs and improve user satisfaction. Combined with user feedback data, the floodlighting system can be managed in a refined manner, the lighting effect can be optimized, and the aesthetics and functionality of the building can be improved. Automatic adjustments can be made based on user feedback and environmental changes, which enhances the adaptability and flexibility of the system. The constructed user feedback data set provides rich data support for building management, which helps 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 time to better meet user needs and improve user satisfaction. Combined with user feedback data, the floodlighting system can be managed in a refined manner, the lighting effect can be optimized, and the aesthetics and functionality of the building can be improved. Automatic adjustments can be made based on user feedback and environmental changes, which enhances the adaptability and flexibility of the system. The constructed user feedback data set provides rich data support for building management, which helps to make more scientific and accurate decisions;

[0048] 3. By analyzing the user's body movements and facial emotions in real time, the system can more accurately understand the user's needs and preferences, thereby providing lighting effects that better meet the user's expectations. The demand adjustment data helps the lighting system allocate resources more efficiently, automatically adjusts according to user feedback and environmental changes, and improves its adaptability in different scenarios and time periods. The user feedback data set provides rich data support for building management, which helps 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 lighting strategies in advance, energy waste can be reduced, especially when the weather changes significantly. The lighting intensity can be adjusted in time to achieve energy saving. The system can automatically adjust the lighting according to environmental changes to provide users with a more comfortable visual experience, especially in scenes with large changes in indoor and outdoor light. It reduces frequent manual adjustments of lighting equipment caused by environmental changes, reduces maintenance costs and human resource consumption, uses machine learning algorithms to process complex environmental data, and generates highly adaptable lighting strategies to improve the intelligence level and adaptive capabilities of the system. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0051] Figure 2 It is a principle block diagram of an intelligent building floodlighting control device in one embodiment of the present application;

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

[0053] Figure Number:

[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 data set construction unit; 6. First adjustment strategy generation unit; 7. User feedback data set construction unit; 8. Demand adjustment data generation unit; 9. Second adjustment strategy generation unit. DETAILED DESCRIPTION

[0055] The present application is further described in detail below in conjunction with the accompanying drawings.

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

[0057] S10: Acquire basic building data, and construct a basic building three-dimensional model according to the basic building data;

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

[0059] S20: acquiring real-time environment data and preliminarily rendering the basic three-dimensional model of the building according to the real-time environment 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;

[0060] Specifically, the weather stations and light sensors installed around the building are used to obtain real-time environmental data, including light intensity, weather conditions, time, etc. Using these data, the basic 3D model of the building is initially rendered through light simulation software to simulate the appearance of the building under natural light. HDRI (high dynamic range imaging) technology is used in this application to simulate complex lighting environments to ensure the authenticity of the rendering effect.

[0061] S30: Obtaining distribution data of all floodlighting devices and 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;

[0062] Specifically, the distribution data and real-time status data of all floodlighting equipment are collected through the building management system. Professional lighting design software is used to render the natural visual 3D model of the building based on these data to simulate the appearance of the building after the floodlighting is turned on. It should be noted that the parameters such as the beam angle, color temperature and brightness of the lighting equipment are taken into consideration to ensure that the rendering effect is consistent with the actual situation.

[0063] S40: scoring the lighting effect three-dimensional model based on preset lighting requirements to generate first lighting satisfaction data;

[0064] Among them, according to the preset lighting requirements, in the embodiment of the present application, the lighting requirements include lighting brightness threshold, wall color temperature threshold and pattern clarity, and an automated scoring system is used to score the three-dimensional model of the lighting effect to generate the first lighting satisfaction data, thereby quantifying the lighting effect and facilitating subsequent data analysis. The scoring system should include multiple evaluation indicators that can comprehensively reflect the pros and cons of the lighting effect.

[0065] S50: Recording historical environmental data, lighting device status data and the corresponding first lighting satisfaction data based on a preset common timeline, and associating the environmental data, lighting device status data and the corresponding first lighting satisfaction data to construct a preliminary adjustment data set;

[0066] Specifically, through a preset public timeline, historical environmental data, lighting equipment status data and first lighting satisfaction data are recorded, and a database management system is used to associate these data, build a preliminary adjustment data set, and ensure the integrity and consistency of the data to facilitate subsequent analysis and adjustment.

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

[0068] 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;

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

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

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

[0072] Specifically, a user feedback analysis model is used to analyze the user feedback data set based on a self-learning algorithm to generate demand adjustment data for updating the preset lighting requirements. The analysis model can recognize and understand the user's subjective description and convert it into specific lighting demand adjustments.

[0073] S90: The preset revised adjustment model analyzes the updated lighting demand and the preset lighting demand and generates demand difference data, and the revised adjustment model generates a second adjustment strategy based on the demand difference data;

[0074] The second adjustment strategy is used to correct and adjust the lighting equipment to improve user satisfaction corresponding to the building floodlighting;

[0075] It is further explained that the correction and adjustment model compares and analyzes the updated lighting demand with the preset lighting demand, generates demand difference data, and generates a second adjustment strategy based on this data for corrective adjustment and control of the lighting equipment. The correction and adjustment model should be able to respond to changes in demand in real time, quickly generate adjustment strategies, and optimize the lighting equipment according to the second adjustment strategy to meet user needs and optimize the effect of building floodlighting.

[0076] In summary, through intelligent control, lighting equipment can be automatically adjusted according to environmental changes and user needs, and lighting equipment can be finely controlled. User feedback can be collected and adjusted in real time to improve user satisfaction with building floodlighting, reduce manual intervention, realize automated management of building floodlighting, and reduce maintenance costs. It can adapt to different environmental conditions and user needs, has good flexibility and adaptability, thereby optimizing the effect of building floodlighting. Adjustment strategies are generated based on data analysis, which improves the scientificity and accuracy of decision-making.

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

[0078] S71: Acquire body behavior data and facial emotion data of the user when passing through the building as user feedback data;

[0079] Specifically, in the embodiment of the present application, high-definition cameras and sensors installed around the building are used to capture the body movements of users when they pass by, such as gait, gestures (raising the mobile phone to take pictures of the outer wall of the building), etc. At the same time, facial recognition technology and emotion recognition algorithms are used to analyze the user's facial expressions to obtain emotional data. These data can be transmitted to the central processing system in real time via a wireless network. In this step, the resolution of the camera and the sensitivity of the sensor are ensured to be 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 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;

[0081] Specifically, we designed an online questionnaire and distributed it through various channels such as building management apps, social media, and emails. The questionnaire included satisfaction surveys on floodlighting in different time periods, festivals, and scenarios. We ensured that the questionnaire design was targeted and comprehensive, and could cover the needs of different user groups.

[0082] In the embodiment of the present application, logic jump and input verification technology are used to ensure that users can complete the questionnaire smoothly and to ensure the integrity and validity of the data. Statistical analysis software is used to organize 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, the collected body behavior data, facial emotion data, and survey data are integrated to build a data set that fully reflects user feedback. The data is stored and indexed using a database management system to facilitate subsequent analysis and application. A reasonable data model is designed to ensure the consistency and scalability of the data set. Data cleaning and preprocessing techniques are used to remove invalid and redundant data and improve data quality.

[0085] In summary, by collecting and analyzing user feedback in real time, we can adjust the floodlighting settings in a timely manner to better meet user needs and improve user satisfaction. Combined with user feedback data, we can manage the floodlighting system in a refined manner, optimize the lighting effect, and improve the aesthetics and functionality of the building. Automatic adjustments based on user feedback and environmental changes enhance the adaptability and flexibility of the system. The constructed user feedback data set provides rich data support for building management, which helps to make more scientific and accurate decisions.

[0086] In S80: the 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 demand. This step includes the following steps:

[0087] S81: Analyze the user's limb behavior data based on a preset limb analysis model to generate first feedback score data;

[0088] Specifically, deep learning algorithms, such as convolutional neural networks (CNNs), are used to analyze captured videos of users’ body movements. The model is trained to recognize different body movements and match them with preset body movement patterns to assess the user’s emotional state and behavioral intentions. For example, a fast pace may indicate a user’s urgency, while a slow pace may indicate relaxation or fatigue.

[0089] In the embodiment of the present application, the algorithm is ensured to be able to process high frame rate videos to accurately capture fast body movements. Data enhancement techniques, such as random cropping and scaling of video frames, are used to improve the generalization ability of the model.

[0090] S82: Analyze the user's facial emotion data based on a preset emotion analysis model to generate second feedback score data;

[0091] Specifically, the user's facial expressions are analyzed through facial key point detection and expression recognition technology. Pre-trained deep learning models, such as ResNet or VGG, are used to identify 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 recognition accuracy. This is done by pre-training the model on a large-scale face dataset and then fine-tuning it for specific applications.

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

[0094] Specifically, the user's comprehensive satisfaction score is calculated by combining the rating data of body behavior and facial emotions, using weighted average or other statistical methods. This comprehensive rating model will consider the importance of different feedback data and assign different weights. Ensure that the rating 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 weight distribution and generate the first user feedback data.

[0095] S84: classify and analyze the user's survey data, and construct a corresponding survey data sub-data set according to the classification results;

[0096] Specifically, the collected survey data is classified according to time periods, festivals, scenes, etc. For example, the lighting satisfaction survey data at night is separated from the data during the day, or the satisfaction data for a specific festival is classified separately.

[0097] Use data mining techniques, such as cluster analysis, to identify patterns and trends in survey data. This helps build more targeted sub-data sets for subsequent analysis.

[0098] S85: Analyze different survey data sub-data sets based on a preset scoring standard to generate second user feedback data;

[0099] Specifically, predefined scoring criteria are applied to each sub-dataset, which may include satisfaction level, emotion intensity level, etc. Statistical analysis methods, such as analysis of variance (ANOVA), are used to evaluate the differences in user feedback in different sub-datasets.

[0100] In the embodiments of the present application, the scoring criteria are ensured to be comparable, and effective comparisons can be made between different sub-data sets. Appropriate statistical tests are used 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 demand adjustment data, where the demand adjustment data is used to adjust the floodlighting device to improve user feedback satisfaction;

[0102] Specifically, the first user feedback data and the second user feedback data are input into a user feedback analysis model, which will comprehensively consider the individual's body 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 that affect user satisfaction. These factors will guide the adjustment strategy of the floodlighting equipment.

[0104] In summary, by analyzing the user's body behavior and facial emotions in real time, the system can more accurately understand the user's needs and preferences, thereby providing lighting effects that are more in line with the user's expectations. Demand adjustment data helps the lighting system allocate resources more efficiently, such as increasing lighting intensity or adjusting color temperature in areas with low user satisfaction, and automatically adjusting according to user feedback and environmental changes to improve its adaptability in different scenarios and time periods. The user feedback dataset provides rich data support for building management, which helps to make more scientific and accurate decisions, such as adjusting lighting strategies to improve energy efficiency and user satisfaction.

[0105] After the step S80: the 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 demand, the following steps are included:

[0106] S801: Acquire weather forecast information, and generate predicted environmental data corresponding to a future time period based on the weather forecast information;

[0107] Specifically, by accessing weather API services, such as the free API provided by the China Meteorological Administration, real-time and predicted weather forecast information can be obtained. The data obtained using the API include temperature, humidity, wind speed, wind direction, precipitation, etc. These data are used as input to generate the predicted environmental data corresponding to the future time period through data processing algorithms, ensuring the stability of the API service and the accuracy of the data, and using appropriate data caching and update mechanisms to ensure the real-time and effectiveness of environmental data.

[0108] S802: generating a preset lighting strategy corresponding to a future time period based on the predicted environment data and the updated lighting demand, wherein the preset lighting strategy is used to set control parameters of the lighting equipment in advance, thereby improving the timeliness of control;

[0109] Specifically, a lighting strategy that adapts to different environmental conditions is developed by using machine learning algorithms, combined with predicted environmental data and updated lighting requirements. For example, when cloudy or rainy days are predicted, the system can automatically adjust the lighting brightness to maintain the uniformity of indoor and outdoor light. The impact of different environmental parameters on lighting requirements is considered in the algorithm design, such as adjusting the brightness and color temperature according to the cloud cover and precipitation probability in the weather forecast. 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 significantly, the lighting intensity can be adjusted in time to achieve energy saving. The system can automatically adjust the lighting according to environmental changes to provide users with a more comfortable visual experience, especially in scenes with large changes in indoor and outdoor light. It reduces frequent manual adjustments of lighting equipment caused by environmental changes, reduces maintenance costs and human resource consumption, uses machine learning algorithms to process complex environmental data, and generates highly adaptable lighting strategies, thereby improving the intelligence level and adaptive capabilities of the system.

[0111] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0112] In one embodiment, an intelligent building floodlighting control device is provided, and the intelligent building floodlighting control device corresponds to the intelligent building floodlighting control method in the above embodiment. Figure 2 As 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 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;

[0114] The 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;

[0115] 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;

[0116] 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;

[0117] A first adjustment strategy generating unit 6, configured to be pre-set with 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 configured 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;

[0118] A user feedback data set construction unit 7, used to obtain user feedback data and construct a user feedback data set based on the feedback data, wherein the users include building users, floodlighting display objects and residents near the building;

[0119] A demand adjustment data generating unit 8, which is 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;

[0120] The second adjustment strategy generating unit 9 is used to pre-set a correction adjustment model to analyze the updated lighting demand and the preset lighting demand, 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 correct and adjust the lighting equipment to improve the user satisfaction corresponding to the building floodlighting.

[0121] The specific definition of the intelligent building floodlighting control device can be found in the definition of the intelligent building floodlighting control method above, which will not be repeated here. Each module in the above intelligent building floodlighting control device can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, 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 may be a server, and its internal structure diagram may be as follows: Figure 3As shown. 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 a 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, an intelligent building floodlighting control method is implemented.

[0123] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

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

[0125] 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;

[0126] 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;

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

[0128] 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;

[0129] 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;

[0130] 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;

[0131] 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;

[0132] The preset correction and adjustment model analyzes the updated lighting demand and the preset lighting demand and generates demand difference data. The correction and adjustment model generates a second adjustment strategy based on the demand difference data. The second adjustment strategy is used to correct and adjust the lighting equipment to improve 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, and when the computer program is executed by a processor, the following steps are implemented:

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

[0135] 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;

[0136] 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;

[0137] Scoring the lighting effect three-dimensional model based on preset lighting requirements to generate first lighting satisfaction data;

[0138] 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;

[0139] 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;

[0140] 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;

[0141] 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;

[0142] The preset correction and adjustment model analyzes the updated lighting demand and the preset lighting demand and generates demand difference data. The correction and adjustment model generates a second adjustment strategy based on the demand difference data. The second adjustment strategy is used to correct and adjust the lighting equipment to improve user satisfaction corresponding to the building floodlighting.

[0143] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many 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).

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

[0145] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in 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 and adjustment model analyzes the updated lighting demand and the preset lighting demand and generates demand difference data. The correction and adjustment model generates a second adjustment strategy based on the demand difference data. The second adjustment strategy is used to correct and adjust the lighting equipment to improve user satisfaction corresponding to the building floodlighting.

2. The intelligent building floodlighting control method according to claim 1 is characterized in that: 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; A user feedback data set is constructed based on the body behavior data, facial emotion data and survey data.

3. The intelligent building floodlighting control method according to claim 2 is characterized in that: The step of analyzing the user feedback data set based on a machine self-learning algorithm in a 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; The first feedback scoring data and the second feedback scoring data are analyzed based on a preset comprehensive scoring model to generate first user feedback data.

4. The intelligent building floodlighting control method according to claim 3 is characterized in that: In 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, wherein the demand adjustment data is used to update the preset lighting demand, the following steps are also included: 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.

5. 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.

6. 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.

7. 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.

8. An intelligent building floodlighting control device, applied to an intelligent building floodlighting control method according to any one of claims 1 to 7, 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.

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 computer program, the steps of the intelligent building floodlighting control method as described in any one of claims 1 to 7 are implemented.

10. 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 7 are implemented.

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