CN119255452B - Intelligent building floodlight lighting control method and system - Google Patents
Intelligent building floodlight lighting control method and system Download PDFInfo
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Abstract
本发明涉及一种智能化楼宇泛光照明控制方法，该方法通过构建楼宇的三维模型并结合实时环境数据，对楼宇照明进行智能化管理，具体的基于楼宇基础数据建立三维模型，并利用实时环境数据进行初步渲染，生成自然视觉三维模型；通过集成泛光照明设备的数据，进行照明渲染，创建照明效果三维模型；根据预设照明需求对模型进行评分，生成满意度数据。通过记录和分析历史环境、设备状态和满意度数据，构建调整数据集，并利用机器学习算法生成调整策略，以适应环境变化；此外，收集用户反馈，更新照明需求，并根据需求差异进行修正调整，以提升用户满意度。本方法实现了照明系统的自动化和智能化控制，提高了照明效果的适应性和用户的满意度。
The present invention relates to an intelligent building floodlighting control method, which intelligently manages building lighting by constructing a three-dimensional model of the building and combining it with real-time environmental data. Specifically, a three-dimensional model is established based on basic building data, and real-time environmental data is used for preliminary rendering to generate a natural visual three-dimensional model; lighting rendering is performed by integrating the data of floodlighting equipment to create a three-dimensional model of lighting effects; the model is scored according to preset lighting requirements to generate satisfaction data. An adjustment data set is constructed by recording and analyzing historical environment, equipment status, and satisfaction data, and an adjustment strategy is generated using a machine learning algorithm to adapt to environmental changes; in addition, user feedback is collected, lighting requirements are updated, and corrections and adjustments are made based on differences in requirements to improve user satisfaction. This method realizes automated and intelligent control of the lighting system, and improves the adaptability of lighting effects and user satisfaction.
Description
Technical Field
The invention relates to the technical field of intelligent illumination, in particular to an intelligent building floodlight illumination control method and system.
Background
With the economic development of cities and the gradual perfection of urban construction, building community floodlighting slowly becomes an important component part of urban lighting and also becomes one of necessary elements of urban building construction, so that a unified and coordinated attractive effect is formed by building elements and floodlighting, and a floodlighting control system plays an indispensable role in the implementation process.
Existing flood lighting for building communities generally uses a preset lighting strategy to control the starting of lighting devices at regular time to illuminate the exterior walls or surrounding environment of the building community.
Aiming at the prior art, the technical problems that the preset illumination strategy is manually set according to experience, and the illumination parameters are difficult to finely adjust, namely, the preset illumination strategy has the condition of being inconsistent with the actual use environment, so that the overall illumination effect of floodlight illumination is reduced, and therefore, improvement is needed.
Disclosure of Invention
In order to optimize the effect of building floodlighting, the application provides an intelligent building floodlighting control method and system.
In a first aspect, the above object of the present application is achieved by the following technical solutions:
an intelligent building floodlighting control method, the method comprising the steps of:
Building basic data are obtained, and a building basic three-dimensional model is built according to the building basic data;
Acquiring real-time environment data and performing preliminary rendering on the building foundation three-dimensional model according to the real-time environment data to generate a building natural vision three-dimensional model, wherein the building natural vision three-dimensional model is used for displaying the corresponding natural illumination condition of a building in a real-time environment;
acquiring distribution data of all the floodlighting devices and real-time state data of each floodlighting device, and performing illumination rendering on the building natural vision three-dimensional model to generate an illumination effect three-dimensional model;
Scoring the lighting effect three-dimensional model based on preset lighting requirements to generate first lighting satisfaction data;
Recording historical environment data, state data of the lighting equipment and corresponding first lighting satisfaction data based on a preset public time axis, and associating the environment data, the state data of the 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 a self-learning machine algorithm to generate a first adjustment strategy according to environment data, wherein the first adjustment strategy is used for enabling the lighting equipment to adjust state data according to environment changes so as to enable floodlight to meet preset lighting requirements, and the first adjustment strategy comprises first lighting angle adjustment data, first brightness adjustment data, first color temperature adjustment data and corresponding energy consumption data;
acquiring feedback data of a user, and constructing a user feedback data set based on the feedback data, wherein the user comprises a user of a building, a floodlighting display object and households nearby the building;
Analyzing the user feedback data set based on a machine self-learning algorithm by a preset user feedback analysis model to generate demand adjustment data, wherein the demand adjustment data is used for updating preset lighting demands;
the preset correction adjustment model analyzes the updated illumination demand and the preset illumination demand and generates demand difference data, and the correction adjustment model generates a second adjustment strategy based on the demand difference data, wherein the second adjustment strategy is used for carrying out correction adjustment control on illumination equipment so as to improve user satisfaction corresponding to building floodlight illumination.
Through adopting above-mentioned technical scheme, through collecting and analyzing user feedback in real time, can in time adjust the floodlight illumination setting, satisfy user's demand better, promote user satisfaction, combine user feedback data, can carry out the fine management to floodlight lighting system, optimize the illuminating effect, promote the pleasing to the eye degree and the functionality of building, according to user feedback and environmental change automatic adjustment, the adaptability and the flexibility of system have been strengthened, the user feedback dataset of construction provides abundant data support for building management, help making more scientific and accurate decision.
The present application may be further configured in a preferred example to include, in the step of acquiring feedback data of a user and constructing a user feedback data set based on the feedback data, the steps of:
Acquiring limb behavior data and facial emotion data of a user when the user passes through a building as feedback data of the user;
Acquiring survey data of a user as feedback data of the user, wherein the survey data comprise flood lighting satisfaction survey data of the user for different time periods, flood lighting satisfaction survey data of the user for different holidays and flood lighting satisfaction survey data of the user for different scenes;
a user feedback dataset is constructed based on the limb behavior data, facial emotion data, and survey data.
Through adopting above-mentioned technical scheme, through collecting and analyzing user feedback in real time, can in time adjust the floodlight illumination setting, satisfy user's demand better, promote user satisfaction, combine user feedback data, can carry out the fine management to floodlight lighting system, optimize the illuminating effect, promote the pleasing to the eye degree and the functionality of building, according to user feedback and environmental change automatic adjustment, the adaptability and the flexibility of system have been strengthened, the user feedback dataset of construction provides abundant data support for building management, help making more scientific and accurate decision.
The present application may be further configured in a preferred example to analyze 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 preset lighting demands, and the method comprises the following steps:
Analyzing the limb behavior data of the user based on a preset limb analysis model to generate first feedback scoring data;
analyzing facial emotion data of the user based on a preset emotion analysis model to generate second feedback scoring data;
And 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.
By adopting the technical scheme, the system can more accurately understand the demands and preferences of the user by analyzing the limb behaviors and facial emotions of the user in real time, so that the lighting effect which is more in line with the demands of the user is provided, and the demand adjustment data is beneficial to the lighting system to more effectively allocate resources.
The application may be further configured in a preferred example to analyze 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 for updating the preset lighting demand, and the method further comprises the following steps:
Classifying and analyzing the investigation data of the user, and constructing a corresponding investigation data sub-data set according to the classification result;
analyzing the different investigation 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, wherein the demand adjustment data is used for adjusting the floodlight lighting device to improve the feedback satisfaction degree of the user.
By adopting the technical scheme, the adaptability of the system in different scenes and time periods is improved by automatically adjusting the system according to the feedback and environmental change of the user, and the user feedback data set provides rich data support for building management, so that more scientific and accurate decisions, such as adjusting the lighting strategy to improve the energy efficiency and the user satisfaction, can be made.
The present application may be further configured in a preferred example to analyze the set of user feedback data based on a machine self-learning algorithm after a preset user feedback analysis model to generate demand adjustment data, the step of updating the preset lighting demand, comprising the steps of:
acquiring weather forecast information and generating prediction environment data corresponding to a future time period based on the weather forecast information;
And generating a preset lighting strategy corresponding to a future time period based on the predicted environment data and the updated lighting requirement, wherein the preset lighting strategy is used for setting control parameters of the lighting equipment in advance, so that the timeliness of control is improved.
Through adopting above-mentioned technical scheme, through predicting environmental change and adjusting the illumination tactics in advance, can reduce the energy waste, especially when weather change is great, can in time adjust illumination intensity, realize energy-conservation, the system can be according to environmental change automatically regulated illumination, provide more comfortable visual experience for the user, especially in the scene that indoor and outdoor light changes greatly, reduce the frequent manual adjustment of lighting equipment that leads to because of environmental change, reduce maintenance cost and manpower resource consumption, utilize the machine learning algorithm to handle complicated environmental data, and generate the illumination tactics that adaptability is strong, improve the intelligent level and the self-adaptation ability of system.
The present application may be further configured in a preferred example such that the lighting requirements include a lighting brightness threshold, a wall color temperature threshold, and pattern definition.
The application may be further configured in a preferred example in that the self-learning machine algorithm includes a neural network algorithm and a random forest algorithm.
In a second aspect, the above object of the present application is achieved by the following technical solutions:
An intelligent building floodlighting control device comprises the following components.
In a third aspect, the above object of the present application is achieved by the following technical solutions:
An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of an intelligent building floodlighting control method as described above when executing the computer program.
In a fourth aspect, the above object of the present application is achieved by the following technical solutions:
a computer readable storage medium storing a computer program which when executed by a processor performs the steps of a method of intelligent building floodlighting control as described above.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the floodlight lighting system can be finely managed by collecting and analyzing user feedback in real time, so that the user requirements are better met, the user satisfaction is improved, the floodlight lighting system can be finely managed by combining user feedback data, the lighting effect is optimized, the attractiveness and the functionality of the building are improved, the adaptability and the flexibility of the system are improved by automatic adjustment according to the user feedback and environmental changes, the constructed user feedback data set provides rich data support for building management, and more scientific and accurate decisions can be made;
2. The floodlight lighting system can be finely managed by collecting and analyzing user feedback in real time, so that the user requirements are better met, the user satisfaction is improved, the floodlight lighting system can be finely managed by combining user feedback data, the lighting effect is optimized, the attractiveness and the functionality of the building are improved, the adaptability and the flexibility of the system are improved by automatic adjustment according to the user feedback and environmental changes, the constructed user feedback data set provides rich data support for building management, and more scientific and accurate decisions can be made;
3. By analyzing the limb behaviors and facial emotions of the user in real time, the system can more accurately understand the demands and preferences of the user, so that the lighting effect which is more in line with the demands of the user is provided, the demand adjustment data is helpful for the lighting system to more effectively allocate resources, the adaptability of the lighting system in different scenes and time periods is improved according to the feedback and environmental change of the user, the user feedback data set provides rich data support for building management, and more scientific and accurate decisions such as adjustment of lighting strategies to improve energy efficiency and user satisfaction are facilitated;
4. The environment change is predicted, the illumination strategy is adjusted in advance, the energy waste can be reduced, particularly when the weather change is large, the illumination intensity can be adjusted in time, the energy saving is realized, the system can automatically adjust the illumination according to the environment change, more comfortable visual experience is provided for a user, particularly in a scene with large indoor and outdoor light change, the frequent manual adjustment of illumination equipment caused by the environment change is reduced, the maintenance cost and the manpower resource consumption are reduced, the complex environment data are processed by using a machine learning algorithm, the illumination strategy with strong adaptability is generated, and the intelligent level and the self-adaption capacity of the system are improved.
Drawings
FIG. 1 is a flow chart of steps of an intelligent building floodlighting control method according to an embodiment of the application;
FIG. 2 is a schematic block diagram of an intelligent building floodlighting control device in accordance with an embodiment of the application;
Fig. 3 is a schematic diagram of an electronic device in an embodiment of the application.
Reference numerals:
1. Building basic three-dimensional model building unit, 2, natural vision three-dimensional model generating unit, 3, lighting effect three-dimensional model generating unit, 4, first lighting satisfaction data generating unit, 5, preliminary adjustment data set building unit, 6, first adjustment strategy generating unit, 7, user feedback data set building unit, 8, demand adjustment data generating unit, 9, second adjustment strategy generating unit.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings.
In an embodiment, as shown in fig. 1, the application discloses an intelligent building floodlight control method, which specifically comprises the following steps:
S10, building basic data are obtained, and a building basic three-dimensional model is built according to the building basic data;
Specifically, a high-precision laser scanner is used for carrying out omnibearing scanning on a building or obtaining a design construction drawing of the building, so that basic data such as the size, the shape and the structure of the building are obtained. These data are input into specialized three-dimensional modeling software to construct a basic three-dimensional model of the building, which includes an accurate representation of all visible portions of the building's exterior walls, windows, balconies, etc.
S20, acquiring real-time environment data and primarily rendering the building foundation three-dimensional model according to the real-time environment data to generate a building natural vision three-dimensional model, wherein the building natural vision three-dimensional model is used for displaying the corresponding natural illumination condition of a building in a real-time environment;
Specifically, environmental data including illumination intensity, weather conditions, time, etc. are acquired in real time through weather stations and illumination sensors installed around the building. And using the data to perform preliminary rendering on the building foundation three-dimensional model through illumination simulation software, and simulating the appearance of the building under natural illumination. In the application, the HDRI (high dynamic range imaging) technology is used for simulating a complex illumination environment, so that the authenticity of a rendering effect is ensured.
S30, acquiring distribution data of all the floodlight lighting devices and real-time state data of each floodlight lighting device, and performing lighting rendering on the building natural vision three-dimensional model to generate a lighting effect three-dimensional model;
In particular, distribution data and real-time status data of all floodlighting devices are collected by a building management system. And using professional lighting design software to carry out lighting rendering on the natural vision three-dimensional model of the building according to the data, simulating the appearance of the building after the floodlighting is started, and considering parameters such as beam angle, color temperature, brightness and the like of lighting equipment to ensure that the rendering effect accords with the actual situation.
S40, scoring the three-dimensional model of the lighting effect based on preset lighting requirements to generate first lighting satisfaction data;
According to preset lighting requirements, in the embodiment of the application, the lighting requirements comprise a lighting brightness threshold value, a wall color temperature threshold value and pattern definition, an automatic scoring system is used for scoring the lighting effect three-dimensional model, and first lighting satisfaction data are generated, so that the lighting effect is quantized, subsequent data analysis is convenient, the scoring system comprises various evaluation indexes, and the advantages and disadvantages of the lighting effect can be comprehensively reflected.
S50, recording historical environment data, state data of the lighting equipment and corresponding first lighting satisfaction data based on a preset public time axis, and associating the environment data, the state data of the lighting equipment and the corresponding first lighting satisfaction data to construct a preliminary adjustment data set;
Specifically, through a preset public time axis, historical environment data, lighting equipment state data and first lighting satisfaction data are recorded, a database management system is used for correlating the data, a preliminary adjustment data set is constructed, the integrity and consistency of the data are ensured, and subsequent analysis and adjustment are facilitated.
S60, analyzing the preliminary adjustment data set based on a self-learning machine algorithm by a preset preliminary adjustment model to generate a first adjustment strategy according to environmental data;
The first adjustment strategy is used for enabling the lighting equipment to adjust the state data according to the environmental change so as to enable the floodlight to meet preset lighting requirements, and comprises first lighting angle adjustment data, first brightness adjustment data, first color temperature adjustment data and corresponding energy consumption data;
In the embodiment of the application, the preliminary adjustment data set is analyzed by using a neural network algorithm and a random forest algorithm to generate a first adjustment strategy, and the control parameters of the floodlighting are changed according to the actual environment change through adjustment of the first adjustment strategy, so that the suitability of the floodlighting is improved, and the effect of the building floodlighting is optimized.
S70, acquiring feedback data of a user, and constructing a user feedback data set based on the feedback data, wherein the user comprises a user of a building, a floodlighting display object and households nearby the building;
s80, analyzing the user feedback data set based on a machine self-learning algorithm by a preset user feedback analysis model to generate demand adjustment data, wherein the demand adjustment data are used for updating preset lighting demands;
Specifically, a user feedback analysis model is used, a user feedback data set is analyzed based on a self-learning algorithm, demand adjustment data are generated and used for updating preset illumination demands, and the analysis model can identify and understand subjective description of a user and is converted into specific illumination demand adjustment.
S90, analyzing the updated illumination demand and the preset illumination demand by a preset correction adjustment model, and generating demand difference data, wherein the correction adjustment model generates a second adjustment strategy based on the demand difference data;
The second adjustment strategy is used for carrying out correction adjustment control on the lighting equipment so as to improve the user satisfaction degree corresponding to building floodlighting;
The correction adjustment model is used for comparing and analyzing the updated illumination demand with preset illumination demand, generating demand difference data, generating a second adjustment strategy based on the data, and carrying out correction adjustment control on illumination equipment, wherein the correction adjustment model can respond to demand change in real time, quickly generate an adjustment strategy, and optimally adjust the illumination equipment according to the second adjustment strategy, so that the demand of a user is met, and the effect of building floodlight is optimized.
In summary, through intelligent control, the lighting equipment is automatically regulated according to environmental changes and user requirements, the lighting equipment is finely controlled, user feedback is collected in real time and regulated, satisfaction of users on building floodlighting is improved, manual intervention is reduced, automatic management of building floodlighting is realized, maintenance cost is reduced, the intelligent control system can adapt to different environmental conditions and user requirements, and has good flexibility and adaptability, so that the effect of building floodlighting is optimized, regulation strategies are generated based on data analysis, and the scientificity and accuracy of decision making are improved.
The step of acquiring feedback data of the user and constructing a user feedback data set based on the feedback data at S70 comprises the following steps:
S71, acquiring limb behavior data and facial emotion data of a user when the user passes through a building as feedback data of the user;
Specifically, in the embodiment of the application, the high-definition camera and the sensor arranged around the building are used for capturing limb behaviors such as gait, gestures (taking pictures of the outer wall of the building by lifting the mobile phone) and the like when the user passes through the high-definition camera. Meanwhile, facial expressions of the user are analyzed by using a facial recognition technology and an emotion recognition algorithm to acquire emotion data. These data can be transmitted in real time to a central processing system via a wireless network, ensuring in this step that the resolution of the camera and the sensitivity of the sensor are sufficient to capture subtle limb movements and expression changes, using deep learning algorithms such as Convolutional Neural Networks (CNNs) to improve the accuracy of facial emotion recognition.
S72, acquiring survey data of a user as feedback data of the user, wherein the survey data comprise flood lighting satisfaction survey data of the user for different time periods, flood lighting satisfaction survey data of the user for different holidays and flood lighting satisfaction survey data of the user for different scenes;
Specifically, an online questionnaire is designed and distributed through various channels such as building management APP, social media, email, and the like. The questionnaire content includes satisfaction surveys of flood lighting at different time periods, holidays, and scenes. The questionnaire design is ensured to have pertinence and comprehensiveness, and the requirements of different user groups can be covered.
In the embodiment of the application, a logic jump and input verification technology is adopted, so that a user can smoothly finish questionnaires, the integrity and the effectiveness of data are ensured, and the collected investigation data are arranged and analyzed by using statistical analysis software.
S73, constructing a user feedback data set based on the limb behavior data, the facial emotion data and the investigation data;
Specifically, the collected limb behavior data, facial emotion data and survey data are integrated to construct a data set which comprehensively reflects user feedback, and a database management system is used for storing and indexing the data so as to facilitate subsequent analysis and application. And a reasonable data model is designed, so that the consistency and expandability of a data set are ensured. And invalid and redundant data are removed by adopting a data cleaning and preprocessing technology, so that the data quality is improved.
In summary, through collecting and analyzing user feedback in real time, the floodlight lighting setting can be adjusted in time, the user requirement is better met, the user satisfaction is improved, the floodlight lighting system can be subjected to fine management by combining user feedback data, the lighting effect is optimized, the attractiveness and the functionality of the building are improved, the adaptability and the flexibility of the system are enhanced according to the user feedback and the environment change automatic adjustment, the constructed user feedback data set provides rich data support for building management, and more scientific and accurate decisions are facilitated.
At 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, wherein the demand adjustment data is used for updating preset lighting demands, and the method comprises the following steps:
S81, analyzing the limb behavior data of the user based on a preset limb analysis model to generate first feedback scoring data;
Specifically, the captured user extremity behavioral videos are analyzed using a deep learning algorithm, such as a Convolutional Neural Network (CNN). Different limb actions are identified through the training model, and the actions are matched with a preset limb behavior mode so as to evaluate the emotional state and the behavioral intention of the user. For example, a fast pace may indicate a user's urge, while a slow pace may indicate relaxation or exhaustion.
In the embodiment of the application, the algorithm is ensured to be capable of processing the high-frame-rate video so as to accurately capture the rapid limb motion. Data enhancement techniques, such as random cropping and scaling of video frames, are used to improve the generalization ability of the model.
S82, analyzing facial emotion data of the user based on a preset emotion analysis model to generate second feedback scoring data;
Specifically, facial expressions of the user are analyzed through a face key point detection and expression recognition technology. Seven basic emotions of facial expression, happiness, sadness, anger, surprise, fear, aversion and contempt, are identified using a pre-trained deep learning model, such as ResNet or VGG. The model will assign a confidence score to each detected expression.
What is needed is to train a model to accommodate different lighting conditions and facial orientations to improve recognition accuracy. By using a transfer learning technique, the model is pre-trained on a large-scale face dataset and then fine-tuned for the specific application.
S83, 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;
specifically, a weighted average or other statistical method is used to calculate the user's overall satisfaction score in combination with the limb behavior and facial emotion scoring data. This composite scoring model may take into account the importance of different feedback data, giving different weights. The scoring model can be ensured to dynamically adjust the weight so as to adapt to the requirements of different user groups and different scenes. The historical data is analyzed using a machine learning algorithm to optimize weight distribution to generate first user feedback data.
S84, classifying and analyzing the survey data of the user, and constructing a corresponding survey data sub-data set according to the classifying result;
Specifically, the collected survey data is classified according to the dimensions of time periods, holidays, scenes and the like. For example, the lighting satisfaction survey data at night is separated from the daytime data, or the satisfaction data for a particular holiday is categorized separately.
Patterns and trends in survey data are identified using data mining techniques, such as cluster analysis. This facilitates the construction of a more targeted sub-data set for subsequent analysis.
S85, analyzing different investigation data sub-data sets based on preset scoring criteria to generate second user feedback data;
In particular, predefined scoring criteria are applied to each sub-dataset, which criteria may include a level of satisfaction, a level of emotional intensity, etc. Statistical analysis methods, such as analysis of variance (ANOVA), are used to evaluate the differences in user feedback in the different sub-data sets.
In the embodiment of the application, the scoring standard is ensured to be comparable, and effective comparison can be carried out between different sub-data sets. Appropriate statistical tests are employed to determine the significance differences of the user feedback.
S86, the user feedback analysis model analyzes the first user feedback data and the second user feedback data to generate demand adjustment data, wherein the demand adjustment data is used for adjusting the floodlight lighting equipment to improve the feedback satisfaction degree of the user;
Specifically, the first user feedback data and the second user feedback data are input into a user feedback analysis model. This model will integrate the limb behavior, facial emotion and survey feedback of the individual to generate demand adjustment data.
Advanced data analysis techniques, such as decision trees or random forests, are used in embodiments of the present application to process complex data sets and identify key factors that affect user satisfaction. These factors will guide the adjustment strategy of the floodlighting.
In summary, by analyzing the limb behaviors and facial emotions of the user in real time, the system can more accurately understand the demands and preferences of the user, so that the lighting system can more effectively distribute resources by the demand adjustment data, such as increasing the lighting intensity or adjusting the color temperature in the area with lower user satisfaction, automatically adjusting according to the feedback and environmental changes of the user, improving the adaptability of the user in different scenes and time periods, and the user feedback data set provides abundant data support for building management, and is helpful for making more scientific and accurate decisions, such as adjusting the lighting strategy to improve the energy efficiency and the user satisfaction.
At 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, wherein the demand adjustment data is used for updating preset lighting demands, and the method comprises the following steps:
S801, weather forecast information is obtained, and prediction environment data corresponding to a future time period is generated based on the weather forecast information;
specifically, the weather forecast information is obtained in real time and predicted by accessing weather API service, such as free API provided by China weather exchange. The data obtained using the API includes temperature, humidity, wind speed, wind direction, precipitation, etc. The data are used as input, the predicted environment data corresponding to the future time period are generated through a data processing algorithm, the stability of the API service and the accuracy of the data are ensured, and the appropriate data caching and updating mechanism is used to ensure the real-time performance and the effectiveness of the environment data.
S802, generating a preset lighting strategy corresponding to a future time period based on the predicted environment data and the updated lighting requirement, wherein the preset lighting strategy is used for setting control parameters of lighting equipment in advance, so that the timeliness of control is improved;
Specifically, a machine learning algorithm is utilized to combine the predicted environmental data and the updated lighting requirements to formulate a lighting strategy suitable for different environmental conditions. For example, the system may automatically adjust the illumination intensity to maintain uniformity of indoor and outdoor light when overcast or rainy days are predicted. The influence of different environmental parameters on lighting requirements is considered in the algorithm design, such as adjusting brightness and color temperature according to cloud cover and precipitation probability in weather prediction. Meanwhile, the algorithm is ensured to be capable of processing the environment data updated in real time and generating corresponding lighting strategies rapidly.
In summary, by predicting the environmental change and adjusting the illumination strategy in advance, the energy waste can be reduced, especially when the weather change is large, the illumination intensity can be adjusted in time, energy saving is realized, the system can automatically adjust illumination according to the environmental change, more comfortable visual experience is provided for users, especially in the scene with large indoor and outdoor light change, frequent manual adjustment of illumination equipment caused by the environmental change is reduced, maintenance cost and manpower resource consumption are reduced, complex environmental data are processed by using a machine learning algorithm, an illumination strategy with strong adaptability is generated, and the intelligent level and self-adaption capability of the system are improved.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
In one embodiment, an intelligent building floodlighting control device is provided, which corresponds to the intelligent building floodlighting control method in the above embodiment one by one. As shown in fig. 2, the intelligent building floodlighting control device comprises a building foundation three-dimensional model construction unit 1, a control unit and a control unit, wherein the building foundation three-dimensional model construction unit is used for acquiring building foundation data and constructing a building foundation three-dimensional model according to the building foundation data;
the natural vision three-dimensional model generating unit 2 is used for acquiring real-time environment data and performing preliminary rendering on the building basic three-dimensional model according to the real-time environment data so as to generate a building natural vision three-dimensional model, wherein the building natural vision three-dimensional model is used for displaying the corresponding natural illumination condition of a building in a real-time environment;
The lighting effect three-dimensional model generating unit 3 is used for acquiring distribution data of all the floodlighting devices and real-time state data of each floodlighting device, and performing lighting rendering on the building natural vision three-dimensional model to generate a lighting effect three-dimensional model;
A first lighting satisfaction data generating unit 4, configured to score the lighting effect three-dimensional model based on a preset lighting requirement, so as to generate first lighting satisfaction data;
A preliminary adjustment data set construction unit 5, configured to record historical environmental data, state data of the lighting device, and corresponding first lighting satisfaction data based on a preset public time axis, and correlate the environmental data, the state data of the lighting device, and the corresponding first lighting satisfaction data to construct a preliminary adjustment data set;
A first adjustment strategy generation unit 6, configured to preset a preliminary adjustment model to analyze the preliminary adjustment data set based on a self-learning machine algorithm, so as to generate a first adjustment strategy according to environmental data, where the first adjustment strategy is used to enable the lighting device to adjust state data according to environmental changes, so that flood lighting meets preset 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;
A user feedback data set construction unit 7, configured to acquire feedback data of a user, and construct a user feedback data set based on the feedback data, where the user includes a user of the building, a display object of flood lighting, and a resident near the building;
A demand adjustment data generating unit 8, configured to preset a user feedback analysis model to analyze the user feedback data set based on a machine self-learning algorithm, so as to generate demand adjustment data, where the demand adjustment data is used to update preset lighting demands;
A second adjustment policy generating unit 9, configured to preset a correction adjustment model to analyze the updated lighting requirement and the preset lighting requirement, and generate requirement difference data, where the correction adjustment model generates a second adjustment policy based on the requirement difference data, and the second adjustment policy is used to perform correction adjustment control on the lighting device, so as to improve user satisfaction corresponding to building floodlighting.
The specific limitation of the intelligent building floodlighting control device can be referred to the limitation of the intelligent building floodlighting control method hereinabove, and the description thereof is omitted here. The modules in the intelligent building floodlighting control device can be fully or partially realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or independent of a processor in the electronic device, or may be stored in software in a memory in the electronic device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, an electronic device is provided, which may be a server, and the internal structure of which may be as shown in fig. 3. The electronic device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the electronic device is for storing the database. The network interface of the electronic device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, implements an intelligent building floodlighting control method.
In one embodiment, an electronic device is provided that includes a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
Building basic data are obtained, and a building basic three-dimensional model is built according to the building basic data;
Acquiring real-time environment data and performing preliminary rendering on the building foundation three-dimensional model according to the real-time environment data to generate a building natural vision three-dimensional model, wherein the building natural vision three-dimensional model is used for displaying the corresponding natural illumination condition of a building in a real-time environment;
acquiring distribution data of all the floodlighting devices and real-time state data of each floodlighting device, and performing illumination rendering on the building natural vision three-dimensional model to generate an illumination effect three-dimensional model;
Scoring the lighting effect three-dimensional model based on preset lighting requirements to generate first lighting satisfaction data;
Recording historical environment data, state data of the lighting equipment and corresponding first lighting satisfaction data based on a preset public time axis, and associating the environment data, the state data of the 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 a self-learning machine algorithm to generate a first adjustment strategy according to environment data, wherein the first adjustment strategy is used for enabling the lighting equipment to adjust state data according to environment changes so as to enable floodlight to meet preset lighting requirements, and the first adjustment strategy comprises first lighting angle adjustment data, first brightness adjustment data, first color temperature adjustment data and corresponding energy consumption data;
acquiring feedback data of a user, and constructing a user feedback data set based on the feedback data, wherein the user comprises a user of a building, a floodlighting display object and households nearby the building;
Analyzing the user feedback data set based on a machine self-learning algorithm by a preset user feedback analysis model to generate demand adjustment data, wherein the demand adjustment data is used for updating preset lighting demands;
the preset correction adjustment model analyzes the updated illumination demand and the preset illumination demand and generates demand difference data, and the correction adjustment model generates a second adjustment strategy based on the demand difference data, wherein the second adjustment strategy is used for carrying out correction adjustment control on illumination equipment so as to improve user satisfaction corresponding to building floodlight illumination.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
Building basic data are obtained, and a building basic three-dimensional model is built according to the building basic data;
Acquiring real-time environment data and performing preliminary rendering on the building foundation three-dimensional model according to the real-time environment data to generate a building natural vision three-dimensional model, wherein the building natural vision three-dimensional model is used for displaying the corresponding natural illumination condition of a building in a real-time environment;
acquiring distribution data of all the floodlighting devices and real-time state data of each floodlighting device, and performing illumination rendering on the building natural vision three-dimensional model to generate an illumination effect three-dimensional model;
Scoring the lighting effect three-dimensional model based on preset lighting requirements to generate first lighting satisfaction data;
Recording historical environment data, state data of the lighting equipment and corresponding first lighting satisfaction data based on a preset public time axis, and associating the environment data, the state data of the 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 a self-learning machine algorithm to generate a first adjustment strategy according to environment data, wherein the first adjustment strategy is used for enabling the lighting equipment to adjust state data according to environment changes so as to enable floodlight to meet preset lighting requirements, and the first adjustment strategy comprises first lighting angle adjustment data, first brightness adjustment data, first color temperature adjustment data and corresponding energy consumption data;
acquiring feedback data of a user, and constructing a user feedback data set based on the feedback data, wherein the user comprises a user of a building, a floodlighting display object and households nearby the building;
Analyzing the user feedback data set based on a machine self-learning algorithm by a preset user feedback analysis model to generate demand adjustment data, wherein the demand adjustment data is used for updating preset lighting demands;
the preset correction adjustment model analyzes the updated illumination demand and the preset illumination demand and generates demand difference data, and the correction adjustment model generates a second adjustment strategy based on the demand difference data, wherein the second adjustment strategy is used for carrying out correction adjustment control on illumination equipment so as to improve user satisfaction corresponding to building floodlight illumination.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile 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. By way of illustration and not limitation, RAM is available in a variety of 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), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The foregoing embodiments are merely illustrative of the technical solutions of the present application, and not restrictive, and although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that modifications may still be made to the technical solutions described in the foregoing embodiments or equivalent substitutions of some technical features thereof, and that such modifications or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (7)
1. The intelligent building floodlighting control method is characterized by comprising the steps of obtaining building basic data and constructing a building basic three-dimensional model according to the building basic data;
Acquiring real-time environment data and performing preliminary rendering on the building foundation three-dimensional model according to the real-time environment data to generate a building natural vision three-dimensional model, wherein the building natural vision three-dimensional model is used for displaying the corresponding natural illumination condition of a building in a real-time environment;
acquiring distribution data of all the floodlighting devices and real-time state data of each floodlighting device, and performing illumination rendering on the building natural vision three-dimensional model to generate an illumination effect three-dimensional model;
Scoring the lighting effect three-dimensional model based on preset lighting requirements to generate first lighting satisfaction data;
Recording historical environment data, state data of the lighting equipment and corresponding first lighting satisfaction data based on a preset public time axis, and associating the environment data, the state data of the 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 a self-learning machine algorithm to generate a first adjustment strategy according to environment data, wherein the first adjustment strategy is used for enabling the lighting equipment to adjust state data according to environment changes so as to enable floodlight to meet preset lighting requirements, and the first adjustment strategy comprises first lighting angle adjustment data, first brightness adjustment data, first color temperature adjustment data and corresponding energy consumption data;
acquiring feedback data of a user, and constructing a user feedback data set based on the feedback data, wherein the user comprises a user of a building, a floodlighting display object and households nearby the building;
Analyzing the user feedback data set based on a machine self-learning algorithm by a preset user feedback analysis model to generate demand adjustment data, wherein the demand adjustment data is used for updating preset lighting demands;
the preset correction adjustment model analyzes the updated illumination demand and the preset illumination demand and generates demand difference data, and the correction adjustment model generates a second adjustment strategy based on the demand difference data, wherein the second adjustment strategy is used for carrying out correction adjustment control on illumination equipment so as to improve user satisfaction corresponding to building floodlight illumination;
The method comprises the following steps of acquiring feedback data of a user and constructing a user feedback data set based on the feedback data:
Acquiring limb behavior data and facial emotion data of a user when the user passes through a building as feedback data of the user;
Acquiring survey data of a user as feedback data of the user, wherein the survey data comprise flood lighting satisfaction survey data of the user for different time periods, flood lighting satisfaction survey data of the user for different holidays and flood lighting satisfaction survey data of the user for different scenes;
constructing a user feedback dataset based on the limb behavior data, facial emotion data, and survey data;
specifically, in the step of analyzing the user feedback data set based on a machine self-learning algorithm in the preset user feedback analysis model to generate demand adjustment data, the demand adjustment data is used for updating the preset lighting demand, the method comprises the following steps:
Analyzing the limb behavior data of the user based on a preset limb analysis model to generate first feedback scoring data;
analyzing facial emotion data of the user based on a preset emotion analysis model to generate second feedback scoring 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;
Classifying and analyzing the investigation data of the user, and constructing a corresponding investigation data sub-data set according to the classification result;
analyzing the different investigation 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, wherein the demand adjustment data is used for adjusting the floodlight lighting device to improve the feedback satisfaction degree of the user.
2. An intelligent building floodlighting control method according to claim 1, characterized in that after the step of analyzing the user feedback data set based on a machine self-learning algorithm by a preset user feedback analysis model to generate demand adjustment data for updating preset lighting demands, the method comprises the steps of:
acquiring weather forecast information and generating prediction environment data corresponding to a future time period based on the weather forecast information;
And generating a preset lighting strategy corresponding to a future time period based on the predicted environment data and the updated lighting requirement, wherein the preset lighting strategy is used for setting control parameters of the lighting equipment in advance, so that the timeliness of control is improved.
3. The intelligent building floodlighting control method of claim 1, wherein the lighting requirement comprises a lighting brightness threshold, a wall color temperature threshold, and a pattern definition.
4. The intelligent building floodlighting control method according to claim 1, wherein the self-learning machine algorithm comprises a neural network algorithm and a random forest algorithm.
5. An intelligent building floodlighting control device applied to the intelligent building floodlighting control method as claimed in any one of claims 1 to 4, wherein the device comprises a building foundation three-dimensional model construction unit (1) for acquiring building foundation data and constructing a building foundation three-dimensional model according to the building foundation data;
The building natural vision three-dimensional model generating unit (2) is used for acquiring real-time environment data and performing preliminary rendering on the building basic three-dimensional model according to the real-time environment data so as to generate a building natural vision three-dimensional model, wherein the building natural vision three-dimensional model is used for displaying the corresponding natural illumination condition of a building in a real-time environment;
The illumination effect three-dimensional model generating unit (3) is used for acquiring distribution data of all the floodlighting devices and real-time state data of each floodlighting device, and performing illumination rendering on the building natural vision three-dimensional model to generate an illumination effect three-dimensional model;
a first lighting satisfaction data generation unit (4) for scoring 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) for recording historical environment data, state data of the lighting device and corresponding first lighting satisfaction data based on a preset public time axis, and associating the environment data, the state data of the lighting device and the corresponding first lighting satisfaction data to construct a preliminary adjustment data set;
A first adjustment strategy generation unit (6) 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 environmental data, the first adjustment strategy being used for adjusting state data of the lighting device according to environmental changes so that the floodlighting meets preset lighting requirements, wherein the first adjustment strategy comprises 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) for acquiring feedback data of a user and constructing a user feedback data set based on the feedback data, wherein the user comprises a user of the building, a floodlighting display object and a resident nearby the building;
a demand adjustment data generation unit (8) for pre-setting a user feedback analysis model to analyze the user feedback data set based on a machine self-learning algorithm to generate demand adjustment data for updating a pre-set lighting demand;
the second adjustment strategy generation unit (9) is used for presetting a correction adjustment model to analyze the updated illumination demand and the preset illumination demand and generate demand difference data, the correction adjustment model is used for generating a second adjustment strategy based on the demand difference data, and the second adjustment strategy is used for carrying out correction adjustment control on illumination equipment so as to improve user satisfaction corresponding to building floodlighting.
6. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of an intelligent building floodlighting control method according to any one of claims 1 to 4.
7. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of an intelligent building floodlighting control method according to any of claims 1 to 4.
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