Intelligent glass remote control management system and method for lighting based on multi-source data

Abstract

The application discloses a multi-source data-based intelligent glass remote control management system and method for lighting, and relates to the technical field of edge computing.The application accurately divides the independent coverage area and the common coverage area of adjacent intelligent glass by analyzing the natural light propagation characteristics, establishes a coordinate mapping system, solves the problem of fuzzy coverage area definition, and provides reliable data basis and spatial positioning basis for collaborative regulation.Based on the dynamic distribution and adjustment weight of the migration proportion of the common coverage area, the migration proportion angle mapping table is constructed relying on historical high-quality data, the traditional single glass independent adjustment mode is abandoned, and the regulation parameter standardization is realized.Through natural light parameter prediction and deviation correction stack closed-loop correction, the adjustment deviation is effectively corrected, the adjacent glass can ensure sufficient natural lighting after adjustment, the common coverage area illumination mean value, variance and standard deviation after light transmission can meet the preset index, the problem of uneven illumination is solved, and the regulation accuracy and stability are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of edge computing technology, specifically to a remote control management system and method for smart glass used in lighting based on multi-source data. Background Technology

[0002] As an important carrier for building lighting and illumination control, smart glass for lighting uses a built-in angle adjustment mechanism to drive the glass body to flexibly adjust the lighting angle. At the same time, relying on its own light control characteristics, it adapts to changes in the intensity and angle of natural light, and adjusts the light transmission effect as needed. The application of this type of glass can not only make efficient use of natural lighting resources and reduce the energy consumption of artificial lighting in the building, but also create a comfortable indoor lighting environment through reasonable control of natural light, thereby meeting the dual needs of buildings for natural lighting and balanced illumination.

[0003] In practical applications of smart glass for lighting, adjacent smart glass units share a common coverage area under natural light. Existing technologies do not accurately define and map the coverage area based on the characteristics of natural light propagation, nor can they calculate the migration ratio of the common coverage area within the coverage range of adjacent smart glass units based on changes in the angle and intensity of natural light and dynamically allocate adjustment weights. As a result, when the common coverage area migrates due to dynamic changes in natural light parameters, uneven light intensity is very likely to occur, which fails to meet the light balance requirements of smart glass lighting applications. Summary of the Invention

[0004] The purpose of this invention is to provide a remote control management system and method for intelligent glass for lighting based on multi-source data, so as to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a remote control and management method for smart glass used in lighting based on multi-source data, the method comprising the following steps: Collect natural light parameters, smart glass installation parameters, operation data, and illumination data of the common coverage area, and combine them with the unique identifier of the corresponding smart glass to construct a structured historical illumination control dataset; The natural light parameters include light intensity, light angle, and light angle change rate; natural light parameters are continuously collected at preset collection intervals, and the collection timestamps are recorded synchronously and the units are converted according to a unified measurement standard; the smart glass installation parameters include installation location coordinates, installation height, effective light-collecting size, and adjustable angle range; the smart glass operation data includes the real-time adjustment angle and adjustment response rate of the smart glass; the common coverage area illumination data includes the real-time illuminance values ​​and illuminance distribution coordinates of multiple collection points within the area; a structured historical illumination control dataset is constructed using a key-value pair storage method, with the unique identifier of the smart glass as the key, and the collected natural light parameters, smart glass installation parameters, operation data, common coverage area illumination data, and the timestamps corresponding to each data point as values; Existing technologies do not systematically collect and standardize the storage of natural light parameters, smart glass installation parameters, operational data, and illumination data of the common coverage area, and lack data support for regulation; It provides a complete, accurate, and traceable data foundation for coverage area analysis, migration ratio calculation, mapping table construction, and deviation correction, ensuring the data reliability of the entire process control logic.

[0006] The lack of a coordinate mapping system resulted in ambiguous regional positioning because the independent and shared coverage areas of adjacent smart glass were not accurately defined by taking into account the characteristics of natural light propagation. Based on the installation parameters of smart glass and the characteristics of natural light propagation, the independent and common coverage areas of two adjacent smart glass panels are analyzed, and a coordinate mapping system for the coverage areas is established. Extract the installation location coordinates, installation height, and effective light-receiving size of the smart glass. Combine this with the rectilinear propagation characteristics of natural light to calculate the boundary coordinates of the coverage area of ​​a single smart glass piece at different adjustment angles. The intersection calculation is performed on the boundary coordinates of the coverage areas of two adjacent smart glass panels to obtain the coordinate range of the overlapping area of ​​the two glass panels, and this overlapping area coordinate range is taken as the common coverage area. The portion of the overall coverage area of ​​a single smart glass piece, excluding the shared coverage area, is defined as an independent coverage area. The boundary coordinates, area, and relative positional relationships of independent coverage areas and shared coverage areas are associated and labeled to establish a coordinate mapping system for coverage areas; To achieve precise positioning and quantitative description of the shared coverage area, providing core spatial coordinate basis for migration calculation and weight allocation adjustment of the shared coverage area.

[0007] It is impossible to calculate the migration ratio of the shared coverage area based on changes in natural light, and it is impossible to dynamically allocate adjustment weights to adjacent smart glass, causing the problem of light imbalance caused by area migration; Real-time acquisition of natural light parameters, calculation of the migration ratio of the common coverage area within the respective coverage areas of two adjacent smart glass panels, and dynamic allocation of adjustment weights for the two glass panels. The illumination angle and the rate of change of the illumination angle of natural light are collected in real time by light sensor and angle sensor, and the real-time coordinate range of the common coverage area is calculated by combining the coordinate mapping system of the coverage area. Calculate the first ratio of the actual area of ​​the shared coverage area to the total area of ​​the first smart glass coverage area, and the second ratio of the actual area of ​​the shared coverage area to the total area of ​​the second smart glass coverage area. Use the first ratio as the migration ratio of the first smart glass and the second ratio as the migration ratio of the second smart glass. The migration ratio of the first smart glass is used as the initial value of its adjustment weight, and the migration ratio of the second smart glass is used as the initial value of its adjustment weight. Normalization is performed on the two initial values ​​of adjustment weight. The normalization process is to calculate the ratio of each initial value of adjustment weight to the sum of the two initial values ​​of adjustment weight, and then obtain the final adjustment weight of the two smart glasses. The adjustment intensity of two adjacent smart glass panels can be dynamically adapted as the location of the shared coverage area moves, laying the foundation for balanced light distribution from the perspective of control logic and avoiding light deviation caused by the adjustment of a single glass panel.

[0008] There is a lack of historical data cleaning and screening mechanisms, a lack of objective light intensity quantification standards, and subjective control measures. Acquire historical illumination control datasets and preprocess them, then filter out high-quality data subsets that meet preset conditions, and calculate the corresponding illumination quantification indexes. Read the historical illumination control dataset of smart glass, and use the three sigma principle to remove outlier data from the dataset. The outlier data includes isolated data caused by sensor interference and invalid data formed by instantaneous changes in illumination. The lighting control dataset after removing outlier data is standardized, and the natural light parameters and illuminance values ​​are normalized according to a unified measurement standard. The illuminance values ​​represent the measured light intensity of natural light at each collection point in the independent coverage area and the common coverage area of ​​the smart glass. Based on the lighting requirements of smart glass lighting applications and the characteristic patterns of historical lighting control data, lighting data screening conditions are set, and the effective data after standardization processing is screened to obtain data that meets the screening conditions and integrate them to form a high-quality data subset. Illuminance values ​​of each collection point in the common coverage area are extracted from the high-quality data subset. The arithmetic mean, variance and standard deviation of the illuminance values ​​are calculated. The calculated arithmetic mean, variance and standard deviation are integrated into a light quantification index. To improve the effectiveness of historical data, establish an objective and unified evaluation standard for illumination status, and provide effective data and quantitative evaluation basis for the construction of migration ratio angle mapping table.

[0009] The lack of correlation mapping relationships based on historical control data and the absence of standardized parameters to guide the coordinated adjustment of smart glass are problems. By associating natural light parameters, migration ratios of common coverage areas, adjustment weights, smart glass adjustment angles, and illumination quantification indicators in a subset of high-quality data, a migration ratio-angle mapping table is constructed. Natural light parameters, migration ratio of common coverage area, adjustment weight of two smart glass panels, adjustment angle of two smart glass panels, and corresponding light quantification indicators are extracted from high-quality data subsets one by one. Using natural light intensity, natural light angle, and migration ratio of shared coverage area as joint index terms, the adjustment weights of the two smart glass panels and the adjustment angles of the two smart glass panels as mapping terms, and light quantification indicators as correlation terms, the correlation relationships between the various data are established. The established relationships are stored in a structured format to form a migration ratio angle mapping table; Transforming historical high-quality regulation experience into standardized regulation basis, realizing the direct conversion from data to regulation parameters, and replacing the traditional single glass independent regulation mode.

[0010] The lack of a natural light parameter prediction mechanism and adjustment deviation correction mechanism results in insufficient control precision and an inability to continuously ensure balanced illumination. Real-time time-series data is constructed based on the collected natural light parameters, and the natural light parameters and migration ratio of the common coverage area in the subsequent preset time period are analyzed and predicted; a deviation correction stack is created and combined with the migration ratio angle mapping table to obtain the target adjustment weight and target adjustment angle of adjacent smart glass, so as to control and manage the smart glass; Arrange the real-time collected natural light parameters in chronological order to construct real-time time series data of natural light parameters. Configure a time sliding window of preset fixed length and preset step size, and extract natural light parameter data within the time sliding window from the real-time time series data. Calculate the statistical characteristics of natural light parameter data within a time sliding window, including mean, trend slope, and fluctuation amplitude. Based on the statistical characteristics, analyze and predict the natural light intensity and natural light angle within a subsequent preset time period using a neural network model. Based on the predicted natural light intensity and illumination angle, combined with the coordinate mapping system of the coverage area, the real-time coordinate range of the common coverage area within the subsequent preset time period is calculated, and the migration ratio of the common coverage area within the respective coverage areas of the two adjacent smart glass panels is obtained. The predicted natural light intensity and light angle are combined with the migration ratio of the common coverage area as a joint index. The migration ratio angle mapping table is queried, and the adjustment weight and adjustment angle of the two smart glasses corresponding to the index in the mapping table are extracted as the initial adjustment weight and initial adjustment angle. The adjustment operation is performed based on the initial adjustment weight and initial adjustment angle. The actual illumination quantification index of the common coverage area after adjustment is collected. The deviation value between the actual illumination quantification index and the standard illumination quantification index is calculated. The deviation value is then entered into the deviation correction stack after being associated with the corresponding timestamp, the predicted natural light intensity, and the illumination angle. Configure a time sliding window for the deviation correction stack, and extract multiple sets of historical deviation values ​​at the same time and the subsequent adjustment data corresponding to each set of deviation values ​​from the deviation correction stack according to the time correspondence. Based on the extracted historical simultaneous deviation values ​​and subsequent adjustment data, the predicted natural light intensity and natural light angle for the subsequent preset time period are corrected. The correction process involves extracting the natural light intensity correction amount and the light angle correction amount corresponding to each set of deviation values, calculating the arithmetic mean of the natural light intensity correction amount and the light angle correction amount of multiple sets of historical simultaneous natural light intensity correction amounts and light angle correction amounts respectively, and performing superposition operations on the arithmetic mean of the two types of correction amounts with the predicted natural light intensity and light angle for the subsequent preset time period respectively. The corrected natural light intensity, light angle, and migration ratio of the common coverage area are used as a joint index. The migration ratio angle mapping table is queried again, and the adjustment weight and adjustment angle of the two smart glasses corresponding to the index in the mapping table are extracted as the target adjustment weight and target adjustment angle of the adjacent smart glasses. Generate control commands containing target adjustment weights and target adjustment angles, and send the control commands to the smart glass execution terminal via a remote control link to control and manage the smart glass's angle adjustment. It enables advance adaptation to changes in natural light and closed-loop correction of adjustment deviations, significantly improving the accuracy and stability of smart glass adjustment; After the angle of two adjacent smart glass panels is adjusted, it can fully adapt to the dynamic changes in the intensity and angle of natural light, ensuring sufficient natural lighting in the building. It can also ensure that the shared coverage area after light transmission strictly matches the preset illuminance quantification index, ensuring that the arithmetic mean of illuminance in the area meets the preset lighting standard, and that the illuminance variance and illuminance standard deviation are maintained in a stable and balanced range. This completely eliminates the technical problems of uneven light intensity and obvious differences in brightness caused by the relocation of the shared coverage area, and simultaneously achieves a dual improvement in the efficiency of natural lighting utilization and the uniformity of indoor lighting.

[0011] Furthermore, a remote control and management system for smart glass used in lighting based on multi-source data is provided, which includes a data acquisition and construction module, a coverage weight calculation module, a data processing and mapping module, and an intelligent control and correction module. The data acquisition and construction module is used to collect various types of regulation-related data and construct a structured historical illumination regulation dataset; the coverage weight calculation module is used to analyze the coverage area of ​​the smart glass and establish a coordinate mapping system, calculate the migration ratio and allocate adjustment weights; the data processing and mapping module is used to preprocess the historical illumination regulation dataset, calculate illumination quantification indicators and construct a migration ratio angle mapping table; the intelligent regulation and correction module is used to predict natural light parameters and migration ratios, combine deviation correction stacks to correct parameters and realize remote control of the smart glass. The data acquisition module includes a multi-source data acquisition unit and a dataset construction unit. The multi-source data acquisition unit is used to acquire natural light parameters, smart glass installation parameters, operating data, and illumination data of the common coverage area, and synchronously record timestamps and complete unit conversion. The dataset construction unit is used to construct a structured historical illumination control dataset using the unique identifier of the smart glass as the key and adopting a key-value pair method. The coverage weight calculation module includes a coverage area analysis unit and a weight allocation calculation unit. The coverage area analysis unit is used to extract smart glass installation parameters, determine independent and common coverage areas, and establish a coverage area coordinate mapping system. The weight allocation calculation unit is used to calculate the migration ratio of the common coverage area and, after normalization, assign the final adjustment weight to adjacent glass. The data processing mapping module includes a data preprocessing unit and a mapping table construction unit. The data preprocessing unit is used to remove abnormal data, standardize the data, select a high-quality data subset, calculate and integrate the light quantification index. The mapping table construction unit is used to associate the high-quality data subset with related data, establish data association relationships, and construct a structured migration ratio angle mapping table. The intelligent control and correction module includes a parameter prediction unit and a deviation correction control unit. The parameter prediction unit is used to construct real-time time-series data of natural light, analyze and predict the natural light parameters and migration ratio in the subsequent preset time period. The deviation correction control unit is used to create a deviation correction stack to correct the predicted parameters, query the mapping table to generate and issue control commands to adjust the smart glass.

[0012] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention standardizes and structures the collection of natural light parameters, smart glass installation parameters, operating data, and illumination data of the common coverage area. It accurately divides the independent coverage area and the common coverage area by combining the propagation characteristics of natural light and establishes a coordinate mapping system. This solves the problems of scattered data and ambiguous coverage area definition in the existing technology, and provides a complete data foundation and accurate spatial positioning basis for the collaborative control of smart glass.

[0013] 2. This invention dynamically allocates the adjustment weight of adjacent smart glass based on the migration ratio of the common coverage area. It constructs a migration ratio angle mapping table based on historical high-quality data, deeply correlates natural light changes, adjustment strategies and light quantification indicators, abandons the traditional mode of independent adjustment of a single glass, realizes the standardization and queryability of control parameters, and avoids the light imbalance caused by regional migration from the control logic.

[0014] 3. This invention uses a closed-loop correction mechanism of natural light parameter prediction and deviation correction stack, combined with a mapping table to complete target parameter query and remote control distribution, effectively correcting the deviation between prediction and actual adjustment. This ensures that after adjustment of adjacent glass, natural lighting effect can be guaranteed, and the mean, variance and standard deviation of illuminance in the light-transmitting area can stably meet the preset indicators, solving the problem of uneven light intensity and greatly improving the accuracy and stability of control. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a remote control and management method for smart glass used in lighting based on multi-source data, according to the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1: As Figure 1 As shown, the present invention provides a technical solution: a remote control and management method for smart glass used in lighting based on multi-source data. The remote control and management method for smart glass used in lighting includes the following steps: Collect natural light parameters, smart glass installation parameters, operation data, and illumination data of the common coverage area, and combine them with the unique identifier of the corresponding smart glass to construct a structured historical illumination control dataset; The natural light parameters include light intensity, light angle, and light angle change rate; natural light parameters are continuously collected at preset collection intervals, and the collection timestamps are recorded synchronously and the units are converted according to a unified measurement standard; the smart glass installation parameters include installation location coordinates, installation height, effective light-collecting size, and adjustable angle range; the smart glass operation data includes the real-time adjustment angle and adjustment response rate of the smart glass; the common coverage area illumination data includes the real-time illuminance values ​​and illuminance distribution coordinates of multiple collection points within the area; a structured historical illumination control dataset is constructed using a key-value pair storage method, with the unique identifier of the smart glass as the key, and the collected natural light parameters, smart glass installation parameters, operation data, common coverage area illumination data, and the timestamps corresponding to each data point as values; In practice, the actual deployment scenario of smart glass on building facades is used as the basis for implementation. The pre-set sensing and acquisition equipment is used to synchronously collect natural light parameters, smart glass related parameters, and illumination data of the common coverage area. Data unit conversion and timestamp recording are completed according to unified measurement standards. The unique identifier of smart glass is used to classify, bind, and store multi-source data in a structured manner, ensuring that various types of data form a precise correspondence with the control scenario of the corresponding glass.

[0018] Based on the installation parameters of smart glass and the characteristics of natural light propagation, the independent and common coverage areas of two adjacent smart glass panels are analyzed, and a coordinate mapping system for the coverage areas is established. Extract the installation location coordinates, installation height, and effective light-receiving size of the smart glass. Combine this with the rectilinear propagation characteristics of natural light to calculate the boundary coordinates of the coverage area of ​​a single smart glass piece at different adjustment angles. The intersection calculation is performed on the boundary coordinates of the coverage areas of two adjacent smart glass panels to obtain the coordinate range of the overlapping area of ​​the two glass panels, and this overlapping area coordinate range is taken as the common coverage area. The portion of the overall coverage area of ​​a single smart glass piece, excluding the shared coverage area, is defined as an independent coverage area. The boundary coordinates, area, and relative positional relationships of independent coverage areas and shared coverage areas are associated and labeled to establish a coordinate mapping system for coverage areas; In practical implementation, the installation parameters of smart glass are extracted based on the actual layout environment of the building's indoor lighting. The coordinate calculation of the boundary of the single glass coverage area is completed according to the physical characteristics of the rectilinear propagation of natural light. The independent coverage area and the common coverage area are divided by comparing the overlapping relationship of adjacent glass coverage areas. The relevant parameters of the area are associated and labeled to form a coverage area coordinate mapping relationship that can be directly adapted to subsequent calculations.

[0019] Real-time acquisition of natural light parameters, calculation of the migration ratio of the common coverage area within the respective coverage areas of two adjacent smart glass panels, and dynamic allocation of adjustment weights for the two glass panels. The illumination angle and the rate of change of the illumination angle of natural light are collected in real time by light sensor and angle sensor, and the real-time coordinate range of the common coverage area is calculated by combining the coordinate mapping system of the coverage area. Calculate the first ratio of the actual area of ​​the shared coverage area to the total area of ​​the first smart glass coverage area, and the second ratio of the actual area of ​​the shared coverage area to the total area of ​​the second smart glass coverage area. Use the first ratio as the migration ratio of the first smart glass and the second ratio as the migration ratio of the second smart glass. The migration ratio of the first smart glass is used as the initial value of its adjustment weight, and the migration ratio of the second smart glass is used as the initial value of its adjustment weight. Normalization is performed on the two initial values ​​of adjustment weight. The normalization process is to calculate the ratio of each initial value of adjustment weight to the sum of the two initial values ​​of adjustment weight, and then obtain the final adjustment weight of the two smart glasses. In practice, the regional location migration caused by the dynamic changes of natural light is used as the control guide. Natural light angle parameters are obtained in real time through light sensors and angle sensors. The common coverage area range is calculated by combining the coverage area coordinate mapping system. The migration ratio is calculated by area ratio and the adjustment weight is determined by normalization processing, so that the adjustment allocation state of adjacent glass is matched with the migration characteristics of the common coverage area.

[0020] Acquire historical illumination control datasets and preprocess them, then filter out high-quality data subsets that meet preset conditions, and calculate the corresponding illumination quantification indexes. Read the historical illumination control dataset of smart glass, and use the three sigma principle to remove outlier data from the dataset. The outlier data includes isolated data caused by sensor interference and invalid data formed by instantaneous changes in illumination. The lighting control dataset after removing outlier data is standardized, and the natural light parameters and illuminance values ​​are normalized according to a unified measurement standard. The illuminance values ​​represent the measured light intensity of natural light at each collection point in the independent coverage area and the common coverage area of ​​the smart glass. Based on the lighting requirements of smart glass lighting applications and the characteristic patterns of historical lighting control data, lighting data screening conditions are set, and the effective data after standardization processing is screened to obtain data that meets the screening conditions and integrate them to form a high-quality data subset. Illuminance values ​​of each collection point in the common coverage area are extracted from the high-quality data subset. The arithmetic mean, variance and standard deviation of the illuminance values ​​are calculated. The calculated arithmetic mean, variance and standard deviation are integrated into a light quantification index. In practice, abnormal data is screened out by combining the overall distribution characteristics of historical illumination control data, data normalization is performed according to unified standards, effective data is selected to form a high-quality data subset based on the actual needs of smart glass lighting applications, and illumination quantitative indicators are formed through statistical calculations of illuminance data, providing an objective and unified evaluation basis for the construction of control mapping relationships.

[0021] By associating natural light parameters, migration ratios of common coverage areas, adjustment weights, smart glass adjustment angles, and illumination quantification indicators in a subset of high-quality data, a migration ratio-angle mapping table is constructed. Natural light parameters, migration ratio of common coverage area, adjustment weight of two smart glass panels, adjustment angle of two smart glass panels, and corresponding light quantification indicators are extracted from high-quality data subsets one by one. Using natural light intensity, natural light angle, and migration ratio of shared coverage area as joint index terms, the adjustment weights of the two smart glass panels and the adjustment angles of the two smart glass panels as mapping terms, and light quantification indicators as correlation terms, the correlation relationships between the various data are established. The established relationships are stored in a structured format to form a migration ratio angle mapping table; In practice, a high-quality subset of data is used as the basis to integrate natural light parameters, migration ratios, adjustment weights, adjustment angles and illumination quantification indicators. Data binding is completed according to the hierarchical structure of the joint index mapping association. The correspondence between data is solidified by a standardized storage form, forming a migration ratio and angle mapping table that can be directly called, thereby improving the parameter matching efficiency in the real-time control stage.

[0022] Real-time time-series data is constructed based on the collected natural light parameters, and the natural light parameters and migration ratio of the common coverage area in the subsequent preset time period are analyzed and predicted; a deviation correction stack is created and combined with the migration ratio angle mapping table to obtain the target adjustment weight and target adjustment angle of adjacent smart glass, so as to control and manage the smart glass; Arrange the real-time collected natural light parameters in chronological order to construct real-time time series data of natural light parameters. Configure a time sliding window of preset fixed length and preset step size, and extract natural light parameter data within the time sliding window from the real-time time series data. Calculate the statistical characteristics of natural light parameter data within a time sliding window, including mean, trend slope, and fluctuation amplitude. Based on the statistical characteristics, analyze and predict the natural light intensity and natural light angle within a subsequent preset time period using a neural network model. Based on the predicted natural light intensity and illumination angle, combined with the coordinate mapping system of the coverage area, the real-time coordinate range of the common coverage area within the subsequent preset time period is calculated, and the migration ratio of the common coverage area within the respective coverage areas of the two adjacent smart glass panels is obtained. The predicted natural light intensity and light angle are combined with the migration ratio of the common coverage area as a joint index. The migration ratio angle mapping table is queried, and the adjustment weight and adjustment angle of the two smart glasses corresponding to the index in the mapping table are extracted as the initial adjustment weight and initial adjustment angle. The adjustment operation is performed based on the initial adjustment weight and initial adjustment angle. The actual illumination quantification index of the common coverage area after adjustment is collected. The deviation value between the actual illumination quantification index and the standard illumination quantification index is calculated. The deviation value is then entered into the deviation correction stack after being associated with the corresponding timestamp, the predicted natural light intensity, and the illumination angle. Configure a time sliding window for the deviation correction stack, and extract multiple sets of historical deviation values ​​at the same time and the subsequent adjustment data corresponding to each set of deviation values ​​from the deviation correction stack according to the time correspondence. Based on the extracted historical simultaneous deviation values ​​and subsequent adjustment data, the predicted natural light intensity and natural light angle for the subsequent preset time period are corrected. The correction process involves extracting the natural light intensity correction amount and the light angle correction amount corresponding to each set of deviation values, calculating the arithmetic mean of the natural light intensity correction amount and the light angle correction amount of multiple sets of historical simultaneous natural light intensity correction amounts and light angle correction amounts respectively, and performing superposition operations on the arithmetic mean of the two types of correction amounts with the predicted natural light intensity and light angle for the subsequent preset time period respectively. The corrected natural light intensity, light angle, and migration ratio of the common coverage area are used as a joint index. The migration ratio angle mapping table is queried again, and the adjustment weight and adjustment angle of the two smart glasses corresponding to the index in the mapping table are extracted as the target adjustment weight and target adjustment angle of the adjacent smart glasses. Generate control commands containing target adjustment weights and target adjustment angles, and send the control commands to the smart glass execution terminal via a remote control link to control and manage the smart glass's angle adjustment. In practice, the parameters for subsequent periods are predicted based on the temporal variation characteristics of natural light parameters. The actual control deviation is obtained through initial adjustment operations and entered into the deviation correction stack. The predicted parameters are corrected based on historical deviation data. The final control parameters are determined by combining the migration ratio angle mapping table and remote control commands are issued, forming a complete closed-loop control process to ensure the accuracy and stability of intelligent glass illumination control.

[0023] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

Claims

1. A remote control and management method for smart glass used in lighting based on multi-source data, characterized in that: The remote control and management method for smart glass used in lighting includes: Collect natural light parameters, smart glass installation parameters, operation data, and illumination data of the common coverage area, and combine them with the unique identifier of the corresponding smart glass to construct a structured historical illumination control dataset; Based on the installation parameters of smart glass and the characteristics of natural light propagation, the independent and common coverage areas of two adjacent smart glass panels are analyzed, and a coordinate mapping system for the coverage areas is established. Real-time acquisition of natural light parameters, calculation of the migration ratio of the common coverage area within the respective coverage areas of two adjacent smart glass panels, and dynamic allocation of adjustment weights for the two glass panels. Acquire historical illumination control datasets and preprocess them, then filter out high-quality data subsets that meet preset conditions, and calculate the corresponding illumination quantification indexes. By associating natural light parameters, migration ratios of common coverage areas, adjustment weights, smart glass adjustment angles, and illumination quantification indicators in a subset of high-quality data, a migration ratio-angle mapping table is constructed. Real-time time-series data is constructed based on the collected natural light parameters, and the natural light parameters and migration ratio of the common coverage area in the subsequent preset time period are analyzed and predicted. A deviation correction stack is created and combined with the migration ratio angle mapping table to obtain the target adjustment weight and target adjustment angle of adjacent smart glass, so as to control and manage the smart glass.

2. The method for remote control and management of smart glass for lighting based on multi-source data according to claim 1, characterized in that: Based on the installation parameters of smart glass and the characteristics of natural light propagation, the independent and shared coverage areas of two adjacent smart glass panels are analyzed, and a coordinate mapping system for the coverage areas is established, including: Extract the installation location coordinates, installation height, and effective light-receiving size of the smart glass. Combine this with the rectilinear propagation characteristics of natural light to calculate the boundary coordinates of the coverage area of ​​a single smart glass piece at different adjustment angles. The intersection calculation is performed on the boundary coordinates of the coverage areas of two adjacent smart glass panels to obtain the coordinate range of the overlapping area of ​​the two glass panels, and this overlapping area coordinate range is taken as the common coverage area. The portion of the overall coverage area of ​​a single smart glass piece, excluding the shared coverage area, is defined as an independent coverage area. The boundary coordinates, area, and relative positional relationships of independent coverage areas and shared coverage areas are associated and labeled to establish a coordinate mapping system for coverage areas.

3. The method for remote control and management of smart glass for lighting based on multi-source data according to claim 2, characterized in that: The real-time acquisition of natural light parameters, calculation of the migration ratio of the shared coverage area within the respective coverage areas of two adjacent smart glass panes, and dynamic allocation of adjustment weights for the two glass panes include: The illumination angle and the rate of change of the illumination angle of natural light are collected in real time by light sensor and angle sensor, and the real-time coordinate range of the common coverage area is calculated by combining the coordinate mapping system of the coverage area. Calculate the first ratio of the actual area of ​​the shared coverage area to the total area of ​​the first smart glass coverage area, and the second ratio of the actual area of ​​the shared coverage area to the total area of ​​the second smart glass coverage area. Use the first ratio as the migration ratio of the first smart glass and the second ratio as the migration ratio of the second smart glass. The migration ratio of the first smart glass is used as the initial value of its adjustment weight, and the migration ratio of the second smart glass is used as the initial value of its adjustment weight. Normalization is performed on the two initial values ​​of adjustment weight. The normalization process is to calculate the ratio of each initial value of adjustment weight to the sum of the two initial values ​​of adjustment weight, so as to obtain the final adjustment weight of the two smart glasses.

4. The method for remote control and management of smart glass for lighting based on multi-source data according to claim 1, characterized in that: The process of acquiring and preprocessing historical illumination control datasets, filtering out high-quality data subsets that meet preset conditions, and calculating corresponding illumination quantification indicators also includes: Read the historical illumination control dataset of smart glass, and use the three sigma principle to remove outlier data from the dataset. The outlier data includes isolated data caused by sensor interference and invalid data formed by instantaneous changes in illumination. The lighting control dataset after removing outlier data is standardized, and the natural light parameters and illuminance values ​​are normalized according to a unified measurement standard. The illuminance values ​​represent the measured light intensity of natural light at each collection point in the independent coverage area and the common coverage area of ​​the smart glass. Based on the lighting requirements of smart glass lighting applications and the characteristic patterns of historical lighting control data, lighting data screening conditions are set, and the effective data after standardization processing is screened to obtain data that meets the screening conditions and integrate them to form a high-quality data subset. Illuminance values ​​from each collection point in the common coverage area are extracted from the high-quality data subset. The arithmetic mean, variance, and standard deviation of the illuminance values ​​are calculated, and the calculated arithmetic mean, variance, and standard deviation are integrated into a luminance quantification index.

5. The method for remote control and management of smart glass for lighting based on multi-source data according to claim 1, characterized in that: The natural light parameters, migration ratios of shared coverage areas, adjustment weights, smart glass adjustment angles, and illumination quantization indicators from the associated high-quality data subset are used to construct a migration ratio-angle mapping table, including: Natural light parameters, migration ratio of common coverage area, adjustment weight of two smart glass panels, adjustment angle of two smart glass panels, and corresponding light quantification indicators are extracted from high-quality data subsets one by one. Using natural light intensity, natural light angle, and migration ratio of shared coverage area as joint index terms, the adjustment weights of the two smart glass panels and the adjustment angles of the two smart glass panels as mapping terms, and light quantification indicators as correlation terms, the correlation relationships between the various data are established. The established relationships are stored in a structured format to form a migration ratio angle mapping table.

6. The method for remote control and management of smart glass for lighting based on multi-source data according to claim 1, characterized in that: The control and management of the smart glass includes: Arrange the real-time collected natural light parameters in chronological order to construct real-time time series data of natural light parameters. Configure a time sliding window of preset fixed length and preset step size, and extract natural light parameter data within the time sliding window from the real-time time series data. Calculate the statistical characteristics of natural light parameter data within a time sliding window, including mean, trend slope, and fluctuation amplitude. Based on the statistical characteristics, analyze and predict the natural light intensity and natural light angle within a subsequent preset time period using a neural network model. Based on the predicted natural light intensity and illumination angle, combined with the coordinate mapping system of the coverage area, the real-time coordinate range of the common coverage area within the subsequent preset time period is calculated, and the migration ratio of the common coverage area within the respective coverage areas of the two adjacent smart glass panels is obtained. The predicted natural light intensity and light angle are combined with the migration ratio of the common coverage area as a joint index. The migration ratio angle mapping table is queried, and the adjustment weight and adjustment angle of the two smart glasses corresponding to the index in the mapping table are extracted as the initial adjustment weight and initial adjustment angle. The adjustment operation is performed based on the initial adjustment weight and initial adjustment angle. The actual illumination quantification index of the common coverage area after adjustment is collected. The deviation value between the actual illumination quantification index and the standard illumination quantification index is calculated. The deviation value is then entered into the deviation correction stack after being associated with the corresponding timestamp, the predicted natural light intensity, and the illumination angle. Configure a time sliding window for the deviation correction stack, and extract multiple sets of historical deviation values ​​at the same time and the subsequent adjustment data corresponding to each set of deviation values ​​from the deviation correction stack according to the time correspondence. Based on the extracted historical simultaneous deviation values ​​and subsequent adjustment data, the predicted natural light intensity and natural light angle for the subsequent preset time period are corrected. The correction process involves extracting the natural light intensity correction amount and the light angle correction amount corresponding to each set of deviation values, calculating the arithmetic mean of the natural light intensity correction amount and the light angle correction amount of multiple sets of historical simultaneous natural light intensity correction amounts and light angle correction amounts respectively, and performing superposition operations on the arithmetic mean of the two types of correction amounts with the predicted natural light intensity and light angle for the subsequent preset time period respectively. The corrected natural light intensity, light angle, and migration ratio of the common coverage area are used as a joint index. The migration ratio angle mapping table is queried again, and the adjustment weight and adjustment angle of the two smart glasses corresponding to the index in the mapping table are extracted as the target adjustment weight and target adjustment angle of the adjacent smart glasses. The system generates control commands that include target adjustment weights and target adjustment angles, and sends these commands to the smart glass actuator via a remote control link to control and manage the smart glass's angle adjustment.

7. The method for remote control and management of smart glass for lighting based on multi-source data according to claim 1, characterized in that: The construction of the structured historical illumination control dataset includes: The natural light parameters include light intensity, light angle, and light angle change rate; natural light parameters are continuously collected at preset collection intervals, and the collection timestamps are recorded synchronously and the units are converted according to a unified measurement standard; the smart glass installation parameters include installation location coordinates, installation height, effective light-collecting size, and adjustable angle range; the smart glass operation data includes the real-time adjustment angle and adjustment response rate of the smart glass; the common coverage area illumination data includes the real-time illuminance values ​​and illuminance distribution coordinates of multiple collection points within the area; a structured historical illumination control dataset is constructed using a key-value pair storage method, with the unique identifier of the smart glass as the key, and the collected natural light parameters, smart glass installation parameters, operation data, common coverage area illumination data, and the timestamps corresponding to each data point as values.

8. A remote control and management system for intelligent lighting glass based on multi-source data, applied to the remote control and management method for intelligent lighting glass based on multi-source data as described in any one of claims 1-7, characterized in that: The intelligent glass remote control and management system for lighting includes a data acquisition and construction module, a coverage weight calculation module, a data processing and mapping module, and an intelligent control and correction module. The data acquisition and construction module is used to collect various types of regulation-related data and construct a structured historical illumination regulation dataset; the coverage weight calculation module is used to analyze the coverage area of ​​the smart glass and establish a coordinate mapping system, calculate the migration ratio and allocate adjustment weights; the data processing and mapping module is used to preprocess the historical illumination regulation dataset, calculate illumination quantification indicators and construct a migration ratio angle mapping table; the intelligent regulation and correction module is used to predict natural light parameters and migration ratios, combine deviation correction stacks to correct parameters and realize remote control of the smart glass.

9. A remote control and management system for intelligent glass for lighting based on multi-source data as described in claim 8, characterized in that: The data acquisition module includes a multi-source data acquisition unit and a dataset construction unit; the multi-source data acquisition unit is used to acquire natural light parameters, smart glass installation parameters, operating data and illumination data of the common coverage area, and synchronously record timestamps and complete unit conversion; The dataset construction unit is used to construct a structured historical illumination control dataset using the unique identifier of the smart glass as the key and adopting a key-value pair method. The coverage weight calculation module includes a coverage area analysis unit and a weight allocation calculation unit. The coverage area analysis unit is used to extract smart glass installation parameters, determine independent and shared coverage areas, and establish a coverage area coordinate mapping system. The weight allocation calculation unit is used to calculate the migration ratio of shared coverage areas and, after normalization, assign final adjustment weights to adjacent glass.

10. A remote control and management system for intelligent glass for lighting based on multi-source data as described in claim 8, characterized in that: The data processing mapping module includes a data preprocessing unit and a mapping table construction unit. The data preprocessing unit is used to remove abnormal data, standardize the data, select a high-quality data subset, calculate and integrate the light quantification index. The mapping table construction unit is used to associate the high-quality data subset with related data, establish data association relationships, and construct a structured migration ratio angle mapping table. The intelligent control and correction module includes a parameter prediction unit and a deviation correction control unit. The parameter prediction unit is used to construct real-time time-series data of natural light, analyze and predict the natural light parameters and migration ratio in the subsequent preset time period. The deviation correction control unit is used to create a deviation correction stack to correct the predicted parameters, query the mapping table to generate and issue control commands to adjust the smart glass.

Images