CN117222069A - Intelligent control method of illumination driving power supply - Google Patents
Intelligent control method of illumination driving power supply Download PDFInfo
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Abstract
An intelligent control method of a lighting driving power supply acquires an ambient brightness value and an ambient temperature value at a plurality of preset time points in a preset time period; performing data preprocessing on the environment brightness values and the environment temperature values of the plurality of preset time points to obtain an up-sampling environment brightness time sequence input vector and an up-sampling environment temperature time sequence input vector; performing feature extraction and feature fusion on the up-sampling ambient brightness time sequence input vector and the up-sampling ambient temperature time sequence input vector to obtain a multi-parameter fusion time sequence feature vector; and determining a recommended output current value of the illumination driving power supply based on the multi-parameter fusion timing feature vector. Thus, the output current of the illumination driving power supply can be intelligently adjusted to realize optimization of illumination effect and saving of energy consumption.
Description
Technical Field
The invention relates to the technical field of intelligent control, in particular to an intelligent control method of a lighting driving power supply.
Background
The illumination driving power supply is a power supply device for supplying electric power required for the illumination apparatus. It converts electrical energy into a current and voltage suitable for the lighting device for its normal operation.
The conventional lighting driving power supply control method generally adopts a fixed output current value, and cannot be dynamically adjusted according to environmental factors. This results in that the illumination brightness may be too high or too low to meet the user's needs under different environmental conditions, and also causes waste of energy.
Thus, an optimized control scheme for the illumination driving power supply is desired.
Disclosure of Invention
The present invention has been made to solve the above-mentioned technical problems. The embodiment of the invention provides an intelligent control method of a lighting driving power supply, which is used for acquiring an ambient brightness value and an ambient temperature value of a plurality of preset time points in a preset time period; performing data preprocessing on the environment brightness values and the environment temperature values of the plurality of preset time points to obtain an up-sampling environment brightness time sequence input vector and an up-sampling environment temperature time sequence input vector; performing feature extraction and feature fusion on the up-sampling ambient brightness time sequence input vector and the up-sampling ambient temperature time sequence input vector to obtain a multi-parameter fusion time sequence feature vector; and determining a recommended output current value of the illumination driving power supply based on the multi-parameter fusion timing feature vector. Thus, the output current of the illumination driving power supply can be intelligently adjusted to realize optimization of illumination effect and saving of energy consumption.
In a first aspect, an intelligent control method for a lighting driving power supply is provided, including:
acquiring an ambient brightness value and an ambient temperature value at a plurality of preset time points in a preset time period;
performing data preprocessing on the environment brightness values and the environment temperature values of the plurality of preset time points to obtain an up-sampling environment brightness time sequence input vector and an up-sampling environment temperature time sequence input vector;
performing feature extraction and feature fusion on the up-sampling ambient brightness time sequence input vector and the up-sampling ambient temperature time sequence input vector to obtain a multi-parameter fusion time sequence feature vector; and
and determining the recommended output current value of the illumination driving power supply based on the multi-parameter fusion time sequence feature vector.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an intelligent control method of a lighting driving power supply according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of an intelligent control method of a lighting driving power supply according to an embodiment of the invention.
Fig. 3 is a block diagram of an intelligent control system for a lighting driving power supply according to an embodiment of the present invention.
Fig. 4 is a schematic view of a scenario of an intelligent control method of a lighting driving power supply according to an embodiment of the present invention.
Detailed Description
The following description of the technical solutions according to the embodiments of the present invention will be given with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Unless defined otherwise, all technical and scientific terms used in the embodiments of the invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the present invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present invention.
In describing embodiments of the present invention, unless otherwise indicated and limited thereto, the term "connected" should be construed broadly, for example, it may be an electrical connection, or may be a communication between two elements, or may be a direct connection, or may be an indirect connection via an intermediate medium, and it will be understood by those skilled in the art that the specific meaning of the term may be interpreted according to circumstances.
It should be noted that, the term "first\second\third" related to the embodiment of the present invention is merely to distinguish similar objects, and does not represent a specific order for the objects, it is to be understood that "first\second\third" may interchange a specific order or sequence where allowed. It is to be understood that the "first\second\third" distinguishing objects may be interchanged where appropriate such that embodiments of the invention described herein may be practiced in sequences other than those illustrated or described herein.
A lighting driving power supply is a power supply device for supplying power required for a lighting device, converting power from a power grid or other power source into a current and voltage suitable for the lighting device for its normal operation. The illumination driving power source is generally used to supply various types of illumination devices including indoor and outdoor illumination systems such as LED lamps, fluorescent lamps, halogen lamps, and the like. These power supplies typically provide a stable current and voltage output to ensure proper operation of the lighting device.
Conventional lighting driving power supplies generally employ a fixed output current value to control the brightness of the lighting device. However, this method cannot be dynamically adjusted according to environmental factors, so that the illumination brightness may be too high or too low under different environmental conditions, so that the requirements of users cannot be met, and meanwhile, energy waste is caused. In order to solve the problem, some advanced illumination driving power supplies adopt intelligent control technology, and the power supplies can sense parameters such as ambient brightness, temperature and the like and dynamically adjust output current according to the parameters so as to realize optimization of illumination effect and saving of energy consumption. Some lighting driving power supplies also have a dimming function, and the level of the lighting brightness can be adjusted as required.
The illumination driving power supply plays a vital role in indoor and outdoor illumination systems, and the optimized illumination driving power supply can provide efficient and reliable electric energy supply, and simultaneously meets the requirements of users on illumination effect and energy conservation. Along with the wide application of the LED lighting technology, a lighting driving power supply is also continuously developed and innovated so as to adapt to different lighting requirements and energy-saving requirements.
The conventional lighting driving power control method generally controls the brightness of the lighting device using a fixed output current value, and this method supplies a constant current to the lighting device based on a set fixed value so that it operates normally. In conventional lighting driving power supplies, a fixed output current value is typically set, which determines the brightness level of the lighting device. The control method is simple and stable, and is suitable for scenes with low lighting requirements or relatively stable environmental conditions.
However, the conventional fixed output current control method has some limitations. The conventional illumination driving power supply generally only provides a fixed output current, and cannot flexibly adjust the brightness according to actual requirements, which results in that the illumination brightness may be too high or too low under different environmental conditions, and the requirements of users cannot be met. Because the conventional illumination driving power supply cannot be dynamically adjusted according to the environmental change, energy waste may be caused, for example, in an environment with strong light, the illumination device still operates with a fixed high brightness, which causes unnecessary consumption of energy. The traditional illumination driving power supply lacks an intelligent function, cannot sense environmental parameters and make corresponding adjustment, which means that illumination brightness cannot be automatically adjusted according to factors such as illumination intensity, temperature and the like, and optimization of energy consumption cannot be achieved. Conventional lighting driving power supplies are generally only suitable for certain types of lamps, and cannot flexibly adapt to different types of lighting devices, which limits the range of choices for users and increases the complexity of system design and maintenance.
In order to solve these problems, new lighting driving power supply control methods, such as intelligent dimming and dimming driving, have been developed in recent years, and the methods can dynamically adjust the output current of the lighting driving power supply based on the sensing and feedback of the ambient brightness, so as to achieve the optimization of the lighting effect and the saving of energy consumption. These new control methods utilize sensors or feedback mechanisms to monitor the ambient brightness and adjust the output current based on real-time brightness information to maintain the proper brightness level of the lighting device under different ambient conditions.
Fig. 1 is a flowchart of an intelligent control method of a lighting driving power supply according to an embodiment of the present invention. Fig. 2 is a schematic diagram of an intelligent control method of a lighting driving power supply according to an embodiment of the invention. As shown in fig. 1 and 2, the intelligent control method of the lighting driving power supply includes: 110, acquiring an ambient brightness value and an ambient temperature value at a plurality of preset time points in a preset time period; 120, performing data preprocessing on the ambient brightness values and the ambient temperature values at the plurality of preset time points to obtain an up-sampling ambient brightness time sequence input vector and an up-sampling ambient temperature time sequence input vector; 130, performing feature extraction and feature fusion on the up-sampling ambient brightness time sequence input vector and the up-sampling ambient temperature time sequence input vector to obtain a multi-parameter fusion time sequence feature vector; and 140, determining a recommended output current value of the illumination driving power supply based on the multi-parameter fusion time sequence feature vector.
In the step 110, ambient brightness values and ambient temperature values are acquired at a plurality of predetermined points in time over a predetermined period of time using a sensor or other ambient monitoring device. And a proper illumination sensor is selected to measure the ambient brightness value, and a temperature sensor is selected to measure the ambient temperature value, so that the accuracy and stability of the sensor are ensured.
Wherein a desired time period, e.g. a specific time period within a day, is determined and sampling is performed at a plurality of time points within the time period. At each predetermined point in time, data acquisition is performed using the sensor, and an ambient brightness value and an ambient temperature value are recorded.
In the step 120, the collected ambient brightness value and the ambient temperature value are preprocessed to obtain an up-sampled ambient brightness time series input vector and an up-sampled ambient temperature time series input vector. The collected data is cleaned, abnormal values and noise are removed, and accuracy and reliability of the data are ensured. The ambient light values and the ambient temperature values are organized and ordered in a time series according to a sequence of predetermined points in time. If the sampling frequency of the predetermined time point is low, interpolation or other up-sampling methods can be used to interpolate the time series data to obtain an ambient brightness time series input vector and an ambient temperature time series input vector with higher frequency.
In the step 130, feature extraction and feature fusion are performed on the up-sampled ambient brightness time sequence input vector and the ambient temperature time sequence input vector to obtain a multi-parameter fused time sequence feature vector. The feature extraction is performed on the ambient brightness time sequence input vector and the ambient temperature time sequence input vector, and various methods such as statistical features, frequency domain features or time domain features can be used to extract representative features. The extracted ambient brightness features and the ambient temperature features are fused, and a simple splicing, weighted average or more complex fusion method can be used to obtain a multi-parameter fused time sequence feature vector.
In the step 140, the recommended output current value of the illumination driving power supply is determined according to the timing characteristic vector of the multi-parameter fusion. Wherein, according to actual requirements and application scenes, a recommendation model or rule based on a machine learning model, a rule engine or other methods can be established to determine an output current value. In determining the output current value, energy efficiency and lighting requirements need to be considered in combination. For example, the output current value is adjusted according to the trend of the ambient brightness and the temperature to achieve the optimization of the power consumption and the satisfaction of the lighting effect.
Aiming at the technical problems, the technical conception of the invention is to dynamically adjust the output current of the illumination driving power supply by acquiring the ambient brightness and the ambient temperature value and utilizing a deep learning algorithm so as to realize the optimization of the illumination effect and the saving of energy consumption.
It should be appreciated that ambient brightness directly affects one's perception of illumination. When ambient brightness is low, it is often desirable for the illumination brightness to be high to provide adequate illumination; while when the ambient brightness is high, one may need a lower illumination brightness to avoid excessive light irritation. In addition, ambient temperature has a certain impact on the performance and lifetime of lighting devices. The high temperature environment may lead to increased heating of the lighting device, while overheating may reduce the efficiency and lifetime of the lighting device. Therefore, in the technical scheme of the invention, the two factors of the ambient brightness and the ambient temperature are expected to be comprehensively considered to carry out self-adaptive control on the output current of the illumination driving power supply, so that the comfortable illumination effect which accords with the perception of human eyes is provided, and the performance of the illumination driving power supply is protected and the service life of the illumination driving power supply is prolonged.
Based on this, in the technical scheme of the present invention, first, the ambient brightness value and the ambient temperature value at a plurality of predetermined time points in a predetermined period of time are acquired. Ambient brightness value refers to a value of the intensity of illumination measured in a particular location or area, typically used to describe the brightness of the environment. The ambient brightness value may be measured by means of an illumination sensor or photometer, etc. The light sensor can sense the ambient light level and convert it into an electrical signal for measurement and recording. Ambient brightness values are typically expressed in units of illumination intensity (e.g., lux).
The ambient temperature value refers to a value of the ambient temperature measured in a particular location or region, describing the heat level of the environment. The ambient temperature value may be measured by a temperature sensor or thermometer, etc. The temperature sensor can sense the ambient temperature and convert it into an electrical signal for measurement and recording. Ambient temperature values are typically expressed in degrees celsius (°c) or degrees fahrenheit (°f).
In the lighting system, the ambient brightness value and the ambient temperature value of a plurality of preset time points in a preset time period are obtained to know the lighting requirement and the temperature change condition of the environment, and the values can be used as the input of the intelligent lighting control system to dynamically adjust the brightness and the temperature of the lighting equipment according to the environment condition so as to improve the energy efficiency and the user experience. By acquiring and analyzing the ambient brightness value and the ambient temperature value, the adaptive adjustment of the lighting system can be realized, a comfortable lighting environment is provided, and the energy consumption is saved.
Acquiring ambient brightness values and ambient temperature values at a plurality of predetermined time points within a predetermined period of time has an important role in the final determination of the recommended output current value of the illumination driving power supply. Ambient brightness and ambient temperature are important factors affecting the energy efficiency of the lighting system, and by obtaining these values, the output current value of the lighting driving power supply can be adjusted according to the actual environmental conditions, so as to achieve the optimization of energy efficiency. For example, at higher ambient brightness, the output current value may be reduced appropriately to save energy. And at lower ambient brightness, the output current value may be increased to provide sufficient lighting effect.
Different ambient brightness and temperature conditions may have an effect on the lighting requirements, and by obtaining these values, the output current value of the lighting driving power supply may be adjusted according to the actual requirements, so as to meet the requirements of the user on the lighting. For example, in darker environments, the output current value may be increased to provide brighter illumination. While in brighter environments, the output current value may be reduced to avoid over-illumination.
The ambient temperature is one of the important factors affecting the comfort of the human body, and by acquiring the ambient temperature value, the output current value of the illumination driving power supply can be adjusted according to the actual temperature condition, so as to provide a more comfortable illumination environment. For example, at a higher ambient temperature, the output current value may be appropriately reduced to reduce the amount of heat generation of the lighting device, thereby improving comfort. The key environment information can be provided by acquiring the environment brightness values and the environment temperature values of a plurality of preset time points in the preset time period, and the output current value of the recommended illumination driving power supply is helped to be determined, so that energy efficiency optimization, illumination demand satisfaction and comfort improvement can be realized, and the performance and user experience of the illumination system are improved.
And then, carrying out data preprocessing on the environment brightness values and the environment temperature values at a plurality of preset time points to obtain an up-sampling environment brightness time sequence input vector and an up-sampling environment temperature time sequence input vector. That is, the data structure processing is performed on the ambient brightness value and the ambient temperature value of the discrete time sequence distribution, so as to facilitate the reading and the identification of the computer.
In a specific example of the present invention, the encoding process for performing data preprocessing on the ambient brightness values and the ambient temperature values at the plurality of predetermined time points to obtain the up-sampling ambient brightness time sequence input vector and the up-sampling ambient temperature time sequence input vector includes: firstly, arranging the ambient brightness values and the ambient temperature values of the plurality of preset time points into an ambient brightness time sequence input vector and an ambient temperature time sequence input vector according to a time dimension; and then up-sampling the ambient brightness time sequence input vector and the ambient temperature time sequence input vector based on linear interpolation to obtain an up-sampling ambient brightness time sequence input vector and an up-sampling ambient temperature time sequence input vector.
Firstly, the arrangement of the ambient brightness value and the ambient temperature value into the time sequence input vector according to the time dimension has the advantage that the dynamic information of the time sequence can be reserved. By organizing the data in time sequence, the change trend of the ambient brightness and the temperature, such as the brightness change in the day and the night, the seasonal change of the temperature and the like, can be captured, so that the change situation of the ambient condition can be better known, and an accurate data basis is provided for subsequent processing and analysis.
Then, the beneficial effect of linear interpolation based up-sampling of ambient brightness and temperature is to increase the time resolution of the data. Sometimes, the sampling frequency for obtaining ambient brightness and temperature may be low, and subtle changes may not be captured. By upsampling, the data points can be refined in time so that the data is smoother and more continuous, which can more accurately analyze trends in changes in ambient brightness and temperature, and provide more accurate input information for subsequent feature extraction and fusion.
The environment brightness and the temperature are arranged into time sequence input vectors according to time dimension, and up-sampling of linear interpolation is carried out, so that dynamic information of time sequence can be reserved, time resolution of data can be increased, the change trend of environment conditions can be better understood, more accurate input data can be provided, and a more reliable basis is provided for subsequent data processing and analysis.
Among other things, up-sampling is understood to be a signal processing technique for increasing the sampling rate or improving the time resolution of a signal. During the up-sampling process, the number of sampling points of the signal increases, thereby making the signal finer and more accurate in time. In the case of the ambient brightness timing input vector and the ambient temperature timing input vector, the up-sampling based on linear interpolation may be performed by: it is first determined how many times the number of sampling points of the signal is to be increased. This may be determined according to specific needs and application scenarios. Then, for each original sampling point, a linear interpolation method is used to calculate the corresponding upsampling point. Linear interpolation is a simple and commonly used interpolation method, and interpolation calculation is performed based on the linear relationship between the original sampling points. Specifically, for two adjacent original sampling points, an upsampling point between them can be calculated by linear interpolation.
The interpolation formula is as follows: interpolation point = original sampling point × upsampling multiple; interpolation point position = original sampling point position + (upsampling factor-1) (original sampling point position-previous original sampling point position)/upsampling factor. The interpolation points are sampling points after up sampling, the interpolation point positions are the positions of each interpolation point on a time axis, and the original sampling point positions are the positions of the original sampling points on the time axis.
Repeating the steps until all up-sampling points are calculated.
Through the steps, the up-sampling based on the linear interpolation can be carried out on the ambient brightness time sequence input vector and the ambient temperature time sequence input vector, so that the corresponding up-sampling ambient brightness time sequence input vector and up-sampling ambient temperature time sequence input vector are obtained, the number of data points can be increased, the time resolution is improved, and more detailed and accurate input data are provided for the subsequent feature extraction and fusion steps.
It is contemplated that ambient brightness and ambient temperature will typically exhibit regular changes as a function of time of day. For example, ambient brightness typically increases gradually during the day, reaches a peak during the day, and then decreases gradually during the night. The ambient temperature may also vary significantly between day and night. In addition, the ambient brightness and ambient temperature may also be affected by other factors to cause short-term fluctuations. For example, changes in weather conditions may result in temporary changes in ambient brightness, and switching on and off of indoor lighting may result in short term fluctuations in ambient temperature. Meanwhile, the control of the output current of the illumination driving power supply needs to consider the cooperative association relationship between the ambient brightness and the ambient temperature. Therefore, in the technical scheme of the invention, feature extraction and feature fusion are expected to be carried out on the up-sampling ambient brightness time sequence input vector and the up-sampling ambient temperature time sequence input vector so as to obtain a multi-parameter fusion time sequence feature vector.
In a specific example of the present invention, the encoding process for performing feature extraction and feature fusion on the up-sampling ambient brightness time sequence input vector and the up-sampling ambient temperature time sequence input vector to obtain a multi-parameter fusion time sequence feature vector includes: firstly, the up-sampling ambient brightness time sequence input vector and the up-sampling ambient temperature time sequence input vector are processed through a time sequence feature extractor based on a multi-scale neighborhood feature extraction module to obtain a multi-scale ambient brightness time sequence feature vector and a multi-scale ambient temperature time sequence feature vector; and fusing the multi-scale environment brightness time sequence feature vector and the multi-scale environment temperature time sequence feature vector to obtain a multi-parameter fusion time sequence feature vector.
Firstly, the time sequence feature extractor based on the multi-scale neighborhood feature extraction module has the advantage that the multi-scale change features of the ambient brightness and the temperature can be captured. There may be different time scales for the ambient brightness and temperature changes, such as short-term rapid changes and long-term slow changes. By using the multi-scale neighborhood feature extraction module, features can be extracted on different time scales, so that changing modes on different scales can be captured, time sequence features of ambient brightness and temperature can be more comprehensively described, and accuracy and robustness of subsequent processing are improved.
Then, the multi-scale environment brightness time sequence feature vector and the multi-scale environment temperature time sequence feature vector are fused, and the beneficial effects are that the information of different feature dimensions is integrated, so that more comprehensive feature representation is provided. The ambient brightness and temperature may have different effects on the output current values of the illumination driving power supply, so that the effects of both can be better comprehensively considered by fusing their timing characteristics. By fusing the time sequence feature vectors of multiple parameters, richer feature information can be provided, and the change condition of the environmental conditions can be described more accurately, so that a more reliable basis is provided for determining the final output current value.
The time sequence feature extractor based on the multi-scale neighborhood feature extraction module can capture multi-scale change features of ambient brightness and temperature, and improves the expression capability of the features. Meanwhile, the multi-scale environment brightness time sequence feature vector and the multi-scale environment temperature time sequence feature vector are fused, information of different feature dimensions can be comprehensively considered, and more comprehensive feature representation is provided. This helps to describe the change in environmental conditions more accurately and provides a more reliable basis for the determination of the final output current value.
It should be understood that the time sequence feature extractor based on the multi-scale neighborhood feature extraction module is a method for extracting multi-scale features from time sequence data, and can be applied to up-sampling environment brightness time sequence input vectors and up-sampling environment temperature time sequence input vectors to obtain multi-scale environment brightness time sequence feature vectors and multi-scale environment temperature time sequence feature vectors.
The time sequence feature extractor based on the multi-scale neighborhood feature extraction module can extract multi-scale environment change features from the up-sampled environment brightness time sequence input vector and the environment temperature time sequence input vector. Thus, brightness and temperature change information on different time scales can be captured, and richer and comprehensive feature representations are provided for subsequent feature distribution optimization and decoder conversion steps.
In one embodiment of the present invention, determining a recommended output current value of the illumination driving power supply based on the multi-parameter fusion timing feature vector includes: performing feature distribution optimization on the multi-parameter fusion time sequence feature vector to obtain an optimized multi-parameter fusion time sequence feature vector; and passing the optimized multi-parameter fusion timing feature vector through a decoder to obtain a decoded value, wherein the decoded value is used for representing the recommended output current value of the illumination driving power supply.
The multi-parameter fusion time sequence feature vector feature distribution optimization method has the beneficial effects of improving the quality and accuracy of feature representation. Through optimizing the feature distribution, the feature vector can be more uniform and reasonable in value in different dimensions, redundancy and correlation between features are reduced, and therefore the distinguishing degree and the expression capability of the features can be improved, and the feature vector can more accurately represent the time sequence features of the ambient brightness and the temperature.
The beneficial effect of passing the optimized multi-parameter fusion time sequence feature vector through the decoder to obtain the decoding value is to convert the abstract feature vector into a specific output current value. The decoder may map the optimized feature vector to the output space to obtain a recommended output current value of the illumination driving power supply. Therefore, reasonable output current values can be provided according to actual environmental conditions and characteristic representations so as to meet the requirements of users on lighting effects. By means of the conversion of the decoder, the abstract characteristic information can be converted into practically usable control parameters, and accurate control of the lighting system is achieved.
The quality and the accuracy of feature representation can be improved by carrying out feature distribution optimization on the multi-parameter fusion time sequence feature vector, and the optimized feature vector is converted into a decoding value through a decoder to realize recommendation and control of an output current value of an illumination driving power supply, so that the energy efficiency performance of an illumination system can be improved, the illumination effect can be optimized, and a reasonable output current value can be provided according to actual environmental conditions so as to meet the demands of users.
The feature distribution optimization is performed on the multi-parameter fusion time sequence feature vector to obtain an optimized multi-parameter fusion time sequence feature vector, which comprises the following steps: calculating an incidence matrix between the multi-scale environment brightness time sequence feature vector and the multi-scale environment temperature time sequence feature vector; performing rank arrangement distribution soft matching with feature scales serving as imitation masks on the correlation matrix to obtain an optimized correlation feature matrix; and fusing the multi-parameter fusion time sequence feature vector with the optimization association feature matrix to obtain the optimization multi-parameter fusion time sequence feature vector.
In the technical scheme of the invention, considering that the multi-scale environment brightness time sequence feature vector and the multi-scale environment temperature time sequence feature vector respectively express multi-scale local time sequence association features of environment brightness values and environment temperature values, the multi-scale environment brightness time sequence feature vector and the multi-scale environment temperature time sequence feature vector are fused point by point in a weighted point adding mode, and meanwhile, global fusion of the multi-scale environment brightness time sequence feature vector and the multi-scale environment temperature time sequence feature vector is expected to be further improved, so that an association matrix between the multi-scale environment brightness time sequence feature vector and the multi-scale environment temperature time sequence feature vector is calculated, and then the multi-parameter fusion time sequence feature vector and the association matrix are fused to optimize the multi-parameter fusion time sequence feature vector.
However, further considering that when the correlation matrix performs vector global correlation expression, if the multi-scale local time sequence correlation feature of the environment brightness value and the environment temperature value expressed by the multi-scale environment brightness time sequence feature vector and the multi-scale environment temperature time sequence feature vector are used as foreground object features, background distribution noise is also introduced when the correlation matrix performs global correlation fusion expression, and the accuracy of the decoding value obtained by the decoder through the optimized multi-parameter fusion time sequence feature vector is affected by the probability density mapping error of the correlation matrix relative to the time sequence distribution of the multi-scale environment brightness time sequence feature vector and the multi-scale environment temperature time sequence feature vector due to the spatial heterogeneous distribution of heterogeneous high-dimensional features of the local time sequence correlation feature of the environment brightness value and the environment temperature value while the high-rank distribution expression between the vector and the matrix is performed.
Based on this, the applicant of the present invention performs soft matching of the rank arrangement distribution of the feature scale, for example denoted as M, as an imitation mask on the correlation matrix, specifically expressed as: performing rank arrangement distribution soft matching with feature scales of the incidence matrix serving as imitation masks by using the following optimization formula to obtain an optimized incidence feature matrix; wherein, the optimization formula is:
wherein M is the incidence matrix, M i，j Is the eigenvalue of the (i, j) th position of the correlation matrix M, S is the scale of the correlation matrix M, i.e. width times height,represents the square of the Frobenius norm of the correlation matrix M, ii M 2 Representing the two norms of the correlation matrix M, and alpha is a weighted hyper-parameter, M ′ ′ ，j Is the eigenvalue of the (i, j) th position of the optimized correlation eigenvalue matrix, exp (·) represents calculating the natural exponential function value with the value as the power.
Here, the feature scale as the soft matching of the rank arrangement distribution of the mimicking mask may focus the feature scale as the mimicking mask for mapping on the foreground object feature and ignore the background distribution noise when mapping the high-dimensional feature of the regression of several codes into the probability density space, and the soft matching of the distribution of the pyramid rank arrangement distribution by different norms of the correlation matrix M effectively captures the correlation between the center region and the tail region of the probability density distribution, avoiding the probability density mapping bias caused by the spatial heterogeneous distribution of the high-dimensional feature of the correlation matrix M, and then fuses the multi-parameter fusion timing feature vector with the correlation matrix, for example, performs matrix multiplication to optimize the multi-parameter fusion timing feature vector, so as to improve the accuracy of the decoding value of the optimized multi-parameter fusion timing feature vector obtained by the decoder.
Further, the multi-parameter fusion timing feature vector is passed through a decoder to obtain a decoded value representing a recommended output current value of the illumination driving power supply. The decoder is a key component for mapping the optimized multi-parameter fusion timing feature vector to the output current value of the illumination driving power supply, and the design of the decoder is usually based on machine learning or optimization algorithm, aiming at establishing the mapping relation between the feature vector and the output current value.
In particular, the decoder may employ various machine learning models, such as neural networks, support vector machines, decision trees, and the like. These models may learn the nonlinear relationship between the feature vectors and the output current values through a training process. In the training process, the decoder receives the optimized multi-parameter fusion time sequence feature vector as input and outputs a corresponding output current value. By comparing with the true output current value, the loss function can be used to evaluate the performance of the decoder and update the parameters of the decoder by a back propagation algorithm, enabling it to more accurately predict the output current value.
The decoder design may also take into account specific lighting system features and requirements. For example, constraints or a priori knowledge may be introduced to guide the learning process of the decoder to ensure that the output current value meets the operating range, power limitations or human eye perceived brightness requirements of the lighting system. The decoder is a key component for converting the optimized multi-parameter fusion timing feature vector into the recommended output current value of the illumination driving power supply. Through machine learning or optimization algorithms, the decoder can learn the mapping relationship between the feature vector and the output current value and provide an accurate output current value to meet the requirements of the lighting system.
In summary, the intelligent control method of the illumination driving power supply according to the embodiment of the invention is explained, which is to dynamically adjust the output current of the illumination driving power supply by obtaining the ambient brightness and the ambient temperature value and utilizing the deep learning algorithm, so as to realize the optimization of the illumination effect and the saving of the energy consumption.
In one embodiment of the present invention, fig. 3 is a block diagram of an intelligent control system for a lighting driving power supply according to an embodiment of the present invention. As shown in fig. 3, an intelligent control system 200 of a lighting driving power supply according to an embodiment of the present invention includes: a data acquisition module 210, configured to acquire an ambient brightness value and an ambient temperature value at a plurality of predetermined time points within a predetermined time period; a data preprocessing module 220, configured to perform data preprocessing on the ambient brightness values and the ambient temperature values at the multiple predetermined time points to obtain an up-sampling ambient brightness time sequence input vector and an up-sampling ambient temperature time sequence input vector; the feature extraction and fusion module 230 is configured to perform feature extraction and feature fusion on the up-sampling ambient brightness time sequence input vector and the up-sampling ambient temperature time sequence input vector to obtain a multi-parameter fusion time sequence feature vector; and an output current value determining module 240, configured to determine a recommended output current value of the illumination driving power supply based on the multi-parameter fusion timing feature vector.
According to the intelligent control system 200 of the lighting driving power supply, in the first aspect, a reasonable output current value can be recommended according to actual environmental conditions and characteristic representation, so that the excessively high or excessively low lighting brightness can be avoided, the waste of energy sources is reduced, and the energy efficiency performance of the whole lighting system is improved. In the second aspect, the characteristics of multi-scale environmental change can be captured according to the time sequence data of the environmental brightness and the temperature, and the output current value is recommended by comprehensively considering the characteristics, so that the lighting effect can be optimized, the lighting system can provide proper brightness and color temperature under different environmental conditions, and the comfort and the visual experience of a user are improved. In a third aspect, the changes in ambient brightness and temperature can be monitored in real time and the output current value can be adaptively adjusted according to these changes, thus ensuring that the lighting system can be automatically adapted at different times and under different scenes and providing optimal lighting effects. According to the fourth aspect, a reasonable output current value can be provided according to actual environmental conditions and user requirements so as to meet the requirements of users on lighting effects, and the users can enjoy more comfortable lighting experience which meets personal preferences, so that the user satisfaction is improved. In a fifth aspect, abnormal conditions can be found in time through real-time monitoring and analysis of ambient brightness and temperature, and corresponding control strategies are adopted, so that reliability of the lighting system can be improved, risks of faults and damage are reduced, and service life of the lighting equipment is prolonged.
The intelligent control system 200 of the lighting driving power supply has various beneficial effects by optimizing the output current value, improving the energy efficiency performance, optimizing the lighting effect, adaptively adjusting and improving the user satisfaction, and improves the performance and the user experience of the lighting system.
Here, it will be understood by those skilled in the art that the specific functions and operations of the respective units and modules in the above-described intelligent control system of the illumination driving power supply have been described in detail in the above description of the intelligent control method of the illumination driving power supply with reference to fig. 1 to 2, and thus, repetitive descriptions thereof will be omitted.
As described above, the intelligent control system 200 of the illumination driving power supply according to the embodiment of the present invention may be implemented in various terminal devices, such as a server or the like for intelligent control of the illumination driving power supply. In one example, the intelligent control system 200 of the lighting driving power supply according to an embodiment of the present invention may be integrated into the terminal device as one software module and/or hardware module. For example, the intelligent control system 200 of the lighting driving power supply may be a software module in the operating system of the terminal device, or may be an application developed for the terminal device; of course, the intelligent control system 200 of the lighting driving power source may also be one of a plurality of hardware modules of the terminal device.
Alternatively, in another example, the intelligent control system 200 of the lighting driving power supply and the terminal device may be separate devices, and the intelligent control system 200 of the lighting driving power supply may be connected to the terminal device through a wired and/or wireless network and transmit the interactive information in a agreed data format.
Fig. 4 is a schematic view of a scenario of an intelligent control method of a lighting driving power supply according to an embodiment of the present invention. As shown in fig. 4, in the application scenario, first, an ambient brightness value (e.g., C1 as illustrated in fig. 4) and an ambient temperature value (e.g., C2 as illustrated in fig. 4) at a plurality of predetermined time points within a predetermined period of time are acquired; the acquired ambient brightness value and ambient temperature value are then input into a server (e.g., S as illustrated in fig. 4) where an intelligent control algorithm of the lighting drive power supply is deployed, wherein the server is capable of processing the ambient brightness value and the ambient temperature value based on the intelligent control algorithm of the lighting drive power supply to determine a recommended output current value of the lighting drive power supply.
It is also noted that in the apparatus, devices and methods of the present invention, the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the invention. Thus, the present invention is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the invention to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (6)
1. An intelligent control method of a lighting driving power supply is characterized by comprising the following steps:
acquiring an ambient brightness value and an ambient temperature value at a plurality of preset time points in a preset time period;
performing data preprocessing on the environment brightness values and the environment temperature values of the plurality of preset time points to obtain an up-sampling environment brightness time sequence input vector and an up-sampling environment temperature time sequence input vector;
performing feature extraction and feature fusion on the up-sampling ambient brightness time sequence input vector and the up-sampling ambient temperature time sequence input vector to obtain a multi-parameter fusion time sequence feature vector; and
and determining the recommended output current value of the illumination driving power supply based on the multi-parameter fusion time sequence feature vector.
2. The intelligent control method of a lighting driving power supply according to claim 1, wherein the data preprocessing of the ambient brightness values and the ambient temperature values at the plurality of predetermined time points to obtain an up-sampled ambient brightness timing input vector and an up-sampled ambient temperature timing input vector, comprises:
arranging the ambient brightness values and the ambient temperature values of the plurality of preset time points into an ambient brightness time sequence input vector and an ambient temperature time sequence input vector according to a time dimension respectively; and
and up-sampling the ambient brightness time sequence input vector and the ambient temperature time sequence input vector based on linear interpolation to obtain the up-sampling ambient brightness time sequence input vector and the up-sampling ambient temperature time sequence input vector.
3. The intelligent control method of a lighting driving power supply according to claim 2, wherein performing feature extraction and feature fusion on the up-sampling ambient brightness time sequence input vector and the up-sampling ambient temperature time sequence input vector to obtain a multi-parameter fusion time sequence feature vector, comprises:
the up-sampling ambient brightness time sequence input vector and the up-sampling ambient temperature time sequence input vector are processed through a time sequence feature extractor based on a multi-scale neighborhood feature extraction module to obtain a multi-scale ambient brightness time sequence feature vector and a multi-scale ambient temperature time sequence feature vector; and
and fusing the multi-scale environment brightness time sequence feature vector and the multi-scale environment temperature time sequence feature vector to obtain the multi-parameter fusion time sequence feature vector.
4. A method of intelligent control of a lighting drive power supply according to claim 3, wherein determining a recommended lighting drive power supply output current value based on the multiparameter fusion timing feature vector comprises:
performing feature distribution optimization on the multi-parameter fusion time sequence feature vector to obtain an optimized multi-parameter fusion time sequence feature vector; and
and the optimized multi-parameter fusion time sequence characteristic vector is passed through a decoder to obtain a decoding value, wherein the decoding value is used for representing the recommended output current value of the illumination driving power supply.
5. The intelligent control method of a lighting driving power supply according to claim 4, wherein performing feature distribution optimization on the multi-parameter fusion timing feature vector to obtain an optimized multi-parameter fusion timing feature vector, comprises:
calculating an incidence matrix between the multi-scale environment brightness time sequence feature vector and the multi-scale environment temperature time sequence feature vector;
performing rank arrangement distribution soft matching with feature scales serving as imitation masks on the correlation matrix to obtain an optimized correlation feature matrix; and
and fusing the multi-parameter fusion time sequence feature vector with the optimization association feature matrix to obtain the optimization multi-parameter fusion time sequence feature vector.
6. The intelligent control method of a lighting driving power supply according to claim 5, wherein performing soft matching of a rank arrangement distribution of feature scales as an imitation mask on the correlation matrix to obtain an optimized correlation feature matrix, comprises: performing rank arrangement distribution soft matching with feature scales of the incidence matrix serving as imitation masks by using the following optimization formula to obtain an optimized incidence feature matrix;
wherein, the optimization formula is:
wherein M is the incidence matrix, M i，j Is the eigenvalue of the (i, j) th position of the correlation matrix M, S is the scale of the correlation matrix M, i.e. width times height,represents the square of the Frobenius norm of the correlation matrix M, ii M 2 Representing the two norms of the correlation matrix M, and alpha is a weighted hyper-parameter, M' i，j Is the eigenvalue of the (i, j) th position of the optimized correlation eigenvector, exp (·) representsA natural exponential function value is calculated that is a power of a numerical value.
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