Intelligent control method of illumination driving power supply

Abstract

The invention provides an intelligent control method for an illumination driving power supply, and relates to the technical field of illumination control, and the method comprises the steps: carrying out the building of a digital twinborn model according to regional information; virtual test points are distributed according to the regional evaluation result, illumination fitting is carried out based on a digital twin model, illumination effect monitoring is carried out through the virtual test points, and a position point monitoring result is established; monitoring and evaluating the position point monitoring result by using an illumination evaluation network, and generating a target control result of each illumination device; establishing a control prediction model according to the historical control information, wherein the control prediction model is a prediction model for outputting a control result according to the power supply parameters; taking the target control result as a control target, performing inverse solution fitting of power supply parameters through the control prediction model, and generating calibrated power supply parameters; driving control of the lighting device is performed. The technical problem that in the prior art, an illumination system is poor in illumination effect can be solved, and the technical effect of improving the illumination effect is achieved.

Description

Technical Field

[0001] The present application relates to the field of lighting control technology, and in particular to an intelligent control method for a lighting driving power supply. Background Art

[0002] With the development of intelligent lighting systems, automation and intelligence of lighting control have become the key to improving energy efficiency and user experience. Existing intelligent control methods for lighting driver power supplies usually involve monitoring of the lighting environment and feedback-based control strategies to adjust parameters such as lighting intensity and color temperature to suit different environments and user needs, including using traditional sensors for physical measurements and controlling based on the measurement results. However, the existing technologies have some shortcomings that affect the overall performance and efficiency of the lighting system.

[0003] At present, traditional methods are limited by fixed sensor networks, resulting in a limited number and location of monitoring points, which cannot fully cover the entire lighting area, thus affecting the uniformity and adaptability of the lighting effect. In addition, there is often a delay in responding to environmental changes, lacking the ability to quickly process and feedback real-time data, and also lacking personalized and context-aware control strategies, resulting in insufficient precision in lighting adjustments and energy waste.

[0004] To sum up, there are technical problems in the prior art due to the limited number and location of monitoring points of traditional lighting systems, which are insufficient in dynamic environmental adaptability, efficiency and personalized control, resulting in poor performance of lighting systems in terms of energy utilization, user satisfaction and operational flexibility, thus affecting the overall lighting quality and economic benefits. Summary of the invention

[0005] The purpose of this application is to provide an intelligent control method for a lighting drive power supply to solve the technical problems in the prior art that the traditional lighting system has limited number and location of monitoring points, and has deficiencies in dynamic environmental adaptability, efficiency and personalized control, resulting in poor performance of the lighting system in terms of energy utilization, user satisfaction and operational flexibility, thereby affecting the overall lighting quality and economic benefits.

[0006] In view of the above problems, the present application provides an intelligent control method for a lighting driving power supply.

[0007] The present application provides an intelligent control method for a lighting driving power supply, which includes: obtaining regional information of a target area, wherein the regional information includes regional structure information and layout information of lighting devices in the area, and building a digital twin model according to the regional information; performing regional evaluation on the target area, distributing virtual test points according to the regional evaluation results, performing lighting fitting based on the digital twin model, and monitoring the lighting effect through the virtual test points to establish position point monitoring results; configuring a lighting evaluation network, monitoring and evaluating the position point monitoring results with the lighting evaluation network, and generating target control results for each lighting device according to the monitoring and evaluation results; obtaining historical control information of the lighting device, and establishing a control prediction model according to the historical control information, wherein the control prediction model is a prediction model for outputting control results according to power supply parameters; taking the target control result as the control target, performing inverse fitting of power supply parameters through the control prediction model to generate calibrated power supply parameters; and performing drive control of the lighting device according to the calibrated power supply parameters.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] By acquiring regional information of the target area, the regional information includes regional structure information and layout information of lighting devices in the area, and building a digital twin model according to the regional information; performing regional evaluation on the target area, distributing virtual test points according to the regional evaluation results, performing lighting fitting based on the digital twin model, and monitoring the lighting effect through the virtual test points to establish location point monitoring results; configuring a lighting evaluation network, monitoring and evaluating the location point monitoring results with the lighting evaluation network, and generating target control results for each lighting device according to the monitoring and evaluation results; acquiring historical control information of the lighting device, and establishing a control prediction model according to the historical control information, the control prediction model being a prediction model for outputting control results according to power supply parameters; taking the target control result as the control target, performing inverse fitting of power supply parameters through the control prediction model to generate calibrated power supply parameters; and driving and controlling the lighting device according to the calibrated power supply parameters, so as to achieve the technical goal of effectively solving the slow response, incomplete monitoring and inaccurate control of the lighting system, and achieve the technical effects of improving the adaptability of the lighting system, reducing energy consumption and enhancing personalized settings.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0012] Figure 1 A schematic diagram of a flow chart of an intelligent control method of a lighting driving power supply for the present application;

[0013] Figure 2 It is a schematic diagram of a flow chart of establishing a control prediction model according to the historical control information in an intelligent control method of a lighting driving power supply of the present application. DETAILED DESCRIPTION

[0014] This application provides an intelligent control method for lighting drive power supply, which solves the technical problem in the prior art that the traditional lighting system has limited monitoring points and locations, and has deficiencies in dynamic environment adaptability, efficiency and personalized control, resulting in poor performance of the lighting system in terms of energy utilization, user satisfaction and operational flexibility, which in turn affects the overall lighting quality and economic benefits. The application achieves the technical goal of effectively solving the slow response, incomplete monitoring and inaccurate control of the lighting system, and achieves the technical effect of improving the adaptability of the lighting system, reducing energy consumption and enhancing personalized settings.

[0015] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0016] For examples, please see the attached Figure 1The present application provides an intelligent control method for a lighting driving power supply, which specifically includes the following steps:

[0017] Step 1: Obtain regional information of the target area, wherein the regional information includes regional structure information and layout information of lighting devices in the area, and build a digital twin model based on the regional information.

[0018] Specifically, the target area is the area to be illuminated. Detailed information about the target area is collected, including the layout structure of the target area and the distribution of lighting devices. Then, the regional information is used to build a digital twin model, which is a virtual, digital copy of the area that can simulate the lighting conditions of the target area in the real environment, realize intelligent control of the lighting drive power supply, and improve the efficiency and comfort of the lighting system.

[0019] Step 2: Perform regional evaluation on the target area, distribute virtual test points according to the regional evaluation results, perform lighting fitting based on the digital twin model, monitor the lighting effect through the virtual test points, and establish location point monitoring results.

[0020] Specifically, after the digital twin model is established, a regional evaluation is performed on the target area, that is, virtual test points are arranged in a grid, with equal distances between each virtual test point, and the virtual test points are evenly distributed to understand the lighting needs and quality. According to the regional evaluation results, a series of virtual test points are distributed in the digital twin model to simulate different locations in the actual environment. Then, the digital twin model is used to perform lighting fitting on the virtual test points, that is, to simulate the lighting effect of the lighting device at the virtual test point. By monitoring the lighting effect of the virtual test point, collecting the lighting data of each virtual test point, and establishing a comprehensive lighting effect monitoring result database, it is helpful to evaluate the performance of the lighting system and ensure that the lighting effect meets the design requirements and standards.

[0021] Step 3: configuring a lighting evaluation network, using the lighting evaluation network to monitor and evaluate the location point monitoring results, and generating target control results for each lighting device based on the monitoring and evaluation results.

[0022] Specifically, in order to evaluate the performance of the lighting system, a lighting evaluation network is designed. The lighting evaluation network will comprehensively consider various indicators of lighting effects, monitor the virtual test points set in the digital twin model, collect lighting data for each virtual test point, monitor and evaluate the lighting effects, and ensure that the lighting system can meet the established performance standards. The monitoring and evaluation process will analyze various indicators in the lighting evaluation network, such as illumination uniformity, color consistency, shadow effect, and visual comfort, to obtain comprehensive evaluation results. Based on the monitoring and evaluation results, a target control result is generated for each lighting device to guide the lighting device on how to adjust its output to optimize the lighting effect. For example, if the illumination uniformity index of a virtual test point is lower than the standard, the corresponding lighting device will be instructed to increase the brightness or adjust the angle of the lamp to improve the lighting uniformity. Similarly, if the color consistency index is not good, it is recommended to adjust the color temperature to achieve a more consistent color performance.

[0023] Step 4: Acquire historical control information of the lighting device, and establish a control prediction model based on the historical control information, wherein the control prediction model is a prediction model for outputting control results based on power supply parameters.

[0024] Specifically, in the process of intelligent control of the lighting system, in order to improve the control efficiency and accuracy, the historical control information of the lighting device is collected, including the past brightness settings, working hours, energy consumption data, etc. of the lighting device. A control prediction model is established based on the historical control information to predict the future output control results according to the power supply parameters. Power supply parameters include voltage, current, power factor, etc., which directly affect the performance and energy consumption of the lighting device. By analyzing the relationship between historical data and power supply parameters, it is predicted how the lighting device should adjust its output under different power supply parameters to achieve the expected lighting effect. For example, if the historical data shows that the lighting demand is low during a specific time period, the control prediction model will recommend reducing the brightness of the lighting device during that period to save energy. Similarly, if the power supply parameters indicate that the lighting effect is unstable due to voltage fluctuations, the corresponding compensation control is predicted and implemented to maintain a stable lighting level.

[0025] By establishing a control prediction model, the lighting system can more intelligently predict and adapt to environmental changes, achieving efficient and precise lighting control.

[0026] Step 5: Using the target control result as the control target, performing inverse fitting of power supply parameters through the control prediction model to generate calibrated power supply parameters.

[0027] Specifically, the target control result is used as the control target, that is, the lighting device generated by the lighting evaluation network is taken as the ideal state, and the control prediction model is used to inversely fit the power supply parameters. The power supply parameters of the lighting device when the target control result is achieved are predicted by analyzing the model. Inverse fitting is a reverse engineering process that aims to find out which power supply parameter settings can make the actual performance of the lighting device closest to the target control result. For example, if the target control result specifies a specific illumination level, the control prediction model is used to predict the voltage and current settings required to achieve the illumination. The lighting device is precisely controlled by generating calibrated power supply parameters. The calibrated power supply parameters can be used to adjust the output of the lighting device to ensure that the actual lighting effect is consistent with the target control result, thereby optimizing the lighting performance and meeting user needs.

[0028] Step six: driving and controlling the lighting device according to the calibrated power supply parameters.

[0029] Specifically, the lighting device is precisely driven and controlled using the calibrated power parameters generated by the control prediction model. The power input of the lighting device is adjusted according to the calibrated power parameters to achieve precise management of brightness, color temperature, power consumption and other key lighting characteristics. For example, if the calibrated power parameters indicate that the brightness needs to be increased to achieve the target control result, the current supplied to the lighting device is increased. Similarly, if power consumption needs to be reduced, the voltage or current supply is reduced. The driving control of the lighting device can ensure that the lighting effect is consistent with the design intent, while optimizing energy use and extending the service life of the lighting equipment.

[0030] The intelligent control method of a lighting driving power supply can achieve the technical goal of effectively solving the slow response, incomplete monitoring and inaccurate control of the lighting system, and achieve the technical effects of improving the adaptability of the lighting system, reducing energy consumption and enhancing personalized settings.

[0031] Furthermore, the present application also includes:

[0032] The configuration lighting evaluation network is as follows: CEI = w1·U composite +w2·CCI+w3·S+W4·VCD; where CEI represents the lighting evaluation network, U composite is the comprehensive illumination uniformity index, E min The minimum brightness value representing the monitoring result of the location point, E max represents the maximum brightness value of the monitoring result of the position point, N is the total number of virtual test points, i represents any virtual test point, E i Represents the brightness value of any virtual test point, E avg Characterizes the average value of the monitoring results of the location point, ω a and ω bis the weight of the illumination uniformity index and the standard deviation uniformity index, CCI is the color consistency index, CT i is the color temperature of the ith virtual test point, CT avg is the average color temperature of the monitoring results of the location point, S is the shadow evaluation value, VCD is the visual comfort, w1, w2, w3, and w4 are the weight coefficients of the comprehensive illumination uniformity index, color consistency index, shadow evaluation value, and visual comfort, respectively.

[0033] Specifically, the minimum brightness value E of the location point monitoring result min and the maximum brightness value E of the monitoring results at the location point max The closer the ratio is to 1, the higher the comprehensive illumination uniformity index U composite The larger it is, the more uniform the lighting is, and vice versa, the worse the illumination uniformity is.

[0034] Next, the brightness value E of any virtual test point i and the average value E of the monitoring results of the location points avg The smaller the difference, The closer it is to 1, the more uniform the lighting is, and vice versa, the worse the illumination uniformity is. i The average color temperature CT of the monitoring results at the location point avg The smaller the difference, The closer the value is to 0, the stronger the consistency of color temperature, the more uniform the color temperature distribution, and the larger the color consistency index CCI. Conversely, the worse the uniformity of color temperature distribution.

[0035] Then, the shadow evaluation value S is obtained by visual observation or calculation of the ratio of the shadow area to the total area. Shadowed Area is the shadow area, and Total Area is the total area.

[0036] Then, the visual comfort VCD is obtained by calculating the average of the flicker index and glare index of each virtual test point. Flicker Index avg Flicker Index is the average flicker index of the monitoring results at the location point. i is the flicker index of the ith virtual test point. Glare Rating avg The average glare index of the monitoring results at the location point, Glare Rating i VCD = 0.5 (1-FlickerIndex avg )+0.5·(1-Glare Rating avg).

[0037] Next, the average flicker index Flicker Index avg and average glare rating avg Get the Visual Comfort VCD.

[0038] Next, the comprehensive illumination uniformity index U composite , color consistency index CCI, shadow evaluation value S and visual comfort VCD are weighted to obtain the lighting evaluation network CEI. Among them, the weight coefficients of the comprehensive illumination uniformity index, color consistency index, shadow evaluation value and visual comfort are set by personnel in this field according to actual conditions. For example, if you want the comprehensive illumination uniformity index and visual comfort to have a higher priority, you can set it to w1=0.3, w2=0.2, w3=0.2, and w4=0.3.

[0039] By systematically evaluating and comparing the overall performance of different lighting design schemes, a quantitative basis is provided for the optimization of the lighting system.

[0040] Furthermore, the present application also includes:

[0041] The control prediction model is as follows: L predicted，j (t+1)=f(V j (t), C j (t), F j (t)); L predicted，j (t+1) represents the control prediction model of the jth lighting device, t represents the time node, V j (t) represents the input voltage of the jth lighting device at time node t, C j (t) represents the input current of the jth lighting device at time node t, F j (t) represents the input frequency parameter of the j-th lighting device at time node t; configure the loss function, and perform inverse solution fitting based on the prediction model through the loss function, as follows: Among them, J is the loss function, k is the sum index, ranging from 0 to M-1, L target，j (t+k) represents the target brightness control result of the j-th lighting device at the future time node t+k, L predicted，j (t+k) represents the predicted brightness of the jth lighting device at the future time node t+k, λ is the regularization parameter, (u j (t+k)) 2 Characterizes the control input power supply parameters of the j-th lighting device at the future time node t+k.

[0042] Specifically, the control prediction model L of the jth lighting devicepredicted，j (t+1), the value of the control prediction model of the jth lighting device represents the predicted lighting intensity or brightness. j (t), the input current Cj(t) of the j-th lighting device at time node t, and the input frequency parameter F of the j-th lighting device at time node t j (t) and other parameters are calculated to obtain the predicted lighting intensity.

[0043] Next, (L target，j (t+k)-L predicted，j (t+k)) 2 It represents the square of the difference between the target and the prediction, and is used to quantify the prediction accuracy of each step. The smaller the value of the squared error term, the smaller the prediction error and control cost, that is, the smaller the loss, and vice versa. By adjusting the regularization parameter λ, the influence of the prediction error and the control input is balanced, and the influence of the control input is controlled to prevent over-control.

[0044] Next, the control input power parameter (u j (t+k)) 2 , used to assess the size of the control input to ensure that the system is not overregulated, thus avoiding possible instability or excessive energy consumption. (u j (t+k)) 2 The smaller, The smaller the value of , the smaller the total amount of control activities in the entire forecast period, and the smaller the degree of loss; otherwise, the greater it is.

[0045] By dynamically adjusting the system to achieve the best performance, a set of optimized control strategies can be found by minimizing the cost function.

[0046] Furthermore, the present application also includes:

[0047] The power data of the lighting driving power supply is read to establish a power steady-state identification; compensation optimization of control is performed according to the power steady-state identification and calibrated power parameters, a dynamic response strategy is generated according to the compensation optimization result, and control compensation of the identified power parameters is performed based on the dynamic response strategy.

[0048] Specifically, in order to ensure that the lighting driving power supply can work stably and effectively, the power supply data is read, including parameters such as voltage, current, and power. A power steady-state flag is established based on the power supply data to determine whether the power supply is in a stable state and whether it can meet the control requirements of the lighting device.

[0049] Next, based on the power steady-state identification and the calculated calibrated power parameters, the control compensation optimization is performed to find the deviation between any power parameters and the calibrated value, and determine how to adjust the deviation to optimize the lighting control. The compensation optimization is performed based on factors such as the real-time status of the power supply, environmental changes, and the response characteristics of the lighting device.

[0050] Based on the results of compensation optimization, a dynamic response strategy is generated, which guides the lighting system on how to adjust power supply parameters according to current conditions and needs to achieve the best control effect. The dynamic response strategy can ensure that the lighting system can maintain stable lighting performance when facing power supply fluctuations or external interference.

[0051] Finally, according to the dynamic response strategy, the sign power parameters are controlled and compensated, which means that the power input of the lighting device is adjusted in real time to offset any detected deviations and ensure that the lighting effect meets the preset goals.

[0052] Through real-time control compensation, the lighting system can not only adapt to dynamic changes in power supply, but also provide a consistent and high-quality lighting experience.

[0053] Furthermore, the present application also includes:

[0054] A first steady-state threshold and a second steady-state threshold are set; it is determined whether the power steady-state identification satisfies the second steady-state threshold; if the power steady-state identification cannot satisfy the second steady-state threshold, an abnormality of the lighting drive power supply is directly reported; if the power steady-state identifications can satisfy the second steady-state threshold, a trigger judgment of the power steady-state identification is performed through the first steady-state threshold to generate a trigger judgment result; an abnormality evaluation of the lighting drive power supply is performed according to the trigger judgment result.

[0055] Specifically, in order to more accurately monitor and control the stability of the lighting driving power supply, a first steady-state threshold and a second steady-state threshold are set. The first steady-state threshold and the second steady-state threshold are predefined standards for determining whether the power supply is in a normal working state.

[0056] Next, it is determined whether the power steady state indicator meets the second steady state threshold. The second steady state threshold is usually set relatively loosely and is used to detect whether there is an obvious power anomaly. If the power steady state indicator shows that the power state cannot meet the second steady state threshold, it indicates that there is a serious power problem, and then directly reports the abnormality of the lighting drive power supply. At this time, emergency measures are taken, such as shutting down the lighting device to prevent further damage or safety risks.

[0057] Next, if the power steady-state flag meets the second steady-state threshold, the first steady-state threshold is used to perform a more detailed power steady-state flag trigger judgment. The first steady-state threshold is usually set more strictly to identify minor power fluctuations or potential problems. Based on the trigger judgment result, the lighting driver power supply is evaluated for abnormalities to determine whether there are stability issues that need attention.

[0058] Through the hierarchical identification method, not only can serious power supply anomalies be identified and handled in a timely manner, but early warning of potential stability problems can also be provided, thereby ensuring the reliability and safety of the lighting system.

[0059] Furthermore, the present application also includes:

[0060] A trigger frequency is obtained, and a first abnormal impact is generated based on the trigger frequency; a trigger value is obtained, and a second abnormal impact is generated according to the trigger value, and a trigger determination result is established according to the first abnormal impact and the second abnormal impact.

[0061] Specifically, when monitoring the lighting driving power supply, the trigger frequency is recorded, that is, the frequency at which the power steady-state flag meets the first steady-state threshold, reflecting the frequency of power state fluctuations. The first abnormal impact is generated based on the trigger frequency, indicating the potential impact of power state fluctuations on the stability of the lighting system.

[0062] At the same time, a trigger value is obtained, which refers to the degree to which the power steady-state indicator exceeds the first steady-state threshold. The magnitude of the trigger value reflects the severity of the power state fluctuation. According to the trigger value, a second abnormal impact is generated, indicating the direct impact of the power state fluctuation on the performance of the lighting system.

[0063] Finally, the first abnormal impact and the second abnormal impact are combined to establish the trigger judgment result, which integrates the frequency and severity of power supply status fluctuations and provides a more comprehensive basis for the abnormal evaluation of lighting drive power supply.

[0064] Through a comprehensive evaluation method, potential problems with lighting driver power supplies can be identified and addressed more accurately, ensuring the stability and reliability of the lighting system.

[0065] Further, if Figure 2 As shown, this application also includes:

[0066] Perform control trusted authentication on the historical control information and establish a control trusted authentication identifier; configure the required data volume, eliminate the data of the historical control information according to the required data volume and the control trusted authentication identifier, and establish a retained data set; perform feature extraction on the retained data set, and establish a control prediction model based on the feature extraction results.

[0067] Specifically, in order to ensure that the historical control information of the lighting device is reliable and accurate, the historical control information is subject to control trust authentication. The control trust authentication process verifies the source, integrity and accuracy of the data, and establishes a control trust authentication logo for the verified data to ensure that the data used for modeling is trustworthy.

[0068] Next, configure the required data volume to determine how much data is needed to build the control prediction model. Based on the configured required data volume and the control trusted certification mark, eliminate the historical control information and remove those data that have not passed the certification or are no longer needed, thereby establishing a reserved data set. The reserved data set contains high-quality, reliable historical data for training the control prediction model.

[0069] Finally, feature extraction is performed on the retained data set to identify and select the most important attributes or variables in the data, which can best predict the control results of the lighting device. Based on the results of feature extraction, a control prediction model is established, which can use the key features in historical data to predict future lighting control needs, thereby improving the intelligent control level and efficiency of the lighting system.

[0070] Furthermore, the present application also includes:

[0071] Acquire structural information of the target area, and generate an initial evaluation result of the area based on the structural information; acquire regional importance information of the target area, and generate an additional evaluation result based on the regional importance information; and generate a regional evaluation result based on the initial evaluation result and the additional evaluation result.

[0072] Specifically, the structural information of the target area is collected, including the layout of the building, the size and shape of the room, etc., and then the structural information is used to generate the initial evaluation results of the target area, reflecting the lighting needs and potential problems based on the regional structure.

[0073] Secondly, obtain the regional importance information of the target area, including the flow of people, activity frequency, functional requirements, etc., to determine the criticality of the area. Based on the regional importance information, generate additional evaluation results to provide a further understanding and refinement of the regional lighting needs.

[0074] Finally, the initial evaluation results and the additional evaluation results are combined to generate a comprehensive regional evaluation result. The additional evaluation result takes into account both the structural characteristics of the region and the importance and functional requirements of the region, providing a comprehensive analysis and guidance for lighting design and control.

[0075] In summary, the intelligent control method of a lighting driving power supply provided by the present application has the following technical effects:

[0076] By acquiring regional information of the target area, the regional information includes regional structure information and layout information of lighting devices in the area, and building a digital twin model according to the regional information; performing regional evaluation on the target area, distributing virtual test points according to the regional evaluation results, performing lighting fitting based on the digital twin model, and monitoring the lighting effect through the virtual test points to establish location point monitoring results; configuring a lighting evaluation network, monitoring and evaluating the location point monitoring results with the lighting evaluation network, and generating target control results for each lighting device according to the monitoring and evaluation results; acquiring historical control information of the lighting device, and establishing a control prediction model according to the historical control information, the control prediction model being a prediction model for outputting control results according to power supply parameters; taking the target control result as the control target, performing inverse fitting of power supply parameters through the control prediction model to generate calibrated power supply parameters; and driving and controlling the lighting device according to the calibrated power supply parameters, so as to achieve the technical goal of effectively solving the slow response, incomplete monitoring and inaccurate control of the lighting system, and achieve the technical effects of improving the adaptability of the lighting system, reducing energy consumption and enhancing personalized settings.

[0077] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0078] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. An intelligent control method for a lighting driving power supply, characterized in that: include: Acquire regional information of the target area, wherein the regional information includes regional structure information and layout information of lighting devices in the area, and build a digital twin model according to the regional information; Performing regional evaluation on the target area, distributing virtual test points according to the regional evaluation results, performing lighting fitting based on the digital twin model, and monitoring the lighting effect through the virtual test points to establish position point monitoring results; Configuring a lighting evaluation network, using the lighting evaluation network to monitor and evaluate the location point monitoring results, and generating target control results for each lighting device based on the monitoring and evaluation results; Acquire historical control information of the lighting device, and establish a control prediction model according to the historical control information, wherein the control prediction model is a prediction model for outputting control results according to power supply parameters; Taking the target control result as the control target, performing inverse fitting of power supply parameters through the control prediction model to generate calibrated power supply parameters; The driving control of the lighting device is performed according to the calibrated power supply parameters.

2. The intelligent control method of a lighting driving power supply according to claim 1, characterized in that: The configuration of the lighting evaluation network is as follows: IEC=w1·U composite +w2·CCI+w3·S+w4·VCD; Among them, CEI represents the lighting evaluation network, U composite is the comprehensive illumination uniformity index, E min The minimum brightness value representing the monitoring result of the location point, E max represents the maximum brightness value of the monitoring result of the position point, N is the total number of virtual test points, i represents any virtual test point, E i Represents the brightness value of any virtual test point, E avg Characterizes the average value of the monitoring results of the location point, ω a and ω b is the weight of the illumination uniformity index and the standard deviation uniformity index, CCI is the color consistency index, CT i is the color temperature of the ith virtual test point, CT avg is the average color temperature of the monitoring results of the location point, S is the shadow evaluation value, VCD is the visual comfort, w1, w2, w3, and w4 are the weight coefficients of the comprehensive illumination uniformity index, color consistency index, shadow evaluation value, and visual comfort, respectively.

3. The intelligent control method of a lighting driving power supply according to claim 1, characterized in that: The method of taking the target control result as the control target, performing inverse fitting of power supply parameters through the control prediction model, and generating calibrated power supply parameters, further includes: The control prediction model is as follows: L predicted,j (t+1)=f(V j (t),C j (t),F j (t)); L predicted,j (t+1) represents the control prediction model of the jth lighting device, t represents the time node, V j (t) represents the input voltage of the jth lighting device at time node t, C j (t) represents the input current of the jth lighting device at time node t, F j (t) represents the input frequency parameter of the j-th lighting device at time node t; Configure the loss function and use the loss function to perform inverse fitting based on the prediction model as follows: Among them, J is the loss function, k is the sum index, ranging from 0 to M-1, L target,j (t+k) represents the target brightness control result of the j-th lighting device at the future time node t+k, L predicted,j (t+k) represents the predicted brightness of the jth lighting device at the future time node t+k, λ is the regularization parameter, (u j (t+k)) 2 Characterizes the control input power supply parameters of the j-th lighting device at the future time node t+k.

4. The intelligent control method of a lighting driving power supply according to claim 1, characterized in that: Also includes: Read the power data of the lighting drive power supply and establish a power steady-state identification; Compensation optimization of control is performed according to the power steady-state identification and the calibrated power parameters, a dynamic response strategy is generated according to the compensation optimization result, and control compensation of the identified power parameters is performed based on the dynamic response strategy.

5. The intelligent control method of a lighting driving power supply according to claim 4, characterized in that: Also includes: Setting a first steady-state threshold and a second steady-state threshold; Determine whether the power steady-state identifier satisfies a second steady-state threshold value, and if the power steady-state identifier fails to satisfy the second steady-state threshold value, directly report that the lighting driving power supply is abnormal; If the power steady-state identifiers all meet the second steady-state threshold, trigger identification of the power steady-state identifier is performed using the first steady-state threshold to generate a trigger identification result; The abnormality of the lighting drive power supply is evaluated based on the trigger judgment result.

6. The intelligent control method of a lighting driving power supply according to claim 5, characterized in that: The triggering and determining of the power steady-state flag by using the first steady-state threshold value to generate a triggering and determining result also includes: Acquire a trigger frequency, and generate a first abnormal impact based on the trigger frequency; A trigger value is acquired, a second abnormal impact is generated according to the trigger value, and a trigger determination result is established according to the first abnormal impact and the second abnormal impact.

7. The intelligent control method of a lighting driving power supply according to claim 1, characterized in that: The acquiring of historical control information of the lighting device and establishing a control prediction model according to the historical control information further includes: Performing control trust authentication on the historical control information and establishing a control trust authentication identifier; Configure the required data volume, eliminate the data of historical control information according to the required data volume and the control trusted authentication identifier, and establish a reserved data set; Feature extraction is performed on the retained data set, and a control prediction model is established according to the feature extraction results.

8. The intelligent control method of a lighting driving power supply according to claim 1, characterized in that: The performing of regional evaluation on the target area and distributing virtual test points according to the regional evaluation result also includes: Acquire structural information of the target area, and generate an initial evaluation result of the area according to the structural information; Acquire regional importance information of the target area, and generate additional evaluation results according to the regional importance information; Generate regional evaluation results based on the initial evaluation results and the additional evaluation results.

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