An intelligent control method and system for an LED lighting lamp

Abstract

This application relates to the technical field of intelligent control of LED lights, and specifically relates to an intelligent control method and system for LED lighting lamps. The method includes: during the process of adjusting the brightness of the LED lighting lamp, a microcontroller with a timer input capture function captures the PWM signal on the drive circuit of the LED lighting lamp, calculates the PWM frequency and PWM duty cycle at each moment, and obtains the digital voltage value of the LED lighting lamp at each moment; calculates the adjustment smoothness, average correlation factor, hysteresis influence degree, and adjustment feedback deviation amount; obtains the adjusted proportional coefficient in the PID control algorithm; adjusts the duty cycle of the PWM, thereby adjusting the brightness of the LED lighting lamp. This application can make the brightness adjustment of the LED lighting lamp smoother, more accurate and stable, which is beneficial to accurately controlling the brightness of the LED lighting lamp and improving the overall lighting effect and user experience.

Description

Technical Field

[0001] This application relates to the technical field of intelligent control of LED lights, and particularly relates to an intelligent control method and system for LED lighting lamps. Background Art

[0002] In the intelligent control of LED lighting lamps, the dimming technology of LED lighting is an important part of it. However, during the dimming process, problems such as reduced light efficiency may occur. Using PWM to dim LED lighting can achieve precise brightness control, while maintaining the stability of color temperature and light efficiency, extending the service life of LEDs, and having high energy efficiency and good compatibility. When using PWM for dimming, the brightness of the LED is adjusted by changing the duty cycle of the PWM. However, the frequency selection of the PWM will affect the adjustment of the duty cycle. If the change amplitude of the duty cycle is too large, it will cause the brightness of the LED to change rapidly, resulting in fluctuations in the brightness of the LED in a short period of time, and the human eye will perceive flicker, leading to health problems such as visual fatigue and headaches. Summary of the Invention

[0003] In order to solve the above technical problems, an intelligent control method and system for LED lighting lamps are provided to solve the existing problems.

[0004] The solution of this application to solve the technical problem is to provide an intelligent control method and system for LED lighting lamps, including the following steps:

[0005] In the first aspect, an embodiment of this application provides an intelligent control method for LED lighting lamps, and the method includes the following steps:

[0006] During the process of regulating the brightness of the LED lighting lamp, a microcontroller with a timer input capture function captures the PWM signal on the drive circuit of the LED lighting lamp, calculates the PWM frequency and PWM duty cycle at each moment, performs analog-to-digital conversion on the voltage signal on the voltage-dividing circuit where the LED lighting lamp is located, and obtains the digital voltage value of the LED lighting lamp at each moment;

[0007] Cluster the PWM frequencies at all moments. By the average change of the PWM frequencies at all moments within each cluster, and the average change of the corresponding PWM duty cycle at the corresponding moments, combined with the complexity of the change between the PWM frequencies at all moments within each cluster and their corresponding PWM duty cycles, calculate the adjustment smoothness of the PWM signal in the LED lighting lamp;

[0008] Analyze the correlation between the digital voltage values, PWM frequency, and PWM duty cycle at all times, and calculate the average correlation factor between voltage and PWM; analyze the lag relationship among the digital voltage values, PWM frequency, and PWM duty cycle at all times, and calculate the lag influence degree between voltage and PWM; based on the average correlation factor, the lag influence degree, and the adjustment smoothness, determine the adjustment feedback deviation amount of the LED lighting lamp;

[0009] Based on the adjustment feedback deviation amount, obtain the adjusted proportional coefficient in the PID control algorithm; combine the deviation situation of the actual brightness of the LED lighting lamp, and adjust the duty cycle of PWM through the PID control algorithm to adjust the brightness of the LED lighting lamp.

[0010] Preferably, the calculation of the adjustment smoothness of the PWM signal in the LED lighting lamp includes:

[0011] Calculate the relative change amount of each clustering cluster according to the average level of the PWM frequency and the average level of the PWM duty cycle at all times within each clustering cluster;

[0012] Calculate the dimension difference of each clustering cluster according to the difference situation of the fractal dimension between the PWM frequency and the PWM duty cycle at all times within each clustering cluster;

[0013] Taking the relative change amount as the weight, calculate the opposite number after weighted summation of the dimension differences of all clustering clusters, and the adjustment smoothness is positively correlated with the opposite number.

[0014] Preferably, the calculation of the relative change amount of each clustering includes:

[0015] Denote the ratio between the mean value of the PWM frequency at all times within each clustering cluster and the maximum PWM frequency within the corresponding clustering cluster as the first ratio;

[0016] Denote the ratio between the mean value of the PWM duty cycle corresponding to all times within each clustering cluster and the maximum PWM duty cycle within the corresponding clustering cluster as the second ratio;

[0017] Calculate the ratio of the first ratio to the second ratio, denote it as the third ratio, and take the difference between the value 1 and the third ratio as the relative change amount.

[0018] Preferably, the dimension difference is the absolute value of the difference between the fractal dimension of the PWM frequency at all times within each clustering cluster and the fractal dimension of the PWM duty cycle at all times.

[0019] Preferably, the calculation of the average correlation factor between voltage and PWM includes:

[0020] Calculate the correlation degrees between the digital voltage values at all times and the PWM frequencies and PWM duty cycles at all times, denoted as the first correlation degree and the second correlation degree respectively;

[0021] The average correlation factor is the mean value of the first correlation degree and the second correlation degree.

[0022] Preferably, calculating the lag influence degree between the voltage and the PWM includes:

[0023] Adopt the cross - correlation algorithm to respectively obtain the cross - correlation diagrams of the digital voltage values at all times and the PWM frequencies and PWM duty cycles at all times, and record the lag numbers corresponding to the peaks in the cross - correlation diagrams as the first lag value and the second lag value respectively;

[0024] Record the difference between the first lag value and the second lag value as the lag difference;

[0025] Correspondingly, obtain the lag number corresponding to the peak in the cross - correlation diagram of the PWM frequencies and PWM duty cycles at all times, denoted as the third lag value;

[0026] Take the difference between the third lag value and the lag difference as the lag influence degree between the voltage and the PWM.

[0027] Preferably, the calculation formula for the adjustment feedback deviation amount of the LED lighting lamp is: , where is the adjustment feedback deviation amount of the LED lighting lamp, is the adjustment smoothness of the PWM signal in the LED lighting lamp, is the lag influence degree, is the average correlation factor, is the exponential function with the natural constant as the base, is the normalization function.

[0028] Preferably, in the PID control algorithm, the adjusted proportionality coefficient The calculation process is as follows: , where is the adjustment feedback deviation amount, is the preset initial proportionality coefficient.

[0029] Preferably, adjusting the duty cycle of the PWM through the PID control algorithm includes:

[0030] During the brightness regulation process of the LED lighting lamp, take the brightness of the LED lighting lamp set by the user as the target brightness;

[0031] Obtain the actual illuminance in the environment in real time through a photoresistor; record the difference between the actual illuminance and the target brightness as the brightness deviation;

[0032] Based on the adjusted proportionality coefficient, use the brightness deviation as the input of the PID controller to adjust the duty cycle of the PWM.

[0033] In a second aspect, an intelligent control system for an LED lighting lamp provided by an embodiment of the present application includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned intelligent control method for an LED lighting lamp are implemented.

[0034] The present application has at least the following beneficial effects:

[0035] In the present application, the PWM frequencies are clustered, so as to subsequently analyze the balance degree of the changes between the PWM frequency and the PWM duty cycle, and the complexity of the changes between the PWM frequency and the PWM duty cycle under a category where the PWM frequency fluctuations are relatively close. The adjustment smoothness is calculated. The beneficial effect is that it considers the balance of the changes between the PWM frequency and the PWM duty cycle, reflects the adjustment coordination consistency of the PWM signal, so as to illustrate the smoothness of the brightness adjustment of the LED lighting lamp and reflect the stability of the brightness change; by calculating the average correlation factor between the digital voltage value and the PWM frequency and the PWM duty cycle, the beneficial effect is that it considers the correlation between the voltage value and the PWM frequency and the correlation between the voltage value and the PWM duty cycle, so as to illustrate that the adjustment of the PWM signal has less significant influence on the brightness of the LED lighting lamp, indicating that the adjustment of the PWM signal has hysteresis on the voltage change of the LED lighting lamp; calculate the hysteresis influence degree between the voltage and the PWM, the beneficial effect is that it considers the influence of the changes in the frequency and duty cycle of the PWM on the hysteresis degree of the voltage change of the LED lamp, indicating the delay phenomenon existing in the transmission process of the PWM adjustment signal or the situation of uncoordinated response; and then obtain the adjustment feedback deviation amount of the LED lighting lamp, the beneficial effect is that it quantifies the hysteresis of the voltage feedback caused by the PWM adjustment and the hysteresis influence of the filter circuit and the like on the voltage change, so as to accurately evaluate the feedback efficiency and deviation degree of the PWM signal adjustment; adjust the proportionality coefficient of the PID algorithm through the adjustment feedback deviation amount and adjust the duty cycle of the PWM. The beneficial effect is that it can avoid the flashing phenomenon caused by the rapid change of the brightness of the LED lighting lamp, so that the brightness adjustment of the LED lighting lamp is smoother, more accurate and stable, which is beneficial to accurately control the brightness of the LED lighting lamp and improve the overall lighting effect and user experience. Description of the Drawings

[0036] The following further elaborates in detail on an intelligent control method for an LED lighting lamp according to this application in conjunction with the accompanying drawings.

[0037] Figure 1 It is a flowchart of the steps of an intelligent control method for an LED lighting lamp provided by an embodiment of this application;

[0038] Figure 2 It is a flowchart of the steps of a method for obtaining the adjustment smoothness of the PWM signal in the LED lighting lamp provided by an embodiment of this application. Specific Embodiments

[0039] In order to make the objectives, technical solutions, and advantages of this application clearer, the following further elaborates in detail on an intelligent control method and system for an LED lighting lamp proposed in this application in conjunction with the accompanying drawings and implementation examples. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0041] Please refer to Figure 1 , which shows a flowchart of the steps of an intelligent control method for an LED lighting lamp provided by an embodiment of this application. The method includes the following steps:

[0042] Step 1, during the process of regulating the brightness of the LED lighting lamp, a microcontroller with a timer input capture function captures the PWM signal on the drive circuit of the LED lighting lamp, calculates the PWM frequency and PWM duty cycle at each moment, and performs analog-to-digital conversion on the voltage signal on the voltage division circuit where the LED lighting lamp is located to obtain the digital voltage value of the LED lighting lamp at each moment.

[0043] PWM (Pulse Width Modulation) is a technology for regulating the output power by controlling the width of the pulse signal. In the LED lighting control system, PWM is mainly used to regulate the brightness of the LED while maintaining its high efficiency and low energy consumption.

[0044] During the process of regulating the brightness of the LED lighting lamp, a microcontroller with a timer input capture function is installed on the drive circuit for controlling the LED lighting lamp, the input capture mode of the timer is configured, and by capturing the rising edge and falling edge signals of the PWM signal, the PWM frequency and PWM duty cycle at each moment are calculated;

[0045] In this embodiment, the microcontroller STM32 is installed in the LED driving circuit to capture the PWM square wave signal and obtain the frequency and duty cycle of the PWM. It should be noted that obtaining the frequency and duty cycle of the PWM by configuring the input capture mode of the timer is a well-known technology and will not be elaborated here.

[0046] The photoresistor is connected to the voltage dividing circuit of the LED lighting lamp, and the change in light intensity is reflected by measuring the voltage across the photoresistor. The resistance value of the photoresistor decreases as the light intensity increases. Therefore, the voltage is inversely proportional to the light intensity. The collected voltage signal is input into the ADC module for analog-to-digital conversion to obtain the digital voltage value.

[0047] The acquisition frequency is set to 1 kHz. Thus, the PWM frequency, PWM duty cycle, and digital voltage value at each moment are obtained. All the collected data are normalized to eliminate the influence of dimension.

[0048] In this embodiment, the maximum-minimum normalization method is used for normalization. Among them, the maximum-minimum normalization method is a well-known technology and will not be elaborated here.

[0049] So far, the PWM frequency and PWM duty cycle at each moment, as well as the digital voltage value of the LED lighting lamp at each moment, are obtained.

[0050] Step 2: Cluster the PWM frequencies at all moments. Calculate the adjustment smoothness of the PWM signal in the LED lighting lamp by the average change of the PWM frequencies at all moments within each cluster, the average change of the corresponding PWM duty cycles at the corresponding moments, and the complexity of the change between the PWM frequencies at all moments within each cluster and their corresponding PWM duty cycles at the corresponding moments.

[0051] When the frequency of the PWM is high enough, for example, when the frequency of the PWM is higher than 100 Hz, the human eye cannot distinguish the flicker of the light and only feels the change in the average brightness. The higher the frequency, the smoother the dimming and the flicker can be avoided. The duty cycle of the PWM is the ratio of the high-level time to the entire pulse period within a pulse cycle. The larger the duty cycle, the longer the LED remains on within the pulse cycle, resulting in a brighter light output. For example, a duty cycle of 90% means that the LED is on for 90% of the pulse cycle and only off for 10% of the pulse cycle, resulting in near full brightness. That is, selecting an appropriate PWM frequency can avoid flicker, and adjusting the PWM duty cycle can control the brightness.

[0052] Secondly, the selection of the PWM frequency affects the adjustment of the duty cycle. When the PWM frequency is high, the time of each pulse cycle is short, which means that more pulse cycles can occur within the same time. Therefore, at high frequencies, a small change in the duty cycle, such as the duty cycle changing from 90% to 91%, will result in a finer change in the LED brightness, thus achieving smoother brightness adjustment.

[0053] Based on the above analysis, when the brightness adjustment of the LED lighting is smoother, the PWM has a higher frequency and a higher duty cycle. Therefore, by analyzing the change of the PWM duty cycle of the LED lighting at the same PWM frequency and calculating the adjustment smoothness to reflect the adjustment of the duty cycle by the frequency change, the step flow chart of the method for obtaining the adjustment smoothness of the PWM signal in the LED lighting provided by the embodiments of the present application is as Figure 2 shown, and specifically includes:

[0054] Cluster the PWM frequencies at all times to obtain multiple clusters;

[0055] In this embodiment, the DBSCAN density clustering algorithm is used for clustering. Among them, the minimum sample size is set to 5 and the neighborhood radius is set to 1 in the DBSCAN density clustering algorithm. As other implementation manners, the implementer can set them according to the actual situation. The DBSCAN density clustering algorithm is a well-known technology and will not be elaborated here. As other implementation manners, the implementer can adopt other methods of the existing technology, such as the hierarchical clustering algorithm, etc. This embodiment does not make special restrictions on this.

[0056] Record the ratio between the mean value of the PWM frequencies at all times within each cluster and the maximum PWM frequency within the corresponding cluster as the first ratio;

[0057] Record the ratio between the mean value of the PWM duty cycles corresponding to the PWM frequencies at all times within each cluster and the maximum PWM duty cycle within the corresponding cluster as the second ratio;

[0058] Calculate the ratio of the first ratio to the second ratio, record it as the third ratio, and take the difference between the value 1 and the third ratio as the relative change amount of each cluster;

[0059] In this embodiment, take the absolute value of the difference between the value 1 and the third ratio as the relative change amount of each cluster.

[0060] Take the absolute value of the difference between the fractal dimension of the PWM frequencies at all times within each cluster and the fractal dimension of the PWM duty cycles at all times as the dimension difference of each cluster;

[0061] In this embodiment, the box-counting method is used to calculate the fractal dimension. The box-counting method is a well-known technique and will not be elaborated here. As other implementation manners, implementers can adopt other methods of the prior art. For example, the Higuchi fractal dimension method, the Hausdorff algorithm, etc. This embodiment does not make special restrictions on this.

[0062] Taking the relative change amount as the weight, calculate the opposite number after weighted summation of the dimension differences of all clustering clusters, and the adjustment smoothness is positively correlated with the opposite number.

[0063] It should be noted that the positive correlation means that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases, which is determined by the actual application, and this application does not make special restrictions.

[0064] In this embodiment, the calculation method of the adjustment smoothness of the PWM signal in the LED lighting lamp is as follows:

[0065]

[0066] Wherein, is the adjustment smoothness of the PWM signal in the LED lighting lamp, is the th dimension difference of the clustering cluster, is the th relative change amount of the clustering cluster, is the number of all clustering clusters, is the exponential function with the natural constant as the base.

[0067] It should be noted that the dimension difference reflects the coordination and consistency of the change of the PWM signal during the adjustment of the frequency and duty cycle. The smaller the dimension difference, the smaller the fractal dimension difference between the frequencies and duty cycles of the PWM at all times within the th clustering cluster, that is, the complexity of the two during the change process is closer, the adjustment of the PWM signal is more coordinated and consistent, and it is more conducive to achieving smoother brightness adjustment; secondly, the smaller the relative change amount, the closer the relative change amount is to 0, then within the th clustering cluster, the change degrees of the frequency and duty cycle of the PWM are relatively balanced, which is conducive to achieving smoother brightness adjustment. The larger the obtained adjustment smoothness, the smoother the adjustment of the PWM signal, the finer and more stable the brightness change of the LED lighting lamp, and the fewer the flashing phenomena.

[0068] Thus, the adjustment smoothness of the PWM signal in the LED lighting lamp is obtained.

[0069] Step 3, analyze the correlation between the digital voltage values at all times and the PWM frequency and PWM duty cycle, and calculate the average correlation factor between the voltage and the PWM; analyze the hysteresis relationship changes among the digital voltage values, PWM frequency, and PWM duty cycle at all times, and calculate the hysteresis influence degree between the voltage and the PWM; based on the average correlation factor, the hysteresis influence degree, and the adjustment smoothness, determine the adjustment feedback deviation amount of the LED lighting lamp.

[0070] Furthermore, in the lighting control link of the LED lighting lamp, due to the inherent non-linear characteristics of the LED lighting lamp itself, the relationship between its brightness and voltage is not completely linear. Due to other control links and feedback mechanisms, changes in the frequency and duty cycle of the PWM may not cause changes in the voltage on the LED lighting lamp. For example, in the driving circuit of the LED lighting lamp, a constant current driver is usually equipped, and this driver can ensure that the current flowing through the LED remains constant. Even if the frequency and duty cycle of the PWM change, the constant current driver will automatically adjust its output voltage to maintain a constant current, thereby keeping the brightness of the LED lighting lamp stable.

[0071] Secondly, since the PWM signal needs to be smoothed by a filtering circuit, such as a capacitor, the filtering circuit has a certain response time to the rapidly changing PWM signal, resulting in the voltage change lagging behind the changes in the frequency and duty cycle of the PWM.

[0072] Therefore, in order to analyze whether the frequency and duty cycle of the PWM can effectively cause changes in the brightness of the LED lighting lamp, by analyzing the relevant change situations between the digital voltage values and the frequency and duty cycle of the PWM respectively, and the hysteresis change situation of the voltage, calculate the adjustment feedback deviation amount, specifically:

[0073] Calculate the correlation degree between the digital voltage values at all times and the frequency of the PWM at all times, and record it as the first correlation degree;

[0074] Calculate the correlation degree between the digital voltage values at all times and the duty cycle of the PWM at all times, and record it as the second correlation degree;

[0075] Calculate the mean value of the first correlation degree and the second correlation degree as the average correlation factor between the voltage and the PWM;

[0076] In this embodiment, the correlation degree is calculated by calculating the Pearson correlation coefficient between the digital voltage values at all times and the frequency of the PWM at all times; calculate the Pearson correlation coefficient between the digital voltage values at all times and the duty cycle of the PWM at all times. Among them, the calculation of the Pearson correlation coefficient is a well-known technology and will not be elaborated here. As other implementation manners, implementers can adopt other methods of existing technologies, such as the Spearman correlation coefficient, etc. This embodiment does not make special restrictions on this.

[0077] Take the digital voltage values at all times and the frequency of PWM at all times as the inputs of the cross-correlation algorithm, and obtain the lag number corresponding to the peak in the cross-correlation graph, denoted as the first lag value;

[0078] Take the digital voltage values at all times and the duty cycle of PWM at all times as the inputs of the cross-correlation algorithm, and obtain the lag number corresponding to the peak in the cross-correlation graph, denoted as the second lag value;

[0079] Denote the difference between the first lag value and the second lag value as the lag difference;

[0080] In this embodiment, denote the absolute value of the difference between the first lag value and the second lag value as the lag difference.

[0081] It should be noted that the cross-correlation algorithm is a well-known technology and will not be elaborated here.

[0082] Take the frequency of PWM at all times and the duty cycle of PWM at all times as the inputs of the cross-correlation algorithm, and obtain the lag number corresponding to the peak in the cross-correlation graph, denoted as the third lag value;

[0083] Take the difference between the third lag value and the lag difference as the lag influence degree between voltage and PWM;

[0084] In this embodiment, take the absolute value of the difference between the third lag value and the lag difference as the lag influence degree between voltage and PWM.

[0085] Based on the regulation smoothness, the average correlation factor, and the lag influence degree, calculate the regulation feedback deviation amount of the LED lighting lamp. The specific calculation formula is:

[0086]

[0087] where, is the regulation feedback deviation amount of the LED lighting lamp, is the regulation smoothness of the PWM signal in the LED lighting lamp, is the lag influence degree between voltage and PWM, is the average correlation factor between voltage and PWM, is the exponential function with the natural constant as the base, is the normalization function. In this embodiment, the sigmoid function is used for normalization processing. Among them, the sigmoid function is a well-known technology and will not be elaborated here. As other implementation manners, implementers can adopt other methods of the existing technology, such as the tanh function, etc. This embodiment does not make special restrictions on this.

[0088] It should be noted that the greater the adjustment smoothness, the smoother the adjustment of the PWM signal, and the finer and more stable the brightness change of the LED lighting fixture should be. Then, the feedback of the LED lamp voltage change should be more obvious, which is used as the smoothing weight of the PWM to amplify the subsequent calculation. The hysteresis influence degree reflects the influence of the changes in the frequency and duty cycle of the PWM on the hysteresis degree of the voltage change of the LED lamp. The greater the hysteresis influence degree, the greater the hysteresis of the influence of the frequency and duty cycle changes on the LED lamp voltage change, indicating that there is a greater delay or uncoordinated response phenomenon in the transmission process of the adjustment signal, and the greater the impact on the accuracy and stability of the adjustment feedback efficiency. The smaller the average correlation factor, the weaker the correlation between the changes in the frequency and duty cycle of the PWM and the voltage change on the LED lighting fixture, and the less significant the influence of the PWM signal adjustment on the brightness of the LED lighting fixture. Secondly, during the brightness adjustment process of the LED lighting, when the frequency and duty cycle of the PWM signal change, theoretically, the brightness of the LED lamp should change accordingly. The adjustment feedback deviation amount reflects the problems of poor voltage feedback or transmission hysteresis during the PWM signal adjustment process.

[0089] Thus, the adjustment feedback deviation amount of the LED lighting fixture is obtained.

[0090] Step 4: Based on the adjustment feedback deviation amount, obtain the adjusted proportional coefficient in the PID control algorithm; combine the deviation situation of the actual brightness of the LED lighting fixture, and adjust the duty cycle of the PWM through the PID control algorithm, thereby adjusting the brightness of the LED lighting fixture.

[0091] During the brightness adjustment process of the LED lighting fixture, when using PWM for brightness control, the PID (Proportion Integration Differentiation) control algorithm is used to control the duty cycle of the PWM. The PID algorithm, based on the deviation between the target brightness and the actual brightness, through the proportional coefficient the integral coefficient and the differential coefficient dynamically adjusts the duty cycle of the PWM, thereby precisely controlling the brightness of the LED lighting fixture.

[0092] Secondly, based on the adjustment feedback deviation amount, adjust the proportional coefficient of the PID control algorithm, specifically:

[0093]

[0094] Among them, is the adjusted proportional coefficient in the PID control algorithm, is the adjustment feedback deviation amount, is a preset initial proportionality coefficient.

[0095] In this embodiment, the value of the preset initial proportionality coefficient is 10. As other implementation manners, the implementer can set it according to the actual situation. Secondly, in the PID control algorithm, the integral coefficient is 1 and the differential coefficient is 0.1. As other implementation manners, the implementer can set them according to the actual situation.

[0096] During the brightness regulation process of the LED lighting lamp, the brightness of the LED lighting lamp set by the user is used as the target brightness;

[0097] The actual illuminance in the environment is obtained in real time through a photosensitive resistor;

[0098] It should be noted that the method of calculating the illuminance through a photosensitive resistor is a well-known technology and will not be elaborated here.

[0099] The difference between the actual illuminance and the target brightness is denoted as the brightness deviation;

[0100] In this embodiment, the absolute value of the difference between the actual illuminance and the target brightness is denoted as the brightness deviation.

[0101] Based on the adjusted proportionality coefficient, the brightness deviation is used as the input of the PID controller to adjust the duty cycle of the PWM, thereby adjusting the brightness of the LED lighting lamp.

[0102] It should be noted that adjusting the proportionality coefficient of the PID control algorithm through the adjustment feedback deviation amount can make the adjustment of the PWM duty cycle more accurate and stable. When the adjustment feedback deviation amount is large, it indicates that there are large voltage feedback problems or transmission lag situations during the PWM signal adjustment process. At this time, increasing the proportionality coefficient can enhance the response speed and adjustment strength of the lighting control system, enable the LED lighting lamp to reach the target brightness faster, and reduce the error and fluctuation of the brightness adjustment; while when the adjustment feedback deviation amount is small, it indicates that the PWM signal adjustment is relatively ideal. At this time, the change of the proportionality coefficient before and after adjustment is small, which can maintain the stability and reliability of the lighting control system, avoid system oscillation or overshoot phenomena caused by excessive adjustment, and ensure the smoothness and comfort of the brightness adjustment of the LED lighting lamp. Secondly, by adjusting the proportionality coefficient, it can adapt to changes in different environments and working conditions, improve the self-adaptability and robustness of the LED lighting control system, and optimize the overall lighting effect and user experience.

[0103] Based on the same inventive concept as the above method, an embodiment of the present application further provides an intelligent control system for an LED lighting lamp, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above methods for an intelligent control method of an LED lighting lamp are implemented.

[0104] It should be understood that although Figure 1 the steps in the flowchart are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, Figure 1 at least a part of the steps in

[0105] can include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0106] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0106] The above-described embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several deformations and improvements can still be made. Therefore, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application all belong to the protection scope of the technical solution of the present application.

Claims

1. An intelligent control method for LED lighting, characterized in that: The method comprises the following steps: In the process of adjusting the brightness of the LED lighting lamp, a microcontroller with a timer input capture function is used to capture the PWM signal on the driving circuit of the LED lighting lamp, calculate the PWM frequency and PWM duty cycle at each moment, perform analog-to-digital conversion on the voltage divider circuit where the LED lighting lamp is located, and obtain the digital voltage value of the LED lighting lamp at each moment; The PWM frequencies at all times are clustered, and the adjustment smoothness of the PWM signal in the LED lighting lamp is calculated by combining the average change of the PWM frequencies at all times in each cluster and the average change of the PWM duty cycle at the corresponding time, and the complexity of the change between the PWM frequencies at all times in each cluster and the PWM duty cycle at the corresponding time. Analyze the correlation between the digital voltage value at all times and the PWM frequency and the PWM duty cycle, and calculate the average correlation factor between the voltage and PWM; analyze the hysteresis relationship between the changes in the digital voltage value at all times, the PWM frequency, and the PWM duty cycle, and calculate the hysteresis influence between the voltage and PWM; determine the adjustment feedback deviation of the LED lighting lamp based on the average correlation factor, the hysteresis influence, and the adjustment smoothness; Based on the adjustment feedback deviation, the adjusted proportional coefficient in the PID control algorithm is obtained; combined with the deviation of the actual brightness of the LED lighting lamp, the duty cycle of the PWM is adjusted through the PID control algorithm, thereby adjusting the brightness of the LED lighting lamp.

2. The LED lighting intelligent control method according to claim 1, characterized in that: The step of calculating the adjustment smoothness of the PWM signal in the LED lighting lamp comprises: According to the average level of PWM frequency and the average level of PWM duty cycle at all times in each cluster, the relative change of each cluster is calculated; According to the difference in fractal dimension between PWM frequency and PWM duty cycle at all times in each cluster, the dimension difference of each cluster is calculated; The relative change is used as a weight to calculate the inverse of the weighted sum of the dimensional differences of all clusters, and the adjustment smoothness is positively correlated with the inverse.

3. The LED lighting intelligent control method according to claim 2, characterized in that: The calculating of the relative change of each cluster includes: The ratio between the mean of the PWM frequencies at all times in each cluster and the maximum PWM frequency in the corresponding cluster is recorded as the first ratio; The ratio between the mean of the PWM duty cycles corresponding to all moments in each cluster and the maximum PWM duty cycle in the corresponding cluster is recorded as the second ratio; The ratio of the first ratio to the second ratio is calculated and recorded as a third ratio, and the difference between the value 1 and the third ratio is taken as the relative change.

4. The LED lighting intelligent control method according to claim 2, characterized in that: The dimension difference is the absolute value of the difference between the fractal dimension of the PWM frequency at all times and the fractal dimension of the PWM duty cycle at all times in each cluster.

5. The LED lighting intelligent control method according to claim 1, characterized in that: The calculating average correlation factor between voltage and PWM includes: Calculate the correlation between the digital voltage value at all times and the PWM frequency at all times and the PWM duty cycle at all times, which are recorded as the first correlation and the second correlation respectively; The average correlation factor is the average of the first correlation and the second correlation.

6. The LED lighting intelligent control method according to claim 1, characterized in that: The calculating the hysteresis influence between the voltage and the PWM comprises: A cross-correlation algorithm is used to obtain cross-correlation graphs of the digital voltage value at all times, the PWM frequency at all times, and the PWM duty cycle at all times, and the hysteresis numbers corresponding to the peak values ​​in the cross-correlation graph are recorded as the first hysteresis value and the second hysteresis value, respectively; Recording the difference between the first hysteresis value and the second hysteresis value as a hysteresis difference; Accordingly, the hysteresis number corresponding to the peak value in the cross-correlation diagram of the PWM frequency at all times and the PWM duty cycle at all times is obtained, and recorded as the third hysteresis value; The difference between the third hysteresis value and the hysteresis difference is used as the hysteresis influence degree between voltage and PWM.

7. The LED lighting intelligent control method according to claim 1, characterized in that: The calculation formula of the adjustment feedback deviation of the LED lighting lamp is: ,in, It is the adjustment feedback deviation of LED lighting. is the adjustment smoothness of the PWM signal in the LED lighting, is the hysteresis influence degree, is the average correlation factor, is an exponential function with a natural constant as base, is the normalization function.

8. The LED lighting intelligent control method according to claim 1, characterized in that: Adjusted proportional coefficient in PID control algorithm The calculation process is: ,in, is the adjustment feedback deviation, The preset initial scale factor.

9. The LED lighting intelligent control method according to claim 1, characterized in that: The step of adjusting the duty cycle of PWM by using a PID control algorithm includes: In the process of adjusting the brightness of the LED lighting lamp, the brightness of the LED lighting lamp set by the user is used as the target brightness; The actual illuminance in the environment is obtained in real time through a photoresistor; the difference between the actual illuminance and the target brightness is recorded as a brightness deviation; Based on the adjusted proportional coefficient, the brightness deviation is used as an input of a PID controller to adjust the duty cycle of the PWM.

10. An intelligent control system for LED lighting, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the LED lighting intelligent control method as described in any one of claims 1-9 are implemented.

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