LED bulb dimming system based on user preferences

Abstract

The present invention relates to the technical field of LED dimming, and specifically to an LED bulb dimming system based on user preferences, comprising: a signal acquisition module for acquiring dimming signal data preferred by the user, the dimming signal data including current, voltage, dimming type, and dimming target during dimming; a signal conversion module for converting and processing the dimming signal data, and determining a data usage range of the dimming signal data under normal use; a range identification module for identifying input conditions of the dimming signal data within the data usage range, acquiring a reference current and a reference voltage within the data usage range, and determining a state influence coefficient and a consistency coefficient corresponding to changes in the reference voltage and reference current when the data usage range changes, thereby improving the accuracy and efficiency of dimming.

Description

Technical Field

[0001] The present invention relates to the technical field of LED dimming, and in particular to an LED bulb dimming system based on user preferences. Background Art

[0002] In the field of LED lighting, as users' demand for lighting control continues to increase, LED bulb dimming systems are gaining increasing attention. Traditional dimming systems often focus solely on basic brightness adjustment functions, neglecting the personalized needs of users in different scenarios. Furthermore, existing dimming systems lack the ability to accurately identify and process user preferences, resulting in less than ideal dimming results.

[0003] For example, Chinese patent publication number CN112911760A discloses a dimming circuit, device and dimming method for improving the dimming accuracy of LEDs. The dimming circuit includes a data distribution module and i current conversion modules connected in parallel, where i is a positive integer greater than 1; the data distribution module is used to divide dimming data with a bit width of N into i input data, and output the i input data to the first to i-th current conversion modules respectively according to the order of high to low bits in the dimming data, where N is a positive integer not less than i; each current conversion module is used to provide an output current for the LED light string according to the received input data.

[0004] The existing technology describes how to implement LED dimming. However, when dimming LEDs, it is also necessary to pay attention to the relationship between current, voltage, and light intensity, and record the appearance of these parameters in user preferences. This ensures that when the LED is adjusted according to user preferences, the adjusted light is more inclined to the user's preferences, thereby improving the dimming effect. Summary of the Invention

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: an LED bulb dimming system based on user preferences, including: a signal acquisition module for obtaining user preferred dimming signal data, the dimming signal data including the current, voltage, dimming type and dimming target during dimming.

[0006] The signal conversion module is used to convert the dimming signal data and determine the data usage range of the dimming signal data under normal use.

[0007] The range identification module is used to identify the input of dimming signal data within the data usage range, obtain the reference current and reference voltage within the data usage range, and determine the state impact coefficient and consistency coefficient corresponding to the changes in the reference voltage and reference current when the data usage range changes.

[0008] The dimming mode determination module is used to compare the changes in the reference voltage and reference current with the dimming type, determine the linear regression model of the current, voltage and light intensity when the current LED bulb is dimming, and determine the currently selected dimming mode based on the output results of the linear regression model.

[0009] The parameter setting module is used to obtain the brightness level, color parameters, and usage frequency of the control parameters of the LED bulb at the corresponding current and voltage of the dimming target, and calculate the parameter bias coefficient.

[0010] The dimming evaluation module is used to verify the relationship between the dimming mode and the parameter bias coefficient during dimming according to the parameter bias coefficient, and obtain the dimming evaluation coefficient.

[0011] The beneficial effects of the present invention are: 1. The present invention uses a signal acquisition module to capture the user's preferred dimming signal data, including current, voltage, dimming type and dimming target; the signal conversion module converts and processes these data to determine the data usage range under normal use; ensures the accuracy and applicability of the dimming signal, and provides reliable basic data for subsequent processing.

[0012] 2. The present invention adopts a range recognition module to identify the input of dimming signal data within the data usage range and obtain the reference current and reference voltage; at the same time, the state influence coefficient and consistency coefficient are calculated through correlation analysis; it can accurately reflect the performance changes of LED bulbs under different dimming states, and provide a basis for determining the dimming method.

[0013] 3. The present invention adopts a dimming mode determination module to determine the currently selected dimming mode based on the output results of the linear regression model; this includes constructing a linear regression model of current, voltage and light intensity, and performing regression analysis; it can automatically select the most appropriate dimming mode according to different user preferences and dimming requirements, thereby improving user satisfaction.

[0014] 4. The present invention uses a parameter setting module to calculate the parameter bias coefficient to reflect the user's preference when using the LED bulb; by comprehensively considering the user's preference and system performance, an objective evaluation of the dimming effect is achieved, providing a basis for optimizing the dimming strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention will be further described below with reference to the accompanying drawings and examples.

[0016] Figure 1 This is a system framework diagram of an LED bulb dimming system based on user preferences.

[0017] Figure 2 This is a system diagram of an LED bulb dimming system based on user preferences.

[0018] Figure 3 It is a flow chart of a range recognition module of an LED bulb dimming system based on user preference.

[0019] Figure 4 FIG. 1 is a flow chart of a dimming evaluation module of an LED bulb dimming system based on user preference. DETAILED DESCRIPTION

[0020] The following embodiments of the present invention are described in detail. The embodiments described below are exemplary and are only used to explain the present invention, and are not to be construed as limiting the present invention. Where specific techniques or conditions are not specified in the embodiments, the techniques or conditions described in the literature in the art or in the product specifications shall be followed.

[0021] See Figure 1 、 Figure 2 ,The LED bulb dimming system based on user preference includes : a signal acquisition module, a signal conversion module, a range recognition module, a dimming mode determination module, a parameter setting module, and a dimming evaluation module.

[0022] The signal acquisition module is used to obtain user preference data and then transmit the corresponding data to the signal conversion module. After setting the data usage range, the signal conversion module transmits the data to the range identification module; the range identification module identifies the data and transmits it to the dimming mode determination module; the dimming mode determination module selects the corresponding dimming mode and transmits the data to the parameter setting module; the parameter setting module outputs the data to the dimming evaluation module after obtaining the parameter bias coefficient.

[0023] The signal acquisition module is used to obtain the dimming signal data preferred by the user. The dimming signal data includes the current, voltage, dimming type and dimming target during dimming.

[0024] The signal conversion module is used to convert the dimming signal data and determine the data usage range of the dimming signal data under normal use.

[0025] The range identification module is used to identify the input of dimming signal data within the data usage range, obtain the reference current and reference voltage within the data usage range, and determine the state impact coefficient and consistency coefficient corresponding to the changes in the reference voltage and reference current when the data usage range changes.

[0026] The dimming mode determination module is used to compare the changes in the reference voltage and reference current with the dimming type, determine the linear regression model of the current, voltage and light intensity when the current LED bulb is dimming, and determine the currently selected dimming mode based on the output results of the linear regression model.

[0027] The parameter setting module is used to obtain the brightness level, color parameters, and usage frequency of the control parameters of the LED bulb at the corresponding current and voltage of the dimming target, and calculate the parameter bias coefficient.

[0028] The dimming evaluation module is used to verify the relationship between the dimming mode and the parameter bias coefficient during dimming according to the parameter bias coefficient, and obtain the dimming evaluation coefficient.

[0029] In one embodiment of the present invention, a signal acquisition module is used to obtain dimming signal data preferred by the user, and the dimming signal data includes the current, voltage, dimming type and dimming target during dimming; wherein the dimming type indicates the current dimming method used, such as PWM dimming, phase control dimming, constant current buck / boost dimming, analog dimming, DMX512 / RDM, etc. Among these methods, the dimming control of multiple LED bulbs or a single LED bulb is mainly performed according to the corresponding situations, and the current and voltage conditions at this time are adjusted according to the bias of the control method to achieve overall dimming.

[0030] The dimming target is used to describe the specific target of the current dimming signal. For example, if there are three LED bulbs and only the second one is adjusted, it is necessary to clearly know the main adjustment target when the dimming signal is executed, the range of current and voltage changes of the target, and the final dimming ratio.

[0031] When obtaining relevant dimming signal data, it is necessary to sort the obtained dimming signal data according to the lighting time, light intensity, number of adjustments, etc. At the same time, it is necessary to determine the specific user behavior corresponding to each user-related data in the dimming signal data to determine the light and method that should be adjusted at this time.

[0032] For example, the dimming type is mainly restricted by the model of the LED bulb used. The purpose of analyzing this dimming type at this time is to be able to identify the type preferred by the current user when using multiple styles of lamps, as well as the specific data of the current light when it is automatically dimmed, to assist in determining the user's preference so that the bulb can be dimmed according to the user's preference; under these different dimming types, the measured current and voltage data will be presented in different forms, such as controlling when the AC power is connected to the bulb, thereby changing the average power input to achieve the dimming effect; quickly switching the current at a fixed frequency, and adjusting the brightness of the lamp by changing the duty cycle; the proportion of the data finally measured and the processing form of this dimming type in different situations will change to a certain extent, resulting in different overall judgments.

[0033] In one embodiment of the present invention, a signal conversion module is used to convert and process dimming signal data and determine the data usage range of the dimming signal data under normal use. The way to convert and process the dimming signal data is to select the current and voltage data of the dimming signal data when it is output to determine the main parameters used in the current dimming. At the same time, the dimming type needs to be obtained. Under a specific dimming type, the current of the current LED bulb will flicker, and at this time there will be a very short current interruption or current change.

[0034] The signal conversion module set up at this time is to simply convert the dimming signal data, so that the range of the dimming signal data can be directly obtained at this time, and the changes in the current system during the light adjustment process can be identified according to this range and the corresponding current and voltage changes. For example, the data usage range can be the time required for each dimming, each light intensity or the frequency and corresponding time ratio of current and voltage changes, as well as the state of the corresponding switch during the corresponding dimming, and the change of the corresponding current under the state of the corresponding switch, so as to quantify the corresponding current and voltage conditions.

[0035] Therefore, the method of obtaining the data usage range includes: extracting features from the dimming signal data, determining the dimming features of the dimming signal data, comparing the dimming features with the dimming type, and determining the change values of the current and voltage under the current dimming type.

[0036] Monitor the changes in current and voltage during dimming, and extract the frequency, amplitude, and duration of the current and voltage changes as the output data usage range.

[0037] When the data usage range is output, the light intensity during dimming, the time required for dimming, and the duration after dimming are recorded.

[0038] In one embodiment of the present invention, a range identification module is used to identify the input condition of dimming signal data within the data usage range, obtain the reference current and reference voltage within the data usage range, and determine the state influence coefficient and consistency coefficient corresponding to the change of the reference voltage and reference current when the data usage range changes.

[0039] In this module, the data used at this time is first clarified based on the obtained data usage range, and the reference current and reference voltage at this time are calculated. After obtaining the reference current and reference voltage, the reference current and reference voltage are calculated with the data within the data usage range to obtain whether there is a correlation between the data usage range and the reference current and reference voltage at this time, and when adjusting the corresponding data, the current state of the LED bulb is affected by the data influence range, so as to obtain whether the data usage range has an impact on the overall dimming; at the same time, the changing trend of the reference voltage and reference current when dimming is performed according to user preferences is verified, and the relevant situation of this part of the changing trend is combined with the relevant impact of the data influence range to obtain the corresponding result of the range identification module.

[0040] like Figure 3 As shown, the implementation of the range identification module further includes: acquiring the frequency, amplitude and duration of changes of current and voltage in the data usage range, and calculating the reference current and reference voltage under the duration of the current and voltage.

[0041] Correlation analysis is performed on the reference current and the reference voltage in turn with the change frequency and amplitude of the current and voltage to obtain a first correlation coefficient corresponding to the reference current and a second correlation coefficient corresponding to the reference voltage.

[0042] The first correlation coefficient and the second correlation coefficient are used to analyze the state of the LED bulb to obtain the state influence coefficient.

[0043] Determine the value of the state influence coefficient under different data usage ranges, verify whether the trend changes of the reference current and reference voltage are consistent with the trend changes of the state influence coefficient, and obtain the consistency coefficient related to the trend changes of the reference current and reference voltage and the trend changes of the state influence coefficient.

[0044] The consistency coefficient of the trend change of the reference current and the reference voltage and the trend change of the state influence coefficient is used as the change of the output.

[0045] The purpose of verifying the state influence coefficient with the reference current and reference voltage at this time is to verify whether the currently identified correlation is consistent with the trend between the parameters set by the user itself under the corresponding settings of the user preferences, so as to identify the trend of the user preference. Based on this trend, the current user's needs when dimming the LED bulb can be understood, thereby further improving the current system's ability to understand user operations.

[0046] For the reference current and reference voltage, the current and voltage within the data usage range will be sorted, and the weighted average of the average values of the top 20% and the bottom 20% of the sorted current and voltage and the average value of the overall current and voltage data will be selected as the reference current and reference voltage used. At this time, the weighted coefficients can be 0.4 and 0.6 to obtain relatively stable values of the current and voltage as the reference current and reference voltage used.

[0047] The first correlation coefficient is obtained by comparing the currently set reference current with the value of the corresponding current at the corresponding change frequency and change amplitude. It can also be understood that the value in the data usage range is the actual current value or the current index value to represent the change situation and relative relationship of the current.

[0048] The first correlation coefficient is expressed as follows: obtaining the current index value of the current in the data usage range at the corresponding change frequency and amplitude, comparing the current index value with the reference current value, and calculating the first correlation coefficient. The first correlation coefficient is calculated by comparing the current index value and the reference current value as input with the average value of the current index value and the average value of the reference current value in the historical data using the Pearson correlation coefficient to analyze the abnormal increase of current when the current changes.

[0049] The second correlation coefficient is calculated in the same way as the first correlation coefficient. The voltage index value of the voltage in the data usage range at the corresponding change frequency and amplitude is obtained, and the voltage index value is compared with the reference voltage value to calculate the second correlation coefficient. The second correlation coefficient uses the Pearson correlation coefficient, takes the reference voltage and the voltage index value as input, and compares them with the average value of the voltage index value and the average value of the reference voltage value in the historical data to obtain the second correlation coefficient.

[0050] The state influence coefficient can be expressed as follows: the obtained first correlation coefficient and the second correlation coefficient are grouped according to the number of identified durations; when the first correlation coefficient is greater than the second correlation coefficient, the corresponding first correlation coefficient and the second correlation coefficient are placed in array M1; when the first correlation coefficient is less than the second correlation coefficient, the corresponding first correlation coefficient and the second correlation coefficient are placed in array M2; the state influence coefficient is calculated based on the average value of the first correlation coefficient and the second correlation coefficient in array M1 and array M2 and the standard deviation of all the first correlation coefficients and the second correlation coefficients; at this time, array M1 and array M2 are used to group the first correlation coefficient and the second correlation coefficient, divide these values into two arrays, and calculate the average value of the two arrays respectively, as well as the standard deviation of all the data at this time to verify the current value of the correlation coefficient.

[0051] Among them, IC represents the state influence coefficient, represents the average value of the first correlation coefficient and the second correlation coefficient in the array M1, and this average value represents the combined average value of the first correlation coefficient and the second correlation coefficient; represents the average value of the first correlation coefficient and the second correlation coefficient in the array M2. In this case, the average value of the first correlation coefficient and the second correlation coefficient represents the average value of all the first correlation coefficients and the second correlation coefficients added together in the corresponding array; Represents the standard deviation of all the first correlation coefficients and the second correlation coefficients of array M1 and array M2. This standard deviation is calculated for all the values in array M1 and array M2. represents the number of elements in array M1, represents the number of elements in array M2, Indicates the total number of elements in arrays M1 and M2.

[0052] In the state influence coefficient, it is mainly used to determine which value of current and voltage has the greatest impact on the current dimming, and which data mainly changes during dimming. This allows the dimming process to be controlled, preventing the current LED bulb from experiencing abnormalities during dimming, and timely monitoring the dimming current and voltage.

[0053] When obtaining the consistency coefficient, the consistency is determined by verifying the correlation between the state influence coefficients of the current and voltage at multiple durations and the reference current and reference voltage in terms of value.

[0054] For example, the consistency coefficient is expressed as obtaining a first probability value of the state influence coefficient and the reference current value, a second probability value of the state influence coefficient and the reference voltage value, and a third probability value of the reference current and the reference voltage value. The consistency coefficient is calculated based on the first probability value, the second probability value, and the third probability value.

[0055] Among them, P IC(I,V) represents the consistency coefficient, P IC,I Represents the first probability value, P IC,V Represents the second probability value, P I,V represents the third probability value.

[0056] After obtaining the consistency coefficient, the changes in the reference voltage and reference current can be clearly identified, and based on this value, the response of the current state impact coefficient to the reference voltage and reference current at different time points can be identified, so that the corresponding changes in the current light when the reference voltage and reference current change can be analyzed.

[0057] In one embodiment of the present invention, a dimming mode determination module is used to compare the changes in the reference voltage and reference current with the dimming type, determine the linear regression model of the current, voltage and light intensity when the current LED bulb is dimming, and determine the currently selected dimming mode based on the output result of the linear regression model.

[0058] This module primarily compares the dimming type with the reference voltage and current, taking the current LED bulb's environment and user preferences as input to calculate the appropriate dimming method. Generally, dimming methods involve changing the lamp's duty cycle, voltage, current, conduction angle, and off-angle. Alternatively, the time corresponding to the on-angle and off-angle periods is divided to determine the lamp's on and off times, allowing for quantification of the specific dimming situation. Therefore, the current environment and user preferences can be expressed as the voltage, current, conduction angle, and off-angle data used during user settings. Dimming method analysis is accomplished by verifying these values.

[0059] The linear regression model in this module determines the calculation results of the current linear analysis by taking current as the dependent variable and light intensity, voltage, duty cycle, conduction angle and turn-off angle as independent variables. It then selects the light intensity and voltage setting values under the current dimming mode according to the current-related coefficients output in the linear regression, and finally obtains the dimming mode that needs to be set.

[0060] When comparing the changes in the reference voltage and reference current with the dimming type, it is necessary to obtain the state influence coefficient and consistency coefficient of the reference current and reference voltage, and compare these two coefficients with the threshold value under the corresponding dimming type to obtain the loss value corresponding to the state influence coefficient and consistency coefficient. When the loss value is the smallest, a linear regression model of the current current, voltage and light intensity is constructed; thereby completing the comparison of current and voltage. At the same time, the output result of the linear regression model needs to be subjected to a t-test to verify whether there is a significant relationship between the current current, voltage and light intensity, and the current, voltage and corresponding light intensity with a significant relationship are used as the parameters that the LED bulb needs to operate at this time, thereby obtaining the corresponding dimming method at this time.

[0061] Therefore, the method of obtaining the dimming mode is expressed as follows: obtain the state influence coefficient and consistency coefficient of the current reference current and reference voltage, calculate the loss value corresponding to the state influence coefficient and consistency coefficient, and when the loss value is the minimum value, obtain the current, voltage, light intensity, duty cycle, conduction angle and disconnection angle corresponding to the current reference current and reference voltage, construct a linear regression model corresponding to the current, voltage and light intensity, take the current as the dependent variable, and take the light intensity, voltage, duty cycle, conduction angle and disconnection angle as independent variables for regression analysis to obtain the regression coefficient of the linear regression model, perform a t test on the regression coefficient, and when the test value of the regression coefficient is greater than the preset test value, output the current, voltage and light intensity of the corresponding regression coefficient to obtain the output result of the linear regression model, and set the dimming mode according to the output result of the linear regression model.

[0062] The loss values corresponding to the state influence coefficient and the consistency coefficient can be expressed as follows: obtain the distribution ratio value corresponding to the consistency coefficient and the distribution ratio value of the state influence coefficient. Here, the distribution ratio value is the ratio of the number of values in the historical data to the total number under the corresponding values of the state influence coefficient and the consistency coefficient; according to the distribution ratio values corresponding to the state influence coefficient and the consistency coefficient, the loss values corresponding to the state influence coefficient and the consistency coefficient are calculated.

[0063] Among them, L s Indicates the loss value corresponding to the state influence coefficient and consistency coefficient, P V1 Indicates the distribution ratio value corresponding to the consistency coefficient, P V2 It represents the distribution ratio value of the state influence coefficient. The loss calculated at this time tends to obtain the relative value relationship between the state influence coefficient and the consistency coefficient, as well as the proportion of these values in the corresponding historical data, to determine the correlation of the current LED bulb operation when dimming control is performed at this time. When the loss value is the smallest, it means that the linear relationship between the current, voltage, and corresponding light intensity represented by the current state influence coefficient and the consistency coefficient is more obvious. At this time, the linear regression model is used to analyze these existing data to set the current dimming mode. At the same time, when calculating the state influence coefficient and the consistency coefficient, some current and voltage related data in a relatively short period of time are used for statistical analysis. At this time, when controlling the LED bulb, the rapid control and monitoring of the LED bulb dimming can be achieved by performing multiple group segmentation experiments and verification on the currently generated data.

[0064] The preset test value is set to 0.05 to verify the significance of the regression coefficient calculated after the analysis, so that the current, voltage and light intensity with significant relationships can be selected to achieve intelligent control of the current LED bulb.

[0065] According to the output results of the linear regression model, the dimming mode is set by calculating the similarity between the current, voltage and light intensity and the preset dimming mode, and outputting the preset dimming mode with the greatest similarity as the currently selected dimming mode.

[0066] At this time, the method of calculating the similarity can convert the current, voltage and light intensity into vector values and calculate the cosine similarity with the vector value of the preset dimming method, and regard the maximum value of the cosine similarity as the maximum value of the similarity at this time, thereby obtaining the preset dimming method with the greatest similarity. At the same time, the preset dimming method includes the current, voltage and light intensity values that need to be adjusted to adjust the light of the current LED bulb; the preset dimming method will be set in the LED bulb dimming system. When adjustment is needed, these preset dimming methods are obtained to complete the overall adjustment control.

[0067] In one embodiment of the present invention, the parameter setting module is used to obtain the brightness level, color parameters, and usage frequency of the control parameters of the LED bulb at the corresponding current and voltage of the dimming target, and calculate the parameter bias coefficient.

[0068] The brightness levels here represent a measure of the light. The main focus is analyzing the number of times these brightness levels appear and their corresponding durations to determine how long the light lasts at the corresponding brightness level, as well as what the user prefers to choose when adjusting these lights.

[0069] Color parameters primarily include color temperature, which determines the color of the light and is typically categorized as warm light, natural light, and cool light. Warm light, with a color temperature of 2700-3000K, is suitable for creating a cozy atmosphere; natural light, with a color temperature of 4000-4500K, is ideal for offices and learning spaces; and cool light, with a color temperature exceeding 5000K, is suitable for locations requiring high brightness. During dimming, the color temperature may be adjusted based on the application scenario. The color parameters represent these available color temperatures using values within the range of 0-1 to perform comprehensive calculations of the corresponding data.

[0070] The control parameters mainly include dimming range; dimming range refers to the adjustable range of the LED bulb from minimum brightness to maximum brightness; this range will indicate the range corresponding to the maximum brightness and minimum brightness during the current dimming, mainly describing the smoothness of the switching when dimming.

[0071] For brightness level, the number of times the current brightness level appears when the user adjusts the light, as well as the duration of the corresponding brightness level, will be recorded; for color temperature, the number of times the current color temperature is selected and the relative time percentage will be recorded, and the control parameter indicates the probability of using the dimming range.

[0072] Therefore, the implementation method of the parameter bias coefficient includes obtaining the number of occurrences and durations of the brightness level, the index value of the color parameter, and the frequency of use of the control parameter, normalizing the number of occurrences of the brightness level, the duration of the brightness level, the index value of the color parameter, and the frequency of use of the control parameter, and then calculating the parameter bias coefficient.

[0073] Among them, PD represents the parameter bias coefficient, δ1 represents the number of occurrences of the normalized brightness level, δ2 represents the duration of the normalized brightness level, δ3 represents the index value of the normalized color parameter, δ4 represents the frequency of use of the normalized control parameter, w1 represents the weight of the number of occurrences of the brightness level, w2 represents the weight of the duration of the brightness level, w3 represents the weight of the index value of the color parameter, and w4 represents the weight of the frequency of use of the control parameter; the weight set here is based on the ratio of the number of values corresponding to the number of occurrences of the brightness level, the duration of the brightness level, the index value of the color parameter and the frequency of use of the control parameter to the total number of corresponding historical data.

[0074] At this time, the parameter bias coefficient is calculated to identify the proportion of these dimming operations in the overall situation after the user dims the light, so as to identify what settings the user prefers. The calculation of the brightness level is to observe whether the user likes the light to be bright. The frequency of use of the control parameter is to find the frequency corresponding to the maximum light and minimum light adjusted by the user when adjusting the light, so as to identify what kind of light range the user likes. The color parameter is used to identify the preferences for warm light, natural light, cold light, etc. A comprehensive value is calculated based on these data to identify whether the light adjusted by the current user meets the user's needs, and adjust the light in real time according to the current parameter bias coefficient, so that the LED bulb can meet the user's preferences when dimming.

[0075] In one embodiment of the present invention, the dimming evaluation module is used to verify the relationship between the dimming mode and the parameter bias coefficient during dimming according to the parameter bias coefficient, and obtain the dimming evaluation coefficient.

[0076] In this module, the purpose of evaluating the dimming method is to compare the parameters used in the dimming method with the parameter bias coefficient to obtain the dimming evaluation coefficient at this time. For example, the parameters in the dimming method are extracted one by one, the observed frequency and predicted frequency of the dimming method are calculated, and the categories of the relevant parameters in the dimming method are verified to test the whole.

[0077] For example, the dimming mode includes multiple values of current, voltage and light intensity, and these values correspond to some labels, such as low current, medium current, low voltage, medium voltage, weak light, medium light, strong light and other labels. These labels can be combined into categories corresponding to multiple dimming modes. Then, when evaluating, the correlation between the dimming parameters in these categories can be obtained, and this correlation can be combined with the parameter bias coefficient to know what form of association exists between these dimming modes and the parameters used for preference, and whether the categories of these dimming modes correspond. Finally, based on the connection between these values, it can be known whether the current LED bulb can be set to the same content as the user's requirements during actual operation; if the system can accurately adjust the current and voltage according to the user's input to achieve the expected light intensity, then the dimming evaluation coefficient will be high, indicating that the system performance is good; if the system frequently has parameter deviations or poor dimming effects during actual operation, then the dimming evaluation coefficient will be low, and further optimization of system performance is required.

[0078] like Figure 4 As shown, the relationship between the dimming mode and the parameter bias coefficient during dimming is verified, and the implementation method of obtaining the dimming evaluation coefficient can be expressed as obtaining the first parameter list and the second parameter list corresponding to the dimming mode. The first parameter list and the second parameter list are used to divide the parameters in the dimming mode to obtain two groups of lists. The data in the two groups of lists correspond to each other. The first parameter list and the second parameter list both include the values of current, voltage and light intensity.

[0079] The first parameter category corresponding to the first parameter list and the second parameter category corresponding to the second parameter list are obtained respectively, the dimming mode is tested according to the first parameter category and the second parameter category, and a preliminary dimming coefficient is calculated; the preliminary dimming coefficient tends to use the form of chi-square test to verify the relationship between the data corresponding to these categories and dimming modes.

[0080] The preliminary dimming coefficient and parameter bias coefficient are calculated according to the voltage values at the time points corresponding to the starting point, middle point and end point of dimming to obtain the dimming evaluation coefficient; when calculating at this time, the starting point, middle point and end point will represent the time points corresponding to dimming, and the voltages at these time points will be mainly identified. At the same time, it is also necessary to obtain the preliminary dimming coefficient and parameter bias coefficient of the dimming implementation, as well as the preliminary dimming coefficient with the relevant initial value to quantify these changes. The initial value of the preliminary dimming coefficient will select the average value of the corresponding settings in the historical data to represent the current relevant dimming situation.

[0081] The preliminary dimming coefficient can be expressed as: Wherein, C represents the preliminary dimming coefficient, r represents the number of the first parameter category, k represents the number of the second parameter category, n represents the total amount of data of the first parameter category and the second parameter category, i ranges from 1 to r, and j ranges from 1 to k; Q ij The observed frequency of the i-th value in the first parameter category and the j-th value in the second parameter category is the number of times each value appears after the first parameter list and the second parameter list are divided into categories; E ij It represents the expected frequency of the co-occurrence of the i-th value in the first parameter category and the j-th value in the second parameter category. The expected frequency is the ratio of the product of the number of occurrences of the i-th value in the first parameter category and the number of occurrences of the j-th value in the second parameter category to the total amount of data.

[0082] The dimming evaluation coefficient can be expressed as follows: obtaining the voltage values of the starting point, middle point and end point during dimming, combining the voltage values with the preliminary dimming coefficient and the parameter bias coefficient, and calculating the dimming evaluation coefficient.

[0083] Among them, DEC represents the dimming evaluation coefficient, PD represents the parameter bias coefficient, and C0 represents the initial value of the preliminary dimming coefficient. The voltage V1 represents the average value of the voltage during dimming, V2 represents the voltage at the middle point of dimming, and V3 represents the voltage at the end point of dimming. To obtain the voltage values and related ratios, it is necessary to verify the stability of the overall dimming process by comparing these values with the previously calculated preliminary dimming coefficient and parameter bias coefficient when the relevant current changes. This allows for the evaluation of the dimming at this time and timely monitoring of whether the dimming process is reasonable.

[0084] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention, which are still covered by the scope of protection of the present invention.

Claims

1. The LED bulb dimming system based on user preference is characterized by: include: A signal acquisition module is used to obtain dimming signal data preferred by the user, where the dimming signal data includes current, voltage, dimming type, and dimming target during dimming; A signal conversion module is used to convert the dimming signal data and determine the data usage range of the dimming signal data under normal use; The range identification module is used to identify the input of the dimming signal data within the data usage range, obtain the reference current and reference voltage within the data usage range, and determine the state impact coefficient corresponding to the change of the reference voltage and reference current when the data usage range changes. and the consistency coefficient ; Correlation analysis is performed on the reference current and the reference voltage in turn with the frequency and amplitude of the current and voltage changes to obtain a first correlation coefficient corresponding to the reference current and a second correlation coefficient corresponding to the reference voltage; The first correlation coefficient and the second correlation coefficient obtained are grouped according to the number of identified durations. When the first correlation coefficient is greater than the second correlation coefficient, the corresponding first correlation coefficient and the second correlation coefficient are put into an array. When the first correlation coefficient is less than the second correlation coefficient, the corresponding first correlation coefficient and second correlation coefficient are placed in the array In the array The average of the first and second correlation coefficients , array The average of the first and second correlation coefficients With array and arrays The standard deviation of all first and second correlation coefficients in , calculated ; ； in, Represents an array The number of elements in , Represents an array The number of elements in , Represents an array and arrays The total number of elements in ; Get The first probability value that appears together with the value of the reference current 、 The second probability value that appears together with the value of the reference voltage , the third probability value of the reference current and reference voltage values appearing together ; ； A dimming mode determination module is used to compare the changes in the reference voltage and reference current with the dimming type, determine the linear regression model of the current, voltage and light intensity when the LED bulb is dimming, and determine the currently selected dimming mode based on the output of the linear regression model; The parameter setting module is used to obtain the brightness level, color parameters, and usage frequency of the control parameters of the LED bulb at the corresponding current and voltage of the dimming target, and calculate the parameter bias coefficient ; Obtain the number of occurrences and durations of brightness levels, the index values of color parameters, and the frequency of use of control parameters, and normalize the number of occurrences of brightness levels, the duration of brightness levels, the index values of color parameters, and the frequency of use of control parameters to record them as 、 、 and ; ； in, The weight representing the number of occurrences of the brightness level, The weight representing the duration of the brightness level, The weight of the index value representing the color parameter, The weight representing the frequency of use of the control parameter; Dimming evaluation module for , verify the relationship between the dimming mode and the parameter bias coefficient during dimming, and obtain the dimming evaluation coefficient ; Obtain a first parameter list and a second parameter list corresponding to the dimming mode; Get the first parameter category corresponding to the first parameter list and the second parameter category corresponding to the second parameter list respectively, check the dimming mode according to the first parameter category and the second parameter category, and calculate the preliminary dimming coefficient ; Will and Calculate the voltage values at the time points corresponding to the starting point, middle point and end point of dimming to get ; ;in, Indicates the initial value of the preliminary dimming coefficient, Indicates the average value of the voltage during dimming. Indicates the voltage value of the starting point of dimming. Indicates the voltage value at the midpoint during dimming. Indicates the voltage value at the end point of dimming.

2. The LED bulb dimming system based on user preference according to claim 1, characterized in that: Methods for obtaining data usage scope include: Extract features from the dimming signal data to determine the dimming features of the dimming signal data, compare the dimming features with the dimming type, and determine the change values of current and voltage under the current dimming type; Monitor the changes in current and voltage during dimming, and extract the frequency, amplitude, and duration of the current and voltage changes as the output data usage range.

3. The LED bulb dimming system based on user preference according to claim 2, characterized in that: The implementation of the range identification module also includes: Acquire the frequency, amplitude and duration of changes of current and voltage in the data usage range, and calculate the reference current and reference voltage under the duration of the current and voltage; Analyze the state of the LED bulb using the first correlation coefficient and the second correlation coefficient to obtain the state influence coefficient; Determine the value of the state influence coefficient under different data usage ranges, verify whether the trend change of the reference current and reference voltage is consistent with the trend change of the state influence coefficient, and obtain the consistency coefficient related to the trend change of the reference current and reference voltage and the trend change of the state influence coefficient; The consistency coefficient of the trend change of the reference current and the reference voltage and the trend change of the state influence coefficient is used as the change of the output.

4. The LED bulb dimming system based on user preference according to claim 1, characterized in that: The dimming mode is obtained by obtaining the state influence coefficient and consistency coefficient of the current reference current and reference voltage, calculating the loss value corresponding to the state influence coefficient and consistency coefficient, and when the loss value is the minimum value, obtaining the current, voltage, light intensity, duty cycle, conduction angle and disconnection angle corresponding to the current reference current and reference voltage, constructing a linear regression model corresponding to the current, voltage and light intensity, taking the current as the dependent variable, and performing regression analysis on the light intensity, voltage, duty cycle, conduction angle and disconnection angle as independent variables to obtain the regression coefficient of the linear regression model, performing a t test on the regression coefficient, and when the test value of the regression coefficient is greater than the preset test value, outputting the current, voltage and light intensity of the corresponding regression coefficient to obtain the output result of the linear regression model, and setting the dimming mode according to the output result of the linear regression model.

5. The LED bulb dimming system based on user preference according to claim 4, characterized in that: The loss values corresponding to the state influence coefficient and the consistency coefficient are expressed as follows: the distribution ratio value corresponding to the consistency coefficient and the distribution ratio value of the state influence coefficient are obtained. Here, the distribution ratio value is the ratio of the number of values corresponding to the historical data and the total number of values under the corresponding values of the state influence coefficient and the consistency coefficient; according to the distribution ratio values corresponding to the state influence coefficient and the consistency coefficient, the loss values corresponding to the state influence coefficient and the consistency coefficient are calculated; ； in, Indicates the loss value corresponding to the state influence coefficient and consistency coefficient, Indicates the distribution ratio value corresponding to the consistency coefficient, Indicates the distribution ratio value of the state influence coefficient.

6. The LED bulb dimming system based on user preference according to claim 1, characterized in that: The preliminary dimming coefficient is expressed as: ； in, represents the preliminary dimming coefficient, represents the number of first parameter categories, Indicates the number of second parameter categories, Indicates the total amount of data in the first parameter category and the second parameter category, where i ranges from 1 to r and j ranges from 1 to k; represents the frequency of observations where the i-th value in the first parameter category and the j-th value in the second parameter category occur together; represents the expected frequency of co-occurrence of the i-th value in the first parameter category and the j-th value in the second parameter category.

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