A control method and control system for energy-saving lighting decorations

Abstract

The application discloses an energy-saving lighting ornament control method and a control system thereof, and relates to the technical field of intelligent lighting control. The method comprises the following steps: dividing a target Christmas tree into a plurality of sub-regions, performing slow start control on the light strings in each sub-region, obtaining a first slow start sequence, acquiring a light string brightness change curve after the light strings are turned on, constructing a light string turning-on judgment threshold set based on the change characteristics of the curve, extracting the light string turning-on time sequence of each sub-region, constructing a plurality of second slow start sequences based on the light string turning-on time sequence, applying the second slow start sequences to the slow start control of each sub-region respectively, extracting control characteristics, and obtaining a first control characteristic set; and iteratively selecting the plurality of second slow start sequences according to the first control characteristic set, obtaining an optimal slow start sequence, and applying the optimal slow start sequence to the Christmas tree ornament control, so that the problem that the turning-on time of the light strings at the remote end or with low voltage is inconsistent due to start delay in a large Christmas tree scene is solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent lighting control technology, and more specifically, to a control method and control system for energy-saving lighting fixtures. Background Technology

[0002] As an important part of holiday decorations, the lighting effects of Christmas trees directly impact the visual experience and festive atmosphere. Modern Christmas trees are often equipped with numerous LED or smart light strings, which use controllers to achieve various lighting modes, such as flashing, gradation, and color switching, to enhance the decorative effect and energy efficiency. Common control methods include timed switching, remote control adjustment, or sensor-based automatic control, all aimed at improving the diversity of lighting performance and energy conservation.

[0003] However, in practical applications, especially in scenarios involving large Christmas trees, multiple trees connected in series, or complex lighting layouts, the long string of lights, uneven impedance distribution, power supply voltage fluctuations, or limited controller driving capabilities often cause delayed startup of lights located at the top, bottom, or far from the controller due to voltage drops. This results in asynchronous lighting, discontinuous transitions, or flickering. This inconsistency not only detracts from the overall aesthetics and visual comfort of the lighting animation but can also increase energy consumption, shorten the lifespan of LEDs, and even cause localized overheating due to some LEDs working at full load for extended periods, posing safety hazards. Moreover, most existing Christmas tree lighting control technologies remain at the level of traditional timed switches, remote control adjustments, or sensor-based control, which are relatively outdated. With the development of intelligent algorithms and smart hardware technologies, it is necessary to innovate these control technologies to improve the intelligence of lighting control and the visual experience.

[0004] To address the above problems, this invention proposes an improved and optimized solution. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a control method and system for energy-saving lighting fixtures. By optimizing the lighting sequence of light strings through zoned soft start and brightness feedback, the method solves the problem of inconsistent lighting times caused by start-up delays in distant or low-voltage light strings in large Christmas tree scenes.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for controlling energy-saving lighting fixtures includes the following steps: dividing the target Christmas tree into several sub-regions, and performing soft-start control on the light strings in each sub-region to obtain a first soft-start sequence; obtaining the brightness change curve of the light strings after they are lit, and constructing a light string lighting judgment threshold set based on the change characteristics of the brightness change curve; extracting the light string lighting timing sequence of each sub-region based on the light string lighting judgment threshold set, and constructing several second soft-start sequences based on the light string lighting timing sequence; applying the several second soft-start sequences to the soft-start control of each sub-region respectively, and extracting control features to obtain a first control feature set; iteratively filtering the several second soft-start sequences according to the first control feature set to obtain the optimal soft-start sequence and applying it to the Christmas tree lighting control.

[0007] In a preferred embodiment, dividing the target Christmas tree into several sub-regions and performing soft-start control on the light strings within each sub-region to obtain a first soft-start sequence specifically involves: acquiring structural distribution data and decorative effect requirements of the target Christmas tree, and dividing the target Christmas tree into several sub-regions; setting different soft-start time parameters in each sub-region to obtain an initial soft-start strategy; controlling the light strings to gradually light up according to a preset time gradient based on the initial soft-start strategy, and recording the start time of each light string; generating a first soft-start sequence based on the start time, wherein the first soft-start sequence includes the start order and time interval of each light string.

[0008] In a preferred embodiment, the step of obtaining the brightness change curve of the light string after the first soft-start sequence is lit, and constructing a set of light string lighting judgment thresholds based on the change characteristics of the brightness change curve, specifically involves: acquiring the brightness data of the light string in each sub-region during the soft-start process in real time, and plotting the brightness change curve over time based on the brightness data to obtain the light string brightness change curve; performing data analysis on the light string brightness change curve and extracting the change characteristics of the light string brightness change curve, the change characteristics including inflection points, slope extreme points, and steady-state brightness values; constructing a dynamic brightness change feature vector based on the inflection points and slope extreme points, and setting a brightness fluctuation tolerance range in combination with the steady-state brightness value; and constructing a set of light string lighting judgment thresholds using a fuzzy algorithm based on the brightness fluctuation tolerance range and the dynamic brightness change feature vector.

[0009] In a preferred embodiment, the step of constructing a threshold set for judging the lighting of the light string using a fuzzy algorithm based on the brightness fluctuation tolerance range and the dynamic brightness change feature vector specifically involves: dividing the brightness fluctuation tolerance range into several sub-ranges with a preset width, and performing statistical distribution analysis on the brightness fluctuations within each sub-range; extracting the mean and variance of the brightness changes in each sub-range based on the statistical distribution analysis results, and constructing a membership function reconstruction parameter set; extracting the weight of each preset component in the dynamic brightness change feature vector, and correcting the membership function reconstruction parameter set based on the weights to obtain a corrected membership function reconstruction parameter set; performing piecewise linear optimization on the preset membership function based on the corrected membership function reconstruction parameter set to obtain an improved membership function; and performing fuzzy inference on the brightness change process based on the improved membership function to construct a threshold set for judging the lighting of the light string.

[0010] In a preferred embodiment, the step of extracting the lighting time sequence of the light strings in each sub-region based on the light string lighting judgment threshold set, and constructing several second soft-start sequences based on the light string lighting time sequence, specifically involves: judging the state of the light strings in each sub-region based on the light string lighting judgment threshold set to obtain the effective lighting time of each light string, and constructing a lighting time sequence map of the sub-region; extracting the temporal distribution features of the lighting time sequence map of the sub-region, and using a clustering algorithm based on the temporal distribution features to identify abnormal light string clusters; and reconstructing the initial soft-start strategy according to the identification results to obtain several second soft-start sequences.

[0011] In a preferred embodiment, the step of applying several second soft-start sequences to the soft-start control of each sub-region and extracting control features to obtain a first control feature set specifically involves: Several second soft-start sequences are applied to each sub-region, and the start-up data of the light string is acquired in real time. The start-up data includes light string energy consumption data, brightness stability index and heat accumulation data. Feature extraction is performed on the start-up data of the light string to obtain control feature vectors. The control feature vectors are integrated into a first control feature set according to a preset sequence number.

[0012] In a preferred embodiment, the step of iteratively screening several second soft-start sequences based on a first control feature set to obtain the optimal soft-start sequence specifically involves: constructing a multi-objective optimization function based on the first control feature set; using a genetic algorithm to perform multiple rounds of iterative optimization on the second soft-start sequences to obtain a Pareto optimal solution set; and screening the Pareto optimal solution set based on the multi-objective optimization function to obtain the optimal soft-start sequence.

[0013] In a preferred embodiment, the application in Christmas tree lighting control specifically involves: compiling the optimal soft-start sequence into a set of control instructions and sending it to the preset lighting controllers of each sub-area; A preset adaptive feedback mechanism is embedded in the preset lighting controller to monitor the lighting status in real time and fine-tune the start-up parameters; energy consumption and brightness data during the actual lighting control process are recorded, and a sequence version management log is established to switch between soft start sequences in multiple scenarios.

[0014] The technical effects and advantages of the energy-saving lighting control method and control system of this invention are as follows: This invention divides the Christmas tree into several sub-regions and performs soft-start control on the light strings within each sub-region. Simultaneously, it collects real-time data on light string brightness, energy consumption, and heat accumulation. By analyzing brightness variation characteristics, it constructs a set of thresholds for light string activation. Then, based on activation timing reconstruction and cluster optimization, it generates a second soft-start sequence. Finally, through multi-objective optimization and iterative selection using a genetic algorithm, it obtains the optimal soft-start sequence and distributes it to the controllers of each region. This method and system can accurately identify the effective activation time of each light string and dynamically adjust activation parameters during execution through an adaptive feedback mechanism. This ensures that even in large Christmas tree scenarios, light strings located at the top, bottom, or in areas with low voltage, they can synchronously reach the predetermined brightness, significantly solving the problem of inconsistent activation times in traditional control. Furthermore, by optimizing the light string activation sequence, time intervals, and energy consumption management, it achieves reduced energy consumption, uniform brightness, and controllable heat accumulation, offering significant technical advantages such as energy efficiency, consistent visual effects, intelligent adaptability, and ease of switching between multiple scenarios. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a control method for energy-saving lighting fixtures according to the present invention.

[0016] Figure 2 This is a schematic diagram of the control system for an energy-saving lighting fixture according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1, Figure 1 The present invention provides a control method for energy-saving lighting fixtures, comprising the following steps: S1, Divide the target Christmas tree into several sub-regions, and perform soft-start control on the string lights in each sub-region to obtain the first soft-start sequence; In this example, the target Christmas tree is divided into several sub-regions, and the string lights are controlled with a soft start in each sub-region to obtain the first soft start sequence, which is as follows: Obtain the structural distribution data and decorative effect requirements of the target Christmas tree, and divide the target Christmas tree into several sub-regions; Different soft-start time parameters are set for each sub-region to obtain the initial soft-start strategy; Based on the initial soft-start strategy, the light strings are controlled to light up gradually according to a preset time gradient, and the start time of each light string is recorded. A first soft-start sequence is generated based on the start time point. The first soft-start sequence includes the start order and time interval of each light string.

[0019] It's important to clarify that the structural distribution data of the target Christmas tree primarily refers to its geometric shape, size proportions, branch layer distribution, density, and the possible placement of the light strings in space. This includes factors such as the tree's height, width, and the number and distribution of branches at different levels. Decorative effect requirements, on the other hand, refer to the lighting aesthetics desired by the user or designer, such as overall brightness levels, color gradients, dynamic flashing rhythms, or highlighting specific areas. Based on this information, the Christmas tree can be divided into several sub-regions: first, according to the tree's spatial structure, it can be layered from bottom to top or from inside to outside, such as the "trunk area," "lower branch area," "middle branch area," and "top star decoration area." Then, combined with decorative effect requirements, different lighting objectives are assigned to different areas; for example, creating a stable atmosphere at the bottom, a gradual dynamic effect in the middle, and emphasizing flashing effects at the top. The final sub-region division not only adheres to physical structural feasibility but also considers aesthetic light distribution, thus providing clear zoning targets for subsequent soft-start control.

[0020] Furthermore, setting a soft-start time parameter for each sub-region essentially involves configuring a delay and gradation time control logic for the lighting process of the light strings. Operationally, different lighting delays can be set based on the importance of the sub-region and the target effect. For example, the light strings in the bottom region can have a longer soft-start time, allowing the light effect to gradually advance from bottom to top, creating a visual transition; while the light strings in the top region can have a shorter soft-start time, quickly highlighting the core highlight at the top of the tree when the entire tree is lit. Soft-start time parameters typically include: initial delay time (i.e., the interval from the start of control to the start of the light strings lighting up), lighting gradient time (i.e., the smooth transition time of the light strings from dark to bright), and interval time between light strings (i.e., the difference in the order of lighting different light strings within the same region). The combined configuration of these parameters constitutes the initial soft-start strategy for that region. By using different time parameters in different regions, the entire Christmas tree can present a layered and dynamic effect during the lighting process.

[0021] Furthermore, when controlling the light strings based on the initial soft-start strategy, lighting signals are sequentially sent to the light strings in each sub-region according to preset delays and gradient times. The specific process is as follows: first, the lighting order of the light strings in each sub-region is determined; then, the light strings are triggered to start according to the set delay time; and finally, a dimming algorithm controls the gradual increase of current or voltage, causing the light strings to gradually brighten from dark to bright within the set gradient time until the target brightness value is reached. During this process, the actual start time of each light string is recorded in real time to ensure that the lighting process conforms to the strategy. These recorded time points are not only used for subsequent analysis and optimization but also serve as comparative data to determine whether there are any delay deviations or anomalies in the actual effect. By gradually lighting up and recording time points, the entire lighting process achieves a rhythmic dynamic transition effect, while providing basic temporal data for generating subsequent sequences.

[0022] Finally, based on the recorded start times, the lighting order and specific start times of each string of lights are organized into a time series, which is the so-called first soft-start sequence. The generation method is as follows: using a timeline as a reference, the actual start times of the light strings are sorted sequentially to obtain the start order of each string. Simultaneously, the time difference between adjacent strings is calculated to obtain the time interval between them. The "start order and time interval of each string" refers to two core pieces of information: first, the order in which the strings light up (e.g., the first lit string is the bottom string, the second is a middle string, and so on); second, the length of the time interval between adjacent strings (e.g., the first and second strings are 0.5 seconds apart, the second and third strings are 1 second apart). This information, combined, constitutes the first soft-start sequence, which fully describes the dynamic lighting process of the light strings across the entire Christmas tree under the initial strategy. This sequence not only visually reflects the start-up pattern of the light strings but also provides a benchmark reference for subsequent threshold determination and optimization.

[0023] S2, obtain the brightness change curve of the first soft start sequence after the light string is lit, and construct a set of light string lighting judgment thresholds based on the change characteristics of the brightness change curve; In this example, the brightness change curve of the light string after the first soft-start sequence is turned on is obtained, and a threshold set for judging whether the light string is turned on is constructed based on the change characteristics of the brightness change curve. Specifically: The brightness data of the light string in each sub-region during the soft start process is acquired in real time, and the brightness change curve over time is plotted based on the brightness data to obtain the brightness change curve of the light string. Data analysis is performed on the brightness variation curve of the light string, and the variation characteristics of the brightness variation curve are extracted. The variation characteristics include inflection points, slope extreme points, and steady-state brightness values. A dynamic brightness change feature vector is constructed based on inflection points and slope extreme points, and a brightness fluctuation tolerance range is set in combination with steady-state brightness values. Based on the tolerance range of brightness fluctuations and the feature vector of dynamic brightness changes, a fuzzy algorithm is used to construct a threshold set for judging whether the light string is lit.

[0024] It's important to note that brightness data refers to the real-time light intensity values ​​collected by photoelectric sensors during the soft-start process of the light string. These values ​​reflect the luminous intensity of the light string at different points in time. Typically, brightness data is stored digitally, for example, in lux or relative brightness percentage. The sampling frequency can be set to milliseconds or seconds to ensure sufficient time resolution. After acquiring the brightness data, by plotting time on the horizontal axis and brightness value on the vertical axis, and sequentially marking and connecting the sampled values ​​at each time point, a curve showing the change in brightness over time can be plotted. This curve is the brightness variation curve of the light string. It visually reflects the entire dynamic process of the light string from startup to reaching stable brightness, including the slow initial rise, the accelerated rise phase, and the final stabilization process.

[0025] Secondly, when analyzing the brightness change curve of the light string, it is first necessary to calculate the slope and curvature of the curve at different time periods to identify the speed and acceleration characteristics of brightness change. Inflection points are the locations where the curve shape undergoes a significant change, typically representing the transition point where the brightness growth trend changes from acceleration to deceleration. Slope extrema refer to the moments when the rate of brightness change on the curve reaches its maximum or minimum; these points reflect the key dynamic stages in the brightness increase process of the light string. The steady-state brightness value is the approximately constant brightness level reached after the curve has stabilized over a long period. These feature points can be extracted by performing differential operations on the curve or by using algorithms that combine smoothing filtering and extremum detection. The resulting feature information not only characterizes the overall features of the light string lighting process but also provides a quantitative basis for subsequent threshold construction.

[0026] Furthermore, the process of constructing a dynamic brightness change feature vector based on inflection points and slope extrema involves transforming the numerical attributes of these key points into a vectorized description. Specifically, this involves: first, extracting the main inflection point positions and their corresponding time and brightness values ​​from the curve; second, extracting the positions of the maximum and minimum slope values, their corresponding time points, and slope values; and finally, combining these features into a multi-dimensional vector in a preset order, such as inflection point time, inflection point brightness, maximum slope value, maximum slope time, minimum slope value, and minimum slope time. This dynamic brightness change feature vector can comprehensively depict the trend and rate characteristics of the light string's changes from the initial lighting stage to near-steady state, and has the advantage of facilitating subsequent fuzzy algorithm calculations and comparisons.

[0027] Finally, the steady-state brightness value refers to the average brightness value of the light string after the soft-start process, when the brightness curve tends to stabilize. It is typically a stable value where the brightness data no longer fluctuates significantly within a certain time interval. For example, if the brightness variation remains within ±2% after the light string has been lit for 5 seconds, the average brightness at this point can be defined as the steady-state brightness value. Based on the steady-state brightness value, a brightness fluctuation tolerance range can be set, which allows the actual brightness to fluctuate within a certain range around the steady-state value while still being considered as being in a normal lighting state. The specific method is as follows: based on statistical analysis results or empirical parameters, set a percentage range (such as ±5% or ±10%), add to and subtract this range from the steady-state brightness value to obtain the brightness fluctuation tolerance range. This allows for the determination of whether the light string has been lit and entered a stable state, while avoiding misjudgments due to slight brightness fluctuations, thus providing a reliable basis for constructing a lighting judgment threshold set.

[0028] In this example, based on the brightness fluctuation tolerance range and the dynamic brightness change feature vector, a fuzzy algorithm is used to construct a threshold set for judging whether the light string is lit, specifically: The brightness fluctuation tolerance range is divided into several sub-ranges with a preset width, and the brightness fluctuation in each sub-range is statistically distributed and analyzed. Based on the statistical distribution analysis results, the mean and variance of the brightness changes in each sub-interval are extracted, and the membership function is constructed to reconstruct the parameter set. The weights of each preset component in the dynamic brightness change feature vector are extracted, and the membership function reconstruction parameter set is corrected based on the weights to obtain the corrected membership function reconstruction parameter set. Based on the modified membership function, the parameter set is reconstructed, and the preset membership function is piecewise linearly optimized to obtain the improved membership function. Based on the improved membership function, fuzzy reasoning is performed on the brightness change process to construct a threshold set for judging whether the light string is lit.

[0029] It should be noted that the brightness fluctuation tolerance range is the allowable fluctuation range after the light string reaches a steady-state brightness, for example, [L_min, L_max]. To analyze the characteristics of different brightness fluctuations, this range can be divided into several sub-ranges with a fixed step size or proportion. For example, if the tolerance range is [90 lux, 110 lux], with a preset width of 5 lux, it can be divided into four sub-ranges: [90–95], [95–100], [100–105], and [105–110]. Each sub-range represents a brightness fluctuation level, which facilitates the statistical analysis of the probability distribution and patterns of brightness changes, providing refined data support for subsequent fuzzy inference and threshold construction.

[0030] Secondly, within each sub-interval, actual brightness data points are collected, and the frequency or probability distribution of brightness occurrences within that interval is calculated. Statistical analysis can include indicators such as mean, variance, kurtosis, and skewness, reflecting the brightness fluctuation characteristics of that sub-interval. For example, if the brightness of the [95–100] sub-interval is mostly concentrated around 97 lux with a small variance, it indicates that the fluctuation within that sub-interval is relatively stable; if the brightness distribution is more dispersed, the fluctuation is larger. Through statistical distribution analysis, the stability and uncertainty of different brightness segments can be understood, providing a basis for subsequent adjustment of membership function parameters.

[0031] Furthermore, the mean and variance of each sub-interval are used as core features to define the shape of the membership function for the corresponding brightness state in that interval. The membership function reconstruction parameter set is a set of parameters used to characterize the fuzzy membership function, including the center value (mean), width or expansion (variance), and possible skew parameters of the brightness in each sub-interval. These parameters allow adjustment of the original membership function to more accurately reflect the fluctuation characteristics of the light string brightness in actual operation. For example, if the brightness mean of the [95–100] sub-interval is 97 lux and the variance is 1.5 lux, then the corresponding membership function center is at 97 lux, and the width matches 1.5 lux, making the fuzzy inference closer to the actual data.

[0032] Furthermore, each component in the dynamic brightness change feature vector (inflection point time, slope extremum, maximum rise rate, etc.) has different importance in determining the lighting status of the light string. Therefore, each component needs to be assigned a weight, which can be calculated automatically based on historical data, expert experience, or algorithms (such as information gain, principal component analysis, etc.). Then, these weights are used to correct the membership function reconstruction parameter set. For example, if a feature component has a high weight, it means that it is more sensitive to determining whether the light string is lit, and the width of the membership function in the corresponding sub-interval can be reduced or its center can be fine-tuned; if the weight is low, the width can be increased to tolerate more fluctuations. Through this correction, a corrected membership function reconstruction parameter set that better reflects the actual brightness changes of the light string can be obtained, ensuring the accuracy of fuzzy inference.

[0033] Secondly, the original membership function is a theoretically defined standard triangular or Gaussian function, while the parameter set corrected by actual data provides information on the actual brightness fluctuation characteristics. Piecewise linear optimization refers to dividing the membership function into several small segments in the brightness range, and using the corrected parameters to linearly adjust the slope, center, and width of each segment, making the function shape closer to the actual brightness distribution. For example, in the [95–100] range, the original membership function is linearly stretched or compressed, so that it reaches a peak of 1 near the mean of 97 lux and decreases to close to 0 at the edges, forming an improved membership function that reflects the true characteristics of the lighting state of the light string.

[0034] Finally, fuzzy inference is performed on the brightness-time curve using the improved membership function. This involves mapping the brightness of each sampling point to a corresponding membership degree, calculating the membership value indicating whether it is "lit" or "not lit," and determining whether the light string has reached a valid lighting state based on the maximum membership degree or a weighted combination. The light string lighting judgment threshold set is a set of thresholds used to determine whether the light string is lit. It typically includes upper and lower limits for brightness in each sub-region or for each light string, as well as time windows or membership thresholds. These are used to subsequently construct the light string lighting sequence and optimize the soft-start sequence. In other words, it defines the standard for "when a light is considered lit," enabling the control system to accurately identify the light string state and perform sequence optimization.

[0035] S3, extract the lighting sequence of each sub-region based on the lighting threshold set, and construct several second soft-start sequences based on the lighting sequence; In this example, the lighting timing of the light strings in each sub-region is extracted based on the threshold set for determining the lighting of the light strings, and several second soft-start sequences are constructed based on the lighting timing of the light strings, specifically as follows: Based on the threshold set for judging the lighting of light strings, the state of the light strings in each sub-region is judged to obtain the effective lighting time of each light string, and a lighting time sequence map of the sub-region is constructed. Extract the temporal distribution features of the lighting time sequence map of the sub-region, and use a clustering algorithm based on the temporal distribution features to identify abnormal light string clusters; Based on the identification results, the initial soft-start strategy is reconstructed in time sequence to obtain several second soft-start sequences.

[0036] It should be noted that after obtaining the threshold set for judging the lighting of light strings, it can be applied to the real-time brightness data of each sub-region to determine the lighting status of the light strings. The specific method is as follows: continuously monitor the brightness curve of the light strings and compare the real-time brightness values ​​with the threshold set. When the brightness exceeds the threshold and remains stably within the tolerance range, the light string is determined to be lit. The recorded time point at this time is the effective lighting moment of the light string. By arranging the effective lighting moments of all light strings in the sub-region in chronological order and visualizing them using charts or matrices, a lighting time sequence graph of the sub-region can be constructed. This graph shows the lighting sequence relationship of different light strings in the sub-region over time, reflecting the overall dynamic law of the lighting process.

[0037] Secondly, after obtaining the lighting time series map of the sub-region, it is necessary to extract its temporal distribution features. The specific method is as follows: First, calculate the statistical indicators of the lighting times of the light strings, such as the average lighting time, time standard deviation, and extreme value distribution (the time difference between the earliest and latest lighting). Second, analyze the interval distribution between adjacent light strings in the time series, extracting the mean, variance, and whether the time intervals exhibit regularity (such as increasing or decreasing trends). Finally, density estimation of the temporal distribution can be performed to determine whether the lighting of the light strings is concentrated within a certain time period. The temporal distribution features obtained through these analyses can reflect the balance, synchronicity, and anomalies of the lighting of light strings in the sub-region, providing a basis for subsequent anomaly identification and strategy optimization.

[0038] Furthermore, based on temporal distribution characteristics, the lighting time and time interval of each light string can be used as feature vectors input into the clustering algorithm. Commonly used clustering methods include K-means, DBSCAN, or hierarchical clustering. Through clustering, light strings with similar lighting times and consistent distribution patterns can be grouped into a normal cluster, while light strings that differ significantly from the overall distribution are identified as abnormal clusters. For example, if the lighting time of some light strings is much later than other light strings in the same area, or if their lighting interval is inconsistent with the overall distribution, they will be marked as abnormal by the clustering algorithm. These abnormal light string clusters often indicate unreasonable time allocation or light string response delays under the initial slow-start strategy, requiring optimization and adjustment.

[0039] Secondly, after identifying abnormal light clusters, the initial soft-start strategy needs to be optimized and reconstructed based on the identification results. The specific method is as follows: First, the lighting time of the abnormal light clusters is corrected, for example, by shortening their delay time or adjusting their lighting sequence to better coordinate with the overall distribution in the same area; second, the lighting sequence of the normal clusters is fine-tuned to ensure the smoothness and balance of the overall lighting process; finally, the adjusted lighting times are recombine to generate a new timing sequence. Since different adjustment strategies may correspond to different optimization objectives (e.g., energy saving priority, visual effect priority, or balanced lighting priority), multiple candidate timing reconstruction schemes, i.e., several second soft-start sequences, will be generated. These sequences will be further optimized in subsequent steps.

[0040] Finally, the first soft-start sequence is directly generated based on the initial strategy. It reflects the lighting order and time interval of the light strings under the set parameters, but it has not been optimized based on actual brightness feedback. Therefore, it may have issues such as uneven lighting in some areas or abnormal delays in individual light strings. In contrast, the second soft-start sequence is obtained by reconstructing the timing based on the first soft-start sequence, combined with actual brightness monitoring results, temporal distribution feature analysis, and clustering recognition results. Its improvements are twofold: firstly, it corrects the lighting timing of abnormal light strings, making the overall lighting process more reasonable and in line with expectations; secondly, by optimizing the temporal distribution, it improves the coordination and visual effect of lighting between different areas. Therefore, the second soft-start sequence is more refined and dynamic than the first sequence, better adapts to actual operating conditions, and provides higher-quality candidate solutions for further optimization.

[0041] S4, apply several second soft-start sequences to the soft-start control of each sub-region respectively, and extract control features to obtain the first control feature set; In this example, several second soft-start sequences are applied to the soft-start control of each sub-region, and control features are extracted to obtain the first control feature set, specifically: Several second soft-start sequences are applied to each sub-region, and the start-up data of the light string is acquired in real time. The start-up data includes light string energy consumption data, brightness stability index and heat accumulation data. Feature extraction is performed on the start-up data of the light string to obtain the control feature vector; The control feature vectors are integrated into the first control feature set according to the preset sequence number.

[0042] It should be noted that when several second soft-start sequences are applied to a sub-region, each sequence is executed sequentially: the controller sends the start time and order of each lamp in the sequence as an instruction to the corresponding sub-region's light string, causing the light string to start softly according to the sequence. During execution, the controller collects the start-up data of each light string in real time, including three aspects: 1. Light string energy consumption data, referring to the electrical energy consumed by the light string from start-up to stable operation (e.g., energy calculated in watt-seconds or current / voltage integrals); 2. Brightness stability index, indicating the degree of brightness fluctuation of the light string during soft start-up and steady state, which can be quantified by brightness variance, slope fluctuation, or steady-state oscillation amplitude; 3. Thermal accumulation data, referring to the temperature change during the light string start-up process, including the temperature rise of the light string core or surface over time and the rate of thermal accumulation. These data comprehensively reflect the energy efficiency, lighting stability, and thermal management of the soft-start sequence.

[0043] Secondly, feature extraction is performed on the startup data of each light string to quantify the performance of each sequence in actual execution. Common methods include: extracting total energy consumption, peak power, and energy consumption curve slope from energy consumption data; extracting brightness rise time, maximum brightness fluctuation, steady-state brightness mean and variance, and overshoot from brightness stability indicators; and extracting temperature rise rate, maximum temperature, and thermal equilibrium time from heat accumulation data. Combining these features in a unified format forms a control feature vector, where each vector represents the overall performance of the sequence in the current sub-region, providing a quantitative basis for subsequent iterative screening and optimization.

[0044] Finally, after several second soft-start sequences are executed in each sub-region and generate corresponding control feature vectors, they are integrated according to preset sequence numbers or sub-region numbers. For example, if five sequences are applied to three sub-regions respectively, there will be 15 control feature vectors. The integration process involves arranging these vectors in sequence order and sub-region order to form a unified matrix or set, called the first control feature set. This feature set comprehensively reflects the execution performance of all second soft-start sequences in each sub-region, including performance indicators such as energy consumption, brightness stability, and heat accumulation, while also retaining sequence number information, providing complete data support for subsequent multi-objective optimization and optimal soft-start sequence selection.

[0045] S5. Based on the first control feature set, several second soft-start sequences are iteratively filtered to obtain the optimal soft-start sequence, which is then applied to the Christmas tree lighting control.

[0046] In this example, the optimal soft-start sequence is obtained by iteratively filtering several second soft-start sequences based on the first control feature set, specifically as follows: Based on the first control feature set, a multi-objective optimization function is constructed; A genetic algorithm was used to perform multiple rounds of iterative optimization on the second slow-start sequence to obtain the Pareto optimal solution set. The optimal soft-start sequence is obtained by filtering the Pareto optimal solution set based on a multi-objective optimization function.

[0047] It should be noted that the first control feature set contains feature vectors for each second soft-start sequence in each sub-region, including energy consumption, brightness stability, and heat accumulation. The multi-objective optimization function quantifies these different performance indicators into mathematical functions that can be optimized simultaneously, used to evaluate the overall performance of each sequence. The common approach is to define an objective function for each performance indicator, such as minimizing total energy consumption, maximizing brightness stability (minimizing fluctuations), and minimizing heat accumulation, and then combine these objective functions into a multi-objective optimization problem. In this function, each parameter of the sequence (such as the light string startup order and startup interval) is a decision variable. The optimization function calculates the performance of each sequence in terms of energy consumption, brightness stability, and thermal management, generating a comprehensive score to guide the optimization algorithm in finding a compromise optimal solution. The core of the multi-objective optimization function is to simultaneously balance multiple conflicting objectives, rather than optimizing a single indicator.

[0048] Furthermore, the genetic algorithm is a global optimization algorithm based on natural selection and genetic mechanisms. Each second slow-start sequence is represented as a chromosome (the starting order and time interval of the light strings in the sequence are treated as genes), and multiple candidate sequences are generated by initializing the population. Then, selection, crossover, and mutation operations are performed in multiple iterations: sequences with high fitness are retained, crossover generates new sequences, and mutation introduces diversity. After each iteration, the performance of each sequence under the multi-objective optimization function is calculated, and sequences not completely dominated by other sequences are selected, forming a Pareto optimal solution set. Each sequence in this set achieves a balance among different objectives, meaning no sequence is superior to other sequences simultaneously in all objectives, providing candidate solutions for subsequent fine-tuning.

[0049] Furthermore, after obtaining the Pareto optimal solution set, it is necessary to select the most suitable sequence for practical application as the optimal soft-start sequence. The selection method typically combines actual weights or preference indicators to weight and summarize the objectives in the multi-objective optimization function, calculating the comprehensive score of each Pareto sequence, or using additional constraints (such as maximum allowable energy consumption, maximum brightness fluctuation, and maximum heat accumulation) to eliminate sequences that do not meet the requirements. Finally, the sequence with the highest comprehensive score or the one that best meets the constraints is selected as the optimal soft-start sequence, used for actual control of the sub-regional light strings.

[0050] Finally, the second soft-start sequence, based on the first soft-start sequence, is an optimized sequence generated through analysis of the actual lighting characteristics and abnormal light strings. It primarily improves the lighting order and time interval of light strings within sub-regions, resolving the problem of asynchronous or abnormally delayed lighting in local light strings. The optimal soft-start sequence, based on the second sequence, is generated through multi-objective optimization functions and iterative selection using a genetic algorithm. It comprehensively considers multiple indicators such as minimizing energy consumption, maximizing brightness stability, and minimizing heat accumulation, and is generated through Pareto optimal selection and weighted selection. Compared to the second sequence, the optimal sequence not only ensures lighting synchronization and reasonable order but also achieves the best compromise in global performance indicators, making it the final optimized solution that can be directly applied to energy-saving lighting control.

[0051] In this example, it is applied to the control of Christmas tree lights, specifically as follows: The optimal soft-start sequence is compiled into a set of control instructions and sent to the preset string light controllers in each sub-area; A preset adaptive feedback mechanism is embedded in the preset light string controller to monitor the status of the light string in real time and fine-tune the start-up parameters. Record energy consumption and brightness data during the actual control of the light string, establish a sequence version management log, and perform soft start sequence switching in multiple scenarios.

[0052] It should be noted that after the optimal soft-start sequence is selected through iterative optimization, it needs to be converted into specific executable control instructions. The specific steps are as follows: First, translate the lighting order, time interval, gradient ascent parameters, and other elements in the optimal sequence into a control language or protocol format, such as PWM dimming instructions, delay trigger instructions, and voltage / current adjustment instructions. Then, based on the controller type of different sub-regions (such as microcontroller controllers, programmable logic controllers (PLCs), or dedicated LED string driver modules), compile these instructions into an instruction set conforming to the communication protocol. Finally, distribute the compiled instruction set to the LED string controllers of each sub-region via wired (such as RS485, CAN bus) or wireless (such as ZigBee, Wi-Fi) methods, enabling each region to execute the lighting process according to the optimal soft-start sequence.

[0053] Furthermore, embedding an adaptive feedback mechanism into the pre-set string light controller ensures that the control strategy can automatically adjust according to the status of the lights during actual operation. Specifically, the controller integrates sensor interfaces (such as current sensors, photosensors, and temperature sensors) to collect real-time data on the energy consumption, brightness, and heat of the string lights. The controller compares the real-time monitoring data with target parameters using a pre-set feedback algorithm (such as PID control, fuzzy control, or threshold-based adaptive adjustment). When a deviation is detected, it automatically adjusts the lighting delay, dimming speed, or voltage and current levels, thus making the string lights operate closer to the ideal state. The pre-set adaptive feedback mechanism refers to a closed-loop adjustment logic set during the system design phase. It automatically corrects the soft-start parameters based on real-time data, ensuring that the string lights maintain stability and energy efficiency under different environments and usage scenarios.

[0054] Furthermore, during the actual operation of the light string, the control system collects energy consumption and brightness data in real time through sampling circuits or sensor modules. Energy consumption data includes values ​​such as current, voltage, and power, obtained through an energy metering chip or current and voltage sensors; brightness data is collected through a photosensor. The system records this data at a fixed frequency (e.g., sampling once every 100ms or 1s) and stores it in the controller's local storage unit or uploads it to a central data management platform. During data recording, the sampled values ​​are timestamped to facilitate subsequent analysis of energy consumption and brightness changes at different time points. This data is used not only for energy efficiency assessment and stability analysis but also provides feedback for subsequent optimization.

[0055] Finally, to adapt to different application scenarios (such as energy-saving mode, holiday mode, and performance mode), version management of the soft-start sequences is necessary. The specific method is to assign a unique version number to each generated and verified soft-start sequence, and record the sequence's core parameters, application scenario, performance indicators (such as energy consumption, brightness stability, and heat accumulation), and actual operating effects in the log. During operation, the control system can automatically call the corresponding version of the soft-start sequence based on user selection or environmental conditions; for example, selecting the low-energy-consumption version in an energy-saving scenario and the version emphasizing visual effects in a holiday scenario. By establishing a sequence version management log, not only can flexible switching between multiple scenarios be achieved, but historical data can also be traced, providing a reference for optimizing subsequent new sequences.

[0056] Example 2, Figure 2 The present invention provides a control system for energy-saving lighting fixtures, comprising a region division module, a threshold construction module, a sequence generation module, a feature extraction module, and a string light control module: The region division module is used to divide the target Christmas tree into several sub-regions and perform soft-start control on the light strings in each sub-region to obtain the first soft-start sequence; The threshold construction module is used to obtain the brightness change curve of the light string after the first soft start sequence is lit, and to construct a set of light string lighting judgment thresholds based on the change characteristics of the brightness change curve. The sequence generation module is used to extract the lighting time sequence of each sub-region based on the lighting threshold set of the light string, and to construct several second soft-start sequences based on the lighting time sequence of the light string; The feature extraction module is used to apply several second soft-start sequences to the soft-start control of each sub-region and extract control features to obtain the first control feature set; The string light control module is used to iteratively filter several second soft-start sequences based on the first control feature set to obtain the optimal soft-start sequence and apply it to the control of Christmas tree lights.

[0057] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0058] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0059] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0060] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0061] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A control method of an energy-saving lighting ornament, characterized by, Includes the following steps: The target Christmas tree is divided into several sub-regions, and the light strings are controlled to start softly in each sub-region to obtain the first soft start sequence; Obtain the brightness change curve of the light string after the first soft-start sequence is turned on, and construct a threshold set for judging whether the light string is turned on based on the change characteristics of the brightness change curve, specifically: The system acquires real-time brightness data of the light strings in each sub-region during the soft start-up process, and plots a brightness change curve over time based on the brightness data to obtain the light string brightness change curve. The system then performs data analysis on the light string brightness change curve and extracts its change features, including inflection points, slope extremes, and steady-state brightness values. A dynamic brightness change feature vector is constructed based on the inflection points and slope extremes, and a brightness fluctuation tolerance range is set in conjunction with the steady-state brightness value. The brightness fluctuation tolerance range is divided into several sub-intervals with a preset width, and the brightness fluctuation within each sub-interval is statistically distributed. Based on the statistical distribution analysis results, the mean and variance of the brightness change in each sub-interval are extracted to construct a membership function reconstruction parameter set. The weights of each preset component in the dynamic brightness change feature vector are extracted, and the membership function reconstruction parameter set is modified based on the weights to obtain the modified membership function reconstruction parameter set. Based on the modified membership function reconstruction parameter set, the preset membership function is piecewise linearly optimized to obtain the improved membership function. Based on the improved membership function, fuzzy reasoning is performed on the brightness change process to construct a threshold set for judging the lighting of light strings; Based on the threshold set for determining the lighting of light strings, the lighting time sequence of each sub-region is extracted, and several second soft-start sequences are constructed based on the lighting time sequence, specifically: Based on the threshold set for judging the lighting of light strings, the state of the light strings in each sub-region is judged to obtain the effective lighting time of each light string, and a lighting time sequence map of the sub-region is constructed. The time distribution features of the lighting time sequence map of the sub-region are extracted, and an abnormal light string cluster is identified based on the time distribution features using a clustering algorithm. Based on the identification results, the initial soft start strategy is reconstructed in time sequence to obtain several second soft start sequences. Several second soft-start sequences are applied to the soft-start control of each sub-region, and control features are extracted to obtain the first control feature set; Based on the first control feature set, several second soft-start sequences are iteratively filtered to obtain the optimal soft-start sequence, which is then applied to the control of Christmas tree lights. The step of iteratively filtering several second soft-start sequences based on the first control feature set to obtain the optimal soft-start sequence is as follows: Based on the first control feature set, a multi-objective optimization function is constructed; a genetic algorithm is used to perform multiple rounds of iterative optimization on the second slow-start sequence to obtain the Pareto optimal solution set; the Pareto optimal solution set is then screened based on the multi-objective optimization function to obtain the optimal slow-start sequence.

2. The control method of the energy saving lighting ornament according to claim 1, characterized in that, The process of dividing the target Christmas tree into several sub-regions and performing soft-start control on the string lights within each sub-region to obtain the first soft-start sequence is as follows: Obtain the structural distribution data and decorative effect requirements of the target Christmas tree, and divide the target Christmas tree into several sub-regions; Different soft-start time parameters are set for each sub-region to obtain the initial soft-start strategy; Based on the initial soft-start strategy, the light strings are controlled to light up gradually according to a preset time gradient, and the start time of each light string is recorded. A first soft-start sequence is generated based on the start time point. The first soft-start sequence includes the start order and time interval of each light string.

3. The control method for energy-saving lighting fixtures according to claim 2, characterized in that, The step involves extracting the lighting timing sequence of each sub-region based on the threshold set for judging the lighting of light strings, and constructing several second soft-start sequences based on the lighting timing sequence, specifically as follows: Based on the threshold set for judging the lighting of light strings, the state of the light strings in each sub-region is judged to obtain the effective lighting time of each light string, and a lighting time sequence map of the sub-region is constructed. Extract the temporal distribution features of the lighting time sequence map of the sub-region, and use a clustering algorithm based on the temporal distribution features to identify abnormal light string clusters; Based on the identification results, the initial soft-start strategy is reconstructed in time sequence to obtain several second soft-start sequences.

4. The control method for energy-saving lighting fixtures according to claim 3, characterized in that, The step of applying several second soft-start sequences to the soft-start control of each sub-region and extracting control features to obtain a first control feature set is as follows: Several second soft-start sequences are applied to each sub-region, and the start-up data of the light string is acquired in real time. The start-up data includes light string energy consumption data, brightness stability index and heat accumulation data. Feature extraction is performed on the start-up data of the light string to obtain the control feature vector; The control feature vectors are integrated into the first control feature set according to the preset sequence number.

5. The control method for energy-saving lighting fixtures according to claim 4, characterized in that, Specifically, this is applied to the control of Christmas tree lights: The optimal soft-start sequence is compiled into a set of control instructions and sent to the preset string light controllers in each sub-area; A preset adaptive feedback mechanism is embedded in the preset light string controller to monitor the status of the light string in real time and fine-tune the start-up parameters. Record energy consumption and brightness data during the actual control of the light string, establish a sequence version management log, and perform soft start sequence switching in multiple scenarios.

6. A control system for an energy-saving lighting fixture, applied to the control method for an energy-saving lighting fixture as described in any one of claims 1-5, characterized in that, It includes a region segmentation module, a threshold construction module, a sequence generation module, a feature extraction module, and a light string control module: The region division module is used to divide the target Christmas tree into several sub-regions and perform soft-start control on the light strings in each sub-region to obtain the first soft-start sequence; The threshold construction module is used to obtain the brightness change curve of the light string after the first soft-start sequence is lit, and to construct a threshold set for judging whether the light string is lit based on the change characteristics of the brightness change curve. Specifically: The system acquires real-time brightness data of the light strings in each sub-region during the soft start-up process, and plots a brightness change curve over time based on the brightness data to obtain the light string brightness change curve. The system then performs data analysis on the light string brightness change curve and extracts its change features, including inflection points, slope extremes, and steady-state brightness values. A dynamic brightness change feature vector is constructed based on the inflection points and slope extremes, and a brightness fluctuation tolerance range is set in conjunction with the steady-state brightness value. The brightness fluctuation tolerance range is divided into several sub-intervals with a preset width, and the brightness fluctuation within each sub-interval is statistically distributed. Based on the statistical distribution analysis results, the mean and variance of the brightness change in each sub-interval are extracted to construct a membership function reconstruction parameter set. The weights of each preset component in the dynamic brightness change feature vector are extracted, and the membership function reconstruction parameter set is modified based on the weights to obtain the modified membership function reconstruction parameter set. Based on the modified membership function reconstruction parameter set, the preset membership function is piecewise linearly optimized to obtain the improved membership function. Based on the improved membership function, fuzzy reasoning is performed on the brightness change process to construct a threshold set for judging the lighting of light strings; The sequence generation module is used to extract the lighting time sequence of the light strings in each sub-region based on the threshold set for judging the lighting of the light strings, and to construct several second soft-start sequences based on the lighting time sequence, specifically: Based on the threshold set for judging the lighting of light strings, the state of the light strings in each sub-region is judged to obtain the effective lighting time of each light string, and a lighting time sequence map of the sub-region is constructed. The time distribution features of the lighting time sequence map of the sub-region are extracted, and an abnormal light string cluster is identified based on the time distribution features using a clustering algorithm. Based on the identification results, the initial soft start strategy is reconstructed in time sequence to obtain several second soft start sequences. The feature extraction module is used to apply several second soft-start sequences to the soft-start control of each sub-region and extract control features to obtain the first control feature set; The string light control module is used to iteratively filter several second soft-start sequences based on the first control feature set to obtain the optimal soft-start sequence and apply it to the Christmas tree light control. The step of iteratively filtering several second soft-start sequences based on the first control feature set to obtain the optimal soft-start sequence is as follows: Based on the first control feature set, a multi-objective optimization function is constructed; a genetic algorithm is used to perform multiple rounds of iterative optimization on the second slow-start sequence to obtain the Pareto optimal solution set; the Pareto optimal solution set is then screened based on the multi-objective optimization function to obtain the optimal slow-start sequence.

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