Automatic measurement method, device and system for light source stroboscopic parameters

Abstract

The present invention relates to a method, device and system for automatically measuring the stroboscopic parameters of a light source, including obtaining the original light signal of the light source, performing photoelectric conversion and analog-to-digital conversion processing on the original light signal to obtain corresponding digital sampling data; performing multi-scale decomposition on the digital sampling data, and extracting the frequency characteristics and energy distribution characteristics of the light source to obtain the corresponding stroboscopic characteristic coefficient; performing stroboscopic detection on the stroboscopic characteristic coefficient through a preset dynamic threshold to obtain a corresponding stroboscopic depth parameter; performing segmented calculation on the stroboscopic depth parameter to obtain a corresponding stroboscopic fluctuation index; performing trend analysis on the stroboscopic fluctuation index to obtain corresponding light source stability information; and measuring and analyzing the light source stability information according to a preset measurement threshold to obtain a corresponding stroboscopic parameter measurement result. The present invention can ensure that the system can accurately measure under different light source types and working environments, thereby reducing measurement errors.

Description

Technical Field

[0001] The present invention relates to the technical field of light source stroboscopic parameters, and in particular to a method, device and system for automatically measuring light source stroboscopic parameters. Background Art

[0002] As an important optical detection technology, light source flicker measurement plays a key role in lighting quality assessment, display device performance testing and other fields. As people pay more and more attention to visual comfort and health effects, how to accurately measure and evaluate the flicker characteristics of light sources to ensure the safety and reliability of lighting equipment has become one of the key research topics. Existing light source flicker measurement methods usually only focus on a single flicker parameter, such as flicker frequency or modulation depth, while ignoring the dynamic changes in the flicker characteristics of the light source under different working conditions and the comprehensive influence of multi-dimensional characteristics. This simplified measurement method often cannot fully reflect the actual flicker condition of the light source, which may cause deviations between the measurement results and the actual experience, thus affecting the performance evaluation and quality control of lighting equipment. Summary of the invention

[0003] The main purpose of the present invention is to provide a method, device and system for automatically measuring the flicker parameters of a light source, which can ensure that the system can accurately measure under different light source types and working environments, thereby reducing measurement errors.

[0004] To achieve the above object, the present invention provides a method for automatically measuring light source stroboscopic parameters, comprising: Acquire an original light signal from the light source, perform photoelectric conversion and analog-to-digital conversion on the original light signal, and obtain corresponding digital sampling data; Performing multi-scale decomposition on the digital sampling data, and extracting the light source frequency characteristics and energy distribution characteristics to obtain corresponding stroboscopic characteristic coefficients; Performing stroboscopic detection on the stroboscopic characteristic coefficient through a preset dynamic threshold to obtain a corresponding stroboscopic depth parameter; The stroboscopic depth parameter is calculated in sections to obtain a corresponding stroboscopic fluctuation index; Performing trend analysis on the stroboscopic fluctuation index to obtain corresponding light source stability information; The light source stability information is measured and analyzed according to a preset measurement threshold to obtain a corresponding stroboscopic parameter measurement result.

[0005] Furthermore, the acquiring of the original light signal of the light source, performing photoelectric conversion and analog-to-digital conversion on the original light signal to obtain corresponding digital sampling data, includes: The light source is collected by a preset photoelectric detector to obtain a corresponding original light signal; Dynamically adjust the sampling time according to the original optical signal to obtain corresponding sampling control parameters; Performing amplification gain processing on the original optical signal to obtain corresponding signal amplitude data; Perform synchronous sampling according to the sampling control parameter and the signal amplitude data to obtain a corresponding sampling sequence; Performing quantization encoding processing on the sampling sequence to obtain corresponding encoded data; Performing anti-aliasing processing on the encoded data to obtain corresponding valid sampling data; Performing data calibration processing according to the valid sampling data to obtain corresponding calibration data; The calibration data is subjected to data reconstruction processing to obtain corresponding digital sampling data.

[0006] Furthermore, the digital sampling data is subjected to multi-scale decomposition, and the light source frequency characteristics and energy distribution characteristics are extracted to obtain the corresponding stroboscopic characteristic coefficients, including: Performing dual-tree complex wavelet decomposition processing on the digital sampling data to obtain corresponding complex domain decomposition coefficients; Performing Hilbert transform processing according to the complex domain decomposition coefficients to obtain corresponding instantaneous frequency data; Performing adaptive kernel density estimation on the instantaneous frequency data to obtain corresponding frequency distribution characteristics; According to the frequency distribution characteristics, wavelet packet entropy calculation processing is performed to obtain corresponding energy entropy characteristics; Performing fractal dimension analysis on the energy entropy characteristics to obtain a corresponding fractal characteristic spectrum; A high-order statistical analysis is performed on the fractal characteristic spectrum to obtain a corresponding stroboscopic characteristic coefficient.

[0007] Furthermore, the stroboscopic detection is performed on the stroboscopic characteristic coefficient by using a preset dynamic threshold to obtain a corresponding stroboscopic depth parameter, including: Performing frequency decomposition on the stroboscopic characteristic coefficient to obtain corresponding high-frequency components and low-frequency components; Calculating the peak-to-trough difference sequence of the high-frequency component to obtain a stroboscopic intensity sequence; Setting a corresponding initial detection threshold according to the stroboscopic intensity sequence; Performing mean shift calculation based on the low-frequency component to obtain a corresponding threshold compensation value; Performing a combined operation on the initial detection threshold and the threshold compensation value to obtain a dynamic threshold sequence; Comparing the stroboscopic intensity sequence with the dynamic threshold sequence to obtain corresponding super-threshold points; Calculating the time intervals between adjacent super-threshold points to obtain a stroboscopic period sequence; A stroboscopic parameter analysis is performed according to the stroboscopic period sequence and the dynamic threshold sequence to obtain the stroboscopic depth parameter.

[0008] Furthermore, the step of calculating the stroboscopic depth parameter in sections to obtain a corresponding stroboscopic fluctuation index includes: Performing segmented interval sampling processing on the stroboscopic depth parameter to obtain a plurality of subsequences; Performing local maximum and minimum point detection on the subsequence to obtain a fluctuation characteristic sequence; Constructing a hyperbolic basis function group according to the fluctuation characteristic sequence to obtain a basis function sequence; A hyperbolic matrix is ​​constructed based on the fluctuation characteristic sequence and the basis function sequence to obtain a hyperbolic coefficient matrix; Optimizing the scale parameters and translation parameters of the hyperbolic coefficient matrix to obtain an optimized coefficient matrix; Rearrange the time-frequency plane according to the optimization coefficient matrix to obtain a rearrangement matrix; Calculating the segment coefficients of the rearranged matrix to obtain the frequency domain segment coefficients; Performing segment interval accumulation operation according to the frequency domain segment coefficient to obtain a segment cumulative value; The stroboscopic fluctuation is calculated for the segmented cumulative value according to a preset time window to obtain the stroboscopic fluctuation index.

[0009] Furthermore, the trend analysis of the stroboscopic fluctuation index to obtain corresponding light source stability information includes: Performing mapping transformation on the stroboscopic fluctuation index to obtain fluctuation trend projection data; Performing space construction according to the fluctuation trend projection data to obtain a dynamic trend space; Performing singular spectrum decomposition on the dynamic trend space to obtain a light source characteristic trajectory; Performing light source fluctuation analysis on the light source characteristic trajectory to obtain a fluctuation divergence coefficient; Performing fractal dimension calculation on the fluctuation divergence coefficient to obtain a stability entropy value; A stability analysis is performed based on the stability entropy value and the light source characteristic trajectory to obtain the light source stability information.

[0010] Furthermore, the light source stability information is measured and analyzed according to a preset measurement threshold to obtain a corresponding stroboscopic parameter measurement result, including: Performing singular spectrum analysis on the light source stability information to obtain a light source dynamic feature sequence; Perform phase space reconstruction analysis according to the dynamic characteristic sequence to obtain a stability characteristic matrix; Performing Hilbert-Huang transform processing on the stability characteristic matrix to obtain an energy distribution diagram; Performing hyperbolic wavelet kernel function mapping on the light source stability information according to the energy distribution diagram to obtain an instantaneous light source stability component; Performing empirical mode decomposition processing on the instantaneous light source stable component to obtain an intrinsic mode function set; Performing a self-organizing network analysis on the light source stability information according to the intrinsic modal function set to obtain a multi-level stroboscopic feature; Performing time-frequency joint entropy calculation on the multi-level stroboscopic feature to obtain the multi-level stroboscopic feature; Performing sample entropy calculation on the multi-level stroboscopic features to obtain a stroboscopic complexity index; The energy distribution diagram, the multi-level stroboscopic characteristics and the stroboscopic complexity index are measured and analyzed to obtain the stroboscopic parameter measurement result.

[0011] The present invention also provides a light source stroboscopic parameter automatic measurement device, which is applied to any one of the above light source stroboscopic parameter automatic measurement methods, comprising: An acquisition module, the acquisition module is used to acquire the original light signal of the light source, perform photoelectric conversion and analog-to-digital conversion on the original light signal, and obtain corresponding digital sampling data; An analysis module, the analysis module is used to perform multi-scale decomposition on the digital sampling data, and extract the frequency characteristics and energy distribution characteristics of the light source to obtain the corresponding stroboscopic characteristic coefficient; An association module, the association module is used to perform stroboscopic detection on the stroboscopic characteristic coefficient through a preset dynamic threshold value to obtain a corresponding stroboscopic depth parameter; A processing module, the processing module is used to perform segmented calculation on the stroboscopic depth parameter to obtain a corresponding stroboscopic fluctuation index; A control module, the control module is used to perform trend analysis on the stroboscopic fluctuation index to obtain corresponding light source stability information; An execution module is used to measure and analyze the light source stability information according to a preset measurement threshold to obtain a corresponding stroboscopic parameter measurement result.

[0012] The present invention also provides a light source stroboscopic parameter automatic measurement system, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of the method for quantifying the heat load of an air source heat pump system as described in any one of the above.

[0013] The present invention provides a method, device and system for automatically measuring light source stroboscopic parameters, which have the following beneficial effects: By performing multi-scale decomposition and feature extraction on the original light signal of the light source, the characteristic performance of the light source at different frequencies and energy distributions can be fully captured, thereby improving the accuracy of flicker parameter measurement and providing a more reliable basis for light source performance evaluation. By introducing dynamic thresholds for flicker detection and combining the segmented calculation of flicker depth parameters, dynamic tracking and refined analysis of the flicker characteristics of the light source are achieved, which helps to accurately reflect the flicker changes of the light source under different working conditions. Trend analysis based on the flicker fluctuation index can ensure that the system can accurately measure under different light source types and working environments, reduce measurement errors, and improve the adaptability of the measurement system. By comprehensively analyzing the light source stability information, a more scientific measurement strategy is formulated, and the entire measurement process is optimized by presetting the measurement threshold, thereby effectively improving the reliability of flicker parameter measurement. At the same time, by considering the multi-dimensional performance of the light source flicker characteristics, the measurement scheme can be flexibly adjusted according to the requirements of different application scenarios, so that the measurement results are more in line with actual application needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of a method for automatically measuring light source stroboscopic parameters provided by the present invention; Figure 2 This is a structural diagram of a light source stroboscopic parameter automatic measurement device provided by the present invention; Figure 3 The present invention provides a structure diagram of a light source stroboscopic parameter automatic measurement system.

[0015] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0018] Reference Figure 1 As shown, the present invention provides 1. a method for automatically measuring light source stroboscopic parameters, characterized in that it includes: Step S1: acquiring an original light signal from a light source, performing photoelectric conversion and analog-to-digital conversion on the original light signal, and obtaining corresponding digital sampling data; Step S2: performing multi-scale decomposition on the digital sampling data, and extracting the frequency characteristics and energy distribution characteristics of the light source to obtain the corresponding stroboscopic characteristic coefficients; Step S3: Performing stroboscopic detection on the stroboscopic characteristic coefficient through a preset dynamic threshold to obtain a corresponding stroboscopic depth parameter; Step S4: Calculate the stroboscopic depth parameter in sections to obtain the corresponding stroboscopic fluctuation index; Step S5: performing trend analysis on the stroboscopic fluctuation index to obtain corresponding light source stability information; Step S6: measuring and analyzing the light source stability information according to a preset measurement threshold to obtain corresponding stroboscopic parameter measurement results.

[0019] Based on the above steps, the detailed process is as follows: Step S1: Receive the light signal emitted by the light source through a photodetector (such as a photodiode, a photoresistor, etc.). During the photoelectric conversion process, the detector converts the captured light signal into a corresponding electrical signal (which can be a voltage or current signal). This process requires special attention to the linear operating range of the detector to avoid signal distortion. The signal is conditioned by an operational amplifier to ensure that the signal amplitude is maintained within an appropriate range. Subsequently, a high-precision ADC is used for analog-to-digital conversion. It is recommended to use a converter with a resolution of 16 bits or more and set a suitable sampling frequency (usually between 10kHz-100kHz). The sampling time window should be long enough to include at least hundreds of light source change cycles to ensure the statistical validity of the data. Finally, the sampled data is preprocessed, including denoising (median filtering or wavelet denoising method can be used), eliminating DC components to highlight AC change characteristics, and performing data normalization processing to prepare for subsequent analysis.

[0020] Step S2: Multi-scale decomposition is performed by using wavelet transform, and wavelet basis functions such as db4 or sym4 are usually selected. The number of decomposition layers is selected to be 3-5 layers according to the actual signal characteristics. Detail coefficients and approximate coefficients of each scale can be obtained through wavelet decomposition, and these coefficients contain characteristic information of the signal in different frequency ranges. Perform FFT transformation on each scale coefficient to obtain its spectrum information, identify the main frequency components and their corresponding amplitudes, and calculate the relative energy ratio of each frequency component. At the same time, it is necessary to calculate the energy distribution of each scale, analyze the concentration of energy in different frequency bands, and extract statistical characteristic parameters such as energy entropy. By integrating frequency and energy characteristics, constructing feature vectors, and normalizing the feature parameters, a complete stroboscopic characteristic coefficient matrix is ​​generated. The key to this process is to accurately capture the time-frequency characteristics of the light source signal and provide a reliable feature basis for subsequent stroboscopic detection.

[0021] Step S3: Set the initial threshold based on the statistical characteristics of the signal, and dynamically adjust the threshold according to the real-time fluctuation of the signal, while taking into account the influence of ambient light to make necessary threshold compensation. During the stroboscopic detection process, calculate the deviation between the actual signal fluctuation and the threshold, mark the time point and corresponding amplitude exceeding the threshold, and count the duration and frequency of stroboscopic events. Determine the modulation depth percentage of the signal by calculating the difference between the peak and valley values ​​of the waveform, and evaluate the intensity level of the stroboscopic light accordingly. Finally, generate a time series of stroboscopic depth, calculate the statistical parameters of each time period, and output a phased stroboscopic feature report. The core of this step is to accurately identify real stroboscopic events through the setting of dynamic thresholds, and quantitatively describe their characteristics, providing basic data for subsequent fluctuation index calculations.

[0022] Step S4: First, it is necessary to determine the appropriate time window length, usually 1-10 seconds as the basic analysis unit. In each time window, the mean, standard deviation and fluctuation range of the stroboscopic depth are calculated. At the same time, the data of adjacent time windows are overlapped and analyzed, usually with an overlap rate of 50%, to avoid losing important information at the window boundary. In the segmented calculation process, attention should be paid to the processing of outliers. For each segment, the normalized stroboscopic fluctuation value is calculated, and the time weighting factor is considered, with newer data being given a higher weight. Finally, the calculation results of each segment are combined into a time series to form a stroboscopic fluctuation index series. This fluctuation index reflects the stability change characteristics of the light source in different time periods.

[0023] Step S5: Stroboscopic fluctuation index trend analysis Based on the stroboscopic fluctuation index sequence obtained in S4, a time series trend analysis is performed. First, the moving average method is used to smooth the data to reduce the impact of short-term fluctuations. Then the linear regression analysis method is used to fit the long-term change trend of the fluctuation index. In the trend analysis process, it is necessary to consider the influence of seasonal and cyclical factors. The time series decomposition method can be used to separate the trend term, seasonal term and random term. The significance of the trend is verified by statistical test methods to ensure the reliability of the trend analysis results. At the same time, the first-order and second-order differences of the fluctuation index are calculated to analyze the change rate and acceleration characteristics of the light source stability. Finally, a light source stability information report containing trend direction, change rate and fluctuation characteristics is generated.

[0024] Step S6: According to the pre-set measurement threshold standard, the obtained light source stability information is comprehensively evaluated. The setting of the measurement threshold needs to take into account the requirements of industry standards and specific application scenarios, usually including two levels: short-term fluctuation threshold and long-term trend threshold. For short-term fluctuations, the main focus is on whether the instantaneous stroboscopic depth exceeds the human eye perception threshold (usually 0.1%); for long-term trends, the focus is on evaluating whether the attenuation rate of the light source stability is within an acceptable range. The impact of environmental factors, such as temperature changes, power supply fluctuations, etc., also needs to be considered during the analysis process, and necessary corrections should be made to the measurement results. Finally, a comprehensive measurement report containing key parameters such as stroboscopic depth, fluctuation frequency, stability rating, etc. is generated, and corresponding improvement suggestions are given. The measurement results need to be output in a standardized format to facilitate subsequent data analysis and quality control.

[0025] The present invention provides a method for automatically measuring the flicker parameters of a light source. By performing multi-scale decomposition and feature extraction on the original light signal of the light source, the characteristic performance of the light source at different frequencies and energy distributions can be fully captured, thereby improving the accuracy of flicker parameter measurement and providing a more reliable basis for light source performance evaluation. By introducing a dynamic threshold for flicker detection and combining the segmented calculation of the flicker depth parameter, dynamic tracking and refined analysis of the flicker characteristics of the light source are achieved, which helps to accurately reflect the flicker changes of the light source under different working conditions. Trend analysis based on the flicker fluctuation index can ensure that the system can accurately measure under different light source types and working environments, reduce measurement errors, and improve the adaptability of the measurement system. By comprehensively analyzing the light source stability information, a more scientific measurement strategy is formulated, and the optimization of the entire measurement process is achieved by presetting the measurement threshold, thereby effectively improving the reliability of flicker parameter measurement. At the same time, by considering the multi-dimensional performance of the flicker characteristics of the light source, the measurement scheme can be flexibly adjusted according to the requirements of different application scenarios, so that the measurement results are more in line with actual application needs.

[0026] In one embodiment, an original light signal of a light source is obtained, and the original light signal is subjected to photoelectric conversion and analog-to-digital conversion to obtain corresponding digital sampling data, including: In the light signal acquisition stage, a highly sensitive photodetector array is used to collect the light source to be tested in real time. The photodetector array contains multiple photosensitive elements, and the response wavelength range of the photosensitive elements is 380-780nm, covering the visible spectrum of the human eye. The field of view of the photodetector array is not less than 120 degrees to ensure effective coverage of a large range of luminous areas. The original light signal output by the detector is an analog voltage signal.

[0027] For dynamic adjustment of sampling time, the system performs adaptive control based on the changing characteristics of the original optical signal. When the signal change rate exceeds the preset threshold (±10%), the system reduces the sampling time interval to 1 / 2 of the original time interval; when the signal remains relatively stable (the change rate is less than ±3%), the sampling time interval is increased to 2 times the original time interval. The adjustment range of the sampling time is limited to between 10μs and 1ms, and the sampling control parameters include the sampling clock frequency and the number of sampling points.

[0028] In the signal conditioning stage, a programmable gain amplifier is used to amplify the original optical signal. The gain range of the gain amplifier is 1-1000 times, and the gain level can be automatically switched between 8 preset values. The system monitors the signal amplitude in real time to keep the output signal within the optimal working range (1-4V) of the AD converter.

[0029] The synchronous sampling process adopts a double buffer structure to ensure seamless switching of sampling control parameters. The system generates a sampling clock based on the sampling control parameters and drives the 16-bit precision AD converter to sample the amplified signal. The sampling sequence is stored in the sampling buffer in sequence according to the first-in-first-out principle.

[0030] Quantization coding uses an adaptive quantization scheme, which automatically selects the optimal quantization bit number according to the dynamic range of the signal. 8-bit quantization is used in areas with large amplitudes, and 12-bit quantization is used in areas with small amplitudes to ensure quantization accuracy under different signal strengths. The encoded data contains the quantization bit number identifier and the quantized value.

[0031] Anti-aliasing is achieved using a digital low-pass filter bank. The filter cutoff frequency is set to 0.4 times the sampling frequency, and the stopband attenuation is greater than 60dB. The effective sampled data after filtering eliminates high-frequency noise and aliasing components.

[0032] The data calibration process combines temperature compensation and nonlinear correction. The system obtains the ambient temperature in real time based on the built-in temperature sensor and obtains the corresponding calibration coefficient by table lookup. The nonlinear correction adopts the 5th-order polynomial fitting method, and the correction coefficient is obtained by pre-calibration. The calibrated data has good linearity and temperature stability.

[0033] Data reconstruction uses a piecewise cubic spline interpolation algorithm to resample and smooth the calibrated data. The reconstruction parameters include node spacing and smoothing factors. The optimal parameter values ​​are determined by the least squares criterion to obtain digital sampling data. The reconstructed digital sampling data has uniform time intervals and continuous and smooth waveform characteristics.

[0034] This embodiment uses a high-sensitivity photodetector array for light signal acquisition, combined with a wide field of view and full-spectrum response characteristics, to achieve comprehensive and accurate monitoring of various light sources, effectively improving the scope of application and reliability of the measurement method. Using a dynamic adaptive sampling time control mechanism, the system can automatically adjust the sampling parameters according to the changing characteristics of the light signal to ensure that stable and reliable measurement results can be maintained when the stroboscopic characteristics of the light source change dramatically. Through the automatic gear adjustment of the programmable gain amplifier and the synchronous sampling scheme of the double buffer structure, seamless switching and optimal signal-to-noise ratio control of the signal acquisition process are achieved. Adaptive quantization coding schemes and digital filter groups are used to effectively solve the problems of quantization accuracy and aliasing interference under different signal intensities. The data calibration method based on temperature compensation and nonlinear correction, combined with the data reconstruction technology of segmented cubic spline interpolation, makes the final digital sampling data have high precision and excellent waveform restoration characteristics, and the relative error and repeatability of the measurement results have reached the industry-leading level.

[0035] In one embodiment, the digital sampling data is decomposed at multiple scales, and the light source frequency characteristics and energy distribution characteristics are extracted to obtain the corresponding stroboscopic characteristic coefficients, including: The light source to be tested is digitally sampled by the data acquisition device, the sampling frequency is set to 2000Hz, and the sampling time is 10s. The collected digital sampling data is processed by double-tree complex wavelet transform, the transformation scale is set to 4 layers, and the Kingsbury Q-shift filter group is used to decompose the data to obtain the corresponding complex domain decomposition coefficients. The complex domain decomposition coefficients contain real coefficients and imaginary coefficients, which represent the amplitude and phase information of the signal respectively.

[0036] The complex domain decomposition coefficients obtained are processed by Hilbert transform, and the transform window length is set to 512 points. The analytical signal form of the signal is obtained by Hilbert transform, and then the instantaneous frequency data is calculated. The instantaneous frequency data reflects the frequency change characteristics of the light source at different times, providing a basis for subsequent feature extraction. The instantaneous frequency data is processed by the adaptive kernel density estimation method, and the kernel function uses the Gaussian kernel function. The bandwidth parameter is determined by the cross-validation method. After kernel density estimation processing, the probability distribution characteristics of the light source frequency are obtained.

[0037] The frequency distribution characteristics are calculated by wavelet packet entropy, and the db4 wavelet basis function is selected, and the number of decomposition layers is set to 3. By calculating the entropy value of each frequency subband, the entropy characteristics reflecting the degree of energy concentration of the frequency distribution are obtained. The entropy characteristics are processed by fractal dimension analysis method, and the fractal dimension is calculated by box dimension algorithm. The scale range is set to 2 to 10, and the corresponding fractal characteristic spectrum is obtained. The fractal characteristic spectrum reflects the self-similarity and complexity characteristics of the frequency distribution.

[0038] The fractal characteristic spectrum is analyzed by high-order statistics, and the third-order moment and fourth-order moment are calculated to obtain the characteristic coefficient reflecting the stroboscopic characteristics. The obtained characteristic coefficient includes the skewness coefficient and the kurtosis coefficient, which respectively characterize the asymmetry and sharpness of the stroboscopic distribution. The characteristic coefficient is used as a quantitative indicator of the stroboscopic parameters of the light source to evaluate the stroboscopic characteristics of the light source.

[0039] This embodiment uses dual-tree complex wavelet transform to process digital sampling data, realizes accurate decomposition of signals in the complex domain, and overcomes the limitations of traditional wavelet transform methods in phase representation. The instantaneous frequency characteristics are obtained by Hilbert transform, and combined with the adaptive kernel density estimation method, the dynamic change characteristics of the light source flicker are accurately captured, and the measurement accuracy is improved. The energy concentration and complexity characteristics of the frequency distribution are fully characterized by combining wavelet packet entropy calculation and fractal dimension analysis, making the measurement results more objective and reliable. The flicker characteristic coefficient obtained by high-order statistical analysis effectively quantifies the asymmetry and sharpness of the light source flicker, providing a reliable basis for light source quality assessment. The entire measurement process has a high degree of automation, no manual intervention is required, and the measurement efficiency is significantly improved. It is suitable for batch light source detection scenarios and has important practical value.

[0040] In one embodiment, the stroboscopic characteristic coefficient is subjected to stroboscopic detection through a preset dynamic threshold to obtain a corresponding stroboscopic depth parameter, including: The stroboscopic characteristic coefficients are subjected to frequency decomposition and decomposed into high-frequency components and low-frequency components through wavelet transform or Fourier transform. The high-frequency component mainly contains the rapid change characteristics of the stroboscopic signal, while the low-frequency component reflects the overall change trend of the signal. The high-frequency component is extracted by setting an appropriate frequency cutoff threshold, which is generally selected as 50Hz, and signals below this frequency are classified as low-frequency components.

[0041] Based on the obtained high-frequency components, the peak-to-valley difference sequence is calculated through the peak detection algorithm. The difference sequence represents the intensity change of the stroboscopic light. The larger the difference, the more obvious the stroboscopic light. The peak detection adopts the sliding window method. The window size is 1 / 4 of the signal period. The peak and trough positions are determined by comparing the extreme points in the window. The difference sequence is the amplitude difference between adjacent peaks and troughs, which constitutes the stroboscopic light intensity sequence.

[0042] Based on the stroboscopic intensity sequence, the initial detection threshold is determined through statistical analysis. The threshold is selected as 1.5 times the mean of the stroboscopic intensity sequence as the benchmark value for judging the occurrence of stroboscopic light. The selection of the threshold needs to consider factors such as the signal-to-noise ratio. Too high a threshold will reduce the detection sensitivity, while too low a threshold will easily lead to misjudgment.

[0043] The mean drift of the low-frequency component is calculated, and the sliding average method is used to obtain the slowly changing trend of the signal. The sliding window size is set to 200ms, the step length is 20ms, and the mean value in the window is calculated as the drift value at that moment. The drift value reflects the change of background light intensity and is used to dynamically compensate the detection threshold.

[0044] The initial detection threshold and the threshold compensation value are weighted and superimposed to obtain a dynamic threshold sequence that changes over time. The weight coefficient is determined by experimental optimization, and the weight of the compensation value is generally between 0.3 and 0.5, so that the threshold can adapt to the slow change of background light intensity.

[0045] By comparing the stroboscopic intensity sequence with the dynamic threshold sequence, the time point exceeding the threshold is determined. The over-threshold point indicates that there is obvious stroboscopic phenomenon at that moment, and these time points are recorded to form the over-threshold point sequence. The comparison process adopts a single-point comparison method, and the intensity value exceeding the dynamic threshold at the corresponding moment is determined as an over-threshold point.

[0046] The time intervals between adjacent threshold-exceeding points are calculated to obtain a sequence describing the changes in the stroboscopic period. The time interval is calculated by the timestamp difference of the threshold-exceeding points, reflecting the temporal characteristics of the stroboscopic. This sequence is used for subsequent stroboscopic parameter analysis.

[0047] According to the stroboscopic period sequence and dynamic threshold sequence, the stroboscopic parameters are analyzed and the stroboscopic depth parameters are calculated. The stroboscopic depth parameters include two indicators: stroboscopic frequency and modulation depth. The frequency is calculated by the inverse of the period sequence, and the modulation depth is determined based on the ratio of the super-threshold amplitude to the dynamic threshold. These parameters together describe the stroboscopic characteristics of the light source.

[0048] This embodiment achieves accurate capture of the stroboscopic signal characteristics by frequency decomposing the stroboscopic characteristic coefficients and extracting high-frequency and low-frequency components respectively, thereby effectively avoiding detection errors caused by signal aliasing. A dynamic threshold detection mechanism is adopted to combine the initial detection threshold with the compensation for background light intensity changes, thereby overcoming the problem that the traditional fixed threshold is easily disturbed by ambient light, and improving the adaptability and reliability of stroboscopic detection. The stroboscopic intensity sequence calculation method based on the peak-to-trough difference accurately reflects the instantaneous change characteristics of stroboscopic. Combined with the optimized sliding window parameter setting, it effectively suppresses noise interference while ensuring detection sensitivity. Through the weighted optimization processing of the threshold compensation value, the adaptive tracking of the detection threshold to the slow change of light intensity is achieved, thereby improving the accuracy of stroboscopic parameter measurement. In the process of extracting the stroboscopic depth parameter, the frequency and modulation depth indicators are comprehensively considered, providing a reliable basis for the comprehensive evaluation of the stroboscopic characteristics of the light source.

[0049] In one embodiment, the stroboscopic depth parameter is calculated in sections to obtain the corresponding stroboscopic fluctuation index, including: The light source stroboscopic depth parameter sequence is sampled at fixed intervals, with the sampling interval set to 1 / N of the original sequence length, where N ranges from 8 to 12, and N subsequences are obtained by sampling. The sampling process ensures that the subsequences are of equal length and cover the full time range of the original sequence.

[0050] Based on the subsequences obtained by sampling, local extreme value detection is performed, and the sampling points that meet the maximum and minimum point judgment conditions are marked in each subsequence through the sliding window method. The maximum point judgment condition is that the current point value is greater than the values ​​of the adjacent points before and after; the minimum point judgment condition is that the current point value is less than the values ​​of the adjacent points before and after. The detected extreme value points constitute the fluctuation feature sequence.

[0051] The wave characteristic sequence is used to construct a hyperbolic basis function group. The basis functions are hyperbolic sine and cosine functions. The function parameters include the scale factor α and the translation factor β. The scale factor α ranges from (0, 1], and the translation factor β ranges from [-π / 2, π / 2]. The basis function sequence contains M basis functions, and the value range of M is 4-8.

[0052] The fluctuation characteristic sequence and the basis function sequence are subjected to matrix operations to generate a hyperbolic coefficient matrix. The matrix operation uses the inner product operation to calculate the projection coefficients of the fluctuation characteristic sequence on each basis function. The dimension of the hyperbolic coefficient matrix is ​​N×M, where N is the number of subsequences and M is the number of basis functions.

[0053] The scale parameter α and translation parameter β in the hyperbolic coefficient matrix are optimized, and the optimization goal is to minimize the reconstruction error. The optimization adopts the gradient descent method, and the parameters are iteratively optimized until the error converges or the maximum number of iterations is reached. After optimization, the optimized coefficient matrix is ​​obtained.

[0054] The time-frequency plane is rearranged based on the optimized coefficient matrix. The rearrangement rule is to arrange the coefficients in order from low to high frequency and from front to back in time. The dimension of the rearrangement matrix is ​​the same as the hyperbolic coefficient matrix, but the order of the coefficients is adjusted to reflect the time-frequency characteristics of the signal.

[0055] The rearranged matrix is ​​segmented and the coefficients are calculated. The matrix is ​​divided into K segments along the frequency dimension, and the value range of K is 3-5. The mean value of the coefficients in each segment is used as the characteristic coefficient of the frequency interval to obtain the frequency domain segment coefficients.

[0056] The segmented cumulative value is calculated based on the frequency domain segment coefficient. The cumulative calculation adopts the weighted summation method, and the weight coefficient decreases with the frequency. The low frequency band weight value range is [0.4, 0.6], the medium frequency band weight value range is [0.2, 0.4], and the high frequency band weight value range is [0.1, 0.2].

[0057] Set the time window to calculate the flicker fluctuation of the segmented cumulative value. The time window length is 1-3s. The standard deviation of the cumulative value is calculated within the window. The ratio of the standard deviation to the preset threshold is the flicker fluctuation index. The preset threshold value range is [0.1, 0.3]. The larger the fluctuation index, the more serious the flicker of the light source.

[0058] This embodiment achieves accurate capture of the stroboscopic characteristics of the light source by sampling the stroboscopic depth parameters in segments and constructing subsequences, avoiding the problem that the traditional sampling method easily misses key fluctuation characteristics. The use of a hyperbolic basis function group for signal decomposition and reconstruction can better reflect the nonlinear characteristics of the stroboscopic signal than the traditional Fourier transform method, and improve the accuracy of stroboscopic feature extraction. The introduction of the adaptive optimization mechanism of scale parameters and translation parameters enables the system to automatically adjust the measurement parameters according to the stroboscopic characteristics of different light sources, thereby enhancing the adaptability and robustness of the measurement method. Through the rearrangement of the time-frequency plane and the calculation of the segmented coefficients, the fine characterization of the stroboscopic signal in different frequency intervals is achieved, overcoming the limitation that the traditional method is difficult to distinguish between high and low frequency flicker components. The fluctuation index calculation method based on the weighted cumulative sum time window not only takes into account the differences in the sensitivity of the human eye to flicker of different frequencies, but also can accurately reflect the dynamic change characteristics of the light source stroboscopic, providing a more reliable quantitative indicator for light source quality assessment.

[0059] In one embodiment, a trend analysis is performed on the stroboscopic fluctuation index to obtain corresponding light source stability information, including: The flicker fluctuation index of the light source is converted into fluctuation trend projection data through mapping transformation, which uses a nonlinear mapping function to perform spatial transformation on the flicker fluctuation index. The key parameters of the mapping transformation include four preset coefficients, and the values ​​of these coefficients directly affect the mapping effect. The purpose of the mapping transformation is to project the flicker fluctuation characteristics into a high-dimensional feature space for subsequent analysis.

[0060] Based on the obtained fluctuation trend projection data, the fluctuation characteristics of the light source are characterized by constructing a dynamic trend space. The construction of the dynamic trend space adopts the phase space reconstruction method, and the fluctuation trend projection data is reconstructed into a three-dimensional phase space trajectory by selecting appropriate embedding dimensions and time delay parameters. The core of this step is to convert the one-dimensional time series data into a dynamic trajectory in a multidimensional space.

[0061] The constructed dynamic trend space is subjected to singular spectrum decomposition to obtain the characteristic trajectory of the light source. The singular spectrum decomposition uses the SVD algorithm to obtain the left singular matrix, the singular value diagonal matrix and the right singular matrix through matrix decomposition. The eigenvector corresponding to the maximum singular value is selected from the decomposition result as the characteristic trajectory of the light source, which reflects the main characteristics of the light source fluctuation.

[0062] The fluctuation analysis of the characteristic trajectory of the light source is realized by calculating the fluctuation divergence coefficient. The calculation of the fluctuation divergence coefficient is based on the local divergence index of the characteristic trajectory and is quantified using the maximum Lyapunov index method. By analyzing the relationship between the distance between adjacent trajectories over time, the divergence degree of the light source fluctuation is evaluated. The fluctuation divergence coefficient reflects the instability of the light source fluctuation.

[0063] The fractal dimension of the fluctuation divergence coefficient is calculated to obtain the stability entropy value. The fractal dimension calculation adopts the correlation dimension method to characterize the complexity of the fluctuation by analyzing the self-similarity of the system at different scales. During the calculation process, it is necessary to determine the appropriate distance scale range. The stability entropy value characterizes the complexity of the light source fluctuation. The larger the entropy value, the more unstable the light source.

[0064] The acquisition of light source stability information is based on a comprehensive analysis of the stability entropy value and the characteristic trajectory of the light source. Stability judgment uses a dual standard: first, the stability entropy value must be less than the preset threshold, and second, the maximum deviation of the characteristic trajectory must be within the preset range. The values ​​of the preset threshold and the preset range must be determined according to the specific application scenario. When both conditions are met at the same time, the light source is judged to be in a stable state, otherwise it is judged to be in an unstable state.

[0065] This embodiment realizes the effective expression of the stroboscopic characteristics in high-dimensional space by performing nonlinear mapping transformation on the stroboscopic fluctuation index of the light source, thereby improving the accuracy and reliability of subsequent analysis. The phase space reconstruction method is used to construct a dynamic trend space, so that the one-dimensional time series data is converted into a multi-dimensional space trajectory, which enhances the characterization ability of the light source fluctuation characteristics. The characteristic trajectory of the light source is extracted by singular spectrum decomposition, which effectively reduces data redundancy and highlights the main characteristic information of the light source fluctuation. The fluctuation divergence coefficient is calculated based on the maximum Lyapunov index method to achieve a quantitative evaluation of the instability of the light source. The fractal dimension is calculated using the correlation dimension method to obtain the stability entropy value, which provides a reliable quantitative indicator for the stability judgment of the light source. The stability judgment is made by dual standards, which takes into account both the entropy value and the trajectory deviation, making the stability evaluation result more comprehensive and accurate, and effectively improving the reliability and practicality of the automatic measurement of the stroboscopic parameters of the light source.

[0066] In one embodiment, the light source stability information is measured and analyzed according to a preset measurement threshold to obtain corresponding stroboscopic parameter measurement results, including: When performing singular spectrum analysis on the stability information of the light source, the Hankel matrix decomposition method is used to construct the dynamic feature sequence of the light source stability information. This process reconstructs the time domain data into a Hankel matrix, performs singular value decomposition on the matrix, and extracts the eigenvectors corresponding to the main singular values ​​as the dynamic feature sequence. The window length of the singular spectrum analysis is set to 1 / 3 of the data length to ensure the accuracy of feature extraction.

[0067] The phase space reconstruction analysis stage is based on the embedding theorem. The time delay is determined by calculating the mutual information, and the embedding dimension m is determined by the GP algorithm. The dynamic feature sequence is reconstructed into a trajectory in the m-dimensional phase space. The selection of reconstruction parameters follows the minimum mutual information criterion, and the embedding dimension is determined by the saturation correlation dimension. The reconstructed stability feature matrix reflects the dynamic characteristics of the light source stability.

[0068] During the Hilbert-Huang transform process, the stability characteristic matrix is ​​Hilbert transformed to obtain the analytical signal, and then the intrinsic mode function is obtained through empirical mode decomposition. The instantaneous frequency of each intrinsic mode function is calculated to construct a three-dimensional time-frequency energy distribution diagram. The transformation process uses an adaptive window length to ensure the balance of time-frequency resolution.

[0069] The hyperbolic wavelet kernel function mapping uses the hyperbolic tangent function as the mother wavelet, and the scale parameter range is set to [2 -4 , 2 4 ], the position parameter step size is 1. The energy distribution map is mapped to the time-frequency plane through wavelet transform to extract the instantaneous light source stable component. The mapping process takes into account the boundary effect and uses the symmetric extension method to process the signal boundary.

[0070] The empirical mode decomposition process uses the envelope interpolation method to construct the upper and lower envelopes, and obtains the intrinsic mode function set through iterative screening. During the decomposition process, the screening termination condition is set as the mean of the number of consecutive screening times is less than 0.1, or the maximum number of iterations is 2000. The obtained intrinsic mode function set contains the stability characteristics of the light source at different frequency scales.

[0071] The self-organizing network analysis adopts the Kohonen network structure. The number of nodes in the input layer is the same as the dimension of the intrinsic mode function, and the output layer adopts a 10×10 two-dimensional grid structure. The network training adopts a batch update method, the initial value of the learning rate is 0.1, the initial value of the neighborhood radius is 5, and the number of training times is 1000. The extraction of multi-level stroboscopic features is achieved through competitive learning.

[0072] The entropy value calculation of time-frequency joint entropy adopts the entropy formula, and the entropy values ​​of multi-level stroboscopic features are calculated in the time domain and frequency domain respectively, and then weighted summation is performed. The time-frequency weight ratio is determined by the particle swarm optimization algorithm, and the objective function is the classification accuracy. The calculated entropy value reflects the complexity of the stroboscopic characteristics.

[0073] The sample entropy calculation selected the mode dimension as 2, and the similarity tolerance was 0.2 times the standard deviation. The complexity of the stroboscopic signal was evaluated by calculating the sample entropy at different scales. The larger the sample entropy value, the higher the signal complexity and the more significant the stroboscopic phenomenon.

[0074] In the final measurement and analysis stage, the energy distribution diagram, multi-level stroboscopic characteristics and stroboscopic complexity index are comprehensively evaluated. The weight of each index is determined by establishing a multi-index evaluation system and combining the fuzzy comprehensive evaluation method. The evaluation matrix is ​​constructed using the hierarchical analysis method, and the consistency ratio is required to be less than 0.1. The evaluation results are used as the stroboscopic parameter measurement results of the stroboscopic parameters.

[0075] This embodiment realizes a comprehensive analysis of light source stability information through the organic combination of multiple signal processing technologies such as singular spectrum analysis, Hilbert-Huang transform and hyperbolic wavelet kernel function mapping. The feature extraction method based on Hankel matrix decomposition and embedding theorem can effectively capture the dynamic characteristics of light source stability and significantly improve the accuracy of flicker parameter measurement. The Hilbert-Huang transform processing with adaptive window and the hyperbolic wavelet kernel function mapping with boundary effect compensation are used to solve the limitations of traditional measurement methods in time-frequency resolution and boundary processing. By combining self-organizing network analysis with multi-level entropy value calculation, not only can multi-scale flicker features be extracted, but also the complexity of flicker signals can be accurately evaluated, providing a reliable basis for the comprehensive evaluation of light source stability. The established multi-index evaluation system has strong adaptability and can be applied to the flicker parameter measurement of different types of light sources, significantly improving the reliability and practicality of the measurement results.

[0076] Reference Figure 2 As shown, a light source stroboscopic parameter automatic measurement device is applied to any one of the above light source stroboscopic parameter automatic measurement methods, comprising: The acquisition module is used to obtain the original light signal of the light source, perform photoelectric conversion and analog-to-digital conversion on the original light signal, and obtain corresponding digital sampling data; An analysis module, which is used to perform multi-scale decomposition on the digital sampling data, and extract the frequency characteristics and energy distribution characteristics of the light source to obtain the corresponding stroboscopic characteristic coefficient; An association module is used to perform stroboscopic detection on the stroboscopic characteristic coefficient through a preset dynamic threshold to obtain a corresponding stroboscopic depth parameter; A processing module, the processing module is used to perform segmented calculation on the stroboscopic depth parameter to obtain a corresponding stroboscopic fluctuation index; A control module, which is used to perform trend analysis on the stroboscopic fluctuation index to obtain corresponding light source stability information; The execution module is used to measure and analyze the light source stability information according to a preset measurement threshold to obtain corresponding stroboscopic parameter measurement results.

[0077] The present invention provides an automatic measurement device for light source stroboscopic parameters. By performing multi-scale decomposition and feature extraction on the original light signal of the light source, the characteristic performance of the light source at different frequencies and energy distributions can be fully captured, thereby improving the accuracy of stroboscopic parameter measurement and providing a more reliable basis for light source performance evaluation. By introducing a dynamic threshold for stroboscopic detection and combining the segmented calculation of the stroboscopic depth parameter, dynamic tracking and refined analysis of the stroboscopic characteristics of the light source are realized, which helps to accurately reflect the stroboscopic changes of the light source under different working conditions. Trend analysis based on the stroboscopic fluctuation index can ensure that the system can accurately measure under different light source types and working environments, reduce measurement errors, and improve the adaptability of the measurement system. By comprehensively analyzing the stability information of the light source, a more scientific measurement strategy is formulated, and the optimization of the entire measurement process is achieved by presetting the measurement threshold, thereby effectively improving the reliability of stroboscopic parameter measurement. At the same time, by considering the multi-dimensional performance of the stroboscopic characteristics of the light source, the measurement scheme can be flexibly adjusted according to the requirements of different application scenarios, so that the measurement results are more in line with actual application needs.

[0078] Reference Figure 3 As shown, the present invention also provides a light source stroboscopic parameter automatic measurement system, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of any one of the above-mentioned methods for quantifying the heat load of an air source heat pump system.

[0079] In this embodiment, the processor and the memory may be connected via a bus or other means. The memory may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk, or a solid-state drive. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application-specific integrated circuit, or one or more integrated circuits configured to implement the embodiments of the present invention.

[0080] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0081] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for automatically measuring light source stroboscopic parameters, characterized in that: include: Acquire an original light signal from the light source, perform photoelectric conversion and analog-to-digital conversion on the original light signal, and obtain corresponding digital sampling data; Performing multi-scale decomposition on the digital sampling data, and extracting the light source frequency characteristics and energy distribution characteristics to obtain corresponding stroboscopic characteristic coefficients; Performing stroboscopic detection on the stroboscopic characteristic coefficient through a preset dynamic threshold to obtain a corresponding stroboscopic depth parameter; The stroboscopic depth parameter is calculated in sections to obtain a corresponding stroboscopic fluctuation index; Performing trend analysis on the stroboscopic fluctuation index to obtain corresponding light source stability information; The light source stability information is measured and analyzed according to a preset measurement threshold to obtain a corresponding stroboscopic parameter measurement result.

2. The method for automatically measuring light source stroboscopic parameters according to claim 1, characterized in that: The step of acquiring an original light signal from a light source, performing photoelectric conversion and analog-to-digital conversion on the original light signal, and obtaining corresponding digital sampling data includes: The light source is collected by a preset photoelectric detector to obtain a corresponding original light signal; Dynamically adjust the sampling time according to the original optical signal to obtain corresponding sampling control parameters; Performing amplification gain processing on the original optical signal to obtain corresponding signal amplitude data; Perform synchronous sampling according to the sampling control parameter and the signal amplitude data to obtain a corresponding sampling sequence; Performing quantization encoding processing on the sampling sequence to obtain corresponding encoded data; Performing anti-aliasing processing on the encoded data to obtain corresponding valid sampling data; Performing data calibration processing according to the valid sampling data to obtain corresponding calibration data; The calibration data is subjected to data reconstruction processing to obtain corresponding digital sampling data.

3. The method for automatically measuring light source stroboscopic parameters according to claim 1, characterized in that: The multi-scale decomposition of the digital sampling data and the extraction of the light source frequency characteristics and energy distribution characteristics to obtain the corresponding stroboscopic characteristic coefficients include: Performing dual-tree complex wavelet decomposition processing on the digital sampling data to obtain corresponding complex domain decomposition coefficients; Performing Hilbert transform processing according to the complex domain decomposition coefficients to obtain corresponding instantaneous frequency data; Performing adaptive kernel density estimation on the instantaneous frequency data to obtain corresponding frequency distribution characteristics; According to the frequency distribution characteristics, wavelet packet entropy calculation processing is performed to obtain corresponding energy entropy characteristics; Performing fractal dimension analysis on the energy entropy characteristics to obtain a corresponding fractal characteristic spectrum; A high-order statistical analysis is performed on the fractal characteristic spectrum to obtain a corresponding stroboscopic characteristic coefficient.

4. The automatic measurement method of light source stroboscopic parameters according to claim 1, characterized in that: The stroboscopic detection is performed on the stroboscopic characteristic coefficient by using a preset dynamic threshold to obtain a corresponding stroboscopic depth parameter, including: Performing frequency decomposition on the stroboscopic characteristic coefficient to obtain corresponding high-frequency components and low-frequency components; Calculating the peak-to-trough difference sequence of the high-frequency component to obtain a stroboscopic intensity sequence; Setting a corresponding initial detection threshold according to the stroboscopic intensity sequence; Performing mean shift calculation based on the low-frequency component to obtain a corresponding threshold compensation value; Performing a combined operation on the initial detection threshold and the threshold compensation value to obtain a dynamic threshold sequence; Comparing the stroboscopic intensity sequence with the dynamic threshold sequence to obtain corresponding super-threshold points; Calculating the time intervals between adjacent super-threshold points to obtain a stroboscopic period sequence; A stroboscopic parameter analysis is performed according to the stroboscopic period sequence and the dynamic threshold sequence to obtain the stroboscopic depth parameter.

5. The method for automatically measuring light source stroboscopic parameters according to claim 1, characterized in that: The step of calculating the stroboscopic depth parameter in sections to obtain a corresponding stroboscopic fluctuation index includes: Performing segmented interval sampling processing on the stroboscopic depth parameter to obtain a plurality of subsequences; Performing local maximum and minimum point detection on the subsequence to obtain a fluctuation characteristic sequence; Constructing a hyperbolic basis function group according to the fluctuation characteristic sequence to obtain a basis function sequence; A hyperbolic matrix is ​​constructed based on the fluctuation characteristic sequence and the basis function sequence to obtain a hyperbolic coefficient matrix; Optimizing the scale parameters and translation parameters of the hyperbolic coefficient matrix to obtain an optimized coefficient matrix; Rearrange the time-frequency plane according to the optimization coefficient matrix to obtain a rearrangement matrix; Calculating the segment coefficients of the rearranged matrix to obtain the frequency domain segment coefficients; Performing segment interval accumulation operation according to the frequency domain segment coefficient to obtain a segment cumulative value; The stroboscopic fluctuation is calculated for the segmented cumulative value according to a preset time window to obtain the stroboscopic fluctuation index.

6. The method for automatically measuring light source stroboscopic parameters according to claim 1, characterized in that: The trend analysis of the stroboscopic fluctuation index to obtain corresponding light source stability information includes: Performing mapping transformation on the stroboscopic fluctuation index to obtain fluctuation trend projection data; Performing space construction according to the fluctuation trend projection data to obtain a dynamic trend space; Performing singular spectrum decomposition on the dynamic trend space to obtain a light source characteristic trajectory; Performing light source fluctuation analysis on the light source characteristic trajectory to obtain a fluctuation divergence coefficient; Performing fractal dimension calculation on the fluctuation divergence coefficient to obtain a stability entropy value; A stability analysis is performed based on the stability entropy value and the light source characteristic trajectory to obtain the light source stability information.

7. The method for automatically measuring light source stroboscopic parameters according to claim 1, characterized in that: The measuring and analyzing the light source stability information according to a preset measurement threshold to obtain a corresponding stroboscopic parameter measurement result includes: Performing singular spectrum analysis on the light source stability information to obtain a light source dynamic feature sequence; Perform phase space reconstruction analysis according to the dynamic characteristic sequence to obtain a stability characteristic matrix; Performing Hilbert-Huang transform processing on the stability characteristic matrix to obtain an energy distribution diagram; Performing hyperbolic wavelet kernel function mapping on the light source stability information according to the energy distribution diagram to obtain an instantaneous light source stability component; Performing empirical mode decomposition processing on the instantaneous light source stable component to obtain an intrinsic mode function set; Performing a self-organizing network analysis on the light source stability information according to the intrinsic modal function set to obtain a multi-level stroboscopic feature; Performing time-frequency joint entropy calculation on the multi-level stroboscopic feature to obtain the multi-level stroboscopic feature; Performing sample entropy calculation on the multi-level stroboscopic features to obtain a stroboscopic complexity index; The energy distribution diagram, the multi-level stroboscopic characteristics and the stroboscopic complexity index are measured and analyzed to obtain the stroboscopic parameter measurement result.

8. An automatic measurement device for light source stroboscopic parameters, characterized in that: The method for automatically measuring the light source stroboscopic parameters as described in any one of claims 1 to 7 above comprises: An acquisition module, the acquisition module is used to acquire the original light signal of the light source, perform photoelectric conversion and analog-to-digital conversion on the original light signal, and obtain corresponding digital sampling data; An analysis module, the analysis module is used to perform multi-scale decomposition on the digital sampling data, and extract the frequency characteristics and energy distribution characteristics of the light source to obtain the corresponding stroboscopic characteristic coefficient; An association module, the association module is used to perform stroboscopic detection on the stroboscopic characteristic coefficient through a preset dynamic threshold value to obtain a corresponding stroboscopic depth parameter; A processing module, the processing module is used to perform segmented calculation on the stroboscopic depth parameter to obtain a corresponding stroboscopic fluctuation index; A control module, the control module is used to perform trend analysis on the stroboscopic fluctuation index to obtain corresponding light source stability information; An execution module is used to measure and analyze the light source stability information according to a preset measurement threshold to obtain a corresponding stroboscopic parameter measurement result.

9. A light source stroboscopic parameter automatic measurement system, characterized in that: include: Memory, used to store programs; The processor is used to execute the program to implement the various steps of the method for quantifying the heat load of an air source heat pump system as described in any one of claims 1 to 7.

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