CN113804417A - Industrial Internet of things-based light flicker detection system with power supply disturbance and detection method thereof - Google Patents
Industrial Internet of things-based light flicker detection system with power supply disturbance and detection method thereof Download PDFInfo
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Abstract
The invention relates to a light flicker detection system with power disturbance based on an industrial Internet of things and a detection method thereof, wherein the light flicker detection system comprises a data acquisition system, a control system and a background processing system, the data acquisition system comprises a darkroom, an oscilloscope and a photoelectric receiver, the control system comprises a programmable power supply and a central control platform, and the background processing system comprises a client, a cloud server and a background processing computer; the system comprises an oscilloscope, a photoelectric receiver and a sample to be detected, wherein the oscilloscope, the photoelectric receiver and the sample to be detected are all arranged in a darkroom, the photoelectric receiver is connected to the oscilloscope through a circuit and collects a high-frequency optical signal emitted by the sample to be detected, the sample to be detected is connected to one end of a programmable power supply through a circuit, the programmable power supply and the other end of the oscilloscope are both connected to a central control platform through circuits, the central control platform is connected with a cloud server through a circuit, and the cloud server is respectively connected with a client and a background processing computer; the invention can realize remote detection and avoid the problem that accurate detection cannot be realized due to the limitation of the technical base of personnel.
Description
[ technical field ]
The invention relates to the technical field of light source stroboscopic testing, in particular to a light flicker detection system with power disturbance based on an industrial Internet of things and a detection method thereof.
[ background art ]
In recent years, with the wider application of the light emitting diode of the new light source, the research on the health lighting is more and more intensive. The flickering is gradually noticed by more people, and related researches show that mental fatigue, migraine and other diseases can be caused in flickering or stroboscopic light for a long time. Meanwhile, both the LED light source and the lamp have flicker or stroboscopic test requirements at home and abroad, and the Chinese quality certification center provides stroboscopic performance certification, and the European Union takes flicker as one of necessary lamp inspection items. Therefore, the flicker or stroboscopic effect is unavoidable in the field of healthy lighting, and is a link that the lamp must be tested, evaluated and optimally designed.
At present, stroboscopic test evaluation work at home and abroad is continuously developed, stroboscopic is required to be a necessity test item in ERP instructions of lighting products exported to European Union, and the national Chinese quality certification center releases 'lighting product stroboscopic performance certification' in 2021 and 3 months. The detection of the light stroboflash mainly involves the standard IEEE Std 1789-2015 IEEE recommended practice for reducing the health risk of observers by the fluctuating current of the high-brightness LED, IEC/TR 61547-1:2020 Equipment for general illumination-part 1 of the requirement on electromagnetic compatibility immunity: an objective optical scintillation meter and a test method of voltage fluctuation immunity, and IEC/TR 63158:2018 Objective test method of stroboscopic effect of common lighting equipment-lighting equipment. At present, the number of manufacturers of devices for stroboscopic testing in China is small, the precision is low, and the test results for the same lighting lamp are greatly different. Moreover, the method is blank in the aspect of remote detection, and cannot meet the detection requirement of the current market.
Based on the problems, if a rapid, efficient and accurate detection method and detection system can be provided, the most advanced technologies such as cloud computing, big data and artificial intelligence are adopted, and the detection is completed in a one-stop mode from the detection report, so that remote detection can be realized, the situation that accurate detection cannot be achieved due to the limitation of personnel technical bases can be avoided, and a bridge for zero-distance detection is built for third-party laboratories and enterprises.
Although patents have been referenced: CN 204789955U-a lighting product stroboscopic test system, but this system only introduces lighttight darkroom design, ensures that the data that the photoelectric detection device gathered are correct, improves the test accuracy of system. According to the requirements of the above standards, the method has certain limitations, can not test flicker under the condition of external voltage fluctuation interference, and can not meet the requirements of the standard IEC/TR 61547-1: 2020. In addition, no corresponding method is given for the algorithms of three standards IEC/TR 61547-1:2020, IEC/TR 63158:2018 and IEEE Std 1789-2015 and the building of the test system of the Internet of things.
[ summary of the invention ]
The invention aims to solve the defects and provide an industrial Internet of things-based optical flicker detection system with power supply disturbance, which can realize remote detection, can avoid the problem that accurate detection cannot be achieved due to the limitation of personnel and technology bases, and builds a zero-distance detection bridge for third-party laboratories and enterprises.
The industrial Internet of things-based light flicker detection system with power disturbance comprises a data acquisition system, a control system and a background processing system, wherein the data acquisition system comprises a darkroom 1, an oscilloscope 2 and a photoelectric receiver 3, the control system comprises a programmable power supply 5 and a central control platform 6, and the background processing system comprises a client 7, a cloud server 8 and a background processing computer 9; the oscilloscope 2, the photoelectric receiver 3 and the tested sample 4 are all arranged in the darkroom 1, the darkroom 1 is a closed space without stray light entering, the photoelectric receiver 3 is connected to the oscilloscope 2 through a circuit, the photoelectric receiver 3 is used for collecting high-frequency optical signals emitted by the tested sample 4, the tested sample 4 is connected to one end of a programmable power supply 5 through a circuit and supplies power to the programmable power supply 5 through the programmable power supply 5, the other end of the programmable power supply 5 and the other end of the oscilloscope 2 are both connected to a central control platform 6 through circuits, the central control platform 6 is connected to a cloud server 8 through a circuit, the cloud server 8 is respectively connected with a client 7 and a background processing computer 9, the client 7 sends related instructions to the central control platform 6 through test requirements to control the programmable power supply 5 to output corresponding electrical signals to light the tested sample 4, the central control platform 6 controls the power output of the programmable power supply 5, receives the high-frequency optical signals collected by the photoelectric receiver 3 through the oscilloscope 2, the central control platform 6 sends the received optical signals to the cloud server 8 through the wireless transmission system, and the background processing system completes the calculation of the data received by the cloud server 8 through the background processing computer 9, presents the final result and issues a detection report.
Further, the oscilloscope 2 and the photoelectric receiver 3 are a set of optical signal acquisition and display system for receiving high-frequency optical signals, the oscilloscope 2 is connected to the central control platform 6 through a network cable, a data line or a wireless network, and transmits detected optical signal data to the central control platform 6, and the photoelectric receiver 3 is a signal collector for receiving high-frequency optical signals.
Further, the programmable power supply 5 is connected to the central control platform 6 through a network cable or a data cable, and the programmable power supply 5 receives a control signal of the central control platform 6 and outputs a response to output an electrical parameter required by the sample 4 to light the sample 4.
Further, the central control platform 6 receives and sends signals in a wired or wireless mode, and sends processed data to the cloud server 8 through the wireless sending module.
Further, the client 7 is a test operation end, the client 7 controls the central control platform 6 to perform a test according to requirements, and the client 7 calculates the test data of the cloud server 8 through the background processing computer 9 and the calculation software to complete the test and issue a report.
Further, the data acquisition system collects light output waveforms of the lamp and the light source and sends the light output waveforms to the background processing system through the control system, the background processing system adopts a micro-service architecture, and whether attacks exist or not and resource use conditions of the cluster are checked through a log system in the system.
Furthermore, deployment of the optical flicker detection system adopts docker and kubernets to deploy, each tiny service is packaged into docker, a mirror image is deployed in a server, and then Kubernets are used for uniformly managing each tiny service; and dynamically adding machines when the hardware resources of the server are not enough to be used so as to expand the overall performance of the system.
The invention also provides a light flicker detection method with power disturbance based on the industrial Internet of things, which comprises the following steps:
1) the detection system is connected, a stable 60W incandescent lamp is taken as a detected sample 4 to be installed on a bracket, and then a programmable power supply 5 is arranged through a client 7 to output 230V and 50Hz and simultaneously apply an electric signal with 8.8Hz modulation frequency and 0.275 percent of relative voltage fluctuation to the detected sample 4;
2) the photoelectric receiver 3 is placed at a place below the tested sample 4 where light can be received, light signals of more than 180 seconds are collected, test data are transmitted to a background processing computer 9, and processing is carried out through computing software to obtain a computing result;
3) based on the four mathematical models, the optical signal data obtained by the test is compiled and calculated and analyzed by using a python language, and then the optical flicker calculation result can be obtained
Other samples tested were then tested by the model.
In the method for detecting the light flicker, four mathematical models are specifically as follows:
module 1: illuminance adapter
Illuminance adaptation circuit:
first order low pass filter transfer function:
and (3) module 2: weighting filter
Eye-brain response weighting filter:
analog frequency response function for reference 60W incandescent lamp:
transfer function of standard voltage scintillation meter:
can be obtained from the above (1), (2), (3) and (4)
And a module 3: squaring multiplier, sliding mean filter and amplifier
Illuminance waveform of incandescent lamp: (d) e (t) {1+ (d)E/2)·sin(2πfmt)} (6)
And (4) module: statistical analysis
E(t)＝{1-(dr/2)·cos(2πfrt)}·{1+(dE/2)·signum (sin(2πfmt))} (7)
Wherein S is a complex Laplace variable; tau isLPSCIs the time constant of the filter, set to 10 s; k ═ 3.57; tau isL1＝0.02ms；τL221.2 ms; e (t) is relative illuminance; f. ofm8.8Hz, 1/T modulation frequencym；dE0.630%, is the percentage of the relative change in the sine wave modulation of the illumination; e (t) is relative illuminance; f. ofr100Hz, the frequency of the illumination ripple superimposed on the dc component; dr22%, is the percentage of relative change in 100Hz illumination ripple; signum (x) is a signum function, and when x is greater than 0, signum (x) is 1; when x is 0, signum (x) is 0; and when x is less than 0, signum (x) is-1.
Compared with the prior art, the invention provides a quick, efficient and accurate detection method and an Internet of things detection system, and the most advanced technologies such as cloud computing, big data, artificial intelligence and the like are adopted, and the detection is completed in a one-stop manner from a detection report, so that the remote detection can be realized, the situation that the accurate detection cannot be achieved due to the limitation of the technical basis of personnel can be avoided, and a bridge for zero-distance detection is built for third-party laboratories and enterprises; moreover, the method can effectively combine the requirements of the current test standard, simply and efficiently complete the light flicker detection work with power supply disturbance, can reduce the workload of testers, can realize remote detection through the Internet of things, solves the complexity of the light flicker test in the traditional laboratory, and is worthy of popularization and application.
[ description of the drawings ]
FIG. 1 is a schematic structural view of the present invention;
in the figure: 1. darkroom 2, oscilloscope 3, photoelectric receiver 4, measured sample 5, programmable power supply 6, central control platform 7, client 8, cloud server 9, background processing computer 11, data line.
[ detailed description of the invention ]
The invention is further described below with reference to the accompanying drawings:
the invention relates to the technical fields of equipment internet of things, remote control, optical signal acquisition, data transmission and analysis, cloud computing, stroboscopic hazard assessment and the like, in particular to an optical flicker detection system with power disturbance based on an industrial internet of things and a detection method thereof.
As shown in fig. 1, the optical flicker detection system with power disturbance based on the industrial internet of things comprises a data acquisition system, a control system and a background processing system, wherein the data acquisition system consists of a darkroom 1, an oscilloscope 2, a photoelectric receiver 3 and a detected sample 4; the control system consists of a programmable power supply 5 and a central control platform 6; the background processing system is composed of a client 7, a cloud server 8, a background processing computer 9 and computing software. The oscilloscope 2, the photoelectric receiver 3 and the sample 4 to be measured are placed in the darkroom 1, and the darkroom 1 is a closed space with enough large space and without stray light entering; the photoelectric receiver 3 is connected with the oscilloscope 2 and is used for collecting a high-frequency optical signal emitted by a sample 4 to be detected; the tested sample 4 is a common sample which is powered by a programmable power supply 5; one end of the programmable power supply 5 is connected to the central control platform 6, and the output of the other end of the programmable power supply 5 is connected to the tested sample 4, so that the client 7 can send a related instruction to the central control platform 6 through a test requirement to control the programmable power supply 5 to output a corresponding electric signal (mainly having voltage, current and frequency) to light the tested sample; the central control platform 6 is connected with a cloud server 8 through a line, and the cloud server 8 is respectively connected with a client 7 and a background processing computer 9; one end of the central control platform 6 is connected to the programmable power supply 5 to control the power supply output, and the other end is connected with the oscilloscope 2 through the network cable to receive the optical signal collected by the high-frequency photoelectric receiver, and in addition, the received optical signal is arranged and then sent to the cloud server 8 through the wireless transmission system; the background processing system completes calculation of the data received by the cloud server through the computer 9 and the calculation software, presents a final result and directly issues a detection report.
The darkroom 1 is a closed space with extremely low internal reflectivity and free from interference of external stray light, and can accommodate the oscilloscope 2, the photoelectric receiver 3, the tested sample 4 and a tester. The oscilloscope 2 and the photoelectric receiver 3 are a set of optical signal acquisition and display system capable of receiving high-frequency optical signals, the oscilloscope 2 can be connected through a network cable, a data line or a wireless network and transmits detected optical signal data to the central control platform 6, and the photoelectric receiver 3 is a signal acquisition device capable of receiving high-frequency optical signals. The sample 4 to be measured is a lamp or light source fixed by a bracket and can be lightened by a programmable power supply 5. The programmable power supply 5 is a power supply with high stability and capable of stably outputting electric signals, corresponding signals can be input through external software to control the programmable power supply to output electric parameters required by the tested sample to light the tested sample 4, and the programmable power supply 5 can be connected to the central control platform 6 through a network cable, receives control signals of the central control platform 6 and makes output response. The optical signal acquisition system formed by the oscilloscope 2 and the electro-optical receiver 3 can be replaced by an optical signal collector, but the collector needs to be provided with a signal acquisition system for receiving high-frequency optical signals and can transmit the acquired signals to the central control platform 6.
The central control platform 6 has signal receiving and sending functions, and can receive and send signals through wire or wireless, the system adopts but not limited to a network cable or a data cable to receive optical signals collected by an oscilloscope or similar equipment, and sends processed data to the cloud server 8 through the wireless transmitting module, and the cloud server 8 is a computer system with larger capacity and higher transmission speed. The central control platform 6 is also connected to the programmable power supply 5 through a network cable or a data cable, and controls the output parameters of the power supply according to the standard test requirements, so that the tested sample 4 is lightened according to the requirements to complete the test. The client 7 is a test operation end and an operation end with test requirements, can control the central control platform 6 to test according to requirements, and meanwhile, calculates the test data of the cloud server 8 through the background processing computer 9 and the computing software to complete the test and issue a report. The calculation software is a software system which can process and calculate the test result according to the standard requirements of IEC/TR 61547-1:2020, and can issue the test report of the relevant parameters and complete WORD edition according to the corresponding requirements.
In the invention, the data acquisition system mainly collects light output waveforms of the lamp and the light source and sends the light output waveforms to the background processing system through the central control system. The background processing system mainly adopts a micro-service architecture, system paralysis can not be caused due to an accident, service is stably provided for the outside for a long time, and the whole situation can be monitored in real time within 7-24 hours. The overall situation of the system can be well checked through a log system in the system, such as: whether there is attack, the resource usage of the cluster, etc. In addition, the cluster independently separates the authentication system, and aims to prevent the authentication system from being exposed in a public network, so that the safety of the whole system is ensured.
For the aspect of data, the invention adopts a master-slave database and a backup database, when the master database is unexpected, the slave database can immediately provide services to the outside, and data backup can be carried out in real time so as to ensure the reliability and the safety of the data. Deployment of the detection system adopts docker and kubernets to deploy, each tiny service is packaged into docker, a mirror image is deployed in a server, and then Kubernets are used for uniformly managing each tiny service; when the hardware resources of the server are not enough to use, the machine can be dynamically added, and the overall performance of the system is expanded.
The invention also provides a light flicker detection method with power disturbance based on the industrial Internet of things, which comprises the following steps:
1) connecting a test system, namely installing a stable 60W incandescent lamp as a tested sample 4 on a bracket, then setting a programmable power supply 5 through a client 7, outputting 230V and 50Hz, and simultaneously applying an electric signal with 8.8Hz modulation frequency and 0.275% relative voltage fluctuation to the tested sample;
2) the photoelectric receiver 3 is placed at a place below a tested sample where light can be received, optical signals of more than 180 seconds are collected, test data are transmitted to the computer 9, and a calculation result is obtained through processing of calculation software;
3) based on the four mathematical models, the optical signal data obtained by the test is compiled and calculated and analyzed by using a python language, and then the optical flicker calculation result can be obtained
Other samples tested can then be tested by the model.
The specific mathematical models of the four modules are as follows:
module 1: illuminance adapter
Illuminance adaptation circuit:
first order low pass filter transfer function:
and (3) module 2: weighting filter
Eye-brain response weighting filter:
analog frequency response function for reference 60W incandescent lamp:
transfer function of standard voltage scintillation meter:
can be obtained from the above (1), (2), (3) and (4)
And a module 3: squaring multiplier, sliding mean filter and amplifier
Illuminance waveform of incandescent lamp: (d) e (t) {1+ (d)E/2)·sin(2πfmt)} (6)
And (4) module: statistical analysis
E(t)＝{1-(dr/2)·cos(2πfrt)}·{1+(dE/2)·signum(sin(2πfmt))} (7)
Wherein S is a complex Laplace variable; tau isLPSCIs the time constant of the filter, set to 10 s; k ═ 3.57; tau isL1＝0.02ms；τL221.2 ms; e (t) is relative illuminance; f. ofm8.8Hz, 1/T modulation frequencym；dE0.630% is the sine wave modulated phase of the illuminationTo the percentage of change; e (t) is relative illuminance; f. ofr100Hz, the frequency of the illumination ripple superimposed on the dc component; dr22%, is the percentage of relative change in 100Hz illumination ripple; signum (x) is a signum function, and when x is greater than 0, signum (x) is 1; when x is 0, signum (x) is 0; and when x is less than 0, signum (x) is-1.
Part of the core program code is as follows:
def ft_example():
data＝pd.read_csv('./50s.csv',sep＝',')
data.columns＝['time','num']
y＝data['num']
x＝data['time']
yy fft (y) # fast fourier transform
Real # gets real part
Imag # gets the imaginary part
Modulus of yf ═ abs (fft (y)) # is taken
yf1 ═ abs (fft (y))/((len (x))/((2)) # normalization process
yf2 ═ yf1[ range (int (len (x)) ] # due to symmetry, only half of the interval is taken
Frequency of (len (y)) # of (xn)
xf1＝xf
xf2 ═ xf [ range (int (len (x))/2) ] #takes a half interval
# original waveform
plt.subplot(221)
plt.plot(x[0:200],y[0:200])
plt.title('Original wave')
FFT of # Mixed wave (bilateral frequency Range)
plt.subplot(222)
plot (xf, yf, 'r') # shows the FFT modulus of the original signal
Title ('FFT of Mixed wave)', font size 7, color '#7a378B') # note that the colors therein may be consulted with a color code table
FFT (normalization) of # Mixed wave
plt.subplot(223)
plt.plot(xf1,yf1,'g')
plt.title('FFT of Mixed wave(normalization)',fontsize＝9, color＝'r')
plt.subplot(224)
plt.plot(xf2,yf2,'b')
plt.title('FFT of Mixed wave)',fontsize＝10,color＝'#F08080')
plt.show()
def percent_flicker(l_vector):
”'
Percentage of flicker in illuminance
Param l vector: # sample vector
Params fs: # sampling frequency
:return:
”'
l_vector＝np.array(l_vector).reshape(1,len(l_vector))
l_av＝np.mean(l_vector)
A＝np.max(l_vector)
B＝np.min(l_vector)
P_fl＝100*(A-B)/(A+B)
return P_fl；
def flickerIndex(l_vector):
avgL＝np.mean(l_vector)
w＝np.array(l_vector).reshape(len(l_vector),1)-np.tile(avgL,(len(l _vector),1))
w＝np.where(w>0,w,0)
a1＝np.sum(w)
a2＝sum(l_vector)
fi＝a1/a2
return fi；
def light_flickermeter_metric_PstLM(l_vector,FS):
l_vector＝np.array(l_vector).reshape(1,len(l_vector))
u_0＝l_vector/np.mean(l_vector)
# settings Filter
HIGHPASS_ORDER＝1
HIGHPASS_CUTOFF＝0.05；
LOWPASS_CUTOFF＝35；
LOWPASS_ORDER＝6；
u _0_ ac ═ u _0-np
The configuration filter 8 indicates the ORDER of the filter, and b _ hp, a _ hp ═ signal
Filtfiltfiltfilt (b _ hp, a _ hp, u _0_ ac) # data is a signal to be filtered
u_hp＝np.array(u_hp)
smooth_limit＝min(round(FS/10),u_hp.shape[1])
smooth_limit＝int(smooth_limit)
linspace_data＝np.linspace(0,1,smooth_limit)[np.newaxis, 0:smooth_limit]
u _ hp [0,0: smooth _ limit ] ═ u _ hp [0,0: smooth _ limit ] _ line _ data # dimension ascending
b_bw,a_bw＝signal.butter(LOWPASS_ORDER,LOWPASS_CUTOFF/(FS/ 2),'lowpass')
u_bw＝signal.filtfilt(b_bw,a_bw,u_hp)
u_bw＝np.array(u_bw)
num1＝[0.041661,44.758,2715.6,29839,0]
den1＝[1,196.32,11781,534820,3505380]
SCALING of SCALING _ FACTOR 1.101603155420234e +06# output
b_w,a_w＝signal.bilinear(num1,den1,FS)
u_w＝signal.filtfilt(b_w,a_w,u_bw)
u_w＝np.array(u_w)
u_q＝u_w**2
# first order Low pass Filter
LOWPASS_2_ORDER＝1
LOWPASS_2_CUTOFF＝1/(2*math.pi*300e-3)
b_lp,a_lp＝signal.butter(LOWPASS_2_ORDER,LOWPASS_2_CUTOFF /(FS/2),'lowpass')
s＝SCALING_FACTOR*lfilter(b_lp,a_lp,u_q)
#np.save('4.txt',s)
P_inst＝s
transient time in seconds for tau _ transient 20#
n_transient＝tau_transient*FS
s＝s[0,int(n_transient):]
NUMOF_CLASSES＝10000
ret＝plt.hist(s,NUMOF_CLASSES)
cum_probability＝100*(1-np.cumsum(ret[0]/np.sum(ret[0])))
# calculation of Each percentile
p_50s＝np.mean([get_percentile(cum_probability,ret[1],30), get_percentile(cum_probability,ret[1],50),get_percentile(cum_probabi lity,ret[1],80)])
p_10s＝np.mean([get_percentile(cum_probability,ret[1],6), get_percentile(cum_probability,ret[1],8), get_percentile(cum_probability,ret[1],10), get_percentile(cum_probability,ret[1],13), get_percentile(cum_probability,ret[1],17)])；
p_3s＝np.mean([get_percentile(cum_probability,ret[1],2.2), get_percentile(cum_probability,
ret[1],3),get_percentile(cum_probability,ret[1],4)])
p_1s＝np.mean([get_percentile(cum_probability,ret[1],0.7), get_percentile(cum_probability,ret[1],1),get_percentile(cum_probabi lity,ret[1],1.5)])
p_0_1＝get_percentile(cum_probability,ret[1],0.1)
PstLM — np (0.0314 × p _0_1+0.0525 × p _1s +0.0657 × p _3s +0.28 × p _10s +0.08 × p _50 s); # calculation result, obtaining Pst
return PstLM,s；
def get_percentile(cum_probability,magnitude,limit):
data＝np.abs(cum_probability-limit)
min_data＝np.min(data)
index＝data.tolist().index(min_data)
return magnitude[index]；
The present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents and are included in the scope of the present invention.
Claims (9)
1. The utility model provides a take light scintillation detecting system of power disturbance based on industry thing networking which characterized in that: the system comprises a data acquisition system, a control system and a background processing system, wherein the data acquisition system comprises a darkroom (1), an oscilloscope (2) and a photoelectric receiver (3), the control system comprises a programmable power supply (5) and a central control platform (6), and the background processing system comprises a client (7), a cloud server (8) and a background processing computer (9); the system comprises an oscilloscope (2), a photoelectric receiver (3) and a tested sample (4), wherein the oscilloscope (2), the photoelectric receiver (3) and the tested sample (4) are all arranged in a darkroom (1), the darkroom (1) is a closed space without stray light entering, the photoelectric receiver (3) is connected to the oscilloscope (2) through a line, the photoelectric receiver (3) is used for collecting high-frequency optical signals emitted by the tested sample (4), the tested sample (4) is connected to one end of a programmable power supply (5) through a line and supplies power to the programmable power supply (5) through the programmable power supply (5), the other end of the programmable power supply (5) and the other end of the oscilloscope (2) are both connected to a central control platform (6) through lines, the central control platform (6) is connected to a cloud server (8) through a line, the cloud server (8) is respectively connected to a client (7) and a background processing computer (9), and the client (7) sends related instructions to the central control platform (6) through test requirements to control the programmable power supply (5) to output corresponding signals The measured sample (4) is lightened by the electric signal, the central control platform (6) controls the power output of the programmable power supply (5), the oscilloscope (2) receives the high-frequency optical signal collected by the photoelectric receiver (3), the central control platform (6) sends the received optical signal to the cloud server (8) through the wireless transmission system, and the background processing system completes the calculation of the data received by the cloud server (8) through the background processing computer (9) and presents the final result and a detection report.
2. The optical flicker detection system of claim 1, wherein: the oscilloscope (2) and the photoelectric receiver (3) are a set of optical signal acquisition and display system for receiving high-frequency optical signals, the oscilloscope (2) is connected with the central control platform (6) through a network cable, a data cable or a wireless network and transmits detected optical signal data to the central control platform (6), and the photoelectric receiver (3) is a signal acquisition device for receiving the high-frequency optical signals.
3. The optical flicker detection system of claim 1, wherein: the programmable power supply (5) is connected to the central control platform (6) through a network cable or a data cable, and the programmable power supply (5) receives a control signal of the central control platform (6) and outputs a response to output an electric parameter required by the tested sample (4) to light the tested sample (4).
4. The optical flicker detection system of claim 1, wherein: the central control platform (6) receives and sends signals in a wired or wireless mode, and sends processed data to the cloud server (8) through the wireless sending module.
5. The optical flicker detection system of claim 1, wherein: the client (7) is a test operation end, the client (7) controls the central control platform (6) to test according to requirements, and the client (7) calculates test data of the cloud server (8) through a background processing computer (9) and calculation software to complete testing and issue a report.
6. The optical flicker detection system of claim 1, wherein: the data acquisition system collects light output waveforms of the lamp and the light source and sends the light output waveforms to the background processing system through the control system, the background processing system adopts a micro-service architecture, and whether attack exists or not and resource use conditions of the cluster are checked through a log system in the system.
7. The optical flicker detection system of claim 1, wherein: the deployment of the optical flicker detection system adopts docker and kubernets to deploy, each tiny service is packaged into docker, a mirror image is deployed in a server, and each tiny service is uniformly managed by Kubernets; and dynamically adding machines when the hardware resources of the server are not enough to be used so as to expand the overall performance of the system.
8. A detection method of a light flicker detection system according to any one of claims 1 to 7, comprising the steps of:
1) the detection system is connected, a stable 60W incandescent lamp is installed on a bracket as a detected sample (4), and then a programmable power supply (5) is arranged through a client (7) to output 230V and 50Hz and simultaneously apply an electric signal with 8.8Hz modulation frequency and 0.275% relative voltage fluctuation to the detected sample (4);
2) the photoelectric receiver (3) is placed at a place below the tested sample (4) where light can be received, light signals of more than 180 seconds are collected, test data are transmitted to a background processing computer (9), and processing is carried out through computing software to obtain a computing result;
3) based on the four mathematical models, the optical signal data obtained by the test is compiled and calculated and analyzed by using a python language, and then the optical flicker calculation result can be obtained
Other samples tested were then tested by the model.
9. The detection method according to claim 8, characterized in that said four mathematical models are in particular,
module 1: illuminance adapter
Illuminance adaptation circuit:
first order low pass filter transfer function:
and (3) module 2: weighting filter
Eye-brain response weighting filter:
analog frequency response function for reference 60W incandescent lamp:
transfer function of standard voltage scintillation meter:
can be obtained from the above (1), (2), (3) and (4)
And a module 3: squaring multiplier, sliding mean filter and amplifier
Illuminance waveform of incandescent lamp: (d) e (t) {1+ (d)E/2)·sin(2πfmt)} (6)
And (4) module: statistical analysis
E(t)＝{1-(dr/2)·cos(2πfrt)}·{1+(dE/2)·signum(sin(2πfmt))} (7)
Wherein S is a complex Laplace variable; tau isLPSCIs the time constant of the filter, set to 10 s; k ═ 3.57; tau isL1＝0.02ms；τL221.2 ms; e (t) is relative illuminance; f. ofm8.8Hz, 1/T modulation frequencym；dE0.630%, is the percentage of the relative change in the sine wave modulation of the illumination; e (t) is relative illuminance; f. ofr100Hz, the frequency of the illumination ripple superimposed on the dc component; dr22%, is the percentage of relative change in 100Hz illumination ripple; signum (x) is a function of signum, x > 0Signum (x) 1; when x is 0, signum (x) is 0; and when x is less than 0, signum (x) is-1.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102548159A (en) * | 2010-12-28 | 2012-07-04 | 通用电气照明解决方案有限责任公司 | Safety flashing detector for traffic lamps |
| CN204789955U (en) * | 2015-05-29 | 2015-11-18 | 上海时代之光照明电器检测有限公司 | Lighting products stroboscopic test system |
| CN204855762U (en) * | 2015-09-08 | 2015-12-09 | 东莞市锐源仪器股份有限公司 | LED drive power supply automatic test system that adjusts luminance |
| EP3098614A1 (en) * | 2015-05-29 | 2016-11-30 | Electricité de France | A method and device for flicker measurements performed on electrical appliances |
| CN205864447U (en) * | 2016-05-20 | 2017-01-04 | 索尔思光电（成都）有限公司 | A kind of optical module cloud test system |
| WO2020237505A1 (en) * | 2019-05-28 | 2020-12-03 | 深圳大学 | Road lamp flicker effect analysis method and terminal |
| CN112904144A (en) * | 2021-01-21 | 2021-06-04 | 深圳市行自迩科技有限公司 | Intelligent testing method and system based on cloud computing |
-
2021
- 2021-10-15 CN CN202111202208.8A patent/CN113804417A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102548159A (en) * | 2010-12-28 | 2012-07-04 | 通用电气照明解决方案有限责任公司 | Safety flashing detector for traffic lamps |
| CN204789955U (en) * | 2015-05-29 | 2015-11-18 | 上海时代之光照明电器检测有限公司 | Lighting products stroboscopic test system |
| EP3098614A1 (en) * | 2015-05-29 | 2016-11-30 | Electricité de France | A method and device for flicker measurements performed on electrical appliances |
| CN204855762U (en) * | 2015-09-08 | 2015-12-09 | 东莞市锐源仪器股份有限公司 | LED drive power supply automatic test system that adjusts luminance |
| CN205864447U (en) * | 2016-05-20 | 2017-01-04 | 索尔思光电（成都）有限公司 | A kind of optical module cloud test system |
| WO2020237505A1 (en) * | 2019-05-28 | 2020-12-03 | 深圳大学 | Road lamp flicker effect analysis method and terminal |
| CN112904144A (en) * | 2021-01-21 | 2021-06-04 | 深圳市行自迩科技有限公司 | Intelligent testing method and system based on cloud computing |
Non-Patent Citations (1)
| Title |
|---|
| INTERNATIONAL ELECTROTECHNICAL COMMISSION: "《IEC/TR 61547-1:2020》", 31 July 2020 * |
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