LED light bar discharge test method

Abstract

The invention discloses an LED light bar discharge test method. The method comprises the following steps: setting test parameters and inputting the test parameters to a multi-channel synchronous data acquisition module; applying a preset voltage to the driving circuit, then triggering power failure and synchronously starting timing; collecting dynamic discharge waveform data of each node in real time; intercepting data in a segmented manner based on a preset time window, and extracting characteristic parameters of a voltage attenuation rate, a current abrupt change amplitude and a waveform distortion coefficient; dynamically comparing the characteristic parameters with a safety threshold range to mark an abnormal interval; and generating a test report according to the abnormal distribution. Through a multi-channel synchronous acquisition and time window segmentation analysis technology, discharge full-period transient response is accurately captured, abnormity caused by non-uniform capacitance distribution or local insulation defects is identified in combination with a dynamic comparison rule, the problem of missing detection in transient abnormity detection of a traditional test method is solved, the test precision and reliability are remarkably improved, and the test efficiency is improved. And efficient data support is provided for safety assessment of the LED light bar.

Description

Technical Field

[0001] The present invention relates to the technical field of LED strip testing, and particularly to a method for discharging test of an LED strip. Background Art

[0002] As an efficient and energy-saving lighting device, the LED strip is widely used in fields such as building decoration, display screens, and automotive lighting. Its electrical performance and reliability directly affect the service life and safety. The discharging test is an important part of evaluating the performance of the LED strip, mainly used to detect the residual charge release characteristics, dynamic response ability, and potential safety hazards after power-off.

[0003] In the prior art, the testing of LED strips mostly focuses on the measurement of static parameters, such as the working voltage, current stability, or light efficiency detection, while lacking systematic testing means for the dynamic characteristics of the discharging process. Conventional testing methods usually use an oscilloscope or a multimeter to sample and record the voltage decay curve of the strip after power-off, or evaluate the capacitance effect through a simple charge-discharge cycle test.

[0004] However, such methods have significant limitations: Since the discharging process of the LED strip involves complex dynamic responses, such as the non-linear decay of residual charges, the coupling effect between distributed capacitance and the driving circuit, it is difficult for existing testing equipment to capture the transient characteristics of the entire discharging cycle in real time. Especially under high-frequency switching or complex driving conditions, instantaneous voltage fluctuations or partial discharge anomalies are easily overlooked. For example, when there are minor insulation defects or uneven capacitance distribution inside the strip, traditional sampling tests cannot fully reflect the abnormal spikes or delays during the discharging process, resulting in potential faults not being effectively identified. This technical defect makes the existing testing methods have blind spots in evaluating the dynamic safety of LED strips, and there is an urgent need for a testing scheme that can accurately analyze the dynamic behavior of the entire discharging cycle and capture transient anomalies in real time. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for discharging test of an LED strip to solve the problem that the existing testing methods have blind spots in evaluating the dynamic safety of LED strips.

[0006] The present invention provides a method for discharging test of an LED strip, including the following steps:

[0007] S1: Set test parameters, including the input voltage threshold, sampling time interval, and discharging monitoring duration, and input the test parameters into a multi-channel synchronous data acquisition module;

[0008] S2: Apply a preset voltage to the driving circuit of the LED strip, trigger a power-off instruction, and simultaneously start the timing function of the multi-channel synchronous data acquisition module;

[0009] S3: Collect the dynamic discharge waveform data of each node of the LED light strip after power failure in real time through the multi-channel synchronous data acquisition module, including the voltage decay curve and the residual current change curve, and send the dynamic discharge waveform data to the data processing module;

[0010] S4: In the data processing module, segment and intercept the dynamic discharge waveform data based on a preset time window, and extract the characteristic parameters within each time window. The characteristic parameters include the voltage decay rate, the current mutation amplitude, and the waveform distortion coefficient;

[0011] S5: Dynamically compare the characteristic parameters with a preset safety threshold range. If any characteristic parameter exceeds the safety threshold range, mark the discharge waveform of the corresponding time window as an abnormal interval;

[0012] S6: Generate a test report including the abnormal type, risk level, and an assessment of the integrity of the discharge process according to the distribution position and duration of the abnormal interval.

[0013] Further, in step S1, the test parameters further include:

[0014] The range of the input voltage threshold is 80% - 120% of the rated voltage of the LED light strip. The sampling time interval is adjustable from 10 microseconds to 1 millisecond, and the discharge monitoring duration is from 0.1 second to 5 seconds after power failure.

[0015] Further, in step S3, the collection of the dynamic discharge waveform data includes:

[0016] Synchronously obtain voltage and current signals through voltage sensors connected in parallel to each node of the LED light strip and current sensors connected in series, perform filtering and noise reduction processing on the signals, and then generate digital waveform data by analog-to-digital conversion of the filtered signals.

[0017] Further, the filtering and noise reduction processing includes:

[0018] Perform baseline drift compensation, high-frequency noise wavelet threshold filtering on the voltage and current signals in sequence, and use a moving average algorithm to smooth signal glitches. The window width of the moving average algorithm is proportional to the sampling time interval.

[0019] Further, in step S4, the characteristic parameters further include:

[0020] The peak voltage, current decay time constant, and waveform oscillation frequency within each time window.

[0021] Further, in step S5, the dynamic comparison includes:

[0022] Calculate the fitting degree between the voltage decay rate and a preset linear decay model. If the fitting degree is lower than the set threshold, it is determined as a non-linear discharge anomaly.

[0023] Compare the current mutation amplitude with the maximum allowable mutation value in the historical normal discharge data. If it exceeds the maximum allowable mutation value, it is determined as a transient overcurrent anomaly.

[0024] Furthermore, step S5 further includes:

[0025] When it is determined as a non-linear discharge anomaly or a transient overcurrent anomaly, automatically trigger the acoustic and optical alarm device, and record the physical position code of the LED light bar corresponding to the abnormal waveform data and the power-off trigger timestamp.

[0026] Furthermore, in step S2, the methods of triggering the power-off instruction include:

[0027] Send a pulse width modulation signal to the drive circuit through a programmable power controller, and cut off the power supply at a preset power-off trigger phase point, where the power-off trigger phase point is dynamically adjusted according to the drive frequency of the LED light bar.

[0028] Furthermore, the method of determining the preset power-off trigger phase point includes:

[0029] Based on the duty cycle of the PWM signal of the drive circuit, trigger the power-off at the zero-crossing point or peak point of the current waveform to match the monitoring requirements of the discharge characteristics under different drive modes.

[0030] Furthermore, the baseline drift compensation includes:

[0031] Fit the signal baseline trend line by the least squares method, and use the trend line to reverse-correct the original signal to eliminate the low-frequency baseline offset caused by environmental temperature or power supply fluctuations.

[0032] The present invention has the following beneficial effects: The LED strip discharge test method of the present invention can capture the dynamic discharge waveform data of each node after the LED strip is powered off in real time through a multi-channel synchronous data acquisition module. Combining the segmented interception and feature parameter extraction technology of a preset time window, it can accurately analyze the transient response characteristics during the entire discharge cycle, especially non-linear change characteristics such as voltage decay rate and waveform distortion coefficient, overcoming the problem of missed detection of abnormal intervals caused by insufficient data sampling rate or single time window in traditional sampling test methods. Further, by dynamically comparing the feature parameters with the safety threshold range, the abnormal discharge interval can be quickly located and its distribution position and duration can be associated, so as to effectively identify potential safety hazards such as transient overcurrent and non-linear attenuation caused by uneven capacitance distribution, insulation defects or drive circuit coupling effects. Compared with the prior art, the present invention significantly improves the accuracy and efficiency of the discharge test, and can still ensure the continuity and integrity of the test results under complex driving conditions or high-frequency switching scenarios, providing highly reliable data support for the safety assessment of LED strips. At the same time, through abnormal type classification and risk level quantification, it provides a direct basis for product optimization design and fault tracing. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0034] Figure 1 It is a flowchart of the LED strip discharge test method provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in combination with the embodiments. It should be pointed out that the following detailed description is illustrative and is intended to provide further description of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the present application belongs.

[0036] Please refer to Figure 1 , the embodiment of the present invention provides an LED strip discharge test method, including:

[0037] S1, setting test parameters including input voltage threshold, sampling time interval and discharge monitoring duration, and inputting the test parameters into a multi-channel synchronous data acquisition module.

[0038] In step S1, the input voltage threshold is defined as 80% - 120% of the rated voltage of the LED strip. For example, for a strip with a rated voltage of 12V, the test voltage range is from 9.6V to 14.4V; the sampling time interval is set to be adjustable from 10 microseconds to 1 millisecond; the discharge monitoring duration is set to be from 0.1 second to 5 seconds after power-off.

[0039] By defining the input voltage threshold range, it is possible to simulate the discharge behavior of the LED strip under abnormal power supply scenarios, such as sudden voltage surges or drops, ensuring that the tests cover extreme working conditions in actual applications. The adjustable sampling time interval supports high-frequency signal capture. For example, microsecond-level sampling can accurately capture transient current mutations, while the dynamically set discharge monitoring duration covers the entire cycle from the moment of power-off to the complete release of residual charges, avoiding late abnormal undetected due to insufficient monitoring time.

[0040] S2, Apply a preset voltage to the driving circuit of the LED strip and trigger a power-off command, and simultaneously start the timing function of the multi-channel synchronous data acquisition module.

[0041] In specific implementation, a programmable power controller is used to apply a preset voltage to the driving circuit, and the power supply is cut off instantly when the power-off command is triggered, while the internal timer of the multi-channel data acquisition module is started. The synchronous timing function ensures strict time alignment between the power-off action and data acquisition, eliminating waveform phase errors caused by power-off trigger and device response delays in traditional tests. When applying the preset voltage, the actual working mode of the driving circuit, such as constant current or PWM driving, is combined to simulate the discharge characteristics under real working conditions, making the test results closer to the actual use scenario.

[0042] S3, The multi-channel synchronous data acquisition module is used to continuously collect the dynamic discharge waveform data of each node of the LED strip after power-off, including the voltage decay curve and the residual current change curve, and send the dynamic discharge waveform data to the data processing module.

[0043] During implementation, high-precision voltage sensors are connected in parallel at the power input end, branch nodes, and ends of the LED strip, and a Hall current sensor is connected in series in the main circuit to synchronously collect multi-node signals; the collected analog signals are processed by filtering and denoising and then converted into digital waveform data by an analog-to-digital converter and transmitted to the data processing module.

[0044] The multi-channel synchronous acquisition technology can comprehensively capture the differences in the discharge behavior of each node of the LED strip. For example, if the voltage decay of a certain branch node is abnormal, the corresponding physical location can be quickly located, solving the problem that single-point detection cannot reflect uneven capacitance distribution or local insulation defects. The filtering and denoising process improves the signal-to-noise ratio of weak discharge signals by eliminating the influence of environmental electromagnetic interference or power supply ripple, ensuring the accuracy of subsequent analysis.

[0045] S4. In the data processing module, segment and intercept the dynamic discharge waveform data based on a preset time window, and extract the characteristic parameters within each time window. The characteristic parameters include the voltage decay rate, the current mutation amplitude, and the waveform distortion coefficient.

[0046] For example, divide the discharge process into a time window every 50 milliseconds, and calculate the voltage decay rate, the current mutation amplitude, and the waveform distortion coefficient within each window. The time window segmentation technology disassembles the continuous discharge process into discrete and analyzable intervals, which is convenient for quantifying abnormal characteristics in stages. For example, a sudden drop in the voltage decay rate within a certain window may indicate capacitor failure. The multi-dimensional extraction of characteristic parameters enhances the ability to identify hidden defects. For example, the waveform distortion coefficient reflects the degree of harmonic distortion, which can effectively detect the oscillation abnormality caused by local breakdown and avoid the one-sidedness of single-parameter analysis.

[0047] S5. Dynamically compare the characteristic parameters with a preset safety threshold range. If any characteristic parameter exceeds the safety threshold range, mark the discharge waveform of the corresponding time window as an abnormal interval.

[0048] In specific implementation, the preset safety threshold range is obtained based on the statistics of historical normal data. For example, the safety threshold of the voltage decay rate is a decrease of 2V to 5V per second; in the dynamic comparison process, a sliding window mechanism is used to update the threshold in real time. For example, it is adaptively adjusted according to the data of the previous time period.

[0049] The dynamic comparison mechanism combines real-time data with a statistical model to solve the problem of insufficient adaptability of the traditional static threshold method under complex working conditions. For example, in a high-temperature environment, the discharge rate of the LED strip naturally slows down. By adaptively adjusting the threshold through a sliding window, false alarms can be reduced, and at the same time, the sensitivity to real abnormalities can be ensured. For example, a sudden drop in the rate caused by insulation defects.

[0050] S6. Generate a test report including the abnormal type, risk level, and evaluation of the integrity of the discharge process according to the distribution position and duration of the abnormal interval.

[0051] During implementation, the position of the abnormal interval is associated with the physical node number of the LED strip. The abnormal type is automatically classified according to the exceeded items of the characteristic parameters. The risk level is determined based on the proportion of the abnormal duration. The integrity of the discharge process is scored by the dispersion degree of the abnormal interval distribution. The test report converts abstract data into actionable engineering conclusions. For example, the PCB area where the faulty lamp bead is located can be located through the abnormal position, and the risk level quantification directly guides the maintenance priority. The integrity score of the discharge process comprehensively evaluates whether the overall discharge behavior meets the safety standards. For example, if the proportion of the abnormal interval is less than 5%, it is judged as qualified, providing a unified criterion for batch acceptance of products.

[0052] In summary, through the multi-channel synchronous acquisition and time-window segmentation analysis technology, the present invention realizes the millisecond-level refined analysis of the full-cycle dynamic response of LED strip discharge, breaking through the limitations of traditional sampling tests; combined with the multi-dimensional extraction of characteristic parameters and the adaptive dynamic comparison rules, it significantly improves the detection accuracy of transient anomalies and local defects; the closed-loop process from data acquisition, processing to report generation upgrades the discharge test from a single-parameter measurement to a comprehensive safety assessment system, providing efficient and reliable technical support for the design optimization and fault tracing of LED strips.

[0053] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for discharging test of an LED light strip, characterized in that, It includes the following steps: S1: Set test parameters, including input voltage threshold, sampling time interval, and discharge monitoring duration, and input the test parameters into a multi-channel synchronous data acquisition module; S2: Apply a preset voltage to the driving circuit of the LED light bar, and trigger a power-off instruction to synchronously start the timing function of the multi-channel synchronous data acquisition module; S3: Real-time collect the dynamic discharge waveform data of each node of the LED light bar after power-off through the multi-channel synchronous data acquisition module, including voltage decay curve and residual current change curve, and send the dynamic discharge waveform data to the data processing module; S4: In the data processing module, segment and intercept the dynamic discharge waveform data based on a preset time window, and extract the characteristic parameters within each time window. The characteristic parameters include voltage decay rate, current mutation amplitude, and waveform distortion coefficient; S5: Dynamically compare the characteristic parameters with a preset safety threshold range. If any characteristic parameter exceeds the safety threshold range, mark the discharge waveform of the corresponding time window as an abnormal interval; S6: Generate a test report including abnormal type, risk level, and discharge process integrity assessment according to the distribution position and duration of the abnormal interval.

2. The LED strip discharge test method according to claim 1, characterized in that, In step S1, the test parameters further include: The range of the input voltage threshold is 80%-120% of the rated voltage of the LED light bar. The sampling time interval is adjustable from 10 microseconds to 1 millisecond, and the discharge monitoring duration is from 0.1 second to 5 seconds after power-off.

3. The LED strip discharge test method according to claim 1, wherein In step S3, the acquisition of the dynamic discharge waveform data includes: Synchronously obtain voltage and current signals through voltage sensors connected in parallel to each node of the LED light bar and current sensors connected in series, perform filtering and denoising processing on the signals, and then generate digital waveform data by analog-to-digital conversion of the filtered signals.

4. The LED light strip discharge test method according to claim 3, wherein, The filtering and denoising processing includes: Perform baseline drift compensation and high-frequency noise wavelet threshold filtering on the voltage and current signals in sequence, and use a moving average algorithm to smooth the signal burrs. The window width of the moving average algorithm is proportional to the sampling time interval.

5. The LED strip discharge test method according to claim 1, wherein In step S4, the characteristic parameters further include: The peak voltage, current decay time constant, and waveform oscillation frequency within each time window.

6. The LED strip discharge test method according to claim 1, wherein In step S5, the dynamic comparison includes: Calculate the fitting degree between the voltage decay rate and a preset linear decay model. If the fitting degree is lower than the set threshold, it is determined as non-linear discharge abnormality; Compare the current mutation amplitude with the maximum allowable mutation value in the historical normal discharge data. If it exceeds the maximum allowable mutation value, it is determined as transient overcurrent abnormality.

7. The LED strip discharge test method according to claim 6, characterized in that, Step S5 further includes: When it is determined as non-linear discharge abnormality or transient overcurrent abnormality, automatically trigger an audible and visual alarm device, and record the physical position code of the LED light bar corresponding to the abnormal waveform data and the power-off trigger timestamp.

8. The LED strip discharge test method according to claim 2, characterized in that, In step S2, the ways to trigger the power-off instruction include: Send a pulse width modulation signal to the driving circuit through a programmable power controller, and cut off the power supply at a preset power-off trigger phase point. The power-off trigger phase point is dynamically adjusted according to the driving frequency of the LED light bar.

9. The LED strip discharge test method according to claim 8, wherein, The determination method of the preset power-off trigger phase point includes: Based on the duty cycle of the PWM signal of the drive circuit, power-off triggering is performed at the zero-crossing point or peak point of the current waveform to match the discharge characteristic monitoring requirements under different drive modes.

10. The LED strip discharge test method according to claim 4, wherein The baseline drift compensation includes: Fitting the signal baseline trend line by the least squares method, and using the trend line to reverse-correct the original signal to eliminate the low-frequency baseline offset caused by environmental temperature or power supply fluctuations.

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