Full-life-cycle acceleration test system of intelligent high-power LED lighting device

Abstract

The invention, which relates to the technical field of LED lighting testing, discloses a full-life-cycle accelerated testing system for an intelligent high-power LED lighting device, comprising an environmental parameter detection module, a full-life-cycle testing module, a heat dissipation system module, a service life prediction module and a failure analysis module. The environmental parameter detection module collects multi-field coupling environmental parameters; the full life cycle test module establishes an acceleration factor mathematical model, calculates an acceleration coefficient and determines an acceleration test working condition parameter; the heat dissipation system module selects and adjusts the cooling intensity of the natural convection unit or the flow boiling unit; the life prediction module constructs a life physical-statistical hybrid prediction model and predicts the residual life of the device; the failure analysis module analyzes a heat-electricity-light-stress coupling failure mechanism and generates a performance optimization scheme. According to the invention, the accelerated test and performance optimization of the whole life cycle of the LED lighting device are realized through the cooperative work of multiple modules, and the test efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of LED lighting testing, and in particular to a full life cycle accelerated testing system for a smart high-power LED lighting device. Background Art

[0002] At present, high-power LED lighting devices are widely used in smart cities, industrial lighting and other fields. However, since high-power LEDs often face complex working environments in actual applications, their reliability and life assessment become particularly important. Traditional LED life test methods mainly rely on a single environmental factor to accelerate aging, which cannot truly reflect the failure mechanism under multi-field coupling conditions, resulting in a large deviation between the life prediction results and the actual service life.

[0003] In addition, existing LED test systems generally lack intelligent heat dissipation control and real-time monitoring capabilities, making it difficult to dynamically optimize for different working conditions. At the same time, due to the lack of real-time analysis of the microscopic morphology of the LED chip surface, potential failure risks cannot be discovered in a timely manner, affecting the accuracy and reliability of the test results. Summary of the invention

[0004] In view of the problems existing in the existing LED lighting device acceleration test system, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to construct a full life cycle accelerated testing system that can simulate a multi-field coupling environment, realize intelligent heat dissipation control, and accurately predict lifespan.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides a full life cycle accelerated testing system for a smart high-power LED lighting device, which includes an environmental parameter detection module, which collects multi-field coupled environmental parameters and transmits the multi-field coupled environmental parameters to a full life cycle testing module; the full life cycle testing module, based on the multi-field coupled environmental parameters, establishes an acceleration factor mathematical model and calculates the acceleration coefficient, determines the accelerated test condition parameters and sends them to the life prediction module and the heat dissipation system module, and dynamically optimizes the test conditions according to the performance optimization scheme of the failure analysis module; the heat dissipation system module selects and adjusts the cooling intensity of the natural convection unit or the flow boiling unit according to the accelerated test condition parameters, generates a heat dissipation performance data packet and feeds it back to the life prediction module; the life prediction module receives the accelerated test condition parameters and the heat dissipation performance data packet, constructs a life physical-statistical hybrid prediction model, dynamically predicts the remaining life of the device and outputs it to the failure analysis module; the failure analysis module extracts the surface morphology characteristics of the LED chip, analyzes the thermal-electrical-optical-stress coupling failure mechanism in combination with the remaining life of the device, generates a performance optimization scheme and feeds it back to the full life cycle testing module.

[0008] As a preferred solution of the full life cycle accelerated testing system of the intelligent high-power LED lighting device described in the present invention, the multi-field coupling environmental parameters include ambient temperature distribution, ambient humidity field, light flux, and structural stress distribution.

[0009] As a preferred solution of the full life cycle accelerated testing system of the intelligent high-power LED lighting device described in the present invention, the working process of the full life cycle testing module includes: analyzing and separating the ambient temperature distribution, light flux, and structural stress distribution in the multi-field coupling environmental parameters to generate a thermoelectric-optical-wet coupling matrix; extracting the eigenvalues ​​of the thermoelectric-optical-wet coupling matrix to generate coupling characteristic parameters; based on the coupling characteristic parameters, calculating the stress intensification factor of the environmental factors on the device life, fitting the stress-life curve, and outputting the stress acceleration model parameters; establishing an acceleration factor mathematical model according to the stress acceleration model parameters, solving the optimal acceleration coefficient, combining the ambient humidity field to correct the optimal acceleration coefficient, and generating a corrected acceleration coefficient; setting the accelerated test working condition parameters according to the corrected acceleration coefficient and the stress intensification factor, and sending them to the life prediction module and the heat dissipation system module.

[0010] As a preferred solution of the full life cycle accelerated test system of the smart high-power LED lighting device described in the present invention, the working process of the heat dissipation system module includes: establishing a heat dissipation demand discrimination matrix under different environmental conditions according to the accelerated test working condition parameters; calculating the critical heat dissipation coefficient according to the heat dissipation demand discrimination matrix, and comparing the critical heat dissipation coefficient with the heat dissipation mode switching threshold to generate a heat dissipation mode selection signal; starting the corresponding heat dissipation unit according to the heat dissipation mode selection signal: collecting the radiator surface temperature field data, and establishing a three-dimensional temperature distribution cloud map of the heat dissipation unit; measuring the local heat dissipation coefficient, and calculating the actual heat dissipation power; constructing a heat dissipation performance evaluation index based on the temperature distribution cloud map and the actual heat dissipation power, and generating a radiator working state feature vector; packaging the radiator working state feature vector and the temperature field data to form a heat dissipation performance data packet output to the life prediction module.

[0011] As a preferred solution of the full life cycle accelerated testing system of the intelligent high-power LED lighting device described in the present invention, the working process of the life prediction module includes: receiving accelerated test working condition parameters and radiator performance data packets, and establishing a multi-source data fusion matrix; extracting the radiator working state characteristic vector and temperature field data, calculating the actual working stress level of the device, and generating a stress time series data set; based on the Arrhenius acceleration model and the stress time series data set, establishing a physical correlation model between temperature stress and life loss, and calculating the temperature stress contribution coefficient; using the Eyring model to analyze the influence of current density on device life, combining the humidity level to establish an electric-wet coupling stress model, and calculating the electric-wet stress contribution coefficient; comprehensively weighting the temperature stress contribution coefficient and the electric-wet stress contribution coefficient to obtain the overall stress influence coefficient; using a deep neural network to construct a historical failure data analysis model, extracting device performance degradation characteristics, and forming a statistical prediction benchmark value; constructing a physical-statistical hybrid prediction model based on the overall stress influence coefficient and the statistical prediction benchmark value; calculating the remaining life of the device according to the physical-statistical hybrid prediction model, and establishing a reliability evaluation system for the prediction results to generate reliability evaluation data; outputting the remaining life of the device and the reliability evaluation data to the failure analysis module.

[0012] As a preferred solution of the full life cycle accelerated testing system of the intelligent high-power LED lighting device described in the present invention, the working process of the failure analysis module is as follows: collect microscopic images of the LED chip surface and construct a chip surface morphology feature library; extract key failure features from the chip surface morphology feature library and establish a surface defect distribution map; receive device remaining life and reliability assessment data, and establish a failure mode recognition matrix in combination with the surface defect distribution map; based on the failure mode recognition matrix, analyze the influence of high temperature on the chip bonding area and calculate the thermal stress failure contribution factor; study the mechanism of current density distribution on the quantum well luminous efficiency and calculate the electrical stress failure contribution factor; analyze the stress synergy effect under the action of thermal-electric-optical coupling and generate the stress failure contribution factor; based on the thermal stress failure contribution factor, the electrical stress failure contribution factor and the stress failure contribution factor, establish a multi-field coupling failure mechanism analysis model and generate the failure mechanism analysis results; based on the failure mechanism analysis results, generate a performance optimization plan and feed it back to the full life cycle test module.

[0013] As a preferred solution of the full life cycle accelerated testing system of the smart high-power LED lighting device described in the present invention, the following steps are included in the dynamic optimization of the test conditions according to the performance optimization scheme of the failure analysis module: receiving the performance optimization scheme output by the failure analysis module and extracting the optimization suggestion parameters; performing a feasibility evaluation on the optimization suggestion parameters and generating an adjustment weight coefficient for each parameter; performing a gradient adjustment on the accelerated test condition parameters based on the adjustment weight coefficient to obtain updated accelerated test condition parameters; performing a stability analysis on the updated accelerated test condition parameters, verifying the dynamic characteristics of the parameter combination, and outputting the stability evaluation results.

[0014] As a preferred solution of the full life cycle accelerated testing system of the intelligent high-power LED lighting device described in the present invention, it is judged whether the termination condition is met according to the stability evaluation result; if the termination condition is met, the final accelerated test operating condition parameters are output; if the termination condition is not met, it returns to re-adjust the gradient.

[0015] The beneficial effects of the present invention are as follows: the present invention constructs a full life cycle accelerated testing system driven by multi-field coupling environmental parameters, realizing intelligent closed-loop management of accelerated testing, heat dissipation control and life prediction. The developed dual-mode intelligent heat dissipation system can be switched in real time according to multi-dimensional parameters, avoiding the limitations of traditional single threshold control and significantly improving the heat dissipation efficiency. The innovative use of a physical-statistical hybrid prediction model, combined with electrical-humidity coupling stress analysis, greatly improves the accuracy of life prediction and effectively reduces maintenance costs. Through multi-field coupling failure mechanism analysis and parameter self-correction methods, dynamic optimization of test conditions is achieved, ensuring the reliability of accelerated test results and test efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 Module connection diagram of the full life cycle acceleration test system for smart high-power LED lighting devices.

[0018] Figure 2 This is the workflow diagram of the full life cycle test module of the full life cycle accelerated test system for smart high-power LED lighting devices.

[0019] Figure 3 Workflow diagram of the failure analysis module of the full life cycle accelerated test system for smart high-power LED lighting devices. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0023] Example 1, reference Figure 1 to Figure 3 , which is the first embodiment of the present invention, and provides a full life cycle accelerated testing system for a smart high-power LED lighting device. The module connection diagram is shown in FIG. Figure 1 As shown, including:

[0024] Environmental parameter detection module, collects multi-field coupled environmental parameters and transmits the multi-field coupled environmental parameters to the full life cycle test module;

[0025] The full life cycle test module establishes the acceleration factor mathematical model and calculates the acceleration coefficient based on the multi-field coupling environment parameters, determines the acceleration test condition parameters and sends them to the life prediction module and the heat dissipation system module, and dynamically optimizes the test conditions according to the performance optimization plan of the failure analysis module;

[0026] The heat dissipation system module selects and adjusts the cooling intensity of the natural convection unit or the flow boiling unit according to the accelerated test condition parameters, generates a heat dissipation performance data packet and feeds it back to the life prediction module;

[0027] The life prediction module receives the accelerated test condition parameters and heat dissipation performance data packets, builds a life physical-statistical hybrid prediction model, dynamically predicts the remaining life of the device and outputs it to the failure analysis module.

[0028] The failure analysis module extracts the surface morphology characteristics of the LED chip, analyzes the thermal-electrical-optical-stress coupling failure mechanism based on the remaining life of the device, and generates a performance optimization plan to feed back to the full life cycle test module.

[0029] Among them, the multi-field coupling environmental parameters include ambient temperature distribution, ambient humidity field, light flux, and structural stress distribution.

[0030] Specifically, the workflow diagram of the full life cycle test module is as follows: Figure 2 As shown, the method includes: analyzing and separating the ambient temperature distribution, light flux, and structural stress distribution in the multi-field coupling environmental parameters to generate a thermoelectric-optical-humidity coupling matrix; using a tensor decomposition method to extract the eigenvalues ​​of the thermoelectric-optical-humidity coupling matrix to generate coupling characteristic parameters; based on the coupling characteristic parameters, using the stress superposition criterion to calculate the stress intensification factor of each environmental factor on the device life, fitting the stress-life curve through a nonlinear regression method, and outputting stress acceleration model parameters, including the slope coefficient, intercept coefficient, and stress sensitivity coefficient of the curve; establishing an acceleration factor mathematical model according to the stress acceleration model parameters, using a numerical iteration method to solve the optimal acceleration coefficient, combining the ambient humidity field to correct the optimal acceleration coefficient, and generating a corrected acceleration coefficient; according to the corrected acceleration coefficient and the stress intensification factor, setting the acceleration test condition parameters and sending them to the life prediction module and the heat dissipation system module.

[0031] Among them, the mathematical model of the acceleration factor is constructed as follows: the stress acceleration model parameters are substituted into the Arrhenius equation, the temperature field acceleration factor sub-model is established, and the initial value of the temperature acceleration factor is calculated; the illumination-structure stress acceleration factor sub-model is constructed based on the inverse power law model, and the stress sensitivity coefficient is used to calculate the initial value of the illumination-structure acceleration factor; the multi-field acceleration factor mathematical model is generated through the sub-model coupling operation, and the initial value of the temperature acceleration factor and the initial value of the illumination-structure acceleration factor are input into the model; the multi-field acceleration factor mathematical model is solved by the Newton iteration method, the convergence threshold is set, and the optimal acceleration coefficient is output; the saturation distribution of the ambient humidity field is obtained, the humidity correction function is established, and the optimal acceleration coefficient is dynamically corrected; the corrected acceleration coefficient is verified with the original environmental parameters, and the corrected acceleration coefficient is output after confirming that the acceleration test boundary conditions are met, including temperature cycle amplitude, current density, humidity level, etc.

[0032] Specifically, the workflow of the heat dissipation system module includes: extracting the target temperature value according to the accelerated test condition parameters, and establishing a heat dissipation demand discrimination matrix under different environmental conditions; calculating the critical heat dissipation coefficient according to the heat dissipation demand discrimination matrix, and comparing the critical heat dissipation coefficient with the heat dissipation mode switching threshold to generate a heat dissipation mode selection signal; starting the corresponding heat dissipation unit according to the heat dissipation mode selection signal: when the heat dissipation mode selection signal is less than the heat dissipation mode switching threshold, starting the natural convection unit, and adjusting the fan speed and heat sink structure parameters according to the temperature gradient; when the heat dissipation mode selection signal is greater than the heat dissipation mode switching threshold, starting the flow boiling unit, and adjusting the coolant flow and microchannel geometry based on the heat flux density; collecting the radiator surface temperature field data through distributed temperature sensors, and establishing a three-dimensional temperature distribution cloud map of the heat dissipation unit; using a heat flux density sensor array to measure the local heat dissipation coefficient, and calculating the actual heat dissipation efficiency in combination with the ambient temperature; constructing a heat dissipation performance evaluation index based on the temperature distribution cloud map and the actual heat dissipation power, and generating a radiator working state feature vector; packaging the radiator working state feature vector and the temperature field data to form a heat dissipation performance data packet output to the life prediction module.

[0033] Among them, the calculation process of the critical heat dissipation coefficient is as follows: perform principal component analysis on the heat dissipation demand discrimination matrix to extract key heat dissipation characteristic parameters; use the heat flux density equation to establish a heat transfer model, substitute the key heat dissipation characteristic parameters, and solve the transient temperature field; construct a heat transfer model based on the convection heat transfer theory, and calculate the comprehensive heat transfer coefficient under different working conditions; substitute the comprehensive heat transfer coefficient and the transient temperature field into the thermal network model to solve the overall thermal resistance; calculate the critical heat dissipation power based on the overall thermal resistance and the target temperature value, and convert it into the critical heat dissipation coefficient.

[0034] Preferably, the method for determining the heat dissipation mode switching threshold includes: establishing a power-temperature rise mapping relationship based on the power consumption of the LED chip and the performance characteristics of the heat sink; calculating the natural convection heat dissipation limit through the difference between the surface temperature of the heat sink and the ambient temperature; determining the starting point of flow boiling heat transfer based on the microchannel heat transfer characteristics; substituting the power-temperature rise mapping relationship into the heat balance equation to solve the switching power; and determining the heat dissipation mode switching threshold based on the switching power and the dynamic characteristics of the heat dissipation system.

[0035] Preferably, the heat dissipation system module significantly improves the heat dissipation efficiency and system adaptability through the intelligent dual-mode heat dissipation system design. The module switches the heat dissipation mode based on the comprehensive evaluation of multi-dimensional parameters, which not only avoids the limitations of the traditional single threshold judgment method, but also dynamically adjusts the heat dissipation intensity according to the actual working conditions, thereby achieving precise control of the heat dissipation performance. This design effectively reduces the thermal resistance of LED devices, prolongs the service life of the devices, improves the energy utilization efficiency of the system, reduces the operating cost, and provides reliable heat dissipation guarantee for the stable operation of high-power LEDs.

[0036] Specifically, the workflow of the life prediction module includes: receiving accelerated test condition parameters and radiator performance data packets, establishing a multi-source data fusion matrix; extracting the radiator working state feature vector and temperature field distribution cloud map, calculating the actual working stress level of the device, and generating a stress time series data set.

[0037] Furthermore, based on the Arrhenius acceleration model and the stress time series data set, a physical correlation model of temperature stress and life loss is established, and the temperature stress contribution coefficient is calculated. The specific steps are as follows: the temperature fluctuation amplitude and frequency are extracted according to the stress time series data set, and the temperature cycle stress distribution is established; the activation energy change rate in different temperature ranges is calculated based on the Arrhenius formula, and the temperature acceleration factor is constructed; the temperature cumulative damage function is established using the activation energy change rate to calculate the thermal stress damage of the device; a life decay rate model is constructed based on thermal stress damage to generate a life degradation curve; the temperature-life correspondence relationship is established by combining the life degradation curve with the temperature cycle stress distribution; the temperature stress contribution coefficient is calculated through the temperature-life correspondence relationship.

[0038] Furthermore, the Eyring model is used to analyze the influence of current density on device life, and an electro-humidity coupling stress model is established in combination with the humidity level to calculate the electro-humidity stress contribution coefficient. The specific steps are as follows: collect current distribution data and environmental humidity data to construct an electro-humidity composite action matrix; establish a current action acceleration factor based on the Eyring model to generate an electric stress function; analyze the influence of humidity on luminous efficiency and establish a humidity stress function; construct a synergistic model of current and humidity to calculate the electro-humidity coupling coefficient; evaluate the device performance attenuation rate based on the electro-humidity coupling coefficient to generate a composite stress damage curve; and calculate the electro-humidity stress contribution coefficient based on the composite stress damage curve.

[0039] Furthermore, the temperature stress contribution coefficient and the electric-humidity stress contribution coefficient are comprehensively weighted to obtain the overall stress influence coefficient; a deep neural network is used to construct a historical failure data analysis model, the device performance degradation characteristics are extracted, and a statistical prediction benchmark value is formed; a physical-statistical hybrid prediction model is constructed based on the overall stress influence coefficient and the statistical prediction benchmark value, and the specific steps are as follows: a stress-life mapping model is constructed according to the overall stress influence coefficient, a stress accumulation evaluation matrix is ​​established, and a physical sub-model is formed; historical performance degradation data is extracted, a performance parameter attenuation curve is established, a life prediction probability distribution is generated, and a statistical sub-model is constructed; the physical model confidence is calculated, the statistical model accuracy is evaluated, and a weight allocation matrix is ​​generated; a physical-statistical bidirectional mapping is established, a hybrid prediction function is constructed, and a life calculation model is formed; a design error evaluation standard is designed, a parameter self-correction method is constructed, and a model optimization index is generated; the remaining life value is calculated, and a reliability evaluation result is generated.

[0040] Finally, the remaining life of the device is calculated based on the physical-statistical hybrid prediction model, and a reliability evaluation system for the prediction results is established to generate reliability evaluation data; the remaining life of the device and the reliability evaluation data are packaged and output to the failure analysis module.

[0041] Preferably, the life prediction module adopts a physical-statistical hybrid prediction model, which organically combines the traditional physical model with the deep learning method, greatly improving the accuracy and reliability of life prediction. By introducing electro-humidity coupling stress analysis and a bidirectional mapping mechanism, the module can fully consider the impact of various environmental factors on the life of the device, and achieve dynamic and accurate prediction of the remaining life of the device. This prediction method not only reduces the prediction error, but also can promptly detect potential failure risks, provide a scientific basis for equipment maintenance and replacement, and effectively reduce equipment maintenance costs and economic losses caused by unexpected downtime.

[0042] Specifically, the failure analysis module workflow diagram is as follows: Figure 3 As shown, it includes: collecting microscopic images of the LED chip surface and building a chip surface morphology feature library; using morphological algorithms to extract key failure features from the chip surface morphology feature library and establish a surface defect distribution map; receiving device remaining life and reliability assessment data, and establishing a failure mode recognition matrix in combination with the surface defect distribution map; analyzing the influence of high temperature on the chip bonding area based on the failure mode recognition matrix, and calculating the thermal stress failure contribution factor; studying the mechanism of current density distribution on the quantum well luminous efficiency, and calculating the electrical stress failure contribution factor in combination with the spectral intensity attenuation data; constructing a multidimensional stress field model, analyzing the stress synergy effect under the thermal-electrical-optical coupling, and generating the stress failure contribution factor.

[0043] Furthermore, the thermal stress failure contribution factor, electrical stress failure contribution factor and stress failure contribution factor are comprehensively evaluated, a multi-field coupling failure mechanism analysis model is established, and the failure mechanism analysis results are generated. The specific steps are as follows: a failure factor weight evaluation matrix is ​​established, and the relative weight values ​​of the three types of failure contribution factors are calculated using the hierarchical analysis method; a failure contribution factor feature space is constructed, and each failure contribution factor is standardized and mapped to a unified evaluation system; the correlation analysis method is used to calculate the interaction strength between failure factors, and a coupling effect quantification model is established; based on the coupling effect quantification model and the relative weight value, the overall failure index is calculated, and a failure development trend curve is generated; according to the changing characteristics of the failure development trend curve and the acceleration change rate, the dominant failure mode is determined, and the failure mechanism analysis results are output.

[0044] Preferably, a performance optimization plan is generated based on the failure mechanism analysis results, including temperature control strategies, current optimization plans and heat dissipation improvement suggestions, and fed back to the full life cycle test module. The steps for generating a performance optimization plan are as follows: input the failure mechanism analysis results into the parameter sensitivity model, identify the core parameters that affect the reliability of the device, and generate a parameter influence matrix; construct an optimization objective function based on the parameter influence matrix, set the reliability index constraints and the working parameter range; use a genetic algorithm to solve the optimization objective function and obtain the optimal range of each core parameter; generate a process adjustment plan based on the optimal range, including chip structure design, thermal management layout, and drive current control; convert the process adjustment plan into the test condition optimization value to form a performance optimization plan.

[0045] Preferably, the failure analysis module realizes accurate identification and quantitative analysis of the failure causes of LED devices through an innovative multi-field coupling failure mechanism analysis method. This module uses a morphological algorithm to analyze the surface features of the chip and combines the analytic hierarchy process to quantify the influence weights of various failure factors, which not only improves the accuracy of failure analysis, but also effectively guides product design optimization and process improvement. This analysis method significantly improves product reliability, shortens product development cycles, reduces development costs, and provides reliable technical support and optimization directions for improving the performance of LED lighting devices.

[0046] Furthermore, dynamically optimizing the test conditions according to the performance optimization scheme of the failure analysis module includes the following steps: receiving the performance optimization scheme output by the failure analysis module, extracting the optimization suggestion parameters, including the temperature cycle adjustment coefficient, the current density correction value, the humidity level correction value, etc.; using the fuzzy evaluation method to perform a feasibility assessment on the optimization suggestion parameters, and generating the adjustment weight coefficient of each parameter; performing a gradient adjustment on the accelerated test condition parameters based on the adjustment weight coefficient to obtain the updated accelerated test condition parameters; performing a stability analysis on the updated accelerated test condition parameters, verifying the dynamic characteristics of the parameter combination through the state space model and evaluating the system response characteristics, and outputting the stability evaluation results; judging whether the termination conditions are met according to the stability evaluation results; if the termination conditions are met, outputting the final accelerated test condition parameters, and if the termination conditions are not met, returning to re-adjust the gradient.

[0047] Among them, the termination conditions include: the temperature cycle amplitude, current density, and humidity level are adjusted three times in a row less than the corresponding set threshold, the preset maximum number of adjustments is reached, or the device performance indicators meet the requirements.

[0048] Moreover, gradient adjustment of the accelerated test condition parameters based on the adjustment weight coefficient includes: constructing a parameter objective function, substituting the temperature cycle adjustment coefficient, the current density correction value, and the humidity level correction value into the calculated performance loss value; calculating the first-order partial derivative of each parameter based on the objective function, and establishing a gradient vector; using an adaptive step size algorithm to calculate the search step size, and multiplying the adjustment weight coefficient with the gradient vector to obtain the parameter update amount; setting upper and lower limit constraints on the parameter update amount to generate a constraint update vector; superimposing the constraint update vector on the original parameter to obtain a preliminary updated parameter; using a quadratic programming method to perform a feasible domain check on the preliminary updated parameter to generate the final updated parameter; performing a difference comparison between the final updated parameter and the original parameter to calculate the adjustment amplitude; and outputting the updated accelerated test condition parameters based on the comparison result between the adjustment amplitude and the preset threshold.

[0049] In addition, the dynamic characteristics of the parameter combination are verified and the system response characteristics are evaluated through the state-space model. The steps are as follows: construct a state-space equation group, including a system input matrix based on the accelerated test condition parameters and a system output matrix based on the LED performance indicators; use the Taylor expansion method to linearize the state-space equation group and construct the system transfer function; calculate the system eigenvalues, extract the system dominant poles, and generate stability judgment marks; construct the system phase diagram, calculate the amplitude margin and phase margin, and judge the closed-loop stability; use the energy function to analyze the large-scale stability of the system, and calculate the energy change rate of the system equilibrium point; analyze the transient response characteristics of the system, and calculate the overshoot, adjustment time and steady-state error; establish a multi-dimensional stability evaluation system, and use the hierarchical analysis method to calculate the weight coefficients of each indicator; package the system eigenvalues, stability margins and transient performance indicators, and output the stability evaluation results.

[0050] In summary, the present invention constructs a full life cycle accelerated testing system driven by multi-field coupling environmental parameters, and realizes intelligent closed-loop management of accelerated testing, heat dissipation control and life prediction. The developed dual-mode intelligent heat dissipation system can be switched in real time according to multi-dimensional parameters, avoiding the limitations of traditional single threshold control and significantly improving the heat dissipation efficiency. The innovative use of a physical-statistical hybrid prediction model, combined with electrical-humidity coupling stress analysis, greatly improves the accuracy of life prediction and effectively reduces maintenance costs. Through multi-field coupling failure mechanism analysis and parameter self-correction methods, dynamic optimization of test conditions is achieved, ensuring the reliability of accelerated test results and test efficiency.

[0051] Example 2, reference Figure 1 to Figure 3 , which is the second embodiment of the present invention, provides a full life cycle accelerated testing system for a smart high-power LED lighting device. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0052] In order to verify the feasibility and advancement of the present invention, the research team selected a batch of 200W LED lighting devices as test objects and conducted detailed accelerated life test experiments. A total of 60 test samples were used, using the American Keithley2400 digital source meter as the LED driving power supply, the Japanese Yokogawa WT310 digital power meter to monitor the input power, the German FLIRT660 infrared thermal imager to collect temperature field data, and the American Ocean Optics QE65000 spectrum analyzer to measure the change in luminous flux. The environmental parameter control uses the German WIKA environmental test chamber, which can achieve temperature adjustment from -40°C to 85°C and humidity control from 20%RH to 95%RH.

[0053] In the initial stage of the experiment, the data collected by the environmental parameter detection module showed that the ambient temperature distribution range was 25±0.5℃, the relative humidity was maintained at 60±2%RH, the initial light flux was 20000±100lm, and the structural stress distribution was uniform. Based on these parameters, the full life cycle test module calculated that the temperature acceleration factor was 1.8, the light-structure acceleration factor was 1.4, and the comprehensive acceleration factor was 2.52. Based on this acceleration factor, the accelerated test condition parameters were set: the temperature cycle range was -30℃ to 75℃, the cycle period was 2 hours, and the current density was 65A / cm 2 , the humidity level is 85% RH.

[0054] The heat dissipation system module showed excellent performance during operation. When the heat dissipation demand was lower than the critical heat dissipation coefficient of 2.8, the natural convection unit adjusted the fan speed to fluctuate within the range of 1200 to 2800 rpm, achieving precise control of the radiator surface temperature. When the heat dissipation demand exceeded the critical value, the flow boiling unit automatically started, the coolant flow adjustment range was 0.8 to 2.5 L / min, and the microchannel cross-sectional area was 0.8 mm × 0.8 mm. During the experiment, the maximum surface temperature of the radiator was controlled within the range of 68 ± 2 ° C, and the thermal resistance was maintained at 0.25 ± 0.02 ° C / W.

[0055] In order to clearly demonstrate the advantages of the present invention over the prior art, the following Table 1 provides comparative data of key performance indicators.

[0056] Table 1 Performance comparison between the present invention and the prior art

[0057] Performance Indicators Prior art The present invention Life expectancy prediction accuracy 82.5％ 94.8％ Cooling system response time 45s 12s System stability ±5.2℃ ±1.8℃ Energy efficiency 76％ 89％ Fault warning accuracy 78.3％ 92.5％ Maintenance cost reduction rate - 25.6％

[0058] It can be seen from the data in Table 1 that the present invention has achieved significant improvements in various key indicators, especially in the response time of the heat dissipation system and the system stability.

[0059] In the life prediction phase, based on 4000 hours of accelerated test data, the analysis of the physical-statistical hybrid prediction model shows that the temperature stress contribution coefficient is 0.45, the electrical-humidity stress contribution coefficient is 0.38, and the comprehensive stress influence coefficient reaches 0.83. The relative error between the statistical prediction benchmark value and the actual life after deep neural network training is controlled within 5.2%. The microscopic images of the chip surface collected by the failure analysis module show that the thermal stress failure contribution factor is 0.42, the electrical stress failure contribution factor is 0.35, and the stress failure contribution factor is 0.23. These data provide important basis for subsequent performance optimization.

[0060] After 3000 hours of continuous operation test, the system showed excellent reliability: the luminous flux maintenance rate remained above 92%, the color temperature deviation was controlled within the range of ±100K, and the power factor was stable above 0.95. The test conditions after dynamic optimization showed that the variation range of the temperature cycle adjustment coefficient was reduced to ±0.8℃, and the fluctuation range of the current density correction value was controlled within ±2A / cm 2 The deviation of the humidity level correction value does not exceed ±3%RH, which fully verifies the technical advantages and innovative value of the present invention in practical applications.

[0061] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. The full life cycle accelerated testing system for intelligent high-power LED lighting devices is characterized by: include: An environmental parameter detection module collects multi-field coupling environmental parameters and transmits the multi-field coupling environmental parameters to a full life cycle test module; The full life cycle test module establishes the acceleration factor mathematical model and calculates the acceleration coefficient based on the multi-field coupling environment parameters, determines the acceleration test condition parameters and sends them to the life prediction module and the heat dissipation system module, and dynamically optimizes the test conditions according to the performance optimization plan of the failure analysis module; A heat dissipation system module selects and adjusts the cooling intensity of the natural convection unit or the flow boiling unit according to the accelerated test condition parameters, generates a heat dissipation performance data packet and feeds it back to the life prediction module; A life prediction module receives the accelerated test condition parameters and heat dissipation performance data packets, constructs a life physical-statistical hybrid prediction model, dynamically predicts the remaining life of the device and outputs it to the failure analysis module; The failure analysis module extracts the surface morphology characteristics of the LED chip, analyzes the thermal-electrical-optical-stress coupling failure mechanism in combination with the remaining life of the device, and generates a performance optimization solution to feed back to the full life cycle test module.

2. The full life cycle accelerated testing system for a smart high-power LED lighting device as claimed in claim 1, characterized in that: The multi-field coupling environmental parameters include environmental temperature distribution, environmental humidity field, light flux, and structural stress distribution.

3. The full life cycle accelerated testing system for a smart high-power LED lighting device as claimed in claim 1, characterized in that: The workflow of the full life cycle test module includes: Analyze and separate the ambient temperature distribution, light flux, and structural stress distribution in the multi-field coupling environmental parameters to generate the thermoelectric-optical-humidity coupling matrix; Extracting the eigenvalues ​​of the thermoelectric-optical wet coupling matrix to generate coupling characteristic parameters; Based on the coupling characteristic parameters, the stress intensification factor of the environmental factors on the device life is calculated, the stress-life curve is fitted, and the stress acceleration model parameters are output; Establishing an acceleration factor mathematical model according to the stress acceleration model parameters, solving the optimal acceleration coefficient, correcting the optimal acceleration coefficient in combination with the ambient humidity field, and generating a corrected acceleration coefficient; According to the modified acceleration coefficient and the stress intensification factor, the accelerated test condition parameters are set and sent to the life prediction module and the heat dissipation system module.

4. The full life cycle accelerated testing system for a smart high-power LED lighting device as claimed in claim 1, characterized in that: The workflow of the heat dissipation system module includes: According to the accelerated test condition parameters, a heat dissipation demand discrimination matrix under different environmental conditions is established; Calculating a critical heat dissipation coefficient according to the heat dissipation demand discrimination matrix, and comparing the critical heat dissipation coefficient with a heat dissipation mode switching threshold to generate a heat dissipation mode selection signal; Start the corresponding cooling unit according to the cooling mode selection signal: Collect the surface temperature field data of the radiator and establish a three-dimensional temperature distribution cloud map of the heat dissipation unit; Measure the local heat dissipation coefficient and calculate the actual heat dissipation power; Constructing a heat dissipation performance evaluation index based on the temperature distribution cloud map and the actual heat dissipation power, and generating a heat sink working state feature vector; The radiator working state feature vector and the temperature field data are packaged to form a heat dissipation performance data packet which is output to the life prediction module.

5. The full life cycle accelerated testing system for a smart high-power LED lighting device as claimed in claim 1, characterized in that: The workflow of the life prediction module includes: Receive acceleration test condition parameters and radiator performance data packets, and establish a multi-source data fusion matrix; Extract the radiator working state feature vector and temperature field data, calculate the actual working stress level of the device, and generate a stress time series data set; Based on the Arrhenius acceleration model and the stress time series data set, a physical correlation model between temperature stress and life loss is established, and the temperature stress contribution coefficient is calculated; The Eyring model is used to analyze the influence of current density on device life, and the electro-wet coupling stress model is established in combination with the humidity level to calculate the electro-wet stress contribution coefficient. The temperature stress contribution coefficient and the electro-humidity stress contribution coefficient are comprehensively weighted to obtain the overall stress influence coefficient; Use deep neural networks to build historical failure data analysis models, extract device performance degradation characteristics, and form statistical prediction benchmark values; Constructing a physical-statistical hybrid prediction model based on the overall stress influence coefficient and the statistical prediction benchmark value; Calculate the remaining life of the device based on the physical-statistical hybrid prediction model, establish a reliability evaluation system for the prediction results, and generate reliability evaluation data; The remaining life of the device and the reliability assessment data are output to a failure analysis module.

6. The full life cycle accelerated testing system for a smart high-power LED lighting device according to claim 1, characterized in that: The workflow of the failure analysis module is as follows: Collect microscopic images of the LED chip surface and build a chip surface morphology feature library; Extract key failure features from the chip surface morphology feature library and establish a surface defect distribution map; Receiving the device remaining life and reliability assessment data, and establishing a failure mode identification matrix in combination with the surface defect distribution map; Based on the failure mode identification matrix, analyzing the influence of high temperature on the chip bonding area, and calculating the thermal stress failure contribution factor; Study the mechanism of the effect of current density distribution on quantum well luminescence efficiency and calculate the contribution factor of electrical stress failure; Analyze the stress synergy effect under thermal-electrical-optical coupling and generate stress failure contribution factors; Based on the thermal stress failure contribution factor, the electrical stress failure contribution factor and the stress failure contribution factor, a multi-field coupling failure mechanism analysis model is established to generate a failure mechanism analysis result; Based on the failure mechanism analysis results, a performance optimization plan is generated and fed back to the full life cycle test module.

7. The full life cycle accelerated testing system for a smart high-power LED lighting device according to claim 1, characterized in that: The dynamic optimization of test conditions according to the performance optimization scheme of the failure analysis module comprises the following steps: Receive the performance optimization solution output by the failure analysis module and extract the optimization suggestion parameters; Conducting feasibility assessment on the optimization suggestion parameters and generating adjustment weight coefficients for each parameter; Gradient-adjust the acceleration test condition parameters based on the adjustment weight coefficient to obtain updated acceleration test condition parameters; Perform stability analysis on the updated acceleration test condition parameters, verify the dynamic characteristics of the parameter combination, and output stability evaluation results.

8. The full life cycle accelerated testing system for a smart high-power LED lighting device as claimed in claim 7, characterized in that: Determining whether a termination condition is met according to the stability evaluation result; If the termination condition is met, the final acceleration test condition parameters are output; if the termination condition is not met, the system returns to re-adjust the gradient.

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