Vehicle-mounted cosmetic mirror light-emitting module detection system and method

Abstract

The application provides a vehicle-mounted cosmetic mirror light-emitting module detection system and method, and relates to the field of quality inspection. The system comprises a lower computer system and an upper computer system. The lower computer system collects color temperature data and illumination data of mirror measurement points through a multi-point array optical parameter acquisition module; a high-precision electrical parameter comprehensive measurement module is used to measure current, voltage, power and transient response characteristic data; an environmental parameter monitoring module acquires temperature, humidity, air pressure and environmental light data through a sensor. A data processing module of the upper computer system filters and environmentally compensates and corrects original parameters to generate correction data under a standard environment; a data analysis module establishes a mapping relationship model of electrical-optical-environmental parameters through a grey correlation degree algorithm, compares preset threshold values, and generates a comprehensive quality evaluation report containing defect codes and process capability indexes. The scheme improves the detection capability of the cosmetic mirror light-emitting module through multi-parameter dynamic correlation analysis.

Description

Technical Field

[0001] This application relates to the field of quality inspection, and particularly to a detection system and method for an in-vehicle makeup mirror lighting module. Background Art

[0002] In the field of quality inspection of in-vehicle makeup mirror lighting modules, traditional inspection methods generally adopt single-dimensional parameter analysis technology. By using discrete inspection equipment to separately collect basic parameters such as electrical characteristics and optical performance, and simply comparing them with preset thresholds to determine the product qualification. Existing technologies mostly rely on static inspections under fixed environmental conditions. For example, using an independent photometer to measure the central point illuminance and a multimeter to obtain the steady-state current value. Although it can complete the verification of the basic parameter compliance, there are core defects: the detection system separates the dynamic coupling relationship between electrical drive characteristics, optical output quality, and environmental disturbance factors. In practical applications, the transient current fluctuation of the module will cause color temperature drift, and the change of environmental temperature and humidity will lead to non-linear attenuation of the LED luminous efficiency. Such composite defects caused by the interaction of multiple parameters cannot be effectively identified through isolated parameter detection. More seriously, traditional methods lack quantitative analysis of the parameter correlation mechanism. When a hidden fault occurs where the light efficiency decreases but the electrical parameters are normal, it is often misjudged as qualified because the correlation between voltage fluctuation and color rendering degree cannot be traced. This results in frequent false detections and missed detections in the actual industrial application of existing detection technologies. Especially for the comprehensive performance indicators such as uniformity and stability required by high-end makeup mirror products, there is a lack of scientific evaluation basis, seriously restricting the improvement of product quality control level. Summary of the Invention

[0003] This application provides a detection system and method for an in-vehicle makeup mirror lighting module to improve the detection ability of the makeup mirror lighting module.

[0004] In a first aspect, this application provides a detection system for an in-vehicle makeup mirror lighting module, the system includes:

[0005] A lower computer system, communicatively connected to the upper computer system, for collecting the electrical parameters, optical parameters, and environmental parameters of the in-vehicle makeup mirror lighting module; an upper computer system, for performing detections based on the electrical parameters, the optical parameters, and the environmental parameters to obtain detection results;

[0006] The lower computer system includes: a multi-point array optical parameter acquisition module, a high-precision electrical parameter comprehensive measurement module, and an environmental parameter monitoring module;

[0007] The multi-point array optical parameter acquisition module is used for simultaneously collecting the optical parameters of the in-vehicle makeup mirror lighting module, and the optical parameters include: multi-point color temperature data and multi-point illuminance data;

[0008] The high-precision electrical parameter comprehensive measurement module is used to measure the electrical parameters of the cosmetic mirror light-emitting module, including: current data, voltage data, power data, and transient response characteristic data.

[0009] The environmental parameter monitoring module is used to measure the environmental parameters of the makeup mirror, including temperature data, humidity data, air pressure data, and ambient light data.

[0010] The host computer system includes: a data processing module, a data analysis module, and a data display module;

[0011] The data processing module is used to perform correction processing on the electrical parameters, the optical parameters, and the environmental parameters to obtain correction data;

[0012] The data analysis module is used to perform correlation analysis on the correction data, establish a mapping relationship model between parameters, and compare the mapping relationship model with a preset standard threshold to generate a comprehensive quality assessment report.

[0013] The data display module is used to receive the comprehensive quality assessment report output by the data analysis module and display the comprehensive quality assessment report on the interface in the form of a combination of charts.

[0014] In the above technical solution, this embodiment uses a 3×3 matrix optical sensor array in the multi-point array optical parameter acquisition module of the lower-level computer system to simultaneously acquire color temperature and illuminance data at nine measurement points on the surface of the cosmetic mirror's light-emitting module. Combined with the high-precision electrical parameter comprehensive measurement module's capture of μs-level transient response characteristic data, a holographic data acquisition network for optical performance and electrical characteristics is constructed in both spatial and temporal dimensions. This, along with real-time acquisition of temperature, humidity, air pressure, and ambient light data by the environmental parameter monitoring module, forms a multi-dimensional parameter synchronous acquisition system. The data processing module of the upper-level computer system uses an environmental compensation calculation unit to perform nonlinear correction on the original optical parameters based on a temperature-humidity-air pressure coupling compensation model, eliminating measurement deviations caused by environmental disturbances and generating correction data under standard environmental conditions. The statistical analysis unit in the data analysis module uses the correction data to calculate the process capability index Cpk / Ppk, quantifying the stability of the production process; the correlation analysis unit uses a grey relational algorithm to establish a mapping relationship model between electrical parameter fluctuations and optical performance degradation, analyzing the influence coefficient of transient current mutations on color temperature drift. When the judgment unit identifies an abnormal correlation pattern in the parameter correlation matrix, the scoring unit triggers a weighted scoring mechanism based on the defect code, generating a performance radar chart reflecting key indicators such as light efficiency and color reproduction. The data display module visually displays the spatially distributed abnormal areas by overlaying a three-dimensional mirror model with a two-color temperature heatmap, and reveals the dynamic correlation path between electrical and optical parameters using a bubble chart of the correlation coefficient matrix. This technical solution achieves accurate evaluation of the comprehensive performance of the cosmetic mirror's luminous module through closed-loop processing of multi-dimensional parameter synchronous acquisition, dynamic environmental compensation, correlation model construction, and visualization. It effectively identifies transient response anomalies and environmentally sensitive defects that are difficult to detect using traditional detection methods, significantly improving the reliability and engineering guidance value of the detection results.

[0015] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0016] By acquiring vibration, acoustic, exhaust composition, and temperature distribution data during engine operation using a multi-source data acquisition model, comprehensive monitoring of engine status is achieved, effectively avoiding the problem of single data sources being easily affected by environmental interference. A heterogeneous data deep fusion module transforms the multi-source data to obtain a unified multi-source feature representation, not only solving the problem of temporal inconsistency in heterogeneous data but also extracting deep correlation features between data. Combined with a multi-level diagnostic module, multi-level diagnosis is performed on the unified multi-source feature representation. Through a progressive approach of feature extraction, temporal analysis, knowledge reasoning, and diagnostic decision-making, the accuracy and interpretability of fault diagnosis are improved. Finally, a predictive maintenance module performs maintenance analysis based on the multi-level diagnostic results. By comprehensively evaluating predicted lifespan values, fault evolution paths, and optimal maintenance times, a shift from passive maintenance to proactive predictive maintenance is achieved, reducing maintenance costs and improving maintenance efficiency. This complete technical solution, from data acquisition and feature fusion to diagnostic prediction, effectively solves the technical problem of low accuracy in traditional single-data-source diagnostic methods. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of a cosmetic mirror light-emitting module detection system provided in an embodiment of this application;

[0018] Figure 2 This is a schematic diagram of the structure of a data analysis module provided in an embodiment of this application;

[0019] Figure 3 This is a flowchart illustrating a method for detecting a light-emitting module in a cosmetic mirror, as provided in an embodiment of this application. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0021] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0022] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0023] Please see Figure 1 , Figure 1 This is a schematic diagram of a vehicle-mounted cosmetic mirror light-emitting module detection system provided in this application embodiment. This system can be implemented using a computer program or run as an independent tool application. Specifically, in this application embodiment, the method can be applied to a server, but it can also be applied to electronic devices such as servers. The vehicle-mounted cosmetic mirror light-emitting module detection system includes the following modules: a lower-level system, communicatively connected to the upper-level system, used to collect electrical, optical, and environmental parameters of the cosmetic mirror light-emitting module; and an upper-level system, used to perform detection based on the electrical, optical, and environmental parameters to obtain detection results.

[0024] The lower-level computer system includes: a multi-point array optical parameter acquisition module, a high-precision electrical parameter comprehensive measurement module, and an environmental parameter monitoring module;

[0025] The multi-point array optical parameter acquisition module is used to simultaneously acquire the optical parameters of the cosmetic mirror light-emitting module, the optical parameters including: multi-point color temperature data and multi-point illuminance data;

[0026] The high-precision electrical parameter comprehensive measurement module is used to measure the electrical parameters of the cosmetic mirror light-emitting module, including: current data, voltage data, power data, and transient response characteristic data.

[0027] The environmental parameter monitoring module is used to measure the environmental parameters of the makeup mirror, including temperature data, humidity data, air pressure data, and ambient light data.

[0028] The host computer system includes: a data processing module, a data analysis module, and a data display module;

[0029] The data processing module is used to perform correction processing on the electrical parameters, the optical parameters, and the environmental parameters to obtain correction data;

[0030] The data analysis module is used to perform correlation analysis on the correction data, establish a mapping relationship model between parameters, and compare the mapping relationship model with a preset standard threshold to generate a comprehensive quality assessment report.

[0031] The data display module is used to receive the comprehensive quality assessment report output by the data analysis module and display the comprehensive quality assessment report on the interface in the form of a combination of charts.

[0032] In this embodiment, the cosmetic mirror luminous module detection system implements detection through the collaborative operation of the lower-level computer system and the upper-level computer system. The multi-point array optical parameter acquisition module of the lower-level computer system adopts a BH1749NUC sensor array arranged in a 3×3 matrix to simultaneously acquire color temperature data and illuminance data at nine measurement points on the surface of the cosmetic mirror luminous module. After filtering out glitches caused by ambient light interference through delayed continuous sampling, the maximum and minimum values ​​are removed using a hash sorting algorithm, and the arithmetic mean of the remaining data is calculated to ensure the reliability of the optical parameters. The high-precision electrical parameter comprehensive measurement module is based on the 16-bit Δ-Σ ADC of the INA237 chip. It captures transient response characteristic data (i.e., the instantaneous fluctuation waveform of current and voltage during power-on / off and gear switching) at a sampling rate of 128 times / second at the moment the cosmetic mirror luminous module is turned on. At the same time, it measures the current data, voltage data, and power data under steady-state conditions. Its built-in temperature sensor controls the bare die temperature measurement error within ±1℃. The environmental parameter monitoring module collects temperature and humidity data through the SHT30 sensor and combines it with air pressure data obtained by the BMP280 sensor to form an environmental parameter set for subsequent compensation calculations.

[0033] It should be noted that the aforementioned delayed continuous sampling refers to a key technology for anti-interference data acquisition through a phased control strategy when a sudden change in ambient light intensity is detected. In specific implementation, the system monitors the rate of change of ambient light in real time. When the rate of change in light intensity exceeds a threshold of 200 lux per second (e.g., a sudden increase from 300 lux to 800 lux within 0.5 seconds), the delayed continuous sampling process is immediately triggered. This process includes three core stages:

[0034] Phase 1: Delayed Waiting

[0035] The system pauses the current sampling task and initiates a 100ms delay. This delay period has been experimentally verified. After a sudden change in ambient light, the first 50ms is the interference signal oscillation period (residual interference >30%), 50-100ms is the signal attenuation and stabilization period (residual interference <5%), and delays exceeding 150ms result in a decrease in detection efficiency of more than 15%. Choosing 100ms as the standard waiting time achieves the optimal balance between interference suppression and detection efficiency.

[0036] Phase Two: Continuous Sampling

[0037] After the delay, the optical sensor performs 50 high-frequency samples at 10ms intervals (total sampling window 500ms). This frequency setting is based on the sensor's response characteristics: the BH1749NUC has a response time of 5ms, and setting the interval to 10ms ensures that the sensor output fluctuation is less than ±1% for each sample (e.g., from 799 lux to 801 lux). The 50 sets of raw data collected in this stage are sufficient to fully capture the steady-state light intensity after interference attenuation.

[0038] Phase 3: Data Processing

[0039] The acquired 50 sets of data underwent two-stage processing: First, a moving average filter was used, calculating the moving average for every 5 consecutive data points (e.g., the data sequence [798, 802, 805, 812, 824] generated a mean of 809 lux); then, a hash sorting method was used to remove the top 10% of maximum values ​​(e.g., 824 lux) and the bottom 10% of minimum values ​​(e.g., 779 lux), and the arithmetic mean of the remaining 40 sets of data was taken as the final measurement. This processing reduced the deviation caused by sudden light interference from ±15% to ±0.5%.

[0040] After receiving the raw parameters transmitted from the lower-level machine, the data processing module of the host computer system performs a Butterworth filter to remove 50Hz power frequency noise from the electrical parameters. The environmental compensation calculation unit, based on the deviation between the measured temperature data and the standard ambient temperature (25℃), performs nonlinear correction on the illuminance data in the optical parameters according to a preset temperature compensation coefficient formula (e.g., ΔL=α×(T-25)), generating corrected data to eliminate environmental interference. The statistical analysis unit of the data analysis module calculates the standard deviation of the corrected data and the process capability index Cpk / Ppk to assess the stability of the production process. The correlation analysis unit uses a grey relational algorithm to establish a mapping model between electrical parameter fluctuations and optical performance changes. For example, it analyzes the correlation weight between the current surge amplitude and color temperature drift in the transient response characteristic data, generating a parameter correlation matrix reflecting the degree of influence between parameters. When the judgment unit detects an abnormal correlation pattern in the parameter correlation matrix where the voltage fluctuation coefficient exceeds 0.15, it triggers the D02 defect code and generates a non-conforming judgment result. The scoring unit assigns weighted scores to key performance indicators such as luminous efficiency and color reproduction based on the weighting rules corresponding to the defect codes, forming a performance evaluation result. The data display module overlays a performance radar chart with a spatial distribution heatmap. The heatmap uses a red-yellow gradient to identify abnormal areas where illuminance data is below 85% of the threshold. Simultaneously, a trend chart displays the rise time and overshoot amplitude of the current waveform in the transient response characteristic data, allowing operators to intuitively identify design defects in the drive circuit. All test data is uploaded to a MySQL database in real time via a star topology network, and a CSV file containing timestamps, product models, and defect codes is generated locally, enabling full lifecycle traceability of quality issues.

[0041] Based on the above embodiments, as an optional embodiment, the data processing module further includes: a signal preprocessing unit, an environmental compensation calculation unit, and a data fusion unit;

[0042] The signal preprocessing unit is used to filter the electrical parameters, the optical parameters, and the environmental parameters to obtain preprocessed electrical parameters, preprocessed optical parameters, and preprocessed environmental parameters.

[0043] In this embodiment, the data processing module works collaboratively with a signal preprocessing unit, an environmental compensation calculation unit, and a data fusion unit to achieve accurate calibration of the detection parameters. The signal preprocessing unit first performs Butterworth low-pass filtering on the raw electrical parameters (including current data, voltage data, and transient response characteristic data) collected by the lower-level system to filter out signal glitches caused by high-frequency electromagnetic interference. For example, it attenuates noise amplitudes above 50Hz at the power frequency to within 5% of the original value, generating preprocessed electrical parameters. Simultaneously, a moving average filtering algorithm is used on the multi-point illuminance data in the optical parameters to eliminate measurement jumps caused by instantaneous changes in ambient light. For example, the raw illuminance data collected by nine BH1749NUC sensors is sampled five times consecutively, and the middle three valid values ​​are taken to generate preprocessed optical parameters with a fluctuation range ≤ ±3%. For the temperature and humidity data acquired by the environmental parameter monitoring module, the signal preprocessing unit uses median filtering to eliminate abnormal values ​​from occasional false alarms from the sensors. For example, when the deviation of three consecutive temperature sampling values ​​exceeds ±0.5℃, abnormal points are automatically removed, generating stable preprocessed environmental parameters.

[0044] The environmental compensation calculation unit establishes a dynamic compensation model based on preprocessed environmental parameters, specifically quantifying and correcting the impact of temperature and air pressure data on optical parameters. For example, when the temperature data is 25°C higher than the standard ambient temperature, the illuminance data in the preprocessed optical parameters is linearly compensated according to a preset temperature attenuation coefficient formula (ΔL=0.12%×(T-25)), eliminating the attenuation effect of LED luminous efficiency with increasing temperature. Simultaneously, based on the deviation between the air pressure data and the standard atmospheric pressure (1013 hPa), the color temperature data is adjusted using the air pressure refractive index correction formula (ΔCCT=0.05K / hPa×P_diff) to compensate for the optical path refraction deviation caused by changes in air density. The data fusion unit performs spatiotemporal alignment processing on the environmentally compensated optical parameters and the preprocessed electrical parameters. For example, it synchronizes transient response characteristic data with corresponding color temperature drift data using timestamps to generate correction data with a unified spatiotemporal reference. This data processing workflow reduces the illuminance measurement error caused by ambient temperature fluctuations from ±8% to ±0.5%, and the color temperature deviation caused by air pressure changes from ±15K to ±1K, significantly improving the accuracy of the detection data.

[0045] The environmental compensation calculation unit is used to calculate the environmental compensation coefficient based on the preprocessed environmental parameters.

[0046] Specifically, in this embodiment, during the implementation of the environmental compensation calculation unit of the vehicle-mounted cosmetic mirror light-emitting module detection system, addressing the core problem of distorted light effect evaluation caused by the lack of quantification of the influence of environmental parameters on optical measurement results in traditional detection methods, this embodiment achieves accurate calculation and dynamic correction of the environmental compensation coefficient through the following steps:

[0047] Step 1: Calculation of Temperature Compensation Coefficient

[0048] Based on the temperature data in the preprocessed environmental parameters, the environmental compensation calculation unit calls the LED luminous efficacy-temperature characteristic curve stored in the system (this curve was obtained through laboratory calibration and reflects the nonlinear decay law of LED luminous efficacy with increasing temperature). First, it calculates the deviation ΔT between the measured temperature and the standard ambient temperature of 25℃. Depending on the positive or negative direction of ΔT, the temperature compensation coefficient formula ΔL=α×ΔT+β×ΔT² is applied for bidirectional compensation, where α=0.12% / ℃ is the linear decay coefficient, and β=0.003% / ℃² is the accelerated decay compensation coefficient in the high-temperature region. For example, when the measured temperature T_measured = 40℃, ΔT = 15℃, then ΔL = 0.12×15+0.003×225 = 1.8+0.675 = 2.475%. At this point, the original illuminance data L_original = 1200 lux becomes L_corrected = 1200×(1+2.475%) = 1229.7 lux after compensation, effectively offsetting the decrease in LED luminous efficacy caused by the high-temperature environment. This step solves the problem of misjudging product performance under high-temperature conditions due to the failure to consider the attenuation of light efficiency in traditional methods.

[0049] Step 2: Pressure Refractive Index Correction

[0050] For the air pressure data acquired by the environmental parameter monitoring module, the environmental compensation calculation unit uses the air pressure refractive index correction formula ΔCCT=0.05K / hPa×P_diff, where P_diff is the difference between the measured air pressure P_measured and the standard atmospheric pressure 1013hPa. When the detection environment is in a low-altitude area (e.g., P_measured = 1000hPa), P_diff = -13hPa, then ΔCCT=0.05×(-13)=-0.65K. At this time, the original color temperature data CCT_original = 4000K is corrected to CCT_corrected = 4000 - 0.65 = 3999.35K, compensating for the optical path refraction deviation caused by changes in air density. This correction process is specifically for the edge measurement points in the nine-point color temperature data (areas significantly affected by the optical path length), reducing the ±15K color temperature measurement error caused by ignoring the influence of air pressure in the traditional method to within ±1K.

[0051] Step 3: Humidity compensation activation determination

[0052] When the measured humidity data RH_ in the preprocessed environmental parameters exceeds the 75% threshold (e.g., RH_measured = 85% in a rainy season testing environment), the environmental compensation calculation unit automatically activates the humidity compensation term ΔL_humid = γ × (RH_measured - RH_reference), where γ = 0.005% / %RH is the luminous flux loss coefficient caused by condensation on the lens surface, and RH_reference = 50% is the calibration reference value. At this time, an additional calculation ΔL_total = ΔL + ΔL_humid is performed. For example, when ΔT = 15℃ and RH_measured = 85%, ΔL_humid = 0.005% × (85 - 50) = 0.175%, and the total compensation ΔL_total = 2.475% + 0.175% = 2.65%, making L_correction = 1200 × 1.0265 = 1231.8 lux. This mechanism compensates for the systematic bias caused by the traditional testing system's failure to consider lens fogging effects in high humidity environments.

[0053] Step 4: Sensor Error Closed-Loop Correction

[0054] To address the nonlinear error of the SHT30 temperature sensor in the environmental parameter monitoring module, the environmental compensation calculation unit performs closed-loop correction of the sensor error: T_true = T_original + k × (T_original - 25), where k = 0.02 is the factory calibration coefficient. For example, when the sensor's original temperature reading T_original = 30℃, after correction, T_true = 30 + 0.02 × (30 - 25) = 30.1℃, reducing the sensor's own error from ±0.5℃ to ±0.1℃. Simultaneously, compensation is applied to the installation height error of the BMP280 barometric pressure sensor, corrected according to the formula P_correction = P_original + h × 12Pa / m (h = 1.5m is the sensor installation height), increasing the barometric pressure measurement value by 18Pa and eliminating the barometric pressure measurement deviation caused by the equipment's mechanical structure.

[0055] The data fusion unit is used to apply the environmental compensation coefficient to the preprocessed optical parameters and preprocessed electrical parameters to obtain correction data under standard environmental conditions.

[0056] In this embodiment, during the implementation of the data fusion unit of the vehicle-mounted cosmetic mirror light-emitting module detection system, to address the problem of inaccurate correction data caused by environmental interference and spatiotemporal misalignment of parameters in traditional detection methods, this embodiment achieves systematic fusion of environmental compensation and multi-source data through the following logical flow:

[0057] Step 1: Spatiotemporal alignment processing

[0058] The data fusion unit first performs a timestamp synchronization operation on the preprocessed optical parameters and preprocessed electrical parameters. Based on the timestamp information embedded during data acquisition by the lower-level machine, the system establishes a strict time correspondence between the illuminance data collected by the optical sensor array at a specific moment and the transient current and voltage values ​​recorded by the electrical parameter acquisition module at the same moment. For example, when detecting the dynamic response of the light-emitting module at the moment of startup, the system ensures that the timing of the optical illuminance change curve and the rising edge of the current waveform are perfectly matched, eliminating the response delay misjudgment caused by sampling time deviation in traditional methods. This process is achieved through timestamp synchronization technology, enabling the accurate capture of the dynamic coupling relationship between optical and electrical parameters.

[0059] Step Two: Application of Environmental Compensation Coefficient

[0060] The data fusion unit utilizes the comprehensive temperature and humidity compensation coefficients and air pressure refractive index compensation coefficients generated by the environmental compensation calculation unit to standardize and correct the spatiotemporally aligned optical parameters. For multi-point illuminance data collected by the optical sensor array, the system proportionally increases the original illuminance values ​​based on the luminous efficiency decay characteristics caused by temperature changes, offsetting the decrease in luminous efficiency due to high-temperature environments. Simultaneously, it adjusts the color temperature data by considering the impact of air pressure changes on light path refraction. For example, in low-pressure environments, the system automatically corrects the color temperature measurement values ​​to eliminate deviations introduced by changes in air density. This process quantifies the mapping relationship between environmental parameters and optical performance, ensuring that the data reflects the true performance under standard environmental conditions.

[0061] Step 3: Dynamic Compensation of Electrical Parameters

[0062] The data fusion unit dynamically corrects the power values ​​in the electrical parameters based on ambient temperature data. The system performs reverse compensation on the original power measurement value according to the temperature drift characteristic model of the drive circuit components, eliminating circuit loss errors caused by temperature increases. For example, when the ambient temperature is significantly higher than standard conditions, the system reduces the calculated power value according to a preset temperature attenuation coefficient, thereby accurately reflecting the actual input power of the drive circuit. This step solves the problem of energy efficiency calculation distortion caused by neglecting the influence of temperature on electrical parameters in traditional methods.

[0063] Step 4: Normalize multi-source data output

[0064] The data fusion unit standardizes and transforms the compensated optical parameters and corrected electrical parameters according to a unified benchmark. For illuminance distribution data in the optical parameters, the system converts it into a percentage relative to the theoretical maximum value; for transient response characteristics in the electrical parameters, it converts them into a fluctuation ratio relative to the rated value. For example, current overshoot amplitude is expressed as a percentage of the rated current, facilitating direct stability assessment by the subsequent scoring unit. This process eliminates scale differences between parameters of different dimensions through normalization, providing a consistent data foundation for constructing the parameter correlation matrix.

[0065] Based on the above embodiments, as an optional embodiment, the multi-point array optical parameter acquisition module includes: a sensor array unit, an optical alignment unit, and an optical signal processing unit;

[0066] The sensor array unit includes multiple BH1749NUC chips arranged in a 3×3 matrix, used to simultaneously acquire initial color temperature data and initial illuminance data at 9 measurement points;

[0067] In this embodiment, during the implementation of the multi-point array optical parameter acquisition module of the vehicle-mounted cosmetic mirror light-emitting module detection system, the sensor array unit synchronously acquires the initial color temperature data and initial illuminance data of nine BH1749NUC chips rigidly packaged in a 3×3 matrix to nine equally divided areas on the module surface. The chip spacing is proportionally distributed according to the size of the light-emitting area to ensure full coverage. The optical alignment unit dynamically calibrates the spatial position of the sensor array using a high-precision six-axis robotic arm equipped with a laser positioner. First, the laser crosshair is aligned with the geometric center of the module, and then the robotic arm is driven to adjust the height and translation amount to ensure that the field of view of each BH1749NUC chip completely covers the preset nine-point grid, eliminating installation deviations to within ±0.1mm. The optical signal processing unit performs anti-interference processing on the initial data. When the ambient light monitoring module detects a sudden change in light intensity, a delayed continuous sampling process is triggered. Acquisition is paused and multiple sets of data are obtained by high-frequency sampling after the interference decays. Outliers are removed by moving average filtering and hash sorting, and the arithmetic mean of the intermediate valid data is calculated, compressing the nine-point illuminance fluctuation range from ±15% to ±0.5%. During the synchronous sampling period, nine data streams are transmitted in parallel to the data processing module via the I²C bus. The module calculates the illuminance range and color temperature spatial distribution characteristics in real time, identifying abnormal areas such as edge points with color temperature deviations exceeding 50K. The positioning accuracy reaches ±3mm, solving the problem of missed edge defects in traditional single-point sampling.

[0068] Based on the above embodiments, as an optional embodiment, the high-precision electrical parameter comprehensive measurement module includes: a current and voltage measurement unit, a multi-level test unit, and a transient characteristic measurement unit;

[0069] The current and voltage measurement unit, based on the INA237 chip, is used to measure the initial current and initial voltage data of the cosmetic mirror's light-emitting module.

[0070] The multi-level testing unit is used to receive control commands, automatically switch the brightness levels of the makeup mirror's light-emitting module according to the control commands, and trigger the current and voltage measurement unit to measure the initial current data and the initial voltage data at different brightness levels.

[0071] The transient characteristic measurement unit is used to sample the initial current data and the initial voltage data at high speed when the makeup mirror light-emitting module is turned on and the gear is switched, and generate transient response characteristic data.

[0072] In this embodiment, during the implementation of the high-precision electrical parameter comprehensive measurement module of the vehicle-mounted cosmetic mirror light-emitting module detection system, the current and voltage measurement unit is built based on the INA237 chip. This chip integrates a high-precision current detection amplifier and a 16-bit Δ-Σ analog-to-digital converter. It captures initial current data in real time through a milliohm-level shunt resistor connected in the power supply circuit of the light-emitting module, and simultaneously measures initial voltage data directly through a voltage divider circuit. During measurement, the system is configured with the INA237 chip at a sampling rate of 128 times / second and an automatic current measurement range switching mode. When the detected current value exceeds a preset threshold, the hardware protection circuit immediately triggers the relay switching action of the multi-range test unit, switching the range from a low-resistance state to a high-resistance state to prevent sensor overload damage. For example, at the instant the cosmetic mirror dimming mode switches, when the current value suddenly increases from a steady-state 500mA to 1.2A, the multi-range test unit completes the range switching within 2ms, ensuring complete recording of the transient process.

[0073] The transient response measurement unit captures transient response data from the current and voltage measurement unit in real time via a high-speed cache module. When a starting command for the light-emitting module or a range switching command is detected, the sampling rate is automatically increased to 10kHz, simultaneously recording the rising edge of the current waveform and the voltage drop process, and eliminating high-frequency switching noise through a digital filter. For example, at the instant the module is turned on, the unit captures a current overshoot of 8% of the rated value, accompanied by a momentary voltage drop of 0.3V, fully restoring the transient response characteristics of the drive circuit. The multi-range test unit dynamically adjusts the measurement sensitivity according to a preset current threshold. When the current value is below 100mA, it switches to a microampere-level high-precision range, using a four-wire Kelvin connection method to eliminate the influence of contact resistance, reducing the measurement error under low current conditions from ±5% in traditional methods to ±0.5%.

[0074] Based on the above embodiments, as an optional embodiment, the environmental parameter monitoring module includes: a temperature and humidity monitoring unit, an air pressure monitoring unit, and an ambient light monitoring unit;

[0075] The temperature and humidity monitoring unit uses an SHT30 sensor to measure the initial temperature and initial humidity data of the environment.

[0076] The air pressure monitoring unit uses a BMP280 sensor to measure the initial air pressure data of the environment.

[0077] The ambient light monitoring unit is used to measure the initial ambient light data of the environment.

[0078] In this embodiment, during the implementation of the environmental parameter monitoring module of the vehicle-mounted cosmetic mirror light-emitting module detection system, the temperature and humidity monitoring unit uses an SHT30 sensor deployed at the center of the detection area. Its built-in capacitive humidity sensing element and bandgap temperature sensor synchronously measure initial temperature and humidity data. The sensor housing is covered with a heat-resistant radiation coating to avoid being affected by the heat generated by the module under test. After power-on, the temperature and humidity monitoring unit performs a self-calibration process: comparing the readings of a standard temperature and humidity meter in a constant temperature and humidity chamber, and using the least squares method to fit correction coefficients, the temperature measurement error is reduced from ±0.3℃ to ±0.1℃, and the humidity measurement error is compressed from ±2%RH to ±0.5%RH. The air pressure monitoring unit uses a BMP280 sensor vertically mounted on the side of the detection platform. An installation height compensation algorithm eliminates air pressure measurement deviations caused by the mechanical structure. For example, when the sensor installation height is 1.5 meters, the system automatically subtracts an 18Pa height correction from the initial air pressure data, ensuring that the air pressure measurement accurately reflects the air pressure conditions of the environment where the module under test is located, with the error controlled within ±0.5hPa.

[0079] The ambient light monitoring unit integrates a photodiode array and optical filters to monitor the initial ambient light data within the detection area in real time. When a sudden change in light intensity is detected, the ambient light monitoring unit triggers a delayed continuous sampling process, pausing current data acquisition and waiting for the ambient light interference signal to attenuate. It then captures multiple sets of raw data in a high-frequency sampling mode, removes outliers through moving average filtering and hash sorting, retains the intermediate valid data, and calculates the arithmetic mean, reducing the measurement deviation caused by sudden changes in ambient light from ±15% to ±0.5%. Data from the temperature and humidity monitoring unit and the air pressure monitoring unit are synchronously transmitted to the data processing module via the I²C bus. The environmental compensation calculation unit generates dynamic compensation coefficients based on the initial temperature, humidity, and air pressure data. For example, when the detected temperature rises to 40℃, the illuminance compensation is increased by 2.65% according to a preset light efficiency attenuation model, eliminating the influence of high-temperature environments on optical parameter measurements. This module, through multi-sensor collaborative calibration and anti-interference processing, reduces the overall error rate of environmental parameter measurements from ±8% using traditional methods to ±0.6%, providing high-precision compensation reference data for the host computer system.

[0080] Based on the above embodiments, as an optional embodiment, please refer to... Figure 2The data analysis module includes: a statistical analysis unit, a correlation analysis unit, a judgment unit, a scoring unit, and a report generation module;

[0081] The statistical analysis unit is used to determine the corresponding statistical characteristics and process capability indicators Cpk and Ppk based on electrical parameters, optical parameters and environmental parameters, and to generate a basic statistical report based on the statistical characteristics and process capability indicators.

[0082] The correlation analysis unit is used to analyze the mutual influence mechanism and degree based on the basic statistical report and generate a parameter correlation matrix.

[0083] The judgment unit is used to determine the qualified or unqualified status of the cosmetic mirror light-emitting module based on the parameter correlation matrix and generate a judgment result.

[0084] The scoring unit is used to quantify and score the key performance indicators of the cosmetic mirror's light-emitting module, such as light efficiency, color reproduction, uniformity, and stability, based on the judgment results, and to form a performance radar chart.

[0085] The report generation module is used to generate a comprehensive quality assessment report based on the basic statistical report, parameter correlation matrix, judgment results, and performance radar chart.

[0086] In this embodiment, during the implementation of the data analysis module of the vehicle-mounted cosmetic mirror light-emitting module detection system, the statistical analysis unit first extracts statistical features from the correction data transmitted by the data processing module, calculates the range and standard deviation of the nine-point illuminance data, the dispersion of the color temperature data, and the peak-to-peak value of the transient response characteristic data in the electrical parameters, and generates a set of statistical features reflecting the fluctuation characteristics of the parameters. Based on the statistical feature values, the statistical analysis unit calls the process capability index algorithm. For example, when the nine-point illuminance range is 200 lux, combined with the module specification limit of upper limit 1200 lux and lower limit 800 lux, the illuminance uniformity process capability index Cpk=1.25 is calculated, and the production process is determined to be at level three control. At the same time, based on the standard deviation of the color temperature data of 15K and the tolerance of ±50K, the color temperature stability process capability index Ppk=1.08 is calculated, and a basic statistical report containing the process capability index is generated, providing a quantitative basis for production line process improvement.

[0087] The correlation analysis unit, based on statistical characteristic values ​​from the basic statistical report, employs a grey relational analysis algorithm to analyze the interaction mechanism between electrical parameter fluctuations and optical parameter changes. For example, it calculates the grey relational degree Γ_current-CCT between the current overshoot amplitude in transient response characteristic data and the standard deviation of nine-point color temperature data. When the Γ value exceeds a preset threshold of 0.75, it determines that there is a strong correlation between current transient characteristics and color temperature stability, and marks the corresponding position in the parameter correlation matrix as a red highlighted area. This analysis process traverses all parameter combinations through a sliding window to construct a complete parameter correlation matrix, revealing the electrical-optical coupling fault mechanism that traditional single-dimensional detection cannot detect.

[0088] The judgment unit scans the parameter correlation matrix in real time for abnormal correlation patterns. When it detects a voltage fluctuation coefficient exceeding 0.15 and a strong correlation with the color temperature standard deviation (Γ_voltage-CCT≥0.7), it triggers the D02 defect code and generates a non-conformance judgment result containing the specific abnormal correlation item. For example, if the voltage fluctuation coefficient of a batch of modules reaches 0.18, and the parameter correlation matrix shows that its Γ value with the color temperature deviation of the edge area is 0.72, the system automatically judges it as a defect of poor matching between the driver circuit and the LED. However, the traditional threshold comparison method has a 67% missed detection rate for such defects because it does not analyze the correlation between parameters.

[0089] The scoring unit dynamically adjusts the weights of key performance indicators based on the defect codes in the judgment results. For example, when a defect exists in the D01 driver circuit, the initial value of the luminous efficiency score is multiplied by a weighting coefficient θ=0.85, and a correction term ΔS=-15 provided by the correlation analysis unit is added to generate a weighted comprehensive score. After all scoring items are normalized and mapped to the 0-100 score range, the scoring unit calls a radar chart generation algorithm to convert the scores of the four dimensions of luminous efficiency, color reproduction, uniformity, and stability into a proportionally scaled performance radar chart. Operators can intuitively identify the module's performance shortcomings by observing the area and shape of the radar chart. For example, a concave area in the radar chart corresponding to a uniformity score of 65 indicates a defect in illuminance distribution control during the production process.

[0090] The report generation module integrates process capability indicators, parameter correlation matrix heatmaps, judgment result details, and performance radar charts from the basic statistical report to generate a comprehensive quality assessment report including timestamps, product models, and serial numbers. The system uploads the report to a MySQL database in real time via a star topology network and generates a traceable CSV format log file locally, achieving full lifecycle quality traceability from raw material batches to factory inspection. This embodiment quantifies production fluctuations through statistical features, locates hidden defects using parameter correlation matrices, and maps quality levels through a dynamic scoring mechanism.

[0091] Based on the above embodiments, as an optional embodiment, the data display module includes: a status display unit, a distribution chart unit, a performance chart unit, and a correlation analysis chart unit;

[0092] The status display unit is used to match colors and icons according to the judgment result and intuitively display the judgment result on the display interface.

[0093] The distribution chart unit is used to generate a spatial distribution heatmap based on the basic statistical report, and to mark abnormal areas exceeding the threshold on the spatial distribution heatmap for intuitive display on the display interface.

[0094] The performance chart unit is used to generate a performance chart based on the performance radar chart and display it intuitively on the display interface.

[0095] The correlation analysis chart unit is used to generate a correlation coefficient matrix based on the parameter correlation matrix and display it intuitively on the display interface.

[0096] In this embodiment, the data display module consists of a status display unit, a distribution chart unit, a performance chart unit, and a correlation analysis chart unit. The functions and implementation methods of each unit are as follows:

[0097] The status display unit generates a circular status indicator with a diameter of 200mm in the central area of ​​the graphical interface. When the judgment result is qualified, a green checkmark icon (diameter accounting for 80%) is displayed. If a D01 type defect exists, it switches to a red exclamation mark icon (diameter accounting for 100%) and superimposed the text label "D01 - Drive Circuit Abnormal" in 14pt bold Song typeface. The distribution chart unit parses the nine-point illuminance data in the basic statistical report and superimposes a two-color temperature heatmap on the surface of the 3D mirror model. The color range is from green (≥800lux, RGB: 0,255,0) to red (≤600lux, RGB: 255,0,0). When the illuminance value of 550lux (threshold 650lux) is detected at the X2Y3 coordinate point, the abnormal marking mechanism is triggered: a flashing red border with a width of 3px (frequency 2Hz) is generated, and the numerical label "550lux (-15.4%)" is superimposed at the corresponding position. The performance chart unit maps the normalized scores of light efficiency (70 points), color reproduction (85 points), uniformity (56 points), and stability (75 points) to a polar coordinate system. The four radiation axes correspond to 0°, 90°, 180°, and 270° respectively. The axis length is scaled proportionally from 0 to 100 points (1 point = 1 mm). Connecting the endpoints of each axis forms a quadrilateral closed area. The fill color is set according to the comprehensive score range (Excellent: light green #90EE90, Good: light yellow #FFFFE0, Poor: light red #FFB6C1). The correlation analysis chart unit generates an 8×8 correlation coefficient matrix. The X-axis is labeled with electrical parameters (such as transient current rise time, in ms), and the Y-axis is labeled with optical parameters (such as edge illumination Cpk value). Each matrix unit is rendered as a bubble chart. The relationship between the bubble diameter d (in mm) and the gray correlation coefficient Γ value is d = 8 × Γ (the diameter is 6.64 mm when Γ = 0.83). The color coding rule is: positive correlation (Γ > 0) uses a blue gradient (dark blue #000080 to light blue #87CEEB), and negative correlation (Γ < 0) uses a red gradient (dark red #8B0000 to light red #FFC0CB). This module reduces the time for operators to identify drive circuit defects from 5.2 minutes to 8 seconds through visualization dimensionality reduction technology. The false detection rate of LED patch offset fault location by X2Y3 heat map anomaly marking has been reduced from 12% to 0.7%. The correlation coefficient matrix can analyze the strong negative correlation between transient current rise time and edge illumination (Γ = -0.79), providing a quantitative decision-making basis for production line process optimization.

[0098] The host computer system also includes a data storage module and a quality traceability module;

[0099] The data storage module is used to store the comprehensive quality assessment report in a local database and a remote cloud server to form historical data records.

[0100] Based on the above embodiments, as an optional embodiment, the host computer system further includes a data storage module and a quality traceability module;

[0101] The data storage module is used to store the comprehensive quality assessment report in a local database and a remote cloud server to form historical data records.

[0102] In this embodiment, the data storage module implements full lifecycle management of the test data through a multi-level storage architecture. After the report generation module outputs the comprehensive quality assessment report, the data storage module first converts the report into a composite file package containing XML format text, JPEG charts, and CSV data tables. The XML file records the defect code (e.g., D01) and the corresponding parameter correlation matrix data (γ=0.83) in the judgment results. The JPEG file stores the performance radar chart and spatial distribution heat map. The CSV file contains basic statistical report data such as the process capability index Cpk=1.45. The local database uses an SQLite relational database and establishes index records for the composite file package by timestamp (e.g., 20240517_142305) and product serial number (SN202405001) through a transaction processing mechanism. The remote cloud server is deployed based on a MySQL cluster. After encapsulating the transmitted data using the AES-256 encryption algorithm, it is synchronized to the cloud historical data record database in real time via the HTTPS protocol. The quality traceability module constructs a reverse query tree, allowing users to quickly retrieve comprehensive quality assessment reports from historical data records by inputting the product serial number. For example, when the market reports a color temperature drift issue with product SN202405001, the traceability module analyzes the parameter correlation matrix during the product's testing, locates the correlation between voltage fluctuation and color temperature drift at γ=0.92 (exceeding the threshold of 0.75), and retrieves the current overshoot waveform (peak value 5.8A / rise time 12ms) from the corresponding transient response characteristic data, automatically generating a traceability report that includes defect cause analysis (voltage regulation failure of the drive circuit).

[0103] Based on the above embodiments, as an optional embodiment, the host computer system further includes: a QR code generation unit, a batch analysis unit, and an early warning generation unit;

[0104] The QR code generation unit is used to receive the comprehensive quality assessment report and product information, and encode the comprehensive quality assessment report and product information to generate a unique QR code identifier containing the test results.

[0105] The batch analysis unit is used to obtain historical data records of products in the same batch from the data storage module, compare and analyze the comprehensive quality assessment report of the current product with the historical data records, and obtain analysis results.

[0106] The warning generation unit is used to generate warning information when the analysis result is abnormal.

[0107] In this embodiment, the host computer system achieves quality traceability and process monitoring through the collaborative operation of the QR code generation unit, batch analysis unit, and early warning generation unit. The QR code generation unit receives the defect code (such as D01) and product information (including serial number SN202405001) from the comprehensive quality assessment report. It encapsulates the XML format report data with Base64 encoded thumbnails (including performance radar charts and heat maps) using the QR code standard, and generates a unique QR code identifier with a side length of 30mm after encryption using the AES-256 algorithm. This identifier is printed on the product label. After scanning the code with a mobile phone, the current overshoot amplitude (5.2A) in the transient response characteristic data and the correlation coefficient matrix generated by the correlation analysis chart unit can be parsed. The batch analysis unit retrieves historical data records from the data storage module for 50 products in the same batch and calculates the average Cpk value of the batch's process capability index (e.g., illuminance uniformity Cpk = 1.62 ± 0.15). When the current product's illuminance Cpk value of 1.21 is detected to be lower than the batch average -3σ, it is judged as abnormal and marked with batch offset code B01. After the batch analysis unit outputs the B01 code, the early warning generation unit automatically retrieves the historical average correlation between voltage fluctuation and color temperature drift γ = 0.68 from the parameter correlation matrix. If the current product's γ = 0.83 exceeds the threshold of 0.75, a level three early warning (orange alert) is triggered, and an early warning email containing an abnormal parameter comparison table (current Cpk = 1.21 vs batch average 1.62) is sent to the quality management email address via SMTP protocol.

[0108] On the other hand, please see Figure 3 This application also provides a method for detecting a cosmetic mirror light-emitting module, the method comprising:

[0109] S101, Simultaneously collect the optical parameters of the cosmetic mirror light-emitting module, the optical parameters including: multi-point color temperature data and multi-point illuminance data;

[0110] S102, Measure the electrical parameters of the light-emitting module of the cosmetic mirror, the electrical parameters including: current data, voltage data, power data and transient response characteristic data;

[0111] S103, Measure the environmental parameters of the makeup mirror, including: temperature data, humidity data, air pressure data and ambient light data;

[0112] S104, The electrical parameters, optical parameters and environmental parameters are corrected to obtain correction data;

[0113] S105, perform correlation analysis on the correction data, establish a mapping relationship model between parameters, and compare the mapping relationship model with the preset standard threshold to generate a comprehensive quality assessment report.

[0114] S106, The comprehensive quality assessment report is displayed on the interface in the form of a combination of charts.

[0115] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will readily conceive of those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0116] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A detection system for a car-mounted vanity mirror light-emitting module, characterized in that, include: The lower-level computer system communicates with the upper-level computer system and is used to collect electrical, optical, and environmental parameters of the cosmetic mirror's light-emitting module. The host computer system is used to perform detection based on the electrical parameters, the optical parameters, and the environmental parameters, and obtain the detection results. The lower-level computer system includes: a multi-point array optical parameter acquisition module, a high-precision electrical parameter comprehensive measurement module, and an environmental parameter monitoring module; The multi-point array optical parameter acquisition module is used to simultaneously acquire the optical parameters of the cosmetic mirror light-emitting module. The optical parameters include: multi-point color temperature data and multi-point illuminance data. The high-precision electrical parameter comprehensive measurement module is used to measure the electrical parameters of the cosmetic mirror light-emitting module. The electrical parameters include: current data, voltage data, power data, and transient response characteristic data. The environmental parameter monitoring module is used to measure the environmental parameters of the makeup mirror, including temperature data, humidity data, air pressure data, and ambient light data. The host computer system includes: a data processing module, a data analysis module, and a data display module; The data processing module is used to correct electrical parameters, optical parameters, and environmental parameters to obtain corrected data; the data processing module also includes: a signal preprocessing unit, an environmental compensation calculation unit, and a data fusion unit; The signal preprocessing unit is used to filter electrical parameters, optical parameters, and environmental parameters to obtain preprocessed electrical parameters, preprocessed optical parameters, and preprocessed environmental parameters. The environmental compensation calculation unit is used to calculate the environmental compensation coefficient based on the preprocessed environmental parameters, specifically including: determining the illuminance compensation coefficient based on the temperature data and humidity data in the preprocessed environmental parameters; and determining the color temperature compensation coefficient based on the air pressure data in the preprocessed environmental parameters. The data fusion unit is used to apply the environmental compensation coefficient to the preprocessed optical parameters and preprocessed electrical parameters to obtain the correction data under standard environmental conditions. The data analysis module is used to perform correlation analysis on the correction data, establish a mapping relationship model between parameters, and compare the mapping relationship model with the preset standard threshold to generate a comprehensive quality assessment report. The data analysis module includes: a statistical analysis unit, a correlation analysis unit, a judgment unit, a scoring unit, and a report generation module. The statistical analysis unit is used to determine the corresponding statistical characteristics and process capability indicators based on electrical and optical parameters, and to generate a basic statistical report based on the statistical characteristics and process capability indicators. The statistical characteristics include the peak-to-peak value of transient response characteristics, the range and standard deviation of illuminance, and the dispersion of color temperature. Among the process capability indicators, Cpk is the process capability indicator for illuminance uniformity, and Ppk is the process capability indicator for color temperature stability. The correlation analysis unit is used to analyze the interaction mechanism between electrical parameter fluctuations and optical parameter changes based on the statistical feature values ​​in the basic statistical report and using the grey relational degree algorithm. Specifically, it traverses all parameter combinations through a sliding window to construct a complete parameter correlation matrix. The judgment unit is used to determine the qualified or unqualified status of the cosmetic mirror light-emitting module based on the parameter correlation matrix and generate a judgment result. The scoring unit is used to quantify and score the key performance indicators of the cosmetic mirror's light-emitting module, such as light efficiency, color reproduction, uniformity, and stability, based on the judgment results, and to form a performance radar chart. The report generation module is used to generate a comprehensive quality assessment report based on the basic statistical report, parameter correlation matrix, judgment results and performance radar chart. The data display module is used to receive the comprehensive quality assessment report output by the data analysis module and display the comprehensive quality assessment report on the interface in the form of a combination of charts.

2. The detection system according to claim 1, characterized in that, The multi-point array optical parameter acquisition module includes: a sensor array unit, an optical alignment unit, and an optical signal processing unit; The sensor array unit includes multiple BH1749NUC chips arranged in a 3×3 matrix, used to simultaneously acquire initial color temperature data and initial illuminance data at 9 measurement points; The optical signal processing unit is used to receive the initial color temperature data and initial illuminance data output by the sensor array unit, and to perform signal stabilization processing on the initial color temperature data and initial illuminance data through a Kalman filter algorithm to obtain optical parameters.

3. The detection system according to claim 1, characterized in that, The high-precision electrical parameter comprehensive measurement module includes: a current and voltage measurement unit, a multi-level test unit, and a transient characteristic measurement unit; The current and voltage measurement unit, based on the INA237 chip, is used to measure the initial current and initial voltage data of the cosmetic mirror's light-emitting module. The multi-level testing unit is used to receive control commands, automatically switch the brightness levels of the makeup mirror's light-emitting module according to the control commands, and trigger the current and voltage measurement unit to measure the initial current and initial voltage data at different brightness levels. The transient characteristic measurement unit is used to sample initial current data and initial voltage data at high speed when the makeup mirror light-emitting module is turned on and the gear is switched, and generate transient response characteristic data.

4. The detection system according to claim 1, characterized in that, The environmental parameter monitoring module includes: a temperature and humidity monitoring unit, an air pressure monitoring unit, and an ambient light monitoring unit; The temperature and humidity monitoring unit uses an SHT30 sensor to measure the initial temperature and initial humidity data of the environment. The air pressure monitoring unit uses a BMP280 sensor to measure the initial air pressure data of the environment. The ambient light monitoring unit is used to measure the initial ambient light data of the environment.

5. The detection system according to claim 1, characterized in that, The data display module includes: a status display unit, a distribution chart unit, a performance chart unit, and a correlation analysis chart unit; The status display unit is used to intuitively display the judgment result on the display interface by matching colors and icons according to the judgment result; The distribution chart unit is used to generate a spatial distribution heatmap based on the basic statistical report, and to mark abnormal areas that exceed the threshold on the spatial distribution heatmap for intuitive display on the display interface. The performance chart unit is used to generate performance charts based on the performance radar chart and display them intuitively on the display interface. The correlation analysis chart unit is used to generate a correlation coefficient matrix based on the parameter correlation matrix and display it intuitively on the display interface.

6. The detection system according to claim 1, characterized in that, The host computer system also includes a data storage module and a quality traceability module; The data storage module is used to store the comprehensive quality assessment report in a local database and a remote cloud server to form historical data records.

7. The detection system according to claim 6, characterized in that, The host computer system also includes: a QR code generation unit, a batch analysis unit, and an early warning generation unit; The QR code generation unit is used to receive the comprehensive quality assessment report and product information, and encode the comprehensive quality assessment report and product information to generate a unique QR code identifier containing the test results. The batch analysis unit is used to obtain historical data records of products in the same batch from the data storage module, compare and analyze the current product's comprehensive quality assessment report with the historical data records, and obtain the analysis results. The warning generation unit is used to generate warning information when the analysis result is abnormal.

8. A detection method based on the vehicle-mounted vanity mirror light-emitting module detection system according to any one of claims 1-7, characterized in that, The method includes: Simultaneously, the optical parameters of the makeup mirror's light-emitting module are collected, including multi-point color temperature data and multi-point illuminance data. Measure the electrical parameters of the light-emitting module of the cosmetic mirror. The electrical parameters include: current data, voltage data, power data, and transient response characteristic data. Measure the environmental parameters of the makeup mirror, including temperature, humidity, air pressure, and ambient light. The electrical, optical, and environmental parameters are calibrated to obtain calibration data. The calibration data is subjected to correlation analysis to establish a mapping relationship model between parameters. Based on this mapping relationship model, the data is compared with the preset standard threshold to generate a comprehensive quality assessment report. The comprehensive quality assessment report is displayed on the interface in a combination of charts and graphs.

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