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

Abstract

The invention provides a vehicle-mounted cosmetic mirror light-emitting module detection system and method, and relates to the field of quality inspection. Comprising a lower computer system and an upper computer system. The lower computer system acquires color temperature data and illumination data of mirror surface measurement points through a multi-point array optical parameter acquisition module; the high-precision electrical parameter comprehensive measurement module is used for measuring current, voltage, power and transient response characteristic data; the environment parameter monitoring module obtains temperature, humidity, air pressure and environment light data through a sensor. A data processing module of the upper computer system carries out filtering and environment compensation correction on the original parameters to generate correction data in a standard environment; and the data analysis module establishes an electrical-optical-environmental parameter mapping relation model through a grey relational degree algorithm, and generates a comprehensive quality evaluation report containing defect codes and process capability indexes after comparison with a preset threshold value. According to the scheme, through multi-parameter dynamic correlation analysis, the detection capability of the cosmetic mirror light-emitting module is improved.

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's compliance. 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 severs 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 cause 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 leads to 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: 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; 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; 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; The high-precision electrical parameter comprehensive measurement module is used for measuring the electrical parameters of the in-vehicle makeup mirror lighting module, and 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 dressing mirror, and the environmental parameters include: 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 perform calibration processing on the electrical parameters, the optical parameters, and the environmental parameters to obtain calibration data; The data analysis module is used to perform correlation analysis processing on the calibration data, establish a mapping relationship model between parameters, and compare it with a preset standard threshold based on the mapping relationship model to generate a comprehensive quality assessment report; 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 a chart combination manner.

[0005] In the above technical solution, in this embodiment, through the 3×3 matrix optical sensor array of the multi-point array optical parameter acquisition module of the lower computer system, the color temperature data and illuminance data of 9 measurement points on the surface of the dressing mirror light-emitting module are synchronously obtained. Combining the capture of μs-level transient response characteristic data by the high-precision electrical parameter comprehensive measurement module, a holographic data acquisition network of optical performance and electrical characteristics is constructed in the spatial dimension and the time dimension. With the temperature, humidity, air pressure, and ambient light data collected in real time by the environmental parameter monitoring module, a multi-dimensional parameter synchronous acquisition system is formed. The data processing module of the host computer system, through the environmental compensation calculation unit, performs non-linear calibration on the original optical parameters based on the temperature humidity-air pressure coupling compensation model to eliminate the measurement deviation caused by environmental disturbance and generate calibration data under standard environmental conditions. The statistical analysis unit in the data analysis module calculates the process capability indexes Cpk / Ppk using the calibration data to quantify the stability of the production process; the correlation analysis unit establishes a mapping relationship model between the electrical parameter fluctuation and the optical performance deterioration through the grey correlation degree algorithm, and analyzes the influence coefficient of the transient current mutation on the color temperature drift. When the determination unit identifies an abnormal correlation pattern in the parameter correlation matrix, the scoring unit triggers a weighted scoring mechanism based on the defect code to generate a performance radar chart reflecting key indexes such as light efficiency and color reducibility. The data display module intuitively displays the abnormal area of the spatial distribution by superimposing a two-color temperature heat map on the three-dimensional mirror model, and reveals the dynamic correlation path between the electrical-optical parameters in combination with the correlation coefficient matrix bubble chart. This technical solution realizes the accurate evaluation of the comprehensive performance of the dressing mirror light-emitting module through the closed-loop processing of multi-dimensional parameter synchronous acquisition, dynamic environmental compensation, correlation model construction, and visualization presentation, effectively identifies transient response anomalies and environment-sensitive defects that are difficult to detect by traditional detection methods, and significantly improves the credibility of the detection results and the engineering guidance value.

[0006] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: By using a multi-source data acquisition model to collect vibration data, acoustic data, exhaust gas composition data, and temperature distribution data during the operation of the engine, comprehensive monitoring of the engine state is achieved, effectively avoiding the problem that a single data source is easily interfered by the environment. Through the heterogeneous data deep fusion module, data conversion is performed on the multi-source data to obtain a unified multi-source feature representation, which not only solves the problem of inconsistent time series of heterogeneous data but also extracts the deep correlation features between the data. Combining with the multi-level diagnosis module to perform multi-level diagnosis on the unified multi-source feature representation, through the progressive process of feature extraction, time series analysis, knowledge reasoning, and diagnostic decision-making, the accuracy and interpretability of fault diagnosis are improved. Finally, the predictive maintenance module performs maintenance analysis based on the multi-level diagnosis results. Through the comprehensive evaluation of the predicted life value, the fault evolution path, and the optimal maintenance time, the transformation from passive maintenance to active predictive maintenance is realized, which not only reduces the maintenance cost but also improves the maintenance efficiency. This complete technical solution from data acquisition, feature fusion to diagnostic prediction effectively solves the technical problem of low accuracy of traditional single-data-source diagnosis methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is a schematic structural diagram of a detection system for a makeup mirror lighting module provided by an embodiment of the present application; Figure 2 It is a schematic structural diagram of a data analysis module provided by an embodiment of the present application; Figure 3 It is a schematic flow diagram of a method for detecting a makeup mirror lighting module provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0008] In order 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 in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments.

[0009] In the description of the embodiments of the present application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design solution described as "for example" or "for instance" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of words such as "for example" or "for instance" is intended to present relevant concepts in a specific manner.

[0010] In the description of the embodiments of the present application, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the technical features indicated. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0011] Please refer to Figure 1 , Figure 1 which is a schematic structural diagram of a vehicle-mounted makeup mirror lighting module detection system provided by the embodiments of the present application. This system can be implemented depending on a computer program or run as an independent tool application. Specifically, in the embodiments of the present application, this method can be applied to a server, but can also be applied to electronic devices such as a server. A vehicle-mounted makeup mirror lighting module detection system includes the following modules: a lower computer system, communicatively connected to the upper computer system, for collecting electrical parameters, optical parameters, and environmental parameters of the makeup mirror lighting module; an upper computer system, for performing detection based on the electrical parameters, the optical parameters, and the environmental parameters to obtain a detection result. 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; The multi-point array optical parameter acquisition module is used for simultaneously acquiring the optical parameters of the makeup mirror lighting 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 for measuring the electrical parameters of the makeup mirror lighting module. The electrical parameters include: current data, voltage data, power data, and transient response characteristic data; The environmental parameter monitoring module is used for measuring the environmental parameters of the makeup mirror. The environmental parameters include: temperature data, humidity data, air pressure data, and ambient light data; The upper computer system includes: a data processing module, a data analysis module, and a data display module; The data processing module is used for performing calibration processing on the electrical parameters, the optical parameters, and the environmental parameters to obtain calibration data; The data analysis module is used for performing correlation analysis processing on the calibration data, establishing a mapping relationship model between parameters, and comparing it with a preset standard threshold based on the mapping relationship model to generate a comprehensive quality assessment report; The data display module is configured to receive the comprehensive quality assessment report output by the data analysis module and display the comprehensive quality assessment report on the interface in a chart combination manner.

[0012] In this embodiment, the detection system of the makeup mirror light-emitting module performs detection through the coordinated operation of the lower computer system and the upper computer system. The multi-point array optical parameter acquisition module of the lower computer system uses a BH1749NUC sensor array arranged in a 3×3 matrix to synchronously collect the color temperature data and illuminance data of nine measurement points on the surface of the makeup mirror light-emitting module. After filtering the glitch signals generated by ambient light interference through delayed continuous sampling, the maximum and minimum values are removed using the 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 a 16-bit Δ-Σ ADC of the INA237 chip, and captures the transient response characteristic data (i.e., the instantaneous fluctuation waveforms of current and voltage during power-on / off and gear switching) at a sampling rate of 128 times per second when the makeup mirror light-emitting module is turned on, and simultaneously measures the current data, voltage data, and power data under steady-state conditions. The built-in temperature sensor of it controls the die temperature measurement error within ±1°C. The environmental parameter monitoring module collects temperature data and humidity data through an SHT30 sensor, and combines with a BMP280 sensor to obtain air pressure data to form an environmental parameter set for subsequent compensation calculations.

[0013] It should be noted that the delayed continuous sampling refers to a key technology for anti-interference data acquisition achieved through a phased control strategy when a sudden change in ambient light intensity is detected. Specifically, during implementation, the system monitors the ambient light change rate in real time. When the light intensity change rate exceeds a threshold of 200 lux per second (for example, suddenly increasing from 300 lux to 800 lux within 0.5 seconds), the delayed continuous sampling process is immediately triggered. This process includes three core stages: The first stage: Delayed waiting The system pauses the current sampling task and starts a 100 ms delay timer. This delay period has been verified through experiments. After the ambient light suddenly changes, the first 50 ms is the interference signal oscillation period (residual interference amount > 30%), the period from 50 to 100 ms is the signal attenuation and stabilization period (residual amount < 5%), and exceeding 150 ms will cause the detection efficiency to decrease by more than 15%. Selecting 100 ms as the standard waiting time can achieve the optimal balance between interference suppression and detection efficiency.

[0014] The second stage: Continuous sampling After the delay ends, the optical sensor continuously performs 50 high-frequency samplings at 10 ms intervals (total sampling window of 500 ms). This frequency setting is based on the sensor response characteristics: the response time of BH1749NUC is 5 ms, and setting a 10 ms interval ensures that the sensor output fluctuation is less than ±1% during each sampling (e.g., from 799 lux to 801 lux). Through the 50 sets of original data collected in this stage, the steady-state light intensity after interference attenuation can be completely captured.

[0015] Stage 3: Data Processing Two-level processing is performed on the obtained 50 sets of data: First, moving average filtering is used, and the moving average is calculated for every 5 consecutive data (e.g., for the data sequence [798, 802, 805, 812, 824], the average value of 809 lux is generated); Subsequently, the first 10% of the maximum values (e.g., 824 lux) and the last 10% of the minimum values (e.g., 779 lux) are removed using the hash sorting method, and the arithmetic average of the remaining 40 sets of data is taken as the final measurement value. Through this processing, the deviation caused by sudden light interference can be reduced from ±15% to ±0.5%.

[0016] After the data processing module of the host computer system receives the original parameters transmitted by the slave computer, the signal preprocessing unit uses a Butterworth filter to filter out 50 Hz power frequency noise from the electrical parameters. The environmental compensation calculation unit performs non-linear correction on the illuminance data in the optical parameters according to the deviation value between the measured temperature data and the standard environmental temperature (25°C) using a preset temperature compensation coefficient formula (e.g., ΔL = α×(T - 25)), generating corrected data that eliminates environmental interference. The statistical analysis unit of the data analysis module calculates the standard deviation and process capability indices Cpk / Ppk of the corrected data to evaluate the stability of the production process; the correlation analysis unit uses the grey correlation degree algorithm to establish a mapping relationship model between the electrical parameter fluctuations and the optical performance changes. For example, it analyzes the correlation weight between the current mutation amplitude and the color temperature drift amount in the transient response characteristic data, generating a parameter correlation matrix that reflects the influence degree between parameters. When the determination 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 determination result. The scoring unit performs weighted scoring on key performance indicators such as light efficiency and color rendering according to the weight assignment rules corresponding to the defect codes, forming a performance evaluation result. The data display module overlays and displays the performance radar chart and the spatial distribution heat map, where the heat map uses a red-yellow two-color gradient to identify abnormal areas where the illuminance data is lower than 85% of the threshold, and at the same time shows the rise time and overshoot amplitude of the current waveform in the transient response characteristic data through a trend chart, enabling operators to intuitively identify design defects in the drive circuit. All detection data is uploaded to the MySQL database in real time through a star topology network, and a CSV file containing timestamps, product models, and defect codes is generated locally to achieve full life cycle traceability of quality problems.

[0017] Based on the above embodiments, as an alternative embodiment, the data processing module further includes: a signal preprocessing unit, an environmental compensation calculation unit, and a data fusion unit; The signal preprocessing unit is configured to perform filtering processing on the electrical parameters, the optical parameters, and the environmental parameters to obtain preprocessed electrical parameters, preprocessed optical parameters, and preprocessed environmental parameters; In this embodiment, the data processing module works in cooperation with the signal preprocessing unit, the environmental compensation calculation unit, and the data fusion unit to achieve precise calibration of the detection parameters. The signal preprocessing unit first performs Butterworth low-pass filtering on the original electrical parameters (including current data, voltage data, and transient response characteristic data) collected by the lower computer system to filter out the signal spikes generated by high-frequency electromagnetic interference. For example, the noise amplitude above 50 Hz of the power frequency is attenuated to less than 5% of the original value to generate preprocessed electrical parameters. At the same time, for the multi-point illuminance data in the optical parameters, a moving average filtering algorithm is used to eliminate the measurement jumps caused by the instantaneous change of the ambient light. For example, the original illuminance data collected by 9 BH1749NUC sensors are sampled continuously 5 times and then the middle 3 effective values are taken to generate preprocessed optical parameters with a fluctuation amplitude ≤ ±3%. For the temperature data and humidity data obtained by the environmental parameter monitoring module, the signal preprocessing unit uses median filtering to eliminate the abnormal values of accidental false alarms of the sensors. For example, when the deviation of the continuous 3 temperature sampling values exceeds ±0.5 °C, the abnormal points are automatically eliminated to generate stable preprocessed environmental parameters.

[0018] The environmental compensation calculation unit establishes a dynamic compensation model based on the preprocessed environmental parameters, and specifically quantifies and corrects the influence of temperature data and air pressure data on the optical parameters. For example, when the temperature data is higher than the standard ambient temperature of 25 °C, the illuminance data in the preprocessed optical parameters is linearly compensated according to the preset temperature attenuation coefficient formula (ΔL = 0.12% × (T - 25)) to eliminate the attenuation effect of the LED luminous efficiency with the increase of temperature. At the same time, according to the deviation value between the air pressure data and the standard atmospheric pressure (1013 hPa), the color temperature data is adjusted through the air pressure refractive index correction formula (ΔCCT = 0.05 K / hPa × P_diff) to compensate for the optical path refraction deviation caused by the change of air density. The data fusion unit performs spatio-temporal alignment processing on the optical parameters after environmental compensation and the preprocessed electrical parameters. For example, the transient response characteristic data and the color temperature drift data at the corresponding moment are synchronized through timestamps to generate calibrated data with a unified spatio-temporal reference. This data processing flow reduces the illuminance measurement error caused by environmental temperature fluctuations from ±8% to ±0.5%, and reduces the color temperature deviation caused by air pressure changes from ±15 K to ±1 K, significantly improving the accuracy of the detection data.

[0019] The environmental compensation calculation unit is used to calculate the environmental compensation coefficient based on the preprocessed environmental parameters; Specifically, in this embodiment, during the implementation of the environmental compensation calculation unit of the in-vehicle makeup mirror lighting module detection system, aiming at the core problem that the traditional detection method leads to distorted light efficiency evaluation because the influence of environmental parameters on the optical measurement results is not quantified, this embodiment realizes the accurate calculation and dynamic correction of the environmental compensation coefficient through the following steps: Step 1: Temperature compensation coefficient calculation Based on the temperature data in the preprocessed environmental parameters, the environmental compensation calculation unit calls the LED light efficiency-temperature characteristic curve stored in the system (this curve is obtained through laboratory calibration and reflects the non-linear attenuation law of LED luminous efficiency with the increase of temperature). First, calculate the deviation value ΔT between the measured temperature and the standard environmental temperature of 25°C. According to the positive and negative directions of ΔT, the temperature compensation coefficient formula ΔL = α×ΔT + β×ΔT² is applied for two-way compensation, where α = 0.12% / °C is the linear attenuation coefficient and β = 0.003% / °C² is the acceleration attenuation compensation coefficient in the high-temperature area. For example, when the measured temperature T_measured = 40°C, ΔT = 15°C, then ΔL = 0.12×15 + 0.003×225 = 1.8 + 0.675 = 2.475%. At this time, 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 light efficiency caused by the high-temperature environment. This step solves the problem that the traditional method misjudges the product performance due to the lack of consideration of light efficiency attenuation under high-temperature working conditions.

[0020] Step 2: Air pressure refractive index correction For the air pressure data obtained by the environmental parameter monitoring module, the environmental compensation calculation unit adopts the air pressure refractive index correction formula ΔCCT = 0.05 K / hPa×P_diff, where P_diff is the difference between the measured air pressure P_measured and the standard atmospheric pressure of 1013 hPa. When the detection environment is in a low-altitude area (such as P_measured = 1000 hPa), P_diff = -13 hPa, then ΔCCT = 0.05×(-13) = -0.65 K. At this time, the original color temperature data CCT_original = 4000 K becomes CCT_corrected = 4000 - 0.65 = 3999.35 K after correction, compensating for the optical path refraction deviation caused by the change in air density. This correction process specifically targets the edge measurement points in the nine-point color temperature data (the area significantly affected by the optical path length), reducing the color temperature measurement error of ±15 K caused by ignoring the air pressure influence in the traditional method to within ±1 K.

[0021] Step 3: Humidity compensation activation determination When the measured humidity data RH_measured in the pre-processed environmental parameters exceeds the 75% threshold (for example, RH_measured = 85% in the detection environment during the plum rain season), 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 light flux loss coefficient caused by lens surface condensation, and RH_reference = 50% is the calibration reference value. At this time, ΔL_total = ΔL + ΔL_humid is additionally calculated. For example, when ΔT = 15℃ and RH_measured = 85%, ΔL_humid = 0.005%×(85 - 50) = 0.175%, and the total compensation amount ΔL_total = 2.475% + 0.175% = 2.65%, making L_corrected = 1200×1.0265 = 1231.8 lux. This mechanism compensates for the systematic deviation in the traditional detection system due to the neglect of the lens atomization effect in high humidity environments.

[0022] Step Four: Closed-loop Correction of Sensor Errors Regarding the non-linear 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 original temperature reading of the sensor 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℃. At the same time, the installation height error of the BMP280 barometric pressure sensor is compensated, and it is corrected according to the formula P_corrected = P_original + h×12 Pa / m (h = 1.5m is the sensor installation height), increasing the barometric pressure measurement value by 18 Pa and eliminating the barometric pressure measurement deviation caused by the equipment's mechanical structure.

[0023] The data fusion unit is used to apply the environmental compensation coefficient to the pre-processed optical parameters and pre-processed electrical parameters to obtain the corrected data under standard environmental conditions.

[0024] In this embodiment, during the implementation of the data fusion unit in the in-vehicle makeup mirror lighting module detection system, aiming at the problem that the corrected data is inaccurate due to environmental interference and parameter spatio-temporal misalignment in the traditional detection method, this embodiment realizes the systematic fusion of environmental compensation and multi-source data through the following logical process: Step One: Spatio-temporal Alignment Processing The data fusion unit first performs a timestamp synchronization operation on the preprocessed optical parameters and the preprocessed electrical parameters. Based on the time marker information embedded during the acquisition by the lower computer, the system establishes a strict time correspondence between the illuminance data collected by the optical sensor array at a specific moment and the current and voltage transient values recorded by the electrical parameter acquisition module at the same moment. For example, when detecting the dynamic response at the moment of the start of the light-emitting module, the system ensures that the timing of the optical illuminance change curve perfectly matches the rising edge of the current waveform, eliminating the misjudgment of response delay caused by sampling time deviation in traditional methods. This process is achieved through timestamp synchronization technology, enabling the precise capture of the dynamic coupling relationship between optical and electrical parameters.

[0025] Step 2: Application of environmental compensation coefficients The data fusion unit calls the comprehensive temperature and humidity compensation coefficient and the air pressure refractive index compensation coefficient generated by the environmental compensation calculation unit to perform a standardized correction on the spatially and temporally aligned optical parameters. For the multi-point illuminance data collected by the optical sensor array, the system proportionally increases the original illuminance value according to the luminous efficiency attenuation characteristics caused by temperature changes to offset the decrease in light efficiency in a high-temperature environment; at the same time, in combination with the influence of air pressure changes on the refraction of the optical path, the system adjusts the color temperature data for offset. For example, in a low-air-pressure environment, the system automatically corrects the color temperature measurement value to eliminate the deviation introduced by the change in air density. This process ensures that the data reflects the true performance under standard environmental conditions by quantifying the mapping relationship between environmental parameters and optical performance.

[0026] Step 3: Dynamic compensation of electrical parameters The data fusion unit dynamically corrects the power value in the electrical parameters based on the environmental temperature data. According to the temperature drift characteristic model of the drive circuit components, the system performs reverse compensation on the original power measurement value to eliminate the circuit loss error caused by the increase in temperature. For example, when the environmental temperature is significantly higher than the standard condition, the system reduces the power calculation value according to the 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 traditional methods ignoring the influence of temperature on electrical parameters.

[0027] Step 4: Normalized output of multi-source data The data fusion unit performs a standardized conversion on the compensated optical parameters and the corrected electrical parameters according to a unified benchmark. For the illuminance distribution data in the optical parameters, the system converts it into a percentage form relative to the theoretical maximum value; for the transient response characteristics in the electrical parameters, it is converted into a fluctuation ratio relative to the rated value. For example, the current overshoot amplitude is expressed as a percentage of the rated current, facilitating the subsequent scoring unit to directly evaluate the stability. This process eliminates the scale difference between parameters with different dimensions through normalization processing, providing a consistent data basis for the construction of the parameter correlation matrix.

[0028] Based on the above embodiments, as an alternative embodiment, 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 a plurality of BH1749NUC chips arranged in a 3×3 matrix, and is used to simultaneously collect the initial color temperature data and initial illuminance data of 9 measurement points; In this embodiment, during the implementation of the multi-point array optical parameter acquisition module of the vehicle-mounted makeup mirror lighting module detection system, the sensor array unit synchronously collects the initial color temperature data and initial illuminance data of nine equally divided areas on the surface of the module through nine BH1749NUC chips rigidly encapsulated in a 3×3 matrix. The chip spacing is distributed proportionally according to the size of the lighting area to ensure full coverage. The optical alignment unit dynamically calibrates the spatial position of the sensor array through a high-precision six-axis robotic arm equipped with a laser locator. First, align the laser crosshair with the geometric center of the module, and then drive the robotic arm to adjust the height and translation amount to ensure that the field of view angles of each BH1749NUC chip completely cover the preset nine-point grid, eliminating the installation deviation within ±0.1 mm. The optical signal processing unit performs anti-interference processing on the initial data. When the ambient light monitoring module detects a sudden light intensity change, it triggers a delayed continuous sampling process, pauses the collection and waits for the interference to decay, then obtains multiple groups of data through high-frequency sampling. After sliding average filtering and hash sorting, the outliers are removed, and the arithmetic mean is calculated from the remaining valid data in the middle, reducing the nine-point illuminance fluctuation range from ±15% to ±0.5%. During the synchronous sampling period, the nine-channel data is transmitted in parallel to the data processing module through the I²C bus, and the illuminance range and color temperature spatial distribution characteristics are calculated in real time to identify abnormal areas where the color temperature deviation of edge points exceeds 50K, with a positioning accuracy of ±3 mm, solving the problem of missed detection of edge defects in traditional single-point sampling. Based on the above embodiments, as an alternative embodiment, the high-precision electrical parameter comprehensive measurement module includes: a current and voltage measurement unit, a multi-gear 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 data and initial voltage data of the makeup mirror lighting module; The multi-gear test unit is used to receive a control instruction, automatically switch the brightness gear of the makeup mirror lighting module according to the control instruction, and trigger the current and voltage measurement unit to measure the initial current data and the initial voltage data at different brightness gears; The transient characteristic measurement unit is used to high-speed sample the initial current data and the initial voltage data when the makeup mirror lighting module is turned on and the gear is switched, and generate transient response characteristic data.

[0029] In an embodiment, during the implementation of the high-precision electrical parameter comprehensive measurement module of the in-vehicle makeup mirror lighting module detection system, the current and voltage measurement unit is constructed 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 the initial current data in real-time through a milliohm-level shunt resistor connected in the power supply circuit of the lighting module, and directly measures the initial voltage data through a voltage division circuit. During measurement, the system configures the sampling rate of the INA237 chip to be 128 times per second, and the current measurement range automatic switching mode. When the detected current value exceeds the preset threshold, the hardware protection circuit immediately triggers the relay switching action of the multi-range test unit, switching the range from the low-resistance state to the high-resistance state to avoid damage to the sensor due to overload. For example, at the moment of switching the makeup mirror dimming mode, when the current value suddenly increases from the steady-state 500 mA to 1.2 A, the multi-range test unit completes the range switching within 2 ms to ensure the complete recording of the transient process.

[0030] The transient characteristic measurement unit captures the transient response characteristic data output by the current and voltage measurement unit in real-time through a cache module. When a lighting module startup or gear switching instruction is detected, it automatically increases the sampling rate to 10 kHz, synchronously records the rising edge of the current waveform and the voltage drop process, and eliminates high-frequency switching noise through a digital filter. For example, at the moment of module startup, this unit captures that the overshoot amplitude of the current reaches 8% of the rated value, and at the same time, there is an instantaneous voltage drop of 0.3 V, fully restoring the transient response characteristics of the drive circuit. The multi-range test unit dynamically adjusts the measurement sensitivity according to the preset current threshold. When the current value is lower than 100 mA, it switches to the microamp-level high-precision gear and uses the four-wire Kelvin connection method to eliminate the influence of contact resistance, reducing the measurement error in the low-current condition from ±5% of the traditional method to ±0.5%.

[0031] Based on the above embodiment, as an optional embodiment, the environmental parameter monitoring module includes: a temperature and humidity monitoring unit, a barometric pressure monitoring unit, and an ambient light monitoring unit; The temperature and humidity monitoring unit uses an SHT30 sensor to measure the initial temperature data and initial humidity data of the environment; The barometric pressure monitoring unit uses a BMP280 sensor to measure the initial barometric pressure data of the environment; The ambient light monitoring unit is used to measure the initial ambient light data of the environment.

[0032] In this embodiment, during the implementation of the environmental parameter monitoring module of the in-vehicle makeup mirror lighting 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 the initial temperature data and initial humidity data. The sensor housing is covered with a heat radiation protection coating to avoid being affected by the heat generated by the module under test. After power-on, the temperature and humidity monitoring unit executes a self-calibration process: comparing the readings of the standard thermometer and hygrometer in a constant temperature and humidity chamber, fitting the correction coefficient through the least squares method, reducing the temperature measurement error from ±0.3°C to ±0.1°C, and compressing the humidity measurement error from ±2%RH to ±0.5%RH. The air pressure monitoring unit uses a BMP280 sensor vertically installed on the side of the test bench, and eliminates the air pressure measurement deviation caused by the mechanical structure through the installation height compensation algorithm. For example, when the installation height of the sensor is 1.5 meters, the system automatically deducts a height correction amount of 18 Pa from the initial air pressure data, so that the air pressure measurement value accurately reflects the air pressure conditions of the environment where the module under test is located, and the error is controlled within ±0.5 hPa.

[0033] The ambient light monitoring unit integrates a photosensitive diode array and an optical filter to continuously monitor the initial ambient light data in the detection area. When a sudden change in light intensity is detected, the ambient light monitoring unit triggers a delayed continuous sampling process, pauses the current data acquisition, and waits for the ambient light interference signal to decay. Then, it captures multiple groups of raw data in a high-frequency sampling mode, eliminates outliers through moving average filtering and hash sorting, retains the intermediate valid data, and calculates the arithmetic mean, reducing the measurement deviation caused by ambient light mutation from ±15% to ±0.5%. The data of the temperature and humidity monitoring unit and the air pressure monitoring unit are synchronously transmitted to the data processing module through the I²C bus. The environmental compensation calculation unit generates a dynamic compensation coefficient based on the initial temperature data, initial humidity data, and initial air pressure data. For example, when the detected temperature rises to 40°C, the illuminance compensation amount is increased by 2.65% according to the preset light effect attenuation model to eliminate the influence of the high-temperature environment on the optical parameter measurement. Through multi-sensor collaborative calibration and anti-interference processing, this module reduces the comprehensive error rate of environmental parameter measurement from ±8% of the traditional method to ±0.6%, providing high-precision compensation reference data for the upper computer system.

[0034] Based on the above embodiment, as an alternative embodiment, please refer to Figure 2 , the data analysis module includes: a statistical analysis unit, a correlation analysis unit, a determination unit, a scoring unit, and a report generation module; The statistical analysis unit is used to determine the corresponding statistical characteristics and process capability indexes of Cpk and Ppk based on electrical parameters, optical parameters, and environmental parameters, and generate a basic statistical report based on the statistical characteristics and process capability indexes; 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; The determination unit is used to determine the qualified or unqualified status of the makeup mirror lighting module according to the parameter correlation matrix and generate a determination result; The scoring unit is used to quantitatively score the key performance indicators of the light efficiency, color rendering property, uniformity, and stability of the makeup mirror lighting module based on the determination result and form a performance radar chart; The report generation module is used to generate a comprehensive quality assessment report according to the basic statistical report, parameter correlation matrix, determination result, and performance radar chart.

[0035] In this embodiment, during the implementation of the data analysis module of the in-vehicle makeup mirror lighting module detection system, the statistical analysis unit first extracts the statistical features of the calibration 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 statistical feature set reflecting the parameter fluctuation characteristics. Based on the statistical feature values, the statistical analysis unit calls the process capability index algorithm. For example, when the range of the nine-point illuminance is 200 lux, combined with the upper limit of the module specification limit of 1200 lux and the lower limit of 800 lux, calculate the process capability index of illuminance uniformity Cpk = 1.25, and determine that the production process is at the third-level control level; at the same time, according to the standard deviation of the color temperature data of 15 K and the tolerance of ±50 K, calculate the process capability index of color temperature stability Ppk = 1.08, and generate a basic statistical report containing the process capability index, providing a quantitative basis for the improvement of the production line process.

[0036] The correlation analysis unit analyzes the interaction mechanism between the electrical parameter fluctuations and the optical parameter changes based on the statistical feature values in the basic statistical report by using the grey correlation degree algorithm. For example, calculate the grey correlation degree Γ_current-CCT between the current overshoot amplitude in the transient response characteristic data and the standard deviation of the nine-point color temperature data. When the Γ value exceeds the preset threshold of 0.75, it is determined that there is a strong correlation mode between the current transient characteristics and the color temperature stability, and the corresponding position in the parameter correlation matrix is marked 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 cannot be found by traditional single-dimensional detection.

[0037] The determination unit scans the abnormal association patterns in the parameter correlation matrix in real time. When it detects that the voltage fluctuation coefficient exceeds 0.15 and there is a strong association with the standard deviation of color temperature (Γ_voltage-CCT≥0.7), it triggers the D02 defect code and generates a non-conforming determination result including specific abnormal association items. For example, for a certain batch of modules, the voltage fluctuation coefficient reaches 0.18, and the parameter correlation matrix shows that the Γ value between it and the color temperature deviation in the edge area is 0.72. The system automatically determines it as a defect of poor matching between the drive circuit and the LED. However, the traditional threshold comparison method fails to analyze the correlation between parameters, resulting in a missed detection rate of 67% for such defects.

[0038] The scoring unit dynamically adjusts the weights of key performance indicators according to the defect codes in the determination results. For example, when there is a D01 drive circuit defect, the initial value of the light efficiency score is multiplied by the weighting coefficient θ = 0.85, and the correction term ΔS = -15 provided by the correlation analysis unit is added to generate a weighted comprehensive score value. After all scoring items are normalized and mapped to the 0-100 score range, the scoring unit calls the radar chart generation algorithm to convert the score values of the four dimensions of light efficiency, color reducibility, uniformity, and stability into a performance radar chart with equal proportion scaling. Operators can intuitively identify the performance short board of the module through the area and shape of the radar chart. For example, the sunken area of the radar chart corresponding to a uniformity score of 65 points indicates a defect in the control of illuminance distribution during the production process.

[0039] The report generation module integrates the process capability indicators, the heat map of the parameter correlation matrix, the details of the determination results, and the performance radar chart in the basic statistical report to generate a comprehensive quality assessment report including the time stamp, product model, and serial number. The system uploads the report to the MySQL database in real time through a star topology network and generates a traceable CSV format log file locally to achieve full life cycle quality traceability from raw material batches to factory inspections. This embodiment quantifies production fluctuations through statistical features, locates hidden defects through the parameter correlation matrix, and maps quality levels through a dynamic scoring mechanism.

[0040] Based on the above embodiment, as an alternative embodiment, the data display module includes: a status display unit, a distribution chart unit, a trend chart unit, and a correlation analysis chart unit; The status display unit is used to intuitively display the determination result on the display interface by matching colors and icons according to the determination result; The distribution chart unit is used to generate a spatial distribution heat map according to the basic statistical report and mark the abnormal areas exceeding the threshold on the spatial distribution heat map for intuitive display on the display interface; The performance chart unit is used to generate a performance chart according to the performance radar chart for intuitive display on the display interface; The associated analysis chart unit is used to generate and visually display a correlation coefficient matrix on the display interface according to the parameter correlation matrix.

[0041] In this embodiment, the data display module is composed of a status display unit, a distribution chart unit, a performance chart unit, and an associated analysis chart unit. The functions and implementation methods of each unit are as follows: The status display unit generates a circular status identifier with a diameter of 200 mm in the central area of the graphical interface. When the judgment result is qualified, a green tick icon (80% of the diameter) is displayed. If there is a D01 type of defect, it is switched to a red exclamation mark icon (100% of the diameter) and the text label "D01 - Abnormal drive circuit" is superimposed, with the font being bold Song typeface of 14 pt. The distribution chart unit analyzes the nine-point illuminance data in the basic statistical report and superimposes a two-color temperature heat map on the surface of the three-dimensional mirror model. The color scale ranges from green (≥800 lux, RGB: 0, 255, 0) to red (≤600 lux, RGB: 255, 0, 0). When the illuminance value at the X2Y3 coordinate point is detected to be 550 lux (threshold 650 lux), an abnormal marking mechanism is triggered: a flashing red border with a width of 3 px (frequency 2 Hz) is generated, and a numerical label "550 lux (-15.4%)" is superimposed at the corresponding position. The performance chart unit maps the normalized score values of luminous efficiency (70 points), color rendering property (85 points), uniformity (56 points), and stability (75 points) to the polar coordinate system. The four radiation axes correspond to 0°, 90°, 180°, and 270° respectively. The axis lengths are scaled proportionally from 0 to 100 points (1 point = 1 mm), and the endpoints of each axis are connected to form a quadrilateral closed area. The filling color is set according to the comprehensive score interval (excellent: light green #90EE90, good: light yellow #FFFFE0, poor: light red #FFB6C1). The associated analysis chart unit generates an 8×8 correlation coefficient matrix diagram. The X-axis is labeled with electrical parameters (such as transient current rise time, unit ms), and the Y-axis is labeled with optical parameters (such as the Cpk value of edge illuminance). Each matrix unit is rendered as a bubble chart. The relationship between the bubble diameter d (unit mm) and the grey correlation degree Γ value is d = 8×Γ (when Γ = 0.83, the diameter is 6.64 mm). The color coding rule is as follows: 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 shortens the time for operators to identify drive circuit defects from 5.2 minutes to 8 seconds through visualization and dimensionality reduction technology. The false detection rate of the X2Y3 heat map abnormal marking for positioning LED patch offset faults is reduced from 12% to 0.7%. The correlation coefficient matrix diagram can analyze the strong negative correlation (Γ = -0.79) between the transient current rise time and the edge illuminance, providing a quantitative decision-making basis for optimizing the production line process.

[0042] The host computer system further includes a data storage module and a quality traceability module; The data storage module is used to store the comprehensive quality assessment report in the local database and the remote cloud server to form historical data records.

[0043] Based on the above embodiments, as an alternative embodiment, the host computer system further includes a data storage module and a quality traceability module; The data storage module is used to store the comprehensive quality assessment report in the local database and the remote cloud server to form historical data records.

[0044] In this embodiment, the data storage module realizes the full life cycle management of the detection 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 codes (such as D01) in the judgment results and the corresponding parameter correlation matrix data (γ = 0.83), the JPEG file stores the performance radar chart and the spatial distribution heat map, and the CSV file contains basic statistical report data such as the process capability index Cpk = 1.45. The local database uses a SQLite relational database, and the composite file package is indexed and recorded by timestamp (such as 20240517_142305) and product serial number (SN202405001) through a transaction processing mechanism; the remote cloud server is deployed based on a MySQL cluster, and after encapsulating the transmission data using the AES-256 encryption algorithm, it is synchronously transmitted to the cloud historical data record library in real time through the HTTPS protocol. The quality traceability module allows quick retrieval of the comprehensive quality assessment report in the historical data records by constructing a reverse query tree. For example, when the market end feedbacks that the SN202405001 product has a color temperature drift problem, the traceability module analyzes the parameter correlation matrix during the detection of this product, locates the correlation degree γ = 0.92 (exceeding the threshold of 0.75) between voltage fluctuation and color temperature drift, and retrieves the current overshoot waveform (peak value 5.8A / rise time 12ms) in the corresponding transient response characteristic data, and automatically generates a traceability report containing the analysis of the defect cause (the voltage stabilization of the drive circuit fails).

[0045] Based on the above embodiments, as an alternative embodiment, the host computer system further includes: a two-dimensional code generation unit, a batch analysis unit, and a warning generation unit; The two-dimensional 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 two-dimensional code identifier containing the test results; The batch analysis unit is used to obtain the 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 the analysis result; The warning generation unit is used to generate a warning message when the analysis result is abnormal.

[0046] In this embodiment, the host computer system realizes quality traceability and process monitoring through the collaborative operation of the QR code generation unit, the batch analysis unit and the warning generation unit. The QR code generation unit receives the defect code (such as D01) and product information (including serial number SN202405001) in the comprehensive quality assessment report, encapsulates the report data in XML format and the Base64-encoded thumbnail (including performance radar chart and heat map) according to the QR code standard, and generates a unique QR code identifier with a side length of 30 mm after encryption by the AES-256 algorithm. This identifier is printed on the product label. After scanning the code with a mobile phone, the current overshoot amplitude (5.2 A) in the transient response characteristic data and the correlation coefficient matrix diagram generated by the correlation analysis chart unit can be parsed. The batch analysis unit retrieves the historical data records of 50 products in the same batch from the data storage module, calculates the average value of the process capability index Cpk of this batch (such as the illuminance uniformity Cpk = 1.62 ± 0.15). When it is detected that the Cpk value of the current product's illuminance is 1.21, which is lower than the batch average value by -3σ, it is determined as abnormal and the batch offset code B01 is marked. After the batch analysis unit outputs the B01 code, the warning generation unit automatically retrieves the historical average value γ = 0.68 of the correlation degree between voltage fluctuation and color temperature drift in the parameter correlation matrix. If the current product's γ = 0.83 exceeds the threshold of 0.75, a level-3 warning (orange alert) is triggered, and a warning email containing the abnormal parameter comparison table (current Cpk = 1.21 vs batch average value 1.62) is sent to the quality management email box through the SMTP protocol.

[0047] On the other hand, please refer to Figure 3 , this application also provides a detection method for a makeup mirror lighting module, and the method includes: S101, simultaneously collecting the optical parameters of the makeup mirror lighting module, where the optical parameters include: multi-point color temperature data and multi-point illuminance data; S102, measuring the electrical parameters of the makeup mirror lighting module, where the electrical parameters include: current data, voltage data, power data and transient response characteristic data; S103, measuring the environmental parameters of the makeup mirror, where the environmental parameters include: temperature data, humidity data, air pressure data and ambient light data; S104, performing calibration processing on the electrical parameters, the optical parameters and the environmental parameters to obtain calibration data; S105. Perform correlation analysis and processing on the calibration data, establish a mapping relationship model between parameters, and compare it with a preset standard threshold based on the mapping relationship model to generate a comprehensive quality assessment report; S106. Display the comprehensive quality assessment report on the interface in a chart combination manner.

[0048] The above are only exemplary embodiments of the present disclosure and should not be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will readily think of other embodiments of the present disclosure after considering the specification and the practice of the present disclosure.

[0049] This application aims to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A detection system for a lighting module of a vehicle-mounted makeup mirror, characterized in that Including: The lower computer system, which is communicatively connected to the upper computer system and is used for collecting the electrical parameters, optical parameters and environmental parameters of the makeup mirror lighting module; The upper computer system, which is used for detecting according to the electrical parameters, the optical parameters and the environmental parameters to obtain a detection result; 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; The multi-point array optical parameter acquisition module is used for simultaneously acquiring the optical parameters of the makeup mirror lighting module, and the optical parameters include: multi-point color temperature data and multi-point illuminance data; The high-precision electrical parameter comprehensive measurement module is used for measuring the electrical parameters of the makeup mirror lighting module, and the electrical parameters include: current data, voltage data, power data and transient response characteristic data; The environmental parameter monitoring module is used for measuring the environmental parameters of the makeup mirror, and the environmental parameters include: temperature data, humidity data, air pressure data and ambient light data; The upper computer system includes: a data processing module, a data analysis module and a data display module; The data processing module is used for performing calibration processing on the electrical parameters, optical parameters and environmental parameters to obtain calibration data; The data analysis module is used for performing correlation analysis processing on the calibration data, establishing a mapping relationship model between parameters, and comparing the mapping relationship model with a preset standard threshold to generate a comprehensive quality assessment report; The data display module is used for receiving the comprehensive quality assessment report output by the data analysis module and displaying the comprehensive quality assessment report on the interface in a chart combination manner.

2. The detection system according to claim 1, wherein The data processing module further includes: a signal preprocessing unit, an environmental compensation calculation unit and a data fusion unit; The signal preprocessing unit is used for performing filtering processing on the electrical parameters, optical parameters and environmental parameters to obtain the preprocessed electrical parameters, the preprocessed optical parameters and the preprocessed environmental parameters; The environmental compensation calculation unit is used for calculating an environmental compensation coefficient based on the preprocessed environmental parameters; The data fusion unit is used for applying the environmental compensation coefficient to the preprocessed optical parameters and the preprocessed electrical parameters to obtain calibration data under standard environmental conditions.

3. The detection system according to claim 1, wherein 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 a plurality of BH1749NUC chips arranged in a 3×3 matrix, and is used for simultaneously collecting the initial color temperature data and the initial illuminance data of 9 measurement points; The optical signal processing unit is used for receiving the initial color temperature data and the initial illuminance data output by the sensor array unit, and performing signal stabilization processing on the initial color temperature data and the initial illuminance data through a Kalman filtering algorithm to obtain optical parameters.

4. 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-gear test unit and a transient characteristic measurement unit; The current and voltage measurement unit, based on the INA237 chip, is used for measuring the initial current data and the initial voltage data of the makeup mirror lighting module; The multi - gear test unit is used to receive control instructions, automatically switch the brightness gear of the makeup mirror lighting module according to the control instructions, and trigger the current - voltage measurement unit to measure the initial current data and initial voltage data at different brightness gears; The transient characteristic measurement unit is used to high - speed sample the initial current data and initial voltage data when the makeup mirror lighting module is turned on and the gear is switched, and generate transient response characteristic data.

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

6. The detection system according to claim 1, wherein The data analysis module includes: a statistical analysis unit, a correlation analysis unit, a determination unit, a scoring unit, and a report generation module; The statistical analysis unit is used to determine the corresponding statistical characteristics and process capability indexes of Cpk and Ppk according to electrical parameters, optical parameters, and environmental parameters, and generate a basic statistical report based on the statistical characteristics and process capability indexes; 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; The determination unit is used to determine the qualified or unqualified status of the makeup mirror lighting module according to the parameter correlation matrix and generate a determination result; The scoring unit is used to quantitatively score the key performance indexes of the light efficiency, color rendering property, uniformity, and stability of the makeup mirror lighting module based on the determination result, and form a performance radar chart; The report generation module is used to generate a comprehensive quality assessment report according to the basic statistical report, the parameter correlation matrix, the determination result, and the performance radar chart.

7. The detection system according to claim 6, wherein The data display module includes: a status display unit, a distribution chart unit, a trend chart unit, and a correlation analysis chart unit; The status display unit is used to match colors and icons according to the determination result and intuitively display the determination result on the display interface; The distribution chart unit is used to generate a spatial distribution heat map according to the basic statistical report, and mark the abnormal areas exceeding the threshold on the spatial distribution heat map and intuitively display it on the display interface; The performance chart unit is used to generate a performance chart according to the performance radar chart and intuitively display it on the display interface; The correlation analysis chart unit is used to generate a correlation coefficient matrix according to the parameter correlation matrix and intuitively display it on the display interface.

8. The detection system according to claim 1, wherein 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 the local database and the remote cloud server to form historical data records.

9. The detection system according to claim 8, wherein, The host computer system also includes: a two - dimensional code generation unit, a batch analysis unit, and a warning generation unit; The two - dimensional 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 two - dimensional code identifier containing the test result; The batch analysis unit is configured to obtain the 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 the analysis result; The warning generation unit is configured to generate a warning message when the analysis result is abnormal.

10. A method for detecting a lighting module of a vehicle-mounted makeup mirror, characterized in that, The method includes: Simultaneously collecting the optical parameters of the makeup mirror lighting module, where the optical parameters include: multi-point color temperature data and multi-point illuminance data; Measuring the electrical parameters of the makeup mirror lighting module, where the electrical parameters include: current data, voltage data, power data, and transient response characteristic data; Measuring the environmental parameters of the makeup mirror, where the environmental parameters include: temperature data, humidity data, air pressure data, and ambient light data; Performing calibration processing on the electrical parameters, optical parameters, and environmental parameters to obtain calibration data; Performing correlation analysis processing on the calibration data, establishing a mapping relationship model between the parameters, and comparing the mapping relationship model with a preset standard threshold to generate a comprehensive quality assessment report; Displaying the comprehensive quality assessment report on the interface in a chart combination manner.

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