Method and system for comprehensively evaluating anti-fogging performance of vehicle lamp based on dynamic optical monitoring

Abstract

The invention relates to the technical field of vehicle lamp monitoring, in particular to a vehicle lamp anti-fogging performance comprehensive evaluation method and system based on dynamic optical monitoring. A comprehensive evaluation method for anti-fogging performance of a vehicle lamp based on dynamic optical monitoring is characterized by comprising the following steps: S1, under a standard environmental condition, injecting saturated steam into the vehicle lamp to be tested according to a plurality of preset gradients through a steam generation device, and simulating moisture invasion under different sealing conditions; s2, the vehicle lamp is turned on after the saturated steam is injected every time, monitoring data are synchronously and continuously obtained, and the monitoring data comprise the change process of optical performance parameters and thermodynamic parameters of the vehicle lamp till the performance is recovered to preset stable conditions; s3, according to the monitoring data, quantitative indexes used for evaluating the anti-fogging performance of the vehicle lamp are extracted, the quantitative indexes comprise a tolerance index, a restoring force index and a stability index, the tolerance index is used for representing the resistance of the vehicle lamp to moisture intrusion, the restoring force index is used for representing the self-healing ability of the vehicle lamp after moisture intrusion, and the stability index is used for representing the anti-fogging ability of the vehicle lamp after moisture intrusion. The stability index is used for representing the performance reliability degree of the vehicle lamp under multiple moisture interferences; and S4, performing normalization processing on the extracted quantitative indexes, inputting the normalized quantitative indexes into a preset weighted evaluation model, performing calculation to obtain an anti-fogging performance comprehensive index, and delimiting the performance grade of the vehicle lamp according to the anti-fogging performance comprehensive index.

Description

Technical Field

[0001] This invention relates to the field of vehicle headlight monitoring technology, specifically to a comprehensive evaluation method and system for the anti-fogging performance of vehicle headlights based on dynamic optical monitoring. Background Technology

[0002] As a crucial safety component of automobiles, vehicle lights directly impact driving safety at night and in adverse weather conditions. However, headlight fogging has long been a prevalent and difficult-to-solve technical problem in the automotive industry. During operation, the internal temperature of the headlight rises, creating a temperature difference between the inside and outside of the lamp body. Combined with moisture seeping in through the ventilation system and the release of moisture absorbed by the lamp housing material during temperature changes, fog easily condenses on the inner surface of the lamp housing. Current technologies address headlight fogging primarily by using desiccants to absorb moisture, coating the inner surface of the lamp housing with a hydrophilic anti-fog coating, and optimizing the ventilation structure design. However, desiccants become ineffective after saturation, and their effective lifespan is typically shorter than the entire lamp's lifespan. Anti-fog coatings also age and peel off over time. Furthermore, simple structural optimization cannot completely eliminate the risk of fogging under all operating conditions.

[0003] Regarding the evaluation of vehicle headlight anti-fogging performance, current industry standards and corporate specifications mainly employ dual-temperature chamber testing or water spraying observation methods. These methods simulate actual usage environments by controlling different temperature and humidity conditions before and after the headlight, observing whether the headlight fogs up and how well the fog dissipates. Some advanced methods are beginning to use simulation analysis based on dew point difference thresholds to determine the location and degree of fogging on the exterior of the headlight.

[0004] However, these existing evaluation methods have significant flaws. Their evaluation dimensions are too singular, primarily focusing on whether the headlights fog up or the static size of the fogged area. They fail to quantify the dynamic attenuation and recovery of the headlights' core optical performance during moisture intrusion. Changes in optical performance such as illuminance attenuation, glare increase, and beam distortion are the key factors directly affecting driving safety. Existing technologies mostly rely on qualitative judgments of pass or fail, lacking a quantitative indicator system capable of accurately distinguishing performance differences between different designs. This makes it difficult to accurately identify design shortcomings and implement targeted optimizations during product development. The industry has not yet established an objective scoring system that integrates multi-dimensional performance parameters into a unified, comparable score. This makes it difficult to compare the anti-fogging performance of different suppliers and design schemes, hindering product quality grading and supply chain management. Furthermore, existing testing methods often overlook the significant impact of the moisture absorption and release characteristics of the headlight's plastic materials on fog formation. Studies have shown that moisture in plastics significantly affects the internal humidity of the headlight, and different materials exhibit significant differences in their moisture absorption capacity. Summary of the Invention

[0005] The technical problem solved by this invention is to provide a comprehensive evaluation method for the anti-fogging performance of vehicle lights based on dynamic optical monitoring. This method can comprehensively, dynamically, and quantitatively evaluate the anti-fogging performance of vehicle lights, thereby making up for the shortcomings of existing technologies and providing a scientific basis for vehicle light design optimization and quality management.

[0006] The basic solution provided by this invention is a comprehensive evaluation method for the anti-fogging performance of vehicle lights based on dynamic optical monitoring, which includes the following steps: S1. Under standard environmental conditions, saturated water vapor is injected into the interior of the vehicle headlight under test through a steam generator at multiple preset gradients to simulate moisture intrusion under different degrees of sealing conditions. S2. After each injection of saturated water vapor, the headlights are turned on, and monitoring data is continuously acquired synchronously. The monitoring data includes the changes in the optical performance parameters and thermodynamic parameters of the headlights until the performance is restored to the preset stable conditions. S3. Based on the monitoring data, extract quantitative indicators for evaluating the anti-fogging performance of vehicle lights. The quantitative indicators include tolerance indicators, resilience indicators and stability indicators. The tolerance indicators are used to characterize the resistance of vehicle lights to moisture intrusion. The resilience indicators are used to characterize the self-healing ability of vehicle lights after moisture intrusion. The stability indicators are used to characterize the performance reliability of vehicle lights under repeated moisture interference. S4. After normalizing the extracted quantitative indicators, input them into the preset weighted evaluation model to calculate the comprehensive anti-fogging performance index, and determine the performance level of the vehicle lights based on the comprehensive anti-fogging performance index.

[0007] The principle and advantages of this invention are as follows: First, under standard laboratory conditions, saturated water vapor is injected into the interior of the vehicle lamp under test through a steam generator at multiple pre-set gradients to simulate different degrees of sealing failure that may occur during actual use, such as minor leakage or more serious water ingress. After the moisture loading is completed, the method requires that the vehicle lamp be turned on immediately after each steam injection, and monitoring data be acquired synchronously and continuously. This data includes not only the optical performance parameters of the vehicle lamp, but also the changes in thermodynamic parameters, and this process continues until the performance of the vehicle lamp recovers to the preset stable conditions. The reason for synchronous and continuous monitoring is that the performance change of the vehicle lamp after moisture intrusion is a dynamic process, from fogging leading to light attenuation to fog dissipation after temperature rise, and the data at each stage is meaningful. Then, three major categories of quantitative indicators are extracted from these continuous monitoring data: tolerance indicators, resilience indicators, and stability indicators. Tolerance indicators are used to evaluate the vehicle lamp's resistance to moisture intrusion, resilience indicators are used to evaluate the vehicle lamp's self-healing ability after moisture intrusion, and stability indicators are used to evaluate the reliability of the vehicle lamp's performance under repeated moisture interference. Finally, all extracted indicators are normalized and input into a preset weighted evaluation model to calculate the comprehensive anti-fogging performance index, and the performance level of the vehicle lights is determined based on this index.

[0008] Compared to existing technologies, which often focus solely on whether headlights eventually fog up or statically measure the fogging area, this evaluation method is too one-sided and fails to establish a strong correlation with driving safety. Our method, however, simulates various levels of moisture intrusion and dynamically monitors multiple key parameters throughout the process, comprehensively and objectively reflecting the headlights' anti-fogging performance in real, complex environments. By integrating multi-dimensional performance parameters into a quantifiable comprehensive index, it also solves the problem of existing technologies relying heavily on qualitative judgments and struggling to accurately distinguish the superiority or inferiority of different design performances, providing clear data support for product optimization.

[0009] Furthermore, in step S1, a bubbling steam generator is controlled by a mass flow controller to sequentially inject at least three gradient levels of saturated water vapor into the interior of the vehicle lamp under test, wherein the gradient levels are set from low to high according to the equivalent water volume.

[0010] Furthermore, the headlights are turned on immediately after each moisture loading is completed, and the dynamic changes of the optical performance parameters and thermodynamic parameters of the headlights are monitored and recorded simultaneously and continuously. The optical performance parameters include at least the illuminance value of the irradiated surface, the glare value, and the spatial distribution of the light pattern, and the thermodynamic parameters include at least the lamp cover temperature. Monitoring continues until all monitored parameters return to stability.

[0011] Furthermore, S3 includes the following steps: S31. Extract tolerance indicators, including moisture intrusion threshold, maximum illuminance attenuation rate, and performance degradation response time; S31 includes the following steps: S311. Determine the moisture intrusion threshold. In the gradient moisture loading test, the stable value of the illuminance at the center point of the headlight illumination surface was monitored after each gradient loading. When the illuminance first drops to the initial illuminance When the value is below 90%, record the corresponding equivalent water vapor loading. As a threshold for moisture intrusion: ,in

[0012] S312, Calculate the maximum illuminance attenuation rate During the entire process of a single gradient test, the lowest value of the average illuminance of the headlight illumination surface is obtained. Calculate its relationship with the initial illuminance. The ratio gives the percentage decay:

[0013] S313, Calculate the performance degradation response time From the time point when a single gradient moisture loading is completed The timer starts and continues until the illuminance at the center of the headlight illumination surface first drops to the preset performance alarm threshold. time point Calculate the time difference: .

[0014] After each gradient of moisture loading, the illuminance at the center point of the headlight illumination surface is monitored until it stabilizes. When this illuminance value first drops below 90% of the initial illuminance, the equivalent water vapor loading amount corresponding to this loading is recorded as the moisture intrusion threshold. This indicator directly characterizes the basic sealing performance of the headlight assembly; a higher threshold means the headlight can withstand more moisture intrusion without significant optical performance degradation. The maximum illuminance attenuation rate is calculated by finding the lowest average illuminance reached by the headlight illumination surface throughout the entire gradient test, subtracting this lowest value from the initial illuminance, and then dividing by the initial illuminance to obtain a percentage. This indicator reflects the extreme degree of performance loss of the headlight under moisture interference; a smaller attenuation rate indicates better optical performance retention of the headlight under the worst conditions. The performance degradation response time is calculated from the moment a single gradient moisture loading is completed until the illuminance at the center point of the headlight's illumination surface first drops to a preset performance alarm threshold. This time difference is the response time, characterizing the speed at which moisture intrusion causes a significant deterioration in optical performance. The longer the response time, the less sensitive the headlight is to moisture intrusion, providing the driver with more reaction time. Existing technologies typically only provide a conclusion about whether fogging occurs, failing to offer such a refined description of resistance. This invention breaks down the headlight's resistance to moisture into three dimensions: sealing performance, extreme performance loss, and rate of deterioration. This allows for precise quantification of subtle differences between different headlight designs, helping researchers accurately pinpoint the product's shortcomings in moisture resistance.

[0015] Furthermore, S3 also includes the following steps: S32. Extract the resilience index, which includes illuminance recovery time, recovery process time constant, and luminance recovery ratio; S2 includes the following steps: S321. Calculate the illuminance recovery time. From the time point when a single gradient moisture loading is completed Start timing until the illuminance at the center point of the illumination returns to its initial value. The time point corresponding to 90% Calculate the time difference:

[0016] S322. Calculate the time constant of the recovery process. An exponential decay model was used to fit the illuminance recovery curve after moisture loading. The exponential decay model is expressed as:

[0017] in, Let be the illuminance value at time t. To restore the stable illuminance value, The illuminance difference to be restored The smaller the characteristic time constant obtained from the fitting, the higher the kinetic efficiency of the dehumidification and heat recovery process inside the headlight. S323, Calculate the luminance recovery ratio After the headlight performance has fully recovered and stabilized, the average illuminance of the illuminated surface is obtained. Calculate its comparison with the initial illuminance before moisture loading. Percentage values:

[0018] This ratio directly reflects whether moisture intrusion has caused permanent optical performance damage to the headlights.

[0019] The restoring power index mainly includes three indicators: illuminance recovery time, recovery process time constant, and luminance recovery ratio. Illuminance recovery time is calculated from the point when a single gradient moisture loading is completed until the illuminance at the center of the illuminated surface recovers to 90% of its initial value. The shorter this time, the faster the headlight can restore its optical performance to an acceptable level after experiencing moisture interference, which is crucial for ensuring driving safety. The recovery process time constant is obtained through mathematical modeling. An exponential decay model is used to fit the illuminance recovery curve after moisture loading, and a characteristic time constant is derived from this model. This constant is a kinetic parameter, and its magnitude directly reflects the efficiency of the dehumidification and thermal recovery processes inside the headlight. The smaller the constant, the more rational the design of the temperature field and airflow organization inside the headlight, enabling more efficient dispersal of fog and restoration of light transmission. The luminance recovery ratio is calculated by measuring the average illuminance of the illuminated surface after the headlight performance has fully stabilized and comparing it with the initial average illuminance before moisture loading, calculating a percentage. This ratio directly reflects whether moisture intrusion has caused permanent optical performance damage to the headlights. If the recovery ratio cannot reach close to 100%, it indicates that even if the fog dissipates, the headlight's light transmission performance has already suffered irreversible degradation. Compared to existing technologies, traditional methods often only observe whether the fog has disappeared, ignoring whether the performance has been fully restored. This method, through these three indicators, not only evaluates the speed of recovery but also the efficiency and quality of recovery, revealing the fundamental differences in self-healing capabilities among different headlights, and providing clear quantitative evidence for improving ventilation structures and optimizing thermal management design.

[0020] Furthermore, S3 also includes the following steps: S33. Extract stability indicators, including long-term brightness retention rate, light shape spatial distribution retention rate, and illuminance recovery process smoothness. S33 includes the following steps: S331. Calculate the long-term luminance retention rate, perform at least three complete gradient test cycles, and record the luminance recovery ratio after each cycle. The brightness recovery ratio of the initial cycle Using this as a baseline, calculate the long-term luminance retention rate after the k-th cycle: Long-term brightness retention rate = ; S332, Calculate the light shape spatial distribution preservation rate During the performance degradation and recovery periods following moisture loading, images of the light spot projected by the vehicle headlights onto a standard screen were acquired using an imaging luminance meter. These images were then processed at various time points. , with the initial reference image After spatial alignment, the structural similarity index is calculated:

[0021] The closer the index value is to 1, the higher the preservation of the spatial distribution of light patterns; S333. Calculate the smoothness S of the illuminance recovery process. During the illuminance recovery stage, continuously record illuminance values ​​at a preset sampling frequency, perform first-order difference calculation on the recovery curve to obtain the instantaneous recovery rate sequence, and calculate the coefficient of variation of this sequence:

[0022] The loss recovery rate is approximately:

[0023] The smaller the value, the smoother the recovery process and the more stable and reliable the thermodynamic and demisting processes.

[0024] Stability metrics are used to evaluate the reliability of vehicle headlights under repeated moisture exposure, specifically including long-term brightness retention rate, light pattern spatial distribution retention rate, and illuminance recovery process smoothness. Calculating the long-term brightness retention rate requires at least three complete gradient test cycles, recording the brightness recovery ratio after each cycle. Then, using the recovery ratio of the first cycle as a baseline, the percentage of subsequent cycle recovery ratios relative to the baseline is calculated. This metric reflects whether the optical performance of the headlight remains stable after multiple moisture exposures and recovery processes, and whether the degree of attenuation is within acceptable limits. A continuous decline in the long-term brightness retention rate indicates potential material aging or structural fatigue issues. Calculating the light pattern spatial distribution retention rate requires acquiring images of the headlight's beam projected onto a standard screen using an imaging luminance meter during the performance degradation and recovery periods after moisture loading. Then, the beam image at each time point is spatially aligned with the initial baseline image, and their structural similarity index is calculated. The closer this index is to one, the smaller the light pattern distortion and the better the retention of key features such as the cutoff line. This is crucial for ensuring that the headlight meets regulatory requirements and provides good lighting performance. The calculation of the smoothness of the illuminance recovery process involves continuously recording illuminance values ​​at a high sampling frequency during the illuminance recovery stage, performing first-order differencing on the recovery curve to obtain an instantaneous recovery rate sequence, and then calculating the coefficient of variation of this sequence. The smaller this coefficient, the smoother the recovery process, without abnormal jitter or fluctuations. A smooth recovery process means that the thermodynamic and defogging processes inside the headlight are stable and controllable, without sudden changes in speed. Current technologies lack effective methods for evaluating the reliability of headlights after repeated use, often only simple durability tests are performed. This method decomposes the long-term reliability of headlights into performance retention capability, optical deformation fidelity capability, and process stability through three indicators, achieving a comprehensive quantitative assessment of the long-term reliability of headlight anti-fogging performance.

[0025] Furthermore, S4 includes the following steps: S41. Map the extracted quantitative indicators to a unified scoring range according to the preset normalization rules to obtain the dimensionless score of each indicator. S42. According to the preset dimensional classification rules, the normalized index scores are classified into the tolerance dimension, resilience dimension, and stability dimension, respectively, and the scores for each dimension are calculated:

[0026] in , , These are the normalized scores for each indicator within the dimensions of tolerance, resilience, and stability. , , These represent the number of indicators included in each dimension. , , These are the preset weight coefficients for the three dimensions, and their sum is 1; S43 represents the preset weight coefficients for the three dimensions; S44. Compare the calculated comprehensive index of anti-fogging performance with the preset level threshold range to determine the level of anti-fogging performance of the vehicle headlights.

[0027] First, the indicators are normalized, mapping the extracted quantitative indicators to a unified scoring range according to preset rules. Since the dimensions and numerical ranges of different indicators may vary greatly—for example, the moisture intrusion threshold might be a volumetric value, while the time constant might be a temporal value—normalization converts them into comparable dimensionless scores. Next, according to preset dimensional classification rules, the normalized indicator scores are assigned to the tolerance, resilience, and stability dimensions, and a score is calculated for each dimension. The calculation method involves first finding the arithmetic mean of all indicator scores within a dimension, then multiplying it by a preset weighting coefficient for that dimension. This weighting coefficient can be adjusted based on different product positioning or customer requirements; for example, if a headlight emphasizes resistance to extreme environments, the weight of the tolerance dimension can be appropriately increased. Then, the scores of the three dimensions are summed to obtain the comprehensive anti-fogging performance index. Finally, this comprehensive index is compared with preset level threshold ranges, such as above 90 points for excellent, and 80 to 90 points for good, thus determining the final level of the headlight's anti-fogging performance. In existing technologies, the evaluation of automotive headlight anti-fogging performance often relies on comparisons of single indicators or subjective qualitative descriptions, making it difficult to conduct horizontal comparisons of products from different designs and manufacturers. This method constructs a scientific weighted evaluation model, condensing complex, multi-dimensional information into an intuitive score and rating, greatly simplifying the interpretation and application of evaluation results. This score can be used not only for solution comparison during product development but also for quality management in the supply chain, providing a possibility for establishing a unified performance benchmark for the industry.

[0028] Furthermore, it also includes S5, which includes the following steps: S51. After each gradient test is completed, execute the preset standard recovery procedure on the headlight under test to restore it to its initial state. S52. Perform a verification test on the vehicle lights after the restoration procedure is completed, and obtain the initial illuminance value after restoration. and compared with the baseline illuminance value before this test. Compare and calculate the illuminance deviation rate:

[0029] S53, if the illuminance deviation rate Exceeding the preset deviation threshold If so, it is determined that the basic performance of the vehicle headlight sample has undergone irreversible changes.

[0030] The present invention also discloses a comprehensive evaluation system for the anti-fogging performance of vehicle lights based on dynamic optical monitoring, which uses the above-mentioned comprehensive evaluation method for the anti-fogging performance of vehicle lights based on dynamic optical monitoring, including a steam loading module, a data acquisition module, a data analysis module and a comprehensive evaluation module; The steam loading module is used to inject saturated water vapor into the interior of the vehicle lamp under test according to multiple preset gradients under standard environmental conditions to simulate moisture intrusion under different degrees of sealing conditions. The data acquisition module is used to turn on the headlights after each injection of saturated water vapor, synchronously and continuously acquire monitoring data and transmit the data to the data analysis module. The monitoring data includes the changes in the optical performance parameters and thermodynamic parameters of the headlights. The data analysis module receives monitoring data transmitted by the data acquisition module, extracts quantitative indicators for evaluating the anti-fogging performance of the vehicle lights based on the monitoring data, including tolerance indicators, resilience indicators and stability indicators, and transmits the extracted quantitative indicators to the comprehensive evaluation module. The comprehensive evaluation module receives various quantitative indicators transmitted by the data analysis module, normalizes each quantitative indicator, inputs it into a preset weighted evaluation model, calculates the comprehensive anti-fogging performance index, and classifies the performance level of the vehicle lights based on the comprehensive anti-fogging performance index. Attached Figure Description

[0031] Figure 1 This is a flowchart illustrating an embodiment of the comprehensive evaluation method for vehicle headlight anti-fogging performance based on dynamic optical monitoring according to the present invention. Figure 2 This is a schematic diagram of the scoring method in an embodiment of the comprehensive evaluation method for the anti-fogging performance of vehicle lights based on dynamic optical monitoring of the present invention; Figure 3 This is a schematic diagram of the AFPI calculation model for an embodiment of the comprehensive evaluation method for vehicle headlight anti-fogging performance based on dynamic optical monitoring of the present invention. Detailed Implementation

[0032] The following detailed description illustrates the specific implementation method: The basic implementation examples are as follows: Figure 1 As shown: The basic solution provided by this invention is a comprehensive evaluation method for the anti-fogging performance of vehicle lights based on dynamic optical monitoring, which includes the following steps: S1. Under standard environmental conditions, saturated water vapor is injected into the interior of the vehicle headlight under test through a steam generator at multiple preset gradients to simulate moisture intrusion under different degrees of sealing conditions.

[0033] In step S1, a bubbling steam generator is controlled by a mass flow controller to sequentially inject at least three gradient levels of saturated water vapor into the interior of the vehicle lamp under test. The gradient levels are set from low to high according to the equivalent water volume.

[0034] Specifically, in this embodiment, a vehicle headlight was selected as the sample. First, the headlight was mounted on a test bench and left to stand for 2 hours under standard laboratory conditions of 25°C and 50% humidity to allow the headlight to reach thermal equilibrium. Then, a bubbling steam generator was controlled by a mass flow controller to sequentially inject three gradients of saturated water vapor into the headlight, with equivalent water volumes of 0.5 ml, 1.0 ml, and 1.5 ml, respectively. After each gradient injection was completed, the steam valve was closed in preparation for subsequent monitoring. During the injection process, uniform steam distribution was ensured to avoid localized condensation.

[0035] S2. After each injection of saturated water vapor, the headlights are turned on, and monitoring data is continuously acquired synchronously. The monitoring data includes the changes in the optical performance parameters and thermodynamic parameters of the headlights until the performance is restored to the preset stable conditions.

[0036] The headlights are turned on immediately after each moisture loading is completed, and the dynamic changes of the optical and thermodynamic parameters of the headlights are monitored and recorded simultaneously and continuously. The optical performance parameters include at least the illuminance value of the irradiated surface, the glare value, and the spatial distribution of the light pattern, and the thermodynamic parameters include at least the lamp cover temperature. Monitoring continues until all monitored parameters return to stability.

[0037] Specifically, after each moisture injection, the vehicle lights are immediately turned on and the data acquisition system is activated simultaneously. An illuminance meter is placed at the center of a standard screen 25 meters from the headlights, continuously recording illuminance values ​​at a frequency of 1 Hz. An imaging luminance meter simultaneously acquires light spot images on the screen for analysis of light distribution and glare values. A thermocouple is attached to the lampshade surface, recording temperature changes at a frequency of 1 Hz. The monitoring process continues until the illuminance values ​​stabilize (changes not exceeding 1% within 5 minutes) and the temperature stabilizes. In this embodiment, the monitoring time for each gradient is approximately 2 hours, recording complete illuminance-time curves, temperature-time curves, and light spot image sequences.

[0038] S3. Based on the monitoring data, extract quantitative indicators for evaluating the anti-fogging performance of vehicle lights. The quantitative indicators include tolerance indicators, resilience indicators, and stability indicators. The tolerance indicators are used to characterize the resistance of vehicle lights to moisture intrusion. The resilience indicators are used to characterize the self-healing ability of vehicle lights after moisture intrusion. The stability indicators are used to characterize the reliability of vehicle lights under repeated moisture interference.

[0039] S3 includes the following steps: S31. Extract tolerance indicators, including moisture intrusion threshold, maximum illuminance attenuation rate, and performance degradation response time; S31 includes the following steps: S311. Determine the moisture intrusion threshold. In the gradient moisture loading test, the stable value of the illuminance at the center point of the headlight illumination surface was monitored after each gradient loading. When the illuminance first drops to the initial illuminance When the value is below 90%, record the corresponding equivalent water vapor loading. As a threshold for moisture intrusion: ,in

[0040] S312, Calculate the maximum illuminance attenuation rate During the entire process of a single gradient test, the lowest value of the average illuminance of the headlight illumination surface is obtained. Calculate its relationship with the initial illuminance. The ratio gives the percentage decay:

[0041] S313, Calculate the performance degradation response time From the time point when a single gradient moisture loading is completed The timer starts and continues until the illuminance at the center of the headlight illumination surface first drops to the preset performance alarm threshold. time point Calculate the time difference: .

[0042] S3 further includes the following steps: S32. Extract the resilience index, which includes illuminance recovery time, recovery process time constant, and luminance recovery ratio; S2 includes the following steps: S321. Calculate the illuminance recovery time. From the time point when a single gradient moisture loading is completed Start timing until the illuminance at the center point of the illumination returns to its initial value. The time point corresponding to 90% Calculate the time difference:

[0043] S322. Calculate the time constant of the recovery process. An exponential decay model was used to fit the illuminance recovery curve after moisture loading. The exponential decay model is expressed as:

[0044] in, Let be the illuminance value at time t. To restore the stable illuminance value, The illuminance difference to be restored The smaller the characteristic time constant obtained from the fitting, the higher the kinetic efficiency of the dehumidification and heat recovery process inside the headlight. S323, Calculate the luminance recovery ratio After the headlight performance has fully recovered and stabilized, the average illuminance of the illuminated surface is obtained. Calculate its comparison with the initial illuminance before moisture loading. Percentage values:

[0045] This ratio directly reflects whether moisture intrusion has caused permanent optical performance damage to the headlights.

[0046] S3 further includes the following steps: S33. Extract stability indicators, including long-term brightness retention rate, light shape spatial distribution retention rate, and illuminance recovery process smoothness. S33 includes the following steps: S331. Calculate the long-term luminance retention rate, perform at least three complete gradient test cycles, and record the luminance recovery ratio after each cycle. The brightness recovery ratio of the initial cycle Using this as a baseline, calculate the long-term luminance retention rate after the k-th cycle: Long-term brightness retention rate = ; S332, Calculate the light shape spatial distribution preservation rate During the performance degradation and recovery periods following moisture loading, images of the light spot projected by the vehicle headlights onto a standard screen were acquired using an imaging luminance meter. These images were then processed at various time points. , with the initial reference image After spatial alignment, the structural similarity index is calculated:

[0047] The closer the index value is to 1, the higher the preservation of the spatial distribution of light patterns; S333. Calculate the smoothness S of the illuminance recovery process. During the illuminance recovery stage, continuously record illuminance values ​​at a preset sampling frequency, perform first-order difference calculation on the recovery curve to obtain the instantaneous recovery rate sequence, and calculate the coefficient of variation of this sequence:

[0048] The loss recovery rate is approximately:

[0049] The smaller the value, the smoother the recovery process and the more stable and reliable the thermodynamic and demisting processes.

[0050] Specifically, in this embodiment, after testing the sample vehicle headlights, the following examples of measured values ​​for each indicator were extracted: Tolerance indicators: Moisture intrusion threshold Under a 1.0 ml gradient, the illuminance first drops below 90% of its initial value, therefore... =1.0ml.

[0051] Maximum illuminance attenuation rate Under a 1.5 ml gradient, the minimum illuminance drops to 65% of the initial value, therefore =35%.

[0052] Performance degradation response time Under a 1.0 ml gradient, it takes 25 seconds for the illuminance to drop to 85% of its initial value (preset alarm threshold) after loading is complete. =25s.

[0053] Resilience indicators: Illuminance recovery time Under a 1.0 ml gradient, it took 45 minutes for the illuminance to recover to 90% of its initial value after loading was completed. =45min.

[0054] Recovery process time constant Exponential fitting was performed on the recovery curve of a 1.0 ml gradient to obtain... =18min.

[0055] Brightness recovery ratio After full recovery, the illuminance stabilized at 98% of the initial value, therefore R=98%.

[0056] Stability metrics (after three cycles of testing): Long-term brightness retention rate: The brightness recovery rate after the third cycle is 95%, and the first cycle is 98%. Therefore, the long-term brightness retention rate = 95 / 98×100%≈96.9%.

[0057] Light shape spatial distribution retention rate At the point of highest humidity, the SSIM index of the light spot and the initial image was 0.92.

[0058] Smoothness S of illuminance recovery process: The coefficient of variation is calculated for the recovery rate sequence. =0.25.

[0059] S4. After normalizing the extracted quantitative indicators, input them into the preset weighted evaluation model to calculate the comprehensive anti-fogging performance index, and determine the performance level of the vehicle lights based on the comprehensive anti-fogging performance index.

[0060] S4 includes the following steps: S41. Map the extracted quantitative indicators to a unified scoring range according to the preset normalization rules to obtain the dimensionless score of each indicator. S42. According to the preset dimensional classification rules, the normalized index scores are classified into the tolerance dimension, resilience dimension, and stability dimension, respectively, and the scores for each dimension are calculated:

[0061] in , , These are the normalized scores for each indicator within the dimensions of tolerance, resilience, and stability. , , These represent the number of indicators included in each dimension. , , These are the preset weight coefficients for the three dimensions, and their sum is 1; S43 represents the preset weight coefficients for the three dimensions; S44. Compare the calculated comprehensive index of anti-fogging performance with the preset level threshold range to determine the level of anti-fogging performance of the vehicle headlights.

[0062] Specifically, the preset benchmark values ​​and tolerance values ​​for each indicator are as follows: Figure 2 As shown, scores for each indicator are calculated based on the measured values. The final result is as follows: Figure 3 The weighted fusion of various indicators yields the comprehensive anti-fogging performance index AFPI, which is then mapped to a preset level range to determine the level of the vehicle headlight's anti-fogging performance.

[0063] It also includes S5, which includes the following steps: S51. After each gradient test is completed, execute the preset standard recovery procedure on the headlight under test to restore it to its initial state. S52. Perform a verification test on the vehicle lights after the restoration procedure is completed, and obtain the initial illuminance value after restoration. and compared with the baseline illuminance value before this test. Compare and calculate the illuminance deviation rate:

[0064] S53, if the illuminance deviation rate Exceeding the preset deviation threshold If so, it is determined that the basic performance of the vehicle headlight sample has undergone irreversible changes.

[0065] Specifically, in this embodiment, after each gradient test, the headlights are placed in a dry environment to cool naturally for 2 hours, then turned on again and the initial illuminance is measured. A preset deviation threshold is used. =3%. After completing the 1.5ml gradient test, the deviation between the recovered illuminance and the pre-test baseline value was 2.1%, which is less than 3%. Therefore, it was determined that the sample performance had not undergone irreversible changes, and subsequent tests could continue. If the deviation exceeds the threshold, the test should be terminated and the result recorded.

[0066] The present invention also discloses a comprehensive evaluation system for the anti-fogging performance of vehicle lights based on dynamic optical monitoring, which uses the above-mentioned comprehensive evaluation method for the anti-fogging performance of vehicle lights based on dynamic optical monitoring, including a steam loading module, a data acquisition module, a data analysis module and a comprehensive evaluation module; The steam loading module is used to inject saturated water vapor into the interior of the vehicle lamp under test according to multiple preset gradients under standard environmental conditions to simulate moisture intrusion under different degrees of sealing conditions. The data acquisition module is used to turn on the headlights after each injection of saturated water vapor, synchronously and continuously acquire monitoring data and transmit the data to the data analysis module. The monitoring data includes the changes in the optical performance parameters and thermodynamic parameters of the headlights. The data analysis module receives monitoring data transmitted by the data acquisition module, extracts quantitative indicators for evaluating the anti-fogging performance of the vehicle lights based on the monitoring data, including tolerance indicators, resilience indicators and stability indicators, and transmits the extracted quantitative indicators to the comprehensive evaluation module. The comprehensive evaluation module receives various quantitative indicators transmitted by the data analysis module, normalizes each quantitative indicator, inputs it into a preset weighted evaluation model, calculates the comprehensive anti-fogging performance index, and classifies the performance level of the vehicle lights based on the comprehensive anti-fogging performance index.

[0067] The above are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A comprehensive evaluation method for the anti-fogging performance of vehicle lights based on dynamic optical monitoring, characterized in that: Includes the following steps: S1. Under standard environmental conditions, saturated water vapor is injected into the interior of the vehicle headlight under test through a steam generator at multiple preset gradients to simulate moisture intrusion under different degrees of sealing conditions. S2. After each injection of saturated water vapor, the headlights are turned on, and monitoring data is continuously acquired synchronously. The monitoring data includes the changes in the optical performance parameters and thermodynamic parameters of the headlights until the performance is restored to the preset stable conditions. S3. Based on the monitoring data, extract quantitative indicators for evaluating the anti-fogging performance of vehicle lights. The quantitative indicators include tolerance indicators, resilience indicators and stability indicators. The tolerance indicators are used to characterize the resistance of vehicle lights to moisture intrusion. The resilience indicators are used to characterize the self-healing ability of vehicle lights after moisture intrusion. The stability indicators are used to characterize the performance reliability of vehicle lights under repeated moisture interference. S4. After normalizing the extracted quantitative indicators, input them into the preset weighted evaluation model to calculate the comprehensive index of anti-fogging performance, and determine the performance level of the vehicle lights based on the comprehensive index of anti-fogging performance.

2. The comprehensive evaluation method for vehicle headlight anti-fogging performance based on dynamic optical monitoring according to claim 1, characterized in that: In step S1, a bubbling steam generator is controlled by a mass flow controller to sequentially inject at least three gradient levels of saturated water vapor into the interior of the vehicle lamp under test. The gradient levels are set from low to high according to the equivalent water volume.

3. The comprehensive evaluation method for vehicle headlight anti-fogging performance based on dynamic optical monitoring according to claim 1, characterized in that: The headlights are turned on immediately after each moisture loading is completed, and the dynamic changes of the optical and thermodynamic parameters of the headlights are monitored and recorded simultaneously and continuously. The optical performance parameters include at least the illuminance value of the irradiated surface, the glare value, and the spatial distribution of the light pattern, and the thermodynamic parameters include at least the lamp cover temperature. Monitoring continues until all monitored parameters return to stability.

4. The comprehensive evaluation method for vehicle headlight anti-fogging performance based on dynamic optical monitoring according to claim 1, characterized in that: S3 includes the following steps: S31. Extract tolerance indicators, including moisture intrusion threshold, maximum illuminance attenuation rate, and performance degradation response time; S31 includes the following steps: S311. Determine the moisture intrusion threshold. In the gradient moisture loading test, the stable value of the illuminance at the center point of the headlight illumination surface was monitored after each gradient loading. When the illuminance first drops to the initial illuminance When the value is below 90%, record the corresponding equivalent water vapor loading. As a threshold for moisture intrusion: ,in S312, Calculate the maximum illuminance attenuation rate During the entire process of a single gradient test, the lowest value of the average illuminance of the headlight illumination surface is obtained. Calculate its relationship with the initial illuminance. The ratio gives the percentage decay: S313, Calculate the performance degradation response time From the time point when a single gradient moisture loading is completed The timer starts and continues until the illuminance at the center of the headlight illumination surface first drops to the preset performance alarm threshold. time point Calculate the time difference: 。 5. The comprehensive evaluation method for vehicle headlight anti-fogging performance based on dynamic optical monitoring according to claim 4, characterized in that: S3 further includes the following steps: S32. Extract the resilience index, which includes illuminance recovery time, recovery process time constant, and luminance recovery ratio; S2 includes the following steps: S321. Calculate the illuminance recovery time. From the time point when a single gradient moisture loading is completed Start timing until the illuminance at the center point of the illumination returns to its initial value. The time point corresponding to 90% Calculate the time difference: S322. Calculate the time constant of the recovery process. An exponential decay model was used to fit the illuminance recovery curve after moisture loading. The exponential decay model is expressed as: in, Let be the illuminance value at time t. To restore the stable illuminance value, The illuminance difference to be restored The smaller the characteristic time constant obtained from the fitting, the higher the kinetic efficiency of the dehumidification and heat recovery process inside the headlight. S323, Calculate the luminance recovery ratio After the headlight performance has fully recovered and stabilized, the average illuminance of the illuminated surface is obtained. Calculate its comparison with the initial illuminance before moisture loading. Percentage values: This ratio directly reflects whether moisture intrusion has caused permanent optical performance damage to the headlights.

6. The comprehensive evaluation method for vehicle headlight anti-fogging performance based on dynamic optical monitoring according to claim 5, characterized in that: S3 further includes the following steps: S33. Extract stability indicators, including long-term brightness retention rate, light shape spatial distribution retention rate, and illuminance recovery process smoothness. S33 includes the following steps: S331. Calculate the long-term luminance retention rate, perform at least three complete gradient test cycles, and record the luminance recovery ratio after each cycle. The brightness recovery ratio of the initial cycle Using this as a baseline, calculate the long-term luminance retention rate after the k-th cycle: Long-term brightness retention rate = ; S332, Calculate the light shape spatial distribution preservation rate During the performance degradation and recovery periods following moisture loading, images of the light spot projected by the vehicle headlights onto a standard screen were acquired using an imaging luminance meter. These images were then processed at various time points. , with the initial reference image After spatial alignment, the structural similarity index is calculated: The closer the index value is to 1, the higher the preservation of the spatial distribution of light patterns; S333. Calculate the smoothness S of the illuminance recovery process. During the illuminance recovery stage, continuously record illuminance values ​​at a preset sampling frequency, perform first-order difference calculation on the recovery curve to obtain the instantaneous recovery rate sequence, and calculate the coefficient of variation of this sequence: The loss recovery rate is approximately: The smaller the value, the smoother the recovery process and the more stable and reliable the thermodynamic and demisting processes.

7. The comprehensive evaluation method for vehicle headlight anti-fogging performance based on dynamic optical monitoring according to claim 6, characterized in that: S4 includes the following steps: S41. Map the extracted quantitative indicators to a unified scoring range according to the preset normalization rules to obtain the dimensionless score of each indicator. S42. According to the preset dimensional classification rules, the normalized index scores are classified into the tolerance dimension, resilience dimension, and stability dimension, respectively, and the scores for each dimension are calculated: in , , These are the normalized scores for each indicator within the dimensions of tolerance, resilience, and stability. , , These represent the number of indicators included in each dimension. , , These are the preset weight coefficients for the three dimensions, and their sum is 1; S43 represents the preset weight coefficients for the three dimensions; S44. Compare the calculated comprehensive index of anti-fogging performance with the preset level threshold range to determine the level of anti-fogging performance of the vehicle headlights.

8. The comprehensive evaluation method for vehicle headlight anti-fogging performance based on dynamic optical monitoring according to claim 7, characterized in that: It also includes S5, which includes the following steps: S51. After each gradient test is completed, execute the preset standard recovery procedure on the headlight under test to restore it to its initial state. S52. Perform a verification test on the vehicle lights after the restoration procedure is completed, and obtain the initial illuminance value after restoration. and compared with the baseline illuminance value before this test. Compare and calculate the illuminance deviation rate: S53, if the illuminance deviation rate Exceeding the preset deviation threshold If so, it is determined that the basic performance of the vehicle headlight sample has undergone irreversible changes.

9. A comprehensive evaluation system for vehicle headlight anti-fogging performance based on dynamic optical monitoring, using the comprehensive evaluation method for vehicle headlight anti-fogging performance based on dynamic optical monitoring as described in any one of claims 1-8, characterized in that: It includes a steam loading module, a data acquisition module, a data analysis module, and a comprehensive evaluation module; The steam loading module is used to inject saturated water vapor into the interior of the vehicle lamp under test according to multiple preset gradients under standard environmental conditions to simulate moisture intrusion under different degrees of sealing conditions. The data acquisition module is used to turn on the headlights after each injection of saturated water vapor, synchronously and continuously acquire monitoring data and transmit the data to the data analysis module. The monitoring data includes the changes in the optical performance parameters and thermodynamic parameters of the headlights. The data analysis module receives monitoring data transmitted by the data acquisition module, extracts quantitative indicators for evaluating the anti-fogging performance of the vehicle lights based on the monitoring data, including tolerance indicators, resilience indicators and stability indicators, and transmits the extracted quantitative indicators to the comprehensive evaluation module. The comprehensive evaluation module receives various quantitative indicators transmitted by the data analysis module, normalizes each quantitative indicator, inputs it into a preset weighted evaluation model, calculates the comprehensive anti-fogging performance index, and classifies the performance level of the vehicle lights based on the comprehensive anti-fogging performance index.

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