LED Vehicle Lighting Operation Monitoring System and Method Based on Data Analysis

Abstract

This invention relates to the field of electrical performance testing technology, specifically disclosing an LED vehicle headlight operation monitoring system and method based on data analysis. This system can accurately acquire the illumination parameters of the environment in which the LED vehicle headlight is located, and then scientifically analyze and determine the reference illumination intensity of the LED vehicle headlight. Based on this, it comprehensively monitors and meticulously collects the circuit information of the LED vehicle headlight. Through data analysis technology, it accurately locates each abnormal LED bead in the LED vehicle headlight. According to the pre-determined reference illumination intensity, it makes targeted adjustments to the abnormal LED beads. After the adjustments are completed, it further monitors the operating status parameters of the LED vehicle headlight, and evaluates the adjustment adaptability index of the LED vehicle headlight accordingly. This intelligently determines whether further performance adjustments are needed and provides timely feedback on the adjustment effects, thereby ensuring that the adjustments made fully meet the predetermined performance indicators.

Description

Technical Field

[0001] This invention relates to the field of electrical performance testing technology, specifically to an LED vehicle lighting operation monitoring system and method based on data analysis. Background Technology

[0002] With the rapid development of automotive technology, LED headlights have gradually become the mainstream choice for modern automotive lighting systems due to their advantages such as high efficiency, long lifespan, and low energy consumption. In recent years, methods for monitoring the operation of LED headlights have emerged. By integrating various sensors and processors, the operating status of LED headlights can be monitored in real time and accurately analyzed.

[0003] For example, the invention patent with announcement number CN116540058B announces an LED vehicle light operation monitoring system based on data analysis, including a processor. The processor is communicatively connected to a vehicle light monitoring module, a fault analysis module, a frequency monitoring module, and a storage module. The vehicle light monitoring module is used to monitor and analyze the operating status of the LED vehicle lights: after the LED vehicle lights are turned on, the LED vehicle lights are marked as monitoring objects; the operating coefficient YX of the monitoring object is obtained by numerically calculating the luminous flux data GT and color temperature data SW; the operating status of the LED vehicle lights can be monitored and analyzed, and the actual operating condition of the LED vehicle lights can be fed back through the operating coefficient.

[0004] For example, the invention patent with publication number CN115561666A discloses a method and system for self-testing electric vehicle lights. It includes acquiring the detection voltage signal of the output circuit of the electric vehicle lights based on a sampling circuit and outputting the sampling voltage; receiving the sampling voltage based on a self-testing circuit and detecting whether the output circuit of the electric vehicle lights is in normal working condition; and controlling the working state of the output circuit of the electric vehicle lights based on a switching module, which can realize circuit self-testing and thus determine whether the output circuit of the electric vehicle lights meets the requirements for safe operation.

[0005] However, in the process of implementing the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems: In the framework of analyzing abnormal operation of vehicle lights, the prior art may only confirm the existence of faults at the circuit level, but fails to fully reveal how these faults specifically interfere with the normal operation mechanism of vehicle lights, and may even ignore the complex situation that some circuits can still maintain basic function operation despite the abnormality, thus failing to take effective measures to adjust the operation of vehicle lights in a timely manner. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an LED vehicle light operation monitoring system and method based on data analysis, which can effectively solve the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of the present invention provides an LED vehicle headlight operation monitoring system based on data analysis, comprising: a reference analysis module, used to acquire the illumination parameters of the environment in which the LED vehicle headlight is located, and analyze the reference illumination intensity of the LED vehicle headlight; an anomaly adjustment module, used to monitor and collect the circuit information of the LED vehicle headlight, locate each abnormal LED bead in the LED vehicle headlight through data analysis, and adjust each abnormal LED bead in the LED vehicle headlight according to the reference illumination intensity of the LED vehicle headlight; and an operation feedback module, used to monitor the operating status parameters of the LED vehicle headlight after adjustment, evaluate the adjustment adaptability index of the LED vehicle headlight, and determine whether performance adjustment and feedback are required.

[0008] As a further solution, the method of locating abnormal LED beads through data analysis involves the following steps: The circuit information of the LED headlight specifically refers to the circuit abnormality index of each LED bead within the detection period. If the circuit abnormality index of a certain LED bead within the detection period is greater than the maximum value of the first circuit abnormality threshold range, then that LED bead is marked as a first-type abnormal LED bead; if the circuit abnormality index of a certain LED bead within the detection period falls within the first circuit abnormality threshold range, then that LED bead is marked as a normal LED bead; if the circuit abnormality index of a certain LED bead within the detection period is less than the maximum value of the first circuit abnormality threshold range... If the circuit abnormality index of a certain LED in the vehicle headlight is less than the minimum value of the second circuit abnormality threshold range or belongs to the second circuit abnormality threshold range during the detection period, and at the same time, the circuit abnormality index of a certain LED in the vehicle headlight is greater than the maximum value of the third circuit abnormality threshold range during the detection period, then the LED in the vehicle headlight is marked as a third type of abnormal LED. If the circuit abnormality index of a certain LED in the vehicle headlight is less than the minimum value of the third circuit abnormality threshold range or belongs to the third circuit abnormality threshold range during the detection period, then the LED in the vehicle headlight is marked as a fourth type of abnormal LED.

[0009] As a further solution, the adjustment of each abnormal LED bead in the LED headlight involves the following steps: acquiring the operating illumination data of the LED headlight during the testing period, and simultaneously analyzing the circuit abnormality index of each LED bead during the testing period to determine the illumination compliance coefficient of the LED headlight during the testing period, and matching the adjustment rate of each abnormal LED bead; counting the total number of first-type, second-type, third-type, and fourth-type abnormal LED beads, as well as the total number of LED beads in the LED headlight; acquiring the first compensation value and the second compensation value of the reference illumination intensity; updating the reference illumination intensity of the LED headlight during the demand period based on the reference illumination intensity, the first compensation value, and the second compensation value; and adjusting the LED headlight according to the reference illumination intensity and the circuit abnormality index of each LED bead during the demand period. The adjustment rate of abnormal LED beads is adjusted once for the first and second types of abnormal LED beads; the total number of first and second type abnormal LED beads is accumulated, and the accumulated result is marked as the total number of adjustable abnormal LED beads; the first compensation value of reference illuminance is updated based on the total number of adjustable and third type abnormal LED beads; the second compensation value of reference illuminance is updated based on the total number of adjustable and fourth type abnormal LED beads; the first and second compensation values ​​of reference illuminance are reinitialized; the reference illuminance of the LED vehicle headlight during the demand period is updated based on the reference illuminance, the first compensation value, and the second compensation value of reference illuminance during the demand period; and the first and second type abnormal LED beads are adjusted a second time based on the reference illuminance of the LED vehicle headlight during the demand period and the adjustment rate of each abnormal LED bead belonging to the LED vehicle headlight.

[0010] As a further solution, the specific determination process for whether to perform performance adjustment and feedback is as follows: extract the adjustment adaptation threshold from the monitoring database; compare the adjustment adaptation index of the LED vehicle light within the adjustment period with the adjustment adaptation threshold; if the adjustment adaptation index of the LED vehicle light within the adjustment period is greater than or equal to the adjustment adaptation threshold, it is determined that no performance adjustment and feedback will be performed; if the adjustment adaptation index of the LED vehicle light within the adjustment period is less than the adjustment adaptation threshold, it is determined that performance adjustment and feedback will be performed.

[0011] As a further solution, the performance adjustment and feedback process is as follows: the adjustment adaptation threshold and the adjustment adaptation index of the LED vehicle light within the adjustment period are processed by difference, and the processing result is compared with the adjustment adaptation threshold to obtain the adjustment adaptation deviation rate of the LED vehicle light; the reference illuminance of the LED vehicle light within the demand period is adjusted according to the adjustment adaptation deviation rate of the LED vehicle light, and an adjustment report is generated for feedback.

[0012] The second aspect of this invention provides a data analysis-based method for monitoring the operation of LED vehicle lights, comprising: Step 1, acquiring the illumination parameters of the environment in which the LED vehicle lights are located, and analyzing the reference illumination intensity of the LED vehicle lights; Step 2, monitoring and collecting the circuit information of the LED vehicle lights, locating each abnormal LED bead in the LED vehicle lights through data analysis, and adjusting each abnormal LED bead in the LED vehicle lights according to the reference illumination intensity of the LED vehicle lights; Step 3, monitoring the operating status parameters of the LED vehicle lights after adjustment, evaluating the adjustment adaptability index of the LED vehicle lights, and determining whether performance adjustment and feedback are required.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0014] (1) This invention provides an LED vehicle light operation monitoring system and method based on data analysis, which can accurately obtain the illumination parameters of the environment in which the LED vehicle light is located, and then scientifically analyze and determine the reference illumination intensity of the LED vehicle light. On this basis, the circuit information of the LED vehicle light is comprehensively monitored and meticulously collected. Through data analysis technology, the abnormal LED beads in the LED vehicle light are accurately located. According to the predetermined reference illumination intensity, the abnormal LED beads are adjusted in a targeted manner. After the adjustment is completed, the operating status parameters of the LED vehicle light are further monitored, and the adjustment adaptability index of the LED vehicle light is evaluated accordingly. Thus, it is intelligently determined whether further performance adjustment is needed, and the adjustment effect is fed back in a timely manner. This greatly enhances the efficiency, stability, adaptability to changing environments, and performance matching degree of the LED vehicle light during operation, thereby ensuring that the adjustments made can fully meet the predetermined performance indicators.

[0015] (2) By initializing the first compensation value and the second compensation value of the reference light intensity, the present invention not only simplifies the process of light intensity adjustment and improves the adjustment efficiency, but also enables the LED vehicle lights to make more precise fine adjustments based on the existing compensation values ​​when adjusting the light intensity next time. This is conducive to adapting to changes in different lighting environments more quickly, ensuring the stability and accuracy of light intensity, thereby improving the overall user experience and effect.

[0016] (3) By analyzing the illumination compliance coefficient of the LED vehicle light during the detection cycle and matching the adjustment rate of each abnormal LED bead of the LED vehicle light, the present invention avoids excessive or insufficient adjustment and effectively reduces the risk of damage to the LED beads due to improper operation. Attached Figure Description

[0017] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the system module connections of the present invention.

[0019] Figure 2 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] Reference Figure 1 As shown, the first aspect of the present invention provides an LED vehicle light operation monitoring system based on data analysis, including: a reference analysis module, an anomaly adjustment module, an operation feedback module, and a monitoring database.

[0022] The reference analysis module is connected to the anomaly adjustment module, the anomaly adjustment module is connected to the operation feedback module, and the reference analysis module, the anomaly adjustment module, and the operation feedback module are all connected to the monitoring database.

[0023] The monitoring database stores the reference current of the LED lamp beads during the demand cycle, the reference power dissipation value of the LED lamp beads during the demand cycle, the reference color temperature of the LED lamp beads during the demand cycle, the circuit anomaly factor corresponding to the average operating current, the circuit anomaly factor corresponding to the average power dissipation value, the circuit anomaly factor corresponding to the average color temperature, the illuminance uniformity threshold value, the threshold brightness fluctuation value, the threshold electrical signal frequency fluctuation value, the illuminance compliance factor corresponding to the illuminance uniformity, the illuminance compliance factor corresponding to the brightness fluctuation value, the illuminance compliance factor corresponding to the electrical signal frequency fluctuation value, the weight value corresponding to the circuit anomaly index, the threshold thermal resistance anomaly rate, the threshold flicker frequency, the adjustment adaptation factor corresponding to the thermal resistance anomaly rate, the adjustment adaptation factor corresponding to the flicker frequency, the adjustment adaptation factor corresponding to the illuminance compliance improvement ratio, the adjustment adaptation factor corresponding to the circuit anomaly index, the adjustment adaptation threshold, the adjustment rate corresponding to each illuminance compliance coefficient interval, the first circuit anomaly threshold interval, the second circuit anomaly threshold interval, and the third circuit anomaly threshold interval.

[0024] The reference analysis module is used to obtain the illumination parameters of the environment in which the LED vehicle lights are located, and to analyze the reference illumination intensity of the LED vehicle lights.

[0025] Specifically, the analysis of the reference illuminance of the LED vehicle headlights involves the following process: The illuminance parameters of the environment surrounding the LED vehicle headlights include the illuminance curve of that environment during the detection period. Based on this curve, the average illuminance of the environment during the demand period is predicted. The illuminance curve represents the change in illuminance over time within the detection period, and can be obtained through the vehicle's built-in light sensor. The average illuminance during the demand period refers to the average illuminance of the environment during the demand period. The prediction process involves inputting the illuminance curve into an autoregressive integral moving average (ARMA) model. The parameters of the ARMA model are determined using correlation functions (e.g., determining the autoregression order using the autocorrelation function and the moving average order using the partial autocorrelation function), resulting in an average illuminance prediction model. This model is then used to predict the average illuminance of the environment during the demand period.

[0026] The reference illuminance of the LED vehicle headlights is matched to the average illuminance of the environment within the demand period. This reference illuminance indicates the illuminance level that the LED vehicle headlights should be adjusted to within the demand period to ensure that they meet the needs of the actual application scenario. The specific matching process is as follows: The reference illuminance corresponding to each average illuminance interval is stored in the monitoring database. The average illuminance interval to which the average illuminance of the environment within the demand period belongs is queried. The reference illuminance corresponding to this average illuminance interval is the reference illuminance of the LED vehicle headlights matched to the average illuminance of the environment within the demand period.

[0027] The aforementioned testing cycle refers to the testing cycle of the environment in which the LED vehicle lights are located. The main purpose of this cycle is to predict the average illuminance of the environment in which the LED vehicle lights are located within the demand cycle. The aforementioned demand cycle refers to the future time period that the LED vehicle lights are expected to face. This embodiment aims to ensure that the LED vehicle lights can adapt to and meet the illuminance requirements of the demand cycle in advance. The specific duration is determined by the data analysis administrator.

[0028] The abnormal adjustment module is used to monitor and collect the circuit information of the LED vehicle lights, locate the abnormal LED beads of the LED vehicle lights through data analysis, and adjust the abnormal LED beads of the LED vehicle lights according to the reference light intensity of the LED vehicle lights.

[0029] Specifically, the process of locating abnormal LED beads in the LED headlight through data analysis is as follows: The circuit information of the LED headlight specifically refers to the circuit abnormality index of each LED bead within the detection period. If the circuit abnormality index of a certain LED bead within the detection period is greater than the maximum value of the first circuit abnormality threshold range, then that LED bead is marked as a first-type abnormal LED bead; if the circuit abnormality index of a certain LED bead within the detection period is within the first circuit abnormality threshold range, then that LED bead is marked as a normal LED bead; if the circuit abnormality index of a certain LED bead within the detection period is less than the minimum value of the first circuit abnormality threshold range... If the circuit abnormality index of a certain LED in the LED headlight is greater than the maximum value of the second circuit abnormality threshold range, then the LED headlight's LED bead is marked as a second-class abnormal LED bead. If the circuit abnormality index of a certain LED headlight within the detection period is less than the minimum value of the second circuit abnormality threshold range or belongs to the second circuit abnormality threshold range, and at the same time the circuit abnormality index of a certain LED headlight within the detection period is greater than the maximum value of the third circuit abnormality threshold range, then the LED headlight's LED bead is marked as a third-class abnormal LED bead. If the circuit abnormality index of a certain LED headlight within the detection period is less than the minimum value of the third circuit abnormality threshold range or belongs to the third circuit abnormality threshold range, then the LED headlight's LED bead is marked as a fourth-class abnormal LED bead.

[0030] It should be explained that the first, second, and third circuit abnormality threshold intervals are used as distinguishing criteria to classify the LED beads in the vehicle lights into different types, all extracted from the monitoring database. Specifically, the first circuit abnormality threshold interval is used to distinguish between first-type and second-type abnormal beads; the second circuit abnormality threshold interval is used to distinguish between second-type and third-type abnormal beads; and the third circuit abnormality threshold interval is used to distinguish between third-type and fourth-type abnormal beads. There are numerical intervals between the first and second circuit abnormality threshold intervals, and also between the second and third circuit abnormality threshold intervals. That is, the minimum value of the first circuit abnormality threshold interval is greater than the maximum value of the second circuit abnormality threshold interval, and the minimum value of the second circuit abnormality threshold interval is greater than the maximum value of the third circuit abnormality threshold interval.

[0031] The first type of abnormal LED bead is characterized by an adjustable abnormal brightness; the second type of abnormal LED bead is characterized by an adjustable dim brightness, but it can still work normally; the third type of abnormal LED bead is characterized by a dim brightness that cannot be adjusted; and the fourth type of abnormal LED bead is characterized by a complete lack of brightness that cannot be adjusted.

[0032] It should be explained that the value in the first circuit abnormality threshold interval is greater than the value in the second circuit abnormality threshold interval, and the value in the second circuit abnormality threshold interval is greater than the value in the third circuit abnormality threshold interval.

[0033] In one specific embodiment, the present invention analyzes the illumination compliance coefficient of the LED vehicle light during the detection cycle and matches the adjustment rate of each abnormal LED bead to the LED vehicle light, thereby avoiding excessive or insufficient adjustment and effectively reducing the risk of damage to the LED beads due to improper operation.

[0034] Specifically, the adjustment of each abnormal LED bead in the LED headlight involves the following process:

[0035] The system acquires the operating illumination data of the LED vehicle headlight during the testing period, and simultaneously analyzes the circuit abnormality index of each LED chip within the testing period to determine the illumination compliance coefficient of the LED vehicle headlight during the testing period. It then matches the adjustment rate of each abnormal LED chip to the system. The adjustment rate of each abnormal LED chip represents the rate at which the illumination intensity of each abnormal LED chip is adjusted to a reference illumination intensity. Specifically, the matching process involves: storing the adjustment rates corresponding to each illumination compliance coefficient range in the monitoring database; querying the illumination compliance coefficient range stored in the monitoring database to which the illumination compliance coefficient of the LED vehicle headlight during the testing period belongs; and determining the adjustment rate corresponding to this illumination compliance coefficient range, which is the adjustment rate of each abnormal LED chip in the LED vehicle headlight matched in this embodiment.

[0036] The total number of LED beads in categories I, II, III, and IV, as well as the total number of LED beads in the LED vehicle lights, can be obtained using data analysis software (such as statistical analysis software).

[0037] Obtain the first and second compensation values ​​of reference illuminance intensity, specifically the most recently initialized first and second compensation values ​​of reference illuminance intensity, which can be extracted from the data update log. The first compensation value is specifically used to adjust the illuminance of LEDs exhibiting Category III anomalies. Specifically, when an LED is classified as Category III, this compensation value will be used to increase or decrease the illuminance of Category I and Category II LEDs to ensure the overall lighting effect is balanced and stable. The second compensation value is specifically used to compensate the illuminance of LEDs exhibiting Category IV anomalies. Similar to the first compensation value, when an LED is determined to be Category IV, this value will be used to adjust the illuminance of Category I and Category II LEDs to achieve the expected lighting effect.

[0038] Based on the reference illuminance, the first compensation value, and the second compensation value of the LED headlights during the demand period, the reference illuminance of the LED headlights during the demand period is updated. Then, based on the reference illuminance of the LED headlights during the demand period and the adjustment rate of each abnormal LED chip, adjustments are made to the first and second types of abnormal LED chips. It should be explained that updating the reference illuminance of the LED headlights during the demand period specifically refers to accumulating the reference illuminance, the first compensation value, and the second compensation value of the LED headlights during the demand period. The accumulated result is the updated reference illuminance of the LED headlights during the demand period.

[0039] The total number of LEDs exhibiting the first type of abnormality is added together with the total number of LEDs exhibiting the second type of abnormality, and the result is marked as the total number of adjustable abnormal LEDs. The first compensation value of the reference illuminance is updated based on the total number of adjustable abnormal LEDs and the total number of LEDs exhibiting the third type of abnormality. The second compensation value of the reference illuminance is updated based on the total number of adjustable abnormal LEDs and the total number of LEDs exhibiting the fourth type of abnormality. Below is a specific example of how to update the reference illuminance compensation value based on the total number of each type of abnormal LED: First, calculate the total number of adjustable abnormal LEDs, that is, add the total number of LEDs exhibiting the first type of abnormality and the total number of LEDs exhibiting the second type of abnormality. Next, update the first compensation value of the reference illuminance based on the total number of adjustable abnormal LEDs and the total number of LEDs exhibiting the third type of abnormality. A new first compensation value of the reference illuminance is set as follows:

[0040] ;

[0041] in, , ;

[0042] Similarly, the second compensation value for the reference illuminance is updated based on the total number of adjustable abnormal LEDs and the total number of fourth-type abnormal LEDs, as follows:

[0043] ;

[0044] in, , ;

[0045] The total number of third-type abnormal LED beads detected in historical adjacent detection periods represents the total number of third-type abnormal LED beads detected in historical adjacent detection periods. The meanings of the total number of adjustable abnormal LED beads detected in historical adjacent detection periods and the total number of fourth-type abnormal LED beads detected in historical adjacent detection periods can be obtained in the same way.

[0046] Reinitializing the first and second compensation values ​​of the reference illuminance means updating these two compensation values ​​based on the latest first and second compensation values ​​of the reference illuminance. In subsequent use, these updated compensation values ​​can be used for more accurate analysis and judgment.

[0047] Based on the reference illuminance of the LED vehicle headlight during the demand period, the first compensation value of the reference illuminance, and the second compensation value of the reference illuminance, the reference illuminance of the LED vehicle headlight during the demand period is updated. Then, based on the reference illuminance of the LED vehicle headlight during the demand period and the adjustment rate of each abnormal LED bead belonging to the LED vehicle headlight, a second adjustment is performed on the first type of abnormal LED beads and the second type of abnormal LED beads. In an example embodiment, assuming the reference illuminance of the LED vehicle headlight during the demand period is I_ref and the adjustment rate of each abnormal LED bead belonging to the LED vehicle headlight is TY, the specific adjustment process is as follows: the illuminance of the first type of abnormal LED beads is reduced to I_ref at a rate of TY, and the illuminance of the second type of abnormal LED beads is increased to I_ref at a rate of TY.

[0048] It should be explained that in the actual adjustment process, current control technology (such as switch-type constant current control technology) is used to adjust the current of the circuit to which each lamp bead belongs until it reaches the reference current value matched by the reference illuminance.

[0049] In one specific embodiment, the present invention simplifies the process of adjusting light intensity and improves adjustment efficiency by initializing a first compensation value and a second compensation value for reference light intensity. It also enables LED vehicle lights to make more precise fine adjustments based on existing compensation values ​​when adjusting light intensity next time, which is beneficial for adapting to changes in different lighting environments more quickly, ensuring the stability and accuracy of light intensity, thereby improving the overall user experience and effect.

[0050] Specifically, the circuit abnormality index of each LED chip in the LED vehicle headlight during the detection period is analyzed as follows: Based on the reference illuminance of the LED vehicle headlight during the demand period, the reference current, reference power dissipation value, and reference color temperature of the LED chip in the demand period are matched from the monitoring database. The matching process is as follows: The monitoring database stores the reference current, reference power dissipation value, and reference color temperature corresponding to each reference illuminance range. The reference illuminance range stored in the monitoring database corresponding to the reference illuminance of the LED vehicle headlight during the demand period is queried. The reference current, reference power dissipation value, and reference color temperature corresponding to this reference illuminance range are the reference current, reference power dissipation value, and reference color temperature of the LED chip in the demand period matched in this embodiment.

[0051] By acquiring and comprehensively analyzing the average operating current, average power dissipation, and average color temperature of each LED chip in the LED headlight during the testing period, a circuit abnormality index for each LED chip in the LED headlight during the testing period is derived. This circuit abnormality index represents quantitative data on the impact of average operating current, average power dissipation, and average color temperature on circuit abnormalities, used to quantify the degree of circuit abnormality. The average operating current of each LED chip in the LED headlight during the testing period represents the average value of the operating current of each LED chip during the testing period, which can be monitored by a current sensor installed on the power supply line of the LED headlight. The average power dissipation of each LED chip in the LED headlight during the testing period represents the average power consumed by each LED chip during operation during the testing period, which can be measured by measuring the power consumption of each LED chip in the LED headlight during the testing period. The real-time voltage and current during operation are measured, and the instantaneous power P is calculated using the power formula (P=UI), where U is the real-time voltage and I is the real-time current. The instantaneous power is then averaged to obtain the value. The real-time voltage can be measured by a voltage sensor, and the real-time current can be measured by a current sensor. The average color temperature of each LED chip in the LED vehicle light during the detection period represents the average color temperature of the light emitted by each LED chip during the detection period. Specifically, it is obtained by matching the average color temperature of each LED chip during the detection period based on the average power dissipation value of each LED chip. The matching process is as follows: the average color temperature corresponding to each average power dissipation value range is stored in the monitoring database. The average power dissipation value range of each LED chip during the detection period is queried from the average power dissipation value range stored in the monitoring database. The average color temperature corresponding to this average power dissipation value range is the average color temperature of the corresponding LED chip during the detection period.

[0052] The circuit anomaly index is specifically obtained by summing the average operating current component, the average power dissipation component, and the average color temperature component. The average operating current component quantifies the impact of changes in average operating current on the circuit anomaly index; the average power dissipation component quantifies the impact of changes in average power dissipation on the circuit anomaly index; and the average color temperature component quantifies the impact of changes in average color temperature on the circuit anomaly index. The average operating current component refers to the indexed analysis of the average operating current, the reference current, and the circuit anomaly factor corresponding to the average operating current. The average power dissipation component refers to the indexed analysis of the average power dissipation value, the reference power dissipation value, and the circuit anomaly factor corresponding to the average power dissipation value. The average color temperature component refers to the indexed analysis of the average color temperature, the reference color temperature, and the circuit anomaly factor corresponding to the average color temperature. The specific expression for the circuit anomaly index of each LED chip in the LED vehicle light within the detection period is as follows:

[0053] ;

[0054] In the formula, This represents the circuit abnormality index of the s-th LED bead in the LED vehicle light during the detection cycle, where s is the LED bead number. d is the total number of LED beads. This represents the average operating current of the s-th LED chip in the LED vehicle light during the detection cycle. This refers to the reference current of the LED chips in the LED automotive light during the demand cycle. This represents the average power dissipation value of the s-th LED chip in the LED vehicle light during the detection cycle. This is the reference power dissipation value of the LED chips in the LED vehicle headlight during the demand cycle. The average color temperature of the s-th LED chip in the LED vehicle light during the testing period. This refers to the reference color temperature of the LED chips used in LED automotive lights during the demand cycle. To monitor the circuit anomaly factor corresponding to the preset average operating current in the database, To monitor circuit anomaly factors corresponding to preset average power dissipation values ​​in the database, To monitor circuit anomaly factors corresponding to the preset average color temperature in the database.

[0055] The reference current of the LED lamp beads in the above-mentioned LED vehicle light during the demand period represents the reference value of the average operating current of the s-th LED lamp bead in the detection period; the reference power dissipation value of the LED lamp beads in the above-mentioned LED vehicle light during the demand period represents the reference value of the average power dissipation value of the s-th LED lamp bead in the detection period; the reference color temperature of the LED lamp beads in the above-mentioned LED vehicle light during the demand period represents the reference value of the average color temperature of the s-th LED lamp bead in the detection period.

[0056] The circuit anomaly factor corresponding to the average operating current is used to quantify the influence of the average operating current unit value on the circuit anomaly index; the circuit anomaly factor corresponding to the average power dissipation value is used to quantify the influence of the average power dissipation value unit value on the circuit anomaly index; the circuit anomaly factor corresponding to the average color temperature is used to quantify the influence of the average color temperature unit value on the circuit anomaly index. The monitoring database stores the correspondence between the average operating current, average power dissipation value, and average color temperature and their corresponding circuit anomaly factors. For example, by inputting the average operating current, average power dissipation value, and average color temperature into the monitoring database, the monitoring database can match the circuit anomaly factor corresponding to the average operating current, the circuit anomaly factor corresponding to the average power dissipation value, and the circuit anomaly factor corresponding to the average color temperature. The values ​​are all between 0 and 1.

[0057] It needs to be explained that when the illuminance of the LED exceeds the reference value, its electrical characteristics also change. The average current and average power dissipation both increase, exceeding their reference values, and the average color temperature also increases. This series of changes can be quantified and evaluated through exponential analysis of the average current, average power dissipation, and average color temperature. If the circuit's abnormality index exceeds the first circuit abnormality threshold range, it indicates that the circuit state has deviated from the normal range and exceeds the requirements for reference average current, etc. Conversely, when the illuminance of the LED is slightly lower than the reference value but still within the adjustable range, its average current, average power dissipation, and average color temperature will also decrease slightly, but still remain within a certain range. At this time, although the circuit's abnormality index is less than the first circuit abnormality threshold range, it is still higher than the second circuit abnormality threshold range, indicating that although the circuit state is slightly lower than the requirements for reference average current, etc., it is still within the adjustable range. Furthermore… If a lamp bead short-circuits, its luminous efficacy will decrease drastically, emitting only a faint light. At this time, the average current, average power dissipation, and average color temperature will all drop significantly, far below their reference values. The circuit's abnormality index will also decrease accordingly, possibly even reaching or falling below the second circuit abnormality threshold range, but still remaining above the third circuit abnormality threshold range. This indicates that the circuit has experienced a significant abnormality. Most seriously, when a lamp bead experiences an open circuit, it will completely lose its luminous efficacy. In this case, the average current, average power dissipation, and average color temperature will all approach zero, far below their reference values. The circuit's abnormality index will also drop below the third circuit abnormality threshold range, clearly indicating that the circuit is in a severely abnormal state. In summary, by monitoring indicators such as the average current, average power dissipation, and average color temperature of the lamp beads, and combining this with the analysis of the circuit abnormality index, the state of the circuit can be effectively assessed, and potential circuit faults can be detected and addressed in a timely manner.

[0058] It should also be explained that in the performance evaluation of LED automotive lights, the stability of the average operating current directly affects the energy efficiency and luminous quality of the LED chips. When the average operating current deviates from the reference current, it may cause the average power dissipation value to deviate from the corresponding reference value. At the same time, the deviation of the current may also cause color temperature fluctuations, causing the average color temperature to deviate from the reference color temperature, thereby affecting the luminous quality of the automotive lights.

[0059] Furthermore, the specific analysis process for the illumination compliance coefficient of the LED vehicle headlights during the testing period is as follows: The operating illumination data of the LED vehicle headlights during the testing period includes the uniformity of illumination intensity of the illumination area to which the LED vehicle headlights belong, the brightness fluctuation value of the illumination area to which the LED vehicle headlights belong during the testing period, and the electrical signal frequency fluctuation value of the LED vehicle headlights during the testing period. The aforementioned uniformity of illumination intensity is used to quantify the uniformity of illumination intensity of the illumination area to which the LED vehicle headlights belong during the testing period. Specifically, it refers to the ratio of the maximum to the minimum illumination intensity of the illumination area to which the LED vehicle headlights belong during the testing period. This can be achieved by collecting real-time illumination intensity at multiple locations within the illumination area using an onboard light sensor, and sorting all illumination intensity data in ascending order. The ratio of the last-ranked illumination intensity to the first-ranked illumination intensity is then extracted. The illumination area refers to the specific area directly illuminated and covered by the LED vehicle headlights, defined by the light emitted by the LED vehicle headlights. The aforementioned brightness fluctuation value is used to characterize the illumination intensity of the LED vehicle headlights during the testing period. The degree of brightness fluctuation within a region can be captured using an onboard camera or light sensor array to obtain real-time illumination distribution images of the illuminated area. Image processing software (such as Matrix Labs) is then used to obtain the brightness variance in the real-time illumination distribution images. The average of the brightness variances of all real-time illumination distribution images within the detection period is then calculated to obtain the brightness fluctuation value. The smaller the brightness fluctuation value, the more stable and uniform the illumination. The aforementioned electrical signal frequency fluctuation value refers to the degree of fluctuation in the frequency of the electrical signal (such as current or voltage) of the LED headlight during operation. It is one of the important indicators for measuring the power supply stability and control accuracy of the LED headlight. The power supply signal of the LED headlight can be monitored through an onboard diagnostic system, recording the frequency value of the electrical signal within the detection period and calculating the standard deviation of these frequency values, which is the electrical signal frequency fluctuation value of the LED headlight within the detection period. The onboard diagnostic system is mainly used to monitor the vehicle's operating status in real time, including the working status of the engine, emission system, fuel system, etc. At the same time, the sensors and controllers in the onboard diagnostic system can monitor the vehicle's power signals, including the power signals of the LED headlights.

[0060] By comprehensively analyzing the circuit anomaly index of each LED chip in the LED vehicle headlight during the testing period, the uniformity of light intensity in the illumination area of ​​the LED vehicle headlight during the testing period, the brightness fluctuation value of the illumination area of ​​the LED vehicle headlight during the testing period, and the electrical signal frequency fluctuation value of the LED vehicle headlight during the testing period, an illumination compliance coefficient of the LED vehicle headlight during the testing period is obtained. The illumination compliance coefficient of the LED vehicle headlight during the testing period represents the quantitative data of the influence of the circuit anomaly index, the uniformity of light intensity, the brightness fluctuation value, and the electrical signal frequency fluctuation value on the illumination compliance of the LED vehicle headlight during the testing period. It is used to quantify the degree to which the illumination performance of the LED vehicle headlight during the testing period conforms to the preset illumination performance.

[0061] The specific expression for the illumination compliance coefficient of the LED vehicle lights during the testing period is as follows:

[0062] ;

[0063] In the formula, The illumination compliance coefficient for LED vehicle lights during the testing period. This refers to the uniformity of light intensity in the illuminated area of ​​the LED vehicle headlight during the testing period. To monitor the preset light intensity uniformity threshold in the database, This represents the brightness fluctuation value of the illuminated area of ​​the LED vehicle headlight during the detection period. To monitor the pre-defined brightness fluctuation values ​​in the database, This refers to the electrical signal frequency fluctuation value of the LED vehicle light during the testing period. To monitor the preset defined electrical signal frequency fluctuation values ​​in the database, To monitor the illumination compliance factor corresponding to the preset illumination intensity uniformity in the database, To monitor the illumination compliance factors corresponding to the preset brightness fluctuation values ​​in the database, To monitor the illumination compliance factor corresponding to the preset electrical signal frequency fluctuation values ​​in the database, This represents the circuit abnormality index of the s-th LED bead in the LED vehicle light during the detection cycle. To monitor the weight values ​​corresponding to the preset circuit anomaly index in the database, where s is the number of each LED bead, d represents the total number of LED beads.

[0064] The above-mentioned light intensity uniformity threshold represents the minimum allowable value for light intensity uniformity; the above-mentioned brightness fluctuation threshold represents the maximum allowable value for brightness fluctuation; the above-mentioned electrical signal frequency fluctuation threshold represents the maximum allowable value for electrical signal frequency fluctuation.

[0065] The aforementioned illumination compliance factor corresponding to the illumination intensity uniformity is used to quantify the influence of the unit value of illumination intensity uniformity on the illumination compliance coefficient; the aforementioned illumination compliance factor corresponding to the brightness fluctuation value is used to quantify the influence of the unit value of brightness fluctuation value on the illumination compliance coefficient; the aforementioned illumination compliance factor corresponding to the electrical signal frequency fluctuation value is used to quantify the influence of the unit value of electrical signal frequency fluctuation value on the illumination compliance coefficient; the aforementioned weight value corresponding to the circuit anomaly index is used to de-unitize the circuit anomaly index and quantify the influence of the unit value of the circuit anomaly index on the illumination compliance coefficient. The monitoring database stores the correspondence between illumination intensity uniformity, brightness fluctuation value, and electrical signal frequency fluctuation value and their corresponding illumination compliance factors, and also stores the correspondence between the circuit anomaly index and its corresponding weight value. For example, by inputting illumination intensity uniformity, brightness fluctuation value, electrical signal frequency fluctuation value, and circuit anomaly index into the monitoring database, the monitoring database can match the illumination compliance factor corresponding to illumination intensity uniformity, the illumination compliance factor corresponding to brightness fluctuation value, the illumination compliance factor corresponding to electrical signal frequency fluctuation value, and the weight value corresponding to circuit anomaly index, all with values ​​ranging from 0 to 1.

[0066] It should be explained that in the performance evaluation of LED vehicle lights, the circuit abnormality index of the s-th LED chip during the testing cycle is a key indicator for measuring its circuit health. It directly affects the uniformity of illumination intensity in the illumination area of ​​the LED vehicle light. When the circuit abnormality index increases, it means that there may be problems such as poor circuit connection, component aging, or unstable drive circuit. These problems will lead to a decrease in illumination intensity uniformity. At the same time, the brightness fluctuation value of the illumination area during the testing cycle will also be affected. Circuit abnormalities may cause brightness instability, resulting in brightness fluctuation values ​​much greater than the corresponding threshold values. In addition, the electrical signal frequency fluctuation value of the LED vehicle light during the testing cycle is also affected by the circuit status. Abnormal circuits may cause electrical signal frequency instability, resulting in electrical signal frequency fluctuation values ​​much greater than the preset threshold electrical signal frequency fluctuation value. The circuit abnormality index, illumination intensity uniformity, brightness fluctuation value, and electrical signal frequency fluctuation value are interrelated and jointly reflect the health status and illumination performance of the LED vehicle light circuit. When any one or more of these parameters are abnormal, the illumination compliance coefficient of the LED vehicle light during the testing cycle will be reduced, which means that the illumination performance of the vehicle light has failed to meet the predetermined compliance standard and needs to be adjusted accordingly.

[0067] The operation feedback module is used to monitor the operating status parameters of the LED headlights after adjustment, evaluate the adjustment adaptability index of the LED headlights, and determine whether to perform performance adjustments and provide feedback.

[0068] Specifically, the determination process for whether to perform performance adjustments and feedback is as follows: An adjustment adaptation threshold is extracted from the monitoring database; the adjustment adaptation index of the LED vehicle light within the adjustment period is compared with the adjustment adaptation threshold. If the adjustment adaptation index of the LED vehicle light within the adjustment period is greater than or equal to the adjustment adaptation threshold, it is determined that no performance adjustments and feedback will be performed; if the adjustment adaptation index of the LED vehicle light within the adjustment period is less than the adjustment adaptation threshold, it is determined that performance adjustments and feedback will be performed. The aforementioned adjustment adaptation threshold represents the minimum value within a reasonable range of the adjustment adaptation index.

[0069] Specifically, the performance adjustment and feedback process involves: processing the difference between the adjustment adaptation threshold and the adjustment adaptation index of the LED vehicle light within the adjustment period; comparing the result with the adjustment adaptation threshold to obtain the adjustment adaptation deviation rate of the LED vehicle light, which is used to quantify the degree of deviation of the adjustment adaptation index of the LED vehicle light from the adjustment adaptation threshold within the adjustment period.

[0070] The reference illuminance of the LED headlights during the demand period is adjusted based on the adjustment adaptation deviation rate of the LED headlights. An adjustment report is generated and fed back. The specific adjustment process is as follows: Assuming the reference illuminance of the LED headlights during the demand period is I_ref and the adjustment adaptation deviation rate of the LED headlights is io, the reference illuminance of the LED headlights during the demand period is adjusted to I_ref*(1-io). Based on I_ref*(1-io), the first type of abnormal LED beads and the second type of abnormal LED beads are readjusted. An adjustment report is generated and the vehicle owner is notified via pop-up window or SMS. The adjustment report includes the adjustment adaptation deviation rate of the LED headlights and a comparison of the illuminance before and after the adjustment (i.e., I_ref and I_ref*(1-io)).

[0071] It should be explained that the first adjustment, the second adjustment, and the readjustment all involve adjusting the light intensity of the first type of abnormal LED beads and the second type of abnormal LED beads to the reference light intensity level.

[0072] Furthermore, the assessment of the LED vehicle headlight adjustment adaptability index is specifically conducted as follows: the adjusted operating status parameters of the LED vehicle headlight include the illumination compliance coefficient of the LED vehicle headlight during the adjustment period, the circuit abnormality index of each LED chip during the adjustment period, the thermal resistance change curve of the LED vehicle headlight during the adjustment period, and the flicker frequency of the LED vehicle headlight during the adjustment period. The adjustment period is a time interval between the testing period and the demand period. The main purpose of the adjustment period is to examine whether the adjusted LED vehicle headlight can meet the performance requirements of the upcoming demand period. The specific duration of the adjustment period is determined by the data analysis administrator. The establishment of this period ensures that the LED vehicle headlight can be adjusted and optimized in a timely manner during continuous operation to meet the standards set by the constantly changing demand period. The illumination compliance coefficient of the LED vehicle headlight during the adjustment period has the same meaning and acquisition method as the illumination compliance coefficient of the LED vehicle headlight during the testing period. The difference lies in the components involved in the illumination compliance coefficient of the LED vehicle headlight during the adjustment period. The parameters are collected within the adjustment period; the circuit abnormality index of each LED bead in the LED vehicle headlight within the adjustment period is consistent with the circuit abnormality index of each LED bead in the detection period in terms of meaning and acquisition method, the difference being that the parameters involved in the circuit abnormality index of each LED bead in the adjustment period are collected within the adjustment period; the thermal resistance change curve of the LED vehicle headlight within the adjustment period is a graphical representation of the change in thermal resistance value of the LED vehicle headlight over time within a specific time period (i.e., the adjustment period), which can be measured by the thermal resistance testing system built into the car; the flicker frequency of the LED vehicle headlight within the adjustment period refers to the ratio of the number of times the LED vehicle headlight flickers within a specific time period (i.e., the adjustment period) to the duration corresponding to the adjustment period. It can be obtained by capturing the light signal of the LED vehicle headlight flickering through a photoelectric sensor, converting it into an electrical signal for recording, amplifying and filtering the electrical signal output by the photoelectric sensor to extract the effective flicker signal, and using a counter or timer to count the flicker signal to obtain the number of flickers within the adjustment period.

[0073] The thermal resistance change curve of the LED vehicle headlight during the adjustment period is compared with the defined thermal resistance change curve to obtain the thermal resistance anomaly rate of the LED vehicle headlight during the adjustment period. The aforementioned defined thermal resistance change curve is a preset thermal resistance change threshold curve used to define the reasonable range of thermal resistance change of the LED vehicle headlight under normal working conditions. It represents the maximum acceptable range of thermal resistance value change over time under expected working conditions. When the actual thermal resistance change of the LED vehicle headlight exceeds this threshold, it may indicate problems such as poor heat dissipation, design defects, or aging. The aforementioned thermal resistance anomaly rate is an indicator used to quantitatively evaluate the degree of abnormality in the thermal resistance change of the LED vehicle headlight. It is calculated by comparing the portion of the thermal resistance change curve that exceeds the defined thermal resistance change curve (i.e., the abnormal portion) with the entire thermal resistance change curve. Specifically, the thermal resistance anomaly rate is equal to the ratio of the area (or length) of the abnormal portion to the area (or length) of the entire thermal resistance change curve. The higher this ratio, the more serious the degree of abnormality in the thermal resistance change of the LED vehicle headlight.

[0074] The difference between the illumination compliance coefficient of the LED vehicle lights during the adjustment period and the illumination compliance coefficient of the LED vehicle lights during the testing period is processed. The result of the difference processing is then compared with the illumination compliance coefficient of the LED vehicle lights during the testing period to obtain the illumination compliance improvement rate of the LED vehicle lights during the adjustment period. This is used to quantify the improvement effect of the illumination compliance of the LED vehicle lights during the adjustment period.

[0075] By comprehensively evaluating the thermal resistance anomaly rate, flicker frequency, illumination compliance improvement rate, and circuit anomaly index of each LED chip within the adjustment period of the LED vehicle headlight, an adjustment adaptation index for the LED vehicle headlight within the adjustment period is derived. This adjustment adaptation index represents quantitative data on the impact of the thermal resistance anomaly rate, flicker frequency, illumination compliance improvement rate, and circuit anomaly index on the degree of adjustment adaptation of the LED vehicle headlight within the adjustment period, and is used to quantify the degree of adjustment adaptation of the LED vehicle headlight within the adjustment period. The specific expression for the adjustment adaptation index of the LED vehicle headlight within the adjustment period is as follows:

[0076] ;

[0077] In the formula, This refers to the adjustment adaptability index of LED vehicle lights within the adjustment cycle. The thermal resistance anomaly rate of LED vehicle lights during the adjustment cycle. The flashing frequency of LED vehicle lights within the adjustment cycle. To improve the compliance rate of LED vehicle lights in terms of illumination during the adjustment period. This represents the circuit abnormality index of the s-th LED chip in the LED vehicle light during the adjustment cycle. To monitor the pre-defined thermal resistance anomaly rate in the database, To monitor the preset flicker frequencies in the database, To monitor the adjustment adaptation factor corresponding to the preset thermal resistance anomaly rate in the database, To monitor the adjustment adaptation factor corresponding to the preset flicker frequency in the database, To monitor the adjustment factor corresponding to the preset illumination compliance improvement rate in the database, To monitor the adjustment and adaptation factors corresponding to the preset circuit anomaly index in the database, where s is the number of each LED bead, d represents the total number of LED beads.

[0078] The above definition of thermal resistance anomaly rate represents the maximum allowable value of thermal resistance anomaly rate; the above definition of flicker frequency represents the maximum allowable value of flicker frequency.

[0079] The adjustment factor corresponding to the aforementioned thermal resistance anomaly rate is used to quantify the influence of the unit value of the thermal resistance anomaly rate on the adjustment factor index; the adjustment factor corresponding to the aforementioned flicker frequency is used to quantify the influence of the unit value of the flicker frequency on the adjustment factor index; the adjustment factor corresponding to the aforementioned illumination compliance improvement ratio is used to de-unitize the illumination compliance improvement ratio and quantify the influence of the unit value of the illumination compliance improvement ratio on the adjustment factor index; the adjustment factor corresponding to the aforementioned circuit anomaly index is used to de-unitize the circuit anomaly index and quantify the influence of the unit value of the circuit anomaly index on the adjustment factor index. The monitoring database stores the correspondence between the thermal resistance anomaly rate, flicker frequency, illumination compliance improvement ratio, and circuit anomaly index and their corresponding adjustment factors. For example, by inputting the thermal resistance anomaly rate, flicker frequency, illumination compliance improvement ratio, and circuit anomaly index into the monitoring database, the monitoring database can match the adjustment factor corresponding to the thermal resistance anomaly rate, the flicker frequency, the illumination compliance improvement ratio, and the circuit anomaly index, with values ​​ranging from 0 to 1.

[0080] It needs to be explained that when LED headlights fail to fully adapt to the adjustment requirements after adjustment, a series of changes in operating parameters will occur. Specifically, LED headlights may exhibit flickering, causing a significant increase in the flicker frequency within the adjustment cycle, indicating potential instability in the LED headlight circuit or the vehicle's power supply. Simultaneously, because the adjustment has failed to effectively improve the headlight's illumination performance, the illumination compliance improvement rate will be low, meaning the headlights have not yet met the expected standards in terms of illumination intensity, uniformity, and stability. Furthermore, the circuit abnormality index of the LED chips will remain high, indicating poor health of the LED chip circuit and potential problems. Problems such as component aging, poor connection, or drive circuit failure can further increase the thermal resistance anomaly rate of the headlights. This means that the heat dissipation performance of the headlights is affected, which may lead to increased headlight temperature, accelerated aging, and performance degradation. In summary, when the flicker frequency of LED headlights is at a high level, the illumination compliance improvement rate is at a low level, the circuit anomaly index is high, and the thermal resistance anomaly rate is at a high level during the adjustment cycle, these parameters interact with each other, resulting in a low degree of adjustment adaptability of the LED headlights. This indicates that the adjustment has not achieved the expected results and needs to be readjusted to improve the performance and adaptability of the headlights.

[0081] In one specific embodiment, the present invention provides an LED vehicle headlight operation monitoring system based on data analysis. This system can accurately acquire the illumination parameters of the environment in which the LED vehicle headlight is located, and then scientifically analyze and determine the reference illumination intensity of the LED vehicle headlight. Based on this, it comprehensively monitors and meticulously collects the circuit information of the LED vehicle headlight. Through data analysis technology, it accurately locates each abnormal LED bead in the LED vehicle headlight. According to the pre-determined reference illumination intensity, it makes targeted adjustments to the abnormal LED beads. After the adjustment is completed, it further monitors the operating status parameters of the LED vehicle headlight and evaluates the adjustment adaptability index of the LED vehicle headlight. This intelligently determines whether further performance adjustments are needed and provides timely feedback on the adjustment effect. This significantly enhances the efficiency, stability, adaptability to changing environments, and performance matching degree of the LED vehicle headlight during operation, thereby ensuring that the adjustments made can fully meet the predetermined performance indicators.

[0082] Reference Figure 2 As shown, the second aspect of the present invention provides a data analysis-based method for monitoring the operation of LED vehicle lights, comprising: Step 1, acquiring the illumination parameters of the environment in which the LED vehicle lights are located, and analyzing the reference illumination intensity of the LED vehicle lights; Step 2, monitoring and collecting the circuit information of the LED vehicle lights, locating each abnormal LED bead in the LED vehicle lights through data analysis, and adjusting each abnormal LED bead in the LED vehicle lights according to the reference illumination intensity of the LED vehicle lights; Step 3, monitoring the operating status parameters of the LED vehicle lights after adjustment, evaluating the adjustment adaptability index of the LED vehicle lights, and determining whether to perform performance adjustments and provide feedback.

[0083] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A data analysis-based LED vehicle light operation monitoring system, characterized in that, include: The reference analysis module is used to obtain the illumination parameters of the environment in which the LED vehicle lights are located, and analyze the reference illumination intensity of the LED vehicle lights. The reference illumination intensity indicates the illumination intensity level that the LED vehicle lights should be adjusted to. The abnormal adjustment module is used to monitor and collect the circuit information of the LED vehicle lights, locate the abnormal LED beads of the LED vehicle lights through data analysis, and adjust the abnormal LED beads of the LED vehicle lights according to the reference light intensity of the LED vehicle lights. The process of locating the abnormal LED beads in the vehicle headlights through data analysis is as follows: The circuit information of the LED vehicle light specifically refers to the circuit abnormality index of each LED bead in the detection period. If the circuit abnormality index of a certain LED bead in the detection period is greater than the maximum value of the first circuit abnormality threshold range, then the LED bead in the LED vehicle light is marked as a first-class abnormal LED bead. If the circuit abnormality index of a certain LED bead in the LED vehicle headlight falls within the first circuit abnormality threshold range during the detection period, then that LED bead in the LED vehicle headlight will be marked as a normal LED bead. If the circuit abnormality index of a certain LED bead in the LED vehicle light is less than the minimum value of the first circuit abnormality threshold range and greater than the maximum value of the second circuit abnormality threshold range during the detection period, then the LED bead in the LED vehicle light will be marked as a second type of abnormal bead. If the circuit abnormality index of a certain LED bead in the LED vehicle light is less than the minimum value of the second circuit abnormality threshold range or belongs to the second circuit abnormality threshold range during the detection period, and at the same time the circuit abnormality index of a certain LED bead in the LED vehicle light is greater than the maximum value of the third circuit abnormality threshold range during the detection period, then the LED bead in the LED vehicle light will be marked as a third type of abnormal LED bead. If the circuit abnormality index of a certain LED bead in the LED vehicle light is less than the minimum value of the third circuit abnormality threshold range or belongs to the third circuit abnormality threshold range during the detection period, then the LED bead in the LED vehicle light will be marked as a fourth type of abnormal bead. The specific analysis process for the circuit abnormality index of each LED chip in the LED vehicle light during the detection period is as follows: Based on the reference illuminance of the LED vehicle headlight during the demand cycle, the reference current, reference power dissipation value, and reference color temperature of the LED vehicle headlight's LED beads during the demand cycle are matched from the monitoring database. The average operating current, average power dissipation, and average color temperature of each LED chip in the LED vehicle light are obtained and comprehensively analyzed during the testing period to obtain the circuit abnormality index of each LED chip in the LED vehicle light during the testing period. The circuit abnormality index represents the quantitative data of the influence of average operating current, average power dissipation, and average color temperature on circuit abnormalities, and is used to quantify the degree of circuit abnormality. The circuit abnormality index of each LED chip in the LED vehicle light during the detection period is specifically expressed as follows: ； In the formula, This represents the circuit abnormality index of the s-th LED bead in the LED vehicle light during the detection cycle, where s is the LED bead number. d is the total number of LED beads. This represents the average operating current of the s-th LED chip in the LED vehicle light during the detection cycle. This refers to the reference current of the LED chips in the LED automotive light during the demand cycle. This represents the average power dissipation value of the s-th LED chip in the LED vehicle light during the detection cycle. This is the reference power dissipation value of the LED chips in the LED vehicle headlight during the demand cycle. The average color temperature of the s-th LED chip in the LED vehicle light during the testing period. This refers to the reference color temperature of the LED chips used in LED automotive lights during the demand cycle. To monitor the circuit anomaly factor corresponding to the preset average operating current in the database, To monitor circuit anomaly factors corresponding to preset average power dissipation values ​​in the database, To monitor circuit anomaly factors corresponding to the preset average color temperature in the database; The adjustment process for each faulty LED chip in the LED headlight is as follows: The system acquires the operating illumination data of the LED vehicle headlights during the testing period, and combines the circuit abnormality index of each LED chip in the LED vehicle headlights during the testing period to analyze the illumination compliance coefficient of the LED vehicle headlights during the testing period, and matches the adjustment rate of each abnormal LED chip in the LED vehicle headlights. The total number of LED beads with abnormality in categories I, II, III, and IV, as well as the total number of LED beads belonging to the LED vehicle lights, are counted. Obtain the first compensation value and the second compensation value of the reference illumination intensity; Based on the reference illuminance of the LED vehicle headlight during the demand period, the first compensation value of the reference illuminance, and the second compensation value of the reference illuminance, update the reference illuminance of the LED vehicle headlight during the demand period. Based on the reference illuminance of the LED vehicle headlight during the demand period and the adjustment rate of each abnormal LED bead of the LED vehicle headlight, adjust the first type of abnormal LED beads and the second type of abnormal LED beads once. The total number of abnormal LEDs of the first type is added together with the total number of abnormal LEDs of the second type, and the result of the addition is marked as the total number of adjustable abnormal LEDs. The first compensation value of the reference illuminance is updated based on the total number of adjustable abnormal LED beads and the total number of third-category abnormal LED beads; The second compensation value of the reference illuminance is updated based on the total number of adjustable abnormal LED beads and the total number of fourth-category abnormal LED beads; Reinitialize the first and second compensation values ​​of the reference illumination intensity; Based on the reference illuminance of the LED vehicle headlight during the demand period, the first compensation value of the reference illuminance, and the second compensation value of the reference illuminance, the reference illuminance of the LED vehicle headlight during the demand period is updated. Based on the reference illuminance of the LED vehicle headlight during the demand period and the adjustment rate of each abnormal LED bead of the LED vehicle headlight, the first type of abnormal LED beads and the second type of abnormal LED beads are adjusted a second time. The first compensation value for the reference illuminance is updated based on the total number of adjustable abnormal LEDs and the total number of third-type abnormal LEDs, as follows: ； in, , ; Similarly, the second compensation value for the reference illuminance is updated based on the total number of adjustable abnormal LEDs and the total number of fourth-type abnormal LEDs, as follows: ； in, , ; The total number of third-type abnormal LEDs detected in historical adjacent detection periods represents the total number of third-type abnormal LEDs detected in historical adjacent detection periods. The total number of adjustable abnormal LEDs detected in historical adjacent detection periods represents the total number of adjustable abnormal LEDs detected in historical adjacent detection periods. The total number of fourth-type abnormal LEDs detected in historical adjacent detection periods represents the total number of fourth-type abnormal LEDs detected in historical adjacent detection periods. The specific analysis process for the illumination compliance coefficient of the LED vehicle lights during the testing period is as follows: The operating illumination data of the LED vehicle headlight during the testing period includes the uniformity of illumination intensity of the illumination area to which the LED vehicle headlight belongs during the testing period, the brightness fluctuation value of the illumination area to which the LED vehicle headlight belongs during the testing period, and the electrical signal frequency fluctuation value of the LED vehicle headlight during the testing period. By comprehensively analyzing the circuit anomaly index of each LED chip in the LED vehicle headlight, the uniformity of illuminance of the illuminated area of ​​the LED vehicle headlight during the testing period, the brightness fluctuation value of the illuminated area of ​​the LED vehicle headlight during the testing period, and the electrical signal frequency fluctuation value of the LED vehicle headlight during the testing period, a lighting compliance coefficient for the LED vehicle headlight during the testing period is obtained. This lighting compliance coefficient represents the quantitative data on the impact of the circuit anomaly index, illuminance uniformity, brightness fluctuation value, and electrical signal frequency fluctuation value on the lighting compliance of the LED vehicle headlight during the testing period. It is used to quantify the degree to which the lighting performance of the LED vehicle headlight during the testing period conforms to the preset lighting performance. The specific expression is as follows: ； In the formula, The illumination compliance coefficient for LED vehicle lights during the testing period. This refers to the uniformity of light intensity in the illuminated area of ​​the LED vehicle headlight during the testing period. To monitor the preset light intensity uniformity threshold in the database, This represents the brightness fluctuation value of the illuminated area of ​​the LED vehicle headlight during the detection period. To monitor the pre-defined brightness fluctuation values ​​in the database, This refers to the electrical signal frequency fluctuation value of the LED vehicle light during the testing period. To monitor the preset defined electrical signal frequency fluctuation values ​​in the database, To monitor the illumination compliance factor corresponding to the preset illumination intensity uniformity in the database, To monitor the illumination compliance factors corresponding to the preset brightness fluctuation values ​​in the database, To monitor the illumination compliance factor corresponding to the preset electrical signal frequency fluctuation values ​​in the database, This represents the circuit abnormality index of the s-th LED bead in the LED vehicle light during the detection cycle. To monitor the weight values ​​corresponding to the preset circuit anomaly index in the database, where s is the number of each LED bead, d is the total number of LED beads; The operation feedback module is used to monitor the operating status parameters of the LED headlights after adjustment, evaluate the adjustment adaptability index of the LED headlights, and determine whether to perform performance adjustments and provide feedback. The assessment process for the adjustment adaptability index of LED vehicle lights is as follows: The adjusted operating status parameters of the LED vehicle lights include the illumination compliance coefficient of the LED vehicle lights during the adjustment period, the circuit abnormality index of each LED chip in the LED vehicle lights during the adjustment period, the thermal resistance change curve of the LED vehicle lights during the adjustment period, and the flicker frequency of the LED vehicle lights during the adjustment period. By comparing the thermal resistance change curve of the LED vehicle headlight during the adjustment period with the defined thermal resistance change curve, the thermal resistance anomaly rate of the LED vehicle headlight during the adjustment period can be obtained. The difference between the illumination compliance coefficient of the LED vehicle headlight during the adjustment period and the illumination compliance coefficient of the LED vehicle headlight during the testing period is processed, and the difference processing result is compared with the illumination compliance coefficient of the LED vehicle headlight during the testing period to finally obtain the illumination compliance improvement rate of the LED vehicle headlight during the adjustment period. The adjustment adaptation index of LED vehicle lights is derived by comprehensively evaluating the thermal resistance anomaly rate, flicker frequency, illumination compliance improvement rate, and circuit anomaly index of each LED chip within the adjustment period. This index represents the quantitative data on the impact of thermal resistance anomaly rate, flicker frequency, illumination compliance improvement rate, and circuit anomaly index on the adjustment adaptation degree of LED vehicle lights within the adjustment period, and is used to quantify the adjustment adaptation degree of LED vehicle lights within the adjustment period. The specific process for determining whether to perform performance adjustments and provide feedback is as follows: Extract the adjustment and adaptation threshold from the monitoring database; The adjustment adaptation index of the LED vehicle headlight within the adjustment cycle is compared with the adjustment adaptation threshold. If the adjustment adaptation index of the LED vehicle headlight within the adjustment cycle is greater than or equal to the adjustment adaptation threshold, it is determined that no performance adjustment and feedback will be performed. If the adjustment adaptation index of the LED vehicle lights is less than the adjustment adaptation threshold within the adjustment cycle, then performance adjustment and feedback will be performed.

2. The LED vehicle light operation monitoring system based on data analysis according to claim 1, characterized in that: The reference illuminance of the LED vehicle headlights was analyzed, and the specific analysis process is as follows: The illumination parameters of the environment to which the LED vehicle lights are located include the illumination intensity curve of the environment to which the LED vehicle lights are located during the detection period. Based on the illumination intensity curve of the environment to which the LED vehicle lights are located during the detection period, the average illumination intensity of the environment to which the LED vehicle lights are located during the demand period is predicted. The reference illuminance of the LED vehicle headlights is matched to the average illuminance of the environment during the demand period.

3. The LED vehicle light operation monitoring system based on data analysis according to claim 1, characterized in that: The performance tuning and feedback process is as follows: The adjustment adaptation threshold and the adjustment adaptation index of the LED vehicle light within the adjustment period are processed by difference, and the processing result is compared with the adjustment adaptation threshold to finally obtain the adjustment adaptation deviation rate of the LED vehicle light. The reference illuminance of the LED vehicle lights is adjusted within the demand cycle based on the adjustment adaptation deviation rate, and an adjustment report is generated for feedback.

4. A method for monitoring the operation of LED vehicle lights based on data analysis as described in any one of claims 1-3, characterized in that: include: Step 1: Obtain the illumination parameters of the environment in which the LED vehicle lights are located, and analyze the reference illumination intensity of the LED vehicle lights; Step 2: Monitor and collect the circuit information of the LED vehicle lights, locate the abnormal LED beads through data analysis, and adjust the abnormal LED beads according to the reference light intensity of the LED vehicle lights. Step 3: Monitor the operating status parameters of the LED headlights after adjustment, evaluate the adjustment adaptability index of the LED headlights, and determine whether performance adjustments and feedback are necessary.

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