LED diode fault self-diagnosis method and system

Abstract

The invention relates to the technical field of LED fault analysis, and discloses an LED fault self-diagnosis method and system, and the method comprises the steps: collecting the data of an environment where an LED is located, calculating an environment factor through environment analysis, correcting the environment factor according to standard environment data, carrying out the preprocessing of collected data parameters, receiving the corrected environment factor, and carrying out the self-diagnosis of the LED fault. The method comprises the following steps: preprocessing data parameters, classifying the preprocessed data parameters, receiving the classified data parameters by an LED detection model, outputting real-time data parameters and prediction data of an LED, carrying out loss optimization on the LED detection model by an LED model optimization unit through a test set, optimizing the LED detection model in real time, classifying the faults of the LED, and carrying out prediction on the faults of the LED. And diode testing and adjustment are carried out through the classified faults and environmental factors, so that LED diode fault self-diagnosis intelligentization is realized.

Description

Technical Field

[0001] The invention relates to the technical field of LED diode fault analysis and discloses an LED diode fault self-diagnosis method and system. Background Art

[0002] The development of LED diode fault self-diagnosis methods and systems is a field of continuous progress, aiming to improve the reliability of LED lamps, extend their service life and reduce maintenance costs. With the popularization of LED technology and the expansion of its application scope, the demand for intelligent management and maintenance of LED lamps is also increasing. The following outlines the current development status and shortcomings of LED fault self-diagnosis methods and systems. Although the impact of the environment on LED diodes is sometimes considered, there is a lack of comprehensive models, and it is impossible to classify LED diode faults according to environmental factors. There is a lack of prediction of the next moment data of the LED diode, so it is impossible to accurately determine the cause of the LED diode failure. The common expert experience in the market to judge the failure of LED diodes lacks objectivity and reality, and subjective factors are prone to misjudgment and omission.

[0003] For example, a Chinese patent application with a publication number of CN118872381A discloses an LED lighting system, an automotive lighting system, and a method for detecting a fault in an LED lighting system. The LED lighting system includes an LED lighting circuit and a fault detection circuit. The LED lighting circuit includes a first string of a first plurality of LEDs electrically coupled in series with a first inductor and a second string of a second plurality of LEDs electrically coupled in series with a second inductor. The first string and the second string each have an equal total forward voltage. The fault detection circuit is configured to detect a fault in the LED lighting circuit based on detection of a voltage difference between the first inductor and the second inductor.

[0004] Although the above patent is simple and easy to implement, it has obvious deficiencies in comprehensiveness and reliability. Users need to manually check and handle faults, which increases maintenance costs. It can only detect the existence of faults but cannot classify the fault types. It lacks a closed-loop control mechanism and cannot automatically adjust the working state of the LED. It does not consider the influence of environmental factors such as temperature and humidity. It mainly relies on the inductor voltage difference to detect faults. This method is relatively simple and can only detect specific types of faults, such as open circuits or short circuits, but cannot fully cover other types of faults. It may cause misjudgment or missed detection in different environments. Summary of the invention

[0005] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In order to solve the above technical problems, the main purpose of the present invention is to provide a LED diode fault self-diagnosis method and system, wherein the LED diode fault self-diagnosis method includes:

[0007] S1. Collect the environmental data of the LED diode, calculate the environmental factors through environmental analysis, and calibrate the environmental factors by comparing with the standard environmental data;

[0008] S2, collecting LED diode data parameters, preprocessing the collected data parameters, receiving the corrected environmental factors, and classifying the preprocessed data parameters;

[0009] S3, the LED diode detection model receives the classified data parameters, establishes a test set and a training set, and outputs the LED diode real-time data parameters and predicted data through the LED diode detection model;

[0010] S4, the LED diode model tuning unit performs loss tuning on the LED diode detection model through the test set, and optimizes the LED diode detection model in real time;

[0011] S5, outputting LED diode real-time data parameters and prediction data through the LED diode detection model, and classifying LED diode faults;

[0012] S6. Perform diode test based on the classified faults and environmental factors. If the tuning test passes, adjust the LED diode through the adjustment unit. If the tuning test fails, a fault alarm is issued.

[0013] As a preferred solution of the LED diode fault self-diagnosis method of the present invention, wherein:

[0014] The environmental data includes temperature data, humidity data, illumination data, power supply voltage data, operating current data, and LED aging data;

[0015] The environmental factors are calculated through the environmental analysis model. The calculation expression of the environmental analysis model is as follows:

[0016]

[0017] in, The comprehensive model outputs the influence coefficient of the environment on the LED diode.

[0018] Preprocess and clean the collected data parameters and prepare the data through data cleaning, data standardization, data transformation and feature selection;

[0019] Data classification is used to classify the preprocessed data according to different environmental conditions;

[0020] Load the preprocessed and classified LED diode parameters from the classified CSV file and randomly divide the data into training and test sets;

[0021] The LED diode detection model is trained using the training set data and evaluated on the test set. The trained model is used to predict new real-time data.

[0022] The convolutional neural network performs convolution operations on the LED diode data parameters through the convolution layer, inputs the maximum pooling layer for dimensionality reduction processing, and inputs the data after dimensionality reduction processing into the flattening layer to flatten the multi-dimensional data into a one-dimensional vector, and the one-dimensional vector is input into the fully connected layer to perform high-level abstract processing on the characteristics of the LED diode.

[0023] As a preferred solution of the LED diode fault self-diagnosis method of the present invention, wherein:

[0024] The convolutional layer calculation expression is as follows:

[0025]

[0026] in, is an element of the output feature map, is the input data of the jth group of LED diodes after classification, b is the bias term, is the weight corresponding to the data of the jth group of LED diode classification, j is the number of data groups after LED diode classification, k is the total number of data groups after LED diode classification, f() is the activation function, and i is the number of feature map groups;

[0027] The maximum pooling layer calculation expression is as follows:

[0028]

[0029] in, is an element of the output feature map, is a local area of ​​the input data, max() is the maximum value function, and s is the step size from the i-th feature map to the next feature map;

[0030] The calculation expression of the fully connected layer is as follows:

[0031]

[0032] Among them, y is the output LED diode vector, x is the input LED diode vector, W is the weight matrix, b is the bias vector, and f() is the activation function.

[0033] As a preferred solution of the LED diode fault self-diagnosis method of the present invention, wherein:

[0034] Performing loss tuning on the LED diode detection model through the test set and optimizing the model in real time, including defining a loss function, selecting an optimization algorithm, performing model training and evaluation, and updating model parameters in real time;

[0035] The loss amount of the LED diode detection model is calculated by defining a loss function, and the loss function calculation expression is as follows:

[0036]

[0037] in, is the loss value of the LED diode detection model output by the loss function, N is the number of samples, p is the number of p-th LED diode samples, is the true value corresponding to the pth LED diode sample number, is the predicted value corresponding to the pth LED diode sample number;

[0038] The optimization algorithm is used to minimize the loss function, calculate the adaptive learning rate by multiplying the output gradient of the LED diode detection model and the hyperparameter product, and then calculate the difference between the adaptive learning rate and the momentum of the real-time output prediction value of the LED diode detection model to update the LED diode detection model. The calculation expression is as follows:

[0039]

[0040] in, is the first-order estimation matrix of the zth group, is the second-order estimation matrix of the zth group, is a first-order matrix hyperparameter, is a second-order matrix hyperparameter, Output gradient value for LED diode detection model, To optimize the first-order estimation matrix, To optimize the second-order estimation matrix, is a small constant used to prevent overfitting. is the LED diode detection model parameter of the hth step, are the optimized model parameters, is the first-order estimation matrix of the z-1th group, is the second-order estimation matrix of the z-1th group.

[0041] As a preferred solution of the LED diode fault self-diagnosis method of the present invention, wherein:

[0042] Fault detection determines whether the LED diode is faulty by comparing the difference between the predicted value and the actual value, including setting a threshold value. When the difference between the predicted value and the actual value exceeds the threshold value, it is considered that the LED diode may be faulty, calculating the difference between the predicted value and the actual value, and determining whether a fault occurs based on the difference value;

[0043] The LED diode is tested according to the classified fault types and environmental factors to verify the accuracy of the fault types and environmental factors.

[0044] LED diode fault self-diagnosis system, including:

[0045] Environmental factor module, including environmental identification unit, environmental factor unit, and environmental testing unit;

[0046] Data module, including data acquisition unit, data processing unit and data classification unit;

[0047] A detection module, including an LED diode detection model and an LED diode model tuning unit;

[0048] A fault identification module, including a data receiving unit and a fault classification unit;

[0049] The adaptive module includes a testing unit and an adjusting unit.

[0050] A preferred solution of the LED diode fault self-diagnosis system of the present invention, wherein:

[0051] The environment identification unit is used to collect environmental data and identify the types of environmental influencing factors;

[0052] The environmental factor unit is used to calculate environmental factors including temperature factor, humidity factor, light intensity factor, power supply voltage factor, operating current factor, and LED aging factor;

[0053] The environmental testing unit is used to analyze the influence of temperature, humidity, light intensity, power supply voltage, operating current and aging effect on LED brightness;

[0054] The data acquisition unit is used to collect the LED diode ambient temperature, ambient humidity, ambient light intensity, power supply voltage, operating current, time and LED brightness;

[0055] The data processing unit is used to pre-process and clean the collected data parameters and prepare data through data cleaning, data standardization, data conversion and feature selection;

[0056] The data classification unit is used to classify the preprocessed data according to different environmental conditions for subsequent analysis and modeling;

[0057] The LED diode detection model is used to output real-time monitoring parameters and prediction parameters of the LED diode;

[0058] The LED diode model tuning unit is used to update the LED diode detection model.

[0059] A preferred solution of the LED diode fault self-diagnosis system of the present invention, wherein:

[0060] The data receiving unit is used to receive the real-time data and prediction data output by the LED diode detection model;

[0061] The fault classification unit is used to identify the received data and detect whether a fault occurs, and if a fault occurs, classify the fault;

[0062] The test unit is used to receive fault classification data and perform LED diode testing according to the fault classification data and environmental factors;

[0063] The adjustment unit is used to adaptively adjust the performance parameters of the LED diode according to the test results.

[0064] Beneficial effects of the present invention:

[0065] The present invention sets environmental factor correction to more accurately reflect the actual working conditions of LED diodes, avoids misjudgment caused by environmental changes, ensures that LED diodes can operate stably in different environments, improves the versatility and adaptability of the system, and can accurately predict the data parameters of LED diodes at the next moment by establishing an LED diode detection model, so as to detect in advance whether the LED diodes are faulty.

[0066] By classifying real-time data parameters and predicted data, faults of different types and degrees can be identified more accurately. By tuning the test and adjustment units, the system achieves closed-loop control to ensure that faults are discovered and handled in a timely manner, reducing unnecessary maintenance and replacement, and reducing maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0068] Figure 1 This is a flow chart of the LED diode fault self-diagnosis method of the present invention;

[0069] Figure 2 This is a composition diagram of the LED diode fault self-diagnosis system of the present invention. DETAILED DESCRIPTION

[0070] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0071] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0072] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0073] Embodiment 1

[0074] like Figure 1 As shown, the LED diode fault self-diagnosis method includes:

[0075] S1. Collect the environmental data of the LED diode, calculate the environmental factors through environmental analysis, and calibrate the environmental factors by comparing with the standard environmental data;

[0076] Among them, environmental data includes temperature data, humidity data, light data, power supply voltage data, working current data, and LED aging data;

[0077] Calculate environmental factors through environmental analysis models;

[0078] Environmental factors include temperature factor, humidity factor, light intensity factor, power supply voltage factor, operating current factor, and LED aging factor;

[0079] Temperature has a significant impact on LEDs, not only affecting brightness, but also changing color temperature and lifespan. The temperature factor calculation expression is as follows:

[0080]

[0081] in, is the LED brightness at temperature T, is the reference temperature The LED brightness under is the maximum tolerable temperature of the LED, and n is the empirical coefficient, which indicates the sensitivity of temperature to brightness;

[0082] As the temperature increases, the energy band gap of semiconductor materials becomes narrower, making it easier for electrons to be excited, but this also increases the recombination rate of carriers, thereby reducing the luminous efficiency;

[0083] Humidity affects the brightness of LEDs indirectly mainly by affecting the performance of LED packaging materials. High humidity may cause moisture absorption and corrosion of packaging materials, thereby reducing the luminous efficiency of LEDs. Humidity may affect the packaging materials of LEDs, causing corrosion or performance degradation. The humidity factor calculation expression is as follows:

[0084]

[0085] in, is the humidity factor, is the humidity sensitivity coefficient, is the relative humidity, is the reference humidity;

[0086] Light intensity may cause the internal temperature of the LED to rise, thus affecting its performance. The light intensity factor calculation expression is as follows:

[0087]

[0088] in, is the incident light intensity, is the light sensitivity coefficient, e is the exponential constant;

[0089] The fluctuation of power supply voltage will affect the working current of LED, thus affecting the brightness. The power supply voltage factor calculation expression is as follows:

[0090]

[0091] in, is the power supply voltage factor, is the voltage sensitivity coefficient, is the power supply voltage, is the reference voltage;

[0092] The change of working current directly affects the brightness and life of LED. The working current factor calculation expression is as follows:

[0093]

[0094] in, is the operating current factor, is the current sensitivity coefficient, I is the working current, is the reference current, m is the empirical coefficient, which indicates the nonlinearity of the effect of current on brightness;

[0095] As time goes by, the performance of LED will gradually decline. The calculation expression of LED aging factor is as follows:

[0096]

[0097] in, is the LED aging factor, λ is the aging rate coefficient, and t is the time;

[0098] The calculation expression of the environmental analysis model is as follows:

[0099]

[0100] in, Output the influence coefficient of the environment on the LED diode for the comprehensive model;

[0101] This environmental analysis model describes the effects of temperature, humidity, light intensity, power supply voltage, operating current and aging effects on LED brightness through a series of mathematical functions. Each function is based on physical principles and experimental data. By combining these functions, a comprehensive and accurate environmental impact parameter for LED performance can be constructed.

[0102] In order to make the model more accurate, it is necessary to adjust various parameters through parameter calibration, and correct environmental factors through parameter calibration;

[0103] Environmental factors take into account the impact of various environmental factors on LED performance and can more accurately calculate the performance of LEDs under different conditions. By continuously optimizing and calibrating model parameters, the calculation accuracy of the model can be further improved, providing strong support for the design and application of LEDs.

[0104] Model validation includes experimental validation and simulation validation;

[0105] The experimental verification measures the actual brightness of the LED under different environmental conditions and compares it with the model prediction value to evaluate the accuracy of the model.

[0106] Simulation verification uses simulation software to simulate LED performance under different environmental conditions to verify the applicability and reliability of the model.

[0107] S2, collecting LED diode data parameters, preprocessing the collected data parameters, receiving the corrected environmental factors, and classifying the preprocessed data parameters;

[0108] The LED diode data parameters are collected through sensors, including:

[0109] The temperature sensor is used to measure the ambient temperature;

[0110] Humidity sensor is used to measure the ambient humidity;

[0111] The light sensor is used to measure the ambient light intensity;

[0112] A voltmeter is used to measure the power supply voltage;

[0113] The ammeter is used to measure the working current;

[0114] The timer is used to record time;

[0115] The brightness sensor is used to measure the brightness of the LED;

[0116] The collected data parameters are preprocessed and cleaned through data cleaning, data standardization, data transformation and feature selection to prepare the data for subsequent analysis and modeling.

[0117] Data cleaning includes removing outliers, filling missing values, and denoising;

[0118] Outlier removal is used to identify and remove obviously erroneous data points, such as temperature or humidity values ​​outside a reasonable range;

[0119] Filling missing values ​​is used to fill missing data points using interpolation methods or other statistical methods;

[0120] Denoising is used to remove noise using filters such as moving average filters;

[0121] Data standardization includes normalization and standardization;

[0122] Normalization is used to scale all numerical features to the same range, such as [0, 1] or [-1, 1].

[0123] Standardization is used to transform the data into a standard normal distribution with zero mean and unit variance.

[0124] Data transformations include logarithmic and polynomial transformations;

[0125] Logarithmic transformation is used for data with nonlinear relationships, and logarithmic transformation can be used to linearize the relationship.

[0126] Polynomial transformation is used for complex nonlinear relationships.

[0127] Feature selection includes correlation analysis and principal component analysis;

[0128] Correlation analysis is used to calculate the correlation between each feature and LED brightness, and select features with high correlation;

[0129] Principal component analysis (PCA) is used to reduce dimensionality and extract the most important features;

[0130] Furthermore, the received corrected environmental factors are used to correct the collected environmental factors according to the rated parameters of the LED diodes to improve the accuracy and reliability of the data.

[0131] The correction temperature is used to correct the temperature data according to the known temperature sensor error model.

[0132] Correction humidity is used to correct the humidity data based on the known humidity sensor error model.

[0133] Correcting the light intensity is used to correct the light intensity data according to a known light sensor error model.

[0134] The correction voltage and current are used to correct the voltage and current data according to the known voltage meter and current meter error models.

[0135] The sensor error model is determined by the sensor rating specifications;

[0136] Furthermore, data classification is used to classify the preprocessed data according to different environmental conditions for subsequent analysis and modeling.

[0137] By defining the classification criteria, the classification concept of LED diodes is set;

[0138] For example:

[0139] Temperature range: for example, low temperature (<20°C), medium temperature (20-30°C), high temperature (>30°C).

[0140] Humidity range: for example, low humidity (<50%), medium humidity (50-70%), high humidity (>70%).

[0141] Light intensity range: for example, low light (<100 lux), medium light (100-1000 lux), high light (>1000 lux).

[0142] Voltage range: for example, low voltage (<4.5V), medium voltage (4.5-5.5V), high voltage (>5.5V).

[0143] Current range: for example, low current (<20mA), medium current (20-30mA), high current (>30mA).

[0144] Time interval: for example, short term (<1 hour), medium term (1-10 hours), long term (>10 hours).

[0145] By writing scripts in programming languages ​​(such as Python), the data is classified according to the defined classification criteria and the classified data is stored in different files or database tables;

[0146] S3, the LED diode detection model receives the classified data parameters, establishes a test set and a training set, and outputs the LED diode real-time data parameters and predicted data through the LED diode detection model;

[0147] Load the preprocessed and classified LED diode parameters from the classified CSV file, and randomly divide the data into training set and test set, with the ratio set to 80% for training set and 20% for test set;

[0148] Using Convolutional Neural Network (CNN) to build an LED diode detection model is an effective method. For the environmental data parameters of LED diodes, which are usually one-dimensional time series data, one-dimensional convolutional neural network (1D CNN) can be used to process them.

[0149] Convert the training set into a data format suitable for inputting the convolutional layer. For 1D CNN, the input data usually needs to be reshaped into the format of (number of samples, time steps, number of features). Use the keras library to build a 1D CNN model, train the model with the training set data, evaluate it on the test set, and use the trained model to predict new real-time data.

[0150] Furthermore, the convolution layer performs convolution operations on the input data through a sliding window and extracts local features. The convolution layer calculation expression is as follows:

[0151]

[0152] in, is an element of the output feature map, is the input data of the jth group of LED diodes after classification, b is the bias term, is the weight corresponding to the data of the jth group of LED diode classification, j is the number of data groups after LED diode classification, k is the total number of data groups after LED diode classification, f() is the activation function, and i is the number of feature map groups;

[0153] Furthermore, the convolution layer performs convolution calculation on the input LED data parameters by multiplying the input vector with the weight matrix and adding the bias term to extract the local eigenvalues ​​of the LED data parameters;

[0154] The maximum value of the local features after convolution is selected through the maximum pooling layer, which reduces the data dimension and retains important features. The calculation expression of the maximum pooling layer is as follows:

[0155]

[0156] in, is an element of the output feature map, is a local area of ​​the input data, max() is the maximum value function, and s is the step size from the i-th feature map to the next feature map;

[0157] The multi-dimensional data is flattened into a one-dimensional vector through the flattening layer, and the one-dimensional vector is input into the fully connected layer. The fully connected layer performs high-level abstract processing on the characteristics of the LED diode through linear combination and activation function. The calculation expression of the fully connected layer is as follows:

[0158]

[0159] Among them, y is the output LED diode vector, x is the input LED diode vector, W is the weight matrix, b is the bias vector, and f() is the activation function;

[0160] Activation functions include linear activation function and ReLU;

[0161] Through the LED diode detection model, a 1D CNN model is constructed to detect and predict the performance of LED diodes. This model uses the convolution layer to extract local features, reduces the data dimension through the maximum pooling layer, and finally performs regression prediction through the fully connected layer. The LED diode detection model can effectively process time series data, improve the accuracy and robustness of predictions, and timely extract features based on the data under the influence of various environments to ensure that the LED diode detection is minimally affected by various environments, the data is more accurate, and the data of the LED diode at the next moment can be accurately predicted based on the current environment, ensuring that the LED diode will accurately detect whether it will fail at the next moment;

[0162] S4, the LED diode model tuning unit performs loss tuning on the LED diode detection model through the test set, and optimizes the LED diode detection model in real time;

[0163] The LED diode detection model is loss tuned through the test set, and the model is optimized in real time, including defining the loss function, selecting the optimization algorithm, training and evaluating the model, and updating the model parameters in real time.

[0164] Furthermore, the loss of the LED diode detection model is calculated by defining a loss function. The loss function is used to measure the difference between the model prediction value and the true value. The loss function calculation expression is as follows:

[0165]

[0166] in, is the loss value of the LED diode detection model output by the loss function, N is the number of samples, p is the number of p-th LED diode samples, is the true value corresponding to the pth LED diode sample number, is the predicted value corresponding to the pth LED diode sample number.

[0167] The optimization algorithm is used to minimize the loss function, calculate the adaptive learning rate by multiplying the output gradient of the LED diode detection model and the hyperparameter, and then calculate the difference between the adaptive learning rate and the momentum of the real-time output prediction value of the LED diode detection model to update the LED diode detection model. The calculation expression is as follows:

[0168]

[0169] in, is the first-order estimation matrix of the zth group, is the second-order estimation matrix of the zth group, is a first-order matrix hyperparameter, is a second-order matrix hyperparameter, Output gradient value for LED diode detection model, To optimize the first-order estimation matrix, To optimize the second-order estimation matrix, is a small constant used to prevent overfitting. is the LED diode detection model parameter of the hth step, are the optimized model parameters, is the first-order estimation matrix of the z-1th group, is the second-order estimation matrix of the z-1th group;

[0170] Calculate the first-order estimation matrix and the second-order estimation matrix of the z group through the first-order estimation matrix and the second-order estimation matrix of the z group, and calculate the optimized model parameters through the first-order estimation matrix and the second-order estimation matrix of the z group;

[0171] Real-time optimization of the model means that the model parameters are continuously adjusted according to new data while the model is running;

[0172] S5, outputting LED diode real-time data parameters and prediction data through the LED diode detection model, and classifying LED diode faults;

[0173] Fault detection determines whether the LED diode is faulty by comparing the difference between the predicted value and the actual value, including setting a threshold value. When the difference between the predicted value and the actual value exceeds the threshold value, it is considered that the LED diode may be faulty, calculating the difference between the predicted value and the actual value, and determining whether a fault occurs based on the difference value;

[0174] If a fault occurs, further fault classification is performed. By further analyzing the fault type, the fault is classified into different categories, and features that are helpful for classification are extracted, such as temperature, humidity, light intensity, voltage, current, etc. The classifier selected according to the influence coefficient of the LED diode is trained using the labeled fault data, and the detected fault is classified using the trained classifier. The real-time data parameters and predicted data are output through the trained LED diode detection model, and the LED diode fault is detected and classified. This method can effectively identify and classify LED diode faults and improve the reliability and maintenance efficiency of the system.

[0175] S6. Perform diode test based on the classified faults and environmental factors. If the tuning test passes, adjust the LED diode through the adjustment unit. If the tuning test fails, a fault alarm is issued.

[0176] Testing the LED diodes according to the classified fault types and environmental factors to verify the accuracy of the fault types and environmental factors, including loading fault data from the classified data, setting test conditions according to the fault types and environmental factors, testing the LED diodes under the set test conditions, and recording the test results;

[0177] Further, based on the test results, the LED diode is tuned and tested to verify the performance after tuning, including selecting appropriate tuning parameters according to the fault type and environmental factors, testing the LED diode under the set tuning parameters, and recording the test results after tuning;

[0178] The adjustment unit is used to adjust the LED diode through the adjustment unit according to the tuning test result;

[0179] If the tuning test fails, a fault alarm is triggered and the fault information is recorded;

[0180] S6 can effectively manage and maintain the performance of LED diodes and improve the reliability and stability of the system.

[0181] Embodiment 2

[0182] like Figure 2 As shown, the LED diode fault self-diagnosis system includes:

[0183] Environmental factor module, including environmental identification unit, environmental factor unit, and environmental testing unit;

[0184] The environmental identification unit is used to collect environmental data and identify the types of environmental influencing factors;

[0185] The environmental factor unit is used to calculate environmental factors including temperature factor, humidity factor, light intensity factor, power supply voltage factor, working current factor, and LED aging factor;

[0186] The environmental test unit is used to analyze the effects of temperature, humidity, light intensity, power supply voltage, operating current and aging effects on LED brightness;

[0187] Data module, including data acquisition unit, data processing unit and data classification unit;

[0188] The data acquisition unit is used to collect the LED diode ambient temperature, ambient humidity, ambient light intensity, power supply voltage, operating current, time and LED brightness;

[0189] The data processing unit is used to pre-process and clean the collected data parameters and prepare data through data cleaning, data standardization, data conversion and feature selection;

[0190] The data classification unit is used to classify the preprocessed data according to different environmental conditions for subsequent analysis and modeling;

[0191] A detection module, including an LED diode detection model and an LED diode model tuning unit;

[0192] The LED diode detection model is used to output the real-time monitoring parameters and prediction parameters of the LED diode;

[0193] The LED diode model tuning unit is used to update the LED diode detection model;

[0194] A fault identification module, including a data receiving unit and a fault classification unit;

[0195] The data receiving unit is used to receive the real-time data and prediction data output by the LED diode detection model;

[0196] The fault classification unit is used to identify the received data and detect whether a fault occurs. If a fault occurs, the fault is classified;

[0197] Adaptive module, including a test unit and an adjustment unit;

[0198] The test unit is used to receive the fault classification data and perform the LED diode test according to the fault classification data and environmental factors;

[0199] The adjustment unit is used to adaptively adjust the performance parameters of the LED diode according to the test results.

[0200] Importantly, it should be noted that the construction and arrangement of the present application shown in a plurality of different exemplary embodiments are only exemplary. Although only two embodiments are described in detail in this disclosure, it should be readily understood by those who refer to this disclosure that many modifications are possible, for example, the size, scale, structure, shape and ratio of various elements, and parameter values ​​(e.g., temperature, pressure, etc.), installation arrangement, use of materials, color, directional changes, etc., without substantially departing from the novel teachings and advantages of the subject matter described in the application. For example, the element shown as integrally formed can be composed of multiple parts or elements, the position of the element can be inverted or otherwise changed, and the nature or number or position of the discrete element can be changed or changed. Therefore, all such modifications are intended to be included in the scope of the present invention. The order or sequence of any process or method steps can be changed or reordered according to alternative embodiments. Any "device plus function" clause is intended to cover the structure of the execution function described in this article, and is not only structurally equivalent but also equivalent structure. Without departing from the scope of the present invention, other replacements, modifications, changes and omissions can be made in the design, operating conditions and arrangement of the exemplary embodiment. Therefore, the invention is not limited to a specific embodiment, but extends to numerous modifications still falling within the scope of the appended claims.

[0201] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment may not be described (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those features that are not relevant to implementing the invention).

[0202] It should be understood that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but for those of ordinary skill having the benefit of this disclosure, the development effort will be a routine task of design, fabrication, and production without undue experimentation.

[0203] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A self-diagnosis method for LED diode faults, characterized in that: include: S1. Collect the environmental data of the LED diode, calculate the environmental factors through environmental analysis, and calibrate the environmental factors by comparing with the standard environmental data; S2, collecting LED diode data parameters, preprocessing the collected data parameters, receiving the corrected environmental factors, and classifying the preprocessed data parameters; S3, the LED diode detection model receives the classified data parameters, establishes a test set and a training set, and outputs the LED diode real-time data parameters and predicted data through the LED diode detection model; S4, the LED diode model tuning unit performs loss tuning on the LED diode detection model through the test set, and optimizes the LED diode detection model in real time; S5, outputting LED diode real-time data parameters and prediction data through the LED diode detection model, and classifying LED diode faults; S6. Perform diode test based on the classified faults and environmental factors. If the tuning test passes, adjust the LED diode through the adjustment unit. If the tuning test fails, a fault alarm is issued.

2. The LED diode fault self-diagnosis method according to claim 1, characterized in that: The environmental data includes temperature data, humidity data, illumination data, power supply voltage data, operating current data, and LED aging data; The environmental factors are calculated through the environmental analysis model. The calculation expression of the environmental analysis model is as follows: ； in, The comprehensive model outputs the influence coefficient of the environment on the LED diode. is the LED brightness at temperature T, T is the temperature, is the humidity sensitivity coefficient, is the relative humidity, is the reference humidity, is the reference temperature The LED brightness under is the light sensitivity coefficient, is the incident light intensity, e is the exponential constant, is the power supply voltage factor, is the current sensitivity coefficient, I is the working current, is the reference current, and m is the empirical coefficient.

3. The LED diode fault self-diagnosis method according to claim 2, characterized in that: Preprocess and clean the collected data parameters and prepare the data through data cleaning, data standardization, data transformation and feature selection; Data classification is used to classify the preprocessed data according to different environmental conditions.

4. The LED diode fault self-diagnosis method according to claim 3, characterized in that: Load the preprocessed and classified LED diode parameters from the classified CSV file and randomly divide the data into training and test sets; The LED diode detection model is trained using the training set data and evaluated on the test set. The trained model is used to predict new real-time data. The convolutional neural network performs convolution operations on the LED diode data parameters through the convolution layer, inputs the maximum pooling layer for dimensionality reduction processing, and inputs the data after dimensionality reduction processing into the flattening layer to flatten the multi-dimensional data into a one-dimensional vector, and the one-dimensional vector is input into the fully connected layer to perform high-level abstract processing on the characteristics of the LED diode.

5. The LED diode fault self-diagnosis method according to claim 4, characterized in that: The convolutional layer calculation expression is as follows: ； in, is an element of the output feature map, is the input data of the jth group of LED diodes after classification, b is the bias term, is the weight corresponding to the data of the jth group of LED diode classification, j is the number of data groups after LED diode classification, k is the total number of data groups after LED diode classification, f() is the activation function, and i is the number of feature map groups; The maximum pooling layer calculation expression is as follows: ； in, is an element of the output feature map, is a local area of ​​the input data, max() is the maximum value function, and s is the step size from the i-th feature map to the next feature map; The calculation expression of the fully connected layer is as follows: ； Among them, y is the output LED diode vector, x is the input LED diode vector, W is the weight matrix, b is the bias vector, and f() is the activation function.

6. The LED diode fault self-diagnosis method according to claim 5, characterized in that: Performing loss tuning on the LED diode detection model through the test set and optimizing the model in real time, including defining a loss function, selecting an optimization algorithm, performing model training and evaluation, and updating model parameters in real time; The loss amount of the LED diode detection model is calculated by defining a loss function, and the loss function calculation expression is as follows: ； in, is the loss value of the LED diode detection model output by the loss function, N is the number of samples, p is the number of p-th LED diode samples, is the true value corresponding to the pth LED diode sample number, is the predicted value corresponding to the pth LED diode sample number; The optimization algorithm is used to minimize the loss function, calculate the adaptive learning rate by multiplying the output gradient of the LED diode detection model and the hyperparameter product, and then calculate the difference between the adaptive learning rate and the momentum of the real-time output prediction value of the LED diode detection model to update the LED diode detection model. The calculation expression is as follows: ； in, is the first-order estimation matrix of the zth group, is the second-order estimation matrix of the zth group, is a first-order matrix hyperparameter, is a second-order matrix hyperparameter, Output gradient value for LED diode detection model, To optimize the first-order estimation matrix, To optimize the second-order estimation matrix, is a small constant used to prevent overfitting. is the LED diode detection model parameter of the hth step, are the optimized model parameters, is the first-order estimation matrix of the z-1th group, is the second-order estimation matrix of the z-1th group.

7. The LED diode fault self-diagnosis method according to claim 6, characterized in that: Fault detection determines whether the LED diode is faulty by comparing the difference between the predicted value and the actual value, including setting a threshold value. When the difference between the predicted value and the actual value exceeds the threshold value, the LED diode is considered to be faulty, calculating the difference between the predicted value and the actual value, and determining whether a fault has occurred based on the difference value; The LED diode is tested according to the classified fault types and environmental factors to verify the accuracy of the fault types and environmental factors.

8. An LED diode fault self-diagnosis system, used to implement the LED diode fault self-diagnosis method according to any one of claims 1 to 7, characterized in that: include: Environmental factor module, including environmental identification unit, environmental factor unit, and environmental testing unit; Data module, including data acquisition unit, data processing unit and data classification unit; A detection module, including an LED diode detection model and an LED diode model tuning unit; A fault identification module, including a data receiving unit and a fault classification unit; The adaptive module includes a testing unit and an adjusting unit.

9. The LED diode fault self-diagnosis system according to claim 8, characterized in that: The environment identification unit is used to collect environmental data and identify the types of environmental influencing factors; The environmental factor unit is used to calculate environmental factors including temperature factor, humidity factor, light intensity factor, power supply voltage factor, operating current factor, and LED aging factor; The environmental testing unit is used to analyze the influence of temperature, humidity, light intensity, power supply voltage, operating current and aging effect on LED brightness; The data acquisition unit is used to collect the LED diode ambient temperature, ambient humidity, ambient light intensity, power supply voltage, operating current, time and LED brightness; The data processing unit is used to pre-process and clean the collected data parameters and prepare data through data cleaning, data standardization, data conversion and feature selection; The data classification unit is used to classify the preprocessed data according to different environmental conditions; The LED diode detection model is used to output real-time monitoring parameters and prediction parameters of the LED diode; The LED diode model tuning unit is used to update the LED diode detection model.

10. The LED diode fault self-diagnosis system according to claim 8, characterized in that: The data receiving unit is used to receive the real-time data and prediction data output by the LED diode detection model; The fault classification unit is used to identify the received data and detect whether a fault occurs, and if a fault occurs, classify the fault; The test unit is used to receive fault classification data and perform LED diode testing according to the fault classification data and environmental factors; The adjustment unit is used to adaptively adjust the performance parameters of the LED diode according to the test results.

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