CN117589427A - Vehicle-mounted flip LED test method - Google Patents

Vehicle-mounted flip LED test method Download PDF

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CN117589427A
CN117589427A CN202410037714.3A CN202410037714A CN117589427A CN 117589427 A CN117589427 A CN 117589427A CN 202410037714 A CN202410037714 A CN 202410037714A CN 117589427 A CN117589427 A CN 117589427A
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vehicle
flip
luminous flux
led
measuring
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黎鹏
邱海胜
黄华龙
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Shenzhen Zhengdong Mingguang Electronic Co ltd
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Shenzhen Zhengdong Mingguang Electronic Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M11/00—Testing of optical apparatus; Testing structures by optical methods not otherwise provided for
    • G01M11/02—Testing optical properties
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/42—Photometry, e.g. photographic exposure meter using electric radiation detectors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
    • G06F18/24155—Bayesian classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/42—Photometry, e.g. photographic exposure meter using electric radiation detectors
    • G01J2001/4247—Photometry, e.g. photographic exposure meter using electric radiation detectors for testing lamps or other light sources
    • G01J2001/4252—Photometry, e.g. photographic exposure meter using electric radiation detectors for testing lamps or other light sources for testing LED's

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Abstract

The invention provides a vehicle-mounted flip LED test method, which comprises the following steps: firstly, detecting the light flux of the vehicle-mounted flip LED through a thermocouple sensor, and then detecting the light flux of the vehicle-mounted flip LED through a plurality of calibration and compensation methods, wherein the application measures the light flux of the same vehicle-mounted flip LED, a plurality of methods are applied, each method has different errors and uncertainties, the weights of different methods adopted for measuring the light flux of the same vehicle-mounted flip LED are determined, the measurement data of the different methods adopted for measuring the light flux of the same vehicle-mounted flip LED are analyzed by using an artificial intelligent algorithm, the weight of the measured value of each method is determined, and the test light flux of the vehicle-mounted flip LED is determined according to weighted summation.

Description

Vehicle-mounted flip LED test method
Technical Field
The invention belongs to the field of LEDs, and particularly relates to a vehicle-mounted flip LED testing method.
Background
For the existing test method of the vehicle-mounted flip LED test, for example, an integrating sphere is used for measuring the vehicle-mounted flip LED, for example, the on-line detection and light receiving test method of the flip LED chip disclosed in patent document CN104502069B can be used for measuring the vehicle-mounted flip LED, and the chip to be tested is moved to the position above a light receiving port of the integrating sphere; the integrating sphere lifting device arranged below the integrating sphere moves upwards, so that the light receiving opening of the integrating sphere is aligned to the light emitting side of the flip LED chip to be tested, the upper plane of the plate arranged at the light receiving opening of the integrating sphere is tightly attached to the plate for loading the flip LED chip to be tested and provides supporting force, the single measurement has larger result error, and the technical test equipment is very complex and is difficult to ensure the authenticity of measurement.
Disclosure of Invention
The invention aims to provide a vehicle-mounted flip LED testing method for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
the vehicle-mounted flip LED testing method comprises the steps of testing a vehicle-mounted flip LED in a plurality of modes, detecting the luminous flux of the vehicle-mounted flip LED through a thermocouple sensor, absorbing the luminous energy of the vehicle-mounted flip LED by the surface of the thermocouple sensor and converting the luminous energy into heat energy when the light of the vehicle-mounted flip LED irradiates the surface of the thermocouple sensor, so that the temperature of the surface of the thermocouple sensor is increased, the temperature change leads to the temperature difference between two conductors of the thermocouple, thereby generating thermoelectric potential, and calculating the luminous flux of the vehicle-mounted flip LED by measuring the thermoelectric potential;
and then detecting the luminous flux of the vehicle-mounted flip LED by various calibration and compensation methods, which specifically comprises the following steps: radiometry, using a radiometer to measure onboard flip-chip LED luminous flux; integrating sphere method: measuring the light flux of the vehicle-mounted flip-chip LED by using an integrating sphere; brightness measurement: measuring the on-board flip-chip LED luminous flux using a luminance meter;
to measure the same onboard flip LED luminous flux, apply multiple methods and each method has different errors and uncertainties, to determine the weights of the different methods used to measure the same onboard flip LED luminous flux, use artificial intelligence algorithms to analyze the measurement data of the different methods used to measure the same onboard flip LED luminous flux and determine the weights of the measurement values of each method, determine the onboard flip LED test luminous flux according to weighted summation, and determine the onboard flip LED test luminous flux according to weighted summation.
Further, using an artificial intelligence algorithm to analyze measurement data of different methods employed to measure the same onboard flip-chip LED luminous flux and determine weights of measurement values of each method, determining onboard flip-chip LED test luminous flux according to a weighted sum, the artificial intelligence algorithm comprising a neural network trainable to identify patterns in the measurement data of different methods employed to measure the same onboard flip-chip LED luminous flux and classify or predict the measurement data of different methods employed to measure the same onboard flip-chip LED luminous flux according to the patterns, taking the measurement value of each measurement method as one input of the neural network, and determining weights of each input using the neural network; the neural network may be trained by a back-propagation algorithm that adjusts the weights of the network by comparing predicted and actual values of the network to improve the performance of the network.
Further, using an artificial intelligence algorithm to analyze measurement data of different methods adopted for measuring the same vehicle-mounted flip LED luminous flux and determine the weight of the measurement value of each method, and determining the vehicle-mounted flip LED test luminous flux according to weighted summation, wherein the artificial intelligence algorithm comprises a support SVM; taking the measured value of each measuring method as one input of the SVM, and determining the weight of each input by using the SVM; training of the SVM is performed by using measurement data of different methods employed by the training to measure the same onboard flip-chip LED luminous flux to maximize the classification interval or minimize the sum of squares of errors.
Further, using an artificial intelligence algorithm to analyze measurement data of different methods adopted for measuring the same vehicle-mounted flip LED luminous flux and determine the weight of the measurement value of each method, and determining the vehicle-mounted flip LED test luminous flux according to weighted summation, wherein the artificial intelligence algorithm comprises a Bayesian network; the bayesian network takes the measured value of each measuring method as one node of the bayesian network, determines probability distribution of each node by using the bayesian network, and then determines the weight of each measured value by using the probability distribution.
Further, the bayesian network takes the measured value of each measuring method as one node of the bayesian network, and uses the bayesian network to determine probability distribution of each node, and then uses the probability distribution to determine the weight of each measured value as follows: collecting data: firstly, measuring data of different methods adopted by the luminous flux of the same vehicle-mounted flip LED are required to be collected; constructing a Bayesian network: taking the measured value of each measuring method as a node of the Bayesian network; determining probability distribution of nodes: training the bayesian network using the collected data and determining a probability distribution for each node, the bayesian network being trained by optimizing network parameters using the known data to maximize accuracy of network predictions; calculating node probability: for each node, calculating the probability of the node on each possible value, and calculating according to the conditional probability and the prior probability of the node in the Bayesian network; calculating weights: the weight of each measured value is calculated from the probability distribution of each node, the probability distribution of each node is taken as the weight of the measured value, and the weights are normalized to ensure that the sum of the weights is 1.
Further, the specific steps for measuring the luminous flux of the vehicle-mounted flip-chip LED by using the radiometer are as follows: the method comprises the steps of opening the radiometer, preheating for a few minutes to enable the instrument to reach a stable state, placing the vehicle-mounted flip LED to be measured at a measuring port of the radiometer, ensuring that light of the vehicle-mounted flip LED can directly irradiate onto the sensor, adjusting the position and angle of the vehicle-mounted flip LED to enable the light of the vehicle-mounted flip LED to furthest irradiate onto the sensor, setting measuring parameters on an operation interface of the radiometer, starting measurement, and waiting for a measuring result.
Further, the specific steps of measuring the luminous flux of the vehicle-mounted flip-chip LED by using the brightness meter are as follows: placing a brightness meter in front of the vehicle-mounted flip-chip LED, and adjusting the angle and the position of the brightness meter to ensure that the luminous flux of the vehicle-mounted flip-chip LED can be measured correctly; turning on the vehicle-mounted flip LED and allowing the vehicle-mounted flip LED to operate until the vehicle-mounted flip LED reaches a stable state; the onboard flip-chip LED was placed in front of the luminance meter and the measurement was recorded.
The beneficial effects are that: according to the method, the same vehicle-mounted flip LED luminous flux is measured, various methods are applied, each method has different errors and uncertainties, in order to determine weights of different methods adopted for measuring the same vehicle-mounted flip LED luminous flux, an artificial intelligent algorithm is used for analyzing measurement data of different methods adopted for measuring the same vehicle-mounted flip LED luminous flux and determining weights of measured values of each method, and vehicle-mounted flip LED test luminous flux is determined according to weighted summation.
Drawings
FIG. 1 is a flow chart of a method for testing a vehicle-mounted flip LED according to the present application
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The application discloses a vehicle-mounted flip LED testing method, as shown in fig. 1, which comprises the steps of testing a vehicle-mounted flip LED in a plurality of modes, firstly detecting the luminous flux of the vehicle-mounted flip LED through a thermocouple sensor, wherein the principle of detecting the luminous flux by the thermocouple sensor is a thermoelectric effect, and the thermocouple is a sensor for converting temperature change into an electric signal, and the working principle of the sensor is based on a Seebeck effect. When two conductors of different materials of the thermocouple form a closed loop, a thermoelectric potential is generated between them if the two conductors differ in temperature. The magnitude of this thermoelectric voltage depends on the material of the two conductors, the temperature and the temperature difference between them. When detecting the luminous flux of the vehicle-mounted flip LED, the thermocouple sensor utilizes the thermoelectric effect caused by the temperature change of the surface of the object when the vehicle-mounted flip LED irradiates the object. When the vehicle-mounted flip LED light irradiates the surface of the object, the surface of the object absorbs light energy and converts the light energy into heat energy, so that the temperature of the surface of the object is increased. This temperature change causes a temperature difference between the two conductors of the thermocouple, thereby generating a thermoelectric potential. By measuring the magnitude of this thermoelectric potential, the magnitude of the luminous flux can be deduced. In particular, thermocouple sensors are typically composed of two conductors of different materials, one of which acts as a transmitter (emitter) and the other as a receiver (detector). When the vehicle-mounted flip-chip LED light irradiates the emitter, the temperature of the surface of the emitter is increased, and the temperature of the receiver is kept unchanged. This can result in a temperature difference between the emitter and the receiver, thereby generating a thermoelectric potential. By measuring the magnitude of the thermoelectric voltage, the magnitude of the luminous flux of the on-board flip LED can be calculated.
And then detecting the luminous flux of the vehicle-mounted flip LED by various calibration and compensation methods, which specifically comprises the following steps: radiometry, using a radiometer to measure onboard flip-chip LED luminous flux; the specific steps for measuring the luminous flux of the vehicle-mounted flip-chip LED by using the radiometer are as follows: the method comprises the steps of opening the radiometer, preheating for a few minutes to enable the instrument to reach a stable state, placing the vehicle-mounted flip LED to be measured at a measuring port of the radiometer, ensuring that light of the vehicle-mounted flip LED can directly irradiate onto the sensor, adjusting the position and angle of the vehicle-mounted flip LED to enable the light of the vehicle-mounted flip LED to furthest irradiate onto the sensor, setting measuring parameters on an operation interface of the radiometer, starting measurement, and waiting for a measuring result. Integrating sphere method: measuring the light flux of the vehicle-mounted flip-chip LED by using an integrating sphere; the use of an integrating sphere to measure the luminous flux of an onboard flip-chip LED is not described in detail in the prior art. Brightness measurement: measuring the on-board flip-chip LED luminous flux using a luminance meter; the specific steps for measuring the luminous flux of the vehicle-mounted flip-chip LED by using the brightness meter are as follows: placing a brightness meter in front of the vehicle-mounted flip-chip LED, and adjusting the angle and the position of the brightness meter to ensure that the luminous flux of the vehicle-mounted flip-chip LED can be measured correctly; turning on the vehicle-mounted flip LED and allowing the vehicle-mounted flip LED to operate until the vehicle-mounted flip LED reaches a stable state; the onboard flip-chip LED was placed in front of the luminance meter and the measurement was recorded. Measuring the same vehicle-mounted flip LED luminous flux, applying a plurality of methods, wherein each method has different errors and uncertainties, analyzing measurement data of different methods adopted for measuring the same vehicle-mounted flip LED luminous flux by using an artificial intelligent algorithm to determine the weights of different methods adopted for measuring the same vehicle-mounted flip LED luminous flux, determining the weights of the measurement values of each method, and determining the vehicle-mounted flip LED test luminous flux according to weighted summation; measuring the same on-board flip-chip LED luminous flux may use a variety of methods, and each method may have different errors and uncertainties.
To determine the weight of these measurements, artificial intelligence algorithms may be used to analyze the data and determine the trustworthiness of each measurement. One possible artificial intelligence algorithm is a neural network. The neural network may be trained to recognize patterns in the data and classify or predict the data based on the patterns. In this case, each measured value may be taken as one input to the neural network, and the neural network is used to determine the weight of each input. The neural network may be trained by a back-propagation algorithm that adjusts the weights of the network by comparing predicted and actual values of the network to improve the performance of the network. Another possible artificial intelligence algorithm is a Support Vector Machine (SVM). SVM is a supervised learning algorithm that can be used for classification and regression tasks. In this case, each measured value may be taken as one input to the SVM, and the weight of each input is determined using the SVM. Training of the SVM is performed by using training data to maximize the classification interval or minimize the sum of squares of errors. In addition, a Bayesian network can be used to analyze the data and determine the trustworthiness of each measurement. A bayesian network is a probabilistic graphical model that can be used for reasoning and classification tasks. In this case, each measured value may be regarded as one node of the bayesian network, and the probability distribution of each node may be determined using the bayesian network. These probability distributions may then be used to determine the weight of each measurement.
The bayesian network takes the measured value of each measuring method as one node of the bayesian network, uses the bayesian network to determine probability distribution of each node, and then uses the probability distribution to determine the weight of each measured value as follows: collecting data: firstly, measuring data of different methods adopted by the luminous flux of the same vehicle-mounted flip LED are required to be collected; constructing a Bayesian network: taking the measured value of each measuring method as a node of the Bayesian network; determining probability distribution of nodes: training the bayesian network using the collected data and determining a probability distribution for each node, the bayesian network being trained by optimizing network parameters using the known data to maximize accuracy of network predictions; calculating node probability: for each node, calculating the probability of the node on each possible value, and calculating according to the conditional probability and the prior probability of the node in the Bayesian network; calculating weights: the weight of each measured value is calculated from the probability distribution of each node, the probability distribution of each node is taken as the weight of the measured value, and the weights are normalized to ensure that the sum of the weights is 1.
Therefore, the method and the device do not need to be very complicated in testing equipment, and the weights of different methods adopted for measuring the luminous flux of the same vehicle-mounted flip LED are determined by means of data processing, so that the result errors can be reduced by organically combining and applying various methods, and the detection accuracy is improved.
Embodiments of the present application that require protection include:
the vehicle-mounted flip LED testing method comprises the following steps:
the method comprises the steps of testing a vehicle-mounted flip LED in multiple modes, detecting the luminous flux of the vehicle-mounted flip LED through a thermocouple sensor, absorbing the luminous energy of the vehicle-mounted flip LED by the surface of the thermocouple sensor and converting the luminous energy into heat energy when the light of the vehicle-mounted flip LED irradiates the surface of the thermocouple sensor, so that the temperature of the surface of the thermocouple sensor is increased, the temperature change causes the temperature difference between two conductors of the thermocouple, thereby generating thermoelectric potential, and calculating the luminous flux of the vehicle-mounted flip LED by measuring the thermoelectric potential; and then detecting the luminous flux of the vehicle-mounted flip LED by various calibration and compensation methods, which specifically comprises the following steps: radiometry, using a radiometer to measure onboard flip-chip LED luminous flux; integrating sphere method: measuring the light flux of the vehicle-mounted flip-chip LED by using an integrating sphere; brightness measurement: measuring the on-board flip-chip LED luminous flux using a luminance meter; measuring the same vehicle-mounted flip LED luminous flux, applying a plurality of methods and each method having different errors and uncertainties, analyzing measurement data of different methods adopted for measuring the same vehicle-mounted flip LED luminous flux by using an artificial intelligence algorithm to determine weights of measurement values of each method, and determining the vehicle-mounted flip LED test luminous flux according to weighted summation.
Preferably, the artificial intelligence algorithm is used to analyze the measurement data of the different methods used to measure the same vehicle-mounted flip-chip LED luminous flux and determine the weight of the measurement value of each method, the vehicle-mounted flip-chip LED test luminous flux is determined according to the weighted summation, the artificial intelligence algorithm comprises a neural network which can be trained to identify patterns in the measurement data of the different methods used to measure the same vehicle-mounted flip-chip LED luminous flux, classify or predict the measurement data of the different methods used to measure the same vehicle-mounted flip-chip LED luminous flux according to the patterns, take the measurement value of each measurement method as one input of the neural network, and determine the weight of each input by using the neural network; the neural network may be trained by a back-propagation algorithm that adjusts the weights of the network by comparing predicted and actual values of the network to improve the performance of the network.
Preferably, the measurement data of different methods adopted for measuring the same vehicle-mounted flip-chip LED luminous flux are analyzed and the weight of the measurement value of each method is determined by using an artificial intelligence algorithm, and the vehicle-mounted flip-chip LED test luminous flux is determined according to weighted summation, wherein the artificial intelligence algorithm comprises a support SVM; taking the measured value of each measuring method as one input of the SVM, and determining the weight of each input by using the SVM; training of the SVM is performed by using measurement data of different methods employed by the training to measure the same onboard flip-chip LED luminous flux to maximize the classification interval or minimize the sum of squares of errors.
Preferably, the measurement data of different methods adopted for measuring the same vehicle-mounted flip-chip LED luminous flux are analyzed and the weight of the measurement value of each method is determined by using an artificial intelligence algorithm, and the vehicle-mounted flip-chip LED test luminous flux is determined according to weighted summation, wherein the artificial intelligence algorithm comprises a Bayesian network; the bayesian network takes the measured value of each measuring method as one node of the bayesian network, determines probability distribution of each node by using the bayesian network, and then determines the weight of each measured value by using the probability distribution.
Preferably, the bayesian network takes the measured value of each measuring method as a node of the bayesian network, and uses the bayesian network to determine probability distribution of each node, and then uses the probability distribution to determine the weight of each measured value as follows: collecting data: firstly, measuring data of different methods adopted by the luminous flux of the same vehicle-mounted flip LED are required to be collected; constructing a Bayesian network: taking the measured value of each measuring method as a node of the Bayesian network; determining probability distribution of nodes: training the bayesian network using the collected data and determining a probability distribution for each node, the bayesian network being trained by optimizing network parameters using the known data to maximize accuracy of network predictions; calculating node probability: for each node, calculating the probability of the node on each possible value, and calculating according to the conditional probability and the prior probability of the node in the Bayesian network; calculating weights: the weight of each measured value is calculated from the probability distribution of each node, the probability distribution of each node is taken as the weight of the measured value, and the weights are normalized to ensure that the sum of the weights is 1.
Preferably, the specific steps for measuring the luminous flux of the flip-chip LED on board the vehicle using the radiometer are as follows: the method comprises the steps of opening the radiometer, preheating for a few minutes to enable the instrument to reach a stable state, placing the vehicle-mounted flip LED to be measured at a measuring port of the radiometer, ensuring that light of the vehicle-mounted flip LED can directly irradiate onto the sensor, adjusting the position and angle of the vehicle-mounted flip LED to enable the light of the vehicle-mounted flip LED to furthest irradiate onto the sensor, setting measuring parameters on an operation interface of the radiometer, starting measurement, and waiting for a measuring result.
Preferably, the specific steps of measuring the luminous flux of the vehicle-mounted flip-chip LED by using the brightness meter are as follows: placing a brightness meter in front of the vehicle-mounted flip-chip LED, and adjusting the angle and the position of the brightness meter to ensure that the luminous flux of the vehicle-mounted flip-chip LED can be measured correctly; turning on the vehicle-mounted flip LED and allowing the vehicle-mounted flip LED to operate until the vehicle-mounted flip LED reaches a stable state; the onboard flip-chip LED was placed in front of the luminance meter and the measurement was recorded.
The embodiment of the application also provides a computer device, which may include a terminal device or a server, and the data computing program of the foregoing method may be configured in the computer device. The computer device is described below.
If the computer device is a terminal device, the embodiment of the present application provides a terminal device, taking the terminal device as a mobile phone as an example:
the mobile phone comprises: radio Frequency (RF) circuitry, memory, input unit, display unit, sensors, audio circuitry, wireless fidelity (Wireless Fidelity, wiFi) module, processor, and power supply.
The RF circuit can be used for receiving and transmitting signals in the process of receiving and transmitting information or communication, particularly, after receiving downlink information of the base station, the downlink information is processed by the processor; in addition, the data of the design uplink is sent to the base station. Typically, RF circuitry includes, but is not limited to, antennas, at least one amplifier, transceivers, couplers, low noise amplifiers (Low NoiseAmplifier, LNA for short), diplexers, and the like. In addition, the RF circuitry may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to global system for mobile communications (Global System of Mobile communication, GSM for short), general packet radio service (GeneralPacket Radio Service, GPRS for short), code division multiple access (Code Division Multiple Access, CDMA for short), wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA for short), long term evolution (Long Term Evolution, LTE for short), email, short message service (Short Messaging Service, SMS for short), and the like.
The memory may be used to store software programs and modules, and the processor executes the software programs and modules stored in the memory to perform various functional applications and data processing of the handset. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
The input unit may be used to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the handset. In particular, the input unit may include a touch panel and other input devices. The touch panel, also referred to as a touch screen, may collect touch operations thereon or thereabout by a user (e.g., operations thereon or thereabout by a user using any suitable object or accessory such as a finger, stylus, etc.), and drive the corresponding connection device according to a predetermined program. Alternatively, the touch panel may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch azimuth of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor, and can receive and execute commands sent by the processor. In addition, the touch panel may be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. The input unit may include other input devices in addition to the touch panel. In particular, other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, mouse, joystick, etc.
The display unit may be used to display information input by a user or information provided to the user and various menus of the mobile phone. The display unit may include a display panel, which may be optionally configured in the form of a liquid crystal display (LiquidCrystal Display, LCD) or an Organic Light-Emitting Diode (OLED) or the like. Further, the touch panel may overlay the display panel, and upon detection of a touch operation thereon or thereabout, the touch panel is transferred to the processor to determine the type of touch event, and the processor then provides a corresponding visual output on the display panel in accordance with the type of touch event. Although in the figures the touch panel and the display panel are shown as two separate components to implement the input and output functions of the cell phone, in some embodiments the touch panel and the display panel may be integrated to implement the input and output functions of the cell phone.
The handset may also include at least one sensor, such as a light sensor, a motion sensor, and other sensors. In particular, the light sensor may include an ambient light sensor that may configure the brightness of the display panel according to the brightness of ambient light, and a proximity sensor that may turn off the display panel and/or backlight when the phone is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and direction when stationary, and can be used for applications of recognizing the gesture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc. that may also be configured with the handset are not described in detail herein.
Audio circuitry, speakers, and microphone may provide an audio interface between the user and the handset. The audio circuit can transmit the received electric signal after the audio data conversion to a loudspeaker, and the loudspeaker converts the electric signal into a sound signal to be output; on the other hand, the microphone converts the collected sound signals into electrical signals, which are received by the audio circuit and converted into audio data, which are processed by the audio data output processor and sent via the RF circuit to, for example, another mobile phone, or which are output to a memory for further processing.
WiFi belongs to a short-distance wireless transmission technology, and a mobile phone can help a user to send and receive an email, browse a webpage, access streaming media and the like through a WiFi module, so that wireless broadband Internet access is provided for the user. Although a WiFi module is illustrated, it is understood that it does not belong to the necessary configuration of the handset, and can be omitted entirely as needed within the scope of not changing the essence of the invention.
The processor is a control center of the mobile phone, and is connected with various parts of the whole mobile phone by various interfaces and lines, and executes various functions and processes data of the mobile phone by running or executing software programs and/or modules stored in the memory and calling data stored in the memory, so that the mobile phone is monitored integrally. In the alternative, the processor may include one or more processing units; preferably, the processor may integrate an application processor and a modem processor, wherein the application processor primarily handles operating systems, user interfaces, applications, etc., and the modem processor primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor.
The handset further includes a power source (e.g., a battery) for powering the various components, preferably in logical communication with the processor through a power management system, such that functions such as managing charge, discharge, and power consumption are performed by the power management system.
Although not shown, the mobile phone may further include a camera, a bluetooth module, etc., which will not be described herein.
If the computer device is a server, the embodiments of the present application further provide a server, where the server may generate a relatively large difference due to different configurations or performances, and may include one or more central processing units (Central Processing Units, abbreviated as CPUs) (e.g., one or more processors) and a memory, one or more storage media (e.g., one or more mass storage devices) storing application programs or data. The memory and storage medium may be transitory or persistent. The program stored on the storage medium may include one or more modules, each of which may include a series of instruction operations on the server. Still further, the central processor may be configured to communicate with a storage medium and execute a series of instruction operations on the storage medium on a server.
The server may also include one or more power supplies, one or more wired or wireless network interfaces, one or more input/output interfaces, and/or one or more operating systems, such as Windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, etc.
In addition, the embodiment of the application also provides a storage medium for storing a computer program for executing the method provided by the embodiment.
The present embodiments also provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method provided by the above embodiments.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, where the above program may be stored in a computer readable storage medium, and when the program is executed, the program performs steps including the above method embodiments; and the aforementioned storage medium may be at least one of the following media: read-only Memory (ROM), RAM, magnetic disk or optical disk, etc.
It should be noted that, in the present specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment is mainly described in a different point from other embodiments. In particular, for the apparatus and system embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, with reference to the description of the method embodiments in part. The apparatus and system embodiments described above are merely illustrative, in which elements illustrated as separate elements may or may not be physically separate, and elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the invention without undue burden.
The foregoing is merely one specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions easily conceivable by those skilled in the art within the technical scope of the present application should be covered in the protection scope of the present application.

Claims (7)

1. The vehicle-mounted flip LED testing method is characterized by comprising the following steps of: the method comprises the steps of testing a vehicle-mounted flip LED in multiple modes, detecting the luminous flux of the vehicle-mounted flip LED through a thermocouple sensor, absorbing the luminous energy of the vehicle-mounted flip LED by the surface of the thermocouple sensor and converting the luminous energy into heat energy when the light of the vehicle-mounted flip LED irradiates the surface of the thermocouple sensor, so that the temperature of the surface of the thermocouple sensor is increased, the temperature change causes the temperature difference between two conductors of the thermocouple, thereby generating thermoelectric potential, and calculating the luminous flux of the vehicle-mounted flip LED by measuring the thermoelectric potential;
and then detecting the luminous flux of the vehicle-mounted flip LED by various calibration and compensation methods, which specifically comprises the following steps: radiometry, using a radiometer to measure onboard flip-chip LED luminous flux; integrating sphere method: measuring the light flux of the vehicle-mounted flip-chip LED by using an integrating sphere; brightness measurement: measuring the on-board flip-chip LED luminous flux using a luminance meter;
to measure the same onboard flip LED luminous flux, apply multiple methods and each method has different errors and uncertainties, to determine the weights of the different methods used to measure the same onboard flip LED luminous flux, use artificial intelligence algorithms to analyze the measurement data of the different methods used to measure the same onboard flip LED luminous flux and determine the weights of the measurement values of each method, determine the onboard flip LED test luminous flux according to weighted summation, and determine the onboard flip LED test luminous flux according to weighted summation.
2. The method of claim 1, wherein the measurement data of the different methods used to measure the same onboard flip-chip LED luminous flux is analyzed and weights of the measurement values of each method are determined using an artificial intelligence algorithm, the artificial intelligence algorithm comprises a neural network which can be trained to identify patterns in the measurement data of the different methods used to measure the same onboard flip-chip LED luminous flux and classify or predict the measurement data of the different methods used to measure the same onboard flip-chip LED luminous flux according to the patterns, the measurement value of each measurement method is taken as one input of the neural network, and the weights of each input are determined using the neural network; the neural network may be trained by a back-propagation algorithm that adjusts the weights of the network by comparing predicted and actual values of the network to improve the performance of the network.
3. The method of claim 1, wherein the measurement data of different methods used to measure the same onboard flip-chip LED luminous flux are analyzed and weights of the measurement values of each method are determined using an artificial intelligence algorithm, and the onboard flip-chip LED test luminous flux is determined according to a weighted sum, the artificial intelligence algorithm including a support SVM; taking the measured value of each measuring method as one input of the SVM, and determining the weight of each input by using the SVM; training of the SVM is performed by using measurement data of different methods employed by the training to measure the same onboard flip-chip LED luminous flux to maximize the classification interval or minimize the sum of squares of errors.
4. The method for testing the flip-chip LEDs on the vehicle according to claim 1, wherein an artificial intelligence algorithm is used to analyze measurement data of different methods adopted to measure the same flip-chip LED luminous flux on the vehicle and determine weights of measurement values of each method, and the flip-chip LED test luminous flux on the vehicle is determined according to a weighted summation, and the artificial intelligence algorithm comprises a bayesian network; the bayesian network takes the measured value of each measuring method as one node of the bayesian network, determines probability distribution of each node by using the bayesian network, and then determines the weight of each measured value by using the probability distribution.
5. The vehicle-mounted flip LED test method of claim 1, wherein the bayesian network takes the measured value of each measuring method as a node of the bayesian network, and uses the bayesian network to determine probability distribution of each node, and then uses the probability distributions to determine the weight of each measured value as follows: collecting data: firstly, measuring data of different methods adopted by the luminous flux of the same vehicle-mounted flip LED are required to be collected; constructing a Bayesian network: taking the measured value of each measuring method as a node of the Bayesian network; determining probability distribution of nodes: training the bayesian network using the collected data and determining a probability distribution for each node, the bayesian network being trained by optimizing network parameters using the known data to maximize accuracy of network predictions; calculating node probability: for each node, calculating the probability of the node on each possible value, and calculating according to the conditional probability and the prior probability of the node in the Bayesian network; calculating weights: the weight of each measured value is calculated from the probability distribution of each node, the probability distribution of each node is taken as the weight of the measured value, and the weights are normalized to ensure that the sum of the weights is 1.
6. The method for testing the flip-chip LEDs on a vehicle according to claim 1, wherein the specific steps of measuring the luminous flux of the flip-chip LEDs on the vehicle using a radiometer are as follows: the method comprises the steps of opening the radiometer, preheating for a few minutes to enable the instrument to reach a stable state, placing the vehicle-mounted flip LED to be measured at a measuring port of the radiometer, ensuring that light of the vehicle-mounted flip LED can directly irradiate onto the sensor, adjusting the position and angle of the vehicle-mounted flip LED to enable the light of the vehicle-mounted flip LED to furthest irradiate onto the sensor, setting measuring parameters on an operation interface of the radiometer, starting measurement, and waiting for a measuring result.
7. The method for testing the flip-chip LEDs on a vehicle according to claim 1, wherein the specific steps of measuring the luminous flux of the flip-chip LEDs on the vehicle using a luminance meter are as follows: placing a brightness meter in front of the vehicle-mounted flip-chip LED, and adjusting the angle and the position of the brightness meter to ensure that the luminous flux of the vehicle-mounted flip-chip LED can be measured correctly; turning on the vehicle-mounted flip LED and allowing the vehicle-mounted flip LED to operate until the vehicle-mounted flip LED reaches a stable state; the onboard flip-chip LED was placed in front of the luminance meter and the measurement was recorded.
CN202410037714.3A 2024-01-10 2024-01-10 Vehicle-mounted flip LED test method Pending CN117589427A (en)

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CN202410037714.3A CN117589427A (en) 2024-01-10 2024-01-10 Vehicle-mounted flip LED test method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202410037714.3A CN117589427A (en) 2024-01-10 2024-01-10 Vehicle-mounted flip LED test method

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Publication Number Publication Date
CN117589427A true CN117589427A (en) 2024-02-23

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