A two-dimensional temperature distribution detection method, system, electronic device and storage medium

Abstract

The application discloses a two-dimensional temperature distribution detection method and system, electronic equipment and storage medium, and relates to the field of temperature detection. The method comprises the following steps: acquiring photon energy data and temperature data of an OLED sample in a stable environment; determining reference point information under different currents according to the photon energy data and the temperature data; constructing a photon energy two-dimensional distribution mathematical model and a temperature two-dimensional distribution mathematical model according to the reference point information, the photon energy data and the temperature data; and constructing a two-dimensional mathematical model according to the photon energy two-dimensional distribution mathematical model and the temperature two-dimensional distribution mathematical model. The two-dimensional mathematical model is used for predicting total photon energy on the surface of an OLED device and surface temperature. The application can accurately realize non-contact two-dimensional temperature distribution detection of an OLED surface.

Description

Technical Field

[0001] This invention relates to the field of temperature detection, and in particular to a two-dimensional temperature distribution detection method, system, electronic device, and storage medium. Background Technology

[0002] For high-power OLED integrated systems, multiple LED chips will form heat sources at different locations within the system. The self-heating effects of the chips are different, and the complex thermal coupling effects between the chips lead to complex variations in chip junction temperature and system temperature distribution, posing significant challenges to system thermal effect analysis and thermal management design. Therefore, researching transient temperature field detection technology for high-power OLED integrated systems and obtaining the temporal and spatial distribution characteristics of chip junction temperature and system temperature field is helpful in exploring the photoelectric-thermal characteristics of OLED integrated systems, and has important practical significance for thermal effect analysis and effective thermal management of high-power OLED integrated systems.

[0003] Thanks to the development of computer technology, numerical simulation and computational calculations have become common methods for studying the thermal characteristics of LED devices and integrated systems, which helps in the study of temperature distribution in LED systems. The dynamic response of LED junction temperature directly affects the variation law of optical color characteristics of devices and integrated systems. Therefore, the study and testing of LED junction temperature is one of the important research contents in the field of LED device and system integration packaging. Some researchers have used simulation to study the relationship between chip size, chip number and junction temperature in high-power LED systems, and showed that non-uniform junction temperature distribution leads to non-uniformity of power of each chip. Some researchers have established a dynamic junction temperature testing method and analyzed the influence of voltage sampling time and switching time on junction temperature test results. Some researchers have obtained an electrical testing method for the junction temperature inside the device by revealing the linear relationship between junction temperature, voltage and current, and constructed a monochromatic LED spectral model with junction temperature as a functional variable. This model includes physical factors such as Boltzmann carrier distribution and carrier temperature. Some researchers have used transient thermal response testing methods to study the thermal response time constant spectrum of each layer of materials in the system and combined it with structure function theory to obtain the thermal resistance and thermal capacity distribution of each layer of materials inside the device. Other researchers have also used transient thermal response analysis to compare the thermal performance of LED systems with different packaging structures, showing that junction temperature and thermal resistance are closely related to the number of chips and their arrangement. Traditional electrical thermal resistance testing methods detect the amplitude of forward voltage changes, calculate the junction temperature based on the voltage temperature coefficient, and ultimately obtain the thermal resistance from the junction to the aluminum substrate. Transient thermal response testing methods, on the other hand, utilize real-time measurements of the transient change curve of the PN junction forward voltage to calculate the thermal resistance-capacitance distribution information of each layer of material within the device. However, both of these thermal resistance methods require calibration of the voltage temperature coefficient and have their limitations: traditional electrical testing methods, because the temperature sensor is placed directly above the aluminum substrate, will, strictly speaking, underestimate the actual thermal resistance value; transient thermal response testing methods rely on complex structure function theory to calculate the thermal resistance-capacitance distribution information of each layer within the device. Furthermore, it is particularly important to note that high-power LED integrated systems, due to their large forward voltage, exceed the measurement range of general thermal resistance testing instruments, resulting in limitations in the testing and evaluation of their thermal characteristics. In the actual temperature measurement of LED devices and integrated systems, most studies employ contact point measurement methods. This involves using thermocouples, resistance thermometers, or fiber optic thermometers in direct contact with the object being measured. Sufficient heat exchange occurs between the two, eventually reaching thermal equilibrium, to achieve temperature measurement. While contact point measurement methods are simple and intuitive, they have several limitations: 1) Direct contact between the temperature sensing element and the object being measured or the ambient temperature can lead to mutual interference, easily causing measurement errors and even affecting the normal operation of the system; 2) The required thermal equilibrium process during measurement results in a general time delay, making it difficult to guarantee real-time performance; 3) One-to-one point measurement can only acquire the temperature of the measured part, or obtain a limited and sparse temperature set through multi-point measurements, failing to reflect two-dimensional or even three-dimensional temperature distribution characteristics.Current research mainly focuses on the intrinsic relationship between the surface temperature and optical properties of LED devices, while there is relatively little research on the testing and analysis of the surface temperature of OLED devices.

[0004] Current OLED temperature measurement methods are mainly divided into contact, semi-contact, and non-contact methods. Commonly used methods include micro-thermocouple (μ-TC), full width at half maximum (FWHM), peak wavelength (Peak Wavelength), and infrared thermal imaging (TI). However, contact temperature measurement methods, which involve direct physical contact between a metal probe and the sample surface, can damage or even destroy the surface structure, altering the OLED's operating state. Furthermore, these methods can only perform single-point temperature measurements. Non-contact temperature measurement methods are not affected by sample encapsulation or operating state and are often used to measure the average temperature of the sample. However, their testing process is significantly affected by the environment, resulting in low spatial resolution and limited data, making high-precision analysis and calculation difficult.

[0005] Contact-based point measurement methods are simple and intuitive, but they have several limitations: 1) The temperature sensing element is in direct contact with the object being measured or the ambient temperature, leading to mutual interference, measurement errors, and even affecting the normal operation of the system; 2) The required thermal equilibrium process during measurement results in a time delay, making it difficult to guarantee real-time performance; 3) One-to-one point measurement can only obtain the temperature of the measured part, or obtain a limited and sparse temperature set through multi-point measurement, failing to reflect the two-dimensional or even three-dimensional temperature distribution characteristics. Current research mainly focuses on the intrinsic relationship between the surface temperature and optical properties of LED devices, while research on the testing and analysis of the surface temperature of OLED devices is relatively limited. Summary of the Invention

[0006] The purpose of this invention is to provide a two-dimensional temperature distribution detection method, system, electronic device, and storage medium that can accurately realize non-contact two-dimensional temperature distribution detection on OLED surfaces.

[0007] To achieve the above objectives, the present invention provides the following solution:

[0008] A two-dimensional temperature distribution detection method, comprising:

[0009] Acquire photon energy and temperature data of OLED samples under stable conditions;

[0010] Based on the photon energy data and the temperature data, determine the reference point information under different currents;

[0011] Based on the reference point information, the photon energy data, and the temperature data, a two-dimensional mathematical model of photon energy distribution and a two-dimensional mathematical model of temperature distribution are constructed.

[0012] A two-dimensional mathematical model is constructed based on the two-dimensional distribution mathematical model of photon energy and the two-dimensional distribution mathematical model of temperature; the two-dimensional mathematical model is used to predict the total photon energy and surface temperature of the OLED device surface.

[0013] Optionally, a two-dimensional mathematical model of photon energy distribution and a two-dimensional mathematical model of temperature distribution are constructed based on the reference point information, the photon energy data, and the temperature data, specifically including:

[0014] Based on the reference point information, the photon energy data, and the temperature data, tests are performed to obtain extended data;

[0015] Based on the extended data and the reference point information, a one-dimensional mathematical model of photon energy distribution and a one-dimensional mathematical model of temperature distribution are constructed.

[0016] By extending the one-dimensional distribution mathematical model of photon energy and the one-dimensional distribution mathematical model of temperature, we obtain a two-dimensional distribution mathematical model of photon energy and a two-dimensional distribution mathematical model of temperature.

[0017] Optionally, a two-dimensional mathematical model is constructed based on the two-dimensional distribution mathematical model of photon energy and the two-dimensional distribution mathematical model of temperature, specifically including:

[0018] Construct a two-dimensional photon energy distribution matrix and a two-dimensional temperature distribution matrix based on the aforementioned two-dimensional photon energy distribution mathematical model and the aforementioned two-dimensional temperature distribution mathematical model;

[0019] The photon energy two-dimensional distribution matrix and the temperature two-dimensional distribution matrix are dimensionless by using a normalization function to obtain the photon energy dimensionless matrix and the temperature dimensionless matrix.

[0020] The coefficient matrix is ​​determined based on the dimensionless matrix of photon energy and the dimensionless matrix of temperature;

[0021] A two-dimensional mathematical model is constructed based on the coefficient matrix, the two-dimensional distribution mathematical model of photon energy, and the two-dimensional distribution mathematical model of temperature.

[0022] The present invention also provides a two-dimensional temperature distribution detection system, comprising:

[0023] The acquisition module is used to acquire photon energy data and temperature data of OLED samples under stable conditions;

[0024] The reference point information determination module is used to determine reference point information under different currents based on the photon energy data and the temperature data.

[0025] The first construction module is used to construct a two-dimensional mathematical model of photon energy distribution and a two-dimensional mathematical model of temperature distribution based on the reference point information, the photon energy data and the temperature data;

[0026] The second construction module is used to construct a two-dimensional mathematical model based on the two-dimensional distribution mathematical model of photon energy and the two-dimensional distribution mathematical model of temperature; the two-dimensional mathematical model is used to predict the total photon energy and surface temperature of the OLED device surface.

[0027] Optionally, the first building module specifically includes:

[0028] The testing unit is used to perform tests based on the reference point information, the photon energy data, and the temperature data to obtain extended data;

[0029] The construction unit is used to construct a one-dimensional mathematical model of photon energy distribution and a one-dimensional mathematical model of temperature distribution based on the extended data and the reference point information.

[0030] The extension unit is used to extend the one-dimensional distribution mathematical model of photon energy and the one-dimensional distribution mathematical model of temperature to obtain a two-dimensional distribution mathematical model of photon energy and a two-dimensional distribution mathematical model of temperature.

[0031] Optionally, the second building module specifically includes:

[0032] The photon energy two-dimensional distribution matrix and temperature two-dimensional distribution matrix construction unit is used to construct the photon energy two-dimensional distribution matrix and the temperature two-dimensional distribution matrix according to the photon energy two-dimensional distribution mathematical model and the temperature two-dimensional distribution mathematical model;

[0033] The dimensionless unit is used to perform dimensionless transformation on the two-dimensional distribution matrix of photon energy and the two-dimensional distribution matrix of temperature using a normalization function, respectively, to obtain a dimensionless matrix of photon energy and a dimensionless matrix of temperature.

[0034] The coefficient matrix determination unit is used to determine the coefficient matrix based on the dimensionless photon energy matrix and the dimensionless temperature matrix.

[0035] The two-dimensional mathematical model construction unit is used to construct a two-dimensional mathematical model based on the coefficient matrix, the two-dimensional distribution mathematical model of photon energy, and the two-dimensional distribution mathematical model of temperature.

[0036] The present invention also provides an electronic device, comprising:

[0037] One or more processors;

[0038] A storage device on which one or more programs are stored;

[0039] When the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the method described above.

[0040] The present invention also provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described above.

[0041] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0042] This invention acquires photon energy and temperature data of OLED samples under stable conditions; determines reference point information under different currents based on the photon energy and temperature data; constructs a two-dimensional mathematical model of photon energy distribution and a two-dimensional mathematical model of temperature distribution based on the reference point information, the photon energy data, and the temperature data; constructs a two-dimensional mathematical model based on the two-dimensional mathematical model of photon energy distribution and the two-dimensional mathematical model of temperature distribution; the two-dimensional mathematical model is used to predict the total photon energy and surface temperature of the OLED device surface, thereby accurately realizing non-contact detection of the two-dimensional temperature distribution of the OLED surface. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 The error diagram between the temperature model and the actual test value when the driving current is 100mA;

[0045] Figure 2 The error diagram between the temperature model and the actual test value when the driving current is 200mA;

[0046] Figure 3 The error diagram between the temperature model and the actual test value when the driving current is 300mA;

[0047] Figure 4 The error diagram between the temperature model and the actual test value when the driving current is 400mA;

[0048] Figure 5 The error diagram between the temperature model and the actual test value when the driving current is 500mA;

[0049] Figure 6 The flowchart of the two-dimensional temperature distribution detection method provided by the present invention is shown. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] The purpose of this invention is to provide a two-dimensional temperature distribution detection method, system, electronic device, and storage medium that can accurately realize non-contact two-dimensional temperature distribution detection on OLED surfaces.

[0052] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] This invention provides a novel non-contact method for measuring the surface temperature or photon energy data of an OLED device. Its purpose is to infer the temperature distribution on the surface of an OLED device based on known optical signal data, thereby analyzing the thermal properties of the OLED device surface; and to infer the photon energy distribution on the surface of an OLED device based on known temperature signal data, thereby analyzing the optical properties of the OLED device surface. This testing method is based on high-speed camera measurement technology and infrared thermal imaging testing technology. It uses a high-speed camera testing system to calculate the photon energy at each point on the OLED device surface, while simultaneously acquiring infrared images of the experimental sample using an infrared thermal imager. The temperature data at each point on the OLED device surface is analyzed and marked, and the relationship between the photon energy and temperature data at each acquisition point is found. The pixel coordinates of the acquired image data are marked as rows and columns, and the relationship between the photon energy data and temperature data in each row and column is found. Furthermore, using the coordinate axes as rows and columns, the acquired temperature data and photon energy data are each constructed into matrices, extending the one-dimensional line test point data to two-dimensional surface data, thereby establishing a theoretical physical model relating the photon energy data of the OLED device surface to its surface temperature, thus realizing the testing method proposed in this invention.

[0054] like Figure 6 As shown, the present invention provides a two-dimensional temperature distribution detection method, comprising:

[0055] Step 101: Acquire photon energy and temperature data of the OLED sample under stable conditions. Collect photon energy and temperature data of the OLED device sample: Place the OLED sample in a stable environment and use a high-speed camera to collect the photon energy P(P1, P2, P3, P4, P5) at various points on the OLED sample under different experimental conditions (changing the OLED driving current from 100mA to 500mA in 100mA steps at room temperature); Additionally, under the same experimental conditions, use an infrared thermal imager to collect the temperature data T(T1, T2, T3, T4, T5) at the same coordinate points under various variables. P1, P2, P3, P4, P5: Photon energy data of the OLED at driving currents of 100mA, 200mA, 300mA, 400mA, and 500mA, respectively; T1, T2, T3, T4, T5: Temperature data of the OLED at driving currents of 100mA, 200mA, 300mA, 400mA, and 500mA, respectively.

[0056] Step 102: Determine reference point information for different currents based on the photon energy data and the temperature data. The parameter point information includes reference point brightness / photon energy and reference point temperature.

[0057] The brightness / photon energy L of the reference point under different currents was determined using the collected photon energy data P. (0) The brightness / photon energy L at the reference point under different currents was determined using the collected data P. (0) Based on the characteristic fitting, the reference point brightness / photon energy L is obtained. (0) Linear relationship with drive current L (0) (i), as shown in equation (1):

[0058] L (0) (i)=ai+b (1)

[0059] In the formula, a and b are data fitting coefficients, and i is the driving current.

[0060] T (0) (i) = a3i + b3, where T (0) (i) represents the linear relationship between the reference point temperature and the driving current, where a3 and b3 are the reference point temperatures T. (0) The data fitting coefficients for the relationship between the current i and the current.

[0061] Step 103: Construct a two-dimensional mathematical model of photon energy distribution and a two-dimensional mathematical model of temperature distribution based on the reference point information, the photon energy data, and the temperature data.

[0062] Step 103 specifically includes: performing tests based on the reference point information, the photon energy data, and the temperature data to obtain extended data.

[0063] Based on the extended data and the reference point information, a one-dimensional mathematical model of photon energy distribution and a one-dimensional mathematical model of temperature distribution are constructed.

[0064] By extending the one-dimensional distribution mathematical model of photon energy and the one-dimensional distribution mathematical model of temperature, we obtain a two-dimensional distribution mathematical model of photon energy and a two-dimensional distribution mathematical model of temperature.

[0065] Data P1 and L(0) are analyzed by testing to determine the current extension length p under different current conditions. sx p sy .

[0066] Taking 100mA as an example, the brightness / photon energy at different pixel coordinates x(y) is tested: Data P1 and L... (0) (100) The current extension length p of the pixel coordinate x on the surface of the OLED device when the driving current is 100mA is obtained by fitting the formula (2). sx (100mA) and the current extension length p at the pixel coordinate y of the OLED device surface when the driving current is 100mA sy (100mA); Using the same method, test, analyze, and fit to obtain the current extension length at 200mA, 300mA, 400mA, and 500mA, and summarize the data to obtain p. sx and p sy .

[0067]

[0068] In the formula L (0) Let L(x) be the reference point brightness / photon energy, and L(x) be the relationship between OLED surface brightness / photon energy and the x-axis pixel coordinate. s denoted as the current extension length, and x as the pixel coordinate x-axis direction.

[0069]

[0070] Where T(x) represents the relationship between temperature and the pixel coordinates along the x-axis; T s These are the parameters obtained through data fitting.

[0071] Data p sx and p sy Fitting analysis revealed an inverse relationship between Psx and Psy and the current. Therefore, linear models Psx(i) and Psy(i) were constructed. Psx(i) is a function of p... sx The negative reciprocal of (i) has a linear relationship with the current; p sx (i) represents the relationship between the parameters obtained through data fitting and the current along the x-axis pixel coordinate direction. Psy(i) is the psy The negative reciprocal of (i) has a linear relationship with the current; p sy (i) shows the relationship between the parameters obtained by data fitting and the current in the y-axis pixel coordinate direction.

[0072]

[0073]

[0074] In the formula, a1, a2, b1, b2 are fitting coefficients.

[0075]

[0076]

[0077] Where Tsx(i) is T sx The negative reciprocal of (i) has a linear relationship with the current; T sx (i) shows the relationship between the parameters obtained by data fitting and the current in the x-axis pixel coordinate direction.

[0078] L (0) Substituting (i), Psx(i) and Psy(i) into formula (2) yields the one-dimensional distribution mathematical model of OLED photon energy, L(i,x), where L(i,y) represents the relationship between the surface photon energy of the OLED device and the pixel coordinates along the x-axis and y-axis under different current i conditions.

[0079]

[0080]

[0081]

[0082]

[0083] Where y represents the y-axis pixel coordinate direction, T(i,x) represents the relationship between the surface temperature of the OLED device and the x-axis pixel coordinate direction under different current i, and T(i,y) represents the relationship between the surface temperature of the OLED device and the y-axis pixel coordinate direction under different current i.

[0084] The one-dimensional distributional mathematical models L(i,x) and L(i,y) are extended to two dimensions based on mathematical knowledge, as follows:

[0085]

[0086] A two-dimensional mathematical model of OLED temperature distribution, T(i,x,y), is obtained using temperature data T.

[0087]

[0088] In the formula, a3 and b3 are the reference point temperatures T. (0) The data fitting coefficients are the relationship between the current i and the data fitting coefficients; a4, b4, a5, a5 are the data fitting coefficients, and i is the driving current.

[0089] Step 104: Construct a two-dimensional mathematical model based on the two-dimensional distribution mathematical model of photon energy and the two-dimensional distribution mathematical model of temperature; the two-dimensional mathematical model is used to predict the total photon energy and surface temperature of the OLED device surface.

[0090] Step 104 specifically includes:

[0091] Based on the two-dimensional distribution mathematical model of photon energy and the two-dimensional distribution mathematical model of temperature, construct the two-dimensional distribution matrix of photon energy and the two-dimensional distribution matrix of temperature.

[0092] The photon energy two-dimensional distribution matrix and the temperature two-dimensional distribution matrix are dimensionless by using a normalization function to obtain the photon energy dimensionless matrix and the temperature dimensionless matrix.

[0093] The coefficient matrix is ​​determined based on the dimensionless matrix of photon energy and the dimensionless matrix of temperature.

[0094] A two-dimensional mathematical model is constructed based on the coefficient matrix, the two-dimensional distribution mathematical model of photon energy, and the two-dimensional distribution mathematical model of temperature.

[0095] A two-dimensional mathematical model is constructed based on the two-dimensional distribution mathematical model of photon energy L(i,x,y) and the two-dimensional distribution mathematical model of temperature T(i,x,y). A two-dimensional distribution matrix P′ of photon energy and a two-dimensional distribution matrix T′ of temperature are created based on L(i,x,y) and T(i,x,y). The matrix P′ and matrix T′ are then normalized using Matlab's normalization function to obtain a dimensionless matrix P″ of photon energy and a dimensionless matrix T″ of temperature. These two matrices are plotted on the same coordinate system. The correlation between the two matrices is observed by substituting P″ and T″ into the correlation coefficient function corrcoef(); the correlation coefficient matrix R is obtained, with the following specific values:

[0096]

[0097]

[0098]

[0099]

[0100]

[0101] It can be seen that matrices P″ and T″ are negatively correlated. Therefore, there exists a coefficient matrix A such that L(i,x,y) and T(i,x,y) are mutually derived, that is:

[0102] L(i,x,y)=A(x,y)T(i,x,y) (9)

[0103]

[0104] In the formula, A(x,y) is the element in the x-th row and y-th column of the coefficient matrix A. Thus, a two-dimensional mathematical model as shown in formulas (9) and (10) is constructed.

[0105] Given a specific data point for an OLED device, this model can be used to deduce other characteristic parameters of the OLED device, thus making it easier to discuss and study the characteristics of the OLED device.

[0106] Analysis of experimental data reveals that the value of A changes under different thermal temperatures and input currents, meaning that the coefficient matrix A is not a single, invariant matrix and varies under different input conditions. Therefore, this invention proposes a functional expression for A in relation to temperature T and current i, namely:

[0107] A=αi 2 +βT 2 +χi+δT 2 +εT+A0 (11)

[0108] Where A0 is the coefficient of the set reference point, and α, β, δ, γ, and ε are undetermined coefficients. Taking the most common 3×3 matrix model as an example, optical signal data and thermal signal data are collected under different input current conditions, nine sampling points are selected, and then the relationship coefficient A is analyzed. The specific data are shown in Tables 1 and 2.

[0109] Table 1. Temperature data at each acquisition point when the driving current is 500mA.

[0110]

[0111] Table 2. Relative photon energy at each acquisition point when the driving current is 500mA.

[0112]

[0113] Therefore, the A value corresponding to each point under this condition can be calculated, and the specific value is as follows:

[0114]

[0115] By calculating in the same way, the values ​​of the A matrix for the other conditions are obtained, and α, β, δ, γ, and ε are determined accordingly. Thus, the two-dimensional mathematical model proposed in this invention is completed.

[0116] A two-dimensional mathematical model was used to predict the total photon energy and surface temperature of OLED devices. Temperature data was generated from the two-dimensional mathematical model using Matlab software, with a 100×100 area as an example. Comparison of the model data with actual test values ​​revealed that the error was extremely small. Figures 1 to 5 As shown in Table 3, relative photon energy data is generated using a two-dimensional mathematical model, also taking 100×100 as an example. The generated data is then compared with actual test values. The average error is found to be relatively small.

[0117] Table 3. Average Error of the Two-Dimensional Distribution Mathematical Model for OLED Photon Energy

[0118]

[0119]

[0120] Error comparison shows that the model can accurately predict the total photon energy and surface temperature of OLED devices.

[0121] This invention constructs a matrix model of OLED chip surface temperature or OLED chip surface photon energy data: the photon energy at each point on the OLED surface is calculated using high-speed camera measurement technology and the surface temperature at each point on the OLED chip is calculated using thermal imaging technology. A coefficient matrix is ​​used to establish a theoretical model relating photon energy and surface temperature, so that the photon energy at each point on the LED chip corresponds one-to-one with the surface temperature at each point.

[0122] The present invention also provides a two-dimensional temperature distribution detection system, comprising:

[0123] The acquisition module is used to acquire photon energy data and temperature data of OLED samples under stable conditions.

[0124] The reference point information determination module is used to determine reference point information under different currents based on the photon energy data and the temperature data.

[0125] The first construction module is used to construct a two-dimensional mathematical model of photon energy distribution and a two-dimensional mathematical model of temperature distribution based on the reference point information, the photon energy data, and the temperature data.

[0126] The second construction module is used to construct a two-dimensional mathematical model based on the two-dimensional distribution mathematical model of photon energy and the two-dimensional distribution mathematical model of temperature; the two-dimensional mathematical model is used to predict the total photon energy and surface temperature of the OLED device surface.

[0127] As an optional implementation, the first building module specifically includes:

[0128] The testing unit is used to perform tests based on the reference point information, the photon energy data, and the temperature data to obtain extended data.

[0129] The construction unit is used to construct a one-dimensional mathematical model of photon energy distribution and a one-dimensional mathematical model of temperature distribution based on the extended data and the reference point information.

[0130] The extension unit is used to extend the one-dimensional distribution mathematical model of photon energy and the one-dimensional distribution mathematical model of temperature to obtain a two-dimensional distribution mathematical model of photon energy and a two-dimensional distribution mathematical model of temperature.

[0131] As an optional implementation, the second building module specifically includes:

[0132] The photon energy two-dimensional distribution matrix and temperature two-dimensional distribution matrix construction unit is used to construct the photon energy two-dimensional distribution matrix and the temperature two-dimensional distribution matrix according to the photon energy two-dimensional distribution mathematical model and the temperature two-dimensional distribution mathematical model.

[0133] The dimensionless unit is used to perform dimensionless transformation on the two-dimensional distribution matrix of photon energy and the two-dimensional distribution matrix of temperature using a normalization function, respectively, to obtain a dimensionless matrix of photon energy and a dimensionless matrix of temperature.

[0134] The coefficient matrix determination unit is used to determine the coefficient matrix based on the dimensionless photon energy matrix and the dimensionless temperature matrix.

[0135] The two-dimensional mathematical model construction unit is used to construct a two-dimensional mathematical model based on the coefficient matrix, the two-dimensional distribution mathematical model of photon energy, and the two-dimensional distribution mathematical model of temperature.

[0136] This invention utilizes a high-speed camera to detect the two-dimensional temperature distribution on the surface of OLED devices. It employs a high-speed camera and an infrared thermal imager to collect brightness and temperature information of pixels on the OLED surface, establishing a physical model for predicting the surface temperature or photon energy of the OLED device. The aim of this technology is to reasonably calculate and predict the two-dimensional temperature distribution or photon energy distribution on the OLED device surface using this physical model, thereby enabling testing without altering the operating state of the OLED device surface and overcoming the shortcomings of traditional testing methods.

[0137] The present invention also provides an electronic device, comprising:

[0138] One or more processors.

[0139] A storage device on which one or more programs are stored.

[0140] When the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the method described above.

[0141] The present invention also provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described above.

[0142] This invention employs a high-speed camera to acquire high-speed image data of the OLED surface and an infrared thermal imager to acquire OLED surface temperature data. The relative photon energy data of the OLED surface is replaced by the grayscale values ​​of the high-speed images. The acquired photon energy and temperature data are analyzed, and two-dimensional distribution models of photon energy and temperature are constructed using formulas. The correlation between the two models is analyzed, and their coefficient matrix A is derived. A mathematical model is then established using A. This mathematical model is used to predict the OLED surface temperature and photon energy, thereby analyzing the thermal and optical properties of the OLED surface. This invention has the following characteristics:

[0143] This invention enables non-contact detection of two-dimensional temperature distribution on an OLED surface. Traditional temperature measurement methods, whether contact, semi-contact, or non-contact, mostly collect light emission information from the entire surface for average temperature testing, or perform single-point temperature measurements. This invention, based on non-contact temperature measurement, overcomes the limitations of traditional methods that can damage device materials, achieving the detection and calculation of two-dimensional temperature distribution.

[0144] The temperature distribution results exhibit high resolution. This invention is based on a series of experiments conducted using a high-speed camera, achieving uncompressed and lossless real-time storage of high-speed images over extended periods. The high-speed images have a resolution of 1.76 million pixels, with a pixel size of 9µm x 9µm. Therefore, the resulting two-dimensional photon energy distribution on the OLED device surface has high resolution, leading to a relatively high-resolution two-dimensional temperature distribution, thus enabling more accurate temperature prediction at various points on the OLED device surface.

[0145] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0146] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A two-dimensional temperature distribution detection method, characterized in that, include: Acquire photon energy and temperature data of OLED samples under stable conditions; Based on the photon energy data and the temperature data, determine the reference point information under different currents; Constructing a two-dimensional mathematical model of photon energy distribution and a two-dimensional mathematical model of temperature distribution based on the reference point information, the photon energy data, and the temperature data; specifically, constructing the two-dimensional mathematical model of photon energy distribution and the two-dimensional mathematical model of temperature distribution based on the reference point information, the photon energy data, and the temperature data includes: conducting tests based on the reference point information, the photon energy data, and the temperature data to obtain extended data; constructing a one-dimensional mathematical model of photon energy distribution and a one-dimensional mathematical model of temperature distribution based on the extended data and the reference point information; and extending the one-dimensional mathematical model of photon energy distribution and the one-dimensional mathematical model of temperature distribution to obtain a two-dimensional mathematical model of photon energy distribution and a two-dimensional mathematical model of temperature distribution. A two-dimensional mathematical model is constructed based on the two-dimensional distribution mathematical model of photon energy and the two-dimensional distribution mathematical model of temperature; the two-dimensional mathematical model is used to predict the total photon energy and surface temperature of the OLED device surface; The expression for the two-dimensional mathematical model is: L(i,x,y)=A(x,y)T(i,x,y); Where L(i,x,y) is a two-dimensional distribution mathematical model, A(x,y) is the element in the x-th row and y-th column of the coefficient matrix A, T(i,x,y) is a two-dimensional distribution mathematical model of OLED temperature, a, a1, a2, a3, a4, b, b1, b2, b3, b4 are all data fitting coefficients, and i is the driving current.

2. The two-dimensional temperature distribution detection method according to claim 1, characterized in that, A two-dimensional mathematical model is constructed based on the aforementioned two-dimensional distribution mathematical model of photon energy and the aforementioned two-dimensional distribution mathematical model of temperature, specifically including: Construct a two-dimensional photon energy distribution matrix and a two-dimensional temperature distribution matrix based on the aforementioned two-dimensional photon energy distribution mathematical model and the aforementioned two-dimensional temperature distribution mathematical model; The photon energy two-dimensional distribution matrix and the temperature two-dimensional distribution matrix are dimensionless by using a normalization function to obtain the photon energy dimensionless matrix and the temperature dimensionless matrix. The coefficient matrix is ​​determined based on the dimensionless matrix of photon energy and the dimensionless matrix of temperature; A two-dimensional mathematical model is constructed based on the coefficient matrix, the two-dimensional distribution mathematical model of photon energy, and the two-dimensional distribution mathematical model of temperature.

3. A two-dimensional temperature distribution detection system, characterized in that, include: The acquisition module is used to acquire photon energy data and temperature data of OLED samples under stable conditions; The reference point information determination module is used to determine reference point information under different currents based on the photon energy data and the temperature data. The first construction module is used to construct a two-dimensional mathematical model of photon energy distribution and a two-dimensional mathematical model of temperature distribution based on the reference point information, the photon energy data, and the temperature data. Specifically, the first construction module includes: a testing unit, used to perform tests based on the reference point information, the photon energy data, and the temperature data to obtain extended data; a construction unit, used to construct a one-dimensional mathematical model of photon energy distribution and a one-dimensional mathematical model of temperature distribution based on the extended data and the reference point information; and an extension unit, used to extend the one-dimensional mathematical model of photon energy distribution and the one-dimensional mathematical model of temperature distribution to obtain a two-dimensional mathematical model of photon energy distribution and a two-dimensional mathematical model of temperature distribution. The second construction module is used to construct a two-dimensional mathematical model based on the two-dimensional distribution mathematical model of photon energy and the two-dimensional distribution mathematical model of temperature; the two-dimensional mathematical model is used to predict the total photon energy and surface temperature of the OLED device surface; The expression for the two-dimensional mathematical model is: L(i,x,y)=A(x,y)T(i,x,y); Where L(i,x,y) is a two-dimensional distribution mathematical model, A(x,y) is the element in the x-th row and y-th column of the coefficient matrix A, T(i,x,y) is a two-dimensional distribution mathematical model of OLED temperature, a, a1, a2, a3, a4, b, b1, b2, b3, b4 are all data fitting coefficients, and i is the driving current.

4. The two-dimensional temperature distribution detection system according to claim 3, characterized in that, The second building module specifically includes: The photon energy two-dimensional distribution matrix and temperature two-dimensional distribution matrix construction unit is used to construct the photon energy two-dimensional distribution matrix and the temperature two-dimensional distribution matrix according to the photon energy two-dimensional distribution mathematical model and the temperature two-dimensional distribution mathematical model; The dimensionless unit is used to perform dimensionless transformation on the two-dimensional distribution matrix of photon energy and the two-dimensional distribution matrix of temperature using a normalization function, respectively, to obtain a dimensionless matrix of photon energy and a dimensionless matrix of temperature. The coefficient matrix determination unit is used to determine the coefficient matrix based on the dimensionless photon energy matrix and the dimensionless temperature matrix. The two-dimensional mathematical model construction unit is used to construct a two-dimensional mathematical model based on the coefficient matrix, the two-dimensional distribution mathematical model of photon energy, and the two-dimensional distribution mathematical model of temperature.

5. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method as described in any one of claims 1 to 2.

6. A storage medium, characterized in that, It stores a computer program thereon, wherein the computer program, when executed by a processor, implements the method as described in any one of claims 1 to 2.

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