MIP LED intelligent temperature control method and system based on dynamic photo-thermal coupling

Abstract

The invention relates to the technical field of hardware temperature control, and provides an MIP LED intelligent temperature control method and system based on dynamic photo-thermal coupling. Acquiring environmental parameters and chip parameters of the LED, generating a photo-thermal coupled partial differential equation set based on the environmental parameters and the chip parameters, and solving the partial differential equation set to generate a photo-thermal coupled field; performing dimension reduction processing on the photo-thermal coupling field to generate a photo-thermal feature vector; acquiring a historical control signal, and performing double-layer nonlinear mapping based on the photo-thermal feature vector, a preset target temperature and the historical control signal to generate a temperature control signal; and the operation of the LED assembly is controlled through the temperature control signal. By generating a photo-thermal coupling partial differential equation set and solving to obtain a photo-thermal coupling field, the photo-thermal state of the LED is comprehensively presented, normal form transformation from passive feedback rectification to active feedforward intervention is achieved, the dynamic response speed is liberated from limitation of physical heat transfer, and efficient, intelligent and accurate regulation and control of the temperature of the LED in different environments are achieved.

Description

Technical Field

[0001] This application relates to the field of hardware temperature control technology, and more specifically, to a method and system for intelligent temperature control of MIP LEDs based on dynamic photothermal coupling. Background Technology

[0002] With the rapid development of semiconductor lighting technology, especially in the process of Micro LED in Package (MIP LED), its high brightness, small size, and design flexibility have made it a core light source in high-end lighting and display fields. However, the MIP structure concentrates extremely high power density in a tiny area, leading to abnormally concentrated heat generation. Traditional static or passive temperature control solutions are unable to cope with its rapid and drastic temperature fluctuations, which not only cause a decrease in luminous efficacy and color drift, but more seriously, drastically shorten the device's lifespan, becoming a major technical bottleneck restricting its development in high-performance applications.

[0003] Traditional PID control relies on temperature feedback for reactive adjustment, depending on feedback delay. This makes it unable to respond in real-time to transient thermal shocks generated by LEDs during dynamic operation, nor can it predict thermal change trends. Furthermore, it is prone to overshoot and oscillations in multivariable coupled systems. Especially during photoelectric conversion, particularly in high-temperature regions, LED parameters such as luminous efficacy and thermal resistance exhibit nonlinear changes. These nonlinear temperature variations lead to spectral drift, decreased luminous efficacy, and reduced lifespan. The linear control model of PID is severely mismatched with the highly nonlinear photothermal conversion process of LEDs. Simple linear control struggles to achieve stable and optimized operation. This lag and inaccurate control can cause chip temperature to fluctuate uncontrollably, resulting in response lag, overshoot, and even exacerbated thermal oscillations. This severely impacts the reliability and stability of LED operation. Summary of the Invention

[0004] This application provides a method and system for intelligent temperature control of MIP LEDs based on dynamic photothermal coupling, which can at least partially solve the problems of low efficiency and accuracy in the temperature control process of MIP LEDs.

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0006] According to one aspect of this application, a method for intelligent temperature control of MIP LEDs based on dynamic photothermal coupling is provided, comprising: acquiring environmental parameters and LED chip parameters, wherein the chip parameters include geometric parameters of the package structure and driving current; generating a system of partial differential equations for photothermal coupling based on the environmental parameters and the chip parameters, and solving the system of partial differential equations to generate a photothermal coupling field; performing dimensionality reduction processing on the photothermal coupling field to generate a photothermal feature vector; acquiring historical control signals, performing a two-layer nonlinear mapping based on the photothermal feature vector, a preset target temperature, and the historical control signals to generate a temperature control signal; and controlling the operation of the LED component through the temperature control signal.

[0007] In this application, based on the aforementioned scheme, the step of generating a set of partial differential equations for photothermal coupling based on the environmental parameters and the chip parameters, and solving the set of partial differential equations to generate a photothermal coupling field, includes: generating a heat conduction equation, an optical transport equation, a heat conduction nonlinear equation, and a temperature-dependent equation based on the environmental parameters and the chip parameters; combining the heat conduction equation, the optical transport equation, the heat conduction nonlinear equation, and the temperature-dependent equation to generate a set of partial differential equations for photothermal coupling; and solving the set of partial differential equations to generate a photothermal coupling field.

[0008] In this application, based on the aforementioned scheme, the step of generating the heat conduction equation, the light transport equation, the heat conduction nonlinear equation, and the temperature-dependent equation based on the environmental parameters and the chip parameters respectively includes: generating the heat conduction equation based on the real-time temperature in the environmental parameters, the thermal conductivity, material density, and specific heat capacity in the chip parameters; generating the light transport equation based on the light intensity information in the environmental parameters, the absorption coefficient, and the heat dissipation factor in the chip parameters; generating the heat conduction nonlinear equation based on the real-time temperature in the environmental parameters and a preset fitting coefficient in the chip parameters; and generating the temperature-dependent equation based on the real-time temperature in the environmental parameters and the absorption coefficient in the chip parameters.

[0009] In this application, based on the aforementioned scheme, the step of generating a heat conduction equation based on the real-time temperature in the environmental parameters, the thermal conductivity, material density, and specific heat capacity in the chip parameters includes: generating a heat conduction equation based on the real-time temperature in the environmental parameters, the thermal conductivity, material density, and specific heat capacity in the chip parameters as follows:

[0010] in, Represents the gradient operator. Indicates temperature T Thermal conductivity at that time The wavelength of light is represented by Temperature is TThe absorption coefficient of converting time energy into heat energy; Indicates light intensity. Indicates the density of the material. This indicates specific heat capacity.

[0011] In this application, based on the aforementioned scheme, the step of generating the optical transmission equation based on the light intensity information in the environmental parameters, the absorption coefficient and the heat dissipation factor in the chip parameters includes: generating the optical transmission equation based on the light intensity information in the environmental parameters, the absorption coefficient and the heat dissipation factor in the chip parameters as follows:

[0012] in, Represents the gradient operator. The wavelength of light is represented by Temperature is T The absorption coefficient of converting time energy into heat energy; The wavelength of light is represented by Temperature is T The scattering coefficient of time energy converted into heat energy; Indicates light intensity. Indicates light intensity Attenuation along the z-direction.

[0013] In this application, based on the aforementioned scheme, the step of reducing the dimensionality of the photothermal coupling field to generate a photothermal feature vector includes: extracting features from the photothermal coupling field based on a feature extraction operator to generate a photothermal feature vector.

[0014] In this application, based on the aforementioned scheme, the step of acquiring historical control signals and generating temperature control signals by performing two-layer nonlinear mapping based on the photothermal feature vector, the preset target temperature, and the historical control signals includes: acquiring historical control signals; constructing a multi-parameter network based on the photothermal feature vector, the preset target temperature, and the historical control signals; and performing two-layer nonlinear mapping in the multi-parameter network to generate temperature control signals.

[0015] In this application, based on the aforementioned scheme, controlling the operation of the LED component through the temperature control signal includes: parsing the temperature control signal to generate a current modulation command and a cooling fan speed command; adjusting the LED driving current based on the current modulation command, and controlling the cooling fan based on the cooling fan speed command.

[0016] In this application, based on the aforementioned scheme, the acquisition of environmental parameters and LED chip parameters includes: acquiring environmental parameters and LED chip parameters through a preset smart sensor.

[0017] According to one aspect of this application, a MIP LED intelligent temperature control system based on dynamic photothermal coupling is provided, comprising: An acquisition module is used to acquire environmental parameters and LED chip parameters, wherein the chip parameters include the geometric parameters of the package structure and the driving current; The coupling module is used to generate a set of partial differential equations for photothermal coupling based on the environmental parameters and the chip parameters, and to solve the set of partial differential equations to generate a photothermal coupling field. The feature module is used to perform dimensionality reduction processing on the photothermal coupling field to generate a photothermal feature vector; The signal module is used to acquire historical control signals and perform a two-layer nonlinear mapping based on the photothermal feature vector, the preset target temperature and the historical control signals to generate a temperature control signal. A control module is used to control the operation of the LED components via the temperature control signal.

[0018] According to one aspect of this application, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the MIP LED intelligent temperature control method based on dynamic photothermal coupling as described in the above embodiments.

[0019] According to one aspect of this application, an electronic device is provided, comprising: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the MIP LED intelligent temperature control method based on dynamic photothermal coupling as described in the above embodiments.

[0020] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the MIP LED intelligent temperature control method based on dynamic photothermal coupling provided in the various optional implementations described above.

[0021] In the technical solution of this application, environmental parameters and LED chip parameters are obtained. Based on the environmental parameters and chip parameters, a system of partial differential equations for photothermal coupling is generated, and the system of partial differential equations is solved to generate a photothermal coupling field. The photothermal coupling field is then subjected to dimensionality reduction processing to generate a photothermal feature vector. Historical control signals are obtained, and a two-layer nonlinear mapping is performed based on the photothermal feature vector, a preset target temperature, and the historical control signals to generate a temperature control signal. The operation of the LED component is controlled by the temperature control signal. By first obtaining environmental and chip parameters, a foundation for accurate analysis is laid. Then, the photothermal coupling partial differential equations are generated and solved to obtain the photothermal coupling field, which can comprehensively present the photothermal state of the LED, effectively suppressing thermal crosstalk and local overheating. The dimensionality reduction of the photothermal coupling field to generate a feature vector simplifies the information. Then, the temperature control signal is generated by combining the historical signal, and finally, the operation of the LED component is controlled according to this signal, realizing intelligent and precise temperature control of the LED in different environments, improving its performance and stability.

[0022] On the other hand, compared with traditional PID control that relies on delayed temperature feedback for post-event remediation, this solution can dynamically predict the future thermal trend of the chip under the current driving and heat dissipation conditions by solving the optical-thermal coupling partial differential equations. This predictive capability enables the system to generate and execute a suppressive temperature control signal in advance through nonlinear mapping before the actual temperature rises sharply. This realizes a paradigm shift from passive feedback correction to active feedforward intervention, thereby freeing the dynamic response speed from the limitations of physical heat transfer and effectively mitigating thermal shock.

[0023] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0025] Figure 1 The flowchart illustrating a method for intelligent temperature control of MIP LEDs based on dynamic photothermal coupling in one embodiment of this application is shown.

[0026] Figure 2 The flowchart illustrating the generation of a photothermal coupling field is shown in one embodiment of this application.

[0027] Figure 3 The illustration shows a schematic diagram of a MIP LED intelligent temperature control system based on dynamic photothermal coupling in one embodiment of this application.

[0028] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0029] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0030] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0031] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0032] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0033] The implementation details of the technical solution of this application are described below: Figure 1 A flowchart illustrating a method for intelligent temperature control of MIP LEDs based on dynamic photothermal coupling according to an embodiment of this application is shown. (Refer to...) Figure 1 As shown, the intelligent temperature control method for MIP LEDs based on dynamic photothermal coupling includes at least steps S110 to S150, which are described in detail below: S110, acquire environmental parameters and LED chip parameters, wherein the chip parameters include the geometric parameters of the package structure and the drive current.

[0034] In practical applications, traditional methods usually rely on a single or a small number of temperature sensors, which can only obtain point temperature information and cannot detect the three-dimensional temperature gradient and thermal crosstalk inside the MIP LED chip and package. This can easily lead to excessively high local junction temperature, causing a sudden drop in luminous efficiency (thermal quenching), wavelength drift and permanent damage.

[0035] In this embodiment, environmental parameters include temperature, humidity, and air pressure, which significantly impact LED performance and lifespan. For example, high temperatures can accelerate LED chip aging, while high humidity can cause the chip to short-circuit due to moisture. LED chip parameters include thermal conductivity, absorption coefficient, geometric dimensions, and electrical characteristics, which determine the chip's photothermal performance and operating characteristics. During data acquisition, a temperature sensor can be used for temperature measurement, placed in a suitable location within the LED's operating environment. After a period of stabilization, the temperature value is read. For humidity measurement, a humidity sensor is used, ensuring its installation location accurately reflects the humidity level of the environment surrounding the LED. Air pressure measurement utilizes a barometer. During measurement, it is crucial to avoid interference from external factors and record the measurement time and other relevant environmental information for subsequent parameter analysis and correlation to understand how environmental parameters change over time or due to other factors.

[0036] In practical applications, obtaining LED chip parameters typically requires precise measurement in a laboratory environment. For thermal conductivity, specific thermal analysis instruments can be used to apply heat to the chip and measure the heat conduction within the chip, thus calculating the thermal conductivity. The absorption coefficient is determined using optical measurement equipment, where different wavelengths of light are irradiated onto the chip, and the absorption is measured to establish the absorption coefficient. Geometric parameters can be measured using high-precision tools such as microscopes and calipers to accurately measure the chip's length, width, and height. Electrical characteristic parameters, such as drive current, can be obtained by applying different voltages across the chip and measuring the corresponding current values. During laboratory measurements, strict control of experimental conditions is crucial to ensure the accuracy and reliability of the measurement results.

[0037] Alternatively, wireless networking can be based on the Z-wave wireless networking specification to generate a sensor network, and chip parameters can be collected through the sensor network.

[0038] In addition, a self-organizing network of micro-electro-mechanical system (MEMS) sensors can be built based on Internet of Things (IoT) technology to form a micro-electro-mechanical system composed of various sensors, and the photothermal correction of LEDs can be realized in the system.

[0039] In one embodiment of this application, after acquiring environmental parameters and LED chip parameters, the acquired parameters can be organized and archived using edge computing nodes deployed near the sensors. The measured data is categorized and stored according to parameter type, measurement time, etc., for easy subsequent retrieval and use. Simultaneously, correlation analysis is performed between parameters to study how environmental parameters affect LED chip parameters and the variation patterns of chip parameters under different environmental conditions. For example, the change in chip thermal conductivity as temperature increases, or the impact of humidity on the chip absorption coefficient, can be analyzed. This correlation analysis allows for a deeper understanding of the LED's working mechanism, providing strong data support for LED design, optimization, and application. Comprehensive information acquired through intelligent sensors provides foundational data for subsequent analysis. These parameters cover both environmental conditions and chip characteristics, enabling subsequent models to better reflect actual conditions and ensuring the accuracy and relevance of temperature control strategies.

[0040] S120, based on the environmental parameters and the chip parameters, generate a set of partial differential equations for photothermal coupling, and solve the set of partial differential equations to generate a photothermal coupling field.

[0041] In one embodiment of this application, after obtaining environmental parameters and LED chip parameters, a set of partial differential equations describing the photothermal coupling relationship is first constructed based on relevant physical principles such as heat conduction and light radiation, combined with the structural characteristics of the chip and the influence of environmental factors on photothermal transmission. This process requires comprehensive consideration of various physical processes such as heat conduction inside the chip, light absorption and scattering in the chip material, and heat exchange with the environment. Then, appropriate numerical solution methods, such as the finite difference method and the finite element method, are used to solve the constructed set of partial differential equations. The continuous partial differential equations are transformed into a discrete set of algebraic equations through discretization processing. Then, iterative calculations are performed with the help of a computer to finally generate a photothermal coupling field that can comprehensively reflect the light intensity and temperature distribution and changes of the LED chip during operation.

[0042] like Figure 2 As shown, in one embodiment of this application, based on the environmental parameters and the chip parameters, a set of partial differential equations for photothermal coupling is generated, and solving the set of partial differential equations generates a photothermal coupling field, including: S210, based on the environmental parameters and the chip parameters, generate the heat conduction equation, the light transmission equation, the heat conduction nonlinear equation, and the temperature dependence equation, respectively; S220, the heat conduction equation, light transmission equation, heat conduction nonlinear equation and temperature dependence equation are combined to generate a set of partial differential equations for photothermal coupling. S230, solve the partial differential equations to generate a photothermal coupling field.

[0043] After collecting all necessary parameters, the dynamic model of the photothermal coupling field was constructed. The core of this process is establishing a fundamental physical model that accurately describes the photothermal coupling phenomenon within the LED chip. This model is based on an understanding of the light-heat interaction mechanism during LED chip operation. When light propagates within the chip, it is absorbed by the material and converted into heat. Simultaneously, this heat affects the optical and thermal properties of the material, thereby altering the light transmission characteristics. By comprehensively considering these complex interactions, a foundation is laid for the subsequent development of a specific mathematical model, ensuring that the model accurately reflects the dynamic photothermal coupling process within the chip.

[0044] Specifically, in one embodiment of this application, based on the real-time temperature among the environmental parameters, and the thermal conductivity, material density, and specific heat capacity among the chip parameters, the heat conduction equation is generated as follows:

[0045] in, Represents the gradient operator. Indicates temperature T Thermal conductivity at that time The wavelength of light is represented by Temperature is T The absorption coefficient of converting time into heat energy. Indicates light intensity. Indicates the density of the material. This represents the specific heat capacity. In this embodiment, the heat conduction equation describes the heat conduction process inside the chip and the conversion of light energy into heat energy, where the source term... This indicates the rate at which light energy is converted into heat energy. The nonlinear term reflects the change in the thermal conductivity of the material with temperature.

[0046] Specifically, in one embodiment of this application, based on the light intensity information in the environmental parameters, the absorption coefficient and heat dissipation factor in the chip parameters, the light transmission equation is generated as follows:

[0047] in, The wavelength of light is represented by Temperature is T The scattering coefficient of time energy converted into heat energy. Indicates light intensity Attenuation along the z-direction (the thickness direction of the encapsulation layer). In this embodiment, the optical transport equation describes the transmission process of light inside the chip.

[0048] In one embodiment of this application, considering the changes in material properties with temperature, this dynamic relationship is fully reflected in the model. For example, the thermal conductivity of a material may change with increasing temperature, and the absorption coefficient will also vary in its ability to absorb light depending on temperature. Simultaneously, the model considers in detail the light transmission within different structures of the chip, as well as the conduction and dissipation of heat under different geometries, in conjunction with the geometric parameters of the packaging structure. These refinements enable the model to more accurately simulate the photothermal coupling behavior of LED chips in actual operation. Specifically, regarding thermal conductivity... and absorption coefficient A complex temperature-dependent model is employed, and a high-precision fitting is achieved through a combination of Taylor expansion, logarithm, and square root terms to more accurately reflect the changes in material properties with temperature.

[0049] Specifically, based on the real-time temperature in the environmental parameters and the preset fitting coefficients in the chip parameters, the nonlinear equation for heat conduction is generated as follows:

[0050] in, Indicates the thermal conductivity at the reference temperature. These represent the preset fitting coefficients in the chip parameters.

[0051] Specifically, in one embodiment of this application, based on the real-time temperature in the environmental parameters and the absorption coefficient in the chip parameters, a temperature dependence equation is generated as follows:

[0052] in, Indicates the absorption coefficient at the reference temperature. This represents the fitting coefficient.

[0053] The characters in the above calculation equations are mainly derived from the physical properties of the materials and experimental data. The corresponding expressions and coefficients are obtained through fitting and theoretical derivation to accurately describe the changes in material properties with temperature.

[0054] In one embodiment of this application, the heat conduction equation, the light transport equation, the heat conduction nonlinear equation, and the temperature dependence equation are combined to generate a set of partial differential equations for photothermal coupling. By solving the above set of equations, the spatiotemporally distributed photothermal coupling field is obtained. This photothermal coupling field comprehensively covers the light and heat distribution information of the LED chip during operation, specifically including light intensity. and temperature This information allows for a clear prediction of the light and heat distribution of an LED chip at different times and locations during operation, specifically the light intensity distribution and temperature variations.

[0055] The above process, by establishing a dynamic model of the photothermal coupling field, allows for a deeper understanding of the mechanism of photothermal interaction within the LED chip, providing a theoretical basis for optimizing chip design. An accurate model can help predict the temperature and light intensity distribution of the chip under different operating conditions, thereby identifying potential thermal problems in advance, such as chip damage or performance degradation caused by overheating. It also provides a reliable data source for subsequent parameter mapping, enabling further research into the relationship between chip parameters and performance, and ultimately achieving chip performance optimization and control.

[0056] S130, the optical-thermal coupling field is reduced in dimensionality to generate an optical-thermal feature vector.

[0057] In this embodiment, the photothermal coupling field data often contains a large amount of information, much of which may be redundant or not critical for analyzing LED chip performance. Through parameter dimensionality reduction and feature extraction, this redundant information can be removed, focusing on the key features that significantly impact chip performance. The spatial weighting function is designed to focus on high-risk regions, such as the vicinity of electrodes, which have a significant impact on chip performance and reliability, thus enabling more targeted feature extraction. The spatiotemporal derivative term captures the changes in the photothermal coupling field over time. Because the light and heat states of the chip are dynamically changing during operation, this information is crucial for understanding the chip's working mechanism and performance.

[0058] In one embodiment of this application, feature extraction is performed on the photothermal coupling field based on a feature extraction operator to generate a photothermal feature vector. This includes: integrating the photothermal coupling field based on a preset spatial weighting function to generate a first feature; differentiating the photothermal coupling field based on a preset spatiotemporal derivative term to generate a second feature; and calculating the vector sum between the first feature and the second feature as the photothermal feature vector. for:

[0059] in, At any moment t The extracted first i One photothermal characteristic; It indicates the spatial region, that is, the three-dimensional spatial range in which the LED chip is located; The spatial weighting function, based on the chip structure and the design of key areas of concern, focuses on high-risk areas, such as near electrodes, and is used to assign different weights to different spatial locations during the integration process; The spatiotemporally distributed photothermal coupled field represents the light intensity and temperature in space (x,y,z) and time. t Information on; Indicates the first iThe adjustable coefficients corresponding to each photothermal feature are used to adjust the weight of the differential term in feature extraction, and can be set according to specific analysis needs and chip characteristics; j and m These represent the index variable and the total number in the summation operation, respectively.

[0060] After the above calculations, the reduced photothermal feature vector is obtained. The photothermal feature vector contains key control parameters such as peak temperature, light intensity non-uniformity, and thermal gradient. These parameters can concisely and effectively characterize the important features of the photothermal coupling field.

[0061] The above process, after dimensionality reduction, significantly reduces the amount of data, lowering the complexity of subsequent calculations and reducing computational resource consumption. This allows for more efficient analysis and optimization of LED chip performance with limited computing resources. The extracted key parameters, such as peak temperature, light intensity non-uniformity, and thermal gradient, directly reflect the important characteristics of the chip during operation. For example, excessively high peak temperatures may damage the chip, light intensity non-uniformity affects the chip's luminous quality, and the thermal gradient is related to internal thermal stress within the chip. These parameters provide crucial information for evaluating chip performance and reliability. Dimensionality reduction removes redundant information and extracts key features. The generated photothermal feature vector is concise and representative, greatly simplifying subsequent analysis while retaining core information, making operations such as double-layer nonlinear mapping more efficient.

[0062] S140: Acquire historical control signals, and perform a two-layer nonlinear mapping based on the photothermal feature vector, the preset target temperature, and the historical control signals to generate a temperature control signal.

[0063] In this embodiment, after acquiring the historical control signals, the previously generated photothermal feature vector, the pre-set target temperature, and these historical control signals are used together as key information. Utilizing a model or algorithm with dual-layer nonlinear mapping capabilities, the first layer comprehensively processes and initially transforms the photothermal feature vector and historical control signals to uncover their intrinsic relationships. The second layer further performs nonlinear adjustments and optimizations on the output of the first layer, closely focusing on the core objective of target temperature. Through this complex dual-layer mapping process, a temperature control signal capable of precisely regulating the LED temperature is finally generated.

[0064] In one embodiment of this application, a historical control signal is acquired, and a temperature control signal is generated by performing a two-layer nonlinear mapping based on the photothermal feature vector, a preset target temperature, and the historical control signal, including: Historical control signals are acquired, and a multi-parameter network is constructed based on the photothermal feature vector, the preset target temperature, and the historical control signals. A two-layer nonlinear mapping is performed in the multi-parameter network to generate a temperature control signal.

[0065] In one embodiment of this application, historical control signals are acquired, and a multi-parameter network is constructed based on the photothermal feature vector, a preset target temperature, and the historical control signals. The photothermal feature vector output from the preceding steps serves as key information input reflecting the photothermal state of the LED chip. The target temperature is the desired temperature value for the chip, providing a target guide for the temperature control strategy. The historical control signals contain relevant information about past chip control, and the time delay considers the time lag effect of the control signals on the chip's state changes. Simultaneously, a two-layer nonlinear mapping mechanism is designed. The inner layer comprehensively considers and integrates the photothermal features and historical control signals, while the outer layer performs nonlinear transformations and reasonable constraints on the integrated information to ensure that the output temperature control signal is within a reasonable physical range, thereby establishing a complex yet effective nonlinear mapping relationship between the photothermal features and the temperature control strategy.

[0066] In one embodiment of this application, a two-layer nonlinear mapping is performed in the multi-parameter network to generate a temperature control signal. for:

[0067] in, Indicates at time t Temperature control signal, Indicates the first k The weight coefficients of the layer network, They represent the first k The bias terms of the layer network are determined during network training by learning the mapping relationship between photothermal features and temperature control strategies. Represents the first eigenvector in the photothermal eigenvector. i These characteristics reflect the chip's time-to-time performance. t Photothermal state information; Indicates historical control signals, l The sequence number representing the time interval. Δt The time interval takes into account the time delay effect of the control signal; Represents the hyperbolic tangent function. This represents the intensity coefficient of the chaotic perturbation. Represents a chaotic sequence. K , n as well as L These represent the total number of weighting coefficients, the total number of features in the photothermal feature vector, and the total number of bias terms, respectively.

[0068] In this embodiment, after network construction, mapping establishment, and optimization mechanisms, a temperature control signal is finally generated. This temperature control signal includes control information such as current modulation amplitude and cooling fan speed. This control information is directly applied to the LED chip's temperature control system, much like operating a machine according to precise instructions. Through these control signals, fine-tuning of the chip temperature can be achieved, ensuring stable operation of the chip in a suitable temperature environment and improving its performance and reliability. By combining historical control signals, photothermal feature vectors, and target temperature to construct a multi-parameter network, a two-layer nonlinear mapping can better fit the complex photothermal and temperature control relationships. The generated temperature control signal comprehensively considers multiple factors, more accurately meeting the LED's temperature control requirements, avoiding local optima, and improving control effectiveness.

[0069] S150, the operation of the LED assembly is controlled by the temperature control signal.

[0070] In this embodiment, upon receiving a temperature control signal, the LED temperature control system quickly receives and interprets the signal. Subsequently, based on the instructions conveyed by the signal, it precisely adjusts the operating state of the components connected to the LED. For example, for components that regulate current, the current is adjusted according to the signal requirements to control the LED's luminous intensity and heat generation; for heat dissipation-related components, the cooling fan speed or the heat sink's operating mode is changed according to the signal indication to ensure that the heat generated by the LED is dissipated effectively and promptly, allowing the LED to operate stably in a suitable temperature environment.

[0071] In one embodiment of this application, controlling the operation of the LED assembly via the temperature control signal includes: The temperature control signal is analyzed to generate current modulation commands and cooling fan speed commands; The driving current of the LED is adjusted based on the current modulation command, and the cooling fan is controlled based on the speed command of the cooling fan.

[0072] In one embodiment of this application, after generating the optimal temperature control signal, the LED temperature control system first enters the signal receiving and parsing stage. The control system acts like a precise information receiving station, timely and accurately capturing the temperature control signal containing key information such as current modulation amplitude and cooling fan speed. Subsequently, the internal signal parsing module begins operation, converting these signals into instruction forms that the control system can understand and process, preparing for subsequent control operations. This step is fundamental to the entire control process, ensuring that the control system can correctly acquire and identify the control requirements from the preceding steps.

[0073] After signal analysis, the driving current of the LED is adjusted according to the current modulation amplitude command obtained from the analysis. Current is a key factor affecting the luminous intensity and heat generation of the LED. By adjusting relevant parameters in the driving circuit, the magnitude of the current input to the LED chip is precisely changed. If the current modulation amplitude indicates that an increase in current is needed, the current output is increased accordingly, enhancing the LED's luminous intensity, but this also increases heat generation; conversely, if the indication is to decrease the current, the current output is reduced, decreasing heat generation. In this way, preliminary control of the LED's luminous intensity and heat generation is achieved.

[0074] In addition to current regulation, the cooling fan is controlled based on the fan speed command in the temperature control signal. The cooling fan plays a crucial role in the LED cooling system, acting as a highly efficient heat transporter. When a command requests an increase in fan speed, a corresponding control signal is sent to the fan drive circuit, causing the fan to accelerate and enhance airflow, thus more quickly removing the heat generated by the LED chip. When a command requests a decrease in fan speed, the signal is adjusted to slow the fan down, reducing energy consumption while meeting cooling requirements. By rationally adjusting the cooling fan speed, it is ensured that the heat generated by the LED chip can be dissipated effectively and promptly.

[0075] After controlling the LED's operation through current and heat dissipation regulation, the control system does not cease operation but enters the operation monitoring and feedback phase. It continuously monitors key parameters of the LED chip, such as temperature and light intensity, and closely monitors the LED's operating status. If a deviation is detected between the actual operating parameters and the target values, the industrial control software will feed this information back to the previous steps, potentially triggering a new round of operations such as photothermal coupling field modeling, feature extraction, and MIP network construction. This regenerates a more suitable temperature control signal, forming a closed-loop control circuit to ensure the LED always operates in a stable temperature environment, achieving optimal performance and reliability.

[0076] This application's technical solution involves acquiring environmental parameters and LED chip parameters, generating a system of partial differential equations for photothermal coupling based on these parameters, and solving these equations to generate a photothermal coupling field. The photothermal coupling field is then dimensionality-reduced to generate a photothermal feature vector. Historical control signals are acquired, and a two-layer nonlinear mapping is performed based on the photothermal feature vector, a preset target temperature, and the historical control signals to generate a temperature control signal. The operation of the LED component is then controlled using this temperature control signal. First, environmental and chip parameters are acquired, laying the foundation for accurate analysis. Then, the photothermal coupling partial differential equations are generated and solved to obtain the photothermal coupling field, which comprehensively presents the LED's photothermal state. Dimensionality reduction of the photothermal coupling field generates a feature vector to simplify information. Finally, a temperature control signal is generated by combining historical signals, and the LED component is controlled accordingly, achieving intelligent and precise temperature control of the LED under different environments, thus improving its performance and stability.

[0077] The following describes embodiments of the MIP LED intelligent temperature control system based on dynamic photothermal coupling, which can be used to execute the MIP LED intelligent temperature control method based on dynamic photothermal coupling described in the above embodiments of this application. It is understood that the MIP LED intelligent temperature control system based on dynamic photothermal coupling can be a computer program (including program code) running on a computer device; for example, the MIP LED intelligent temperature control system based on dynamic photothermal coupling is an application software. This MIP LED intelligent temperature control system based on dynamic photothermal coupling can be used to execute the corresponding steps in the method provided in the embodiments of this application. For details not disclosed in the embodiments of the MIP LED intelligent temperature control system based on dynamic photothermal coupling of this application, please refer to the embodiments of the MIP LED intelligent temperature control method based on dynamic photothermal coupling described above.

[0078] Figure 3 A block diagram of a MIP LED intelligent temperature control system based on dynamic photothermal coupling according to an embodiment of this application is shown.

[0079] Reference Figure 3 As shown, a MIP LED intelligent temperature control system based on dynamic photothermal coupling according to an embodiment of this application includes: The acquisition module 310 is used to acquire environmental parameters and LED chip parameters, wherein the chip parameters include the geometric parameters of the package structure and the driving current; The coupling module 320 is used to generate a set of partial differential equations for photothermal coupling based on the environmental parameters and the chip parameters, and solve the set of partial differential equations to generate a photothermal coupling field. Feature module 330 is used to perform dimensionality reduction processing on the photothermal coupling field to generate a photothermal feature vector; Signal module 340 is used to acquire historical control signals and perform a two-layer nonlinear mapping based on the photothermal feature vector, the preset target temperature and the historical control signals to generate a temperature control signal; The control module 350 is used to control the operation of the LED components through the temperature control signal.

[0080] In this application, based on the aforementioned scheme, the step of generating a set of partial differential equations for photothermal coupling based on the environmental parameters and the chip parameters, and solving the set of partial differential equations to generate a photothermal coupling field, includes: generating a heat conduction equation, an optical transport equation, a heat conduction nonlinear equation, and a temperature-dependent equation based on the environmental parameters and the chip parameters; combining the heat conduction equation, the optical transport equation, the heat conduction nonlinear equation, and the temperature-dependent equation to generate a set of partial differential equations for photothermal coupling; and solving the set of partial differential equations to generate a photothermal coupling field.

[0081] In this application, based on the aforementioned scheme, the step of generating the heat conduction equation, the light transport equation, the heat conduction nonlinear equation, and the temperature-dependent equation based on the environmental parameters and the chip parameters respectively includes: generating the heat conduction equation based on the real-time temperature in the environmental parameters, the thermal conductivity, material density, and specific heat capacity in the chip parameters; generating the light transport equation based on the light intensity information in the environmental parameters, the absorption coefficient, and the heat dissipation factor in the chip parameters; generating the heat conduction nonlinear equation based on the real-time temperature in the environmental parameters and a preset fitting coefficient in the chip parameters; and generating the temperature-dependent equation based on the real-time temperature in the environmental parameters and the absorption coefficient in the chip parameters.

[0082] In this application, based on the aforementioned scheme, the step of generating a heat conduction equation based on the real-time temperature in the environmental parameters, the thermal conductivity, material density, and specific heat capacity in the chip parameters includes: generating a heat conduction equation based on the real-time temperature in the environmental parameters, the thermal conductivity, material density, and specific heat capacity in the chip parameters as follows:

[0083] in, Represents the gradient operator. Indicates temperature T Thermal conductivity at that time The wavelength of light is represented by Temperature is T The absorption coefficient of converting time energy into heat energy; Indicates light intensity. Indicates the density of the material. This indicates specific heat capacity.

[0084] In this application, based on the aforementioned scheme, the step of generating the optical transmission equation based on the light intensity information in the environmental parameters, the absorption coefficient and the heat dissipation factor in the chip parameters includes: generating the optical transmission equation based on the light intensity information in the environmental parameters, the absorption coefficient and the heat dissipation factor in the chip parameters as follows:

[0085] in, Represents the gradient operator. The wavelength of light is represented by Temperature is T The absorption coefficient of converting time energy into heat energy; The wavelength of light is represented by Temperature is T The scattering coefficient of time energy converted into heat energy; Indicates light intensity. Indicates light intensity Attenuation along the z-direction.

[0086] In this application, based on the aforementioned scheme, the step of reducing the dimensionality of the photothermal coupling field to generate a photothermal feature vector includes: extracting features from the photothermal coupling field based on a feature extraction operator to generate a photothermal feature vector.

[0087] In this application, based on the aforementioned scheme, the step of acquiring historical control signals and generating temperature control signals by performing two-layer nonlinear mapping based on the photothermal feature vector, the preset target temperature, and the historical control signals includes: acquiring historical control signals; constructing a multi-parameter network based on the photothermal feature vector, the preset target temperature, and the historical control signals; and performing two-layer nonlinear mapping in the multi-parameter network to generate temperature control signals.

[0088] In this application, based on the aforementioned scheme, controlling the operation of the LED component through the temperature control signal includes: parsing the temperature control signal to generate a current modulation command and a cooling fan speed command; adjusting the LED driving current based on the current modulation command, and controlling the cooling fan based on the cooling fan speed command.

[0089] In this application, based on the aforementioned scheme, the acquisition of environmental parameters and LED chip parameters includes: acquiring environmental parameters and LED chip parameters through a preset smart sensor.

[0090] This application's technical solution involves acquiring environmental parameters and LED chip parameters, generating a system of partial differential equations for photothermal coupling based on these parameters, and solving these equations to generate a photothermal coupling field. The photothermal coupling field is then dimensionality-reduced to generate a photothermal feature vector. Historical control signals are acquired, and a two-layer nonlinear mapping is performed based on the photothermal feature vector, a preset target temperature, and the historical control signals to generate a temperature control signal. The operation of the LED component is then controlled using this temperature control signal. First, environmental and chip parameters are acquired, laying the foundation for accurate analysis. Then, the photothermal coupling partial differential equations are generated and solved to obtain the photothermal coupling field, which comprehensively presents the LED's photothermal state. Dimensionality reduction of the photothermal coupling field generates a feature vector to simplify information. Finally, a temperature control signal is generated by combining historical signals, and the LED component is controlled accordingly, achieving intelligent and precise temperature control of the LED under different environments, thus improving its performance and stability.

[0091] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0092] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not impose any limitations on the function and scope of use of the embodiments of this application.

[0093] In this embodiment, the computer system includes a central processing unit 401, which can perform various appropriate actions and processes based on a program stored in the read-only memory 402 or a program loaded from the storage section 408 into the random access memory 403, such as executing the MIP LED intelligent temperature control method based on dynamic photothermal coupling described in the above embodiment. The random access memory 403 also stores various programs and data required for system operation. The central processing unit 401, the read-only memory 402, and the random access memory 403 are interconnected via a bus 404. An input / output interface 405 is also connected to the bus 404.

[0094] The following components are connected to the input / output interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 410 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0095] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit 401, it performs various functions defined in the system of this application.

[0096] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0097] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0098] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0099] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.

[0100] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the MIP LED intelligent temperature control method based on dynamic photothermal coupling described in the above embodiments.

[0101] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0102] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the methods according to the embodiments of this application.

[0103] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0104] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for intelligent temperature control of MIP LEDs based on dynamic photothermal coupling, characterized in that, include: Obtain environmental parameters and LED chip parameters, wherein the chip parameters include the geometric parameters of the package structure and the drive current; Based on the environmental parameters and the chip parameters, a set of partial differential equations for photothermal coupling is generated, and the set of partial differential equations is solved to generate a photothermal coupling field. The photothermal coupling field is reduced in dimension to generate a photothermal feature vector; Acquire historical control signals, and perform a two-layer nonlinear mapping based on the photothermal feature vector, the preset target temperature, and the historical control signals to generate a temperature control signal; The operation of the LED components is controlled by the temperature control signal.

2. The intelligent temperature control method for MIP LEDs based on dynamic photothermal coupling according to claim 1, characterized in that, Based on the environmental parameters and the chip parameters, a set of partial differential equations for photothermal coupling is generated. Solving the set of partial differential equations generates a photothermal coupling field, including: Based on the environmental parameters and the chip parameters, heat conduction equations, light transmission equations, heat conduction nonlinear equations, and temperature-dependent equations are generated respectively. The heat conduction equation, light transport equation, heat conduction nonlinear equation, and temperature dependence equation are combined to generate a set of partial differential equations for photothermal coupling. Solve the set of partial differential equations to generate a photothermal coupling field.

3. The intelligent temperature control method for MIP LEDs based on dynamic photothermal coupling according to claim 2, characterized in that, Based on the environmental parameters and the chip parameters, heat conduction equations, optical transport equations, heat conduction nonlinear equations, and temperature-dependent equations are generated, including: Based on the real-time temperature in the environmental parameters, and the thermal conductivity, material density, and specific heat capacity in the chip parameters, a heat conduction equation is generated; Based on the light intensity information in the environmental parameters, the absorption coefficient and heat dissipation factor in the chip parameters, an optical transmission equation is generated; Based on the real-time temperature in the environmental parameters and the preset fitting coefficients in the chip parameters, a nonlinear equation for heat conduction is generated. A temperature-dependent equation is generated based on the real-time temperature in the environmental parameters and the absorption coefficient in the chip parameters.

4. The intelligent temperature control method for MIP LEDs based on dynamic photothermal coupling according to claim 3, characterized in that, Based on the real-time temperature in the environmental parameters, and the thermal conductivity, material density, and specific heat capacity in the chip parameters, a heat conduction equation is generated, including: Based on the real-time temperature from the environmental parameters, and the thermal conductivity, material density, and specific heat capacity from the chip parameters, the heat conduction equation is generated as follows: in, Represents the gradient operator. Indicates temperature T Thermal conductivity at that time The wavelength of light is represented by Temperature is T The absorption coefficient of converting time energy into heat energy; Indicates light intensity. Indicates the density of the material. This indicates specific heat capacity.

5. The intelligent temperature control method for MIP LEDs based on dynamic photothermal coupling according to claim 3, characterized in that, Based on the light intensity information in the environmental parameters, the absorption coefficient and heat dissipation factor in the chip parameters, an optical transmission equation is generated, including: Based on the light intensity information in the environmental parameters, the absorption coefficient and heat dissipation factor in the chip parameters, the optical transmission equation is generated as follows: in, Represents the gradient operator. The wavelength of light is represented by Temperature is T The absorption coefficient of converting time energy into heat energy; The wavelength of light is represented by Temperature is T The scattering coefficient of time energy converted into heat energy; Indicates light intensity. Indicates light intensity Attenuation along the z-direction.

6. The intelligent temperature control method for MIP LEDs based on dynamic photothermal coupling according to claim 1, characterized in that, The photothermal coupling field is subjected to dimensionality reduction processing to generate a photothermal feature vector, including: Based on the feature extraction operator, the optical-thermal coupling field is subjected to feature extraction to generate an optical-thermal feature vector.

7. The intelligent temperature control method for MIP LEDs based on dynamic photothermal coupling according to claim 1, characterized in that, Acquire historical control signals, and generate temperature control signals by performing a two-layer nonlinear mapping based on the photothermal feature vector, the preset target temperature, and the historical control signals, including: Historical control signals are acquired, and a multi-parameter network is constructed based on the photothermal feature vector, the preset target temperature, and the historical control signals. A two-layer nonlinear mapping is performed in the multi-parameter network to generate a temperature control signal.

8. The intelligent temperature control method for MIP LEDs based on dynamic photothermal coupling according to claim 1, characterized in that, Controlling the operation of the LED components via the temperature control signal includes: The temperature control signal is analyzed to generate current modulation commands and cooling fan speed commands; The driving current of the LED is adjusted based on the current modulation command, and the cooling fan is controlled based on the speed command of the cooling fan.

9. The intelligent temperature control method for MIP LEDs based on dynamic photothermal coupling according to claim 1, characterized in that, Obtain environmental parameters and LED chip parameters, including: Environmental parameters and LED chip parameters are acquired through preset smart sensors.

10. A MIP LED intelligent temperature control system based on dynamic photothermal coupling, characterized in that, include: An acquisition module is used to acquire environmental parameters and LED chip parameters, wherein the chip parameters include the geometric parameters of the package structure and the driving current; The coupling module is used to generate a set of partial differential equations for photothermal coupling based on the environmental parameters and the chip parameters, and to solve the set of partial differential equations to generate a photothermal coupling field. The feature module is used to perform dimensionality reduction processing on the photothermal coupling field to generate a photothermal feature vector; The signal module is used to acquire historical control signals and perform a two-layer nonlinear mapping based on the photothermal feature vector, the preset target temperature and the historical control signals to generate a temperature control signal. A control module is used to control the operation of the LED components via the temperature control signal.

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