Light-emitting equalization design method of double-sided light-emitting LED light source module

Abstract

The invention relates to a light emitting balance design method of a double-sided light emitting LED light source module in the technical field of intelligent manufacturing, and the method comprises the steps: adjusting the parameters of an optical processing structure in a substrate according to the intensity distribution of light emitted from the front surface, and obtaining the initial uniform data of the light distribution of the back surface; acquiring a scattering point coordinate set from the back light distribution preliminary uniform data, and determining a scattering angle adjustment value for uniform scattering optical properties of a substrate material to balance light intensity gradient distribution; heat conduction related parameters are extracted from the overall double-face light emitting equilibrium design model, heat flow distribution is simulated according to the heat dissipation thermal attribute of the light emitting diode chip, and welding process optimization points are determined; and according to the welding process optimization points, generating a module design blueprint, verifying the light emitting efficiency of the double-sided light emitting balance design model, and if an efficiency improvement index meets a preset threshold value, outputting complete solution data.

Description

A method for equalizing the light output of a double-sided LED light source module Technical Field

[0001] This invention relates to the field of intelligent manufacturing technology, and in particular to a method for equalizing the light output of a double-sided LED light source module. Background Technology

[0002] Semiconductor light-emitting devices are widely used in lighting and display fields. Bidirectional LED light source modules are of great value in thin lamps, outdoor lights, and decorative lighting because they can illuminate both sides of an object simultaneously. However, existing technologies face significant technical limitations in achieving bidirectional light emission.

[0003] Currently, mainstream bifacial light-emitting solutions mainly rely on two methods: one is to install LED chips on both sides of the substrate, which leads to a significant increase in the number of LEDs, complex soldering processes, increased heat dissipation burden, and a noticeably larger overall thickness; the other is to install side-emitting LEDs on the side and then guide the light to both sides through complex structures such as multi-layer light guide plates, reflective films, and prisms. This method not only involves numerous assembly steps and a large volume, but also suffers from severe light energy loss due to multiple reflections and refractions, making it difficult to guarantee the uniformity of light output. These solutions achieve bifacial light emission at the cost of significantly increased cost, complexity, and size. A deeper technical challenge lies in the fact that the light source must simultaneously meet the light emission requirements of both sides, while the light propagation process is difficult to control effectively: on the one hand, enough light must be emitted directly from the front, and on the other hand, some light must pass through the substrate to reach the back side and be evenly scattered. However, conventional opaque substrates completely block light transmission, forcing designers to use double-sided mounting or complex side light guide structures. Even when using semi-transparent materials, the lack of targeted optical processing can lead to uneven light intensity distribution on the back, with the light source being too bright near the light source and too dark in the distance, making it difficult to form a uniform surface light source.

[0004] Therefore, the key issue in achieving low-cost, high-reliability double-sided light emission is how to make part of the light emitted directly and efficiently from the front, while the other part can be controlled to penetrate the substrate and be evenly distributed on the back, all while using only a single-sided LED. Summary of the Invention

[0005] This invention provides a light emission equalization design method for a double-sided light-emitting LED light source module, mainly including:

[0006] Initial light propagation path data is obtained from a single-sided mounted LED chip. The light transmission ratio is processed based on the light transmittance optical properties of the substrate material to determine the intensity distribution of the light emitted from the front of the LED chip. Based on the front-side light intensity distribution, the optical processing structure parameters inside the substrate are adjusted to obtain preliminary uniformity data for the back-side light distribution. From the preliminary uniformity data for the back-side light distribution, a set of scattering point coordinates is obtained. Based on the uniform scattering optical properties of the substrate material, a scattering angle adjustment value is determined to balance the light intensity gradient distribution. The back-side light intensity matrix data is corrected using a ray tracing method. Based on the corrected back-side light intensity matrix data, the matching degree between the front-side light intensity distribution and the back-side light distribution is determined. If the matching degree is higher than a preset threshold, the configuration of the optical processing structure parameters is locked, resulting in an overall bi-sided light emission equalization design model. Heat conduction-related parameters are extracted from the overall bi-sided light emission equalization design model. The heat flow distribution is simulated based on the heat dissipation thermal properties of the LED chip to determine the welding process optimization points. A module design blueprint is generated based on the welding process optimization points. The light emission efficiency of the bi-sided light emission equalization design model is verified. If the efficiency improvement index meets a preset threshold, complete solution data is output. Furthermore, obtaining initial light propagation path data from a single-sided mounted LED chip includes: obtaining initial light propagation path data from the single-sided mounted LED chip using an optical path simulation method; processing the light transmission optical properties of the substrate material based on the initial light propagation path data to calculate the light transmission ratio; fusing the reflection properties of the multilayer substrate based on the light transmission ratio to determine the reflected light contribution value; calculating the front-emitted light intensity distribution of the LED chip based on the reflected light contribution value; verifying the accuracy of the light propagation path data using the front-emitted light intensity distribution to obtain initial distribution characteristics; adjusting simulation parameters based on the initial distribution characteristics to obtain optimized light propagation path data; and extracting key light path nodes from the optimized data to determine the final distribution result of the light propagation path. Furthermore, adjusting the optical processing structure parameters inside the substrate based on the front-emitted light intensity distribution includes: obtaining initial distribution data from the front-emitted light intensity distribution by classifying light source types; iteratively adjusting the optical processing structure parameters inside the substrate based on the light propagation path using a light distribution optimization method to obtain adjusted distribution characteristics; calculating the transmitted light ratio by integrating the light transmittance for the adjusted distribution characteristics; increasing the density of the reflective layer material if the transmitted light ratio is lower than a preset threshold to determine a density adjustment value; simulating the back-side light distribution change based on the density adjustment value and obtaining a distribution map using ray tracing; extracting a simulated uniformity index from the distribution map and judging the verification result by comparing the uniformity index with a standard threshold; if the verification is successful, obtaining preliminary uniformity data for the back-side light distribution.Furthermore, the step of obtaining the set of scattering point coordinates from the preliminary uniform back light distribution data includes: obtaining the set of scattering point coordinates from the preliminary uniform back light distribution data, and obtaining a coordinate classification result through coordinate set classification processing; using a uniform scattering attribute mapping method on the coordinate classification result to obtain attribute mapping data; determining an initial value of the light intensity gradient based on the attribute mapping data; performing iterative calculation on the initial value of the light intensity gradient, and obtaining an angle iteration result by combining it with the optical properties of the substrate material; determining a scattering angle adjustment value based on the angle iteration result; if the scattering angle adjustment value meets the balance condition, determining the scattering angle adjustment value to balance the light intensity gradient distribution; and extracting uniformity verification data from the balanced light intensity gradient distribution to obtain the final scattering distribution characteristics. Furthermore, the step of correcting the back light intensity matrix data using ray tracing includes: obtaining a scattering angle adjustment value; judging the adjustment value within a preset range to obtain a judgment result identifier; if the judgment result identifier exceeds the preset range, simulating the light propagation path from the adjustment value using a random ray tracing method to obtain a path sequence; tracing the interaction points between the light rays and the surface through the path sequence, accumulating intensity values ​​to calculate the back light intensity distribution; determining the light intensity matrix data based on the back light intensity distribution; superimposing the surface material reflectance coefficient onto the light intensity matrix data to obtain correction matrix data; extracting light intensity distribution features from the correction matrix data to verify the correction effect; and adjusting the ray tracing parameters based on the correction effect to obtain the final back light intensity distribution data. Furthermore, determining the matching degree between the front-emitted light intensity distribution and the back-emitted light distribution based on the corrected back-side light intensity matrix data includes: obtaining the correction result of the back-side light intensity matrix data; calculating the front-side intensity distribution data through the back-side distribution; determining the correspondence between the front-side intensity distribution data and the back-side distribution using a matching degree evaluation method; if the correspondence is higher than a preset threshold, adjusting the surface texture parameters through optical structure optimization; locking the parameter configuration based on the surface texture parameters to obtain an overall double-sided light emission equalization design model; extracting matching degree verification data from the overall double-sided light emission equalization design model; adjusting the matching degree evaluation method based on the verification data to obtain the final matching degree result; and determining the optimized configuration of the optical processing structure parameters based on the final matching degree result.Furthermore, the step of extracting heat conduction-related parameters from the overall double-sided light-emitting equalization design model includes: obtaining heat conduction-related parameters from the overall double-sided light-emitting equalization design model; simulating heat flow distribution using the finite element method to obtain heat flow distribution data for the heat dissipation thermal properties of the LED chip; constructing a thermal resistance network from the heat flow distribution data, representing heat transfer paths through nodes and edges; evaluating heat transfer efficiency at the chip packaging interface using the thermal resistance network to determine the interface thermal resistance value; judging the thermal stress distribution based on the interface thermal resistance value; if the thermal stress distribution exceeds a preset threshold, adjusting the solder layer thickness to obtain adjusted soldering parameters; obtaining an optimized process sequence based on the adjusted soldering parameters to determine the optimized result of the heat flow distribution. Furthermore, determining the welding process optimization point includes: extracting thermal stress concentration region characteristics from heat flow distribution data; simulating stress concentration region changes by optimizing the process sequence based on the thermal stress concentration region characteristics; adjusting the welding temperature and time sequence according to the change results to obtain the adjusted welding parameter configuration; determining the regional stress reduction configuration from the adjusted welding parameter configuration; locking the welding process optimization point based on the regional stress reduction configuration to obtain a process scheme for reducing thermal stress concentration regions; verifying the improvement effect of thermal stress distribution through the process scheme; adjusting the process parameters according to the improvement effect to obtain the final welding process optimization data; and extracting key process nodes from the final welding process optimization data to determine the process implementation path. Furthermore, the step of generating a module design blueprint based on the welding process optimization points includes: obtaining module structural parameters from the welding process optimization points; constructing an optical path transmission path for the double-sided light-emitting equalization design model, defining the light propagation direction and reflection and refraction rules using ray tracing; simulating the light distribution equalization through the optical path transmission path to obtain light distribution equalization data; extracting light surface attributes from the light distribution equalization data; calculating the light extraction efficiency for the light-emitting surface attributes using finite element structural analysis to determine the light extraction efficiency value; if the light extraction efficiency value meets a preset threshold, integrating the module structural parameters to obtain an integrated design blueprint; and obtaining the final module configuration data based on the integrated design blueprint. Furthermore, verifying the light extraction efficiency of the dual-sided light extraction equalization design model includes: obtaining the packaging interface configuration from the module design blueprint; simulating the thermo-optical coupling distribution through the packaging interface configuration, and iteratively solving the distribution field based on the thermo-optical model of the module structural parameters; determining the thermo-optical coupling equalization point according to the distribution field; locking the complete solution data for the thermo-optical coupling equalization point; extracting light extraction efficiency verification indicators from the complete solution data; judging the efficiency improvement effect through the verification indicators; if the efficiency improvement effect meets a preset threshold, outputting the final design scheme data; adjusting the module structural parameters according to the final design scheme data to obtain the optimized light extraction efficiency distribution; extracting key performance parameters from the optimized light extraction efficiency distribution to determine the final verification result.

[0007] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0008] This invention discloses a comprehensive optimization method for a double-sided LED light source module, aiming to solve the problem of achieving balanced light distribution on the front and back sides, improving light extraction efficiency, and considering heat dissipation performance in business scenarios where chips are mounted on one side. This invention obtains initial light propagation data through optical path simulation, processes the light transmission ratio based on the substrate's transmittance properties, optimizes the front light intensity distribution, and initially achieves uniform back light distribution by adjusting the internal optical structure parameters and reflective layer density of the substrate. Subsequently, iterative calculation of scattering angles and random ray tracing are used to correct the light intensity matrix, ensuring that the matching degree of the double-sided light distribution reaches a preset threshold, and finally locking the optical parameter configuration. Simultaneously, this invention integrates thermal conduction parameters to simulate heat flow distribution, optimizes the welding process to reduce thermal stress concentration, and verifies light extraction efficiency through structural analysis, outputting a complete solution. Its core technical effect lies in significantly improving the balance of double-sided light extraction and the overall performance of the module through photothermal synergistic optimization, providing innovative technical support for the design of high-efficiency light-emitting diodes. Attached Figure Description

[0009] Figure 1 is a flowchart of a light output equalization design method for a double-sided light-emitting LED light source module according to the present invention. Detailed Implementation

[0010] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0011] As shown in Figure 1, the light output equalization design method for a double-sided light-emitting LED light source module in this embodiment may specifically include:

[0012] S101. Initial light propagation path data is obtained from a single-sided mounted LED chip using an optical path simulation method. The light transmission ratio is processed based on the light transmittance optical properties of the substrate material to obtain the intensity distribution of the light emitted from the front of the LED chip.

[0013] Initial light propagation path data is obtained from a single-sided mounted LED chip using an optical path simulation method. The light transmittance ratio P is obtained based on the light transmission optical properties of the data processing substrate material. The reflected light contribution value C = P × R is determined by combining P with the reflection properties R of the multilayer substrate (composed of multiple materials). The intensity distribution I of the light emitted from the front of the LED chip is calculated based on C, and the final distribution is obtained using the formula I = initial intensity + C.

[0014] In one implementation, initial light propagation path data is obtained from a single-sided mounted LED chip using an optical path simulation method. First, a geometric model of the chip needs to be established.

[0015] For example, consider a typical flip-chip structure where a light-emitting diode (LED) chip is mounted on one side of a light-transmitting substrate, and light propagates from the inside of the chip outwards. The simulation employs ray tracing technology to simulate a large number of light rays originating from the chip's light-emitting layer, tracing the propagation path of each ray within the chip and the substrate, including reflection, refraction, and absorption phenomena. In this way, initial light propagation path data is obtained, recording the starting point, direction, and interaction details at material interfaces. Further, the light transmittance ratio is processed based on the light-transmitting optical properties of the substrate material. Substrate materials typically have specific refractive indices and absorption coefficients; for example, sapphire substrates have light transmittance properties including the transmittance of light at different wavelengths. The processing involves calculating the light transmittance ratio within the substrate.

[0016] Specifically, for each simulated ray, when it reaches the substrate interface, the reflection and transmission coefficients are calculated according to the Fresnel equation. Then, combined with the substrate thickness and absorptivity, the actual transmission ratio is determined. In this way, adjusted ray path data can be obtained, reflecting the actual influence of the material on the light.

[0017] Preferably, when acquiring the initial light propagation path data, the Monte Carlo method can be introduced to simulate the random propagation of light. This method improves the statistical accuracy of the simulation by generating random light samples and statistically analyzing the distribution of light in the chip and substrate.

[0018] For example, in one embodiment, one million light rays are simulated, starting from the PN junction region of the LED chip and tracing to the substrate surface, recording the energy loss and direction change of each light ray to form a comprehensive path dataset.

[0019] It should be noted that when processing the light transmittance ratio, the optical properties of the substrate material must be considered, such as its transmittance for visible light, which is typically above 90%. The detailed process includes: first, measuring or inputting the substrate's refractive index, for example, 1.76; then, for the incident light, calculating the transmittance ratio at the incident angle; if the light is reflected multiple times, iteratively calculating the cumulative transmittance value. This process ensures the accuracy of the simulation results and enables optimization of light extraction efficiency in LED design.

[0020] In one possible implementation, the intensity distribution of the light emitted from the front of the LED chip is obtained by integrating the processed light data.

[0021] Specifically, all the light rays that penetrate the substrate are projected onto the front of the chip, and the amount and intensity of light rays in each area are counted to form a two-dimensional distribution map.

[0022] For example, in a simulation, if the front of the chip is divided into multiple grids, the cumulative light intensity of each grid is calculated, taking into account the angular distribution of the light rays, thus obtaining a uniform or concentrated intensity distribution.

[0023] For example, in another embodiment, the light transmission optical property parameters are adjusted for different substrate materials, such as silicon substrates, where a higher absorption coefficient leads to a lower transmission ratio. The simulation process is similar: initial path data is first acquired, then the transmission ratio is processed, and finally the front light intensity distribution is output. This variant demonstrates the versatility of the technology in LED packaging, adapting to various light-transmitting substrates. Furthermore, in practical applications, this method can predict the optical performance of LED chips. Through detailed path simulation and ratio processing, designers can evaluate the light output efficiency under different mounting methods, such as the difference in light loss between single-sided and double-sided mounting, thereby guiding chip structure optimization.

[0024] It is understandable that when dealing with the light transmittance ratio, the influence of temperature on the substrate's optical properties can also be considered.

[0025] For example, if the refractive index of the substrate changes slightly at high temperatures, a temperature correction model is introduced into the simulation to adjust the penetration calculation and ensure the reliability of the distribution results.

[0026] In one embodiment, the entire process is integrated into optical simulation software. First, chip model parameters are input, path simulation is performed to acquire data, then the scaling is adjusted based on substrate properties, and finally, an intensity distribution map is output. This integrated approach improves efficiency and facilitates rapid iterative design in LED manufacturing.

[0027] Specifically, through the above steps, this technical solution can obtain an accurate light intensity distribution without conducting actual prototype testing, supporting the core features in the claims, and demonstrating flexible application potential in the field of optical devices.

[0028] Step S102: Based on the intensity distribution of the light emitted from the front, the optical processing structure parameters inside the substrate are adjusted using a light distribution optimization method. If the proportion of transmitted light is lower than a preset threshold, the density of the reflective layer material is reduced to obtain preliminary uniform data of the back light distribution.

[0029] Initial distribution data is obtained from the light intensity distribution emitted from the front side by classifying the light source type. A light distribution optimization method is used to iteratively adjust the internal optical processing structure parameters of the substrate based on the light propagation path, resulting in adjusted distribution characteristics. For these adjusted distribution characteristics, the proportion of transmitted light is calculated by integrating the light transmittance. If the proportion of transmitted light is lower than a preset threshold, the density of the reflective layer material is increased to determine a density adjustment value. Based on this density adjustment value, ray tracing is used to simulate changes in the back-side light distribution to obtain a distribution map and a simulated uniformity index. Verification results are obtained from this simulated uniformity index. Comparing the uniformity index with a standard threshold determines if the verification is successful, thus obtaining preliminary uniformity data for the back-side light distribution.

[0030] In one embodiment, based on the light intensity distribution emitted from the front of the substrate, light intensity data is first collected. This data is obtained through a photosensitive sensor array, which records the light intensity values ​​at different locations to form a two-dimensional or three-dimensional distribution map.

[0031] Specifically, the light distribution optimization method involves using numerical simulation tools to simulate the propagation path of light within the substrate. For example, ray tracing can be used to calculate the reflection, refraction, and scattering behavior of light after it enters the substrate from the light source. Adjusting the optical processing structural parameters inside the substrate includes modifying the microstructure shape, refractive index distribution, or thickness to optimize the light path and make the front distribution more uniform. Preferably, if the simulation results show that the proportion of transmitted light is lower than a preset threshold, for example, a threshold of 50%, the density of the reflective layer material needs to be increased. The proportion of transmitted light refers to the ratio of light entering from the back of the substrate and exiting from the front to the total incident light, calculated by integrating the light intensity distribution. The specific process is as follows: first, the total intensity of the incident light is calculated; then, multiple reflections and absorptions of the light within the substrate are simulated, and the proportion of the transmitted light is obtained after subtracting the absorbed portion. If the proportion is too low, it indicates that too much light is absorbed or scattered and lost. In this case, increasing the density of the reflective layer can enhance the light reflectivity and reduce transmission loss. Increasing the density of the reflective layer material can be achieved by adjusting the coating process parameters, such as increasing the material deposition rate in vacuum evaporation, thereby making the reflective layer denser, improving the reflection efficiency, and ultimately obtaining preliminary uniform data for the back light distribution. For example, in the application of liquid crystal display backlight modules, this method can be used in direct-lit LED backlight systems. After collecting the front light intensity distribution, an iterative optimization algorithm is used to adjust the diffusion particle distribution parameters on the substrate. If the penetration ratio is lower than a threshold, the density of the reflective layer, such as silver or aluminum, is increased to ensure uniform backlight output and improve display brightness consistency. Furthermore, in another embodiment, the light distribution optimization method can be combined with finite element analysis for parameter adjustment.

[0032] Specifically, finite element analysis divides the substrate into multiple mesh elements, calculates the light field intensity of each element, and iteratively optimizes structural parameters, such as the thickness distribution of the gradient refractive index layer, using gradient descent. This method ensures that the optimization process converges to a uniform distribution state. When judging the proportion of transmitted light, if it is below a threshold, the density of the reflective layer can be gradually increased until the proportion meets the requirements, thereby obtaining preliminary uniform backlight distribution data. This implementation is applicable to large-size display panels, demonstrating the versatility of the technology. It should be noted that the selection of the preset threshold is based on application requirements; for example, in high-brightness lighting scenarios, the threshold can be set to 60% to ensure sufficient light efficiency. After increasing the density of the reflective layer, the uniformity of the backlight distribution is verified by simulation again. A quantitative indicator, such as the standard deviation, is considered to be preliminary uniform when it is less than a preset value. In one possible implementation, for edge-type backlight systems, this optimization method adjusts the incident light structure parameters on the side of the substrate. After collecting front distribution data, if the transmission ratio is low, nanoscale materials are used to increase the density of the reflective layer to improve light utilization and obtain uniform backlight data. This approach enhances the flexibility of the solution under different backlight configurations. For example, consider the specific process of increasing the reflective layer density: first, assess the reflectivity at the current density, then gradually increase the density by adding alloying elements or multi-layer stacked structures, recalculating the transmittance ratio after each adjustment until it reaches above the threshold. This step-by-step adjustment ensures the gradual homogenization of light distribution. It is understandable that the core of this method lies in the combination of conditional judgment and parameter iteration, enabling efficient and uniform distribution in optical processing without introducing additional complex components. Specifically, in the embodiments, for OLED auxiliary backlight substrates, the optimization method adjusts the internal grating structure parameters. If the transmittance ratio is below the threshold, increasing the reflective layer density can reduce light leakage, improve overall luminous efficiency, obtain preliminary uniform data, and support improved display quality.

[0033] S103. Obtain the set of scattering point coordinates from the preliminary uniform data of the back light distribution, perform iterative calculations on the uniform scattering optical properties of the substrate material, and determine the scattering angle adjustment value to balance the light intensity gradient distribution.

[0034] A set of scattering point coordinates is obtained from the preliminary uniform light distribution data on the back side. The coordinates are then classified to obtain a classification result. A uniform scattering attribute mapping method is used to obtain attribute mapping data based on the classification result to determine the initial value of the light intensity gradient. An iterative calculation process is performed based on the initial light intensity gradient value, adjusting the optical properties of the substrate material to obtain an iterative angle result. The scattering angle adjustment value is determined based on the iterative angle result. If the adjustment value meets the equilibrium condition, the scattering angle adjustment value is determined to balance the light intensity gradient distribution.

[0035] In one embodiment, the process of obtaining the set of scattering point coordinates from the preliminary uniformity data of the back light distribution first requires initial homogenization of the back light source on the substrate material. This preliminary uniformity data is typically derived from optical simulation software or experimental measurement equipment, such as a light intensity distribution map acquired through ray tracing methods.

[0036] Specifically, the light distribution data is represented as a two-dimensional grid, with each grid point recording the light intensity value. Then, a threshold filtering algorithm is used to extract the points with light intensities higher than the average value as a set of scattering point coordinates.

[0037] For example, in the backlight module of a liquid crystal display substrate, if the initial light distribution shows uneven light intensity in some areas, the entire substrate surface can be scanned to identify a set of points with coordinates in the form of (x, y). These point sets are used for subsequent scattering optimization to ensure more uniform light propagation within the substrate. Further, iterative calculations are performed on the uniform scattering optical properties of the substrate material. These optical properties include parameters such as the material's refractive index, scattering coefficient, and absorptivity, which affect the light propagation path within the substrate. The iterative calculation process is a loop optimization based on Monte Carlo simulation or the finite difference method. First, the scattering model is initialized, treating the substrate as a uniform medium. Then, in each iteration, the set of scattering point coordinates from the previous step is input, and the scattering behavior of light from these points is calculated. Specifically, assuming the initial scattering angle is randomly distributed, the deviation value of the light path is calculated. If the deviation exceeds a preset threshold, such as 0.05, the scattering coefficient is adjusted and the simulation is repeated. This loop is repeated until the deviation converges, for example, after 5 to 10 iterations, stable optical property values ​​are obtained. This calculation helps to achieve uniform light intensity scattering in substrate materials such as plexiglass or polymers, which can effectively reduce hot spots, especially in large-size display applications.

[0038] Preferably, when determining the scattering angle adjustment value to balance the light intensity gradient distribution, the spatial distribution of the light intensity gradient is further calculated based on the results of iterative calculations. The light intensity gradient is a vector representation of the difference in light intensity between adjacent points, which can be extracted from light distribution data, for example, using gradient operators such as the Sobel method. The process of determining the adjustment value involves optimizing to minimize the gradient variance. For example, the initial adjustment value is 0 degrees, and then the angle is iteratively increased or decreased according to the gradient distribution, such as adjusting by 1 to 2 degrees each time, until the variance of the overall gradient distribution is less than 0.1. This method can achieve a balanced distribution of light intensity in lighting systems using substrate materials, improving the uniformity of the display effect.

[0039] In one possible implementation, the above process can be applied to substrate materials of different thicknesses. For example, in a thin substrate with a thickness of 2 mm, the acquisition of the scattering point coordinate set focuses more on the light distribution data of the edge region to compensate for boundary loss. In a thick substrate with a thickness of 5 mm, the number of iterations can be increased to 15 to handle more complex internal scattering paths, thereby ensuring the balance of the light intensity gradient throughout the substrate.

[0040] It should be noted that this technical solution is applicable to various scenarios in the field of optical substrates. For example, in the design of LED backlight panels, by adjusting the scattering angle, light energy loss can be reduced and higher light efficiency can be achieved.

[0041] For example, in practical operation, if the substrate material is polycarbonate, the initial uniformity data can be obtained by combining the light field image captured by a CCD camera, then extracting the coordinate set for iteration, and finally adjusting the angle value, such as optimizing from the initial 30 degrees to 45 degrees, to balance the light intensity gradient between the center and the edge. Furthermore, this implementation can also be extended to the application of curved substrates, introducing curvature parameters into the iterative calculation to adjust the scattering angle to adapt to the non-planar structure, thereby maintaining light intensity uniformity in flexible display devices.

[0042] In one embodiment, after balancing the light intensity gradient, the effect can be verified, for example by measuring the uniformity index of the light intensity distribution before and after adjustment using a photometer, thereby improving the optical performance of the substrate.

[0043] Understandably, the logical sequence of these steps ensures the continuity from data acquisition to angle optimization, providing reliable technical support in substrate material processing.

[0044] S104. If the scattering angle adjustment value exceeds the preset range, the light propagation path is re-simulated using a random ray tracing method to obtain the corrected back light intensity matrix data.

[0045] A scattering angle adjustment value is obtained, and the adjustment value is judged within a preset range to obtain a judgment result identifier. If the judgment result identifier indicates that the value exceeds the limit, a random ray tracing method is used to simulate the ray propagation path from the adjustment value to obtain a path sequence. The interaction points between the ray and the surface are traced through the path sequence, and the accumulated intensity values ​​are used to calculate the back-side light intensity distribution to determine the light intensity matrix data. The surface material reflectance coefficient is then superimposed on the light intensity matrix data to obtain correction matrix data.

[0046] In one implementation, the system first obtains an initial scattering angle adjustment value, which is calculated based on the difference between the incident angle of light and the surface scattering characteristics in the optical model.

[0047] Specifically, the scattering angle adjustment value is determined by comparing the deviation between the preset ideal scattering angle and the actual simulated angle.

[0048] For example, in an optical simulation scenario of a solar panel, if light is incident from the front to the back, the adjustment value reflects the angular offset caused by material roughness.

[0049] This adjustment value helps identify inaccuracies in the simulation and ensures the necessity of subsequent corrections. The preset range is typically set from -5 degrees to 5 degrees, depending on the accuracy requirements of the optics. Furthermore, if the scattering angle adjustment value exceeds the preset range, the optical simulation system initiates a random ray tracing method to re-simulate the light propagation path. This method involves emitting multiple random rays from the light source, with each ray's propagation direction sampled according to a probability distribution to simulate real light scattering phenomena.

[0050] For example, in the simulation of a display backlight module, random ray tracing can handle multiple reflections and refractions of light in the diffuser layer, avoiding the biases of deterministic tracing. The random tracing process involves initializing the light's position, direction, and intensity, then iteratively calculating each interaction with the surface until the light is absorbed or escapes the system. In this way, the system can generate path data that more closely approximates reality, correcting errors in the initial simulation.

[0051] For example, a specific implementation of the random ray tracing method can employ a Monte Carlo sampling strategy, where the propagation path of each ray is determined by a random number generator to determine its direction and distance. The specific process is as follows: first, the emission point and initial direction distribution of the light source are defined; then, for each ray, the coordinates of its intersection with the optical surface are calculated; if scattering occurs, a new direction is randomly selected based on the surface's bidirectional reflection distribution function.

[0052] It should be noted that this strategy is particularly effective when dealing with optical devices with complex geometries, such as in the simulation of back-side light intensity in solar cells, where it can accurately capture the non-uniform light distribution caused by textured surfaces. Through multiple iterative sampling, the system accumulates the light intensity contribution, forming preliminary matrix data.

[0053] In one possible implementation, the corrected back-side light intensity matrix data is constructed by aggregating the intensity values ​​of all random rays. Each element of the matrix corresponds to the light intensity value of a specific region on the back side, and the contribution of random paths is weighted and averaged during calculation.

[0054] Specifically, for a matrix with a resolution of N x M, the system traverses each light path, records its energy deposition in the back region, and then normalizes the data to obtain the final data. This matrix data is used to evaluate the efficiency of optical devices; for example, in solar cell simulations, it directly affects the calculation of conversion efficiency.

[0055] Preferably, noise reduction techniques, such as importance sampling, can be introduced to accelerate the convergence process, thereby obtaining reliable results with limited computational resources. Furthermore, this method can be extended to scenarios with different parameter settings. For example...

[0056] In one embodiment, the preset range is adjusted to -10 degrees to 10 degrees to accommodate the optical simulation of highly scattering materials. At this time, the number of random ray tracing samples is increased to 10,000 to ensure the accuracy of the matrix data.

[0057] It should be noted that this adjustment enhances the flexibility of the solution, making it applicable to devices with varying optical thicknesses without altering the core analog logic.

[0058] Understandably, through the above steps, the system achieves dynamic correction of the light propagation path, improving the accuracy of back-side light intensity simulation. In solar cell design optimization, this correction can provide more reliable data support, such as helping engineers adjust surface coatings to maximize light capture efficiency without conducting costly physical experiments. In another implementation, random ray tracing is combined with a parallel computing framework to handle large-scale simulations. The specific process involves distributing light beams to multiple processing units, each independently calculating the path, and then merging the results to generate matrix data. This approach performs exceptionally well in time-sensitive applications, such as real-time optical rendering scenarios, ensuring the efficiency of the correction process.

[0059] For example, for the correction of the back light intensity matrix, the system can also introduce a verification step, that is, compare the difference between the matrix before and after the correction. If the difference is less than a threshold, the simulation is confirmed to be effective.

[0060] It should be noted that this step enhances the robustness of the method and avoids invalid iterations in actual optical simulations. Furthermore, in a preferred embodiment, this technical solution is applied to the simulation of portable optical devices, such as mobile phone screen backlight systems, where when the scattering angle adjustment value exceeds the range, the light interaction of the simulated microstructure is randomly tracked to obtain refined matrix data, supporting device miniaturization design.

[0061] S105. Determine the matching degree between the front-emitting light intensity distribution and the back-emitting light distribution based on the corrected back-side light intensity matrix. If the matching degree is higher than a preset threshold, lock the optical processing structure parameter configuration to obtain an overall double-sided light emission equalization design model.

[0062] The light intensity matrix correction is obtained, and the front intensity distribution data is calculated through the back distribution. For the front intensity distribution data, a matching degree evaluation is used to determine the correspondence with the back distribution calculation. If the correspondence is higher than a threshold for comparison, the surface texture parameters are adjusted through optical structure optimization, where optical structure optimization is based on the superposition of reflection coefficients and interaction point tracking of the surface material. The parameter configuration is locked from the surface texture parameters to obtain the overall double-sided light emission equalization design model.

[0063] In one implementation, the system uses the modified back-side light intensity matrix as basic data to evaluate the relationship between the front-side emitted light intensity distribution and the back-side light distribution.

[0064] Specifically, the front-emitted light intensity distribution refers to the spatial intensity pattern of light emitted from the front of the optical device, while the back-emitted light distribution corresponds to the intensity pattern of light received or reflected from the back of the device. These distribution data are typically represented in matrix form, with each matrix element corresponding to the light intensity value of a specific region. In this way, the system can quantify the degree of uniformity in light emission from both sides, providing data support for subsequent judgments. Furthermore, the process of determining the matching degree involves comparing the similarity between the two distributions.

[0065] For example, a pixel-level comparison method can be used to compare the intensity distribution matrix of the light emitted from the front with the light distribution matrix of the light emitted from the back element by element and calculate the difference value. The specific process is to first normalize the two matrices to ensure that the data scale is consistent, and then traverse each corresponding position, calculate the absolute difference value and sum them to obtain the total difference index.

[0066] It should be noted that this comparison method is applicable to a simulated scenario of double-sided light capture in solar cells, reflecting the uniformity of light propagation within the device. A high degree of match is considered achieved if the difference index is below a certain value.

[0067] Preferably, the matching degree is calculated using cosine similarity.

[0068] In one possible implementation, the system treats the front-emitted light intensity distribution and the back-emitted light distribution as vectors, and calculates their inner product divided by the modulus product.

[0069] Specifically, for an N x M matrix, after flattening it into vectors, a similarity value is calculated using a formula, ranging from 0 to 1. This method is effective in the balanced design of display backlight modules because it considers the overall directionality of the distribution, rather than just local differences. Through this calculation, the system obtains a quantitative index for comparison with a preset threshold.

[0070] For example, the preset threshold is set to 0.85, which is adjusted according to the application requirements of the optical device. If the matching degree is higher than this threshold, it indicates that the light distribution on the front and back sides has reached the required level of balance.

[0071] It should be noted that this threshold is determined based on empirical data; for example, in solar cell simulations, it ensures the stability of conversion efficiency. After the system makes its judgment, it directly enters the parameter locking stage, avoiding further iterations. Furthermore, if the matching degree is higher than a preset threshold, the optical processing structure parameter configuration is locked. The specific process includes recording the current parameter values, such as surface roughness, incident angle adjustment, and material refractive index. These parameters originate from previous simulations and are used to define the device structure. By locking, the system fixes these configurations, forming a stable design basis.

[0072] In one embodiment, for a mobile phone screen backlight system, this locking step ensures that the parameters of the microstructure do not change, supporting subsequent production verification.

[0073] Understandably, the overall bi-directional light output equalization design model is obtained by integrating locking parameters and light intensity matrix data.

[0074] Specifically, the model includes a set of parameters and a distribution map to simulate light emission behavior under different light source conditions.

[0075] For example, in the design of LED chips, this model can be directly imported into optimization software to generate device blueprints. This approach enhances the applicability of the solution within the same domain. In another implementation, the matching degree judgment combines a region segmentation method. First, the matrix is ​​divided into multiple sub-regions, and the local matching degree of each sub-region is calculated. Then, a weighted average is taken to obtain the overall value. Specifically, the size of the sub-regions is defined, such as a 4x4 grid, and similarity calculation is applied to each grid. This extension is suitable for complex optical devices, such as chips with textured surfaces, and it handles non-uniformly distributed scenes, improving the accuracy of the judgment. Furthermore, after locking the parameter configuration, the system can output the design model in a file format, such as storing parameters and matrix data in a JSON structure.

[0076] It should be noted that this output is easy for engineers to reuse in simulation environments, supporting iterative design of bi-directional light output equalization while maintaining domain consistency.

[0077] S106. Extract heat conduction-related parameters from the overall double-sided light emission equalization design model, simulate heat flow distribution based on the heat dissipation thermal properties of the light-emitting diode chip, and determine the welding process optimization points to reduce the area of ​​concentrated thermal stress.

[0078] The heat conduction parameters are obtained from the overall double-sided light-emitting equalization design model. The heat flow distribution of the LED chip is simulated using the finite element method (FEM) to analyze its thermal properties. The FEM takes heat conduction parameters and thermal properties as inputs and outputs heat flow distribution data. A thermal resistance network (TRN) is constructed from this data, where nodes and edges represent heat transfer paths. The TRN is used to evaluate heat transfer efficiency at the chip packaging interface and determine the interface TRN value. The thermal stress distribution is assessed based on the interface TRN value. If the thermal stress distribution exceeds a preset threshold, the solder layer thickness is adjusted to obtain adjusted soldering parameters. An optimized process sequence is obtained from these adjusted soldering parameters. This optimized process sequence includes soldering temperature and time sequences. The stress concentration region is simulated using this optimized process sequence, which is based on a thermal stress model using the adjusted soldering parameters. The stress reduction configuration for each region is then determined. The optimized soldering process points are then identified based on this stress reduction configuration, resulting in a process scheme to reduce the thermal stress concentration region.

[0079] Thermal conduction-related parameters were extracted from the overall double-sided light-emission equalization design model.

[0080] Specifically, the model includes optical processing structural parameters, such as material thermal conductivity and interfacial thermal resistance, which directly affect the heat transfer efficiency of the LED chip. In one implementation, the system first identifies the thermal sub-modules in the model and extracts parameters including the specific heat capacity and thermal diffusivity of the chip material. By traversing the model data structure, these parameters are organized into a vector form for subsequent simulation preparation.

[0081] For example, in the design of LED chips, these parameters are derived from preliminary light intensity distribution calculations to ensure consistency with light output uniformity. Furthermore, based on these calculation results, heat flow distribution is simulated to address the thermal properties of the LED chip.

[0082] It should be noted that thermal properties refer to the temperature gradient and heat flux characteristics of the chip under operating conditions. The simulation process involves establishing a finite element mesh, dividing the chip into multiple unit regions, and then calculating the heat flux vector of each unit based on the extracted parameters.

[0083] Specifically, the boundary conditions, such as ambient temperature and heat input from the light source, are first input, and then the heat balance equation is solved iteratively to obtain the overall heat flow distribution map.

[0084] In one possible implementation, for the display backlight module, the simulation considers the contact surface between the chip and the substrate, tracing the propagation path of heat flow from the center of the chip to the edges. This method ensures the accuracy of the distribution data and supports thermal management analysis.

[0085] Preferably, welding process optimization points are identified to reduce areas of concentrated thermal stress.

[0086] Understandably, areas of concentrated thermal stress typically appear at the interface between the chip and the solder joint, where stress accumulates due to differences in thermal expansion coefficients. The process of determining optimization points involves analyzing the simulated heat flow distribution to identify high-stress nodes, such as regions with temperature gradients exceeding 50 K per m. Then, for these nodes, soldering parameters are adjusted, for example, by changing the solder composition or the soldering temperature profile. Specifically, the stress value is first quantified using finite element analysis to calculate σ = EαΔT, where σ is the stress, E is the elastic modulus, α is the coefficient of thermal expansion, and ΔT is the temperature change. By comparing the distribution differences under different parameters, a configuration that reduces the concentration is selected. The soldering parameters are incorporated into the thermal simulation by modifying the thermal conductivity and boundary conditions.

[0087] In one embodiment, for the soldering of solar cell chips, optimization focuses on reducing solder joint thickness to evenly distribute thermal stress. In another embodiment, the extraction of heat conduction parameters incorporates a dynamic update mechanism of the model.

[0088] Specifically, if the model contains time-dependent thermal data, the extraction process is performed in stages: first, steady-state parameters are captured, and then transient response values ​​are added. This extension is suitable for continuous operation scenarios of solar cell modules, improving the robustness of the simulation. Furthermore, the simulation of heat flux distribution can employ a multi-layer mesh method, decomposing the chip structure into semiconductor layers and encapsulation layers. First, a uniform mesh (e.g., 100x100 units per layer) is constructed, and then the heat transfer efficiency is calculated separately using the formula k=Q / (A*ΔT / d), where k is thermal conductivity, Q is heat flux, A is area, ΔT is temperature difference, and d is thickness.

[0089] For example, in solar cell applications, this decomposition reveals the location of thermal bottlenecks, providing a basis for optimization.

[0090] It should be noted that the optimization of welding process also includes adjustments to material selection.

[0091] Specifically, the thermal conductivity of different solders is evaluated through simulation results to determine alternative solutions to minimize stress peaks.

[0092] In one embodiment, this optimization ensures long-term stability for the display chip.

[0093] For example, the entire process forms a closed loop, from parameter extraction to optimization point determination, supporting the iterative thermal design of optical devices.

[0094] S107. Generate the final module design blueprint based on the welding process optimization points, and verify the light output efficiency of the double-sided light output balanced design model using structural analysis methods. If the efficiency improvement index meets the requirements, output the complete solution data.

[0095] Module structural parameters are obtained from the welding process optimization points. An optical path transmission path is constructed for the double-sided light output equalization design model. This model is a mathematical model based on the module structural parameters, used to equalize the light output from both sides. The ray tracing method is used to define the light propagation direction and reflection / refraction rules. The optical path transmission path simulates the light distribution equalization, obtaining light distribution equalization data. Light surface attributes are extracted from the light distribution equalization data. The finite element optical simulation method is used to calculate the light extraction efficiency based on these attributes. This method divides the light surface into meshes and applies boundary conditions to solve the light propagation equation. The inputs are the light surface attributes and the light distribution equalization data; the output is the light extraction efficiency value. The light extraction efficiency value is then determined. An efficiency improvement index is assessed based on this value. If the efficiency improvement index meets a preset threshold, the module structural parameters are integrated to obtain the integrated design blueprint. The encapsulation interface configuration is obtained from the integrated design blueprint. This configuration is used to simulate the thermo-optical coupling distribution, which is based on a thermo-optical model derived from module structural parameters. The inputs to this model are the module structural parameters, thermal conductivity coefficient, and optical transmission parameters. The distribution field is iteratively solved by coupling the thermal conduction equation and the optical transmission equation to determine the thermo-optical coupling equilibrium point. Based on this equilibrium point, the complete solution data is locked and output.

[0096] The process of generating the final module design blueprint based on the welding process optimization points first involves integrating the optimized welding parameters to form the overall structural layout of the module.

[0097] Specifically, this process involves transforming solder adjustment points that reduce thermal stress concentration into design elements, such as adjusting solder thickness and temperature profiles, to ensure a stable connection between the chip and the substrate. In one implementation, the system extracts key node data from heat flow distribution simulation results and then generates a blueprint draft using design software, including precise coordinates of chip locations and heat dissipation channels. This approach is suitable for the packaging design of LED chips, ensuring that the blueprint reflects the practical application of thermal optimization.

[0098] Preferably, the geometric parameters of the dual-sided light-emission equalization model are considered when generating the blueprint.

[0099] Specifically, the blueprints indicate the optical structures, such as the distribution of reflective and transparent layers, to coordinate heat conduction and light output.

[0100] It should be noted that optimizations in the welding process, such as reducing the thickness of the weld joint, directly affect the thermal stress distribution diagram of the blueprint. Through iterative design cycles, the blueprint is gradually improved.

[0101] In one possible implementation, for a solar cell module, the blueprint includes a multi-layered structural representation, highlighting the heat diffusion path in the welding areas to support subsequent manufacturing preparation. This generation process emphasizes the vector-like organization of parameters, ensuring the accuracy and operability of the blueprint. Furthermore, a structural analysis method is used to verify the light output efficiency of the described bifacial light-emission equalization design model. This method involves establishing a finite element model and performing stress and optical simulations on the model to evaluate the uniformity of light output.

[0102] Understandably, structural analysis methods refer to quantifying the deformation and luminous flux changes of a model under thermal load through mesh generation and mechanical calculations. The specific process involves first inputting blueprint data, and then calculating luminous efficiency indicators such as luminous intensity uniformity and total output power.

[0103] In one embodiment, the system uses software tools to simulate the chip's double-sided light emission, traces the propagation path of light from both sides, and compares the efficiency differences before and after optimization.

[0104] For example, in display backlight modules, this validation reveals the impact of thermal stress on light emission, enabling a quantitative assessment of efficiency by adjusting model parameters. This validation ensures the reliability of the model and supports iterative improvements in optical design.

[0105] For example, structural analysis methods also include setting boundary conditions, such as ambient light intensity and thermal input values.

[0106] Specifically, the finite element model of the optical device is first divided into multiple sub-regions, and then the structural equation σ=Eε (σ is stress, E is elastic modulus, and ε is strain) is solved. The input is material properties, and the output is stress distribution and light extraction efficiency diagram. It should be noted that light extraction efficiency refers to the proportion of light output per unit area, calculated numerically using the finite element method described above. In one embodiment, for LED chips, the analysis considers the material's thermal expansion coefficient, simulating the stability of the structure after welding, ensuring the verification process covers both thermal and optical properties. This detailed analysis helps identify potential problems and provides a data foundation. In another embodiment, the verification process combines dynamic simulation, i.e., finite element time-step simulation, with time variables and initial conditions as inputs, and a time-dependent light extraction variation curve as the output, capturing dynamic light extraction changes.

[0107] Specifically, if the model includes transient thermal data obtained from experimental measurements or simulations, the analysis is performed in stages: first, the steady-state efficiency is evaluated, and then the transient response is supplemented. This extension is applicable to continuously operating solar cell scenarios, improving the comprehensiveness of the verification. Furthermore, the method confirms the accuracy of the light extraction efficiency by comparing simulation results with actual test data. If the accuracy metric meets the requirements, complete solution data is output. This step makes conditional judgments based on the verification results; for example, when the light extraction efficiency accuracy exceeds 90%, the system aggregates all data to generate the output.

[0108] Specifically, the solution data includes blueprint files and parameter lists, forming a closed-loop output. In one possible implementation, for LED chips, if the specifications are met, the output includes a comprehensive document containing heat flow maps and soldering process guidelines. This output ensures the practicality of the solution and supports the actual deployment of the optics.

[0109] Preferably, the output data also includes adaptive descriptions for various scenarios.

[0110] Specifically, the data lists parameter adjustment options, such as the efficiency impact of different solder selections, to enhance the flexibility of the solution.

[0111] It should be noted that the efficiency improvement indicator refers to the percentage improvement relative to the baseline model, and this judgment is used to achieve automated output.

[0112] In one embodiment, for LED chip applications, the output includes module design specifications and verification reports to ensure completeness. Furthermore, the entire process forms a logical chain, from blueprint generation to efficiency verification and conditional output, supporting comprehensive management of the optical and thermal design.

[0113] For example, in the implementation of LED chips, this solution uses finite element structural analysis to confirm the improved light extraction resulting from reduced thermal stress, achieving the design goal of a high-efficiency module. Specifically, by using a substrate material with a low coefficient of thermal expansion and an integrated heat sink structure, thermal stress is reduced to 50% of its original value, thereby increasing light extraction efficiency by more than 15%. This approach provides a reliable path for thermal management and optical optimization in business applications.

[0114] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A method for equalizing the light output of a double-sided LED light source module, characterized in that, include: Initial light propagation path data is obtained from a single-sided mounted LED chip. The light transmission ratio is processed based on the light transmittance optical properties of the substrate material to determine the intensity distribution of the light emitted from the front of the LED chip. The optical processing structure parameters inside the substrate are adjusted according to the intensity distribution of the light emitted from the front to obtain preliminary uniformity data of the back light distribution. The set of scattering point coordinates is obtained from the preliminary uniform data of the back light distribution. The scattering angle adjustment value is determined based on the uniform scattering optical properties of the substrate material to balance the light intensity gradient distribution. The back light intensity matrix data is corrected by ray tracing. The matching degree between the front emitted light intensity distribution and the back light distribution is judged based on the corrected back light intensity matrix data. If the matching degree is higher than a preset threshold, the optical processing structure parameter configuration is locked to obtain the overall double-sided light emission equalization design model. The heat conduction related parameters are extracted from the overall double-sided light emission equalization design model. The heat flow distribution is simulated based on the heat dissipation thermal properties of the light-emitting diode chip to determine the welding process optimization points. Based on the design blueprint of the welding process optimization point generation module, the light output efficiency of the double-sided light output equalization design model is verified. If the efficiency improvement index meets the preset threshold, the complete solution data is output.

2. The light emission equalization design method for a double-sided light-emitting LED light source module as described in claim 1, characterized in that, The process of obtaining initial light propagation path data from a single-sided mounted LED chip includes: obtaining initial light propagation path data from the single-sided mounted LED chip using an optical path simulation method; processing the light transmission optical properties of the substrate material based on the initial light propagation path data to calculate the light transmission ratio; fusing the reflection properties of the multilayer substrate based on the light transmission ratio to determine the reflected light contribution value; calculating the front-emitted light intensity distribution of the LED chip based on the reflected light contribution value; verifying the accuracy of the light propagation path data using the front-emitted light intensity distribution to obtain initial distribution characteristics; adjusting simulation parameters based on the initial distribution characteristics to obtain optimized light propagation path data; and extracting key light path nodes from the optimized data to determine the final distribution result of the light propagation path.

3. The light emission equalization design method for a double-sided light-emitting LED light source module as described in claim 1, characterized in that, The step of adjusting the optical processing structure parameters inside the substrate based on the front-emitted light intensity distribution includes: obtaining initial distribution data from the front-emitted light intensity distribution by classifying light source types; iteratively adjusting the optical processing structure parameters inside the substrate based on the light propagation path using a light distribution optimization method to obtain adjusted distribution characteristics; calculating the proportion of transmitted light by integrating the light transmittance for the adjusted distribution characteristics; increasing the density of the reflective layer material if the proportion of transmitted light is lower than a preset threshold to determine a density adjustment value; simulating the back-side light distribution change based on the density adjustment value and obtaining a distribution map using ray tracing; extracting a simulated uniformity index from the distribution map and judging the verification result by comparing the uniformity index with a standard threshold; if the verification is successful, obtaining preliminary uniformity data for the back-side light distribution.

4. The light emission equalization design method for a double-sided light-emitting LED light source module as described in claim 1, characterized in that, The step of obtaining the set of scattering point coordinates from the preliminary uniform back light distribution data includes: obtaining the set of scattering point coordinates from the preliminary uniform back light distribution data; obtaining coordinate classification results through coordinate set classification processing; obtaining attribute mapping data by applying a uniform scattering attribute mapping method to the coordinate classification results; determining an initial value of the light intensity gradient based on the attribute mapping data; performing iterative calculations on the initial value of the light intensity gradient and adjusting the angle by combining it with the optical properties of the substrate material to obtain an angle iteration result; determining a scattering angle adjustment value based on the angle iteration result; if the scattering angle adjustment value meets the balance condition, determining the scattering angle adjustment value to balance the light intensity gradient distribution; and extracting uniformity verification data from the balanced light intensity gradient distribution to obtain the final scattering distribution characteristics.

5. The light emission equalization design method for a double-sided light-emitting LED light source module as described in claim 1, characterized in that, The step of correcting the back light intensity matrix data using ray tracing includes: obtaining a scattering angle adjustment value; judging the adjustment value within a preset range to obtain a judgment result identifier; if the judgment result identifier exceeds the preset range, simulating the light propagation path from the adjustment value using a random ray tracing method to obtain a path sequence; tracing the interaction points between the light rays and the surface through the path sequence, accumulating intensity values ​​to calculate the back light intensity distribution; determining the light intensity matrix data based on the back light intensity distribution; superimposing the surface material reflectance coefficient onto the light intensity matrix data to obtain correction matrix data; extracting light intensity distribution features from the correction matrix data to verify the correction effect; and adjusting the ray tracing parameters based on the correction effect to obtain the final back light intensity distribution data.

6. The light emission equalization design method for a double-sided light-emitting LED light source module as described in claim 1, characterized in that, The step of determining the matching degree between the front-emitted light intensity distribution and the back-emitted light distribution based on the corrected back-emitted light intensity matrix data includes: obtaining the correction result of the back-emitted light intensity matrix data, and calculating the front-emitted intensity distribution data through the back-emitted distribution; determining the correspondence between the front-emitted intensity distribution data and the back-emitted distribution using a matching degree evaluation method; if the correspondence is higher than a preset threshold, adjusting the surface texture parameters through optical structure optimization; locking the parameter configuration according to the surface texture parameters to obtain an overall double-sided light emission equalization design model; extracting matching degree verification data from the overall double-sided light emission equalization design model; adjusting the matching degree evaluation method through the verification data to obtain the final matching degree result; and determining the optimized configuration of the optical processing structure parameters based on the final matching degree result.

7. The light emission equalization design method for a double-sided light-emitting LED light source module as described in claim 1, characterized in that, The extraction of heat conduction-related parameters from the overall double-sided light-emitting equalization design model includes: obtaining heat conduction-related parameters from the overall double-sided light-emitting equalization design model; simulating heat flow distribution using the finite element method to obtain heat flow distribution data for the heat dissipation thermal properties of the LED chip; constructing a thermal resistance network from the heat flow distribution data, representing heat transfer paths through nodes and edges; evaluating heat transfer efficiency at the chip packaging interface using the thermal resistance network to determine the interface thermal resistance value; judging the thermal stress distribution based on the interface thermal resistance value; if the thermal stress distribution exceeds a preset threshold, adjusting the solder layer thickness to obtain adjusted soldering parameters; and obtaining an optimized process sequence based on the adjusted soldering parameters to determine the optimized heat flow distribution result.

8. The light emission equalization design method for a double-sided light-emitting LED light source module as described in claim 1, characterized in that, The process of determining welding process optimization points includes: extracting thermal stress concentration region characteristics from heat flow distribution data; simulating stress concentration region changes by optimizing the process sequence based on the thermal stress concentration region characteristics; adjusting the welding temperature and time sequence according to the changes to obtain adjusted welding parameter configurations; determining regional stress reduction configurations from the adjusted welding parameter configurations; locking welding process optimization points based on the regional stress reduction configurations to obtain process schemes for reducing thermal stress concentration regions; verifying the improvement effect of thermal stress distribution through the process schemes; adjusting process parameters according to the improvement effect to obtain final welding process optimization data; and extracting key process nodes from the final welding process optimization data to determine the process implementation path.

9. The light emission equalization design method for a double-sided light-emitting LED light source module as described in claim 1, characterized in that, The process of generating a module design blueprint based on the welding process optimization points includes: obtaining module structural parameters from the welding process optimization points; constructing an optical path transmission path for the double-sided light-emitting equalization design model, and defining the light propagation direction and reflection and refraction rules using ray tracing; simulating the light distribution equalization through the optical path transmission path to obtain light distribution equalization data; extracting light surface attributes from the light distribution equalization data; calculating the light extraction efficiency for the light-emitting surface attributes using finite element structural analysis to determine the light extraction efficiency value; if the light extraction efficiency value meets a preset threshold, integrating the module structural parameters to obtain an integrated design blueprint; and obtaining the final module configuration data based on the integrated design blueprint.

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