Backlight guide plate brightness optimization method

Abstract

The invention aims to provide a backlight light guide plate brightness optimization method, and relates to the technical field of light guide plates. The method comprises the following steps: constructing a first optical model according to structural parameters of the light guide plate, and performing simulation through the first optical model to determine an arc cutting structure; and constructing a second optical model according to the light guide plate structure parameters and the arc cutting structure, and performing simulation through the second optical model to determine a dot distribution model. According to the method, the first optical model and the second optical model are constructed in stages, the arc cutting structure and the lattice point distribution model are optimized respectively, systematic optimization of the brightness of the backlight light guide plate is achieved, and the problems that the light energy utilization rate of the light guide plate is low and the brightness is uneven are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of light guide plates, and particularly to an optimization method for the brightness of a backlight light guide plate. Background Art

[0002] The light guide plate is a core component of the backlight module, and its optical performance directly determines the brightness and uniformity of the backlight module. As Figure 1 shown, the light incident surface of the traditional light guide plate adopts a structure with dots processed on a mirror surface or a ground surface. Due to the reflection of this structure, a large amount of light is totally reflected inside the light guide plate and cannot be effectively exported, resulting in a low light energy utilization rate and affecting the display effect.

[0003] To achieve brightness improvement and uniformity optimization, as Figure 2 and Figure 3 shown, an existing solution is to adopt a combined structure of an arc cutting structure (R-CUT) plus dots on the light incident surface, that is, to first process the arc cutting structure on the light incident surface and then distribute dots thereon. This method initially guides the light path through the arc cutting structure and then fine-tunes the light distribution through the dots, improving the light energy utilization rate to a certain extent. However, the parameters of the arc cutting structure (such as depth, width) and the dot distribution lack collaborative optimization, resulting in relatively high light flux loss and local brightness non-uniformity problems in actual applications. Summary of the Invention

[0004] The purpose of the present invention is to provide an optimization method for the brightness of a backlight light guide plate, realizing the systematic optimization of the brightness of the backlight light guide plate and solving the problems of low light energy utilization rate and brightness non-uniformity in the design of the light guide plate.

[0005] In a first aspect, the present invention provides an optimization method for the brightness of a backlight light guide plate, including the steps of: Constructing a first optical model according to the light guide plate structure parameters, and determining the arc cutting structure through simulation of the first optical model; Constructing a second optical model according to the light guide plate structure parameters and the arc cutting structure, and determining the dot distribution model through simulation of the second optical model; The step of determining the dot distribution model through simulation of the second optical model includes the steps of: Dividing the light incident surface and the light emitting surface of the second optical model into a plurality of light incident regions and light emitting regions respectively through a plurality of grid dividing lines; Using the particle swarm optimization algorithm to obtain the dot filling rate of each light incident region; Setting the number of dots according to the dot filling rate of each light incident region to obtain the dot distribution model.

[0006] As a preferred solution of the present invention, the light guide plate structure parameters include the three-dimensional dimensions of the light guide plate and the light source position; Constructing the first optical model according to the structural parameters of the light guide plate specifically includes: Selecting a vertex of the light guide plate as the origin to construct a three-dimensional coordinate system of the light guide plate; Setting z = 0 as the light-incident surface of the light guide plate and z = H as the light-emitting surface of the light guide plate; Setting the light source position on the single-side light-incident surface where x = 0, or setting it on the first light-incident surface where x = 0 and the second light-incident surface where x = L.

[0007] As a preferred embodiment of the present invention, determining the arc cutting structure through simulation by the first optical model includes the steps of: Setting the light flux of the light-incident surface of the first optical model; Initializing and generating a number of sample points within the value range of the maximum depth and width through Latin hypercube sampling, and constructing a sample data set and a Gaussian process model according to the number of sample points; Obtaining the light flux of the light-emitting surface corresponding to each sample point in the sample data set through the first optical model, and calculating the simulation value of the first total light-emitting rate corresponding to the sample point according to the light flux of the light-emitting surface and the light flux of the light-incident surface; Processing the Gaussian process model through the acquisition function to obtain the sample point corresponding to the optimal prediction value of the first total light-emitting rate and adding it to the sample data set; Iteratively optimizing until the first iteration number is reached or the first convergence condition is satisfied, and determining the arc cutting structure according to the sample point with the largest predicted value of the first total light-emitting rate output.

[0008] As a preferred embodiment of the present invention, the acquisition function is expressed as: , where, represents the expected improvement acquisition function; represents the expected value operation, which is used to integrate the prediction distribution of the first total light-emitting rate; represents the simulation value of the first total light-emitting rate; represents the optimal prediction value of the first total light-emitting rate.

[0009] As a preferred embodiment of the present invention, after setting the number of dots according to the dot filling rate of each light-incident area, it further includes: Setting the regular arrangement of dots in each light-incident area according to the number of dots, and processing each light-incident area using the local randomization algorithm to obtain the dot distribution model.

[0010] As a preferred embodiment of the present invention, using the particle swarm optimization algorithm to obtain the dot filling rate of each light-incident area includes the steps of: Initializing the particle swarm parameters; The particle swarm parameters include the position and velocity of each particle; The position of the particle represents the dot filling rate vector; Obtaining the objective function value corresponding to the position of each particle; Updating the update velocity and position of each particle according to the objective function value; Iteratively optimize until the second iteration number is reached or the objective function value is less than the set threshold, and output the global best position as the dot filling rate of each light-receiving area.

[0011] As a preferred embodiment of the present invention, the obtaining of the objective function value corresponding to the position of each particle includes the steps of: Update the second optical model according to the position of the particle, and obtain the output light flux corresponding to each light-emitting area through the second optical model; Calculate the light-emitting surface light flux and the output light flux variance according to the output light flux corresponding to each light-emitting area, and calculate the objective function value; The objective function value is expressed as: , where, represents the output light flux of the i-th light-emitting area, represents the average value of the output light fluxes of all light-emitting areas; n represents the number of light-receiving areas; and are the first weighting coefficient and the second weighting coefficient respectively.

[0012] As a preferred embodiment of the present invention, the initialization of the particle swarm parameters includes setting the initial position of each particle; the initial position of the particle satisfies the dot filling rate range corresponding to each light-receiving area; the dot filling rate range corresponding to each light-receiving area is set by a floating coefficient and a filling rate reference value; The obtaining method of the filling rate reference value is: Set the light flux of the light-incident surface of the second optical model, and obtain the input light flux of each light-receiving area and the output light flux corresponding to the light-emitting area through the second optical model; Obtain the simulation value of the regional light-emitting rate according to the input light flux of each light-receiving area and the output light flux corresponding to the light-emitting area; Obtain the light-emitting rate coefficient according to the simulation value of the regional light-emitting rate, and set the filling rate reference value corresponding to each light-receiving area according to the light-emitting rate coefficient.

[0013] As a preferred embodiment of the present invention, the number of dots is expressed as: , where, represents the number of dots in the i-th light-receiving area, S represents the dot area, represents the dot filling rate of the i-th light-receiving area, represents the width of the light guide plate, represents the spacing of the grid division lines.

[0014] In a second aspect, the present invention further provides a light guide plate, which includes a light incident surface, a light-facing surface, and a light-emitting surface; the light-facing surface and the light-emitting surface are arranged opposite to each other; the light-facing surface is provided with an arc cutting structure and a plurality of light points; the plurality of light points are arranged through a light point distribution model; the arc cutting structure and the light point distribution model are determined by the foregoing method for optimizing the brightness of a backlight light guide plate.

[0015] The beneficial effects of the present invention are as follows: In the embodiment of the present invention, the first optical model determines the arc cutting structure through simulation, and initially ensures the light energy utilization rate through the arc cutting structure; the second optical model dynamically adjusts the light point filling rate in combination with the particle swarm optimization algorithm, significantly improving the brightness and uniformity of the light-emitting surface. By constructing the first optical model and the second optical model in stages and respectively optimizing the arc cutting structure and the light point distribution model, the present invention realizes the systematic optimization of the brightness of the backlight light guide plate, and solves the problems of low light energy utilization rate and uneven brightness of the light guide plate.

[0016] In the embodiment of the present invention, the maximum depth and width of the arc cutting structure are optimized by using Latin hypercube sampling and Gaussian process model, and the optimal solution is quickly converged through the iterative acquisition function. While reducing the number of sample points, the global nature of parameter optimization is ensured, significantly reducing the calculation cost and improving the light flux output efficiency of the arc cutting structure.

[0017] After the light point filling rate is set in the embodiment of the present invention, a local randomization algorithm is introduced to fine-tune the light points, breaking the moiré fringe phenomenon that may be caused by regular arrangement, so as to further optimize the light point distribution with less complexity under limited computing resources, taking into account the requirements of high uniformity and low calculation cost. Description of the Drawings

[0018] The drawings here are incorporated into the specification and form a part of this specification, indicating the embodiments that conform to the present invention, and are used together with the specification to explain the principles of the present invention.

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a schematic plan view of processing light points on a mirror surface or a ground surface in the prior art; Figure 2 It is a schematic plan view of the light-facing surface adopting a combination of an arc cutting structure and light points in the prior art; Figure 3Schematic diagram of a three-dimensional structure of a light-facing surface adopting an arc cutting structure plus dot combination in the prior art; Figure 4 Flowchart of a method for optimizing the brightness of a backlight light guide plate according to an embodiment of the present invention; Figure 5 Flowchart of a method for simulating and determining a dot distribution model through a second optical model according to an embodiment of the present invention; Figure 6 Flowchart of a method for simulating and determining an arc cutting structure through a first optical model according to an embodiment of the present invention. Detailed implementation manners

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] It should be noted that all directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative position relationship and movement conditions between components in a specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0023] In addition, the descriptions involving "first", "second", etc. in the present invention are only for descriptive purposes, and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0024] Embodiment 1 Please refer to Figure 4 and Figure 5 , the present invention provides a method for optimizing the brightness of a backlight light guide plate, including the steps of: Construct a first optical model according to the light guide plate structure parameters, and determine the arc cutting structure through simulation by the first optical model; Construct a second optical model according to the light guide plate structure parameters and the arc cutting structure, and determine the dot distribution model through simulation by the second optical model; The step of determining the dot distribution model through simulation by the second optical model includes the steps of: The light-receiving surface and the light-emitting surface of the second optical model are respectively divided into a plurality of light-receiving regions and light-emitting regions by a plurality of grid division lines; The particle swarm optimization algorithm is used to obtain the dot filling rate of each light-receiving region; The number of dots is set according to the dot filling rate of each light-receiving region to obtain a dot distribution model.

[0025] In the present invention, the first optical model determines the arc cutting structure through simulation, and preliminarily ensures the light energy utilization rate through the arc cutting structure; the second optical model dynamically adjusts the dot filling rate in combination with the particle swarm optimization algorithm, significantly improving the brightness and uniformity of the light-emitting surface. By constructing the first optical model and the second optical model in stages, and respectively optimizing the arc cutting structure and the dot distribution model, the present invention realizes the systematic optimization of the brightness of the backlight light guide plate, and solves the problems of low light energy utilization rate and uneven brightness in the design of the light guide plate.

[0026] Specifically, the following content is used to elaborate on each step of an optimization method for the brightness of a backlight light guide plate in Embodiment 1 of the present invention: An optimization method for the brightness of a backlight light guide plate includes the steps of: S1. Construct a first optical model according to the light guide plate structure parameters, and determine the arc cutting structure through simulation of the first optical model; The light guide plate structure parameters include the three-dimensional dimensions of the light guide plate and the light source position; Constructing the first optical model according to the light guide plate structure parameters specifically includes: selecting a vertex of the light guide plate as the origin to construct a three-dimensional coordinate system of the light guide plate, and the coordinates in the light guide plate are represented by (x, y, z). The x-axis represents the width direction of the light guide plate, the y-axis represents the length direction of the light guide plate, and the z-axis represents the height direction of the light guide plate. 0 ≤ x ≤ W, 0 ≤ y ≤ L, 0 ≤ z ≤ H, where W, L, and H respectively represent the width, length, and height of the three-dimensional dimensions. Based on the three-dimensional coordinate system of the light guide plate, it is set that z = 0 represents the light-receiving surface of the light guide plate, and z = H represents the light-emitting surface of the light guide plate. In this embodiment, the light source is set on the first light-incident surface x = 0, and can also be set on the first light-incident surface x = 0 and the second light-incident surface x = L.

[0027] By defining the three-dimensional dimensions of the light guide plate and the light source position in the three-dimensional coordinate system of the light guide plate, a mathematical basis is provided for the construction of the first optical model. The flexibility of the light source to be set unidirectionally or bidirectionally adapts to the requirements of different application scenarios, enhancing the versatility and adaptability of the method.

[0028] The arc cutting structure in this embodiment is based on a sine curve, and the arc cutting structure is expressed as: ; Wherein, represents the cutting depth of the light-incident surface of the light guide plate, A represents the maximum depth of the arc cutting structure, and D represents the width of the arc cutting structure. represents the phase of the arc cutting structure (which can be default set to 0).

[0029] Based on the above content, when designing the arc cutting structure, the key lies in optimizing the settings of the maximum depth A and the width D according to the structural parameters of the light guide plate. In this embodiment, through the simulation of the first optical model, it helps to evaluate the influence of different structural schemes on the light flux distribution, and then optimize the design of the arc cutting structure of the light guide plate.

[0030] In one embodiment, please refer to Figure 6 , the method for determining the arc cutting structure by simulating through the first optical model includes the steps of: S11. Set the light flux of the light-incident surface of the first optical model; In this embodiment, the light source position can usually be set at x = 0 on the single-sided light-incident surface of the first optical model, or can also be set at x = 0 and x = L on the double-sided light-incident surfaces. By setting the light flux of the light-incident surface, it can provide the initial conditions for the optical simulation. The light flux refers to the light energy passing through a certain surface of the light guide plate per unit time, which determines the total light amount that the light guide plate can output.

[0031] S12. Initialize and generate a number of sample points within the value ranges of the maximum depth and the width through Latin hypercube sampling, and construct a sample data set and a Gaussian process model according to the number of sample points; Generate multiple sample points within the value ranges of the maximum depth (A) and the width (D) through the Latin hypercube sampling method. For example, generate 20 groups of initial parameter combinations. The Latin hypercube sampling method ensures uniform sampling of the parameter space, which helps to obtain diverse optical performance data. These sample points will be used for subsequent Gaussian process modeling and optimization. Then, construct a Gaussian Process (GP) model, and the Gaussian process model can provide the predicted value of the first total light output rate in the subsequent steps.

[0032] S13. Obtain the light flux of the light-emitting surface corresponding to each sample point in the sample data set through the first optical model, and calculate the simulation value of the first total light output rate corresponding to the sample point according to the light flux of the light-emitting surface and the light flux of the light-incident surface; In this embodiment, the first optical model is constructed based on optical simulation software (such as LightTools, TracePro), and the calculation of the light flux of the light-emitting surface and the light flux of the light-incident surface is completed through the optical simulation software. During the implementation process, automatically run the simulation for all sample (A, D) combinations in the sample data set, and use parallel computing (such as a multi-node cluster) to accelerate the ray tracing and reduce the single simulation time. Output and save it to the database.

[0033] The simulation value of the first total light extraction efficiency is expressed as: ; Wherein, represents the light flux of the light extraction surface corresponding to the sample point and represents the light flux of the light incident surface.

[0034] S14. Optimize the Gaussian process model according to the simulation value of the first total light extraction efficiency corresponding to each sample point; The basic idea of the Gaussian process model is to infer the output value and its uncertainty of an unknown point based on the existing sample point data. In this application, according to the simulation value of the first total light extraction efficiency corresponding to each sample point, the Gaussian process model can train a probability distribution model to predict the first total light extraction efficiency of other unmeasured points and calculate the credibility of the prediction.

[0035] Specifically, the Gaussian process model includes a mean function and a kernel function. Each sample point and its corresponding simulation value of the first total light extraction efficiency are used as known data to optimize the parameters of the kernel function of the Gaussian process model. In one embodiment, the kernel function adopts a radial basis function, and the maximum likelihood estimation is used to optimize the parameters of the kernel function, so that the Gaussian process model has the best fitting degree for the known simulation value of the first total light extraction efficiency.

[0036] S15. Process the Gaussian process model through an acquisition function to obtain the sample point corresponding to the optimal prediction value of the first total light extraction efficiency and add it to the sample data set; In one embodiment, the acquisition function is an expected improvement function (EI), and the acquisition function is expressed as: ; Wherein, represents the expected improvement acquisition function; represents the expected value operation, which is used to integrate the prediction distribution of the first total light extraction efficiency; represents the optimal prediction value of the first total light extraction efficiency.

[0037] Based on the foregoing, the sample point with the maximum first total light extraction efficiency obtained through the acquisition function is expressed as .

[0038] In this embodiment, the expected improvement function quantifies the potential gain of each step in the optimization process, ensuring that the most potential sample point is selected to be added to the data set in each iteration. This optimization strategy based on probability distribution effectively avoids the local optimum trap and improves the efficiency and accuracy of the arc cutting structure optimization.

[0039] S16. Iteratively optimize until the first iteration number is reached or the first convergence condition is satisfied, and determine the arc cutting structure according to the sample point with the largest predicted value of the first total light output rate.

[0040] Iterative optimization refers to repeatedly executing steps S13 - S15. The first iteration number and the first convergence condition are set during the initialization process.

[0041] In the present invention, the maximum depth and width of the arc cutting structure are optimized by using Latin hypercube sampling and Gaussian process model. The iterative acquisition function quickly converges to the optimal solution, which not only reduces the number of sample points but also ensures the global nature of parameter optimization, significantly reduces the computational cost, and improves the light flux output efficiency of the arc cutting structure.

[0042] S2. Construct a second optical model based on the light guide plate structure parameters and the arc cutting structure, and determine the dot distribution model through simulation with the second optical model.

[0043] In the present invention, the first optical model and the second optical model are respectively used to determine the arc cutting structure and the dot distribution model. The light incident surface of the first optical model changes the "overall morphology" of the light incident surface by adjusting the arc cutting structure, adjusts the initial reflection path of light in the light guide plate, and directly affects the total light output efficiency. Based on the determined arc cutting structure, the second optical model finely regulates the local light scattering through the dot distribution, further improves the overall brightness, and solves the problem of uneven brightness.

[0044] In one embodiment, the determining the dot distribution model through simulation with the second optical model includes the steps of: S21. Divide the light incident surface and the light output surface of the second optical model into a number of light incident regions and light output regions respectively by a number of grid division lines; the distances between the grid division lines are equal; The light incident surface is the lower surface of the light guide plate, that is, the plane represented by z = 0 in the second optical model; the light output surface is the upper surface of the light guide plate, that is, the plane represented by z = H in the second optical model. Since the light source in the light guide plate is arranged on the single - side light incident surface where x = 0, therefore, for two points with equal y values in the light incident surface, their illuminance values can be regarded as equal. Therefore, in this embodiment, it is defined that the illuminance value of the dots in the light incident surface only changes with the coordinate x.

[0045] For the sake of simplicity in calculation, in this step, the light incident surface and the light output surface of the second optical model are divided into n regions by n + 1 grid division lines parallel to the y - axis. The grid division lines are represented as , where , , and the distances between the grid division lines are equal, represented as , . It can be understood that the areas of any two light incident regions and light output regions are equal.

[0046] S22. Obtain the dot filling rate of each light-facing area using the particle swarm optimization algorithm; In this embodiment, the particle swarm optimization algorithm is used to simulate the flight of a number of particles in the solution space to find the optimal solution. Each particle represents a possible solution, and the global optimal solution is found by updating the position and velocity. In this process, each particle moves in the search space and adjusts its velocity and position according to its own experience and the experience of the global optimal particle.

[0047] Further, the step of obtaining the dot filling rate of each light-facing area using the particle swarm optimization algorithm includes the steps: S221. Initialize the particle swarm parameters; the particle swarm parameters include the position and velocity of each particle; the position of the particle represents the dot filling rate vector; In this embodiment, the particle swarm parameters include the number of particles, the position and velocity of the particles, the maximum number of iterations, the inertia weight, the individual learning factor, and the swarm learning factor. The inertia weight is used to control the inertia of the particle at the current position, the individual learning factor is used to control the attraction of the particle to its own optimal solution, and the swarm learning factor is used to control the attraction of the particle to the global optimal solution. The position of the particle represents the dot filling rate vector, denoted as , is the dot filling rate of the i-th light-facing area, = 1, 2,..., n. The velocity of the particle is used to determine the direction and amplitude of the particle's movement in the solution space. Based on the foregoing representation method of the grid dividing line, the i-th light-facing area is and The light-facing area between.

[0048] In this step, the dot filling rate vector represented by the initial position of the particle can be randomly generated within the dot filling rate range corresponding to the light-facing area. The preset range is set by the minimum and maximum values of the dot filling rate of each light-facing area.

[0049] Further, initializing the particle swarm parameters includes setting the initial position of each particle; the initial position of the particle satisfies the dot filling rate range corresponding to each light-facing area; the dot filling rate range corresponding to each light-facing area is set by the floating coefficient and the filling rate reference value.

[0050] If the light source is set on the single-sided light-incident surface, according to prior knowledge, the filling rate reference value satisfies . The preset range of the light-facing area is expressed as , where is the floating coefficient, usually taking values in [0.1 - 0.4].

[0051] If the light source is symmetrically set on the double-sided light-incident surface, according to prior knowledge, the filling rate reference value satisfies Monotonically increasing within Monotonically decreasing within, and .

[0052] In one embodiment, the method for obtaining the reference value of the filling rate is as follows: S2211. Set the light flux of the incident light surface of the second optical model, and obtain the input light flux of each light-receiving area and the output light flux corresponding to the light-emitting area through the second optical model; S2212. Obtain the simulated value of the regional light extraction rate according to the input light flux of each light-receiving area and the output light flux corresponding to the light-emitting area; The simulated value of the regional light extraction rate is expressed as: ; Wherein, Represents the simulated value of the regional light extraction rate of the i-th light-emitting area, Represents the output light flux of the i-th light-emitting area, Is the input light flux of the i-th light-receiving area.

[0053] S2213. Obtain the light extraction rate coefficient according to the simulated value of the regional light extraction rate, and set the reference value of the filling rate corresponding to each light-receiving area according to the light extraction rate coefficient.

[0054] The light extraction rate coefficient is expressed as , and based on the light extraction rate coefficient, the reference value of the filling rate corresponding to each light-receiving area is expressed as: ; Wherein, Represents the simulated value of the regional light extraction rate of the i-th light-emitting area, Is a preset filling rate constant; n represents the number of light-receiving areas; Represents the minimum value of the simulated value of the regional light extraction rate.

[0055] In one embodiment, the light source is arranged on the single-sided incident light surface, ; In another embodiment, the light sources are symmetrically arranged on the double-sided incident light surfaces, , where And Respectively represent Rounding down and rounding up for

[0056] Based on the basic principle of the light guide plate, and the areas of any two light-receiving regions and light-emitting regions are equal. Then, in an ideal state, the relationship between the regional light-emitting rate of the light-emitting region between the same two grid division lines and the filling rate of the light-receiving region should be approximately linear. Therefore, after determining the ratio relationship between the filling rate reference values corresponding to the light-receiving regions through the light-emitting rate coefficient, a preset filling rate constant can be set according to prior knowledge, that is, the filling rate reference value corresponding to the light-receiving region farthest from the point light source. Then, the filling rate reference value corresponding to each light-receiving region is determined according to the ratio relationship between the preset filling rate constant and the filling rate reference value corresponding to the light-receiving region.

[0057] However, due to the complex influence of the arc cutting structure and the dot pattern on the light path, as well as the theoretical error caused by the grid division accuracy, the filling rate reference value is not the optimal solution of the dot pattern filling rate. Therefore, the dot pattern filling rate is optimized through subsequent particle swarm optimization steps.

[0058] S222. Obtain the objective function value corresponding to the position of each particle; the objective function value is expressed as: ; Wherein, represents the output light flux of the i-th light-emitting region, represents the average value of the output light fluxes of all light-emitting regions; n represents the number of light-receiving regions, and are the first weighting coefficient and the second weighting coefficient respectively.

[0059] In the formula, the of the first term represents the light flux on the light-emitting surface, that is, the sum of the output light fluxes of all light-emitting regions; the of the second term represents the variance of the output light flux, which is used to measure the light-emitting uniformity. In this embodiment, the importance of the light flux on the light-emitting surface and the light-emitting uniformity in the objective function is weighed through the weighting coefficients and .

[0060] Further, the obtaining the objective function value corresponding to the position of each particle includes the steps of: S2221. Update the second optical model according to the position of the particle, and obtain the output light flux corresponding to each light-emitting region through the second optical model; S2222. Calculate the light flux on the light-emitting surface and the variance of the output light flux according to the output light flux corresponding to each light-emitting region, and calculate the objective function value.

[0061] In this embodiment, through the mapping relationship between the simulated regional light-emitting rate value and the filling rate reference value, a scientific initial solution range is provided for the particle swarm optimization algorithm. Combining the single-sided / double-sided light source position method to dynamically adjust the filling rate distribution trend greatly reduces the search space of the optimization algorithm and improves the convergence speed.

[0062] S223. Update the update velocity and position of each particle according to the objective function value; Update the update velocity and position of each particle according to the objective function value, specifically: update the personal best position and the global best position of each particle according to the objective function value, and update the velocity and position according to the personal best position and the global best position.

[0063] The personal best position refers to the position corresponding to the minimum objective function value among the positions experienced by the particle in history. Each particle will save this information during the search process as its best position in history. The global best position refers to the position with the minimum objective function value among the individual optimal positions of all particles in the entire particle swarm. The global best position is global information, and all particles in the particle swarm will use the global best position to update their velocities and positions. After obtaining the objective function value, compare the current objective function value of the particle with the objective function value corresponding to the personal best position. If the current objective function value is larger, then set the current position of the particle as the personal best position. Then, compare the objective function values corresponding to the personal best positions of all particles, and set the personal best position with the largest objective function value as the global best position.

[0064] Based on the foregoing, the velocity of the particle is expressed as: , The position of the particle is expressed as: = + , where is the inertia weight, used to represent the influence of the velocity of the previous generation of particles on the velocity of the current generation of particles; t represents the iteration round; j represents the serial number of the particle; specifically represents the velocity of the i-th particle at the t-th iteration; specifically represents the position of the i-th particle at the t-th iteration; and are respectively the preset individual learning factor and the global learning factor, represents the personal best position of the particle, and g represents the global best position. and are both random numbers within the interval [0, 1], used to provide randomness for each iteration.

[0065] S224. Iteratively optimize until the second iteration number is reached or the objective function value is less than the set threshold, and output the global best position as the dot filling rate of each light-receiving area.

[0066] The iterative optimization specifically involves repeatedly executing steps S222 to S224. The second iteration count and the objective function value are set while initializing the particle swarm parameters.

[0067] In this embodiment, the particle swarm optimization algorithm is used to dynamically adjust the dot filling rate, and the swarm intelligence is utilized to quickly approach the global optimal solution. The initialization parameters are combined with the filling rate range and reference values to ensure that the algorithm operates efficiently within a reasonable search space, avoiding the blindness of the traditional trial-and-error method and significantly shortening the optimization cycle.

[0068] S23. Set the number of dots according to the dot filling rate of each light-receiving area to obtain a dot distribution model; The number of dots is expressed as: ; Where, represents the number of dots in the i-th light-receiving area, S represents the area of a single dot, represents the dot filling rate of the i-th light-receiving area, represents the width of the light guide plate, represents the spacing of the grid division lines.

[0069] The dot distribution model includes the dot filling rate of each light-receiving area. Based on the dot filling rate, it is possible to choose to set regularly arranged dots or irregularly arranged dots within the light-receiving area.

[0070] In the actual application process, when the grid division accuracy is low, that is, the number of grid areas is small, regularly arranged dots are likely to form moiré fringes. However, if the number of set grid areas is large, making the grid division accuracy in the simulation process higher, it will result in greater consumption of computing resources.

[0071] To solve this problem, in one embodiment, after setting the number of dots according to the dot filling rate of each light-receiving area, it further includes: S24. Set the regular arrangement of dots in each light-receiving area according to the number of dots, and use the local randomization algorithm to process each light-receiving area to obtain a dot distribution model.

[0072] The purpose of the local randomization algorithm is to generate a new dot pattern distribution by means of random perturbation based on the current dot pattern distribution. In this step, the local randomization algorithm makes small random adjustments near the current solution. Specifically, the spacing between adjacent dots in each light-facing area with regular dot arrangement is equal. The local randomization algorithm makes small-range random perturbations to the dots in each light-facing area to change the positions of the dots, and then uses the second optical model to simulate the perturbed dot pattern distribution model and calculate the objective function value. If the objective function value is better, the dot pattern distribution model is accepted as the dot pattern distribution model. It should be noted that the objective function value corresponding to the dot pattern distribution model obtained by processing with the local randomization algorithm is not necessarily significantly better than the objective function value corresponding to the dot pattern distribution model output by the particle swarm optimization algorithm in the previous step. However, due to the limitation of the grid division accuracy on computing resources, the local randomization algorithm can quickly eliminate the moiré fringes formed by regularly arranged dots, and further make the dot pattern distribution model more reasonable with a relatively small computational complexity. In this embodiment, after setting the dot filling rate, the local randomization algorithm is introduced to fine-tune the dots to break the moiré fringe phenomenon that may be caused by regular arrangement. Under limited computing resources, this embodiment further optimizes the dot pattern distribution with a relatively small complexity, taking into account the requirements of high uniformity and low computational cost.

[0073] Embodiment 2 The present invention also provides a light guide plate, which includes a light incident surface, a light-facing surface, and a light-emitting surface; the light-facing surface and the light-emitting surface are oppositely arranged; the light-facing surface is provided with an arc cutting structure and a plurality of dots; the plurality of dots are arranged through a dot pattern distribution model; the arc cutting structure and the dot pattern distribution model are determined by the foregoing optimization method for the brightness of a backlight light guide plate.

[0074] The arc cutting structure and the dot pattern distribution model of the light guide plate provided in this embodiment are highly matched with the optical simulation results in physical implementation, and have the characteristics of high light efficiency, high uniformity, and low moiré fringes. They are applicable to high-precision backlight modules and can significantly improve the user experience.

[0075] In the embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or component libraries can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the modules can be in electrical, mechanical, or other forms.

[0076] The module described as a separation component may or may not be physically separated. The component shown as a module may or may not be a physical module, that is, it may be located in one place or distributed across multiple grid modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0077] In addition, in each embodiment of the present application, each functional module can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module.

[0078] If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a grid device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media such as USB flash drives, dynamic hard disks, read-only memories (ROM, read-only memory), random access memories (RAM, random access memory), magnetic disks, or optical discs that can store program codes.

[0079] The above are only specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. An optimization method for the brightness of a backlight light guide plate, characterized in that: Including the steps: Construct a first optical model according to the light guide plate structure parameters, and determine the arc cutting structure through simulation of the first optical model; Construct a second optical model according to the light guide plate structure parameters and the arc cutting structure, and determine the dot distribution model through simulation of the second optical model; The step of determining the dot distribution model through simulation of the second optical model includes the steps: Divide the light incident surface and the light emitting surface of the second optical model into a plurality of light incident regions and light emitting regions respectively through a plurality of grid division lines; Use the particle swarm optimization algorithm to obtain the dot filling rate of each light incident region; Set the number of dots according to the dot filling rate of each light incident region to obtain the dot distribution model.

2. The optimization method for the brightness of a backlight light guide plate according to claim 1, wherein: The light guide plate structure parameters include the three-dimensional size of the light guide plate and the light source position; The specific method of constructing the first optical model according to the light guide plate structure parameters is: select a vertex of the light guide plate as the origin to construct a three-dimensional coordinate system of the light guide plate; set z = 0 as the light incident surface of the light guide plate, and z = H as the light emitting surface of the light guide plate; set the light source position on the single-sided light incident surface where x = 0, or set it on the first light incident surface x = 0 and the second light incident surface x = L.

3. The optimization method for the brightness of a backlight light guide plate according to claim 1, wherein: The step of determining the arc cutting structure through simulation of the first optical model includes the steps: Set the light flux of the light incident surface of the first optical model; Initialize and generate a plurality of sample points within the value range of the maximum depth and width through Latin hypercube sampling, and construct a sample data set and a Gaussian process model according to the plurality of sample points; Obtain the light flux of the light emitting surface corresponding to each sample point in the sample data set through the first optical model, and calculate the simulation value of the first total light emission rate corresponding to the sample point according to the light flux of the light emitting surface and the light flux of the light incident surface; Process the Gaussian process model through the acquisition function to obtain the sample point corresponding to the optimal predicted value of the first total light emission rate and add it to the sample data set; Iteratively optimize until the first iteration number is reached or the first convergence condition is satisfied, and determine the arc cutting structure according to the sample point with the largest predicted value of the first total light emission rate output.

4. The optimization method for the brightness of a backlight light guide plate according to claim 3, characterized in that: The acquisition function is expressed as: ， Among them, represents the expected improvement acquisition function; represents the expected value operation, which is used to integrate the predictive distribution of the first total light output rate; represents the simulation value of the first total light output rate; represents the optimal predicted value of the first total light output rate.

5. The optimization method for the brightness of a backlight light guide plate according to claim 1, wherein: After setting the number of dots according to the dot filling rate of each light incident region, it further includes: setting the regular arrangement of dots in each light incident region according to the number of dots, and processing each light incident region with the local randomization algorithm to obtain the dot distribution model.

6. The optimization method for the brightness of a backlight light guide plate according to claim 1, wherein: The step of using the particle swarm optimization algorithm to obtain the dot filling rate of each light incident region includes the steps: Initialize the particle swarm parameters; the particle swarm parameters include the position and velocity of each particle; the position of the particle represents the dot filling rate vector; Obtain the objective function value corresponding to the position of each particle; Update the update velocity and position of each particle according to the objective function value; Iteratively optimize until the second iteration number is reached or the objective function value is less than the set threshold, and output the global best position as the dot filling rate of each light incident region.

7. An optimization method for the brightness of a backlight light guide plate according to claim 6, characterized in that: The step of obtaining the objective function value corresponding to the position of each particle includes the steps: Update the second optical model according to the position of the particle, and obtain the output light flux corresponding to each light emitting region through the second optical model; Calculate the light flux of the light emitting surface and the variance of the output light flux according to the output light flux corresponding to each light emitting region, and calculate the objective function value; The objective function value is expressed as: ， Among them, represents the output luminous flux of the i-th light-emitting area, represents the average value of the output luminous fluxes of all light-emitting areas; n represents the number of light-facing areas; and are the first weighting coefficient and the second weighting coefficient respectively.

8. The optimization method for the brightness of a backlight light guide plate according to claim 6, characterized in that: The initialized particle swarm parameters include setting the initial position of each particle; the initial position of the particle satisfies the dot filling rate range corresponding to each light-receiving area; the dot filling rate range corresponding to each light-receiving area is set by a floating coefficient and a filling rate reference value; The obtaining method of the filling rate reference value is as follows: Set the light flux on the light-incident surface of the second optical model, and obtain the input light flux of each light-receiving area and the output light flux corresponding to the light-emitting area through the second optical model; Obtain the simulation value of the area light-emitting rate according to the input light flux of each light-receiving area and the output light flux corresponding to the light-emitting area; Obtain the light-emitting rate coefficient according to the simulation value of the area light-emitting rate, and set the filling rate reference value corresponding to each light-receiving area according to the light-emitting rate coefficient.

9. The optimization method for the brightness of a backlight light guide plate according to claim 1, wherein: The number of dots is expressed as: ， Among them, represents the number of dots in the i-th light-receiving area, S represents the dot area, represents the dot filling rate of the i-th light-receiving area, represents the width of the light guide plate, represents the spacing of the grid division lines.

10. A light guide plate, characterized in that: It includes a light-incident surface, a light-receiving surface and a light-emitting surface; the light-receiving surface and the light-emitting surface are arranged opposite to each other; the light-receiving surface is provided with an arc cutting structure and a plurality of dots; the plurality of dots are set by a dot distribution model; the arc cutting structure and the dot distribution model are determined by an optimization method for the brightness of a backlight light guide plate according to any one of claims 1-9.

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