A method for optimizing brightness of backlight guide plate

Abstract

The purpose of the present invention is to provide a method for optimizing the brightness of a backlight guide plate, which relates to the technical field of light guide plates. The method comprises the steps of: constructing a first optical model based on the structural parameters of the light guide plate, and performing simulation to determine the arc cutting structure using the first optical model; constructing a second optical model based on the structural parameters of the light guide plate and the arc cutting structure, and performing simulation to determine the dot distribution model using the second optical model. By constructing the first optical model and the second optical model in stages, respectively optimizing the arc cutting structure and the dot distribution model, the present invention achieves systematic optimization of the brightness of the backlight guide plate, thereby solving the problems of low light energy utilization and uneven brightness of the light guide plate.

Description

Technical Field

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

[0002] The light guide plate is the core component of the backlight module, and its optical performance directly determines the brightness and uniformity of the backlight module. Figure 1 As shown in FIG, the light-facing surface of a conventional light guide plate adopts a structure in which dots are machined on a mirror or polished surface. Due to the reflection of this structure, a large amount of light is totally reflected within the light guide plate and cannot be effectively guided out, resulting in low light energy utilization and affecting the display effect.

[0003] To achieve brightness improvement and uniformity optimization, such as Figure 2 and Figure 3 As shown, one existing solution is to use a combination of an arc-cut structure (R-CUT) and a dot pattern on the light-facing surface. Specifically, the arc-cut structure is first machined on the light-facing surface, and then the dots are distributed over it. This approach uses the arc-cut structure to initially guide the light path, and then fine-tunes the light distribution using the dots, improving light energy utilization to a certain extent. However, the parameters of the arc-cut structure (such as depth and width) and the dot pattern distribution are not optimized in a coordinated manner, resulting in high light flux loss and localized brightness unevenness in practical applications. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for optimizing the brightness of a backlight light guide plate, to achieve systematic optimization of the brightness of the backlight light guide plate, and to solve the problems of low light energy utilization and uneven brightness in the design of the light guide plate.

[0005] In a first aspect, the present invention provides a method for optimizing the brightness of a backlight light guide plate, comprising the steps of:

[0006] Constructing a first optical model according to the structural parameters of the light guide plate, and determining the arc cutting structure through simulation using the first optical model;

[0007] Constructing a second optical model based on the structural parameters of the light guide plate and the arc cutting structure, and determining a dot distribution model through simulation using the second optical model;

[0008] The method of determining the dot distribution model by simulating the second optical model includes the steps of:

[0009] Dividing the light-facing surface and the light-emitting surface of the second optical model into a plurality of light-facing areas and light-emitting areas respectively by a plurality of grid division lines;

[0010] Use particle swarm optimization algorithm to obtain the dot filling rate of each light-facing area;

[0011] The number of dots is set according to the dot filling rate of each light-facing area to obtain a dot distribution model.

[0012] As a preferred solution of the present invention, the structural parameters of the light guide plate include the three-dimensional size of the light guide plate and the position of the light source;

[0013] The first optical model is constructed according to the structural parameters of the light guide plate, specifically: a vertex of the light guide plate is selected as the origin to construct a three-dimensional coordinate system of the light guide plate; z=0 is set as the light-facing surface of the light guide plate, and z=H is set as the light-emitting surface of the light guide plate; the light source position is set to the single-sided light incident surface of x=0, or to the first light incident surface x=0 and the second light incident surface x=L.

[0014] As a preferred solution of the present invention, the method of determining the arc cutting structure by simulating the first optical model includes the steps of:

[0015] Setting the luminous flux of the incident surface of the first optical model;

[0016] Initialize and generate a number of sample points within a maximum depth and width range through Latin hypercube sampling, and construct a sample data set and a Gaussian process model based on the sample points;

[0017] Obtaining the light-exiting surface luminous flux corresponding to each sample point in the sample data set through a first optical model, and calculating a first total light-exiting rate simulation value corresponding to the sample point according to the light-exiting surface luminous flux and the light-incident surface luminous flux;

[0018] Processing the Gaussian process model through an acquisition function to obtain sample points corresponding to the optimal predicted value of the first total light output rate and adding the sample points to the sample data set;

[0019] Iterative optimization is performed until a first number of iterations is reached or a first convergence condition is satisfied, and an arc cutting structure is determined according to a sample point outputting the maximum first total light output rate prediction value.

[0020] As a preferred solution of the present invention, the acquisition function is expressed as:

[0021] ,

[0022] in, Indicates the expectation to improve the acquisition function; represents an expected value operation, which is used to integrate the predicted distribution of the first total light extraction rate; represents a first total light output rate simulation value; Indicates the optimal predicted value of the first total light output rate.

[0023] As a preferred solution of the present invention, after setting the number of dots according to the dot filling rate of each light-facing area, it also includes: setting the regular arrangement of dots in each light-facing area according to the number of dots, and using a local randomization algorithm to process each light-facing area to obtain a dot distribution model.

[0024] As a preferred solution of the present invention, the method of obtaining the dot filling rate of each light-facing area using a particle swarm optimization algorithm includes the following steps:

[0025] Initialize 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;

[0026] Get the objective function value corresponding to the position of each particle;

[0027] Update the updated speed and position of each particle according to the objective function value;

[0028] Iterate the optimization until the second iteration number is reached or the objective function value is less than the set threshold, and output the optimal position of the group as the dot filling rate of each light-facing area.

[0029] As a preferred solution of the present invention, the step of obtaining the objective function value corresponding to the position of each particle includes the following steps:

[0030] Update the second optical model according to the position of the particles, and obtain the output luminous flux corresponding to each light-emitting area through the second optical model;

[0031] Calculate the light output surface luminous flux and the output luminous flux variance according to the output luminous flux corresponding to each light output area, and calculate the objective function value;

[0032] The objective function value is expressed as:

[0033] ,

[0034] in, represents the output luminous flux of the i-th light-emitting area, It represents the average output luminous flux 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.

[0035] As a preferred embodiment of the present invention, the initialization of the particle group 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 a floating coefficient and a filling rate reference value;

[0036] The filling rate reference value is obtained as follows:

[0037] Setting the light incident surface luminous flux of the second optical model, and obtaining the input luminous flux of each light-facing area and the output luminous flux corresponding to the light-emitting area through the second optical model;

[0038] Obtain the regional light output rate simulation value according to the input light flux of each light-facing area and the output light flux corresponding to the light-emitting area;

[0039] A light extraction coefficient is obtained according to the light extraction coefficient of the area, and a fill rate reference value corresponding to each light-facing area is set according to the light extraction coefficient.

[0040] As a preferred solution of the present invention, the number of network points is expressed as:

[0041] ,

[0042] in, represents the number of dots in the i-th light-facing area, S represents the dot area, represents the dot filling rate of the i-th light-facing area, Indicates the width of the light guide plate, Indicates the spacing between grid lines.

[0043] In the second aspect, the present invention also provides a light guide plate, comprising a light incident surface, a light facing surface and a light emitting surface; the light facing surface and the light emitting surface are arranged relative to each other; 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 distribution model; the arc cutting structure and the dot distribution model are determined by the aforementioned method for optimizing the brightness of a backlight light guide plate.

[0044] The beneficial effects of the present invention are:

[0045] In this embodiment of the present invention, a first optical model is used to determine the arc cutting structure through simulation, which initially ensures light energy utilization. A second optical model, combined with a particle swarm optimization algorithm, dynamically adjusts the dot fill rate, significantly improving the brightness and uniformity of the light-emitting surface. By constructing the first and second optical models in stages, respectively optimizing the arc cutting structure and dot distribution model, the present invention achieves systematic optimization of the backlight light guide plate's brightness, resolving the issues of low light energy utilization and uneven brightness of the light guide plate.

[0046] The embodiment of the present invention optimizes the maximum depth and width of the arc cutting structure by adopting Latin hypercube sampling and Gaussian process model, and quickly converges to the optimal solution through iterative acquisition function. While reducing the number of sample points, it ensures the globality of parameter optimization, significantly reduces the computational cost, and improves the luminous flux output efficiency of the arc cutting structure.

[0047] After the dot fill rate is set, the embodiment of the present invention introduces a local randomization algorithm to fine-tune the dots, breaking the moiré phenomenon that may be caused by regular arrangement, thereby further optimizing the dot distribution with less complexity under limited computing resources, taking into account the requirements of high uniformity and low computing cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0050] Figure 1 A schematic diagram of the plane structure of processing dots on a mirror surface or a polished surface in the prior art;

[0051] Figure 2 This is a schematic diagram of the plane structure of the light-facing surface using an arc cutting structure plus a dot combination in the prior art;

[0052] Figure 3 This is a schematic diagram of the three-dimensional structure of the light-facing surface using an arc cutting structure plus a dot combination in the prior art;

[0053] Figure 4 1 is a flow chart of a method for optimizing the brightness of a backlight light guide plate according to an embodiment of the present invention;

[0054] Figure 5 Schematic diagram of a process for determining a dot distribution model by simulating a second optical model according to an embodiment of the present invention;

[0055] Figure 6 FIG. 1 is a flow chart of determining an arc cutting structure by simulating a first optical model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0057] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0058] In addition, the descriptions of "first", "second", etc. in the present invention are for descriptive purposes only and should not be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0059] Example 1

[0060] Please refer to Figure 4 and Figure 5 The present invention provides a method for optimizing the brightness of a backlight guide plate, comprising the steps of:

[0061] Constructing a first optical model according to the structural parameters of the light guide plate, and determining the arc cutting structure through simulation using the first optical model;

[0062] Constructing a second optical model based on the structural parameters of the light guide plate and the arc cutting structure, and determining a dot distribution model through simulation using the second optical model;

[0063] The method of determining the dot distribution model by simulating the second optical model includes the steps of:

[0064] Dividing the light-facing surface and the light-emitting surface of the second optical model into a plurality of light-facing areas and light-emitting areas respectively by a plurality of grid division lines;

[0065] Use particle swarm optimization algorithm to obtain the dot filling rate of each light-facing area;

[0066] The number of dots is set according to the dot filling rate of each light-facing area to obtain a dot distribution model.

[0067] This invention uses a first optical model to determine the arc cutting structure through simulation, which initially ensures light energy utilization. A second optical model, combined with a particle swarm optimization algorithm, dynamically adjusts the dot fill rate, significantly improving the brightness and uniformity of the light-emitting surface. By constructing the first and second optical models in stages, optimizing the arc cutting structure and dot distribution model respectively, this invention achieves systematic optimization of the backlight guide plate brightness, resolving the issues of low light energy utilization and uneven brightness in light guide plate design.

[0068] Specifically, the steps of a method for optimizing the brightness of a backlight guide plate according to embodiment 1 of the present invention are described in detail through the following content:

[0069] A method for optimizing the brightness of a backlight guide plate comprises the following steps:

[0070] S1. Constructing a first optical model according to the structural parameters of the light guide plate, and performing simulation to determine the arc cutting structure through the first optical model;

[0071] The structural parameters of the light guide plate include the three-dimensional size of the light guide plate and the position of the light source;

[0072] A first optical model is constructed based on the structural parameters of the light guide plate. Specifically, a vertex of the light guide plate is selected as the origin to construct a three-dimensional coordinate system for the light guide plate. The coordinates in the light guide plate are represented by (x, y, z), where 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≤x≤H, where W, L, and H represent the width, length, and height of the three-dimensional dimensions, respectively. Based on the three-dimensional coordinate system of the light guide plate, z=0 is set to represent the light-facing surface of the light guide plate, and z=H represents the light-emitting surface of the light guide plate. The light source of this embodiment is set at the first light incident surface x=0, and can also be set at the first light incident surface x=0 and the second light incident surface x=L.

[0073] By defining the three-dimensional dimensions of the light guide plate and the position of the light source within the light guide plate's three-dimensional coordinate system, a mathematical foundation is provided for constructing the first optical model. The flexibility of unilateral or bilateral light source placement allows for adaptability to diverse application scenarios, enhancing the method's versatility and adaptability.

[0074] The arc cutting structure of this embodiment is based on a sine curve, and the arc cutting structure is expressed as follows:

[0075] ;

[0076] in, Indicates the cutting depth of the light guide plate facing the light surface, A indicates the maximum depth of the arc cutting structure, and D indicates the width of the arc cutting structure. Indicates the phase of the arc cutting structure (can be set to 0 by default).

[0077] Based on the above, the key to designing the arc-cut structure is to optimize the maximum depth A and width D according to the structural parameters of the light guide plate. This embodiment uses the first optical model simulation to help evaluate the impact of different structural solutions on the luminous flux distribution, thereby optimizing the arc-cut structure design of the light guide plate.

[0078] In one embodiment, please refer to Figure 6 The method of simulating and determining the arc cutting structure by using the first optical model comprises the steps of:

[0079] S11, setting the luminous flux of the incident surface of the first optical model;

[0080] In this embodiment, the light source position can generally be set at x=0 on the single-side incident surface of the first optical model, or x=0 and x=L on both sides. By setting the luminous flux at the incident surface, initial conditions can be provided for the optical simulation. Luminous flux refers to the amount of light energy passing through a specific surface of the light guide plate per unit time, and determines the total amount of light that the light guide plate can output.

[0081] S12. Initializing and generating a number of sample points within the range of maximum depth and width through Latin hypercube sampling, and constructing a sample data set and a Gaussian process model based on the sample points;

[0082] Latin hypercube sampling is used to generate multiple sample points within the maximum depth (A) and width (D) range, for example, generating 20 initial parameter combinations. This method ensures uniform sampling of the parameter space, helping to obtain diverse optical performance data. These sample points are then used for subsequent Gaussian process modeling and optimization. A Gaussian process (GP) model is then constructed, which provides a first prediction of the total light output in subsequent steps.

[0083] S13, obtaining the light-exiting surface luminous flux corresponding to each sample point in the sample data set through the first optical model, and calculating a first total light extraction rate simulation value corresponding to the sample point according to the light-exiting surface luminous flux and the light-incident surface luminous flux;

[0084] 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 on the light surface and the light incident surface is completed by the optical simulation software. During the implementation process, the simulation is automatically run for all sample (A, D) combinations in the sample data set, and parallel computing (such as multi-node clustering) is used to accelerate ray tracing and reduce the time of a single simulation. Output And save it to the database.

[0085] The first total light extraction rate simulation value is expressed as:

[0086] ;

[0087] in, Represents sample points The corresponding luminous flux of the light-emitting surface is Indicates the luminous flux of the incident surface.

[0088] S14, optimizing the Gaussian process model according to the first total light extraction rate simulation value corresponding to each sample point;

[0089] The basic idea of ​​the Gaussian process model is to infer the output value and its uncertainty at an unknown point based on the existing sample point data. In this application, based on the simulated first total light output rate value corresponding to each sample point, the Gaussian process model can be trained to obtain a probability distribution model to predict the first total light output rate of other unmeasured points and calculate the reliability of this prediction.

[0090] Specifically, the Gaussian process model includes a mean function and a kernel function. Each sample point and its corresponding first total light output rate simulated value are used as known data to optimize the parameters of the Gaussian process model's kernel function. In one embodiment, the kernel function employs a radial basis function, and maximum likelihood estimation is used to optimize the kernel function parameters to achieve optimal Gaussian process model fit to the known first total light output rate simulated value.

[0091] S15. Processing the Gaussian process model through an acquisition function to obtain sample points corresponding to the first optimal predicted value of the total light output efficiency and adding the sample points to the sample data set;

[0092] In one embodiment, the acquisition function is an expected improvement function (EI), which is expressed as:

[0093] ;

[0094] in, Indicates the expectation to improve the acquisition function; represents an expected value operation, which is used to integrate the predicted distribution of the first total light extraction rate; Indicates the optimal predicted value of the first total light output rate.

[0095] Based on the above content, the sample point with the largest first total light output rate obtained by the acquisition function is expressed as .

[0096] This example quantifies the potential gain at each step in the optimization process using an expected improvement function, ensuring that the most promising sample points are selected for inclusion in the dataset at each iteration. This probability-based optimization strategy effectively avoids local optimality and improves the efficiency and accuracy of arc cutting structure optimization.

[0097] S16. Iterate the optimization until a first number of iterations is reached or a first convergence condition is satisfied, and determine the arc cutting structure according to the sample point with the largest output first total light output rate prediction value.

[0098] Iterative optimization refers to repeatedly executing steps S13 to S15. The first number of iterations and the first convergence condition are set during the initialization process.

[0099] The present invention optimizes the maximum depth and width of the arc cutting structure by adopting Latin hypercube sampling and Gaussian process model, and quickly converges to the optimal solution through iterative acquisition function. While reducing the number of sample points, it ensures the globality of parameter optimization, significantly reduces the computational cost, and improves the luminous flux output efficiency of the arc cutting structure.

[0100] S2. Construct a second optical model according to the structural parameters of the light guide plate and the arc cutting structure, and determine a dot distribution model by performing simulation on the second optical model.

[0101] In this invention, the first and second optical models are used to determine the arc-cutting structure and dot distribution model, respectively. The first optical model modifies the overall topography of the light-facing surface by adjusting the arc-cutting structure, adjusting the initial reflection path of light within the light guide plate, and directly affecting the overall light extraction efficiency. Based on the determined arc-cutting structure, the second optical model finely controls local light scattering through dot distribution, further improving overall brightness and addressing brightness unevenness.

[0102] In one embodiment, the determining of the dot distribution model by simulating the second optical model includes the steps of:

[0103] S21, dividing the light-facing surface and the light-emitting surface of the second optical model into a plurality of light-facing areas and light-emitting areas respectively by a plurality of grid division lines, wherein the distances between the grid division lines are equal;

[0104] The light-facing surface is the lower surface of the light guide plate, i.e., the plane represented by z = 0 in the second optical model. The light-exiting surface is the upper surface of the light guide plate, i.e., the plane represented by z = H in the second optical model. Because the light source in the light guide plate is located on the single-side incident surface at x = 0, two points on the light-facing surface with the same y value can be considered to have equal illuminance values. Therefore, in this embodiment, the illuminance value of the grid points on the light-facing surface is defined to vary only with the x coordinate.

[0105] For the sake of simplicity, this step divides the light-facing surface and light-emitting surface of the second optical model into n regions by n+1 grid division lines parallel to the y-axis. The grid division lines are represented as ,in , , and the distances between the grid division lines are equal, which can be expressed as , It can be understood that the areas of any two light-facing areas and light-emitting areas are equal.

[0106] S22. Using a particle swarm optimization algorithm to obtain the dot filling rate of each light-facing area;

[0107] This example uses a particle swarm optimization algorithm to simulate the movement of several particles through the solution space to find the optimal solution. Each particle represents a possible solution, and the global optimal solution is searched for by updating its position and velocity. During this process, each particle moves through the search space, adjusting its velocity and position based on its own experience and that of the globally optimal particle.

[0108] Furthermore, the method of using the particle swarm optimization algorithm to obtain the dot filling rate of each light-facing area includes the following steps:

[0109] S221, initializing 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;

[0110] In this embodiment, the particle swarm parameters include the number of particles, particle positions and velocities, the maximum number of iterations, inertia weight, individual learning factor, and group 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 group 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, which is recorded as , is the dot filling rate of the i-th light-facing area, =1,2,...,n. The particle velocity is used to determine the direction and amplitude of the particle movement in the solution space. Based on the above-mentioned grid division line representation, the i-th light-facing area is and The light-facing area between them.

[0111] In this step, the dot fill rate vector representing the initial position of the particle can be randomly generated within the dot fill rate range corresponding to the light-facing area. The preset range is set by the minimum and maximum dot fill rates of each light-facing area.

[0112] Furthermore, initializing the particle group 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 a floating coefficient and a filling rate reference value.

[0113] If the light source is set on a single-side incident surface, according to prior knowledge, the fill rate reference value satisfies The preset range of the front light area is expressed as ,in It is a floating coefficient, usually taking a value of [0.1-0.4].

[0114] If the light source is symmetrically set on both sides of the light incident surface, according to prior knowledge, the fill rate reference value satisfies Monotonically increasing internally, is monotonically decreasing, and .

[0115] In one embodiment, the filling rate reference value is obtained as follows:

[0116] S2211: Set the light incident surface luminous flux of the second optical model, and obtain the input luminous flux of each light-facing area and the output luminous flux corresponding to the light-emitting area through the second optical model;

[0117] S2212, obtaining a regional light output rate simulation value according to the input light flux of each light-facing area and the output light flux corresponding to the light-emitting area;

[0118] The simulated value of the regional light output rate is expressed as:

[0119] ;

[0120] in, represents the simulated value of the regional light output rate of the i-th light output area, represents the output luminous flux of the i-th light-emitting area, is the input luminous flux of the i-th light-facing area.

[0121] S2213: Obtain a light extraction coefficient according to the light extraction coefficient simulation value of the area, and set a fill rate reference value corresponding to each light-facing area according to the light extraction coefficient.

[0122] The light extraction coefficient is expressed as Based on the light output coefficient, the fill rate reference value of each light-facing area is expressed as:

[0123] ;

[0124] in, represents the simulated value of the regional light output rate of the i-th light output area, is the preset fill rate constant; n represents the number of light-facing areas; Indicates the minimum value of the regional light output rate simulation value.

[0125] In one embodiment, the light source is disposed on a single-side light incident surface. In another embodiment, the light sources are symmetrically arranged on both sides of the light incident surface. ,in and Respectively express Perform floor and ceiling rounding.

[0126] Based on the basic principles of light guide plates, and provided that the areas of any two light-facing areas and light-emitting areas are equal, ideally, the relationship between the regional light output rate of the light-emitting area between the same two grid division lines and the fill rate of the light-facing area should be approximately linear. Therefore, after determining the ratio between the fill rate reference values ​​corresponding to the light-facing areas using the light output rate coefficient, a preset fill rate constant can be set based on prior knowledge, i.e., the fill rate reference value corresponding to the light-facing area farthest from the point light source. The fill rate reference value corresponding to each light-facing area can then be determined based on the ratio between the preset fill rate constant and the fill rate reference value corresponding to the light-facing area.

[0127] However, due to the complex influence of the arc cutting structure and dots on the optical path, as well as the theoretical error caused by the grid division accuracy, the filling rate reference value is not the optimal solution for the dot filling rate. Therefore, the dot filling rate is optimized through the subsequent particle swarm optimization step.

[0128] S222. Obtain the objective function value corresponding to the position of each particle; the objective function value is expressed as:

[0129] ;

[0130] in, represents the output luminous flux of the i-th light-emitting area, It represents the average output luminous flux 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.

[0131] In the formula, the first term Represents the luminous flux of the light-emitting surface, that is, the sum of the output luminous flux of all light-emitting areas; the second term Represents the output luminous flux variance, which is used to measure the uniformity of light output. and Weigh the importance of light flux and light uniformity in the objective function.

[0132] Furthermore, obtaining the objective function value corresponding to the position of each particle includes the steps of:

[0133] S2221: Update the second optical model according to the position of the particle, and obtain the output luminous flux corresponding to each light-emitting area through the second optical model;

[0134] S2222. Calculate the light output surface luminous flux and the output luminous flux variance according to the output luminous flux corresponding to each light output area, and calculate the objective function value.

[0135] This embodiment provides a scientific initial solution range for the particle swarm optimization algorithm by mapping the simulated regional light output rate values ​​to the fill rate reference values. By dynamically adjusting the fill rate distribution trend based on the position of single-sided or double-sided light sources, the optimization algorithm's search space is significantly reduced, improving convergence speed.

[0136] S223, updating the update speed and position of each particle according to the objective function value;

[0137] The update speed and position of each particle are updated according to the objective function value, specifically: the personal best position and the group best position of each particle are updated according to the objective function value, and the speed and position are updated according to the personal best position and the group best position.

[0138] The individual best position is the position with the lowest objective function value among all the positions a particle has ever taken. Each particle saves this information during the search process as its own historical best position. The group best position is the position with the lowest objective function value among the individual best positions of all particles in the swarm. The group best position is global information, and all particles in the swarm use it to update their speed and position. After obtaining the objective function value, the particle's current objective function value is compared with the objective function value corresponding to the individual best position. If the current objective function value is greater, the particle's current position is set as the individual best position. Then, the objective function values ​​corresponding to the individual best positions of all particles are compared, and the individual best position with the largest objective function value is set as the group best position.

[0139] Based on the above, the velocity of the particle is expressed as:

[0140] ,

[0141] The position of the particle is expressed as:

[0142] = + ,

[0143] in, is the inertia weight, which is 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 sequence number of the particle; Specifically, it 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 the preset individual learning factor and group learning factor, represents the individual optimal position of the particle, and g represents the group optimal position. and are all random numbers in the interval [0,1], used to provide randomness for each iteration.

[0144] S224 , iterative optimization is performed until the second iteration number is reached or the objective function value is less than a set threshold, and the optimal position of the group is output as the dot filling rate of each light-facing area.

[0145] Specifically, the iterative optimization is to repeat steps S222 to S224. The second iteration number and the objective function value are set while initializing the particle swarm parameters.

[0146] This implementation uses a particle swarm optimization algorithm to dynamically adjust the dot fill rate, leveraging swarm intelligence to rapidly approach the global optimal solution. Initialization parameters are combined with the fill rate range and reference values ​​to ensure the algorithm operates efficiently within a reasonable search space, avoiding the blindness of traditional trial-and-error methods and significantly shortening the optimization cycle.

[0147] S23, setting the number of dots according to the dot filling rate of each light-facing area to obtain a dot distribution model;

[0148] The number of network points is expressed as:

[0149] ;

[0150] in, represents the number of dots in the i-th light-facing area, S represents the area of ​​a single dot, represents the dot filling rate of the i-th light-facing area, Indicates the width of the light guide plate, Indicates the spacing between grid lines.

[0151] The dot distribution model includes the dot filling rate of each light-facing area. Based on the dot filling rate, you can choose to set regularly arranged dots or irregularly arranged dots in the light-facing area.

[0152] In practical applications, when meshing accuracy is low (i.e., the number of mesh regions is small), regularly arranged dots are more likely to form moiré fringes. However, setting a larger number of mesh regions to achieve higher meshing accuracy during simulation results in a greater consumption of computing resources.

[0153] To solve this problem, in one embodiment, after setting the number of dots according to the dot filling rate of each light-facing area, the method further includes:

[0154] S24. Arrange the dots regularly in each light-facing area according to the number of dots, and use a local randomization algorithm to process each light-facing area to obtain a dot distribution model.

[0155] The purpose of the local randomization algorithm is to generate a new dot distribution based on the current dot distribution through random perturbations. In this step, the local randomization algorithm performs small random adjustments near the current solution. Specifically, in each light-facing area where the dots are arranged regularly, the spacing between adjacent dots is equal. The local randomization algorithm performs a small-scale random perturbation on the dots in each light-facing area to change the position of the dots. Then, the second optical model is used to simulate the dot distribution model after the perturbation and calculate the objective function value. If the objective function value is better, the dot distribution model is accepted as the dot distribution model. It should be noted that the objective function value corresponding to the dot distribution model obtained by the local randomization algorithm is not necessarily significantly better than the objective function value corresponding to the dot distribution model output by the particle swarm optimization algorithm in the aforementioned step. However, due to the limitation of 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 distribution model more reasonable through lower computational complexity. After the dot filling rate is set, this embodiment introduces a local randomization algorithm to fine-tune the dots to break the moiré fringes that may be caused by the regular arrangement. This embodiment further optimizes the network point distribution with less complexity under limited computing resources, taking into account the requirements of high uniformity and low computing cost.

[0156] Example 2

[0157] The present invention also provides a light guide plate, comprising a light incident surface, a light facing surface and a light emitting surface; the light facing surface and the light emitting surface are arranged relative to each other; 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 distribution model; the arc cutting structure and the dot distribution model are determined by the aforementioned method for optimizing the brightness of a backlight light guide plate.

[0158] The arc cutting structure and dot distribution model of the light guide plate provided in this embodiment highly match the optical simulation results in physical realization, and have the characteristics of high light efficiency, high uniformity and low moiré fringes. It is suitable for high-precision backlight modules and can significantly improve user experience.

[0159] In the embodiments provided in this 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 merely illustrative. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of modules, which can be electrical, mechanical or other forms.

[0160] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple grid modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0161] In addition, the functional modules in the various embodiments of the present application may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The above-mentioned integrated modules may be implemented in the form of hardware or software functional modules.

[0162] 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 this application, or the part that contributes to the existing technology, or all or part of the 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 for enabling a computer device (which can be a personal computer, server, or grid device, etc.) to execute all or part of the steps of the method described in each embodiment of this application. The aforementioned storage medium includes: USB flash drives, dynamic hard drives, read-only memories (ROM), random access memories (RAM), magnetic disks, optical disks, and other media that can store program code.

[0163] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is intended to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for optimizing the brightness of a backlight guide plate, characterized in that: Including steps: Constructing a first optical model according to the structural parameters of the light guide plate, and determining the arc cutting structure through simulation using the first optical model; Constructing a second optical model based on the structural parameters of the light guide plate and the arc cutting structure, and determining a dot distribution model through simulation using the second optical model; The method of determining the dot distribution model by simulating the second optical model includes the steps of: Dividing the light-facing surface and the light-emitting surface of the second optical model into a plurality of light-facing areas and light-emitting areas respectively by a plurality of grid division lines; Use particle swarm optimization algorithm to obtain the dot filling rate of each light-facing area; The number of dots is set according to the dot filling rate of each light-facing area to obtain a dot distribution model; The method of simulating and determining the arc cutting structure by using the first optical model comprises the following steps: Setting the luminous flux of the incident surface of the first optical model; Initialize and generate a number of sample points within the maximum depth and width range through Latin hypercube sampling, and construct a sample data set and a Gaussian process model based on the sample points; Obtaining the light-exiting surface luminous flux corresponding to each sample point in the sample data set through a first optical model, and calculating a first total light-exiting rate simulation value corresponding to the sample point according to the light-exiting surface luminous flux and the light-incident surface luminous flux; Processing the Gaussian process model through an acquisition function to obtain sample points corresponding to the optimal predicted value of the first total light output rate and adding the sample points to the sample data set; Iterative optimization is performed until a first number of iterations is reached or a first convergence condition is satisfied, and an arc cutting structure is determined according to a sample point outputting a maximum first total light output rate prediction value.

2. The method for optimizing the brightness of a backlight guide plate according to claim 1, wherein: The structural parameters of the light guide plate include the three-dimensional size of the light guide plate and the position of the light source; The first optical model is constructed according to the structural parameters of the light guide plate, specifically: a vertex of the light guide plate is selected as the origin to construct a three-dimensional coordinate system of the light guide plate; z=0 is set as the light-facing surface of the light guide plate, and z=H is set as the light-emitting surface of the light guide plate; the light source position is set to the single-sided light incident surface of x=0, or to the first light incident surface x=0 and the second light incident surface x=L.

3. The method for optimizing the brightness of a backlight guide plate according to claim 1, wherein: The acquisition function is expressed as: ， in, Indicates the expectation to improve the acquisition function; represents an expected value operation, which is used to integrate the predicted distribution of the first total light extraction rate; represents a first total light output rate simulation value; Indicates the optimal predicted value of the first total light output rate.

4. The method for optimizing the brightness of a backlight guide plate according to claim 1, wherein: After setting the number of dots according to the dot filling rate of each light-facing area, the method further includes: setting a regular arrangement of dots in each light-facing area according to the number of dots, and using a local randomization algorithm to process each light-facing area to obtain a dot distribution model.

5. The method for optimizing the brightness of a backlight guide plate according to claim 1, wherein: The method of using the particle swarm optimization algorithm to obtain the dot filling rate of each light-facing area includes the following steps: Initialize 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; Get the objective function value corresponding to the position of each particle; Update the updated speed and position of each particle according to the objective function value; Iterate the optimization until the second iteration number is reached or the objective function value is less than the set threshold, and output the optimal position of the group as the dot filling rate of each light-facing area.

6. The method for optimizing the brightness of a backlight guide plate according to claim 5, wherein: The step of obtaining the objective function value corresponding to the position of each particle comprises the following steps: Update the second optical model according to the position of the particles, and obtain the output luminous flux corresponding to each light-emitting area through the second optical model; Calculate the light output surface luminous flux and the output luminous flux variance according to the output luminous flux corresponding to each light output area, and calculate the objective function value; The objective function value is expressed as: ， in, represents the output luminous flux of the i-th light-emitting area, It represents the average output luminous flux 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.

7. The method for optimizing the brightness of a backlight guide plate according to claim 5, wherein: The initialization of the particle group 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 a floating coefficient and a filling rate reference value; The filling rate reference value is obtained as follows: Setting the light incident surface luminous flux of the second optical model, and obtaining the input luminous flux of each light-facing area and the output luminous flux corresponding to the light-emitting area through the second optical model; Obtain the regional light output rate simulation value according to the input light flux of each light-facing area and the output light flux corresponding to the light-emitting area; A light extraction coefficient is obtained according to the light extraction coefficient of the area, and a fill rate reference value corresponding to each light-facing area is set according to the light extraction coefficient.

8. The method for optimizing the brightness of a backlight guide plate according to claim 1, wherein: The number of network points is expressed as: ， in, represents the number of dots in the i-th light-facing area, S represents the dot area, represents the dot filling rate of the i-th light-facing area, Indicates the width of the light guide plate, Indicates the spacing between grid lines.

9. A light guide plate, characterized in that: It 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 dots; the plurality of dots are arranged through a dot distribution model; the arc cutting structure and the dot distribution model are determined by a backlight light guide plate brightness optimization method as described in any one of claims 1-8.

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