Monte carlo ray tracing method based on spectral distribution

Abstract

The application relates to a Monte Carlo light tracing method based on spectral distribution, which is related to far-field light source space distribution simulation. The spectral radiant power distribution data of the three-dimensional space distribution of a near-field light source is acquired, and the radiant power at a current position is calculated. The radiant power data of each three-dimensional coordinate position is proportionally divided into spectral radiant power distribution data of a plurality of micro-surface light sources, a large number of light rays are randomly generated by using the Monte Carlo algorithm and taking the micro-surface light sources as new starting points to simulate the light source distribution, the corresponding light ray direction is calculated according to the random angle, the spectral information of each light ray is extrapolated to the far-field space according to the direction of the light ray, the far-field three-dimensional coordinates are calculated according to the Monte Carlo light tracing method, and the radiometry, photometry and colorimetry parameters are calculated according to the far-field spectral data. The application overcomes the disadvantages of a traditional far-field light source detection system, such as large volume, high cost, low flexibility, long time consumption and single function, and realizes comprehensive analysis of the space distribution of a light source at an arbitrary distance.

Description

Technical Field

[0001] This invention relates to the simulation of far-field light source spatial distribution, and more particularly to a Monte Carlo ray tracing method based on spectral distribution that extrapolates the near-field spatial distribution of a light source to its far-field spatial distribution, while incorporating spectral information. Background Technology

[0002] In daily life and work environments, light sources are ubiquitous, and accurately determining the spatial distribution of light sources is crucial for the light distribution of luminaires. The most commonly used instrument for detecting the spatial distribution of light sources is the goniophotometer, which can measure optical data such as spatial luminous intensity and luminous flux of a light source. Near-field goniophotometers treat the light source under test as a surface source, with light rays emanating from its surface, resulting in parameters that more closely approximate the actual distribution of the light source. Far-field goniophotometers, on the other hand, require a sufficiently large distance between the light source under test and the measuring probe, treating the light source as a point source in this mode. Conventional testing systems can only measure the spatial distribution data of light sources at fixed distances. If it is necessary to measure the spatial distribution at different distances, the distance between the light source under test and the photometer probe must be adjusted each time. This means that the space required for the entire measuring equipment increases dramatically, and more importantly, it cannot obtain colorimetric parameters at arbitrary distances. Therefore, traditional photometer measurement methods are very limited.

[0003] The Monte Carlo algorithm, also known as the statistical simulation algorithm, was first proposed by John von Neumann in the 1940s. With a sufficiently large sample size, it can perform probabilistic estimation and analysis of random problems, obtaining results close to reality. Yu Ying et al. used the Monte Carlo method to simulate LEDs with different optical encapsulation structures, establishing a simulation model of low-power LEDs. They used quadratic surface equations to describe the LED encapsulation structure and simulated and statistically analyzed its light intensity distribution (Yu Ying, Yan Jun. "Monte Carlo Simulation and Experimental Analysis on Optical Encapsulated Structure of Light EmittingDiode." Journal of Semiconductors, 2004(12):1685-1689). However, this method is limited to ray tracing of known, fixed encapsulation surface structures, and the results only include the photometric parameter of light intensity, failing to calculate and analyze various radiometric, photometric, and colorimetric parameters of the light source under test. Song Weihao et al. used the Monte Carlo method to randomly sample the emission position and direction of light rays from the luminous body, determine the equation of the emitted light rays, trace a large number of emitted light rays, obtain the coordinates of the light rays landing on the receiving plane, divide the two-dimensional receiving plane into grids and count the number of light rays landing in each grid, and calculate the irradiance distribution of the receiving plane (Song Weihao, Zhou Xianwen, Zhang Wei. "Simulation for irradiation characteristics of infrared light source based on Monte Carlo method" Laser & Infrared, 2018, 48(02):198-204). However, this method uses a two-dimensional receiving plane to calculate the irradiance, which cannot obtain the three-dimensional spatial distribution of the light source. In addition, each emitted light ray does not carry its own spectral information, so it is impossible to analyze photometric, colorimetric and other parameters in the future.

[0004] Gao Limin et al. proposed a method for solving the optical path layout based on a spherical coordinate system (Chinese Invention Patent, Publication No. CN112393884 A). This method uses the Monte Carlo method to randomly generate rays from different emission positions and directions, and then uses ray tracing simulation software to calculate the irradiance distribution on a spherical receiver. However, the far-field results obtained by this method are limited to the irradiance distribution and do not include the spectral information of each ray, making it impossible to obtain colorimetric parameters such as color temperature and color gamut. Therefore, current researchers lack the ability to simulate near-field light sources and obtain a far-field spatial distribution of the light source with complete radiometric, photometric, and colorimetric parameters, while incorporating the spectral information of the light source. Summary of the Invention

[0005] This invention addresses the problems of current far-field light source spatial detection equipment, such as large size, high cost, low flexibility, long time consumption, limited functionality, and inability to obtain accurate spectral information and radiometric, photometric, and colorimetric parameters. It proposes a Monte Carlo ray tracing method based on spectral distribution, which can flexibly obtain the distribution of far-field light sources at arbitrary distances by setting different tracking distances R.

[0006] This invention first divides the acquired near-field spectral distribution of the light source into micro-surface light sources. Then, it establishes certain sampling rules and assigns weights to the emitting surface, using random sampling to generate a large number of rays. Next, it tracks the spectral information contained in each ray. Finally, it divides the receiving surface into grid cells and statistically analyzes the ray reception of each grid. Simultaneously, according to the sampling rules, it statistically analyzes the spectral information corresponding to each ray at a distant receiving surface, obtaining the light source distribution characteristics and detailed spectral information of the entire receiving surface at a distant distance. Further calculations can then yield the complete radiometric, photometric, and colorimetric parameters of the far-field light source.

[0007] This invention includes the following steps:

[0008] 1) Obtain the spectral radiant power distribution data of the three-dimensional spatial distribution of the near-field light source, and calculate the radiant power at the current location;

[0009] 2) Each position in the near field is determined by three-dimensional coordinates (x0, y0, z0), which serve as the initial exit coordinates;

[0010] 3) Divide the radiation power data at each (x0, y0, z0) position into several micro-surface light source spectral radiation power distribution data according to the proportion. Using these micro-surface light sources as a new starting point, use the Monte Carlo algorithm to randomly generate a large number of light rays to simulate the light source distribution.

[0011] 4) Define a random angle and calculate the corresponding ray direction;

[0012] 5) Extrapolate the spectral information of each ray to the far-field space based on the direction of the ray, and calculate the far-field three-dimensional coordinates using the Monte Carlo ray tracing method;

[0013] 6) Calculate far-field spectral data to calculate radiometric, photometric, and colorimetric parameters, enabling comprehensive analysis of the spatial distribution of light sources at any distance.

[0014] In step 1), the spectral radiant power distribution Φ(λ) of the three-dimensional spatial distribution of the near-field light source is obtained as follows:

[0015] Φ(λ)=Φ E (λ)*S (1)

[0016] Where λ is the wavelength, Φ E (λ) represents the spectral irradiance, and S represents the surface element at the near-field measurement location. Then, the radiant power P at the current location is:

[0017] P=∫Φ(λ)dλ (2)

[0018] In step 2), each position in the near field is determined using three-dimensional coordinates (x0, y0, z0), calculated as follows:

[0019]

[0020] Where β and θ are the angle values ​​of two directions describing the spatial distribution of the near-field light source, R0 is the distance from (x0,y0,z0) in the spatial distribution of the near-field light source to the center coordinate O(0,0,0) of the luminaire, and h is the height of the support rod of the luminaire under test.

[0021] In step 3), the radiant power data at each (x0, y0, z0) position is proportionally divided into several micro-surface light source spectral radiant power distribution data. This involves taking the radiant power P at the current position as a surface light source and using unit power as the basis for dividing each micro-surface light source, and then making a fine division according to the requirements. If the surface light source is divided into K parts, then each tiny surface light source represents a radiant power of P / K, corresponding to a spectral radiant power distribution of Φ(λ) / K. Starting from these micro-surface light sources, a Monte Carlo algorithm is used to perform far-field ray tracing.

[0022] In step 4), a random angle is defined and the corresponding ray direction is calculated; the random angle φ R θ R Represents the angle between a ray and the x-axis and z-axis in spherical coordinates; the direction vector in three-dimensional coordinates corresponding to a randomly sampled ray. It can be represented as:

[0023]

[0024] Where δ is the angle between the current plane normal and the Z-axis, and (l,m,n) satisfies l 2 +m 2 +n 2 =1.

[0025] In step 5), the spectral information of each ray is extrapolated to the far-field space according to the direction of the ray, the three-dimensional coordinates of the far field are calculated according to the Monte Carlo ray tracing method, and the spectral information corresponding to each ray is statistically analyzed to the distant receiving surface according to the random sampling rule, so as to obtain the spatial distribution characteristics of the light source and detailed spectral information of the entire receiving surface at the distant distance; the receiving surface is divided into a sphere or a plane.

[0026] When the receiving surface is spherical, the specific steps are as follows;

[0027] A.1 Define the far-field radius to be extrapolated as R, and the far-field three-dimensional rectangular coordinates as (x, y, z). Based on the principle, the following relationship can be obtained:

[0028]

[0029] Meanwhile, the coordinates on the far-field derivation surface satisfy:

[0030]

[0031] The results obtained by combining equations (5) and (6) are as follows:

[0032] t=[(I*x0+m*y0+n*z0) 2 +R 2 -r 2 ] 1 / 2 -(I*x0+m*y0+n*z0) (7)

[0033] Substitute equation (7) into equation (6) to obtain the three-dimensional coordinates (x, y, z) of the far field;

[0034] A.2 Define statistical ray and spectral power distribution data for the far-field receiving surface;

[0035] Using direction angle α, The far-field three-dimensional spherical coordinates are defined by the radius R. From the (x, y, z) coordinates obtained above, we can get:

[0036]

[0037] The far-field angle α and With α and The number of tracked rays and the spectral radiant power distribution at different angles are statistically analyzed for the two dimensions of the table to obtain simulated matrix table data. Different angle ranges are selected from the table for plotting to analyze the spectral radiant power distribution at different angles in the far field.

[0038] When the receiving surface is a plane, the specific steps are as follows:

[0039] B.1 Define the far-field vertical distance to be extrapolated as R, and the far-field rectangular coordinates as (x, y, z). Based on the principle, the following relationship can be obtained:

[0040]

[0041] Meanwhile, the coordinates on the far-field two-dimensional plane satisfy:

[0042] The results obtained by combining equations (9) and (10) are as follows:

[0043]

[0044] Substituting equation (11) into equation (10) yields the coordinates (x, y, z) of the far-field plane.

[0045] B.2 Statistical analysis of ray and spectral power distribution data on the far-field receiving plane

[0046] By using x and y as the two dimensions of a table to statistically analyze the amount of light and the distribution of spectral radiant power per unit area, simulated far-field plane data can be obtained, which can then be plotted as a two-dimensional graph.

[0047] In step 6), the radiometric parameters include radiant power, radiant intensity, and irradiance; the photometric parameters include luminous flux, luminous intensity, and irradiance; and the colorimetric parameters include the color rendering index (CCT).

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0049] 1. The spectral Monte Carlo ray tracing method extrapolates from the spatial distribution of near-field light sources to the spatial distribution of far-field light sources at arbitrary distances, overcoming many drawbacks of traditional far-field light source detection systems, such as large size, high cost, low flexibility, long time consumption, and single function.

[0050] 2. The spatial distribution of far-field light sources includes complete information on spherical and planar light fields, which is equivalent to obtaining a four-dimensional far-field light source distribution containing the spectral dimension;

[0051] 3. Calculate complete photometric and colorimetric parameters such as radiant power, light intensity, illuminance, brightness, and color temperature based on the extrapolated far-field spectral data. Attached Figure Description

[0052] Figure 1 The diagram shows the division of the micro-source, the direction of the light rays, and the receiving surface; where (a) is a sphere and (b) is a plane.

[0053] Figure 2 This is an intensity distribution diagram of a two-dimensional planar receiving surface for orthophotos.

[0054] Figure 3 This is the central unfolded diagram of the orthophoto two-dimensional planar receiving surface.

[0055] Figure 4 This is a three-dimensional intensity distribution map of the far field.

[0056] Figure 5 This is a polar coordinate plot of the far-field intensity distribution. Where (a) is... Direction, (b) is the α direction.

[0057] Figure 6 For far field A rectangular coordinate diagram showing the intensity distribution along a direction.

[0058] Figure 7 This is a far-field spectral distribution diagram.

[0059] Figure 8 This is a two-dimensional diagram of the far-field color temperature distribution. Among them, (a) is... Direction, (b) is the α direction. Detailed Implementation

[0060] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described examples are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0061] Existing far-field light source spatial distribution photometers occupy a large space. Measuring the light source distribution at different distances requires manual adjustment each time, and the measurement results are greatly affected by human and environmental factors. This invention proposes a Monte Carlo ray tracing method based on spectral distribution. It utilizes the spectral irradiance data of the three-dimensional spatial distribution of near-field light sources to calculate the spatial radiant power of the near-field light source. Each position in the near field is defined by three-dimensional coordinates (x0, y0, z0) and used as the initial emission coordinates. The radiant power data at each (x0, y0, z0) position is then proportionally divided into several micro-facet light source spectral radiant power distribution data. Using these micro-facet light sources as new starting points, a large number of rays are randomly generated using the Monte Carlo algorithm to simulate the light source distribution. The direction of the randomly generated rays is represented by a vector. Let (x, y, z) represent the far-field three-dimensional coordinates of a random ray emitted and arriving at a distant receiving surface. Then, the near-field radius... Light direction Far-field radius There is a geometric relationship between the three vectors, i.e. like Figure 1 As shown in (a), by extrapolating the spectral information of each ray to the far-field space based on its direction of travel, each ray obtained in the far field contains the spectral information carried over from the near field. By statistically superimposing the spectral distributions of all rays on the far-field receiving surface grid, the radiometric, photometric, and colorimetric parameters of the far field can be further calculated, enabling a comprehensive analysis of the spatial distribution of light sources at arbitrary distances. This method only requires the spectral information of the spatial distribution of the near-field light source, greatly reducing the volume; it can simulate at arbitrary distances, offering high flexibility and saving costs and time; and it can achieve multifunctional and comprehensive analysis of the spatial distribution of spherical or planar light fields.

[0062] A Monte Carlo ray tracing method based on spectral distribution includes the following steps:

[0063] 1) Obtain the spectral radiant power distribution Φ(λ) of the three-dimensional spatial distribution of the near-field light source;

[0064] Φ(λ)=Φ E (λ)*S (1)

[0065] λ is the wavelength, Φ E (λ) represents the spectral irradiance, and S represents the surface element at the near-field measurement location. Then, the radiant power P at the current location is:

[0066] P=∫Φ(λ)dλ (2)

[0067] 2) Calculate the three-dimensional coordinates (x0, y0, z0) of the near-field light source spatial distribution. The calculation formula is as follows:

[0068]

[0069] Where β and θ are the angle values ​​of two directions describing the spatial distribution of the near-field light source, R0 is the distance from (x0,y0,z0) in the spatial distribution of the near-field light source to the center coordinate O(0,0,0) of the luminaire, and h is the height of the support rod of the luminaire under test. Figure 1 As shown in (a).

[0070] 3) Divide the radiated power into several micro-surface light sources;

[0071] The current location's radiant power P is taken as the surface light source, and unit power is used as the basis for dividing each micro-surface light source into smaller parts, allowing for finer division as needed. For example, if the surface light source is divided into K parts, then each micro-surface light source represents a radiant power of P / K, corresponding to a spectral radiant power distribution of Φ(λ) / K. Then, starting with these micro-surface light sources, a Monte Carlo algorithm is used for far-field ray tracing.

[0072] 4) Define a random angle and calculate the corresponding ray direction;

[0073] random angle φ R θ R This represents the angle between the ray and the x-axis and z-axis in the spherical coordinate system.

[0074] Therefore, the direction vector corresponding to a randomly sampled ray in three-dimensional coordinates It can be represented as:

[0075]

[0076] δ is the angle between the current plane normal and the Z-axis, where (l,m,n) satisfies l 2 +m 2 +n 2 =1.

[0077] 5) Calculate the far-field three-dimensional coordinates using the Monte Carlo ray tracing method;

[0078] A. When the receiving surface is a sphere.

[0079] A.1 Define the far-field radius to be extrapolated as R, and the far-field three-dimensional rectangular coordinates as (x, y, z). Based on the derivation principle, the following relationship can be obtained:

[0080]

[0081] Meanwhile, the coordinates on the far-field derivation surface satisfy:

[0082]

[0083] By combining equations (5) and (6), we can calculate the result as follows:

[0084] t=[(I*x0+m*y0+n*z0) 2 +R 2 -r 2 ] 1 / 2 -(I*x0+m*y0+n*z0) (7)

[0085] Substituting equation (7) into equation (6) will give us the three-dimensional coordinates (x, y, z) of the far field.

[0086] A.2 Define statistical ray and spectral power distribution data for the far-field receiving surface;

[0087] Using direction angle α, The far-field three-dimensional spherical coordinates are defined by the radius R. From the (x, y, z) coordinates obtained above, we can get:

[0088]

[0089] The far-field angle α and With α and The table is used to statistically analyze the number of light rays tracked and the spectral radiant power distribution at different angles in two dimensions, resulting in a simulated matrix table of data. Different angle ranges can be selected from the table to plot the data and analyze the spectral radiant power distribution at different angles in the far field.

[0090] B. When the receiving surface is a plane.

[0091] B.1 Define the far-field vertical distance to be extrapolated as R, and the far-field rectangular coordinates as (x, y, z). Based on the derivation principle, the following relationship can be obtained:

[0092]

[0093] Meanwhile, the coordinates on the far-field two-dimensional plane satisfy:

[0094]

[0095] By combining equations (9) and (10), we can calculate the result as follows:

[0096]

[0097] Substituting equation (11) into equation (10) yields the coordinates (x, y, z) of the far-field plane.

[0098] B.2 Statistical analysis of ray and spectral power distribution data on the far-field receiving plane

[0099] By using x and y as the two dimensions of a table to statistically analyze the amount of light and the distribution of spectral radiant power per unit area, simulated far-field plane data can be obtained, which can then be plotted as a two-dimensional graph.

[0100] 6) Calculate radiometric (radiative power, radiative intensity, irradiance), photometric (luminous flux, luminous intensity, irradiance), and colorimetric (color rendering index, CCT) parameters based on far-field spectral data.

[0101] The following example uses a common 5W white LED bulb as the light source to be tested.

[0102] First, calculate the corresponding radiated power for each band. This can be done using formulas (1) and (2). Taking R0 = 500mm as the near-field distance and h = 100mm as the height of the luminaire support rod,... Figure 1 (a) A schematic diagram showing the relationship between the far-field derivation point coordinates and the near-field initial coordinates is given. The three-dimensional coordinates (x0, y0, z0) of the near-field light source spatial distribution are calculated using equation (3), and the three-dimensional distribution of the near-field light source is obtained.

[0103] Furthermore, each wavelength band is divided into several micro-surface light sources based on radiant power. The radiant power is used as the basis for dividing the micro-surface light sources based on near-field illuminance data. Ray tracing is then performed using these tiny surface light sources, with each ray carrying its own spectral information. The number of rays emitted from each micro-surface can be set according to the required accuracy, and the distance to the far field can be adjusted as needed.

[0104] Furthermore, two independent random variables are generated as random angles φ. R θ R And calculate the emission direction corresponding to each random ray according to equation (4). Then, the far-field derivation point coordinates (x, y, z) and the outgoing direction of the random ray are... The relationship between the near-field initial coordinates (x0, y0, z0) and the graph is as follows: Figure 1 As shown. The near-field coordinates (x0, y0, z0) are calculated based on the known far-field radius R = 10000 mm to be extrapolated and the originally defined near-field radius R0 = 500 mm.

[0105] When the receiving surface is spherical, the far-field three-dimensional rectangular coordinates are defined as (x, y, z). Following the Monte Carlo ray tracing principle, the far-field three-dimensional coordinates (x, y, z) are calculated using equations (5), (6), and (7). Angle α, ... Let the radius R define the far-field light receiving surface. Let the two angles between the defined receiving surface and the Z-axis be α1 and α2, and the angle with the Y-axis be α1 and α2. According to Equation (8), the number of light rays falling in the specified area and the distribution of spectral radiation power can be statistically obtained, thereby obtaining the three-dimensional coordinates of the outgoing light rays reaching the far-field spherical receiving surface. By analogy, the distribution of light rays generated by a large number of random light sources can be obtained to be closest to the actual light source. Figure 4 The image shows the far-field three-dimensional intensity distribution, revealing that the light source primarily illuminates the upper hemisphere. To more clearly analyze the details of the three-dimensional distribution, the three-dimensional distribution characteristics are... The two angles, α and β, are dissected and transformed into a two-dimensional diagram for analysis. Figure 5 (a) and (b) are Polar coordinate plot of far-field light intensity at angle α and angle α. Figure 6 This is a rectangular coordinate graph of light intensity. Figure 7 for Figure 6 Spectral power distribution at α = 0 degrees and α = 45 degrees.

[0106] When the receiving surface is a plane, the far-field three-dimensional rectangular coordinates are defined as (x, y, R), such as Figure 1As shown in (b), the distribution of light rays illuminating the far-field two-dimensional plane can be obtained at a distance R from the orthographic direction of the light source. Here, the radius of the orthographic receiving surface is set to be within 1000 mm, and the far-field distance R = 10000 mm. The coordinates (x, y, R) of the far-field plane are calculated according to equations (9), (10), and (11). The number of rays and the spectral radiant power distribution are statistically analyzed, and a two-dimensional distribution diagram is drawn, as shown in the figure. Figure 2 The image shows the intensity distribution of a two-dimensional orthophoto receiver. It can be seen that the intensity is higher in the central region and gradually decreases as it expands outwards. Figure 3 for Figure 2 Intensity distribution diagrams extending along the transverse and longitudinal centers.

[0107] Furthermore, by precisely tracing the spectral information carried by each emitted ray from its precise source, the complete spectral distribution of a far-field light source at any distance can be obtained, allowing for the calculation of colorimetric parameters. Here, we take the calculation of correlated color temperature (CCT) as an example. Figure 8 The two-dimensional CCT distribution map in the far field is given, from Figure 8 (a) It can be seen that, given The CCT distribution of the angled lighting fixtures exhibits symmetry both vertically and horizontally. Figure 8 (b) It can be seen that the CCT of the LED lamp at a given α angle fluctuates slightly around 4000K.

Claims

1. A Monte Carlo ray tracing method based on spectral distribution, characterized in that... Includes the following steps: 1) Obtain the spectral radiant power distribution data of the three-dimensional spatial distribution of the near-field light source, and calculate the radiant power at the current location; 2) Each position in the near field is determined by three-dimensional coordinates (x0, y0, z0), which serve as the initial exit coordinates; 3) Divide the radiation power data at each (x0, y0, z0) position into several micro-surface light source spectral radiation power distribution data according to the proportion. Using these micro-surface light sources as a new starting point, use the Monte Carlo algorithm to randomly generate a large number of light rays to simulate the light source distribution. The step of dividing the radiation power data at each (x0, y0, z0) position into several micro-surface light source spectral radiation power distribution data proportionally involves taking the radiation power P at the current position as the surface light source and using the unit power as the basis for dividing each micro-surface light source, and making a fine division according to the requirements; if the surface light source is divided into K parts, then each tiny surface light source represents P / K of radiation power, and the corresponding spectral radiation power distribution is Φ(λ) / K; Starting with these micro-faceted light sources, far-field ray tracing is performed using the Monte Carlo algorithm; 4) Define a random angle and calculate the corresponding ray direction; 5) Extrapolate the spectral information of each ray to the far-field space according to the direction of the ray, calculate the three-dimensional coordinates of the far field according to the Monte Carlo ray tracing method, and statistically analyze the spectral information corresponding to each ray to the distant receiving surface according to the random sampling rule, so as to obtain the light source distribution characteristics and detailed spectral information of the entire receiving surface at the far distance; the receiving surface is divided into a sphere or a plane. When the receiving surface is spherical, the specific steps are as follows; A.1 Define the far-field radius to be extrapolated as R, and the far-field three-dimensional rectangular coordinates as (x, y, z). Based on the principle, the following relationship is obtained: Meanwhile, the coordinates on the far-field derivation surface satisfy: Where (l,m,n) satisfies l 2 +m 2 +n 2 =1; The results obtained by combining equations (5) and (6) are as follows: t=[(I*x0+m*y0+n*z0) 2 +R 2 -r 2 ] 1 / 2 -(I*x0+m*y0+n*z0) (7) Substitute equation (7) into equation (6) to obtain the three-dimensional coordinates (x, y, z) of the far field; A.2 Define statistical ray and spectral power distribution data for the far-field receiving surface; Using direction angle α, The far-field three-dimensional spherical coordinates are defined by the radius R. From the (x, y, z) coordinates obtained above, we can derive: The far-field angle α and With α and The number of tracked rays and the spectral radiant power distribution at different angles are statistically analyzed for the two dimensions of the table to obtain simulated matrix table data. Different angle ranges are selected from the table for plotting to analyze the spectral radiant power distribution at different angles in the far field. When the receiving surface is a plane, the specific steps are as follows: B.1 Define the far-field vertical distance to be extrapolated as R, and the far-field rectangular coordinates as (x, y, z). Based on the principle, the following relationship can be obtained: Meanwhile, the coordinates on the far-field two-dimensional plane satisfy: The results obtained by combining equations (9) and (10) are as follows: Substitute equation (11) into equation (10) to obtain the coordinates (x, y, z) of the far-field plane; B.2 Statistical analysis of ray and spectral power distribution data on the far-field receiving plane Using x and y as the two dimensions of the table, the number of rays and the distribution of spectral radiant power per unit area are statistically analyzed to obtain simulated far-field plane data, which is then plotted into a two-dimensional graph for display. 6) Calculate far-field spectral data to calculate radiometric, photometric, and colorimetric parameters, enabling comprehensive analysis of the spatial distribution of light sources at any distance.

2. The Monte Carlo ray tracing method based on spectral distribution as described in claim 1, characterized in that... In step 1), the spectral radiant power Φ(λ) of the three-dimensional spatial distribution of the near-field light source is obtained as follows: Φ(λ)=Φ E (l)*S (1) Where λ is the wavelength, Φ E (λ) represents the spectral irradiance, and S represents the surface element at the near-field measurement location. Then, the radiant power P at the current location is: P=∫Φ(λ)dλ (2).

3. The Monte Carlo ray tracing method based on spectral distribution as described in claim 1, characterized in that... In step 2), each position in the near field is determined using three-dimensional coordinates (x0, y0, z0), calculated as follows: Where β and θ are the angular values ​​of two directions describing the spatial distribution of the near-field light source, and R0 is the distance between the near-field light source and the distance between the points (x0, y0, ..., θ) in the spatial distribution. The distance from z0) to the center coordinate O(0,0,0) of the lamp is the distance, and h is the height of the support rod of the lamp to be tested.

4. The Monte Carlo ray tracing method based on spectral distribution as described in claim 1, characterized in that... In step 4), a random angle is defined and the corresponding ray direction is calculated; the random angle φ R θ R Represents the angle between a ray and the x-axis and z-axis in spherical coordinates; the direction vector in three-dimensional coordinates corresponding to a randomly sampled ray. Represented as: Where δ is the angle between the current plane normal and the Z-axis, and (l,m,n) satisfies l 2 +m 2 +n 2 =1.

5. The Monte Carlo ray tracing method based on spectral distribution as described in claim 1, characterized in that... In step 6), the radiometric parameters include radiant power, radiant intensity, and irradiance; the photometric parameters include luminous flux, luminous intensity, and irradiance. The colorimetric parameters include the color rendering index (CCT).

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