An oyster farm illumination distribution optimization method based on 3D modeling

Abstract

This invention discloses a method for optimizing the illumination distribution in oyster farms based on 3D modeling, comprising the following steps: collecting spatial structure data and water flow data of the oyster farm and unifying them to the same coordinate system; performing preprocessing to generate a standardized 3D modeling scene of the oyster farm; calculating the propagation distribution of light energy in the medium to obtain an initial photon energy density field; performing scattering correction on the initial photon energy density field; generating an ecologically corrected photon energy density field based on the photosensitivity feedback data of the oyster population; generating a time-balanced photon energy density field according to the temporal variation law of the ecologically corrected photon energy density field; calculating the photon energy density differences in different light source areas and adjusting the light source power and illumination angle; and generating illumination distribution results and a photon energy density heatmap. This invention utilizes 3D modeling and multi-source data fusion technology to achieve intelligent optimization of illumination distribution in oyster farms, possessing the advantages of accuracy, efficiency, and ecological adaptability.

Description

Technical Field

[0001] This invention relates to the field of light environment control, and in particular to a method for optimizing light distribution in oyster farms based on 3D modeling. Background Technology

[0002] Existing oyster farm lighting management mainly relies on manual experience or a single lighting monitoring device for adjustment. Usually, the brightness and switching time of the light source are controlled by measuring illuminance or transparency, which is difficult to accurately reflect the propagation characteristics of light in the water medium. Due to the presence of suspended particles, flow disturbances and spatial structure obstruction in the water, light energy undergoes complex refraction, scattering and attenuation phenomena during transmission, resulting in a large difference between the actual lighting distribution and the expected distribution, causing local uneven lighting and unstable oyster growth environment.

[0003] Furthermore, existing studies often neglect the physiological response patterns of oyster populations to light stimulation, fail to combine biological behavioral feedback with light field modeling, and cannot achieve adaptive optimization of light energy distribution. Existing methods generally lack a unified modeling mechanism for light, fluid, and ecological feedback, and have not established a photon energy density regulation system in three-dimensional space. This results in a lack of dynamism and precision in the light optimization process, making it difficult to meet the refined management needs of modern intelligent marine aquaculture environments. Summary of the Invention

[0004] One objective of this invention is to propose a method for optimizing the light distribution in oyster farms based on 3D modeling. This invention utilizes 3D modeling and multi-source data fusion technology to achieve intelligent optimization of the light distribution in oyster farms, possessing the advantages of precision, efficiency, and ecological adaptability.

[0005] An oyster farm illumination distribution optimization method based on 3D modeling according to an embodiment of the present invention includes the following steps:

[0006] Spatial structure data and water flow data of oyster farms were collected, light source illuminance parameters were set, and unified to the same coordinate system to obtain the original three-dimensional dataset;

[0007] Preprocessing is performed on the original 3D dataset to generate a standardized 3D model scene of an oyster farm;

[0008] In the 3D modeling scene of a standardized oyster farm, the light propagation path and the water flow path are spatially mapped, and the transmission distribution of light energy in the medium is calculated to obtain the initial photon energy density field.

[0009] The concentration, particle size distribution and refractive index of suspended particles in the water of oyster farms were measured, and the initial photon energy density field was scattered and corrected to generate an optically corrected photon energy density field.

[0010] The photosensitivity response data of oyster populations is collected to form photosensitivity feedback data of oyster populations, which is then applied to the optically corrected photon energy density field to generate the ecologically corrected photon energy density field.

[0011] Based on the temporal variation law of the ecologically modified photon energy density field, the energy distribution is adjusted to generate a time-balanced photon energy density field;

[0012] Based on the time-equilibrium photon energy density field, the photon energy density difference in different light source regions is calculated, and the light source power and illumination angle are adjusted to generate a spatially balanced photon energy density field.

[0013] Based on the spatially balanced photon energy density field, the illumination distribution results and photon energy density thermogram are generated.

[0014] Optionally, the generation of the original 3D dataset specifically includes:

[0015] Spatial structure data acquisition nodes were set up in the oyster farming area to scan the farming racks, pontoons, support structures and water boundaries to collect spatial structure data;

[0016] Water flow data collection nodes were set up within the oyster farm to measure water flow data at each sampling point;

[0017] Set the light source illuminance parameters;

[0018] A raw three-dimensional dataset is generated by combining spatial structure data and water flow data. The raw three-dimensional dataset is a set of three-dimensional information composed of spatial structure data and water flow data, based on a unified coordinate system, and is used to represent the geometric shape and fluid state of oyster farms.

[0019] Optionally, the generation of the standardized oyster farm 3D model scene includes:

[0020] Noise removal and outlier elimination are performed on the spatial structure data in the original 3D dataset.

[0021] Perform time synchronization and interpolation resampling on the water flow data in the original 3D dataset;

[0022] Coordinate registration is performed between spatial structure data and water flow data;

[0023] Based on coordinate unification, the registered spatial structure data and water flow data are reconstructed into grids and divided into voxel units. The average spatial coordinates and average velocity vector within each voxel unit are calculated to generate a grid data field.

[0024] A standardized oyster farm 3D modeling scene is obtained based on a grid data field. The standardized oyster farm 3D modeling scene refers to a 3D virtual aquaculture environment model with continuous topological relationships constructed using preprocessed spatial structure data and water flow data under a unified coordinate system.

[0025] Optionally, the generation of the initial photon energy density field includes:

[0026] In the 3D modeling scenario of a standardized oyster farm, the pre-processed spatial structure data and water flow data are used as inputs. The light propagation path set is generated according to the light source illuminance parameters and the spatial position of the light source. The propagation length and refraction attenuation information of light in the medium are calculated, and the two are combined to form the energy attenuation coefficient.

[0027] Based on the preprocessed water flow data, the velocity vector of each voxel unit is calculated to generate water flow path data;

[0028] In a unified coordinate system, the light propagation path data and the water flow path data are spatially mapped. For each voxel unit, the cosine of the angle between the light propagation direction and the water flow direction and the coupling coefficient are calculated to form a spatial mapping matrix of light and flow.

[0029] A joint calculation is performed on the spatial mapping matrix of illumination and flow, taking into account the illumination propagation direction, flow velocity vector, energy attenuation coefficient, medium refractive index and absorption coefficient, to calculate the instantaneous photon energy transfer value of each voxel unit;

[0030] Time accumulation and spatial smoothing are performed on the instantaneous photon energy transfer value to generate the initial photon energy density field.

[0031] Optionally, the generation of the spatial mapping matrix between illumination and flow specifically includes:

[0032] The coordinate component difference is calculated for the propagation direction vector of each ray in the light propagation path data, and the square root of the sum of the squares of the three-dimensional components is obtained to obtain the unit vector of the light propagation direction.

[0033] Normalize the velocity vector of each voxel in the water flow path data to obtain the unit vector of the water flow direction;

[0034] Within each voxel, the unit vector in the direction of light propagation and the unit vector in the direction of water flow are multiplied by their components and summed. The result is then divided by the product of the magnitudes of the two vectors to obtain the cosine of the voxel-level angle.

[0035] The cosine values ​​of the included angles of all voxel units are weighted and summed, and the weighted sum is divided by the number of voxels to obtain the coupling coefficient between the direction of light propagation and the direction of water flow.

[0036] The cosine of the included angle and the coupling coefficient obtained from each voxel element are sequentially filled into the corresponding positions of the three-dimensional matrix according to the arrangement order of the voxel elements in the three-dimensional coordinates, to obtain the spatial mapping matrix of illumination and flow.

[0037] Optionally, the generation of the optically corrected photon energy density field specifically includes:

[0038] Multiple water sampling points were set up in the 3D modeling scene of a standardized oyster farm. The water samples at each sampling point were tested to obtain the concentration of suspended particles, particle size distribution and refractive index of the medium. The test results were numbered and time-marked according to the 3D coordinates of the sampling points to generate the original optical dataset of the water body.

[0039] Normalization is performed on the original optical dataset of the water body. Taking each sampling point as the center, the average product of the normalized suspended particle concentration and the normalized particle size of its neighboring sampling points is calculated. Based on the concentration gradient change of neighboring sampling points, abrupt correction is performed on the abrupt points to obtain the average scattering intensity at each spatial location.

[0040] The average scattering intensity is proportionally integrated with the refractive index of the medium. The average scattering intensity is adjusted according to the local variation of the refractive index of the medium, and the optical scattering correction coefficient at each spatial location is calculated.

[0041] The optical scattering correction coefficient is input into the initial photon energy density field, and a voxel-by-voxel multiplication operation is performed on the photon energy density value of each voxel unit and the corresponding optical scattering correction coefficient to obtain the scattering correction photon energy density value.

[0042] The scattered correction photon energy density value is smoothed and normalized to generate an optical correction photon energy density field.

[0043] Optionally, the generation of the ecologically corrected photon energy density field specifically includes:

[0044] In the 3D modeling scene of a standardized oyster farm, the shell valve opening and closing frequency, physiological electrical signal amplitude and local water flow disturbance intensity of the oyster population were collected. The collected data were synchronized in time, noise removed and outlier corrected, and normalized and scaled according to a unified time reference to form oyster population photosensitive feedback data.

[0045] The optically corrected photon energy density field and the oyster population photosensitivity feedback data are matched in spatial position in a three-dimensional coordinate system. The photon energy density value and the corresponding photosensitivity feedback vector of each voxel are read. The shell valve opening and closing frequency, physiological electrical signal amplitude and local water flow disturbance intensity of the photosensitivity feedback vector are weighted and averaged to obtain the normalized photosensitivity feedback intensity value. The normalized photosensitivity feedback intensity value and the photon energy density value are multiplied to obtain the voxel photosensitivity response energy value. All voxel photosensitivity response energy values ​​are smoothed in three-dimensional spatial coordinate order to generate the ecologically corrected photon energy density field.

[0046] For each voxel unit in the photosensitive response energy matrix, calculate the arithmetic mean of its photosensitive response energy value with that of its neighboring voxels, and then average the current voxel photosensitive response energy value with the arithmetic mean to obtain the voxel equilibrium photosensitive energy value;

[0047] All voxel balanced photosensitive energy values ​​are smoothed according to the three-dimensional spatial coordinate order to generate an ecologically corrected photon energy density field.

[0048] Optionally, the generation of the time-balanced photon energy density field specifically includes:

[0049] Based on the time series data of the ecologically corrected photon energy density field, the continuously sampled time series is divided into several time windows of equal length, and the average photon energy density of each voxel in each time window is extracted to form a time window energy mean sequence.

[0050] Perform a difference operation on the average photon energy density of adjacent time windows to calculate the average photon energy change rate of each voxel between adjacent time windows, forming a voxel energy change rate sequence.

[0051] Based on the voxel energy change rate sequence, the energy fluctuation amplitude of each voxel over the entire time range is calculated, and the normalized result of the energy fluctuation amplitude is used as a correction factor to generate an updated voxel energy distribution.

[0052] The updated voxel energy distribution is smoothed and normalized across the entire field to obtain a time-balanced photon energy density field.

[0053] Optionally, the generation of the spatially equalized photon energy density field specifically includes:

[0054] Using the time-balanced photon energy density field as input, statistical calculations are performed on the voxel photon energy density values ​​in the illumination areas of each light source to obtain the average photon energy density value of each light source area, and the average photon energy density value of the entire field is calculated.

[0055] The difference between the average photon energy density value of each light source region and the average photon energy density value of the whole field is calculated to obtain the photon energy offset of each light source region. When the photon energy offset is positive, it is marked as an area with excessive energy, and when the photon energy offset is negative, it is marked as an area with insufficient energy, thus forming photon energy density difference data.

[0056] Based on the photon energy density difference data, the ratio of the absolute value of the photon energy offset in each light source region to the average photon energy density value of the whole field is calculated. This ratio is used as the power adjustment coefficient. The power of the light source in the region with excessive energy is decreased proportionally according to the power adjustment coefficient, and the power of the light source in the region with insufficient energy is increased proportionally according to the power adjustment coefficient, thus generating the light source power correction parameter.

[0057] Using the light source power correction parameter as input, the angle between the light source illumination direction and the normal of the illuminated area is calculated. The light source illumination angle correction parameter is determined by the cosine change of the angle, and the light source illumination angle correction data is generated.

[0058] The light source power correction parameters and light source illumination angle correction data are applied to the time-balanced photon energy density field, and power weighting and angle weighting are performed on the photon energy density values ​​of each voxel to generate a spatially balanced photon energy density field.

[0059] Optionally, the generation of the illumination distribution results and the photon energy density heatmap specifically includes:

[0060] Using the spatially balanced photon energy density field as input, the photon energy density values ​​of each voxel are traversed, and the three-dimensional coordinates and photon energy density values ​​of each voxel are extracted to generate a photon energy density dataset.

[0061] Based on the spatial coordinate distribution in the photon energy density dataset, a three-dimensional interpolation method is used to calculate the estimated photon energy density at unsampled locations, generating a continuous spatial photon energy density field.

[0062] Normalize the continuous spatial photon energy density field, calculate the difference between the photon energy density of each voxel and the average photon energy density of the whole field, and assign color gradients according to the sign and amplitude of the difference to generate a photon energy density heatmap.

[0063] Based on the energy gradient distribution of the photon energy density heatmap, the spatial range of high-energy and low-energy regions is identified, the energy coverage and energy concentration of the corresponding regions of each light source are calculated, and the energy coverage and photon energy density data are weighted and superimposed to generate the illumination distribution results.

[0064] The illumination distribution results and photon energy density heatmap are visualized and output in a unified coordinate system.

[0065] The beneficial effects of this invention are:

[0066] This invention introduces a comprehensive computing framework that integrates 3D modeling and multi-source data fusion to achieve precise modeling and intelligent optimization of light distribution in oyster farms. It effectively overcomes the technical shortcomings of existing light control methods, such as reliance on experience, slow response, and coarse adjustment. By deploying spatial structure data acquisition nodes and water flow data acquisition nodes in the farm, a standardized 3D modeling scene under a unified coordinate system is constructed, enabling a spatial mapping between light propagation paths and water flow paths. This forms a light energy transfer model that truly reflects the propagation, refraction, and attenuation characteristics of light in the water medium, providing a stable physical basis for photon energy density calculation.

[0067] This invention further utilizes water optical parameters such as suspended particle concentration, particle size distribution, and medium refractive index to perform scattering correction on the initial photon energy density field, improving the model's ability to express the optical properties of complex water bodies. By constructing an optically corrected photon energy density field, it can accurately characterize the actual transmission state of light energy in particulate media, avoiding simulation biases caused by neglecting the microstructure of water bodies in traditional illumination models. At the same time, this invention introduces photosensitive feedback data of the oyster community's shell valve opening and closing frequency, physiological electrical signals, and local water flow disturbance intensity to achieve coupled calculation of the light energy density field and biological response signals, generating an ecologically corrected photon energy density field. This gives the illumination optimization process a biological feedback adaptive characteristic, enabling dynamic adjustment of the light distribution according to the physiological state of the oysters.

[0068] Furthermore, this invention constructs a time-balanced photon energy density field in the time dimension, and achieves time stabilization of light intensity by normalizing the energy fluctuation amplitude. In the spatial dimension, it introduces light source power correction parameters and illumination angle correction data, and achieves spatial light energy distribution equalization through dual weighting of power and angle. The resulting spatially balanced photon energy density field and illumination distribution results can be intuitively presented in a three-dimensional visualization heat map, making the light source configuration more scientific and reasonable. Attached Figure Description

[0069] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0070] Figure 1 This is a flowchart of a method for optimizing light distribution in oyster farms based on 3D modeling, as proposed in this invention.

[0071] Figure 2 This is a schematic diagram of the generation of an ecologically modified photon energy density field for an oyster farm illumination distribution optimization method based on 3D modeling proposed in this invention.

[0072] Figure 3 This is a schematic diagram illustrating the method for optimizing the illumination distribution in oyster farms based on 3D modeling proposed in this invention, which involves jointly adjusting the light source power and illumination angle to generate a spatially balanced photon energy density field. Detailed Implementation

[0073] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0074] refer to Figures 1-3 A method for optimizing light distribution in oyster farms based on 3D modeling includes the following steps:

[0075] Spatial structure data and water flow data of oyster farms were collected, light source illuminance parameters were set, and unified to the same coordinate system to obtain the original three-dimensional dataset;

[0076] Preprocessing is performed on the original 3D dataset to generate a standardized 3D model scene of an oyster farm;

[0077] In the 3D modeling scene of a standardized oyster farm, the light propagation path and the water flow path are spatially mapped, and the transmission distribution of light energy in the medium is calculated to obtain the initial photon energy density field.

[0078] The concentration, particle size distribution and refractive index of suspended particles in the water of oyster farms were measured, and the initial photon energy density field was scattered and corrected to generate an optically corrected photon energy density field.

[0079] The photosensitivity response data of oyster populations is collected to form photosensitivity feedback data of oyster populations, which is then applied to the optically corrected photon energy density field to generate the ecologically corrected photon energy density field.

[0080] Based on the temporal variation law of the ecologically modified photon energy density field, the energy distribution is adjusted to generate a time-balanced photon energy density field;

[0081] Based on the time-equilibrium photon energy density field, the photon energy density difference in different light source regions is calculated, and the light source power and illumination angle are adjusted to generate a spatially balanced photon energy density field.

[0082] Based on the spatially balanced photon energy density field, the illumination distribution results and photon energy density thermogram are generated.

[0083] In this embodiment, the generation of the original three-dimensional dataset specifically includes:

[0084] Spatial structure data acquisition nodes were set up in the oyster farming area to scan the farming racks, pontoons, support structures and water boundaries to collect spatial structure data;

[0085] Water flow data acquisition nodes are set up in the oyster farm to measure water flow data at each sampling point. The water flow data includes velocity components in three dimensions.

[0086] Set the light source illuminance parameters, which are used to characterize the light energy intensity per unit area;

[0087] A raw three-dimensional dataset is generated by combining spatial structure data and water flow data. The raw three-dimensional dataset is a set of three-dimensional information composed of spatial structure data and water flow data, based on a unified coordinate system, and is used to represent the geometric shape and fluid state of oyster farms.

[0088] In this embodiment, the generation of the standardized oyster farm 3D modeling scene includes:

[0089] Noise removal and outlier elimination are performed on the spatial structure data in the original 3D dataset.

[0090] Time synchronization and interpolation resampling are performed on the water flow data in the original 3D dataset to ensure the integrity and smoothness of the water flow data in the time dimension.

[0091] Coordinate registration is performed between spatial structure data and water flow data;

[0092] Based on coordinate unification, the registered spatial structure data and water flow data are reconstructed into grids and divided into voxel units. The average spatial coordinates and average velocity vectors within each voxel unit are calculated to generate a grid data field with continuous spatial distribution and consistent density characteristics.

[0093] A standardized oyster farm 3D modeling scene is obtained based on a grid data field. The standardized oyster farm 3D modeling scene refers to a 3D virtual aquaculture environment model with continuous topological relationships constructed using preprocessed spatial structure data and water flow data under a unified coordinate system.

[0094] In this embodiment, the generation of the initial photon energy density field includes:

[0095] In the 3D modeling scenario of a standardized oyster farm, the pre-processed spatial structure data and water flow data are used as inputs. The light propagation path set is generated according to the light source illuminance parameters and the spatial position of the light source. The propagation length and refraction attenuation information of light in the medium are calculated. The two are combined to form an energy attenuation coefficient, which is used to characterize the distance attenuation of light energy in the water medium and the interface energy loss.

[0096] The generation of the energy attenuation coefficient specifically includes: using the light source illuminance parameter as the initial light energy intensity value, using the spatial position of the light source as the path starting point, dividing the light emission direction according to a preset angle step size, and generating a set of light emission directions; in each light emission direction, searching for the intersection points of the light and the grid unit layer by layer based on the preprocessed spatial structure data, calculating and accumulating the spatial distance from the light source to each intersection point to obtain the propagation length value of the light in the medium; on the propagation path, calculating the interface energy transmission ratio using the refractive index of the medium and the incident angle of the light, and representing the refraction attenuation value by multiplying the transmission ratio by the number of interfaces; multiplying the propagation length value by the refraction attenuation value to obtain the energy attenuation of each light during propagation, and then calculating the energy attenuation coefficient by the ratio of the energy attenuation to the initial light energy intensity value, which is used to represent the overall attenuation degree of light energy propagation in the medium, wherein the number of interfaces refers to the number of times the light passes through different media during propagation;

[0097] Based on the preprocessed water flow data, the velocity vector of each voxel unit is calculated to generate water flow path data, which is used to describe the dynamic flow state of water in the spatial and temporal dimensions.

[0098] In a unified coordinate system, the light propagation path data and the water flow path data are spatially mapped. For each voxel unit, the cosine of the angle between the light propagation direction and the water flow direction and the coupling coefficient are calculated to form a spatial mapping matrix of light and flow.

[0099] A joint calculation is performed on the spatial mapping matrix of illumination and flow, taking into account the illumination propagation direction, flow velocity vector, energy attenuation coefficient, medium refractive index and absorption coefficient, to calculate the instantaneous photon energy transfer value of each voxel unit;

[0100] The generation of the instantaneous photon energy transfer value specifically includes: matching the light propagation direction vector and the velocity vector within each voxel unit, and calculating the dot product of the two vectors as the energy direction coupling value; multiplying the energy direction coupling value with the magnitude of the velocity vector to obtain the light energy flow influence within the voxel, which is used to characterize the correction of the light energy transfer intensity by fluid disturbance; multiplying this light energy flow influence with the energy attenuation coefficient to superimpose the energy loss effect caused by propagation length and refraction attenuation, generating the voxel corrected energy value; and then dividing the voxel corrected energy value by the refractive index of the medium to obtain... The refraction correction energy value is used to reflect the refraction adjustment ratio of the medium interface on energy transfer. Then, the refraction correction energy value is multiplied by the medium absorption coefficient, and the negative of the natural logarithm is taken as the energy absorption correction term, which is used to represent the degree of energy attenuation of light energy under the action of medium absorption. The energy direction coupling value, voxel correction energy value, refraction correction energy value and energy absorption correction term are weighted and summed, and the weighted result is divided by the number of voxels to obtain the instantaneous photon energy transfer value of each voxel unit. The instantaneous photon energy transfer value is used to quantify the local transmission intensity of light energy in the 3D modeling scene of a standardized oyster farm.

[0101] The instantaneous photon energy transfer values ​​are subjected to temporal accumulation and spatial smoothing to generate an initial photon energy density field. Specifically, this involves: performing hourly summation of the instantaneous photon energy transfer values ​​for consecutive time windows in chronological order, and dividing the sum by the number of time windows to obtain the voxel-level average photon energy value, which represents the cumulative intensity of light energy in the time dimension; selecting the average photon energy values ​​of adjacent voxels centered on each voxel, calculating the square of the energy difference between adjacent voxels and summing the results, then dividing by the number of adjacent voxels and taking the square root to obtain the voxel energy gradient amplitude value; and then applying the voxel energy gradient amplitude... The average photon energy value of each voxel is weighted to obtain a spatially smoothed photon energy value. The average photon energy value of each voxel is averaged with the spatially smoothed photon energy value to obtain a voxel composite light energy value, which is used to simultaneously reflect the energy effect of time accumulation and spatial smoothing. The voxel composite light energy values ​​of all voxels are arranged and smoothed according to the three-dimensional coordinate order and normalized to generate a photon energy density value, thereby obtaining an initial photon energy density field. The initial photon energy density field is used to characterize the stable transmission state of light energy in the standardized oyster farming 3D modeling scene.

[0102] In this embodiment, the generation of the spatial mapping matrix between illumination and flow specifically includes:

[0103] The coordinate component difference is calculated for the propagation direction vector of each ray in the light propagation path data, and the square root of the sum of the squares of the three-dimensional components is obtained to obtain the unit vector of the light propagation direction.

[0104] Normalize the velocity vector of each voxel in the water flow path data to obtain the unit vector of the water flow direction;

[0105] Within each voxel, the unit vector of the light propagation direction and the unit vector of the water flow direction are multiplied by their components and summed. The result is then divided by the product of the magnitudes of the two vectors to obtain the cosine value of the voxel-level angle, which is used to characterize the angle distribution between the light propagation direction and the water flow direction.

[0106] The cosine values ​​of the included angles of all voxel units are weighted and summed, and the weighted sum is divided by the number of voxels to obtain the coupling coefficient between the direction of light propagation and the direction of water flow.

[0107] The cosine of the included angle and the coupling coefficient obtained from each voxel element are sequentially filled into the corresponding positions of the three-dimensional matrix according to the arrangement order of the voxel elements in the three-dimensional coordinates, to obtain the spatial mapping matrix of illumination and flow.

[0108] In this embodiment, the generation of the optically corrected photon energy density field specifically includes:

[0109] Multiple water sampling points were set up in the 3D modeling scene of a standardized oyster farm. The water samples at each sampling point were tested to obtain the concentration of suspended particles, particle size distribution and refractive index of the medium. The test results were numbered and time-marked according to the 3D coordinates of the sampling points to generate the original optical dataset of the water body.

[0110] Normalization is performed on the original optical dataset of the water body. Taking each sampling point as the center, the average product of the normalized suspended particle concentration and the normalized particle size of its neighboring sampling points is calculated. The abrupt change points are smoothed according to the concentration gradient change of neighboring sampling points to obtain the average scattering intensity at each spatial location, which is used to characterize the scattering response characteristics of the water body to light propagation in three-dimensional space.

[0111] The generation of the average scattering intensity specifically includes: selecting several adjacent sampling points in a three-dimensional coordinate system with each sampling point as the center; reading the normalized suspended particle concentration and normalized particle size of the adjacent sampling points respectively; multiplying the two to obtain the local scattering contribution value of the adjacent sampling points; summing all the local scattering contribution values ​​of the sampling point and its adjacent sampling points by addition; dividing the summation result by the number of sampling points to obtain the average scattering product of the sampling point, which is used to characterize the basic scattering intensity of the local area; calculating the concentration gradient change amplitude of the sampling point; for abrupt changes where the concentration gradient change amplitude exceeds a preset threshold, adding the average scattering product of the point to the average scattering product of the adjacent sampling points and taking the average value, replacing the original abrupt change point's average scattering product to achieve local smoothing correction; after all sampling points have completed smoothing correction, reordering the corrected average scattering product of each sampling point according to the three-dimensional spatial coordinates to generate average scattering intensity data corresponding to each spatial location, which is used to describe the local scattering characteristics of water body for light propagation in three-dimensional space;

[0112] The average scattering intensity is proportionally integrated with the refractive index of the medium. The average scattering intensity is adjusted according to the local variation ratio of the refractive index of the medium. The optical scattering correction coefficient at each spatial location is calculated. The optical scattering correction coefficient is used to reflect the comprehensive correction degree of the light propagation path by the water body under local medium conditions.

[0113] The generation of the optical scattering correction coefficient specifically includes: taking each spatial location as a unit, reading the corresponding average scattering intensity and the refractive index of the medium, performing a division operation between the two to obtain a refractive correction ratio, which is used to characterize the proportion of the influence of refractive index change on scattering intensity; calculating the difference in refractive index of the medium between adjacent spatial locations, taking the absolute value of the difference and dividing it by the average refractive index of the medium to obtain the local change ratio of refractive index; multiplying the average scattering intensity by the refractive correction ratio, and performing an additive correction on the result according to the local change ratio of refractive index to obtain the refractive adjusted scattering intensity value; summing the refractive adjusted scattering intensity values ​​of adjacent spatial locations, and dividing the sum by the number of spatial locations to obtain the optical scattering correction coefficient;

[0114] The optical scattering correction coefficient is input into the initial photon energy density field. A voxel-by-voxel multiplication operation is performed on the photon energy density value of each voxel unit and the corresponding optical scattering correction coefficient to obtain the scattering correction photon energy density value, which is used to reflect the energy attenuation result after scattering and refraction of the medium.

[0115] Smoothing is performed on the scattered photon energy density value to eliminate local energy abrupt changes and enhance the continuity of spatial transition. Then, it is normalized to generate an optically corrected photon energy density field. The optically corrected photon energy density field is used to characterize the spatial transmission state of light energy after correction under the combined effects of suspended particle concentration, particle size distribution and medium refractive index.

[0116] In this embodiment, the generation of the ecologically corrected photon energy density field specifically includes:

[0117] In the 3D modeling scene of a standardized oyster farm, the shell valve opening and closing frequency, physiological electrical signal amplitude and local water flow disturbance intensity of the oyster population were collected. The collected data were synchronized in time, noise removed and outlier corrected, and normalized and scaled according to a unified time reference to form oyster population photosensitive feedback data, which is used to characterize the comprehensive response characteristics of the oyster population to light stimulation.

[0118] The intensity of local water flow disturbance is obtained by calculating the absolute value of the difference between the instantaneous value of the flow velocity and the average flow velocity within a fixed time window and taking the average value, which is used to reflect the average amplitude of water flow change.

[0119] The optically corrected photon energy density field and the oyster population photosensitivity feedback data are matched in spatial position in a three-dimensional coordinate system. The photon energy density value and the corresponding photosensitivity feedback vector of each voxel are read. The shell valve opening and closing frequency, physiological electrical signal amplitude and local water flow disturbance intensity of the photosensitivity feedback vector are weighted and averaged to obtain the normalized photosensitivity feedback intensity value. The normalized photosensitivity feedback intensity value and the photon energy density value are multiplied to obtain the voxel photosensitivity response energy value. All voxel photosensitivity response energy values ​​are smoothed in three-dimensional spatial coordinate order to generate the ecologically corrected photon energy density field.

[0120] For each voxel unit in the photosensitive response energy matrix, calculate the arithmetic mean of its photosensitive response energy value with that of its neighboring voxels, and then average the current voxel photosensitive response energy value with the arithmetic mean to obtain the voxel equilibrium photosensitive energy value;

[0121] All voxel balanced photosensitive energy values ​​are smoothed according to the three-dimensional spatial coordinate order to generate an ecologically corrected photon energy density field, which is used to describe the light energy distribution state under the photosensitive feedback of the oyster population.

[0122] In this embodiment, the generation of the time-balanced photon energy density field specifically includes:

[0123] Based on the time series data of the ecologically corrected photon energy density field, the continuously sampled time series is divided into several time windows of equal length. The average photon energy density of each voxel in each time window is extracted to form a time window energy mean sequence, which is used to represent the distribution state of light energy in different time intervals.

[0124] Perform a difference operation on the average photon energy density of adjacent time windows to calculate the average photon energy change rate of each voxel between adjacent time windows, forming a voxel energy change rate sequence, which is used to characterize the average evolution trend of light energy in the time dimension.

[0125] Based on the voxel energy change rate sequence, the energy fluctuation amplitude of each voxel over the entire time range is calculated, and the normalized result of the energy fluctuation amplitude is used as a correction factor to generate an updated voxel energy distribution.

[0126] The generation of the updated voxel energy distribution data specifically includes: for each voxel in the voxel energy change rate sequence, taking the absolute value of the photon energy density change rate over all time windows, and averaging the results over all time windows to obtain the average energy fluctuation amplitude of that voxel; normalizing the average energy fluctuation amplitude of each voxel to generate a normalized fluctuation amplitude; using the average value of the normalized fluctuation amplitude of all voxels as the energy balance benchmark, calculating the difference between the normalized fluctuation amplitude of each voxel and the energy balance benchmark to obtain the voxel energy offset value; adjusting according to the positive or negative direction of the voxel energy offset value, proportionally reducing the current photon energy density value of voxels with positive energy offset values, and proportionally increasing the current photon energy density value of voxels with negative energy offset values, with the adjustment ratio being proportional to the absolute value of the energy offset value; the adjusted photon energy density values ​​constitute the updated voxel energy distribution, reflecting the balancing result of light energy in the time dimension;

[0127] The updated voxel energy distribution is smoothed and normalized across the entire field to obtain a time-balanced photon energy density field, which is used to characterize the time-stable distribution of light energy in the three-dimensional space of a standardized oyster farm.

[0128] In this embodiment, the generation of the spatially balanced photon energy density field specifically includes:

[0129] Using the time-balanced photon energy density field as input, statistical calculations are performed on the voxel photon energy density values ​​in the illumination areas of each light source to obtain the average photon energy density value of each light source area, and the average photon energy density value of the entire field is calculated.

[0130] The difference between the average photon energy density value of each light source region and the average photon energy density value of the whole field is calculated to obtain the photon energy offset of each light source region. When the photon energy offset is positive, it is marked as an area with excessive energy, and when the photon energy offset is negative, it is marked as an area with insufficient energy, thus forming photon energy density difference data.

[0131] Based on the photon energy density difference data, the ratio of the absolute value of the photon energy offset in each light source region to the average photon energy density value of the whole field is calculated. This ratio is used as the power adjustment coefficient. The power of the light source in the region with excessive energy is decreased proportionally according to the power adjustment coefficient, and the power of the light source in the region with insufficient energy is increased proportionally according to the power adjustment coefficient, thus generating the light source power correction parameter.

[0132] Using the light source power correction parameter as input, the angle between the light source illumination direction and the normal of the illuminated area is calculated. The light source illumination angle correction parameter is determined by the cosine change of the angle, and the light source illumination angle correction data is generated.

[0133] The generation of the light source illumination angle correction data specifically includes: obtaining the spatial position and illumination direction vector corresponding to each light source, and determining the normal direction of each voxel unit in the illuminated area; calculating the angle between the light source illumination direction vector and the normal direction of the illuminated area; comparing the cosine value of the angle with a set threshold; when the cosine value is less than the set threshold, calculating the angle difference and performing an angle decrement operation, using the angle decrement as the illumination angle adjustment value; when the cosine value is greater than the set threshold, calculating the angle difference and performing an angle increment operation, using the angle increment as the illumination angle adjustment value; multiplying the angle adjustment value of each light source with the corresponding light source power correction parameter to obtain the light source angle correction intensity data; averaging all the light source angle correction intensity data to obtain the light source illumination angle correction parameter; applying it to the illumination direction of each light source to update the light source illumination angle and generate the light source illumination angle correction data.

[0134] The light source power correction parameters and light source illumination angle correction data are applied to the time-balanced photon energy density field. Power weighting and angle weighting are performed on the photon energy density values ​​of each voxel to generate a spatially balanced photon energy density field. Specifically, this includes: for each voxel unit, calculating the power weight value based on the ratio of the light source power correction parameter of the light source corresponding to its illuminated area to the original light source power; obtaining the angle weight value based on the ratio of the change in the incident angle of the voxel in the light source illumination angle correction data to a set threshold; averaging the original voxel photon energy density value by multiplying it by the power weight value and the angle weight value respectively to obtain the voxel weighted photon energy value; and performing smoothing correction on the weighted photon energy values ​​of all voxels in three-dimensional spatial coordinate order to generate a spatially balanced photon energy density field. The spatially balanced photon energy density field is used to characterize the balanced distribution state of light energy under the combined effect of light source power and illumination angle.

[0135] In this embodiment, the generation of the illumination distribution result and the photon energy density heatmap specifically includes:

[0136] Using the spatially balanced photon energy density field as input, the photon energy density values ​​of each voxel are traversed, and the three-dimensional coordinates and photon energy density values ​​of each voxel are extracted to generate a photon energy density dataset.

[0137] Based on the spatial coordinate distribution in the photon energy density dataset, the estimated photon energy density at unsampled locations is calculated using a three-dimensional interpolation method, generating a continuous spatial photon energy density field to characterize the continuous distribution of light energy in the three-dimensional space of the aquaculture farm.

[0138] Normalize the continuous spatial photon energy density field, calculate the difference between the photon energy density of each voxel and the average photon energy density of the whole field, and assign color gradients according to the sign and amplitude of the difference to generate a photon energy density heatmap.

[0139] Based on the energy gradient distribution of the photon energy density heatmap, the spatial range of high-energy and low-energy regions is identified, the energy coverage and energy concentration of the corresponding regions of each light source are calculated, and the energy coverage and photon energy density data are weighted and superimposed to generate the illumination distribution results.

[0140] The illumination distribution results and photon energy density heatmap are visualized and output in a unified coordinate system.

[0141] Example 1:

[0142] To verify the feasibility of this invention in practice, it was applied to the lighting environment control practice of a coastal oyster farming base. This farming base has a wide distribution and complex water flow. Traditional lighting methods generally suffer from uneven light energy distribution, local overexposure, or insufficient illuminance, which affects the physiological metabolism and shell development of the oyster population. This invention, based on three-dimensional modeling technology, obtains spatial geometric information of the farming racks, floats, support structures, and water boundaries by setting up spatial structure data acquisition nodes and water flow data acquisition nodes. At the same time, it collects water flow velocity and direction data at multiple time periods to construct an original three-dimensional dataset under a unified coordinate system. After data cleaning, outlier removal, and time synchronization processing, a standardized three-dimensional modeling scene of the oyster farm is generated, providing a basic environmental model for lighting optimization calculations.

[0143] In this scenario, by inputting the illuminance parameters and spatial location of the light source, a set of light propagation paths is generated. This is then combined with water flow path data to achieve spatial mapping, thereby calculating the propagation and attenuation distribution of light energy in the medium and obtaining an initial photon energy density field. Subsequently, multiple water sampling points are set up in the actual aquaculture area to measure the concentration of suspended particles, particle size distribution, and refractive index of the medium. These optical parameters are used to scatter and correct the initial photon energy density field, obtaining an optically corrected photon energy density field. Then, by collecting the shell valve opening and closing frequency, physiological electrical signals, and local water flow disturbance signals of the oyster population, photosensitive feedback data is generated and applied to the optically corrected photon energy density field to form an ecologically corrected photon energy density field, thereby reflecting the real-time photophysiological response characteristics of the oyster population.

[0144] During the dynamic adjustment of illumination, the energy distribution is balanced based on the temporal variation law of the ecologically modified photon energy density field, generating a time-balanced photon energy density field to ensure the stability of light energy input at different time periods. For areas with uneven spatial distribution, the differences in photon energy density in different light source areas are further calculated, and the power of the light source and the illumination angle are automatically adjusted to make the spatial illumination tend to be balanced, ultimately generating a spatially balanced photon energy density field. Based on this result, the illumination distribution results and photon energy density heat map can be output, providing a visual basis for light energy regulation in the aquaculture area.

[0145] To verify the performance of the present invention in practice, it was compared with the traditional fixed-point lighting method, and the results are shown in Table 1.

[0146] Table 1. Performance Comparison of the Method of the Present Invention and Traditional Fixed-Point Lighting Method

[0147]

[0148] As can be clearly seen from Table 1, the method of this invention outperforms the traditional method in all key performance indicators. In the experimental comparison, the traditional fixed-point illumination method, due to the lack of three-dimensional environment modeling and dynamic light energy control mechanism, resulted in uneven distribution of light in space, with a light intensity standard deviation coefficient as high as 0.42, and obvious local overexposure and shadow areas. This invention adopts a combination of three-dimensional modeling and light propagation path calculation, and achieves a balanced distribution of light energy in the spatial and temporal dimensions by constructing a photon energy density field, which improves the uniformity of illumination by 57.1%, reduces the attenuation of light energy under the action of water flow, and increases the energy utilization rate from 64.5% to 89.2%. This shows that this method can effectively reduce light energy waste and improve illumination coverage.

[0149] From an ecological perspective, the average growth rate of oysters increased by 54%, and the shell color and thickness were more balanced during the growth cycle, indicating that the role of light regulation in promoting physiological metabolism was enhanced. At the same time, the stability of light energy distribution was improved by 61.3%, indicating that good energy consistency could still be maintained under external environmental fluctuations. This is attributed to the dynamic fluctuation correction mechanism introduced in the calculation of time-balanced photon energy density field in this invention.

[0150] In terms of energy consumption, this invention makes real-time adjustments based on the light source power correction parameters and illumination angle correction data, reducing the overall power consumption of the lighting system by 29%. In addition, by introducing the ecological correction photon energy density field, the lighting control is more in line with the photosensitive response characteristics of the oyster population, increasing the population health index by 16.7% and shortening the lighting adjustment response time to about half of the original.

[0151] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for optimizing the light distribution in an oyster farm based on 3D modeling, characterized in that, Includes the following steps: Spatial structure data and water flow data of oyster farms were collected, light source illuminance parameters were set, and unified to the same coordinate system to obtain the original three-dimensional dataset; Preprocessing is performed on the original 3D dataset to generate a standardized 3D model scene of an oyster farm; In the 3D modeling scene of a standardized oyster farm, the light propagation path and the water flow path are spatially mapped, and the transmission distribution of light energy in the medium is calculated to obtain the initial photon energy density field. The concentration, particle size distribution and refractive index of suspended particles in the water of oyster farms were measured, and the initial photon energy density field was scattered and corrected to generate an optically corrected photon energy density field. The photosensitivity response data of oyster populations is collected to form photosensitivity feedback data of oyster populations, which is then applied to the optically corrected photon energy density field to generate the ecologically corrected photon energy density field. Based on the temporal variation law of the ecologically modified photon energy density field, the energy distribution is adjusted to generate a time-balanced photon energy density field; Based on the time-equilibrium photon energy density field, the photon energy density difference in different light source regions is calculated, and the light source power and illumination angle are adjusted to generate a spatially balanced photon energy density field. Based on the spatially balanced photon energy density field, the illumination distribution results and photon energy density heatmap are generated. The generation of the ecologically corrected photon energy density field specifically includes: In the 3D modeling scene of a standardized oyster farm, the shell valve opening and closing frequency, physiological electrical signal amplitude and local water flow disturbance intensity of the oyster population were collected. The collected data were synchronized in time, noise removed and outlier corrected, and normalized and scaled according to a unified time reference to form oyster population photosensitive feedback data. The optically corrected photon energy density field and the oyster population photosensitivity feedback data are matched in spatial position in a three-dimensional coordinate system. The photon energy density value and the corresponding photosensitivity feedback vector of each voxel are read. The shell valve opening and closing frequency, physiological electrical signal amplitude and local water flow disturbance intensity of the photosensitivity feedback vector are weighted and averaged to obtain the normalized photosensitivity feedback intensity value. The normalized photosensitivity feedback intensity value and the photon energy density value are multiplied to obtain the voxel photosensitivity response energy value. All voxel photosensitivity response energy values ​​are smoothed in three-dimensional spatial coordinate order to generate the ecologically corrected photon energy density field. For each voxel unit in the photosensitive response energy matrix, calculate the arithmetic mean of its photosensitive response energy value with that of its neighboring voxels, and then average the current voxel photosensitive response energy value with the arithmetic mean to obtain the voxel equilibrium photosensitive energy value; All voxel balanced photosensitive energy values ​​are smoothed according to the three-dimensional spatial coordinate order to generate an ecologically corrected photon energy density field.

2. The method for optimizing the light distribution in an oyster farm based on 3D modeling according to claim 1, characterized in that, The generation of the original 3D dataset specifically includes: Spatial structure data acquisition nodes were set up in the oyster farming area to scan the farming racks, pontoons, support structures and water boundaries to collect spatial structure data; Water flow data collection nodes were set up within the oyster farm to measure water flow data at each sampling point; Set the light source illuminance parameters; A raw three-dimensional dataset is generated by combining spatial structure data and water flow data. The raw three-dimensional dataset is a set of three-dimensional information composed of spatial structure data and water flow data, based on a unified coordinate system, and is used to represent the geometric shape and fluid state of oyster farms.

3. The method of claim 1, wherein the method is characterized by: The generation of the standardized oyster farm 3D model scene includes: Noise removal and outlier elimination are performed on the spatial structure data in the original 3D dataset. Perform time synchronization and interpolation resampling on the water flow data in the original 3D dataset; Coordinate registration is performed between spatial structure data and water flow data; Based on coordinate unification, the registered spatial structure data and water flow data are reconstructed into grids and divided into voxel units. The average spatial coordinates and average velocity vector within each voxel unit are calculated to generate a grid data field. A standardized oyster farm 3D modeling scene is obtained based on a grid data field. The standardized oyster farm 3D modeling scene refers to a 3D virtual aquaculture environment model with continuous topological relationships constructed using preprocessed spatial structure data and water flow data under a unified coordinate system.

4. The method for optimizing the light distribution in an oyster farm based on 3D modeling according to claim 1, characterized in that, The generation of the initial photon energy density field includes: In the 3D modeling scenario of a standardized oyster farm, the pre-processed spatial structure data and water flow data are used as inputs. The light propagation path set is generated according to the light source illuminance parameters and the spatial position of the light source. The propagation length and refraction attenuation information of light in the medium are calculated, and the two are combined to form the energy attenuation coefficient. Based on the preprocessed water flow data, the velocity vector of each voxel unit is calculated to generate water flow path data; In a unified coordinate system, the light propagation path data and the water flow path data are spatially mapped. For each voxel unit, the cosine of the angle between the light propagation direction and the water flow direction and the coupling coefficient are calculated to form a spatial mapping matrix of light and flow. A joint calculation is performed on the spatial mapping matrix of illumination and flow, taking into account the illumination propagation direction, flow velocity vector, energy attenuation coefficient, medium refractive index and absorption coefficient, to calculate the instantaneous photon energy transfer value of each voxel unit; Time accumulation and spatial smoothing are performed on the instantaneous photon energy transfer value to generate the initial photon energy density field.

5. The method for optimizing light distribution in oyster farms based on 3D modeling according to claim 4, characterized in that, The generation of the spatial mapping matrix between illumination and flow specifically includes: The coordinate component difference is calculated for the propagation direction vector of each ray in the light propagation path data, and the square root of the sum of the squares of the three-dimensional components is obtained to obtain the unit vector of the light propagation direction. Normalize the velocity vector of each voxel in the water flow path data to obtain the unit vector of the water flow direction; Within each voxel, the unit vector in the direction of light propagation and the unit vector in the direction of water flow are multiplied by their components and summed. The result is then divided by the product of the magnitudes of the two vectors to obtain the cosine of the voxel-level angle. The cosine values ​​of the included angles of all voxel units are weighted and summed, and the weighted sum is divided by the number of voxels to obtain the coupling coefficient between the direction of light propagation and the direction of water flow. The cosine of the included angle and the coupling coefficient obtained from each voxel element are sequentially filled into the corresponding positions of the three-dimensional matrix according to the arrangement order of the voxel elements in the three-dimensional coordinates, to obtain the spatial mapping matrix of illumination and flow.

6. The method for optimizing light distribution in oyster farms based on 3D modeling according to claim 1, characterized in that, The generation of the optically corrected photon energy density field specifically includes: Multiple water sampling points were set up in the 3D modeling scene of a standardized oyster farm. The water samples at each sampling point were tested to obtain the concentration of suspended particles, particle size distribution and refractive index of the medium. The test results were numbered and time-marked according to the 3D coordinates of the sampling points to generate the original optical dataset of the water body. Normalization is performed on the original optical dataset of the water body. Taking each sampling point as the center, the average product of the normalized suspended particle concentration and the normalized particle size of its neighboring sampling points is calculated. Based on the concentration gradient change of neighboring sampling points, abrupt correction is performed on the abrupt points to obtain the average scattering intensity at each spatial location. The average scattering intensity is proportionally integrated with the refractive index of the medium. The average scattering intensity is adjusted according to the local variation of the refractive index of the medium, and the optical scattering correction coefficient at each spatial location is calculated. The optical scattering correction coefficient is input into the initial photon energy density field, and a voxel-by-voxel multiplication operation is performed on the photon energy density value of each voxel unit and the corresponding optical scattering correction coefficient to obtain the scattering correction photon energy density value. The scattered correction photon energy density value is smoothed and normalized to generate an optical correction photon energy density field.

7. The method for optimizing light distribution in oyster farms based on 3D modeling according to claim 1, characterized in that, The generation of the time-balanced photon energy density field specifically includes: Based on the time series data of the ecologically corrected photon energy density field, the continuously sampled time series is divided into several time windows of equal length, and the average photon energy density of each voxel in each time window is extracted to form a time window energy mean sequence. Perform a difference operation on the average photon energy density of adjacent time windows to calculate the average photon energy change rate of each voxel between adjacent time windows, forming a voxel energy change rate sequence. Based on the voxel energy change rate sequence, the energy fluctuation amplitude of each voxel over the entire time range is calculated, and the normalized result of the energy fluctuation amplitude is used as a correction factor to generate an updated voxel energy distribution. The updated voxel energy distribution is smoothed and normalized across the entire field to obtain a time-balanced photon energy density field.

8. The method for optimizing light distribution in oyster farms based on 3D modeling according to claim 1, characterized in that, The generation of the spatially balanced photon energy density field specifically includes: Using the time-balanced photon energy density field as input, statistical calculations are performed on the voxel photon energy density values ​​in the illumination areas of each light source to obtain the average photon energy density value of each light source area, and the average photon energy density value of the entire field is calculated. The difference between the average photon energy density value of each light source region and the average photon energy density value of the whole field is calculated to obtain the photon energy offset of each light source region. When the photon energy offset is positive, it is marked as an area with excessive energy, and when the photon energy offset is negative, it is marked as an area with insufficient energy, thus forming photon energy density difference data. Based on the photon energy density difference data, the ratio of the absolute value of the photon energy offset in each light source region to the average photon energy density value of the whole field is calculated. This ratio is used as the power adjustment coefficient. The power of the light source in the region with excessive energy is decreased proportionally according to the power adjustment coefficient, and the power of the light source in the region with insufficient energy is increased proportionally according to the power adjustment coefficient, thus generating the light source power correction parameter. Using the light source power correction parameter as input, the angle between the light source illumination direction and the normal of the illuminated area is calculated. The light source illumination angle correction parameter is determined by the cosine change of the angle, and the light source illumination angle correction data is generated. The light source power correction parameters and light source illumination angle correction data are applied to the time-balanced photon energy density field, and power weighting and angle weighting are performed on the photon energy density values ​​of each voxel to generate a spatially balanced photon energy density field.

9. The method for optimizing light distribution in oyster farms based on 3D modeling according to claim 1, characterized in that, The generation of the illumination distribution results and the photon energy density heatmap specifically includes: Using the spatially balanced photon energy density field as input, the photon energy density values ​​of each voxel are traversed, and the three-dimensional coordinates and photon energy density values ​​of each voxel are extracted to generate a photon energy density dataset. Based on the spatial coordinate distribution in the photon energy density dataset, a three-dimensional interpolation method is used to calculate the estimated photon energy density at unsampled locations, generating a continuous spatial photon energy density field. Normalize the continuous spatial photon energy density field, calculate the difference between the photon energy density of each voxel and the average photon energy density of the whole field, and assign color gradients according to the sign and amplitude of the difference to generate a photon energy density heatmap. Based on the energy gradient distribution of the photon energy density heatmap, the spatial range of high-energy and low-energy regions is identified, the energy coverage and energy concentration of the corresponding regions of each light source are calculated, and the energy coverage and photon energy density data are weighted and superimposed to generate the illumination distribution results. The illumination distribution results and photon energy density heatmap are visualized and output in a unified coordinate system.

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