Underwater LED fish gathering lamp control method and system based on light field scattering model

Abstract

The invention discloses an underwater LED fish gathering lamp control method based on a light field scattering model, and the method comprises the following steps: obtaining water optical characteristic parameters and environmental parameters of a target water area in real time, and building an underwater light field scattering model through employing a radiation transfer equation in combination with a Monte Carlo simulation method; with improvement of target fish school gathering efficiency and reduction of energy consumption as optimization objectives, environment parameters are taken as constraint conditions, illumination effects under different LED parameter combinations are simulated through a light field scattering model, and LED control parameters with optimal comprehensive performance are determined; a control instruction is generated according to the LED control parameters, and the working state of each LED unit is adjusted; meanwhile, fish school gathering data and current change values of water temperature, salinity and turbidity are monitored in real time and fed back to the light field scattering model for parameter correction, and closed-loop control is formed. The underwater LED fish gathering lamp control method solves the technical problems of low light field distribution prediction precision, insufficient fish school visual matching performance, poor environmental adaptability and unbalanced energy consumption optimization in the existing underwater LED fish gathering lamp control method.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater lighting control, and in particular to a method and system for controlling an underwater LED fish-attracting lamp based on a light field scattering model. Background Art

[0002] Underwater fish-attracting lights are critical equipment in marine fishing and aquaculture, and their performance directly impacts fish-attracting efficiency and energy consumption. Traditional fish-attracting lights typically use high-pressure mercury lamps and metal halide lamps, which suffer from high energy consumption, short lifespans, and poor spectral adjustment capabilities. In recent years, LED light sources have become the mainstream choice for underwater fish-attracting lights due to their high energy efficiency, adjustable wavelength, and fast response speed. However, their control strategies still face significant technical bottlenecks.

[0003] Existing control methods for underwater LED fish-attracting lights generally rely on empirical parameter settings or simple manual adjustments of light intensity and wavelength to attract fish. These methods fail to fully account for the complexities of underwater light field propagation. In underwater environments, light propagation is significantly affected by the optical properties of water (such as attenuation, scattering, and absorption) and environmental parameters (such as water temperature, salinity, and turbidity). Temperature fluctuations alter the thermal motion of water molecules, affecting the light scattering cross section. Salinity fluctuations cause changes in the water's refractive index, disrupting the uniformity of the light field distribution. Increased turbidity exacerbates light scattering and attenuation due to the increase in suspended particles, reducing light penetration depth. These factors combine to prevent traditional control methods from accurately predicting the actual underwater light field distribution. This can lead to problems such as mismatches between the illuminated area and the fish's activity range, and insufficient coverage of the sensitive spectrum, resulting in low fish-attracting efficiency.

[0004] At the same time, the phototactic behavior of target fish is species-specific, and their visual systems are sensitive to specific wavelengths. However, existing technologies lack dynamic recognition and matching mechanisms for the spectra to which fish are visually sensitive. Instead, they often use fixed-wavelength combinations of lighting, making it difficult to adapt to the needs of different fish species or growth stages. Furthermore, traditional systems are mostly open-loop control systems, unable to provide feedback and adjustments based on real-time fish aggregation data and changes in environmental parameters. This makes it difficult to optimize the balance between energy consumption and fish attraction effectiveness. This is especially true in complex and dynamic water environments, where over- or under-illumination is prone to occur, resulting in energy waste or ineffective fish attraction.

[0005] Therefore, developing a control method and system for LED fish-attracting lights that can accurately sense underwater environmental parameters, dynamically simulate light field scattering laws, and achieve adaptive optimization based on the phototaxis characteristics of fish schools has become a key technical requirement for improving underwater fish-attracting efficiency and reducing energy consumption. Summary of the Invention

[0006] In order to overcome the shortcomings of the existing technology, the purpose of the present invention is to provide an underwater LED fish-attracting light control method and system based on the light field scattering model, which is used to solve the technical problems of low light field distribution prediction accuracy, insufficient visual matching of fish schools, poor environmental adaptability and unbalanced energy consumption optimization in the existing underwater LED fish-attracting light control method.

[0007] In order to solve the above problems, the technical solutions adopted by the present invention are as follows: A method for controlling an underwater LED fish-attracting light based on a light field scattering model comprises the following steps: Acquire the optical characteristic parameters and environmental parameters of the target water area in real time. The optical characteristic parameters include: water attenuation coefficient, scattering phase function and absorption coefficient; the environmental parameters include: water temperature, salinity, turbidity and the visual sensitivity spectrum range of the target fish school; Based on the optical characteristics of water and environmental parameters, the underwater light field scattering model is established by using the radiation transfer equation combined with the Monte Carlo simulation method. With the optimization goals of improving the efficiency of target fish aggregation and reducing energy consumption, and taking environmental parameters as constraints, the light field scattering model is used to simulate the lighting effects under different LED parameter combinations to determine the LED control parameters with the best overall performance; Control instructions are generated based on LED control parameters to adjust the working status of each LED unit. At the same time, fish aggregation data and changes in current water temperature, salinity, and turbidity are monitored in real time and fed back to the light field scattering model for parameter correction to form a closed-loop control.

[0008] Preferably, the real-time acquisition of the water optical characteristic parameters and environmental parameters of the target water area includes: The water attenuation coefficient was measured using a dual-wavelength transmissometer; the scattering phase function was obtained using a multi-angle laser scattering instrument; and the absorption coefficient was measured using an ultraviolet-visible spectrophotometer. Water temperature is monitored in real time using a platinum resistance temperature sensor; salinity is measured using a four-electrode conductivity sensor and corrected to practical salinity units based on water temperature data; turbidity is detected using a 90° scattered light turbidimeter; A high-speed camera was used to record the phototaxis behavior of target fish schools towards LED light spots of different wavelengths. The corresponding relationship between the fish school aggregation density and wavelength was statistically analyzed through image analysis software, and the sensitive spectrum peak interval was extracted to obtain the visual sensitivity spectrum range of the target fish school.

[0009] Preferably, establishing an underwater light field scattering model includes: Construct the radiation transfer equation based on the water attenuation coefficient, scattering phase function and absorption coefficient; Generate initial photon packets based on LED light source parameters, each photon packet carries energy, position, direction and wavelength information; Each photon packet is tracked, and the water molecule scattering cross-section parameters and suspended particle scattering coefficient are corrected according to the water optical characteristics parameters and environmental parameters to calculate the free path length and scattering direction of the photon.

[0010] Preferably, when establishing the underwater light field scattering model, the following steps are also included: When the energy of the photon packet decays to a threshold or exceeds the target water area, the tracking is terminated, and the energy deposition of the photon packet at each position is recorded to obtain the light intensity distribution, spectral composition and light intensity attenuation gradient of the effective illumination area; The light intensity distribution of the simulated output is compared with the measured data of the light intensity sensor, and the model prediction accuracy is optimized by adjusting the scattering coefficient of suspended particles.

[0011] Preferably, the correction of the water molecule scattering cross section parameter includes: Based on Mie scattering theory, the correlation equation between the scattering cross section of water molecules and temperature and salinity is established; The multi-angle laser scattering instrument was used to measure the scattering cross section of water molecules in standard seawater at various temperature-salinity combinations, and the temperature correction coefficient k in the correlation equation was obtained by least squares fitting. t and salinity correction factor k s Experience value; Substitute the current water temperature T and salinity S into the correlation equation to calculate the corrected water molecule scattering cross section σ s (T,S) and used as input parameters for Monte Carlo simulation.

[0012] Preferably, when correcting the suspended particle scattering coefficient, the method includes: Based on the turbidity value TUR and the preset turbidity-suspended particle concentration calibration curve, the mass concentration C of suspended particles in the water body is inverted; Measure the particle size distribution and volume fraction of suspended particles, and determine the refractive index and absorption coefficient of the particles based on the detected particle composition; Based on Mie scattering theory, for particle groups in different particle size ranges, the single particle scattering cross section in each interval is combined with the particle number concentration in each interval to obtain the benchmark suspended particle scattering coefficient t; The turbidity correction coefficient k (TUR) is obtained by fitting the field experimental data to compensate the reference suspended particle scattering coefficient t, and the corrected suspended particle scattering coefficient t1 is obtained and used as the input parameter of the Monte Carlo simulation.

[0013] Preferably, simulating the lighting effects under different LED parameter combinations includes: Set the adjustable range of LED parameters, including wavelength combination coverage range, light intensity value range, beam divergence angle range and spatial distribution pattern; Construct a dual-objective optimization function F=(f1,f2), where f1 is the evaluation index of fish aggregation efficiency and f2 is the energy consumption index; The effective illumination area boundary defined by water temperature, the light field uniformity threshold affected by salinity, and the light penetration depth limited by turbidity are used as hard constraints. The NSGA-II multi-objective genetic algorithm is used to search the parameter space. The f1 and f2 values corresponding to each parameter combination are calculated using the light field scattering model. After iteration, the Pareto optimal solution set is obtained. Select the LED control parameter combination with the best comprehensive performance from the Pareto optimal solution set.

[0014] Preferably, when feeding back to the light field scattering model for parameter correction, it includes: The YOLO target detection algorithm is used to identify fish schools in underwater video frames, extract the number, distribution density and activity range of fish schools in the effective illumination area, and calculate the actual sensitive spectrum matching degree based on the collected spectral distribution data; Perform sliding average filtering on the real-time collected water temperature, salinity, and turbidity change values; If the water temperature change or salinity change reaches or exceeds the set value, the temperature correction coefficient k is adjusted using the PID control algorithm based on the fish aggregation efficiency deviation. t and salinity correction factor k s , update the water molecule scattering cross section σ s (T,S).

[0015] Preferably, when feeding back to the light field scattering model for parameter correction, the method further includes: If the turbidity change value reaches or exceeds the set value, the turbidity correction coefficient k(TUR) is corrected according to the deviation between the measured light penetration depth and the model prediction value, and the suspended particle scattering coefficient t1 is updated; Based on the revised light field scattering model, the NSGA-II multi-objective genetic algorithm is re-run to search the parameter space. If there is a parameter combination with comprehensive performance improvement exceeding the threshold in the newly generated Pareto optimal solution set, a new control instruction is generated to dynamically adjust the luminescence parameters.

[0016] An underwater LED fish-attracting light control system based on a light field scattering model, comprising: An environmental parameter acquisition module is installed in the target water area and is configured to acquire the water optical characteristic parameters and environmental parameters of the target water area in real time; the water optical characteristic parameters include: water attenuation coefficient, scattering phase function and absorption coefficient; the environmental parameters include: water temperature, salinity, turbidity and the visual sensitivity spectrum range of the target fish school; a light field scattering model building module, which is in communication with the environmental parameter acquisition module and is configured to establish an underwater light field scattering model based on water optical characteristic parameters and environmental parameters using a radiation transfer equation combined with a Monte Carlo simulation method; a control parameter optimization module, communicatively connected to the light field scattering model construction module, configured to optimize the target fish aggregation efficiency and reduce energy consumption, use environmental parameters as constraints, simulate the lighting effects under different LED parameter combinations through the light field scattering model, and determine the LED control parameters with the best overall performance; The control execution and feedback module is in communication with the above modules and is configured to generate control instructions based on the LED control parameters to adjust the working status of each LED unit; at the same time, it receives real-time monitored fish aggregation data and changes in current water temperature, salinity, and turbidity, and feeds them back to the light field scattering model for parameter correction to form a closed-loop control.

[0017] Compared with the prior art, the present invention has the following beneficial effects: (1) Improve the accuracy of light field distribution prediction and optimize fish gathering efficiency This method uses real-time data from water optical properties (attenuation coefficient, scattering phase function, absorption coefficient) and environmental parameters (water temperature, salinity, turbidity), then combines the radiative transfer equation with Monte Carlo simulation to establish an underwater light field scattering model. This model accurately predicts the light intensity distribution, spectral composition, and attenuation gradient within the effective illumination area. Compared to traditional empirical parameter settings, this light field simulation significantly reduces error, ensuring a close match between the illumination area and the activity range of fish schools, significantly increasing the density of target fish clusters.

[0018] (2) Dynamically matching the visual characteristics of fish schools to enhance phototaxis The system uses high-speed cameras and image analysis technology to extract the visually sensitive spectral range of target fish schools, and combines it with light field scattering models to optimize LED wavelength combinations and light intensity distribution, achieving dynamic matching of the spectrum with the visual preferences of fish schools, thereby increasing the speed of fish school's phototaxis response and solving the problem of poor adaptability of traditional fixed spectrum lighting.

[0019] (3) Environmental adaptive closed-loop control to improve system stability This system utilizes a closed-loop control mechanism of "parameter acquisition - model modification - optimization execution - feedback iteration." Using a PID algorithm, it adjusts water temperature, salinity correction coefficients, and turbidity compensation coefficients in real time, dynamically updating the light field scattering model. Even with significant changes in environmental parameters (such as turbidity fluctuations of ±50 NTU or water temperature fluctuations of ±1°C), the system responds quickly and maintains high light field prediction accuracy, ensuring continuous and stable operation in complex water environments (such as estuaries, offshore, and pelagic).

[0020] (4) Balance fish gathering efficiency and energy consumption to reduce operating costs The NSGA-II multi-objective genetic algorithm solves the Pareto optimal solution for fish aggregation efficiency and energy consumption, achieving dynamic optimization of LED parameters (wavelength, light intensity, and divergence angle). While ensuring fish aggregation, it reduces system energy consumption and extends LED light source life, making it particularly suitable for long-term deep-sea fishing operations.

[0021] (5) Modular design and strong compatibility make it easy to promote and apply The system utilizes a modular architecture consisting of an environmental parameter acquisition module, a light field scattering model construction module, a control parameter optimization module, and an execution feedback module. It is compatible with LED light sources from different brands. Its standardized interface design enables rapid integration into existing fishing vessels or aquaculture platforms, reducing deployment costs and ensuring broad industry applicability.

[0022] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a step diagram of a method for controlling an underwater LED fish attracting light according to an embodiment of the present invention; Figure 2 This is a flowchart of simulating lighting effects under different LED parameter combinations according to an embodiment of the present invention; Figure 3 This is a flow chart of parameter correction for the light field scattering model according to an embodiment of the present invention; Figure 4 This is an interactive diagram of the underwater LED fish attracting light control system modules according to an embodiment of the present invention.

[0024] Explanation of the accompanying figures: 200, underwater LED fish-attracting light control system; 201, environmental parameter acquisition module; 202, light field scattering model construction module; 203, control parameter optimization module; 204, control execution and feedback module. DETAILED DESCRIPTION

[0025] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0026] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0027] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0028] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0029] Example 1, see Figure 1 The present invention provides a method for controlling underwater LED fish attracting lights. Figure 1 The method for controlling an underwater LED fish-attracting light based on a light field scattering model includes the following steps: S101. Underwater Environmental Parameter Acquisition: The environmental parameter acquisition module 201 located in the target water area acquires the optical and environmental parameters of the target water area in real time. The optical parameters include the water attenuation coefficient, scattering phase function, and absorption coefficient. The environmental parameters include the water temperature, salinity, turbidity, and the visual sensitivity spectrum of the target fish. S102. Light Field Scattering Model Construction: Based on water optical properties and environmental parameters, an underwater light field scattering model is established using the radiation transfer equation combined with Monte Carlo simulation. The input of the underwater light field scattering model is the wavelength, light intensity, beam divergence angle, and spatial distribution parameters of the LED light source. The output is the light intensity distribution, spectral composition, and light intensity attenuation gradient of the effective illumination area in the target water area. Water temperature and salinity are included in the model calculation by modifying the water molecule scattering cross section parameter, and turbidity is included in the model iteration by adjusting the suspended particle scattering coefficient. S103. Control Parameter Optimization Calculation: With the optimization goals of improving the efficiency of target fish aggregation and reducing energy consumption, and using environmental parameters as constraints, a light field scattering model is used to simulate the illumination effects of different LED parameter combinations to determine the LED control parameters with the best overall performance. These LED control parameters include the wavelength combination of each LED unit, the dynamic light intensity modulation curve, and the spatial distribution pattern. Water temperature affects the activity range of target fish, thereby limiting the boundaries of the effective illumination area. Salinity affects the uniformity of the light field distribution by changing the water's refractive index. Turbidity affects the light intensity attenuation gradient by limiting light penetration depth. S104. Control command execution and feedback: Generate control commands based on the LED control parameters with the best overall performance, driving the LED driver circuit to adjust the operating status of each LED unit. Simultaneously, the light intensity sensor and image recognition module installed in the fish monitoring device monitors the aggregation data of fish schools in the actual illuminated area in real time, and synchronously collects the current changes in water temperature, salinity, and turbidity. These values are fed back to the light field scattering model for parameter correction, forming a closed-loop control based on the dynamic changes of environmental parameters.

[0030] Background: The propagation characteristics of underwater light fields are directly affected by the optical properties of water (such as light attenuation, scattering, and absorption). Environmental parameters (water temperature, salinity, turbidity, etc.) change the physical and chemical properties of water, thereby affecting the scattering pattern of light and the phototaxis of fish. Therefore, it is necessary to collect key parameters in real time through multiple types of data acquisition equipment to provide accurate data support for subsequent model construction and parameter optimization. Based on this: In the above step S101, the real-time acquisition of the water optical characteristic parameters and environmental parameters of the target water area includes: The water attenuation coefficient was measured using a dual-wavelength transmissometer, where characteristic wavelengths of 450nm and 650nm were selected to correspond to the penetration characteristics of blue light and red light, respectively. The scattered light intensity distribution data were collected at intervals of 15° within the range of 0°-180° by using a multi-angle laser scattering instrument to obtain the scattered light phase function; The absorption coefficient was measured using a UV-visible spectrophotometer, with the scanning range covering the 350-800 nm spectral interval; Real-time monitoring of water temperature through platinum resistance temperature sensor; Salinity is measured by a four-electrode conductivity sensor and corrected to practical salinity units (PSU) based on water temperature data; Turbidity was detected using a 90° scattered light turbidimeter with a measuring range of 0-1000 NTU; High-speed cameras were used to record the phototaxis of target fish schools to LED light spots of different wavelengths (400-700nm). Image analysis software was used to calculate the correspondence between fish school density and wavelength, extract the sensitive spectrum peak range, and obtain the visual sensitivity spectrum range of the target fish school. Data synchronization and calibration: All collected data are synchronized with the clock through the underwater acoustic communication unit, and after noise removal, they are sent to the light field scattering model construction module 202.

[0031] In the embodiments of the present invention, it is necessary to further explain the water attenuation coefficient: a dual-wavelength transmissometer (450nm blue light, 650nm red light) is used to measure this. This principle is based on the Beer-Lambert Law, which states that when light propagates through water, the ratio of the transmitted light intensity to the incident light intensity is related to the water's attenuation coefficient. By measuring the attenuation of the transmitted light intensity at a specific wavelength, the water attenuation coefficient corresponding to that wavelength (total attenuation capacity, resulting from the combined effects of absorption and scattering) can be calculated. Blue and red light are chosen as characteristic wavelengths because they have significantly different penetrabilities in water (blue light has strong penetrability, red light is easily absorbed), which comprehensively reflects the attenuation characteristics of light of different colors.

[0032] Scattering phase function: The scattered light intensity distribution data is collected by a multi-angle laser scattering instrument in the range of 0°-180° at intervals of 15°. The principle is that when the laser is incident on the water, the photons will be scattered by the water molecules or suspended particles. The distribution of the scattered light intensity at different angles reflects the distribution of the photons from the incident direction ( ) and scattered to the outgoing direction ( ). By counting the scattered light intensity at each angle, the scattering phase function can be constructed , describes the scattering direction distribution characteristics of light and provides key parameters for the light field scattering model.

[0033] Absorption coefficient: The measurement is performed using a UV-Vis spectrophotometer (scanning the 350-800nm spectral range). The principle is that when light of different wavelengths enters the water, part of the light is absorbed by dissolved substances (such as humus) or particles in the water, while the unabsorbed light passes through the water. By comparing the difference between the incident light intensity and the transmitted light intensity, the absorption coefficient corresponding to each wavelength can be calculated. , reflecting the water's ability to absorb light of a specific wavelength.

[0034] Water temperature: Measured using a platinum resistance temperature sensor. The principle is that the resistance of platinum metal is linearly related to temperature (its temperature coefficient of resistance is stable). By measuring the change in sensor resistance, the real-time water temperature can be calculated. Water temperature affects the thermal motion of water molecules (changing the molecular scattering cross section) and the range of fish activity (limiting the effective light area).

[0035] Salinity: Measured using a four-electrode conductivity sensor and corrected for water temperature to practical salinity units (PSU). The principle is that water conductivity and salinity are positively correlated (higher salinity, higher ion concentration, and greater conductivity). The sensor measures water conductivity and corrects for this with temperature (temperature affects ion mobility) to obtain an accurate salinity value. Salinity contributes to the model calculation by changing the water's refractive index, which affects the uniformity of the light field distribution.

[0036] Turbidity: Measured using a 90° scattered light turbidimeter (range 0-1000 NTU). The principle is that suspended particles scatter incident light, and the intensity of scattered light at a 90° angle is positively correlated with particle concentration. By measuring the intensity of scattered light at a 90° angle, the turbidity value (NTU) can be converted. Turbidity reflects the concentration of suspended particles in the water and directly affects the penetration depth of light (light intensity attenuation gradient).

[0037] The visual sensitivity spectrum of target fish schools: High-speed cameras were used to record the phototaxis of fish schools in response to LED light spots of different wavelengths (400-700nm). Image analysis software was then used to analyze the relationship between fish school density and wavelength. The principle is that fish schools have a preference for specific wavelengths of light (due to the varying sensitivity of retinal photoreceptors to different wavelengths). By observing the density of fish schools in response to light spots of different wavelengths, the peak range of their sensitivity spectrum can be extracted, providing a basis for optimizing LED wavelength combinations.

[0038] Background: The propagation characteristics of underwater light fields are directly affected by the optical properties of water (such as light attenuation, scattering, and absorption). Environmental parameters (water temperature, salinity, turbidity, etc.) can change the physical and chemical properties of water, thereby affecting the scattering pattern of light and the phototaxis of fish. Therefore, establishing an accurate underwater light field scattering model is key to achieving precise control of LED fish-attracting lights. This model needs to quantitatively describe the propagation process of light in water and reflect the impact of changes in environmental parameters on the light field distribution, thereby providing a reliable simulation basis for subsequent LED parameter optimization. Based on this: In the above step S102, when establishing the underwater light field scattering model, it includes: constructing a radiation transfer equation, initializing a Monte Carlo simulation, simulating photon transmission, energy deposition and termination judgment, and model calibration.

[0039] In a possible embodiment, a radiation transfer equation is constructed based on the water attenuation coefficient, the scattering phase function, and the absorption coefficient, as shown in the following equation: ; Where, For the spatial position At, along the direction The radiation intensity of the transmission (unit: W·sr -1 ·m -2 ), that is, the light energy transmitted per unit time, per unit solid angle, and per unit vertical area; is a spatial position vector, which is used to describe the specific coordinate position of the photon in the target water area; is the propagation direction of the photon (unit vector), which indicates the direction information of the photon movement; is the path length, which refers to the propagation direction of photons in water. Distance moved (unit: m); For spatial location Total attenuation coefficient at (unit: m -1 ), which is equal to the water attenuation coefficient and scattering coefficient sum ( ), reflecting the total attenuation ability of water to light (combined effects of absorption and scattering); is the scattering phase function, which represents the spatial position At the incident direction, the photon Scattered to the current direction (outgoing direction) The probability distribution function (dimensionless), usually obtained by multi-angle laser scattering measurement; For spatial location Scattering coefficient at (unit: m -1 ), which represents the probability of a photon deviating from its original direction due to scattering within a unit length; For the spatial position At, along the direction the intensity of the radiation transmitted; is the incident direction The solid angle element (unit: sr) is used to integrate all possible incident directions with respect to the outgoing direction Contribution to radiation intensity.

[0040] In the embodiment of the present invention, it is necessary to further explain that the left side item: represents the radiation intensity (light energy transmitted per unit time, per unit solid angle, per unit vertical area) along the propagation path The rate of change reflects the dynamic energy change of light when it propagates in water. The first term on the right: Describes the attenuation process of light. Among them, The total attenuation coefficient is determined by the water attenuation coefficient (the ability of water to absorb light) and the scattering coefficient (The probability of a photon deviating from its original direction due to scattering) is jointly determined. This term indicates that during the propagation of light, the radiation intensity in the original direction will be attenuated due to absorption or scattering by water (deviating from the original direction). The second term on the right is: Describes the scattering supplement process of light. This term indicates that photons in other directions may enter the current direction due to scattering. , thereby supplementing the radiation intensity in the current direction.

[0041] This equation quantitatively describes the energy distribution of light propagating through water by balancing attenuation (absorption + scattering losses) with scattering compensation. This provides a theoretical basis for subsequent simulations of light field distributions (such as the intensity distribution, spectral composition, and attenuation gradient within the effective illumination area) using methods such as Monte Carlo simulation. Combined with measured parameters such as the water attenuation coefficient, scattering phase function, and absorption coefficient, it accurately reflects the propagation characteristics of light in different water environments.

[0042] Background: In the construction of underwater light field scattering models, Monte Carlo simulation is a key technology that simulates the transmission process of photons in water through random sampling methods. Because the propagation of underwater light fields involves complex optical behaviors such as scattering and absorption, it is extremely difficult to directly solve the light intensity distribution through mathematical equations. Monte Carlo simulation can statistically approximate the actual light field distribution. Based on this: In a possible embodiment, the Monte Carlo simulation is initialized by generating initial photon packets based on LED light source parameters (wavelength, light intensity, beam divergence angle, and spatial distribution), where each photon packet carries energy, position, direction, and wavelength information.

[0043] It is important to further explain that the initialization step of the Monte Carlo simulation in this embodiment of the present invention is the starting point of the entire simulation process. The LED light source serves as the initial energy source for the light field, and its parameters (wavelength, intensity, beam divergence, and spatial distribution) directly determine the initial characteristics of the light field. To simulate continuous light radiation in a discrete manner, the light emitted by the LED light source must be decomposed into a large number of "photon packets" (each photon packet represents a certain number of photons). Each photon packet carries key information such as energy (reflecting light intensity), position (the initial emission point of the light source), direction (the initial propagation direction determined by the beam divergence), and wavelength (which influences the attenuation and scattering properties of light in water). The generation of these initial photon packets is the foundation for subsequent photon transmission simulations: by tracing the scattering and absorption paths of each photon packet in water and calculating its energy deposition, key parameters such as the intensity distribution, spectral composition, and attenuation gradient in the effective illumination area are determined.

[0044] In a possible embodiment, photon transmission simulation: each photon packet is tracked, and the optical characteristic parameters of the water body ( 、 、 ) and environmental parameters to correct the water molecule scattering cross-section parameters (determined by water temperature and salinity) and the suspended particle scattering coefficient (determined by turbidity), and calculate the free path length and scattering direction of photons.

[0045] It is important to further explain the embodiments of the present invention that when photons propagate through water, their energy and propagation direction may change due to absorption or scattering. The free path length (i.e., the distance a photon travels between two scattering / absorption events) is determined by the total attenuation coefficient of the water. The greater the total attenuation coefficient (the greater the water's ability to attenuate light), the shorter the photon's free path length, meaning that the photon is more susceptible to absorption or scattering.

[0046] When a photon deviates from its original direction due to scattering, its new propagation direction is determined by the scattering phase function. The scattering phase function describes the direction of the photon from the incident direction. Scattered in the outgoing direction The probability distribution of the photon's scattering direction is obtained by random sampling of the scattering phase function (obtained through field measurements using a multi-angle laser scattering instrument). In Monte Carlo simulations, the new direction of a scattered photon is determined by randomly sampling the scattering phase function. For example, if the scattering phase function has a high probability of scattering at small angles (such as forward scattering), the photon is more likely to scatter along an angle close to the original direction. Conversely, when the probability of scattering at large angles is high, the photon's direction changes more dramatically.

[0047] Water temperature and salinity correction: Water temperature affects the thermal motion of water molecules (changing the molecular scattering cross section), and salinity changes the refractive index of water (affecting the scattering law of light), which together correct the scattering cross section parameter (σ s (T,S)). These corrected parameters will be substituted into the total attenuation coefficient The calculation of the water body makes the simulation more consistent with the temperature-salinity characteristics of the actual water environment.

[0048] Turbidity correction: Turbidity reflects the concentration of suspended particles. Through turbidity-particle concentration inversion, particle characteristic parameter acquisition (particle size, composition, etc.) and turbidity dynamic correction (k(TUR)), the corrected suspended particle scattering coefficient t1 is finally obtained. This parameter directly affects the total attenuation coefficient The scattering coefficient in , thereby adjusting the calculation of photon free path length and scattering direction to adapt to the light field scattering characteristics under different turbidity environments.

[0049] Scattering coefficient Contains two contributions: Water molecular scattering: water molecular scattering cross section σ corrected by water temperature and salinity s (T,S) decision; Suspended particle scattering: determined by the suspended particle scattering coefficient t1 after turbidity correction.

[0050] Water molecule scattering cross section σ s (T,S) is used to calculate the molecular scattering pair scattering coefficient Specifically: Based on Mie scattering theory, the scattering cross section σ of water molecules s Multiplying (T,S) by the molecular number density (the number of molecules per unit volume) gives the water molecular scattering coefficient t2.

[0051] The scattering coefficient is obtained by adding the water molecule scattering coefficient t2 and the suspended particle scattering coefficient t1. .

[0052] In a possible embodiment, energy deposition and termination judgment: when the energy of the photon packet decays to a threshold value (such as 10% of the initial energy), -6) or exceeds the target water area, the tracking is terminated, the energy deposition of the photon packet at each position is recorded, and the light intensity distribution, spectral composition and light intensity attenuation gradient of the effective illumination area are statistically obtained.

[0053] It is further explained in the embodiment of the present invention that for each initial photon packet, the simulation repeats the following steps: The total attenuation coefficient based on the current position Calculate the free path length (i.e. path length ), determines the photon movement New position after distance; Determine whether the photon is absorbed (probability / ) is removed (energy deposition), or is scattered (probability / ) Change direction; If it is scattered, the new direction is determined by sampling the scattered phase function and the tracking is continued; if it is absorbed or the energy decays to a threshold (such as 10 of the initial energy), ... tracking is continued. -6 ), the tracking is terminated and the energy deposition is recorded.

[0054] By counting a large number of photon packets, we can eventually obtain the light intensity distribution, spectral composition and light intensity attenuation gradient of the effective illumination area in the target water area, providing a basis for the optimization of LED control parameters.

[0055] In a possible embodiment, model calibration involves comparing the light intensity distribution of the simulated output with the measured data of the light intensity sensor in the fish monitoring device, and optimizing the model prediction accuracy by adjusting the suspended particle scattering coefficient.

[0056] Background description: The scattering characteristics of underwater light fields are not only affected by the optical properties of the water itself (such as the scattering phase function and attenuation coefficient), but are also closely related to the physical and chemical environment of the water. Changes in water temperature will change the thermal motion state of water molecules (when the temperature rises, the thermal motion of molecules intensifies, and the molecular spacing and arrangement change), thereby affecting the scattering cross section of the molecules to light; changes in salinity will cause fluctuations in the refractive index of the water (when the salinity increases, the refractive index of the water increases), directly changing the propagation path and scattering probability of light in the water. The dynamic changes of these environmental parameters will significantly affect the prediction accuracy of the light field scattering model. If not corrected, the model will not be able to accurately reflect the distribution law of the actual underwater light field. Based on this: In the above step S102, when correcting the water molecule scattering cross section parameter, it includes: Constructing a temperature-salinity correction coefficient model: Based on Mie scattering theory, a correlation equation is established between the scattering cross section of water molecules and temperature and salinity; Experimental calibration correction coefficient: By adjusting the water temperature of the thermostatic bath (10-30°C) and artificially preparing standard seawater with different salinities (25-40PSU), a multi-angle laser scattering instrument was used to measure the scattering cross section of water molecules under various temperature-salinity combinations. The temperature correction coefficient k in the correlation equation was obtained by fitting using the least squares method. t and salinity correction factor k s The empirical value of k t =0.002℃ -1 , k s =0.001PSU -1 ); Real-time parameter correction: Substitute the current water temperature T and salinity S obtained by the environmental parameter acquisition module 201 into the correlation equation to calculate the corrected water molecule scattering cross section σ s (T,S), and this value is used as the input parameter for the calculation of photon scattering probability in Monte Carlo simulation to compensate for the influence of molecular thermal motion differences caused by water temperature changes and water refractive index fluctuations caused by salinity changes on the light field scattering characteristics.

[0057] It is necessary to further explain in the embodiment of the present invention that, in actual application, after the environmental parameter acquisition module 201 obtains the current water temperature T and salinity S in real time, it substitutes the above temperature and salinity correlation equation to calculate the corrected water molecule scattering cross section σ s (T, S) is used as the input parameter for the photon scattering probability in the Monte Carlo simulation. This correction process dynamically compensates for the effects of changes in water temperature (differences in molecular thermal motion) and salinity (refractive index fluctuations) on the light field scattering characteristics, enabling the light field scattering model to more accurately reflect the light propagation laws in actual underwater environments, thereby improving the reliability of subsequent LED control parameter optimization.

[0058] Background description: The scattering characteristics of underwater light fields are not only affected by the optical properties of the water itself, but are also closely related to the state of suspended particles in the water. Factors such as the concentration of suspended particles (reflected by turbidity), particle size distribution, and composition will significantly change the scattering law of light: for example, in a high turbidity environment, the concentration of suspended particles is high, and light will quickly attenuate due to frequent scattering during propagation, resulting in a decrease in light penetration depth; in a low turbidity environment, there are fewer particles, the scattering loss of light is reduced, and the penetration depth is increased. In addition, the dynamic changes of suspended particles (such as flocculation and aggregation caused by water flow disturbances or dissolution due to sedimentation) will change their effective scattering area, thereby affecting the accuracy of the scattering coefficient. If the suspended particle scattering coefficient is not adjusted according to the actual turbidity environment, the light field scattering model will not be able to accurately reflect the light propagation characteristics under different turbidity conditions, resulting in large deviations between the model's predicted light intensity distribution, spectral composition and other parameters and the actual situation, ultimately affecting the reliability of LED control parameter optimization. Based on this: In the above step S102, when correcting the suspended particle scattering coefficient, it includes: turbidity-particle concentration inversion, particle characteristic parameter acquisition, reference scattering coefficient calculation and turbidity dynamic correction.

[0059] In one possible embodiment, turbidity-particle concentration inversion: based on the turbidity value (TUR) measured by a 90° scattered light turbidimeter, combined with a preset turbidity-suspended particle concentration calibration curve, the mass concentration C (unit: mg / L) of suspended particles in the water body is inverted.

[0060] It is important to further explain the embodiments of the present invention that, in actual applications, after measuring the turbidity value (TUR) of the target water area using a turbidimeter, the value is inserted into a preset calibration curve to invert the corresponding suspended particle mass concentration C (mg / L). This process utilizes the positive correlation between turbidity value and particle concentration, achieving a quantitative conversion from light scattering signal (turbidity) to particle concentration using the calibration curve.

[0061] In a possible embodiment, the particle characteristic parameters are obtained by measuring the particle size distribution and volume fraction of the suspended particles online using a laser particle size analyzer, and combining the particle composition detected by an X-ray fluorescence spectrometer to determine the refractive index and absorption coefficient of the particles.

[0062] It is important to further explain in the present embodiments that the refractive index of a particle is primarily determined by its chemical composition (e.g., component type and molecular structure). Substances of different chemical compositions (e.g., quartz, clay, and organic matter) have known refractive indices (which can be queried using an optical properties database). The absorption coefficient, on the other hand, is related to the particle's ability to absorb light of a specific wavelength and is also dependent on its chemical composition (e.g., metal oxides absorb red light more strongly, while organic matter absorbs ultraviolet light more strongly). Therefore, after determining the particle composition using an X-ray fluorescence spectrometer, combined with the particle size distribution obtained by a laser particle size analyzer (particle size affects light scattering and absorption efficiency), the refractive index and absorption coefficient of the particle can be comprehensively determined, providing key parameters for calculating the scattering coefficient in the subsequent light field scattering model.

[0063] Background description: The scattering characteristics of underwater light fields are not only affected by the optical properties of the water itself, but are also closely related to the state of suspended particles. Factors such as the concentration of suspended particles (reflected by turbidity), particle size distribution, and composition will significantly change the scattering law of light: for example, small-sized particles (such as 0.5-10μm) and large-sized particles (such as 50-100μm) have very different light scattering efficiencies - small particles are more likely to cause forward scattering, while large particles may produce more complex scattering angle distributions. If the particles are not calculated by particle size, and only the average particle size or a single parameter is used to describe them, it will lead to deviations in the calculation of the scattering coefficient, affecting the accuracy of the light field model. Based on this: In one possible embodiment, the benchmark scattering coefficient is calculated as follows: based on Mie scattering theory, for particle groups in different particle size ranges (divided into 0.5-10μm, 10-50μm, and 50-100μm), the single particle scattering cross section σi (calculated from the refractive index and absorption coefficient of the particles, where i is the particle size range number) in each interval is combined with the particle number concentration Ni (calculated from C and particle size distribution) in each interval to obtain the benchmark suspended particle scattering coefficient t=Σ(Ni·σi).

[0064] It should be further explained in the embodiments of the present invention that, through the above-mentioned binning calculation steps, the benchmark suspended particle scattering coefficient (t=Σ(Ni·σi)) can more realistically reflect the scattering characteristics of suspended particles in actual water bodies, thereby improving the adaptability of the light field scattering model to different turbidity environments and providing reliable light field distribution simulation data for subsequent LED control parameter optimization.

[0065] Background: The scattering properties of underwater light fields are influenced not only by the optical properties of the water itself but also by the state of suspended particles. Factors such as the concentration (reflected by turbidity), particle size distribution, and composition of suspended particles can significantly alter the scattering behavior of light. For example, in high turbidity environments, where suspended particle concentrations are high, light propagates rapidly due to frequent scattering, resulting in reduced light penetration depth. In low turbidity environments, where particles are fewer, light scattering losses are reduced, leading to increased penetration depth. Furthermore, suspended particles exhibit dynamic characteristics: When turbidity exceeds 500 NTU, particles may flocculate and aggregate due to interactions, increasing the effective scattering area. Conversely, when turbidity falls below 100 NTU, some particles may dissolve, reducing the effective scattering area. These dynamic changes can cause the scattering coefficient calculated based on the initial particle characteristics to deviate from the actual situation. Without adjusting the suspended particle scattering coefficient based on the actual turbidity, the light field scattering model will not accurately reflect the light propagation characteristics under varying turbidity conditions. This can lead to significant deviations between the model's predicted parameters, such as light intensity distribution and spectral composition, and the actual situation, ultimately impacting the reliability of LED control parameter optimization. Based on this: In one possible embodiment, dynamic correction of turbidity: a nonlinear relationship model k(TUR)=a·TUR^b+c (where a=0.002, b=0.8, and c=0.95 are empirical coefficients) between the turbidity correction coefficient k(TUR) and the turbidity value is obtained by fitting field experimental data. This model is used to compensate for the scattering coefficient deviation caused by particle flocculation (particle agglomeration causes changes in the effective scattering area when the turbidity is greater than 500 NTU) or dissolution (partial particle dissolution when the turbidity is less than 100 NTU). The corrected suspended particle scattering coefficient t1=t·k(TUR) is obtained, and this value is used as the input parameter for the photon scattering probability calculation in the Monte Carlo simulation to improve the adaptability of the light field scattering model to different turbidity environments.

[0066] Further explanation is required regarding this embodiment of the present invention. By establishing a nonlinear relationship model between the turbidity correction coefficient and the turbidity value and fitting empirical coefficients based on field experimental data, the suspended particle scattering coefficient is dynamically corrected to compensate for deviations in the scattering coefficient caused by particle flocculation or dissolution. The corrected suspended particle scattering coefficient t1 is substituted into the Monte Carlo simulation to calculate the photon free path length and scattering direction. This correction enables the light field scattering model to more accurately reflect light propagation characteristics (such as light intensity distribution and penetration depth) in varying turbidity environments, thereby improving the reliability of LED control parameter optimization and ensuring that fish attracting lights can effectively attract fish while reducing energy consumption even in complex water environments.

[0067] Background: In the practical application of underwater LED fish-attracting lights, the LED's luminous parameters (such as wavelength combination, light intensity, beam divergence angle, and spatial distribution pattern) directly influence the distribution characteristics of the underwater light field (light intensity distribution, spectral composition, attenuation gradient, etc.), which in turn determine the fish-attracting efficiency and system energy consumption. However, the underwater environment is highly dynamic and complex: environmental parameters such as water temperature, salinity, and turbidity vary significantly across different waters, and the visual sensitivity spectrum of target fish is also species-specific. Furthermore, adjusting LED parameters must strike a balance between improving fish-attracting efficiency and reducing energy consumption.

[0068] Relying solely on experience or the adjustment of a single parameter will make it difficult to accurately judge the impact of different parameter combinations on the actual effect, and may lead to inefficient fish aggregation under high energy consumption or insufficient aggregation effect under low energy consumption. See Figure 2 In the above step S103, the lighting effect simulation flow chart of FIG. 1 includes: simulating the lighting effect under different LED parameter combinations by using the light field scattering model; Parameter space definition: Set the adjustable range of LED parameters, where the wavelength combination covers the target fish's visual sensitivity spectrum peak range of ±20nm (for example, when the sensitive spectrum peak is 520nm, the wavelength range is 500-540nm), the light intensity value is 0-800W (in steps of 50W), the beam divergence angle is 10°-60° (in steps of 5°), and the spatial distribution mode includes uniform distribution, center-enhanced distribution, and annular distribution. Multi-objective optimization modeling: A dual-objective optimization function F = (f1, f2) was constructed, where f1 is the fish aggregation efficiency evaluation index (defined as the product of the fish density in the effective illumination area and the sensitive spectrum matching degree, where the sensitive spectrum matching degree = Σ(the proportion of light intensity in the overlapping range of the LED wavelength and the sensitive spectrum × weight coefficient)), and f2 is the energy consumption index (defined as the mean of the sum of the squares of the light intensities of each LED unit). Constraint setting: The effective illumination area boundary limited by water temperature (for example, the illumination area radius does not exceed 30m when the water temperature is 15-25℃), the light field uniformity threshold affected by salinity (for example, the standard deviation of light intensity distribution is ≤15% when the salinity is 30-35PSU), and the light penetration depth limited by turbidity (for example, the light penetration depth is ≥10m when the turbidity is 500NTU) are set as hard constraints; Simulation iterative optimization: The NSGA-II multi-objective genetic algorithm is used to search the parameter space. The f1 and f2 values corresponding to each parameter combination are calculated using the light field scattering model. After 50-100 generations of iteration, the Pareto optimal solution set is obtained. Optimal parameter screening: Based on actual application requirements (such as prioritizing aggregation efficiency in offshore aquaculture and low energy consumption in deep-sea fishing), select the LED control parameter combination with the best overall performance from the Pareto optimal solution set.

[0069] What needs to be further explained in the embodiment of the present invention is that this embodiment systematically simulates different LED parameter combinations through a light field scattering model, combines environmental parameter constraints and multi-objective optimization strategies, quantitatively evaluates the lighting effects of each combination, and finally screens out the LED control parameters with the best comprehensive performance in a complex underwater environment to achieve the goal of accurate, efficient and energy-saving fish attracting.

[0070] Background: In the practical application of underwater LED fish-attracting lights, the environmental parameters of the target waters (such as water temperature, salinity, and turbidity) and the phototaxis of fish are not static but change dynamically with time, location in the waters, or external conditions (such as currents and weather). Although the initially constructed light field scattering model is calibrated based on the initial environmental parameters and the fish's sensitive spectral range, significant changes in these parameters (such as a temperature increase of more than 1°C, a salinity fluctuation of more than 1 PSU, or a turbidity change of more than 50 NTU) or in fish activity (such as a decrease in aggregation density or a shift in sensitive spectral preferences) can cause key parameters describing the light field scattering characteristics in the model (such as the water molecule scattering cross section and the suspended particle scattering coefficient) to deviate from their initial values. This can cause the model's predicted light field characteristics, such as light intensity distribution and spectral composition, to deviate from the actual conditions. If the model parameters are not promptly corrected, the LED control parameters optimized based on the old model will no longer accurately match the light propagation patterns and fish's phototaxis requirements in the current environment, potentially resulting in reduced fish aggregation efficiency or increased energy consumption. Based on this: See Figure 3 The parameter correction flow chart of FIG. 1 includes the following steps: in the above step S104, when the light field scattering model is fed back to perform parameter correction: Fish aggregation data processing: The image recognition module uses the YOLO target detection algorithm to identify fish schools in underwater video frames, extract the number, distribution density, and activity range of fish schools within the effective illumination area, and calculate the actual sensitive spectrum matching degree (actual sensitive spectrum matching degree = Σ(the proportion of light intensity in the sensitive wavelength range in the measured spectrum) × the corresponding weight coefficient) based on the spectral distribution data collected by the light intensity sensor. Dynamic calibration of environmental parameters: Perform sliding average filtering on the real-time collected water temperature, salinity, and turbidity change values to eliminate high-frequency noise interference; the water temperature change value ΔT = current water temperature T - initial water temperature T0, the salinity change value ΔS = current salinity S - initial salinity S0, and the turbidity change value ΔTUR = current turbidity TUR - initial turbidity TUR0; Model parameter correction: Temperature-salinity correction coefficient adjustment: If |ΔT| ≥ 1°C or |ΔS| ≥ 1PSU, the temperature correction coefficient k is adjusted using the PID control algorithm based on the deviation of fish aggregation efficiency (actual matching degree - predicted matching degree). t and salinity correction factor k s , update the water molecule scattering cross section σ s (T,S); Optimization of turbidity correction coefficient: If |ΔTUR| ≥ 50 NTU, the turbidity correction coefficient k(TUR) is corrected according to the deviation between the measured light penetration depth and the model predicted value (ΔD = measured depth D - predicted depth D0), and the suspended particle scattering coefficient t1 = t·k(TUR) is updated; Iterative optimization of control parameters: The corrected light field scattering model parameters are input into the control parameter optimization module 203, and the NSGA-II multi-objective genetic algorithm is re-run to search the parameter space. If there is a parameter combination in the newly generated Pareto optimal solution set that improves the overall performance by more than 5%, a new control instruction is generated to drive the LED unit to dynamically adjust the light-emitting parameters.

[0071] What needs to be further explained in the embodiment of the present invention is that this embodiment collects the change values of environmental parameters and fish aggregation data in real time, feeds back to the light field scattering model for parameter correction, and re-optimizes the LED control parameters. It can dynamically adapt to changes in the underwater environment, ensure that the fish-attracting lamp always operates in the optimal state, and take into account the dual goals of efficiently aggregating fish and reducing energy consumption.

[0072] Example 2, see Figure 4 The present invention provides a multi-channel output state real-time monitoring system module interaction diagram. Figure 4 The underwater LED fish attracting light control system 200 based on the light field scattering model shown includes: an environmental parameter acquisition module 201, a light field scattering model construction module 202, a control parameter optimization module 203 and a control execution and feedback module 204.

[0073] The environmental parameter acquisition module 201 is set in the target water area and is configured to obtain the water optical characteristic parameters and environmental parameters of the target water area in real time; the water optical characteristic parameters include: water attenuation coefficient, scattering phase function and absorption coefficient; the environmental parameters include: water temperature, salinity, turbidity and the visual sensitivity spectrum range of the target fish school.

[0074] The light field scattering model construction module 202 is communicatively connected to the environmental parameter acquisition module 201, and is configured to establish an underwater light field scattering model based on the optical characteristic parameters of the water body and the environmental parameters, using the radiation transfer equation combined with the Monte Carlo simulation method; the input of the underwater light field scattering model is the wavelength, light intensity, beam divergence angle and spatial distribution parameters of the LED light source, and the output is the light intensity distribution, spectral composition and light intensity attenuation gradient of the effective illumination area in the target water area; among them, water temperature and salinity participate in the model calculation by correcting the scattering cross-section parameters of water body molecules, and turbidity participates in the model iteration by adjusting the scattering coefficient of suspended particles.

[0075] The control parameter optimization module 203 is in communication with the light field scattering model construction module 202, and is configured to optimize the target fish aggregation efficiency and reduce energy consumption. The environmental parameters are used as constraints. The light field scattering model is used to simulate the lighting effects under different LED parameter combinations to determine the LED control parameters with the best overall performance. The LED control parameters include the emission wavelength combination of each LED unit, the dynamic light intensity modulation curve and the spatial distribution pattern. Among them, water temperature affects the activity range of the target fish, thereby limiting the boundary of the effective lighting area. Salinity affects the uniformity of the light field distribution by changing the refractive index of the water body. Turbidity affects the light intensity attenuation gradient by limiting the light penetration depth.

[0076] The control execution and feedback module 204 is in communication with the environmental parameter acquisition module 201, the light field scattering model construction module 202, and the control parameter optimization module 203, and is configured to generate control instructions based on the LED control parameters with the best overall performance, drive the LED drive circuit to adjust the working state of each LED unit; at the same time, it receives the light intensity sensor and image recognition module in the fish monitoring equipment to monitor the fish aggregation data in the actual illuminated area in real time, as well as the current water temperature, salinity, and turbidity changes, and feeds back to the light field scattering model for parameter correction, thereby forming a closed-loop control based on the dynamic changes of environmental parameters.

[0077] In a possible embodiment, the environmental parameter acquisition module 201 is configured with: a dual-wavelength transmissometer, a multi-angle laser scattering instrument, a UV-visible spectrophotometer, a platinum resistance temperature sensor, a four-electrode conductivity sensor, a 90° scattered light turbidimeter, and a high-speed camera.

[0078] In a possible embodiment, the dual-wavelength transmissometer selects characteristic wavelengths of 450 nm and 650 nm, corresponding to the penetration characteristics of blue light and red light, respectively.

[0079] In a possible embodiment, the multi-angle laser scattering instrument collects scattered light intensity distribution data within a range of 0°-180° at intervals of 15°.

[0080] In a possible embodiment, the scanning range of the UV-visible spectrophotometer covers the 350-800 nm spectral interval.

[0081] In a possible embodiment, the measuring range of the 90° scattered light turbidimeter is 0-1000 NTU.

[0082] In a possible embodiment, the high-speed camera is equipped with image analysis software for calculating the correspondence between the density of fish gathering and the wavelength and extracting the sensitive spectrum peak interval.

[0083] In a possible embodiment, the environmental parameter acquisition module 201 is further configured with an underwater acoustic communication unit for synchronizing the clocks of all collected data and sending the data to the light field scattering model construction module 202 after removing noise. Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling underwater LED fish-attracting lights based on a light field scattering model, characterized in that: The following steps are involved: Acquire the optical characteristic parameters and environmental parameters of the target water area in real time. The optical characteristic parameters include: water attenuation coefficient, scattering phase function and absorption coefficient; the environmental parameters include: water temperature, salinity, turbidity and the visual sensitivity spectrum range of the target fish school; Based on the optical characteristics of water and environmental parameters, the underwater light field scattering model is established by using the radiation transfer equation combined with the Monte Carlo simulation method. With the optimization goals of improving the efficiency of target fish aggregation and reducing energy consumption, and taking environmental parameters as constraints, the light field scattering model is used to simulate the lighting effects under different LED parameter combinations to determine the LED control parameters with the best overall performance; Control instructions are generated based on LED control parameters to adjust the working status of each LED unit. At the same time, fish aggregation data and changes in current water temperature, salinity, and turbidity are monitored in real time and fed back to the light field scattering model for parameter correction to form a closed-loop control.

2. The method according to claim 1, characterized in that When obtaining the optical characteristic parameters and environmental parameters of the target water area in real time, it includes: The water attenuation coefficient was measured using a dual-wavelength transmissometer; the scattering phase function was obtained using a multi-angle laser scattering instrument; and the absorption coefficient was measured using an ultraviolet-visible spectrophotometer. Water temperature is monitored in real time using a platinum resistance temperature sensor; salinity is measured using a four-electrode conductivity sensor and corrected to practical salinity units based on water temperature data; turbidity is detected using a 90° scattered light turbidimeter; A high-speed camera was used to record the phototaxis behavior of target fish schools towards LED light spots of different wavelengths. The corresponding relationship between the fish school aggregation density and wavelength was statistically analyzed through image analysis software, and the sensitive spectrum peak interval was extracted to obtain the visual sensitivity spectrum range of the target fish school.

3. The method according to claim 1, characterized in that When establishing an underwater light field scattering model, it includes: Construct the radiation transfer equation based on the water attenuation coefficient, scattering phase function and absorption coefficient; Generate initial photon packets based on LED light source parameters, each photon packet carries energy, position, direction and wavelength information; Each photon packet is tracked, and the water molecule scattering cross-section parameters and suspended particle scattering coefficient are corrected according to the water optical characteristics parameters and environmental parameters to calculate the free path length and scattering direction of the photon.

4. The method according to claim 3, characterized in that When establishing an underwater light field scattering model, it also includes: When the energy of the photon packet decays to a threshold or exceeds the target water area, the tracking is terminated, and the energy deposition of the photon packet at each position is recorded to obtain the light intensity distribution, spectral composition and light intensity attenuation gradient of the effective illumination area; The light intensity distribution of the simulated output is compared with the measured data of the light intensity sensor, and the model prediction accuracy is optimized by adjusting the scattering coefficient of suspended particles.

5. The method according to claim 3, characterized in that When correcting the scattering cross section parameters of water molecules, it includes: Based on Mie scattering theory, the correlation equation between the scattering cross section of water molecules and temperature and salinity is established; The multi-angle laser scattering instrument was used to measure the scattering cross section of water molecules in standard seawater at various temperature-salinity combinations, and the temperature correction coefficient k in the correlation equation was obtained by least squares fitting. t and salinity correction factor k s Experience value; Substitute the current water temperature T and salinity S into the correlation equation to calculate the corrected water molecule scattering cross section σ s (T,S) and used as input parameters for Monte Carlo simulation.

6. The method according to claim 3, characterized in that When correcting the suspended particle scattering coefficient, include: Based on the turbidity value TUR and the preset turbidity-suspended particle concentration calibration curve, the mass concentration C of suspended particles in the water body is inverted; Measure the particle size distribution and volume fraction of suspended particles, and determine the refractive index and absorption coefficient of the particles based on the detected particle composition; Based on Mie scattering theory, for particle groups in different particle size ranges, the single particle scattering cross section in each interval is combined with the particle number concentration in each interval to obtain the benchmark suspended particle scattering coefficient t; The turbidity correction coefficient k (TUR) is obtained by fitting the field experimental data to compensate the reference suspended particle scattering coefficient t, and the corrected suspended particle scattering coefficient t1 is obtained and used as the input parameter of the Monte Carlo simulation.

7. The method according to any one of claims 4 to 6, characterized in that When simulating the lighting effects under different LED parameter combinations, it includes: Set the adjustable range of LED parameters, including wavelength combination coverage range, light intensity value range, beam divergence angle range and spatial distribution pattern; Construct a dual-objective optimization function F=(f1,f2), where f1 is the evaluation index of fish aggregation efficiency and f2 is the energy consumption index; The effective illumination area boundary defined by water temperature, the light field uniformity threshold affected by salinity, and the light penetration depth limited by turbidity are used as hard constraints. The NSGA-II multi-objective genetic algorithm is used to search the parameter space. The f1 and f2 values corresponding to each parameter combination are calculated using the light field scattering model. After iteration, the Pareto optimal solution set is obtained. Select the LED control parameter combination with the best comprehensive performance from the Pareto optimal solution set.

8. The method according to claim 7, characterized in that Feedback to the light field scattering model for parameter correction includes: The YOLO target detection algorithm is used to identify fish schools in underwater video frames, extract the number, distribution density and activity range of fish schools in the effective illumination area, and calculate the actual sensitive spectrum matching degree based on the collected spectral distribution data; Perform sliding average filtering on the real-time collected water temperature, salinity, and turbidity change values; If the water temperature change or salinity change reaches or exceeds the set value, the temperature correction coefficient k is adjusted using the PID control algorithm based on the fish aggregation efficiency deviation. t and salinity correction factor k s , update the water molecule scattering cross section σ s (T,S).

9. The method according to claim 8, characterized in that When feeding back to the light field scattering model for parameter correction, it also includes: If the turbidity change value reaches or exceeds the set value, the turbidity correction coefficient k(TUR) is corrected according to the deviation between the measured light penetration depth and the model prediction value, and the suspended particle scattering coefficient t1 is updated; Based on the revised light field scattering model, the NSGA-II multi-objective genetic algorithm is re-run to search the parameter space. If there is a parameter combination with comprehensive performance improvement exceeding the threshold in the newly generated Pareto optimal solution set, a new control instruction is generated to dynamically adjust the luminescence parameters.

10. An underwater LED fish attracting light control system based on a light field scattering model, characterized in that: include: An environmental parameter acquisition module is provided in the target water area and is configured to obtain water optical characteristic parameters and environmental parameters of the target water area in real time; Water optical characteristic parameters include: water attenuation coefficient, scattering phase function and absorption coefficient; environmental parameters include: water temperature, salinity, turbidity and the visual sensitivity spectrum range of target fish; a light field scattering model building module, which is in communication with the environmental parameter acquisition module and is configured to establish an underwater light field scattering model based on water optical characteristic parameters and environmental parameters using a radiation transfer equation combined with a Monte Carlo simulation method; a control parameter optimization module, communicatively connected to the light field scattering model construction module, configured to optimize the target fish aggregation efficiency and reduce energy consumption, use environmental parameters as constraints, simulate the lighting effects under different LED parameter combinations through the light field scattering model, and determine the LED control parameters with the best overall performance; The control execution and feedback module is in communication with the above modules and is configured to generate control instructions based on the LED control parameters to adjust the working status of each LED unit; at the same time, it receives real-time monitored fish aggregation data and changes in current water temperature, salinity, and turbidity, and feeds them back to the light field scattering model for parameter correction to form a closed-loop control.

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