LED lamp energy-saving control method and system suitable for algae light supplement

Abstract

The invention discloses an LED lamp energy-saving control method and system suitable for algae light supplement, and the method comprises the steps: obtaining the three-dimensional distribution and water depth data of algae in real time through image recognition, calculating the influence of water refraction on the light intensity according to the water depth, obtaining the LED light intensity after refraction compensation, collecting the ambient light parameters, and obtaining the LED light intensity after reflection compensation based on the data. And dynamically determining the spatial position and illumination parameters of the LED lamp group by using a preset algorithm, monitoring the algae microbubble condition, calculating the photosynthesis intensity, comparing the photosynthesis intensity with a preset threshold value, and adjusting the illumination parameters according to a nonlinear positive correlation gradient when the photosynthesis intensity exceeds the preset threshold value. According to the invention, data related to algae growth are accurately acquired in real time, the parameters of the LED lamp group are dynamically determined and flexibly adjusted by comprehensively considering multiple factors, the algae growth requirements are accurately matched, and the algae photosynthesis efficiency is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of LED control, and in particular to an energy-saving control method and system of an LED lamp suitable for algae supplementary lighting. Background Art

[0002] In the current algae farming industry, light plays a decisive role in the growth and reproduction of algae, but the existing algae lighting technology has obvious defects. In terms of obtaining algae growth information, traditional methods cannot provide appropriate light for algae in different positions, resulting in uneven algae growth and affecting the overall yield and quality. When calculating light intensity, the refraction effect of water bodies and the dynamic changes of ambient light parameters are generally ignored. Either the light is too strong to cause energy waste, or the light is insufficient to inhibit algae photosynthesis. In addition, traditional methods make it difficult to timely and intelligently adjust the spatial position and lighting parameters of LED light groups according to the real-time growth status of algae, such as changes in photosynthesis intensity, which greatly limits the efficiency and benefits of algae farming. Summary of the invention

[0003] In order to solve at least one of the above-mentioned technical problems, the present invention provides an energy-saving control method and system of an LED lamp suitable for algae supplementary lighting.

[0004] In a first aspect, the present invention provides an energy-saving control method for an LED lamp suitable for algae supplementary lighting, the method comprising:

[0005] The three-dimensional distribution information of algae in the culture pond is obtained in real time by image recognition technology, wherein the three-dimensional distribution information includes vertical distribution data of algae biomass and corresponding water depth data;

[0006] The influence of water refraction effect on light intensity is calculated based on water depth data, and the LED light intensity after refraction compensation is obtained;

[0007] Collecting current ambient light parameters, wherein the ambient light parameters include light intensity and incident angle;

[0008] Based on the three-dimensional distribution information of algae, ambient light parameters and LED light intensity, the spatial position parameters and lighting parameters of the LED light group are dynamically determined through a preset parameter optimization algorithm; the spatial position parameters include installation height and angle;

[0009] Monitor the generation rate and volume changes of microbubbles on the surface of algae, and deduce the photosynthesis intensity based on the generation rate and volume changes;

[0010] The photosynthesis intensity is compared with a preset threshold interval. When the threshold interval is exceeded, the lighting parameters of the LED lamp group are adjusted according to a preset gradient, and the adjustment amplitude is nonlinearly positively correlated with the deviation value.

[0011] Preferably, the calculation of the LED light intensity after refraction compensation satisfies:

[0012]

[0013] In the formula, I adj represents the LED light intensity after refraction compensation, I0 represents the calibrated light intensity, k d represents the extinction coefficient of the water body, z represents the water depth, α represents the temperature influence coefficient, and ΔT is the difference between the real-time water temperature and the reference water temperature.

[0014] Preferably, the dynamically determining the spatial position parameters and illumination parameters of the LED light group by a preset parameter optimization algorithm includes:

[0015] Collecting historical data, the historical data including historical algae three-dimensional distribution information, historical ambient light parameters, historical LED light intensity, historical spatial position parameters and historical illumination parameters;

[0016] Constructing a neural network model, wherein the neural network model uses the historical data as a training sample and outputs optimal spatial position parameters and illumination parameters for current three-dimensional distribution information of algae, ambient light parameters, and LED light intensity;

[0017] Introducing constraints into the neural network model, wherein the constraints include a maximum power limit of the LED lamp group, a safe range of lamp installation height, and a limit on the impact of lighting parameters on the human body and equipment;

[0018] Optimizing the neural network model through iterative training so that the spatial position parameters and light parameters output by the model can minimize energy consumption and maximize the photosynthesis efficiency of algae;

[0019] The optimized neural network model is applied to real-time calculation to dynamically adjust the spatial position parameters and lighting parameters of the LED light group.

[0020] Preferably, the photosynthesis intensity is obtained based on a photosynthesis efficiency evaluation algorithm, which is expressed as:

[0021]

[0022] Where P(t) is the quantitative index of photosynthesis intensity per unit time, T is the integration time window, the default T value is 300 seconds, N is the number of bubbles released by algae in the integration time window, and r is k represents the equivalent radius of the th bubble, Represents the sum of the projected areas of all bubbles in the vertical direction.

[0023] In a second aspect, the present invention further provides an LED lamp energy-saving control system suitable for algae supplementary lighting, the system comprising:

[0024] An algae distribution information acquisition module is used to acquire three-dimensional distribution information of algae in the culture pond in real time through image recognition technology, wherein the three-dimensional distribution information includes vertical distribution data of algae biomass and corresponding water depth data;

[0025] The light intensity refraction compensation calculation module is used to calculate the influence of water refraction effect on light intensity according to water depth data, and obtain the LED light intensity after refraction compensation;

[0026] An ambient light parameter acquisition module is used to acquire current ambient light parameters, wherein the ambient light parameters include light intensity and incident angle;

[0027] The LED lamp group parameter determination module is used to dynamically determine the spatial position parameters and illumination parameters of the LED lamp group through a preset parameter optimization algorithm based on the three-dimensional distribution information of algae, ambient light parameters and LED light intensity; the spatial position parameters include installation height and angle;

[0028] The photosynthesis intensity estimation module is used to monitor the generation rate and volume change of microbubbles on the surface of algae, and estimate the photosynthesis intensity based on the generation rate and volume change;

[0029] The illumination parameter adjustment module is used to compare the photosynthesis intensity with a preset threshold interval. When the threshold interval is exceeded, the illumination parameter of the LED lamp group is adjusted according to a preset gradient, and the adjustment amplitude is nonlinearly positively correlated with the deviation value.

[0030] In a third aspect, the present invention further provides an electronic device comprising a processor and a memory, wherein the memory is used to store computer program code, and the computer program code comprises computer instructions. When the processor executes the computer instructions, the electronic device executes the method as described in the first aspect above and any possible implementation thereof.

[0031] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, wherein the computer program includes program instructions, and when the program instructions are executed by a processor of an electronic device, the processor executes the method as described in the first aspect above and any possible implementation thereof.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1) The present invention obtains the three-dimensional distribution information of algae through image recognition technology, including the vertical distribution of biomass and water depth data, which lays the foundation for subsequent precise supplementary lighting. The influence of the refraction effect of the water body on the light intensity is calculated and the light intensity after refraction compensation is obtained to ensure that the algae receive appropriate light intensity, avoid the light problem caused by refraction affecting growth, collect ambient light parameters and combine algae distribution and LED light intensity, optimize the spatial position and light parameters of the LED lamp group, this process integrates multiple factors, makes the lamp group setting more scientific and reasonable, not only meets the algae light demand but also improves energy utilization, monitors the microbubble generation rate and volume change on the algae surface to infer the photosynthesis intensity, and then adjusts the light parameters according to the comparison result of the photosynthesis intensity and the preset threshold value, and the adjustment range is nonlinearly positively correlated with the deviation value. This precise adjustment ensures that the algae is in the best growth state, helps to improve the growth rate, yield and quality of the algae, and at the same time achieves energy saving and reduces the breeding cost. The present embodiment not only improves the detection efficiency, but also significantly enhances the monitoring accuracy of potential problem areas.

[0034] 2) The present invention achieves precise adjustment of LED light intensity by introducing a calculation formula for LED light intensity after refraction compensation that takes into account factors such as water extinction coefficient, temperature influence coefficient and water depth. On the one hand, it can more accurately take into account the changes in light intensity caused by factors such as refraction, attenuation and temperature change when light propagates in water, thereby avoiding the impact of inaccurate light intensity on algae photosynthesis, thereby improving the accuracy of the lighting conditions of the algae growth environment; on the other hand, compared with the traditional simple light intensity setting method, this precise adjustment can avoid unnecessary energy waste while meeting the lighting requirements of algae growth, achieving energy-saving effects, effectively improving the rationality and economy of light resource utilization in the algae cultivation process, and providing a strong guarantee for the efficient and stable growth of algae.

[0035] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0037] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0038] Figure 1 A schematic flow chart of an energy-saving control method for an LED lamp suitable for algae supplementary lighting provided by an embodiment of the present invention;

[0039] Figure 2A schematic diagram of the structure of an LED lamp energy-saving control system suitable for algae supplementary lighting provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0042] The existing technology uniformly controls the LED light group by pre-setting time and power, but cannot grasp the three-dimensional distribution of algae in real time, does not consider the refraction of water and changes in ambient light, and cannot be flexibly adjusted according to the photosynthesis intensity of algae, resulting in uneven growth of algae and hindering the improvement of breeding efficiency and benefits.

[0043] See also Figure 1 , Figure 1 The present invention provides a schematic flow chart of an energy-saving control method for an LED lamp suitable for algae supplementary lighting. Figure 1 As shown, the method includes:

[0044] S100, obtaining three-dimensional distribution information of algae in the culture pond in real time by image recognition technology, wherein the three-dimensional distribution information includes vertical distribution data of algae biomass and corresponding water depth data;

[0045] A multi-camera stereo vision imaging system is used, and high-definition cameras are installed at different positions and heights in the culture pond. These cameras have high-resolution and low-light shooting capabilities, and can produce clear images under different lighting conditions. The images collected by the camera are preprocessed to remove noise interference, and then the morphological characteristics of the algae are identified through the feature extraction algorithm. Then, the stereo matching algorithm is used to calculate the position information of the algae in three-dimensional space based on the parallax between the images taken by different cameras, thereby obtaining the vertical distribution data of the algae biomass and the corresponding water depth data. It should be noted that the three-dimensional distribution information of algae refers to the spatial distribution of algae in the culture pond, including the biomass distribution in the vertical direction and the corresponding water depth position, which is an important basis for the subsequent control of LED lights.

[0046] S200, calculating the influence of water refraction effect on light intensity according to the water depth data, and obtaining the LED light intensity after refraction compensation;

[0047] In this embodiment, based on Snell's law, combined with the refractive index data of the water body (obtaining the refractive index of the water body at different temperatures and salinities through experimental measurement or consulting relevant information), a water body refraction model is established. Using the water depth data of the aquaculture pond, the model is used to calculate the change in the refraction angle of the light after it enters the water body from the air, and then the attenuation of the light intensity in the water body is calculated. According to the attenuation calculation results, the initial light intensity of the LED lamp is adjusted so that the light intensity that finally reaches the algae meets the requirements, and the LED light intensity after refraction compensation is obtained. It should be noted that the water body refraction effect refers to the phenomenon that the propagation direction of light changes when it enters another medium (such as water) from one medium (such as air), which will cause the light intensity to change in the water body. The LED light intensity after refraction compensation takes into account the water body refraction effect, and the initial light intensity of the LED lamp is adjusted so that the light intensity that actually reaches the algae can meet the light intensity required for algae growth.

[0048] S300, collecting current ambient light parameters, where the ambient light parameters include light intensity and incident angle;

[0049] In this embodiment, a high-precision ambient light sensor is used, which integrates multiple functional modules and can measure light intensity and incident angle respectively. The light intensity measurement module uses a photodiode to convert the light signal into an electrical signal, and converts it into a digital signal for processing and analysis through a precise analog-to-digital conversion circuit. The incident angle measurement module calculates the incident angle based on the intensity distribution of light in different directions. It should be noted that the ambient light parameters refer to the lighting characteristics in the current environment, including light intensity and incident angle. These parameters will affect the photosynthesis of algae and are an important reference for adjusting the parameters of LED lights. Light intensity refers to the luminous flux of visible light received per unit area, which is used to measure the intensity of light. The incident angle refers to the angle between the light and the surface normal when it is incident on the surface of an object, which affects the reflection and refraction of light on the surface of the object, and then affects the absorption of light by algae.

[0050] S400, based on the three-dimensional distribution information of algae, ambient light parameters and LED light intensity, dynamically determine the spatial position parameters and lighting parameters of the LED light group through a preset parameter optimization algorithm; the spatial position parameters include installation height and angle, and the lighting parameters include emission light intensity and lighting period;

[0051] In this embodiment, a genetic algorithm is used as a preset parameter optimization algorithm. This is a computational model that simulates the biological evolution process and seeks the optimal solution through mechanisms such as natural selection, inheritance and mutation. First, a set of initial LED light group spatial position parameters (installation height and angle) and lighting parameters (emission light intensity and lighting cycle) are randomly generated as the initial population. The population size is set according to the actual situation, for example, it is set to 50 individuals, each of which represents a possible combination of LED light group parameters.

[0052] In order to evaluate the quality of each individual, the fitness value of each individual is calculated according to the algae growth model and photosynthesis efficiency model. The algae growth model comprehensively considers factors such as the growth characteristics, nutrient concentration, and light conditions of the algae, and describes the growth rate of algae under different environments through mathematical formulas. The photosynthesis efficiency model is based on the basic principles of photosynthesis, combined with parameters such as light intensity and spectral composition, to calculate the photosynthesis efficiency of algae under specific light conditions. The fitness value reflects the growth and photosynthesis efficiency of the algae under the current parameter settings. For example, the fitness value can be defined as the ratio of the increase in algae biomass to energy consumption within a certain period of time. The higher the ratio, the better the parameter combination. Then the genetic operation is carried out. The first is the selection operation, using the roulette selection method, that is, the probability of being selected is calculated according to the fitness value of each individual. The higher the fitness value, the greater the probability of being selected. By simulating the rotation of the roulette wheel with random numbers, a certain number of individuals are selected from the population as parents. After that, the crossover operation is performed. For the selected parent individuals, the crossover point is randomly selected, and the genes of the parent individuals at the crossover point are exchanged to generate new offspring individuals. For example, for two parent individuals A and B, the spectral ratio part of their illumination parameters is crossed at the third gene position. The first three genes of individual A and the last part of the genes of individual B form a new offspring individual, and the first three genes of individual B and the last part of the genes of individual A form another offspring individual. The newly generated offspring individuals are mutated with a certain mutation probability. The mutation operation is to randomly change a gene value of an individual, such as mutating the installation height gene of a certain offspring individual so that it changes randomly within a certain range, thereby introducing new gene combinations and increasing the diversity of the population. By repeatedly repeating genetic operations such as selection, crossover and mutation, the population is optimized after multiple rounds of iterations until a set of parameters with the best fitness value is found, which is the dynamically determined LED light group parameters. Generally, the number of iterations can be set to 100 times or the iteration can be stopped based on the convergence of the fitness value.

[0053] S500, monitors the generation rate and volume change of microbubbles on the surface of algae, and estimates the photosynthesis intensity based on the generation rate and volume change;

[0054] Preferably, the photosynthesis intensity is obtained based on a photosynthesis efficiency evaluation algorithm, which is expressed as:

[0055]

[0056] Where P(t) is the quantitative index of photosynthesis intensity per unit time, T is the integration time window, the default T value is 300 seconds, N is the number of bubbles released by algae in the integration time window, and r is k represents the equivalent radius of the th bubble, Represents the sum of the projected areas of all bubbles in the vertical direction.

[0057] Monitor the generation rate and volume change of microbubbles on the surface of algae, and infer the photosynthesis intensity based on the generation rate and volume change. Specific technical means: Install image acquisition equipment with high resolution and high frame rate, such as high-speed cameras, above the algae growth area of ​​the aquaculture pond. To ensure the shooting effect, the shooting area is evenly illuminated to avoid shadows. The algae surface is continuously photographed at fixed time intervals by the device to capture the generation and change process of microbubbles. The collected image sequence is processed. Using digital image processing technology, the image is first pre-processed by grayscale, noise reduction and other pre-processing operations to enhance the image quality and highlight the characteristics of microbubbles. Further, an algorithm based on contour detection and morphological analysis is used to identify microbubbles in the image and mark each microbubble. By comparing the position and morphology of microbubbles in continuous frame images, the generation time and movement trajectory of each microbubble are determined, thereby calculating the generation rate of microbubbles. In determining the equivalent radius of microbubbles, the principle of perspective transformation is used, combined with the intrinsic and extrinsic parameters of the camera (obtained by prior calibration), to convert the microbubbles in the two-dimensional image into size information in three-dimensional space. According to the projection shape of the microbubble in the image, it is approximately regarded as a sphere. By measuring its projection size in the image and combining the conversion relationship, the equivalent radius r of each microbubble is calculated. k When calculating the photosynthesis intensity, the given photosynthesis efficiency evaluation algorithm is used: The default value of the integration time window T is 300 seconds. Within this time window, the number of bubbles N released by algae is counted, and the sum of the projection areas of all bubbles in the vertical direction is calculated. Combined with the microbubble generation rate By performing an integral operation, we can obtain the quantitative index P(t) of the photosynthesis intensity per unit time.

[0058] In the algae farming scenario, the photosynthesis efficiency evaluation algorithm breaks through the limitations of traditional evaluation methods by accurately measuring the bubble area and generation rate, and realizes the quantification of photosynthesis efficiency. It not only improves the evaluation accuracy and can keenly capture subtle changes in photosynthesis efficiency, but also has the advantage of rapid response and can output results in real time. Based on these accurate and rapid quantitative data, farmers can adjust key factors such as light and nutrition in a timely manner, effectively improve the yield and quality of algae farming, reduce resource waste, and significantly improve farming efficiency.

[0059] S600, comparing the photosynthesis intensity with a preset threshold interval, and when it exceeds the threshold interval, adjusting the illumination parameters of the LED light group according to a preset gradient, and the adjustment amplitude is nonlinearly positively correlated with the deviation value.

[0060] The photosynthesis intensity is compared with a preset threshold interval. When it exceeds the threshold interval, the lighting parameters of the LED light group are adjusted according to a preset gradient, and the adjustment amplitude is nonlinearly positively correlated with the deviation value. The preset threshold interval is determined based on the photosynthesis intensity range under the optimal growth state of algae (this range is obtained through a large number of experiments and data statistics). When the calculated photosynthesis intensity exceeds this interval, it is necessary to adjust the lighting parameters of the LED light group. The preset gradient refers to adjusting the lighting parameters according to a certain step size, such as adjusting the light intensity by 5% each time, adjusting the spectral ratio by 2% each time, etc. The adjustment amplitude is nonlinearly positively correlated with the deviation value, which means that when the deviation of the photosynthesis intensity from the threshold interval is greater, the adjustment amplitude is not a simple linear increase, but is based on a nonlinear function (for example, the quadratic function y=ax 2 +bx+c, where x is the deviation value and y is the adjustment amplitude), so that the lighting parameters can be controlled more precisely, avoiding the negative impact of excessive adjustment of lighting parameters on algae growth, ensuring that the algae are always in the best growth environment, and improving the yield and quality of algae.

[0061] In this embodiment, the three-dimensional distribution information of algae is obtained through image recognition technology, including the vertical distribution of biomass and water depth data, which lays the foundation for subsequent precise supplementary lighting. The influence of the refraction effect of the water body on the light intensity is calculated and the light intensity after refraction compensation is obtained to ensure that the algae receive appropriate light intensity and avoid the light problem caused by refraction affecting growth. The ambient light parameters are collected and combined with the algae distribution and LED light intensity to optimize the spatial position and lighting parameters of the LED light group. This process integrates multiple factors to make the light group setting more scientific and reasonable, which not only meets the algae lighting needs but also improves energy utilization. The microbubble generation rate and volume change on the algae surface are monitored to infer the photosynthesis intensity, and then the light parameters are adjusted according to the comparison results of the photosynthesis intensity and the preset threshold value. The adjustment range is nonlinearly positively correlated with the deviation value. This precise adjustment ensures that the algae are in the best growth state, which helps to improve the growth rate, yield and quality of the algae, while achieving energy saving and reducing breeding costs.

[0062] Preferably, the calculation of the LED light intensity after refraction compensation satisfies:

[0063]

[0064] In the formula, I adj represents the LED light intensity after refraction compensation, I0 represents the calibrated light intensity, k d represents the extinction coefficient of the water body, z represents the water depth, α represents the temperature influence coefficient, and ΔT is the difference between the real-time water temperature and the reference water temperature.

[0065] In an actual algae culture pond, the relevant parameters must first be measured and calibrated. For the calibration light intensity I0, when there is no algae in the culture pond and the water is clear, set the LED lamp to the standard working state, and use a high-precision light intensity meter to measure at a certain distance from the LED lamp and at a position without refraction interference (such as near the water surface in the air) to obtain the calibration light intensity I0. For example, the calibration light intensity of a certain LED lamp is measured to be I0 = 1000 lux, and the extinction coefficient of the water is k d , can be measured experimentally by selecting multiple different positions in the aquaculture pond, vertically placing light intensity measuring instruments downward, and recording the light intensity values ​​at different water depths z. According to the principle that light intensity attenuates with water depth, the least squares method is used to fit the measured data to obtain the extinction coefficient k of the aquaculture pond water body. d . Assume that after measurement and calculation, the extinction coefficient k of the aquaculture pond water is d =0.15m -1 , water depth z = 2m, temperature influence coefficient α, needs to be determined in advance according to the characteristics of the aquaculture pond water body. Generally speaking, it can be obtained by consulting relevant literature or conducting targeted experiments. Assume that the temperature influence coefficient of the aquaculture pond water body α = 0.02℃ -1The real-time water temperature can be obtained through the temperature sensor installed in the culture pond. The reference water temperature is usually set to the most suitable water temperature for the growth of the algae. For example, if the reference water temperature is set to 25°C and the real-time water temperature sensor measures 28°C, then ΔT = 3°C. Substitute the parameters into Get I adj =785lux, that is, the LED light intensity after refraction compensation is about 785lux.

[0066] In this embodiment, by introducing a calculation formula for LED light intensity after refraction compensation that takes into account factors such as the water extinction coefficient, temperature influence coefficient, and water depth, precise adjustment of the LED light intensity is achieved. On the one hand, it can more accurately take into account the changes in light intensity caused by factors such as refraction, attenuation, and temperature changes when light propagates in the water body, thereby avoiding the impact of algae photosynthesis due to inaccurate light intensity, thereby improving the accuracy of the lighting conditions of the algae growth environment; on the other hand, compared with the traditional simple light intensity setting method, this precise adjustment can avoid unnecessary energy waste while meeting the lighting requirements of algae growth, achieving energy-saving effects, and effectively improving the rationality and economy of light resource utilization in the algae cultivation process, providing a strong guarantee for the efficient and stable growth of algae.

[0067] Preferably, the dynamically determining the spatial position parameters and illumination parameters of the LED light group by a preset parameter optimization algorithm includes:

[0068] Collecting historical data, the historical data including historical algae three-dimensional distribution information, historical ambient light parameters, historical LED light intensity, historical spatial position parameters and historical illumination parameters;

[0069] Constructing a neural network model, wherein the neural network model uses the historical data as a training sample and outputs optimal spatial position parameters and illumination parameters for current three-dimensional distribution information of algae, ambient light parameters, and LED light intensity;

[0070] Introducing constraints into the neural network model, wherein the constraints include a maximum power limit of the LED lamp group, a safe range of lamp installation height, and a limit on the impact of lighting parameters on the human body and equipment;

[0071] Optimizing the neural network model through iterative training so that the spatial position parameters and light parameters output by the model can minimize energy consumption and maximize the photosynthesis efficiency of algae;

[0072] The optimized neural network model is applied to real-time calculation to dynamically adjust the spatial position parameters and lighting parameters of the LED light group.

[0073] In this embodiment, the historical data collection work is first carried out. Over a period of time, such as one month, the historical three-dimensional distribution information of algae, historical ambient light parameters, historical LED light intensity, historical spatial position parameters and historical lighting parameters are continuously collected using image recognition equipment, ambient light sensors, etc. These data come from a wide range of sources. The image recognition equipment takes images of algae distribution from different angles, and the ambient light sensor monitors the ambient light in all directions, recording the light intensity, spectral composition and incident angle of different weather and time. At the same time, the spatial position parameters of the LED light group at different times are recorded, such as the installation height is accurate to centimeters, the angle is accurate to degrees, and the lighting parameters, the emission light intensity is accurate to lux, the spectral ratio is refined to the proportion of light of different wavelengths, and the lighting cycle is accurate to minutes.

[0074] After completing data collection, the neural network model was constructed. Multilayer perceptron (MLP) was selected as the basic architecture. The input layer received historical data, and multiple hidden layers were set in the middle. Feature extraction and data processing were performed through nonlinear activation functions, and the output layer output the optimal spatial position parameters and illumination parameters for the current three-dimensional distribution information of algae, ambient light parameters, and LED light intensity. For example, the hidden layer is set to 3 layers, and the number of neurons in each layer is determined according to the data characteristics and model complexity, such as 100, 80, and 60 respectively. The neural network model was optimized through iterative training. The stochastic gradient descent (SGD) algorithm was used, the learning rate was set to 0.01, and the amount of training data per batch was 50. During the training process, the weights and biases of the model were continuously adjusted so that the spatial position parameters and illumination parameters output by the model could minimize energy consumption and maximize the photosynthesis efficiency of algae. After 1000 iterations of training, the loss function of the model converged, achieving a good optimization effect. The optimized neural network model was applied to real-time computing. In actual operation, the current three-dimensional distribution information of algae, ambient light parameters and LED light intensity are collected in real time and input into the model. The model quickly outputs the spatial position parameters and lighting parameters of the dynamically adjusted LED light group to achieve precise control of the LED light group.

[0075] In summary, the method provided in this embodiment can at least achieve the following effects:

[0076] 1) The present invention obtains the three-dimensional distribution information of algae through image recognition technology, including the vertical distribution of biomass and water depth data, which lays the foundation for subsequent precise supplementary lighting. The influence of the refraction effect of the water body on the light intensity is calculated and the light intensity after refraction compensation is obtained to ensure that the algae receive appropriate light intensity, avoid the light problem caused by refraction affecting growth, collect ambient light parameters and combine algae distribution and LED light intensity, optimize the spatial position and lighting parameters of the LED lamp group. This process integrates multiple factors to make the lamp group setting more scientific and reasonable, which not only meets the algae lighting needs but also improves energy utilization. The microbubble generation rate and volume change on the algae surface are monitored to infer the photosynthesis intensity, and then the light parameters are adjusted according to the comparison results of the photosynthesis intensity and the preset threshold value. The adjustment range is nonlinearly positively correlated with the deviation value. This precise adjustment ensures that the algae is in the best growth state, which helps to improve the growth rate, yield and quality of the algae, while achieving energy saving and reducing breeding costs.

[0077] 2) The present invention achieves precise adjustment of LED light intensity by introducing a calculation formula for LED light intensity after refraction compensation that takes into account factors such as water extinction coefficient, temperature influence coefficient and water depth. On the one hand, it can more accurately take into account the changes in light intensity caused by factors such as refraction, attenuation and temperature change when light propagates in water, thereby avoiding the impact of inaccurate light intensity on algae photosynthesis, thereby improving the accuracy of the lighting conditions of the algae growth environment; on the other hand, compared with the traditional simple light intensity setting method, this precise adjustment can avoid unnecessary energy waste while meeting the lighting requirements of algae growth, achieving energy-saving effects, effectively improving the rationality and economy of light resource utilization in the algae cultivation process, and providing a strong guarantee for the efficient and stable growth of algae.

[0078] See also Figure 2 In one embodiment, an energy-saving control system of an LED lamp suitable for supplementary lighting for algae is also provided, the system comprising:

[0079] The algae distribution information acquisition module 100 is used to acquire the three-dimensional distribution information of the algae in the culture pond in real time through image recognition technology, wherein the three-dimensional distribution information includes the vertical distribution data of the algae biomass and the corresponding water depth data;

[0080] The light intensity refraction compensation calculation module 200 is used to calculate the influence of the water body refraction effect on the light intensity according to the water depth data, and obtain the LED light intensity after refraction compensation;

[0081] The ambient light parameter acquisition module 300 is used to acquire current ambient light parameters, wherein the ambient light parameters include light intensity and incident angle;

[0082] The LED lamp group parameter determination module 400 is used to dynamically determine the spatial position parameters and illumination parameters of the LED lamp group through a preset parameter optimization algorithm based on the three-dimensional distribution information of algae, ambient light parameters and LED light intensity; the spatial position parameters include installation height and angle;

[0083] The photosynthesis intensity estimation module 500 is used to monitor the generation rate and volume change of microbubbles on the surface of algae, and estimate the photosynthesis intensity according to the generation rate and volume change;

[0084] The illumination parameter adjustment module 600 is used to compare the photosynthesis intensity with a preset threshold interval. When the threshold interval is exceeded, the illumination parameter of the LED light group is adjusted according to a preset gradient, and the adjustment amplitude is nonlinearly positively correlated with the deviation value.

[0085] It can be understood that the functions or modules included in the system provided in this embodiment can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0086] The present invention also provides an electronic device, including a processor and a memory, wherein the memory is used to store computer program code, and the computer program code includes computer instructions. When the processor executes the computer instructions, the electronic device executes a method as described in any possible implementation manner.

[0087] The present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes a method as described in any possible implementation manner.

[0088] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0089] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. Those skilled in the art can also clearly understand that the descriptions of the various embodiments of the present invention have different focuses. For the convenience and brevity of description, the same or similar parts may not be repeated in different embodiments. Therefore, for parts not described or not described in detail in a certain embodiment, refer to the records of other embodiments.

Claims

1. An energy-saving control method for LED lamps suitable for algae supplementary lighting, characterized in that: The method comprises: The three-dimensional distribution information of algae in the culture pond is obtained in real time by image recognition technology, wherein the three-dimensional distribution information includes vertical distribution data of algae biomass and corresponding water depth data; The influence of water refraction effect on light intensity is calculated based on water depth data, and the LED light intensity after refraction compensation is obtained; Collecting current ambient light parameters, wherein the ambient light parameters include light intensity and incident angle; Based on the three-dimensional distribution information of algae, ambient light parameters and LED light intensity, the spatial position parameters and lighting parameters of the LED light group are dynamically determined through a preset parameter optimization algorithm; the spatial position parameters include installation height and angle; Monitor the generation rate and volume changes of microbubbles on the surface of algae, and deduce the photosynthesis intensity based on the generation rate and volume changes; The photosynthesis intensity is compared with a preset threshold interval. When the threshold interval is exceeded, the lighting parameters of the LED lamp group are adjusted according to a preset gradient, and the adjustment amplitude is nonlinearly positively correlated with the deviation value.

2. The energy-saving control method of LED lamp suitable for algae supplementary lighting according to claim 1, characterized in that: The calculation of the LED light intensity after refraction compensation satisfies: In the formula, I adj represents the LED light intensity after refraction compensation, I0 represents the calibrated light intensity, k d represents the extinction coefficient of the water body, z represents the water depth, α represents the temperature influence coefficient, and ΔT is the difference between the real-time water temperature and the reference water temperature.

3. The energy-saving control method of LED lamp suitable for algae supplementary lighting according to claim 1, characterized in that: The method of dynamically determining the spatial position parameters and illumination parameters of the LED light group by using a preset parameter optimization algorithm includes: Collecting historical data, the historical data including historical algae three-dimensional distribution information, historical ambient light parameters, historical LED light intensity, historical spatial position parameters and historical illumination parameters; Constructing a neural network model, wherein the neural network model uses the historical data as a training sample and outputs optimal spatial position parameters and illumination parameters for current three-dimensional distribution information of algae, ambient light parameters, and LED light intensity; Introducing constraints into the neural network model, wherein the constraints include a maximum power limit of the LED lamp group, a safe range of lamp installation height, and a limit on the impact of lighting parameters on the human body and equipment; Optimizing the neural network model through iterative training so that the spatial position parameters and light parameters output by the model can minimize energy consumption and maximize the photosynthesis efficiency of algae; The optimized neural network model is applied to real-time calculation to dynamically adjust the spatial position parameters and lighting parameters of the LED light group.

4. The energy-saving control method of LED lamp suitable for algae supplementary lighting according to claim 1, characterized in that: The photosynthesis intensity is obtained based on a photosynthesis efficiency evaluation algorithm, which is expressed as: Where P(t) is the quantitative index of photosynthesis intensity per unit time, T is the integration time window, the default T value is 300 seconds, N is the number of bubbles released by algae in the integration time window, and r is k represents the equivalent radius of the th bubble, Represents the sum of the projected areas of all bubbles in the vertical direction.

5. An energy-saving control system for LED lamps suitable for algae supplementary lighting, characterized in that: The system comprises: An algae distribution information acquisition module is used to acquire three-dimensional distribution information of algae in the culture pond in real time through image recognition technology, wherein the three-dimensional distribution information includes vertical distribution data of algae biomass and corresponding water depth data; The light intensity refraction compensation calculation module is used to calculate the influence of water refraction effect on light intensity according to water depth data, and obtain the LED light intensity after refraction compensation; An ambient light parameter acquisition module is used to acquire current ambient light parameters, wherein the ambient light parameters include light intensity and incident angle; The LED lamp group parameter determination module is used to dynamically determine the spatial position parameters and illumination parameters of the LED lamp group through a preset parameter optimization algorithm based on the three-dimensional distribution information of algae, ambient light parameters and LED light intensity; the spatial position parameters include installation height and angle; The photosynthesis intensity estimation module is used to monitor the generation rate and volume change of microbubbles on the surface of algae, and estimate the photosynthesis intensity based on the generation rate and volume change; The illumination parameter adjustment module is used to compare the photosynthesis intensity with a preset threshold interval. When the threshold interval is exceeded, the illumination parameter of the LED lamp group is adjusted according to a preset gradient, and the adjustment amplitude is nonlinearly positively correlated with the deviation value.

6. An electronic device, characterized in that: include: A processor and a memory, wherein the memory is used to store computer program codes, wherein the computer program codes include computer instructions. When the processor executes the computer instructions, the electronic device executes the energy-saving control method for LED lamps suitable for algae lighting supplement as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes the energy-saving control method of an LED lamp suitable for algae lighting supplement as described in any one of claims 1 to 4.

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