Optical fiber-based intelligent lighting system for submerged plants and its control method

Abstract

The present invention discloses an optical fiber-based intelligent light-filling system for submerged plants and a control method thereof. The system comprises the following steps: obtaining an environmental image and environmental data of the submerged plant; segmenting the environmental image to obtain a regional environmental image; performing image recognition on the regional environmental image; calculating the biodiversity based on the image recognition result; determining the regional lighting requirement; performing feature extraction on the growth environment; determining a first light-filling requirement and a second light-filling requirement; constructing an intelligent light-filling model for submerged plants; inputting the first light-filling requirement and the second light-filling requirement into the intelligent light-filling model for submerged plants to obtain a light-filling strategy; and performing intelligent light-filling on the submerged plants according to the light-filling strategy. This method not only improves the accuracy and efficiency of optical fiber-based intelligent light-filling for submerged plants, but also has good interpretability and can be directly applied to an optical fiber-based intelligent light-filling system for submerged plants.

Description

Technical Field

[0001] The present invention relates to the field of intelligent lighting supplement, and in particular to an optical fiber-based submerged plant intelligent lighting supplement system and a control method thereof. Background Art

[0002] Submerged plants absorb nutrients from the water during their growth, alleviating eutrophication. They also release oxygen through photosynthesis, increasing the dissolved oxygen content in the water. They also have significant economic value, playing an important role in ecological restoration, aquatic plant cultivation, and aquarium viewing. Fiber optic lighting systems offer excellent light-guiding performance and durability. Using fiber as a transmission medium, they can transmit light signals from a light source to areas requiring supplemental lighting over long distances or that are difficult to directly illuminate.

[0003] Because natural light decays rapidly underwater and is blocked by emergent plants, submerged plants often grow slowly or even die due to insufficient light, necessitating supplemental lighting for submerged plants. Traditional submerged plant supplemental lighting equipment often relies on manual placement and salvage, requiring frequent maintenance and replacement, and making it difficult to adjust light intensity, spectral range, and illuminated area in a timely and flexible manner. By fully considering the growth environment of submerged plants, understanding their supplemental lighting needs, and combining fiber optic technology, a timely, accurate, and efficient fiber-based intelligent supplemental lighting system for submerged plants and its control method are designed to overcome the shortcomings of existing supplemental lighting systems and control technologies. This provides accurate and efficient supplemental lighting for submerged plants, which is of great significance for maintaining the balance of the ecosystem and creating economic value. Summary of the Invention

[0004] The purpose of the present invention is to provide an optical fiber-based intelligent lighting system for submerged plants and a control method thereof.

[0005] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0006] The present invention comprises the following steps:

[0007] Acquire environmental images and environmental data of submerged plants, and pre-process the environmental images and environmental data; the environmental data includes growth environment and lighting environment;

[0008] Segmenting the environmental image according to submerged plant growth areas to obtain regional environmental images, performing image recognition on the regional environmental images to obtain image recognition results, and calculating biological richness based on the image recognition results; the image recognition results include plant quantity, plant category, plant growth stage, and water quality environment;

[0009] Matching the plant category and the plant growth stage with a standard plant information database to obtain the lighting conditions of submerged plants, and determining the regional lighting requirements based on the lighting conditions, the number of plants, and the lighting environment data;

[0010] Extracting features of the growth environment to obtain underwater temperature, oxygen content, and carbon dioxide content, determining a first supplemental lighting requirement based on the underwater temperature, the biological richness, and the regional lighting requirement, and determining a second supplemental lighting requirement based on the oxygen content, the carbon dioxide content, the water quality environment, and the regional lighting requirement;

[0011] An intelligent fill light model for submerged plants is constructed, the first fill light requirement and the second fill light requirement are input into the intelligent fill light model for submerged plants to obtain a fill light strategy, and intelligent fill light is performed on the submerged plants according to the fill light strategy.

[0012] Furthermore, the method of segmenting the environmental image according to the submerged plant growth area to obtain a regional environmental image includes:

[0013] The growth range of submerged plants is projected onto a two-dimensional plane, the cell area is determined according to the fill light range of the optical fiber fill light device, and the submerged plant growth area is divided according to the cell area;

[0014] The environmental image is segmented according to the submerged plant growth area to obtain a regional environmental image, and regional coordinates are set for the regional environmental image.

[0015] Furthermore, the method for performing image recognition on the regional environment image includes:

[0016] (1) Construct a support vector machine image classification and counting model, input the regional environmental image into the support vector machine image classification and counting model, output the classification and counting results of plants and animals in the regional environmental image, and record the categories and numbers of submerged plants;

[0017] (2) The phenological extraction method of time series NDVI is used to obtain the growth stages of submerged plants, including:

[0018] Draw a region of interest enclosed by 3 or more points on the regional environment image, and calculate the average value of the three color bands in the region of interest;

[0019] Extract vegetation indices that characterize the growth characteristics of submerged plants, including the normalized difference vegetation index NDVI, relative greenness index GI, relative blueness index BI, relative redness index RI, and relative blue-red brightness BRI index;

[0020] The inverse Fourier transform function ifft was used to reconstruct the time series NDVI. The NDVI points within the interval were fitted to obtain the seasonal trajectory curve of submerged plant growth. The root mean square error was used to evaluate the fitting effect.

[0021] The expression of the submerged plant growth season trajectory curve is:

[0022]

[0023] Where c1 is the mean of winter light and vegetation fraction, c2 is the difference between summer and winter light and vegetation fraction, c3 is the trend of summer greenness, t is time, x1 is the inflection point of spring greenness increase, x3 is the inflection point of autumn greenness decrease, x2 and x4 are parameters for adjusting the seasonal growth curves in spring and autumn, x2, x4∈(0,3];

[0024] According to the seasonal trajectory curve of submerged plants, phenological parameters are extracted to obtain plant growth stages and corresponding growth parameters;

[0025] (3) Identify the impurities in the water in the regional environmental image to determine the water quality environment, perform Gaussian filtering to denoise the regional environmental image, and convert it into a grayscale image. Calculate the three Tamura texture features of the grayscale image, namely, roughness, directionality, and uniformity. Perform a comprehensive evaluation based on the set roughness threshold, directionality threshold, and uniformity threshold to obtain a water quality score. Combine the water quality score, pH value, and salt ion concentration to determine the water quality environment.

[0026] Furthermore, the method for calculating biological richness based on the image recognition result includes:

[0027] Substitute the species categories and species numbers obtained by image recognition into the biological richness function to obtain biological richness. The expression of the biological richness function is:

[0028]

[0029] Where B is the biological richness, T is the number of all organisms in the area, C is the total number of all biological categories in the area, A is the area of the area, and n is the total number of all organisms in the area. i 、n j 、n k are the number of aquatic plants of category i, aquatic animals of category j and microorganisms of category k, w1, w2 and w3 are the category weights of aquatic plants, aquatic animals and microorganisms, l, m and n are the number of categories of aquatic plants, aquatic animals and microorganisms, respectively.

[0030] Furthermore, the method for determining the regional lighting requirements includes:

[0031] According to the plant category and plant growth stage, the plant category and plant growth stage are matched with the plant category and plant growth stage of the standard plant information database, and the similarity between the submerged plants to be matched and the submerged plants in the standard plant information database is calculated one by one, and the light conditions of the submerged plants in the standard plant information database corresponding to the matching group with the highest similarity are extracted;

[0032] Lighting conditions include ideal spectral range and ideal light intensity. Feature extraction of lighting environment data is performed to obtain light intensity, spectral range, photon flux density, and radiation flux density. The ideal light intensity is adjusted according to the lighting environment data and the number of plants, and the regional light demand is calculated. The expression of regional light demand is:

[0033]

[0034] Among them I y ′ is the required light intensity of the region, α and β are the light parameter influencing factors, PPFD is the light quantum flux density, N is the number of all submerged plants in the region, l is the number of submerged plant categories, I yi is the ideal light intensity for the i-th type of submerged plants, PAR is the radiation flux density, w 1i is the weight coefficient of the i-th type of submerged plants, n i is the number of submerged plants in category i;

[0035] The spectral ranges of all submerged plants are sorted according to the size of the values, the largest starting wavelength is taken as the spectrum end judgment point, and the smallest ending wavelength is taken as the spectrum start judgment point. The regional required spectral range is determined based on the spectrum end judgment point and the spectrum start judgment point, and the regional required light intensity and the regional required spectral range are combined to determine the regional light demand.

[0036] Furthermore, the method for determining the first fill light requirement includes:

[0037] The environmental factors that affect the photosynthesis of submerged plants are determined based on the underwater temperature and biological richness. The underwater temperature, the ideal temperature of submerged plants and biological richness are substituted into the environmental function to obtain the environmental factors. The environmental function expression is:

[0038]

[0039] Where E is the environmental factor, x1 and x3 are the positive coefficients of the influence of the adjustment factor, T is the underwater temperature, T y is the ideal temperature corresponding to the maximum efficiency of submerged plants' photosynthesis, x2 is the sensitivity of temperature changes to environmental factors, and there is no linear relationship between environmental factors and the first term, B is the biodiversity, x4 is the sensitivity of biodiversity to environmental factors, and the second term is the attenuation coefficient, which is negatively correlated with environmental factors;

[0040] The first fill light requirement is determined based on environmental factors and regional lighting requirements. The corresponding expression for the light intensity of the first fill light requirement is:

[0041]

[0042] Where ΔI1 is the light intensity required for the first fill light, I y ′ is the required light intensity of the area, I t is the light intensity corresponding to the underwater lighting environment;

[0043] The spectral range required by the first fill light is consistent with the spectral range required by the region.

[0044] Furthermore, the method for determining the second fill light requirement includes:

[0045] Substituting the oxygen content, carbon dioxide content and water quality environment in the water into the second fill light demand function, the light intensity required for the second fill light is obtained. The expression of the second fill light demand function is:

[0046]

[0047] Where ΔI2 is the light intensity required for the second supplementary light, k1 and k2 are the influence coefficients of oxygen and carbon dioxide, d1 and d2 are the oxygen content and carbon dioxide content in water, r1 and r2 are the response coefficients of oxygen and carbon dioxide, p and q are the changes in oxygen content and carbon dioxide content, PH and PH y are the pH value in water and the ideal pH value, c, c y is the nutrient ion concentration and ideal nutrient ion concentration in water, o, o y to rate water quality and ideal water quality;

[0048] The spectral range required by the second fill light is consistent with the spectral range required by the area.

[0049] Furthermore, the method for obtaining the fill light strategy includes:

[0050] The first step light requirement, the second fill light requirement and the area coordinates are combined into a fill light dataset, and the fill light dataset is divided into a training set and a test set;

[0051] Constructing a submerged plant intelligent lighting model, which includes an input layer, a hidden layer, a parameter selection layer, and a data output layer;

[0052] The training set data is input into the input layer for preprocessing, and the preprocessed data is input into the hidden layer for feature extraction to obtain feature data. The feature data is input into the parameter selection layer to select the fill light output fusion feature;

[0053] The parameter selection layer includes the attention layer and the feature fusion layer. The feature data enters the attention layer to weight the features, and the weighted features and original features are input into the feature fusion layer for integration and output of the fused features to the data output layer.

[0054] The data output layer consists of two fully connected layers. The fusion feature input is sent to the first fully connected layer and the output data is sent to the second fully connected layer. The second fully connected layer outputs the predicted value of the fill light parameter.

[0055] The root mean square error loss function is used to evaluate the difference between the predicted value and the true value of the lighting parameter. The Adam optimizer is used to optimize the model weights. The test set is used to evaluate the model and output the intelligent lighting model for submerged plants.

[0056] The environmental data and environmental images of the area to be supplemented with light are input into the intelligent supplementary lighting model of submerged plants to obtain the supplementary lighting strategy and mobilize the supplementary lighting equipment for supplementary lighting.

[0057] The second aspect is the optical fiber-based intelligent lighting system for submerged plants, including:

[0058] Data acquisition module: used to obtain environmental images and environmental data of submerged plants, pre-process the environmental images and environmental data, and transmit them to the image module and data module; the environmental images are obtained through a high-definition underwater camera, and the environmental data are obtained through a temperature sensor, a spectrum sensor, a dissolved oxygen meter, and a water quality carbon dioxide concentration meter;

[0059] Image module: used to segment the environmental image according to the growth area to obtain the regional environmental image, and perform image recognition on the regional environmental image to obtain the plant quantity, plant category, plant growth stage and water quality environment;

[0060] Data module: used to determine the regional lighting requirement based on the lighting conditions, the number of plants and the lighting environment data, determine the first supplementary lighting requirement based on the underwater temperature, the biodiversity and the regional lighting requirement, and determine the second supplementary lighting requirement based on the oxygen content, the carbon dioxide content, the water quality environment and the regional lighting requirement;

[0061] A fill light model module is configured to construct an intelligent fill light model for submerged plants based on the first fill light requirement, the second fill light requirement, and the regional coordinates, and process the environmental data and environmental image of the area to be filled with light and input them into the intelligent fill light model for submerged plants to obtain fill light parameters; the fill light parameters include color temperature parameters, fill light intensity, fill light power, and regional coordinates;

[0062] Intelligent monitoring module: used to store, view and manage the environmental image, the environmental data and the fill light parameters, determine the fill light strategy according to the fill light parameters, and the controller sends adjustment signals of luminous intensity, color temperature and power according to the fill light strategy; used to convert the submerged plant growth area into a cloud image through regional coordinates, and view and manage the fill light strategy of the submerged plant growth area with different regional coordinates;

[0063] Fill light module: used to execute the adjustment signal of the controller to perform intelligent fill light on the submerged plant growth area; includes a light source group, an optical fiber transmission group, a fill light group and a power supply group; the light source group consists of an optical fiber light source and RGB-LED lamp beads, and is used to change the luminous intensity, color temperature and power of the light source according to the adjustment signal; the optical fiber transmission group consists of an optical fiber bundle and an optical fiber connector, and is used to transmit the light source underwater; the fill light group is used to evenly disperse the light source transmitted by the optical fiber to the submerged plant growth area for intelligent fill light; the power supply group consists of a solar panel and a battery pack, and is used to provide power for the submerged plant intelligent fill light system.

[0064] The beneficial effects of the present invention are:

[0065] The present invention is an optical fiber-based intelligent light-supplementing system for submerged plants and a control method thereof. Compared with the prior art, the present invention has the following technical effects:

[0066] The present invention can improve the accuracy of intelligent fill lighting through image segmentation, image recognition, determination of regional lighting requirements, determination of first fill lighting requirements, determination of second fill lighting requirements and model construction steps, thereby improving the speed and precision of intelligent fill lighting for submerged plants, greatly saving resources, improving fill lighting efficiency, realizing intelligent, efficient and remote controllable fill lighting for submerged plants, performing fill lighting control on submerged plants in real time, providing guarantee for the timeliness and precision of intelligent fill lighting for submerged plants, having important significance for intelligent fill lighting for submerged plants, and adapting to different intelligent fill lighting systems for submerged plants and the intelligent fill lighting requirements of submerged plants of different users, and having certain universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a flowchart of the steps of the optical fiber-based submerged plant intelligent lighting system and its control method of the present invention. DETAILED DESCRIPTION

[0068] The present invention will be further described below through specific examples. The illustrative examples and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0069] The optical fiber-based intelligent light-supplementing system for submerged plants and the control method thereof of the present invention comprise the following steps:

[0070] like Figure 1 As shown, in this embodiment, the following steps are included:

[0071] Acquire environmental images and environmental data of submerged plants, and pre-process the environmental images and environmental data; the environmental data includes growth environment and lighting environment;

[0072] Segmenting the environmental image according to submerged plant growth areas to obtain regional environmental images, performing image recognition on the regional environmental images to obtain image recognition results, and calculating biological richness based on the image recognition results; the image recognition results include plant quantity, plant category, plant growth stage, and water quality environment;

[0073] Matching the plant category and the plant growth stage with a standard plant information database to obtain the lighting conditions of submerged plants, and determining the regional lighting requirements based on the lighting conditions, the number of plants, and the lighting environment data;

[0074] Extracting features of the growth environment to obtain underwater temperature, oxygen content, and carbon dioxide content, determining a first supplemental lighting requirement based on the underwater temperature, the biological richness, and the regional lighting requirement, and determining a second supplemental lighting requirement based on the oxygen content, the carbon dioxide content, the water quality environment, and the regional lighting requirement;

[0075] An intelligent fill light model for submerged plants is constructed, the first fill light requirement and the second fill light requirement are input into the intelligent fill light model for submerged plants to obtain a fill light strategy, and intelligent fill light is performed on the submerged plants according to the fill light strategy.

[0076] In this embodiment, the method for segmenting the environmental image according to the submerged plant growth area to obtain the regional environmental image includes:

[0077] The growth range of submerged plants is projected onto a two-dimensional plane, the cell area is determined according to the fill light range of the optical fiber fill light device, and the submerged plant growth area is divided according to the cell area;

[0078] The environmental image is segmented according to the submerged plant growth area to obtain a regional environmental image, including:

[0079] A Transformer model improved with a windowed multi-head attention mechanism is used for image edge detection. A ConvNext network is used to extract global and local features. A progressive upsampling decoder and a stepwise aggregation module are used for feature fusion to complete image segmentation. The regional environment image is output and the regional coordinates are set for the regional environment image.

[0080] In the actual evaluation, taking the fiber optic intelligent lighting system for submerged plants in the XX Town Wetland Reserve as an example, the lighting range of a single fiber optic lighting device is 9㎡. The corresponding regional environmental image shows the image content of the submerged plant growth range projected within 9㎡ on a two-dimensional plane. The environmental image of the submerged plant growth area with the regional coordinates of (2, 3) is taken for image segmentation.

[0081] In this embodiment, the method for performing image recognition on the regional environment image includes:

[0082] (1) Construct a support vector machine image classification and counting model. The color histogram, local binary pattern, and contour analysis of the regional environment image are used to obtain the color features, texture features, and shape features of the regional environment image. The extracted features are preprocessed, and the radial basis function is used as the kernel function to process the data. The model is trained using the training set, and the model performance is evaluated using the test set. The support vector machine image classification and counting model is output.

[0083] Input the regional environmental image into the support vector machine image classification and counting model to output the classification and counting results of plants and animals in the regional environmental image, and record the categories and numbers of submerged plants;

[0084] In the actual evaluation, the regional environmental image with the coordinates of (2, 3) was input into the support vector machine image classification and counting model for image recognition. The output animal categories were 7, with a total of 84 animal individuals / species, and the plant categories were 9, with a total of 251 plant individuals / clumps. The submerged plants were: 100 plants of Vallisneria, 30 clumps of Ceratophyllum, 20 plants of Myriophyllum, 50 plants of Hydrilla, and 40 plants of Potamogeton.

[0085] (2) The phenological extraction method of time series NDVI is used to obtain the growth stage of submerged plants. The specific steps include:

[0086] Draw a region of interest enclosed by three or more points on the regional environment image and calculate the average value of the three color bands in the region of interest:

[0087]

[0088] Where DN is the pixel brightness value of the red, blue and green bands, c is one of the red, blue and green bands, SUMD is the sum of all pixels in a certain band in the area of interest, and N is the number of pixels in the regional environment image;

[0089] Extract the vegetation index that characterizes the growth characteristics of submerged plants. The calculation formula of the vegetation index is:

[0090]

[0091] NDVI, GI, BI, RI, and BRI are normalized vegetation index, relative greenness index, relative blueness index, relative redness index, and relative blue-red brightness index, respectively. DN , G DN 、R DN 、B DN They are the near-infrared band pixel brightness value, green band pixel brightness value, red band pixel brightness value and blue band pixel brightness value respectively;

[0092] The inverse Fourier transform function ifft was used to reconstruct the time series NDVI. The NDVI points within the interval were fitted to obtain the seasonal trajectory curve of submerged plant growth. The root mean square error was used to evaluate the fitting effect.

[0093] The expression of the submerged plant growth season trajectory curve is:

[0094]

[0095] Where c1 is the mean of winter light and vegetation fraction, c2 is the difference between summer and winter light and vegetation fraction, c3 is the trend of summer greenness, t is time, x1 is the inflection point of spring greenness increase, x3 is the inflection point of autumn greenness decrease, x2 and x4 are parameters for adjusting the seasonal growth curves in spring and autumn, x2, x4∈(0,3];

[0096] Extracting phenological parameters based on the submerged plant growth season trajectory curve to obtain plant growth stages and corresponding growth parameters; the plant growth stages include greening period, maturity period, leaf fall period and dormancy period;

[0097] In the actual evaluation, image recognition was performed on the regional environmental image with regional coordinates (2, 3) and the submerged plant growth season trajectory curve was fitted, resulting in: Vallisneria: 10 plants (greening period), 60 plants (mature period), 30 plants (leaf-falling period); Ceratophyllum: 2 clumps (greening period), 15 clumps (mature period), 13 clumps (leaf-falling period); Myriophyllum: 4 plants (greening period), 12 plants (mature period), 4 plants (leaf-falling period); Hydrilla: 15 plants (greening period), 30 plants (mature period), 5 plants (leaf-falling period); Potamogeton pectinata: 7 plants (greening period), 24 plants (mature period), 9 plants (leaf-falling period).

[0098] (3) Identify impurities in the water within the regional environmental image to determine the water quality environment, perform Gaussian filtering on the regional environmental image to denoise it, and convert it into a grayscale image. Calculate the three Tamura texture features of the grayscale image, namely, roughness, directionality, and uniformity. Perform a comprehensive evaluation based on the set roughness threshold, directionality threshold, and uniformity threshold to obtain a water quality score. Combine the water quality score, pH value, and salt ion concentration to determine the water quality environment.

[0099] In the actual assessment, the water quality score of the regional environmental image with regional coordinates (2, 3) was 7 points after image recognition, the pH value was 7.5, and the salt ion concentration was 110 mg / L. The water quality environment was relatively good.

[0100] In this embodiment, the method for calculating biological richness based on the image recognition result includes:

[0101] Substitute the species categories and species numbers obtained by image recognition into the biological richness function to obtain biological richness. The expression of the biological richness function is:

[0102]

[0103] Where B is the biological richness, T is the number of all organisms in the area, C is the total number of all biological categories in the area, A is the area of the area, and n is the total number of all organisms in the area. i 、n j 、n k are the number of aquatic plants of category i, aquatic animals of category j, and microorganisms of category k, respectively; w1, w2, w3 are the category weights of aquatic plants, aquatic animals, and microorganisms, respectively; l, m, n are the number of categories of aquatic plants, aquatic animals, and microorganisms, respectively;

[0104] In the actual evaluation, the biological richness was calculated to be 7 based on the image recognition results of the regional environmental image with the regional coordinates (2, 3).

[0105] In this embodiment, the method for determining the regional lighting requirement includes:

[0106] According to the plant category and plant growth stage, the plant category and plant growth stage are matched with the plant category and plant growth stage of the standard plant information database, and the similarity between the submerged plants to be matched and the submerged plants in the standard plant information database is calculated one by one, and the light conditions of the submerged plants in the standard plant information database corresponding to the matching group with the highest similarity are extracted;

[0107] Lighting conditions include ideal spectral range and ideal light intensity. Feature extraction of lighting environment data is performed to obtain light intensity, spectral range, photon flux density, and radiation flux density. The ideal light intensity is adjusted according to the lighting environment data and the number of plants, and the regional light demand is calculated. The expression of regional light demand is:

[0108]

[0109] Among them I y ′ is the required light intensity of the region, α and β are the light parameter influencing factors, PPFD is the light quantum flux density, N is the number of all submerged plants in the region, l is the number of submerged plant categories, I yi is the ideal light intensity for the i-th type of submerged plants, PAR is the radiation flux density, w 1i is the weight coefficient of the i-th type of submerged plants, n i is the number of submerged plants in category i;

[0110] The spectral ranges of all submerged plants are sorted by value, the largest starting wavelength is taken as the spectrum end judgment point, and the smallest ending wavelength is taken as the spectrum start judgment point. The regional required spectral range is determined based on the spectrum end judgment point and the spectrum start judgment point, and the regional required light intensity and regional required spectral range are combined to determine the regional light demand;

[0111] In the actual evaluation, the illumination environment data of the area coordinates (2, 3) are: illumination intensity 5000 lux, spectral range 400-700 nm, and light quantum flux density of about 250 μmol / m 2 / s, combined with the ideal spectral range and ideal light intensity of submerged plants, when α is 0.8 and β is 1.1, the regional required light intensity is 7900 lux and the regional required spectral range is 350-890nm.

[0112] In this embodiment, the method for determining the first fill light requirement includes:

[0113] The environmental factors that affect the photosynthesis of submerged plants are determined based on the underwater temperature and biological richness. The underwater temperature, the ideal temperature of submerged plants and biological richness are substituted into the environmental function to obtain the environmental factors. The environmental function expression is:

[0114]

[0115] Where E is the environmental factor, x1 and x3 are the positive coefficients of the influence of the adjustment factor, T is the underwater temperature, T y is the ideal temperature corresponding to the maximum efficiency of submerged plants' photosynthesis, x2 is the sensitivity of temperature changes to environmental factors, and there is no linear relationship between environmental factors and the first term, B is the biodiversity, x4 is the sensitivity of biodiversity to environmental factors, and the second term is the attenuation coefficient, which is negatively correlated with environmental factors;

[0116] The first fill light requirement is determined based on environmental factors and regional lighting requirements. The corresponding expression for the light intensity of the first fill light requirement is:

[0117]

[0118] Where ΔI1 is the light intensity required for the first fill light, I y ′ is the required light intensity of the area, I t is the light intensity corresponding to the underwater lighting environment;

[0119] The spectral range required by the first fill light is consistent with the spectral range required by the region;

[0120] In the actual evaluation, T is set to 20℃, T yTaking 22°C, x1 as 1.221, x2 as 0.1, x3 as 0.5, x4 as 0.1, and combining the biodiversity B as 7, the calculated environmental factor E is 1.55;

[0121] Combining the regional required light intensity of 7900 lux and the light intensity of 5000 lux, the light intensity ΔI1 required for the first fill light is calculated to be 4247 lux, and the spectral range of the first fill light requirement is 350-890 nm.

[0122] In this embodiment, the method for determining the second fill light requirement includes:

[0123] Substituting the oxygen content, carbon dioxide content and water quality environment in the water into the second fill light demand function, the light intensity required for the second fill light is obtained. The expression of the second fill light demand function is:

[0124]

[0125] Where ΔI2 is the light intensity required for the second supplementary light, k1 and k2 are the influence coefficients of oxygen and carbon dioxide, d1 and d2 are the oxygen content and carbon dioxide content in water, r1 and r2 are the response coefficients of oxygen and carbon dioxide, p and q are the changes in oxygen content and carbon dioxide content, PH and PH y are the pH value in water and the ideal pH value, c, c y is the nutrient ion concentration and ideal nutrient ion concentration in water, o, o y to rate water quality and ideal water quality;

[0126] The spectral range required by the second fill light is consistent with the spectral range required by the area;

[0127] In the actual evaluation, the oxygen content and carbon dioxide content were 10 mg / L and 7 mg / L respectively, the corresponding influence coefficients k1 and k2 were both 1, the corresponding response coefficients r1 and r2 were 0.1 and 0.01 respectively, the pH value and the ideal pH value were both 7.5, the nutrient ion concentration c in water and the ideal nutrient ion concentration c y is 110mg / L and 220mg / L, the water quality score and ideal water quality score are 7 and 7.5, and the value in the brackets is 0.87;

[0128] Combining the regional required light intensity of 7900 lux and the light intensity of 5000 lux, the light intensity ΔI1 required for the second fill light is calculated to be 1873 lux, and the spectral range of the second fill light requirement is 350-890 nm.

[0129] In this embodiment, the method for obtaining the fill light strategy includes:

[0130] The first step light requirement, the second fill light requirement and the area coordinates are combined into a fill light dataset, and the fill light dataset is divided into a training set and a test set;

[0131] Constructing a submerged plant intelligent lighting model, which includes an input layer, a hidden layer, a parameter selection layer, and a data output layer;

[0132] The training set data is input into the input layer for preprocessing, and the preprocessed data enters the hidden layer. The activation function uses ReLU to extract the data features to obtain feature data. The feature data is input into the parameter selection layer to select the fill light parameters; the feature data includes light intensity, maximum wavelength, minimum wavelength and area coordinates;

[0133] The parameter selection layer includes the attention layer and the feature fusion layer. The feature data enters the attention layer, and the multi-layer perceptron MLP is used for parameter learning to calculate the attention weight. The activation function uses softmax, and the attention weight is used to weight the features. The weighted features and the original features are input into the feature fusion layer, and ASFF is used to integrate the features and output the fused features to the data output layer.

[0134] The data output layer consists of two fully connected layers. The fused features are input into the first fully connected layer. The number of neurons in the first fully connected layer is set to 8, and the activation function uses Leaky ReLU. The data is output to the second fully connected layer. The number of neurons in the second fully connected layer is set to 4, and the tanh activation function is used to output continuous color temperature parameters. The sigmoid activation function is used to output fill light intensity and fill light power. The data output layer outputs the predicted values of fill light parameters including color temperature parameters, fill light intensity, fill light power and area coordinates.

[0135] The root mean square error loss function was used to evaluate the difference between the predicted and true values of the lighting parameters. The Adam optimizer was used to optimize the weights of the submerged plant intelligent lighting model. The test set was used to evaluate the submerged plant intelligent lighting model.

[0136] Input the environmental data and environmental image of the area to be lighted into the intelligent light-filling model of submerged plants to obtain light-filling parameters, and determine the light-filling strategy based on the light-filling parameters to mobilize the light-filling equipment for light-filling;

[0137] In the actual evaluation, the area coordinates (2, 3), the first fill light requirement of 4247 lux / 350-890 nm, and the second fill light requirement of 1873 lux / 350-890 nm were input into the submerged plant intelligent fill light model to obtain the specific fill light parameters: fill light intensity of 3297 lux, fill light color temperature of 350-400 nm and 700-890 nm, and fill light power of 12 W.

[0138] The second aspect is the optical fiber-based intelligent lighting system for submerged plants, including:

[0139] Data acquisition module: used to obtain environmental images and environmental data of submerged plants, pre-process the environmental images and environmental data, and transmit them to the image module and data module; the environmental images are obtained through a high-definition underwater camera, and the environmental data are obtained through a temperature sensor, a spectrum sensor, a dissolved oxygen meter, and a water quality carbon dioxide concentration meter;

[0140] Image module: used to segment the environmental image according to the growth area to obtain the regional environmental image, and perform image recognition on the regional environmental image to obtain the plant quantity, plant category, plant growth stage and water quality environment;

[0141] Data module: used to determine the regional lighting requirement based on the lighting conditions, the number of plants and the lighting environment data, determine the first supplementary lighting requirement based on the underwater temperature, the biodiversity and the regional lighting requirement, and determine the second supplementary lighting requirement based on the oxygen content, the carbon dioxide content, the water quality environment and the regional lighting requirement;

[0142] A fill light model module is configured to construct an intelligent fill light model for submerged plants based on the first fill light requirement, the second fill light requirement, and the regional coordinates, and process the environmental data and environmental image of the area to be filled with light and input them into the intelligent fill light model for submerged plants to obtain fill light parameters; the fill light parameters include color temperature parameters, fill light intensity, fill light power, and regional coordinates;

[0143] Intelligent monitoring module: used to store, view and manage the environmental image, the environmental data and the fill light parameters, determine the fill light strategy according to the fill light parameters, and the controller sends adjustment signals of luminous intensity, color temperature and power according to the fill light strategy; used to convert the submerged plant growth area into a cloud image through regional coordinates, and view and manage the fill light strategy of the submerged plant growth area with different regional coordinates;

[0144] Fill light module: used to execute the adjustment signal of the controller to perform intelligent fill light on the submerged plant growth area; includes a light source group, an optical fiber transmission group, a fill light group and a power supply group; the light source group consists of an optical fiber light source and RGB-LED lamp beads, and is used to change the luminous intensity, color temperature and power of the light source according to the adjustment signal; the optical fiber transmission group consists of an optical fiber bundle and an optical fiber connector, and is used to transmit the light source underwater; the fill light group is used to evenly disperse the light source transmitted by the optical fiber to the submerged plant growth area for intelligent fill light; the power supply group consists of a solar panel and a battery pack, and is used to provide power for the submerged plant intelligent fill light system.

[0145] 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. The optical fiber-based intelligent light supplement control method for submerged plants is characterized by: The following steps are involved: S1. Acquire environmental images and environmental data of submerged plants, and pre-process the environmental images and environmental data; the environmental data includes growth environment and lighting environment; S2. Segmenting the environmental image according to the submerged plant growth area to obtain a regional environmental image, performing image recognition on the regional environmental image to obtain an image recognition result, and calculating the biodiversity based on the image recognition result; the image recognition result includes the number of plants, plant type, plant growth stage, and water quality environment; S3. Matching the plant category and the plant growth stage with a standard plant information database to obtain the lighting conditions of submerged plants, and determining the regional lighting requirements based on the lighting conditions, the number of plants, and the lighting environment data; S4. Extracting features of the growth environment to obtain underwater temperature, oxygen content, and carbon dioxide content, determining a first supplemental lighting requirement based on the underwater temperature, the biodiversity, and the regional lighting requirement, and determining a second supplemental lighting requirement based on the oxygen content, the carbon dioxide content, the water quality environment, and the regional lighting requirement; S5. Constructing an intelligent fill light model for submerged plants, inputting the first fill light requirement and the second fill light requirement into the intelligent fill light model for submerged plants to obtain a fill light strategy, and performing intelligent fill light on the submerged plants according to the fill light strategy; The method for performing image recognition on the regional environment image includes: Construct a support vector machine image classification and counting model, input the regional environmental image into the support vector machine image classification and counting model, output the classification and counting results of plants and animals in the regional environmental image, and record the types and numbers of submerged plants; The phenological extraction method of time series NDVI is used to obtain the growth stages of submerged plants, including: Draw a region of interest enclosed by 3 or more points on the regional environment image, and calculate the average value of the three color bands in the region of interest; Extract vegetation indices that characterize the growth characteristics of submerged plants, including the normalized difference vegetation index NDVI, relative greenness index GI, relative blueness index BI, relative redness index RI, and relative blue-red brightness BRI index; The inverse Fourier transform function ifft was used to reconstruct the time series NDVI. The NDVI points within the interval were fitted to obtain the seasonal trajectory curve of submerged plant growth. The root mean square error was used to evaluate the fitting effect. The expression of the submerged plant growth season trajectory curve is: Where c1 is the mean of winter light and vegetation fraction, c2 is the difference between summer and winter light and vegetation fraction, c3 is the trend of summer greenness, t is time, x1 is the inflection point of spring greenness increase, x3 is the inflection point of autumn greenness decrease, x2 and x4 are parameters for adjusting the seasonal growth curves in spring and autumn, x2, x4∈(0,3]; According to the seasonal trajectory curve of submerged plants, phenological parameters are extracted to obtain plant growth stages and corresponding growth parameters; The water quality environment is determined by identifying impurities in the water within the regional environmental image. The regional environmental image is de-noised through Gaussian filtering and converted into a grayscale image. The three Tamura texture features of the grayscale image (roughness, directionality, and uniformity) are calculated. A comprehensive evaluation is performed based on the set roughness threshold, directionality threshold, and uniformity threshold to obtain a water quality score. The water quality environment is determined by combining the water quality score, pH value, and salt ion concentration. The method for calculating the biological richness according to the image recognition result includes: Substitute the species categories and species numbers obtained by image recognition into the biological richness function to obtain biological richness. The expression of the biological richness function is: Where B is the biological richness, T is the number of all organisms in the area, C is the total number of all biological categories in the area, A is the area of the area, and n is the total number of all organisms in the area. i 、n j 、n k are the number of aquatic plants of category i, aquatic animals of category j and microorganisms of category k, w1, w2 and w3 are the category weights of aquatic plants, aquatic animals and microorganisms, l, m and n are the number of categories of aquatic plants, aquatic animals and microorganisms, respectively.

2. The optical fiber-based intelligent light supplement control method for submerged plants according to claim 1, characterized in that: The method for obtaining a regional environmental image by segmenting the environmental image according to the submerged plant growth area includes: The growth range of submerged plants is projected onto a two-dimensional plane, the cell area is determined according to the fill light range of the optical fiber fill light device, and the submerged plant growth area is divided according to the cell area; The environmental image is segmented according to the submerged plant growth area to obtain a regional environmental image, and regional coordinates are set for the regional environmental image.

3. The optical fiber-based intelligent light supplement control method for submerged plants according to claim 1, characterized in that: The method for determining the lighting requirements of the area comprises: According to the plant category and plant growth stage, the plant category and plant growth stage are matched with the plant category and plant growth stage of the standard plant information database, and the similarity between the submerged plants to be matched and the submerged plants in the standard plant information database is calculated one by one, and the light conditions of the submerged plants in the standard plant information database corresponding to the matching group with the highest similarity are extracted; Lighting conditions include ideal spectral range and ideal light intensity. Feature extraction of lighting environment data is performed to obtain light intensity, spectral range, photon flux density, and radiation flux density. The ideal light intensity is adjusted according to the lighting environment data and the number of plants, and the regional light demand is calculated. The expression of regional light demand is: Among them I' y is the required light intensity of the region, α and β are light parameter influencing factors, PPEF is the light quantum flux density, N is the number of all submerged plants in the region, l is the number of submerged plant categories, I yi is the ideal light intensity for the i-th type of submerged plants, PAR is the radiation flux density, w 1i is the weight coefficient of the i-th type of submerged plants, n i is the number of submerged plants in category i; The spectral ranges of all submerged plants are sorted according to the size of the values, the largest starting wavelength is taken as the spectrum end judgment point, and the smallest ending wavelength is taken as the spectrum start judgment point. The regional required spectral range is determined based on the spectrum end judgment point and the spectrum start judgment point, and the regional required light intensity and the regional required spectral range are combined to determine the regional light demand.

4. The optical fiber-based intelligent light supplement control method for submerged plants according to claim 1, characterized in that: The method for determining the first fill light requirement includes: The environmental factors that affect the photosynthesis of submerged plants are determined based on the underwater temperature and biological richness. The underwater temperature, the ideal temperature of submerged plants and biological richness are substituted into the environmental function to obtain the environmental factors. The environmental function expression is: Where E is the environmental factor, x1 and x3 are the positive coefficients of the influence of the adjustment factor, T is the underwater temperature, T y is the ideal temperature corresponding to the maximum efficiency of submerged plants' photosynthesis, x2 is the sensitivity of temperature changes to environmental factors, and there is no linear relationship between environmental factors and the first term, B is the biodiversity, x4 is the sensitivity of biodiversity to environmental factors, and the second term is the attenuation coefficient, which is negatively correlated with environmental factors; The first fill light requirement is determined based on environmental factors and regional lighting requirements. The corresponding expression for the light intensity of the first fill light requirement is: Where ΔI1 is the light intensity required for the first fill light, I ' y is the required light intensity of the area, I t is the light intensity corresponding to the underwater lighting environment; The spectral range required by the first fill light is consistent with the spectral range required by the region.

5. The optical fiber-based intelligent light supplement control method for submerged plants according to claim 1, characterized in that: The method for determining the second fill light requirement includes: Substituting the oxygen content, carbon dioxide content and water quality environment in the water into the second fill light demand function, the light intensity required for the second fill light is obtained. The expression of the second fill light demand function is: Where ΔI2 is the light intensity required for the second fill light, I ' y is the required light intensity of the area, k1 and k2 are the influence coefficients of oxygen and carbon dioxide, d1 and d2 are the oxygen content and carbon dioxide content in water, r1 and r2 are the response coefficients of oxygen and carbon dioxide, p and q are the change values of oxygen content and carbon dioxide content, PH and PH y are the pH value in water and the ideal pH value, c, c y is the nutrient ion concentration and ideal nutrient ion concentration in water, o, o y to rate water quality and ideal water quality; The spectral range required by the second fill light is consistent with the spectral range required by the area.

6. The optical fiber-based intelligent light supplement control method for submerged plants according to claim 1, characterized in that: The method for obtaining the fill light strategy includes: The first step light requirement, the second fill light requirement and the area coordinates are combined into a fill light dataset, and the fill light dataset is divided into a training set and a test set; Constructing a submerged plant intelligent lighting model, which includes an input layer, a hidden layer, a parameter selection layer, and a data output layer; The training set data is input into the input layer for preprocessing, and the preprocessed data is input into the hidden layer for feature extraction to obtain feature data. The feature data is input into the parameter selection layer to select the fill light output fusion feature; The parameter selection layer includes the attention layer and the feature fusion layer. The feature data enters the attention layer to weight the features, and the weighted features and original features are input into the feature fusion layer for integration and output of the fused features to the data output layer. The data output layer consists of two fully connected layers. The fusion feature input of the first fully connected layer outputs the data to the second fully connected layer, and the second fully connected layer outputs the predicted value of the fill light parameter. The root mean square error loss function was used to evaluate the difference between the predicted and true values of the lighting parameters. The Adam optimizer was used to optimize the weights of the submerged plant intelligent lighting model. The test set was used to evaluate the submerged plant intelligent lighting model. The environmental data and environmental images of the area to be supplemented with light are input into the intelligent supplementary lighting model of submerged plants to obtain the supplementary lighting strategy and mobilize the supplementary lighting equipment for supplementary lighting.

7. An optical fiber-based intelligent lighting system for submerged plants, used to implement the method of claims 1-6, characterized in that: include: Data acquisition module: used to obtain environmental images and environmental data of submerged plants, pre-process the environmental images and environmental data, and transmit them to the image module and data module; the environmental images are obtained through a high-definition underwater camera, and the environmental data are obtained through a temperature sensor, a spectrum sensor, a dissolved oxygen meter, and a water quality carbon dioxide concentration meter; Image module: used to segment the environmental image according to the growth area to obtain the regional environmental image, and perform image recognition on the regional environmental image to obtain the plant quantity, plant category, plant growth stage and water quality environment; Data module: used to determine the regional lighting requirement based on the lighting conditions, the number of plants and the lighting environment data, determine the first supplementary lighting requirement based on the underwater temperature, the biodiversity and the regional lighting requirement, and determine the second supplementary lighting requirement based on the oxygen content, the carbon dioxide content, the water quality environment and the regional lighting requirement; A fill light model module is configured to construct an intelligent fill light model for submerged plants based on the first fill light requirement, the second fill light requirement, and the regional coordinates, and process the environmental data and environmental image of the area to be filled with light and input them into the intelligent fill light model for submerged plants to obtain fill light parameters; the fill light parameters include color temperature parameters, fill light intensity, fill light power, and regional coordinates; Intelligent monitoring module: used to store, view and manage the environmental image, the environmental data and the fill light parameters, determine the fill light strategy according to the fill light parameters, and the controller sends adjustment signals of luminous intensity, color temperature and power according to the fill light strategy; used to convert the submerged plant growth area into a cloud image through regional coordinates, and view and manage the fill light strategy of the submerged plant growth area with different regional coordinates; Fill light module: used to execute the adjustment signal of the controller to perform intelligent fill light on the submerged plant growth area; includes a light source group, an optical fiber transmission group, a fill light group and a power supply group; the light source group consists of an optical fiber light source and RGB-LED lamp beads, and is used to change the luminous intensity, color temperature and power of the light source according to the adjustment signal; the optical fiber transmission group consists of an optical fiber bundle and an optical fiber connector, and is used to transmit the light source underwater; the fill light group is used to evenly disperse the light source transmitted by the optical fiber to the submerged plant growth area for intelligent fill light; the power supply group consists of a solar panel and a battery pack, and is used to provide power for the submerged plant intelligent fill light system.

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