Mangrove forest planting method in oyster reef region, electronic equipment and medium

Abstract

The invention discloses a mangrove forest planting method in an oyster reef region, electronic equipment and a medium. The method comprises the following steps: constructing a root system topology model based on a preset CT scanning result of mangrove forest plants; based on the three-dimensional grid data of the root system topology model and a plurality of matrix matching parameters, anti-shearing force corresponding to each matrix matching parameter is predicted through a graph neural network, and target matrix materials corresponding to each matrix matching parameter comprise oyster shell powder, biochar and filamentous fungus mycelia; the honeycomb bionic matrix corresponding to the target matrix matching parameters meeting the target conditions is manufactured through 3D printing, and mangrove forest planting is conducted through the honeycomb bionic matrix. According to the method, the honeycomb bionic matrix is constructed on the basis of the target matrix matching parameters meeting the target conditions obtained through prediction of the combination diagram neural network of the oyster shell powder, the biochar and the filamentous fungus mycelia of the degradable material, and compared with a traditional simple mixed matrix obtained by depending on empirical matching parameters, the honeycomb bionic matrix has the advantages that the operation is simple and convenient; the lodging-resistant capability of the mangrove forest can be effectively improved.

Description

Technical Field

[0001] The present application relates to, but is not limited to, the field of ecological technology, and in particular to a mangrove planting method, electronic equipment, and medium in an oyster reef area. Background Art

[0002] The substrate for mangrove planting is the fundamental environment for the plants' survival. Traditional substrate mixes rely on empirical formulas (such as pure silt or simple mixed substrates), lacking a scientifically optimized match between root mechanical properties and substrate shear resistance. This, particularly in oyster reef areas, hinders root anchorage. Consequently, substrates produced using traditional substrate mixes suffer from poor air permeability and drainage, hindering root development and erosion resistance. Mangroves planted using these substrates are less resistant to wind and waves and are prone to lodging. Summary of the Invention

[0003] The embodiments of the present application provide a method, electronic equipment, and medium for planting mangroves in oyster reef areas, which can effectively improve the mangroves' ability to resist lodging.

[0004] In a first aspect, an embodiment of the present application provides a method for planting mangroves in an oyster reef area, comprising:

[0005] Perform CT scans on pre-set mangrove plants and construct root system topology models based on the CT scan results;

[0006] Based on the three-dimensional grid data of the root topology model and multiple substrate ratio parameters, the shear resistance corresponding to each substrate ratio parameter is predicted by a graph neural network, wherein all the substrate ratio parameters correspond to the same type of target substrate material, which includes oyster shell powder, biochar, and filamentous fungal mycelium;

[0007] Determining the matrix ratio parameter corresponding to the shear resistance corresponding to the target condition as the target matrix ratio parameter, and using 3D printing technology to produce a honeycomb biomimetic matrix corresponding to the target matrix ratio parameter;

[0008] The honeycomb bionic matrix is ​​used to plant mangroves.

[0009] In some embodiments, a CT scan is performed on a predetermined mangrove plant, and a root system topology model is constructed based on the CT scan results, including:

[0010] Fixing the mangrove plant using a low-temperature fixation technique or an epoxy resin embedding technique;

[0011] The salinized soil with fixed mangrove plants was subjected to gradient ethanol dehydration treatment;

[0012] The target sample was obtained by soaking the dehydrated mangrove plants in sodium iodide for a preset time;

[0013] The target sample is scanned by a micro-CT device to obtain a CT scan result, and the root system topology model is constructed based on the CT scan result after noise correction and geometric calibration.

[0014] In some embodiments, the CT scan result includes a three-dimensional grayscale image of the root system, and constructing a root system topology model based on the CT scan result includes:

[0015] Calculating an image histogram of the three-dimensional grayscale image of the root system, and segmenting the root system and soil of the mangrove plant based on the image histogram and a preset density threshold to obtain a segmentation result, wherein the segmentation result includes initial root system data of the mangrove plant;

[0016] Strengthening the bifurcation region features of the initial root system data based on a preset Mask R-CNN model to obtain target root system data;

[0017] Extracting root system skeleton features of the target root system data;

[0018] The root system topology model is constructed based on the root system skeleton characteristics.

[0019] In some embodiments, based on the three-dimensional grid data of the root system topology model and multiple substrate ratio parameters, predicting the shear resistance corresponding to each of the substrate ratio parameters through a graph neural network includes:

[0020] Constructing the graph neural network based on the three-dimensional grid data;

[0021] Each of the matrix ratio parameters is sequentially input into the graph neural network to obtain each of the shearing resistances, wherein any of the shearing resistances includes the sub-shearing resistances of each node in the corresponding graph neural network.

[0022] In some embodiments, the target condition is greater than a reference shearing resistance, and determining the matrix ratio parameter corresponding to the shearing resistance that satisfies the target condition as the target matrix ratio parameter includes:

[0023] Based on the parent-child relationship of the root topology corresponding to the graph neural network, the path from the root node to the leaf node of the graph neural network is recursively traversed to obtain the overall anti-shear force, wherein, in the process of recursively calculating the reference anti-shear force, for any bifurcation node in the path, the first anti-shear force obtained by accumulating the sub-anti-shear forces of the child nodes corresponding to the bifurcation node is used as the target anti-shear force of the bifurcation node, and for any continuous segment node in the path, the sub-anti-shear force with the smallest value in the target continuous segment corresponding to the continuous segment node is used as the target anti-shear force of the target continuous segment, and the overall anti-shear force is determined based on all the target anti-shear forces;

[0024] The matrix ratio parameter corresponding to the overall shear resistance greater than the reference shear resistance is determined as the target matrix ratio parameter.

[0025] In some embodiments, a 3D printing technique is used to produce a honeycomb biomimetic matrix corresponding to the target matrix ratio parameters, including:

[0026] Obtaining the oyster shell powder, the biochar, and the filamentous fungal mycelium corresponding to the target matrix ratio parameters;

[0027] Grinding the oyster shell powder and the biochar in a ball mill to obtain a mixed powder;

[0028] The filamentous fungal mycelium is sterilized and pre-cultured in a liquid culture medium for a preset time to obtain a mycelium suspension;

[0029] The mixed powder and the mycelium suspension are mixed in a preset ratio, sodium alginate is added, and the mixture is stirred to obtain a printable slurry;

[0030] generating a random honeycomb topology structure based on the CT scan result, and constructing a printing path of a 3D printer based on the random honeycomb topology structure;

[0031] The 3D printer loaded with the printing slurry and the water-soluble support material is controlled to perform 3D printing at a preset temperature based on the printing path to obtain the honeycomb biomimetic matrix.

[0032] In some embodiments, before using the honeycomb biomimetic matrix to plant mangroves, the method further includes:

[0033] The honeycomb biomimetic matrix was placed in an environment with a humidity of 95% and a temperature of 28° C. and cultured for 72 hours;

[0034] The honeycomb biomimetic matrix is ​​subjected to UV curing for 30 minutes;

[0035] Electrostatically spraying the honeycomb biomimetic matrix to uniformly deposit an oyster shell powder-chitosan composite coating on the surface of the honeycomb biomimetic matrix;

[0036] The honeycomb biomimetic matrix is ​​mechanically polished to remove residues of the water-soluble support material on the honeycomb biomimetic matrix.

[0037] In some embodiments, the number of the honeycomb biomimetic matrix is ​​multiple, and mangrove planting using the honeycomb biomimetic matrix includes:

[0038] Determining elevation data of a target tidal flat, arranging each of the honeycomb biomimetic matrices in the target tidal flat within an elevation range of -0.5 m to 1.2 m, wherein the target tidal flat is located in an oyster reef area, and the spacing between each of the honeycomb biomimetic matrices is 25 cm×25 cm;

[0039] embedding biodegradable anchoring pins at the bottom of each honeycomb biomimetic matrix;

[0040] 72 hours before planting the mangrove plants, injecting nutrient solution into the pores of each of the honeycomb-shaped bionic matrices;

[0041] Inserting the selected mangrove seedlings into the corresponding honeycomb channels of the honeycomb biomimetic matrix by a planting machine, wherein the honeycomb channels are pre-filled with an oyster shell powder-peat mixed matrix;

[0042] A mycorrhizal fungus spore suspension is sprayed on each of the honeycomb biomimetic substrates.

[0043] In a second aspect, an embodiment of the present application provides an electronic device comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the mangrove planting method in the oyster reef area as described in the first aspect.

[0044] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the mangrove planting method in the oyster reef area as described in the first aspect.

[0045] The embodiment of the present application provides a method for planting mangroves in oyster reef areas, the method comprising: performing a CT scan on a preset mangrove plant and constructing a root topology model based on the CT scan results; predicting the shear resistance corresponding to each of the matrix ratio parameters through a graph neural network based on the three-dimensional grid data of the root topology model and a plurality of matrix ratio parameters, wherein the target matrix materials corresponding to all of the matrix ratio parameters are of the same type, and the target matrix materials include oyster shell powder, biochar, and filamentous fungal mycelium; determining the matrix ratio parameters corresponding to the shear resistance corresponding to the target conditions as target matrix ratio parameters, and using 3D printing technology to produce a honeycomb biomimetic matrix corresponding to the target matrix ratio parameters; and using the honeycomb biomimetic matrix to plant mangroves. According to the scheme provided in the embodiment of the present application, a honeycomb biomimetic matrix is ​​constructed based on the target matrix ratio parameters that meet the target conditions obtained by combining the degradable materials of oyster shell powder, biochar, and filamentous fungal mycelium with the graph neural network prediction. Compared with the traditional simple mixed matrix obtained by relying on empirical ratio parameters, the anti-lodging ability of mangroves can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flowchart of the steps of a method for planting mangroves in an oyster reef area provided by one embodiment of the present application;

[0047] Figure 2 This is a structural diagram of an electronic device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0049] It is understood that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and the like in the specification, claims, or accompanying drawings are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0050] The substrate for mangrove planting is the fundamental environment for the plants' survival. Traditional substrate mixes rely on empirical formulas (such as pure silt or simple mixed substrates), lacking a scientifically optimized match between root mechanical properties and substrate shear resistance. This, particularly in oyster reef areas, hinders root anchorage. Consequently, substrates produced using traditional substrate mixes suffer from poor air permeability and drainage, hindering root development and erosion resistance. Mangroves planted using these substrates are less resistant to wind and waves and are prone to lodging.

[0051] To solve the above-mentioned problems, an embodiment of the present application provides a method for planting mangroves in oyster reef areas, the method comprising: performing a CT scan on a preset mangrove plant and constructing a root topology model based on the CT scan results; based on the three-dimensional grid data of the root topology model and a plurality of matrix ratio parameters, predicting the shear resistance corresponding to each of the matrix ratio parameters through a graph neural network, wherein the target matrix materials corresponding to all the matrix ratio parameters are of the same type, and the target matrix materials include oyster shell powder, biochar, and filamentous fungal mycelium; determining the matrix ratio parameter corresponding to the shear resistance corresponding to the target condition as the target matrix ratio parameter, and using 3D printing technology to produce a honeycomb biomimetic matrix corresponding to the target matrix ratio parameter; and using the honeycomb biomimetic matrix to plant mangroves. According to the scheme provided in the embodiment of the present application, a honeycomb biomimetic matrix is ​​constructed based on the target matrix ratio parameters that meet the target conditions obtained by combining the degradable materials of oyster shell powder, biochar, and filamentous fungal mycelium with the graph neural network prediction. Compared with the traditional simple mixed matrix obtained by relying on empirical ratio parameters, it can effectively improve the anti-lodging ability of mangroves.

[0052] The embodiments of the present application are further described below with reference to the accompanying drawings.

[0053] refer to Figure 1 , Figure 1 This is a flowchart of the steps of a method for planting mangroves in an oyster reef area provided by an embodiment of the present application. The embodiment of the present application provides a method for planting mangroves in an oyster reef area, which includes but is not limited to the following steps:

[0054] Step S10: Perform a CT scan on the preset mangrove plants, and construct a root system topology model based on the CT scan results.

[0055] Specifically, in some embodiments, Figure 1 Step S10 includes but is not limited to the following steps:

[0056] Step S11, fixing the mangrove plants using a low-temperature fixation technique or an epoxy resin embedding technique;

[0057] Step S12, performing gradient ethanol dehydration treatment on the salinized soil of the fixed mangrove plants;

[0058] Step S13, using sodium iodide soaking solution to dehydrate the mangrove plants for a preset time to obtain a target sample;

[0059] Step S14 , performing a CT scan on the target sample using a micro-CT device to obtain a CT scan result, and constructing a root system topology model based on the CT scan result after noise correction and geometric calibration.

[0060] Specifically, the mangrove plant of this embodiment may be Kandelia candel or Sonneratia salsa, etc., which can be determined by those skilled in the art according to actual needs.

[0061] Specifically, in this embodiment, the temperature corresponding to the fixation of mangrove plants using the low-temperature fixation technology is -20°C.

[0062] It should be noted that, in this embodiment, before the mangrove plants are fixed using the low-temperature fixation technique or the epoxy resin embedding technique, the in-situ sampling technique is first used to collect the mangrove plant roots and the surrounding 30 cm 3 The original soil is dug out as a whole to avoid breaking the capillary roots.

[0063] It can be understood that in this embodiment, the mangrove plants after in situ sampling are fixed using low-temperature fixation technology or epoxy resin embedding technology, and the salinized soil of the fixed mangrove plants is subjected to gradient ethanol dehydration treatment (gradient dehydration from 30% ethanol concentration to 100% ethanol concentration) to achieve medium optimization and reduce the scanning noise of subsequent CT scanning operations; then, the dehydrated mangrove plants are soaked in sodium iodide for a preset time (the preset soaking time in this embodiment is 48 hours) to obtain the target sample, which can improve the penetration efficiency of X-rays during subsequent CT scanning operations.

[0064] It should be noted that the micro-CT device of this embodiment is a submicron micro-CT device with a resolution of 10μm, which can ensure clear imaging of root hair-level structures. Before using the micro-CT device for CT scanning, the X-ray source parameters of the micro-CT device are adjusted to: voltage 120kV, current 150μA, exposure time 1.5s / frame, so as to balance the signal-to-noise ratio and radiation damage risk; the scanning path of the micro-CT device is set to spiral scanning + 360° rotation, with a step angle of 0.3°, and 5000 to 8000 projection images are obtained for a single sample; the detector pixel matrix of the micro-CT device is set to 4096×4096, the layer thickness is 5μm, and the overlap rate is 40%, which can ensure the continuity of three-dimensional data.

[0065] It can be understood that when obtaining the CT scan results, this embodiment simultaneously acquires dark field (no X-rays) and bright field (no samples) images while scanning to obtain the CT scan results, which can correct and eliminate the detector response differences, and use tungsten alloy calibration balls to verify the system geometric accuracy and control errors to ensure the accuracy of the CT scan results, thereby ensuring the accuracy of the root topology model.

[0066] Specifically, in some embodiments, the CT scan results include a three-dimensional grayscale image of the root system. Figure 1 Step S10 of constructing a root system topology model based on the CT scan results includes but is not limited to the following steps:

[0067] Step S15, calculating an image histogram of the three-dimensional grayscale image of the root system, and segmenting the root system and soil of the mangrove plant based on the image histogram and a preset density threshold to obtain a segmentation result, wherein the segmentation result includes initial root system data of the mangrove plant;

[0068] Step S16, enhancing the bifurcation region features of the initial root system data based on a preset Mask R-CNN model to obtain target root system data;

[0069] Step S17, extracting root system skeleton features of the target root system data;

[0070] Step S18: constructing a root system topology model based on the root system skeleton features.

[0071] Specifically, the CT scan results include a three-dimensional grayscale image of the root system. Different scanning energies are used to scan different areas of the target sample. For example, dual-energy scanning (low energy 30kV + high energy 100kV) is used for high-salt area samples of the target sample, which can effectively eliminate salt crystallization artifacts.

[0072] Specifically, the Mask R-CNN model of this embodiment is added with a CBAM attention module that can enhance the features of the bifurcation region.

[0073] It can be understood that the specific steps of constructing the root topology model based on the CT scan results in this embodiment include: (1) using the density threshold method to perform multi-level segmentation to accurately separate the roots of mangrove plants from the soil / sediment, ensuring that the main root positioning error is ≤1.2%. The specific steps of multi-level segmentation are as follows: calculating the image histogram of the three-dimensional grayscale image of the root system, determining the root system (density>1.8g / cm 3 ) and soil (density <1.3g / cm 3) density threshold, which is dynamically optimized by the Otsu algorithm, and the roots and soil of mangrove plants are segmented based on the image histogram and the density threshold to obtain the segmentation result, wherein the segmentation result includes the initial root data of the mangrove plants; error correction is achieved through manual review; (2) the bifurcation area features of the initial root data are enhanced based on the preset Mask R-CNN model to obtain the target root data. Specifically, a labeled dataset containing 2000 groups of mangrove root bifurcation points is constructed (for example, including 1200 groups of Kandelia ovata and 800 groups of Salicornia heracleifolia); the labeled dataset is randomly rotated in the range of -15° to 15°, the brightness is adjusted in the range of -20% to 20%, and the labeled dataset is enhanced with Gaussian noise with a standard deviation σ=0.01; (3) the initial Mask R-CNN model is trained using the labeled dataset with 500 iterations and a preset loss function to obtain the trained Mask R-CNN model. The R-CNN model enhances the bifurcation region features of the initial root data to obtain the target root data; (4) The improved MAT medial axis transformation method is used to perform binarization processing, prune short branches, and topological repair processing on the target root data in sequence, and realize neighborhood connectivity detection, repair broken skeletons (maximum repair spacing 0.2 mm), and obtain root skeleton features, where the root skeleton features include root length density L, fractal dimension D, and specific surface area S. Root length density L = ∑ (number of skeleton pixels × resolution) / volume, root length density L can characterize the root extension capacity per unit volume; fractal dimension D = lim (lnN (ε) / ln (1 / ε)), fractal dimension D can characterize the root space complexity, where N (ε) represents the minimum number of boxes required to cover the fractal with a small box with a side length of ε; specific surface area S = root surface area / biomass dry weight, specific surface area S can characterize the material exchange efficiency. (4) A root topology model was constructed based on the root skeleton characteristics. Specifically, the root skeleton characteristics were converted into a pore network model using Avizo software. The key parameters of the pore network model were then obtained, including the pore throat diameter distribution (corresponding to a distribution range of 2 μm to 50 μm) and the path tortuosity T (value range of 1.6 to 2.4), where path tortuosity T = actual diffusion path / straight-line distance. The root topology model was constructed by multi-physics field coupling based on the oxygen diffusion equation and the sediment retention model combined with the above key parameters. The expression corresponding to the oxygen diffusion equation is as follows:

[0074]

[0075] Where Deff is the effective diffusion coefficient, Rroot is the root oxygen consumption rate, is the Laplace operator of oxygen concentration; the expression of sediment retention model is as follows:

[0076] F max =0.87·e 0.32D ·ρ 1.5 ;

[0077] Where D is the fractal dimension and ρ is the root density.

[0078] In step S20, based on the three-dimensional grid data of the root topology model and multiple substrate ratio parameters, the shear resistance corresponding to each substrate ratio parameter is predicted through a graph neural network, wherein all the substrate ratio parameters correspond to the same type of target substrate material, which includes oyster shell powder, biochar, and filamentous fungal mycelium.

[0079] Specifically, in some embodiments, Figure 1 Step S20 includes but is not limited to the following steps:

[0080] Step S21, constructing a graph neural network based on the three-dimensional grid data;

[0081] In step S22, each matrix ratio parameter is sequentially input into the graph neural network to obtain each shearing resistance, wherein any shearing resistance includes the sub-shearing resistance of each node in the corresponding graph neural network.

[0082] It can be understood that the matrix materials selected in the present invention (including oyster shell powder, biochar, and filamentous fungal mycelium) are all degradable materials, and no hard materials (such as concrete modules) are used. This can improve the disaster resistance of mangroves while effectively avoiding the damage of hard materials to the ecological balance of mudflats, increase the abundance of microorganisms, and ensure that ecological compatibility is met.

[0083] It can be understood that this embodiment sets multiple matrix ratio parameters, and different matrix ratio parameters correspond to different mixing ratios of oyster shell powder, biochar, and filamentous fungal mycelium. The shear resistance of the matrix under different mixing ratios is predicted respectively through the graph neural network. Specifically, a graph neural network is constructed based on three-dimensional grid data, and each matrix ratio parameter is input into the graph neural network in turn to obtain each shear resistance. Among them, any shear resistance includes the sub-shear resistance of each node in the corresponding graph neural network, which can provide an effective data basis for the subsequent determination of the target matrix ratio parameters corresponding to high shear resistance and the manufacture of a bionic matrix with strong anti-lodging ability.

[0084] Step S30 , determining the matrix ratio parameter corresponding to the shear resistance that meets the target condition as the target matrix ratio parameter, and using 3D printing technology to manufacture a honeycomb biomimetic matrix corresponding to the target matrix ratio parameter.

[0085] Specifically, in some embodiments, the target condition is greater than a reference shear resistance, Figure 1The step S30 of determining the matrix ratio parameter corresponding to the shear resistance corresponding to the target condition as the target matrix ratio parameter includes but is not limited to the following steps:

[0086] Step S31: Based on the parent-child relationship of the root topology corresponding to the graph neural network, recursively traverse the path from the root node to the leaf node of the graph neural network to obtain the overall shear resistance. In the process of recursively calculating the reference shear resistance, for any bifurcation node in the path, the first shear resistance obtained by accumulating the sub-shear resistances of the sub-nodes corresponding to the bifurcation node is used as the target shear resistance of the bifurcation node. For any continuous segment node in the path, the sub-shear resistance with the smallest value in the target continuous segment corresponding to the continuous segment node is used as the target shear resistance of the target continuous segment. The overall shear resistance is determined based on all the target shear resistances.

[0087] Step S32: determining the matrix ratio parameter corresponding to the overall shear resistance greater than the reference shear resistance as the target matrix ratio parameter.

[0088] It can be understood that in this embodiment, the step of determining the substrate ratio parameter corresponding to the shear resistance corresponding to the target condition as the target substrate ratio parameter includes: based on the parent-child relationship of the root topology corresponding to the graph neural network, recursively traversing the path from the root node to the leaf node of the graph neural network to obtain the overall shear resistance, wherein, in the process of recursively calculating the reference shear resistance, for any bifurcation node in the path, the first shear resistance obtained by accumulating the sub-shear resistances of the sub-nodes corresponding to the bifurcation node is used as the target shear resistance of the bifurcation node, for any continuous segment node in the path, the sub-shear resistance with the smallest value in the target continuous segment corresponding to the continuous segment node is used as the target shear resistance of the target continuous segment, and the overall shear resistance is determined based on all the target shear resistances, so as to obtain the overall shear resistance of the mangrove planted corresponding to each substrate ratio parameter, and then, the substrate ratio parameter corresponding to the overall shear resistance greater than the reference shear resistance is determined as the target substrate ratio parameter. The graph neural network can realize automatic prediction of the overall shear resistance corresponding to each substrate ratio parameter, which can improve the screening efficiency of the substrate ratio parameter and reduce the experimental cost compared with the traditional method of expert experience judgment.

[0089] In addition, the substrate ratio parameters of this embodiment are dynamically optimized based on environmental factors such as tide and salinity, which can effectively reduce the interference of environmental variables and ensure the availability of the target substrate ratio parameters.

[0090] Additionally, in some embodiments, Figure 1 The step S30 of using 3D printing technology to produce a honeycomb biomimetic matrix corresponding to the target matrix ratio parameters includes but is not limited to the following steps:

[0091] Step S33, obtaining oyster shell powder, biochar, and filamentous fungal mycelium corresponding to the target matrix ratio parameters;

[0092] Step S34, grinding the oyster shell powder and biochar in a ball mill to obtain a mixed powder;

[0093] Step S35, sterilizing the filamentous fungal mycelium and pre-culturing it in a liquid culture medium for a preset time to obtain a mycelium suspension;

[0094] Step S36, obtaining oyster shell powder, biochar, and filamentous fungal mycelium corresponding to the target matrix ratio parameters;

[0095] Step S37, mixing the mixed powder and the mycelium suspension according to a preset ratio, adding sodium alginate, and stirring to obtain a printable slurry;

[0096] Step S38, generating a random honeycomb topology structure based on the CT scanning result, and constructing a printing path of the 3D printer based on the random honeycomb topology structure;

[0097] Step S39 , controlling the 3D printer loaded with printing slurry and water-soluble support material to perform 3D printing at a preset temperature based on the printing path to obtain a honeycomb biomimetic matrix.

[0098] Specifically, the target matrix ratio parameters of oyster shell powder, biochar and filamentous fungal mycelium in this embodiment are 6:2.5:1.5.

[0099] It can be understood that after determining the target matrix ratio parameters, the raw materials are pretreated first to obtain oyster shell powder, biochar and filamentous fungal mycelium corresponding to the target matrix ratio parameters, and then the oyster shell powder with a particle size of less than or equal to 75 μm and the biochar with a particle size of less than or equal to 100 μm are mixed in a ratio of 6:2.5, and then placed in a ball mill for grinding to obtain a mixed powder. The grinding time is 2 hours, which can ensure that the particles are evenly distributed; the filamentous fungal mycelium is sterilized and pre-cultured in a liquid culture medium for a preset time (the pre-culture time in this embodiment is 48 hours) to obtain a mycelium suspension, which is used as a biological adhesive; then the slurry is prepared, the mixed powder and the mycelium suspension are mixed in a preset ratio (8:2 in this embodiment), and added Sodium alginate with a concentration of 2% is stirred to obtain a printable slurry; then a bionic model is constructed, a random honeycomb topology structure is generated based on the CT scanning results, and a printing path of a 3D printer is constructed based on the random honeycomb topology structure, wherein the surface porosity of the random honeycomb topology structure is 50%, the pore size of the surface pores ranges from 0.5 mm to 1.2 mm, the deep porosity is 30%, and the pore size of the deep pores ranges from 0.3 mm to 0.6 mm, thereby simulating the gradient pore distribution formed by tidal scouring; further, based on the printing path, a 3D printer loaded with printing slurry and water-soluble support material is controlled to perform 3D printing at a preset temperature (the preset temperature in this embodiment is 25°C, which can prevent the mycelium from inactivating) to obtain a honeycomb bionic matrix.

[0100] Step S40: Planting mangroves using the honeycomb bionic matrix.

[0101] Additionally, in some embodiments, when executing Figure 1 Before step S40, the method for planting mangroves in the oyster reef area of ​​the embodiment of the present application further includes but is not limited to the following steps:

[0102] Step S51, placing the honeycomb biomimetic matrix in an environment with a humidity of 95% and a temperature of 28° C. for 72 hours;

[0103] Step S52, performing UV curing treatment on the honeycomb biomimetic matrix for 30 minutes;

[0104] Step S53, electrostatically spraying the honeycomb biomimetic matrix to uniformly deposit an oyster shell powder-chitosan composite coating on the surface of the honeycomb biomimetic matrix;

[0105] Step S54 , mechanically grinding the honeycomb biomimetic matrix to remove residues of the water-soluble support material on the honeycomb biomimetic matrix.

[0106] It can be understood that after printing is completed to obtain the honeycomb bionic matrix, the honeycomb bionic matrix is ​​placed in an environment with a humidity of 95% and a temperature of 28°C for 72 hours, so that the mycelium forms a three-dimensional network structure and improves the shear strength; then, the honeycomb bionic matrix is ​​subjected to UV curing for 30 minutes to enhance the surface anti-scouring performance; further, the honeycomb bionic matrix is ​​electrostatically sprayed to uniformly deposit an oyster shell powder-chitosan composite coating on the surface of the honeycomb bionic matrix, and the honeycomb bionic matrix is ​​mechanically polished to remove the residue of the water-soluble support material on the honeycomb bionic matrix, which can reduce fluid resistance, thereby providing effective support for the subsequent planting of mangroves with strong lodging resistance.

[0107] Specifically, Figure 1 Step S40 includes but is not limited to the following steps:

[0108] Step S41, determining the elevation data of the target tidal flat, arranging each honeycomb biomimetic matrix in the target tidal flat within an elevation range of -0.5m to 1.2m, the target tidal flat being located in an oyster reef area, and the spacing between each honeycomb biomimetic matrix being 25cm×25cm;

[0109] Step S42, embedding biodegradable anchoring pins at the bottom of each honeycomb biomimetic matrix;

[0110] Step S43, 72 hours before planting the mangrove plants, injecting nutrient solution into the pores of each honeycomb-shaped bionic matrix;

[0111] Step S44: inserting the selected mangrove seedlings into the corresponding honeycomb channels of the honeycomb biomimetic matrix using a planting machine, wherein the honeycomb channels are pre-filled with an oyster shell powder-peat mixed matrix;

[0112] Step S45 , spraying the mycorrhizal fungus spore suspension onto each honeycomb biomimetic matrix.

[0113] It can be understood that the specific steps of using the honeycomb bionic matrix for mangrove planting in this embodiment include: determining the elevation data of the target mudflat, arranging each honeycomb bionic matrix in the target mudflat within the elevation range of -0.5m to 1.2m, the target mudflat is located in the oyster reef area, the spacing between each honeycomb bionic matrix is ​​25cm×25cm, and biodegradable anchor nails are embedded in the bottom of each honeycomb bionic matrix to improve the anti-scouring ability; then, 72 hours before planting mangrove plants, nutrient solution is injected into the pores of each honeycomb bionic matrix to promote the symbiosis of mycelium and local microorganisms; the selected mangrove seedlings are inserted into the honeycomb pores of the corresponding honeycomb bionic matrix through a planting machine, wherein the honeycomb pores are pre-filled with an oyster shell powder-peat mixed matrix; after planting, each honeycomb bionic matrix is ​​promptly sprayed with a mycorrhizal fungal spore suspension, and a stable mangrove-colony-intertidal animal ecosystem can be formed after 20 days. This embodiment can effectively reduce the loss rate of the substrate through scientific root model topology and substrate ratio optimization, which not only improves the planting effect of mangroves, but also reduces subsequent maintenance costs, providing strong guarantees for the ecological restoration and sustainable development of mangroves.

[0114] like Figure 2 As shown, Figure 2 : is a structural diagram of an electronic device provided by an embodiment of the present application. The present invention also provides an electronic device 200, including:

[0115] The processor 210 may be implemented as a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0116] The memory 220 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 220 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 220 and is called by the processor 210 to execute the mangrove planting method in the oyster reef area of ​​the embodiment of the present application;

[0117] Input / output interface 230, used to implement information input and output;

[0118] Communication interface 240, used to implement communication interaction between the apparatus and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0119] bus 250 , which transmits information between the various components of the device (e.g., processor 210 , memory 220 , input / output interface 230 , and communication interface 240 );

[0120] The processor 210 , the memory 220 , the input / output interface 230 and the communication interface 240 are connected to each other in communication within the device via the bus 250 .

[0121] In addition, an embodiment of the present application further provides a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned mangrove planting method in the oyster reef area is implemented.

[0122] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory optionally includes a memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of the above-mentioned networks include but are not limited to the Internet, an intranet, a local area network, a mobile communication network and a combination thereof. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and are located in one place, or may be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0123] Those skilled in the art will appreciate that all or some of the steps and systems in the method disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include computer storage media (or non-transitory media) and communication media (or temporary media). As known to those skilled in the art, the term computer storage media is included in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data) and is volatile and non-volatile, removable, and non-removable. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technology, CD-ROM, digital versatile disks (DVD), or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage, or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically includes computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0124] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above implementation. Those skilled in the art can also make various equivalent modifications or substitutions under the shared conditions that do not violate the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A method for planting mangroves in oyster reef areas, characterized in that: include: Perform CT scans on pre-set mangrove plants and construct root system topology models based on the CT scan results; Based on the three-dimensional grid data of the root topology model and multiple substrate ratio parameters, the shear resistance corresponding to each substrate ratio parameter is predicted by a graph neural network, wherein all the substrate ratio parameters correspond to the same type of target substrate material, which includes oyster shell powder, biochar, and filamentous fungal mycelium; Determining the matrix ratio parameter corresponding to the shear resistance corresponding to the target condition as the target matrix ratio parameter, and using 3D printing technology to produce a honeycomb biomimetic matrix corresponding to the target matrix ratio parameter; The honeycomb bionic matrix is ​​used to plant mangroves.

2. The method for planting mangroves in oyster reef areas according to claim 1, wherein: Perform CT scans on pre-set mangrove plants and construct a root system topology model based on the CT scan results, including: Fixing the mangrove plant using a low-temperature fixation technique or an epoxy resin embedding technique; The salinized soil with fixed mangrove plants was subjected to gradient ethanol dehydration treatment; The target sample was obtained by soaking the dehydrated mangrove plants in sodium iodide for a preset time; The target sample is scanned by a micro-CT device to obtain a CT scan result, and the root system topology model is constructed based on the CT scan result after noise correction and geometric calibration.

3. The method for planting mangroves in oyster reef areas according to claim 1, wherein: The CT scan result includes a three-dimensional grayscale image of the root system. Constructing a root system topology model based on the CT scan result includes: Calculating an image histogram of the three-dimensional grayscale image of the root system, and segmenting the root system and soil of the mangrove plant based on the image histogram and a preset density threshold to obtain a segmentation result, wherein the segmentation result includes initial root system data of the mangrove plant; Strengthening the bifurcation region features of the initial root system data based on a preset Mask R-CNN model to obtain target root system data; Extracting root system skeleton features of the target root system data; The root system topology model is constructed based on the root system skeleton characteristics.

4. The method for planting mangroves in oyster reef areas according to claim 1, wherein: Based on the three-dimensional grid data of the root system topology model and multiple substrate ratio parameters, the shear resistance corresponding to each substrate ratio parameter is predicted through a graph neural network, including: Constructing the graph neural network based on the three-dimensional grid data; Each of the matrix ratio parameters is sequentially input into the graph neural network to obtain each of the shearing resistances, wherein any of the shearing resistances includes the sub-shearing resistances of each node in the corresponding graph neural network.

5. The method for planting mangroves in oyster reef areas according to claim 4, characterized in that: The target condition is that the shear resistance is greater than a reference shear resistance, and the matrix ratio parameter corresponding to the shear resistance that satisfies the target condition is determined as the target matrix ratio parameter, including: Based on the parent-child relationship of the root topology corresponding to the graph neural network, the path from the root node to the leaf node of the graph neural network is recursively traversed to obtain the overall anti-shear force, wherein, in the process of recursively calculating the reference anti-shear force, for any bifurcation node in the path, the first anti-shear force obtained by accumulating the sub-anti-shear forces of the child nodes corresponding to the bifurcation node is used as the target anti-shear force of the bifurcation node, and for any continuous segment node in the path, the sub-anti-shear force with the smallest value in the target continuous segment corresponding to the continuous segment node is used as the target anti-shear force of the target continuous segment, and the overall anti-shear force is determined based on all the target anti-shear forces; The matrix ratio parameter corresponding to the overall shear resistance greater than the reference shear resistance is determined as the target matrix ratio parameter.

6. The method for planting mangroves in oyster reef areas according to claim 1, wherein: The honeycomb biomimetic matrix corresponding to the target matrix ratio parameters is produced using 3D printing technology, including: Obtaining the oyster shell powder, the biochar, and the filamentous fungal mycelium corresponding to the target matrix ratio parameters; Grinding the oyster shell powder and the biochar in a ball mill to obtain a mixed powder; The filamentous fungal mycelium is sterilized and pre-cultured in a liquid culture medium for a preset time to obtain a mycelium suspension; The mixed powder and the mycelium suspension are mixed in a preset ratio, sodium alginate is added, and the mixture is stirred to obtain a printable slurry; generating a random honeycomb topology structure based on the CT scan result, and constructing a printing path of a 3D printer based on the random honeycomb topology structure; The 3D printer loaded with the printing slurry and the water-soluble support material is controlled to perform 3D printing at a preset temperature based on the printing path to obtain the honeycomb biomimetic matrix.

7. The method for planting mangroves in oyster reef areas according to claim 6, characterized in that: Before using the honeycomb bionic matrix to plant mangroves, the method further includes: The honeycomb biomimetic matrix was placed in an environment with a humidity of 95% and a temperature of 28° C. and cultured for 72 hours; The honeycomb biomimetic matrix is ​​subjected to UV curing for 30 minutes; Electrostatically spraying the honeycomb biomimetic matrix to uniformly deposit an oyster shell powder-chitosan composite coating on the surface of the honeycomb biomimetic matrix; The honeycomb biomimetic matrix is ​​mechanically polished to remove residues of the water-soluble support material on the honeycomb biomimetic matrix.

8. The method for planting mangroves in oyster reef areas according to claim 1, wherein: There are multiple honeycomb bionic matrices, and mangrove planting using the honeycomb bionic matrices includes: Determining elevation data of a target tidal flat, arranging each of the honeycomb biomimetic matrices in the target tidal flat within an elevation range of -0.5 m to 1.2 m, wherein the target tidal flat is located in an oyster reef area, and the spacing between each of the honeycomb biomimetic matrices is 25 cm×25 cm; embedding biodegradable anchoring pins at the bottom of each honeycomb biomimetic matrix; 72 hours before planting the mangrove plants, injecting nutrient solution into the pores of each of the honeycomb-shaped bionic matrices; Inserting the selected mangrove seedlings into the corresponding honeycomb channels of the honeycomb biomimetic matrix by a planting machine, wherein the honeycomb channels are pre-filled with an oyster shell powder-peat mixed matrix; A mycorrhizal fungus spore suspension is sprayed on each of the honeycomb biomimetic substrates.

9. An electronic device, characterized in that: comprising at least one control processor and a memory for communicatively connecting with the at least one control processor; The memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to perform the mangrove planting method in the oyster reef area according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the mangrove planting method in an oyster reef area according to any one of claims 1 to 8.

Images