Submerged plant restoration and monitoring operation method in ecological restoration

Abstract

The application discloses an ecological restoration submerged plant recovery and monitoring operation method, and belongs to the technical field of submerged plants, and comprises the following steps: screening submerged plant planting and cultivation materials, rapidly cultivating submerged plant seedlings, determining underwater planting points, and automatically planting the submerged plant seedlings by using an underwater robot; inputting a plurality of initial planting point allocation results into an efficiency and benefit prediction score model to obtain a prediction score value of each initial planting point allocation result, determining the underwater planting points based on the initial planting point allocation result with the maximum prediction score value, and monitoring and managing the recovered submerged plants. The application solves the problems of low planting efficiency, high cost and low survival rate. The application establishes a systematic system of submerged plant seedling cultivation, modular propagation, automated and efficient planting, intelligent monitoring and operation, so that the submerged plants can be efficiently and modularly cultivated, the planting efficiency and the survival rate of the submerged plants are improved, and the planting cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of submerged plant technology, specifically to methods for the restoration, monitoring, and maintenance of submerged plants in ecological restoration. Background Technology

[0002] Aquatic plants can be divided into emergent plants, floating-leaved plants, and submerged plants according to their different growth characteristics. As the main primary producers in the ecosystem, they play an irreplaceable role. Submerged plants, in particular, have more ecological functions due to their specific living habits. When submerged plants are abundant, the water body exhibits characteristics such as high biodiversity, clear water quality, high dissolved oxygen content, and low algae density, which are of great value in maintaining the clear water stability of rivers and lakes.

[0003] Submerged plants are fundamental to maintaining biodiversity in aquatic bodies. As primary producers, they provide essential food sources for fish and other aquatic organisms, offer refuge for small zooplankton or fish, and maintain the integrity and complexity of the ecosystem's food web structure. Furthermore, submerged plants live entirely in water, with their roots, stems, and leaves all having absorption functions, playing a crucial role in controlling nitrogen and phosphorus nutrients and preventing the accumulation of heavy metals. Simultaneously, by adsorbing biotic and abiotic suspended matter in the water, they mitigate the resuspension of bottom sediment caused by water flow and waves, improve underwater light conditions, and increase water transparency. Compared to phytoplankton, submerged plants are larger, have longer lifecycles, and possess a stronger capacity for nutrient absorption and storage, effectively inhibiting the growth of phytoplankton. Submerged plants play a crucial "bridging" role in aquatic ecosystems, thereby maintaining the normal ecological cycle and balance of water bodies. Submerged plants are almost entirely submerged in water throughout their life cycle. Compared to terrestrial plants, submerged plants are more delicate, and their roots are sometimes underdeveloped or degenerate, serving only to anchor the plant. The fibrous vascular bundles and mechanical tissues of submerged plants are extremely underdeveloped, which makes the plants very soft and makes planting and harvesting relatively difficult.

[0004] Currently, the cultivation of submerged plants is mainly done manually, using methods such as dry cutting, fork cutting, direct planting, and seed bag planting. These methods are not only inefficient and costly, but also frequently affected by water depth and waves, resulting in low survival rates for the planted submerged plants. Furthermore, due to the needs of ecological restoration projects, the water bodies requiring aquatic plant restoration are often polluted, leading to a large demand for submerged plants of various species. The preparation work before planting is also relatively complex, all of which significantly impact the rapid cultivation of aquatic plants. Summary of the Invention

[0005] The purpose of this invention is to provide a method for the restoration, monitoring, and operation of submerged plants in ecological restoration. By establishing a systematic system for the cultivation of submerged plant seedlings, modular propagation, automated and efficient planting, and intelligent monitoring and operation, submerged plants can be cultivated efficiently and modularly, improving planting efficiency and survival rate, reducing planting costs, and solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] Methods for the restoration, monitoring, and maintenance of submerged plants in ecological restoration include:

[0008] Select submerged plant cultivation materials, quickly cultivate submerged plant seedlings, determine underwater planting points, and use underwater robots to automatically plant submerged plant seedlings;

[0009] Among them, an efficiency and benefit prediction scoring model is established based on historical planting efficiency and its efficiency weight, historical cost-benefit and its benefit weight, and combined with artificial intelligence.

[0010] The allocation results of multiple initial planting points are input into the efficiency and benefit prediction scoring model to obtain the predicted score value of each initial planting point allocation result. The underwater planting point is determined based on the initial planting point allocation result with the largest predicted score value.

[0011] The restored submerged plants are monitored, and the monitoring results are analyzed and predicted to enable operation and maintenance management of the submerged plants.

[0012] Preferably, underwater planting sites are identified, including:

[0013] Based on the underwater planting area image obtained by the camera, the bottom type of the underwater planting area image is identified by the AI ​​algorithm to obtain the underwater bottom type, and based on the underwater bottom type, the type of plant cultivated in the underwater planting area is obtained.

[0014] The historical planting conditions and historical planting situation of the plant type are obtained, and the artificial intelligence model is trained based on the historical planting conditions and historical planting situation to obtain the planting point allocation model.

[0015] The camera continuously captures multiple frames of images of the underwater planting area. Based on these multiple frames of images, the water depth, water flow velocity, and bottom depth of the underwater planting area are determined, and the illumination characteristics of the underwater planting area are obtained.

[0016] The water depth, water flow velocity, bottom depth, and light characteristics of the underwater planting area, as well as the planting requirements, are input into the planting point allocation model to obtain multiple initial planting point allocation results for the underwater planting area;

[0017] Obtain the historical growth data corresponding to the historical planting situation of the plant type, determine the historical planting efficiency of the historical planting situation, obtain the historical cost-effectiveness of the historical growth situation, and establish the efficiency weight of historical planting efficiency and the benefit weight of historical cost-effectiveness based on actual needs.

[0018] Based on historical planting efficiency and its efficiency weight, historical cost-effectiveness and its benefit weight, and combined with artificial intelligence, an efficiency and benefit prediction scoring model is established.

[0019] The results of multiple initial planting point allocations are input into the efficiency and benefit prediction scoring model to obtain the predicted score value of each initial planting point allocation result.

[0020] Underwater planting sites are determined based on the initial planting site allocation result with the highest predicted score.

[0021] Preferably, based on historical planting efficiency and its efficiency weight, historical cost-effectiveness and its benefit weight, and combined with artificial intelligence, an efficiency and benefit prediction scoring model is established, including:

[0022] Based on the historical growth data corresponding to the historical planting conditions, the historical planting conditions are divided into different planting areas, and each planting area corresponds to different local growth conditions.

[0023] Set the efficiency coefficient and yield coefficient for the corresponding planting area based on the local growth conditions;

[0024] Efficiency prediction rules and benefit prediction rules for the planting conditions to be tested are established based on efficiency coefficients and benefit coefficients.

[0025] The planting efficiency and planting benefits of the planting situation to be tested are obtained based on the efficiency prediction rules and the benefit prediction rules.

[0026] Based on historical planting efficiency and its efficiency weight, and historical cost-benefit and its benefit weight, a predictive scoring rule is established.

[0027] Based on efficiency prediction rules, benefit prediction rules, and prediction scoring rules, and combined with artificial intelligence, an efficiency and benefit prediction scoring model is established.

[0028] Preferably, underwater robots are used for automated planting of submerged plant seedlings, including:

[0029] The underwater planting points are divided into planting grids. A modular design combining woven grids and planting baskets is adopted to divide the underwater planting points into multiple grid units, establishing a four-level grid of water area-zone-unit-point. Multiple grid units are managed through a combination of physical isolation and digital monitoring.

[0030] The water quality and bottom sediment are detected by sensors to assess the planting environment of submerged plants. When the planting environment is suitable for the seedlings of submerged plants, the operator sends instructions to the underwater robot through a computer and sends the underwater planting point to the underwater robot.

[0031] Submerged plant seedlings are loaded into the seedling container of an underwater robot. The underwater planting point is located using GPS and sonar systems. The underwater robot then finds the underwater planting point according to the planned movement path.

[0032] The underwater robot uses a robotic arm to dig a pit and place the submerged plant seedlings into the pit. The seedlings are planted using the cutting method, and then planted in the bottom mud, covered with soil to fix them, thus completing the underwater planting task of the submerged plants. After the community stabilizes, it enters the maintenance stage.

[0033] Preferred materials for cultivating submerged plants include:

[0034] Based on the differences in plant growth conditions, common submerged plants in ecological restoration were selected as typical representative species, mainly using easily obtainable plant seeds, stone buds, dormant buds and broken stems.

[0035] Choose environmentally friendly embedding materials suitable for plant cultivation;

[0036] By comparing the advantages and disadvantages of different cultivation materials through germination experiments, high-quality cultivation materials suitable for the cultivation of submerged plants can be screened out.

[0037] Preferred methods for rapidly cultivating submerged plant seedlings include:

[0038] Submerged plants with faster rooting were selected from Vallisneria natans, Potamogeton malaianus, and Myriophyllum spicatum.

[0039] Based on the selected submerged plants, the substrate type, water level, light intensity and water temperature conditions were regulated, and inducing hormones were added appropriately. Germination experiments were used to quantify various environmental indicators for the cultivation of submerged plants, and a cultivation site for submerged plants was established.

[0040] By combining the selected high-quality cultivation materials, the submerged plants are cultivated in a submerged plant cultivation site, enabling modular cultivation of submerged plants and rapid cultivation of submerged plant seedlings.

[0041] Preferably, monitoring of restored submerged plants includes:

[0042] Real-time monitoring and data collection of community composition, coverage and biomass of submerged plants are conducted using remote sensing technology and sensors to obtain plant growth data.

[0043] Based on remote sensing technology and sensors, the water temperature, pH and dissolved oxygen of the underwater submerged plant's living environment are monitored and collected in real time to obtain water quality change data.

[0044] Real-time monitoring and data collection of changes in fish and plankton in the habitat of submerged plants are conducted using remote sensing technology and sensors to obtain ecological response data.

[0045] Among them, based on plant growth data, water quality change data and ecological response data, real-time monitoring data of submerged plants after restoration during ecological restoration were determined.

[0046] Preferably, analysis and prediction are performed based on monitoring data, including:

[0047] Clean the real-time monitoring data of submerged plants to remove noise and identify and delete duplicate, missing and outlier values.

[0048] The real-time monitoring data of submerged plants is transformed to remove the dimensional differences between the real-time monitoring data of submerged plants and to determine standardized real-time monitoring data of submerged plants.

[0049] Feature extraction is performed on the real-time monitoring data of submerged plants. Feature vectors related to the monitoring and maintenance of submerged plants are extracted from the real-time monitoring data of submerged plants to determine the characteristic monitoring data of submerged plants.

[0050] Preferably, the analysis and prediction based on monitoring data also includes:

[0051] Collect historical data on the growth of submerged plants, and use the historical data on the growth of submerged plants to train and optimize the deep learning model to determine the risk prediction model for the growth of submerged plants.

[0052] Submerged plant characteristic monitoring data are input into the submerged plant growth risk prediction model. The submerged plant characteristic monitoring data are analyzed based on the submerged plant growth risk prediction model, and the growth risk behavior of submerged plants is predicted to determine the submerged plant growth risk prediction results.

[0053] Preferably, the operation and maintenance management of submerged plants includes:

[0054] When risky behaviors in the growth of submerged plants are predicted, maintenance and management of submerged plants are carried out, including removing invasive species, pruning overly dense plants, and optimizing the growth environment. At the same time, the submerged plant restoration plan is dynamically adjusted based on the monitoring of submerged plants to ensure the recovery of submerged plants in the ecological restoration process.

[0055] Compared with the prior art, the beneficial effects of the present invention are:

[0056] This invention employs germination experiments to screen submerged plant cultivation materials, quantifies various environmental indicators for submerged plant cultivation, establishes submerged plant cultivation sites, and conducts modular cultivation of submerged plants to rapidly cultivate seedlings. Underwater robots are used for automated planting of these seedlings, completing the underwater planting task. After the community stabilizes, it enters the maintenance phase, where the recovered submerged plants are monitored. Real-time monitoring data is collected, processed, and used to determine the characteristic monitoring data of the submerged plants. This data is analyzed and predicted, and operation and maintenance management is implemented based on the prediction results. By establishing a systematic system for submerged plant seedling cultivation, modular propagation, automated and efficient planting, and intelligent monitoring and operation and maintenance, efficient modular cultivation of submerged plants can be achieved, improving planting efficiency and survival rate while reducing planting costs. Attached Figure Description

[0057] Figure 1 This is a flowchart of the submerged plant restoration and monitoring operation and maintenance method in the ecological restoration of the present invention;

[0058] Figure 2 This is a flowchart illustrating the determination of underwater planting sites according to the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] To address the problems of low planting efficiency, high cost, and low survival rate of submerged plants due to the influence of water depth and waves, please refer to [link to relevant documentation]. Figure 1-2 This embodiment provides the following technical solution:

[0061] Methods for the restoration, monitoring, and maintenance of submerged plants in ecological restoration include:

[0062] Germination experiments were used to screen submerged plant cultivation materials;

[0063] In this embodiment, a germination experiment is used to screen submerged plant cultivation materials, including:

[0064] Based on the differences in plant growth conditions, common submerged plants in ecological restoration were selected as typical representative species, mainly using easily obtainable plant seeds, stone buds, dormant buds and broken stems.

[0065] Choose environmentally friendly embedding materials suitable for plant cultivation. Among them, environmentally friendly embedding materials should be environmentally friendly and pollution-free, biodegradable, low cost, easy to obtain, have good water permeability and a certain strength.

[0066] By comparing the advantages and disadvantages of different cultivation materials through germination experiments, high-quality cultivation materials suitable for the cultivation of submerged plants can be screened out.

[0067] Modular cultivation of submerged plants enables rapid cultivation of submerged plant seedlings;

[0068] In this embodiment, the rapid cultivation of submerged plant seedlings includes:

[0069] Submerged plants with faster rooting were selected from Vallisneria natans, Potamogeton malaianus, and Myriophyllum spicatum.

[0070] Based on the selected submerged plants, the substrate type, water level, light intensity and water temperature conditions were regulated, and inducing hormones were added appropriately. Germination experiments were used to quantify various environmental indicators for the cultivation of submerged plants, and a cultivation site for submerged plants was established.

[0071] By combining the selected high-quality cultivation materials, the submerged plants are cultivated in a submerged plant cultivation site, enabling modular cultivation of submerged plants and rapid cultivation of submerged plant seedlings.

[0072] It should be noted that, in order to accelerate seed germination, achieve seed recovery in polluted water bodies, and enable the self-restoration of submerged plant density, extensive research on artificial seeds has been conducted based on different seed forms and germination methods of various submerged plants. This research involved quantitatively adding various types of hormones to promote rapid seed germination, sprouting, and rooting; creating diverse embedding materials to enhance seed resistance to adverse environmental influences; and improving seed storage, transport, and germination capacity under suitable conditions. The optimal germination rates for different plant seeds were also investigated. Suitable growth conditions, such as water temperature, depth, transparency, and light conditions, are crucial. Studies show that the optimal temperature for seed germination of submerged plants is generally around 20℃. The germination rate generally increases with increasing temperature, while the germination rate decreases. For example, the germination rate of *Potamogeton crispus*, *Gnaphalium affine*, and *Vallisneria natans* is highest at 20℃, but the germination rate is 28℃ > 20℃ > 10℃. Light intensity is another important factor affecting the cultivation of submerged plants. Research indicates that germination can occur when the light intensity in the water reaches 5-10% of the incident light intensity, with a light intensity of 40 μmol / m². 2 In addition, numerous studies on tissue culture have been conducted, utilizing fixed-point nutrient solution addition and plant fragments or tissue structures to rapidly cultivate submerged plants indoors.

[0073] Underwater robots are used to automate the planting of submerged plant seedlings;

[0074] In this embodiment, an underwater robot is used to automatically plant submerged plant seedlings, including:

[0075] Based on the identification of underwater substrate types using cameras and AI algorithms, underwater planting points are determined and planting grids are divided. A modular design combining woven grids and planting baskets is adopted to divide the underwater planting points into multiple grid units, establishing a four-level grid of water area-zone-unit-point. Multiple grid units are managed through a combination of physical isolation and digital monitoring.

[0076] The water quality and bottom sediment are detected by sensors to assess the planting environment of submerged plants. When the planting environment is suitable for the seedlings of submerged plants, the operator sends instructions to the underwater robot through a computer and sends the underwater planting point to the underwater robot.

[0077] Submerged plant seedlings are loaded into the seedling container of an underwater robot. The underwater planting point is located using GPS and sonar systems. The underwater robot then finds the underwater planting point according to the planned movement path.

[0078] The underwater robot uses a robotic arm to dig a pit and place the submerged plant seedlings into the pit. The seedlings are planted using the cutting method, which involves planting them in the bottom mud, covering them with soil to fix them, and stabilizing the seedlings. This completes the underwater planting task of the submerged plants. After the community has stabilized, it enters the maintenance stage.

[0079] It should be noted that cutting propagation is more conducive to rapid rooting, establishment, and growth of plants compared to other methods. While the throwing method reduces the impact of wind, waves, and water flow on the planted seedlings, the root system cannot effectively break through the netting or non-woven fabric covering the seedlings to contact the bottom mud, leading to a sharp decline in plant survival rate and making rapid growth more difficult, thus failing to guarantee recovery. Sowing and bagging are the most efficient methods, utilizing plant seeds, buds, dormant buds, etc., directly dispersing them into the water body with the aid of other methods. This method is suitable for shallow water bodies without wind and waves, with a lower germination rate but more stable plant establishment. For cutting propagation, in order to improve rapid plant growth, a study compared forward, reverse, and horizontal cutting propagation methods for different plants. The results showed that horizontal cutting increased the number of adventitious roots and branches. Therefore, when planting cuttings, it is necessary to distinguish the morphological upper and lower ends to maximize the contact area with the bottom mud, which is beneficial to enhance the establishment and propagation ability.

[0080] It should be noted that traditional seedling cultivation methods are relatively extensive, and the planting cost is between 60-150 yuan / m². 2 Planting efficiency is 2-5 m per person per hour. 2 This automated planting method is 3-6 times more efficient than manual labor, reducing the need for 2-5 workers and lowering the planting cost to approximately 20-30 yuan / m².2 In addition, most restoration projects have short construction periods and limited optimal planting time. Automated mechanical planting can handle various working conditions and ensure good planting efficiency and survival rate. It can not only save a lot of costs, but also effectively improve planting efficiency and plant survival rate, reduce rework, and achieve restoration results faster. It is the best choice at present.

[0081] Specifically, the process of identifying underwater substrate types based on cameras and AI algorithms, and determining underwater planting sites based on the identified substrate types, includes:

[0082] Based on the underwater planting area image obtained by the camera, the bottom type of the underwater planting area image is identified by the AI ​​algorithm to obtain the underwater bottom type, and based on the underwater bottom type, the type of plant cultivated in the underwater planting area is obtained.

[0083] The historical planting conditions and historical planting situation of the plant type are obtained, and the artificial intelligence model is trained based on the historical planting conditions and historical planting situation to obtain the planting point allocation model.

[0084] The camera continuously captures multiple frames of images of the underwater planting area. Based on these multiple frames of images, the water depth, water flow velocity, and bottom depth of the underwater planting area are determined, and the illumination characteristics of the underwater planting area are obtained.

[0085] The water depth, water flow velocity, bottom depth, and light characteristics of the underwater planting area, as well as the planting requirements, are input into the planting point allocation model to obtain multiple initial planting point allocation results for the underwater planting area;

[0086] Obtain the historical growth data corresponding to the historical planting situation of the plant type, determine the historical planting efficiency of the historical planting situation, obtain the historical cost-effectiveness of the historical growth situation, and establish the efficiency weight of historical planting efficiency and the benefit weight of historical cost-effectiveness based on actual needs.

[0087] Based on historical planting efficiency and its efficiency weight, historical cost-effectiveness and its benefit weight, and combined with artificial intelligence, an efficiency and benefit prediction scoring model is established.

[0088] The results of multiple initial planting point allocations are input into the efficiency and benefit prediction scoring model to obtain the predicted score value of each initial planting point allocation result.

[0089] Underwater planting sites are determined based on the initial planting site allocation result with the highest predicted score.

[0090] In this embodiment, planting requirements are set according to actual conditions, and actual needs are also preset according to actual conditions, which can be achieved by adjusting the model parameters.

[0091] In this embodiment, a higher efficiency and benefit prediction score indicates a greater degree of fulfillment of actual needs.

[0092] The beneficial effects of the above design scheme are as follows: by acquiring underwater planting area images based on cameras, identifying the substrate type based on AI algorithms, and obtaining the underwater substrate type, the types of plants to be cultivated in the underwater planting area are determined based on the underwater substrate type, thus realizing the determination of the plant type. In addition, by combining the historical planting and growth data of the plant type with the artificial intelligence model, the planting point is determined and the efficiency of the planting point is evaluated. Finally, the initial planting point allocation result with the highest predicted score is selected to determine the underwater planting point, thus achieving the optimal determination of the underwater planting point and providing a foundation for the efficient and modular cultivation of submerged plants.

[0093] Specifically, the efficiency and benefit prediction scoring model, based on historical planting efficiency and its efficiency weight, historical cost-effectiveness and its benefit weight, and combined with artificial intelligence, includes:

[0094] Based on the historical growth data corresponding to the historical planting conditions, the historical planting conditions are divided into different planting areas, and each planting area corresponds to different local growth conditions.

[0095] Set the efficiency coefficient and yield coefficient for the corresponding planting area based on the local growth conditions;

[0096] Efficiency prediction rules and benefit prediction rules for the planting situation to be analyzed are established based on efficiency coefficients and benefit coefficients.

[0097] The formula for calculating the efficiency prediction rule is as follows:

[0098]

[0099] in, σ a τ represents the planting efficiency of the planting situation to be analyzed, n represents the number of regions to be divided for the planting situation to be analyzed, and τ represents the planting efficiency of the planting situation to be analyzed. i M represents the area weight of the i-th partitioned region. i This represents the efficiency coefficient of the historical planting region that has the greatest similarity to the i-th partitioned region. α i This represents the similarity value between the i-th partitioned region and its corresponding historical planting region with the highest similarity, where C represents a constant and its value is less than 1. α i When the i-th partitioned region and its corresponding historical planting region with the highest similarity are considered to be efficiency-oriented similar, Take the + value from the middle, when the i-th partitioned region and its corresponding historical planting region with the highest similarity are considered to be downwardly similar in efficiency. Take - in the middle, where M0 represents the reference efficiency coefficient;

[0100] The calculation formula for the benefit prediction rule is as follows:

[0101]

[0102] in, K a This indicates the planting efficiency of the planting situation to be analyzed. β i This represents the similarity value between the i-th partitioned region and its corresponding historical planting region, which has the highest similarity. Y i This represents the benefit coefficient of the historical planting area that has the highest similarity to the i-th partitioned area. Z Represents a constant, with values ​​less than 100%. β i When the i-th partitioned region and its corresponding historical planting region with the highest similarity are similar in terms of benefits, Take the positive sign from the middle, where the i-th partitioned region is the most similar to its corresponding historical planting region, indicating downward similarity in terms of benefits. Take -, Y 0 Indicates the reference benefit coefficient;

[0103] The planting efficiency and planting benefits of the planting situation to be tested are obtained based on the efficiency prediction rules and the benefit prediction rules.

[0104] Based on historical planting efficiency and its efficiency weight, and historical cost-benefit and its benefit weight, a predictive scoring rule is established.

[0105] The formula for calculating the prediction scoring rule is as follows:

[0106]

[0107] Where H represents the predicted score, σ represents the standardized historical planting efficiency, K represents the standardized historical cost-benefit ratio, δ0 represents the efficiency weight, and ε K Indicates the benefit weight;

[0108] Based on efficiency prediction rules, benefit prediction rules, and prediction scoring rules, and combined with artificial intelligence, an efficiency and benefit prediction scoring model is established.

[0109] In this embodiment, upward efficiency similarity means that the efficiency coefficient of the i-th partitioned region is greater than the efficiency coefficient of the historical planting region with the highest similarity to it, and downward efficiency similarity means that the efficiency coefficient of the i-th partitioned region is less than or equal to the efficiency coefficient of the historical planting region with the highest similarity to it.

[0110] In this embodiment, upward similarity of benefits means that the efficiency coefficient of the i-th partitioned region is greater than the efficiency coefficient of the historical planting region with the highest similarity to it, and downward similarity of benefits means that the efficiency coefficient of the i-th partitioned region is less than or equal to the efficiency coefficient of the historical planting region with the highest similarity to it.

[0111] In this embodiment, The smaller the value, the more synchronized the historical planting efficiency and historical cost-effectiveness are, and the higher the corresponding prediction score.

[0112] In this embodiment, the division of the planting situation to be analyzed is obtained based on the matching of artificial intelligence with the corresponding historical planting areas.

[0113] The beneficial effects of the above design scheme are as follows: By dividing the historical planting situation into different planting areas based on the historical growth situation, each planting area corresponds to a different local growth situation. Based on the local growth situation, efficiency coefficients and benefit coefficients are set for the corresponding planting areas. Based on the efficiency coefficients and benefit coefficients, efficiency prediction rules and benefit prediction rules for the planting situation to be tested are established. Based on the efficiency prediction rules and benefit prediction rules, the planting efficiency and planting benefit of the planting situation to be tested are obtained. Based on the historical planting efficiency and its efficiency weight, historical cost-benefit and its benefit weight, prediction scoring rules are established. Based on the efficiency prediction rules, benefit prediction rules and prediction scoring rules, combined with artificial intelligence, an efficiency and benefit prediction scoring model is established. Based on artificial intelligence, the prediction calculation of efficiency and benefit, as well as the calculation of prediction scores, and the addition of the pre-division of planting areas, the accuracy of the obtained efficiency and benefit prediction scoring model is ensured, providing a basis for determining the optimal underwater planting point.

[0114] Specifically, the restoration of submerged plants in ecological restoration involves carrying out water pollution control and water ecological restoration work, reducing nutrients in water bodies, improving the water environment, restoring aquatic plants, and rebuilding a healthy ecosystem.

[0115] The restored submerged plants were monitored, and real-time monitoring data of the submerged plants were collected, processed, and then the characteristic monitoring data of the submerged plants were determined.

[0116] In this embodiment, the restored submerged plants are monitored, and real-time monitoring data of the submerged plants is collected, including:

[0117] Real-time monitoring and data collection of community composition, coverage and biomass of submerged plants are conducted using remote sensing technology and sensors to obtain plant growth data.

[0118] Based on remote sensing technology and sensors, the water temperature, pH and dissolved oxygen of the underwater submerged plant's living environment are monitored and collected in real time to obtain water quality change data.

[0119] Real-time monitoring and data collection of changes in fish and plankton in the habitat of submerged plants are conducted using remote sensing technology and sensors to obtain ecological response data.

[0120] Among them, based on plant growth data, water quality change data and ecological response data, real-time monitoring data of submerged plants after restoration during ecological restoration were determined.

[0121] In this embodiment, the real-time monitoring data of the collected submerged plants is processed, including:

[0122] Clean the real-time monitoring data of submerged plants to remove noise and identify and delete duplicate, missing and outlier values.

[0123] The real-time monitoring data of submerged plants is transformed to remove the dimensional differences between the real-time monitoring data of submerged plants and to determine standardized real-time monitoring data of submerged plants.

[0124] Feature extraction is performed on the real-time monitoring data of submerged plants. Feature vectors related to the monitoring and maintenance of submerged plants are extracted from the real-time monitoring data of submerged plants to determine the characteristic monitoring data of submerged plants.

[0125] It should be noted that by monitoring the restored submerged plants, collecting real-time monitoring data, and processing it to determine the characteristic monitoring data of the submerged plants, it is easier to analyze and predict the characteristic monitoring data of the submerged plants in the future. This will enable better operation and maintenance management of the submerged plants and improve their survival rate.

[0126] Analyze and predict the characteristics of submerged plants based on monitoring data, and conduct operation and maintenance management of submerged plants based on the prediction results.

[0127] In this embodiment, the analysis and prediction of submerged plant characteristic monitoring data includes:

[0128] Collect historical data on the growth of submerged plants, and use the historical data on the growth of submerged plants to train and optimize the deep learning model to determine the risk prediction model for the growth of submerged plants.

[0129] Submerged plant characteristic monitoring data are input into the submerged plant growth risk prediction model. The submerged plant characteristic monitoring data are analyzed based on the submerged plant growth risk prediction model, and the growth risk behavior of submerged plants is predicted to determine the submerged plant growth risk prediction results.

[0130] In this embodiment, the operation and maintenance management of submerged plants based on the prediction results includes:

[0131] When risky behaviors in the growth of submerged plants are predicted, maintenance and management of submerged plants are carried out, including removing invasive species, pruning overly dense plants, and optimizing the growth environment. At the same time, the submerged plant restoration plan is dynamically adjusted based on the monitoring of submerged plants to ensure the recovery of submerged plants in the ecological restoration process.

[0132] Specifically, by establishing a systematic system for the cultivation of submerged plant seedlings, modular propagation, automated and efficient planting, and intelligent monitoring and operation and maintenance, submerged plants can be cultivated efficiently and modularly, improving planting efficiency and survival rate, and reducing planting costs.

[0133] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0134] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for the restoration, monitoring, and maintenance of submerged plants in ecological restoration, characterized in that, include: Select submerged plant cultivation materials, quickly cultivate submerged plant seedlings, determine underwater planting points, and use underwater robots to automatically plant submerged plant seedlings; Among them, an efficiency and benefit prediction scoring model is established based on historical planting efficiency and its efficiency weight, historical cost-benefit and its benefit weight, and combined with artificial intelligence. The allocation results of multiple initial planting points are input into the efficiency and benefit prediction scoring model to obtain the predicted score value of each initial planting point allocation result. The underwater planting point is determined based on the initial planting point allocation result with the largest predicted score value. The restored submerged plants are monitored, and the monitoring results are analyzed and predicted to enable operation and maintenance management of the submerged plants. Determine underwater planting sites, including: Based on the underwater planting area image obtained by the camera, the bottom type of the underwater planting area image is identified by the AI ​​algorithm to obtain the underwater bottom type, and based on the underwater bottom type, the type of plant cultivated in the underwater planting area is obtained. The historical planting conditions and historical planting situation of the plant type are obtained, and the artificial intelligence model is trained based on the historical planting conditions and historical planting situation to obtain the planting point allocation model. The camera continuously captures multiple frames of images of the underwater planting area. Based on these multiple frames of images, the water depth, water flow velocity, and bottom depth of the underwater planting area are determined, and the illumination characteristics of the underwater planting area are obtained. The water depth, water flow velocity, bottom depth, and light characteristics of the underwater planting area, as well as the planting requirements, are input into the planting point allocation model to obtain multiple initial planting point allocation results for the underwater planting area; Obtain the historical growth data corresponding to the historical planting situation of the plant type, determine the historical planting efficiency of the historical planting situation, obtain the historical cost-effectiveness of the historical growth situation, and establish the efficiency weight of historical planting efficiency and the benefit weight of historical cost-effectiveness based on actual needs. Based on historical planting efficiency and its efficiency weight, historical cost-effectiveness and its benefit weight, and combined with artificial intelligence, an efficiency and benefit prediction scoring model is established. The results of multiple initial planting point allocations are input into the efficiency and benefit prediction scoring model to obtain the predicted score value of each initial planting point allocation result. Underwater planting sites are determined based on the initial planting site allocation result with the highest predicted score.

2. The method for restoration and monitoring of submerged plants in ecological restoration as described in claim 1, characterized in that, Based on historical planting efficiency and its efficiency weight, historical cost-effectiveness and its benefit weight, and combined with artificial intelligence, an efficiency-benefit prediction and scoring model is established, including: Based on the historical growth data corresponding to the historical planting conditions, the historical planting conditions are divided into different planting areas, and each planting area corresponds to different local growth conditions. Set the efficiency coefficient and yield coefficient for the corresponding planting area based on the local growth conditions; Efficiency prediction rules and benefit prediction rules for the planting conditions to be tested are established based on efficiency coefficients and benefit coefficients. The planting efficiency and planting benefits of the planting situation to be tested are obtained based on the efficiency prediction rules and the benefit prediction rules. Based on historical planting efficiency and its efficiency weight, and historical cost-benefit and its benefit weight, a predictive scoring rule is established. Based on efficiency prediction rules, benefit prediction rules, and prediction scoring rules, and combined with artificial intelligence, an efficiency and benefit prediction scoring model is established.

3. The method for restoration and monitoring of submerged plants in ecological restoration as described in claim 1, characterized in that, The use of underwater robots for automated planting of submerged plant seedlings includes: The underwater planting points are divided into planting grids. A modular design combining woven grids and planting baskets is adopted to divide the underwater planting points into multiple grid units, establishing a four-level grid of water area-zone-unit-point. Multiple grid units are managed through a combination of physical isolation and digital monitoring. The water quality and bottom sediment are detected by sensors to assess the planting environment of submerged plants. When the planting environment is suitable for the seedlings of submerged plants, the operator sends instructions to the underwater robot through a computer and sends the underwater planting point to the underwater robot. Submerged plant seedlings are loaded into the seedling container of an underwater robot. The underwater planting point is located using GPS and sonar systems. The underwater robot then finds the underwater planting point according to the planned movement path. The underwater robot uses a robotic arm to dig a pit and place the submerged plant seedlings into the pit. The seedlings are planted using the cutting method, and then planted in the bottom mud, covered with soil to fix them, thus completing the underwater planting task of the submerged plants. After the community stabilizes, it enters the maintenance stage.

4. The method for restoration and monitoring of submerged plants in ecological restoration as described in claim 1, characterized in that, Screening of submerged plant cultivation materials, including: Based on the differences in plant growth conditions, common submerged plants in ecological restoration were selected as typical representative species, mainly using easily obtainable plant seeds, stone buds, dormant buds and broken stems. Choose environmentally friendly embedding materials suitable for plant cultivation; By comparing the advantages and disadvantages of different cultivation materials through germination experiments, high-quality cultivation materials suitable for the cultivation of submerged plants can be screened out.

5. The method for restoration and monitoring of submerged plants in ecological restoration as described in claim 1, characterized in that, Rapid cultivation of submerged plant seedlings, including: Submerged plants with faster rooting were selected from Vallisneria natans, Potamogeton malaianus, and Myriophyllum spicatum. Based on the selected submerged plants, the substrate type, water level, light intensity and water temperature conditions were regulated, and inducing hormones were added appropriately. Germination experiments were used to quantify various environmental indicators for the cultivation of submerged plants, and a cultivation site for submerged plants was established. By combining the selected high-quality cultivation materials, the submerged plants are cultivated in a submerged plant cultivation site, enabling modular cultivation of submerged plants and rapid cultivation of submerged plant seedlings.

6. The method for restoration and monitoring of submerged plants in ecological restoration as described in claim 1, characterized in that, Monitoring of restored submerged plants includes: Real-time monitoring and data collection of community composition, coverage and biomass of submerged plants are conducted using remote sensing technology and sensors to obtain plant growth data. Based on remote sensing technology and sensors, the water temperature, pH and dissolved oxygen of the underwater submerged plant's living environment are monitored and collected in real time to obtain water quality change data. Real-time monitoring and data collection of changes in fish and plankton in the habitat of submerged plants are conducted using remote sensing technology and sensors to obtain ecological response data. Among them, based on plant growth data, water quality change data and ecological response data, real-time monitoring data of submerged plants after restoration during ecological restoration were determined.

7. The method for restoration and monitoring of submerged plants in ecological restoration as described in claim 6, characterized in that, Analysis and prediction based on monitoring data include: Clean the real-time monitoring data of submerged plants to remove noise and identify and delete duplicate, missing and outlier values. The real-time monitoring data of submerged plants is transformed to remove the dimensional differences between the real-time monitoring data of submerged plants and to determine standardized real-time monitoring data of submerged plants. Feature extraction is performed on the real-time monitoring data of submerged plants. Feature vectors related to the monitoring and maintenance of submerged plants are extracted from the real-time monitoring data of submerged plants to determine the characteristic monitoring data of submerged plants.

8. The method for restoration and monitoring of submerged plants in ecological restoration as described in claim 7, characterized in that, Analysis and prediction based on monitoring data also include: Collect historical data on the growth of submerged plants, and use the historical data on the growth of submerged plants to train and optimize the deep learning model to determine the risk prediction model for the growth of submerged plants. Submerged plant characteristic monitoring data are input into the submerged plant growth risk prediction model. The submerged plant characteristic monitoring data are analyzed based on the submerged plant growth risk prediction model, and the growth risk behavior of submerged plants is predicted to determine the submerged plant growth risk prediction results.

9. The method for restoration and monitoring of submerged plants in ecological restoration as described in claim 1, characterized in that, Operation and maintenance management of submerged plants includes: when risky behavior of submerged plant growth is predicted, maintenance and control of submerged plants are carried out, invasive species are removed, overly dense plants are pruned, and the growth environment is optimized. At the same time, the submerged plant restoration plan is dynamically adjusted according to the monitoring of submerged plants to ensure the restoration of submerged plants in ecological restoration.

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