Composite water ecological restoration method

Abstract

The invention relates to the technical field of water ecology, in particular to a composite water ecology restoration method. The method comprises the following steps that layered water quality monitoring is carried out on a target restoration water body, so that water quality monitoring data are obtained, and the water quality monitoring data comprise physicochemical index detection data and biological monitoring data; acquiring water meter full image data of the target restoration water body; performing water meter floating garbage identification on the target restoration water body according to the water meter full image data so as to obtain water meter garbage data; according to the water meter garbage data, performing water meter garbage removal scheme design on the target restoration water body to obtain a water meter garbage removal scheme; according to the physicochemical index detection data, heavy metal pollution evaluation is conducted on the target remediation water body, and therefore heavy metal pollution evaluation data are obtained. The advantages of various ecological means can be fully exerted, mutual promotion is achieved, and a better comprehensive remediation effect is achieved.

Description

Technical Field

[0001] This invention relates to the field of water ecology technology, and in particular to a composite water ecology restoration method. Background Technology

[0002] With economic development and population growth, the rapid development of industry, agriculture, and urbanization has brought serious environmental pollution problems, among which water pollution has become one of the most urgent issues to be addressed. Traditional water remediation methods rely solely on single ecological restoration approaches, resulting in limited effectiveness. Summary of the Invention

[0003] Therefore, it is necessary for the present invention to provide a composite aquatic ecological restoration method to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a composite aquatic ecosystem restoration method includes the following steps:

[0005] Step S1: Conduct stratified water quality monitoring on the target remediation water body to obtain water quality monitoring data, which includes physicochemical index detection data and biological monitoring data;

[0006] Step S2: Obtain the overall image data of the water meter of the target water body to be restored; identify floating debris on the water meter of the target water body based on the overall image data of the water meter to obtain water meter debris data; design a water meter debris removal plan for the target water body based on the water meter debris data to obtain a water meter debris removal plan;

[0007] Step S3: Assess the heavy metal pollution of the target remediation water body based on the physicochemical index detection data to obtain heavy metal pollution assessment data; calculate the amount of adsorbent to be added and design the addition route based on the heavy metal pollution assessment data to obtain adsorbent material addition data and material addition route data.

[0008] Step S4: Based on the physicochemical index detection data and biological monitoring data, harmful bacteria are detected in the target remediation water body to obtain harmful bacteria detection data; the inhibitory microorganisms of harmful bacteria are searched through the pre-set microbial database to obtain initial inhibitory microbial species data; based on the initial inhibitory microbial species data, intelligent fixed-point controlled-release microcapsule loading is designed for the target remediation water body to obtain the microcapsule loading design scheme.

[0009] Step S5: Based on the water meter waste removal plan, adsorption material dosage data, material delivery route data, and microcapsule loading design plan, a composite water ecological restoration plan is designed to obtain a composite restoration plan.

[0010] This invention utilizes stratified water quality monitoring to comprehensively understand the water quality of the target remediation water body, including physicochemical indicators such as pH, dissolved oxygen content, turbidity, and conductivity, as well as biological monitoring data on algae, zooplankton, and benthic organisms. The water quality monitoring data provides comprehensive information, helping to assess the health of the water body, identify pollution sources, and understand ecosystem changes. Acquiring this data provides a scientific basis for subsequent remediation plan design, determining the focus and direction of remediation, and developing targeted measures to improve remediation effectiveness. Obtaining a complete image of the water surface of the target remediation water body provides a visual understanding of the water surface's condition, including surface debris, contaminants, and potential hazards. Floating debris identification accurately identifies and acquires surface debris data, helping to determine the type, distribution, and density of debris, providing a basis for debris removal. Designing a surface debris removal plan based on the surface debris data allows for the development of specific removal steps and methods, effectively removing debris from the water surface and improving the overall water quality. Assessing heavy metal pollution through physicochemical index testing data allows us to understand the content and degree of heavy metals in the target remediation water body, such as lead, mercury, and cadmium. Based on this heavy metal pollution assessment data, the dosage of adsorbent materials can be calculated, and a reasonable deployment route can be designed to reduce heavy metal pollution levels. The data on adsorbent dosage and deployment route provide quantitative guidance for the remediation process, ensuring the accuracy of dosage and the effectiveness of deployment. Harmful bacteria detection allows for the timely identification of harmful bacteria in the target remediation water body, such as E. coli and Salmonella. Using a pre-set microbial database, we can identify inhibitory microorganisms for harmful bacteria, providing a reference for subsequent remediation and selecting suitable microorganisms to inhibit their growth. Intelligent, targeted, controlled-release microcapsule loading design based on inhibitory microorganism data allows for precise microcapsule deployment, achieving the inhibition of harmful bacteria and improving the hygienic condition of the water body. By comprehensively considering various factors, including the water surface waste removal plan, adsorbent dosage data, deployment route data, and microcapsule loading design, a comprehensive aquatic ecological remediation plan can be designed. This comprehensive approach integrates various data and design schemes, employing integrated measures to address different problems and pollution sources, achieving complete restoration of the target water body. The composite remediation scheme combines different remediation measures, employing multiple remediation methods to address various problems and pollution sources. Through the implementation of this comprehensive remediation scheme, not only can surface waste be addressed, but heavy metal pollution can also be reduced, and the growth of harmful bacteria controlled. This improves the overall water quality and ecological environment restoration effect. The comprehensive remediation scheme design considers multiple environmental factors and remediation objectives to achieve comprehensive water body restoration. By comprehensively utilizing various remediation measures, the self-purification capacity of the water body can be promoted, enhancing the stability and sustainable development of the aquatic ecosystem. This provides a sustainable foundation for future water body protection and management.In summary, this invention employs a comprehensive remediation scheme that integrates multiple remediation measures, addressing different pollution sources and problems with diverse approaches to enhance remediation effectiveness. The implementation of this comprehensive remediation scheme can simultaneously resolve multiple pollution issues, achieving a more comprehensive remediation effect. By continuously monitoring and evaluating the remediation effect, and adjusting and optimizing remediation measures in a timely manner, the remediation outcome can be continuously improved. This ensures the controllability of the remediation process and the continuous improvement of remediation results, providing support for the long-term protection and management of water bodies. By adopting a comprehensive remediation scheme, the resources required for various remediation methods can be fully utilized, maximizing resource utilization efficiency. This not only improves the economic efficiency of the remediation effect but also reduces resource waste, achieving sustainable development. The comprehensive remediation scheme comprehensively considers the interactions between different pollution problems, adopting integrated solutions for multiple issues. This enhances the comprehensiveness and completeness of the remediation effect, addressing the diverse challenges of water pollution problems. Attached Figure Description

[0011] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0012] Figure 1 A schematic flowchart of the steps of a composite aquatic ecosystem restoration method according to an embodiment is shown;

[0013] Figure 2 A detailed flowchart of step S2 of one embodiment is shown;

[0014] Figure 3 A vector diagram of a water body layout according to an embodiment is shown. Detailed Implementation

[0015] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0016] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0017] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0018] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a composite aquatic ecosystem restoration method, the method comprising the following steps:

[0019] Step S1: Conduct stratified water quality monitoring on the target remediation water body to obtain water quality monitoring data, which includes physicochemical index detection data and biological monitoring data;

[0020] Step S2: Obtain the overall image data of the water meter of the target water body to be restored; identify floating debris on the water meter of the target water body based on the overall image data of the water meter to obtain water meter debris data; design a water meter debris removal plan for the target water body based on the water meter debris data to obtain a water meter debris removal plan;

[0021] Step S3: Assess the heavy metal pollution of the target remediation water body based on the physicochemical index detection data to obtain heavy metal pollution assessment data; calculate the amount of adsorbent to be added and design the addition route based on the heavy metal pollution assessment data to obtain adsorbent material addition data and material addition route data.

[0022] Step S4: Based on the physicochemical index detection data and biological monitoring data, harmful bacteria are detected in the target remediation water body to obtain harmful bacteria detection data; the inhibitory microorganisms of harmful bacteria are searched through the pre-set microbial database to obtain initial inhibitory microbial species data; based on the initial inhibitory microbial species data, intelligent fixed-point controlled-release microcapsule loading is designed for the target remediation water body to obtain the microcapsule loading design scheme.

[0023] Step S5: Based on the water meter waste removal plan, adsorption material dosage data, material delivery route data, and microcapsule loading design plan, a composite water ecological restoration plan is designed to obtain a composite restoration plan.

[0024] Preferably, step S1 includes the following steps:

[0025] Step S11: Obtain depth data of the target water body to be restored;

[0026] Specifically, depth measurement equipment, such as sonar depth sounders, laser rangefinders, or multibeam sonar systems, can be prepared. A sampling point layout plan for depth measurement is determined based on the size and shape of the water body to be restored. This may include determining the number and location of sampling points to ensure coverage of all areas of the water body. Measurements are then performed using the depth measurement equipment within the water body. The equipment is moved along the predetermined sampling point layout plan, and depth data is recorded at each sampling point. For larger bodies of water, vessels or floating platforms may be needed to support the movement of the depth measurement equipment and the measurement operations. The depth data is recorded in the order or location of the measurements for subsequent processing and analysis.

[0027] Step S12: Divide the target water body into layers based on the depth data to obtain the first layer water intake depth data, the second layer water intake depth data, and the third layer water intake depth data;

[0028] Specifically, for example, the acquired depth data can be used for stratification. Based on project requirements and the depth distribution of the water body, the number of strata and the depth range of each stratum are determined. The depth data is then classified according to the defined strata, assigning the depth data of each sampling point to the corresponding stratum. For each stratum, the depth data within that stratum is extracted to obtain the first stratum water sampling depth data, the second stratum water sampling depth data, and the third stratum water sampling depth data.

[0029] Step S13: Based on the water depth data of the first layer, the water depth data of the second layer, and the water depth data of the third layer, sampling points are set up for the corresponding layers to obtain the sampling point location data of each layer.

[0030] Specifically, for example, the number and location distribution of sampling points at each level can be determined based on the water depth data from the first, second, and third levels. The position of the sampling points relative to the water surface is determined based on the water depth data for each level. For example, if the water depth is 2 meters, the sampling points should be located 2 meters below the water surface. Within each level, sampling points are deployed according to the predetermined number and location distribution. GPS positioning technology or other positioning methods can be used to determine the precise location of the sampling points. The location data of each sampling point, including latitude and longitude coordinates or other applicable coordinate systems, is recorded for subsequent sampling and monitoring operations.

[0031] Step S14: Collect water samples from the corresponding layers based on the sampling point location data of each layer to obtain water samples from the corresponding layers;

[0032] Specifically, for example, the locations of water samples to be collected within each layer can be determined based on the sampling point location data of the first, second, and third layers. Prepare water sampling tools and containers, such as water sample bottles or sampling bags. At each sampling point location within each layer, use appropriate tools to collect water samples into the corresponding containers. Ensure that the collected water samples are representative and avoid contamination or alteration of water quality. Collect a sufficient number of water samples within each layer according to the sampling plan and requirements for subsequent analysis and monitoring.

[0033] Step S15: Perform physicochemical analysis and biological testing on the water samples at the corresponding levels to obtain water quality monitoring data for the first, second, and third layers. Each layer of water quality monitoring data includes physicochemical index testing data and biological monitoring data.

[0034] Specifically, this can be achieved by setting up a water quality monitoring laboratory or commissioning a professional laboratory to conduct analysis and testing. Collected water samples are sent to the laboratory, ensuring accurate labeling and recording. In the laboratory, physicochemical analysis is performed on water samples at each level, including measuring indicators such as temperature, pH, turbidity, dissolved oxygen, and total dissolved solids. Simultaneously, biological testing is conducted, such as measuring plankton density, algae concentration, and total bacterial count. Based on the laboratory's analytical methods and equipment, first-level, second-level, and third-level water quality monitoring data are obtained, including physicochemical and biological monitoring data for each level.

[0035] Step S16: Perform a weighted average calculation on the physicochemical index detection data and biological monitoring data in the first layer of water quality monitoring data, the second layer of water quality monitoring data and the third layer of water quality monitoring data respectively to obtain water quality monitoring data, which includes physicochemical index detection data and biological monitoring data.

[0036] Specifically, for example, weighted averages can be calculated for the physicochemical indicator test data at each level, based on their importance and weight. Weights can be determined according to relevant standards or expert opinions. For biological monitoring data, weighted averages can be calculated based on the importance and weight of different biological indicators. Similarly, weights can be determined with reference to standards or expert opinions. The weighted average results from each level are then integrated to obtain water quality monitoring data for the first, second, and third levels, including physicochemical indicator test data and biological monitoring data. As needed, the water quality monitoring data can be visualized or further analyzed to assess water quality and facilitate subsequent decision-making, planning, or remediation measures.

[0037] Preferably, step S2 includes the following steps:

[0038] Step S21: Take surface images of the target water body to be repaired using a drone to obtain a multi-view water surface image set;

[0039] Specifically, for example, drone equipment can be prepared, including the drone itself, remote controller, camera, etc. Based on the size and shape of the target water body to be restored, an appropriate flight path and altitude are determined. The drone is launched and flies along the predetermined flight path, ensuring that it can cover the entire surface of the target water body. During the flight, the camera is used to continuously capture images of the water surface from multiple perspectives. The quality and clarity of the captured images are ensured for subsequent processing and analysis.

[0040] Step S22: Perform orthorectification and non-destructive enhancement on the multi-view water meter image set to obtain an equivalent horizontal projection image set;

[0041] Specifically, for example, the acquired multi-view water meter image set can be imported into a computer or image processing software. Orthorectification is then performed on the images to correct their projection and align them with the horizontal plane of the water body. Geographic Information System (GIS) tools or image processing software can be used for this correction. The corrected images are then non-destructively enhanced to improve image quality and detail. Enhancement features in image processing software, such as contrast adjustment, color correction, and noise reduction, can be used.

[0042] Step S23: Stitch the overlapping areas of the equivalent horizontal projection image set to obtain the overall image data of the water meter;

[0043] Specifically, for example, image processing software can be used to perform image stitching on an equivalent horizontal projection image set. First, overlapping regions in the image set are identified and matched and corrected to ensure alignment and consistency between the images. During the stitching operation, the image processing software's stitching tools or algorithms are used to merge the images of the overlapping regions, generating a complete overall image of the water meter. Necessary post-processing is then performed on the generated overall water meter image, such as removing image edge artifacts and adjusting brightness and contrast, to improve image quality and visualization. Finally, the overall water meter image data is saved and further analyzed and applied as needed.

[0044] Step S24: Extract the floating debris area of ​​the target water body based on the overall image data of the water meter to obtain the distribution data of the floating debris on the water meter;

[0045] Specifically, for example, image processing software can be used to load a full-view image of the water meter. For floating debris in the image, image segmentation techniques can be used for region extraction. For instance, methods such as thresholding, edge detection, or color segmentation can be used to identify and extract areas of floating debris. In the image processing software, an appropriate image segmentation algorithm is applied to distinguish the floating debris from the background and generate a binary image or segmentation result of the floating debris. Based on the binary image or segmentation result of the floating debris, the area, quantity, or other relevant statistical data of the floating debris are calculated to obtain the distribution data of the floating debris on the water meter.

[0046] Step S25: Based on the overall image data of the water meter, identify plastic waste and dead algae in the distribution data of floating matter on the water meter to obtain water meter waste data;

[0047] Specifically, for example, image processing software can be used to load water surface planktonic distribution data into a comprehensive water meter image. Plastic waste and dead algae can then be identified from this data. Machine learning and computer vision algorithms can be used for classification and identification. A labeled training dataset is prepared, including water surface planktonic image samples with known categories (plastic waste and dead algae). Machine learning algorithms, such as convolutional neural networks (CNNs) or support vector machines (SVMs), are used to train the training dataset to build a classification model. The established classification model is then used to predict and classify the water surface planktonic distribution data, identifying areas containing plastic waste and dead algae. Based on the classification results, the area, quantity, or other relevant statistical data of plastic waste and dead algae are calculated to obtain water surface waste data.

[0048] Step S26: Based on the overall image data of the water meter and the data on the waste on the water meter, design a waste removal plan for the target water body to be restored, thereby obtaining the waste removal plan for the water meter.

[0049] Specifically, for example, a water meter debris removal plan can be designed by combining overall water meter image data and water meter debris data for the target water body being restored. Based on the distribution of floating debris in the overall water meter image data, the areas and types of debris requiring removal (such as plastic waste and dead algae) can be identified. Based on statistical information such as the area and quantity of debris in the water meter debris data, the scale and complexity of the debris removal can be assessed, taking into account factors such as removal time, resources, and costs. Based on the assessment results, a reasonable water meter debris removal plan can be developed. The plan may include removal methods, tools and equipment, a removal schedule, and necessary safety measures during the removal process. When designing the plan, consultation and cooperation with relevant departments or professionals can also be considered to ensure the effectiveness and sustainability of the removal process. Finally, the water meter debris removal plan should be recorded and presented for subsequent implementation and monitoring.

[0050] Preferably, step S26 includes the following steps:

[0051] Step S261: Based on the water meter waste data, label the waste type areas in the overall water meter image data to obtain labeled water meter image data;

[0052] Specifically, for example, garbage area information from water meter garbage data can be used to label the corresponding full-view water meter image data with garbage type areas. In image processing software, the full-view water meter image data is loaded. Based on the garbage area information in the garbage data, image annotation tools or drawing tools are used to label the garbage areas on the full-view water meter image. Each garbage area is labeled and corresponding labels or category information are added to distinguish different types of garbage. The labeled water meter image data is saved as the result of the labeled water meter image data.

[0053] Step S262: Extract and calculate the area of ​​dead algae from the labeled water meter image data to obtain statistical data on the area of ​​dead algae;

[0054] Specifically, for example, image processing software can be used to load labeled water meter image data. For the labeled water meter image data, the area of ​​the dead algae region is extracted and calculated. In the image processing software, methods such as image segmentation or thresholding are used to separate the dead algae region from other regions. The area of ​​the separated dead algae region is calculated. Area calculation tools or algorithms provided by the image processing software can be used, or the area can be estimated by pixel counting. Steps 3 and 4 are repeated for each labeled water meter image data to obtain the area of ​​the dead algae region in each image.

[0055] Step S263: Calculate the oxygen consumption of algae through algal respiration based on the statistical data of the dead algal area to obtain algal oxygen consumption data;

[0056] Specifically, for example, statistical data on the area of ​​dead algae can be used to calculate the oxygen consumption of algal respiration. Based on known algal biological parameters and the oxygen consumption formula for respiration, the area of ​​dead algae is converted into the oxygen consumption of algal respiration. Using the oxygen consumption per unit area from the algal biological parameters, the area of ​​dead algae is multiplied by the oxygen consumption per unit area to obtain the oxygen consumption of algal respiration. This calculation is performed for each area of ​​dead algae to obtain the corresponding algal oxygen consumption data. The algal oxygen consumption data is then statistically analyzed and summarized to obtain overall algal oxygen consumption data. Alternatively, the algal oxygen consumption calculation formula can be used to calculate the oxygen consumption of all dead algae through algal respiration to obtain overall algal oxygen consumption data.

[0057] Step S264: Compare the algal oxygen consumption data with the preset oxygen saturation lower limit threshold. When the algal oxygen consumption data is greater than the preset oxygen saturation lower limit threshold, the area corresponding to the dead algae is taken as the priority removal area, thereby obtaining the algal priority removal area data; when the algal oxygen consumption data is less than the preset oxygen saturation lower limit threshold, the corresponding area is marked as the natural recovery area.

[0058] Specifically, for example, algal oxygen consumption data and a preset lower oxygen saturation threshold can be obtained. For labeled water meter image data, the corresponding algal oxygen consumption data is acquired. This data is then compared to the preset lower oxygen saturation threshold. If the algal oxygen consumption data is greater than the preset threshold, the area is marked as a priority algal removal area. If the data is less than the threshold, the area is marked as a natural recovery area. This process is repeated to obtain data on both the priority algal removal area and the natural recovery area.

[0059] Step S265: Perform statistical analysis of plastic waste areas on the labeled water meter image data to obtain plastic waste distribution data;

[0060] Specifically, for example, image processing software can be used to load labeled water meter image data. Statistical analysis can then be performed on the labeled water meter image data, focusing on areas with plastic waste. In the image processing software, methods such as image segmentation or thresholding can be used to separate the plastic waste areas from other areas. The separated plastic waste areas can then be counted or their areas statistically analyzed to obtain distribution data for the plastic waste.

[0061] Step S266: Based on the data of priority algae removal areas and plastic waste distribution data, design a water surface waste removal plan for the target water body to obtain the water surface waste removal plan.

[0062] Specifically, for example, data on priority algae removal areas and plastic waste distribution can be used as input. Based on the algae priority removal area data, areas requiring priority removal are identified; these areas may include areas with dead algae and other priority removal areas. Based on the plastic waste distribution data, the distribution of plastic waste is determined, including its location, quantity, and density. Combining the algae priority removal area data and the plastic waste distribution data, a water meter waste removal plan is designed. This plan may include removing dead algae areas, removing other priority removal areas, and removing plastic waste areas. Based on the designed water meter waste removal plan, a removal schedule is developed.

[0063] Preferably, in step S263, the oxygen consumption of algae respiration is calculated using the algal oxygen consumption calculation formula, wherein the algal oxygen consumption calculation is as follows:

[0064] ;

[0065] ;

[0066] ;

[0067] In the formula, This represents the oxygen consumption of algae. For time, The water depth is a constant. Water depth is a variable. For water depth The time is Dissolved oxygen concentration at that time The diffusion coefficient of dissolved oxygen is given. This is the efficiency coefficient of photosynthesis. For water depth The time is The rate of photosynthesis at that time This represents the rate coefficient of algal respiration. This represents the inhibition coefficient of algal respiration. This refers to the dissolved oxygen concentration at the water surface. is the base of the natural logarithm. This represents the attenuation coefficient of dissolved oxygen with water depth. is the coefficient of dissolved oxygen variation over time. This represents the initial phase of dissolved oxygen. The rate of photosynthesis on the water surface. This represents the attenuation coefficient of photosynthesis with water depth.

[0068] Preferably, step S266 includes the following steps:

[0069] Step S2661: Perform numerical simulation of water flow on the target water body to obtain water flow simulation result data;

[0070] Specifically, data such as the geographic information, shape, and boundary conditions of the target water body can be collected. A flow simulation model is then built using numerical simulation software (such as fluid dynamics software), and model parameters are set. The collected geographic information and boundary conditions are applied to the flow simulation model. The flow simulation model is then run to simulate the water flow motion within the water body. Based on the simulation results, data such as water velocity, direction, and flow path are obtained.

[0071] Step S2662: Based on the water flow simulation results data, algae priority removal area data, and plastic waste distribution data, evaluate and predict the waste drift trajectory to obtain waste drift prediction data;

[0072] Specifically, for example, water flow simulation data can be used as input to determine the water flow velocity and direction in the water body. Combining this with data on algae-preferred removal areas and plastic waste distribution data, the initial location of the waste can be determined. Based on the water flow velocity and direction, the drift trajectory of the initial waste location can be assessed and predicted to obtain waste drift prediction data.

[0073] Step S2663: Mark key interception locations on the waste drift prediction data to obtain interception location data;

[0074] Specifically, for example, water flow simulation data and debris drift prediction data can be used to determine the drift path of each piece of debris. Based on the time information in the debris drift prediction data, the predicted endpoint location for each piece of debris is determined. Criteria or conditions for critical interception locations are determined based on the interception strategy and the characteristics of the target remediation water body. For example, factors such as the distance between the interception location and the target remediation area, and areas of high debris density can be considered. Based on these criteria or conditions, the critical interception locations along the debris drift path are identified. Information about these identified critical interception locations is then labeled in the debris drift prediction data. Specific markers or data structures can be used to represent critical interception locations in the debris drift prediction data. The labeled critical interception location data, including location coordinates and time information, is extracted. The interception location data is then formatted for subsequent processing and analysis.

[0075] Step S2664: Calculate the length of the interception net based on the interception location data, the algae priority removal area data, and the plastic waste distribution data, thereby obtaining the interception net length data;

[0076] Specifically, for example, data on interception locations, priority areas for algae removal, and plastic waste distribution can be combined to determine the areas that need to be covered. The length of the interception net is then calculated based on the shape and size of the coverage area. This calculated net length is used as the net length data.

[0077] Step S2665: Calculate the optimal route for the removal vessel based on the interception location data, the algae priority removal area data, the plastic waste distribution data, and the interception net length data to obtain the removal vessel route data; design a water surface waste removal scheme for the target water body based on the removal vessel route data to obtain the water surface waste removal scheme.

[0078] Specifically, for example, the starting and ending points of the removal vessels can be determined based on interception location data. Combining data on priority algae removal areas and plastic waste distribution, routes can be determined to avoid algae-covered areas and cover plastic waste areas. Route planning algorithms (such as A* algorithm or genetic algorithm) are used to calculate the optimal route for the removal vessels, considering factors such as the shortest path, vessel speed, and maneuverability. Based on the calculation results, the route data for the removal vessels is obtained, including waypoint coordinates, route path, and navigation instructions. Based on the route data, the distribution of waste along the route and the length of the interception net are analyzed. A surface waste removal scheme is designed based on the characteristics of the target water body and the waste removal requirements. The route and speed of the removal vessels are considered to determine the order, frequency, and method of waste removal. Visualization tools or graphics software are used to create schematic diagrams or operational guidelines for the surface waste removal scheme.

[0079] Preferably, step S3 includes the following steps:

[0080] Step S31: Obtain the evaluation standard limit data of heavy metals in the surface water environmental quality standards;

[0081] Specifically, for example, applicable surface water environmental quality standards can be determined based on the laws and regulations of the country or region where the target remediation water body is located. Ensure that the selected standards are up-to-date and comply with the regulatory requirements of the area where the target remediation water body is located. Obtain surface water environmental quality standards by referring to the official websites, announcements, or documents of relevant environmental protection departments, water resource management agencies, or research institutions. Ensure that the obtained standards include evaluation standard limit data for heavy metals. For the required heavy metal elements (such as lead, cadmium, mercury, etc.), consult the relevant sections or chapters in the surface water environmental quality standards. Extract the evaluation standard limit data for heavy metals, including information such as the permissible concentration limits for each heavy metal element under different water quality categories.

[0082] Step S32: Based on the physicochemical index detection data and the evaluation standard limit data, conduct a heavy metal pollution assessment of the target remediation water body to obtain heavy metal pollution assessment data;

[0083] Specifically, for example, physicochemical indicator test data can be compared with the heavy metal evaluation standard limits in the surface water environmental quality standards. This determines whether the concentration of each heavy metal element exceeds the corresponding evaluation standard limit. Based on the comparison results, heavy metal elements exceeding the evaluation standard limits are identified as pollutants. Quantitative or qualitative methods can be used to assess the degree of heavy metal pollution, such as calculating the exceedance multiple and classifying pollution levels. The heavy metal pollution assessment results are then compiled, including information on pollutant types, concentration exceedances, and pollution levels. The heavy metal pollution assessment data is saved as a dataset or report for subsequent analysis and decision-making.

[0084] Step S33: Obtain emission source location data of heavy metal pollutants and sampling point location data corresponding to the heavy metal pollution assessment data;

[0085] Specifically, potential sources of heavy metal pollutant emissions can be identified, for example, through environmental monitoring data, industrial pollution source surveys, or relevant literature. Ensure that all heavy metal pollutant emission sources related to the target remediation water body are covered. Conduct on-site investigations or refer to relevant investigation reports to obtain location data of heavy metal pollutant emission sources. Record the geographic coordinates or detailed address information for each heavy metal pollution source. Collect corresponding location data based on the sampling point locations used in previous heavy metal pollution assessment steps. Record the geographic coordinates or detailed address information for each sampling point.

[0086] Step S34: Determine the adsorbent placement points based on the emission source location data and sampling point location data to obtain a placement point dataset, which includes multiple placement point data, all of which are located between the emission source and the sampling point;

[0087] Specifically, requirements for adsorbent placement sites, such as placement distance and density, can be determined based on remediation goals and strategies. Distance constraints between emission sources and sampling points should be considered to ensure placement sites are located between them. Emission source location data and sampling point location data should be overlaid or spatially analyzed in a Geographic Information System (GIS). Candidate placement sites located between emission sources and sampling points should be selected based on distance constraints and adsorbent placement site requirements. On-site investigations of candidate placement sites should be conducted to examine their geographical environment, soil conditions, water flow dynamics, and other factors. Based on the on-site conditions, the final adsorbent placement sites should be further confirmed to ensure they meet requirements and can effectively remediate heavy metal pollution. The location data of the determined adsorbent placement sites should be compiled into a dataset, including the geographic coordinates of each placement site and information on the added substance. The placement site dataset should be saved as a data file or marked in the GIS for future use and management.

[0088] Step S35: Spatial annotation of the water flow simulation results data is performed using the deployment point dataset to obtain annotated water flow simulation results data;

[0089] Specifically, for example, a deployment point dataset can be used, including information such as the geographic coordinates of the deployment points. This dataset is then spatially overlaid or matched with the flow simulation results. Based on the location information of the deployment points, the corresponding flow simulation results are labeled to determine the flow characteristics at each deployment point. The labeled results are then processed, and the flow simulation results corresponding to each deployment point are extracted. The labeled flow simulation results are then saved for subsequent analysis and use.

[0090] Step S36: Calculate the amount of adsorbent to be added to the target remediation water body based on the heavy metal pollution assessment data, thereby obtaining the data on the amount of adsorbent added at the sampling points;

[0091] Specifically, for example, heavy metal pollution assessment data can be used, including information such as pollutant types and concentration exceedances. Based on the characteristics of the adsorbent material and the remediation goals, a method for calculating the adsorbent dosage is determined. For instance, the dosage can be calculated based on the heavy metal concentration and the adsorption capacity of the adsorbent material. Based on the concentration exceedances in the heavy metal pollution assessment data and the adsorbent dosage calculation method, the required adsorbent dosage for each sampling point is calculated. Considering factors such as adsorbent efficiency and remediation cycle, a reasonable dosage is determined. The adsorbent dosage calculation results are then compiled, and the adsorbent dosage data for each sampling point is extracted. The adsorbent dosage data for each sampling point is saved for subsequent analysis and use.

[0092] Step S37: Based on the labeled water flow simulation results data roots, calculate the amount of adsorbent material to be added to the corresponding addition points of the addition point dataset according to the preset gradient incremental data, and gradually increase the allocation gradient from the sampling point to the pollution source.

[0093] Specifically, for example, labeled water flow simulation results data can be used, including water flow characteristic information at each delivery point location. A delivery point dataset, including the geographic coordinates of the delivery points, can be used. Based on the labeled water flow simulation results data, the delivery amount is calculated and allocated in a progressively increasing manner from the sampling point to the pollution source, according to the location of the delivery points and gradient increment data. The delivery amount is gradually increased according to the preset gradient increment data to ensure that the delivery amount gradually increases during water flow transmission. Preset gradient increment data is set to progressively increase the delivery amount from the sampling point to the pollution source. The delivery amount allocation calculation results are then processed, and the delivery amount data for each delivery point is extracted. The delivery amount data is saved for subsequent analysis and use.

[0094] Step S38: Based on the labeled water flow simulation results data, design the delivery route for the target remediation water body according to the adsorption material delivery amount data, thereby obtaining the material delivery route data.

[0095] Specifically, for example, labeled water flow simulation data can be used, including water flow characteristics at the placement points. Adsorbent dosage data, including the dosage for each placement point, can also be used. Based on the labeled water flow simulation data and adsorbent dosage data, the placement path of the adsorbent in the target remediation water body is determined. Considering factors such as water flow velocity, flow direction, and adsorbent dosage, a reasonable placement path is designed to ensure effective coverage of the target remediation water body. The placement path design results are then compiled, and the adsorbent placement path data is extracted. This data is then saved for subsequent analysis and use.

[0096] See Figure 3 This diagram illustrates the overall spatial planning of adsorbent material placement and surface waste removal in a target water body, taking into account pollution sources, sampling points, water flow conditions, and navigation constraints.

[0097] As shown in the figure, a pollution source outlet is located in the target remediation water body, serving as the main source of heavy metal pollutants, and a pollution diffusion impact zone is formed around it. Along the main water flow direction, multiple adsorption material placement points are sequentially set between the pollution source and each sampling point. Each placement point is located within the line connecting the pollution source and the corresponding sampling point, and is used to intercept and adsorb heavy metal pollutants migrating with the water flow in a graded manner.

[0098] In this embodiment, the adsorbent material placement points are connected by dashed lines to form a material placement route. This route is designed based on water flow simulation data and follows a gradient principle of gradually increasing placement amount from the sampling point towards the pollution source. That is, the placement amount of adsorbent material at placement points closer to the pollution source is greater than that at placement points closer to the sampling point, in order to improve the overall pollution reduction efficiency. The size of the placement points in the figure is used to illustrate the difference in adsorbent material placement amount at different placement points.

[0099] Preferably, step S38 includes the following steps:

[0100] Step S381: Obtain cargo route data passing through the target repaired water body;

[0101] Specifically, one can contact relevant shipping companies, airlines, or logistics companies to obtain cargo route data for the target waterway restoration area. If waterway management departments or relevant agencies provide such data, one can request it from them. The obtained cargo route data should be organized and cleaned to ensure its accuracy and completeness. The cargo route data should include the origin and destination locations of the route, route information, etc.

[0102] Step S382: Use the specified color to spatially annotate the water flow simulation result data with the specified cargo route based on the cargo route data, thereby obtaining the water flow simulation result data containing the route.

[0103] Specifically, for example, based on cargo route data, the portion of the waterway passing through the target restoration water body in the annotated water flow simulation results data can be spatially annotated. A specified color or other visualization method can be selected to combine the waterway and water flow simulation results data for display. The annotated water flow simulation results data and the spatially annotated cargo route results are merged to generate water flow simulation results data containing the waterway. The water flow simulation results data containing the waterway should include the annotated waterway information and water flow characteristic information.

[0104] Step S383: Connect the drop points marked in the simulation results of the water flow containing the route in pairs to obtain the initial material drop route data;

[0105] Specifically, for example, the drop points marked in the simulation data of the water flow along the route can be connected in pairs sequentially to form an initial material drop route. The order of connection can be determined based on factors such as the location of the drop points, the direction of the water flow, and the route path. The order of the drop points and the connection path after connection are recorded to generate the initial material drop route data. The initial material drop route data should include the order of the drop points, information on the connection paths, etc.

[0106] Step S384: Based on the simulation results of water flow including the shipping route, perform route intersection detection on the initial material delivery route data and the freight route data. When there is no route intersection between the initial material delivery route data and the freight route data, the initial material delivery route data is used as the material delivery route data.

[0107] Specifically, for example, based on the simulated water flow results including shipping routes, initial material delivery route data and freight route data can be mapped to corresponding water flow simulation areas. Intersection detection is then performed on the initial material delivery route data and freight route data. Geometric calculation methods, such as line segment intersection judgment algorithms, can be used to determine whether the two routes intersect. If the initial material delivery route data and freight route data do not intersect, the initial material delivery route data is used as the final material delivery route data. If the initial material delivery route data and freight route data do intersect, further decision-making is required. Either the initial material delivery route data or the freight route data can be adjusted to avoid intersection. Alternatively, corresponding decision rules can be formulated according to specific needs, such as prioritizing the retention of freight route data or adjusting based on the degree of route intersection. Based on the final material delivery route data, subsequent planning and operations are carried out, such as the installation and adjustment of material delivery equipment and the determination of delivery timing.

[0108] Step S385: When the initial material delivery route data and the cargo route data intersect, perform collision risk prediction on the material delivery vessel and the cargo vessel based on the initial material delivery route data and the cargo route data to obtain collision risk prediction result data; adjust and optimize the initial material delivery route data based on the collision risk prediction result data to obtain material delivery route data.

[0109] Specifically, for example, assuming that the initial material delivery route data intersects with the cargo route data, collision risk prediction is performed on parameters such as speed, direction, position, and weight of the material delivery vessel and the cargo vessel based on both data, thereby obtaining collision risk prediction results. These results can include information such as collision probability, collision time, collision location, and collision consequences. Based on these results, the initial material delivery route data is adjusted and optimized to obtain the final material delivery route data. Various adjustment and optimization methods can be used, such as changing the speed, direction, starting point, and ending point of the material delivery vessel, or changing the speed, direction, starting point, and ending point of the cargo vessel, or both, to reduce collision risk.

[0110] Preferably, step S4 includes the following steps:

[0111] Step S41: Based on the physicochemical index detection data and biological monitoring data, harmful bacteria detection data are obtained for the target remediation water body;

[0112] Specifically, for example, biological monitoring can be conducted based on physicochemical indicators, including pH, dissolved oxygen, and turbidity. This involves collecting water samples and testing for harmful bacteria, such as E. coli and Salmonella. The harmful bacteria test data is then analyzed to determine the types and concentrations of harmful bacteria in the water.

[0113] Step S42: Search for inhibitory microorganisms of harmful bacteria in the harmful bacteria detection data through a preset microbial database to obtain initial inhibitory microbial species data;

[0114] Specifically, for example, a pre-prepared microbial database can be established, containing information on various microbial species and their inhibitory abilities. For each harmful bacterial species in the harmful bacterial detection data, the corresponding inhibitory microbial species are searched in the microbial database. The found inhibitory microbial species are recorded to form the initial inhibitory microbial species data.

[0115] Step S43: Based on the physicochemical index detection data, conduct an adaptability assessment of the initial inhibitory microbial strain data. When there are strains in the initial inhibitory microbial strain data that are not easy to grow in the environmental conditions of the target remediation water body, remove the corresponding strains from the inhibitory microbial strain data to obtain the inhibitory microbial strain data.

[0116] Specifically, for example, environmental conditions of the target water body, such as temperature and oxygen content, can be analyzed based on the physicochemical index data. For each species in the initial inhibitory microbial strain data, its adaptability to the water environment conditions is examined. If some species are found to be unsuitable for the target water body's environmental conditions, these species are removed from the initial inhibitory microbial strain data, resulting in an adaptively assessed inhibitory microbial strain data set.

[0117] Step S44: Based on the data of inhibitory microbial species, design intelligent fixed-point controlled-release microcapsule loads for the target remediation water body to obtain a microcapsule load design scheme.

[0118] Specifically, for example, data on inhibitory microbial strains can be used to determine the types of microorganisms that need to be introduced into the target remediation water body. Based on the characteristics and needs of the target remediation water body, intelligent, targeted controlled-release microcapsules are designed to protect and maintain the activity and inhibitory properties of the microorganisms. The microcapsule payload is designed, which involves culturing and encapsulating the inhibitory microbial strains into the microcapsules. Considering factors such as the material, size, and release rate of the microcapsules, a microcapsule payload design scheme is formulated to achieve continuous and appropriate microbial release.

[0119] Preferably, step S44 includes the following steps:

[0120] Step S441: Determine the types of microcapsule loads based on the data of inhibitory microbial strains, thereby obtaining candidate load strain data;

[0121] Specifically, for example, inhibitory microbial species can be identified based on data on inhibitory microbial strains. Candidate payload strains suitable for encapsulation in microcapsules are then selected from these species. Based on microcapsule design requirements and the characteristics of the target remediation water, the adaptability and stability of each candidate payload strain are evaluated, ultimately determining the microcapsule payload species—that is, selecting the suitable microbial strains for encapsulation in microcapsules.

[0122] Step S442: Obtain water flow velocity data and microbial contamination distribution data for the target remediation water body;

[0123] Specifically, for example, water flow velocity measurement equipment can be deployed to measure water flow velocity in the target remediation water body and obtain water flow velocity data. Sampling and analysis of microbial contamination distribution can also be performed to collect data on the distribution of microbial contamination in different areas of the target remediation water body. This can be achieved through laboratory analysis of water samples and sediment samples, or through real-time monitoring using on-site monitoring equipment.

[0124] Step S443: Perform multi-target fixed-point optimization based on the water flow velocity data and microbial contamination distribution data of the target remediation water body to obtain the ideal release node coordinate data;

[0125] Specifically, for example, water flow velocity data and microbial contamination distribution data can be input into a multi-objective fixed-point optimization algorithm. Optimization objectives can be set, such as maximum coverage area or minimum dose concentration. The multi-objective fixed-point optimization algorithm is then run to generate a set of ideal release node coordinates, representing the optimal release location in the target remediation water body.

[0126] Step S444: Perform cell proliferation kinetics equation simulation calculations based on the ideal release node coordinate data to obtain ideal release time-dose curve data;

[0127] Specifically, for example, a cell proliferation kinetics model can be established based on the coordinates of ideal release nodes. This model considers the processes of microbial proliferation, degradation, and diffusion in the target remediation water. Based on the model parameters and initial conditions, simulations are performed for each ideal release node to obtain the corresponding release time-dose curve data. By combining the simulation results of multiple release nodes, comprehensive ideal release time-dose curve data can be obtained, which can guide the specific release time and dosage arrangements of the microcapsules.

[0128] Step S445: Design shell material parameters based on candidate load strain data to obtain shell material property data;

[0129] Specifically, for example, based on candidate microbial strain data, the specific properties and requirements that the microcapsules need to possess can be determined, such as protecting the microbial strain and controlling the release rate. A suitable shell material, such as polymers or nanomaterials, can be selected, and its physical and chemical properties can be determined. Based on the microcapsule design requirements and the characteristics of the selected shell material, shell material parameters, including shell thickness and porosity, can be designed.

[0130] Step S446: Design microcapsule structure parameters based on ideal release time-dose curve data and shell material property data to obtain microcapsule structure parameters;

[0131] Specifically, for example, the structural parameters of the microcapsule, such as microcapsule diameter and core-shell ratio, can be determined by combining ideal release time-dose curve data and the properties of the selected shell material. These microcapsule structural parameters can then be adjusted to achieve ideal release time and dose control while simultaneously meeting the protection and stability requirements of the shell material.

[0132] Step S447: Based on the microcapsule structure parameters, use the water flow simulation results to simulate and predict the migration and release of microcapsules in water, thereby obtaining microcapsule release prediction data;

[0133] Specifically, for example, based on microcapsule structural parameters and water flow velocity data of the target remediation water body, water flow simulation methods (such as CFD simulation) can be used to simulate the transport behavior of microcapsules in water. Considering factors such as the shape, density, and hydrodynamics of the microcapsules, the trajectory and settling velocity of the microcapsules in water can be predicted. Based on the release mechanism and release time-dose curve data of the microcapsules, combined with the water flow simulation results, the release location and release rate of the microcapsules can be predicted, obtaining microcapsule release prediction data.

[0134] Step S448: Develop an overall delivery plan based on the ideal release time-dose curve data and microcapsule release prediction data to obtain the microcapsule load design scheme.

[0135] Specifically, for example, ideal release time-dose curve data and microcapsule release prediction data can be combined to analyze the release behavior and effects of microcapsules at different release points. Considering the pollution level and remediation needs of the target water body, an overall deployment plan should be developed, including the number, distribution, and deployment strategy of microcapsules. The microcapsule load design should be optimized to ensure that the dosage of microcapsules at different release points meets the remediation requirements, while also considering factors such as the stability and controllability of the microcapsules.

[0136] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0137] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A composite aquatic ecosystem restoration method, characterized in that, Includes the following steps: Step S1: Conduct stratified water quality monitoring on the target remediation water body to obtain water quality monitoring data, which includes physicochemical index detection data and biological monitoring data; Step S2: Obtain the overall image data of the water meter of the target water body to be restored; identify floating debris on the water meter of the target water body based on the overall image data of the water meter to obtain water meter debris data; Based on the water meter waste data, a water meter waste removal plan is designed for the target water body to be restored, thereby obtaining the water meter waste removal plan; Step S3: Based on the physicochemical index detection data, conduct a heavy metal pollution assessment of the target remediation water body to obtain heavy metal pollution assessment data; Based on the heavy metal pollution assessment data, the amount of adsorbent to be added to the target remediation water body is calculated and the placement route is designed, thereby obtaining data on the amount of adsorbent to be added and the placement route. Step S4: Based on the physicochemical index detection data and biological monitoring data, harmful bacteria are detected in the target remediation water body to obtain harmful bacteria detection data; the inhibitory microorganisms of harmful bacteria are searched through the pre-set microbial database to obtain initial inhibitory microbial species data; based on the initial inhibitory microbial species data, intelligent fixed-point controlled-release microcapsule loading is designed for the target remediation water body to obtain the microcapsule loading design scheme. Step S5: Based on the water meter waste removal plan, adsorption material dosage data, material delivery route data, and microcapsule loading design plan, a composite water ecological restoration plan is designed to obtain a composite restoration plan.

2. The composite aquatic ecosystem restoration method according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain depth data of the target water body to be restored; Step S12: Divide the target water body into layers based on the depth data to obtain the first layer water intake depth data, the second layer water intake depth data, and the third layer water intake depth data; Step S13: Based on the water depth data of the first layer, the water depth data of the second layer, and the water depth data of the third layer, sampling points are set up for the corresponding layers to obtain the sampling point location data of each layer. Step S14: Collect water samples from the corresponding layers based on the sampling point location data of each layer to obtain water samples from the corresponding layers; Step S15: Perform physicochemical analysis and biological testing on the water samples at the corresponding levels to obtain water quality monitoring data for the first, second, and third layers. Each layer of water quality monitoring data includes physicochemical index testing data and biological monitoring data. Step S16: Perform a weighted average calculation on the physicochemical index detection data and biological monitoring data in the first layer of water quality monitoring data, the second layer of water quality monitoring data and the third layer of water quality monitoring data respectively to obtain water quality monitoring data, which includes physicochemical index detection data and biological monitoring data.

3. The composite aquatic ecosystem restoration method according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Take surface images of the target water body to be repaired using a drone to obtain a multi-view water surface image set; Step S22: Perform orthorectification and non-destructive enhancement on the multi-view water meter image set to obtain an equivalent horizontal projection image set; Step S23: Stitch the overlapping areas of the equivalent horizontal projection image set to obtain the overall image data of the water meter; Step S24: Extract the floating debris area of ​​the target water body based on the overall image data of the water meter to obtain the distribution data of the floating debris on the water meter; Step S25: Based on the overall image data of the water meter, identify plastic waste and dead algae in the distribution data of floating matter on the water meter to obtain water meter waste data; Step S26: Based on the overall image data of the water meter and the data on the waste on the water meter, design a waste removal plan for the target water body to be restored, thereby obtaining the waste removal plan for the water meter.

4. The composite aquatic ecosystem restoration method according to claim 3, characterized in that, Step S26 includes the following steps: Step S261: Based on the water meter waste data, label the waste type areas in the overall water meter image data to obtain labeled water meter image data; Step S262: Extract and calculate the area of ​​dead algae from the labeled water meter image data to obtain statistical data on the area of ​​dead algae; Step S263: Calculate the oxygen consumption of algae through algal respiration based on the statistical data of the dead algal area to obtain algal oxygen consumption data; Step S264: Compare the algal oxygen consumption data with the preset oxygen saturation lower limit threshold. When the algal oxygen consumption data is greater than the preset oxygen saturation lower limit threshold, the area corresponding to the dead algae is taken as the priority removal area, thereby obtaining the algal priority removal area data; when the algal oxygen consumption data is less than the preset oxygen saturation lower limit threshold, the corresponding area is marked as the natural recovery area. Step S265: Perform statistical analysis of plastic waste areas on the labeled water meter image data to obtain plastic waste distribution data; Step S266: Based on the data of priority algae removal areas and plastic waste distribution data, design a water surface waste removal plan for the target water body to be restored, thereby obtaining the water surface waste removal plan.

5. The composite aquatic ecological restoration method according to claim 4, characterized in that, In step S263, the oxygen consumption of algae respiration is calculated using the algal oxygen consumption calculation formula, as shown below: ； ； ； In the formula, This represents the oxygen consumption of algae. For time, The water depth is a constant. Water depth is a variable. For water depth The time is Dissolved oxygen concentration at that time The diffusion coefficient of dissolved oxygen is given. This is the efficiency coefficient of photosynthesis. For water depth The time is The rate of photosynthesis at that time This represents the rate coefficient of algal respiration. This represents the inhibition coefficient of algal respiration. This refers to the dissolved oxygen concentration at the water surface. is the base of the natural logarithm. This represents the attenuation coefficient of dissolved oxygen with water depth. is the coefficient of dissolved oxygen variation over time. This represents the initial phase of dissolved oxygen. The rate of photosynthesis on the water surface. This represents the attenuation coefficient of photosynthesis with water depth.

6. The composite aquatic ecosystem restoration method according to claim 4, characterized in that, Step S266 includes the following steps: Step S2661: Perform numerical simulation of water flow on the target water body to obtain water flow simulation results data; Step S2662: Based on the water flow simulation results data, algae priority removal area data, and plastic waste distribution data, evaluate and predict the waste drift trajectory to obtain waste drift prediction data; Step S2663: Mark key interception locations on the waste drift prediction data to obtain interception location data; Step S2664: Calculate the length of the interception net based on the interception location data, the algae priority removal area data, and the plastic waste distribution data, thereby obtaining the interception net length data; Step S2665: Calculate the optimal route for the removal vessel based on the interception location data, the algae priority removal area data, the plastic waste distribution data, and the interception net length data to obtain the removal vessel route data; design a water surface waste removal scheme for the target water body based on the removal vessel route data to obtain the water surface waste removal scheme.

7. The composite aquatic ecosystem restoration method according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Obtain the evaluation standard limit data of heavy metals in the surface water environmental quality standards; Step S32: Based on the physicochemical index detection data and the evaluation standard limit data, conduct a heavy metal pollution assessment of the target remediation water body to obtain heavy metal pollution assessment data; Step S33: Obtain emission source location data of heavy metal pollutants and sampling point location data corresponding to the heavy metal pollution assessment data; Step S34: Determine the adsorbent placement points based on the emission source location data and sampling point location data to obtain a placement point dataset, which includes multiple placement point data, all of which are located between the emission source and the sampling point; Step S35: Spatial annotation of the water flow simulation results data is performed using the deployment point dataset to obtain annotated water flow simulation results data; Step S36: Calculate the amount of adsorbent to be added to the target remediation water body based on the heavy metal pollution assessment data, thereby obtaining the data on the amount of adsorbent added at the sampling points; Step S37: Based on the labeled water flow simulation results data roots, calculate the amount of adsorbent material to be added to the corresponding addition points of the addition point dataset according to the preset gradient incremental data, and gradually increase the allocation gradient from the sampling point to the pollution source. Step S38: Based on the labeled water flow simulation results data, design the delivery route for the target remediation water body according to the adsorption material delivery amount data, thereby obtaining the material delivery route data.

8. The composite aquatic ecological restoration method according to claim 7, characterized in that, Step S38 includes the following steps: Step S381: Obtain cargo route data passing through the target repaired water body; Step S382: Use the specified color to spatially annotate the water flow simulation result data with the specified cargo route based on the cargo route data, thereby obtaining the water flow simulation result data containing the route. Step S383: Connect the drop points marked in the simulation results of the water flow containing the route in pairs to obtain the initial material drop route data; Step S384: Based on the simulation results of water flow including the shipping route, perform route intersection detection on the initial material delivery route data and the freight route data. When there is no route intersection between the initial material delivery route data and the freight route data, the initial material delivery route data is used as the material delivery route data. Step S385: When the initial material delivery route data and the cargo route data intersect, perform collision risk prediction on the material delivery vessel and the cargo vessel based on the initial material delivery route data and the cargo route data to obtain collision risk prediction result data; adjust and optimize the initial material delivery route data based on the collision risk prediction result data to obtain material delivery route data.

9. The composite aquatic ecosystem restoration method according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Based on the physicochemical index detection data and biological monitoring data, harmful bacteria detection data are obtained for the target remediation water body; Step S42: Search for inhibitory microorganisms of harmful bacteria in the harmful bacteria detection data through a preset microbial database to obtain initial inhibitory microbial species data; Step S43: Based on the physicochemical index detection data, conduct an adaptability assessment of the initial inhibitory microbial strain data. When there are strains in the initial inhibitory microbial strain data that are not easy to grow in the environmental conditions of the target remediation water body, remove the corresponding strains from the inhibitory microbial strain data to obtain the inhibitory microbial strain data. Step S44: Based on the data of inhibitory microbial species, design intelligent fixed-point controlled-release microcapsule loads for the target remediation water body to obtain a microcapsule load design scheme.

10. The composite aquatic ecological restoration method according to claim 9, characterized in that, Step S44 includes the following steps: Step S441: Determine the types of microcapsule loads based on the data of inhibitory microbial strains, thereby obtaining candidate load strain data; Step S442: Obtain water flow velocity data and microbial contamination distribution data for the target remediation water body; Step S443: Perform multi-target fixed-point optimization based on the water flow velocity data and microbial contamination distribution data of the target remediation water body to obtain the ideal release node coordinate data; Step S444: Perform cell proliferation kinetics equation simulation calculations based on the ideal release node coordinate data to obtain ideal release time-dose curve data; Step S445: Design shell material parameters based on candidate load strain data to obtain shell material property data; Step S446: Design microcapsule structure parameters based on ideal release time-dose curve data and shell material property data to obtain microcapsule structure parameters; Step S447: Based on the microcapsule structure parameters, use the water flow simulation results to simulate and predict the migration and release of microcapsules in water, thereby obtaining microcapsule release prediction data; Step S448: Develop an overall delivery plan based on the ideal release time-dose curve data and microcapsule release prediction data to obtain the microcapsule load design scheme.

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