Large-area water area dynamic monitoring three-stage ecological management method

Abstract

The present application relates to the technical field of water environment ecological management and restoration, in particular to a three-stage ecological management method for large-area water dynamic monitoring, comprising the following steps: laying monitoring buoys to analyze phytoplankton density and construct a water quality anomaly block diagram, screening animal community imbalance positions to generate a fish laying area diagram, identifying the intersection zone of algae and floating objects to construct a cleaning facility laying diagram, combining water depth and plant degradation distribution to generate a submerged plant re-planting area distribution diagram, and comprehensively constructing a three-stage linkage management structure to output an ecological intervention deployment diagram. In the present application, the distribution of algae is accurately positioned based on the water quality anomaly block to guide the differentiated release of fish, the intersection zone of algae and floating objects is identified to ensure accurate interception of pollution sources, water depth is combined to screen stable re-planting zones to improve plant survival rate, a multi-zone linkage management structure is constructed to form a closed-loop ecological cycle, and multi-dimensional parameter collaborative optimization enhances the water endogenous pollution control capability and self-restoration potential.

Description

Technical Field

[0001] This invention relates to the field of water environment ecological governance and restoration technology, and in particular to a three-stage ecological governance method for dynamic monitoring of large-area water bodies. Background Technology

[0002] The field of water environment ecological governance and restoration technology involves the treatment of various types of water pollution and the restoration of ecosystems, mainly covering core aspects such as eutrophication control, algal bloom control, ecosystem structure adjustment, water quality improvement, and restoration of aquatic ecological functions. In addressing the ecological degradation of large-scale water bodies such as lakes, reservoirs, and urban rivers, this technology utilizes various methods including aquatic plant planting, ecological bank protection construction, hydrodynamic regulation, and water quality monitoring to build a healthy aquatic ecosystem cycle, enhance the water body's self-purification capacity, reduce pollutant concentrations, restore the original ecological functions of the water body, and establish a long-term stable governance mechanism. Among these methods, the traditional three-stage ecological governance approach for large-area water bodies refers to ecological governance systems for large-scale water bodies such as lakes, reservoirs, and urban landscape water bodies. It typically employs a combination of physical barriers, artificial intervention, and ecological construction to divide functional zones and coordinate water pollution control and ecological restoration through multiple measures. In traditional governance methods, the outer areas are usually cleaned by setting up debris barriers and deploying cleaning boats to reduce external pollution sources; the middle areas are mostly controlled by releasing fish to control the plankton community structure and adjust the food chain to balance the aquatic ecosystem; and the core areas rely on the planting of submerged plants to absorb nitrogen and phosphorus nutrients in the water, increase dissolved oxygen content, and stabilize the bottom sediment structure.

[0003] Traditional large-area water ecological management relies on fixed physical zoning and static artificial intervention measures. The cleaning operations in the outer areas often lack precise guidance from water quality monitoring data, resulting in blind surface cleaning. In the middle area, biological regulation simply involves releasing fish without quantitative analysis of the coupling relationship between phytoplankton density and transparency, resulting in low algae feeding efficiency. In the core area, phytoremediation ignores the specific differences in water depth and degree of degradation, leading to low survival rates of replanted plants. The fragmented management measures in each functional area hinder the formation of a synergistic ecological purification mechanism, resulting in poor overall ecosystem structural stability and difficulty in maintaining long-term self-purification capacity. Summary of the Invention

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a three-stage ecological governance method for dynamic monitoring of large-area waters, comprising the following steps: S1: Deploy water quality monitoring buoys to obtain water temperature, dissolved oxygen, turbidity, transparency and chlorophyll a concentration in differentiated zones, analyze the coupling relationship between phytoplankton growth density and transparency changes, and construct a distribution map of water quality anomalies. S2: Based on the distribution map of the abnormal water quality blocks, screen the phytoplankton-dominant areas, call the aquatic animal monitoring data of the corresponding areas, analyze key indicators such as community density, species diversity index and trophic level structure, determine whether there is an imbalance in the aquatic animal community structure, and generate a distribution map of filter-feeding fish and carnivorous fish. S3: Based on the layout map of filter-feeding fish and carnivorous fish, identify the spatial intersection zone between algae-dominated water bodies and floating debris accumulation points, calculate the layout density and operational coverage area of ​​cleaning vessels and debris-blocking floating grids, and construct a layout map of water surface cleaning facilities. S4: Call the water surface cleaning facility layout map, combine the water depth layer and the plant degradation distribution, screen the stable replanting zone in the area where submerged plants are missing, and generate a submerged plant replanting area distribution map. S5: Based on the distribution map of the submerged plant replanting area, the distribution map of the filter-feeding fish and carnivorous fish, the distribution map of the water quality abnormality block, and the distribution map of the water surface cleaning facilities, construct a three-level linkage structure of ecological management area, ecological regulation area and ecological restoration area, and output an ecological intervention deployment map.

[0005] As a further aspect of the present invention, the water quality anomaly distribution map includes water temperature difference blocks, dissolved oxygen change blocks, turbidity distribution blocks, transparency difference blocks, and chlorophyll a concentration anomaly blocks; the filter-feeding fish and carnivorous fish deployment area map includes phytoplankton-dominated areas, filter-feeding fish-dense areas, carnivorous fish-dense areas, and aquatic animal structural imbalance areas; the water surface cleaning facility deployment map includes algae-dense areas, floating debris accumulation points, cleaning vessel deployment density and coverage areas, and debris-blocking floating grid deployment density and coverage areas; the submerged plant replanting area distribution map includes stable replanting zones, suitable water depth zones, and plant degradation blank areas; and the ecological intervention deployment map includes ecological management zones, ecological regulation zones, and ecological restoration zones.

[0006] As a further aspect of the present invention, the stable replanting zone refers to an area in the submerged plant absence zone that has a suitable water depth and is free from strong disturbance.

[0007] As a further aspect of the present invention, the spatial intersection zone refers to the spatial overlap between the high-density algae distribution area and the concentrated accumulation point of floating matter, and is determined to be a related neighborhood region in spatial analysis.

[0008] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Acquire water quality monitoring buoy data deployed at monitoring points in the target water area, extract parameters such as water temperature, dissolved oxygen, turbidity, transparency and chlorophyll a concentration, synchronize multi-source data according to timestamps, construct a parameter matrix of differentiated monitoring points, and generate a multi-parameter water quality feature matrix. S102: Based on the multi-parameter water quality feature matrix, extract transparency and chlorophyll a concentration, construct coupling judgment rules based on the negative correlation between the two, screen the set of monitoring points that meet the coupling characteristics, and generate a set of phytoplankton and transparency coupled distribution areas. S103: Call the set of phytoplankton and transparency coupled distribution areas, judge the outlier values ​​of water temperature, dissolved oxygen and turbidity at the corresponding monitoring points, mark the outlier points and aggregate them according to the geographical adjacency relationship to generate a water quality anomaly block distribution map.

[0009] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Obtain the spatial boundary and monitoring point information of the corresponding ecological regulation area in the distribution map of the water quality anomaly block, extract the transparency and chlorophyll a concentration parameters, screen the grid area dominated by algae according to the difference in distribution intensity, and generate a set of phytoplankton-dominated blocks. S202: Based on the phytoplankton-dominant block set, obtain aquatic animal monitoring data in the corresponding area, extract key indicators such as community density, species diversity index and trophic level structure, and mark structurally abnormal areas when the biomass ratio of carnivorous fish to filter-feeding fish is less than a preset balance coefficient, thereby generating a set of aquatic animal structural imbalance blocks. S203: Call the set of phytoplankton-dominant blocks and the set of aquatic animal structural imbalance blocks, and based on the spatial intersection and fish nutrient structure regulation rules, that is, when the biomass ratio of carnivorous fish and filter-feeding fish in the water area deviates significantly from the preset balance coefficient, it is determined to be an area that needs intervention, verify the intervention target area of ​​filter-feeding fish and carnivorous fish, and generate a layout area map of filter-feeding fish and carnivorous fish by combining regional connectivity and boundary accessibility.

[0010] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Obtain the spatial boundary of the target water body in the distribution area map of filter-feeding fish and carnivorous fish, combine remote sensing images and UAV monitoring images to identify the location of algae-dominant water bodies and high-frequency aggregation points of floating objects in the area, and extract the spatially continuous distribution zone formed by the intersection based on the grid number and coordinate overlap relationship to generate a set of spatial intersection zone locations. S302: Based on the set of spatial intersections, call the operation data and the water surface obstacle distribution map, calculate the unit deployment efficiency of the water surface cleaning vessel and the debris barrier floating grid in each intersection, combine the maximum daily cleaning volume of a single vessel and the achievable area of ​​the waterway, obtain the minimum coverage unit and coverage radius respectively, calculate the unit density and coverage range of the facilities, and generate a set of facility deployment parameters. S303: Call the set of spatial intersection locations and the set of facility layout parameters, and based on the positional relationship of the intersection in the target area and the calculation results of the equipment layout density, combine spatial connectivity to perform grid overlay, draw the facility configuration topology, and generate a water surface cleaning facility layout map.

[0011] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Call the spatial boundary of the ecological restoration area covered by the water surface cleaning facility layout map, obtain the corresponding water depth layer and plant degradation distribution layer in the area, overlay raster data according to the coordinate consistency rule, extract raster units with water depth less than the preset safe replanting threshold and plant coverage of zero, and generate a set of submerged plant missing blocks. S402: Based on the set of missing submerged plant blocks, analyze the spatial correlation between the blocks and the facility nodes in the water surface cleaning facility layout diagram, screen out areas with high operational interference, and combine shoreline buffer distance, water flow stability and substrate type environmental factors to screen out sub-regions that meet the preset stable replanting conditions and generate a set of stable replanting zones. S403: Call the set of stable replanting zones, aggregate the spatial topology in the ecological restoration area, construct a grid index relationship based on coordinate number and block attributes, mark the range of plots with replanting feasibility, and generate a distribution map of submerged plant replanting areas.

[0012] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Call the distribution map of the submerged plant replanting area, the layout map of the filter-feeding fish and carnivorous fish, the distribution map of the water quality abnormal area and the layout map of the water surface cleaning facility, extract the spatial boundaries and functional labels in the map, unify the coordinate system and spatially overlay the layers, establish the coverage relationship matrix between the regions, and generate a multi-source functional area association layer. S502: Based on the multi-source functional area association layer, identify the combination of ecological restoration area, ecological regulation area and ecological management area that have spatial continuity and boundary nesting relationship, establish the functional priority sequence of the three types of areas according to the difference of governance objectives, mark the mutual response boundary, and generate a three-level linkage governance structure model. S503: Invoke the three-level linkage governance structure model, construct the intervention task mapping relationship in the spatial layer according to the governance objects and intervention methods corresponding to each type of area, and draw the ecological governance diagram structure by combining the grid number and governance path to generate the ecological intervention deployment map.

[0013] As a further aspect of the present invention, the coverage relationship matrix between regions refers to a two-dimensional matrix structure that represents the spatial overlapping, inclusion, and adjacent relationships of multiple functional area layers.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, the distribution of algal populations is accurately located based on the analysis of abnormal water quality areas, the specific location of the imbalance in the aquatic animal community structure is determined to guide the differentiated release of filter-feeding and carnivorous fish, the spatial intersection zone between algae-dominated water bodies and floating debris accumulation points is identified, and the density of cleaning facilities is scientifically calculated to ensure the precise interception of pollution sources. Stable replanting zones are selected by combining water depth and plant degradation distribution to improve the survival rate of submerged plants. A linkage governance structure of ecological management, regulation and restoration zones is constructed to form a closed-loop ecological cycle. The synergistic optimization of multi-dimensional parameters enhances the ability to control internal pollution and the self-repair potential of the water body. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0019] Please see Figure 1 This invention provides a three-stage ecological governance method for dynamic monitoring of large-area water bodies, comprising the following steps: S1: By deploying water quality monitoring buoys, we can obtain water temperature, dissolved oxygen, turbidity, transparency and chlorophyll a concentration in different zones of a large water area, analyze the coupling distribution area between phytoplankton growth density and transparency changes, and construct a distribution map of water quality anomalies. S2: Based on the distribution map of water quality anomalies, screen the phytoplankton-dominant areas in the ecological regulation zone, determine the distribution range of algal populations and the structural imbalance of aquatic animal communities, and generate a map of the distribution areas of filter-feeding fish and carnivorous fish. S3: Based on the layout map of filter-feeding fish and carnivorous fish, identify the spatial intersection zone between algae-dominant water bodies and floating debris accumulation points within the target area, calculate the layout density and operational coverage area of ​​surface cleaning vessels and debris-blocking floating screens, and construct a layout map of surface cleaning facilities. S4: Call the water surface cleaning facility layout map, combine it with the water depth layer and plant degradation distribution in the ecological restoration area, screen the stable replanting zone in the area where submerged plants are missing, and generate a submerged plant replanting area distribution map. S5: Based on the distribution map of submerged plant replanting areas, the distribution map of filter-feeding fish and carnivorous fish, the distribution map of abnormal water quality areas, and the distribution map of water surface cleaning facilities, construct a three-tiered linkage governance structure between ecological management areas, ecological regulation areas, and ecological restoration areas, and output an ecological intervention deployment map.

[0020] The water quality anomaly distribution map includes blocks with differences in water temperature, dissolved oxygen changes, turbidity distribution, transparency differences, and abnormal chlorophyll a concentration. The filter-feeding and carnivorous fish deployment map includes phytoplankton-dominated areas, densely populated filter-feeding fish areas, densely populated carnivorous fish areas, and areas with imbalanced aquatic animal structure. The surface cleaning facility deployment map includes areas with dense algae, floating debris accumulation points, the deployment density and coverage area of ​​cleaning vessels, and the deployment density and coverage area of ​​floating debris barriers. The submerged plant replanting area distribution map includes stable replanting zones, suitable water depth zones, and areas with plant degradation. The ecological intervention deployment map includes ecological management zones, ecological regulation zones, and ecological restoration zones.

[0021] Please see Figure 2 The specific steps of S1 are as follows: S101: Acquire water quality monitoring buoy data deployed at monitoring points in the target water area, extract parameters such as water temperature, dissolved oxygen, turbidity, transparency and chlorophyll a concentration, synchronize multi-source data according to timestamps, construct a parameter matrix of differentiated monitoring points, and generate a multi-parameter water quality feature matrix. High-frequency data acquisition was initiated using 50 fixed-point water quality monitoring buoys and 3 mobile monitoring unmanned vessels deployed in target water areas (such as large shallow lakes or urban landscape waterways). High-precision multi-parameter water quality analyzers (such as YSIEXO2 or equivalent precision instruments) were used for monitoring, with a sampling frequency of once every 10 minutes and a continuous acquisition duration of 24 hours. Physical and biochemical indicators of the aquatic environment were directly read using a sensor array, specifically including water temperature data (degrees Celsius) obtained using a thermistor sensor, dissolved oxygen data (mg / L) obtained using an optical dissolved oxygen sensor, turbidity data (NTU) obtained using a scattering turbidimeter, transparency data (cm) obtained using a Seymophilic disk depth converter or photoelectric sensor, and chlorophyll a concentration data (µg / L) obtained using a fluorescence sensor. After data transmission to the server, timestamp synchronization was performed: using the master clock of monitoring buoy A as a reference, the upload timestamps of all mobile monitoring data were iterated, and linear interpolation was used to correct data items with time deviations exceeding 30 seconds. Specifically, if the data value at the previous time T1 is V1, the data value at the next time T2 is V2, and the target synchronization time is Tx, then the value at time Tx is determined by calculating the time-weighted average of V1 and V2, thereby eliminating time drift between multiple sources. Subsequently, a three-dimensional data cube is constructed based on the geographical coordinates (longitude and latitude) of the monitoring points and the unified time series. Water temperature, dissolved oxygen, turbidity, transparency, and chlorophyll a concentration under the same spatial coordinates are arranged in columns, and null values ​​(i.e., data with NaN values) or noise points significantly exceeding physical extremes (e.g., data with water temperatures exceeding 40 degrees Celsius or below 0 degrees Celsius) caused by sensor malfunctions are removed. Finally, the cleaned data is integrated to form a multi-parameter water quality characteristic matrix containing spatial, temporal, and five attribute dimensions. For example, the matrix row data for monitoring point P1 at a certain time is: water temperature 22.5 degrees Celsius, dissolved oxygen 6.8 mg / L, turbidity 15 NTU, transparency 45 cm, and chlorophyll a concentration 35.2 μg / L.

[0022] S102: Based on the multi-parameter water quality feature matrix, transparency and chlorophyll a concentration are extracted. Based on the negative correlation between the two, a coupling judgment rule is constructed, a set of monitoring points that meet the coupling characteristics is selected, and a set of phytoplankton and transparency coupling distribution areas is generated. The transparency and chlorophyll a concentration columns from the multi-parameter water quality feature matrix are read, and the Pearson correlation coefficient is calculated to quantify the strength of their negative correlation. The calculation process is as follows: First, the arithmetic mean of the transparency and chlorophyll a data sequences are calculated separately. Next, for each sampling point in the sequence, the difference between the transparency value and the mean transparency value, and the difference between the chlorophyll a value and the mean chlorophyll a value, are calculated, and these two differences are multiplied to obtain the covariance component. Simultaneously, the standard deviations of the transparency and chlorophyll a sequences are calculated separately. Finally, the sum of all covariance components is divided by the total number of samples, and then divided by the product of the standard deviations of the two sets of data to obtain the correlation coefficient R. A strong negative correlation threshold of -0.7 is set. For example, when the calculated correlation coefficient is -0.85, it indicates a significant negative correlation between the two, meaning that an increase in chlorophyll a concentration directly leads to a decrease in transparency. Based on this rule, the system iterates through the time-series data of all monitoring points, selecting those with a correlation coefficient less than -0.7 and an average chlorophyll a concentration exceeding a preset algal bloom threshold (e.g., 20 μg / L). Assuming that only P2, P5, and P8 among monitoring points P1 to P10 meet the above conditions, these three monitoring points are marked as "phytoplankton-controlled" locations. The spatial coordinates of these locations are extracted to form a discrete set of points, and a geofence is constructed using a convex hull algorithm or a buffer expansion method (e.g., a 200-meter radius around the monitoring point) to generate a set of phytoplankton and transparency coupled distribution areas.

[0023] S103: Call the set of phytoplankton and transparency coupled distribution areas, judge the outlier values ​​of water temperature, dissolved oxygen and turbidity at the corresponding monitoring points, mark the outlier points and aggregate them according to the geographical adjacency relationship to generate a water quality anomaly block distribution map. The system calls upon the generated set of phytoplankton and transparency coupled distribution areas to lock all monitoring data records within that area. For each monitoring point within the area, Z-Score standardized anomaly detection is performed on the water temperature, dissolved oxygen, and turbidity parameters. The specific calculation logic is as follows: the real-time measured value of a parameter is subtracted from its historical average value for the current season, and the difference is then divided by the historical standard deviation of that parameter. An anomaly threshold of 2.0 is set. For example, if a point has a dissolved oxygen measurement of 3.0 mg / L, a historical average of 7.0 mg / L, and a standard deviation of 1.5 mg / L, the calculated Z-value is -2.66, whose absolute value is greater than 2.0, thus indicating abnormally low dissolved oxygen. The system iterates through all points within the area. When a monitoring point has at least one parameter deemed abnormal within three consecutive sampling periods, it is marked as a "water quality anomaly point." Subsequently, a density-based spatial clustering algorithm (such as a logical variant of DBSCAN) is introduced, setting the neighborhood search radius to 500m and the minimum number of points contained in the core point to be 3. The system calculates the Euclidean distance between anomaly points and groups anomaly points with a distance of less than 500m into the same cluster. For example, anomaly points A, B, and C are all within 400m of each other and are aggregated into an anomaly block. The system generates a bounding polygon boundary for the aggregated point group and records the main anomaly characteristics of the block (such as "low oxygen-high turbidity") in the attribute table, ultimately generating a water quality anomaly block distribution map.

[0024] Please see Figure 3 The specific steps of S2 are as follows: S201: Obtain the spatial boundary and monitoring point information of the corresponding ecological regulation area in the distribution map of water quality anomalies, extract transparency and chlorophyll a concentration parameters, screen the grid areas dominated by algae based on the differences in distribution intensity, and generate a set of phytoplankton-dominated blocks. Load the distribution map of water quality anomalies and read the vector boundary coordinates of the areas marked as ecological control zones (i.e., areas requiring biological manipulation). Within this spatial range, retrieve historical hyperspectral remote sensing imagery and on-site monitoring data to extract rasterized data of transparency and chlorophyll a concentration, with a raster resolution of 10 meters by 10 meters. Perform gridd difference analysis: for each grid cell, calculate the ratio of its chlorophyll a concentration to the lake-wide average chlorophyll a concentration, and simultaneously calculate the ratio of its transparency to the lake-wide average transparency. Set the algal dominance criterion as follows: when the chlorophyll a concentration ratio is greater than 1.5 and the transparency ratio is less than 0.6, the grid is determined to be an "algal-dominant grid." For example, if the lake-wide average chlorophyll a is 20 μg / L and the lake-wide average transparency is 50 cm, and a certain grid has chlorophyll a of 40 μg / L (ratio of 2.0) and transparency of 25 cm (ratio of 0.5), then this grid meets the screening criteria. The system traverses all grids within the ecological regulation zone, spatially merges adjacent grids that meet the conditions, removes isolated fragmented patches with an area of ​​less than 1000 square meters, retains large areas with high contiguousness, and defines these areas as a set of phytoplankton-dominant blocks.

[0025] S202: Based on the phytoplankton-dominant block set, obtain aquatic animal monitoring data in the corresponding area, extract key indicators such as community density, species diversity index and trophic level structure, mark structurally abnormal areas when the biomass ratio of carnivorous fish to filter-feeding fish is less than the preset balance coefficient, and generate a set of aquatic animal structural imbalance blocks. Within the spatial area covered by the dominant phytoplankton clusters, the most recent aquatic animal survey data were imported. Data sources included acoustic scanning data from high-frequency fish finders and environmental DNA (eDNA) sequencing results. Three key indicators were extracted from the data: community density (unit: fish / thousand cubic meters), species diversity index (calculated using the Shannon-Wiener index logic), and trophic level structure ratio. The Shannon-Wiener index was calculated by multiplying the proportion of each fish species to the total number of individuals by the natural logarithm of that proportion, then summing the products of all species and taking the negative value. Trophic level structure was quantified by calculating the ratio of filter-feeding fish biomass to carnivorous fish biomass. A rule for judging functional group imbalance was established: when the Shannon-Wiener index was less than 1.5, or the ratio of filter-feeding fish to carnivorous fish biomass was less than 0.5 (i.e., carnivorous fish excess, filter-feeding fish deficiency) or greater than 10.0 (i.e., filter-feeding fish excess, carnivorous fish deficiency), it was considered structurally abnormal. For example, if monitoring data in a certain area shows that the biomass of filter-feeding silver carp and bighead carp is 500 kg and the biomass of carnivorous topmouth culter is 20 kg, with a ratio as high as 25.0 and a diversity index of only 1.2, then this area is marked as "structurally imbalanced". The system vectorizes and stores the boundaries of all marked areas, generating a set of aquatic animal structurally imbalanced blocks.

[0026] S203: Call the set of phytoplankton-dominant blocks and the set of aquatic animal structural imbalance blocks. Based on the spatial intersection and fish trophic structure regulation rules, that is, when the biomass ratio of carnivorous fish and filter-feeding fish in the water area deviates significantly from the preset balance coefficient, it is determined as an area that needs intervention. Verify the intervention target areas of filter-feeding fish and carnivorous fish. Combine regional connectivity and boundary accessibility to generate a deployment area map of filter-feeding fish and carnivorous fish. Spatial overlay analysis was performed on phytoplankton-dominant blocks (representing sources of algal excess) and aquatic animal structure imbalance blocks (representing areas with insufficient or unbalanced predation pressure). Classification was based on the "functional demand allocation principle": if an area is both a phytoplankton-dominant block and its aquatic animal structure shows a low proportion of filter-feeding fish (ratio less than 0.5), then the area is designated as a "filter-feeding fish release target area," aiming to release silver carp and bighead carp to feed on algae; if an area only shows an imbalanced aquatic animal structure and a low proportion of carnivorous fish (ratio greater than 10.0), then it is designated as a "carnivorous fish release target area," aiming to control small, unwanted fish. When verifying regional connectivity, a water depth topographic map was used, and shallow areas with a depth less than 0.8 meters were removed to prevent fish stranding, and enclosed ponds with no water exchange with the main lake area were also removed. Finally, by combining boundary accessibility (i.e. the distance that transport ships can reach, such as within 5 kilometers of the dock), the final deployment boundary is determined, and a deployment area map of filter-feeding fish and carnivorous fish is generated.

[0027] Please see Figure 4 The specific steps of S3 are as follows: S301: Obtain the spatial boundaries of target water bodies in the distribution area map of filter-feeding fish and carnivorous fish. Combine remote sensing images and UAV monitoring images to identify the location of algae-dominant water bodies and high-frequency aggregation points of floating objects in the area. Based on the grid number and coordinate overlap relationship, extract the spatially continuous distribution zone formed by the intersection and generate a set of spatial intersection zone locations. The system loads the distribution maps of filter-feeding and carnivorous fish, defining the maximum bounding rectangle of the target water body. Within this range, it acquires UAV orthophotos (spatial resolution better than 0.1 meters) and satellite multispectral imagery from the past 24 hours. A deep convolutional neural network (CNN) model is used to identify the images. The model structure employs a fully convolutional neural network (such as FCN or a modified U-Net), consisting of multiple convolutional layers and upsampling layers. This preserves spatial resolution and performs class-based discrimination for each pixel, ultimately outputting a pixel-level classification probability map of the same size as the original image, achieving semantic segmentation of two target categories: "cyanobacterial blooms" and "floating debris / dead branches." The model segments based on these two features. Simultaneously, it maps the identified high-frequency clusters to a geographic coordinate system based on raster numbers. The overlap between the identified area and the fish distribution area is calculated, and their intersection is extracted. Only spatially continuous distribution zones with an overlapping area greater than 50 square meters and a strip-like shape (length-to-width ratio greater than 3:1) are retained. These areas are the areas that urgently need treatment because they require fish regulation and have obvious surface aggregates. They are defined as the set of spatially intersecting zones.

[0028] S302: Based on the spatial intersection zone location set, call the operation data and water surface obstacle distribution map, calculate the unit deployment efficiency of water surface cleaning vessels and debris barrier floating grids in each intersection zone, combine the maximum daily cleaning volume of a single vessel and the navigable area of ​​the channel, obtain the minimum coverage unit and coverage radius respectively, calculate the unit density and coverage range of the facilities, and generate a set of facility deployment parameters. For each spatial intersection zone, the feasibility of physical removal operations is assessed. The cleaning vessel operation parameter table (as shown in Table 1) and the water surface obstacle distribution map (including vector data of bridge piers, shoals, aquaculture enclosures, etc.) are used. The calculation logic is as follows: First, obtain the area value A (unit: square meters) of the intersection zone. Based on the single-vessel operation efficiency E (unit: square meters / hour) of the cleaning vessel in Table 1, calculate the theoretical operation time T = A / E. Combined with the maximum daily cleaning volume per vessel (e.g., 6 hours of effective daily operation time), calculate the minimum number of coverage units required (i.e., the required number of vessels or operation frequency). For example, if the area of ​​a certain intersection zone is 30,000 square meters and the single-vessel efficiency is 5,000 square meters / hour, then a single vessel needs 6 hours to complete the task. If the completion time is limited to 3 hours, then 2 minimum coverage units need to be deployed. Simultaneously, based on the obstacle distribution map, a safety buffer zone is established by extending 50 meters outward from the obstacle outline. The buffer zone area is subtracted from the intersection zone, and the minimum inscribed circle radius of the remaining area is the effective coverage radius of the facility. Calculate the unit density of the facility: divide the required number of coverage units by the effective operating area. Finally, encapsulate the calculated data such as the number of vessels, the length of the debris barrier (equal to 1.2 times the length of the water-facing boundary of the cross strip), and the coverage radius to generate a set of facility deployment parameters.

[0029] Table 1 Basic Parameters of Surface Operation Facilities

[0030] Table 1 lists the basic performance parameters of the equipment used in the calculation process to determine the deployment density.

[0031] S303: Call the spatial intersection zone location set and facility layout parameter set, and based on the positional relationship of the intersection zone in the target area and the equipment layout density calculation results, combine spatial connectivity to perform grid overlay, draw the facility configuration topology structure, and generate a water surface cleaning facility layout map. Based on the geographical locations determined by the spatial intersection zone location set and the calculated equipment quantity and density from the facility deployment parameter set, a spatial topology is constructed. Each intersection zone is considered a "operation node," and an adjacency matrix is ​​constructed based on the waterway distance between intersection zones. If the waterway distance between two operation nodes is less than 2 kilometers, a "scheduling connection" is established between them. Based on the facility deployment density, a gridded cruise path or fixed-point mooring coordinate is generated within each operation node. For example, if a node requires the deployment of two ships, the node area is divided into two sub-grids by area, and the centroid of each sub-grid is the initial deployment point of the ships. For debris-blocking floating barriers, linear fitting is performed along the upstream boundary of the water flow of the intersection zone based on the calculated length. All nodes, connections, and specific equipment coordinate points are superimposed to draw a network-structured facility configuration topology, generating a surface cleaning facility deployment map.

[0032] Please see Figure 5 The specific steps of S4 are as follows: S401: Call the spatial boundary of the ecological restoration area covered by the water surface cleaning facility layout map, obtain the corresponding water depth layer and plant degradation distribution layer in the area, overlay raster data according to the coordinate consistency rule, extract raster units with water depth less than the preset safe replanting threshold and plant coverage of zero, and generate a set of submerged plant missing blocks. In the layout map of water surface cleaning facilities, the spatial boundary marked as "ecological restoration zone" is extracted. Within this boundary, a high-precision underwater topographic map (water depth layer) and a vegetation degradation distribution layer generated from historical vegetation surveys are overlaid. Raster operations are performed: First, both layers are uniformly resampled into 2m x 2m raster cells. Then, each raster is traversed, and its water depth and vegetation cover values ​​are checked. A safe replanting water depth threshold of 2.5 meters is set (because below this depth, insufficient light makes it difficult for submerged plants to survive). The filtering logic is as follows: if the water depth value of a raster is less than 2.5 meters and the vegetation cover value is equal to 0 (i.e., no vegetation cover at all), then the raster is selected. For example, raster G1 has a water depth of 1.8 meters and a cover of 0, so G1 is selected; raster G2 has a water depth of 3.0 meters and a cover of 0, so G2 is removed. All raster cells that meet the conditions are spatially aggregated to generate a set of submerged plant missing blocks.

[0033] S402: Based on the set of missing submerged plant blocks, analyze the spatial correlation between the nodes of the facilities in the water surface cleaning facility layout diagram, screen out areas with high operational interference, and combine environmental factors such as shoreline buffer distance, water flow stability and substrate type to screen out sub-regions that meet the preset stable replanting conditions and generate a set of stable replanting zones. The dataset of submerged plant-deficient areas was retrieved, and channel and operation point information from the surface cleaning facility layout map was incorporated. Spatial correlation analysis was performed to avoid interference: a "high-interference buffer zone" was established by extending 30 meters to both sides of the cruising path of each cleaning vessel as the center line; a 20-meter buffer zone was established centered on each debris-blocking floating gate. These buffer zones were removed from the submerged plant-deficient areas because these areas are frequently subjected to mechanical disturbance and are unsuitable for plant establishment. Subsequently, environmental factors were introduced for secondary screening: the distance of each block from the shoreline was calculated, and wave erosion areas less than 5 meters away were removed; the substrate type layer was retrieved, retaining areas with substrates marked as "silty clay" or "silty soil" and removing areas marked as "hard rock" or "quicksand"; flow field data was retrieved, and areas with an annual average flow velocity greater than 0.4 m / s were removed. For example, if a sub-area has suitable substrates but a flow velocity of 0.8 m / s year-round, it was removed from the dataset. The selected areas are the replanting sites that meet the conditions for stable growth, thus generating a set of stable replanting zones.

[0034] S403: Call the stable replanting zone set, aggregate the spatial topology in the ecological restoration area, construct a grid index relationship based on coordinate number and block attribute, mark the area of ​​plots with replanting feasibility, and generate a distribution map of submerged plant replanting area. The stable replanting zones are mapped back onto the spatial base map of the ecological restoration area. Quadtree spatial indexing technology is used to encode and aggregate the scattered replanting zones. A grid index is constructed based on the size and shape of the replanting zones: for large plots larger than 10,000 square meters, internal planting cells (e.g., 20 meters by 20 meters) are divided and assigned unique block IDs (e.g., Block_A_01); for smaller, scattered plots, they are merged into the nearest large plot index according to proximity. The suitable planting species (e.g., Vallisneria natans, Hydrilla verticillata, determined by water depth and substrate) and recommended planting density (e.g., 30 plants per square meter) for each plot are marked in the attribute table. Finally, the plot ranges with spatial coordinates, block numbers, and planting attributes are visualized and rendered to generate a distribution map of submerged plant replanting areas.

[0035] Please see Figure 6 The specific steps of S5 are as follows: S501: Call the distribution map of submerged plant replanting area, the layout map of filter-feeding fish and carnivorous fish, the distribution map of water quality abnormality block and the layout map of water surface cleaning facilities, extract the spatial boundaries and functional labels in the map, unify the coordinate system and spatially overlay the layers, establish the coverage relationship matrix between regions, and generate multi-source functional area association layers. Summarize all thematic layers generated in the preceding steps: distribution map of submerged plant replanting areas, distribution map of filter-feeding and carnivorous fish areas, distribution map of water quality anomalies, and distribution map of water surface cleaning facilities. Unify the geographic coordinate system of all layers to the WGS84UTM projected coordinate system to ensure accurate spatial alignment. Perform polygon overlay analysis: overlay all layers vertically to cut out the smallest common geometric unit. Establish an overlay relationship matrix, where rows represent the smallest geometric unit and columns represent the attribute values ​​of the four functional layers (e.g., whether replanting, whether fish release, whether anomaly, whether cleaning). If a unit exists in the corresponding layer, the corresponding position in the matrix is ​​marked as 1; otherwise, it is marked as 0. For example, the vector of unit U1 is [1,1,0,0], indicating that the area is both a replanting area and a fish release area. Merge spatial units containing at least one functional attribute and retain all attribute labels to generate a multi-source functional area association layer.

[0036] S502: Based on the multi-source functional area association layer, identify the combination of ecological restoration area, ecological regulation area and ecological management area that have spatial continuity and boundary nesting relationship, establish the functional priority sequence of the three types of areas according to the difference of governance objectives, mark the mutual response boundary, and generate a three-level linkage governance structure model. This study analyzes the spatial topological relationships in the multi-source functional area association layer, identifying the spatial adjacency and nesting relationships among the "ecological restoration zone" (plant replanting), "ecological regulation zone" (fish release), and "ecological management zone" (mechanical cleaning). Based on the urgency of the governance objectives and the laws of ecological succession, a functional priority sequence for the three types of zones is established, as follows: the ecological restoration zone has the highest priority (as the ecological base, stability must be prioritized to avoid disturbance), followed by the ecological regulation zone (as a biological control measure), and the ecological management zone has the lowest priority (as an emergency auxiliary measure). Based on this sequence, a linkage rule is established: when the ecological restoration zone and the ecological management zone overlap spatially or the boundary distance is less than 10 meters, an "avoidance boundary" is automatically generated, requiring management facilities to operate outside the restoration zone. When the ecological regulation zone overlaps with the ecological restoration zone, a "symbiotic boundary" is generated, indicating that this area requires the release of carnivorous fish that do not graze on grass, rather than herbivorous fish. By identifying these mutually responsive boundary lines, a three-tiered linkage governance structure model is generated, comprising a core area, a buffer zone, and an outer zone.

[0037] S503: Invoke the three-level linkage governance structure model, construct the intervention task mapping relationship in the spatial layer according to the governance objects and intervention methods corresponding to each type of area, combine the grid number and governance path to draw the ecological governance diagram structure and generate the ecological intervention deployment map; A three-tiered linkage governance structure model is loaded to generate specific intervention task instructions for each type of governance area. For ecological restoration areas, a "planting-maintenance" task chain is generated (e.g., planting Vallisneria natans in October at a density of 30 plants / square meter); for ecological regulation areas, a "release-monitoring" task chain is generated (e.g., releasing silver carp in November at a density of 50 fish / acre); for ecological management areas, a "cruise-retrieval" task chain is generated (e.g., patrolling twice daily, focusing on cleaning the downwind area). These tasks are bound to specific grid numbers (Grid_ID), and the shortest path algorithm in graph theory is used to plan the governance paths between areas (e.g., the cleaning vessel departs from the dock, avoiding the restoration area and arriving at each management area in sequence). Finally, various areas are drawn on the map with different colors and fill patterns, and streamlines with arrows are overlaid to represent the governance paths and material flow directions, generating the final visualized ecological intervention deployment map. This map serves as a direct guide for on-site engineering implementation, clarifying the complete plan of "where to do it, what to do, and how to do it."

[0038] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the technical solution.

Claims

1. A three-stage ecological governance method for dynamic monitoring of large-area water bodies, characterized in that, Includes the following steps: S1: Deploy water quality monitoring buoys to obtain water temperature, dissolved oxygen, turbidity, transparency and chlorophyll a concentration in differentiated zones, analyze the coupling relationship between phytoplankton growth density and transparency changes, and construct a distribution map of water quality anomalies. S2: Based on the distribution map of the abnormal water quality blocks, screen the phytoplankton-dominant areas, call the aquatic animal monitoring data of the corresponding areas, analyze key indicators such as community density, species diversity index and trophic level structure, determine whether there is an imbalance in the aquatic animal community structure, and generate a distribution map of filter-feeding fish and carnivorous fish. S3: Based on the layout map of filter-feeding fish and carnivorous fish, identify the spatial intersection zone between algae-dominated water bodies and floating debris accumulation points, calculate the layout density and operational coverage area of ​​cleaning vessels and debris-blocking floating grids, and construct a layout map of water surface cleaning facilities. S4: Call the water surface cleaning facility layout map, combine the water depth layer and the plant degradation distribution, screen the stable replanting zone in the area where submerged plants are missing, and generate a submerged plant replanting area distribution map. S5: Based on the distribution map of the submerged plant replanting area, the distribution map of the filter-feeding fish and carnivorous fish, the distribution map of the water quality abnormality block, and the distribution map of the water surface cleaning facilities, construct a three-level linkage structure of ecological management area, ecological regulation area and ecological restoration area, and output an ecological intervention deployment map.

2. The three-stage ecological governance method for dynamic monitoring of large-area waters according to claim 1, characterized in that, The water quality anomaly distribution map includes blocks of water temperature differences, dissolved oxygen changes, turbidity distribution, transparency differences, and chlorophyll a concentration anomalies. The filter-feeding and carnivorous fish deployment map includes phytoplankton-dominated areas, densely populated filter-feeding fish areas, densely populated carnivorous fish areas, and areas of aquatic animal structural imbalance. The water surface cleaning facility deployment map includes algae-dense areas, floating debris accumulation points, the deployment density and coverage area of ​​cleaning vessels, and the deployment density and coverage area of ​​debris-blocking floating barriers. The submerged plant replanting area distribution map includes stable replanting zones, suitable water depth zones, and plant degradation blank areas. The ecological intervention deployment map includes ecological management zones, ecological regulation zones, and ecological restoration zones.

3. The three-stage ecological governance method for dynamic monitoring of large-area waters according to claim 1, characterized in that, The stable replanting zone refers to an area within the submerged plant absence zone that has a suitable water depth and is free from strong disturbance.

4. The three-stage ecological governance method for dynamic monitoring of large-area waters according to claim 1, characterized in that, The spatial intersection zone refers to the spatial overlap between high-density algal distribution areas and concentrated floating debris accumulation points, and is identified as a related neighboring region in spatial analysis.

5. The three-stage ecological governance method for dynamic monitoring of large-area waters according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Acquire water quality monitoring buoy data deployed at monitoring points in the target water area, extract parameters such as water temperature, dissolved oxygen, turbidity, transparency and chlorophyll a concentration, synchronize multi-source data according to timestamps, construct a parameter matrix of differentiated monitoring points, and generate a multi-parameter water quality feature matrix. S102: Based on the multi-parameter water quality feature matrix, extract transparency and chlorophyll a concentration, construct coupling judgment rules based on the negative correlation between the two, screen the set of monitoring points that meet the coupling characteristics, and generate a set of phytoplankton and transparency coupled distribution areas. S103: Call the set of phytoplankton and transparency coupled distribution areas, judge the outlier values ​​of water temperature, dissolved oxygen and turbidity at the corresponding monitoring points, mark the outlier points and aggregate them according to the geographical adjacency relationship to generate a water quality anomaly block distribution map.

6. The three-stage ecological governance method for dynamic monitoring of large-area waters according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Obtain the spatial boundary and monitoring point information of the corresponding ecological regulation area in the distribution map of the water quality anomaly block, extract the transparency and chlorophyll a concentration parameters, screen the grid area dominated by algae according to the difference in distribution intensity, and generate a set of phytoplankton-dominated blocks. S202: Based on the phytoplankton-dominant block set, obtain aquatic animal monitoring data in the corresponding area, extract key indicators such as community density, species diversity index and trophic level structure, and mark structurally abnormal areas when the biomass ratio of carnivorous fish to filter-feeding fish is less than a preset balance coefficient, thereby generating a set of aquatic animal structural imbalance blocks. S203: Call the set of phytoplankton-dominant blocks and the set of aquatic animal structural imbalance blocks, and based on the spatial intersection and fish nutrient structure regulation rules, that is, when the biomass ratio of carnivorous fish and filter-feeding fish in the water area deviates significantly from the preset balance coefficient, it is determined to be an area that needs intervention, verify the intervention target area of ​​filter-feeding fish and carnivorous fish, and generate a layout area map of filter-feeding fish and carnivorous fish by combining regional connectivity and boundary accessibility.

7. The three-stage ecological governance method for dynamic monitoring of large-area waters according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Obtain the spatial boundary of the target water body in the distribution area map of filter-feeding fish and carnivorous fish, combine remote sensing images and UAV monitoring images to identify the location of algae-dominant water bodies and high-frequency aggregation points of floating objects in the area, and extract the spatially continuous distribution zone formed by the intersection based on the grid number and coordinate overlap relationship to generate a set of spatial intersection zone locations. S302: Based on the set of spatial intersections, call the operation data and the water surface obstacle distribution map, calculate the unit deployment efficiency of the water surface cleaning vessel and the debris barrier floating grid in each intersection, combine the maximum daily cleaning volume of a single vessel and the achievable area of ​​the waterway, obtain the minimum coverage unit and coverage radius respectively, calculate the unit density and coverage range of the facilities, and generate a set of facility deployment parameters. S303: Call the set of spatial intersection locations and the set of facility layout parameters, and based on the positional relationship of the intersection in the target area and the calculation results of the equipment layout density, combine spatial connectivity to perform grid overlay, draw the facility configuration topology, and generate a water surface cleaning facility layout map.

8. The three-stage ecological governance method for dynamic monitoring of large-area waters according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Call the spatial boundary of the ecological restoration area covered by the water surface cleaning facility layout map, obtain the corresponding water depth layer and plant degradation distribution layer in the area, overlay raster data according to the coordinate consistency rule, extract raster units with water depth less than the preset safe replanting threshold and plant coverage of zero, and generate a set of submerged plant missing blocks. S402: Based on the set of missing submerged plant blocks, analyze the spatial correlation between the blocks and the facility nodes in the water surface cleaning facility layout diagram, screen out areas with high operational interference, and combine shoreline buffer distance, water flow stability and substrate type environmental factors to screen out sub-regions that meet the preset stable replanting conditions and generate a set of stable replanting zones. S403: Call the set of stable replanting zones, aggregate the spatial topology in the ecological restoration area, construct a grid index relationship based on coordinate number and block attributes, mark the range of plots with replanting feasibility, and generate a distribution map of submerged plant replanting areas.

9. The three-stage ecological governance method for dynamic monitoring of large-area water bodies according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Call the distribution map of the submerged plant replanting area, the layout map of the filter-feeding fish and carnivorous fish, the distribution map of the water quality abnormal area and the layout map of the water surface cleaning facility, extract the spatial boundaries and functional labels in the map, unify the coordinate system and spatially overlay the layers, establish the coverage relationship matrix between the regions, and generate a multi-source functional area association layer. S502: Based on the multi-source functional area association layer, identify the combination of ecological restoration area, ecological regulation area and ecological management area that have spatial continuity and boundary nesting relationship, establish the functional priority sequence of the three types of areas according to the difference of governance objectives, mark the mutual response boundary, and generate a three-level linkage governance structure model. S503: Invoke the three-level linkage governance structure model, construct the intervention task mapping relationship in the spatial layer according to the governance objects and intervention methods corresponding to each type of area, and draw the ecological governance diagram structure by combining the grid number and governance path to generate the ecological intervention deployment map.

10. The three-stage ecological governance method for dynamic monitoring of large-area waters according to claim 9, characterized in that, The coverage relationship matrix between regions refers to a two-dimensional matrix structure that represents the spatial overlapping, inclusion, and adjacent relationships of multiple functional area layers.

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