Reservoir ecological scheduling method and system considering water quality dynamic response

Abstract

The invention relates to the technical field of water environment management, in particular to a reservoir ecological scheduling method and system considering water quality dynamic response. The method comprises the following steps: acquiring reservoir water quality monitoring data; performing water eutrophication analysis according to the reservoir water quality monitoring data to obtain water eutrophication data; pollution source tracking is carried out based on the water eutrophication data to obtain pollution source data; the pollutant emission intensity is evaluated based on the pollution source data; a high-pollution area is calibrated according to the pollutant emission intensity; carrying out algae density detection according to the high-pollution area to obtain algae density data; performing water algal toxin analysis according to the algae density data to obtain water algal toxin data; and obtaining reservoir hydrological data, and carrying out maximum rainfall feature extraction to obtain maximum rainfall data. The response rate and the pollution risk identification rate of reservoir water quality dynamic scheduling are improved based on the water environment management technology.

Description

Technical Field

[0001] The present invention relates to the technical field of water environment management, and in particular to a reservoir ecological scheduling method and system considering the dynamic response of water quality. Background Art

[0002] Traditional reservoir ecological scheduling lacks the ability to identify fine-grained pollution sources and cannot accurately identify point source and non-point source pollution types based on dynamic changes in time and space; it ignores the chain ecological response process caused by eutrophication of water bodies and fails to fully establish the dynamic relationship between changes in nutrient concentration, algae growth, toxin release and dissolved oxygen changes; it lacks the coordinated modeling of hydrodynamic parameters and hydrological processes, making it difficult to accurately simulate the diffusion path and dilution process of pollutants in different sections and under different bottom roughness conditions; the ecological scheduling mechanism is rigid, and generally adopts fixed thresholds or preset time triggering methods, which cannot achieve intelligent response based on real-time water quality evolution trends; the internal modules of the system are loose, and the monitoring, analysis, simulation and control links have not formed an efficient linkage, which cannot support the rapid identification and linkage scheduling control of reservoir water quality abnormalities, resulting in low overall water environment regulation efficiency, delayed response, and insufficient ecological restoration capabilities. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a reservoir ecological scheduling method and system that takes into account the dynamic response of water quality to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a reservoir ecological operation method considering the dynamic response of water quality is provided, comprising the following steps:

[0005] Step S1: Acquire reservoir water quality monitoring data; perform water eutrophication analysis based on the reservoir water quality monitoring data to obtain water eutrophication data; and track pollution sources based on the water eutrophication data to obtain pollution source data;

[0006] Step S2: Evaluate pollutant emission intensity based on pollution source data; calibrate high-pollution areas based on pollutant emission intensity; perform algae density detection in high-pollution areas to obtain algae density data; perform water algae toxin analysis based on the algae density data to obtain water algae toxin data;

[0007] Step S3: Acquire reservoir hydrological data and extract maximum rainfall characteristics to obtain maximum rainfall data; perform flow conversion based on the maximum rainfall data to obtain flow data; perform toxin diffusion simulation based on the flow data and water body algae toxin data to obtain algae toxin diffusion data;

[0008] Step S4: Identify high-concentration toxin retention areas based on algal toxin diffusion data; perform dissolved oxygen anomaly detection based on high-concentration toxin retention areas to obtain dissolved oxygen anomaly data; perform reservoir ecological water release scheduling based on the dissolved oxygen anomaly data to obtain reservoir ecological water release data.

[0009] By acquiring reservoir water quality monitoring data and conducting eutrophication analysis, the present invention can timely grasp the situation of excessive nutrients in water bodies, and provide a data basis for subsequent tracing of the causes of pollution. The introduction of pollution source tracking enhances the ability to identify pollution input paths, helps to distinguish point source and non-point source pollution, and improves the accuracy of pollution type determination. Emission intensity assessment and high-pollution area identification based on pollution source data further refine the granularity of pollution spatial distribution and achieve quantitative assessment of local water body risks. Combined with algae density detection and toxin analysis in highly polluted areas, a comprehensive correlation chain between changes in nutrient concentration, abnormal algae proliferation and toxin release can be constructed, thereby improving the dynamic characterization of the ecological evolution process of water bodies. Hydrological data acquisition and maximum rainfall feature extraction enhance the dynamic perception of external hydraulic inputs, making the quantitative conversion between rainfall-runoff-inflow more field-responsive, and ensuring that the initial boundary conditions of toxin diffusion simulation are accurate and reasonable. The toxin diffusion simulation couples hydrodynamic parameters such as water velocity, water depth, and bottom roughness with the rainfall inflow process to construct a fine-scale, multi-dimensional diffusion path analysis mechanism, thereby improving the accuracy of predictions of toxin retention locations and dilution capacity. Based on the abnormal dissolved oxygen change information identified in high-concentration toxin retention areas, real-time perception of the impact of abnormal algae toxin release on the ecosystem can be achieved, enhancing the intelligent early warning capability of ecological risks. Finally, through the ecological scheduling mechanism triggered by dissolved oxygen anomalies, the traditional static threshold-oriented water release control method is broken, and a dynamic scheduling decision-making model dominated by water quality is realized, which improves the efficiency of reservoir water quality linkage control and the timeliness of ecological scheduling response, and overall enhances the comprehensive regulation capability of the water environment under complex pollution evolution processes.

[0010] Preferably, this specification also provides a reservoir ecological scheduling system considering the dynamic response of water quality, which is used to execute the reservoir ecological scheduling method considering the dynamic response of water quality as described above. The reservoir ecological scheduling system considering the dynamic response of water quality includes:

[0011] The pollution source tracking module is used to obtain reservoir water quality monitoring data; conduct water eutrophication analysis based on the reservoir water quality monitoring data to obtain water eutrophication data; and track pollution sources based on water eutrophication data to obtain pollution source data;

[0012] The water algae toxin analysis module is used to evaluate the pollutant emission intensity based on pollution source data; calibrate high-pollution areas according to pollutant emission intensity; conduct algae density detection in high-pollution areas to obtain algae density data; and perform water algae toxin analysis based on the algae density data to obtain water algae toxin data;

[0013] The toxin diffusion simulation module is used to obtain reservoir hydrological data and extract the maximum rainfall characteristics to obtain the maximum rainfall data; convert the flow rate based on the maximum rainfall data to obtain the flow rate data; and simulate the toxin diffusion based on the flow rate data and the water body algae toxin data to obtain the algae toxin diffusion data;

[0014] The reservoir ecological water release scheduling module is used to identify high-concentration toxin retention areas based on algal toxin diffusion data; perform dissolved oxygen anomaly detection based on high-concentration toxin retention areas to obtain dissolved oxygen anomaly data; and perform reservoir ecological water release scheduling based on dissolved oxygen anomaly data to obtain reservoir ecological water release data.

[0015] The reservoir ecological scheduling system of the present invention taking into account the dynamic response of water quality can implement any one of the reservoir ecological scheduling methods of the present invention taking into account the dynamic response of water quality. It is used to combine the operations and signal transmission media between various modules to complete the reservoir ecological scheduling method taking into account the dynamic response of water quality. The internal modules of the system cooperate with each other to improve the response rate of the dynamic scheduling of reservoir water quality and the pollution risk identification rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0017] Figure 1 This is a schematic flow chart of the steps of a reservoir ecological scheduling method considering the dynamic response of water quality according to the present invention;

[0018] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0019] Figure 3 Detailed flowchart of step S16 in the present invention;

[0020] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0021] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work are within the scope of protection of the present invention.

[0022] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0023] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0024] To achieve this, please refer to Figures 1 to 3 The present invention provides a reservoir ecological scheduling method considering the dynamic response of water quality, the method comprising the following steps:

[0025] Step S1: Acquire reservoir water quality monitoring data; perform water eutrophication analysis based on the reservoir water quality monitoring data to obtain water eutrophication data; and track pollution sources based on the water eutrophication data to obtain pollution source data;

[0026] In this embodiment, a fixed online multi-parameter water quality monitoring buoy platform was used to set up at least nine monitoring points upstream, midstream, and downstream of the reservoir. Monitoring parameters included total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH3-N), dissolved oxygen (DO), pH, electrical conductivity (EC), and water temperature (T). The sampling frequency was set to once per hour for a period of at least 30 days. Ammonia nitrogen was detected using ultraviolet-visible spectroscopy, total phosphorus concentration was detected using ammonium molybdate spectrophotometry, and total nitrogen content was detected using ultraviolet spectrophotometry. The monitoring data were input into a water eutrophication assessment process based on the national surface water Class V water quality limit. Nutrient index thresholds were set as follows: TP>0.2mg / L and TN>1.0mg / L, indicating eutrophication. Within the eutrophic area, the location of potential pollution sources was determined using a linear backpropagation method by comparing the direction of the temporal and spatial concentration gradients of pollutants and combining the spatial location data of sewage outlets, agricultural irrigation outlets, and livestock and poultry breeding areas marked in the geographic information system (GIS). On-site sampling and verification of pollution points are carried out to analyze the matching between nitrogen and phosphorus morphological characteristics and source data to confirm the location of the pollution source and its emission properties.

[0027] Step S2: Evaluate pollutant emission intensity based on pollution source data; calibrate high-pollution areas based on pollutant emission intensity; perform algae density detection in high-pollution areas to obtain algae density data; perform water algae toxin analysis based on the algae density data to obtain water algae toxin data;

[0028] In this embodiment, the identified pollution sources are classified into point sources (such as domestic sewage outlets, industrial outlets) and non-point sources (such as agricultural non-point sources, exposed slopes, etc.). The emission intensity calculation method is set separately: the point source is calculated based on the actual flow rate of pollutants (m 3 The emission flux is calculated by multiplying the annual average fertilization intensity per unit area (kg / ha / yr) by the pollutant concentration (mg / L). The total annual emission from non-point sources is estimated by multiplying the annual average fertilization intensity per unit area (kg / ha / yr) by the runoff coefficient (set according to terrain slope, soil type and rainfall data). Areas with an annual emission flux greater than 500kgTP or 5000kgTN are calibrated as high-pollution areas. A 3×3 grid sampling point layout is set in the area, and 1L of water sample is collected. After 12 hours of natural sedimentation, the upper water layer is extracted, and the algae sample is concentrated with a filter membrane and an inverted microscope is used to analyze the algae. Cell counting was performed using the algae morphology identification manual, and the algae species were confirmed according to the algae morphology identification manual. 1000 cells / mL was used as the criterion for judging algae-rich areas. Toxin data was further collected in areas where the algae concentration exceeded 3000 cells / mL, and the concentration of microcystin (MC-LR) was determined using enzyme-linked immunosorbent assay (ELISA). If the toxin concentration exceeded 1 μg / L, a temperature-pH combined analysis was performed on the toxin formation process (temperature measurement point every 0.5°C, pH value measurement point every 0.2 unit gradient), and the toxin release and degradation kinetic curves were set through acid-base adjustment experiments. After the degradation curve stabilized, the adsorption coefficient was calculated based on the initial concentration and residual concentration, and this coefficient was used to simulate the migration of toxins under different hydrodynamic conditions, ultimately forming the water body algae toxin data.

[0029] Step S3: Acquire reservoir hydrological data and extract maximum rainfall characteristics to obtain maximum rainfall data; perform flow conversion based on the maximum rainfall data to obtain flow data; perform toxin diffusion simulation based on the flow data and water body algae toxin data to obtain algae toxin diffusion data;

[0030] In this embodiment, radar rainfall echo fusion ground rain gauge data is used to obtain 24-hour minute-by-minute rainfall data in the reservoir basin. The maximum continuous 1-hour, 3-hour and 24-hour cumulative values ​​are selected, and the maximum value is taken as the maximum rainfall in mm / h. The threshold value of >50 mm / h is set as heavy rainfall. Combined with the reservoir catchment area A (km 2 ) and the surface runoff coefficient C (dimensionless, set according to soil type and vegetation coverage, such as 0.25-0.45 for sandy loam), and the runoff calculation formula Q = C·A·P (Q is the runoff volume m 3 , P is the rainfall m) converted to rainfall runoff. Combined with the reservoir basin area S (km 2 ), convert the inflow per unit time into flow (m 3 This flow rate was coupled with the initial distribution grid of algal toxins in the water body. Toxin diffusion was simulated using the two-dimensional shallow water convection-diffusion equation. The boundary conditions were set to fixed boundaries (no toxin input), and the initial conditions were imported based on the toxin distribution matrix. The simulation time was 48 hours, the time step was 300 seconds, and the spatial resolution was no higher than 50m × 50m. The Crank–Nicolson numerical scheme was used to solve the temporal and spatial evolution of toxin concentrations, and the toxin concentration distribution layer was output as the algal toxin diffusion data.

[0031] Step S4: Identify high-concentration toxin retention areas based on algal toxin diffusion data; perform dissolved oxygen anomaly detection based on high-concentration toxin retention areas to obtain dissolved oxygen anomaly data; perform reservoir ecological water release scheduling based on the dissolved oxygen anomaly data to obtain reservoir ecological water release data.

[0032] In this embodiment, the continuous water area with a toxin concentration greater than 1.5μg / L in the output data of the algal toxin diffusion simulation is calibrated as a high-concentration toxin retention area. An underwater DO probe monitoring point is set based on the center point of the area, and an optical dissolved oxygen probe (measuring range 0-20mg / L, accuracy 0.1mg / L) is used to record continuously for 72 hours, with a frequency of once every 5 minutes. The water temperature (℃) and water flow rate (m / s) are measured synchronously, and the DO concentration change rate per unit time is calculated using linear difference. If the DO decreases by more than 2mg / L or drops below 4mg / L within 6 hours, it is determined to be a dissolved oxygen abnormal area. For the identified dissolved oxygen abnormal area, a drainage scheduling model is constructed based on the terrain elevation map and the reservoir drainage structure parameters (such as bottom hole diameter, bottom hole elevation, gate opening range). The scheduling strategy is based on the target area volume, the current toxin concentration and DO concentration and the total water storage capacity of the reservoir area, and adopts the method of giving priority to opening the adjacent discharge structure, controlling the discharge flow rate (0.2-1.5m 3 Strategies such as ( / s) were implemented to maintain a drainage period of no less than 4 hours and no more than 24 hours. During this period, water DO concentrations were re-measured every hour, and drainage volumes were adjusted appropriately based on concentration increases. Finally, data on discharge periods, flow rates, gate opening numbers, and regional water quality changes during the scheduling period were recorded and compiled into reservoir ecological release data.

[0033] Preferably, step S1 includes the following steps:

[0034] Step S11: obtaining reservoir water quality monitoring data, and calculating nutrient concentration to obtain nutrient concentration data;

[0035] In this embodiment, no less than 10 online automatic water quality monitoring stations are set up in the upstream, midstream and downstream of the reservoir. Each station uses a multi-channel flow injection analyzer to measure the total nitrogen (TN), total phosphorus (TP), nitrate nitrogen (NO3 - -N), nitrite nitrogen (NO2 - -N), ammonium nitrogen (NH4 + -N) and soluble reactive phosphorus (SRP) are monitored simultaneously. Total nitrogen is determined by alkaline potassium persulfate digestion ultraviolet spectrophotometry, and the absorbance is read at a wavelength of 220nm; total phosphorus is detected by ammonium molybdate ascorbic acid reduction method, and the absorbance measurement wavelength is set to 700nm. The units of nitrogen and phosphorus concentrations are both mg / L, the sampling frequency is set to once every 6 hours, and the continuous collection period is set to 15 days. The daily data collected at each station are averaged as the representative nutrient concentration of the station; the final output data table uses the reservoir area grid number as the primary key, and each parameter is listed independently. Abnormal values ​​(such as total nitrogen>15mg / L, TP>2.5mg / L) are eliminated and confirmed by manual re-inspection of water samples to ensure data reliability.

[0036] Step S12: performing algae growth simulation according to the nutrient concentration data to obtain algae growth data; calculating algae biomass based on the algae growth data to obtain algae biomass data;

[0037] In this example, the total nitrogen, total phosphorus, nitrate nitrogen and soluble phosphorus concentrations were used as input factors to construct a table of algae unit nutrient salt proliferation rate. It was assumed that the algae growth response was significant when the total phosphorus content was within the range of 0.02-0.2 mg / L; the calculation was based on the fact that 0.1 mg / LTP per liter of water could increase the daily growth rate of algae by about 5%. The nutrient salt data were compared with the local measured daily average light duration (measured by a photosynthetic active radiation PAR sensor, in mol / m 2 / d), water temperature (measured by CTD sensor, unit is ℃), and set the light intensity to be >1200μmol / m 2 / s, and the water temperature range of 15-28℃ is the suitable growth range. On this basis, the algebraic iteration method is used to gradually calculate the cell proliferation every day. The calculation cycle is 10 consecutive days, and the daily algae cell growth number (cells / mL) in each area is generated. The algae biomass calculation is based on the unit volume cell volume accumulation method. The cell volume of dominant algae species (such as Microcystis, Scenedesmus, and Oscillatoria) is set (for example, Microcystis is 110μm 3 / cell), multiplied by the daily cell count to obtain biomass (μg / L). The resulting data is output as three columns: grid number, algal species, and biomass.

[0038] Step S13: calibrating high algae biomass waters based on the algae biomass data;

[0039] In this embodiment, the daily maximum value is screened in the algae biomass data, and a high algae biomass judgment threshold is set. When the biomass exceeds 1000 μg / L, it is calibrated as a high algae biomass area. The areas that meet the conditions are numbered and classified, and spatial cluster analysis is performed, and adjacent areas with a center distance of less than 100 meters are regarded as the same high algae block. Use the GIS platform to render the layer, output a vector map of high algae waters (shapefile format), and mark its boundary coordinates on the elevation map to provide an accurate coordinate basis for subsequent water body sampling and positioning. Generate independent identification numbers, area, center coordinates, biomass maximum value, minimum value and other fields for each block, so that they can be called and overlaid for analysis in subsequent steps.

[0040] Step S14: performing water turbidity detection according to the high algae biomass water area to obtain water turbidity data;

[0041] In this embodiment, artificial sampling positions are arranged in the center and edge points of high algae water areas, 1L of water sample is extracted at each sampling point, and the sample is measured on site using a portable turbidity meter (measuring range 0-1000NTU, resolution 0.1NTU). The measurement environment requires no direct sunlight. The water sample is fully shaken 5 times before measurement and then allowed to stand for 15 seconds. The average value is taken as the final turbidity value (unit NTU) for 3 consecutive measurements. In addition, the diameter distribution of suspended particles is detected in conjunction with the use of a laser particle size analyzer to analyze the turbidity composition. More than 30 sampling points are arranged in the area to form a spatial isovalue distribution map of water turbidity. Areas with turbidity greater than 30NTU are recorded as abnormal turbidity areas. The corresponding high algae water area numbers of each sampling point are sorted out, and a turbidity data table is output, including fields such as sampling number, location coordinates, corresponding algae biomass, turbidity value, and particle size range.

[0042] It is particularly important that step S14 includes the following steps:

[0043] Step S141: identifying the high algae water area boundary according to the high algae biomass water area to obtain high algae water area boundary data;

[0044] In this embodiment, high-frequency water color remote sensing images (such as Sentinel-2MSI images, with a resolution of 10 meters and collected within the past 7 days on sunny days) are called, and the high algae biomass distribution data obtained in advance are derived from the historical statistics of water quality monitoring and the processing results of the near-infrared vegetation index (NDVI), wherein the area with NDVI greater than 0.2 can be preliminarily determined to be a high algae covered water body. A spatial vectorization tool (such as a Python vectorization script based on GDAL) is used to convert the binary mask of the high NDVI area into a vector boundary, and the high algae water area boundary data stored in the shape file format (shp) is obtained. The boundary coordinate system uniformly adopts the WGS84 projection to ensure the spatial consistency of the subsequent operation data. In order to ensure the boundary closure and calculation accuracy, the boundary simplification threshold should be set to Douglas-Peucker compression within 1 meter during the processing process, and a topological check should be performed to eliminate geometric errors such as self-intersection and multi-side collinearity, and the standardized high algae water area boundary data is output.

[0045] Step S142: performing spatial cropping of the remote sensing image according to the high algae water area boundary data to obtain a remote sensing image of the high algae water area;

[0046] In this embodiment, the remote sensing image portion of the high algae water area is extracted by a spatial clipping operation. Using the remote sensing image clipping module, the input is set to the Sentinel-2 original remote sensing image (including B2, B3, B4, and B8 bands) in a geographic information processing platform (such as QGIS or ArcGIS), the output format is GeoTIFF, and the output resolution is set to 10 meters to ensure consistency with the original image. In the clipping operation, a vector boundary file is used for mask clipping, and the boundary buffer distance is set to 0 meters to ensure that the output image completely covers the algae water area and does not include the land area. If there is cloud interference in the image, it is necessary to combine the cloud mask data (QA60 band) for pixel removal processing. The removal condition is that the cloud coverage probability is greater than 20% of the pixels. The cropped image is stored in an uncompressed format to facilitate subsequent reflectivity calculations. Finally, the remote sensing image of the high algae water area is output as the basic data for reflectivity processing.

[0047] Step S143: extracting effective band reflectance values ​​based on the remote sensing image of the high algae water area;

[0048] In this example, cropped remote sensing images of high algae-rich waters are used to calculate effective band reflectance values. The required bands include visible blue light (B2, center wavelength 490nm), green light (B3, center wavelength 560nm), red light (B4, center wavelength 665nm), and near-infrared light (B8, center wavelength 842nm). All band reflectance calculations are based on Top-of-Atmosphere (TOA) correction. First, the calibration coefficient of the Sentinel-2 Level-1C image is called to convert the digital value (DN) into apparent reflectance. The reflectance R is calculated as R = DN × 0.0001. After reflectance correction, Python NumPy arrays are used to store the values ​​separately by band, and pixel values ​​with reflectance less than 0 or greater than 1 are removed as invalid values. The effective band reflectance extraction results are output in CSV format, recording the geographic location (latitude and longitude) of each pixel and its B2, B3, B4, and B8 reflectance values ​​for subsequent spectral ratio calculations.

[0049] Step S144: Calculate the water body spectrum ratio according to the effective band reflectance value; estimate the suspended particulate matter concentration according to the water body spectrum ratio to obtain the water body suspended matter concentration;

[0050] In this embodiment, the spectral ratio of the water body is calculated, and the ratio indicators used are the red-green ratio (RBR) and the red-blue ratio (RBBR), RBR = B4 / B3, RBBR = B4 / B2, and the reflectance data obtained in step S143 are used to perform pixel-by-pixel calculations. If the denominator value is less than 0.01 during the calculation, the corresponding ratio result is set to an invalid value to avoid abnormal amplification. After the calculation is completed, the result is saved in a raster data format (RBR and RBBR are recorded for each pixel). Subsequently, the particulate matter concentration is estimated based on the existing empirical relationship for the concentration of suspended matter in remotely sensed water bodies, using the relationship CSPM = 43.5×RBR^1.12 (unit: mg / L), which is derived from the fitting analysis of the measured sample points (r 2 >0.85, sample size >50). The final output is a raster map of suspended solids concentration in water bodies, with a spatial resolution of 10 meters and a range of 0 to 500 mg / L. Pixels with concentrations greater than 500 mg / L are marked as outliers and removed to prevent subsequent calculation bias.

[0051] Step S145: Perform standard turbidity conversion based on the suspended matter concentration in the water body to obtain water turbidity data.

[0052] In this embodiment, the internationally accepted suspended solids and turbidity conversion function is used, and the conversion formula is: NTU = 0.9 × CSPM ^ 0.62. The CSPM value obtained in the previous step is directly used in the calculation, and the conversion is performed pixel by pixel. During the conversion process, the maximum NTU value must be controlled not to exceed 2000NTU. Pixels exceeding this value are uniformly assigned a value of 2000NTU to maintain data standardization. All calculation results are output in GeoTIFF format with a spatial resolution of 10 meters and the projection coordinate system maintained in WGS84. To facilitate subsequent ecological scheduling analysis, all turbidity data are resampled and aligned with the flow grid. The sampling method uses bilinear interpolation, and finally a standardized water turbidity data grid map is output, and the turbidity average value, maximum value, abnormally high value pixel location and its latitude and longitude coordinates are recorded, and the output is a dual-format result in table and raster.

[0053] Step S15: Calculating the chlorophyll content based on the water turbidity data to obtain chlorophyll concentration data; performing eutrophication assessment based on the chlorophyll concentration data and the nutrient concentration data to obtain water eutrophication data;

[0054] In the present embodiment, on the basis of having obtained turbidity data point, using water sample 2L as standard measurement volume, use GF / C glass fiber filter membrane to filter, filtration pressure is controlled within 0.3atm, prevents cell rupture.Filter membrane is put into 90% acetone, and after 4 ℃ of dark environments extraction 24 hours, uses spectrophotometer to read absorbance under 664nm, 647nm, 630nm three wavelengths, presses Lorenzen formula calculation chlorophyll a concentration (μ g / L).Each measuring point records concentration after and carries out coupled analysis with corresponding nutrient salt data, sets standard by OECD eutrophication evaluation system: chlorophyll a>25μ g / L, TN>1.5mg / L, TP>0.1mg / L and is judged as eutrophic water body.Use three index weighting method (each index weight is respectively 0.4,0.3,0.3) to form comprehensive eutrophication index, index is greater than 0.6 grid record as severe eutrophication area. Finally, the eutrophication level of the water body is output as a grid number, along with the original values ​​of the three parameters and the comprehensive index value, and a eutrophication spatial distribution map is generated.

[0055] Step S16: Tracing pollution sources based on water eutrophication data to obtain pollution source data.

[0056] In this embodiment, based on the spatial distribution map of eutrophication, terrain tracing analysis is performed on a 1:10000 hydrological topographic map upstream of each eutrophic block. A water flow path inversion tool is used to construct a water flow direction map based on DEM data, track the water flow path in the eutrophic area, and mark all possible confluence points on the path. Combined with vector data such as land use maps, river discharge outlet data sets, and farmland irrigation and drainage system layers, the source of the eutrophic area is locked. Water sample collection points are set up on the spot in the potential source area, and the same nutrient analysis process is used for comparative analysis. The correlation coefficient between the source pollution concentration and the concentration in the downstream eutrophic area is calculated. If the correlation is greater than 0.85, it is determined to be the main source of pollution. The pollution source data table is finally output, and the content includes pollution source type, geographic coordinates, pollution load (unit kg / d), corresponding eutrophic area number, and time series concentration change trend chart. All data are synchronously updated to the reservoir ecological scheduling database to provide data support for subsequent responsive scheduling.

[0057] Preferably, step S16 includes the following steps:

[0058] Step S161: determining high nutrient load areas based on water eutrophication data;

[0059] In this embodiment, the spatial distribution of three indicators, chlorophyll a concentration (μg / L), total nitrogen (TN, mg / L), and total phosphorus (TP, mg / L), contained in the water eutrophication data, is used to divide the reservoir area into several grid cells, with the grid side length set to 50 meters. For each grid cell, the values ​​of chlorophyll a exceeding 25μg / L, TN exceeding 1.5mg / L, and TP exceeding 0.1mg / L are used as eutrophication judgment thresholds, and the three indicators are screened grid by grid. Grid cells that meet at least two of the three indicators exceeding the threshold are marked as high nutrient load areas. This process is achieved through GIS spatial overlay analysis, using the "raster calculator" function of ArcGIS 10.8 software to perform logical operations on the three-parameter raster layer to obtain the vector boundaries of the high nutrient load area. In order to avoid interference from isolated points, an area with at least three consecutive grid cells exceeding the threshold and a total area greater than 0.05 square kilometers is set as an effective high nutrient load area. The boundary polygons are exported in Shapefile format to facilitate subsequent data extraction and on-site verification.

[0060] Step S162: Detecting nitrogen and phosphorus content in the high nutrient load area to obtain nitrogen and phosphorus content data;

[0061] In this embodiment, for areas with high nutrient load, water sampling points are arranged in a uniform grid of 100 meters × 100 meters within each area. The amount of water sample collected at each point is 2 liters, and a polyethylene sampling bottle without nitrogen and phosphorus pollution is used. A portable water quality detector is used on site to conduct a preliminary and rapid determination of the nitrogen and phosphorus indicators in the water sample. The specific detection uses a flow injection analyzer (FIA) for quantitative determination, and the ammonium molybdate spectrophotometry method is used to determine the total phosphorus (TP), with a detection range of 0.005mg / L to 10mg / L and a resolution of 0.001mg / L; the total nitrogen (TN) is detected by alkaline potassium persulfate digestion combined with an ultraviolet spectrophotometer, with a detection range of 0.01mg / L to 20mg / L and a resolution of 0.01mg / L. The sampling process ensures that the water samples at each sampling point are delivered to the laboratory immediately and transported in a 4°C refrigerated container, with the transportation time controlled within 4 hours. After sampling, all measurement results were entered into the database, and a continuous nitrogen and phosphorus concentration distribution map in the high nutrient load area was generated using the spatial interpolation method (inverse distance weighted method IDW, with the power parameter set to 2). The data was stored in a grid format with a cell size of 50m×50m.

[0062] Step S163: Performing drainage outlet pollution detection based on the nitrogen and phosphorus content data to obtain drainage outlet pollution data;

[0063] In this embodiment, first, based on the registration information of the drainage outlets in the reservoir basin, the coordinates and basic discharge parameters of all drainage outlets are extracted. Water samples are collected within 50 meters around these drainage outlets, with a sampling volume of 2 liters, using the same sampling bottles and refrigerated transportation specifications. The nitrogen and phosphorus content in the water samples is analyzed using a flow injection analyzer, and the results are used as the baseline data of the pollution load of the drainage outlet. The drainage outlet flow data is collected synchronously, and the flow measurement uses an ultrasonic flowmeter with a measurement accuracy of ±1%. The measurement frequency is set to sample once every 15 minutes, and the continuous monitoring period is not less than 7 days. According to the collected nitrogen and phosphorus concentrations (mg / L) and the corresponding flow (m 3 / s) to calculate the emission load, the calculation formula is: Pollution load (kg / d) = nitrogen and phosphorus concentration (mg / L) × flow rate (m 3 Pollution load data for all outlets is aggregated into a database. The outlet pollution data table includes outlet number, location coordinates, nitrogen concentration, phosphorus concentration, average flow rate, and corresponding pollution load. Time series statistical analysis is performed to eliminate outliers and ensure data robustness.

[0064] Step S164: performing pollution source matching based on the drainage outlet pollution data to obtain pollution source data.

[0065] In the present embodiment, the nitrogen and phosphorus spatial concentration distribution map in the high nutrient load area is combined with the drainage outlet pollution load data to implement pollution source matching analysis. Based on the drainage outlet geographical location coordinates, according to the hydrological flow direction and terrain slope, the digital elevation model (DEM) data is used to calculate the influence range of each drainage outlet on the grid unit in the high nutrient load area. Using spatial buffer analysis, the buffer radius is set to 1000 meters, and the nitrogen and phosphorus concentrations of the high nutrient load grid unit in the buffer zone are subjected to correlation analysis with the pollution load of the corresponding drainage outlet. The specific correlation is calculated using the Pearson correlation coefficient, and the drainage outlet with a calculation result greater than 0.85 is determined to be the main pollution source. For the drainage outlet determined to be the main pollution source, its discharge time series characteristics are further analyzed, and the drainage outlet flow and nitrogen and phosphorus concentration change data are used to draw the pollution load time series curve, and the nitrogen and phosphorus concentration change curve in the high nutrient load area is subjected to time lag analysis to verify the rationality of the pollution transmission path. The final output pollution source data includes pollution source type (domestic sewage, industrial discharge, agricultural non-point source, etc.), geographical location, pollution load, influence range, and time series characteristics, which are used as the data basis for the subsequent management and response measures of reservoir ecological scheduling.

[0066] Preferably, evaluating pollutant emission intensity based on pollution source data in step S2 includes:

[0067] Based on the pollution source data, the pollution types are classified to obtain point source pollution data and non-point source pollution data;

[0068] In the present embodiment, according to the pollution source spatial coordinates, emission characteristics and emission mode information contained in the pollution source data, classification is carried out according to the physical form and emission mode of the pollution source.Point source pollution refers to a single clear discharge outlet, such as factory wastewater discharge outlet, sewage treatment plant outlet and centralized discharge well, and non-point source pollution refers to the non-centralized emission area without obvious discharge point, such as agricultural farmland runoff, living area scattered discharge and urban rainwater runoff, etc.First, pollution source data is imported into geographic information system (GIS) software (ArcGIS10.8), spatial buffer is set according to pollution source coordinates, point source pollution adopts radius 50 meters buffer range, non-point source pollution area is obtained by comprehensive land use data and watershed topography division, and continuous agricultural land and residential area with an area of ​​not less than 0.1 square kilometer are as non-point source pollution area. Combined with surface slope information and land cover type data, buffer overlay analysis, spatial cluster analysis (DBSCAN algorithm, radius parameter is set to 100 meters, minimum number of points is 3) are used to classify pollution source points, automatically distinguish point source and non-point source area. Point source pollution data records include pollution source number, geographic coordinates, pollution type and emission intensity, while non-point source pollution data include pollution area boundaries, polygonal area and its corresponding land use type.

[0069] Conduct ammonia nitrogen pollution detection based on point source pollution data to obtain ammonia nitrogen pollution data;

[0070] In this embodiment, for point source pollution locations, on-site water sampling is carried out for 7 consecutive days, with a daily sampling frequency of no less than 3 times to ensure that the data covers different emission periods. Sampling uses a polyethylene sampling bottle with a sampling volume of 2 liters. A portable multi-parameter water quality analyzer (Hach HQ40d) is used on-site to predict the ammonia nitrogen concentration. After the water sample is sent to the laboratory, the ammonia nitrogen concentration is determined by Nessler's reagent spectrophotometry. The instrument model is a UV-Vis spectrophotometer (such as Shimadzu UV-2600), with a measurement wavelength of 420nm, a detection range of 0.01mg / L to 10mg / L, and a resolution of 0.005mg / L. The sample storage and processing temperature is strictly controlled to 4°C, and the measurement error is controlled at ±3%. The measurement results record the ammonia nitrogen concentration data at specific time points to form a time series database. For data outliers, the 3σ principle is used to eliminate them. Finally, the ammonia nitrogen concentrations in multiple time periods are weighted averaged by time to generate point source ammonia nitrogen concentration data in mg / L.

[0071] Phosphorus nutrient concentration data were obtained by testing the phosphorus nutrient concentration based on the non-point source pollution data;

[0072] In the present embodiment, sampling points were arranged in a 100 m × 100 m grid within the non-point source pollution area. Water samples from drainage ditches, low-lying areas in fields, and water body edges were preferentially collected based on land use type. The sampling period covered no less than three sampling cycles in both the rainy and dry seasons. A 3-liter sample was collected at each sampling point. The samples were refrigerated on-site using polyethylene sampling bottles and transported to the laboratory for determination of total phosphorus (TP) and dissolved phosphorus (DIP). Total phosphorus was determined using ammonium molybdate spectrophotometry with a detection wavelength of 880 nm and a detection limit of 0.002 mg / L. Dissolved phosphorus was determined after filtering through a 0.45 μm filter membrane. The data was spatially interpolated (using the inverse distance weighted method with a power parameter of 2) to generate a distribution map of phosphorus nutrient concentration in the non-point source area. The data format was a grid with each grid 50 m × 50 m and the unit mg / L. The rainy season sampling data and the dry season sampling data were statistically analyzed to generate the spatiotemporal distribution characteristics of seasonal phosphorus nutrient concentration.

[0073] Calculate ammonia nitrogen emission flux based on ammonia nitrogen pollution data;

[0074] In this embodiment, the calculation of ammonia nitrogen emission flux is based on the discharge outlet of the point source pollution water body as the unit, combining the ammonia nitrogen concentration (mg / L) and the emission flow rate (m 3 The flow rate is measured by an online ultrasonic flow meter with a measurement frequency of once every 5 minutes and the data storage duration is not less than 7 days. The hourly ammonia nitrogen emission load is calculated using the formula: Emission flux (kg / d) = ammonia nitrogen concentration (mg / L) × flow rate (m 3 Emission flux values ​​for each time period were calculated by multiplying the values ​​by 86400 (seconds / day) / 1000 (mg / kg). Time series data were accumulated to calculate the average daily emission flux. For time inconsistencies between ammonia nitrogen concentration and flow data, linear interpolation was used to align the data. After calculation, the average daily emission flux was compared with the maximum emission flux, defined as the highest value within a seven-day period.

[0075] Calculate total phosphorus flux based on phosphorus nutrient concentration data;

[0076] In this embodiment, the runoff volume is estimated using rainfall data from standard rain gauges in the region and the surface runoff coefficient method. The rainfall data is collected at an hourly frequency, and the runoff coefficient is calculated based on soil type, slope, and vegetation coverage, with a value range of 0.1 to 0.5. The calculation formula is: Total phosphorus flux (kg / d) = Total phosphorus concentration (mg / L) × runoff volume (m 3 The phosphorus concentration for each grid cell within the space is multiplied by the corresponding runoff volume ( / day) / 1000, and the sum is calculated to obtain the total phosphorus flux for the entire nonpoint source area. To ensure accurate calculations, runoff volumes were verified using the rainfall-runoff model SWAT, and model parameters were repeatedly adjusted using historical flow observations. Calculated results include the average daily total phosphorus flux and the maximum daily flux, both in kg / day.

[0077] The pollutant emission intensity was evaluated based on ammonia nitrogen emission flux and total phosphorus flux.

[0078] In the present embodiment, the ammonia nitrogen emission flux (kg / d) and the total phosphorus flux (kg / d) are respectively divided by the environmental capacity threshold of the corresponding pollutants, the ammonia nitrogen environmental capacity threshold is set to 10kg / d, and the total phosphorus environmental capacity threshold is set to 1kg / d to obtain a standardized emission intensity index. The comprehensive emission intensity index is calculated using the weighted average method, with the weight ammonia nitrogen being 0.6 and the total phosphorus being 0.4. The calculation formula is: comprehensive emission intensity = 0.6 × (ammonia nitrogen emission flux / 10) + 0.4 × (total phosphorus flux / 1). The emission intensity level is distinguished according to the size of the comprehensive index, and the specific level division threshold is: low intensity <0.5, medium intensity 0.5-1.0, and high intensity >1.0. The assessment is implemented using Excel or MATLAB calculation tools, and the output results include single pollutant emission fluxes and a comprehensive emission intensity index, providing quantitative data support for subsequent ecological scheduling decisions.

[0079] Preferably, the algae density detection in the high-pollution area in step S2 includes:

[0080] Algae samples were collected from highly polluted areas and concentrated by sedimentation to obtain enriched algae suspension data;

[0081] In the present embodiment, utilize high nutrient load area as sampling location, select the surface layer (0-0.5 meter) water sample of water body in this area to collect. Use transparent polyethylene sampling bottle with sampling bottle volume of 5 liters, avoid disturbing sediment when water sample is collected. Sampling is set to 3 replicates per point to ensure the representativeness of sampling data. After sampling, water sample is transported to laboratory, and storage temperature is maintained at 4 ℃. Centrifugal sedimentation method is adopted in concentration process: collect water sample and put it into 500mL centrifuge bottle, set centrifuge speed to 3000 rev / min, centrifugation time 20 minutes, centrifugation temperature is controlled at 20 ℃, centrifuge model is selected to meet national standard high-speed refrigerated centrifuge (such as Beckman Coulter Allegra X-15R). After centrifugation, discard supernatant, retain about 20mL sedimentation liquid at the bottom, be enriched algae suspension. This suspension concentration multiple is about 25 times, and volume is calculated by the ratio of initial sampling volume to volume after concentration, and detailed record concentration volume and concentration multiple is recorded. To avoid damage to algal cells, no chemical fixatives were used before centrifugation, and subsequent processing was performed quickly after centrifugation.

[0082] Prepare slide samples based on the enriched algae suspension data to obtain algae microscopic sample data;

[0083] In this embodiment, the enriched algae suspension is thoroughly mixed, and 10 μL of the suspension is drawn with a micropipette and evenly coated on the center of a pre-cleaned, dust-free glass slide. After the sample is coated, it is gently covered with a sterile cover slip to avoid bubbles and ensure that the sample thickness is uniform, about 0.1 mm thick. The prepared slide is immediately sent to the microscope sample holder for observation, or placed in a 4°C refrigerator for short-term storage, and the storage time does not exceed 1 hour. Sample preparation must be completed in a dust-free environment, and the operator uses dust-free gloves to reduce the risk of sample contamination. The temperature during sample preparation is controlled at room temperature (20±2°C) and the humidity is controlled at 40%-60% to keep the cell morphology stable.

[0084] Collect algae images based on algae microscopic sample data;

[0085] In this embodiment, a compound microscope with two magnifications of 100 times (objective lens 10× eyepiece 10) and 400 times (objective lens 40× eyepiece 10) is selected, and the model meets the national standard (such as Olympus CX23). The microscope is equipped with a digital camera with a resolution of not less than 2048×1536 pixels. At least 10 fields of view are randomly selected for each slide for image capture to ensure that the fields of view are evenly distributed. The image acquisition uses automatic exposure and white balance functions to avoid image overexposure or color shift. Each image is saved in lossless format TIFF, and the file naming format includes the sample number, acquisition date and field of view number to facilitate subsequent data management. After the image acquisition is completed, a basic quality inspection is performed to ensure that the image is clear, the focus is accurate, the background is uniform, and there is no obvious interference from impurities.

[0086] Identify algae cell morphology according to the algae image to obtain algae cell morphology data;

[0087] In this embodiment, digital image processing technology is used to perform cell morphology recognition on the collected algae images. The image is subjected to grayscale conversion and background removal processing, and the image threshold segmentation method is used (the threshold is set to the first 5% grayscale value range in the image grayscale histogram) to achieve separation of algae cells and background. Subsequently, morphological processing is performed, including expansion and corrosion operations, and the size of the structural element is set to 3×3 pixels to remove image noise. For the segmented algae area, the cell outline is extracted, and its morphological parameters such as area, major axis, minor axis, and roundness (defined as 4π×area / perimeter2) are calculated. The cell morphology data table includes the number of each cell, area (μm 2 ), major axis length (μm), minor axis length (μm), circularity value, and cell position coordinates. During measurement, the image ruler was based on the microscope calibration scale, with 1 pixel corresponding to 0.2 μm. These operations were performed using a Matlab-based image processing toolbox, ensuring fixed parameters and workflow for the processing steps.

[0088] Calculating the number of algae cells according to the algae cell morphology data to obtain algae cell number data;

[0089] In this embodiment, the number of independent cells in each image is counted using the connected domain analysis algorithm based on the cell outline. The statistical results of each image are summarized in a data table, including the image number and the corresponding total number of cells. Since the acquisition field of view area is known (for example, 100×100μm 2 ) and average the cell counts across each field of view to obtain the average algal cell count per field of view. To improve statistical accuracy, at least 100 images were analyzed, and the overall mean and standard deviation were calculated. Cell count data were expressed as unitless counts, and the data storage format included sampling time, sample number, field of view number, cell count, and statistical results.

[0090] The algae concentration data was obtained by converting the unit volume according to the algae cell number data;

[0091] In this example, the algae cell concentration per unit volume was calculated by combining the concentration factor of the algae suspension and the field of view area of ​​the sample slide. The specific conversion formula is: algae concentration (cells / mL) = (average number of cells in the field of view × slide sample area magnification × concentration factor) / (field of view volume). The field of view volume is calculated by the slide sample thickness (0.1mm) and the field of view area (e.g., 100μm×100μm), and converted to 0.001mm 3 The concentration factor is calculated by the ratio of the sample volume (e.g., 5 L) to the concentration volume (20 mL) in step 1. During this conversion process, all length units are converted to metric units. The cell concentration results are stored in the database, annotated with the sampling time, sample number, and calculation parameters.

[0092] Algae density detection is performed based on the algae concentration data to obtain algae density data.

[0093] In this embodiment, algae density detection is based on algae concentration data and is calculated in combination with water flow velocity and water volume data. Water flow velocity is measured using an acoustic Doppler current meter with a measurement accuracy of ±0.01m / s, a sampling frequency of 1 time per minute, and measurement points are evenly distributed in high nutrient load areas. Water volume is calculated using reservoir depth profile and water surface area data. Water depth is measured using a sonar depth sounder with an accuracy of ±0.05m. Algae density is calculated using the formula: algae density (cells / m 3 )=algae concentration (cells / mL)×1000(mL / L)×total volume of water (m 3 ) / monitoring water volume (m 3 The algae density data at the monitoring time and location are fully recorded, and the storage format includes time, spatial coordinates, water depth, water volume and calculation results. The data is used for dynamic response analysis of reservoir ecological regulation.

[0094] Preferably, step S2 of performing algal toxin analysis in water based on algal density data includes:

[0095] Demarcate high-density algae areas based on algae density data;

[0096] In this embodiment, the algae density data (unit: cells / m 3 ), imported into a geographic information system (GIS) platform (such as ArcGIS 10.8), and established a spatial database based on the spatial coordinates of different sampling points in the reservoir and the corresponding algae density data. The inverse distance weighted (IDW) method in the spatial interpolation method was used, with an interpolation radius of 500 meters and a weight index of 2 to generate a continuous algae density spatial distribution map. The high-density algae threshold was set to 1×10^6 cells / m 3 Using this threshold as a criterion, continuous regions within the spatial distribution map with a density greater than or equal to the threshold are automatically extracted as high-density algae areas. Region boundaries are vectorized to an accuracy of 10 meters. The extracted area, boundaries, and spatial location data are stored in a database for subsequent temperature and pH measurement and positioning. All coordinates are in the WGS-84 standard.

[0097] Measuring water temperature based on high-density algae areas to obtain water temperature data;

[0098] In the present embodiment, multiple water temperature measurement stations are set up in the high-density algae area, and the distance between the measurement points is controlled within 100 meters to ensure coverage of the entire high-density area. Water temperature measurement uses a high-precision digital water quality multi-parameter instrument (model such as YSI Pro DSS) with a measurement accuracy of ± 0.05 ° C and a resolution of 0.01 ° C. The water temperature at the measurement point is sampled along the water depth profile. The sampling depth includes the surface layer (0-0.5m), the middle layer (0.5-2m) and the bottom layer (2-5m). The sampling frequency is once an hour, and the data is collected continuously for 72 hours to ensure that the data has dynamic timeliness. The data is uploaded to the data management platform in real time via a wireless transmission module. All temperature data are accompanied by a sampling timestamp and measuring point coordinates, and the data storage format is a CSV file. In order to eliminate outliers, a sliding window method is adopted with a window width of 3 hours to calculate the moving average temperature value as the final temperature data.

[0099] The pH value of the water body is measured based on the high-density algae area to obtain the pH value data of the water body;

[0100] In this embodiment, the pH measurement corresponds to the temperature measurement point. A water quality multi-parameter measuring instrument equipped with a pH electrode (model such as Hach HQ40d) is used. The instrument calibration is strictly in accordance with the instrument manual. The standard buffer solution pH4.00, 7.00 and 10.00 is used to calibrate the electrode. Calibration is completed before each measurement. The pH measurement range is 0-14, the resolution is 0.01, and the accuracy is ±0.02 pH units. Sampling also covers the surface, middle and bottom layers, with a sampling frequency of once per hour for 72 consecutive hours. The pH value and the corresponding time and coordinates are recorded in real time on site, and the data upload storage format is the same as the temperature data. To prevent instrument drift, the standard buffer solution is compared every day after the measurement to confirm that the measurement error does not exceed ±0.02, otherwise the instrument is recalibrated. Abnormal pH value data are eliminated using the 3σ method. The final data are provided in the form of a time series for subsequent toxin degradation rate analysis.

[0101] Toxin degradation analysis is performed based on water temperature data and water pH data to obtain the toxin degradation rate;

[0102] In this embodiment, the toxin degradation rate was determined using a laboratory water sample toxin degradation kinetics test method based on the spatiotemporal data of water body temperature and pH. Representative water samples were selected and the temperature and pH conditions were maintained consistent with the field monitoring data. The toxin degradation rate is commonly expressed using a first-order kinetic model: dC / dt = -kC, where C is the toxin concentration and k is the degradation rate constant. The degradation rate constant k is affected by temperature and pH and is adjusted using the Arrhenius equation: k = k_ref × exp[-Ea / R × (1 / T-1 / T_ref)], where T is the absolute temperature (K), Ea is the activation energy (J / mol, set to 50,000 J / mol), R is the gas constant 8.314 J / (mol·K), and k_ref is the degradation rate constant at the reference temperature (T_ref = 298K). The effect of pH on k was determined experimentally by a specific adjustment factor, and the adjustment factor was calculated by linear interpolation within the pH range of 6.5 to 9.0. The final result of the toxin degradation rate k is expressed in units of 1 / d, generating dynamic degradation rate data with a time resolution of hours. In the experimental steps, the toxin concentration was determined by high performance liquid chromatography-mass spectrometry (HPLC-MS), and the measurement error was less than 5%.

[0103] Toxin adsorption simulation is performed based on the toxin degradation rate to obtain toxin adsorption data;

[0104] In this embodiment, the adsorption process is described using the Langmuir isotherm adsorption model, with the formula Q = (Q_max × K × C) / (1 + K × C), where Q is the amount of toxin adsorbed per unit mass of sediment (mg / kg), Q_max is the maximum adsorption capacity (set to 2.5 mg / g), K is the adsorption equilibrium constant (unit: L / mg, value: 0.3), and C is the toxin concentration in the water (mg / L). The toxin concentration is dynamically adjusted over time based on the degradation rate, and the simulation time step is 1 hour. Sediment adsorption capacity data was experimentally determined using batch adsorption experiments. The dry weight of sediment samples was determined using the drying method (drying at 105°C to constant weight), and the toxin content was determined using HPLC-MS. The adsorption model calculation process was implemented based on MATLAB programming, and the input parameters included the instantaneous toxin concentration, sediment content, and ambient temperature. The toxin adsorption data was output as a time series in mg / kg with a timestamp.

[0105] Perform toxin migration analysis based on toxin adsorption data to obtain toxin migration data;

[0106] In this example, toxin migration analysis is implemented using a hydrodynamic combined with a diffusion model, taking into account water transport and sediment toxin re-release. Hydrodynamic parameters are measured using an ADCP (Acoustic Doppler Current Profiler) deployed in the reservoir, with a measurement time resolution of 10 minutes and a velocity accuracy of ±0.01 m / s. The diffusion coefficient is set based on the on-site temperature and flow state, with a typical value of 1×10^-5m 2 / s. The toxin migration process was numerically solved using a two-dimensional convection-diffusion equation, discretized using the finite difference method, with a time step of 600 seconds and a spatial grid size of 50 m × 50 m. The boundary conditions were the toxin concentration input at the reservoir inlet and an open boundary set at the outlet. The sediment re-release rate was based on first-order kinetics, and the release rate constant was set to 0.1 / d. The model input included the toxin adsorption amount output from step 5, the on-site water flow rate, and the diffusion parameters, and the output was the spatiotemporal distribution concentration of the toxin in the water (mg / L). Data storage is marked with time and space coordinates, and the format supports import from GIS platforms.

[0107] The algal toxin content in water bodies is evaluated based on toxin migration data to obtain algal toxin data in water bodies.

[0108] In this embodiment, the toxin migration concentration data and the algae spatial distribution data are combined to calculate the algae toxin content in each sampling unit by spatial superposition analysis. The algae toxin content calculation formula is: Tox_content = C_toxin × B_mass, where C_toxin is the toxin concentration in the water body of the unit (mg / L), and B_mass is the algae biomass, which is the algae cell density multiplied by the mass of a single cell (the mass of a single cell is set to 1×10^-10mg / cell). The unit volume of the water body is calculated based on the water depth and area, and the unit is L. During the calculation, the total toxin mass is estimated in mg in combination with the unit water volume. The data of each unit are summarized to generate a spatial distribution map of algae toxins for the entire reservoir. The data results are labeled with time and space for support of ecological scheduling decisions.

[0109] Preferably, step S3 includes the following steps:

[0110] Step S31: Acquire reservoir hydrological data and extract maximum rainfall features to obtain maximum rainfall data;

[0111] In this embodiment, rainfall data is collected by setting up multiple automatic weather stations in the reservoir basin, and the density of weather stations is controlled at one point per 100 square kilometers to ensure uniform data spatial coverage. The type of rain gauge used is a weighing automatic rain gauge with an accuracy of ±0.1mm and a resolution of 0.2mm. It automatically records rainfall time series and the sampling frequency is once every 10 minutes. Rainfall data for one consecutive year is collected, historical extreme value events are screened, and the maximum rainfall data is extracted using the time series extreme value analysis method (such as the annual maximum rainfall method). The maximum rainfall is in millimeters (mm), and the time scale is set to 24-hour cumulative rainfall. To avoid the influence of abnormal data, the maximum rainfall value is subjected to a 3σ test to eliminate extreme outliers. The data recording time mark uses the UTC standard, and the data storage format is a structured database format, which supports subsequent automatic call.

[0112] Step S32: Calculating rainfall runoff according to the maximum rainfall data;

[0113] In this embodiment, the calculation of rainfall runoff is based on the empirical formula in hydrology: Q = P × A × C, where Q is the runoff (cubic meters, m 3 ), P is the maximum rainfall (meters, m), A is the basin area (square meters, m 2), C is the runoff coefficient (dimensionless). The runoff coefficient C is determined according to the land use type and soil permeability, and the value range is 0.3 to 0.9. The C value of 0.5 for a typical agricultural basin is specifically selected as the basis for calculation. The maximum rainfall P is obtained by step S31, and the unit is converted to meters (mm / 1000). In the calculation of the runoff Q, the basin area A is obtained by measuring the reservoir basin, and the unit is converted to square meters. By substituting the above parameters into the formula, the runoff caused by rainfall is calculated, and the result is retained to two decimal places, and the unit is cubic meters. The calculation process is implemented through programming to ensure the correctness of each parameter input and unit conversion.

[0114] Step S33: Obtaining reservoir basin area data; performing flow conversion based on rainfall runoff and reservoir basin area data to obtain flow data;

[0115] In this embodiment, the reservoir basin area data is derived from the high-precision digital elevation model (DEM) analysis in the geographic information system (GIS). The basin boundaries are extracted using digital hydrological analysis tools, and the area measurement accuracy reaches ±1%. The basin area unit is square kilometers (km 2 ), converted to square meters (m 2 ) to participate in subsequent calculations. Based on the rainfall runoff Q and basin area A calculated in step S32, further calculate the flow data per unit time (cubic meters per second, m 3 / s), the calculation formula is: flow rate = rainfall runoff / time interval, the time interval is determined to be 24 hours (86400 seconds) based on the rainfall data time scale. 3 ) divided by the time in seconds to obtain the average flow rate, rounded to two decimal places. Flow data is synchronized with reservoir water level observation data and recorded with consistent time stamps. This data is stored in a database to ensure subsequent access and analysis.

[0116] Step S34: obtaining reservoir cross-section data; identifying cross-section bottom bed material according to the reservoir cross-section data to obtain cross-section bottom bed material data; determining cross-section roughness based on the cross-section bottom bed material data;

[0117] In this embodiment, the reservoir cross-section topographic profile is obtained by underwater sonar sounding technology (such as the multi-beam sounding system MBES) with a resolution of 0.5 meters. The cross-section data includes water depth and bottom bed morphology information. The bottom bed sediment samples collected on site are used to detect the sediment particle size distribution using a particle size analyzer. The particle size range is 0.001 to 2 mm, and the particle distribution is detailed. The bottom bed material is divided into categories such as sand, gravel, and silt based on the particle size distribution. The cross-section bottom bed material data corresponds to each cross-section sampling point, and the data storage contains the bottom bed material category code. The cross-section roughness is represented by the Manning roughness (n) coefficient. Different materials correspond to different Manning coefficients. The sandy bottom bed takes 0.025, the gravel takes 0.035, and the silt takes 0.012. According to the cross-section bottom bed material distribution, the overall roughness of the cross-section is calculated by the area weighted method. The specific calculation is to multiply the Manning coefficient of each material by the corresponding area ratio and then sum it up. The roughness data is saved in the form of dimensionless Manning coefficient values.

[0118] Step S35: Calculating the bottom bed undulation based on the reservoir cross-section data; calculating the water flow resistance coefficient based on the bottom bed undulation and the cross-section roughness; performing flow correction on the flow data based on the water flow resistance coefficient to obtain corrected flow data;

[0119] In this embodiment, the cross-section bottom undulation is calculated using multi-beam bathymetry data. The specific calculation formula is: undulation = (water depth at the highest point of the cross-section - water depth at the lowest point of the cross-section) / average water depth of the cross-section. The calculation result is a unitless ratio, usually ranging from 0.01 to 0.2. The flow resistance coefficient C_d is calculated by combining the Manning roughness n and the bottom undulation, using the empirical formula C_d = (g×n 2 ) / R^(1 / 3), where g is the acceleration due to gravity, 9.81 m / s 2 , R is the hydraulic radius (m), and the hydraulic radius is calculated from the cross-sectional area and the wetted perimeter. Then, C_d is corrected for fluctuation, and the correction formula is C_d_corrected = C_d × (1 + 5 × fluctuation), to ensure that the influence of the bottom bed morphology on the water flow resistance is taken into account. The corrected flow Q_corrected is adjusted according to the flow resistance relationship, and the calculation formula is Q_corrected = Q / (1 + C_d_corrected), where Q is the flow data obtained in step S33, and Q_corrected is the corrected flow data. All calculated values ​​are rounded to three decimal places, and the flow unit is cubic meters per second (m 3 The calculation results are saved in the database for subsequent water quality analysis.

[0120] Step S36: performing toxin dilution analysis on the water body algae toxin data according to the corrected flow data to obtain toxin dilution data;

[0121] In this embodiment, the toxin dilution analysis is based on the dilution model C_diluted = C_initial × (Q_initial / Q_corrected), where C_diluted is the toxin concentration after dilution (mg / L), C_initial is the initial toxin concentration (mg / L), and Q_initial is the original flow rate (m 3 / s), Q_corrected is the flow rate corrected in step S35 (m 3 The input parameter C_initial is provided by monitoring algal toxin concentration data, accurate to 0.001 mg / L. Flow data are averaged over a 24-hour time window. Flow units are maintained consistent throughout the calculation, and dilution concentration results are rounded to four decimal places. This calculation is performed in the MATLAB environment, using vector processing to achieve rapid calculations across multiple time periods and spatial points. Toxin dilution data serves as the basis for dynamic changes in water toxins and is directly referenced in subsequent simulations.

[0122] Step S37: Perform toxin diffusion simulation based on the toxin dilution data to obtain algae toxin diffusion data.

[0123] In this example, a two-dimensional diffusion transport numerical model was used for calculations. First, toxin dilution data was input into the system as the initial concentration conditions for the simulation, ensuring that the starting point of the simulation accurately reflected the current distribution of toxins in the water. Water velocity data was obtained from an acoustic Doppler current profiler (ADCP), which provides a flow velocity time resolution of 10 minutes and a spatial resolution of 50 meters, specifically including the horizontal velocity component of the water flow. The diffusion coefficient used in the diffusion process was set to 1 times 10 to the negative fifth power square meters per second. The diffusion coefficient was dynamically adjusted based on on-site water temperature measurements, with an adjustment range limited to ±10% to reflect the effect of temperature on the diffusion rate. The numerical calculations used an explicit finite difference method, dividing the space into a 50-by-50-meter grid, with a time step of 300 seconds to ensure numerical stability and computational accuracy. The model boundaries used no-flux boundary conditions to prevent toxins from diffusing into the model from outside the boundaries. The simulation process ran continuously for 48 hours, and the migration and diffusion of toxins in the water during the complete simulation period were analyzed. The final output of toxin concentration distribution results is stored in the form of raster data, which supports the subsequent visualization using geographic information systems and provides a basis for reservoir ecological scheduling.

[0124] Preferably, step S37 includes the following steps:

[0125] Step S371: uploading the toxin dilution data to the water environment diffusion simulation platform;

[0126] In this embodiment, the toxin dilution data storage format adopts CSV file, and the fields include timestamp (UTC time, format is YYYY-MM-DD HH:MM:SS), monitoring point longitude and latitude (using WGS84 coordinate system), toxin concentration (unit mg / L, accurate to four decimal places). It is uploaded to the water environment diffusion simulation platform server through an encrypted FTP channel using the network transmission protocol (TCP / IP). Before uploading, the data is checked for integrity, and the MD5 hash algorithm is used to verify that the file has not been tampered with to ensure that the uploaded data is consistent with the local data. The upload process is automatically divided into blocks, each block size is 10MB, and breakpoint resumption is supported to ensure upload stability in a network fluctuation environment. After the upload is completed, the server returns a confirmation message to confirm that the data has been completely received and stored in the specified database table. The upload interface adopts REST API call, and the transmission rate is automatically adjusted according to the network bandwidth to ensure real-time performance. The time series of the toxin dilution data is sorted during uploading to ensure time continuity and facilitate subsequent simulation calls.

[0127] Step S372: setting the water flow rate range to 0.05m / s-1.2m / s, the water depth range to 0.5m-10.0m, and the water temperature range to 5°C-35°C;

[0128] In this embodiment, in the parameter setting interface of the water environment diffusion simulation platform, the flow rate parameter is set to an interval input format, with a minimum input value of 0.05m / s and a maximum input value of 1.2m / s. The unit is strictly limited to meters per second (m / s), and the value is rounded to two decimal places. The water depth parameter is in meters (m), with a setting range of 0.5m (shallowest) to 10.0m (deepest). The data source is the statistical interval of measured data from cross-section water depth monitoring equipment (such as sonar depth sounder). The water temperature range is measured in degrees Celsius (℃), with a minimum input value of 5℃ and a maximum value of 35℃. Data collection comes from an automatic water quality monitoring station using a platinum resistance thermometer with a temperature resolution of 0.1℃ and an error of ±0.2℃. The settings of the above three parameters are used to initialize the simulation boundary conditions. The platform generates a parameter field through a parameter interpolation algorithm to ensure that the simulation process covers the dynamic changes of the entire set range. After the parameters are set, they are verified by the preprocessing module to ensure that the input values ​​meet physical and engineering specifications and there are no abnormal exceeding data.

[0129] Step S373: setting the cross-section bottom bed roughness n value to 0.015-0.035 and the cross-section undulation to 0.3m-2.0m;

[0130] In this embodiment, the cross-section bottom bed roughness is measured in units of Manning roughness n, and an interval numerical input method is used, with a minimum value of 0.015 and a maximum value of 0.035, which is derived from the cross-section bottom bed material analysis results, specifically corresponding to the roughness range of sandy to fine gravel bottom beds. The cross-section undulation is measured in meters (m), with a set variation range of 0.3m to 2.0m. It is calculated using multi-beam bathymetric data, and the calculation formula is the difference between the maximum water depth and the minimum water depth of the cross section. The above parameters serve as key parameters for water flow resistance and turbulence characteristics when input into the simulation platform, and directly affect the hydrodynamic conditions in the diffusion model. After the parameters are input, the system performs a numerical stability test to ensure that there is no divergence or numerical instability in the simulation calculation process within the parameter range. The settings of the cross-section bottom bed roughness and undulation are transferred as quantitative parameters in the simulation calculation, and participate in the dynamic calculation of the water flow resistance coefficient and the diffusion coefficient.

[0131] Step S374: Set the simulation time step to 1 min-15 min, the total simulation time to 6 h-72 h, and the diffusion scale to 50 m-500 m in the local area;

[0132] In the present embodiment, the simulation time step is in seconds, and the setting interval is 60 seconds (1 minute) to 900 seconds (15 minutes). The step size is determined according to the dynamic characteristics of the water body and the numerical stability requirement. The smaller the value, the higher the simulation accuracy, but the amount of calculation increases. The total simulation duration is in hours, and the setting range is 6 hours to 72 hours, which meets the short-term and medium-term diffusion process simulation requirements. The diffusion scale is defined as the simulation space range, which is set to a local area of ​​50 meters to 500 meters. The spatial unit is divided in the form of a two-dimensional grid. The grid resolution is controlled between 10 meters and 50 meters, and the number of grids is automatically calculated according to the diffusion scale. After the above parameters are input through the simulation platform interface, the numerical solver preconfiguration module is called, and the numerical stability condition (such as CFL condition) is set according to the time step and the spatial grid, and the maximum time step allowed is automatically calculated. The total number of steps is calculated according to the total duration and the time step during the simulation initialization to ensure that the simulation time is accurate. The diffusion scale and the spatial grid jointly determine the model calculation scale and the output data accuracy.

[0133] Step S375: Run the water convection-diffusion numerical calculation module to obtain algal toxin diffusion data.

[0134] In this embodiment, when the numerical calculation module is started, the uploaded toxin dilution data is first loaded as the initial toxin concentration field input. The boundary conditions and initial conditions of the convection-diffusion equation are constructed by combining the flow velocity, water depth, water temperature, roughness, undulation, time step, simulation duration, and diffusion scale parameters set in steps S372 to S374. The calculation uses the finite difference method to discretize the two-dimensional convection-diffusion partial differential equation. Time marching uses explicit or implicit time integration, and spatial discretization uses central difference or upwind difference schemes. The water velocity vector field is generated by linear interpolation of parameters within the input range. The effect of water temperature on the diffusion coefficient is adjusted using the empirical formula D_T = D_20 × θ^(T-20), where D_20 is the diffusion coefficient at 20°C, θ is 1.03, and T is the current water temperature. Water depth and cross-section bottom parameters participate in the hydrodynamic calculations, affecting the calculation of water velocity and turbulent diffusion coefficient. During the calculation process, an adaptive time step is used to monitor residuals to ensure numerical convergence. The calculated results are the spatiotemporal distribution of toxin concentrations within each grid cell in a two-dimensional space. The output format is a time-series raster data format with units of mg / L and values ​​retained to four decimal places. After the simulation is complete, the results are stored in a database to support subsequent analysis and scheduling applications.

[0135] Preferably, step S4 includes the following steps:

[0136] Step S41: identifying high-concentration toxin retention areas based on algal toxin diffusion data;

[0137] In the present embodiment, for the input algae toxin diffusion data, the toxin concentration data of the two-dimensional spatial distribution are first imported into the GIS spatial analysis system, and the data format is the concentration value of the time series grid point, the unit is mg / L, and the time resolution is consistent with the simulation time step. Set the concentration threshold to 0.05mg / L, screen the toxin concentration of the grid unit, and identify the area where the concentration exceeds the threshold in the continuous time period. Utilize the spatial clustering algorithm (connected region identification based on the neighboring relationship) to spatially merge the grid points that exceed the standard to form the polygonal boundaries of several high-concentration toxin retention areas. For each area, its area (unit square meter), maximum concentration value and average concentration value are counted and stored as regional characteristic parameters. This process realizes data reading and spatial calculation through libraries such as Python's GDAL and Shapely to ensure that the spatial accuracy reaches 10 meters of grid resolution.

[0138] Step S42: Calculating the water flow rate based on the high-concentration toxin retention area to obtain water flow rate data;

[0139] In the present embodiment, according to the high concentration toxin retention area, the data interface of the reservoir flow rate measuring device is called, the measured flow rate data in the retention area are obtained, the flow rate unit is meter per second (m / s), and the measuring point adopts ADCP (acoustic Doppler current profiler), lays out at least 3 cross-region sections, measures the vertical and horizontal distribution of water flow velocity. The measuring point flow rate data are carried out to spatial interpolation processing, and the continuous flow velocity field in the retention area is generated using Kriging interpolation method. In the data processing process, setting the interpolation radius is 50 meters, ensures data space continuity, and time synchronization precision is controlled within 5 minutes. The flow rate data are through verification, and outliers are eliminated (the data point of flow velocity exceeds 2m / s or is judged to be abnormal below 0.01m / s), and 3 times of standard deviation methods are adopted to carry out outlier elimination. The mean value, maximum value and the distribution matrix of the flow rate in the final output retention area, as water body flow rate data, provide basis for subsequent exchange capacity calculation.

[0140] Step S43: Evaluate the water exchange capacity based on the water flow rate data to obtain water exchange capacity data; identify the hydrodynamic stagnation area based on the water exchange capacity data;

[0141] In this embodiment, the water exchange capacity uses the water exchange rate as an evaluation indicator, and the calculation formula is E = Q / V, where E is the water exchange rate (unit d-1), Q is the flow rate in the retention area (m 3 / d), V is the volume of water in the retention area (m 3 ). The flow rate Q is calculated by multiplying the velocity data by the cross-sectional area of ​​the retention area. The area is expressed in square meters, and the integral is calculated using the numerical integration method. The water volume V is obtained by multiplying the area of ​​the retention area by the average water depth (obtained by a depth meter, in meters, with an accuracy of 0.1 meters). The water exchange rate threshold is set to 0.1d-1, and areas below this value are defined as hydrodynamic stagnation areas. Spatial positioning is performed using a geographic information system to generate a vector map of the stagnation area boundary. The characteristics of the hydrodynamic stagnation area include the area, average water exchange rate, and its spatial distribution. The data are saved in a database table format for reference in the subsequent layout of monitoring sections.

[0142] Step S44: Dissolved oxygen monitoring sections are arranged according to the hydrodynamic stagnation area to obtain dissolved oxygen monitoring data; the rate of change of oxygen concentration is calculated according to the dissolved oxygen monitoring data; dissolved oxygen anomaly detection is determined according to the rate of change of oxygen concentration to obtain dissolved oxygen anomaly data;

[0143] In this embodiment, based on the boundary of the hydrodynamic sluggish area, at least three sections are selected in the sluggish area to arrange dissolved oxygen monitoring points. The section layout refers to the uniformity of the flow velocity distribution in the reference area. The section length and point spacing are 50 meters and 10 meters respectively. Dissolved oxygen monitoring uses a portable electrochemical dissolved oxygen probe with a measurement unit of mg / L, an accuracy of ±0.1mg / L, and a sampling frequency of 15 minutes. The collected data is uploaded to the data processing system and sorted according to the time series. The oxygen concentration change rate is calculated using the central difference method, and the calculation formula is ΔDO / Δt=(DO_t+1-DO_t-1) / (2×Δt), where Δt is the time interval (15 minutes) and DO is the dissolved oxygen concentration. The oxygen concentration change rate abnormal threshold is set to ±0.05mg / (L·min), and data points outside this range are judged as dissolved oxygen abnormalities. The dissolved oxygen abnormality data is stored in the form of abnormal time, location, and abnormal amplitude to facilitate the location of potential hypoxia or hyperoxia in the water body.

[0144] Step S45: Perform reservoir ecological water release scheduling based on the abnormal dissolved oxygen data to obtain reservoir ecological water release data.

[0145] In this embodiment, the amount of ecological water release required for scheduling is calculated based on the abnormal dissolved oxygen data, the current water level of the reservoir, the inflow and outflow, and the historical scheduling rules. The outflow flow (unit: m) is measured using a control sluice flow meter. 3 / s), the scheduling time resolution is set to 1 hour. The minimum ecological water release flow threshold is set to 5m 3 / s, the maximum allowable ecological water discharge flow is 50m 3 / s, the water release volume is proportionally distributed according to the size and severity of the abnormal area. The scheduling plan includes the water release flow, water release period and water release section location. The data format includes time, flow (m 3 Water release scheduling instructions are remotely issued and executed through the control system, and scheduling status and real-time flow data are synchronously transmitted back to the database for subsequent effect evaluation and water quality dynamic response analysis. All scheduling data is archived hourly to ensure historical tracking and subsequent scheduling adjustments.

[0146] It is particularly important that step S45 includes the following steps:

[0147] Step S451: determining the water quality risk level based on the abnormal dissolved oxygen data to obtain water quality risk data;

[0148] In this embodiment, the dissolved oxygen abnormality data is collected from multiple dissolved oxygen monitoring sections arranged in the hydrodynamic sluggish area. The section layout is determined according to the previous toxin migration path and the hydrodynamic slowdown area. The dissolved oxygen electrode sensor is used with an interval depth of 0.5m and a horizontal interval of 25m. The monitoring frequency is set to once every 10 minutes. The dissolved oxygen concentration value collected at each monitoring point is compared with the national surface water environmental quality standard GB3838-2002. The dissolved oxygen threshold value of Class I water bodies is set to 7.5mg / L, Class II is 6.0mg / L, and Class III is 5.0mg / L. The value below 5.0mg / L is considered abnormal. For abnormal points, the water quality risk value is calculated using the risk scoring method according to the decrease in oxygen concentration, duration, and impact range. The scoring standards are as follows: 4 points for a concentration below 4.0mg / L and 6 points for a concentration below 3.0mg / L; 3 points for a continuous abnormality lasting more than 2 hours and 5 points for more than 6 hours; and an affected area of ​​more than 2500m 2 2 points for running over 5000m 2 A total of 4 points are awarded. Areas with a comprehensive score of more than 10 points are marked as high-risk areas, areas with a score of 6 to 10 points are marked as medium-risk areas, and areas with a score below 6 points are marked as low-risk areas. Finally, a spatially distributed water quality risk level map is generated in the form of a GIS raster, and water quality risk data is output.

[0149] Step S452: Divide the target water body control area based on the water quality risk data to obtain ecological control area data;

[0150] In this embodiment, the water quality risk level grid data output in step S451 is called, and high-risk and medium-risk areas are taken as the main intervention targets. A spatial clustering algorithm is used to identify connected areas, and risk areas with horizontal spacing of ≤50m and vertical spacing of ≤0.5m between adjacent grids are merged. Risk areas with an area of ​​less than 400m are eliminated. 2 After delineation, the control priorities are assigned according to the risk level. The high-risk area has a priority of 1, the medium-risk area has a priority of 2, and the low-risk area is not included in the current scheduling range. Then the water body boundary vector diagram within the control area is obtained, and the regional volume is analyzed in combination with the DEM water depth data. The boundary closure algorithm is used to convert the control area into a closed water body space entity for subsequent water volume regulation calculations. All regional boundaries, priorities and spatial positioning information are uniformly output as ecological control area data. The format uses Shapefile and a supporting DBF data table containing fields such as number, area, average depth, and priority.

[0151] Step S453: Calculate the dispatchable water volume based on the ecological regulation area data;

[0152] In this embodiment, the lower limit of the dispatching water level is set to the ecological water demand line, which is set to 1.2m above the dead water level; the upper limit of the dispatching water level is set to the pre-flood limit water level or the real-time water level, whichever is lower. The current reservoir capacity is determined based on the water level-reservoir capacity curve; the water volume in the dispatching interval is calculated using the triangle approximation method, water surface area × (upper limit water level - lower limit water level) / 2. The current dispatching period is set to a 24-hour cycle, with hours as the basic calculation unit, combined with historical evaporation (the average daily evaporation is about 4mm, calculated at 0.17m 3 / m 2 d), deducting evaporation losses to calculate the total available water volume. All available water volume values ​​are associated with regional control objects, and a table of available water volume data is output containing fields such as region number, control priority, region volume, and available water volume.

[0153] Step S454: Calculate the water release flow rate during the time period based on the schedulable water volume;

[0154] In this embodiment, the scheduling cycle is set to 24 hours, and the scheduling cycle is divided into 8 water release periods, each lasting 3 hours. Based on the adjustable water volume data obtained in step S453, the total water transfer volume is allocated according to the priority of each control area, and the high-priority area is not less than 60% of the total water transfer volume. The water transfer demand in each area is reversed based on the oxygen concentration change rate to replace the volume (the oxygen concentration recovery target is 6.0 mg / L, corresponding to a dilution ratio>1.8), and the regional volume is divided by the dilution ratio to determine the target water release volume for the area. Evenly distribute the target water release volume in the 8 time periods and divide it by 3 hours to obtain the target flow rate per time period, in units of m 3 / s. The actual discharge port structural parameters are verified by calculating the flow capacity of the gate section width (such as 3m), opening height (such as 1.5m) and head difference to ensure that the target flow does not exceed the maximum safe discharge capacity. Finally, the flow information of each period is summarized to form a period discharge flow data table, which includes the period number, control area number, target flow (m 3 / s), dispatching time (h), total dispatching water volume (m 3 ).

[0155] Step S455: Perform reservoir ecological water release scheduling according to the water release flow rate during the time period to obtain reservoir ecological water release data.

[0156] In this embodiment, the dispatching instruction is sent to the gate execution unit by the reservoir dispatching automatic control system. The execution command is started 10 minutes before the start of each dispatching period, and the gate opening height is determined based on the flow data of step S454. The discharge is verified by using the head in front of the gate and the gate opening area, using the flow formula Q = μ·B·H·√(2gH), where μ is 0.6, B is the gate width, and H is the head in front of the gate. The water level meter and flow meter installed on site record the actual discharge flow and water level fluctuations in a 1-minute cycle to ensure that the error with the planned value does not exceed ±5%. During the execution of the dispatching process, the dispatching console records the operation time, opening height, duration and actual flow in real time, and stores the data in the dispatching log. After each period, the dispatching feedback data is recorded and integrated into the complete reservoir ecological water release data. The data content includes fields such as the control area number, discharge period, target flow, actual flow, dispatched water volume, execution error, execution status, etc., and is uniformly exported in Excel and CSV formats.

[0157] Preferably, this specification also provides a reservoir ecological scheduling system considering the dynamic response of water quality, which is used to execute the reservoir ecological scheduling method considering the dynamic response of water quality as described above. The reservoir ecological scheduling system considering the dynamic response of water quality includes:

[0158] The pollution source tracking module is used to obtain reservoir water quality monitoring data; conduct water eutrophication analysis based on the reservoir water quality monitoring data to obtain water eutrophication data; and track pollution sources based on water eutrophication data to obtain pollution source data;

[0159] The water algae toxin analysis module is used to evaluate the pollutant emission intensity based on pollution source data; calibrate high-pollution areas according to pollutant emission intensity; conduct algae density detection in high-pollution areas to obtain algae density data; and perform water algae toxin analysis based on the algae density data to obtain water algae toxin data;

[0160] The toxin diffusion simulation module is used to obtain reservoir hydrological data and extract the maximum rainfall characteristics to obtain the maximum rainfall data; convert the flow rate based on the maximum rainfall data to obtain the flow rate data; and simulate the toxin diffusion based on the flow rate data and the water body algae toxin data to obtain the algae toxin diffusion data;

[0161] The reservoir ecological water release scheduling module is used to identify high-concentration toxin retention areas based on algal toxin diffusion data; perform dissolved oxygen anomaly detection based on high-concentration toxin retention areas to obtain dissolved oxygen anomaly data; and perform reservoir ecological water release scheduling based on dissolved oxygen anomaly data to obtain reservoir ecological water release data.

[0162] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0163] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present 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 present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A reservoir ecological scheduling method considering the dynamic response of water quality, characterized in that: The following steps are involved: Step S1: Obtaining reservoir water quality monitoring data; Conduct water eutrophication analysis based on reservoir water quality monitoring data to obtain water eutrophication data; Tracing pollution sources based on water eutrophication data to obtain pollution source data; Step S2: Evaluate pollutant emission intensity based on pollution source data; Demarcate high-pollution areas based on pollutant emission intensity; conduct algae density detection in high-pollution areas to obtain algae density data; conduct water body algae toxin analysis based on algae density data to obtain water body algae toxin data; Step S3: Obtain reservoir hydrological data and extract maximum rainfall features to obtain maximum rainfall data; Flow conversion is performed based on the maximum rainfall data to obtain flow data; toxin diffusion simulation is performed based on the flow data and water body algae toxin data to obtain algae toxin diffusion data; Step S4: Identify high-concentration toxin retention areas based on algal toxin diffusion data; perform dissolved oxygen anomaly detection based on high-concentration toxin retention areas to obtain dissolved oxygen anomaly data; perform reservoir ecological water release scheduling based on the dissolved oxygen anomaly data to obtain reservoir ecological water release data.

2. The reservoir ecological scheduling method considering the dynamic response of water quality according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: obtaining reservoir water quality monitoring data, and calculating nutrient concentration to obtain nutrient concentration data; Step S12: performing algae growth simulation according to the nutrient concentration data to obtain algae growth data; calculating algae biomass based on the algae growth data to obtain algae biomass data; Step S13: calibrating high algae biomass waters based on the algae biomass data; Step S14: performing water turbidity detection according to the high algae biomass water area to obtain water turbidity data; Step S15: Calculating the chlorophyll content based on the water turbidity data to obtain chlorophyll concentration data; performing eutrophication assessment based on the chlorophyll concentration data and the nutrient concentration data to obtain water eutrophication data; Step S16: Tracing pollution sources based on water eutrophication data to obtain pollution source data.

3. The reservoir ecological scheduling method considering the dynamic response of water quality according to claim 2 is characterized in that: Step S16 includes the following steps: Step S161: determining high nutrient load areas based on water eutrophication data; Step S162: Detecting nitrogen and phosphorus content in the high nutrient load area to obtain nitrogen and phosphorus content data; Step S163: Performing drainage outlet pollution detection based on the nitrogen and phosphorus content data to obtain drainage outlet pollution data; Step S164: performing pollution source matching based on the drainage outlet pollution data to obtain pollution source data.

4. The reservoir ecological scheduling method considering the dynamic response of water quality according to claim 1 is characterized in that: The evaluation of pollutant emission intensity based on pollution source data in step S2 includes: Based on the pollution source data, the pollution types are classified to obtain point source pollution data and non-point source pollution data; Conduct ammonia nitrogen pollution detection based on point source pollution data to obtain ammonia nitrogen pollution data; Phosphorus nutrient concentration data were obtained by testing the phosphorus nutrient concentration based on the non-point source pollution data; Calculate ammonia nitrogen emission flux based on ammonia nitrogen pollution data; Calculate total phosphorus flux based on phosphorus nutrient concentration data; The pollutant emission intensity was evaluated based on ammonia nitrogen emission flux and total phosphorus flux.

5. The reservoir ecological scheduling method considering the dynamic response of water quality according to claim 1 is characterized in that: The algae density detection in the high pollution area in step S2 includes: Algae samples were collected from highly polluted areas and concentrated by sedimentation to obtain enriched algae suspension data; Prepare slide samples based on the enriched algae suspension data to obtain algae microscopic sample data; Collect algae images based on algae microscopic sample data; Identify algae cell morphology according to the algae image to obtain algae cell morphology data; Calculating the number of algae cells according to the algae cell morphology data to obtain algae cell number data; The algae concentration data was obtained by converting the unit volume according to the algae cell number data; Algae density detection is performed based on the algae concentration data to obtain algae density data.

6. The reservoir ecological scheduling method considering the dynamic response of water quality according to claim 1 is characterized in that: Step S2 of performing algae toxin analysis in water based on algae density data includes: Demarcate high-density algae areas based on algae density data; Measuring water temperature based on high-density algae areas to obtain water temperature data; The pH value of the water body is measured based on the high-density algae area to obtain the pH value data of the water body; Toxin degradation analysis is performed based on water temperature data and water pH data to obtain the toxin degradation rate; Toxin adsorption simulation is performed based on the toxin degradation rate to obtain toxin adsorption data; Perform toxin migration analysis based on toxin adsorption data to obtain toxin migration data; The algal toxin content in water bodies is evaluated based on toxin migration data to obtain algal toxin data in water bodies.

7. The reservoir ecological scheduling method considering the dynamic response of water quality according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Acquire reservoir hydrological data and extract maximum rainfall features to obtain maximum rainfall data; Step S32: Calculating rainfall runoff according to the maximum rainfall data; Step S33: Obtaining reservoir basin area data; performing flow conversion based on rainfall runoff and reservoir basin area data to obtain flow data; Step S34: obtaining reservoir cross-section data; identifying cross-section bottom bed material according to the reservoir cross-section data to obtain cross-section bottom bed material data; determining cross-section roughness based on the cross-section bottom bed material data; Step S35: Calculating the bottom bed undulation based on the reservoir cross-section data; calculating the water flow resistance coefficient based on the bottom bed undulation and the cross-section roughness; performing flow correction on the flow data based on the water flow resistance coefficient to obtain corrected flow data; Step S36: performing toxin dilution analysis on the water body algae toxin data according to the corrected flow data to obtain toxin dilution data; Step S37: Perform toxin diffusion simulation based on the toxin dilution data to obtain algae toxin diffusion data.

8. The reservoir ecological scheduling method considering the dynamic response of water quality according to claim 7 is characterized in that: Step S37 The following steps are involved: Step S371: uploading the toxin dilution data to the water environment diffusion simulation platform; Step S372: setting the water flow rate range to 0.05m / s-1.2m / s, the water depth range to 0.5m-10.0m, and the water temperature range to 5°C-35°C; Step S373: setting the cross-section bottom bed roughness n value to 0.015-0.035 and the cross-section undulation to 0.3m-2.0m; Step S374: Set the simulation time step to 1 min-15 min, the total simulation time to 6 h-72 h, and the diffusion scale to 50 m-500 m in the local area; Step S375: Run the water convection-diffusion numerical calculation module to obtain algal toxin diffusion data.

9. The reservoir ecological scheduling method considering the dynamic response of water quality according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: identifying high-concentration toxin retention areas based on algal toxin diffusion data; Step S42: Calculating the water flow rate based on the high-concentration toxin retention area to obtain water flow rate data; Step S43: Evaluate the water exchange capacity based on the water flow rate data to obtain water exchange capacity data; identify the hydrodynamic stagnation area based on the water exchange capacity data; Step S44: Dissolved oxygen monitoring sections are arranged according to the hydrodynamic stagnation area to obtain dissolved oxygen monitoring data; the rate of change of oxygen concentration is calculated according to the dissolved oxygen monitoring data; dissolved oxygen anomaly detection is determined according to the rate of change of oxygen concentration to obtain dissolved oxygen anomaly data; Step S45: Perform reservoir ecological water release scheduling based on the abnormal dissolved oxygen data to obtain reservoir ecological water release data.

10. A reservoir ecological dispatching system considering the dynamic response of water quality, characterized in that: For executing the reservoir ecological dispatching method considering the dynamic response of water quality as claimed in claim 1, the reservoir ecological dispatching system considering the dynamic response of water quality comprises: The pollution source tracking module is used to obtain reservoir water quality monitoring data; conduct water eutrophication analysis based on the reservoir water quality monitoring data to obtain water eutrophication data; and track pollution sources based on water eutrophication data to obtain pollution source data; The water algae toxin analysis module is used to evaluate the pollutant emission intensity based on pollution source data; calibrate high-pollution areas according to pollutant emission intensity; conduct algae density detection in high-pollution areas to obtain algae density data; and perform water algae toxin analysis based on the algae density data to obtain water algae toxin data; The toxin diffusion simulation module is used to obtain reservoir hydrological data and extract the maximum rainfall characteristics to obtain the maximum rainfall data; convert the flow rate based on the maximum rainfall data to obtain the flow rate data; and simulate the toxin diffusion based on the flow rate data and the water body algae toxin data to obtain the algae toxin diffusion data; The reservoir ecological water release scheduling module is used to identify high-concentration toxin retention areas based on algal toxin diffusion data; perform dissolved oxygen anomaly detection based on high-concentration toxin retention areas to obtain dissolved oxygen anomaly data; and perform reservoir ecological water release scheduling based on dissolved oxygen anomaly data to obtain reservoir ecological water release data.

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