Lake ecological water level dynamic regulation method and system based on multi-element data and medium

Abstract

The present application relates to the technical field of water resources management, and more particularly to a lake ecological water level dynamic regulation method and system based on multi-element data and a medium. The method comprises the following steps: obtaining lake remote sensing monitoring data; identifying the water body boundary according to the lake remote sensing monitoring data to obtain water body boundary data; calculating the water body area based on the water body boundary data to obtain water body area data; analyzing the water storage capacity according to the water body area data to obtain water storage capacity data; detecting water body dilution according to the water storage capacity data to obtain water body dilution data; analyzing water pollution according to the water body dilution data to obtain water pollution data; obtaining lake aquatic organism data; identifying dissolved oxygen anomalies according to the lake aquatic organism data to obtain dissolved oxygen anomaly data. The present application realizes accurate management of lake ecological water level based on water resources management technology, effectively improves water environmental quality and ecological system stability.

Description

Technical Field

[0001] This invention relates to the field of water resource management technology, and in particular to a method, system and medium for dynamic regulation of lake ecological water level based on multivariate data. Background Technology

[0002] Traditional water body dynamic regulation models mainly rely on single data sources, such as water level or meteorological data, lacking comprehensive consideration of ecological factors such as aquatic organisms, water pollution, and carbon cycle, resulting in weak ecological response capabilities of the regulation models. Inlet and outlet control strategies are mostly based on fixed thresholds or rules, making it difficult to flexibly adjust according to pollutant diffusion characteristics and disturbance responses, resulting in non-adaptive regulation results. Disturbance optimization usually ignores the coupling relationship between lake carbon cycle and phytoplankton metabolism, failing to accurately identify disturbance-sensitive areas and response mechanisms, making it difficult to effectively intervene in ecological disorder. Existing systems mainly rely on static parameter inputs, lacking the ability to integrate and process multi-source dynamic data such as remote sensing, aquatic organisms, and water dilution, leading to delayed water level regulation feedback and poor overall regulation effects. Summary of the Invention

[0003] Therefore, it is necessary for the present invention to provide a method, system and medium for dynamic regulation of lake ecological water level based on multivariate data, in order to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for dynamic regulation of lake ecological water levels based on multivariate data is proposed, comprising the following steps: Step S1: Obtain lake remote sensing monitoring data; identify water body boundaries based on lake remote sensing monitoring data to obtain water body boundary data; calculate water body area based on water body boundary data to obtain water body area data; Step S2: Analyze the water storage capacity based on the water body area data to obtain water storage capacity data; conduct water dilution testing based on the water storage capacity data to obtain water dilution data; and analyze water pollution based on the water dilution data to obtain water pollution data. Step S3: Obtain lake aquatic organism data; identify dissolved oxygen anomalies based on lake aquatic organism data to obtain dissolved oxygen anomaly data; detect phytoplankton metabolic imbalance based on dissolved oxygen anomaly data to obtain phytoplankton metabolic imbalance data; assess lake carbon cycle disorder based on phytoplankton metabolic imbalance data to obtain lake carbon cycle disorder data. Step S4: Optimize water disturbance based on lake carbon cycle disorder data to obtain optimized water disturbance data; optimize inlet and outlet control strategies based on water pollution data to obtain inlet and outlet control strategy data; construct an ecological water level regulation model based on the optimized water disturbance data and inlet and outlet control strategy data, and dynamically regulate the lake's ecological water level based on the ecological water level regulation model to obtain dynamic water level regulation data.

[0005] This invention acquires remote sensing monitoring data of lakes and identifies water body boundaries, accurately determining the actual extent and changing trends of water bodies, providing a high-precision foundation for subsequent area calculation and water storage capacity analysis. By combining water body area calculation with water depth measurement, it enables dynamic assessment of the actual water storage capacity of lakes, facilitating a comprehensive understanding of water resource carrying capacity. Based on this, water dilution detection and water pollution analysis not only reflect the impact of external pollution input and internal pollution release but also dynamically reveal the dilution and enrichment processes of pollutants in the lake. Introducing dissolved oxygen anomaly identification using lake aquatic biological data allows for real-time monitoring of localized hypoxia or hyperxia caused by biological activity, improving the accuracy of ecological response identification. Combined with phytoplankton metabolic imbalance detection, it further reveals the metabolic imbalance process between primary production and respiration in the aquatic ecosystem, providing a basis for... The carbon cycle status provides key criteria; through carbon cycle disorder assessment, quantitative analysis of comprehensive ecological processes such as eutrophication, water body organic carbon load, and sediment carbon release can be achieved; water disturbance optimization not only combines carbon dioxide release rate with disturbance-sensitive area identification, but also achieves precise intervention in disordered areas through water flow regulation, improving the targeting of ecological regulation; inlet and outlet control strategy optimization integrates pollutant concentration and diffusion simulation results, making water exchange more ecologically adaptable and pollution control capable; the finally constructed ecological water level regulation model integrates two core levels: hydrodynamic simulation and inlet and outlet scheduling, achieving precise dynamic regulation of lake ecological water level, improving the system's ability to integrate multi-source ecological data and the real-time responsiveness of regulation strategies, thereby effectively solving the problems of weak response, inaccurate regulation, and data fragmentation in traditional models.

[0006] Preferably, step S1 specifically includes: Step S11: Acquire remote sensing monitoring data of the lake and perform image preprocessing to obtain remote sensing data of the lake to be processed; Step S12: Extract water spectral features from the remote sensing data of the lake to be processed to obtain water spectral data; Step S13: Calculate the normalized difference water index based on the water spectral data; identify water regions based on the normalized difference water index to obtain water region images; fill in the broken edges of the water region images to obtain water body edge contour images. Step S14: Perform geographic coordinate correction based on the water body edge contour image to obtain water body boundary data; Step S15: Calculate the water area based on the water body boundary data to obtain the water body area data.

[0007] This invention significantly improves the quality and resolvability of remote sensing images through the acquisition and image preprocessing of lake remote sensing monitoring data, providing a high-quality data foundation for subsequent analysis. Extracting water spectral features effectively distinguishes water bodies from surrounding non-water areas, improving the accuracy and robustness of water body identification. Calculating the normalized difference water index and performing regional identification allows for rapid and accurate determination of water body distribution. Furthermore, the invention repairs discontinuities in remote sensing images caused by cloud cover or reflection differences by connecting broken edges, thereby improving the integrity of water body boundaries. Geographic coordinate correction ensures that the remote sensing image aligns with the actual geographical location. The one-to-one correspondence provides a standardized reference coordinate system for subsequent spatial analysis and multi-source data fusion; the accurate calculation of water area lays the foundation for the assessment and dynamic regulation of the entire lake's water volume, enabling reliable data support for hydrological modeling, ecological simulation, and inlet / outlet scheduling; the overall process helps to break through the limitations of traditional single-data-driven regulation based on water level or meteorology, constructing a multi-source integrated lake ecological response mechanism, providing accurate and dynamically updated spatial boundary basis for subsequent coupled analysis of water dilution, water pollution, aquatic organism distribution, and carbon cycle, thereby effectively improving the intelligence, precision, and ecological adaptability of lake ecological water level regulation.

[0008] Preferably, step S2 specifically includes: Step S21: Measure the water depth based on the water area data to obtain the water depth data; Step S22: Perform water body stratification based on water depth data to obtain water body stratification data; Step S23: Calculate the stratified volume based on the water stratification data to obtain the stratified water volume; perform volume summation calculation based on the stratified water volume to obtain the total water storage volume data; Step S24: Evaluate the water storage capacity based on the total water storage volume data to obtain water storage capacity data; Step S25: Conduct water dilution testing based on water storage capacity data to obtain water dilution data; Step S26: Analyze water pollution based on water dilution data to obtain water pollution data.

[0009] This invention enables precise perception of the three-dimensional structure of lakes by measuring water depth based on water area, providing data support for identifying stratified hydrological characteristics. Stratified water treatment not only reveals differences in physicochemical conditions such as temperature and dissolved oxygen at different depths but also supports stratified pollution source tracing and ecological process modeling. Stratified volume calculation combined with volume accumulation accurately quantifies the total water storage volume of lakes, providing fundamental data for water resource assessment and regulation capacity prediction. Water storage capacity assessment based on accurate total water storage volume helps identify the regulation carrying capacity limits under different seasons and rainfall scenarios. Introducing a water dilution detection mechanism enables the identification of dynamic trends in pollutant concentrations, providing spatiotemporal guidance for water quality risk prevention and control. By analyzing the impact mechanism of changes in water dilution capacity on water pollution, it is possible to dynamically identify enrichment areas or migration paths of pollutants under different water depths and stratification conditions, improving the depth and accuracy of water pollution analysis. This provides key pollution intervention basis for dynamic ecological water level regulation strategies, enhancing the response capability and regulation accuracy of water level regulation systems to complex water quality disturbances.

[0010] Preferably, step S3 specifically includes: Step S31: Obtain lake aquatic organism data; Step S32: Analyze the distribution of aquatic organisms in the lake based on the aquatic organism data to obtain the distribution data of aquatic organisms in the lake; set up water sampling points based on the distribution data of aquatic organisms in the lake to obtain the water sampling point data; Step S33: Perform dissolved oxygen sampling based on water sampling point data to obtain dissolved oxygen data; identify low dissolved oxygen areas based on dissolved oxygen data; Step S34: Detect iron and manganese precipitates in the low dissolved oxygen area to obtain iron and manganese precipitate data; determine dissolved oxygen anomalies based on the iron and manganese precipitate data to obtain dissolved oxygen anomaly data; Step S35: Detect phytoplankton metabolic imbalance based on dissolved oxygen anomaly data to obtain phytoplankton metabolic imbalance data; Step S36: Assess lake carbon cycle disorder based on phytoplankton metabolic imbalance data to obtain lake carbon cycle disorder data.

[0011] This invention, by introducing aquatic organism data collection and distribution analysis, enables precise characterization of the spatial heterogeneity of lake ecological states, providing a basis for the scientific layout of subsequent water sampling points, thereby improving the representativeness and accuracy of data acquisition. Setting sampling points based on aquatic organism distribution effectively avoids sampling blind spots, making the collected dissolved oxygen data more ecologically relevant and responsive. Identifying low dissolved oxygen areas based on dissolved oxygen data helps in early warning of eutrophication and habitat degradation risks. Furthermore, combining this with the detection of iron and manganese precipitates in low dissolved oxygen areas can effectively identify element migration and reduction reactions caused by bottom hypoxia. This process enhances the comprehensive ability to identify dissolved oxygen anomalies. Based on this, phytoplankton metabolic imbalance detection not only reveals the physiological response of phytoplankton communities to environmental stresses but also reflects the stable operation of the lake's primary productivity system. By assessing carbon cycle disorder through metabolic imbalance, dynamic quantification of changes in carbon source / sink mechanisms can be achieved, thus providing key ecological constraints and feedback control basis for ecological water level regulation. The overall process improves the systematic nature of ecological state identification, the refinement of response mechanisms, and the adaptability of water level regulation models to ecological disturbances, compensating for the shortcomings of traditional models in ecological process integration and feedback accuracy.

[0012] Preferably, step S35 specifically includes: Step S351: Identify areas of abnormal dissolved oxygen based on the dissolved oxygen anomaly data; Step S352: Determine the photosynthetic rate of phytoplankton based on areas of abnormal dissolved oxygen; Step S353: Determine the phytoplankton respiration rate based on areas of abnormal dissolved oxygen; Step S354: Calculate net primary productivity based on phytoplankton photosynthetic rate and phytoplankton respiration rate to obtain net primary productivity data; Step S355: Assess phytoplankton metabolic imbalance based on net primary productivity data to obtain phytoplankton metabolic imbalance data.

[0013] This invention, through field measurements of phytoplankton photosynthetic and respiration rates in areas of abnormal dissolved oxygen, helps to construct a dynamic index system reflecting the metabolic state of phytoplankton under real ecological stress, significantly improving the accuracy of metabolic process modeling and the sensitivity of ecological response analysis. Quantitative calculation of net primary productivity effectively reflects the production capacity and ecological stability of the aquatic autotrophic system, thereby identifying the risk of decreased primary productivity or carbon source release caused by metabolic disorders. Assessment of metabolic imbalance based on net primary productivity not only reveals the physiological response mechanism of phytoplankton to low dissolved oxygen environments but also provides a basis for early intervention in carbon cycle disorders, further enhancing the predictive ability and accuracy of response decisions of the regulatory system to ecological changes. This method constructs an ecological feedback chain from dissolved oxygen anomaly identification to metabolic imbalance assessment, opening up a dynamic coupling path between ecological perception and regulatory models, improving the identification ability of ecological driving mechanisms for lake ecological water level regulation and the ecological fit of intervention decisions, effectively overcoming the technical shortcomings of traditional models where ecological processes and regulatory parameters are disconnected.

[0014] Preferably, step S36 specifically includes: Step S361: Perform eutrophication analysis on the phytoplankton metabolic imbalance data to obtain eutrophication data of the water body; Step S362: Identify the sources of eutrophication in water bodies based on eutrophication data and obtain eutrophication source data. Step S363: Detect organic carbon content based on eutrophication source data of the water body to obtain organic carbon content data; Step S364: Perform carbon flow simulation based on organic carbon content data to obtain carbon flow data; identify the carbon flow direction based on the carbon flow data to obtain carbon flow direction data; Step S365: Perform sediment analysis based on carbon flow direction data to obtain sediment data; calculate bottom carbon flux based on sediment data; Step S366: Perform lake carbon cycle disorder analysis based on bottom carbon flux to obtain lake carbon cycle disorder data.

[0015] This invention, by introducing eutrophication analysis following phytoplankton metabolic imbalance, can promptly identify nutrient accumulation problems caused by metabolic imbalance, providing data support for early ecological risk intervention. Further identification of eutrophication sources helps to accurately locate the input pathways and spatial distribution characteristics of pollutants, avoiding the limitations of traditional models in clearly identifying pollution diffusion areas. Combined with organic carbon content detection, the enrichment degree of key carbon sources in water bodies can be quantified, thereby assessing their promoting or inhibiting effects on primary producers, providing key input variables for dynamic carbon cycle modeling. Based on carbon flow simulation and direction identification, the migration and diffusion processes of carbon in water bodies can be effectively characterized. This study reveals the coupling relationship between carbon and hydrodynamic disturbances and ecological processes. Sediment detection and bottom carbon flux calculation can reflect the deposition or resuspension characteristics of organic carbon at the water-sediment interface, constructing a complete chain of carbon flux coupling and enhancing the system's ability to monitor carbon sink / carbon source processes. Finally, the analysis of lake carbon cycle disorder can accurately identify the driving factors and dynamic evolution paths of structural imbalances in the carbon cycle, thereby providing carbon process constraints and ecological feedback basis for ecological water level regulation. This makes up for the technical shortcomings of traditional models in lacking ecological closed-loop regulation and accurate carbon element tracking, and enhances the ecological adaptability, dynamic responsiveness and system closed-loop nature of the regulation strategy.

[0016] Preferably, step S4 specifically includes: Step S41: Calculate the carbon dioxide release rate based on lake carbon cycle disorder data; Step S42: Identify disturbance-sensitive areas based on carbon dioxide release rate; regulate water flow velocity based on disturbance-sensitive areas to obtain optimized water disturbance data; Step S43: Calculate pollutant concentration based on water pollution data; perform diffusion simulation based on pollutant concentration to obtain pollutant diffusion simulation data; Step S44: Optimize the inlet and outlet control strategies based on pollutant diffusion simulation data to obtain inlet and outlet control strategy data; Step S45: Construct an ecological water level regulation model based on water disturbance optimization data and inlet / outlet control strategy data, and conduct dynamic regulation of lake ecological water level based on the ecological water level regulation model to obtain dynamic water level regulation data.

[0017] This invention, by calculating carbon dioxide release rate and introducing carbon emission intensity as a core parameter to measure the degree of ecological disturbance in lakes, enhances the ability of the control system to quantitatively express the results of carbon cycle disturbances and identify ecological feedback. Based on carbon release intensity, it identifies disturbance-sensitive areas, enabling precise location of concentrated carbon sources in lakes and improving the targeting and ecological adaptability of water disturbance control. Water flow velocity regulation can effectively alleviate problems such as carbon accumulation, metabolic anomalies, and pollutant enrichment in disturbance-sensitive areas, promoting the recovery of lakes to a stable carbon flux state. Pollutant concentration calculation and diffusion simulation can construct a dynamic spatial evolution map of pollutants, providing a basis for pollution response and diffusion path judgment for inlet and outlet regulation, solving the problems of traditional rule-driven strategies in complex situations. The problem of slow response in pollution scenarios is addressed; by introducing pollutant concentration gradient and diffusion trend constraints, the control strategy optimization can dynamically adjust the inlet and outlet flux and opening and closing logic to achieve synergistic optimization of pollution reduction and enhanced water body self-purification capacity; based on comprehensive water body disturbance optimization data and control strategy data, an ecological water level regulation model is constructed, which not only strengthens the real-time linkage mechanism between water level regulation and ecological processes, but also synchronously corrects the regulation target based on carbon cycle response and pollution response, realizing multi-objective coordinated control of water body stability, ecological balance and environmental quality, significantly improving the overall scientificity, accuracy and adaptability of lake ecological water level regulation, and overcoming the technical bottlenecks of fragmented regulation variables, lagging ecological feedback and inefficient disturbance response in existing models.

[0018] Preferably, step S45 specifically includes: Step S451: Construct a hydrodynamic simulation layer based on optimized water disturbance data; Step S452: Construct the inlet and outlet scheduling layer based on the inlet and outlet control strategy data; Step S453: Construct an ecological water level regulation model based on the hydrodynamic simulation layer and the inlet / outlet scheduling layer; Step S454: Dynamically regulate the lake's ecological water level according to the ecological water level regulation model to obtain dynamic water level regulation data.

[0019] This invention, by constructing a hydrodynamic simulation layer, enables dynamic simulation of water flow paths, velocity distribution, and disturbance responses within lakes. This effectively quantifies the coupled impact of hydrological disturbances on ecological processes (such as phytoplankton metabolism, pollutant migration, and carbon flux changes), enhancing the model's ability to analyze disturbance propagation chains and ecological feedback paths. Constructing an inlet / outlet scheduling layer allows for refined management of fluxes, opening sequences, and spatial distributions at different inlets and outlets. This enables dynamic intervention during periods of concentrated pollutant diffusion and ecological risk, improving the efficiency of scheduling strategies in responding to real-time pollution diffusion characteristics and disturbance evolution processes. Furthermore, by integrating the hydrodynamic model... Based on the pseudo-layer and the inlet / outlet scheduling layer, an ecological water level regulation model is constructed. This model can establish a multi-factor coupling mechanism between hydrodynamic processes, pollutant migration, biological responses, and water level regulation, significantly improving the model's ability to match water level regulation targets under complex ecological conditions. Ultimately, through dynamic water level regulation using the ecological water level regulation model, the coordinated optimization of water quality improvement, carbon cycle balance, and aquatic organism suitable environment can be achieved while ensuring the ecological stability of the lake. This solves the technical bottleneck of traditional water level control strategies lacking ecosystem constraints and dynamic perception capabilities, thereby comprehensively improving the systematicness, adaptability, and ecological driving capacity of lake water level regulation.

[0020] Optionally, this specification also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any one of the lake ecological water level dynamic control methods based on multi-source data.

[0021] Preferably, this specification also provides a lake ecological water level dynamic control system based on multivariate data, used to execute the lake ecological water level dynamic control method based on multivariate data as described above. The lake ecological water level dynamic control system based on multivariate data includes: The water area calculation module is used to acquire lake remote sensing monitoring data; identify water body boundaries based on the lake remote sensing monitoring data to obtain water body boundary data; and calculate the water body area based on the water body boundary data to obtain water body area data. The water pollution analysis module is used to analyze water storage capacity based on water body area data to obtain water storage capacity data; to perform water dilution detection based on water storage capacity data to obtain water dilution data; and to perform water pollution analysis based on water dilution data to obtain water pollution data. The lake carbon cycle disorder assessment module is used to acquire lake aquatic organism data; identify dissolved oxygen anomalies based on the lake aquatic organism data to obtain dissolved oxygen anomaly data; detect phytoplankton metabolic imbalance based on the dissolved oxygen anomaly data to obtain phytoplankton metabolic imbalance data; and assess lake carbon cycle disorder based on the phytoplankton metabolic imbalance data to obtain lake carbon cycle disorder data. The ecological water level regulation model construction module is used to optimize water body disturbance based on lake carbon cycle disorder data to obtain optimized water body disturbance data; optimize inlet and outlet control strategies based on water pollution data to obtain inlet and outlet control strategy data; construct an ecological water level regulation model based on the optimized water body disturbance data and inlet and outlet control strategy data; and perform dynamic regulation of lake ecological water level based on the ecological water level regulation model to obtain dynamic water level regulation data.

[0022] The present invention relates to a dynamic control system for lake ecological water levels based on multivariate data. This system can implement any of the dynamic control methods for lake ecological water levels based on multivariate data of the present invention. It is used to connect the operation and signal transmission media between various modules to complete the dynamic control method for lake ecological water levels based on multivariate data. The internal modules of the system cooperate with each other to achieve precise management of lake ecological water levels and effectively improve water environment quality and ecosystem stability. Attached Figure Description

[0023] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the steps of a method for dynamic regulation of lake ecological water level based on multivariate data according to the present invention. Figure 2 This is a detailed flowchart of step S1 in the present invention; Figure 3 This is a detailed flowchart of step S2 in the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0024] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0025] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0026] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0027] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a method for dynamic regulation of lake ecological water level based on multivariate data, the method comprising the following steps: Step S1: Obtain lake remote sensing monitoring data; identify water body boundaries based on lake remote sensing monitoring data to obtain water body boundary data; calculate water body area based on water body boundary data to obtain water body area data; In this embodiment, multi-temporal high-resolution image data of the target lake area is acquired using a satellite remote sensing platform (e.g., Sentinel-2, Landsat 8). The acquired image bands include visible light (blue, green, and red bands) and near-infrared bands. Radiometric, geometric, and atmospheric corrections are performed on the acquired raw remote sensing image data. Standardization is then performed using image preprocessing software such as ENVI or dedicated remote sensing processing tools to ensure spatial and spectral accuracy. Subsequently, water bodies are extracted using the spectral characteristics of the remote sensing images and the Normalized Difference Water Index (NDWI) calculated using the formula NDWI = (green band - near-infrared band) / (green band + near-infrared band). An NDWI threshold of 0.3 is set; pixels below this threshold are considered non-water bodies, while pixels above it are considered water bodies. To eliminate water body edge breaks and noise interference, morphological closure operations are used to fill voids at the water body edges. Specifically, expansion followed by erosion is performed, with a 3×3 square core selected as the structural element. Based on the completed water body boundary outline, and combined with Geographic Information System (GIS) tools, the water body boundary was calibrated using geographic coordinates. Ground control points (GCPs) and digital elevation models (DEMs) were used to assist in adjusting spatial errors, ensuring that the water body boundary accurately corresponds to the geographic coordinate system. Finally, using the polygon area calculation tool in GIS, the water body area was calculated based on the calibrated water body boundary polygons. The result is in square meters and is rounded to two decimal places.

[0028] Step S2: Analyze the water storage capacity based on the water body area data to obtain water storage capacity data; conduct water dilution testing based on the water storage capacity data to obtain water dilution data; and analyze water pollution based on the water dilution data to obtain water pollution data. In this embodiment, based on the water area data calculated in step S1, combined with the average water depth data (obtained by sonar depth sounder or historical hydrological stations, with depth unit in meters and accuracy of 0.01 meters), the lake's water storage capacity is calculated using the formula: Water storage capacity (cubic meters) = Water area (square meters) × Average water depth (meters). Historical hydrological data is used to verify the lake's water storage capacity, ensuring the measurement error does not exceed ±5%. Next, water dilution testing is performed, collecting water quality parameters at the lake's inlet and outlet, primarily including conductivity (μS / cm), dissolved oxygen (mg / L), and total dissolved solids (TDS, mg / L), using a portable multi-parameter water quality analyzer. Dilution assessment is performed based on the rate of change in conductivity; a rate of change exceeding 10% is defined as a significant dilution state. The dilution test results serve as a key indicator for determining the mixing and flow state of the water body. Subsequently, water pollution analysis was conducted based on the diluted detection data. The analyzed pollutant indicators included ammonia nitrogen (NH3-N, mg / L), total phosphorus (TP, mg / L), chemical oxygen demand (COD, mg / L), and total nitrogen (mg / L). Pollution thresholds were set at 1.0 mg / L for ammonia nitrogen, 0.05 mg / L for total phosphorus, 15 mg / L for COD, and 1 mg / L for TN. Exceeding any of these thresholds was considered exceeding the pollution limits. By comparing the pollution indicators at each sampling point with the thresholds, the pollution distribution and pollution level were determined, and a spatial distribution map of water pollution was established.

[0029] Step S3: Obtain lake aquatic organism data; identify dissolved oxygen anomalies based on lake aquatic organism data to obtain dissolved oxygen anomaly data; detect phytoplankton metabolic imbalance based on dissolved oxygen anomaly data to obtain phytoplankton metabolic imbalance data; assess lake carbon cycle disorder based on phytoplankton metabolic imbalance data to obtain lake carbon cycle disorder data. In this embodiment, aquatic biological data were collected from different sampling points in the lake, including phytoplankton concentration (cell count / mL), zooplankton species and quantities. High-power microscopy and flow cytometry were used for counting to ensure a phytoplankton cell count accuracy of ±5%. Then, dissolved oxygen concentration was measured using a dissolved oxygen analyzer, with the unit being mg / L, and data was collected hourly. The dissolved oxygen data was compared with the regional standard dissolved oxygen range (typically 6-10 mg / L) to identify abnormal areas; values ​​below 5 mg / L were considered abnormal. Based on the abnormal dissolved oxygen data, phytoplankton metabolic imbalance was detected by measuring phytoplankton chlorophyll a content (μg / L) and photosynthetic rate (μmolO2 / m2 / s) using a photosynthetic rate meter. If the chlorophyll a content exceeded 30 μg / L and the photosynthetic rate decreased by more than 20%, a metabolic imbalance was identified. Subsequently, based on phytoplankton metabolic imbalance data, the carbon cycle disorder in the lake was assessed, using dissolved organic carbon (DOC, mg / L) and carbon dioxide release rate (CO2 mg / m² / h) as evaluation indicators. A carbon cycle disorder was considered to exist when the DOC concentration exceeded 5 mg / L and the CO2 release rate exceeded 1 mg / m² / h. Data was collected using fixed automated samplers and laboratory analytical methods to ensure data continuity and accuracy.

[0030] Step S4: Optimize water disturbance based on lake carbon cycle disorder data to obtain optimized water disturbance data; optimize inlet and outlet control strategies based on water pollution data to obtain inlet and outlet control strategy data; construct an ecological water level regulation model based on the optimized water disturbance data and inlet and outlet control strategy data, and dynamically regulate the lake's ecological water level based on the ecological water level regulation model to obtain dynamic water level regulation data.

[0031] In this embodiment, lake carbon cycle disturbance indicators are used in conjunction with water body physical parameters (such as water temperature, flow velocity, dissolved oxygen, etc.) to optimize water body disturbance using numerical calculation methods. By setting disturbance parameter thresholds, when the water temperature exceeds 25 degrees Celsius and dissolved oxygen is below 5 mg / L, the disturbance optimization algorithm is activated to calculate the optimal water flow path and water distribution scheme. The optimization objective is to reduce the carbon release rate and restore ecological balance. For inlet and outlet control strategy optimization, a water pollution threshold trigger mechanism is set based on the spatial distribution map of water pollution and flow velocity data. When areas where pollutant indicators exceed standards appear, the inlet and outlet opening degrees are automatically adjusted (the threshold is set within an adjustment range of 0%-100%) to adjust the inflow and outflow rates of water into and out of the lake in real time. Dynamic adjustment is achieved using flow meters and control valve actuators, with a control accuracy requirement of ±2%. Combining disturbance optimization data and control strategy data, an ecological water level regulation model is constructed. The model parameters include water area, water storage capacity, pollutant concentration, dissolved oxygen level, and carbon cycle indicators. Based on the dynamic control commands output by the model, water level control devices (such as gates and pumping stations) are driven to achieve real-time adjustment of the water level. The adjustment accuracy is ±0.05 meters, and the adjustment cycle is set from 30 minutes to 1 hour according to real-time monitoring data. Through the linkage of the data acquisition system and the control execution system, dynamic and precise control of the lake's ecological water level is achieved.

[0032] Preferably, step S1 specifically includes: Step S11: Acquire remote sensing monitoring data of the lake and perform image preprocessing to obtain remote sensing data of the lake to be processed; In this embodiment, high-resolution satellite remote sensing data is used as the primary source of remote sensing monitoring data for lakes. Specifically, Sentinel-2 or Landsat 8 multispectral imagery is employed, encompassing blue (wavelength approximately 0.45-0.52 μm), green (approximately 0.53-0.59 μm), red (approximately 0.64-0.67 μm), and near-infrared (approximately 0.85-0.88 μm) bands. After acquiring the imagery data, image preprocessing is performed, including radiometric correction, geometric correction, and atmospheric correction. Radiometric correction uses a radiative transfer model to calculate the radiance value received by the sensor and converts it to surface reflectance, employing parameters such as solar altitude angle and observation angle. Geometric correction, based on ground control points (GCPs) and digital elevation models (DEMs), combines precise projection transformation algorithms (such as polynomial transformations or RPC models) to perform spatial correction of the imagery, ensuring that the geospatial location error of the remote sensing imagery is controlled within 1 pixel (resolution is typically 10 meters or 30 meters). Atmospheric correction employs atmospheric correction models (such as the 6S or DOS algorithm) to remove the influence of atmospheric scattering and absorption on the image, ensuring that the image reflectivity data accurately reflects the true surface reflectivity. After image preprocessing, high spatial and spectral accuracy are achieved, resulting in GeoTIFF data with a spatial resolution of 10 meters and using the WGS84 geographic coordinate system.

[0033] Step S12: Extract water spectral features from the remote sensing data of the lake to be processed to obtain water spectral data; In this embodiment, spectral features of the lake are extracted from the remote sensing data. Using reflectance data from the blue, green, red, and near-infrared bands of the multispectral image, and considering the strong near-infrared absorption and high visible light reflectance of the water body, reflectance values ​​for each band are extracted. A band-combined feature vector is constructed based on the spectral band reflectance for subsequent water body identification calculations. During the extraction process, spectral data is acquired strictly at the pixel level, recording the reflectance values ​​of the four bands for each pixel. The data storage format adopts a multi-band raster data format to ensure the spatial and band correspondence of the data. Spectral feature extraction is performed using image processing tools, generating a water body spectral data set through pixel traversal calculations, ensuring no missing data and that the reflectance values ​​are within the range of 0 to 1, with an accuracy of three decimal places.

[0034] Step S13: Calculate the normalized difference water index based on the water spectral data; identify water regions based on the normalized difference water index to obtain water region images; fill in the broken edges of the water region images to obtain water body edge contour images. In this embodiment, the Normalized Difference Water Index (NDWI) is calculated based on water spectral data for water body region identification. The NDWI calculation formula is NDWI = (green band reflectance - near-infrared band reflectance) / (green band reflectance + near-infrared band reflectance). The calculation process iterates through all pixels, performing the calculation pixel by pixel. An NDWI threshold of 0.3 is set; all pixels with an NDWI value greater than or equal to 0.3 are identified as water bodies, while those with a value less than 0.3 are considered non-water bodies. This binarization result is used to construct a water body region image. To address the issues of edge breaks and clutter noise in water body regions, a morphological closing operation is used to fill the edges of the water body region image. Specifically, this involves image dilation using a 3×3 structuring element, followed by erosion to eliminate small holes and breaks. After the closing operation, a complete water body edge contour image is generated. Image processing utilizes professional remote sensing image analysis software with strictly fixed parameters to ensure the repeatability and accuracy of each step.

[0035] Step S14: Perform geographic coordinate correction based on the water body edge contour image to obtain water body boundary data; In this embodiment, geographic coordinate correction is performed on the water body edge contour image to ensure the spatial accuracy of the water body boundary data. Spatial registration is performed using a digital elevation model (DEM) and ground control point (GCP) data in conjunction with an image registration algorithm. The registration process employs an automatic matching method based on similar triangles to calculate the affine transformation matrix between the image and the geographic coordinate system, controlling the transformation accuracy to within one pixel of error. Combined with projection transformation, the image coordinate system is converted to the geographic coordinate system using the WGS84 geographic coordinate system to ensure consistency in subsequent GIS analysis. The correction results are exported in vector polygon format with meter-level accuracy, facilitating subsequent area calculations.

[0036] Step S15: Calculate the water area based on the water body boundary data to obtain the water body area data.

[0037] In this embodiment, the spatial analysis function of GIS software is utilized to call the polygon area calculation module, automatically identify the coordinates of the vertices of the boundary polygons, and calculate the actual land surface area using an ellipsoidal projection model. The calculation result is in square meters, and the area value is retained to two decimal places. During the calculation process, the boundary vertices are checked sequentially to ensure that the boundaries are closed and do not self-intersect, preventing area calculation errors. The area calculation is repeated on remote sensing images at multiple time points to achieve dynamic monitoring of water body area.

[0038] Preferably, step S2 specifically includes: Step S21: Measure the water depth based on the water area data to obtain the water depth data; In this embodiment, water depth data is obtained based on the water area data acquired in previous steps, combined with water depth measurement technology. Water depth measurement employs a multibeam sonar system (MBES), which consists of a surface buoy and an underwater sonar probe. It calculates water depth by transmitting and receiving sound signals. During measurement, the buoy is equipped with a GPS receiver to acquire the water surface coordinates in real time, achieving meter-level positioning accuracy. The sonar probe frequency is set to 200kHz to ensure fine resolution of the water depth measurement, covering a depth range of 0.5 meters to 50 meters with an accuracy of ±0.1 meters. The sonar equipment is deployed across the lake surface at fixed intervals (e.g., 10 meters) to form a dense grid of water depth sampling points. The collected water depth data is combined with the corresponding GPS coordinates. Outliers (such as measurement points with abrupt changes greater than twice the standard deviation) are removed through data cleaning. An interpolation algorithm (Kriging interpolation) is used to fill in data-deficient areas, generating a continuous bathymetric digital elevation model (BathymetricDEM). The water depth data accuracy is ensured to meet measurement accuracy requirements. The output format is raster data, and the spatial resolution is consistent with the water area data.

[0039] Step S22: Perform water body stratification based on water depth data to obtain water body stratification data; In this embodiment, an equal-depth layering method is used to divide the water depth range into several water layers of fixed thickness, with each layer thickness uniformly set to 1 meter. For example, when the maximum water depth is 20 meters, the layering is divided into 20 layers. Each layer is considered an independent depth interval. Using BathymetricDEM, the depth value of each pixel is mapped to the corresponding water layer number, forming water layer data. The water layer data is stored in the form of a three-dimensional array, recording the spatial location and depth information of each pixel at different depth layers. Efficient storage and retrieval of the layer data are achieved through spatial database management.

[0040] Step S23: Calculate the stratified volume based on the water stratification data to obtain the stratified water volume; perform volume summation calculation based on the stratified water volume to obtain the total water storage volume data; In this embodiment, for each water layer, the area of ​​all pixels within that layer is multiplied by the layer thickness (1 meter), and the volume of that layer is obtained by summing the results. The area of ​​a single pixel is determined based on the water area data, assumed to be 100 square meters (corresponding to a 10-meter × 10-meter pixel). The formula for calculating the volume of a single layer is: Volume = Pixel Area × Layer Thickness × Proportion of Water Depth. For some edge pixels with incomplete layer depth, a layer depth refinement adjustment is adopted, and the actual water depth is converted according to the layer thickness ratio to ensure the accuracy of the volume calculation. The total water volume of the entire lake is obtained by summing the volumes of each water layer, with the volume unit being cubic meters, and the calculation result is retained to two decimal places. The calculation process is completed using spatial analysis tools of a Geographic Information System (GIS) to ensure that the spatial information corresponds accurately with the volume data.

[0041] Step S24: Evaluate the water storage capacity based on the total water storage volume data to obtain water storage capacity data; In this embodiment, water storage capacity is defined as the ratio of the lake's maximum water storage volume to its historical average water volume. Historical average water volume data is derived from continuous hydrological measurements over the past five years, with the unit being cubic meters. The water storage capacity index is obtained through comparative calculations. A water storage capacity threshold of 0.8 is set during the analysis process; that is, when the current total water storage volume is less than 80% of the historical average water volume, the water storage capacity is considered insufficient. The calculation process utilizes a database to manage hydrological data and dynamically calculates the current water storage volume, with the result accurate to three decimal places. This index is used as a reference for subsequent water body management and regulation.

[0042] Step S25: Conduct water dilution testing based on water storage capacity data to obtain water dilution data; In this embodiment, a chemical sensor array is used to determine water dilution, and dilution detection is performed based on water storage capacity data. Multiple water sampling devices are deployed to collect water samples at different depths and locations, and measurements are taken using conductivity (EC), total dissolved solids (TDS), and salinity sensors. Measurement data are recorded in micro-Siemens units per centimeter (μS / cm). The normal conductivity range for water is set at 50-500 μS / cm. An anomaly is determined when three consecutive conductivity measurements show a change exceeding 50 μS / cm. Data is collected hourly, and data is uploaded to a database in real time via wireless transmission. Based on the collected data, a water dilution coefficient is calculated, defined as the ratio of the on-site conductivity to the historical average conductivity. A dilution coefficient greater than 1.2 indicates a high degree of water dilution. Dilution detection is achieved through data statistics and curve analysis to ensure the accuracy and timeliness of the results.

[0043] Step S26: Analyze water pollution based on water dilution data to obtain water pollution data.

[0044] In this embodiment, a multi-parameter water quality monitoring instrument was used to measure key pollution indicators such as dissolved oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen (NH3-N), total phosphorus (TP), and total nitrogen (TN). The measurement accuracy met the requirements of national environmental protection standards, with an accuracy of 0.01 mg / L for DO and 0.1 mg / L for COD. Water samples were collected at the same depth distribution as for dilution testing, and sampling was conducted twice daily. The monitoring data were combined with the water dilution coefficient, and a weighted statistical method was used to calculate the water pollution index, ranging from 0 to 100. An index exceeding 60 was considered excessive pollution. Pollution analysis was based on standard limits. Quality control was implemented during the analysis process to exclude outliers and ensure that the pollution data reflected the actual water quality.

[0045] Preferably, step S3 specifically includes: Step S31: Obtain lake aquatic organism data; In this embodiment, a combination of on-site sampling and laboratory testing was used to obtain aquatic biological data for the lake. A fixed sampling cycle was established (e.g., once per quarter), and continuous trawling sampling was conducted on the lake surface (0-2 meters deep) using a phytoplankton sampling net (30 micrometers mesh diameter, 30 cm diameter). The trawl speed was controlled at 1 m / s, and the trawl distance was set at 50 meters. The collected aquatic biological samples were placed in sealed containers and transported to the laboratory at a low temperature of 4°C. In the laboratory, microscopic counting was used to classify and statistically analyze the species and quantities of phytoplankton. A standard counting chamber (such as a Neubauer hemocytometer) was used to calculate the biological volume concentration in the samples, expressed as individuals per liter (ind / L). A complete aquatic biological dataset was formed by combining biodiversity index calculation methods (such as the Shannon index) with biological abundance data, including species distribution, quantity density, and biodiversity indicators.

[0046] Step S32: Analyze the distribution of aquatic organisms in the lake based on the aquatic organism data to obtain the distribution data of aquatic organisms in the lake; set up water sampling points based on the distribution data of aquatic organisms in the lake to obtain the water sampling point data; In this embodiment, Geographic Information System (GIS) technology is used to spatially locate the sampling points and their corresponding biological data. The accuracy of the sampling point coordinates is achieved using a GPS positioning device, with a positioning error not exceeding 3 meters. For the biological abundance and species data at each sampling point, a spatial interpolation algorithm (such as Inverse Distance Weighted Wavelet) is used to perform spatial continuity analysis, generating an aquatic organism distribution data layer. This layer displays the trend of biological abundance changes in different areas. Based on the spatial distribution map, water body sampling points are strategically placed to cover densely populated biological areas and areas with significant distribution changes, with the spacing between sampling points controlled within 100 meters to ensure the spatial representativeness of the data. The water body sampling point data includes point coordinates, sampling time, and corresponding biological indicator information. The data format adopts the standard GIS point data format (such as Shapefile) for easy subsequent data management and analysis.

[0047] Step S33: Perform dissolved oxygen sampling based on water sampling point data to obtain dissolved oxygen data; identify low dissolved oxygen areas based on dissolved oxygen data; In this embodiment, dissolved oxygen (DO) sampling was conducted at water sampling points. A portable dissolved oxygen meter was used, with a measurement range of 0-20 mg / L and an accuracy of 0.01 mg / L. Calibration employed a two-point calibration method using zero oxygen and saturated air to ensure measurement accuracy. During the measurement process, the sampling instrument probe was immersed at different depths at the water sampling points, with depths set at 0 meters, 1 meter, and 2 meters, and the corresponding DO values ​​were recorded. Sampling was uniformly scheduled within 2 to 4 hours after sunrise to avoid the influence of temperature fluctuations. The collected DO data was compiled in spreadsheet format, and a critical DO threshold of 2 mg / L was set according to the Chinese Water Quality Standard GB3838-2002; areas below this value were identified as low dissolved oxygen areas. Spatial statistical analysis was performed on the DO values ​​at the sampling points to identify the range and distribution of low DO areas. The data was output in the form of a GIS vector layer, including the boundary information of low dissolved oxygen areas.

[0048] Step S34: Detect iron and manganese precipitates in the low dissolved oxygen area to obtain iron and manganese precipitate data; determine dissolved oxygen anomalies based on the iron and manganese precipitate data to obtain dissolved oxygen anomaly data; In this embodiment, an underwater sampler was used to collect sediments from the bottom of the low-DO area, with the sampling depth controlled within 0-10 cm. The collected sediment samples were placed in sealed containers and transported at low temperature to the laboratory for chemical analysis. Atomic absorption spectrometry (AAS) was used to determine the iron (Fe) and manganese (Mn) content, with detection limits of 0.01 mg / kg and measurement accuracy ±2%. Sample pretreatment included drying, grinding, and acid digestion, and the instrument was calibrated using standard solutions. Samples with iron content exceeding 5,000 mg / kg and manganese content exceeding 1,000 mg / kg were considered to have abnormal iron-manganese precipitate abundance. The measurement results were statistically analyzed to form iron-manganese precipitate data, which were then matched with spatial coordinates to construct a precipitate distribution data layer.

[0049] Step S35: Detect phytoplankton metabolic imbalance based on dissolved oxygen anomaly data to obtain phytoplankton metabolic imbalance data; In this embodiment, metabolic index analysis was employed using collected phytoplankton species and quantity data, combined with DO content and sediment concentration. The photosynthetic rate and respiration rate of the phytoplankton were calculated. The photosynthetic rate was based on chlorophyll a concentration (measured by water sample fluorescence method, unit μg / L) and light intensity data (measured by a photometer, unit μmol photons·m). -2 ·s -1The respiration rate was calculated based on the rate of change in dissolved oxygen (DO). Metabolic imbalance was defined as a photosynthetic rate to respiration rate ratio (P / R) of less than 1, indicating that respiration was stronger than photosynthesis. Data was collected daily, and time-series statistical methods were used for analysis. Periods with P / R < 1 for more than 3 consecutive days were identified to obtain phytoplankton metabolic imbalance data. The data format was a time-space three-dimensional table.

[0050] Step S36: Assess lake carbon cycle disorder based on phytoplankton metabolic imbalance data to obtain lake carbon cycle disorder data.

[0051] In this embodiment, water carbon composition determination technology was employed to measure the concentrations of dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), and total organic carbon (TOC) using a high-temperature combustion non-dispersive infrared detector with a detection accuracy of 0.1 mg / L. Sampling points were strategically located based on areas of metabolic imbalance, with water samples collected at depths including the surface (0-1 meter) and the bottom (5-10 meters), and sampling was conducted monthly. The carbon cycle status was assessed using carbon balance calculations, incorporating data on water temperature, pH, and dissolved oxygen. The net ecological carbon flux of the lake was calculated, and positive or negative values ​​were used to distinguish between carbon sinks and carbon sources. The assessment results were presented as a numerical matrix, with spatial resolution corresponding to sampling point density and temporal resolution at the monthly level. Based on standard reference values, if the DOC concentration exceeded 3 mg / L, combined with metabolic imbalance indicators, carbon cycle disorder was confirmed, forming a comprehensive assessment dataset.

[0052] Preferably, step S35 specifically includes: Step S351: Identify areas of abnormal dissolved oxygen based on the dissolved oxygen anomaly data; In this embodiment, dissolved oxygen (DO) data is organized into a two-dimensional spatial-temporal data matrix according to the spatial coordinates of sampling points and sampling time, with units of mg / L. Based on the National Surface Water Environmental Quality Standard (GB3838-2002), a DO anomaly threshold of 2.0 mg / L is set; areas below this value are considered DO anomaly areas. Spatial clustering analysis is used to cluster all sampling points with DO below 2.0 mg / L according to their neighborhood relationships of no more than 50 meters, forming several anomaly areas. Each anomaly area is represented by a boundary polygon, and the boundary calculation uses the Alpha shape algorithm to ensure that the boundary closely matches the actual distribution of sampling points. The temporal continuity of the anomaly areas is confirmed using the time window method, requiring the anomaly state to last for at least 48 hours. The above processing is completed using spatial analysis tools on a GIS platform, outputting a vector data file containing the boundary coordinates and start and end time information of the anomaly areas in GeoJSON format for easy subsequent processing.

[0053] Step S352: Determine the photosynthetic rate of phytoplankton based on areas of abnormal dissolved oxygen; In this embodiment, a combination of on-site sampling and laboratory analysis was used to determine the photosynthetic rate of phytoplankton. First, at least three sampling points were set up in the abnormal area, with the distance between sampling points controlled within 30 meters. The rate of change in oxygen produced by photosynthesis at different water depths (0 m, 0.5 m, 1 m) was measured using an underwater photosynthesis analyzer (such as a Clark oxygen electrode). The sampling time was fixed at 30 minutes, and the change in dissolved oxygen concentration per minute was recorded. The net oxygen production per unit time (mgO2·L) was calculated. -1 ·h -1 Chlorophyll a concentration was measured simultaneously using the water sample fluorescence method. The sample volume was 1 liter, filtered, and then counted for fluorescence, with an accuracy of ±0.1 μg / L. The photosynthetic rate was calculated using the formula: P = ΔDO / Δt × V, where ΔDO is the change in dissolved oxygen concentration during the measurement period, Δt is the time interval, and V is the unit volume. The laboratory data were compiled into a photosynthetic rate data table, including the coordinates and depth of the sampling points, photosynthetic rate, and chlorophyll a concentration information.

[0054] Step S353: Determine the phytoplankton respiration rate based on areas of abnormal dissolved oxygen; In this embodiment, the dissolved oxygen consumption method is used to determine the phytoplankton respiration rate. The sampling point and depth are the same as in step S352. Using the light-blocking water sample bottle method, the collected water sample is sealed in an opaque container and placed in a constant-temperature water bath to maintain the water temperature at the sampling site (±0.5°C error). The linear decrease of DO concentration in the container over time is recorded. The sampling time is set to 2 hours, and DO values ​​are recorded every 15 minutes. The respiration rate is calculated using the formula R = -ΔDO / Δt × V, where the negative sign indicates a decrease in DO concentration. The unit of respiration rate is mgO2·L. -1 ·h -1 Data processing excluded outliers and used linear regression to fit the trend of DO concentration changes. The final result was a respiratory rate dataset containing sampling point coordinates, sampling depth, respiratory rate values, and ambient temperature information.

[0055] Step S354: Calculate net primary productivity based on phytoplankton photosynthetic rate and phytoplankton respiration rate to obtain net primary productivity data; In this embodiment, net primary productivity (NPP) is calculated using photosynthetic rate (P) and respiration rate (R) data. The calculation formula is NPP=PR, with units of mgO2·L. -1 ·h -1 The net primary productivity (NPP) and R values ​​at each sampling point and corresponding depth were calculated point-by-point to form a matrix. The data were processed using statistical software, and a weighted average was calculated for data from different depths at the same sampling point. The weights were determined based on the distribution of light intensity data at different depths. Light intensity was measured using an underwater illuminometer, with a measurement range of 0-2000 μmol photons·m. -2·s -1 Data was collected every 5 minutes, using the average daily irradiance. The weighted average formula was NPP_avg=Σ(NPP_i×w_i), where w_i is the proportion of irradiance at depth i. The calculated net primary productivity data was organized according to spatial coordinates and output as a spatial data table for subsequent ecological analysis.

[0056] Step S355: Assess phytoplankton metabolic imbalance based on net primary productivity data to obtain phytoplankton metabolic imbalance data.

[0057] In this embodiment, metabolic imbalance is defined as a negative net primary productivity (NPP < 0), i.e., a state where the respiration rate is greater than the photosynthetic rate. Based on the NPP values ​​in the spatial data table, all sampling points with NPP < 0 are marked using spatial statistical methods. A spatial clustering algorithm is then used to merge adjacent negative value points, forming the boundary of the metabolic imbalance region. The region boundary is calculated using a Voronoi diagram segmentation and point density threshold (≥ 3 points / 100 square meters). The analysis results are output as a vector layer of the metabolic imbalance region, including the region boundary coordinates, the intensity of metabolic imbalance (represented by the absolute value of the mean negative NPP), and a time label. The data format is GeoTIFF or Shapefile to ensure compatibility with other spatial data and support subsequent ecological regulation.

[0058] Preferably, step S36 specifically includes: Step S361: Perform eutrophication analysis on the phytoplankton metabolic imbalance data to obtain eutrophication data of the water body; In this embodiment, spatial vector data of the aforementioned phytoplankton metabolic imbalance area is used in conjunction with water quality monitoring data on total phosphorus (TP), total nitrogen (TN), and chlorophyll a (Chl-a) concentrations for determination. Eutrophication standards are set based on the combined UPSA and my country's "Technical Regulations for Surface Water Resources Quality Assessment": TP ≥ 0.02 mg / L, TN ≥ 0.5 mg / L, and Chl-a ≥ 10 μg / L are considered eutrophic. Data extraction and statistical analysis of these indicators are performed on the corresponding water quality monitoring points within the phytoplankton metabolic imbalance area, calculating the mean and standard deviation of each indicator. A multi-indicator joint determination method is used; if all three indicators exceed the threshold, the area is defined as a eutrophication area. Eutrophication data is output as a spatial vector layer, including the eutrophication area boundary, the mean concentrations of multiple indicators, and the monitoring time.

[0059] Step S362: Identify the sources of eutrophication in water bodies based on eutrophication data and obtain eutrophication source data. In this embodiment, topographic data, land use data, pollutant discharge point information, and hydrological flow data of the surrounding watershed are collected. Topographic data uses a digital elevation model (DEM) with a resolution of at least 10 meters; land use data is derived from remote sensing image classification results within the past year; discharge point information includes industrial discharge outlets, agricultural non-point sources, and domestic sewage discharge points. Pollutant load distribution is calculated based on watershed zoning, combined with hydrodynamic flow field simulation to determine pollutant transport paths. Pollutant transport paths are calculated using a two-dimensional hydrodynamic model (such as a two-dimensional shallow water wave equation solver), with parameter settings including flow velocity (0-1 m / s), water depth (1-5 m), and pollutant attenuation coefficient (0.1-0.3 d). -1 The source tracing results are based on the superposition of pollutant concentration gradients and flow directions to form the coordinates and intensity distribution of pollution sources. The output is a spatial distribution vector map layer of pollution sources, including the location coordinates, type classification, and pollutant contribution ratio of each source.

[0060] Step S363: Detect organic carbon content based on eutrophication source data of the water body to obtain organic carbon content data; In this embodiment, sampling points were set at the locations of each pollution source and within 500 meters downstream. Water samples were collected at stratified depths (0.5 meters, 1 meter, and 2 meters), with a sampling volume of 1 liter per layer. The total organic carbon (TOC) content of the water samples was determined using the potassium dichromate redox method (Walkley-Black method), with a measurement accuracy of ±0.1 mg / L. Water sample pretreatment included filtration (using a 0.45 μm filter membrane) and dilution to ensure that the concentration measurement was within the linear range. All data were labeled with sampling time, GPS coordinates of the sampling location, and water depth. The measurement results were compiled into a spatial data table of organic carbon content, including TOC values ​​and their spatial distribution.

[0061] Step S364: Perform carbon flow simulation based on organic carbon content data to obtain carbon flow data; identify the carbon flow direction based on the carbon flow data to obtain carbon flow direction data; In this embodiment, a two-dimensional transport model is constructed using flow field data combined with organic carbon concentration data. The finite volume method is applied to discretize the governing equations, which include the convective diffusion transport and degradation reactions of organic carbon. Parameter settings include the water velocity vector field (m / s) and the diffusion coefficient (1×10⁻⁶). -5 m² / s), organic carbon degradation rate constant (k=0.05d). -1 The simulation time step was set to 10 minutes, and the calculation period covered at least 7 days to ensure complete capture of the dynamic process of carbon flow. The simulation output is spatiotemporal distribution data of organic carbon concentration. Based on the simulation results, the carbon flow direction was identified by calculating the concentration gradient direction and the velocity vector direction. The carbon flow direction data is represented in the form of a vector field, including the flow angle (0°-360°) and the flow velocity magnitude (m / s), and the output format is a GIS-recognizable vector data file.

[0062] Step S365: Perform sediment analysis based on carbon flow direction data to obtain sediment data; calculate bottom carbon flux based on sediment data; In this embodiment, sampling points were selected at areas of significant sediment deposition and the end of carbon inflow. A handheld multi-parameter water quality analyzer and sediment sampler were used to collect bottom sediment samples. The sediment sampling depth was the first 10 cm, and the samples were stored in a clean inorganic carbon container to prevent organic degradation. The organic carbon content was determined in the laboratory using the high-temperature furnace recalcination method, with an accuracy of ±0.05%. Simultaneously, a particle size analyzer was used to determine the sediment particle size distribution, and the carbon enrichment characteristics were inferred based on the carbon content. The bottom carbon flux was calculated based on the sediment thickness and organic carbon concentration using the formula F = D × C × R, where F is the bottom carbon flux (mgC·m³). -2 ·d -1 D is the sediment settling rate (cm·d). -1 C represents the organic carbon concentration (mgC·g). -1 R is the density of the sediment (g·cm³). -3 Each parameter was obtained through field measurements and laboratory analysis. Bottom carbon flux data are stored in spatial raster format.

[0063] Step S366: Perform lake carbon cycle disorder analysis based on bottom carbon flux to obtain lake carbon cycle disorder data.

[0064] In this embodiment, bottom carbon flux data is comprehensively analyzed in conjunction with upper layer organic carbon input and phytoplankton metabolic imbalance data. Time-series statistical analysis is used to calculate the balance between carbon input and sedimentary carbon emissions. Carbon cycle disorder is defined as a bottom carbon flux greater than 40 mg C·m³. -2 ·d -1 This is accompanied by areas of abnormal dissolved oxygen in the water. Turbulent regions are identified through spatial overlay analysis, and vector boundary data is output, including turbulence intensity indicators (such as bottom carbon flux and dissolved oxygen concentration), time stamps, and spatial coordinates. The data format is GeoTIFF, facilitating integration with remote sensing and water quality monitoring data.

[0065] Preferably, step S4 specifically includes: Step S41: Calculate the carbon dioxide release rate based on lake carbon cycle disorder data; In this embodiment, bottom carbon flux data is obtained, with units of mgC·m. -2 ·d -1The data includes the corresponding water area. Using the organic carbon release rate measured by carbon flux into the water body, and combining this with the stoichiometric relationships in the organic carbon oxidation process, the carbon flux is converted into CO2 release. The conversion factor is calculated based on the ratio of the molecular weight of carbon (12 g / mol) to the molecular weight of CO2 (44 g / mol), specifically using the formula: CO2 release rate (mgCO2·m³ / mol). -2 ·d -1 = Bottom carbon flux × (44 / 12). Simultaneously, combining water temperature, pH, and dissolved oxygen concentration data, Henry's law is used to calculate the CO2 gas-water exchange rate in the water body. The gas-water exchange rate coefficient is adjusted according to environmental parameters, typically ranging from 0.1 to 0.3 m / d. By integrating the bottom release rate and the gas-water exchange rate, the total CO2 release rate is calculated. The results are output as daily average CO2 release (unit: kgCO2 / d) in a spatial grid format, with the grid size generally set to 100m × 100m for ease of subsequent analysis.

[0066] Step S42: Identify disturbance-sensitive areas based on carbon dioxide release rate; regulate water flow velocity based on disturbance-sensitive areas to obtain optimized water disturbance data; In this embodiment, a threshold screening method is used to select CO2 release rates higher than 50 mg CO2·m -2 ·d -1 The areas were marked as disturbance-sensitive zones. Based on the spatial distribution of these zones, and combined with water flow velocity monitoring data (measured by an ultrasonic current meter with an accuracy of ±0.01 m / s and a sampling frequency of once per hour), spatial interpolation of the flow velocity was performed, and the water flow velocity field was obtained using the Kriging interpolation method. For abnormal flow velocity values ​​within the disturbance-sensitive zones, the flow velocity was regulated using regulating sluice gates or pumps, with the regulation range limited to between 0.05 m / s and 0.3 m / s, ensuring that the flow velocity met the water agitation requirements while avoiding excessive disturbance that could lead to ecological damage. After the regulation measures were implemented, optimized water disturbance data was generated, including the adjusted flow velocity field data and corresponding time labels. The regulation parameters and flow velocity changes were stored in a time-series database for easy historical tracking and dynamic analysis.

[0067] Step S43: Calculate pollutant concentration based on water pollution data; perform diffusion simulation based on pollutant concentration to obtain pollutant diffusion simulation data; In this embodiment, pollutants include nitrogen, phosphorus, heavy metals, and organic pollutants. The concentration of each pollutant is measured in mg / L. Sampling points cover the main areas of the entire lake, with sampling depths including the surface (0.5m) and middle layer (2m). After water sample collection, standard analytical methods were used for determination. Nitrogen and phosphorus were measured using spectrophotometry, with detection limits of 0.01 mg / L and 0.005 mg / L, respectively; heavy metals were measured using atomic absorption spectrometry, with a detection limit of 0.001 mg / L; and organic pollutants were measured using gas chromatography-mass spectrometry, with a detection limit of 0.0001 mg / L. When calculating the average pollutant concentration, a spatially weighted average method was used, with weighting coefficients determined based on the influence of water depth and distance from the shore at the sampling points. Pollutant concentration data underwent time synchronization correction to ensure all data correspond to the same time window. The pollutant diffusion simulation employed a two-dimensional water quality diffusion equation, solved numerically using the finite difference method. The simulation grid size was 50m × 50m, the time step was 10 minutes, and the total simulation duration was 72 hours. The boundary conditions are set to pollutant concentration values ​​at the lake's inlet and outlet, using observed data as input. The diffusion coefficient is adjusted based on water temperature and wind speed, typically ranging from 0.5 to 1.5 m² / s. The simulation output is spatiotemporally distributed pollutant concentration data in NetCDF format, supporting multi-period queries.

[0068] Step S44: Optimize the inlet and outlet control strategies based on pollutant diffusion simulation data to obtain inlet and outlet control strategy data; In this embodiment, the geographical locations of the inlet and outlet and their water flow regulation capacity parameters, such as maximum flow rate (m³ / s) and opening angle range (0°-90°), are determined. The concentration distribution of pollutants near the inlet and outlet in the simulation results is used to analyze pollutant retention and outflow. A multi-objective optimization function is established based on the control strategy objectives, including minimizing pollutant concentration and achieving water flow equilibrium. Constraints include the maximum regulation capacity of the inlet and outlet and upper and lower limits for water level control. A genetic algorithm is used for optimization, with parameters set as follows: population size 50, number of iterations 100, crossover probability 0.8, and mutation probability 0.05. During optimization, the control variables are the opening angle of the inlet and outlet and the flow rate. Finally, the optimal combination of control parameters for the inlet and outlet is obtained, forming the inlet and outlet control strategy data. The data is saved in time series format, including regulation time points, opening angles, and flow rate values, facilitating implementation.

[0069] Step S45: Construct an ecological water level regulation model based on water disturbance optimization data and inlet / outlet control strategy data, and conduct dynamic regulation of lake ecological water level based on the ecological water level regulation model to obtain dynamic water level regulation data.

[0070] In this embodiment, the model employs a combination of physical and empirical methods. Input variables include the optimized flow velocity field after water disturbance, water level (in meters), inlet and outlet flow rates, and opening angles. The model establishment process includes a dynamic water level equilibrium equation, a water quality transport equation, and an ecological response equation. The dynamic water level equilibrium equation, based on the principle of continuity, uses a discrete time step of 0.1 hours for water volume calculation and combines inlet and outlet flow rate adjustment data to achieve dynamic water level adjustment. The ecological response equation links water velocity, water level, phytoplankton growth, and water quality change parameters through empirical formulas. These parameters are obtained through regression analysis of historical monitoring data. The model achieves the goal of dynamic water level control through time series calculations. The output results are spatiotemporally continuous dynamic water level control data, including water level (meters), flow velocity (m / s), inlet and outlet opening status, and flow rates. The data format is CSV and GIS compatible, facilitating on-site control and subsequent analysis.

[0071] Preferably, step S45 specifically includes: Step S451: Construct a hydrodynamic simulation layer based on optimized water disturbance data; In this embodiment, the input water disturbance optimization data includes regulated flow velocity field data, with a spatial resolution of 100 m × 100 m and a temporal resolution of 10 minutes per frame. A two-dimensional hydrodynamic grid is constructed based on the lake's digital elevation model (DEM) and lakebed topographic measurement data, with the grid cell size corresponding to the spatial resolution of the flow velocity data. The two-dimensional shallow water equations are discretized using the finite volume method to calculate the water flow state. The parameters used in the model include the hydrodynamic viscosity coefficient ν, typically ranging from 1 × 10⁻⁶. -6 Up to 1×10 -4 The flow rate is m² / s, and the water density ρ is fixed at 1000 kg / m³. Boundary conditions are set as fixed water level and flow rate, with upper and lower limits for the water level determined based on observation station data, ranging from ±0.3 meters. A time step of 60 seconds is selected to ensure numerical stability. An explicit time-progression method is used for iterative calculations, with the number of iterations determined by the simulation duration, typically 10,080 steps per week of simulation. The hydrodynamic simulation layer outputs spatiotemporally continuous water level distribution, flow velocity vector field, and flow direction angle, in a two-dimensional matrix and vector field file format, compatible with GIS systems. This simulation layer serves as the foundational data source for subsequent ecological water level regulation.

[0072] Step S452: Construct the inlet and outlet scheduling layer based on the inlet and outlet control strategy data; In this embodiment, the inlet and outlet control strategy data includes a control time series, opening angle (in degrees, range 0°-90°), and corresponding flow rate adjustment (in m³ / s). The scheduling layer establishes a time-series control table based on this data, with a time resolution of once every 10 minutes, covering a period of at least 7 days. The actual flow rate adjustment capability of the inlet and outlet is obtained through physical measurements, with a maximum flow rate limit set at 10 m³ / s and a minimum adjustable flow rate of 0.1 m³ / s. The scheduling layer encodes the opening status of the inlet and outlet within each time period to ensure accurate mapping between scheduling parameters and actual flow output. Based on the flow balance principle, the scheduling layer calculates the flow rate adjustment of all inlet and outlet, and interacts in real-time with the water body flow boundary conditions in the hydrodynamic simulation layer. The scheduling layer inputs the inlet and outlet flow rate parameters to the hydrodynamic simulation layer in real-time through a program interface to achieve dynamic adjustment. The output data is a time-series flow rate adjustment parameter file, containing timestamps, opening angles, and flow rate values, stored in a structured text format, and supports dynamic retrieval.

[0073] Step S453: Construct an ecological water level regulation model based on the hydrodynamic simulation layer and the inlet / outlet scheduling layer; In this embodiment, the model architecture adopts a layered design. The bottom layer is the hydrodynamic simulation module, responsible for calculating water level changes and flow conditions. The middle layer is the inlet / outlet scheduling module, responsible for dynamically adjusting the flow rate. The top layer is the ecological regulation module, which makes water level adjustment decisions based on hydrological and water quality information. Input parameters include the water level height (in meters), flow velocity (m / s) output from the hydrodynamic simulation layer, the flow rate regulation parameters from the inlet / outlet scheduling layer, and ecological index parameters (such as water temperature and dissolved oxygen concentration). The model calculates dynamic changes in water volume based on continuity and equilibrium equations. The equations use an explicit numerical discretization method, with a time step set to 30 seconds to meet the computational accuracy requirements. Inlet / outlet scheduling is implemented through closed-loop feedback control, adjusting the flow rate regulation strategy based on the current water level deviation and ecological indicators. The ecological regulation module combines phytoplankton metabolic data and eutrophication indicators to set the water level regulation range, typically ±0.2 meters from the baseline water level. The model integrates a real-time data input interface to achieve dynamic operation. Outputs include dynamic water level regulation schemes and corresponding flow rate regulation instructions. Data formats support XML and CSV for easy system access and manual viewing.

[0074] Step S454: Dynamically regulate the lake's ecological water level according to the ecological water level regulation model to obtain dynamic water level regulation data.

[0075] In this embodiment, the dynamic control process adopts a time-series simulation method, with a control cycle set to 24 hours and a time step of 5 minutes. Each calculation is based on the water level and flow status of the previous moment, and updated by combining real-time meteorological data (wind speed range 0-10 m / s, air temperature range 0-35℃) and water quality monitoring data (dissolved oxygen concentration range 3-10 mg / L). The control system outputs spatiotemporally continuous water level height (unit: meters) data, covering all key areas of the lake, with a spatial resolution corresponding to the grid size of the hydrodynamic simulation layer. The control data also includes the real-time opening status of the inlet and outlet and the flow value, with numerical accuracy reaching three decimal places. All control data is stored in timestamp order and managed using a time-series database. The dynamic control results are transmitted to the on-site sluice gate execution system via an interface, with an execution frequency of once every 10 minutes. The data is also backed up in CSV and GIS compatible formats for easy subsequent analysis and verification.

[0076] Optionally, this specification also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any one of the lake ecological water level dynamic control methods based on multi-source data.

[0077] Preferably, this specification also provides a lake ecological water level dynamic control system based on multivariate data, used to execute the lake ecological water level dynamic control method based on multivariate data as described above. The lake ecological water level dynamic control system based on multivariate data includes: The water area calculation module is used to acquire lake remote sensing monitoring data; identify water body boundaries based on the lake remote sensing monitoring data to obtain water body boundary data; and calculate the water body area based on the water body boundary data to obtain water body area data. The water pollution analysis module is used to analyze water storage capacity based on water body area data to obtain water storage capacity data; to perform water dilution detection based on water storage capacity data to obtain water dilution data; and to perform water pollution analysis based on water dilution data to obtain water pollution data. The lake carbon cycle disorder assessment module is used to acquire lake aquatic organism data; identify dissolved oxygen anomalies based on the lake aquatic organism data to obtain dissolved oxygen anomaly data; detect phytoplankton metabolic imbalance based on the dissolved oxygen anomaly data to obtain phytoplankton metabolic imbalance data; and assess lake carbon cycle disorder based on the phytoplankton metabolic imbalance data to obtain lake carbon cycle disorder data. The ecological water level regulation model construction module is used to optimize water body disturbance based on lake carbon cycle disorder data to obtain optimized water body disturbance data; optimize inlet and outlet control strategies based on water pollution data to obtain inlet and outlet control strategy data; construct an ecological water level regulation model based on the optimized water body disturbance data and inlet and outlet control strategy data; and perform dynamic regulation of lake ecological water level based on the ecological water level regulation model to obtain dynamic water level regulation data.

[0078] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0079] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for dynamic regulation of lake ecological water level based on multivariate data, characterized in that, Includes the following steps: Step S1: Acquire remote sensing monitoring data of the lake; Water body boundary identification is performed based on lake remote sensing monitoring data to obtain water body boundary data; The water body area is calculated based on the water body boundary data to obtain the water body area data. Step S2: Analyze the water storage capacity based on the water body area data to obtain water storage capacity data; conduct water dilution testing based on the water storage capacity data to obtain water dilution data; and analyze water pollution based on the water dilution data to obtain water pollution data. Step S3: Obtain lake aquatic organism data; identify dissolved oxygen anomalies based on lake aquatic organism data to obtain dissolved oxygen anomaly data; detect phytoplankton metabolic imbalance based on dissolved oxygen anomaly data to obtain phytoplankton metabolic imbalance data; assess lake carbon cycle disorder based on phytoplankton metabolic imbalance data to obtain lake carbon cycle disorder data. Step S4: Optimize water disturbance based on lake carbon cycle disorder data to obtain optimized water disturbance data; Based on water pollution data, the control strategies for inlets and outlets are optimized to obtain inlet and outlet control strategy data. Based on water disturbance optimization data and inlet and outlet control strategy data, an ecological water level regulation model is constructed, and the lake's ecological water level is dynamically regulated based on the ecological water level regulation model to obtain dynamic water level regulation data.

2. The method for dynamic regulation of lake ecological water level based on multivariate data according to claim 1, characterized in that, Step S1 is as follows: Step S11: Acquire remote sensing monitoring data of the lake and perform image preprocessing to obtain remote sensing data of the lake to be processed; Step S12: Extract water spectral features from the remote sensing data of the lake to be processed to obtain water spectral data; Step S13: Calculate the normalized difference water index based on the water spectral data; identify water regions based on the normalized difference water index to obtain water region images; fill in the broken edges of the water region images to obtain water body edge contour images. Step S14: Perform geographic coordinate correction based on the water body edge contour image to obtain water body boundary data; Step S15: Calculate the water area based on the water body boundary data to obtain the water body area data.

3. The method for dynamic regulation of lake ecological water level based on multivariate data according to claim 1, characterized in that, Step S2 is as follows: Step S21: Measure the water depth based on the water area data to obtain the water depth data; Step S22: Perform water body stratification based on water depth data to obtain water body stratification data; Step S23: Calculate the stratified volume based on the water stratification data to obtain the stratified water volume; perform volume summation calculation based on the stratified water volume to obtain the total water storage volume data; Step S24: Evaluate the water storage capacity based on the total water storage volume data to obtain water storage capacity data; Step S25: Conduct water dilution testing based on water storage capacity data to obtain water dilution data; Step S26: Analyze water pollution based on water dilution data to obtain water pollution data.

4. The method for dynamic regulation of lake ecological water level based on multivariate data according to claim 1, characterized in that, Step S3 is as follows: Step S31: Obtain lake aquatic organism data; Step S32: Analyze the distribution of aquatic organisms in the lake based on the aquatic organism data to obtain the distribution data of aquatic organisms in the lake; set up water sampling points based on the distribution data of aquatic organisms in the lake to obtain the water sampling point data; Step S33: Perform dissolved oxygen sampling based on water sampling point data to obtain dissolved oxygen data; identify low dissolved oxygen areas based on dissolved oxygen data; Step S34: Detect iron and manganese precipitates in the low dissolved oxygen area to obtain iron and manganese precipitate data; determine dissolved oxygen anomalies based on the iron and manganese precipitate data to obtain dissolved oxygen anomaly data; Step S35: Detect phytoplankton metabolic imbalance based on dissolved oxygen anomaly data to obtain phytoplankton metabolic imbalance data; Step S36: Assess lake carbon cycle disorder based on phytoplankton metabolic imbalance data to obtain lake carbon cycle disorder data.

5. The method for dynamic regulation of lake ecological water level based on multivariate data according to claim 4, characterized in that, Step S35 is as follows: Step S351: Identify areas of abnormal dissolved oxygen based on the dissolved oxygen anomaly data; Step S352: Determine the photosynthetic rate of phytoplankton based on areas of abnormal dissolved oxygen; Step S353: Determine the phytoplankton respiration rate based on areas of abnormal dissolved oxygen; Step S354: Calculate net primary productivity based on phytoplankton photosynthetic rate and phytoplankton respiration rate to obtain net primary productivity data; Step S355: Assess phytoplankton metabolic imbalance based on net primary productivity data to obtain phytoplankton metabolic imbalance data.

6. The method for dynamic regulation of lake ecological water level based on multivariate data according to claim 4, characterized in that, Step S36 is as follows: Step S361: Perform eutrophication analysis on the phytoplankton metabolic imbalance data to obtain eutrophication data of the water body; Step S362: Identify the sources of eutrophication in water bodies based on eutrophication data and obtain eutrophication source data. Step S363: Detect organic carbon content based on eutrophication source data of the water body to obtain organic carbon content data; Step S364: Perform carbon flow simulation based on organic carbon content data to obtain carbon flow data; identify the carbon flow direction based on the carbon flow data to obtain carbon flow direction data; Step S365: Perform sediment analysis based on carbon flow direction data to obtain sediment data; calculate bottom carbon flux based on sediment data; Step S366: Perform lake carbon cycle disorder analysis based on bottom carbon flux to obtain lake carbon cycle disorder data.

7. The method for dynamic regulation of lake ecological water level based on multivariate data according to claim 1, characterized in that, Step S4 is as follows: Step S41: Calculate the carbon dioxide release rate based on lake carbon cycle disorder data; Step S42: Identify disturbance-sensitive areas based on carbon dioxide release rate; regulate water flow velocity based on disturbance-sensitive areas to obtain optimized water disturbance data; Step S43: Calculate pollutant concentration based on water pollution data; perform diffusion simulation based on pollutant concentration to obtain pollutant diffusion simulation data; Step S44: Optimize the inlet and outlet control strategies based on pollutant diffusion simulation data to obtain inlet and outlet control strategy data; Step S45: Construct an ecological water level regulation model based on water disturbance optimization data and inlet / outlet control strategy data, and conduct dynamic regulation of lake ecological water level based on the ecological water level regulation model to obtain dynamic water level regulation data.

8. The method for dynamic regulation of lake ecological water level based on multivariate data according to claim 7, characterized in that, Step S45 is as follows: Step S451: Construct a hydrodynamic simulation layer based on optimized water disturbance data; Step S452: Construct the inlet and outlet scheduling layer based on the inlet and outlet control strategy data; Step S453: Construct an ecological water level regulation model based on the hydrodynamic simulation layer and the inlet / outlet scheduling layer; Step S454: Dynamically regulate the lake's ecological water level according to the ecological water level regulation model to obtain dynamic water level regulation data.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the lake ecological water level dynamic regulation method based on multivariate data as described in any one of claims 1 to 8.

10. A dynamic control system for lake ecological water level based on multivariate data, characterized in that, For executing the lake ecological water level dynamic control method based on multivariate data as described in claim 1, the lake ecological water level dynamic control system based on multivariate data comprises: The water area calculation module is used to acquire lake remote sensing monitoring data; identify water body boundaries based on the lake remote sensing monitoring data to obtain water body boundary data; and calculate the water body area based on the water body boundary data to obtain water body area data. The water pollution analysis module is used to analyze water storage capacity based on water body area data to obtain water storage capacity data; to perform water dilution detection based on water storage capacity data to obtain water dilution data; and to perform water pollution analysis based on water dilution data to obtain water pollution data. The lake carbon cycle disorder assessment module is used to acquire lake aquatic organism data; identify dissolved oxygen anomalies based on the lake aquatic organism data to obtain dissolved oxygen anomaly data; detect phytoplankton metabolic imbalance based on the dissolved oxygen anomaly data to obtain phytoplankton metabolic imbalance data; and assess lake carbon cycle disorder based on the phytoplankton metabolic imbalance data to obtain lake carbon cycle disorder data. The ecological water level regulation model construction module is used to optimize water body disturbance based on lake carbon cycle disorder data to obtain optimized water body disturbance data; optimize inlet and outlet control strategies based on water pollution data to obtain inlet and outlet control strategy data; construct an ecological water level regulation model based on the optimized water body disturbance data and inlet and outlet control strategy data; and perform dynamic regulation of lake ecological water level based on the ecological water level regulation model to obtain dynamic water level regulation data.

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