Lake ecological water level dynamic regulation and control method and system based on multivariate data and medium

Abstract

The invention relates to the technical field of water resource management, in particular to a lake ecological water level dynamic regulation and control method and system based on multivariate data and a medium. The method comprises the following steps: acquiring lake remote sensing monitoring data; performing water body boundary identification according to the lake remote sensing monitoring data to obtain water body boundary data; performing water body area calculation based on the water body boundary data to obtain water body area data; performing water storage capacity analysis according to the water body area data to obtain water storage capacity data; performing water body dilution detection according to the water storage capacity data to obtain water body dilution data; performing water quality pollution analysis according to the water body dilution data to obtain water quality pollution data; acquiring lake aquatic organism data; and carrying out dissolved oxygen abnormity identification according to the lake aquatic organism data to obtain dissolved oxygen abnormity data. Precise management of the lake ecological water level is achieved based on the water resource management technology, and the water environment quality and the ecological system stability are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water resource management, and particularly relates to a lake ecological water level dynamic regulation method and system based on multi-element data and a medium. BACKGROUND

[0002] Traditional water body dynamic regulation model construction mainly relies on a single data source, such as water level or meteorological data, lacks comprehensive consideration of ecological factors such as aquatic organisms, water pollution and carbon cycle, and has weak ecological response ability; the in-out water inlet control strategy design is mainly based on fixed threshold or rules, and it is difficult to flexibly adjust according to the pollutant diffusion characteristics and disturbance response, and the regulation result does not have self-adaptability; the disturbance optimization usually ignores the coupling relationship between lake carbon cycle and phytoplankton metabolism, cannot accurately identify the disturbance sensitive area and response mechanism, and is difficult to effectively intervene in the ecological disorder state; the existing system is mainly based on static parameter input, lacks fusion processing capability of multi-source dynamic data such as remote sensing, aquatic organisms and water body dilution, causes water level regulation feedback lag, and the overall regulation effect is not good. SUMMARY

[0003] Therefore, it is necessary to provide a lake ecological water level dynamic regulation method and system based on multi-element data to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a lake ecological water level dynamic regulation method based on multi-element data comprises the following steps: Step S1: obtaining lake remote sensing monitoring data; identifying water body boundary according to the lake remote sensing monitoring data to obtain water body boundary data; calculating water body area based on the water body boundary data to obtain water body area data; Step S2: analyzing 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; Step S3: obtaining lake aquatic organism data; identifying dissolved oxygen anomaly according to the lake aquatic organism data to obtain dissolved oxygen anomaly data; detecting phytoplankton metabolism imbalance according to the dissolved oxygen anomaly data to obtain phytoplankton metabolism imbalance data; evaluating lake carbon cycle disorder according to the phytoplankton metabolism imbalance data to obtain lake carbon cycle disorder data; Step S4: optimizing water body disturbance according to the lake carbon cycle disorder data to obtain water body disturbance optimization data; optimizing in-out water inlet control strategy according to the water pollution data to obtain in-out water inlet control strategy data; constructing an ecological water level regulation model according to the water body disturbance optimization data and the in-out water inlet control strategy data, and dynamically regulating the lake ecological water level according to the ecological water level regulation model to obtain water level dynamic regulation data.

[0005] The present application can accurately obtain the actual range and variation trend of the water body by acquiring lake remote sensing monitoring data and identifying the water body boundary, providing a high-precision basis for subsequent area calculation and water storage capacity analysis; by combining water area calculation with water depth measurement, the dynamic evaluation of the actual water storage capacity of the lake can be realized, which helps to comprehensively master the water resources carrying capacity; on this basis, water body dilution detection and water quality pollution analysis can not only reflect the influence of exogenous pollution input and endogenous release, but also dynamically reveal the dilution and enrichment process of pollutants in the lake body; by introducing lake aquatic organism data to carry out dissolved oxygen anomaly identification, the local anoxia or hyperoxia phenomenon caused by biological activity can be mastered in real time, and the ecological response identification accuracy is improved; combined with phytoplankton metabolic imbalance detection, the metabolic imbalance process between primary production and respiration in the aquatic ecosystem is further revealed, providing a key criterion for carbon cycle state; through carbon cycle disorder evaluation, the quantitative analysis of comprehensive ecological processes such as eutrophication, water body organic carbon load and sediment carbon release can be realized; water body disturbance optimization not only combines carbon dioxide release rate and disturbance sensitive area identification, but also realizes precise intervention on the disorder area through flow regulation, improving the ecological regulation pertinence; the optimization of inlet and outlet control strategy integrates pollutant concentration and diffusion simulation results, making the water exchange more ecological adaptability and pollution prevention and control capacity; the finally constructed ecological water level regulation model integrates hydrodynamic simulation and inlet and outlet regulation two core levels, realizes the accurate dynamic regulation of the lake ecological water level, improves the system's fusion ability of multi-source ecological data and the real-time responsiveness of the regulation strategy, thereby effectively solving the problems of weak response, inaccurate regulation and data fragmentation of traditional models.

[0006] Preferably, step S1 is specifically: Step S11: Obtain lake remote sensing monitoring data and perform image preprocessing to obtain the to-be-processed lake remote sensing data; Step S12: Extract water body spectral features according to the to-be-processed lake remote sensing data to obtain water body spectral data; Step S13: Calculate the normalized difference water index based on the water body spectral data; identify the water body region according to the normalized difference water index to obtain a water body region image; and fill in the broken edges based on the water body region image to obtain a water body edge contour image; Step S14: Correct the geographic coordinates according to the water body edge contour image to obtain water body boundary data; Step S15: Calculate the water body area based on the water body boundary data to obtain water body area data.

[0007] The present application can significantly improve the quality and resolvability of remote sensing images by obtaining and pre-processing the remote sensing monitoring data of the lake, providing high-quality data basis for subsequent analysis; extracting water spectral features can effectively distinguish water body from surrounding non-water body area, improve the accuracy and robustness of water body recognition; by calculating the normalized difference water index and performing regional recognition, the water body distribution range can be quickly and accurately marked, and the discontinuity problem caused by cloud cover or reflection difference in the remote sensing image can be repaired by connecting the broken edge filling operation, thereby improving the integrity of the water body boundary; the geographic coordinate correction operation makes the remote sensing image one-to-one corresponding to the actual geographic location, providing a standardized reference coordinate system for subsequent spatial analysis and multi-source data fusion; accurate calculation of water area lays a foundation for the whole lake water volume evaluation and dynamic regulation, making hydrological modeling, ecological simulation and water inlet and outlet scheduling have reliable data support; the overall steps are beneficial to break through the limitations of traditional water level or weather single data driven regulation, and construct a multi-source fusion lake ecological response mechanism, which provides accurate and dynamically updated spatial boundary basis for subsequent water dilution, water pollution, water organism distribution and carbon cycle coupling analysis, thereby effectively improving the intelligentization, refinement and ecological adaptability of lake ecological water level regulation.

[0008] Preferably, step S2 is specifically: Step S21: measuring water depth according to the water area data to obtain water depth data; Step S22: layering the water body based on the water depth data to obtain water layering data; Step S23: calculating the layering volume according to the water layering data to obtain water layering volume; and performing volume accumulation calculation based on the water layering volume to obtain total water storage volume data; Step S24: evaluating water storage capacity according to the total water storage volume data to obtain water storage capacity data; Step S25: detecting water dilution according to the water storage capacity data to obtain water dilution data; Step S26: analyzing water pollution according to the water dilution data to obtain water pollution data.

[0009] The present application can realize accurate perception of the three-dimensional structure of the lake by measuring the water depth on the basis of the water area, and provide data support for identifying the layered hydrological characteristics; the layered processing of the water body can not only reveal the differences in physical and chemical conditions such as temperature and dissolved oxygen of the water body at different depths, but also support the layered pollution tracing and ecological process modeling; the layered volume calculation combined with volume accumulation can accurately quantify the total water storage volume of the lake, and provide basic data guarantee for water resource assessment and regulation capacity prediction; based on the accurate total water storage volume, the water storage capacity assessment is helpful to identify the regulation and storage carrying limit under different seasons and rainfall scenarios; on this basis, the water body dilution detection mechanism is introduced, which can realize the identification of the dynamic change trend of the pollutant concentration, and then provide the spatio-temporal directionality for water quality risk prevention and control; by analyzing the influence mechanism of the change of the water body dilution capacity on water quality pollution, the enrichment area or migration path of the pollutant under different water depths and layered conditions can be dynamically identified, the depth and accuracy of water quality pollution analysis are improved, and thus the key pollution intervention basis for the dynamic regulation strategy of the ecological water level is provided, and the response ability and regulation accuracy of the water level regulation system to complex water quality disturbance are enhanced.

[0010] Preferably, step S3 is specifically: Step S31: obtaining lake aquatic organism data; Step S32: performing biological distribution analysis according to the lake aquatic organism data to obtain lake aquatic organism distribution data; setting water sampling points according to the lake aquatic organism distribution data to obtain water sampling point data; Step S33: performing dissolved oxygen sampling according to the water sampling point data to obtain dissolved oxygen data; identifying a low dissolved oxygen area according to the dissolved oxygen data; Step S34: performing iron and manganese precipitate detection according to the low dissolved oxygen area to obtain iron and manganese precipitate data; performing dissolved oxygen anomaly determination according to the iron and manganese precipitate data to obtain dissolved oxygen anomaly data; Step S35: performing phytoplankton metabolic imbalance detection according to the dissolved oxygen anomaly data to obtain phytoplankton metabolic imbalance data; Step S36: performing lake carbon cycle disorder evaluation according to the phytoplankton metabolic imbalance data to obtain lake carbon cycle disorder data.

[0011] The present application can realize the accurate characterization of the spatial heterogeneity of the ecological state of the lake by introducing the aquatic organism data collection and distribution analysis, provide the basis for the scientific layout of the subsequent water sampling points, and thus improve the representativeness and analysis accuracy of data acquisition; the sampling point setting according to the distribution of aquatic organisms can effectively avoid the sampling blind area, so that the collected dissolved oxygen data is more ecological and response sensitive; based on the dissolved oxygen data, the low dissolved oxygen area is identified, which helps to early warning of the risk area of water eutrophication and biological habitat deterioration; further combined with the detection of iron and manganese precipitates in the low dissolved oxygen area, the element migration and reduction reaction process caused by bottom anoxia can be effectively identified, and the comprehensive discrimination ability of dissolved oxygen anomaly is enhanced; on this basis, the detection of phytoplankton metabolic imbalance is carried out, which not only reveals the physiological response of phytoplankton community to environmental stress, but also reflects whether the primary productivity system of the lake is stable; through the metabolic imbalance state evaluation of carbon cycle disorder, the dynamic quantification of carbon source / carbon sink mechanism change can be realized, and then the key ecological constraint index and feedback control basis for ecological water level regulation are provided; the whole process improves the systematization of ecological state recognition, the refinement of response mechanism and the adaptability of water level regulation model to ecological disturbance, and makes up for the deficiency of traditional model in ecological process fusion and feedback accuracy.

[0012] Preferably, step S35 is specifically: Step S351: determining the dissolved oxygen anomaly area according to the dissolved oxygen anomaly data; Step S352: determining the phytoplankton photosynthetic rate based on the dissolved oxygen anomaly area; Step S353: determining the phytoplankton respiration rate based on the dissolved oxygen anomaly area; Step S354: calculating the net primary productivity according to the phytoplankton photosynthetic rate and the phytoplankton respiration rate to obtain net primary productivity data; Step S355: evaluating the phytoplankton metabolic imbalance according to the net primary productivity data to obtain phytoplankton metabolic imbalance data.

[0013] The present application is helpful to construct a dynamic index system reflecting the metabolic state of phytoplankton under real ecological stress by measuring the photosynthetic rate and respiratory rate of phytoplankton in the dissolved oxygen abnormal area, significantly improving the accuracy of metabolic process modeling and the sensitivity of ecological response analysis; through the quantitative calculation of net primary productivity, the production capacity and ecological stability of the autotrophic system of the water body can be effectively reflected, so as to identify the risk of primary production decline or carbon source release caused by metabolic disorder; based on the net primary productivity, the metabolic imbalance is evaluated, which can not only reveal the physiological response mechanism of phytoplankton to low dissolved oxygen environment, but also provide a basis for early intervention of carbon cycle disorder, further strengthen the prediction ability of the regulation system to ecological change and the accuracy of response decision; the method constructs an ecological feedback chain from dissolved oxygen anomaly identification to metabolic imbalance evaluation, opens up the dynamic coupling path between ecological perception and regulation model, improves the identification ability of ecological driving mechanism of lake ecological water level regulation and the ecological fitness of intervention decision, and effectively overcomes the technical short board that the ecological process and regulation parameters are disconnected in the traditional model.

[0014] Preferably, step S36 is specifically: Step S361: analyzing water body eutrophication according to the phytoplankton metabolic imbalance data to obtain water body eutrophication data; Step S362: identifying the water body eutrophication source based on the water body eutrophication data to obtain water body eutrophication source data; Step S363: detecting organic carbon content according to the water body eutrophication source data to obtain organic carbon content data; Step S364: simulating carbon flow based on the organic carbon content data to obtain carbon flow data; identifying the carbon flow direction according to the carbon flow data to obtain carbon flow direction data; Step S365: detecting sediment according to the carbon flow direction data to obtain sediment data; calculating the bottom carbon flux according to the sediment data; Step S366: analyzing lake carbon cycle disorder according to the bottom carbon flux to obtain lake carbon cycle disorder data.

[0015] The present application can timely identify the accumulation of nutrients caused by metabolic imbalance by introducing eutrophication analysis after the metabolic imbalance of phytoplankton, provide data support for early ecological risk intervention; by further identifying the source of eutrophication, it is helpful to accurately locate the input path and spatial distribution characteristics of the pollutant, and to avoid the limitation of unclear identification of pollution diffusion area in traditional models; combined with the detection of organic carbon content, the enrichment degree of key carbon sources in water body can be quantified, and then the promotion or inhibition effect on primary producers is evaluated, which provides key input variables for carbon cycle dynamic modeling; based on carbon flow simulation and direction identification, the migration and diffusion process of carbon in water body can be effectively described, and the coupling relationship between carbon and hydrodynamic disturbance and ecological process is revealed; sediment detection and bottom carbon flux calculation can reflect the deposition or resuspension characteristics of organic carbon at the water-sediment interface, build a complete chain of carbon flux coupling, and enhance the response monitoring ability of the system to carbon sink / source process; finally, the analysis of lake carbon cycle disorder can accurately identify the driving factors and dynamic evolution path of the structural imbalance of carbon cycle, so as to provide carbon process constraint conditions and ecological feedback basis for ecological water level regulation, make up for the technical short board of traditional models lacking ecological closed-loop regulation and accurate tracking of carbon elements, and overall enhance the ecological adaptability, dynamic responsiveness and system closed-loop of the regulation strategy.

[0016] Preferably, step S4 is specifically: Step S41: calculating the carbon dioxide release rate according to the lake carbon cycle disorder data; Step S42: identifying the disturbance sensitive area based on the carbon dioxide release rate; based on the disturbance sensitive area, the water body flow rate is regulated to obtain water body disturbance optimization data; Step S43: calculating the pollutant concentration according to the water pollution data; based on the pollutant concentration, diffusion simulation is carried out to obtain pollutant diffusion simulation data; Step S44: optimizing the inlet and outlet control strategy according to the pollutant diffusion simulation data to obtain inlet and outlet control strategy data; Step S45: constructing an ecological water level regulation model according to the water body disturbance optimization data and the inlet and outlet control strategy data, and dynamically regulating the lake ecological water level according to the ecological water level regulation model to obtain water level dynamic regulation data.

[0017] The application can enhance the quantification expression and ecological feedback identification ability of the control system to the carbon cycle disorder result by calculating the carbon dioxide release rate and introducing carbon emission intensity as the core parameter for measuring the disturbance degree of the lake ecology; the carbon release intensity can be used to identify the disturbance sensitive area, so as to realize the accurate positioning of the carbon source concentration area in the lake and improve the targeting and ecological adaptability of the water disturbance control; the water flow speed regulation can effectively alleviate the problems such as carbon accumulation, metabolic abnormality and pollutant enrichment in the disturbance sensitive area, and promote the lake to restore to a stable carbon flux state; the pollutant concentration calculation and diffusion simulation can construct the dynamic evolution map of the pollutant in space, provide the pollution response basis and diffusion path judgment for the inlet and outlet control, and solve the problem of slow reaction of the traditional rule-driven strategy in the complex pollution scene; the control strategy optimization can dynamically adjust the inlet and outlet flux and the opening and closing logic by introducing the pollutant concentration gradient and diffusion trend constraint, realize the collaborative optimization of pollution reduction and water self-purification capacity enhancement; the ecological water level regulation model is constructed on the basis of the comprehensive water disturbance optimization data and control strategy data, which not only strengthens the real-time linkage mechanism of water level regulation and ecological process, but also can correct the regulation target based on the carbon cycle response and pollution response, realize the multi-objective coordinated control of water stability, ecological balance and environmental quality, and significantly improve the overall scientific nature, accuracy and self-adaptive ability of the lake ecological water level regulation, and overcome the technical bottlenecks of the existing model such as the fragmentation of regulation variables, lagging ecological feedback and low-efficiency disturbance response.

[0018] Preferably, step S45 specifically comprises: Step S451: constructing a hydrodynamic simulation layer according to the water disturbance optimization data; Step S452: constructing an inlet and outlet scheduling layer according to the inlet and outlet control strategy data; Step S453: constructing an ecological water level regulation model according to the hydrodynamic simulation layer and the inlet and outlet scheduling layer; Step S454: performing dynamic regulation of the lake ecological water level according to the ecological water level regulation model, to obtain water level dynamic regulation data.

[0019] The present application can realize dynamic simulation of the flow path, flow velocity distribution and disturbance response of the water body in the lake by constructing a hydrodynamic simulation layer, effectively quantifies the coupling effect of hydrological disturbance on ecological processes (such as phytoplankton metabolism, pollutant migration and carbon flux change), and enhances the analysis ability of the model to the disturbance propagation chain and ecological feedback path; by constructing an inlet and outlet scheduling layer, the fine management of different inlet and outlet fluxes, opening time sequence and spatial distribution is realized, and dynamic intervention can be realized during the period of pollutant diffusion and ecological risk concentration, and the response efficiency of the scheduling strategy to the real-time pollution diffusion characteristics and disturbance evolution process is improved; on the basis of integrating the hydrodynamic simulation layer and the inlet and outlet scheduling layer, the ecological water level regulation model is constructed, the multi-factor coupling mechanism between the hydrodynamic process, pollutant migration, biological response and water level regulation is established, and the matching ability of the model to the water level regulation target under complex ecological state is significantly improved; finally, through the dynamic water level regulation of the ecological water level regulation model, the linkage optimization of water quality improvement, carbon cycle balance and suitable environment for aquatic organisms can be realized under the premise of ensuring the ecological stability of the lake, and the technical bottleneck of the traditional water level control strategy lacking ecological system constraints and dynamic sensing ability is solved, thereby comprehensively improving the systematicness, adaptability and ecological driving ability of the lake water level regulation.

[0020] Optionally, the present specification also provides a computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement any one of the lake ecological water level dynamic regulation methods based on multi-element data.

[0021] Preferably, the present specification also provides a lake ecological water level dynamic regulation system based on multi-element data for executing the lake ecological water level dynamic regulation method based on multi-element data as described above, which comprises: a water body area calculation module configured to obtain lake remote sensing monitoring data, identify a water body boundary based on the lake remote sensing monitoring data to obtain water body boundary data, and calculate a water body area based on the water body boundary data to obtain water body area data; a water quality pollution analysis module configured to analyze water storage capacity based on the water body area data to obtain water storage capacity data, detect water body dilution based on the water storage capacity data to obtain water body dilution data, and analyze water quality pollution based on the water body dilution data to obtain water quality pollution data; a lake carbon cycle disorder evaluation module configured to obtain lake aquatic organism data, identify dissolved oxygen abnormalities based on the lake aquatic organism data to obtain dissolved oxygen abnormality data, detect phytoplankton metabolism imbalance based on the dissolved oxygen abnormality data to obtain phytoplankton metabolism imbalance data, and evaluate lake carbon cycle disorder based on the phytoplankton metabolism imbalance data to obtain lake carbon cycle disorder data; The ecological water level regulation model construction module is configured to obtain water body disturbance optimization data by optimizing water body disturbance according to the lake carbon cycle disorder data, obtain water inlet and outlet control strategy data by optimizing water inlet and outlet control strategy according to the water pollution data, and construct an ecological water level regulation model according to the water body disturbance optimization data and the water inlet and outlet control strategy data, and perform dynamic regulation of the lake ecological water level according to the ecological water level regulation model to obtain water level dynamic regulation data.

[0022] The lake ecological water level dynamic regulation system based on multi-element data can realize any one of the lake ecological water level dynamic regulation methods based on multi-element data, and is used as a medium for joint operation and signal transmission between modules to complete the lake ecological water level dynamic regulation method based on multi-element data. BRIEF DESCRIPTION OF DRAWINGS

[0023] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-restrictive embodiments made with reference to the accompanying drawings: Fig. 1 A step flowchart of the lake ecological water level dynamic regulation method based on multi-element data is shown in the figure. Fig. 2 A detailed step flowchart of step S1 in the present application is shown in the figure. Fig. 3 A detailed step flowchart of step S2 in the present application is shown in the figure. The implementation of the present application, functional features and advantages will be further described with reference to the accompanying drawings. DETAILED DESCRIPTION

[0024] The technical method of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0025] In addition, the accompanying drawings are only schematic illustrations of the present application, and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated description thereof will be omitted. Some block diagrams shown in the drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or 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" or the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the example embodiments, a first element can be termed a second element, and similarly, a second element can be termed a first element. 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 object, please refer to Figs. 1 to 3 The application provides a lake ecological water level dynamic regulation method based on multi-element data, which comprises the following steps: Step S1: acquiring lake remote sensing monitoring data; identifying water body boundary based on the lake remote sensing monitoring data to obtain water body boundary data; and calculating water body area based on the water body boundary data to obtain water body area data; In this embodiment, multi-temporal high-resolution image data of a target lake region is acquired by using a satellite remote sensing platform (for example, Sentinel-2 or Landsat 8). The collected image bands include visible light (blue, green and red bands) and near-infrared bands. The original remote sensing image data collected is subjected to radiation correction, geometric correction and atmospheric correction processing, and is subjected to standardized processing by using image preprocessing software such as ENVI or a special remote sensing processing tool, so as to ensure the accuracy of the data in space and spectrum. Subsequently, the water body region is extracted by using the spectral feature information of the remote sensing image through a normalized difference water index (NDWI) calculation formula NDWI=(green band-near-infrared band) / (green band+near-infrared band). The NDWI threshold is set to 0.3, and the pixels lower than the threshold are determined as non-water bodies, and the pixels higher than the threshold are determined as water bodies. In order to eliminate water body edge cracking and noise interference, morphological closing operation is used to fill the holes in the water body edge, and the specific operation is to perform inflation first and then corrosion, and the structure element is selected to be a square kernel of 3x3. Based on the filled complete water body edge contour, geographic coordinate correction is performed on the water body boundary by using a geographic information system (GIS) tool, spatial error is adjusted by using a ground control point (GCP) and a digital elevation model (DEM), and it is ensured that the water body boundary is accurately corresponding to the geographic coordinate system. Finally, the water body area is calculated based on the corrected water body boundary polygon by using a polygon area calculation tool in GIS, the result unit is square meters, and the calculation result is kept to two decimal places.

[0028] Step S2: analyzing the water storage capacity based on the water body area data to obtain water storage capacity data; detecting water body dilution based on the water storage capacity data to obtain water body dilution data; and analyzing water pollution based on the water body dilution data to obtain water pollution data; In this embodiment, based on the water area data calculated in step S1, combined with the average depth data of the water body (obtained by a sounder or historical hydrological station measurement, the depth unit is meter, the precision is 0.01 meter), the water storage capacity of the lake is calculated, and the calculation formula is water storage capacity (cubic meter) = water area (square meter) x average water depth (meter). The water storage capacity of the lake is verified by historical hydrological data to ensure that the measurement error is not more than ± 5%. Then, water body dilution detection is carried out, and the water quality parameters of the lake inlet and outlet are collected, mainly including conductivity (μS / cm), dissolved oxygen (mg / L) and total dissolved solids (TDS, mg / L), and a portable multi-parameter water quality analyzer is used for sampling and determination. According to the conductivity change rate, the dilution evaluation is carried out, and when the conductivity change rate exceeds 10%, it is judged as significant dilution state. The dilution detection result is used as a key index to judge the mixing and flow state of the water body. Subsequently, based on the dilution detection data, water pollution analysis is carried out, and the pollution indexes including ammonia nitrogen (NH3-N, mg / L), total phosphorus (TP, mg / L), chemical oxygen demand (COD, mg / L), and total nitrogen (mg / L) are analyzed. The pollution threshold values are set as ammonia nitrogen 1.0 mg / L, total phosphorus 0.05 mg / L, COD 15 mg / L, and TN 1 mg / L, and any threshold value exceeding is considered as pollution exceeding the standard. By comparing the pollution indexes of each sampling point with the threshold value, the pollution distribution and pollution grade are determined, and the spatial distribution map of water pollution is established.

[0029] Step S3: obtaining lake aquatic organism data; performing dissolved oxygen anomaly identification according to the lake aquatic organism data to obtain dissolved oxygen anomaly data; performing phytoplankton metabolism imbalance detection according to the dissolved oxygen anomaly data to obtain phytoplankton metabolism imbalance data; performing lake carbon cycle disorder evaluation according to the phytoplankton metabolism imbalance data to obtain lake carbon cycle disorder data; In this embodiment, aquatic organism data at different sampling points in the lake is collected, including phytoplankton concentration (cell number / mL), zooplankton species and quantity. High-power microscope counting method and flow cytometry are used for counting to ensure that the detection accuracy of phytoplankton cell number reaches ±5%. Then, the dissolved oxygen concentration is measured, and a dissolved oxygen analyzer is used to measure the dissolved oxygen value of the water body at the sampling point, with the unit being mg / L, and the data collection frequency being once per hour. The dissolved oxygen data is compared with the regional standard dissolved oxygen range (usually 6-10 mg / L) to identify abnormal areas, and it is determined that the dissolved oxygen is abnormal when it is lower than 5 mg / L. Based on the dissolved oxygen abnormal data, phytoplankton metabolic imbalance is detected, and by measuring the phytoplankton chlorophyll a content (μg / L) and photosynthesis rate (μmolO2 / m2 / s), a photosynthesis meter is used to obtain photosynthesis rate data. If the chlorophyll a content exceeds 30 μg / L and the photosynthesis rate decreases by more than 20%, it is determined that there is metabolic imbalance. Subsequently, based on the phytoplankton metabolic imbalance data, lake carbon cycle disorder is evaluated, and dissolved organic carbon (DOC, mg / L) and carbon dioxide release rate (CO2 mg / m2 / h) in the water body are used as evaluation indexes. When the DOC concentration is higher than 5 mg / L and the CO2 release rate is higher than 1 mg / m2 / h, it is determined that there is disorder in carbon cycle. Data collection is obtained through fixed automatic samplers and laboratory analysis methods to ensure the continuity and accuracy of the data.

[0030] Step S4: According to the lake carbon cycle disorder data, water body disturbance optimization data is obtained; according to the water quality pollution data, inlet and outlet control strategy optimization data is obtained; according to the water body disturbance optimization data and the inlet and outlet control strategy data, an ecological water level regulation model is constructed, and dynamic regulation of the lake ecological water level is carried out according to the ecological water level regulation model, to obtain water level dynamic regulation data.

[0031] In this embodiment, the disturbance optimization algorithm is started when the water temperature exceeds 25 degrees Celsius and the dissolved oxygen is lower than 5 mg / L, and the optimal water flow path and water distribution scheme are calculated to reduce the carbon release rate and restore ecological balance. The inlet and outlet control strategy optimization sets the water pollution threshold trigger mechanism according to the water quality pollution spatial distribution map and flow rate data. When the pollution index exceeds the standard area appears, the opening degree of the inlet and outlet is automatically adjusted (the threshold is set to 0%-100% adjustment range), and the flow rate of the water inflow and outflow is adjusted in real time. The water flow meter and control valve actuator are used to realize dynamic adjustment, and the control accuracy requirement is ±2%. Combined with the disturbance optimization data and control strategy data, an ecological water level regulation model is constructed, and the model parameters include water area, water storage capacity, pollutant concentration, dissolved oxygen level and carbon cycle index. Based on the dynamic regulation instructions output by the model, the water level control device (such as gate, pump station) is driven to realize real-time adjustment of the water level, and the adjustment accuracy is ±0.05 meters, and the adjustment period is set to 30 minutes to 1 hour according to the real-time monitoring data. Through the linkage of data acquisition system and control execution system, the dynamic and accurate regulation of lake ecological water level is realized.

[0032] Preferably, step S1 is specifically: Step S11: Obtain lake remote sensing monitoring data and perform image preprocessing to obtain the to-be-processed lake remote sensing data; In this embodiment, high-resolution satellite remote sensing data is used as the main source of lake remote sensing monitoring data. When acquiring satellite data, multispectral image data of Sentinel-2 or Landsat8 is used, which includes blue (wavelength about 0.45-0.52 μm), green (about 0.53-0.59 μm), red (about 0.64-0.67 μm) and near-infrared (about 0.85-0.88 μm) bands. After obtaining the image data, image preprocessing operations are performed, including radiation correction, geometric correction and atmospheric correction. The radiation correction uses a radiation transfer model to calculate the conversion of the radiation brightness value received by the sensor into the ground reflectivity, and uses parameters such as solar elevation angle and observation angle during the correction process. The geometric correction is based on ground control points (GCP) and digital elevation models (DEM), combined with precise projection transformation algorithms (such as polynomial transformation or RPC model) to correct the spatial position of the image, ensuring that the geographical spatial position error of the remote sensing image is controlled within 1 pixel (the resolution is usually 10 meters or 30 meters). The atmospheric correction uses an atmospheric correction model (such as 6S or DOS algorithm) to remove the influence of atmospheric scattering and absorption on the image, so that the image reflectivity data reflects the true ground reflectance characteristics. After image preprocessing, the processed lake remote sensing data with high spatial accuracy and spectral accuracy is obtained, the data format is GeoTIFF, the spatial resolution is 10 meters, and the geographic coordinate system uses WGS84.

[0033] Step S12: Extracting water spectral features from the processed lake remote sensing data to obtain water spectral data; In this embodiment, water spectral features are extracted from the processed lake remote sensing data. Using the blue, green, red and near-infrared band reflectivity data of the multispectral image, the reflectivity values of each band are extracted based on the strong near-infrared absorption characteristics and high visible light reflection characteristics of water bodies. Based on the spectral band reflectivity, a band combination feature vector is constructed for subsequent water body recognition calculation. During the extraction process, the spectral data is strictly obtained at the pixel level, and the four band reflectivity values of each pixel are recorded. The data storage format uses a multi-band raster data format to ensure the spatial and band correspondence of the data. The spectral feature extraction is based on an image processing tool, and the water spectral data set is generated through pixel traversal calculation to ensure that the data is complete and the reflectivity value range is between 0 and 1, with an accuracy of three decimal places.

[0034] Step S13: Calculating the normalized difference water index based on the water spectral data; identifying the water area based on the normalized difference water index to obtain a water area image; filling the broken edges based on the water area image to obtain a water edge contour image; In this embodiment, the normalized difference water index (NDWI) is calculated based on the water body spectral data for water body area identification. The NDWI calculation formula is NDWI=(green band reflectance-near infrared band reflectance) / (green band reflectance+ near infrared band reflectance), and the calculation process is performed pixel by pixel. The NDWI threshold is set to 0.3, and all pixels with NDWI value greater than or equal to 0.3 are determined as water body, otherwise as non-water body. The binary result is used to form a water body area image. To solve the problem of water body edge area fracture and clutter noise, a morphological closing operation method is used to fill the edge of the water body area image. The specific operation includes image dilation operation with a 3x3 structure element, followed by erosion operation to eliminate small holes and fractures. After closing operation, a complete water body edge contour image is generated. The image processing uses professional remote sensing image analysis software with strictly fixed parameters to ensure the repeatability and accuracy of each step of operation.

[0035] Step S14: Geographical coordinate correction is performed according to the water body edge contour image to obtain water body boundary data; In this embodiment, the geographical coordinate correction is performed on the water body edge contour image to ensure the spatial accuracy of the water body boundary data. Digital elevation model (DEM) and ground control point (GCP) data are used in combination with image registration algorithm for spatial registration. The registration process uses an automatic matching method based on similar triangles to calculate the affine transformation matrix between the image and the geographical coordinate system, and the transformation accuracy is controlled to be less than 1 pixel at the pixel level. Combined with projection conversion, the image coordinate system is converted to the geographical coordinate system, and the coordinate system uses WGS84 geographical coordinate system to ensure the consistency of subsequent GIS analysis. The correction result is exported in vector polygon format with an accuracy of meters, which is convenient for subsequent area calculation.

[0036] Step S15: Water body area calculation is performed based on the water body boundary data to obtain water body area data.

[0037] In this embodiment, the spatial analysis function of the GIS software is used to call the polygon area calculation module, automatically identify the boundary polygon vertex coordinates, and calculate the actual ground surface area using the ellipsoid projection model. The calculation result is in square meters, and the area value is rounded to two decimal places. The boundary vertices are checked in sequence during the calculation process to ensure that the boundary is closed and has no self-intersection, preventing area calculation errors. The area calculation is repeatedly performed on multiple time point remote sensing images to realize dynamic monitoring of water body area.

[0038] Preferably, step S2 specifically comprises: Step S21: Water depth measurement is performed according to the water body area data to obtain water body depth data; In this embodiment, based on the water area data obtained in the previous step, water depth data is obtained by combining water depth measurement technology. The water depth measurement uses a multi-beam echo sounder system (MBES), which is composed of a water surface buoy and an underwater sonar probe. The system calculates the water depth by transmitting and receiving acoustic signals. During the measurement process, the buoy is equipped with a GPS receiver to obtain the real-time position coordinates of the water surface, with a positioning accuracy of meters. The sonar probe frequency is set to 200 kHz to ensure fine resolution of the water depth measurement. The measurement range of water depth is 0.5 meters to 50 meters, with an accuracy of ±0.1 meters. The sonar equipment is arranged on 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. After data cleaning to remove outliers (e.g., measurement points with sudden changes greater than 2 times the standard deviation), an interpolation algorithm (Kriging interpolation) is used to fill in the data gaps, generating a continuous water depth digital elevation model (Bathymetric DEM). The water depth data accuracy is ensured to meet the measurement accuracy requirements, and the output format is raster data with consistent spatial resolution as the water area data.

[0039] Step S22: Based on the water depth data, the water is stratified to obtain water stratification data; In this embodiment, the isobath layer division method is used to divide the water depth range into several fixed-thickness water layers, with a uniform layer thickness of 1 meter. For example, when the maximum water depth is 20 meters, the water is divided into 20 layers. Each layer is an independent depth interval, and the depth value of each pixel is mapped to the corresponding water layer number using the Bathymetric DEM to form water stratification data. The water stratification data is stored in a three-dimensional array form, recording the spatial position and depth information of each pixel at different depth layers. Through spatial database management, efficient storage and retrieval of stratification data is achieved.

[0040] Step S23: Based on the water stratification data, stratification volume calculation is performed to obtain water stratification volume; and based on the water stratification volume, volume accumulation calculation is performed to obtain total water storage volume data; In this embodiment, for each water layer, the area of all pixels in the layer is multiplied by the layer thickness (1 meter) to obtain the volume value of the layer. The area of a single pixel is determined based on the water area data, assuming it is 100 square meters (corresponding to a 10 meter x 10 meter pixel). The single-layer volume calculation formula is volume = pixel area x layer thickness x water depth proportion. For non-full layer depths of some edge pixels, the stratification depth is refined to ensure the accuracy of the volume calculation. The volume of each water layer is summed to obtain the total water storage volume of the entire lake. The volume unit is cubic meters, and the calculation result is rounded to two decimal places. The calculation process is completed with the help of spatial analysis tools of geographic information system (GIS), ensuring accurate correspondence between spatial information and 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, the water storage capacity is defined as the ratio of the maximum water storage volume of the lake to the historical average water volume. The historical average water volume data comes from the continuous hydrological measurement records of the past 5 years, and the water volume unit is cubic meters. Through comparative calculation, the water storage capacity index value is obtained. The threshold of water storage capacity is set to 0.8 in the analysis process, that is, when the current total water storage volume is lower than 80% of the historical average water volume, it is determined that the water storage capacity is insufficient. The calculation process uses database to manage hydrological data and combines with dynamic calculation of current water storage volume, and the result is accurate to three decimal places. This index is used for subsequent water body management and regulation reference.

[0042] Step S25: Perform water body dilution detection based on the water storage capacity data to obtain water body dilution data; In this embodiment, chemical sensor array is used to determine the water body dilution, and dilution detection is performed based on the water storage capacity data. Multi-point water sample collection devices are deployed to collect water samples at different depths and positions, and conductivity (EC), total dissolved solids (TDS) and salinity sensors are used for measurement. The measurement data is recorded in units of microsiemens per centimeter (μS / cm), and the normal water body conductivity range is set to 50-500 μS / cm. The abnormal value judgment standard is that the conductivity changes more than 50 μS / cm in three consecutive measurements, which is determined as dilution anomaly. The data collection frequency is set to once per hour, and the data is uploaded to the database in real time through wireless transmission. Based on the collected data, the water body dilution coefficient is calculated, which is defined as the ratio of the on-site conductivity to the average historical conductivity. A dilution coefficient greater than 1.2 indicates a high degree of water body dilution. Dilution detection is achieved through data statistics and curve analysis to ensure the accuracy and timeliness of the results.

[0043] Step S26: Perform water quality pollution analysis based on the water body dilution data to obtain water quality pollution data.

[0044] In this embodiment, multi-parameter water quality monitoring instruments are 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 meets the requirements of national environmental protection standards, with DO accuracy of 0.01 mg / L and COD accuracy of 0.1 mg / L. The water sample collection depth distribution is the same as that of the dilution detection, and the sampling frequency is twice a day. The monitoring data is combined with the water body dilution coefficient, and the water quality pollution index is calculated using a weighted statistical method. The index range is 0-100, and a value greater than 60 is determined as pollution exceeding the standard. Pollution analysis is based on standard limits, and measurement data is quality controlled during the analysis process to exclude outliers and ensure that pollution data reflects the actual water quality conditions.

[0045] Preferably, step S3 is specifically: Step S31: Obtain lake aquatic organism data; In this embodiment, the method of field sampling combined with laboratory detection is used to obtain the data of aquatic organisms in the lake. By setting a fixed sampling period (for example, once every quarter), the phytoplankton sampling net (mesh diameter 30 microns, diameter 30 centimeters) is used to continuously drag-net sampling in the surface layer (0-2 meters deep) of the lake, the drag-net speed is controlled at 1 meter / second, and the drag-net distance is set to 50 meters. The collected aquatic organism samples are placed in sealed containers and transported to the laboratory at 4°C. The laboratory uses microscopic counting method to classify and count the species and quantity of phytoplankton, and uses standard counting chamber (such as Neubauer hemocytometer) to calculate the biological volume concentration in the sample, with the unit of individual number per liter (ind / L). Through the biological diversity index calculation method (such as Shannon index), combined with the biological abundance data, a complete set of aquatic organism data is formed, including species distribution, quantity density and biological diversity index.

[0046] Step S32: analyzing the biological distribution according to the data of aquatic organisms in the lake to obtain the data of aquatic organisms distribution in the lake; setting the water sampling points according to the data of aquatic organisms distribution in the lake to obtain the data of water sampling points; In this embodiment, the geographic information system (GIS) technology is used to position the sampling points and the corresponding biological data in space coordinates. The sampling point coordinates are positioned by GPS positioning instrument, and the positioning error is not more than 3 meters. For the biological abundance and species data of each sampling point, the spatial continuity analysis is carried out by using the spatial interpolation algorithm (such as inverse distance weighted method IDW) to generate the data layer of aquatic organisms distribution. The layer shows the trend of biological abundance change in different regions. According to the spatial distribution map, the water sampling points are set to focus on covering the biological dense area and the area with significant distribution change, and the sampling point spacing is controlled within 100 meters to ensure the spatial representativeness of the data. The data of water sampling points include point coordinates, sampling time and corresponding biological index information, and the data format adopts the standard format of GIS point data (such as Shapefile), which is convenient for subsequent data management and analysis.

[0047] Step S33: sampling dissolved oxygen according to the data of water sampling points to obtain the data of dissolved oxygen; identifying the low dissolved oxygen area according to the data of dissolved oxygen; In this embodiment, dissolved oxygen (DO) sampling is carried out around the water sampling point. A portable dissolved oxygen meter is used, with a measurement range of 0-20 mg / L and an accuracy of 0.01 mg / L. Calibration is performed using zero oxygen and saturated air two-point calibration method to ensure measurement accuracy. During measurement, the sampling instrument probe is immersed in the water at different depths of the sampling point, with depth distribution set at 0 meters, 1 meter and 2 meters, and the corresponding DO values are recorded. The sampling time is uniformly arranged within 2-4 hours after sunrise to avoid the influence of temperature fluctuations. The collected DO data is arranged in electronic table format, and the DO critical threshold is set as 2 mg / L according to the Chinese water quality standard GB3838-2002, and a value lower than this is determined as a low dissolved oxygen area. Spatial statistical analysis is performed on the DO values of the sampling points to identify the range and distribution of the low DO area, and the data is output in the form of GIS vector layer, including the boundary information of the low dissolved oxygen area.

[0048] Step S34: iron and manganese precipitate detection according to the low dissolved oxygen area to obtain iron and manganese precipitate data; dissolved oxygen anomaly determination according to the iron and manganese precipitate data to obtain dissolved oxygen anomaly data; In this embodiment, underwater samplers are used to collect low DO area sediments at a depth of 0-10 cm. The collected sediment samples are placed in sealed containers and transported to the laboratory for chemical analysis. The analysis method uses atomic absorption spectrometry (AAS) to measure the content of iron (Fe) and manganese (Mn), with detection limits of 0.01 mg / kg and measurement accuracy of ±2%. In the experiment, sample pretreatment includes drying, grinding and acid digestion, and standard solution calibration instrument is used. Samples with iron content exceeding 5,000 mg / kg and manganese content exceeding 1,000 mg / kg are determined as iron and manganese precipitate abundance anomalies. The measurement results are statistically arranged to form iron and manganese precipitate data, which are matched with spatial coordinates to construct a precipitate distribution data layer.

[0049] Step S35: phytoplankton metabolic imbalance detection according to the dissolved oxygen anomaly data to obtain phytoplankton metabolic imbalance data; In this embodiment, the collected phytoplankton species and quantity data are used in combination with DO content and precipitate concentration to perform metabolic index analysis. The photosynthetic rate and respiration rate of phytoplankton are calculated based on chlorophyll a concentration (measured by water sample fluorescence method, unit μg / L) and light intensity data (measured by light intensity meter, unit μmolphotons·m -2 ·s -1) calculation. Respiratory rate is monitored according to the rate of change of DO. Metabolic imbalance is defined as the ratio of photosynthetic rate to respiratory rate (P / R) being less than 1, indicating that respiration is stronger than photosynthesis. The monitoring data collection frequency is once a day, and the analysis uses time series statistical methods to confirm the period of P / R <1 for more than 3 days to obtain the phytoplankton metabolic imbalance data, and the data format is a three-dimensional table of time-space.

[0050] Step S36: Lake carbon cycle disorder evaluation is performed according to the phytoplankton metabolic imbalance data to obtain the lake carbon cycle disorder data.

[0051] In this embodiment, water body carbon component determination technology is used to measure dissolved organic carbon (DOC), dissolved inorganic carbon (DIC) and total organic carbon (TOC) concentrations, and a high-temperature combustion non-dispersive infrared detector is used with a detection accuracy of 0.1 mg / L. The sampling points are arranged according to the metabolic imbalance area, and the water sample collection depth includes the surface layer (0-1 meters) and the bottom layer (5-10 meters), and the sampling frequency is once a month. Combined with water temperature, pH and dissolved oxygen data, the carbon cycle state is evaluated by carbon balance calculation method. The net ecological carbon flux of the lake is calculated, and the positive and negative values of the carbon flux are used to distinguish the carbon sink or carbon source state. The evaluation results are presented in the form of a numerical matrix, the spatial resolution corresponds to the sampling point density, and the time resolution is monthly. According to the standard reference value, such as the DOC concentration exceeding 3 mg / L, combined with the metabolic imbalance index, the carbon cycle disorder is confirmed, and a comprehensive evaluation data set is formed.

[0052] Preferably, step S35 specifically comprises: Step S351: Dissolved oxygen abnormal area is marked according to dissolved oxygen abnormal data; In this embodiment, the dissolved oxygen data is arranged according to the spatial coordinates of the sampling points and the sampling time into a two-dimensional space-time data matrix, with units of mg / L. According to the national surface water environmental quality standard (GB3838-2002), the dissolved oxygen abnormal threshold is set to 2.0 mg / L, and the area below this value is considered to be a dissolved oxygen abnormal area. Using spatial clustering analysis method, all sampling points with DO lower than 2.0 mg / L are clustered according to the neighborhood relationship with a distance of not more than 50 meters, forming several abnormal areas. Each abnormal area is expressed in the form of a boundary polygon, and the boundary calculation uses the Alpha shape algorithm to ensure that the boundary fits the actual sampling point distribution. The time continuity of the abnormal area is confirmed by the time window method, which requires that the abnormal state lasts at least 48 hours. The above processing is completed based on the spatial analysis tool of the GIS platform, and a vector data file containing the boundary coordinates and time start-stop information of the abnormal area is output, and the data format uses GeoJSON, which is convenient for subsequent processing.

[0053] Step S352: Phytoplankton photosynthetic rate is determined based on the dissolved oxygen abnormal area; In this embodiment, the dissolved oxygen anomaly area is determined by field sampling and laboratory analysis. First, at least three sampling points are arranged in the abnormal area, and the distance between sampling points is controlled within 30 meters. The oxygen production rate of photosynthesis at different depths (0 meters, 0.5 meters, 1 meter) is measured by underwater photosynthesis instrument (such as Clark oxygen electrode). The sampling time is fixed for 30 minutes, and the change of dissolved oxygen concentration per minute is recorded, and the net oxygen generation amount per unit time (mgO2·L -1 ·h -1 ) is calculated. The concentration of chlorophyll a is measured synchronously by water sample fluorescence method, the sample volume is 1 liter, and the fluorescence counting is carried out after filtration, and the accuracy is ±0.1 μg / L. The photosynthetic rate is calculated according to the formula: P=ΔDO / Δt×V, wherein ΔDO is the change of dissolved oxygen concentration in the measurement period, Δt is the time interval, and V is the unit volume. After the laboratory data is arranged, the photosynthetic rate data table is formed, which contains the sampling point coordinates, depth, photosynthetic rate and chlorophyll a concentration information.

[0054] Step S353: determining the phytoplankton respiration rate based on the dissolved oxygen anomaly area; In this embodiment, the phytoplankton respiration rate is determined by dissolved oxygen consumption method. The sampling points and depths are consistent with step S352. The collected water samples are sealed in light-tight containers by water sample bottle method of isolating light, and placed in a constant temperature water bath to maintain the water temperature of the sampling site (±0.5°C error). The linear decrease of DO concentration in the container with time is recorded, and the sampling time is set to 2 hours, and the DO value is recorded every 15 minutes. The formula for calculating the respiration rate is R=-ΔDO / Δt×V, and the negative sign indicates the decrease of DO concentration. The unit of respiration rate is mgO2·L -1 ·h -1 . Data processing excludes abnormal points, and linear regression is used to fit the change trend of DO concentration. Finally, a set of respiration rate data is formed, which contains the sampling point coordinates, sampling depth, respiration rate value and environmental temperature information.

[0055] Step S354: calculating the net primary productivity according to the phytoplankton photosynthetic rate and the phytoplankton respiration rate, and obtaining the net primary productivity data; In this embodiment, the net primary productivity (NPP) is calculated by using the photosynthetic rate (P) and the respiration rate (R) data. The calculation formula is NPP=P-R, and the unit is mgO2·L -1 ·h -1 . According to the P and R values of each sampling point and corresponding depth, the net primary productivity data matrix is formed. The data is arranged by statistical software, and the weighted average of different depth data of the same sampling point is calculated, and the weight is determined according to the depth layer light intensity data distribution. The light intensity is measured by underwater light instrument, and the measurement range is 0-2000 μmolphotons·m -2·s -1 The data acquisition frequency is once every 5 minutes, and the average sunlight of the sampling day is taken. The weighted average formula is NPP_avg =∑(NPP_i x w_i), wherein w_i is the proportion of the light intensity of the i-th depth layer. The calculated net primary productivity data is arranged according to the spatial coordinates, and a spatial data table is output, which is convenient for subsequent ecological analysis.

[0056] Step S355: According to the net primary productivity data, phytoplankton metabolic imbalance is evaluated, and phytoplankton metabolic imbalance data is obtained.

[0057] In this embodiment, metabolic imbalance is defined as net primary productivity being negative (NPP < 0), i.e. the state that the respiration rate is greater than the photosynthetic rate. According to the NPP value in the spatial data table, all sampling points with NPP < 0 are marked using a spatial statistical method, and adjacent negative points are combined using a spatial clustering algorithm to form the boundary of the metabolic imbalance region. The region boundary calculation uses Voronoi diagram segmentation combined with a point density threshold (≥ 3 points / 100 square meters) for judgment. The analysis result is output as a metabolic imbalance region vector layer, including region boundary coordinates, metabolic imbalance intensity (represented by the absolute value of the average negative value of NPP) and a time label. The result data format is GeoTIFF or Shapefile, which is compatible with other spatial data and supports subsequent ecological regulation.

[0058] Preferably, step S36 specifically comprises: Step S361: According to the phytoplankton metabolic imbalance data, water body eutrophication analysis is performed, and water body eutrophication data is obtained; In this embodiment, the spatial vector data of the aforementioned phytoplankton metabolic imbalance region is used in combination with total phosphorus (TP), total nitrogen (TN) and chlorophyll a (Chl-a) concentration data in water quality monitoring to make a judgment. The water body eutrophication standard is set according to the comprehensive UPSA and the “Technical Regulations for Evaluation of Surface Water Resources Quality” of China and other standards: when TP ≥ 0.02 mg / L, TN ≥ 0.5 mg / L, and Chl-a ≥ 10 μg / L, it is determined to be in a eutrophic state. The data of these indicators of the corresponding water quality monitoring points in the phytoplankton metabolic imbalance region are extracted and statistically analyzed, and the mean and standard deviation of each indicator are calculated. A multi-index joint determination method is used, and if all three indicators exceed the threshold, the region is defined as a water body eutrophication region. The water body eutrophication data is output as a spatial vector layer, including the eutrophication region boundary, the mean concentration of multiple indicators, and the monitoring time.

[0059] Step S362: Based on the water body eutrophication data, the water body eutrophication source is identified, and water body eutrophication source data is obtained; In this embodiment, the terrain data, land use data, pollutant discharge point information and hydrological flow data of the watershed around the lake are collected. The terrain data uses a digital elevation model (DEM) with a resolution of not less than 10 meters; the land use data is derived from the classification results of remote sensing images within nearly one year; the discharge point information includes industrial discharge outlets, agricultural non-point sources, domestic sewage discharge points, etc. The pollutant load distribution calculation based on watershed zoning is adopted, and the pollutant transport path is determined by combining the hydrodynamic flow field simulation. The pollutant transport path is calculated by a two-dimensional hydrodynamic model (such as a two-dimensional shallow water wave equation solver), and the parameter settings include flow velocity (0-1 m / s), water depth (1-5 m), pollutant decay coefficient (0.1-0.3 d -1 ). The source tracing result is superimposed according to the pollutant concentration gradient and flow direction to form the coordinates and intensity distribution of the pollution source. The output is a vector layer of spatial distribution of pollution sources, including the coordinates, type classification and pollution contribution proportion of each source position.

[0060] Step S363: detecting the organic carbon content according to the eutrophication source data of the water body to obtain organic carbon content data; In this embodiment, the sampling points are set at the positions of each pollution source and within a range of 500 meters downstream thereof, and the water samples are collected by layered sampling (0.5 meters, 1 meter, 2 meters) with a sampling volume of 1 liter per layer. The total organic carbon (TOC) content of the water sample is measured by the potassium dichromate oxidation-reduction method (Walkley-Black method) with a measurement accuracy of ±0.1 mg / L. The water sample pretreatment includes filtration (0.45 μm filter membrane) and dilution to ensure that the concentration measurement is within the linear range. All data are labeled with sampling time, sampling position GPS coordinates and water depth. The measurement results form a spatial data table of organic carbon content, including TOC values and their spatial distribution.

[0061] Step S364: simulating carbon flow based on the organic carbon content data to obtain carbon flow data; and identifying 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 by using the flow field data in combination with the organic carbon concentration data, and the finite volume method is used to discretize the control equation, which includes the convection-diffusion transport and degradation reaction of organic carbon. The parameter settings include the water flow velocity vector field (m / s), the diffusion coefficient (1×10 -5 m² / s), and the organic carbon degradation rate constant (k=0.05 d -1 ). The simulation time step is set to 10 minutes, and the calculation duration covers at least 7 days to ensure complete capture of the carbon flow dynamic process. The simulation output is the spatio-temporal distribution data of the organic carbon concentration. Based on the simulation results, the carbon flow direction is identified by calculating the concentration gradient direction and the flow velocity vector direction. The carbon flow direction data are represented in the form of a vector field, including the flow direction angle (0°-360°) and the flow velocity size (m / s), and the output format is a vector data file that can be recognized by GIS.

[0062] Step S365: sediment detection is performed according to the carbon flow direction data to obtain sediment data; and a bottom carbon flux is calculated according to the sediment data; In this embodiment, the sampling points are selected in the significant sediment deposition area and the carbon inflow end, and a handheld multi-parameter water quality detector and a sediment sampler are used to collect the sediment samples. The sediment sampling depth is the first 10 centimeters, and the samples are stored in an inorganic carbon clean container to prevent organic matter degradation. In the laboratory, the organic carbon content is measured by high-temperature furnace re-burning method, with an accuracy of ± 0.05%. At the same time, the particle size analyzer is used to measure the particle size distribution of the sediment, and the carbon enrichment characteristics are inferred in combination with the carbon content. According to the sediment thickness and the organic carbon concentration, the bottom carbon flux is calculated, and the calculation formula is F=DxCxR, wherein F is the bottom carbon flux (mgC·m -2 ·d -1 ), D is the sediment settling rate (cm·d -1 ), C is the organic carbon concentration (mgC·g -1 ), and R is the sediment density (g·cm -3 ). Each parameter is obtained by field measurement and laboratory analysis. The bottom carbon flux data is stored in a spatial grid format.

[0063] Step S366: lake carbon cycle disorder analysis is performed according to the bottom carbon flux to obtain lake carbon cycle disorder data.

[0064] In this embodiment, the bottom carbon flux data is comprehensively analyzed with the upper water body organic carbon input and the phytoplankton metabolism imbalance data. The time series statistical analysis method is used to calculate the balance of carbon input and sediment carbon emission. The carbon cycle disorder is defined as the bottom carbon flux greater than 40 mgC·m -2 ·d -1 and accompanied by an abnormal area of water body dissolved oxygen. The disorder area is determined by spatial superposition analysis, and the output vector boundary data includes disorder intensity indicators (such as bottom carbon flux value, dissolved oxygen concentration, etc.), time marker and spatial coordinates. The data format uses GeoTIFF, which is convenient for integration with remote sensing and water quality monitoring data.

[0065] Preferably, step S4 specifically comprises: Step S41: calculating a carbon dioxide release rate according to the lake carbon cycle disorder data; In this embodiment, the bottom carbon flux data is obtained, with a unit of mgC·m -2 ·d -1and corresponding water area data. The carbon flux is converted to CO2 release rate by using the release rate of organic carbon to the water body and the stoichiometric relationship in the oxidation process of organic carbon. The conversion coefficient is calculated according to the ratio of the molecular weight of carbon (12 g / mol) to the molecular weight of CO2 (44 g / mol), and the specific calculation formula is: CO2 release rate (mgCO2·m -2 ·d -1 ) = bottom carbon flux × (44 / 12). At the same time, combined with water temperature, pH value and dissolved oxygen concentration data, the gas-water exchange rate of CO2 in the water body is calculated by Henry's law, and the gas-water exchange rate coefficient is adjusted according to environmental parameters, usually taking the range of 0.1-0.3 m / d. The total CO2 release rate is calculated by integrating the bottom release rate and the gas-water exchange rate. The results are output in the form of daily average CO2 release (unit: kgCO2 / d) according to the spatial grid, and the grid size is generally set to 100 m × 100 m, which is convenient for subsequent analysis.

[0066] Step S42: identifying the disturbance sensitive area based on the carbon dioxide release rate; and adjusting the water flow rate based on the disturbance sensitive area to obtain water body disturbance optimization data; In this embodiment, the threshold screening method is used to mark the area with CO2 release rate higher than 50 mgCO2·m -2 ·d -1 as the disturbance sensitive area. Based on the spatial distribution of these areas, combined with the water flow rate monitoring data (measured by ultrasonic flowmeter, accuracy ± 0.01 m / s, sampling frequency is once per hour), the flow rate is spatially interpolated, and the Kriging interpolation method is used to obtain the water flow rate field. For the abnormal value of flow rate in the disturbance sensitive area, the flow rate is adjusted by adjusting the water gate or water pump adjustment means, and the adjustment range is limited to 0.05 m / s to 0.3 m / s, to ensure that the flow rate meets the water stirring demand and avoids excessive disturbance leading to ecological destruction. After the adjustment measures are executed, the water body disturbance optimization data is formed, including the adjusted flow rate field data and the corresponding time label. The adjustment parameters and flow rate change are stored in the form of time series database, which is convenient for historical tracking and dynamic analysis.

[0067] Step S43: calculating the pollutant concentration according to the water quality pollution data; and simulating the diffusion of the pollutant according to the pollutant concentration to obtain pollutant diffusion simulation data; In this embodiment, the pollutants include nitrogen, phosphorus, heavy metals and organic pollutants. The concentration of each pollutant is measured in mg / L, and the sampling points are arranged to cover the main areas of the lake. The sampling depth includes the surface layer (0.5 m) and the middle layer (2 m). After the water sample is collected, the standard analysis method is used for determination. Nitrogen and phosphorus are determined by spectrophotometry, and the detection limits are 0.01 mg / L and 0.005 mg / L, respectively. Heavy metals are determined by atomic absorption spectrometry, and the detection limit is 0.001 mg / L. Organic pollutants are determined by gas chromatography-mass spectrometry, and the detection limit is 0.0001 mg / L. When calculating the average concentration of pollutants, the spatial weighted average method is used, and the weight coefficient is determined based on the influence factors of water depth and distance from the shore. The pollutant concentration data is time-synchronized and corrected to ensure that all data correspond to the same time window. The pollutant diffusion simulation uses a two-dimensional water quality diffusion equation, which is numerically solved based on the finite difference method. The simulation grid size is 50 m x 50 m, the time step is 10 minutes, and the total simulation time is 72 hours. The boundary conditions are set as the pollutant concentration values at the inlet and outlet of the lake, which are input using observation data. The diffusion coefficient is adjusted according to water temperature and wind speed, usually ranging from 0.5 to 1.5 m² / s. The simulation output is the spatial and temporal distribution of pollutant concentration data in NetCDF format, which supports multi-time period query.

[0068] Step S44: optimizing the inlet and outlet control strategy based on the pollutant diffusion simulation data to obtain inlet and outlet control strategy data; In this embodiment, the geographical location of the inlet and outlet and its water quantity regulation ability parameters, such as maximum flow (unit 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 the retention and outflow of pollutants. Combined with the control strategy target, a multi-objective optimization function is established, which includes the minimization of pollutant concentration and the balance of water flow, and the constraint conditions include the maximum regulation ability of the inlet and outlet and the upper and lower limits of water level control. Genetic algorithm is used for optimization, and the algorithm parameters are set as population size 50, iteration number 100, crossover probability 0.8, and mutation probability 0.05. In the optimization process, the control variables are the opening angle and flow size of the inlet and outlet. The optimal control parameter combination of the inlet and outlet is finally obtained, forming the inlet and outlet control strategy data. The data is saved in time series format, containing regulation time points, opening angles and flow values, which is convenient for implementation and execution.

[0069] Step S45: constructing an ecological water level regulation model based on the water body disturbance optimization data and the inlet and outlet control strategy data, and dynamically regulating the lake ecological water level based on the ecological water level regulation model to obtain water level dynamic regulation data.

[0070] In this embodiment, the model adopts a combination of physical and empirical methods, and the input variables include the optimized flow velocity field of water disturbance, water level height (unit: meters), inflow and outflow rate, and opening angle. The model establishment process includes water level dynamic balance equation, water quality transport equation, and ecological response equation. The water level dynamic balance equation is based on the continuity principle, and the water quantity calculation is performed with a discrete time step of 0.1 hour, combined with the inflow and outflow rate adjustment data to realize dynamic adjustment of water level. The ecological response equation links the water flow velocity, water level, and phytoplankton growth and water quality change parameters through empirical formula, and the parameters are obtained by regression based on historical monitoring data. The model realizes the water level dynamic regulation target through time series calculation. The calculation result is output as a time and space continuous water level dynamic regulation data, including water level height (meters), water flow velocity (m / s), inflow and outflow opening state and flow rate, and the data format is CSV and GIS compatible format, which is convenient for on-site control and subsequent analysis.

[0071] Preferably, step S45 specifically comprises: Step S451: constructing a hydrodynamic simulation layer according to the water disturbance optimization data; In this embodiment, the input water disturbance optimization data includes adjusted water flow rate field data, with a spatial resolution of 100 meters x 100 meters and a time resolution of 10 minutes per frame. According to the digital elevation model (DEM) of the lake and the lake bottom topographic measurement data, a two-dimensional hydrodynamic grid is constructed, and the grid cell size corresponds to the spatial resolution of the water flow rate data. The two-dimensional shallow water equation is discretized using the finite volume method to calculate the water flow state. The parameters used in the model include the hydrodynamic viscosity coefficient v, which is usually in the range of 1 x 10 -6 to 1 x 10 -4 m² / s, and the water density p is fixed at 1000 kg / m³. The boundary condition is set as fixed water level and flow rate, and the upper and lower limits of water level are determined according to the observation station data, with a range of ±0.3 meters. The time step is selected as 60 seconds to ensure numerical stability. The explicit time marching method is used for iterative calculation, and the number of iterations is determined according to the simulation time, usually 10,080 steps for one week of simulation. The hydrodynamic simulation layer outputs include time and space continuous water level height distribution, water flow velocity vector field and flow direction angle, and the data format is two-dimensional matrix and vector field file, compatible with GIS system. This simulation layer serves as the basis for subsequent ecological water level regulation.

[0072] Step S452: constructing an inflow and outflow scheduling layer according to the inflow and outflow control strategy data; In this embodiment, the inlet and outlet control strategy data includes control time sequence, opening angle (unit degree, range 0°-90°) and corresponding flow adjustment amount (unit m³ / s). The scheduling layer establishes a time control table according to the data, and the time resolution is 10 minutes once, covering at least 7 days. The actual flow adjustment capacity of the inlet and outlet is obtained by physical measurement, the maximum flow limit is set to 10 m³ / s, and the minimum adjustable flow is 0.1 m³ / s. The scheduling layer encodes the opening state of the inlet and outlet in each period to ensure the accurate mapping relationship between the scheduling parameters and the actual flow output. The scheduling layer calculates the flow adjustment amount of all inlets and outlets according to the flow balance principle, and interacts with the hydrodynamic simulation layer in real time. The scheduling layer inputs the inlet and outlet flow parameters to the hydrodynamic simulation layer in real time through the program interface to realize dynamic adjustment. The output data is a time sequence flow adjustment parameter file, including timestamp, opening angle and flow value, stored in structured text format, supporting dynamic calling.

[0073] Step S453: constructing an ecological water level regulation model according to the hydrodynamic simulation layer and the inlet and outlet scheduling layer; In this embodiment, the model architecture adopts a hierarchical design, the bottom layer is a hydrodynamic simulation module responsible for calculating water level changes and flow state, the middle layer is an inlet and outlet scheduling module responsible for dynamic flow adjustment, and the top layer is an ecological regulation module combining hydrological and water quality information to make water level adjustment decisions. The input parameters include the water level height (unit meter) and flow velocity (m / s) output by the hydrodynamic simulation layer, the flow adjustment parameters of the inlet and outlet scheduling layer, and the ecological index parameters (such as water temperature, dissolved oxygen concentration, etc.). The model calculates the dynamic changes of water quantity based on the continuity equation and the balance equation, and uses an explicit numerical discretization method with a time step of 30 seconds to meet the calculation accuracy requirements. The inlet and outlet scheduling is realized through closed-loop feedback control, and the flow adjustment strategy is adjusted according to the current water level deviation and ecological index. The ecological regulation module sets the water level regulation range based on the phytoplankton metabolism data and the eutrophication index of the water body, which is usually ±0.2 meters of the reference water level. The model integrates real-time data input interface to realize dynamic operation. The output includes water level dynamic regulation scheme and corresponding flow adjustment instruction, and the data format supports XML and CSV, which is convenient for system calling and manual checking.

[0074] Step S454: dynamically regulating the lake ecological water level according to the ecological water level regulation model to obtain water level dynamic regulation data.

[0075] In this embodiment, the dynamic regulation process adopts time sequence simulation method, the regulation period is set to 24 hours, and the time step is 5 minutes. Each step calculation is based on the water level and flow state at the previous moment, combined with 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) for updating. The regulation system outputs the spatial and temporal continuous water level height (unit: meter) data, covering all key areas of the lake, and the spatial resolution corresponds to the grid size of the hydrodynamic simulation layer. The regulation data also includes the real-time opening state and flow value of the water inlet and outlet, and the numerical precision reaches three decimal places. All regulation data are stored in time sequence according to the time stamp, and a time sequence database is used for management. The dynamic regulation results are transmitted to the on-site water gate execution system through the interface, and the execution frequency is every 10 minutes. The data is also backed up in CSV and GIS compatible format for subsequent analysis and review.

[0076] Optionally, the present specification also provides a computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement any one of the lake ecological water level dynamic regulation methods based on multi-element data.

[0077] Preferably, the present specification also provides a lake ecological water level dynamic regulation system based on multi-element data, for executing the lake ecological water level dynamic regulation method based on multi-element data as described above, which comprises: a water body area calculation module, configured to obtain lake remote sensing monitoring data; perform water body boundary identification according to the lake remote sensing monitoring data to obtain water body boundary data; and perform water body area calculation based on the water body boundary data to obtain water body area data; a water quality pollution analysis module, configured to perform water storage capacity analysis according to the water body area data to obtain water storage capacity data; perform water body dilution detection according to the water storage capacity data to obtain water body dilution data; and perform water quality pollution analysis according to the water body dilution data to obtain water quality pollution data; a lake carbon cycle disorder evaluation module, configured to obtain lake aquatic organism data; perform dissolved oxygen anomaly identification according to the lake aquatic organism data to obtain dissolved oxygen anomaly data; perform phytoplankton metabolism imbalance detection according to the dissolved oxygen anomaly data to obtain phytoplankton metabolism imbalance data; and perform lake carbon cycle disorder evaluation according to the phytoplankton metabolism imbalance data to obtain lake carbon cycle disorder data; The ecological water level regulation model construction module is configured to obtain water body disturbance optimization data by optimizing water body disturbance according to the lake carbon cycle disorder data; obtain water inlet and outlet control strategy data by optimizing the water inlet and outlet control strategy according to the water pollution data; construct an ecological water level regulation model according to the water body disturbance optimization data and the water inlet and outlet control strategy data, and perform dynamic regulation of the lake ecological water level according to the ecological water level regulation model to obtain water level dynamic regulation data.

[0078] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the application being defined by the appended claims and not by the above description, and it is intended to encompass all variations falling within the meaning and the scope of the equivalent elements of the claims.

[0079] The foregoing is considered only as a specific implementation of the present application, enabling one of ordinary skill in the art to understand or implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for dynamic regulation of ecological water level of a lake based on multi-element data, characterized in that, The method comprises the following steps: Step S1: obtaining lake remote sensing monitoring data; According to the lake remote sensing monitoring data, the water body boundary recognition is performed to obtain water body boundary data; Based on the water body boundary data, the water body area calculation is performed to obtain water body area data; Step S2: According to the water body area data, the water storage capacity analysis is performed to obtain water storage capacity data; According to the water storage capacity data, the water body dilution detection is performed to obtain water body dilution data; According to the water body dilution data, the water quality pollution analysis is performed to obtain water quality pollution data; Step S3: obtaining lake aquatic organism data; According to the lake aquatic organism data, the dissolved oxygen anomaly recognition is performed to obtain dissolved oxygen anomaly data; According to the dissolved oxygen anomaly data, the phytoplankton metabolism imbalance detection is performed to obtain phytoplankton metabolism imbalance data; According to the phytoplankton metabolism imbalance data, the lake carbon cycle disorder evaluation is performed to obtain lake carbon cycle disorder data; Step S4: According to the lake carbon cycle disorder data, the water body disturbance optimization is performed to obtain water body disturbance optimization data; According to the water quality pollution data, the inlet and outlet water control strategy optimization is performed to obtain inlet and outlet water control strategy data; According to the water body disturbance optimization data and the inlet and outlet water control strategy data, the ecological water level regulation model is constructed, and the lake ecological water level dynamic regulation is performed according to the ecological water level regulation model to obtain water level dynamic regulation data.

2. The method according to claim 1, wherein, Step S1 is specifically: Step S11: obtaining lake remote sensing monitoring data and performing image preprocessing to obtain to-be-processed lake remote sensing data; Step S12: performing water body spectral feature extraction according to the to-be-processed lake remote sensing data to obtain water body spectral data; Step S13: calculating a normalized difference water index based on the water body spectral data; performing water body region recognition according to the normalized difference water index to obtain a water body region image; and performing connection broken edge filling based on the water body region image to obtain a water body edge contour image; Step S14: performing geographic coordinate correction according to the water body edge contour image to obtain water body boundary data; Step S15: performing water body area calculation based on the water body boundary data to obtain water body area data.

3. The method according to claim 1, wherein, Step S2 is specifically: Step S21: performing water depth measurement according to the water body area data to obtain water body depth data; Step S22: performing water body layering based on the water body depth data to obtain water body layering data; Step S23: performing layering volume calculation according to the water body layering data to obtain water body layering volume; and performing volume accumulation calculation based on the water body layering volume to obtain total water storage volume data; Step S24: evaluating water storage capacity according to the total water storage volume data to obtain water storage capacity data; Step S25: performing water body dilution detection according to the water storage capacity data to obtain water body dilution data; Step S26: performing water quality pollution analysis according to the water body dilution data to obtain water quality pollution data.

4. The method according to claim 1, wherein, Step S3 is specifically: Step S31: obtaining lake aquatic organism data; Step S32: performing biological distribution analysis according to the lake aquatic organism data to obtain lake aquatic organism distribution data; and setting water sampling points according to the lake aquatic organism distribution data to obtain water sampling point data; Step S33: Dissolved oxygen sampling is performed according to the water body sampling point data to obtain dissolved oxygen data; and a low dissolved oxygen area is identified according to the dissolved oxygen data; Step S34: Iron and manganese precipitate detection is performed according to the low dissolved oxygen area to obtain iron and manganese precipitate data; and dissolved oxygen anomaly determination is performed according to the iron and manganese precipitate data to obtain dissolved oxygen anomaly data; Step S35: Phytoplankton metabolic imbalance detection is performed according to the dissolved oxygen anomaly data to obtain phytoplankton metabolic imbalance data; Step S36: Lake carbon cycle disorder evaluation is performed according to the phytoplankton metabolic imbalance data to obtain lake carbon cycle disorder data.

5. The method according to claim 4, wherein, Step S35 specifically comprises: Step S351: Dissolved oxygen anomaly areas are demarcated according to the dissolved oxygen anomaly data; Step S352: Phytoplankton photosynthetic rate is determined based on the dissolved oxygen anomaly areas; Step S353: Phytoplankton respiration rate is determined based on the dissolved oxygen anomaly areas; Step S354: Net primary productivity data is obtained by calculating net primary productivity according to the phytoplankton photosynthetic rate and the phytoplankton respiration rate; Step S355: Phytoplankton metabolic imbalance data is obtained by evaluating phytoplankton metabolic imbalance according to the net primary productivity data.

6. The method according to claim 4, wherein, Step S36 specifically comprises: Step S361: Water body eutrophication data is obtained by performing water body eutrophication analysis according to the phytoplankton metabolic imbalance data; Step S362: Water body eutrophication source data is obtained by identifying water body eutrophication sources based on the water body eutrophication data; Step S363: Organic carbon content data is obtained by performing organic carbon content detection according to the water body eutrophication source data; Step S364: Carbon flow data is obtained by performing carbon flow simulation based on the organic carbon content data; and carbon flow direction data is obtained by identifying carbon flow directions according to the carbon flow data; Step S365: Sediment data is obtained by performing sediment detection according to the carbon flow direction data; and bottom carbon flux is calculated according to the sediment data; Step S366: Lake carbon cycle disorder data is obtained by performing lake carbon cycle disorder analysis according to the bottom carbon flux.

7. The method according to claim 1, wherein, Step S4 specifically comprises: Step S41: Carbon dioxide release rate is calculated according to the lake carbon cycle disorder data; Step S42: Disturbance sensitive area is identified based on the carbon dioxide release rate; and water body disturbance optimization data is obtained by performing water body flow rate regulation based on the disturbance sensitive area; Step S43: Pollutant concentration is calculated according to the water quality pollution data; and pollutant diffusion simulation data is obtained by performing diffusion simulation according to the pollutant concentration; Step S44: Inlet and outlet water control strategy data is obtained by performing inlet and outlet water control strategy optimization according to the pollutant diffusion simulation data; Step S45: An ecological water level regulation model is constructed according to the water body disturbance optimization data and the inlet and outlet water control strategy data; and water level dynamic regulation data is obtained by performing lake ecological water level dynamic regulation according to the ecological water level regulation model.

8. The method according to claim 7, wherein, Step S45 specifically comprises: Step S451: A hydrodynamic simulation layer is constructed according to the water body disturbance optimization data; Step S452: An inlet and outlet water scheduling layer is constructed according to the inlet and outlet water control strategy data; Step S453: An ecological water level regulation model is constructed according to the hydrodynamic simulation layer and the inlet and outlet water scheduling layer; Step S454: performing dynamic regulation of the lake ecological water level according to the ecological water level regulation model to obtain water level dynamic regulation data.

9. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed by a processor to implement the multi-element data-based lake ecological water level dynamic regulation method of any one of claims 1 to 8.

10. A multi-element data-based lake ecological water level dynamic regulation system, characterized in that, The multi-element data-based lake ecological water level dynamic regulation system for executing the multi-element data-based lake ecological water level dynamic regulation method of claim 1 comprises: a water body area calculation module configured to obtain lake remote sensing monitoring data, perform water body boundary identification according to the lake remote sensing monitoring data to obtain water body boundary data, and perform water body area calculation based on the water body boundary data to obtain water body area data; a water quality pollution analysis module configured to perform water storage capacity analysis according to the water body area data to obtain water storage capacity data, perform water body dilution detection according to the water storage capacity data to obtain water body dilution data, and perform water quality pollution analysis according to the water body dilution data to obtain water quality pollution data; a lake carbon cycle disorder evaluation module configured to obtain lake aquatic organism data, perform dissolved oxygen anomaly identification according to the lake aquatic organism data to obtain dissolved oxygen anomaly data, perform phytoplankton metabolism imbalance detection according to the dissolved oxygen anomaly data to obtain phytoplankton metabolism imbalance data, and perform lake carbon cycle disorder evaluation according to the phytoplankton metabolism imbalance data to obtain lake carbon cycle disorder data; an ecological water level regulation model construction module configured to perform water body disturbance optimization according to the lake carbon cycle disorder data to obtain water body disturbance optimization data, perform inlet and outlet water port control strategy optimization according to the water quality pollution data to obtain inlet and outlet water port control strategy data, construct an ecological water level regulation model according to the water body disturbance optimization data and the inlet and outlet water port control strategy data, and perform dynamic regulation of the lake ecological water level according to the ecological water level regulation model to obtain water level dynamic regulation data.

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