A reservoir ecological regulation method, system and storage medium

Abstract

This invention relates to the field of reservoir ecological management technology, and more particularly to a reservoir ecological scheduling method, system, and storage medium. The method includes the following steps: acquiring multi-source parameters of the reservoir's vertical profile using a sensor array to obtain an original profile dataset; characterizing the sediment-water mass exchange intensity of the original profile dataset to obtain a comprehensive exchange flux index; stratifying the reservoir according to the comprehensive exchange flux index to obtain a reservoir stratification structure map; performing seasonal-depth partitioning based on the reservoir stratification structure map to obtain a stratified statistical feature set; correlating the stratified statistical feature set with ecological security responses to obtain an ecological security domain map; and analyzing the exchange flux and scheduling constraints based on the ecological security domain map to obtain an exchange flux deviation table and a set of scheduling constraints. This invention provides a scientific scheduling method for the health of the reservoir ecosystem through the synergistic optimization of water level control and stratified water release.

Description

Technical Field

[0001] This invention relates to the field of reservoir ecological management technology, and in particular to a reservoir ecological scheduling method, system and storage medium. Background Technology

[0002] Existing reservoir ecological regulation methods typically focus only on single parameters such as dissolved oxygen, temperature, or nutrients in the water, neglecting the comprehensive characterization of the sediment-water interface as a key area for material and energy exchange. This results in incomplete assessments of endogenous pollution release and benthic ecological conditions, making it impossible to accurately grasp the key regulatory parameters of the reservoir ecosystem.

[0003] Current water quality assessments often use uniform and fixed threshold standards, failing to consider seasonal stratification changes in reservoirs and differences in the ecological characteristics of different water layers. This results in significant differences in the ecological significance of the same threshold in different seasons and water layers, leading to a disconnect between threshold determination results and actual ecological needs, and affecting the scientific nature of scheduling decisions.

[0004] Traditional scheduling treats reservoirs as a homogeneous whole, neglecting the differentiated needs of different functional zones and vertical stratifications, especially the varying sensitivities of benthic ecosystems to water level changes and water quality conditions. This results in scheduling measures improving water quality in one area while negatively impacting benthic ecosystems in other areas, making it difficult to achieve synergistic optimization of water quality and benthic ecosystems.

[0005] In summary, existing technologies suffer from several problems that urgently need to be addressed, including incomplete monitoring of sediment-water interface exchange processes, inaccurate determination of ecological thresholds, and a lack of spatial differentiation in scheduling decisions. Summary of the Invention

[0006] Therefore, it is necessary to provide a reservoir ecological scheduling method, system, and storage medium to solve at least one of the above-mentioned technical problems.

[0007] To achieve the above objectives, a reservoir ecological management method includes the following steps:

[0008] Step S1: Collect multi-source parameters of the vertical profile of the reservoir using a sensor array to obtain the original profile dataset; characterize the sediment-water mass exchange intensity of the original profile dataset to obtain the comprehensive exchange flux index.

[0009] Step S2: Stratify the reservoir according to the comprehensive exchange flux index to obtain the reservoir stratification structure map; perform seasonal-depth partitioning based on the reservoir stratification structure map to obtain the partition statistical feature set; perform ecological security response correlation on the partition statistical feature set to obtain the ecological security domain map;

[0010] Step S3: Analyze the exchange flux and scheduling constraints based on the ecological security domain map to obtain the exchange flux deviation table and scheduling constraint set; calculate the water level control strategy based on the exchange flux deviation table and scheduling constraint set to obtain the water level control sequence; optimize the stratified water release ratio based on the water level control sequence to obtain the stratified water release configuration table; determine the reservoir water exchange scheduling cycle based on the stratified water release configuration table to obtain the optimized scheduling parameter set.

[0011] Step S4: Based on the optimized scheduling parameter set, continuously track and monitor the changes in the exchange flux index, assess the actual impact of scheduling measures on water quality and benthic ecology, and generate scheduling response curves.

[0012] This invention utilizes a multi-point vertical profile integrated sensor array deployed at the sediment-water interface in key reservoir areas. This allows for the simultaneous high-frequency acquisition of vertical distribution data for various key parameters, such as heat flux, gas release rate, and dissolved substance diffusion rate, overcoming the limitations of traditional methods that focus only on a single water parameter or lack detailed interface monitoring. Rigorous calibration and anomaly identification of the raw data ensure its accuracy and reliability. Based on this, by calculating various interface flux values ​​and applying an adaptive weighted nonlinear fusion algorithm that considers seasonal characteristics and parameter interactions, multidimensional flux information is integrated into a single, dynamically characterizing the intensity of material and energy exchange at the sediment-water interface—the Comprehensive Exchange Flux Index (CEIS). This provides a crucial quantitative indicator for a comprehensive and scientific assessment of reservoir-endogenous pollution release and benthic ecological status, laying a precise data foundation for subsequent ecological management decisions. Based on the CEIS, and combined with historical hydrological and water quality data, the reservoir is vertically segmented, and seasonal characteristic periods are defined according to meteorological and hydrological patterns, constructing a seasonal-depth dual-dimensional zoning framework that refines the descriptive units of the reservoir's ecological state. By precisely pairing and segmenting historical CEIS data with synchronous ecological environment monitoring data, key inflection points in the response relationship between CEIS values ​​and ecological indicators such as benthic communities and water quality were identified, thus establishing a direct link between CEIS changes and ecosystem state changes. Based on these ecological response relationships, three levels of thresholds—normal operation, alert, and critical—were set for each seasonal-depth zone, forming a dynamically adjusted threshold hierarchy matrix and generating an intuitive and visualized Ecological Security Domain Map (ESDM). This overcomes the shortcomings of traditional methods using uniform fixed thresholds, enabling threshold determination results to accurately reflect the actual ecological security status under specific seasonal and water depth conditions, providing a scientific and accurate threshold judgment basis for subsequent ecological scheduling plans. The reservoir was divided into functional areas with different management priorities, and based on the comparison between the real-time exchange flux composite index (CEIS) and the ecological security domain map (ESDM), the CEIS deviation of each water layer in each functional area was quantitatively calculated, clarifying the key areas and ecological issues requiring priority regulation. Based on the constraints of the reservoir's basic functions such as flood control, water supply, and power generation, as well as engineering operation conditions, and combined with water balance prediction, an ideal water level change path was calculated that can address high-priority ecological issues without violating rigid constraints. An executable water level control sequence was obtained through smoothing and step-wise processing. Simultaneously, based on the vertical stratification of water quality and the impact assessment of the sediment interface, the opening combination and release ratio of multiple water intakes were optimized, achieving precise intervention in the water quality and sediment environment of specific strata through stratified release. By comprehensively considering regional response, water volume renewal, ecological process timescales, seasonal sensitivity, and the severity of deviations, the optimal scheduling frequency and duration were determined. Finally, the commands for water level control, stratified release ratios, and scheduling cycles were integrated and optimized through numerical simulation to generate a refined scheduling parameter set.This represents a shift in reservoir management from a holistic approach to a more refined, zoned, and tiered one. It innovatively uses the sediment-water exchange process as the core control objective, effectively addressing the challenge of balancing water quality management and benthic ecosystem protection, and achieving synergistic optimization between the two. A comprehensive management effectiveness evaluation system was constructed. By deploying high-density monitoring points within the management implementation area and impact range, and establishing a "dense-sparse" tiered sampling strategy, continuous and accurate monitoring of the management operation process and its ecological response was ensured. Simultaneous collection of the Comprehensive Exchange Flux Index (CEIS) and various parameters such as water quality and benthic biological activity yielded multi-dimensional response data. By calculating the standardized deviation of CEIS relative to the pre-management baseline and identifying spatiotemporal variation patterns, the actual impact range and intensity of management measures on the sediment-water exchange process were quantified. Correlation analysis between CEIS variation patterns and simultaneously collected ecological and environmental indicators quantified the promoting effect of management measures on water quality improvement and benthic ecosystem restoration, resulting in an ecologically significant ecological effectiveness index. Finally, by constructing an intuitive scheduling response curve, the study clearly demonstrated how scheduling operations affect the CEIS and how this impact is transmitted to water quality and the benthic ecosystem. This provides a solid data and analytical foundation for scientifically assessing the ecological benefits of this scheduling measure, forming a closed-loop management system for reservoir ecological scheduling, and providing key feedback information for the continuous improvement and optimization of subsequent scheduling schemes. Therefore, a reservoir ecological scheduling method is proposed. This method achieves multi-parameter synchronous monitoring by constructing a vertical profile integrated sensor array, establishing a comprehensive exchange flux index to characterize the intensity of material and energy exchange at the sediment-water interface; it replaces the traditional single threshold determination with a seasonal-depth dual-dimensional threshold matrix to achieve dynamic and accurate delineation of the ecological safety zone; and it constructs a refined scheduling parameter set based on functional zone division and vertical stratification characteristics to achieve synergistic optimization of water level control and stratified water release. This effectively solves the technical challenge of balancing reservoir water quality management and benthic ecosystem protection, providing a scientific scheduling method for the health of the reservoir ecosystem.

[0013] Preferably, the present invention also provides a reservoir ecological scheduling system for executing the reservoir ecological scheduling method described above, the reservoir ecological scheduling system comprising:

[0014] The exchange flux monitoring module is used to collect multi-source parameters of the vertical profile of the reservoir through a sensor array to obtain the raw profile dataset; the raw profile dataset is then used to characterize the material exchange intensity between sediment and water to obtain the comprehensive exchange flux index.

[0015] The ecological threshold determination module is used to stratify reservoirs according to the comprehensive exchange flux index to obtain a reservoir stratification structure map; perform seasonal-depth partitioning based on the reservoir stratification structure map to obtain a partition statistical feature set; and perform ecological security response correlation on the partition statistical feature set to obtain an ecological security domain map.

[0016] The scheduling scheme construction module is used to analyze the exchange flux and scheduling constraints based on the ecological security domain map to obtain the exchange flux deviation table and the scheduling constraint condition set; to calculate the water level control strategy based on the exchange flux deviation table and the scheduling constraint condition set to obtain the water level control sequence; to optimize the stratified water release ratio based on the water level control sequence to obtain the stratified water release configuration table; and to determine the reservoir water exchange scheduling cycle based on the stratified water release configuration table to obtain the optimized scheduling parameter set.

[0017] The performance evaluation and feedback module is used to continuously track and monitor changes in the exchange flux index based on the optimized scheduling parameter set, evaluate the actual impact of scheduling measures on water quality and benthic ecology, and generate scheduling response curves.

[0018] This reservoir ecological scheduling system, through the collaborative operation of its constituent modules, achieves refined management of reservoir ecological processes. The exchange flux monitoring module comprehensively, in real-time, and quantitatively acquires the intensity of material and energy exchange at the sediment-water interface (comprehensive exchange flux index), providing an unprecedented data foundation for a deeper understanding of endogenous pollution and benthic habitat status. Based on this comprehensive index and combined with the reservoir's stratification and seasonal variation characteristics, the ecological threshold determination module constructs a dynamic and accurate ecological security domain map, overcoming the limitations of traditional static threshold determination and ensuring that scheduling decisions are based on an accurate assessment of the actual ecological state. The scheduling scheme construction module utilizes the problem areas identified by the ecological security domain map to generate refined water level control sequences and stratified water release configuration tables, considering the reservoir's multifunctional needs and engineering constraints. This enables targeted interventions for ecological problems in different regions and water layers, particularly balancing the needs of water quality improvement and benthic ecosystem protection. The effectiveness evaluation and feedback module continuously monitors the ecological response after scheduling implementation, quantitatively evaluates the actual effects of scheduling measures, and uses the evaluation results as feedback information to guide the adjustment and optimization of subsequent scheduling schemes, forming a closed-loop management system for continuous improvement. Therefore, by integrating monitoring, assessment, decision-making and feedback, this system provides a scientific, efficient and eco-friendly overall solution for reservoir operation, effectively solving the technical challenge of balancing endogenous pollution control and benthic ecosystem protection in traditional reservoir operation.

[0019] Preferably, a computer-readable storage medium stores a computer program that, when executed, implements the reservoir ecological scheduling method as described above. Attached Figure Description

[0020] Figure 1 This is a schematic diagram illustrating the steps of a reservoir ecological management method.

[0021] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0023] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0024] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0025] In this embodiment of the invention, reference Figure 1 The diagram shown is a flowchart illustrating the steps of the reservoir ecological scheduling method of the present invention. In this example, the reservoir ecological scheduling method includes the following steps:

[0026] Step S1: Collect multi-source parameters of the vertical profile of the reservoir using a sensor array to obtain the original profile dataset; characterize the sediment-water mass exchange intensity of the original profile dataset to obtain the comprehensive exchange flux index.

[0027] In this embodiment of the invention, reservoir topographic and hydrological data are acquired, and based on this, a multi-point vertical profile integrated sensor array, including heat flux, gas, and electrochemical elements, is deployed at the key sediment-water interface to form a monitoring network topology. Multi-source parameters are synchronously collected based on the topology to obtain the original profile dataset. Sensor calibration and anomaly identification using the sliding window method are performed on the original data to obtain a calibrated monitoring sequence. Classification flux values ​​such as heat flux, gas release rate, and dissolved substance diffusion rate are calculated based on the calibration data. Classification fluxes are normalized, seasonal types are determined, basic weights are established, flux interaction terms are calculated, and weights are adaptively optimized. Finally, through nonlinear fusion calculations such as weighted power averaging, the Comprehensive Exchange Flux Index (CEIS), characterizing the sediment-water exchange intensity, is obtained.

[0028] Step S2: Stratify the reservoir according to the comprehensive exchange flux index to obtain the reservoir stratification structure map; perform seasonal-depth partitioning based on the reservoir stratification structure map to obtain the partition statistical feature set; perform ecological security response correlation on the partition statistical feature set to obtain the ecological security domain map;

[0029] In this embodiment of the invention, based on the Comprehensive Exchange Flux Index (CEIS) and water temperature, density, and dissolved oxygen profile data, a gradient analysis adaptive algorithm is used to divide the vertical layers of the reservoir, resulting in a reservoir stratigraphic structure map. Historical meteorological and hydrological data are analyzed to define the characteristic periods of spring, summer, autumn, and winter, establishing a seasonal characteristic periodic table. Based on the stratigraphic map and periodic table, historical CEIS data is partitioned in a seasonal-depth dual-dimensional manner, and statistical characteristics are calculated to form a set of partitioned statistical characteristics. Historical ecological environment monitoring data is matched with the set of partitioned statistical characteristics, and piecewise regression is used to identify inflection points in the relationship between CEIS and ecological indicator responses, resulting in an ecological inflection point dataset. A three-dimensional data matrix based on partitions, CEIS intervals, and ecological indicators is constructed, and smooth interpolation is performed to obtain a response surface. Gradient change areas on the surface are identified as threshold candidates, and bidirectional verification is performed using original ecological data to confirm the normal, warning, and critical three-level thresholds, resulting in a verification threshold dataset. An Ecological Security Domain Map (ESDM) and a cluster of ecological response curves are then generated.

[0030] Step S3: Analyze the exchange flux and scheduling constraints based on the ecological security domain map to obtain the exchange flux deviation table and scheduling constraint set; calculate the water level control strategy based on the exchange flux deviation table and scheduling constraint set to obtain the water level control sequence; optimize the stratified water release ratio based on the water level control sequence to obtain the stratified water release configuration table; determine the reservoir water exchange scheduling cycle based on the stratified water release configuration table to obtain the optimized scheduling parameter set.

[0031] In this embodiment of the invention, basic geographic information and ecological safety domain map (ESDM) of the reservoir are acquired. Reservoir functional zones are divided according to functional requirements and ecological sensitivity, resulting in a functional zone distribution map. Real-time CEIS and ESDM are compared to calculate the CEIS deviation of each water layer in each functional zone, generating an exchange flux deviation table. Reservoir engineering parameters and scheduling procedures are acquired, and a set of scheduling constraints is determined based on the deviation table. An ideal water level change path sketch is determined based on the priority of the deviation table and hydrological forecast water balance. After constraint screening, correction smoothing, and step transformation, a water level control sequence that meets the constraints is obtained. Based on engineering parameters, the water level control sequence, and the water quality stratification structure map, the impact of stratified water release on the sediment interface is evaluated. An optimization objective function is constructed based on water quality targets, constraints are set, a candidate water release scheme set is generated, risks are assessed, and a stratified water release configuration table is determined. Integrating regional response, water volume updates, ecological time scales, seasonal sensitivity, and deviation levels, the optimal scheduling frequency and duration are determined, generating a time-series scheduling plan. The plan is input into a model for simulation, and the parameter set is optimized to obtain the final optimized scheduling parameter set.

[0032] Step S4: Based on the optimized scheduling parameter set, continuously track and monitor the changes in the exchange flux index, assess the actual impact of scheduling measures on water quality and benthic ecology, and generate scheduling response curves.

[0033] In this embodiment of the invention, monitoring points are densely deployed in the scheduling implementation and impact areas based on the optimized scheduling parameter set and functional area distribution map, forming a monitoring point deployment scheme. Based on the deployment scheme and scheduling time, a "dense-sparse" graded sampling plan is formulated, characterized by high frequency before scheduling, denser deployment in the initial stage of scheduling, and restoration to normal operation in the later stage. The Combined Exchange Flux Index (CEIS) and parameters such as water temperature, dissolved oxygen, nutrients, and benthic organism activity are collected synchronously according to the plan to obtain a multidimensional response dataset. Baseline data before scheduling is acquired, and the standardized deviation of CEIS relative to the baseline in the multidimensional response dataset is calculated to obtain a standardized deviation matrix. Based on the standardized deviation matrix, spatiotemporal variation patterns of CEIS are identified, forming a response pattern map. The response pattern map is correlated with synchronous ecological indicators to quantitatively evaluate the effectiveness of scheduling on water quality and benthic ecology, obtaining an ecological effectiveness index. Based on the ecological effectiveness index and the response pattern map, curves of the standardized deviation of CEIS over time in representative areas are plotted, key events are marked, and a scheduling response curve is constructed.

[0034] Preferably, step S1 includes:

[0035] Step S11: Obtain reservoir morphology and hydrological data; Based on the reservoir morphology and hydrological data, deploy multiple monitoring points at the sediment-water interface in key areas. Each monitoring point is equipped with a vertical profile integrated sensor, which includes three core components: a heat flow sensor, a gas chromatography detector, and an electrochemical diffusion sensor. The sensors are arranged vertically, covering the key exchange area from the upper 5cm of the sediment to the lower 10cm of the water, thus obtaining a sensor array and forming a monitoring network topology.

[0036] Step S12: Based on the monitoring network topology, multi-parameter synchronous acquisition is performed through a sensor array to obtain the original profile dataset;

[0037] Step S13: Perform data calibration and anomaly identification on the original profile dataset to obtain the calibrated monitoring sequence;

[0038] Step S14: Calculate flux parameters based on the calibrated monitoring sequence to obtain a classification flux value table;

[0039] Step S15: Perform weighted fusion calculation on the classified flux value table to obtain the comprehensive exchange flux index.

[0040] In this embodiment of the invention, reservoir morphology data is collected using UAV aerial surveying combined with a multibeam echo sounder to generate a high-resolution three-dimensional terrain model and a bottom elevation map. Historical hydrological data, including daily inflow, outflow, water level, and rainfall records for the past ten years, are obtained from the reservoir management department. Based on the three-dimensional terrain model, key areas are identified as severely silted-up areas, tributary confluences, the bottom of deep-water areas, and known benthic habitat sensitive areas. For example, a monitoring point is deployed at the bottom of the main reservoir's deep water, in the delta region formed by a tributary confluence, and in a specific area near the intake. Each monitoring point is anchored to a vertical profile integrated sensor. The sensor consists of the following core components: a miniature heat flow plate sensor (e.g., Hukseflux HFP01) for measuring heat exchange between sediment and water; a membrane injection-gas sensor integrated module (e.g., an electrochemical or optical sensor equipped with CH4 and CO2 selective membranes) for real-time measurement of dissolved gas concentration at the interface; and a multi-channel electrochemical sensor array (e.g., using ion-selective electrodes or voltammetric sensors) for measuring dissolved ammonia nitrogen (NH4). + ), total phosphorus (PO4) 3- ) and specific heavy metal ions (such as Cd) 2+ Pb 2+The concentration of the sensor was measured. The sensor elements were vertically fixed to a rigid support rod. The heat flux sensor was located 2 cm below the sediment surface, while the gas and electrochemical sensors were located 0 cm, 2 cm, 5 cm, and 10 cm above the sediment surface, respectively, forming a continuous monitoring profile covering the upper 5 cm of the sediment to the lower 10 cm of the water. The sensors were connected to an underwater data acquisition unit via waterproof connectors. The locations of the three monitoring points (based on GPS positioning) and the sensor connection methods were entered into the system to generate a monitoring network topology map describing the spatial distribution of the sensors and the data transmission paths.

[0041] Based on the monitoring network topology, the underwater data logger is activated to perform high-frequency synchronous data acquisition. The data logger's internal clock is synchronized with the GPS time signal. The heat flux sensor is configured to send instantaneous heat flux values ​​to the data logger every 10 minutes. The gas sensor integrated module is configured to trigger membrane sampling and concentration measurement every 30 minutes, and send the measured CH4 and CO2 concentration values ​​to the data logger. The electrochemical sensor array is configured to sequentially measure and transmit the values ​​of each channel (NH4) every 15 minutes. + ,PO43-,Cd 2+ Pb 24 The concentration values ​​of NH4+ are measured. All sensor data, with precise timestamps, are transmitted to the data acquisition unit via an RS485 underwater data bus. The data acquisition unit aligns the data from different sensors according to the timestamps, forming a set of records for each monitoring point at each sampling time, including location, depth, timestamp, and raw sensor readings, constituting the raw profile dataset. For example, a record might contain: Point ID = P1, Timestamp = 2023-10-27, 10:00:00, Depth = -2cm, Raw heat flux reading = X1; Point ID = P1, Timestamp = 2023-10-27, 10:00:00, Depth = 0cm, Raw CH1 reading = Y1, Raw CO2 reading = Y2; Point ID = P1, Timestamp = 2023-10-27, 10:00:00, Depth = 2cm, Raw NH4+ reading = Y2. + Original readings = Z1, PO4 3- Original reading = Z2, Cd 2+ Original reading = Z3, Pb 2+ The initial reading is Z4, and so on, up to all sensor readings up to a depth of 10 cm.

[0042] Data calibration and anomaly identification are performed on the raw profile dataset. Electrochemical sensors are calibrated periodically (e.g., every two weeks) in-situ using standard solutions of NH4Cl, KH2PO4, CdCl2, and Pb(NO3)2 at known concentrations to establish calibration curves (e.g., voltage-concentration relationships) between raw readings and actual concentrations. Heat flux sensors undergo zero-point and sensitivity calibration; zero-point calibration is performed in a temperature-difference-free environment, and sensitivity calibration is performed using a known heat flux source. Gas sensors are calibrated using standard mixtures of CH4 / N2 and CO2 / N2 at known concentrations. Calibration curve parameters are stored in the data processing module. Upon receiving the raw profile dataset, the data processing module converts the raw readings to physical quantity units (e.g., W / m³) based on the timestamp and sensor type, applying the appropriate calibration curve. 2 (mg / L). Outlier screening was performed using a sliding window method. For each parameter (heat flux, CH4, CO2, NH4), ... + PO4 3- Cd 2+ Pb 2+ A fixed-size sliding time window (e.g., 24 hours) and a threshold (e.g., 3 standard deviations) are set. The window slides across the time series, and the mean (μ) and standard deviation (σ) of the data within the window are calculated. If the current data point (x) satisfies |x-μ|>3σ, the data point is marked as an outlier. Outliers are not removed from the dataset; they are only marked. The calibrated data and the outlier markings together constitute the calibrated monitoring sequence.

[0043] The three types of interface flux parameters were calculated based on the calibrated monitoring sequences. Heat flux (q) was calculated using Fourier's law of thermal conductivity: q = -λ·(dT / dz). Where λ is the typical thermal conductivity value of saturated sediment or water (e.g., saturated sediment λ≈1.0 W / (m·K), water λ≈0.6 W / (m·K)). dT / dz is obtained by calculating the temperature gradient by dividing the difference between the sensor temperature 2 cm below the sediment surface and the water temperature at 0 cm below the sediment surface by the vertical distance (e.g., 2 cm), or by using linear regression to calculate the gradient using temperature data from more points. Gas release rate (R) and dissolved substance diffusion rate (J) were calculated using the discrete form of Fick's first law: J≈-D·(ΔC / Δz). ΔC is the concentration difference measured by the sensor at different depths (e.g., 0 cm and 2 cm, or 2 cm and 5 cm), and Δz is the corresponding depth difference. D is the molecular diffusion coefficient of the gas or dissolved substance in water (e.g., CH4 in water at 20℃ D≈1.8×10⁻⁶). -9 m 2 / s, NH4 + In water at 20℃, D≈1.9×10 -9 m 2 / s), and corrections are made for sediment porosity and tortuosity. For gas release, if the concentration decreases rapidly in the water, the interfacial mass transfer coefficient (k) needs to be considered, with a rate R≈k·(C_interface-C_bulk), where k can be estimated using empirical formulas based on water flow velocity and water depth. In this example, the diffusion flux is mainly calculated using the concentration gradient of the vertical profile as a characterization of the release / diffusion rate. The calculation results are compiled into a table, including timestamps, monitoring point IDs, calculated instantaneous heat flux, CH4 flux, CO2 flux, and NH4 flux. + Flux, PO4 3- Flux, Cd 2+ Flux, Pb 2+ Flux is used to create a categorized flux value table. For example, one row in the table might be: Time = 2023-10-27, 10:00:00, Location ID = P1, Heat Flux = -55W / m 2 CH4 flux = 0.12 g / (m 2 ·d), NH4 + Flux = 0.05 g / (m 2 ·d),...

[0044] For the numerical tables of classified fluxes, the heat flux, gas flux (sum of CH4 and CO2 or calculated separately), and dissolved substance flux (NH4) are listed. + ,PO4 3- Cd 2+ Pb 2+Normalization is performed on the fluxes (either summed or calculated separately). The Min-Max normalization method is used to scale the values ​​of each flux category to the [0,1] interval: Normalized_Value = (Value - Min_Value) / (Max_Value - Min_Value). Min_Value and Max_Value are the minimum and maximum values ​​of that flux category in historical monitoring data. Seasonal differential weighting coefficients are set. For example, during the summer stratification period, considering the significant impact of high temperature and hypoxia on water quality due to increased phosphorus and methane release from sediment, the weights for gas flux and phosphorus flux are set higher. During the winter circulation period, water is more uniformly mixed, and endogenous release is relatively low, resulting in a more even weight distribution. For example, the summer weight set is: wheat = 0.1, wgas = 0.4, wphosphorus = 0.3, wother dissolved substances = 0.2. The winter weight set is: wheat = 0.2, wgas = 0.2, wphosphorus = 0.2, wother dissolved substances = 0.4. A weighted fusion algorithm is applied to calculate the Exchange Flux Integrated Index (CEIS). For each monitoring point at each time stamp, CEIS = w_heat·Normalized_heat flux + w_gas·Normalized_gas flux + w_phosphorus·Normalized_phosphorus flux + w_other dissolved substances·Normalized_other dissolved substances flux. This results in a set of CEIS values ​​representing both the time series and spatial distribution, i.e., the comprehensive exchange flux index. For example, the CEIS value for point P1 at 10:00:00 on October 27, 2023 is 0.78.

[0045] Of particular importance is that the weighted fusion calculation in step S15 is specifically as follows:

[0046] Normalize the flux parameters of the categorical flux numerical table to obtain a normalized flux dataset;

[0047] Seasonal feature determination is performed on the normalized flux dataset to obtain the seasonal feature type;

[0048] The weighting coefficients are determined based on seasonal characteristics, resulting in a basic weighting coefficient table.

[0049] The interaction term coefficients are calculated based on the basic weight coefficient table to obtain the parameter adjustment coefficient set;

[0050] Adaptive weight optimization is performed based on the parameter adjustment coefficient set to obtain the optimized weight coefficient table;

[0051] The comprehensive exchange flux index is obtained by nonlinear fusion calculation based on the optimized weight coefficient table and the normalized flux dataset.

[0052] In this embodiment of the invention, the flux parameters in the categorized flux value table are normalized to eliminate the influence of different physical units and magnitudes. For each flux type in the categorized flux value table (e.g., heat flux, CH4 flux, NH4 flux...), the normalization process is applied. + For fluxes (e.g., heat fluxes from water to sediment), the Min-Max normalization method is used, with the minimum (Min_Value) and maximum (Max_Value) values ​​in historical monitoring records used as the scaling range. The current monitored value of this type of flux (Value) is converted to a normalized value between 0 and 1 using the formula: Normalized_Value = (Value - Min_Value) / (Max_Value - Min_Value). If the flux value is negative (e.g., heat flux from water to sediment), then based on its physical meaning (e.g., a negative value indicates heat flowing into the sediment, exacerbating stratification), an appropriate Min_Value and Max_Value range is determined for scaling to ensure that the normalized value reflects its relative intensity and ecological significance. For example, if the historical range of a parameter is [-100, 200] and the current value is 50, then the normalized value is (50 - (-100)) / (200 - (-100)) = 150 / 300 = 0.5. This normalization operation is performed on all flux parameters in the categorical flux value table at each monitoring point and at each timestamp, generating a normalized flux dataset containing all normalized flux values. Normalized_Value represents the normalized flux value, Value represents the original flux value, Min_Value represents the historical minimum value for this type of flux, and Max_Value represents the historical maximum value for this type of flux. Obtain the timestamp of the current data collection, for example, 10:00 AM on October 27, 2023. Based on a pre-established seasonal characteristic periodic table, which defines the time range of each seasonal characteristic period (e.g., Spring Mixed Period: March 1 to May 31; Summer Stratification Period: June 1 to September 30; Autumn Transition Period: October 1 to November 30; Winter Cycle Period: December 1 to February 28 / 29 of the following year), compare the current timestamp with the seasonal characteristic periodic table to determine the seasonal characteristic type to which the current time belongs. For example, if October 27, 2023, falls within the autumn transition period, then the current seasonal characteristic type is determined to be "autumn transition period." Based on the determined seasonal characteristic type, the corresponding basic weight coefficient is retrieved from a pre-stored basic weight coefficient table. The basic weight coefficient table is a matrix where rows represent different seasonal characteristic periods and columns represent different normalized flux parameters (such as normalized heat flux, normalized CH4 flux, and normalized NH4 flux). +(Fluctuations, etc.). The values ​​in the table are determined based on expert knowledge or historical ecological response analysis, reflecting the initial relative importance of different flux parameters on the overall sediment-water exchange ecology in a specific season. For example, if the current season is determined to be the "autumn transition period," the base weights of all flux parameters corresponding to the "autumn transition period" row are extracted from the table to obtain a base weight coefficient table, for example: [w_heat_base=0.15,w_CH4_base=0.2,w_CO2_base=0.1,w_NH4 + _base=0.25,w_PO4 3- _base=0.2,w_Cd 2+ _base=0.05,w_Pb 2+ `_base = 0.05`. `w_parameter_base` represents the base weight coefficient of the corresponding parameter. Based on the normalized flux dataset of the current data points, the interaction term coefficients reflecting the strength of the interaction between different flux parameters are calculated. For example, sediment heat release (positive heat flux) and gas release (such as CH4) have a synergistic effect; high temperature promotes microbial gas production, and heat transfer facilitates upward gas migration. One interaction term is defined as `I1 = Normalized_Heat_Flux × Normalized_CH4_Flux`. Another interaction term reflects dissolved nutrients (such as NH4). + ,PO4 3- The synergistic effect of anaerobic gas production and gas release (high nutrient salts lead to hypoxia, which in turn promotes anaerobic gas production) is defined as I2 = Normalized NH4. + _Flux×Normalized_PO4 3- _Flux × Normalized_CH4_Flux. The values ​​of these interaction terms reflect the cooperative or antagonistic strength of different flux combinations at the current time. These predefined interaction terms are calculated to form a set of parameter adjustment coefficients. For example, if the current Normalized_Heat_Flux = 0.8, Normalized_CH4_Flux = 0.7, Normalized_NH4... + _Flux = 0.6, Normalized_PO4 3-If _Flux = 0.5, then I1 = 0.8 × 0.7 = 0.56, and I2 = 0.6 × 0.5 × 0.7 = 0.21. Parameter_Adjustment_Coefficient represents the calculated interaction term values. The base weight coefficient table is adaptively adjusted using the parameter adjustment coefficient set to generate an optimized weight coefficient table. Adaptive adjustment is achieved through a preset adjustment function, which converts the interaction term coefficients into increments or multipliers on the base weights. For example, for the CH4 flux, its optimized weight w_CH4_optimized can be calculated using the base weight w_CH4_base and interaction terms I1 and I2: w_CH4_optimized = w_CH4_base × (1 + α1 × I1 + α2 × I2). Here, α1 and α2 are preset adjustment factors whose values ​​determine the sensitivity of the interaction terms to the weights, determined through historical data fitting or an expert system. The weights of other parameters are also adjusted according to specific interaction terms. For example, the optimized weights for heat flux are w_heat_optimized = w_heat_base × (1 + β1 × I1). All optimized weight coefficients are ensured to be non-negative. After adjustment, a set of optimized weights reflecting the actual flux interactions is obtained, forming an optimized weight coefficient table. Optimized_Weight represents the optimized weight coefficients, Base_Weight represents the base weight coefficients, I represents the interaction term coefficients, and α and β represent adjustment factors. Using the optimized weight coefficient table and the normalized flux dataset, a nonlinear fusion calculation is performed to obtain the Exchange Flux Integrated Index (CEIS).

[0053] Nonlinear fusion methods can employ weighted power mean. The formula is: CEIS = (Σ i (Optimized_Weight i ×Normalized_Flux i p ))^(1 / p). Among them, Normalized_Flux i Optimized_Weight represents the value of the i-th normalized flux parameter. i Σ represents the corresponding optimization weight coefficient. iThis represents the summation over all normalized flux parameters. `p` is a parameter greater than 1 used to control the degree of nonlinearity. The larger the value of `p`, the closer the fusion result is to the parameter value with the highest weight, thus highlighting the impact of abnormally high individual flux values ​​on the composite index. For example, `p = 2` (weighted double average). Each weight in the optimized weight coefficient table and its corresponding value in the normalized flux dataset are substituted into the formula for calculation. For example, `CEIS = (w_heat_optimized × Normalized_Heat_Flux)`. 2 +w_CH4_optimized×Normalized_CH4_Flux 2 +...+w_Pb 2+ _optimized×Normalized_Pb 2+ _Flux 2 )^(1 / 2). A single value is calculated, which is the combined exchange throughput index for the current monitoring point and timestamp. CEIS represents the combined exchange throughput index, Optimized_Weight. i represents the optimized weight coefficient of the i-th parameter, Normalized_Fluxi represents the normalized flux value of the i-th parameter, and p represents the exponent of the power average.

[0054] Preferably, step S2 includes the following steps:

[0055] Step S21: Divide the vertical profile of the reservoir into layers according to the comprehensive exchange flux index to obtain the reservoir layer structure map, which includes the surface mixing zone, thermocline and deep water layer.

[0056] Step S22: Obtain meteorological and hydrological data of the reservoir area; define the seasonal characteristic period based on the meteorological and hydrological data to obtain the seasonal characteristic periodic table, which includes the spring mixed period, summer stratification period, autumn transition period and winter cycle period;

[0057] Step S23: Perform seasonal-depth dual-dimensional data partitioning based on the seasonal characteristic periodic table and reservoir stratigraphic structure map to obtain the partitioned statistical feature set;

[0058] Step S24: Obtain reservoir ecological environment monitoring data; perform ecological response correlation analysis on reservoir ecological environment monitoring data and zonal statistical feature sets to obtain ecological response curve clusters;

[0059] Step S25: Construct a threshold level system based on the ecological response curve cluster to obtain a threshold level matrix;

[0060] Step S26: Generate an ecological security domain map based on the threshold grading matrix.

[0061] In this embodiment of the invention, the reservoir water body is divided into layers based on long-term collected Combined Exchange Flux Index (CEIS) and synchronously monitored vertical profile data of water temperature, density, and dissolved oxygen. The data processing system analyzes the gradient changes of each parameter with water depth. For example, the water temperature gradient is calculated as ΔT / Δz, and the density gradient is calculated as Δρ / Δz. The depth range where the water temperature or density gradient is largest and persistent is identified as the thermocline. The surface mixing zone is defined as an area with small vertical gradients of water temperature, density, and dissolved oxygen, and significantly affected by wind disturbance, typically located above the thermocline. The deep water layer is defined as an area below the thermocline, with lower water temperature, significantly decreased dissolved oxygen concentration, and typically higher CEIS. An adaptive algorithm based on gradient analysis and threshold determination is used. This algorithm receives multiple sets of vertical profile data and calculates the gradient values ​​at each depth interval. For example, if the ΔT / Δz of a certain layer interval exceeds 0.1℃ / m, and this gradient persists within a certain thickness range, then that range is determined to be a thermocline. The depth ranges of the surface mixing zone and the deep water layer are determined based on the upper and lower boundaries of the thermocline. This algorithm dynamically adjusts the stratification criteria based on the specific morphology of the reservoir and measured data from different periods, generating a map describing the vertical strata structure of the reservoir at different times and locations, i.e., a reservoir strata structure map. For example, at a certain point on a certain day, the surface mixing zone is 0-5m, the thermocline is 5-10m, and the deep water layer extends from 10m to the reservoir bottom. ΔT represents the temperature change, Δz represents the vertical distance change, and Δρ represents the density change.

[0062] Acquire nearly 30 years of meteorological and hydrological data for the reservoir area, including daily average temperature, maximum / minimum temperature, rainfall, evaporation, wind speed, sunshine duration, and historical records of reservoir inflow, outflow, water level, and vertical water temperature profiles. Analyze the interannual and seasonal variations of these data. For example, by analyzing the changes in the multi-year average water temperature vertical profile, identify key time points for the reservoir's initiation of stratification, stable stratification, initiation of mixing, and complete mixing. The spring mixing period is defined as the period when water temperature begins to rise, the vertical temperature difference gradually decreases until the water body is uniformly mixed (e.g., when the temperature difference between the surface and bottom layers is less than 1°C and begins to rise continuously, it marks the beginning of the spring mixing period). The summer stratification period is defined as the period with the largest vertical temperature gradient and the most stable stratification (e.g., the thermocline reaches its maximum thickness and is most stable in position). The autumn transition period is defined as the period when surface water temperature begins to decline, the thermocline moves downwards until the water body mixes again. The winter cycle period is defined as the period when water temperature approaches 4°C and the water body is uniformly mixed across the entire depth. Based on these characteristic changes, the start and end dates of each characteristic period are statistically averaged over many years, and a standard seasonal characteristic cycle table is established. For example: Spring mixed period: March 10 - May 25; Summer stratification period: May 26 - September 30; Autumn transition period: October 1 - November 20; Winter cycle period: November 21 - March 9.

[0063] Based on a seasonal periodic table and a reservoir stratigraphic structure map, a seasonal-depth dual-dimensional partitioning framework is constructed. This framework is a logical structure that divides the spatiotemporal range of the reservoir into multiple non-overlapping units. For example, the depth dimension is divided into three layers: "surface mixing zone," "thermocline," and "deep water layer," while the time dimension is divided into four seasons: "spring mixing period," "summer stratification period," "autumn transition period," and "winter cycle period." This divides the spatiotemporal state of the reservoir into 3×4=12 partitions (e.g., the "summer stratification period - deep water layer" partition). All historically collected Combined Exchange Flux Index (CEIS) data points are categorized into the corresponding seasonal-depth partitions based on their collection time and depth. For example, a CEIS data point collected in July at a water depth of 15m (assuming that this is the deepest layer at that time) will be categorized into the "summer stratification period - deep water layer" partition. For all historical CEIS data points collected within each partition, their statistical characteristics are calculated, including the mean, standard deviation, minimum, maximum, median, and 25% and 75% quantiles. These statistical results are compiled into a set of zonal statistical features, such as a table where each row represents a zonal region, listing the name of the zonal region and its corresponding CEIS statistical value.

[0064] Long-term ecological and environmental monitoring data of the reservoir are acquired, including benthic biodiversity indices (such as the Shannon-Wiener index), dominant species composition, dissolved oxygen concentration, pH, oxidation-reduction potential (ORP), ammonia nitrogen, total phosphorus, chlorophyll a concentration, sediment organic matter content, and heavy metal concentration. These ecological data are then synchronized temporally and spatially with CEIS data from the zonal statistical feature set. For example, the benthic organism survey results for a specific layer in a given season are matched with the average CEIS value for the same layer during the same period. Statistical analysis methods are used to determine the correlation between CEIS values ​​and ecological indicators. For instance, a correlation analysis of the mean CEIS value and the mean dissolved oxygen value in the deep water layer during the summer stratification period reveals a strong negative correlation. Scatter plots are generated, with CEIS values ​​on the horizontal axis and ecological indicator values ​​on the vertical axis. Data points for each season-depth zone are fitted to obtain curves reflecting changes in ecological indicators with CEIS. For example, one curve shows that during the summer stratification period in the deep water layer, benthic biodiversity index rapidly declines after CEIS exceeds a certain value. These curves together form the ecological response curve cluster.

[0065] Based on the ecological response curve cluster, three levels of CEIS thresholds are set for each seasonal-depth zone: normal operation threshold, warning threshold, and critical threshold. For each zone, its ecological response curve is analyzed: The upper limit of the normal operation threshold is set as the upper limit range of CEIS when the corresponding ecological indicator is in a healthy or stable state. For example, based on the curve, it is set as the maximum CEIS value (or a high percentile of the historical CEIS distribution of the zone, such as 90%) when the benthic diversity index is higher than a certain preset health value and dissolved oxygen is higher than 4 mg / L. The upper limit of the warning threshold is set as the CEIS value when the ecological indicator begins to show adverse changes but is still within the recoverable range. For example, it is set as the CEIS value corresponding to the beginning of a decline in the benthic diversity index or the drop in dissolved oxygen to the 2-4 mg / L range. This usually corresponds to a turning point or a point of significant change in the slope on the ecological response curve. The critical threshold is set as the CEIS value when the ecosystem undergoes severe and irreversible changes. For example, it is set as the CEIS value corresponding to significant changes in the benthic community structure (such as the dominance of pollution-tolerant species), dissolved oxygen remaining below 2 mg / L or even anaerobic conditions, or a rapid increase in the concentration of toxic substances. This corresponds to a critical point on the ecological response curve. These threshold values ​​are organized into a matrix according to season and stratum, i.e., a threshold classification matrix. For example, the matrix includes the normal upper limit of 0.6, the warning upper limit of 0.8, and the critical value of 0.95 for "summer stratification period - deep water layer".

[0066] Data from the threshold grading matrix is ​​integrated to generate an ecological safety domain map. A three-dimensional visualization model is constructed, with water depth as one axis, season as another, and CEIS threshold values ​​as a third axis. On the water depth-seasonal plane, various seasonal-depth zones are divided according to the stratigraphic structure map and the seasonal periodic table. For each zone, isosurfaces or regional boundaries representing the upper limits of the normal operating threshold and the upper limits of the warning threshold are drawn according to the threshold grading matrix. Different risk levels are visually represented using color coding: for example, areas with CEIS below the upper limit of the normal threshold are marked in green (safe zone), areas with CEIS between the upper limit of the normal threshold and the upper limit of the warning threshold are marked in yellow (warning zone), and areas with CEIS above the upper limit of the warning threshold (or reaching the critical threshold) are marked in red (danger zone). This three-dimensional or hierarchical two-dimensional map clearly shows the safe operating range and potential risk areas of CEIS under different seasons and water depths, forming an intuitive decision support tool, namely the ecological safety domain map.

[0067] Of particular importance is the ecological response correlation analysis in step S24, which specifically includes:

[0068] Ecological data pairing and organization were performed on reservoir ecological environment monitoring data and zonal statistical feature sets to obtain paired ecological data tables;

[0069] The paired ecological data table is sorted and grouped according to data sequence to obtain a sorted and grouped dataset.

[0070] Piecewise regression inflection point identification is performed on the sorted and grouped dataset to obtain the ecological inflection point dataset;

[0071] A three-dimensional data matrix was constructed from the ecological inflection point dataset to obtain a three-dimensional ecological matrix;

[0072] Smooth response surface interpolation is performed on the three-dimensional ecological matrix to obtain a smooth response surface;

[0073] Gradient change regions are identified on the smooth response surface to obtain a threshold candidate distribution map;

[0074] Based on the paired ecological data table, the threshold candidate distribution map is bidirectionally validated and the threshold is confirmed to obtain the validation threshold dataset.

[0075] Generate a cluster of ecological response curves based on the validation threshold dataset and the smooth response surface;

[0076] In this embodiment of the invention, historical reservoir ecological environment monitoring data is matched with zonal statistical feature sets using timestamps and spatial locations to generate paired ecological data tables. The ecological environment monitoring data includes benthic organism survey data (such as species richness, biomass, and diversity index), water quality vertical profile data (such as dissolved oxygen concentration, ammonia nitrogen, total phosphorus, and redox potential), and sediment data (such as sediment organic matter content and heavy metal concentration). For each monitoring point and each sampling time, the Exchange Flux Integrated Index (CEIS) value of the same time and layer is paired with the corresponding ecological environment indicator value. For example, if a benthic organism sample is collected from a deep-water layer at a monitoring point on a certain day and the diversity index is calculated, and the CEIS value of that layer at that point is 0.75, the dissolved oxygen in the deep-water layer is 1.5 mg / L, and the sediment organic matter content is 8%, then a row is recorded in the paired ecological data table: Seasonal Zoning = Summer Stratification Period - Deep-water Layer, CEIS = 0.75, Benthic Diversity Index = 1.2, Dissolved Oxygen = 1.5 mg / L, Sediment Organic Matter = 8%. All historical paired data were organized to form a paired ecological data table containing seasonal partitions, CEIS values, and various ecological indicator values. The data in the paired ecological data table was then sorted and grouped to obtain a sorted and grouped dataset. First, the data was grouped by seasonal-depth partitions. Within each partition, the paired data were sorted in ascending order by CEIS value. For example, within the "Summer Stratification Period - Deep Water Layer" partition, all data points were arranged according to their CEIS values ​​from smallest to largest. Then, the sorted data was further grouped according to CEIS value or the number of data points, for example, dividing into subgroups of 10 data points or every 0.05 CEIS interval. This grouping facilitates subsequent analysis and reduces the impact of single-point noise. The sorted and grouped dataset is a structured representation of the paired ecological data table, facilitating segmented analysis. For each seasonal-depth partition and each ecological indicator in the sorted and grouped dataset, segmented regression inflection point identification was performed to obtain an ecological inflection point dataset. For example, for the "Benthic Diversity Index" data within the "Summer Stratification Period - Deep Water Layer" zone, a piecewise linear regression model is fitted with the CEIS value as the independent variable and the diversity index as the dependent variable. This model seeks one or more CEIS values ​​(inflection points) where the slope of the linear regression changes significantly before and after these points. For example, the model might be in the form: Y = a1 × CEIS + b1 (CEIS ≤ C1), Y = a2 × CEIS + b2 (CEIS > C1). Statistical methods (such as least squares combined with a breakpoint detection algorithm) are used to determine the optimal inflection point (C1) and the slopes (a1, a2) for each segment. The CEIS value with the most significant slope change is identified as the potential ecological response inflection point for this ecological index within that zone. For example, it was found that when the CEIS reaches 0.7, the rate of decline in the diversity index accelerates significantly.Record all identified inflection point CEIS values ​​and their corresponding ecological index values ​​to form an ecological inflection point dataset. For example: zoning = summer stratification period - deep water layer, index = benthic diversity index, inflection point CEIS = 0.70, corresponding index value = 1.8. Y represents the ecological index value, CEIS represents the comprehensive exchange flux index, a1, a2 represent the piecewise regression slope, b1, b2 represent the intercept, and C1 represents the inflection point CEIS value. Construct a three-dimensional data matrix based on the sorted and grouped dataset. The three dimensions of this matrix represent the seasonal-depth zoning, the CEIS value range, and the ecological index type, respectively. For example, the first dimension is the zoning ID (e.g., 1 represents spring - surface layer, 2 represents spring - thermocline, ..., 12 represent winter - deep water layer), the second dimension is dividing the CEIS range into several intervals (e.g., 0-0.1, 0.1-0.2, ..., 0.9-1.0), and the third dimension is the ecological index ID (e.g., 1 represents benthic diversity index, 2 represents dissolved oxygen, 3 represents ammonia nitrogen). Each cell in the matrix stores the average or median of the corresponding partition, CEIS interval, and ecological indicator. For example, matrix element M[5,7,2] stores the average dissolved oxygen concentration within the "summer stratification period - deep water layer" partition, with CEIS values ​​in the range of 0.6-0.7. This structured data representation transforms scattered paired data into a regular three-dimensional grid, facilitating subsequent surface interpolation. M[i,j,k] represents the element with index (i,j,k) in the three-dimensional data matrix. For each ecological indicator dimension in the three-dimensional data matrix, response surface smoothing interpolation is performed to obtain a smooth response surface. For a specific ecological indicator (e.g., dissolved oxygen), using the partition ID and the midpoint of the CEIS interval as input coordinates, and the average value of the indicator stored in the matrix cell as the output value, an interpolation algorithm (such as bicubic spline interpolation or Kriging interpolation) is applied to generate a continuous and smooth surface in the entire partition-CEIS two-dimensional space. This surface represents the predicted response of the ecological indicator under different partitions and different CEIS values. For example, for dissolved oxygen, a surface O(partition ID, CEIS) is obtained, representing the predicted dissolved oxygen concentration under a given partition and CEIS value. This process is performed on all important ecological indicators, generating a set of smoothed response surfaces, i.e., smoothed response surfaces. O represents the ecological indicator response surface. Gradient change regions are identified on the smoothed response surfaces to obtain a threshold candidate distribution map. For each smoothed response surface O(partition ID, CEIS), its partial derivative (gradient) with respect to CEIS is calculated. The magnitude of the gradient value reflects the sensitivity of ecological indicators to changes in CEIS. Regions with large absolute gradient values ​​indicate that ecological indicators change rapidly with small changes in CEIS within that range; these regions are sensitive areas of ecological response and correspond to potential threshold ranges. For example, calculating the gradient field of dissolved oxygen surface O with respect to CEIS. The CEIS range exceeding a certain preset threshold (e.g., a decrease of more than 1 mg / L for every 0.1 CEIS unit increase in dissolved oxygen). These high-gradient regions are marked on a partition-CEIS two-dimensional plane to form a threshold candidate distribution map. `Threshold_Candidate_Map` represents the threshold candidate distribution map. The threshold candidate distribution map is bidirectionally validated and the threshold confirmed based on the original paired ecological data table to obtain a validation threshold dataset. The threshold candidate range is compared with the actual data points in the original paired ecological data table. For example, if a partition in the candidate map shows a high gradient within the CEIS range of 0.7 ± 0.05, the data points in the original data table with CEIS in this range are checked to verify whether this range truly corresponds to a significant change in the ecological indicator (e.g., dissolved oxygen actually drops below the critical level, or the benthic diversity index significantly decreases). Simultaneously, the actual CEIS values ​​of the ecological indicators showing significant changes in the original data table are checked to verify whether these values ​​fall within the candidate threshold range. Through iterative adjustments and manual judgment, combined with ecological knowledge, the specific CEIS values ​​for the three-level thresholds (normal, alert, critical) of each season-depth partition are finally confirmed. For example, it is confirmed that in the "summer stratification period - deep water layer", CEIS = 0.6 is the normal upper limit, CEIS = 0.75 is the warning upper limit, and CEIS = 0.9 is the critical value. This set of confirmed threshold values ​​constitutes the validation threshold dataset. `Validated_Threshold_Dataset` represents the validation threshold dataset. An ecological response curve cluster is generated based on the validation threshold dataset and the smoothed response surface. For each season-depth zone and each important ecological indicator, a 2D slice of the CEIS-ecological indicator corresponding to that zone is extracted from the smoothed response surface, forming a smoothed ecological response curve. For example, the curve corresponding to the "summer stratification period - deep water layer" is extracted from the dissolved oxygen surface O. On this curve, the CEIS points or areas corresponding to the normal operation threshold upper limit, the warning threshold upper limit, and the critical threshold are marked according to the validation threshold dataset. These threshold-marked curves for all key ecological indicators across all season-depth zones are collected to form the ecological response curve cluster.

[0077] For example, the curve cluster includes benthic diversity index response curves and dissolved oxygen response curves for the "summer stratification period - deep water layer," and each curve is labeled with the corresponding level 3 CEIS threshold. Ecological_Response_Curve_Cluster represents the ecological response curve cluster.

[0078] Preferably, the parsing of exchange throughput and scheduling constraints in step S3 includes:

[0079] Obtain basic geographic information of the reservoir; divide the reservoir into functional zones based on the basic geographic information and ecological security zone map to obtain a functional zone distribution map, which includes water intake area, flood discharge area and ecological protection area.

[0080] The exchange flux deviation was calculated from the comprehensive exchange flux index and the ecological security domain map to obtain the exchange flux deviation table.

[0081] Obtain the reservoir engineering parameters and determine the set of scheduling constraints based on the exchange flux deviation table.

[0082] In this embodiment of the invention, basic geographic information of the reservoir is acquired, including the reservoir shoreline, water depth contour lines, the location of the inflow river, the location of outflow structures (dam, spillway, intake, hydropower station, etc.), the distribution of reservoir bays and tributaries, and the spatial coordinates and extent of important ecologically sensitive areas (such as fish spawning grounds and benthic habitats). This information is typically stored in GIS (Geographic Information System) format, forming a basic geographic information database for the reservoir. Based on the basic geographic information of the reservoir and the ecological security domain map, the reservoir is functionally zoned. A certain area around the water intake structures (e.g., the area determined by considering the radius of influence of flow velocity and water depth requirements centered on the intake) is designated as the water intake area. The area around the outflow structures such as the dam, spillway, and flood discharge tunnel is designated as the flood discharge area. Areas identified in the ecological security domain map that have important ecological value or high ecological sensitivity (e.g., reservoir bays where historical data show that the CEIS has been within the normal range for a long time, or areas known to have rare benthic species) are designated as ecological protection areas. Other areas are designated as general reservoir areas. The boundaries of these functional zones are drawn on the reservoir map using Geographic Information System (GIS) tools, forming a functional zone distribution map. The boundaries of each functional zone are stored as polygon layers in the GIS.

[0083] Compare the real-time collected Comprehensive Exchange Index of Flux (CEIS) data with the ecological security domain map, calculate the exchange flux deviation values for each functional area and each water layer, and obtain the exchange flux deviation table. First, match the real-time CEIS data points to the corresponding functional areas and water layers according to their spatial positions and water depths (determined according to the current layer structure diagram). Then, according to the current date and the matched water layer, query the upper limit of the normal operation threshold (T_normal_upper) and the upper limit of the warning threshold (T_alert_upper) corresponding to this partition from the ecological security domain map. For each data point, calculate its CEIS deviation. If CEIS ≤ T_normal_upper, the deviation is recorded as 0. If T_normal_upper < CEIS ≤ T_alert_upper, the deviation is recorded as CEIS - T_normal_upper. If CEIS > T_alert_upper, the deviation is recorded as (CEIS - T_alert_upper) + (T_alert_upper - T_normal_upper) × β, where β is a coefficient greater than 1, used to amplify the weight of the deviation above the warning threshold, reflecting its more serious ecological risk. Summarize the deviations of all data points in the same water layer within each functional area (such as calculating the average deviation or the maximum deviation) to generate the exchange flux deviation table. This table lists the current average CEIS values, the corresponding upper limits of the normal / warning thresholds, and the calculated deviation values for each functional area and each water layer (such as the intake area - deep water layer, ecological protection area - thermocline, etc.). Deviation = max(0, CEIS - T_normal_upper) + max(0, CEIS - T_alert_upper) × (β - 1). Deviation represents the deviation value, CEIS represents the current comprehensive exchange index of flux, T_normal_upper represents the upper limit of the normal operation threshold, T_alert_upper represents the upper limit of the warning threshold, and β represents the amplification coefficient of the deviation above the warning threshold.

[0084] Obtain reservoir engineering parameters, including dam height, total reservoir capacity, dead storage capacity, normal storage level, flood control limit level, beneficial storage capacity, number, diameter, and maximum discharge capacity of intakes at different elevations, discharge capacity curves (flow-water level relationship) of spillways and bottom discharge outlets, range of water diversion and power generation flow for hydropower stations, and upper and lower limits of permissible daily water level change rates. These parameters are typically stored in reservoir operation regulations and engineering design documents. Based on the exchange flux deviation table, determine the set of operation constraints for the current operation cycle. For example, if the exchange flux deviation table shows that the CEIS of the deep water layer in the ecological protection zone is severely exceeded (high deviation value), then it is necessary to prioritize reducing the CEIS of the area through deep water discharge. In this case, the set of operation constraints will include an additional constraint: deep water discharge flow ≥ Q_min, where Q_min is the minimum required flow calculated based on the severity of the deviation, but Q_min cannot exceed the total maximum discharge capacity of the deep water intake. If the deviation table indicates that the surface CEIS of the water intake area is in a warning state, the constraint set includes: ensuring that the water level near the surface intake is maintained within a safe range to avoid disturbing the bottom sediment. Simultaneously, all scheduling operations must comply with the hard constraints of the reservoir's basic functional requirements for flood control, water supply, and power generation: such as the current water level must not exceed the flood control limit; the water level at the water intake must be higher than the minimum intake elevation; the power generation flow rate must be within the unit's allowable range; and the downstream ecological base flow must be guaranteed. These hard constraints and the targeted constraints determined based on the CEIS deviation together constitute the scheduling constraint set for the current scheduling cycle. Q_min represents the minimum required flow rate.

[0085] Preferably, the water level control strategy calculation in step S3 includes:

[0086] Based on the exchange throughput deviation table, the critical areas are prioritized to obtain the area priority table;

[0087] The water balance within the scheduling cycle is calculated based on hydrological forecast data to obtain a water balance table.

[0088] Based on the regional priority table and water balance table, the ideal change path is determined, and an ideal path sketch is obtained.

[0089] The ideal path sketch and the set of scheduling constraints are screened to obtain a constraint violation flag table;

[0090] Based on the constraint violation flag table, the ideal path sketch is corrected and smoothed to obtain a smoothed adjusted path.

[0091] The smooth adjustment path is decomposed and transformed in a stepwise manner to obtain a stepwise water level sequence;

[0092] Based on the stepped water level sequence, the scheduling effect is predicted, and an effect prediction report is obtained;

[0093] Based on the effect prediction report and the regional priority table, the water level sequence was optimized collaboratively to obtain the water level control sequence.

[0094] In this embodiment of the invention, an exchange flux deviation table is obtained. This table lists the Comprehensive Exchange Flux Index (CEIS) deviation values ​​of different water layers (such as the surface mixing zone, thermocline, and deep water layer) for each functional zone of the reservoir (e.g., water intake zone, flood discharge zone, and ecological protection zone). The functional zones are prioritized based on the magnitude of the deviation values. A higher deviation value indicates that the sediment-water exchange state of the area is further from the ecological safety threshold, requiring priority intervention through scheduling measures. The total regional deviation is determined using a weighted summation method or a maximum deviation method. For example, for each functional zone, the sum of the deviation values ​​of all water layers is calculated as the total ecological deviation index for that area. Then, the functional zones are ranked from highest to lowest according to the total ecological deviation index, generating a regional priority table. For example, the ecological protection zone has the highest total deviation, ranked 1; the water intake zone is second, ranked 2; and the flood discharge zone has the lowest, ranked 3. Priority_Rank represents the regional priority ranking.

[0095] Obtain hydrological forecast data, including daily inflow, regional rainfall, reservoir evaporation, and expected non-ecological outflow (such as planned water supply and power generation) for the future scheduling cycle (e.g., the next 7 days). Calculate the reservoir's water balance within the scheduling cycle based on this data. For each forecast day, calculate the net change in reservoir water volume: ΔV = (Inflow + Regional Rainfall) × 86400 - (Reservoir Evaporation + Expected Non-Ecological Outflow) × 86400. ΔV represents the daily net change in water volume (m³). 3 Using the reservoir water level-capacity (HV) curve, the daily net water volume change is converted into the corresponding water level change ΔH. For example, based on the current water level H, the reservoir capacity V is found from the HV curve, and the new capacity V' = V + ΔV is calculated. Then, the new water level H' is found by looking up the new capacity H' from the HV curve, so ΔH = H' - H. The daily ΔH value is recorded in the water balance table, reflecting the natural water level change trend without ecological intervention. ΔV represents the daily net water volume change, H represents the water level, V represents the reservoir capacity, and ΔH represents the daily water level change.

[0096] The ecological problems requiring priority are identified based on a regional priority table, along with their corresponding regions and water layers (e.g., CEIS exceedance in deep water layers within an ecological reserve). Natural water level trends predicted by the water balance sheet are referenced. An ideal water level change path is determined by considering the needs of the priority ecological problems. For example, if deep water CEIS exceedance is the highest priority issue, and hydrological forecasts indicate sufficient future inflow, the ideal path is to initially lower the water level appropriately to increase deep-layer discharge capacity and promote water exchange, followed by a gradual rise in water level based on the water balance trend. If inflow is insufficient, the ideal path is to maintain a lower water level or allow it to decline slowly to maximize the utilization of deep reservoir capacity. The ideal path sketch is a smoothed water level-time curve based on ecological needs and water conditions, without considering specific engineering constraints. `Ideal_Path_Sketch` represents the ideal path sketch.

[0097] The ideal path sketch is compared and screened against the set of scheduling constraints to identify the moments when constraints are violated. The set of scheduling constraints includes hard constraints on reservoir operation, such as: upper water level limits (e.g., flood control limits), lower water level limits (e.g., dead water level, minimum operating water level at the intake), and maximum permissible daily water level change rate. For each time point on the ideal path sketch, the water level value is checked to see if it falls within the upper or lower limits. For adjacent time points, the water level change rate is calculated and checked to see if it exceeds the maximum permissible daily change rate. For example, if the ideal path sketch shows that the water level drop rate exceeds the permissible 10 cm / d on a certain day, then a constraint violation is marked for that day. All time points and violation types are recorded in the constraint violation marking table. `Constraint_Violation_Marking_Table` represents the constraint violation marking table.

[0098] The ideal path sketch is corrected and smoothed based on the constraint violation flag table to obtain a smoothed adjustment path. At the time points indicated in the constraint violation flag table, the water level values ​​on the ideal path sketch are adjusted to meet the corresponding constraints. For example, if the water level drops too quickly on a certain day, the target water level for that day is adjusted to the previous day's water level minus the maximum allowable drop. If the water level is below the minimum intake elevation, the target water level is adjusted to the minimum intake elevation. After adjustment, the original smoothed curve shows sharp inflections. A smoothing algorithm (e.g., using Loess local weighted regression or Savitzky-Golay filter) is applied to process the corrected path to eliminate sharp changes, generating a relatively smooth water level-time curve that satisfies all constraints—the smoothed adjustment path. `Smoothed_Adjustment_Path` represents the smoothed adjustment path.

[0099] The smooth adjustment path is decomposed into a stepped water level sequence. The entire scheduling cycle is divided into several fixed-length scheduling intervals (e.g., one interval per day). Within each scheduling interval, a constant water level change rate or a target water level at the end of the interval is determined based on the smooth adjustment path. For example, the average slope of the smooth adjustment path within each daily interval is calculated and used as the constant water level change rate command for that day. Alternatively, the water level value at the end of each day can be directly used as the final target water level for that day.

[0100] This process transforms a continuous, smooth path into a series of discrete, easily manipulated, step-like instructions, forming a step-water level sequence. Stepped_Water_Level_Sequence represents the step-water level sequence.

[0101] Using a stepped water level sequence as input, a coupled numerical model of reservoir hydrodynamic and ecological processes is employed to predict the effects of water management. This model can simulate changes in the water flow field, temperature, dissolved oxygen, nutrient distribution, and sediment-water exchange flux within the reservoir under given water level variations and inflow / outflow conditions. The stepped water level sequence is input into the model, and the simulation is run. Predicted values ​​for CEIS, dissolved oxygen, and benthic habitat indicators are extracted from key areas (especially those ranked high in the regional priority table). The comparison between these predicted values ​​and ecological safety thresholds is analyzed to assess the potential effects of the water management sequence on improving ecological conditions. The simulation results and assessment conclusions are summarized to generate an effect prediction report. `Effect_Estimation_Report` represents the effect prediction report.

[0102] The ecological benefits of the stepped water level sequence are assessed based on the effect prediction report, and then coordinated with the regional priority table for optimization to obtain the final water level regulation sequence. If the effect prediction report shows that the highest priority ecological problems have not been effectively improved, or there is room for improvement, the stepped water level sequence is fine-tuned without violating constraints. For example, if the predicted deep-water CEIS is still too high, and the water level change rate constraint allows, the water level decline rate corresponding to deep-water water release can be slightly increased; if the improvement of benthic habitat in a certain area is not significant, the length of the water level stabilization period can be adjusted. According to regional priorities, the improvement of ecological effects in high-priority areas is given priority. Through a small number of iterative adjustments and effect predictions, a stepped water level sequence that maximizes ecological benefits (especially in priority areas) while satisfying all constraints is found. This final determined sequence is the water level regulation sequence. Water_Level_Regulation_Sequence represents the water level regulation sequence.

[0103] Preferably, the optimization of the stratified water discharge ratio in step S3 includes:

[0104] Determine the intake parameter table based on reservoir engineering parameters and water level control sequence;

[0105] A vertical stratification analysis of the water quality in the reservoir was conducted to obtain a water quality stratification structure diagram;

[0106] Based on the water quality stratification diagram, an impact assessment of the sediment interface was conducted, resulting in an interface impact assessment table.

[0107] Obtain water quality target parameters and construct the objective function based on the interface impact assessment table to obtain the optimized objective function;

[0108] Based on the water intake parameter table and the optimization objective function, the constraints are set to obtain the optimization constraint set.

[0109] Generate a set of candidate optimization solutions based on the set of optimization constraints;

[0110] An interface disturbance risk assessment and scheme determination were performed on the candidate optimization scheme set to obtain a tiered water release configuration table.

[0111] In this embodiment of the invention, an intake parameter table is determined based on reservoir engineering parameters and a water level control sequence. The reservoir engineering parameters include the name of each intake, its elevation (expressed as absolute elevation, e.g., meters above sea level), and its maximum design discharge capacity (e.g., m³). 3 / s). The water level control sequence provides target water levels at different time points within the future control cycle. For each time point in the water level control sequence, the current target water level is compared with the elevation of each intake. If the target water level is higher than the elevation of a certain intake, the intake is submerged and usable at that time point; if the target water level is lower than or equal to the intake elevation, the intake is dry and unusable at that time point. Record the name, elevation, maximum design discharge capacity, and usability status of each intake at different time points in the water level control sequence. Compile this information into an intake parameter table. For example, the table contains: Intake Name = Deep Intake 1, Elevation = 150.0m, Maximum Capacity = 50m 3 / s, available time period = [T1,T2] (when the water level is higher than 150.0m). Outlet_Parameter_Table represents the water intake parameter table.

[0112] Acquire real-time vertical profile monitoring data of water quality in the reservoir, including water temperature, dissolved oxygen (DO), oxidation-reduction potential (ORP), and ammonia nitrogen (NH4). + ), total phosphorus (PO4) 3-The variation of parameters such as DO (dissolved oxygen) with water depth is analyzed. This data is then used to identify gradient changes and stratification characteristics of water quality parameters with depth. For example, the rate of change of DO concentration with depth, ΔDO / Δz, is calculated. Depth ranges where DO and ORP values ​​rapidly decrease are identified, which typically indicate the onset of anoxic or anaerobic layers. NH4+ is also identified. + PO4 3- Significantly elevated concentrations at various depths are typically associated with sediment release and anaerobic environments. Based on the vertical distribution characteristics of these water quality parameters, the reservoir's vertical profile is divided into layers with different water quality characteristics, such as a high-DO surface oxidation layer, a rapidly decreasing DO middle transition zone, and a low-DO / anaerobic bottom reduction layer with high nutrient concentrations. A water quality stratification diagram reflecting the current vertical stratification structure of the reservoir is generated; for example, the diagram shows an oxidation layer from 0-5m, a transition zone from 5-8m, and a reduction layer from 8m to the reservoir bottom. ΔDO represents the change in dissolved oxygen, and Δz represents the change in vertical distance.

[0113] Based on the water quality stratification diagram and the previous exchange flux deviation table, an impact assessment of the sediment interface was conducted, resulting in an interface impact assessment table. The exchange flux deviation table indicates which areas and water layers have CEIS exceeding the ecological safety threshold, suggesting problems with sediment-water exchange in these areas (e.g., excessive release of endogenous pollutants). Combined with the water quality stratification diagram, the water quality characteristics of the layers containing these high CEIS areas were analyzed. For example, if a high CEIS area is located in the bottom reducing layer, and this layer has extremely low DO and NH4... + and PO4 3- High concentrations indicate a close relationship between endogenous pollutant release and the anoxic environment of the bottom layer in this area. The potential impact of releasing water from different intake layers on improving water quality and the sediment interface near these high CEIS areas was assessed. For example, releasing water from the bottom layer intake can directly remove some of the heavily polluted bottom water; releasing water from the surface layer intake (if it affects the bottom flow field or through mixing) introduces oxygen; and releasing water from the middle layer intake has different effects. The assessment results were quantified as potential impact scores or descriptive assessments and recorded in the Interface Impact Assessment Table, for example: High CEIS area A (bottom layer), deep layer release: high potential impact (removal of pollutants, improvement of DO), middle layer release: medium potential impact (causing local mixing), surface layer release: low potential impact (small direct impact). The Interface_Impact_Assessment_Table represents the Interface Impact Assessment Table.

[0114] Obtain water quality target parameters. These parameters are set based on reservoir ecological management objectives, such as requiring dissolved oxygen concentration in deep water layers to be no less than 4 mg / L during the summer stratification period, or ammonia nitrogen concentration in the water above the sediment to be no more than 1 mg / L. Based on the interface impact assessment table, identify the key water quality parameters and areas that need to be prioritized for improvement through stratified water release. Construct an optimization objective function based on these key parameters and their target values. The objective function is a mathematical expression whose value reflects the merits of the current stratified water release scheme. For example, the objective function can be set to minimize the deviation between the water quality parameters in key areas and the target values, while considering the reduction in CEIS. For example, the minimized function F = w1 × max(0, DO_target - Predicted_DO_deep) + w2 × max(0, Predicted_NH4) + _bottom-NH4 + _target)-w3×Predicted_CEIS_Reduction_weighted. Among them, Predicted_DO_deep, Predicted_NH4 + `_bottom` and `Predicted_CEIS_Reduction_weighted` are the predicted key water quality parameter values ​​and CEIS reduction amounts (estimated through simplified models or empirical formulas) based on the stratified water release scheme. `w1`, `w2`, and `w3` are weighting coefficients reflecting the importance of different objectives. `Objective_Function` represents the optimization objective function, `w` represents the weighting coefficients, `Predicted_Parameter` represents the predicted parameter values, and `Target_Parameter` represents the target parameter values.

[0115] Based on the intake parameter table and the optimization objective function, a set of optimization constraints is set. These constraints limit the feasibility of the stratified water release scheme. The main constraints include: 1. Total water release flow constraint: The sum of the water release flows from all opened intakes must equal the total outflow required by the water level control sequence at the current time step (to achieve the target water level change). 2. Individual intake capacity constraint: The water release flow from each opened intake cannot exceed its maximum design water release capacity. 3. Intake availability constraint: Only intakes that are submerged and available at the current water level can be opened for water release. 4. Minimum water release requirement (if present): The scheduling constraint set will enforce a minimum water release flow for a certain intake or a certain stratum (such as deep layers) to ensure specific needs (such as ecological baseflow or bottom scouring). These constraints are expressed using mathematical equations or inequalities to form the optimization constraint set. For example, Σ iOutlet_Dischargei = Required_Outflow; 0 ≤ Outlet_Dischargei ≤ Max_Capacityi (if outlet i is available); Outlet_Dischargei = 0 (if outlet i is unavailable); Σ j Deep_Outlet_Discharge j ≥Min_Deep_Discharge (if present). Outlet_Discharge i Let represent the discharge flow rate of the i-th intake, Required_Outflow represent the total outflow required by the water level control sequence, Max_Capacityi represent the maximum discharge capacity of the i-th intake, Deep_Outlet_Dischargej represent the discharge flow rate of the j-th deep intake, and Min_Deep_Discharge represent the minimum deep water discharge requirement.

[0116] Based on the set of optimization constraints, a set of candidate optimization schemes is generated. A candidate optimization scheme is a specific combination of water intake discharge flows (i.e., the discharge flow value of each available water intake), which must satisfy all equality and inequality in the set of optimization constraints. This can be achieved by solving a constrained optimization problem. If the objective function and constraints are linear, linear programming methods can be used. If they are nonlinear, nonlinear programming algorithms can be used. Alternatively, for a finite number of water intakes, combinations of discharge ratios with a certain precision can be enumerated, for example, in steps of 5% of the total flow, and assigned to different available water intakes to generate all combinations that satisfy the total flow and individual capacity constraints. These discharge flow combinations that satisfy the constraints constitute the set of candidate optimization schemes. `Candidate_Schemes` represents the set of candidate optimization schemes.

[0117] An interface disturbance risk assessment and scheme determination were performed on the candidate optimization scheme set to obtain a stratified water release configuration table. For each scheme (i.e., each intake water flow combination) in the candidate optimization scheme set, its interface disturbance risk was first assessed. For example, the water flow velocity near each opened bottom intake was calculated. If the flow velocity exceeded the critical initiation velocity of the bottom sediment (determined by sediment characteristics), the scheme was considered to have a bottom sediment disturbance risk. Then, each scheme was substituted into the optimization objective function to calculate its objective function value (e.g., the predicted water quality improvement and CEIS reduction). The objective function values ​​of all schemes with acceptable risks were compared. The scheme with the optimal objective function value (e.g., minimizing deviation or maximizing benefit) and whose interface disturbance risk was within a controllable range was selected as the final stratified water release configuration. If the objective function values ​​of multiple schemes were close, the scheme with the simplest operation or the lowest disturbance risk was selected first. The selected scheme determined the specific water flow rate of each intake within the current time step. These flow rate values ​​were compiled into a table, i.e., the stratified water release configuration table.

[0118] For example, the table includes: Intake Name = Deep Intake 1, Discharge Flow Rate = 35m³ 3 / s; Intake name = Surface intake A, discharge flow rate = 15m³ 3 / s. Stratified_Discharge_Configuration represents the stratified discharge configuration table.

[0119] Preferably, the determination of the reservoir water exchange scheduling cycle in step S3 includes:

[0120] The regional response timetable is estimated based on the stratified water release configuration table; the water volume renewal cycle is calculated based on the stratified water release configuration table to obtain the stratified renewal cycle table; and the ecological process timescale analysis is performed based on the regional response timetable to obtain the ecological timescale table.

[0121] Obtain current seasonal data and conduct a seasonal sensitivity assessment to obtain the seasonal sensitivity index;

[0122] The exchange flux deviation table is classified into exchange flux deviations to obtain the deviation classification results;

[0123] A basic cycle plan is set according to the regional response timetable, the hierarchical update cycle table, and the ecological timescale table;

[0124] Based on the seasonal sensitivity index and deviation classification results, the scheduling frequency of the basic cycle scheme is optimized to obtain the optimized frequency scheme.

[0125] The scheduling duration of the optimized frequency scheme is determined to obtain the duration scheme.

[0126] A time-series scheduling plan is generated based on the optimized frequency scheme, duration scheme, and tiered water release configuration table.

[0127] Numerical simulation and optimization of the time-series scheduling plan are performed to obtain an optimized scheduling parameter set.

[0128] In this embodiment of the invention, the regional response time of different functional zones and water layers is estimated based on the stratified water release configuration table (which specifies the specific water release flow rate of each available water intake within the scheduling cycle). This requires utilizing the hydrodynamic characteristics of the reservoir. For example, for the deep water layer of a certain functional zone (such as an ecological protection zone), if the water release flow rate of the deep water intake is known, its hydraulic residence time or response time can be preliminarily estimated by calculating the ratio of the effective volume of the deep water layer in that area to the water release flow rate: T_response≈V_region / Q_discharge_deep. For other regions or water layers, water flow velocity and distance can be considered. For example, the average flow velocity from the water intake to a key monitoring area can be estimated, and then the water arrival time can be calculated based on the distance. A more accurate method is to use a simplified two-dimensional or three-dimensional hydrodynamic model, input the flow rate from the stratified water release configuration table, simulate the water flow field in the reservoir area, and estimate the time required for the tracer to diffuse from the water intake to each region. The estimated response times of each region / water layer are recorded in the regional response time table. For example, the table includes: Ecological Protection Zone - Deep Water Response Time = 3 days; Water Intake Area - Surface Response Time = 1 day. T_response represents the regional response time, V_region represents the effective volume of the region, and Q_discharge_deep represents the deep water discharge flow rate.

[0129] Based on the stratified water release configuration table, the water renewal cycle for different water layers or functional areas is calculated, resulting in a stratified renewal cycle table. The water renewal cycle (or average water age) refers to the time required for a water body to be replaced by newly entering water. For the entire reservoir, the renewal cycle is approximately the total reservoir capacity divided by the total outflow. For a specific water layer or area, if its volume V_layer / region and the net flow through that layer / region Q_net_through_layer / region can be estimated, then the renewal cycle ≈ V_layer / region / Q_net_through_layer / region. Based on the water release flow of each layer in the stratified water release configuration table, combined with a simplified model of inflow and inter-layer exchange, the average flow of different water layers or functional areas is estimated, and then their renewal cycles are calculated. For example, to calculate the renewal cycle of the deep water layer, the deep water release flow and the inter-layer vertical exchange flow are considered. The calculation results are recorded in the stratified renewal cycle table. For example, the table contains: deep water layer renewal cycle = 10 days; surface layer renewal cycle = 5 days. Renewal_Period represents the water volume update cycle, V_layer / region represents the volume of the water layer / region, and Q_net_through_layer / region represents the net flow through the layer / region.

[0130] Based on the regional response timeline, the timescales of ecological processes related to each region are analyzed to obtain an ecological timescale table. For example, if a region has a short response time (e.g., 1 day), it is necessary to focus on rapid-response ecological processes, such as diurnal variations in dissolved oxygen and the photosynthetic rate of phytoplankton. If a region has a long response time (e.g., 10 days), it is necessary to focus on medium- and long-term ecological processes, such as the rate of nutrient release from sediment, seasonal variations in benthic communities, and water stratification evolution. Combining historical ecological monitoring data and ecological knowledge, one or more key ecological processes and their typical timescales are identified for each region. For example, for deep water layers, the timescale for sediment oxygen consumption and nutrient accumulation is several days to several weeks; for severely eutrophic surface layers, the timescale for algal blooms is several days to one week. This information is compiled into an ecological timescale table. For example, the table includes: deep water layer - sediment oxygen consumption timescale = 7 days; surface layer - algal growth response timescale = 3 days. `Ecological_Time_Scale` represents the timescale of ecological processes.

[0131] The current season's data is obtained by querying the current date stored internally within the system and comparing it with the seasonal characteristic periodic table (obtained from S2.2). For example, the current date belongs to the summer stratification period. A seasonal sensitivity assessment is conducted based on the typical meteorological, hydrological, and ecological characteristics of the current season. For example, during the summer stratification period, water temperatures are high, bottom oxygen levels are prone to depletion, sediment release is vigorous, and the system responds faster or is more sensitive to disturbances (such as mixing caused by bottom water release) and improvement measures (such as increasing bottom dissolved oxygen). During the winter circulation period, water mixing is homogeneous, water temperatures are low, ecological processes are slow, and the system is relatively insensitive. The assessment results are quantified into a seasonal sensitivity index; for example, each seasonal characteristic period is assigned a score from 1 to 5, with summer stratification period = 5 (highest sensitivity) and winter circulation period = 1 (lowest sensitivity). `Seasonal_Sensitivity_Index` represents the seasonal sensitivity index.

[0132] Obtain the exchange flux deviation table. This table contains the current CEIS deviation values ​​for each functional zone and water layer. Classify the deviation values ​​according to preset threshold ranges (e.g., based on normal, warning, and critical thresholds in the ecological security domain map). For example, areas with deviation values ​​less than the upper limit of the normal threshold are classified as "low risk"; areas with deviation values ​​between the upper limit of the normal and warning thresholds are classified as "medium risk"; and areas with deviation values ​​greater than the upper limit of the warning threshold are classified as "high risk" or "severe risk". Record the deviation classification results for each zone / water layer to form the deviation classification results. For example, the deviation classification results show: Ecological Protection Zone - Deep Water Layer: Severe Risk; Water Intake Area - Surface Layer: Medium Risk. Deviation_Grading_Result represents the deviation classification result.

[0133] Based on the regional response timetable, stratified update cycle table, and ecological timescale table, a basic scheduling cycle scheme is established. The aim is to determine an initial scheduling operation repetition interval. The minimum timescale associated with high-risk areas (determined based on deviation classification results) in all tables is considered. For example, if the deep water layer of the ecological reserve is a severely risky area, its response time is 3 days, the update cycle is 10 days, and the sediment oxygen consumption timescale is 7 days. The base cycle can be set as a representative value among these timescales, such as the minimum response time (3 days) or a time slightly longer than the response time but shorter than the timescale of the main ecological processes (e.g., 5-7 days). A standard base cycle length, such as 7 days, is set as the starting point for subsequent optimizations. `Base_Period_Scheme` represents the base cycle scheme.

[0134] Based on the seasonal sensitivity index and deviation classification results, the scheduling frequency of the basic cycle scheme is optimized to obtain the optimized frequency scheme. If the current seasonal sensitivity is high and the deviation classification results indicate the existence of severely risky areas, the scheduling frequency needs to be increased (i.e., the scheduling cycle is shortened) for more timely response and intervention. For example, if the basic cycle scheme is 7 days, the seasonal sensitivity index is 5, and severely risky areas exist, the scheduling cycle is shortened to 3 days. If the seasonal sensitivity is low and the highest deviation classification is low risk, the scheduling frequency can be reduced (i.e., the scheduling cycle is extended), for example, extending the cycle to 14 days. A lookup table or function based on the seasonal sensitivity index and the highest deviation level is used to determine the cycle adjustment factor. The cycle length of the optimized frequency scheme is obtained by multiplying the basic cycle scheme by the adjustment factor. `Optimized_Frequency_Scheme` represents the optimized frequency scheme.

[0135] Based on the optimized frequency scheme, determine the scheduling duration scheme. The scheduling duration refers to the total time expected to be required to continuously perform scheduling operations in order to address the currently identified ecological problem. This should match the timescale of the ecological processes required to solve the problem. For example, if ecological problems in a severely risky area (such as deep-water anoxic conditions and pollutant accumulation) are expected to require one month to be significantly improved through water exchange and mixing (refer to the ecological timescale table), and the optimized frequency scheme determines a cycle of 7 days, then the total duration scheme can be set to 30 days. Alternatively, a dynamic duration can be set, i.e., continuous scheduling until the CEIS of the critical area continuously decreases and remains below the warning threshold for a period of time. In this step, a predetermined fixed duration is set. Duration_Scheme represents the duration scheme.

[0136] Based on the optimized frequency scheme (cycle length), duration scheme (total duration), and tiered water release configuration table (operation instructions within a single cycle), a time-series scheduling plan is generated. The time-series scheduling plan is a detailed operation schedule. It divides the total duration of the duration scheme into several scheduling cycles, the length of which is determined by the optimized frequency scheme. Within each scheduling cycle, the operation instructions from the tiered water release configuration table are repeatedly applied (i.e., specific intake water release flows are executed during specific time periods within the cycle). For example, if the duration is 30 days, the cycle is 7 days, and the tiered water release configuration table specifies the daily water release flow, then the time-series scheduling plan will list the specific water release flow instructions for each intake for each day from day 1 to day 30. `Temporal_Scheduling_Plan` represents the time-series scheduling plan.

[0137] Numerical simulation and optimization of the time-series scheduling plan yields an optimized scheduling parameter set. The generated time-series scheduling plan is used as input to drive a coupled numerical model of reservoir hydrodynamic-ecological processes. The simulation measures the reservoir's ecological response (e.g., spatiotemporal variations in CEIS, dissolved oxygen, and nutrients) throughout the entire duration of the plan. The simulation results are evaluated, for example, checking whether CEIS in high-risk areas has significantly decreased and reached the expected target, and whether new ecological problems exist (e.g., excessive sediment disturbance). If the simulation results are unsatisfactory or there is room for improvement, specific parameters in the time-series scheduling plan (e.g., fine-tuning of discharge flow in certain cycles, fine-tuning of the duration of specific operations) are optimized without violating the scheduling constraint set. For example, using a genetic algorithm or particle swarm optimization algorithm, with ecological improvement as the optimization objective and scheduling constraints as the constraints, the optimal combination of time-series scheduling parameters is searched. After simulation verification and optimization iterations, a final, executable set of detailed scheduling operation instructions is obtained, i.e., the optimized scheduling parameter set. `Optimized_Scheduling_Parameter_Set` represents the optimized scheduling parameter set.

[0138] Preferably, step S4 includes the following steps:

[0139] Step S41: Deploy monitoring points for scheduling execution based on the optimized scheduling parameter set to obtain a monitoring point deployment scheme;

[0140] Step S42: Formulate a time-series sampling strategy based on the monitoring point deployment plan to obtain a hierarchical sampling plan;

[0141] Step S43: Collect synchronous parameters according to the hierarchical sampling plan to obtain a multidimensional response dataset;

[0142] Step S44: Obtain baseline data before scheduling, and calculate the baseline deviation by combining it with the multidimensional response dataset to obtain the standardized deviation matrix;

[0143] Step S45: Based on the standardized deviation matrix, perform spatiotemporal variation pattern recognition on the comprehensive index of exchange flux to obtain the response pattern map;

[0144] Step S46: Perform ecological index correlation analysis on the response pattern map to obtain the ecological effectiveness index;

[0145] Step S47: Construct the scheduling response curve based on the ecological efficiency index and response pattern map.

[0146] In this embodiment of the invention, an optimized scheduling parameter set (which specifies in detail the scheduling operation time, water level changes, stratified water release flow, etc.) and a reservoir functional zone distribution map are obtained. Based on the implementation area of ​​the optimized scheduling parameter set (e.g., instructions mainly targeting the deep water layer of the ecological protection zone and the surface layer of the water intake area) and the expected impact range (e.g., the water flow impact range predicted through numerical simulation), a monitoring point deployment scheme is designed. The density of monitoring points is increased in high-risk areas (determined according to the exchange flux deviation table) and their downstream potential impact areas. For example, if key deep water release is carried out in the deep water layer of the ecological protection zone, vertical profile integrated sensors are densely deployed at the bottom of this area and along its downstream flow path. If water level regulation is carried out in the surface layer of the water intake area, sensors are densely deployed in the surface layer and near the thermocline of this area. Meanwhile, regular monitoring points are retained in the control area (similar areas not directly affected by scheduling). The deployment scheme specifies the geographical coordinates of each monitoring point and the type of vertical profile integrated sensor installed. This location information is recorded in the monitoring point deployment scheme. `Monitoring_Point_Layout_Plan` represents the monitoring point deployment scheme.

[0147] Based on the monitoring point deployment plan and the optimized scheduling operation schedule, a time-series sampling strategy was formulated, resulting in a graded sampling plan. The optimized scheduling parameter set clarified the start time and duration of the scheduling operation. A "dense-sparse" progressive sampling strategy was designed. In the 24 hours prior to the start of the scheduling operation, all monitoring points collected baseline data at a high frequency (e.g., hourly for CEIS and environmental parameters). In the initial stage after the start of the scheduling operation (e.g., the first 72 hours), the sampling frequency was increased to once every 2 hours at the key monitoring points most directly affected by the scheduling (e.g., downstream of deep water outlets). Subsequently, as the scheduling effect gradually became apparent and the rate of change slowed, the sampling frequency was gradually reduced. For example, in the 4-7 days after the start of the scheduling, the sampling frequency was adjusted to once daily; after 7 days until the end of the scheduling, the sampling frequency returned to the regular monitoring frequency (e.g., 1-2 times per week). The graded sampling plan detailed the specific sampling time and sampling parameter list for each monitoring point throughout the entire scheduling duration. `Graded_Sampling_Plan` represents the graded sampling plan.

[0148] Following a tiered sampling plan, synchronous parameter acquisition was performed at designated monitoring points and sampling times to obtain a multidimensional response dataset. Using a vertical profile integrated sensor array and other auxiliary monitoring equipment, the Combined Exchange Flux Index (CEIS) and a series of key environmental parameters were synchronously acquired at each sampling time. In addition to CEIS, the synchronously acquired parameters included, but were not limited to: water temperature vertical profile, dissolved oxygen (DO) vertical profile, oxidation-reduction potential (ORP) vertical profile, pH value, conductivity, and ammonia nitrogen (NH4+). + ), total phosphorus (PO4) 3- The data includes chlorophyll a concentration and indicators of benthic activity intensity (e.g., monitored via acoustic or optical sensors). Ensure all parameter acquisitions include accurate timestamps and location / depth information, and align them to the desired time using the data acquisition system. Integrate all acquired parameter data to create a multidimensional response dataset containing time, location, depth, and all parameter values. The Multi_Dimensional_Response_Dataset represents the multidimensional response dataset.

[0149] Obtain pre-scheduling baseline data. This data consists of baseline data collected frequently during the tiered sampling plan before the scheduling operation begins (e.g., data from the 24 hours prior to scheduling). Compare the CEIS data in the multidimensional response dataset with the pre-scheduling baseline data, calculate the baseline deviation, and obtain the standardized deviation matrix. For each monitoring point and each sampling time, calculate the difference between the current CEIS value and the average CEIS baseline value before scheduling at that point: Deviation = CEIS_current - CEIS_baseline_average. To eliminate the influence of differences in baseline levels among different monitoring points, standardize the deviation values. For example, calculate the standard deviation (σ_baseline) of CEIS for each monitoring point during the pre-scheduling baseline period. Then, standardization deviation = Deviation / σ_baseline. Organize the standardized deviation values ​​of all monitoring points and all time points into a matrix, where rows represent time, columns represent monitoring points, and matrix cells store the corresponding standardized deviation values, forming the standardized deviation matrix. Standardized_Deviation_Matrix represents the standardized deviation matrix, Deviation represents the deviation value, CEIS_current represents the current CEIS value, CEIS_baseline_average represents the baseline average of CEIS before scheduling, and σ_baseline represents the baseline standard deviation of CEIS before scheduling.

[0150] Based on the standardized deviation matrix, the spatiotemporal variation patterns of the Comprehensive Exchange Flux Index (CEIS) are identified to obtain a response pattern map. The standardized deviation matrix is ​​analyzed to identify the time-varying trend of CEIS (e.g., whether the CEIS deviation increases, decreases, or fluctuates after scheduling begins) and spatial distribution characteristics (e.g., whether the deviation change is confined to the scheduling area or spreads outwards). Spatiotemporal data analysis methods are used, such as trend analysis of the deviation time series for each monitoring point, to identify the rate of change and duration. Spatial interpolation is performed on the deviation values ​​at different monitoring points to generate CEIS deviation distribution maps at different time points. Key spatiotemporal patterns are identified, such as the time and location of the maximum deviation, the time required for the deviation to recover to the baseline level, and the spatial extent of the deviation's impact. These spatiotemporal variation characteristics are visualized, for example, by plotting time series graphs, spatial distribution heatmaps, or three-dimensional spatiotemporal evolution maps, forming a response pattern map reflecting the scheduling response process. `Response_Pattern_Map` represents the response pattern map.

[0151] A correlation analysis is performed between the response pattern map (spatiotemporal variation of CEIS deviation) and other ecological indicators (such as dissolved oxygen, ammonia nitrogen, and benthic activity) collected synchronously in the multidimensional response dataset to obtain an ecological effectiveness index. For example, regions and time periods with significant decreases in CEIS deviation are identified in the response pattern map, and then data on dissolved oxygen concentration, ammonia nitrogen concentration, and benthic activity in the same region during the same period are retrieved in the multidimensional response dataset. The correlation coefficients and time-lag correlations between changes in CEIS deviation and changes in these ecological indicators are calculated. For example, if a decrease in CEIS deviation shows a strong positive correlation and short time lag with increases in dissolved oxygen concentration, decreases in ammonia nitrogen concentration, and enhancement of benthic activity, it indicates that the scheduling measures have effectively improved water quality and benthic habitat by reducing CEIS. The strength of this correlation and the magnitude of ecological improvement are quantified. For example, an ecological effectiveness index E is defined, the calculation formula of which can take into account the degree of improvement of key ecological indicators and their proximity to target values, as well as the magnitude of the reduction in CEIS.

[0152] E=w_DO×(Predicted_DO_improved-DO_baseline) / (DO_target-DO_baseline)+w_NH4 + ×(NH4 + _baseline-Predicted_NH4 + _improved) / (NH4 + _baseline-NH4 +The calculated ecological effectiveness index is recorded to evaluate the overall ecological effect of this scheduling. Ecological_Effectiveness_Index represents the ecological effectiveness index, w represents the weighting coefficient, Predicted_Parameter_improved represents the predicted improved parameter value, Parameter_baseline represents the parameter baseline value, and Parameter_target represents the parameter target value.

[0153] Based on the ecological effectiveness index and response pattern map, a dispatch response curve is constructed. Key monitoring points or representative areas are selected, and their CEIS standardized deviation is plotted over time. For example, a time series plot of the CEIS standardized deviation for monitoring point P1 in the deep water layer of an ecological reserve is plotted. The start and end times of the dispatch operation, as well as the occurrence times of key ecological events assessed according to the ecological effectiveness index (e.g., the moment when dissolved oxygen in the deep water layer begins to rise significantly), are marked on this curve. Simultaneously, curves showing the changes of key ecological indicators (such as dissolved oxygen) over time at this monitoring point, or their standardized deviation curves, can be plotted and compared with the CEIS deviation curve. These curves are then combined to form a set of charts that visually demonstrate how dispatch operations affect CEIS and how CEIS changes relate to ecological responses—the dispatch response curve. These curves provide direct evidence for evaluating the actual effectiveness of this dispatch, understanding the response mechanism, and guiding future dispatch optimization. `Dispatch_Response_Curve` represents the dispatch response curve.

[0154] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0155] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A reservoir ecological regulation method, characterized in that, The method comprises the following steps: Step S1: collecting multi-source parameters of a vertical profile of the reservoir by a sensor array to obtain an original profile data set; and performing sediment-water body material exchange intensity characterization on the original profile data set to obtain an exchange flux comprehensive index; wherein, step S1 comprises the following steps: Step S11: obtaining reservoir morphology and hydrological data; and arranging a plurality of monitoring points at a sediment-water body interface in a key area according to the reservoir morphology and hydrological data, each monitoring point being configured with a vertical profile integrated sensor, which comprises three core elements, namely, a heat flow sensor, a gas chromatograph detector and an electrochemical diffusion sensor, the sensors being vertically arranged to cover a key exchange area from the upper 5 cm of the sediment to the lower 10 cm of the water body, to obtain a sensor array and form a monitoring network topology graph; Step S12: performing multi-parameter synchronous collection by the sensor array based on the monitoring network topology graph to obtain the original profile data set; Step S13: performing data calibration and abnormality identification on the original profile data set to obtain a calibrated monitoring sequence; Step S14: performing flux parameter calculation according to the calibrated monitoring sequence to obtain a classified flux numerical table; Step S15: performing weighted fusion calculation on the classified flux numerical table to obtain the exchange flux comprehensive index; Step S2: stratifying the reservoir according to the exchange flux comprehensive index to obtain a reservoir stratigraphic structure graph; performing seasonal-depth partitioning according to the reservoir stratigraphic structure graph to obtain a partition statistical feature set; and performing ecological safety response correlation on the partition statistical feature set to obtain an ecological safety domain graph; wherein, step S2 comprises the following steps: Step S21: stratigraphically dividing the vertical profile of the reservoir according to the exchange flux comprehensive index to obtain the reservoir stratigraphic structure graph, wherein the reservoir stratigraphic structure graph comprises a surface mixed zone, a thermocline and a deep water layer; Step S22: obtaining meteorological and hydrological data of an area where the reservoir is located; and defining a seasonal characteristic period according to the meteorological and hydrological data to obtain a seasonal characteristic period table, wherein the seasonal characteristic period table comprises a spring mixing period, a summer stratification period, an autumn transition period and a winter circulation period; Step S23: performing seasonal-depth two-dimensional data partitioning according to the seasonal characteristic period table and the reservoir stratigraphic structure graph to obtain a partition statistical feature set; Step S24: obtaining reservoir ecological environment monitoring data; and performing ecological response correlation analysis on the reservoir ecological environment monitoring data and the partition statistical feature set to obtain an ecological response curve cluster; Step S25: constructing a threshold level system based on the ecological response curve cluster to obtain a threshold classification matrix; Step S26: generating an ecological safety domain graph according to the threshold classification matrix; Step S3: performing exchange flux and scheduling constraint analysis according to the ecological safety domain graph to obtain an exchange flux deviation table and a scheduling constraint condition set; wherein, the exchange flux and scheduling constraint analysis in step S3 comprises: obtaining reservoir basic geographic information; and dividing the reservoir into functional areas according to the reservoir basic geographic information and the ecological safety domain graph to obtain a functional area distribution graph, wherein the functional area distribution graph comprises a water intake area, a flood discharge area and an ecological protection area; performing exchange flux deviation calculation on the exchange flux comprehensive index and the ecological safety domain graph to obtain an exchange flux deviation table; and Obtaining reservoir engineering parameters, and determining a set of scheduling constraint conditions according to the exchange flux deviation table; performing water level regulation strategy calculation according to the exchange flux deviation table and the set of scheduling constraint conditions to obtain a water level regulation sequence; performing hierarchical water release proportion optimization according to the water level regulation sequence to obtain a hierarchical water release configuration table; and performing reservoir water exchange scheduling period determination according to the hierarchical water release configuration table to obtain an optimized scheduling parameter set; Step S4: continuously tracking and monitoring the exchange flux index change according to the optimized scheduling parameter set, evaluating the actual influence of the scheduling measures on water quality and benthic ecology, and generating a scheduling response curve.

2. The reservoir ecological regulation method according to claim 1, characterized in that, The water level regulation strategy calculation in step S3 includes: performing key area priority ranking according to the exchange flux deviation table to obtain an area priority table; calculating water balance in the scheduling period according to the hydrological forecast data to obtain a water balance table; determining an ideal change path according to the area priority table and the water balance table to obtain an ideal path sketch; performing constraint condition screening on the ideal path sketch and the set of scheduling constraint conditions to obtain a constraint violation marking table; performing path correction and smoothing on the ideal path sketch according to the constraint violation marking table to obtain a smoothed adjustment path; performing stepwise decomposition and conversion on the smoothed adjustment path to obtain a stepwise water level sequence; performing scheduling effect estimation according to the stepwise water level sequence to obtain an effect estimation report; performing water level sequence collaborative optimization according to the effect estimation report and the area priority table to obtain the water level regulation sequence.

3. The reservoir ecological regulation method according to claim 1, characterized in that, The hierarchical water release proportion optimization in step S3 includes: determining a water intake parameter table according to the reservoir engineering parameters and the water level regulation sequence; performing water quality vertical layering analysis on the reservoir to obtain a water quality layering structure diagram; performing bottom mud interface influence evaluation according to the water quality layering structure diagram to obtain an interface influence evaluation table; obtaining water quality target parameters and constructing a target function according to the interface influence evaluation table to obtain an optimization target function; setting constraint conditions according to the water intake parameter table and the optimization target function to obtain an optimization constraint condition set; generating a set of candidate optimization schemes according to the optimization constraint condition set; performing interface disturbance risk evaluation and scheme determination on the set of candidate optimization schemes to obtain the hierarchical water release configuration table.

4. The reservoir ecological regulation method according to claim 1, characterized in that, The reservoir water exchange scheduling period determination in step S3 includes: estimating a regional response time table according to the hierarchical water release configuration table; calculating a hierarchical update period table according to the hierarchical water release configuration table; and performing ecological process time scale analysis according to the regional response time table to obtain an ecological time scale table; obtaining current seasonal data and performing seasonal sensitivity evaluation to obtain a seasonal sensitivity index; performing exchange flux deviation classification on the exchange flux deviation table to obtain a deviation classification result; setting a basic period scheme according to the regional response time table, the hierarchical update period table, and the ecological time scale table; optimizing the scheduling frequency according to the seasonal sensitivity index and the deviation classification result to obtain an optimized frequency scheme; determining the scheduling duration according to the optimized frequency scheme to obtain a duration scheme; generating a timing scheduling plan according to the optimized frequency scheme, the duration scheme, and the hierarchical water release configuration table; performing numerical simulation and optimization on the timing scheduling plan to obtain the optimized scheduling parameter set.

5. The reservoir ecological regulation method according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: According to the optimized scheduling parameter set, the scheduling execution monitoring point layout is performed to obtain a monitoring layout scheme; Step S42: According to the monitoring layout scheme, a timing sampling strategy is formulated to obtain a hierarchical sampling plan; Step S43: According to the hierarchical sampling plan, a synchronous parameter is collected to obtain a multi-dimensional response data set; Step S44: A baseline data before scheduling is obtained, and a baseline deviation is calculated in combination with the multi-dimensional response data set to obtain a standardized deviation matrix; Step S45: Based on the standardized deviation matrix, a time-space variation pattern of the exchange flux comprehensive index is identified to obtain a response mode atlas; Step S46: An ecological index correlation analysis is performed on the response mode atlas to obtain an ecological efficiency index; Step S47: According to the ecological efficiency index and the response mode atlas, a scheduling response curve is constructed.

6. A reservoir ecological regulation system, characterized in that, The reservoir ecological scheduling system for performing the reservoir ecological scheduling method of claim 1 comprises: An exchange flux monitoring module for collecting multi-source parameters of a vertical profile of the reservoir through a sensor array to obtain an original profile data set; and performing a sediment-water material exchange strength characterization on the original profile data set to obtain an exchange flux comprehensive index; An ecological threshold determination module for dividing the reservoir into layers according to the exchange flux comprehensive index to obtain a reservoir layer structure diagram; performing a seasonal-depth partitioning according to the reservoir layer structure diagram to obtain a partitioning statistical feature set; and performing an ecological safety response correlation on the partitioning statistical feature set to obtain an ecological safety domain atlas; A scheduling scheme construction module for performing an exchange flux and scheduling constraint analysis according to the ecological safety domain atlas to obtain an exchange flux deviation table and a scheduling constraint condition set; performing a water level regulation strategy calculation according to the exchange flux deviation table and the scheduling constraint condition set to obtain a water level regulation sequence; performing a hierarchical water release proportion optimization according to the water level regulation sequence to obtain a hierarchical water release configuration table; and performing a reservoir water exchange scheduling period determination according to the hierarchical water release configuration table to obtain an optimized scheduling parameter set; An efficiency evaluation feedback module for continuously tracking a change of the exchange flux index according to the optimized scheduling parameter set, evaluating an actual influence of the scheduling measure on water quality and benthic ecology, and generating a scheduling response curve.

7. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed to implement the reservoir ecological scheduling method of any one of claims 1 to 5. The computer program is executed to implement the reservoir ecological scheduling method of any one of claims 1 to 5.

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