Reservoir ecological scheduling method and system and storage medium

Abstract

The invention relates to the technical field of reservoir ecological management, in particular to a reservoir ecological scheduling method and system and a storage medium. The method comprises the following steps: carrying out vertical section multi-source parameter acquisition on a reservoir through a sensor array to obtain an original section data set; carrying out bottom mud-water body substance exchange strength characterization on the original profile data set to obtain an exchange flux comprehensive index; layering the reservoir according to the exchange flux comprehensive index to obtain a reservoir horizon structure chart; performing season-depth partition according to the reservoir horizon structure chart to obtain a partition statistical feature set; performing ecological safety response association on the partitioned statistical feature set to obtain an ecological safety domain map; and performing exchange flux and scheduling constraint analysis according to the ecological security domain atlas to obtain an exchange flux deviation table and a scheduling constraint condition set. According to the invention, a scientific scheduling method is provided for health of the reservoir ecosystem through collaborative optimization of water level regulation and control and layered drainage.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir ecological management, and in particular to a reservoir ecological dispatching method, system and storage medium. Background Art

[0002] Existing reservoir ecological scheduling monitoring methods usually only focus on single parameters such as dissolved oxygen, temperature or nutrients in the water body, but ignore the comprehensive characterization of the sediment-water interface as a key area for material and energy exchange, resulting in incomplete assessment of endogenous pollution release and benthic ecological conditions, and unable to accurately grasp the key regulatory parameters of the reservoir ecosystem.

[0003] Existing water quality evaluations mostly use a unified and fixed threshold standard, which fails to take into account the seasonal stratification changes of reservoirs and the differences in ecological characteristics of different water layers. As a result, the ecological significance of the same threshold in different seasons and different water layers varies significantly. The threshold judgment results are out of touch with actual ecological needs, affecting the scientific nature of scheduling decisions.

[0004] Traditional scheduling operates the reservoir as a homogeneous whole, ignoring the differentiated needs of different functional areas and vertical stratifications, especially the differences in the sensitivity of benthic ecosystems to water level changes and water quality conditions. As a result, scheduling measures may improve the water quality in one area while having a negative impact on the benthic ecology in other areas, making it difficult to achieve coordinated optimization of water quality and benthic ecology.

[0005] In summary, existing technologies have problems such as incomplete monitoring of the sediment-water interface exchange process, inaccurate ecological threshold determination, and lack of spatial differentiation considerations in scheduling decisions, which need to be urgently addressed. Summary of the Invention

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

[0007] To achieve the above-mentioned purpose, a reservoir ecological scheduling method includes the following steps:

[0008] Step S1: Collect multi-source parameters of a vertical profile of the reservoir through a sensor array to obtain an original profile data set; characterize the sediment-water material exchange intensity of the original profile data set to obtain a comprehensive exchange flux index;

[0009] Step S2: The reservoir is stratified according to the exchange flux comprehensive index to obtain a reservoir layer structure map; seasonal-depth zoning is performed based on the reservoir layer structure map to obtain a zoning statistical feature set; the zoning statistical feature set is correlated with the ecological security response to obtain an ecological security domain map;

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

[0011] Step S4: Continuously track and monitor the changes in the exchange flux index based on the optimized scheduling parameter set, evaluate the actual impact of the scheduling measures on water quality and benthic ecology, and generate a scheduling response curve.

[0012] By deploying a multi-point vertical profile integrated sensor array at the sediment-water interface in key reservoir areas, the present invention is able to simultaneously and frequently collect vertical distribution data on multiple key parameters, such as heat flux, gas release rate, and dissolved substance diffusion rate. This overcomes 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 ensures data accuracy and reliability. On this basis, by calculating various interface flux values ​​and applying an adaptive weighted nonlinear fusion algorithm that considers seasonal characteristics and parameter interactions, the multidimensional flux information is integrated into a single, dynamic exchange flux index (CEIS) that can characterize the intensity of material and energy exchange at the sediment-water interface. This provides a key quantitative indicator for a comprehensive and scientific assessment of endogenous pollution release and benthic ecological status in reservoirs, laying a precise data foundation for subsequent ecological scheduling decisions. Based on the CEIS, the reservoir is vertically layered in combination with historical hydrological and water quality data, and seasonal characteristic periods are defined according to meteorological and hydrological laws. A dual-dimensional seasonal-depth partitioning framework is constructed, refining the descriptive units of the reservoir's ecological status. By precisely matching historical CEIS data with synchronized ecological and environmental monitoring data and conducting segmented correlation analysis, key inflection points in the response relationships between CEIS values ​​and ecological indicators such as benthic communities and water quality were identified, thereby establishing a direct link between CEIS changes and changes in ecosystem status. Based on these ecological response relationships, three threshold levels—normal operation, warning, and critical—were set for each season-depth zone, forming a dynamically adjusted threshold grading matrix and generating an intuitive and visual ecological security domain map (ESDM). This overcomes the shortcomings of the traditional use of fixed thresholds, enabling threshold determination results to accurately reflect the actual ecological security status under specific seasons and water depths, providing a scientific and accurate basis for subsequent ecological scheduling plans. The reservoir was divided into functional zones with different management priorities. Based on a comparison of the real-time exchange flux comprehensive index (CEIS) with the ecological security domain map (ESDM), CEIS deviations were quantified for each water layer in each functional zone, identifying key areas and ecological issues requiring priority regulation. Taking into account the basic functional constraints of reservoirs such as flood control, water supply, and power generation, as well as the operating conditions of the project, combined with water balance prediction, the ideal water level change path that can solve high-priority ecological problems without violating hard constraints is calculated, and an executable water level control sequence is obtained through smoothing and step-by-step processing. At the same time, based on the vertical stratification structure of water quality and the impact assessment of the sediment interface, the opening combination and release ratio of multi-layer water intakes are optimized, and precise intervention in the water quality and sediment environment of specific layers is achieved through stratified water release. By comprehensively considering regional response, water volume renewal and ecological process time scales, as well as seasonal sensitivity and deviation severity, the optimal scheduling frequency and duration are determined. Finally, the instructions such as water level control, stratified water release ratio, and scheduling period are integrated and optimized through numerical simulation verification to generate a refined scheduling parameter set.This shift has achieved a shift in reservoir operation from holistic to refined, zoned, and layered management. It innovatively prioritizes the sediment-water exchange process as a core regulatory objective, effectively addressing the difficulty in balancing water quality management and benthic ecological protection, achieving synergistic optimization of the two. A comprehensive system for evaluating reservoir operation effectiveness was established. By deploying a high-density monitoring point system within the operation implementation area and impact zone and developing a "dense-sparse" graded sampling strategy, this system ensures continuous and accurate monitoring of the operation process and its ecological responses. The Comprehensive Exchange Flux Index (CEIS) was simultaneously collected, along with multiple parameters such as water quality and benthic biomass activity, to obtain multidimensional response data. By calculating the standardized deviation of the CEIS relative to the pre-operation baseline and identifying spatiotemporal patterns of change, the actual extent and intensity of the impact of the operation measures on the sediment-water exchange process were quantified. Correlating CEIS patterns with changes in simultaneously collected ecological and environmental indicators, the effects of the operation measures on water quality improvement and benthic ecological restoration were quantitatively assessed, resulting in an ecologically meaningful ecological effectiveness index. Finally, by constructing an intuitive dispatch response curve, it is clearly demonstrated how the dispatch operation affects CEIS and how this impact is transmitted to water quality and benthic ecosystems. This provides a solid data and analytical basis for the scientific evaluation of the ecological benefits of this dispatch measure, forms a closed-loop management of reservoir ecological dispatch, and provides key feedback information for the continuous improvement and optimization of subsequent dispatch plans. Therefore, a reservoir ecological dispatch method is provided, which realizes multi-parameter synchronous monitoring by constructing a vertical profile integrated sensor array, establishes an exchange flux comprehensive index to characterize the intensity of material and energy exchange at the sediment-water interface; replaces the traditional single threshold judgment with a seasonal-depth dual-dimensional threshold matrix to achieve dynamic and accurate delineation of the ecological security zone; based on the functional area division and vertical stratification characteristics, a refined dispatch parameter set is constructed to achieve coordinated optimization of water level regulation and stratified water release, effectively solving the technical problem of balancing reservoir water quality management and benthic ecological protection, and providing a scientific dispatch method for the health of the reservoir ecosystem.

[0013] Preferably, the present invention further provides a reservoir ecological dispatching system for executing the reservoir ecological dispatching method described above, the reservoir ecological dispatching 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 original profile data set; the original profile data set is used to characterize the sediment-water material exchange intensity to obtain the exchange flux comprehensive index;

[0015] The ecological threshold determination module is used to stratify the reservoir according to the exchange flux comprehensive index to obtain a reservoir layer structure map; perform season-depth zoning based on the reservoir layer structure map to obtain a zoning statistical feature set; and associate the zoning statistical feature set with ecological security responses 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; 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; 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;

[0017] The performance evaluation 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 a scheduling response curve.

[0018] The reservoir ecological operation system, through the coordinated operation of its component modules, enables refined management of reservoir ecological processes. The exchange flux monitoring module provides comprehensive, real-time, and quantitative data on the intensity of material and energy exchange at the sediment-water interface (the exchange flux composite index), providing an unprecedented data foundation for a deeper understanding of endogenous pollution and benthic habitat status. Based on this composite index and incorporating reservoir stratification and seasonal variations, the ecological threshold determination module constructs a dynamic and precise ecological safety zone map. This overcomes the limitations of traditional static threshold determination and ensures that operation decisions are based on an accurate assessment of the actual ecological state. The operation plan construction module utilizes the problem areas identified in the ecological safety zone map. Taking into account the multifunctional needs of the reservoir and engineering constraints, it generates a refined water level control sequence and stratified water release configuration table. This enables targeted interventions for ecological issues in different regions and water layers, balancing the needs of improving water quality with protecting benthic ecosystems. The effectiveness evaluation and feedback module continuously monitors the ecological responses after operation implementation, quantitatively assesses the actual effectiveness of operation measures, and uses the evaluation results as feedback to guide subsequent adjustments and optimization of the operation plan, forming a closed-loop management system with continuous improvement. Therefore, by integrating monitoring, evaluation, decision-making and feedback, the system provides a scientific, efficient and eco-friendly overall solution for reservoir operation, effectively solving the technical difficulties of balancing endogenous pollution control and benthic ecological protection in traditional reservoir operation.

[0019] Preferably, a computer-readable storage medium stores a computer program, and when the computer program is executed, the reservoir ecological scheduling method as described above is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flowchart of the steps of a reservoir ecological scheduling method.

[0021] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0022] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0023] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

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

[0025] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart showing 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 a vertical profile of the reservoir through a sensor array to obtain an original profile data set; characterize the sediment-water material exchange intensity of the original profile data set to obtain a comprehensive exchange flux index;

[0027] In an embodiment of the present invention, reservoir topographic and hydrological data are obtained, and based on this, a multi-point vertical profile integrated sensor array is deployed at the key sediment-water interface, including heat flow, gas, and electrochemical elements, to form a monitoring network topology map. Based on the topology map, multi-source parameters are synchronously collected to obtain an original profile data set. Sensor calibration and sliding window method anomaly identification 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. The classification flux is normalized, the seasonal type is determined, the basic weight is determined, the flux interaction term is calculated, and the weight is adaptively optimized. Finally, through nonlinear fusion calculations such as weighted power average, the comprehensive exchange flux index (CEIS) that characterizes the sediment-water exchange intensity is obtained.

[0028] Step S2: The reservoir is stratified according to the exchange flux comprehensive index to obtain a reservoir layer structure map; seasonal-depth zoning is performed based on the reservoir layer structure map to obtain a zoning statistical feature set; the zoning statistical feature set is correlated with the ecological security response to obtain an ecological security domain map;

[0029] In an embodiment of the present invention, a gradient analysis adaptive algorithm is used to divide the vertical layers of the reservoir according to the comprehensive exchange flux index (CEIS) and water temperature, density, and dissolved oxygen profile data to obtain a reservoir layer structure map. Historical meteorological and hydrological data are analyzed to define the characteristic periods of spring, summer, autumn, and winter, and a seasonal characteristic periodic table is established. According to the layer map and the periodic table, the historical CEIS data are partitioned in the season-depth dual dimension and statistical characteristics are calculated to form a partition statistical feature set. The historical ecological environment monitoring data is matched with the partition statistical feature set, and the inflection point of the response relationship between CEIS and ecological indicators is identified using piecewise regression to obtain an ecological inflection point data set. 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. The surface gradient change area is identified as a threshold candidate, and a two-way verification is performed in combination with the original ecological data to confirm the normal, warning, and critical thresholds to obtain a verification threshold data set, and an ecological security domain map (ESDM) and an ecological response curve cluster are generated.

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

[0031] In an embodiment of the present invention, basic reservoir geographic information and an ecological security domain map (ESDM) are obtained, and the functional areas of the reservoir are divided according to functional requirements and ecological sensitivity to obtain a functional area distribution map. The real-time CEIS is compared with the ESDM, the CEIS deviation of each water layer in each functional area is calculated, and an exchange flux deviation table is generated. The reservoir engineering parameters and scheduling procedures are obtained, and the scheduling constraint set is determined in combination with the deviation table. According to the deviation table priority and the hydrological forecast water balance, a draft of the ideal water level change path is determined. After constraint screening, correction smoothing, and step conversion, a water level control sequence that meets the constraints is obtained. According to the engineering parameters, the water level control sequence, and the water quality stratification structure diagram, the impact of stratified water release on the sediment interface is evaluated. Based on the water quality target, an optimization objective function is constructed, constraints are set, a set of candidate water release schemes is generated, risks are assessed, and a stratified water release configuration table is determined. The optimal scheduling frequency and duration are determined by comprehensively considering regional response, water volume update, ecological time scale, seasonal sensitivity, and deviation level, and a time-series scheduling plan is generated. The plan is input into the model simulation, the parameter set is optimized, and the final optimized scheduling parameter set is obtained.

[0032] Step S4: Continuously track and monitor the changes in the exchange flux index based on the optimized scheduling parameter set, evaluate the actual impact of the scheduling measures on water quality and benthic ecology, and generate a scheduling response curve.

[0033] In an embodiment of the present invention, based on the optimized scheduling parameter set and the functional area distribution map, monitoring points are densely deployed in the scheduling implementation and impact areas to form a monitoring point layout plan. According to the point layout plan and the scheduling time, a "dense-sparse" hierarchical sampling plan is formulated with high frequency before scheduling, denseness in the initial scheduling, and normal recovery in the later period. According to the plan, the comprehensive exchange flux index (CEIS) and parameters such as water temperature, dissolved oxygen, nutrients, and benthic biological activity are synchronously collected to obtain a multidimensional response data set. The baseline data before scheduling is obtained, and the standardized deviation of CEIS relative to the baseline in the multidimensional response data set is calculated to obtain a standardized deviation matrix. Based on the standardized deviation matrix, the spatiotemporal variation pattern of CEIS is identified to form a response pattern map. The response pattern map is correlated with the synchronous ecological indicators for quantitative evaluation of the effectiveness of scheduling on water quality and benthic ecology to obtain an ecological efficiency index. Based on the ecological efficiency index and the response pattern map, a curve of the change of CEIS standardized deviation over time in representative areas is drawn, key events are marked, and a scheduling response curve is constructed.

[0034] Preferably, step S1 includes:

[0035] Step S11: Acquire reservoir morphology and hydrological data; deploy multiple monitoring points at the sediment-water interface in key areas based on the reservoir morphology and hydrological data. Each monitoring point is equipped with a vertical profile integrated sensor, including three core components: a heat flow sensor, a gas chromatograph detector, and an electrochemical diffusion sensor. The sensors are arranged vertically, covering the key exchange area from 5 cm above the sediment to 10 cm below the water layer, to obtain a sensor array and form a monitoring network topology map.

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

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

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

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

[0040] In an embodiment of the present invention, reservoir morphology data is collected using drone aerial surveys combined with a multi-beam echo sounder to generate a high-resolution three-dimensional terrain model and bottom elevation map. Historical hydrological data is obtained from the reservoir management department, including daily records of inflow and outflow, water level, rainfall, and the like over the past decade. Based on the three-dimensional terrain model, critical areas are identified, including areas of severe reservoir siltation, tributary confluences, the bottom of the deepwater area, and areas known to be sensitive to benthic organisms. For example, a monitoring point is deployed at the deepwater bottom of the main reservoir, in the delta formed by the confluence of a tributary, and in a specific area near the water intake. Each monitoring point is anchored with an integrated vertical profile sensor. The sensor consists of the following core components: a micro hot flow plate sensor (such as Hukseflux HFP01) for measuring the heat exchange between the sediment and the water column; a membrane sampling-gas sensor integrated module (such as an electrochemical or optical sensor equipped with a CH4 and CO2 selective membrane) for real-time measurement of the dissolved gas concentration at the interface; and a multi-channel electrochemical sensor array (such as an ion-selective electrode or a voltammetric sensor) for measuring dissolved ammonia nitrogen (NH4 + ), total phosphorus (PO4 3- ) and certain heavy metal ions (such as Cd 2+ ,Pb 2+) concentration. The sensor elements are fixed vertically on a rigid support rod. The heat flow sensor is located 2 cm below the surface of the sediment, and the gas sensor and electrochemical sensor are located at 0 cm, 2 cm, 5 cm and 10 cm above the surface of the sediment, respectively, forming a continuous monitoring profile covering 5 cm from the upper layer of the sediment to 10 cm from the lower layer of the water body. The sensors are connected to the underwater data collector through a waterproof connector. The positions of the three monitoring points (located by GPS) and the sensor connection method are entered into the system to generate a monitoring network topology diagram describing the spatial distribution of sensors and data transmission paths.

[0041] Based on the monitoring network topology, the underwater data collector is started to perform high-frequency synchronous acquisition tasks. The internal clock of the data collector is synchronized with the GPS time signal. The heat flow sensor is configured to send an instantaneous heat flow value to the data collector 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 collector. The electrochemical sensor array is configured to measure and send each channel (NH4 + ,PO43-,Cd 2+ ,Pb 24 ) concentration value. The data of all sensors are accurately timestamped and transmitted to the data collector via the RS485 underwater data bus. The data collector aligns the data of different sensors according to the timestamp to form a record set containing the position, depth, timestamp and raw sensor reading of each monitoring point at each sampling moment, forming the original profile data set. For example, a record contains: point ID = P1, timestamp = 2023-10-27, 10:00:00, depth = -2cm, heat flow raw reading = X1; point ID = P1, timestamp = 2023-10-27, 10:00:00, depth = 0cm, CH1 raw reading = Y1, CO2 raw reading = Y2; point ID = P1, timestamp = 2023-10-27, 10:00:00, depth = 2cm, NH4 + Original reading = Z1, PO4 3- Original reading = Z2, Cd 2+ Original reading = Z3, Pb 2+ Raw reading = Z4, and so on, for all sensor readings up to a depth of 10 cm.

[0042] Data calibration and anomaly identification are performed on the original profile data set. Electrochemical sensors are calibrated on-site regularly (for example, every two weeks). Standard solutions of NH4Cl, KH2PO4, CdCl2, and Pb(NO3)2 with known concentrations are used to perform two-point or multi-point calibration of the sensors to establish a calibration curve between the original readings and the actual concentrations (for example, the voltage-concentration relationship). The heat flow sensor is calibrated for zero point and sensitivity. The zero point calibration is performed in an environment with no temperature difference, and the sensitivity calibration is performed using a known heat flow source. The gas sensor is calibrated using standard mixed gases of CH4 / N2 and CO2 / N2 with known concentrations. The calibration curve parameters are stored in the data processing module. After receiving the original profile data set, the data processing module applies the corresponding calibration curve according to the timestamp and sensor type to convert the original readings into physical units (such as W / m 2 , mg / L). Sliding window method is used for outlier screening. For each parameter (heat flow, CH4, CO2, NH4 + PO4 3- 、Cd 2+ , Pb 2+ ), setting a fixed-size sliding time window (e.g., 24 hours) and a threshold (e.g., 3 standard deviations). 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 outlier markers together constitute the calibrated monitoring sequence.

[0043] Three types of interface flux parameters are calculated based on the calibrated monitoring sequence. The heat flux (q) is calculated using Fourier's law of heat conductivity: q = -λ·(dT / dz). Here, λ is the typical thermal conductivity value of saturated sediment or water (e.g., λ≈1.0W / (m·K) for saturated sediment and λ≈0.6W / (m·K) for water), and dT / dz is the temperature gradient obtained by dividing the temperature difference between the sensor 2 cm below the sediment surface and the water temperature at 0 cm from the sediment surface by the vertical distance (e.g., 2 cm). Alternatively, the gradient can be calculated by linear regression using temperature data from more points. The gas release rate (R) and the diffusion rate of dissolved substances (J) are calculated using the discrete form of Fick's first law: J≈-D·(ΔC / Δz). ΔC is the difference in concentration 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., D≈1.8×10 -9 m 2 / s, NH4 + D≈1.9×10 in water at 20℃ -9 m 2 / s), and make corrections considering the porosity and tortuosity of the sediment. For gas release, if the concentration drops rapidly in the water body, the interfacial mass transfer coefficient (k) needs to be considered, and the rate R≈k·(C_interface-C_bulk), where k can be estimated based on empirical formulas such as water velocity and water depth. In this example, the concentration gradient of the vertical profile is mainly used to calculate the diffusion flux as a representation of the release / diffusion rate. The calculation results are organized into a table, including timestamp, monitoring point ID, calculated instantaneous heat flux, CH4 flux, CO2 flux, NH4 + Flux, PO4 3- Flux, Cd 2+ Flux, Pb 2+ Flux, forming a classified flux value table. For example, a row in the table is: Time = 2023-10-27, 10:00:00, Point 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 heat flux, gas flux (CH4 and CO2 total or calculated separately), dissolved substance flux (NH4 + ,PO4 3- ,Cd 2+ ,Pb 2+Normalization is performed (summed or calculated separately). The Min-Max normalization method is used to scale each flux type to the interval [0,1]: Normalized_Value = (Value - Min_Value) / (Max_Value - Min_Value). Min_Value and Max_Value are the minimum and maximum values ​​of that flux type in the historical monitoring data. Seasonal weighting coefficients are set. For example, during the summer stratification period, higher weights are assigned to gas and phosphorus fluxes, given that high temperatures and hypoxia significantly impact water quality, leading to increased phosphorus and methane release from sediments. During the winter cycle, when water mixing is more uniform and endogenous release is relatively low, weights are more evenly distributed. For example, the summer weight set is: wHeat = 0.1, wGas = 0.4, wP = 0.3, and wOther Dissolved Matter = 0.2. The winter weight set is: wHeat = 0.2, wGas = 0.2, wP = 0.2, and wOther Dissolved Matter = 0.4. A weighted fusion algorithm is used to calculate the Comprehensive Exchange Flux Index (CEIS). For each monitoring point at each timestamp, CEIS = wheat·Normalized_heat_flux + wgas·Normalized_gas_flux + wphosphorus·Normalized_phosphorus_flux + wother_dissolved_substances·Normalized_other_dissolved_substances_flux. This ultimately yields a time series and spatially distributed CEIS value set, known as the exchange flux composite index. For example, the CEIS value for point P1 at 10:00:00 on October 27, 2023, was 0.78.

[0045] It is particularly important that the weighted fusion calculation in step S15 is specifically as follows:

[0046] Normalize the flux parameters of the classification flux value table to obtain a normalized flux data set;

[0047] The seasonal characteristics of the normalized flux dataset are determined to obtain the seasonal characteristic type;

[0048] Determine the weight coefficient according to the seasonal characteristic type and obtain a basic weight coefficient table;

[0049] Calculate the interaction term coefficients according to the basic weight coefficient table to obtain the parameter adjustment coefficient set;

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

[0051] Nonlinear fusion calculation is performed based on the optimized weight coefficient table and the normalized flux data set to obtain the exchange flux comprehensive index;

[0052] In the embodiment of the present invention, the various flux parameters in the classification flux value table are normalized to eliminate the influence of different physical units and magnitudes. For each flux type in the classification flux value table (such as heat flux, CH4 flux, NH4 + For fluxes, etc., the Min-Max normalization method is used, using the minimum (Min_Value) and maximum (Max_Value) values ​​of that type of flux in the historical monitoring records as the scaling range. The current value of that 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 (for example, heat flux from the water column to the sediment), the appropriate Min_Value and Max_Value ranges are determined based on their physical significance (for example, negative values ​​indicate heat influx into the sediment, exacerbating stratification) and scaled 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, the normalized value is (50 - (-100)) / (200 - (-100)) = 150 / 300 = 0.5. This normalization operation is performed on all flux parameters in the categorized flux value table at each monitoring point and each timestamp, generating a normalized flux dataset containing all normalized flux values. Normalized_Value represents the normalized flux value, Value represents the raw flux value, Min_Value represents the historical minimum value for that type of flux, and Max_Value represents the historical maximum value for that type of flux. Obtain the timestamp of the current data collection, for example, October 27, 2023, 10:00 AM. Based on a pre-established seasonal characteristic period table, which defines the time range for each seasonal characteristic period (e.g., spring mixing 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 period table to determine the seasonal characteristic type to which the current time belongs. For example, October 27, 2023 is in the autumn transition period, so the current seasonal characteristic type is determined to be "autumn transition period". According to the determined seasonal characteristic type, the corresponding basic weight coefficient is found from the pre-stored basic weight coefficient table. The basic weight coefficient table is a matrix, whose rows represent different seasonal characteristic periods and columns represent different normalized flux parameters (such as normalized heat flux, normalized CH4 flux, normalized NH4 +Flux, 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 to the overall sediment-water exchange ecology in a specific season. For example, if the current season is determined to be "Autumn Transition Period", the basic weights of all flux parameters corresponding to the "Autumn Transition Period" row are extracted from the table to obtain the basic 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 basic weight coefficient of the corresponding parameter. Based on the normalized flux data set of the current data point, the interaction term coefficient reflecting the interaction strength between different flux parameters is 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 helps gas migration upward. Define an interaction term I1=Normalized_Heat_Flux×Normalized_CH4_Flux. Another interaction term reflects the dissolved nutrients (such as NH4 + ,PO4 3- ) and the synergistic effect of gas release (high nutrients lead to hypoxia, which in turn promotes anaerobic gas production), and I2 = Normalized_NH4 is defined + _Flux×Normalized_PO4 3- _Flux×Normalized_CH4_Flux. The values ​​of these interaction terms reflect the synergistic or antagonistic strength of different flux combinations at the current moment. Calculate the values ​​of these predefined interaction terms to form a parameter adjustment coefficient set. 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 value. The parameter adjustment coefficient set is used to adaptively adjust the base weight coefficient table to generate an optimized weight coefficient table. Adaptive adjustment is achieved using a preset adjustment function, which converts the interaction term coefficient into an increment or multiplier to the base weight. For example, for CH4 flux, its optimized weight w_CH4_optimized can be calculated using the base weight w_CH4_base and the interaction terms I1 and I2: w_CH4_optimized = w_CH4_base × (1 + α1 × I1 + α2 × I2). α1 and α2 are preset adjustment factors whose values ​​determine the sensitivity of the interaction term to the weight and are determined through historical data fitting or an expert system. The weights of other parameters are also adjusted based on the specific interaction terms. For example, the optimized heat flux weight, w_heat_optimized, = w_heat_base × (1 + β1 × I1). Ensure that all optimized weight coefficients are non-negative. After adjustment, a set of optimized weights reflecting the current actual flux interactions is obtained, forming an optimized weight coefficient table. Optimized_Weight represents the optimized weight coefficient, Base_Weight represents the base weight coefficient, I represents the interaction coefficient, and α and β represent the adjustment factors. The optimized weight coefficient table is used with the normalized flux dataset for nonlinear fusion calculations to obtain the Comprehensive Exchange Flux Index (CEIS).

[0053] The nonlinear fusion method can use the weighted power mean. The formula is: CEIS = (Σ i (Optimized_Weight i ×Normalized_Flux i p ))^(1 / p). Among them, Normalized_Flux i Indicates the value of the i-th normalized flux parameter, Optimized_Weight i Represents the corresponding optimization weight coefficient, Σ iIndicates the sum of all normalized flux parameters. p is a parameter greater than 1 and is used to control the degree of nonlinearity. The larger the p value, the closer the fusion result is to the parameter value with the highest weight, thereby highlighting the impact of abnormally high single flux values ​​on the comprehensive index. For example, take p = 2 (weighted quadratic average). Substitute each weight in the optimization weight coefficient table and the corresponding value in the normalized flux data set 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 exchange flux comprehensive index of the current monitoring point and timestamp. CEIS represents the exchange flux comprehensive index, Optimized_Weight i represents the optimization 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: dividing the vertical section of the reservoir into layers according to the exchange flux comprehensive index to obtain a reservoir layer structure diagram, wherein the reservoir layer structure diagram includes a surface mixing zone, a thermocline, and a deep water layer;

[0056] Step S22: Acquire meteorological and hydrological data of the area where the reservoir is located; define seasonal characteristic periods based on the meteorological and hydrological data to obtain a seasonal characteristic periodic table, wherein the seasonal characteristic periodic table includes a spring mixing period, a summer stratification period, an autumn transition period, and a winter cycle period;

[0057] Step S23: performing season-depth dual-dimensional data partitioning processing according to the seasonal characteristic periodic table and the reservoir layer structure map to obtain a partition statistical feature set;

[0058] Step S24: Acquire reservoir ecological environment monitoring data; perform ecological response correlation analysis on the reservoir ecological environment monitoring data and the partition statistical feature set to obtain an ecological response curve cluster;

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

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

[0061] In an embodiment of the present invention, reservoir water is divided into strata based on the long-term collected Comprehensive Exchange Flux Index (CEIS) and simultaneously 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 the largest and persists 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 significant influence of wind disturbances, usually 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 generally higher CEIS. An adaptive algorithm based on gradient analysis and threshold determination is used. The algorithm receives multiple sets of vertical profile data and calculates the gradient value for each depth interval. For example, if the ΔT / Δz of a layer interval exceeds 0.1°C / m and this gradient persists within a certain thickness range, the range is determined to be a thermocline. The depth range of the surface mixing zone and the deep water layer is determined based on the upper and lower boundaries of the thermocline. The algorithm dynamically adjusts the horizon division criteria based on the reservoir's specific morphology and measured data from different periods. This generates a map describing the vertical stratum structure of the reservoir at different times and locations, known as a reservoir stratigraphic map. For example, at a specific point on a particular day, the surface mixing zone (0-5 m), the thermocline (5-10 m), and the deep water layer (10 m to the reservoir bottom) are shown. ΔT represents the change in temperature, Δz represents the change in vertical distance, and Δρ represents the change in density.

[0062] Obtain meteorological and hydrological data for the reservoir area over the past 30 years, including daily average temperature, maximum and minimum temperatures, rainfall, evaporation, wind speed, sunshine hours, and historical records of reservoir inflow, outflow, water level, and vertical profiles of water temperature. Analyze these data for interannual and seasonal variations. For example, by analyzing the changes in the multi-year average vertical profile of water temperature, key time points for the onset of stratification, stable stratification, the onset of mixing, and complete mixing in the reservoir can be identified. The spring mixing period is defined as the period when water temperature begins to rise and the vertical temperature gradient gradually decreases until the water is evenly mixed (for example, when the temperature difference between the surface and bottom layers is less than 1°C and begins to rise continuously, marking the onset of the spring mixing period). The summer stratification period is defined as the period when the vertical temperature gradient is the greatest and the stratification is most stable (for example, when the thermocline reaches its maximum thickness and is most stable). The autumn transition period is defined as the period when surface water temperature begins to drop and the thermocline moves downward until the water re-mixes. The winter circulation period is defined as the period when the water temperature approaches 4°C and mixing is evenly distributed throughout the water depth. Based on these characteristic changes, the start and end dates of each characteristic period averaged over many years are calculated to establish a standard seasonal characteristic periodic table, for example: spring mixing 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 the seasonal characteristic periodic table and the reservoir stratigraphic structure diagram, a seasonal-depth dual-dimensional partitioning framework was constructed. This framework is a logical structure that divides the spatiotemporal extent of the reservoir into multiple non-overlapping units. For example, the depth dimension is divided into three layers: the "surface mixing zone," the "thermocline," and the "deep water layer," while the temporal dimension is divided into four seasons: the "spring mixing period," the "summer stratification period," the "autumn transition period," and the "winter cycle period." This divides the spatiotemporal state of the reservoir into 3 × 4 = 12 partitions (for example, the "summer stratification period-deep water layer" partition). All historically collected Comprehensive Exchange Flux Index (CEIS) data points are classified into 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 15 meters (assuming the depth of the water layer at that time) would be classified into the "summer stratification period-deep water layer" partition. For all historical CEIS data points collected within each partition, statistical characteristics are calculated, including the mean, standard deviation, minimum, maximum, median, and 25% and 75% quantiles. These statistical results are aggregated to form a partition statistical feature set, such as a table, where each row represents a partition and lists the name of the partition and its corresponding CEIS statistical value.

[0064] Long-term reservoir ecological and environmental monitoring data are collected, including benthic biodiversity indices (such as the Shannon-Wiener index), dominant species composition, dissolved oxygen concentrations in various water layers, pH, redox potential (ORP), ammonia nitrogen, total phosphorus, chlorophyll a concentrations, sediment organic matter content, and heavy metal concentrations. These ecological data are then temporally synchronized and spatially matched with CEIS data from the regional statistical feature set. For example, benthic biodiversity survey results for a particular layer in a particular 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 example, a correlation analysis between the mean CEIS value for the "Summer Stratification-Deep Water" zone and the mean dissolved oxygen value for the deep water zone during the same period revealed a strong negative correlation. A scatter plot is then plotted, with the CEIS value on the horizontal axis and the ecological indicator value on the vertical axis. The data points for each season-depth zone are fitted to generate curves that reflect the changes in ecological indicators with CEIS. For example, one curve shows that in the "Summer Stratification-Deep Water" zone, when the CEIS exceeds a certain value, the benthic biodiversity index decreases rapidly. The collection of these curves constitutes the ecological response curve cluster.

[0065] Based on the cluster of ecological response curves, three levels of CEIS thresholds were set for each season-depth zone: normal operating threshold, warning threshold, and critical threshold. For each zone, the ecological response curve was analyzed. The upper limit of the normal operating threshold was set as the upper range of the CEIS value when the corresponding ecological indicator is healthy or stable. For example, based on the curve, it was set as the maximum CEIS value (or a high percentile of the historical CEIS distribution for that zone, such as the 90th percentile) corresponding to when the benthic diversity index is above a preset healthy value and the dissolved oxygen is above 4 mg / L. The upper limit of the warning threshold was set as the CEIS value at which the ecological indicator begins to show adverse changes but is still within the recoverable range. For example, it was set as the CEIS value when the benthic diversity index begins to decline or when the dissolved oxygen drops to the range of 2-4 mg / L. This typically corresponds to a turning point or a significant change in slope on the ecological response curve. The critical threshold was set as the CEIS value when the ecosystem undergoes severe and irreversible changes. For example, it was set as the CEIS value corresponding to significant changes in the benthic community structure (such as the dominance of pollution-tolerant species), dissolved oxygen levels persistently below 2 mg / L or even anaerobic conditions, or a rapid increase in toxic concentrations. This corresponds to a critical tipping point on the ecological response curve. These threshold values ​​are organized into a matrix by season and layer, known as the threshold grading matrix. For example, the matrix includes a normal upper limit of 0.6, a warning upper limit of 0.8, and a critical value of 0.95 for the "Summer Stratification Period - Deep Water Layer."

[0066] Data from the threshold classification matrix are integrated to generate an ecological security zone map. A three-dimensional visualization model is constructed, with water depth as one axis, season as another, and CEIS threshold value as the third axis. On the depth-season plane, seasonal-depth zones are delineated based on the stratigraphic structure map and seasonal period table. For each zone, isosurfaces or regional boundaries representing the upper and lower limits of normal operation and warning thresholds are drawn according to the threshold classification matrix. Color coding is used to visually represent different risk levels: for example, areas with CEIS values ​​below the upper limit of the normal threshold are marked green (safe zone), areas with CEIS values ​​between the upper limit of the normal threshold and the upper limit of the warning threshold are marked yellow (warning zone), and areas with CEIS values ​​above the upper limit of the warning threshold (or reaching the critical threshold) are marked red (danger zone). This three-dimensional or layered two-dimensional map clearly demonstrates the safe operating range and potential risk areas of CEIS under different seasons and water depths, forming an intuitive decision-making support tool, the ecological security zone map.

[0067] It is particularly important that the ecological response correlation analysis in step S24 is specifically as follows:

[0068] The reservoir ecological environment monitoring data and the regional statistical feature set are paired and sorted to obtain the paired ecological data table;

[0069] Sort and group the data sequence of the paired ecological data table to obtain a sorted and grouped data set;

[0070] Perform segmented regression inflection point identification on the sorted grouped data set to obtain the ecological inflection point data set;

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

[0072] Perform smooth interpolation of the response surface on the three-dimensional ecological matrix to obtain a smooth response surface;

[0073] Identify the gradient change area of ​​the smooth response surface and obtain the threshold candidate distribution map;

[0074] According to the paired ecological data table, the threshold candidate distribution map is bidirectionally verified and the threshold is confirmed to obtain the verification threshold data set;

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

[0076] In an embodiment of the present invention, historical reservoir ecological environment monitoring data is matched with a partitioned statistical feature set by time stamp and spatial location to generate a paired ecological data table. The ecological environment monitoring data includes benthic 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 layer during the same period is paired with the corresponding ecological environment index value. For example, if a monitoring point collects benthic samples in the deep water layer and calculates the diversity index on a certain day, and the CEIS value of the 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 partition = 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 divisions, CEIS values, and values ​​for various ecological indicators. The data in the paired ecological data table were sorted and grouped to produce a sorted and grouped dataset. First, the data were grouped by season-depth divisions. Within each division, the paired data were sorted in ascending order by CEIS value. For example, within the "Summer Stratification-Deep Water" division, all data points were sorted from smallest to largest CEIS value. The sorted data were then further grouped by CEIS value or number of data points, for example, every 10 data points or every 0.05 CEIS interval was divided into a subgroup. This grouping facilitated subsequent analysis by reducing the impact of single-point noise. The sorted and grouped dataset is a structured representation of the paired ecological data table, facilitating segmented analysis. Segmented regression inflection point identification was performed for each season-depth division and each ecological indicator in the sorted and grouped dataset, resulting in 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 was fitted, using the CEIS value as the independent variable and the diversity index as the dependent variable. This model searches for one or more CEIS values ​​(inflection points) where the slope of the linear regression changes significantly before and after this point. For example, the model form is: Y = a1 × CEIS + b1 (CEIS ≤ C1) and Y = a2 × CEIS + b2 (CEIS > C1). Statistical methods (such as the least squares method combined with a breakpoint detection algorithm) are used to determine the optimal inflection point location (C1) and the slopes of each segment (a1, a2). The CEIS value with the most significant slope change is identified as the potential ecological response inflection point for this ecological indicator in this zone. For example, it was identified that when the CEIS reaches 0.7, the rate of decline of the diversity index significantly accelerates.All identified inflection point CEIS values ​​and their corresponding ecological indicator values ​​were recorded to form an ecological inflection point dataset. For example, if: partition = summer stratification period - deep water, indicator = benthic diversity index, inflection point CEIS = 0.70, corresponding indicator value = 1.8. Y represents the ecological indicator value, CEIS represents the exchange flux index, a1 and a2 represent the regression slopes, b1 and b2 represent the intercepts, and C1 represents the inflection point CEIS value. A three-dimensional data matrix was constructed based on the sorted and grouped dataset. The three dimensions of this matrix represent the season-depth partition, CEIS value range, and ecological indicator type, respectively. For example, the first dimension is the partition ID (e.g., 1 represents spring-surface, 2 represents spring-thermocline, ..., 12 represents winter-deep water), the second dimension divides 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 indicator ID (e.g., 1 represents benthic diversity index, 2 represents dissolved oxygen, and 3 represents ammonia nitrogen). Each cell in the matrix stores the mean or median value for the corresponding partition, CEIS interval, and ecological indicator. For example, the matrix element M[5,7,2] stores the average dissolved oxygen concentration for CEIS values ​​between 0.6 and 0.7 within the "Summer Stratification-Deep Water" partition. This structured data representation transforms the scattered paired data into a regular three-dimensional grid, facilitating subsequent surface interpolation. M[i,j,k] represents the element indexed by (i,j,k) in the three-dimensional data matrix. A smooth response surface is interpolated for each ecological indicator dimension in the three-dimensional data matrix to produce a smoothed response surface. For a specific ecological indicator (e.g., dissolved oxygen), an interpolation algorithm (such as bicubic spline interpolation or kriging interpolation) is applied, using the partition ID and CEIS interval midpoint as input coordinates and the mean value of the indicator stored in the matrix cell as the output value. This surface is generated across the entire partition-CEIS two-dimensional space, representing the predicted response of the ecological indicator across different partitions and CEIS values. For example, for dissolved oxygen, a surface O(partition ID, CEIS) is generated, representing the predicted dissolved oxygen concentration for a given partition and CEIS value. This operation is repeated for all important ecological indicators, generating a set of smoothed response surfaces, known as smoothed response surfaces. O represents the ecological indicator response surface. Gradient change regions are identified on the smoothed response surface to obtain a candidate threshold 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 reflects the sensitivity of the ecological indicator to changes in CEIS. Areas with large absolute gradient values ​​indicate that the ecological indicator will change rapidly with small changes in CEIS within the CEIS range. These areas are sensitive areas of ecological response and correspond to potential threshold ranges. For example, calculate the gradient field of the dissolved oxygen surface O with respect to CEIS. Identify The CEIS range exceeds a 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 the two-dimensional zone-CEIS plane to form a threshold candidate distribution map. The threshold candidate distribution map is bidirectionally verified and threshold-confirmed using the original paired ecological data table to generate a validation threshold dataset. The threshold candidate range is compared with actual data points in the original paired ecological data table. For example, if a zone in the candidate map shows a high gradient within the CEIS range of 0.7 ± 0.05, the original data table is checked for data points within this CEIS range for that zone to verify whether this range corresponds to a significant change in ecological indicators (e.g., a drop in dissolved oxygen below the critical level or a significant decrease in the benthic diversity index). Furthermore, actual CEIS values ​​in the original data table showing significant changes in ecological indicators are checked to verify whether these values ​​fall within the candidate threshold range. Through iterative adjustment and manual judgment, combined with ecological knowledge, the specific CEIS values ​​for the three threshold levels (normal, warning, and critical) for each season-depth zone are ultimately determined. For example, in the "Summer Stratification Period - Deep Water Layer," CEIS = 0.6 is confirmed as the upper limit of normal operation, CEIS = 0.75 is the upper limit of warning, 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. Based on the validation threshold dataset and the smoothed response surface, a cluster of ecological response curves is generated. For each season-depth partition and each important ecological indicator, a two-dimensional slice of the CEIS-ecological indicator corresponding to that partition is extracted from the smoothed response surface to form 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 upper limit of the normal operating threshold, the upper limit of the warning threshold, and the critical threshold are marked based on the validation threshold dataset. These threshold-marked curves for all key ecological indicators in all season-depth partitions are combined to form a cluster of ecological response curves.

[0077] For example, the curve cluster includes the benthic diversity index response curve and the dissolved oxygen response curve for the "Summer Stratification - Deep Water Layer" zone. Each curve is labeled with the corresponding three-level CEIS threshold for that zone. Ecological_Response_Curve_Cluster represents an ecological response curve cluster.

[0078] Preferably, the exchange flux and scheduling constraint analysis in step S3 includes:

[0079] Obtain basic geographic information of the reservoir; divide the reservoir functional area according to the basic geographic information of the reservoir and the ecological security zone map, and obtain a functional area distribution map, where the functional area distribution map includes water intake area, flood discharge area and ecological protection area;

[0080] The exchange flux deviation is calculated for the exchange flux comprehensive index and the ecological security zone map to obtain the exchange flux deviation table;

[0081] Obtain reservoir project parameters and determine the scheduling constraint set based on the exchange flux deviation table.

[0082] In an embodiment of the present invention, basic reservoir geographic information is obtained, including the reservoir shoreline, water depth contours, the location of the inlet river, the location of outflow structures (dams, spillways, water intakes, hydropower stations, etc.), the distribution of reservoir bays and tributaries, and the spatial coordinates and extents of important ecologically sensitive areas (such as fish spawning grounds and benthic habitats). This information is typically stored in a GIS (Geographic Information System) format to form a basic reservoir geographic information database. Based on the reservoir basic geographic information and the ecological security zone map, the reservoir is divided into functional zones. A certain area around the water intake structure (for example, an area centered on the water intake, taking into account the flow velocity influence radius and water depth requirements) is designated as the water intake zone. The area around outflow structures such as dams, spillways, and spillway tunnels is designated as the flood discharge zone. Areas of significant ecological value or high ecological sensitivity identified in the ecological security zone map (for example, reservoir bays where historical data shows CEIS to be within the normal range for a long time, or areas known to harbor rare benthic species) are designated as ecological protection zones. Other areas are designated as general reservoir areas. These functional area boundaries were drawn on the reservoir map using geographic information system tools to form a functional area distribution map. The boundaries of each functional area were stored as polygon layers in 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 water intake area - deep water layer, ecological protection area - thermocline layer, 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] Reservoir engineering parameters are obtained, including dam height, total storage capacity, dead capacity, normal storage level, flood control limit level, beneficial storage capacity, number, diameter, and maximum discharge capacity of water intakes at different elevations, discharge capacity curves (flow-water level relationship) for spillways and bottom outlets, hydropower station diversion and power generation flow ranges, and upper and lower limits on the daily rate of change of water levels. These parameters are typically stored in reservoir operation procedures and engineering design documents. Based on the exchange flux deviation table, the scheduling constraint set for the current scheduling cycle is determined. For example, if the exchange flux deviation table indicates that the CEIS of the deep water layer in an ecological protection zone is seriously exceeding the standard (high deviation value), it is necessary to prioritize reducing the CEIS in this area through deep water release. In this case, the scheduling constraint set will include an additional constraint: deep water release 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 intakes. If the deviation table indicates that the surface CEIS in the water intake area is in an alert state, the constraint set includes ensuring that the water level near the surface water intake remains within a certain safe range to avoid disturbing the bottom sediment. At the same time, all scheduling operations must comply with the hard constraints required by the reservoir's basic functions, such as flood control, water supply, and power generation. For example, the current water level must not exceed the flood control limit; the water level at the water supply intake must be above the minimum water 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, along with the targeted constraints determined based on the CEIS deviation, 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] Prioritize the key areas according to the exchange flux deviation table to obtain a regional priority table;

[0087] Calculate the water balance within the dispatching period based on the hydrological forecast data to obtain a water balance table;

[0088] Determine the ideal change path based on the regional priority table and water balance table, and obtain a sketch of the ideal path;

[0089] Perform constraint screening on the ideal path sketch and the scheduling constraint set to obtain a constraint violation mark table;

[0090] Perform path correction and smoothing on the ideal path sketch according to the constraint violation mark table to obtain a smoothed adjusted path;

[0091] Perform step-by-step decomposition transformation on the smooth adjustment path to obtain a step-by-step water level sequence;

[0092] Estimate the dispatching effect based on the stepped water level sequence and obtain an effect estimation report;

[0093] The water level sequence is collaboratively optimized based on the effect estimation report and regional priority table to obtain the water level control sequence.

[0094] In an embodiment of the present invention, an exchange flux deviation table is obtained. The table lists the exchange flux comprehensive index (CEIS) deviation values ​​of each functional area of ​​the reservoir (such as the water intake area, the flood discharge area, and the ecological protection area) in different water layers (such as the surface mixing zone, the thermocline, and the deep water layer). The functional areas are prioritized according to the size of the deviation value. The higher the deviation value, the farther the sediment-water exchange state in the area is from the ecological safety threshold, and it is necessary to intervene through scheduling measures in priority. The weighted summation method or the maximum deviation method is used to determine the total regional deviation. For example, for each functional area, the sum of the deviation values ​​of all its water layers is calculated as the total ecological deviation index of the area. Then, the functional areas are sorted from high to low according to the total ecological deviation index to generate a regional priority table. For example, the ecological protection area has the highest total deviation and is ranked 1; the water intake area is second and is ranked 2; the flood discharge area is the lowest and is 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 dispatch outflow (such as water supply and power generation plan flow) for the future dispatch period (e.g., the next 7 days). Calculate the water balance of the reservoir within the dispatch period based on these data. For each forecast day, calculate the change in net water volume of the reservoir: ΔV = (inflow + regional rainfall) × 86400 - (reservoir evaporation + expected non-ecological dispatch outflow) × 86400. ΔV represents the daily net water volume change (m 3 ). Use the reservoir water level-storage capacity (HV) relationship curve to convert the daily net water volume change 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 reservoir capacity V' is calculated as V+ΔV. Then, the new water level H' is obtained by reversing the HV curve, and ΔH=H'-H. The daily ΔH value is recorded in the water balance table to reflect the natural water level change trend without ecological scheduling 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] Based on the regional priority table, identify the ecological issues that require priority, along with their respective regions and water layers (for example, CEIS exceeding the standard in deep water layers in ecological protection areas). Reference the natural water level trends predicted in the water balance table. Determine an ideal water level change path based on the needs of the prioritized ecological issues. For example, if CEIS exceeding the standard in deep water layers is the highest priority, and hydrological forecasts indicate sufficient future water inflow, the ideal path is to initially lower the water level appropriately to increase deep water release capacity and promote water exchange, then gradually raise the water level based on the water balance trend. If water inflow is insufficient, the ideal path is to maintain a lower water level or slowly lower it to maximize the use of deep reservoir capacity for treatment. The ideal path sketch is a smooth 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 with the scheduling constraint set to identify the time when the constraints are violated. The scheduling constraint set contains hard constraints on reservoir operation, such as: water level upper limit (such as flood control limit water level), water level lower limit (such as dead water level, minimum operating water level of water intake), maximum allowable daily water level change rate, etc. For each time point on the ideal path sketch, check whether its water level value is within the upper and lower limits. For adjacent time points, calculate its water level change rate and check whether it exceeds the maximum allowable daily change rate. For example, if the ideal path sketch shows that the water level drop rate on a certain day exceeds the allowed 10cm / d, the constraint violation is marked on that day. All time points and violation types that violate the constraints 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 according to the constraint violation mark table to obtain a smoothed adjustment path. At the time point indicated in the constraint violation mark table, the water level value on the ideal path sketch is adjusted to meet the corresponding constraint conditions. For example, if the water level drops too fast on a certain day, the target water level for that day is adjusted to the water level of the previous day minus the maximum allowable drop. If the water level is lower than the minimum water intake elevation, the target water level is adjusted to the minimum water intake elevation. After the adjustment, the original smooth curve has a sharp turn. A smoothing algorithm (for example, using Loess local weighted regression or Savitzky-Golay filter) is applied to the corrected path to eliminate sharp changes and generate a relatively smooth water level-time curve that meets all constraints, namely the smoothed adjustment path. Smoothed_Adjustment_Path represents the smoothed adjustment path.

[0099] Perform a step-by-step decomposition transformation on the smooth adjustment path to obtain a step-by-step water level sequence. Divide the entire scheduling cycle into a number of fixed-length scheduling intervals (e.g., one daily interval). Within each scheduling interval, determine a constant water level change rate or a target water level at the end of an interval based on the smooth adjustment path. For example, calculate the average slope of the smooth adjustment path within each daily interval and use it as the constant water level change rate instruction for that day. Alternatively, directly use the water level value of the smooth adjustment path at the end of each day as the final target water level for that day.

[0100] This process converts the continuous smooth path into a series of discrete, easy-to-operate step-like instructions, forming a stepped water level sequence. Stepped_Water_Level_Sequence represents a stepped water level sequence.

[0101] The stepped water level sequence is used as input drive, and the scheduling effect is estimated using the reservoir hydrodynamic-ecological process coupling numerical model. The model can simulate the water flow field, temperature, dissolved oxygen, nutrient distribution and sediment-water exchange flux changes within the reservoir under given water level changes and inflow and outflow conditions. The stepped water level sequence is input into the model and the simulation is run. The predicted values ​​of CEIS, dissolved oxygen, benthic habitat indicators and other indicators of key areas in the simulation results (especially the areas ranked high in the regional priority table) are extracted. The comparison of these predicted values ​​with the ecological safety threshold is analyzed to evaluate the potential effect of the scheduling sequence on improving the ecological situation. The simulation results and evaluation conclusions are summarized to generate an effect estimation report. Effect_Estimation_Report represents the effect estimation report.

[0102] The ecological benefits of the stepped water-level sequence are evaluated based on the impact estimation report and optimized collaboratively with the regional priority table to determine the final water-level regulation sequence. If the impact estimation report indicates that the highest-priority ecological issue has not been effectively addressed or that there is room for improvement, the stepped water-level sequence is fine-tuned without violating any constraints. For example, if the estimated CEIS of the deep water layer is still too high and the water-level change rate constraint allows, a slight increase in the corresponding water-level drawdown rate for deep-water release can be attempted. If benthic habitat improvements in a particular area are not significant, the duration of the water-level stabilization period can be adjusted. Based on regional priorities, priority is given to improving the ecological effects in high-priority areas. Through a small number of iterative adjustments and impact estimations, a stepped water-level sequence is found that maximizes ecological benefits (especially in priority areas) while satisfying all constraints. This finalized 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 release ratio in step S3 includes:

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

[0105] Carry out vertical stratification analysis of water quality in reservoirs to obtain a water quality stratification structure diagram;

[0106] Conduct sediment interface impact assessment based on the water quality layered structure diagram to obtain an interface impact assessment table;

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

[0108] According to the water intake parameter table and the optimization objective function, the constraint conditions are set to obtain the optimization constraint condition set;

[0109] Generate a set of candidate optimization solutions according to the set of optimization constraints;

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

[0111] In the embodiment of the present invention, the water intake parameter table is determined according to the reservoir engineering parameters and the water level control sequence. The reservoir engineering parameters include the name of each water intake, the elevation (expressed in absolute elevation, such as meters above sea level), the maximum design discharge capacity (such as m 3 / s). The water level control sequence provides target water levels at different time points in the future scheduling cycle. For each time point in the water level control sequence, the current target water level is compared with the elevation of each water intake. If the target water level is higher than the elevation of a water intake, the water intake is in a flooded and available state at that time point; if the target water level is lower than or equal to the water intake elevation, the water intake is in a dry and unavailable state at that time point. Record the name, elevation, maximum design discharge capacity of each water intake, and the available status at different time points in the water level control sequence. Organize this information into a water intake parameter table. For example, the table contains: Water Intake Name = Deep Water 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] Obtain real-time vertical profile monitoring data of water quality in reservoirs, including water temperature, dissolved oxygen (DO), oxidation-reduction potential (ORP), ammonia nitrogen (NH4 + ), total phosphorus (PO4 3-) and other parameters with water depth. Analyze these data to identify gradient changes in water quality parameters with depth and stratification characteristics. For example, calculate the rate of change of DO concentration with depth, ΔDO / Δz. Identify the depth range where DO and ORP values ​​drop rapidly, which usually marks the beginning of anoxic or anaerobic layers. Identify NH4 + PO4 3- Depths with significantly elevated concentrations are typically associated with sediment release and anaerobic conditions. Based on the vertical distribution of these water quality parameters, the reservoir's vertical profile is divided into layers with distinct water quality characteristics, such as a surface oxidized layer with high DO, a mid-layer transition zone with rapidly decreasing DO, and a bottom reduced layer with low DO / anaerobic conditions and high nutrient concentrations. A water quality stratification diagram is generated to reflect the current vertical stratification of the reservoir's water quality. For example, the diagram shows the oxidized layer from 0-5 m, the transition zone from 5-8 m, and the reduced layer from 8 m to the reservoir bottom. ΔDO represents the change in dissolved oxygen, and Δz represents the change in vertical distance.

[0113] According to the water quality stratification structure diagram and the previous exchange flux deviation table, the sediment interface impact assessment is carried out to obtain the interface impact assessment table. The exchange flux deviation table indicates which areas and water layers have CEIS exceeding the ecological safety threshold, indicating that there are problems with sediment-water exchange in these areas (for example, the release of endogenous pollution is too high). Combined with the water quality stratification structure diagram, the water quality characteristics of the layers where these high CEIS areas are located are analyzed. For example, if a high CEIS area is located in the bottom reduction layer, and the DO of this layer is extremely low and the NH4 + and PO4 3- High concentrations indicate that endogenous pollution releases in this area are closely related to the hypoxic environment of the bottom layer. The potential impact of releasing water from different intakes on improving water quality and the sediment interface near these high-CEIS areas was assessed. For example, releasing water from a bottom intake can directly remove some heavily polluted bottom water; releasing water from a surface intake can introduce oxygen (if it affects the bottom flow field or through mixing); releasing water from a mid-layer intake has different impacts. The assessment results are quantified as a potential impact score or descriptive assessment and recorded in the Interface Impact Assessment Table. For example: High-CEIS Area A (bottom layer), deep water release: high potential impact (removes pollutants, improves DO), mid-layer water release: medium potential impact (induces local mixing), surface water release: low potential impact (small direct impact). Interface_Impact_Assessment_Table represents the Interface Impact Assessment Table.

[0114] Obtain water quality target parameters. These parameters are set based on the ecological management objectives of the reservoir. For example, the dissolved oxygen concentration in the deep water layer must not be lower than 4 mg / L during the summer stratification period, or the ammonia nitrogen concentration in the water body above the sediment must not exceed 1 mg / L. According to the interface impact assessment table, determine the key water quality parameters and areas that need to be improved first through stratified water release. Based on these key parameters and their target values, construct an optimization objective function. The objective function is a mathematical expression whose value reflects the pros and cons 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 the key areas and the target values, while taking into account the reduction of CEIS. For example, the minimization 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 represent the predicted key water quality parameter values ​​and CEIS reduction (estimated using a simplified model or empirical formula) based on the tiered water release scheme. w1, w2, and w3 are weight coefficients reflecting the importance of different objectives. Objective_Function represents the optimization objective function, w represents the weight coefficient, Predicted_Parameter represents the predicted parameter value, and Target_Parameter represents the target parameter value.

[0115] According to the water intake parameter table and the optimization objective function, set the optimization constraint 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 of all open water intakes must be equal to the total outflow flow required by the water level control sequence at the current time step (to achieve the target water level change). 2. Single water intake capacity constraint: The water release flow of each open water intake cannot exceed its maximum design water release capacity. 3. Water intake availability constraint: Only water intakes that are in a flooded and available state at the current water level can be opened for water release. 4. Minimum water release requirement (if any): The scheduling constraint set will force a water intake or a layer (such as a deep layer) to have a minimum water release flow to ensure specific needs (such as ecological base flow or flushing the bottom). These constraints are expressed as mathematical equations or inequalities to form an optimization constraint set. For example, Σ iOutlet_Dischargei=Required_Outflow;0≤Outlet_Dischargei≤Max_Capacityi (if water intake i is available); Outlet_Dischargei=0 (if water intake i is not available);Σ j Deep_Outlet_Discharge j ≥Min_Deep_Discharge (if present). Outlet_Discharge i represents the discharge flow of the i-th water intake, Required_Outflow represents the total outflow required by the water level control sequence, Max_Capacityi represents the maximum discharge capacity of the i-th water intake, Deep_Outlet_Dischargej represents the discharge flow of the j-th deep water intake, and Min_Deep_Discharge represents the minimum deep discharge requirement.

[0116] Based on the set of optimization constraints, a set of candidate optimization schemes is generated. A candidate optimization scheme is a set of specific water intake discharge flow combinations (i.e., the discharge flow value of each available water intake), which must satisfy all equations and inequalities in the set of optimization constraints. This can be achieved by solving a constrained optimization problem. If the objective function and constraints are linear, a linear programming method can be used. If they are nonlinear, a nonlinear programming algorithm can be used. Alternatively, for a limited number of water intakes, the discharge ratio combinations with a certain accuracy can be enumerated, for example, with a step size of 5% of the total flow, and allocated 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 candidate optimization scheme set. Candidate_Schemes represents the candidate optimization scheme set.

[0117] The interface disturbance risk assessment and solution selection for the candidate optimization scheme set are performed to generate a stratified water release configuration table. For each scheme in the candidate optimization scheme set (i.e., each set of water intake flow rate combinations), its interface disturbance risk is first assessed. For example, the discharge flow rate near each open bottom water intake is calculated. If the flow rate exceeds the critical starting flow rate of the sediment (determined by sediment characteristics), the scheme is considered to have a sediment disturbance risk. Then, each scheme is substituted into the optimization objective function, and its objective function value (e.g., predicted water quality improvement and CEIS reduction) is calculated. The objective function values ​​of all risk-acceptable schemes are compared. The scheme with the optimal objective function value (e.g., minimizing deviation or maximizing benefit) and a controllable interface disturbance risk is selected as the final stratified water release configuration. If the objective function values ​​of multiple schemes are similar, the scheme with the simplest operation or the lowest disturbance risk is preferred. The selected scheme determines the specific discharge flow rate for each water intake in the current time step. These flow values ​​are organized into a table, namely the stratified water release configuration table.

[0118] For example, the table contains: Intake Name = Deep Intake 1, Discharge Flow = 35m 3 / s; Intake Name = Surface Intake A, Discharge Flow = 15m 3 / s. Stratified_Discharge_Configuration indicates the stratified discharge configuration table.

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

[0120] Estimate the regional response timetable based on the stratified water release configuration table; calculate the water volume update period based on the stratified water release configuration table to obtain a stratified update period table; perform ecological process timescale analysis based on the regional response timetable to obtain an ecological timescale table;

[0121] Obtain the current season data and perform seasonal sensitivity assessment to obtain the seasonal sensitivity index;

[0122] Performing exchange flux deviation grading on the exchange flux deviation table to obtain a deviation grading result;

[0123] Set a base cycle plan based on the regional response timeline, stratified renewal cycle table, and ecological time scale table;

[0124] The scheduling frequency of the basic cycle plan is optimized according to the seasonal sensitivity index and deviation classification results to obtain the optimized frequency plan;

[0125] Determine the scheduling duration of the optimized frequency plan to obtain a duration plan;

[0126] Generate a time sequence scheduling plan based on the optimized frequency plan, duration plan and tiered water release configuration table;

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

[0128] In an embodiment of the present invention, the regional response times for different functional zones and water layers are estimated based on a stratified water discharge configuration table (which specifies the specific discharge flow rates for 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 functional zone (such as an ecological protection zone), if the discharge flow rate of the deep water intake is known, the hydraulic residence time or response time can be preliminarily estimated by calculating the ratio of the effective volume of the deep water layer in the region to the discharge flow rate: T_response ≈ V_region / Q_discharge_deep. For other regions or water layers, water velocity and distance can be considered. For example, the average flow velocity from the discharge port 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 discharge configuration table, simulate the reservoir water flow field, and estimate the time required for the tracer to diffuse from the discharge port to each zone. The estimated response time for each zone / water layer is recorded in the regional response time table. For example, the table contains: Ecological Protection Area - Deep Water Response Time = 3 days; Water Intake Area - Surface Water 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, calculate the water volume renewal cycle for different water layers or functional regions, creating a stratified water release cycle table. The water volume renewal cycle (or average water age) refers to the time required for a water body to be replaced by newly incoming water. For the entire reservoir, the renewal cycle is approximately the total storage capacity divided by the total outflow. For a specific water layer or region, if its volume V_layer / region and the net flow through it 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 rates for each layer in the stratified water release configuration table, combined with a simplified model of inflow and interlayer exchange, estimate the average flow rates for each water layer or functional region, and then calculate its renewal cycle. For example, to calculate the renewal cycle of the deep water layer, consider the deep water release flow and the vertical exchange flow between layers. Record the calculated results in the stratified water release cycle table. For example, the table may include: deep water layer renewal cycle = 10 days; surface layer renewal cycle = 5 days. Renewal_Period represents the water volume renewal period, 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, analyze the timescales of ecological processes relevant to each region to create an ecological timescale table. For example, if a region's response time is short (e.g., one day), focus on rapidly responding ecological processes, such as diurnal variations in dissolved oxygen and phytoplankton photosynthesis rates. If a region's response time is longer (e.g., 10 days), focus on medium- and long-term ecological processes, such as sediment nutrient release rates, seasonal variations in benthic communities, and the evolution of water stratification. Combining historical ecological monitoring data and ecological knowledge, identify one or more key ecological processes and their typical timescales for each region. For example, in the deep water layer, the timescales for sediment oxygen consumption and nutrient accumulation range from days to weeks; in the severely eutrophic surface layer, the timescales for the occurrence and development of algal blooms range from days to weeks. This information is organized into an ecological timescale table. For example, the table might include: 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] Obtain the current season data. This can be determined by querying the current date stored internally in the system and comparing it with the seasonal characteristic period table (obtained from S2.2). For example, the current date belongs to the summer stratification period. Based on the typical meteorological and hydrological characteristics and ecological characteristics of the current season, a seasonal sensitivity assessment is performed. For example, during the summer stratification period, the water temperature is high, the bottom layer is prone to hypoxia, and the sediment release is vigorous. The system responds faster or more sensitively to disturbances (such as mixing caused by bottom water release) and improvement measures (such as increasing bottom DO). During the winter circulation period, the water body is evenly mixed, the water temperature is low, the ecological process rate is slow, and the system is relatively insensitive. The assessment results are quantified as a seasonal sensitivity index. For example, each seasonal characteristic period is assigned a score of 1-5, with the summer stratification period = 5 (highest sensitivity) and the 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 each water layer. Deviation values ​​are graded based on a preset threshold range (e.g., based on the 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 graded as "low risk"; areas with deviation values ​​between the upper limit of the normal and warning thresholds are graded as "medium risk"; and areas with deviation values ​​greater than the upper limit of the warning threshold are graded as "high risk" or "severe risk." Record the deviation grading results for each area / water layer to form a deviation grading result. For example, the deviation grading result shows: Ecological Protection Area - Deep Water Layer: Severe Risk; Water Intake Area - Surface Layer: Medium Risk. Deviation_Grading_Result represents the deviation grading result.

[0133] According to the regional response timetable, the hierarchical update period table and the ecological time scale table, a basic scheduling cycle scheme is set. The purpose is to determine a preliminary repetition interval of the scheduling operation. Consider the minimum time scale associated with the high-risk area (determined according to the deviation classification results) in all tables. For example, if the deep water layer of the ecological protection area is a serious risk area, its response time is 3 days, the update period is 10 days, and the sediment oxygen consumption time scale is 7 days. The basic period can be set to a representative value among these time scales, such as the minimum response time (3 days) or a time slightly longer than the response time but less than the time scale of the main ecological process (such as 5-7 days). Set a standard basic period length, such as 7 days, as the starting point for subsequent optimization. Base_Period_Scheme represents the basic period scheme.

[0134] According to the seasonal sensitivity index and the deviation grading results, the scheduling frequency of the basic cycle scheme is optimized to obtain an optimized frequency scheme. If the current seasonal sensitivity is high and the deviation grading results show that there are serious risk areas, it is necessary to increase the scheduling frequency (i.e. shorten the scheduling cycle) for more timely response and intervention. For example, if the basic cycle scheme is 7 days, the seasonal sensitivity index is 5, and there are serious risk areas, the scheduling cycle is shortened to 3 days. If the seasonal sensitivity is low and the highest deviation grade is low risk, the scheduling frequency can be reduced (i.e., the scheduling cycle is extended), for example, the cycle can be extended 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, and the basic cycle scheme is multiplied by the adjustment factor to obtain the cycle length of the optimized frequency scheme. 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 length of time that the scheduling operation is expected to be continuously performed in order to solve the currently identified ecological problem. This should match the time scale of the ecological process required to solve the problem. For example, if the ecological problems in the serious risk area (such as hypoxia and pollutant accumulation in the deep water layer) are expected to take a month to be significantly improved by water exchange and mixing (refer to the ecological time scale table), and the optimized frequency scheme determines a cycle of 7 days, the total duration scheme can be set to 30 days. Alternatively, a dynamic duration can be set, that is, the scheduling continues until the CEIS in the key area continues to decrease 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] A temporal scheduling plan is generated based on the optimized frequency scheme (cycle length), duration scheme (total duration) and stratified water release configuration table (operation instructions within a single cycle). The temporal scheduling plan is a detailed operation schedule. It divides the total duration of the duration scheme into several scheduling cycles, and the length of each cycle is determined by the optimized frequency scheme. In each scheduling cycle, the operation instructions in the stratified water release configuration table are repeatedly applied (that is, the specific water intake release flow is executed at a specific time period within the cycle). For example, if the duration is 30 days, the cycle is 7 days, and the stratified water release configuration table specifies the daily release flow, then the temporal scheduling plan will list the specific release flow instructions for each water intake every day from day 1 to day 30. Temporal_Scheduling_Plan represents the temporal scheduling plan.

[0137] Numerical simulation and optimization are performed on the sequential scheduling plan to obtain an optimized scheduling parameter set. The generated sequential scheduling plan is used as input to drive a numerical simulation of the coupled reservoir hydrodynamic-ecological process model. The reservoir's ecological response (e.g., spatiotemporal variations in CEIS, dissolved oxygen, and nutrients) is simulated throughout the entire duration plan. The simulation results are evaluated, for example, to check whether CEIS in high-risk areas has significantly decreased and achieved the expected target, and whether new ecological issues (e.g., excessive sediment disturbance) exist. If the simulation results are unsatisfactory or there is room for improvement, specific parameters in the sequential scheduling plan (e.g., fine-tuning the release flow rate during certain periods or the duration of specific operations) are optimized without violating the scheduling constraints. For example, a genetic algorithm or particle swarm optimization algorithm is used, with ecological improvement as the optimization objective and scheduling constraints as the constraints, to search for the optimal sequential scheduling parameter combination. After simulation verification and optimization iterations, a final, executable set of detailed scheduling operation instructions, namely the optimized scheduling parameter set, is obtained. The optimized scheduling parameter set is represented by Optimized_Scheduling_Parameter_Set.

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

[0139] Step S41: scheduling and executing monitoring point layout according to the optimized scheduling parameter set to obtain a monitoring point layout plan;

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

[0141] Step S43: synchronously collecting parameters according to the hierarchical sampling plan to obtain a multi-dimensional response data set;

[0142] Step S44: obtaining pre-dispatching benchmark data, and performing baseline deviation calculation in combination with the multi-dimensional response data set to obtain a standardized deviation matrix;

[0143] Step S45: performing spatiotemporal variation pattern recognition on the exchange flux comprehensive index based on the standardized deviation matrix to obtain a response pattern map;

[0144] Step S46: performing ecological index correlation analysis on the response pattern map to obtain an ecological efficiency index;

[0145] Step S47: Constructing a dispatch response curve based on the eco-efficiency index and the response pattern map.

[0146] In an embodiment of the present invention, an optimized scheduling parameter set (which specifies in detail the timing of scheduling operations, water level changes, stratified water release flow rates, and other instructions) and a reservoir functional zone distribution map are obtained. A monitoring point layout plan is designed based on the implementation area of ​​the optimized scheduling parameter set (for example, if the instructions are primarily targeted at the deep water layer of the ecological protection zone and the surface water intake area) and the expected impact range (for example, the water flow impact range predicted by numerical simulation). The density of monitoring points is increased in high-risk areas (determined based on the exchange flux deviation table) and their downstream potential impact areas. For example, if a focused 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 the area and along the downstream water flow path. If water level regulation is carried out in the surface water intake area, sensors are densely deployed in the surface layer of the area and near the thermocline. At the same time, conventional monitoring points are retained in the control area (similar areas not directly affected by the scheduling). The layout plan specifies the geographic coordinates of each monitoring point and the type of vertical profile integrated sensor installed. This location information is recorded in the monitoring point layout plan. Monitoring_Point_Layout_Plan represents the monitoring point layout plan.

[0147] Based on the monitoring point layout plan and the optimized scheduling schedule, a sequential sampling strategy was developed to create a graded sampling plan. The optimized scheduling parameter set specifies the start time and duration of the scheduling operation. A "dense-sparse" progressive sampling strategy was designed. Within 24 hours before the start of the scheduling operation, baseline data were collected at all monitoring points at a high frequency (e.g., collecting CEIS and environmental parameters hourly). Initially after the scheduling operation began (e.g., the first 72 hours), the sampling frequency was increased to every two hours at key monitoring points most directly affected by the scheduling (e.g., the area downstream of the deep water outlet). Thereafter, as the scheduling effects became apparent and the rate of change slowed, the sampling frequency was gradually reduced. For example, the sampling frequency was adjusted to daily for 4-7 days after the start of the scheduling operation; after 7 days until the end of the scheduling operation, the sampling frequency returned to the regular monitoring frequency (e.g., once or twice per week). The graded sampling plan details the specific sampling times and sampling parameters for each monitoring point throughout the duration of the scheduling operation. Graded_Sampling_Plan represents the graded sampling plan.

[0148] According to the hierarchical sampling plan, synchronous parameter collection is performed at the designated monitoring points and sampling times to obtain a multi-dimensional response data set. Using a vertical profile integrated sensor array and other auxiliary monitoring equipment, the exchange flux comprehensive index (CEIS) and a series of key environmental parameters are synchronously collected at each sampling moment. In addition to CEIS, the parameters collected synchronously include but are not limited to: water temperature vertical profile, dissolved oxygen (DO) vertical profile, redox potential (ORP) vertical profile, pH value, conductivity, ammonia nitrogen (NH4 + ), total phosphorus (PO4 3- ), chlorophyll a concentration, and indicators of benthic biomass (e.g., monitored by acoustic or optical sensors). Ensure that all parameter data are accurately time-stamped and have location / depth information, and are time-aligned by the data acquisition system. Combine all collected parameter data to form a multidimensional response dataset containing time, location, depth, and values ​​for all parameters. Multi_Dimensional_Response_Dataset represents a multidimensional response dataset.

[0149] Obtain pre-dispatch baseline data. These data are baseline data collected at high frequency in a graded sampling plan before the start of the scheduling operation (for example, data 24 hours before scheduling). Compare the CEIS data in the multidimensional response data set with the pre-dispatch baseline data, calculate the baseline deviation, and obtain a 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. In order to eliminate the impact of baseline level differences at different monitoring points, the deviation value is standardized. For example, calculate the standard deviation (σ_baseline) of CEIS for each monitoring point during the pre-dispatch baseline period. Then, standardized deviation = Deviation / σ_baseline. Arrange the standardized deviation values ​​of all monitoring points and all time points into a matrix, whose rows represent time and columns represent monitoring points. The matrix cells store the corresponding standardized deviation values ​​to form a 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 average CEIS baseline value before scheduling, and σ_baseline represents the standard deviation of the CEIS baseline before scheduling.

[0150] Based on the standardized deviation matrix, the spatiotemporal variation pattern of the exchange flux comprehensive index is identified to obtain a response pattern map. The standardized deviation matrix is ​​analyzed to identify the temporal variation trend of CEIS (for example, whether the CEIS deviation increases, decreases or fluctuates after the start of scheduling) and the spatial distribution characteristics (for example, whether the deviation change is confined to the scheduling area or spreads outward). Spatiotemporal data analysis methods are used, for example, trend analysis is performed on the deviation time series of each monitoring point to identify the rate of change and duration. Spatial interpolation is performed on the deviation values ​​of 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 return to the baseline level, and the spatial range of the deviation impact. These spatiotemporal variation characteristics are visualized, such as drawing a time series graph, a spatial distribution heat map, or a three-dimensional spatiotemporal evolution graph to form a response pattern map reflecting the scheduling response process. Response_Pattern_Map represents the response pattern map.

[0151] An ecological effectiveness index (EPI) is derived by correlating the response pattern map (the spatiotemporal variation in CEIS deviation) with other ecological indicators (such as dissolved oxygen, ammonia nitrogen, and benthic activity) collected simultaneously in the multidimensional response dataset. For example, the response pattern map identifies regions and time periods where CEIS deviation significantly decreased. Then, the multidimensional response dataset contains data on dissolved oxygen, ammonia nitrogen, and benthic activity for the same regions over the same period. Correlation coefficients and time-lagged 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 with an increase in dissolved oxygen concentration, a decrease in ammonia nitrogen concentration, and an increase in benthic activity, with a short time lag, this indicates that the regulation measures have effectively improved water quality and benthic habitats by reducing CEIS. The strength of this correlation and the magnitude of the ecological improvement can be quantified. For example, an EPI (Ecological Effectiveness Index) can be defined, whose calculation formula can take into account the degree of improvement in key ecological indicators relative to target values ​​and the magnitude of the CEIS reduction.

[0152] E=w_DO×(Predicted_DO_improved-DO_baseline) / (DO_target-DO_baseline)+w_NH4 + ×(NH4 + _baseline-Predicted_NH4 + _improved) / (NH4 + _baseline-NH4 +_target)+.... The calculated ecological effectiveness index is recorded and used to evaluate the overall ecological effect of this dispatch. Ecological_Effectiveness_Index represents the ecological effectiveness index, w represents the weight 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 efficiency index and response pattern map, a dispatch response curve is constructed. Key monitoring points or representative areas are selected and a curve showing the changes in their CEIS standardized deviation over time is plotted. For example, a time series graph of the CEIS standardized deviation is plotted for deep-water monitoring point P1 in the ecological protection zone. This curve is used to mark the start and end times of the dispatch operation, as well as the time of occurrence of key ecological events assessed based on the ecological efficiency index (for example, the moment when dissolved oxygen in the deep-water layer begins to rise significantly). At the same time, a curve showing the changes in key ecological indicators (such as dissolved oxygen) over time at this monitoring point or its standardized deviation curve can be plotted for comparison with the CEIS deviation curve. These curves are combined to form a set of charts that intuitively show how dispatch operations affect CEIS and how CEIS changes are related to ecological responses, namely, dispatch response curves. These curves provide a direct basis for evaluating the actual effects of this dispatch, understanding the response mechanism, and guiding future dispatch optimization. Dispatch_Response_Curve represents the dispatch response curve.

[0154] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0155] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A reservoir ecological scheduling method, characterized in that: The following steps are involved: Step S1: Collect multi-source parameters of a vertical profile of the reservoir through a sensor array to obtain an original profile data set; characterize the sediment-water material exchange intensity of the original profile data set to obtain a comprehensive exchange flux index; Step S2: stratify the reservoir according to the exchange flux comprehensive index to obtain a reservoir layer structure map; perform season-depth partitioning based on the reservoir layer structure map to obtain a partition statistical feature set; The ecological security response is correlated with the regional statistical feature set to obtain the ecological security domain map; Step S3: Analyze the exchange flux and scheduling constraints based on the ecological security domain map to obtain an exchange flux deviation table and a scheduling constraint set; calculate the water level control strategy based on the exchange flux deviation table and the scheduling constraint set to obtain a water level control sequence; optimize the stratified water release ratio based on the water level control sequence to obtain a stratified water release configuration table; determine the reservoir water exchange scheduling cycle based on the stratified water release configuration table to obtain an optimized scheduling parameter set; Step S4: Continuously track and monitor the changes in the exchange flux index based on the optimized scheduling parameter set, evaluate the actual impact of the scheduling measures on water quality and benthic ecology, and generate a scheduling response curve.

2. The reservoir ecological scheduling method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire reservoir morphology and hydrological data; deploy multiple monitoring points at the sediment-water interface in key areas based on the reservoir morphology and hydrological data. Each monitoring point is equipped with a vertical profile integrated sensor, including three core components: a heat flow sensor, a gas chromatograph detector, and an electrochemical diffusion sensor. The sensors are arranged vertically, covering the key exchange area from 5 cm above the sediment to 10 cm below the water layer, to obtain a sensor array and form a monitoring network topology map. Step S12: Based on the monitoring network topology, multi-parameter synchronous acquisition is performed through the sensor array to obtain an original profile data set; Step S13: performing data calibration and anomaly identification on the original profile data set to obtain a calibrated monitoring sequence; Step S14: Calculating flux parameters based on the calibrated monitoring sequence to obtain a classification flux value table; Step S15: Perform weighted fusion calculation on the classification flux value table to obtain the exchange flux comprehensive index.

3. The reservoir ecological scheduling method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: dividing the vertical section of the reservoir into layers according to the exchange flux comprehensive index to obtain a reservoir layer structure diagram, wherein the reservoir layer structure diagram includes a surface mixing zone, a thermocline, and a deep water layer; Step S22: Acquire meteorological and hydrological data of the area where the reservoir is located; define seasonal characteristic periods based on the meteorological and hydrological data to obtain a seasonal characteristic periodic table, wherein the seasonal characteristic periodic table includes a spring mixing period, a summer stratification period, an autumn transition period, and a winter cycle period; Step S23: performing season-depth dual-dimensional data partitioning processing according to the seasonal characteristic periodic table and the reservoir layer structure map to obtain a partition statistical feature set; Step S24: Acquire reservoir ecological environment monitoring data; perform 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 rating system based on the ecological response curve cluster to obtain a threshold rating matrix; Step S26: Generate an ecological security domain map based on the threshold classification matrix.

4. The reservoir ecological scheduling method according to claim 1, characterized in that: The exchange flux and scheduling constraint analysis in step S3 includes: Obtain basic geographic information of the reservoir; divide the reservoir functional area according to the basic geographic information of the reservoir and the ecological security zone map, and obtain a functional area distribution map, where the functional area distribution map includes water intake area, flood discharge area and ecological protection area; The exchange flux deviation is calculated for the exchange flux comprehensive index and the ecological security zone map to obtain the exchange flux deviation table; Obtain reservoir project parameters and determine the scheduling constraint set based on the exchange flux deviation table.

5. The reservoir ecological scheduling method according to claim 1, characterized in that: The water level control strategy calculation in step S3 includes: Prioritize the key areas according to the exchange flux deviation table to obtain a regional priority table; Calculate the water balance within the dispatching period based on the hydrological forecast data to obtain a water balance table; Determine the ideal change path based on the regional priority table and water balance table, and obtain a sketch of the ideal path; Perform constraint screening on the ideal path sketch and the scheduling constraint set to obtain a constraint violation mark table; Perform path correction and smoothing on the ideal path sketch according to the constraint violation mark table to obtain a smoothed adjusted path; Perform step-by-step decomposition transformation on the smooth adjustment path to obtain a step-by-step water level sequence; Estimate the dispatching effect based on the stepped water level sequence and obtain an effect estimation report; The water level sequence is collaboratively optimized based on the effect estimation report and regional priority table to obtain the water level control sequence.

6. The reservoir ecological scheduling method according to claim 1, characterized in that: The optimization of the stratified water release ratio in step S3 includes: Determine the water intake parameter table based on reservoir engineering parameters and water level control sequence; Carry out vertical stratification analysis of water quality in reservoirs to obtain a water quality stratification structure diagram; Conduct sediment interface impact assessment based on the water quality layered structure diagram to obtain an interface impact assessment table; Obtain water quality target parameters and construct the objective function according to the interface impact assessment table to obtain the optimized objective function; According to the water intake parameter table and the optimization objective function, the constraint conditions are set to obtain the optimization constraint condition set; Generate a set of candidate optimization solutions according to the set of optimization constraints; The interface disturbance risk assessment and scheme determination are performed on the candidate optimization scheme set to obtain a tiered water release configuration table.

7. The reservoir ecological scheduling method according to claim 1, characterized in that: The determination of the reservoir water exchange scheduling period in step S3 includes: Estimate the regional response timetable based on the stratified water release configuration table; calculate the water volume update period based on the stratified water release configuration table to obtain a stratified update period table; perform ecological process timescale analysis based on the regional response timetable to obtain an ecological timescale table; Obtain the current season data and perform seasonal sensitivity assessment to obtain the seasonal sensitivity index; Performing exchange flux deviation grading on the exchange flux deviation table to obtain a deviation grading result; Set a base cycle plan based on the regional response timeline, stratified renewal cycle table, and ecological time scale table; The scheduling frequency of the basic cycle plan is optimized according to the seasonal sensitivity index and deviation classification results to obtain the optimized frequency plan; Determine the scheduling duration of the optimized frequency plan to obtain a duration plan; Generate a time sequence scheduling plan based on the optimized frequency plan, duration plan and tiered water release configuration table; Numerical simulation and optimization of the time series scheduling plan are performed to obtain the optimized scheduling parameter set.

8. The reservoir ecological dispatching method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: scheduling and executing monitoring point layout according to the optimized scheduling parameter set to obtain a monitoring point layout plan; Step S42: Formulate a time-series sampling strategy based on the monitoring point layout plan to obtain a hierarchical sampling plan; Step S43: synchronously collecting parameters according to the hierarchical sampling plan to obtain a multi-dimensional response data set; Step S44: obtaining pre-dispatching benchmark data, and performing baseline deviation calculation in combination with the multi-dimensional response data set to obtain a standardized deviation matrix; Step S45: performing spatiotemporal variation pattern recognition on the exchange flux comprehensive index based on the standardized deviation matrix to obtain a response pattern map; Step S46: performing ecological index correlation analysis on the response pattern map to obtain an ecological efficiency index; Step S47: Constructing a dispatch response curve based on the eco-efficiency index and the response pattern map.

9. A reservoir ecological dispatching system, characterized in that: For executing the reservoir ecological dispatching method according to claim 1, the reservoir ecological dispatching system comprises: 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 original profile data set; the original profile data set is used to characterize the sediment-water material exchange intensity to obtain the exchange flux comprehensive index; The ecological threshold determination module is used to stratify the reservoir according to the exchange flux comprehensive index to obtain a reservoir layer structure map; perform season-depth zoning based on the reservoir layer structure map to obtain a zoning statistical feature set; and associate the zoning statistical feature set with ecological security responses to obtain an ecological security domain map; 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; 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; 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; The performance evaluation 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 a scheduling response curve.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the reservoir ecological scheduling method according to any one of claims 1 to 8 is implemented.

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