Method and device for researching carbon transport characteristics under reservoir operation and computer equipment

Abstract

The present application belongs to the technical field of environmental hydraulics, and relates to a reservoir operation scheduling carbon transport characteristic research method, device and computer equipment. The method comprises the following steps: constructing a three-dimensional reservoir carbon transport monitoring network to obtain basic data of the carbon transport process; based on the basic data, a water dynamics-water quality coupling model for simulating the carbon transport process is constructed, and the water dynamics-water quality coupling model is verified by using monitoring data; the operation scheduling characteristics are quantified, and scheduling characteristic parameters affecting the carbon transport are extracted; a carbon flux accounting model considering the influence of scheduling is established to quantify the influence of scheduling on carbon budget; a quantitative relationship between the carbon transport characteristic parameters and the scheduling characteristic parameters is established; based on carbon environmental constraints, scheduling optimization evaluation is carried out, the carbon target is included in the scheduling optimization system, and a low-carbon scheduling scheme is proposed. The present application has systematicness and dynamicity, realizes quantitative evaluation of carbon transport, has practicability, can be popularized to carbon cycle research of other types of water bodies, and has expansibility.

Description

Technical Field

[0001] This invention relates to the field of environmental hydraulics technology, and in particular to methods, apparatus and computer equipment for studying carbon transport characteristics under reservoir scheduling and operation. Background Technology

[0002] Reservoirs, as important water conservancy projects, play a crucial role in flood control, water supply, and power generation. In recent years, with the advent of global climate change and the introduction of "dual carbon" targets, the carbon emission and carbon sink functions of reservoirs have received increasing attention. Studies have shown that processes such as water temperature stratification, water mixing, and sediment resuspension during reservoir operation can affect carbon transport and transformation, thereby impacting their carbon source / sink functions.

[0003] Existing technologies have proposed stratified methods for calculating carbon emissions from lakes and reservoirs based on the relationship between reservoir thermal structure and carbon emissions. However, existing methods still have the following problems: limited monitoring methods: mostly fixed-point observations, which are difficult to reflect the impact of scheduling operations on the spatiotemporal distribution of carbon transport; insufficient mechanism research: lack of research methods on the coupling mechanism between scheduling operations and carbon transport; poor model adaptability: traditional carbon flux calculation methods do not fully consider the dynamic changes in scheduling methods; lack of assessment techniques: lack of quantitative assessment techniques for carbon transport characteristics under different scheduling strategies.

[0004] Therefore, a systematic, dynamic, and quantitative research method is urgently needed to reveal the intrinsic link between reservoir scheduling and carbon transport, and to provide a scientific basis for low-carbon scheduling. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for studying carbon transport characteristics under reservoir operation and scheduling, employing the following technical solution, including the following steps:

[0006] Construct a three-dimensional carbon transport monitoring network for reservoirs to obtain basic data on carbon transport processes;

[0007] Based on the aforementioned basic data, a hydrodynamic-water quality coupling model simulating carbon transport processes was constructed, and the hydrodynamic-water quality coupling model was validated using monitoring data.

[0008] Quantify scheduling operation characteristics and extract scheduling characteristic parameters that affect carbon transport;

[0009] Establish a carbon flux accounting model that takes into account the impact of scheduling, and quantify the impact of scheduling on carbon balance;

[0010] Establish a quantitative relationship between carbon transport characteristic parameters and the scheduling characteristic parameters;

[0011] Based on carbon environmental constraints, scheduling optimization assessments are conducted, carbon targets are incorporated into the scheduling optimization system, and low-carbon scheduling schemes are proposed.

[0012] Preferably, the step of constructing a three-dimensional carbon transport monitoring network for reservoirs and obtaining basic data on carbon transport processes specifically includes:

[0013] Set up vertical stratified monitoring points;

[0014] Set up horizontal zone monitoring points;

[0015] Set up cross-sectional monitoring points.

[0016] Preferably, the step of constructing a hydrodynamic-water quality coupling model to simulate carbon transport based on the basic data, and verifying the hydrodynamic-water quality coupling model using monitoring data, specifically includes:

[0017] A three-dimensional hydrodynamic model is used as the basic framework, coupled with a carbon transport module to form the hydrodynamic-water quality coupled model. The carbon transport control equation is constructed based on the Navier-Stokes equation.

[0018] The parameters of the hydrodynamic-water quality coupling model were calibrated using monitoring data.

[0019] The accuracy of the hydrodynamic-water quality coupling model was verified using measured data.

[0020] Preferably, the step of quantifying the scheduling operation characteristics and extracting the scheduling characteristic parameters affecting carbon transport specifically includes:

[0021] Define flow characteristic parameters;

[0022] Define water level characteristic parameters;

[0023] Classify scheduling conditions.

[0024] Preferably, the step of establishing a carbon flux accounting model that considers the impact of scheduling, and quantifying the impact of scheduling on the carbon budget, specifically includes:

[0025] Construct the carbon mass balance equation;

[0026] Define a quantum model for carbon processes;

[0027] A dimensionless correction coefficient is introduced to perform model calibration on the quantum model of the carbon process.

[0028] Preferably, the step of establishing the quantitative relationship between the carbon transport characteristic parameters and the scheduling characteristic parameters specifically includes:

[0029] Define carbon transport characteristic parameters;

[0030] Extracting carbon transport characteristic parameters under different scheduling conditions;

[0031] A statistical relationship model between carbon transport characteristic parameters and scheduling characteristic parameters is established based on multiple linear regression.

[0032] Preferably, the step of conducting scheduling optimization evaluation based on carbon environmental constraints, incorporating carbon targets into the scheduling optimization system, and proposing a low-carbon scheduling scheme specifically includes:

[0033] Construct a scheduling and constraint system that includes carbon environmental goals;

[0034] Establish a multi-objective scheduling optimization model;

[0035] A genetic algorithm is used to solve the multi-objective scheduling optimization model and evaluate the proposed solutions.

[0036] To address the aforementioned technical problems, this invention also provides a device for studying carbon transport characteristics under reservoir operation, employing the following technical solution, including:

[0037] The acquisition module is used to construct a three-dimensional carbon transport monitoring network for reservoirs and acquire basic data on carbon transport processes.

[0038] The module is used to construct a hydrodynamic-water quality coupling model simulating carbon transport processes based on the aforementioned basic data, and to verify the hydrodynamic-water quality coupling model using monitoring data.

[0039] The extraction module is used to quantify scheduling operation characteristics and extract scheduling characteristic parameters that affect carbon transport;

[0040] The quantification module is used to establish a carbon flux accounting model that takes into account the impact of scheduling, and to quantify the impact of scheduling on carbon balance.

[0041] A module is established to establish the quantitative relationship between carbon transport characteristic parameters and the scheduling characteristic parameters;

[0042] The evaluation module is used to evaluate scheduling optimization based on carbon environmental constraints, incorporate carbon targets into the scheduling optimization system, and propose low-carbon scheduling schemes.

[0043] To address the aforementioned technical problems, the present invention also provides a computer device that employs the technical solution described below, comprising a memory and a processor. The memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the above-described method for studying carbon transport characteristics under reservoir scheduling and operation.

[0044] To address the aforementioned technical problems, the present invention also provides a computer-readable storage medium, which employs the technical solution described below. The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the aforementioned method for studying carbon transport characteristics under reservoir scheduling and operation.

[0045] Compared with existing technologies, this invention has the following main advantages: it constructs a complete technical chain from monitoring, modeling, accounting to optimization, which is systematic; it considers the impact of spatiotemporal changes in scheduling on carbon transport, which is dynamic; it achieves quantitative assessment of carbon transport through parameterization and modeling; it can provide direct decision support for low-carbon scheduling and carbon emission management of reservoirs, which is practical; and it can be extended to carbon cycle research in other types of water bodies, which is scalable. Attached Figure Description

[0046] To more clearly illustrate the solutions in this invention, the accompanying drawings used in the description of the embodiments of this invention will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0047] Figure 1 This is a flowchart of an embodiment of the method for studying carbon transport characteristics under reservoir scheduling and operation according to the present invention;

[0048] Figure 2 This is a schematic diagram of the structure of an embodiment of the carbon transport characteristics research device under reservoir scheduling operation of the present invention;

[0049] Figure 3 This is a schematic diagram of the structure of an embodiment of the computer device of the present invention. Detailed Implementation

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to limit the invention; the terms “comprising” and “having”, and any variations thereof, in the specification, claims and foregoing description of the invention, are intended to cover non-exclusive inclusion.

[0051] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0052] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0053] It should be noted that the carbon transport characteristics research method under reservoir scheduling and operation provided in the embodiments of the present invention is generally executed by server / terminal equipment, and correspondingly, the carbon transport characteristics research device under reservoir scheduling and operation is generally set in the server / terminal equipment.

[0054] It should be understood that the number of terminal devices, networks, and servers is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be used.

[0055] Example 1

[0056] Please refer to Figure 1 The flowchart illustrates an embodiment of the method for studying carbon transport characteristics under reservoir operation according to the present invention. The method for studying carbon transport characteristics under reservoir operation includes the following steps:

[0057] Step S1: Construct a three-dimensional carbon transport monitoring network for the reservoir and obtain basic data on the carbon transport process.

[0058] In this embodiment, the electronic equipment (e.g., server / terminal device) on which the carbon transport characteristics research method under reservoir operation is run can receive research requests for carbon transport characteristics under reservoir operation via wired or wireless connection. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future-developed wireless connection methods.

[0059] The basic data specifically includes, but is not limited to, data on dissolved organic carbon, particulate organic carbon, CO2 partial pressure, water temperature, dissolved oxygen, flow velocity, and suspended sediment concentration.

[0060] In this embodiment, step S1 may specifically include the following steps:

[0061] S11, Set up vertical stratified monitoring points.

[0062] The vertical stratification monitoring points were set up using a combination of systematic deployment and dynamic adjustment. First, based on the maximum water depth and stratification characteristics of the reservoir, the vertical stratification interval was determined to be 5 to 10 meters, with the specific interval fine-tuned based on preliminary thermocline detection results. In the sample reservoir, 3-5 monitoring sections were deployed along the main channel and typical bays. At each section, a deep-water sampler made of plexiglass (such as the KC Denmark model or similar) was used for stratified sampling. The sampler was equipped with a trigger-activated closure device that automatically closed at a preset depth to prevent mixing of different water layers. During sampling, a temperature and depth profile was simultaneously recorded using a thermo-depth meter (such as the Sea-Bird SBE19plus), and dissolved oxygen was measured on-site using a dissolved oxygen meter (such as the YSI ProODO). The water sample collected from each layer was divided into three parts: one part was filtered on-site (using a 0.45 μm filter membrane) and stored in a brown glass bottle for dissolved organic carbon analysis; one part was unfiltered and stored in a glass bottle for particulate organic carbon and suspended sediment analysis; and one part was injected into a pre-treated gas sampling bottle for subsequent CO2 partial pressure measurement. The coordinates and depth information of the vertical stratification points are recorded using GPS and echo sounders and incorporated into a geographic information system for spatial management.

[0063] In this embodiment, YSI EXO is used for temperature, depth, and dissolved oxygen monitoring. YSI EXO is a multi-parameter water quality monitoring platform designed specifically for natural aquatic environments and widely used for temperature, depth, and dissolved oxygen monitoring in rivers, lakes, oceans, estuaries, and groundwater. The platform employs a titanium alloy anti-biofouling system and wet-plug connector technology, supports operation at depths of up to 250 meters, and has a battery life of 60-90 days, enabling long-term continuous monitoring. Its core sensors include a fluorescence lifetime optical dissolved oxygen sensor (accuracy ±0.1 mg / L), a NIST-calibrated thermistor temperature sensor (accuracy ±0.01℃), and a non-air-permeable depth sensor (resolution 0.001 meters), which can simultaneously collect key parameters such as temperature, depth, and dissolved oxygen. The device integrates a smart cleaning brush, Bluetooth wireless transmission, and 512MB of memory, supporting direct sensor replacement in the field after laboratory calibration, providing high-precision, low-maintenance real-time data support for environmental research, pollution monitoring, and ecological protection.

[0064] The purpose of step S11 is to accurately capture the vertical gradient distribution of reservoir water temperature, dissolved oxygen, and carbon composition, especially to identify the location and thickness of the thermocline and anoxic layer, revealing the characteristics of vertical carbon transport and stratified retention. The encrypted vertical data provides high-resolution initial field and validation data for subsequent hydrodynamic-carbon transport models, forming the basis for understanding the impact of thermal stratification on the carbon cycle.

[0065] S12, Set up horizontal zone monitoring points.

[0066] The horizontal zoning is systematically divided based on the reservoir's capacity-water level curve and topography. First, using a digital elevation model (DEM), the reservoir area is divided into upstream (backwater zone), midstream (transition zone), dam-front (deep water zone), and downstream impact zone (a certain area downstream of the dam). Within each zone, 2-4 representative monitoring points are deployed based on area and flow characteristics. In the upstream zone, monitoring is primarily conducted near the main inflow points to monitor inflow load; in the midstream zone, monitoring is conducted in areas where the river widens and flow velocity decreases; in the dam-front zone, monitoring is conducted in deep water areas near the dam; and in the downstream zone, monitoring is conducted downstream of the power generation tailrace or spillway. The horizontal monitoring points are combined with vertical stratified points to form a three-dimensional grid. The monitoring indicators for each horizontal point are consistent with those for the vertical points, with particular attention paid to horizontal flow velocity and direction (using an Acoustic Doppler Current Profiler (ADCP) for mobile measurements) to characterize the horizontal carbon transport path. All point information is integrated into the reservoir operation and scheduling diagram, achieving spatial correspondence between the monitoring network and the scheduling zones.

[0067] The purpose of step S12 is to reveal the spatial heterogeneity of carbon transport through horizontal zoning monitoring, quantifying the different roles of different reservoir areas in the input, transformation, and output of carbon. By comparing the carbon concentration and speciation in the upstream, midstream, and upstream areas, processes such as sedimentation and mineralization during carbon transport can be assessed. Zoning data is a key input for constructing carbon mass balance models and calculating regional carbon fluxes.

[0068] S13, set up cross-section monitoring points.

[0069] The key section monitoring points are for enhanced monitoring of carbon input and output, as well as key interface processes in the reservoir. Specific setups include:

[0070] Main inflow sections: Fixed monitoring sections are established 500 meters upstream of the inlets of all first-level tributaries, equipped with multi-parameter online water quality monitoring instruments (such as the YSI EXO series) to continuously monitor flow rate, water temperature, turbidity, dissolved oxygen, pH, etc., and are supplemented by periodic manual sampling to determine carbon composition. Simultaneously, automatic samplers are installed at the sections to trigger the collection of mixed water samples based on changes in flow rate.

[0071] Front section of the dam: A profile monitoring system is set up 100-300 meters away from the dam. This system may include a vertical anchored observation chain with high-frequency sensors mounted on it to continuously monitor vertical water temperature, dissolved oxygen, chlorophyll, CDOM (colored dissolved organic matter), etc., and to collect vertical water samples regularly.

[0072] Downstream flow sections: Monitoring points are set up in the stable section downstream of the power station's tailrace channel or spillway outlet. Water samples are collected synchronously with the power plant's flow records to directly obtain the concentration and flux of carbon released downstream. The sampling frequency of all key sections is higher than that of conventional zonal monitoring points (such as inlet and outlet points, which can be sampled daily or every day), and intensive monitoring is ensured before and after scheduling events (such as flood discharge, increase or decrease in power generation load).

[0073] The purpose of step S13 is to: monitor key cross-sections to directly quantify the boundary fluxes (inputs and outputs) of the reservoir carbon cycle, which is the most critical and uncertain part of carbon mass balance accounting. High-frequency vertical data from the upstream cross-section can capture the impact of short-term water mixing events (such as cold surges) caused by scheduling on carbon distribution. Step S13 provides reliable boundary condition data for achieving accurate carbon budget accounting.

[0074] Step S2: Based on the basic data, construct a hydrodynamic-water quality coupling model to simulate the carbon transport process, and use monitoring data to verify the hydrodynamic-water quality coupling model.

[0075] In this embodiment, step S2 may specifically include the following steps:

[0076] S21 uses a three-dimensional hydrodynamic model as the basic framework, coupled with a carbon transport module to form a hydrodynamic-water quality coupled model, and constructs carbon transport control equations based on the Navier-Stokes equations.

[0077] A three-dimensional hydrodynamic model (such as EFDC, Delft3D, or a self-developed model based on an unstructured mesh) is used as the basic framework. The hydrodynamic module solves the Navier-Stokes equations with the Boussinesq approximation and the hydrostatic assumption to calculate the velocity field, water level, and water temperature. Based on this, a carbon transport module is coupled.

[0078] The Navier-Stokes equations, or NS equations for short, are a set of partial differential equations describing the motion of viscous Newtonian fluids. Essentially, they apply Newton's second law (F=ma) to infinitesimal fluid elements. The equations comprehensively consider fluid pressure, viscous forces (internal friction), and inertial forces, precisely expressing the law of conservation of momentum in fluid motion. These equations are the most fundamental pillar of fluid mechanics.

[0079] Carbon is considered as dissolved or particulate matter in various forms (such as DIC, DOC, POC), and its governing equations add source and sink terms to the three-dimensional convection-diffusion equations:

[0080] ;

[0081] in, This indicates that at a fixed point in space, the first... The rate of change of the concentration of each carbon form over time. express Flow rate gradient in direction (usually east-west), velocity concentration along Directional transport; express The lateral flux gradient (usually in the north-south direction); express The lateral flux gradient in the vertical direction. The sum of these three terms represents the rate at which the net increase or decrease of matter per unit volume is caused by water flow (including horizontal and vertical flow). express Turbulent diffusion term in the direction; express Turbulent diffusion term in the direction (using the same horizontal diffusion coefficient) ); express Turbulent diffusion term in the direction. Represents the concentration of the i-th carbon form; , , For three-dimensional flow velocity components; and The horizontal and vertical turbulent diffusion coefficients are provided by the turbulence closure model (such as the k-ε model) of the hydrodynamic module; The source and sink terms for the i-th carbon form include biochemical and physical processes such as photosynthesis / respiration, organic matter mineralization, carbonate balance, particle sedimentation and resuspension. The three-dimensional hydrodynamic model treats DOC and POC as independent variables and establishes their conversion relationship with dissolved inorganic carbon (DIC, used to calculate CO2 partial pressure).

[0082] The purpose of step S21 is as follows: This governing equation is the mathematical core for simulating the spatiotemporal evolution of carbon in water. It physically describes the transport of carbon with water flow (convection), mixing due to concentration gradients (diffusion), and transformation due to biogeochemical reactions (source and sink). By solving this equation, the concentration distribution of carbon at any location and time in the reservoir can be predicted under different scheduling scenarios.

[0083] S22, Using monitoring data, the parameters of the hydrodynamic-water quality coupled model are calibrated.

[0084] Parameter calibration is an iterative optimization process. First, a set of parameters sensitive to carbon transport simulation is determined, primarily including: rate constants of the carbon cycle (such as DOC mineralization rate and phytoplankton growth rate), settling velocity of particulate carbon, resuspension flux at the bottom of the water body, and empirical coefficients in the gas-liquid mass transfer coefficient. Then, a period with complete monitoring data (covering different seasons and hydrological conditions) is selected as the calibration period. The measured hydro-meteorological boundary conditions (inflow, outflow, wind speed, temperature, humidity, etc.) are input into the model, and the simulation is run. The spatiotemporal distributions of variables such as water temperature, dissolved oxygen, DOC, POC, and CO2 partial pressure obtained from the simulation are compared with measured data acquired from the monitoring network during the same period. The parameters to be calibrated are repeatedly adjusted, either manually or in combination with automatic optimization algorithms (such as genetic algorithms and particle swarm optimization), until the error indices (such as root mean square error RMSE and Nash efficiency coefficient NSE) between the simulation results and the measured data are optimal. The calibration process needs to be carried out in steps. First, the hydrodynamic and temperature modules are calibrated to ensure the accuracy of the flow field and thermal structure. Then, the carbon transport module is calibrated.

[0085] Step S22 serves the purpose of parameter calibration, a crucial step in "localizing" a general model. By adjusting process parameters, it ensures that the model's description of the physical and biochemical processes conforms to the actual conditions of a specific reservoir, thereby significantly improving the accuracy and reliability of the model's carbon transport simulation for that reservoir. An uncalibrated model possesses only theoretical value; only a well-calibrated model has predictive capability.

[0086] S23 verifies the accuracy of the hydrodynamic-water quality coupling model using measured data.

[0087] Step S3: Quantify the scheduling operation characteristics and extract the scheduling characteristic parameters that affect carbon transport.

[0088] In this embodiment, step S3 may specifically include the following steps:

[0089] S31 defines the flow characteristic parameters.

[0090] The flow characteristic parameters are calculated based on the daily or hourly time series of the reservoir outflow (or inflow, selected according to the research objectives).

[0091] Flow Rate Variable Ratio (FRR): Within a selected statistical period (such as a scheduling phase, a month, or a typical scheduling process), extract all flow data within that period and calculate its maximum value. Minimum value and arithmetic mean Substitute into the formula Perform the calculation. This parameter is dimensionless; a larger value indicates a greater fluctuation in flow relative to the average level, reflecting the severity of the scheduling operation.

[0092] Flow Sustainability Index (FPI): First, the flow time series is preprocessed, defining a threshold for similar flow as ±10% of the current flow. Then, the entire time series is scanned to identify all periods where the flow change within a continuous time frame is less than 10%, and the durations of these periods are summed to obtain the FPI. The total length of the statistical period is... .calculate FPI is a value between 0 and 1. The closer it is to 1, the more stable the traffic is, and the scheduling operation will mainly focus on smooth operation.

[0093] Step S31 serves to quantify the characteristics of flow disturbances caused by scheduling from both amplitude and time dimensions, using FRR and FPI. Strong flow fluctuations (high FRR) exacerbate sediment resuspension and alter water mixing intensity, thus affecting carbon migration and transformation. Stable flow (high FPI), on the other hand, is conducive to the formation of a stable hydrodynamic and biochemical environment. These two parameters are key quantitative indicators connecting scheduling operations with the hydrodynamic environment of carbon transport.

[0094] S32 defines the water level characteristic parameters.

[0095] Water level characteristic parameters are calculated based on daily process data of water level in front of the reservoir dam or data on reservoir capacity changes.

[0096] Daily Water Level Variation (DWLF): Select a typical day and read the highest value of the water level in front of the dam on that day. and minimum value The reservoir's design parameters include a normal water level. As a reference benchmark. Calculation. This parameter is dimensionless and reflects the intensity of diurnal regulation. Frequent and large diurnal regulation (high DWLF) can lead to periodic flooding and exposure of the shoreline, affecting the carbon cycle in the region.

[0097] Storage capacity utilization (SUR): Get the current storage capacity. Dead storage capacity Total storage capacity (Usually obtained from the storage capacity curve). Calculation SUR represents the proportion of current effective reservoir capacity (adjustable reservoir capacity) to total effective reservoir capacity, and is a value between 0 and 1. The higher the SUR, the more water the reservoir stores, the greater the water depth, and the easier it is to maintain thermal stratification; the lower the SUR, the lower the water level of the reservoir, the shallower the water depth, and the stronger the water mixing.

[0098] The purpose of step S32 is as follows: DWLF quantifies the degree of disturbance to the riparian ecosystem caused by daily water level regulation, while SUR characterizes the overall water storage status and geometry of the reservoir. Both together affect the reservoir's hydraulic residence time, water thermal stratification stability, sediment-water interface area, and redox conditions, thus profoundly influencing carbon burial and release.

[0099] S33, classify scheduling working conditions.

[0100] Based on long-term (e.g., over 10 years) reservoir operation logs and hydrological data, combined with scheduling procedures, cluster analysis (such as K-means clustering) or rule-based discrimination methods are used to summarize the complex continuous scheduling process into several typical operating condition types with distinct hydrodynamic characteristics. The discrimination rules are typically based on threshold combinations of flow and water level characteristic parameters.

[0101] Flood discharge conditions during the flood season: These occur during the main flood season to cope with floods. They are characterized by: FRR > 2.5 (drastic flow changes), FPI < 0.3 (unstable flow), DWLF may be large (>0.02) due to flood control pre-discharge and flood storage, and SUR dynamically changes within the medium to high range (0.4-0.9).

[0102] Low water level operation: This usually occurs during the pre-flood drawdown period or at the end of the water supply period. Characteristics include: low FRR (<0.8), high FPI (>0.8, stable flow), low DWLF (<0.005), and low SUR (<0.2).

[0103] Water storage conditions during the dry season: the water storage stage from the end of the flood season to the beginning of the dry season. Characteristics include: moderate FRR (0.8-2.0), moderate FPI (0.4-0.7), gradually decreasing DWLF (0.005-0.015), and increasing SUR from low to high (0.2-0.8).

[0104] High water level operation: After water storage is completed, the system maintains a high water level (e.g., for power generation or water supply). Characteristics include: low FRR (0.6-1.5), high FPI (>0.6), low DWLF (<0.01), and high SUR (>0.8).

[0105] The purpose of step S33 is to standardize and typicalize the continuous and complex scheduling process by dividing the operating conditions, so that subsequent carbon transport characteristic analysis, flux accounting, and relationship modeling can be carried out for different and representative "scenarios". This greatly simplifies the complexity of the problem, makes the research conclusions more general and instructive, and facilitates the formulation of differentiated carbon management strategies for different operating conditions.

[0106] Step S4: Establish a carbon flux accounting model that takes into account the impact of scheduling, and quantify the impact of scheduling on carbon balance.

[0107] In this embodiment, step S4 may specifically include the following steps:

[0108] S41, construct the carbon mass balance equation.

[0109] Treating the reservoir as a controlled volume system, a dynamic balance equation for its carbon mass is established. The system's carbon inputs include external inputs ( ) and net internal generation of the system ( Carbon output includes sedimentary burial ( ); ), released into the atmosphere ( ) and the output through the downstream water flow ( The accumulation or consumption of carbon within the system constitutes the total carbon flux. Therefore, the basic mass balance equation is: To correct for systematic errors in the model and adapt to different reservoirs, a dimensionless correction coefficient is introduced for each term. Thus, the accounting model adopted in this invention is formed:

[0110] .

[0111] The time step for model calculations is consistent with the scheduling decision step, typically in days. All flux units are uniformly set to kgC / d.

[0112] The purpose of step S41 is to provide the overall framework and integration tool for carbon flux accounting. It integrates dispersed carbon fluxes from different processes into a unified accounting system, enabling the quantitative calculation of the net carbon source and sink intensity of a reservoir at any given time period. (The sign and magnitude of the positive and negative values). Introducing correction coefficients enhances the model's flexibility and calibration capabilities.

[0113] S42 defines the quantum model of carbon processes.

[0114] Establish specific, mechanism-based, or empirical sub-models for each term in the mass balance equation:

[0115] For each tributary j flowing into the reservoir, multiply its average daily flow by the corresponding carbon concentration (DOC+POC+DIC) and sum the results. Total carbon flux into the reservoir (mass / time, e.g., kg C / d), which is the sum of carbon carried by all tributaries. : No. The average daily flow rate (volume / time, e.g., m³ / d) of the tributary flowing into the reservoir. : No. The total carbon concentration (mass / volume, e.g., kg C / m³) in a tributary is the sum of the concentrations of dissolved organic carbon (DOC), particulate organic carbon (POC), and dissolved inorganic carbon (DIC). Data are from inflow section monitoring.

[0116] Internal net production flux is the CO2 produced from the mineralization of organic matter in the water body and the absorption by primary production processes. The net value. It can be established in relation to water temperature (WT, affecting microbial activity), dissolved oxygen (DO, distinguishing aerobic / anaerobic mineralization efficiency), and hydraulic retention time (W). This can be achieved through an empirical function (affecting reaction time) or by directly utilizing net ecosystem productivity (NEP) data from a hydrodynamic-water quality model. The net carbon flux (mass / time) generated or consumed within a water body, resulting from organic matter mineralization. absorption of primary production The net value. A positive value indicates net carbon release (source), and a negative value indicates net carbon absorption (sink). Water temperature (°C) affects microbial activity and metabolic rate. Dissolved oxygen concentration (mg / L or g / m³) is used to distinguish the efficiency of aerobic and anaerobic mineralization processes. Hydraulic residence time (time, e.g., d) is defined as follows: ,in For the reservoir capacity, This refers to the outflow rate. It reflects the timescale of water exchange with the external environment and influences the degree of biogeochemical reactions.

[0117] : Deposition flux of particulate organic carbon (POC). Among them... The effective settlement velocity of POC can be estimated using Stokes' formula or determined on-site. This represents the area of ​​sediment at the current water level (obtained from the DEM). This represents the POC concentration in the near-bottom water. : The flux (mass / time) of particulate organic carbon (POC) that enters the sediment through sedimentation and is permanently buried. The effective settling velocity of POC (length / time, e.g., m / d) can be estimated using the Stokes formula or determined using an in-situ sediment trap. The effective area (area, e.g., m²) of sediments at the current water level is usually obtained from a digital elevation model (DEM). The concentration of POC in near-bottom water (mass / volume, e.g., kg C / m³) represents the amount of settling particulate matter.

[0118] Gas-liquid interface Exchange flux. Among them, Between the reservoir and the atmosphere The net exchange flux (mass / time). A positive value indicates the release of water. A negative value indicates that the water body absorbed atmospheric energy. k is the piston velocity, calculated using a parameterized scheme that includes the effects of wind speed and water temperature (via the Schmidt number Sc); surface water concentration; This is the atmospheric equilibrium concentration, calculated using Henry's Law. The area of ​​the reservoir's water surface (area, such as m²) varies with the water level and can be obtained from the water level-area relationship curve.

[0119] Carbon flux outflow is calculated by multiplying the average daily outflow by the carbon concentration in the surface water upstream of the dam or at the corresponding depth of the spillway. Total carbon flux (mass / time) carried by the outflow. Average daily outbound flow (volume / time, e.g., m³ / d). The total carbon concentration (mass / volume, e.g., kg C / m³) of the outflowing water can be determined based on the measured carbon concentration (DOC+POC+DIC) of the surface water or the corresponding depth of the spillway in front of the dam.

[0120] The purpose of step S42 is to concretize and computable these sub-models from abstract carbon processes. They combine monitoring data, hydrodynamic parameters, and fundamental physicochemical principles to achieve independent quantification of each carbon migration and transformation pathway. This is the core step in moving from conceptual models to quantitative accounting.

[0121] S43 introduces a dimensionless correction coefficient to perform model calibration on the quantum model of carbon processes.

[0122] Correction coefficient to Determining the accuracy is a systematic calibration process. A sufficiently long historical period (e.g., 2-3 years) is collected, containing complete boundary conditions (flow, meteorology) and sufficient frequency of reservoir carbon concentration spatial distribution monitoring data. First, uncalibrated (i.e., all...) data are used. Using the model and the aforementioned sub-model, and taking boundary conditions as input, the simulation calculates the process fluxes for each calculation step (e.g., day) during that historical period. , ... and total flux On the other hand, using detailed monitoring data from this period, a set of "observed values" or "reference values" can be independently estimated through monitoring-based spatial integration or water chemical mass balance methods. , Then, through multiple regression or optimization algorithms, a set of... The value that makes the total flux calculated by the model... Compared with reference value This minimizes the error while ensuring the consistency of the process flux with its reference value in terms of magnitude. This set... The value is the correction coefficient for the reservoir.

[0123] The purpose of step S43 is to use correction coefficients to correct system biases. These biases may stem from: limitations in the universality of the sub-model formulas, errors in the values ​​of key parameters (such as settling velocity and mineralization rate), and input errors caused by insufficient representativeness of monitoring data. By introducing these coefficients, the results of the accounting model can be made closer to the "real" situation of a specific reservoir, thereby improving the accuracy of carbon budget assessment.

[0124] Step S5: Establish the quantitative relationship between carbon transport characteristic parameters and scheduling characteristic parameters.

[0125] In this embodiment, step S5 may specifically include the following steps:

[0126] S51 defines the characteristic parameters of carbon transport.

[0127] Carbon Retention Rate (CRR): This parameter is defined as the net carbon retention efficiency of a reservoir system. The calculation formula is: .

[0128] The molecule represents the total carbon flux. The net carbon increment of the system is represented by , and the denominator is the total amount of carbon entering the system (external input + internal generation). CRR is a dimensionless ratio ranging from negative infinity to 1.

[0129] CRR > 0: This indicates a positive net increase, meaning the reservoir accumulates carbon and acts as a carbon sink.

[0130] CRR < 0: This indicates a negative net increase, meaning the reservoir releases carbon and acts as a carbon source.

[0131] CRR = 0: Carbon balance.

[0132] Carbon Emission Intensity (CRI): This parameter is defined as the rate at which carbon is released into the atmosphere per unit surface area of ​​a reservoir. The calculation formula is: ,in This refers to the reservoir surface area corresponding to the calculated time period. The unit of CRI is usually g·cm³. -2 ·d -1It eliminates the influence of reservoir size, making it easier to compare the "intensity" or "efficiency" of carbon emissions between reservoirs of different sizes.

[0133] The purpose of step S51 is as follows: CRR, from the perspective of the overall system function, quantitatively answers the core question of "whether the reservoir is a carbon source or a carbon sink, and how efficient it is." CRI, on the other hand, focuses on the atmospheric emission processes of greatest environmental concern and quantifies their spatial intensity. These two parameters together constitute the core indicator system for assessing the environmental effects of reservoir carbon transport, condensing complex carbon flux data into intuitive and comparable characteristic values.

[0134] S52, extract carbon transport characteristic parameters under different scheduling conditions.

[0135] Using a validated and calibrated hydrodynamic-carbon transport coupling model, or directly using a monitoring-based carbon flux accounting model, simulations were performed on a series of typical historical scheduling events or periods. First, based on the classification criteria in step S3, multiple representative periods belonging to four typical operating conditions—"flood season discharge," "low water level operation," "dry season water storage," and "high water level operation"—were identified and selected from historical records (at least 3-5 cases for each condition). Then, the model was run, inputting the precise boundary conditions (flow rate, water level, meteorological conditions, etc.) corresponding to these periods, to simulate and calculate the daily-scale carbon flux sequence for each period. , Next, according to the formula in step S51, the average CRR and CRI values ​​for each period are calculated. Finally, a dataset is formed, in which each row of data represents a working case, including the scheduling characteristic parameters (FRR, FPI, DWLF, SUR) and the corresponding carbon transport characteristic parameters (CRR, CRI) for that case.

[0136] The purpose of step S52 is to generate a training sample dataset for constructing a "scheduling-carbon transport" relationship model. Through model simulation, it systematically and quantitatively links scheduling operations (represented as feature parameters) with the resulting carbon environmental effects (represented as feature parameters), providing a foundation for subsequent statistical analysis.

[0137] S53, a statistical relationship model between carbon transport characteristic parameters and scheduling characteristic parameters is established based on multiple linear regression.

[0138] Based on the sample dataset obtained in step S52, a statistical relationship model is established between carbon transport characteristic parameters (dependent variable Y) and dispatch characteristic parameters (independent variable X). Multiple linear regression is employed.

[0139] Model Form: Assuming the relationship is linear, the equation is established as follows: ,in, It can be CRR or CRI; , , , These represent FRR, FPI, DWLF, and SUR (or some parameters selected based on correlation analysis), respectively. It is the intercept. , ..., It is the regression coefficient; This is the error term.

[0140] Regression analysis: Regression calculations are performed using statistical software (such as SPSS, R, and Python's statsmodels library). The software will output estimated regression coefficients, significance tests of the coefficients (p-values), goodness of fit of the model (R²), adjusted R², and root mean square error (RMSE), etc.

[0141] Model validation and optimization: Check whether the residuals conform to a normal distribution, whether heteroscedasticity exists, and whether there is severe multicollinearity among the independent variables (determined by the variance inflation factor, VIF). If necessary, interaction terms or polynomial terms (such as FRR) of the independent variables can be introduced. 2 Alternatively, stepwise regression can be used to screen variables and optimize the model. Ultimately, the equation in the example is obtained:

[0142] CRR=22.08+0.45×SUR+0.32×FRR+0.18×FPI+0.23×DWLF×100+0.15×FRR 2 .

[0143] The purpose of step S53 is to: This statistical model quantitatively reveals the mathematical laws governing the impact of scheduling operations on carbon transport. The magnitude and sign of the regression coefficients indicate the direction and intensity of the influence of each scheduling characteristic on the CRR or CRI. For example, a positive coefficient for SUR may indicate that the more water stored (increased SUR), the stronger the carbon sequestration function (increased CRR). This model can be used for prediction: given a set of scheduling characteristic parameters (representing a scheduling strategy), it can predict the potential carbon sequestration rate or carbon emission intensity, thus providing a rapid assessment tool for developing low-carbon scheduling schemes.

[0144] Step S6: Based on carbon environmental constraints, conduct scheduling optimization assessment, incorporate carbon targets into the scheduling optimization system, and propose low-carbon scheduling schemes.

[0145] In this embodiment, step S6 may specifically include the following steps:

[0146] S61, Construct a scheduling constraint system that includes carbon environmental goals.

[0147] Based on the traditional reservoir optimization scheduling model, a new carbon environmental constraint is added, forming a multi-objective constraint system. The specific constraint equations are as follows:

[0148] Traditional constraints include water level constraints, flow constraints, and reservoir capacity balance.

[0149] Water level constraints: This ensures that the water level remains between the dead water level and the flood control limit level (which may vary over time). The reservoir is at all times Water level (unit: m). Dead water level: The lowest water level that a reservoir is allowed to drop under normal operating conditions. The flood control limit water level is the upper limit water level set during the flood season for flood control safety, and it can be time-varying.

[0150] Flow constraints: The outflow meets the downstream ecological base flow requirements and does not exceed the flood discharge capacity. Among them, The reservoir is at all times Outflow rate (unit: m³ / s). Ecological baseflow is the minimum flow required to maintain the health of the downstream ecosystem. The maximum flood discharge capacity of a reservoir is limited by its flood discharge facilities.

[0151] Storage capacity balance: Water volume is conserved. Among them, The reservoir is at all times Storage capacity (unit: m³). The reservoir is at all times Storage capacity (unit: m³). Inbound flow rate (unit: m³ / s). Outbound flow rate (unit: m³ / s). Loss of flow, including evaporation, leakage, etc. (unit: m³ / s). Time step (unit: seconds).

[0152] Carbon environmental constraints include daily carbon emission intensity constraints and annual carbon sink function constraints.

[0153] Daily carbon emission intensity constraints: .in, Carbon intensity per day (unit: tCO2e / (km²·d)) is the amount of carbon emitted per unit area of ​​water per day. The carbon emission intensity threshold is set based on regional carbon reduction targets or reservoir type. This means that the daily calculated carbon emissions per unit area cannot exceed a set threshold. The threshold can be determined based on regional carbon reduction targets, reservoir type, or background values.

[0154] Annual carbon sequestration function constraint: .in, Annual carbon retention rate reflects the reservoir's ability to absorb or fix carbon throughout the year. The minimum carbon retention rate requirement ensures that the reservoir has a certain carbon sequestration function on an annual scale. This means that the annual carbon retention rate must not be lower than a minimum value to ensure that the reservoir plays a certain carbon sequestration role on an annual scale.

[0155] The purpose of step S61 is to formally incorporate carbon environmental goals into the mathematical framework of dispatching decisions. It is no longer a post-event evaluation indicator, but a hard constraint or optimization target with equal status to traditional goals such as flood control, water supply, and power generation. This marks a fundamental shift in dispatching from a purely economic and social benefit-oriented approach to a synergistic approach encompassing economic, social, and ecological environmental benefits.

[0156] S62, Establish a multi-objective scheduling optimization model.

[0157] Construct a sequence of decision variables (usually daily water levels) over a scheduling period (e.g., one year). Or daily outbound flow () is a multi-objective optimization model for the optimization object.

[0158] Objective functions include maximizing power generation ( Maximize water supply guarantee rate (or minimize water shortage) and minimize carbon emissions (such as minimizing annual total emissions). Or minimize the number of days exceeding the CRI limit) and maximize carbon retention (maximize) ) etc. Among them, Total power generation during the dispatch period (unit: kWh or J). The overall efficiency of the hydro-generator unit (dimensionless, typically 0.8 to 0.95). : The density of water (usually taken as 1000 kg / m³). : Gravitational acceleration (approximately 9.81 m / s²). :time The net head (unit: m) is the difference in water level between upstream and downstream minus head loss. The flow rate used for power generation (unit: m³ / s) is typically less than or equal to... . Time step (unit: seconds).

[0159] These objectives are often conflicting. Therefore, a multi-objective optimization method is used to seek the Pareto optimal solution set, which is the solution that cannot be improved for any one objective without compromising the other objectives.

[0160] Constraints: the complete constraint system constructed in step S61.

[0161] Decision variables: (t=1,2,…,T), or directly is .

[0162] The purpose of step S62 is to provide a mathematical tool for optimizing carbon-constrained scheduling schemes. It transforms the complex decision-making problem of "how to operate the reservoir under various constraints" into a scientific problem that can be solved automatically using mathematical algorithms. By solving this model, we can systematically explore the compromises that traditional benefit objectives may make under carbon constraints, as well as the achievable carbon emission reduction potential.

[0163] S63 uses a genetic algorithm to solve the multi-objective scheduling optimization model and evaluate the scheme.

[0164] Because the reservoir scheduling optimization model is a complex problem with high dimensions, nonlinearity, nonconvexity, and complex constraints, traditional mathematical programming methods are difficult to solve. Therefore, intelligent optimization algorithms such as genetic algorithms (GA) are adopted. The algorithm flow includes the following steps:

[0165] 1. Encoding: Decision sequence of daily outflow over a year. , ..., The data is encoded as a "chromosome", with each flow value acting as a "gene".

[0166] 2. Initialize the population: Randomly generate a certain number (e.g., 100) of legal scheduling schemes (chromosomes) to form the initial population.

[0167] 3. Fitness Evaluation: For each individual in the population (scheduling plan), run the reservoir simulation model (including hydrodynamic and carbon accounting modules) to calculate its compliance with various constraints and calculate its multiple objective function values. Based on the degree of constraint violation and the quality of the objective values, calculate a comprehensive fitness score.

[0168] 4. Genetic manipulation:

[0169] Selection: Based on fitness scores, select the best individuals to enter the next generation.

[0170] Crossover: Randomly pair selected individuals, exchange a portion of their chromosomes, and produce new individuals.

[0171] Mutation: Randomly altering the magnitude of certain genes (flow values) in a new individual with a certain probability.

[0172] 5. Iteration: Repeat steps 3 and 4 until the preset number of iterations (e.g., 500 generations) is reached or the fitness converges.

[0173] 6. Output and Evaluation: After the algorithm finishes, it outputs a set of Pareto optimal solutions. Decision-makers can choose a balanced solution from these solutions. For example, the carbon-optimized scheduling scheme achieves 85% of the power generation revenue of the traditional scheme, but reduces annual carbon emissions by 10% and increases the carbon retention rate to 60%. By comparing the comprehensive index tables of multiple schemes such as traditional scheduling, carbon-constrained scheduling, and carbon-optimized scheduling, the benefit-cost (including environmental costs) trade-offs under different carbon management intensities can be clearly evaluated.

[0174] Step S63 serves the purpose of providing a feasible and efficient approach to solving complex multi-objective scheduling optimization models using genetic algorithms. It enables global search, avoiding local optima, and directly handles nonlinear and discrete variables. Through this step, a specific, operable sequence of optimized scheduling schemes considering carbon objectives is obtained, and their overall effectiveness is quantitatively evaluated, providing a direct basis for final management decisions.

[0175] A certain reservoir (capacity 15.2 × 10⁻⁶) 8 Taking a water volume of m³, with a normal storage level of 185 m and a flood control limit level of 180.4 m as an example, the method of this embodiment is applied as follows:

[0176] 1. Monitoring Network: A monitoring network is established with monitoring points at 5-meter intervals vertically, 4 horizontal zones, and 3 key cross-sections to obtain data for one year.

[0177] 2. Model validation: A three-dimensional hydrodynamic-carbon transport model was established and calibrated. R² > 0.85, RMSE < 0.3.

[0178] 3. Scheduling parameter extraction: Calculate the FRR, FPI, DWLF, and SUR values ​​for each operating condition, as shown in Table 1.

[0179] Table 1. FRR, FPI, DWLF, and SUR values ​​under various operating conditions

[0180]

[0181] 4. Carbon flux calculation: Calculate the carbon flux for each operating condition. The correction factor is shown in Table 2. The results show that flood discharge during the flood season is the carbon source, and high water level operation is the carbon sink.

[0182] Table 2 Carbon flux and correction factor for each operating condition, unit: kg C / d

[0183]

[0184] 5. Relationship modeling: Establish regression models for CRR, CRI and scheduling parameters, with R² values ​​of 0.82 and 0.78, respectively.

[0185] Simulation results from May 2022 to April 2023 for a certain reservoir were selected for carbon flux calculation and analysis. The mathematical relationship between carbon transport characteristic parameters and reservoir scheduling and operation characteristic parameters is constructed as follows:

[0186] CRR = 22.08+ 0.45×SUR +0.32×FRR + 0.18×FPI + 0.23×DWLF×100 +0.15×FRR² (R²=0.82, RMSE=0.28).

[0187] CRI = 12.5 - 8.3×SUR + 15.7×FRR - 2.1×FPI + 0.8×DWLF + 3.2×FRR² (R²=0.78, RMSE=0.31).

[0188] 6. Optimization and Evaluation: A genetic algorithm was used to solve the carbon-constrained scheduling model, resulting in an optimized carbon scheduling scheme that reduced annual carbon emissions by 10% and increased the carbon retention rate to 60%. The evaluation results are shown in Table 3 for different scheme types.

[0189] Table 3 Evaluation results for different scheme types

[0190]

[0191] The specific value of the benchmark can be determined according to actual needs.

[0192] The beneficial effects of implementing this embodiment are as follows: it constructs a complete technical chain from monitoring, modeling, accounting to optimization, which is systematic; it considers the impact of the spatiotemporal changes of scheduling operations on carbon transport, which is dynamic; it realizes quantitative assessment of carbon transport through parameterization and modeling; it can provide direct decision support for low-carbon scheduling and carbon emission management of reservoirs, which is practical; and it can be extended to carbon cycle research in other types of water bodies, which is scalable.

[0193] This invention can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0194] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0195] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0196] Example 2

[0197] Further reference Figure 2 As a response to the above Figure 1 The present invention provides an embodiment of a device for studying carbon transport characteristics under reservoir scheduling and operation, which is similar to the method shown. Figure 1 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0198] like Figure 2 As shown, the carbon transport characteristics research device 70 under reservoir scheduling operation in this embodiment includes: an acquisition module 71, a construction module 72, an extraction module 73, a quantification module 74, a setup module 75, and an evaluation module 76. Wherein:

[0199] Module 71 is used to construct a three-dimensional carbon transport monitoring network for reservoirs and acquire basic data on carbon transport processes.

[0200] Module 72 is used to build a hydrodynamic-water quality coupling model to simulate carbon transport processes based on basic data, and to validate the hydrodynamic-water quality coupling model using monitoring data.

[0201] Extraction module 73 is used to quantify scheduling operation characteristics and extract scheduling characteristic parameters that affect carbon transport;

[0202] Quantification module 74 is used to establish a carbon flux accounting model that takes into account the impact of scheduling, and to quantify the impact of scheduling on carbon balance.

[0203] Module 75 is established to create a quantitative relationship between carbon transport characteristic parameters and scheduling characteristic parameters;

[0204] Evaluation module 76 is used to evaluate scheduling optimization based on carbon environmental constraints, incorporate carbon targets into the scheduling optimization system, and propose low-carbon scheduling schemes.

[0205] The beneficial effects of implementing this embodiment are as follows: it constructs a complete technical chain from monitoring, modeling, accounting to optimization, which is systematic; it considers the impact of the spatiotemporal changes of scheduling operations on carbon transport, which is dynamic; it realizes quantitative assessment of carbon transport through parameterization and modeling; it can provide direct decision support for low-carbon scheduling and carbon emission management of reservoirs, which is practical; and it can be extended to carbon cycle research in other types of water bodies, which is scalable.

[0206] Example 3

[0207] To address the aforementioned technical problems, embodiments of the present invention also provide a computer device. Please refer to [link / reference needed]. Figure 3 , Figure 3 This is a basic structural block diagram of the computer device in this embodiment.

[0208] The aforementioned computer device 8 includes a memory 81, a processor 82, and a network interface 83 that are interconnected via a system bus. It should be noted that only the computer device 8 with components 81, 82, and 83 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described herein is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0209] The aforementioned computer devices can be desktop computers, laptops, handheld computers, and cloud servers, among other computing devices. These devices can facilitate human-computer interaction with users through keyboards, mice, remote controls, touchpads, or voice-activated devices.

[0210] The aforementioned memory 81 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the aforementioned memory 81 may be an internal storage unit of the aforementioned computer device 8, such as the hard disk or memory of the computer device 8. In other embodiments, the aforementioned memory 81 may also be an external storage device of the aforementioned computer device 8, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 8. Of course, the aforementioned memory 81 may also include both the internal storage unit and its external storage device of the aforementioned computer device 8. In this embodiment, the aforementioned memory 81 is typically used to store the operating system and various application software installed on the aforementioned computer device 8, such as computer-readable instructions for studying carbon transport characteristics under reservoir scheduling and operation. In addition, the aforementioned memory 81 can also be used to temporarily store various types of data that have been output or will be output.

[0211] In some embodiments, the processor 82 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 82 is typically used to control the overall operation of the computer device 8. In this embodiment, the processor 82 is used to execute computer-readable instructions stored in the memory 81 or to process data, such as executing computer-readable instructions for the method of studying carbon transport characteristics under reservoir scheduling.

[0212] The network interface 83 may include a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the computer device 8 and other electronic devices.

[0213] The beneficial effects of implementing this embodiment are as follows: it constructs a complete technical chain from monitoring, modeling, accounting to optimization, which is systematic; it considers the impact of the spatiotemporal changes of scheduling operations on carbon transport, which is dynamic; it realizes quantitative assessment of carbon transport through parameterization and modeling; it can provide direct decision support for low-carbon scheduling and carbon emission management of reservoirs, which is practical; and it can be extended to carbon cycle research in other types of water bodies, which is scalable.

[0214] Example 4

[0215] The present invention also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the method for studying carbon transport characteristics under reservoir scheduling operation as described above.

[0216] The beneficial effects of implementing this embodiment are as follows: it constructs a complete technical chain from monitoring, modeling, accounting to optimization, which is systematic; it considers the impact of the spatiotemporal changes of scheduling operations on carbon transport, which is dynamic; it realizes quantitative assessment of carbon transport through parameterization and modeling; it can provide direct decision support for low-carbon scheduling and carbon emission management of reservoirs, which is practical; and it can be extended to carbon cycle research in other types of water bodies, which is scalable.

[0217] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0218] Obviously, the embodiments described above are merely some embodiments of the present invention, not all embodiments. The accompanying drawings show preferred embodiments of the present invention, but do not limit the patent scope of the present invention. The present invention can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the patent protection scope of this invention.

Claims

1. A method for studying carbon transport characteristics under reservoir operation and scheduling, characterized in that, Includes the following steps: A three-dimensional carbon transport monitoring network for reservoirs is constructed to obtain basic data on carbon transport processes. The basic data includes: dissolved organic carbon, particulate organic carbon, CO2 partial pressure, water temperature, dissolved oxygen, flow velocity, and suspended sediment concentration. Based on the aforementioned basic data, a hydrodynamic-water quality coupled model simulating the carbon transport process is constructed, and the hydrodynamic-water quality coupled model is validated using monitoring data. The construction of the hydrodynamic-water quality coupled model includes: using a three-dimensional hydrodynamic model as the basic framework, coupling a carbon transport module, and constructing a carbon transport control equation based on the Navier-Stokes equation. The quantitative scheduling operation characteristics are extracted by defining flow characteristic parameters, water level characteristic parameters, and classifying scheduling operation types. A carbon flux accounting model considering the impact of scheduling is established to quantify the impact of scheduling on carbon budget. The carbon flux accounting model is as follows: ,in, Total carbon flux, For external input carbon flux. This represents the net carbon flux generated within the system. For carbon flux deposited and buried. This refers to the carbon flux released into the atmosphere. Carbon flux is output from the downstream water flow. to This is a dimensionless correction factor; A quantitative relationship is established between carbon transport characteristic parameters and the scheduling characteristic parameters, wherein the carbon transport characteristic parameters include carbon retention rate (CRR) and carbon release intensity (CRI), wherein: CRR represents the net carbon retention efficiency of a reservoir system, and is dimensionless. ,in, To calculate the reservoir surface area for a given time period, CRI represents the rate at which carbon is released into the atmosphere per unit surface area of ​​the reservoir. Scheduling optimization evaluation based on carbon environmental constraints incorporates carbon objectives into the scheduling optimization system and proposes low-carbon scheduling schemes. The scheduling optimization evaluation based on carbon environmental constraints includes: constructing a scheduling constraint system that includes carbon environmental objectives, establishing a multi-objective scheduling optimization model, and using a genetic algorithm to solve the multi-objective scheduling optimization model and evaluate the schemes.

2. The method for studying carbon transport characteristics under reservoir operation according to claim 1, characterized in that, The steps for constructing a three-dimensional carbon transport monitoring network for reservoirs and obtaining basic data on carbon transport processes specifically include: Set up vertical stratified monitoring points; Set up horizontal zone monitoring points; Set up cross-sectional monitoring points.

3. The method for studying carbon transport characteristics under reservoir operation according to claim 1, characterized in that, The steps for validating the hydrodynamic-water quality coupling model using monitoring data specifically include: The parameters of the hydrodynamic-water quality coupling model were calibrated using monitoring data. The accuracy of the hydrodynamic-water quality coupling model was verified using measured data.

4. The method for studying carbon transport characteristics under reservoir operation according to claim 1, characterized in that, The steps for extracting scheduling characteristic parameters affecting carbon transport by quantifying scheduling operation characteristics specifically include: Define flow characteristic parameters, including flow variation rate (FRR) and flow persistence index (FPI), where: FRR represents the magnitude of flow fluctuation relative to the average level; it is dimensionless. , , These represent the maximum, minimum, and average flow rates within the statistical period. FPI represents the percentage of time that traffic remains stable, ranging from 0 to 1. The total length of the period during which the flow rate change is less than 10%. This represents the total length of the statistical period. Define water level characteristic parameters, including daily water level variation (DWLF) and reservoir capacity utilization rate (SUR), where: DWLF represents the daily regulation intensity, which is dimensionless. , These are the highest and lowest water levels on a typical day. This is the normal water level. SUR represents the proportion of current available storage capacity to total available storage capacity, ranging from 0 to 1. For the current storage capacity, For dead storage capacity, Total storage capacity; Based on the threshold combination of the flow characteristic parameters and water level characteristic parameters, the scheduling conditions are classified into flood discharge conditions, low water level operation conditions, dry season water storage conditions, and high water level operation conditions.

5. The method for studying carbon transport characteristics under reservoir operation according to claim 1, characterized in that, The steps for establishing a carbon flux accounting model that considers the impact of scheduling, and quantifying the impact of scheduling on the carbon budget, specifically include: Construct a carbon mass balance equation. ; Define a quantum model for carbon processes, including: ,in Let j be the average daily flow of the j-th tributary flowing into the reservoir. For the corresponding carbon concentration, , represents the net flux of organic matter mineralization and primary production in water, where WT is water temperature and DO is dissolved oxygen. V is the hydraulic residence time, and V is the reservoir capacity. For outbound flow, ,in The effective settling velocity of particulate organic carbon. For the area of ​​the sediment, This refers to the concentration of particulate organic carbon in the near-bottom water. Where k is the piston speed, The CO2 concentration in surface water. Atmospheric equilibrium concentration, For water surface area, ,in Carbon concentration in the outflow water; Introducing dimensionless correction coefficients to The quantum model of the carbon process is corrected to form the carbon flux accounting model.

6. The method for studying carbon transport characteristics under reservoir operation according to claim 1, characterized in that, The step of establishing the quantitative relationship between carbon transport characteristic parameters and scheduling characteristic parameters specifically includes: Define carbon transport characteristic parameters, including carbon retention rate (CRR) and carbon release intensity (CRI); Carbon transport characteristic parameters under different scheduling conditions are extracted to form a dataset containing scheduling characteristic parameters and carbon transport characteristic parameters; A statistical relationship model between carbon transport characteristic parameters and scheduling characteristic parameters is established based on multiple linear regression: Where Y is CRR or CRI, They are FRR, FPI, DWLF, and SUR, respectively. The intercept is... For regression coefficients, This is the error term.

7. The method for studying carbon transport characteristics under reservoir operation according to any one of claims 1 to 6, characterized in that, The steps of conducting scheduling optimization evaluation based on carbon environmental constraints, incorporating carbon targets into the scheduling optimization system, and proposing low-carbon scheduling schemes specifically include: Construct a scheduling constraint system that incorporates carbon environmental targets, including water level constraints, flow constraints, reservoir capacity balance, daily carbon emission intensity constraints, and annual carbon sink function constraints, among which: water level constraints: , Dead water level For time-varying flood control water levels, flow constraints: , As the downstream ecological base flow, To achieve maximum flood discharge capacity and reservoir balance: , Daily carbon emission intensity constraints to account for lost flow: , Annual carbon sink function constraints are set based on the carbon emission intensity threshold. , Minimum carbon retention rate; A multi-objective scheduling optimization model is established, with daily outflow or daily water level as decision variables, and the objective functions being to maximize power generation, maximize water supply guarantee rate, minimize carbon emissions, and maximize carbon retention rate. A genetic algorithm is used to solve the multi-objective scheduling optimization model and evaluate the proposed solutions.

8. A device for studying carbon transport characteristics under reservoir operation scheduling, characterized in that, include: The acquisition module is used to construct a three-dimensional carbon transport monitoring network for reservoirs and acquire basic data on the carbon transport process. The basic data includes: dissolved organic carbon, particulate organic carbon, CO2 partial pressure, water temperature, dissolved oxygen, flow velocity, and suspended sediment concentration. The module is used to construct a hydrodynamic-water quality coupled model to simulate carbon transport processes based on the basic data, and to verify the hydrodynamic-water quality coupled model using monitoring data. The construction of the hydrodynamic-water quality coupled model includes: using a three-dimensional hydrodynamic model as the basic framework, coupling a carbon transport module, and constructing carbon transport control equations based on the Navier-Stokes equations. The extraction module is used to quantify the scheduling operation characteristics and extract scheduling characteristic parameters that affect carbon transport. The quantified scheduling operation characteristics include: defining flow characteristic parameters, defining water level characteristic parameters, and classifying scheduling operation types. The quantification module is used to establish a carbon flux accounting model that considers the impact of scheduling, quantifying the impact of scheduling on the carbon budget. The carbon flux accounting model is as follows: ,in, Total carbon flux, For external input carbon flux. This represents the net carbon flux generated within the system. For carbon flux deposited and buried. This refers to the carbon flux released into the atmosphere. Carbon flux is output from the downstream water flow. to This is a dimensionless correction factor; A module is established to create a quantitative relationship between carbon transport characteristic parameters and the scheduling characteristic parameters, wherein the carbon transport characteristic parameters include carbon retention rate (CRR) and carbon emission intensity (CRI), wherein: CRR represents the net carbon retention efficiency of a reservoir system, and is dimensionless. ,in, To calculate the reservoir surface area for a given time period, CRI represents the rate at which carbon is released into the atmosphere per unit surface area of ​​the reservoir. The evaluation module is used to evaluate scheduling optimization based on carbon environmental constraints, incorporate carbon targets into the scheduling optimization system, and propose low-carbon scheduling schemes. The scheduling optimization evaluation based on carbon environmental constraints includes: constructing a scheduling constraint system that includes carbon environmental targets, establishing a multi-objective scheduling optimization model, and using a genetic algorithm to solve the multi-objective scheduling optimization model and evaluate the schemes.

9. A computer device, characterized in that, The method includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the method for studying carbon transport characteristics under reservoir scheduling operation as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the method for studying carbon transport characteristics under reservoir scheduling operation as described in any one of claims 1 to 7.

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