Dynamic evaluation method and system for carbon sink effect in hydropower station operation period

Abstract

The invention relates to the technical field of hydropower station operation management, and discloses a hydropower station operation period carbon sink effect dynamic evaluation method and system, and the method comprises the steps: carrying out the deep integration and probabilistic processing of basic data through a mixed probability distribution model and a kernel density estimation method, generating a probability distribution sequence including key parameters such as generating capacity, water level and land utilization conversion probability; the carbon emission reduction amount of hydroelectric power generation is calculated through the IPCC standard, the reservoir water surface carbon emission is calculated according to the climate type, and the vegetation carbon reserve loss of the submerged area is quantified based on the carbon density table, namely the net fixed carbon amount of the vegetation of the submerged area is dynamically monitored; gathering the four results into net carbon sink probability distribution of the hydropower station; the carbon sink economic value of the hydropower station in the whole life cycle is automatically aggregated, and a visual carbon sink effect time sequence curve is generated. The system comprises a probability distribution sequence acquisition module, a carbon sink quantity convergence module and a data visualization display module. According to the invention, an extensible technical framework is provided for carbon sink evaluation of the watershed scale.

Description

Technical Field

[0001] This invention relates to the field of hydropower station operation and management technology, and in particular to a method and system for dynamic assessment of the carbon sequestration effect during the operation of a hydropower station. Background Technology

[0002] In recent years, global net greenhouse gas emissions caused by human activities have increased year by year. Statistics show that the annual growth rate of global carbon dioxide emissions was approximately 0.5% from 2012 to 2022, reaching a total of 57.4 billion tons in 2022. Hydropower development is one of the main forces in optimizing the energy supply structure and reducing carbon emissions in the energy sector. As of the end of 2024, my country's installed hydropower capacity reached 435.95 million kW, accounting for 13.02% of the country's total installed capacity and 22.89% of non-thermal power installed capacity. Vigorously developing hydropower has become an important choice for my country to address global warming. Therefore, accurately quantifying the carbon sink effect during the operation period of hydropower stations is of great significance for assessing the contribution of hydropower development to dual carbon targets. Currently, the calculation of carbon emissions during the operation of hydropower stations mainly refers to the emission factor method, which involves multiplying the hydropower generation and the water surface area below the normal reservoir level by the corresponding emission factors and then summing the results, while also considering the carbon source sink of the submerged soil and terrestrial vegetation. Current assessments use the installed capacity and normal water level of hydropower stations as basic data. In fact, the power generation of hydropower stations varies dynamically under different water inflow frequencies. Secondly, the water surface area changes due to the dynamic adjustment of the reservoir water level, and the area and type of soil and terrestrial vegetation in the corresponding reservoir inundation area (normally flooded area and drawdown area) also differ. In addition, the carbon storage and carbon sequestration of vegetation vary greatly in different seasons. Therefore, it is impossible to accurately measure the carbon sink effect of hydropower stations during the operation period by only considering basic data under static conditions.

[0003] Prior art 1, application number: 202510392619.X, discloses a method and system for calculating the carbon sink value of water conservancy projects. This method involves collecting basic engineering data and multi-dimensional ecological data from water conservancy projects; constructing a soil carbon sink sub-model, a water body carbon balance sub-model, and a vegetation biological quantum model to form a dynamic carbon sink accounting model; and dynamically calculating the total carbon sink amount and converting it into economic value based on the basic engineering data, multi-dimensional ecological data, and the dynamic carbon sink accounting model. While this method quantifies the carbon sink contribution of water conservancy projects to ecosystems such as soil, water bodies, and vegetation, and converts it into economic value assessment to overcome the shortcomings of existing technologies, accurately and comprehensively calculating the carbon sink value of water conservancy projects and providing a scientific basis for environmental benefit assessment and sustainable development of water conservancy projects, it uses a fixed carbon sink accounting model and considers multiple factors such as soil, water bodies, and vegetation, but fails to address the inherent randomness and uncertainty of monitoring data.

[0004] Prior art two, application number: 202510820190.X, discloses a method and system for dynamic assessment of carbon sequestration potential of natural resources, including: collecting monitoring data of the main terrain in the target area, performing slope fitting to obtain terrain slope and fitting factors, and processing to obtain a first dynamic coefficient; obtaining historical terrain slope, processing to obtain a second dynamic coefficient, and combining the first dynamic coefficient to obtain a dynamic coefficient; using a carbon sequestration capacity prediction model to predict carbon sequestration capacity, obtaining carbon sequestration capacity parameters, performing confidence compensation based on the fitting factors to obtain a first compensation parameter; and dynamically compensating based on the dynamic coefficient to obtain a second compensation parameter as the dynamic assessment result. Although this solves the problem of low accuracy in carbon sequestration assessment in hilly areas, and improves the accuracy and dynamic adaptability of the assessment by considering dynamic changes in terrain and combining slope dynamic rate with the prediction model, providing a reliable solution for accurate assessment of carbon sequestration potential, it focuses on the impact of terrain changes in hilly areas on carbon sequestration, and its technical solution is limited to the assessment of carbon sequestration in terrestrial ecosystems.

[0005] Existing technology three, application number: 202510640783.8, discloses a method and system for quantifying the carbon emission reduction effect of electricity demand-side response, including the following steps: acquiring user-side data and grid-side data; performing local data preprocessing through edge computing nodes and then uploading it to a cloud database; obtaining the adjusted load based on the preprocessed user-side data; combining the preprocessed grid-side data with real-time generation curves and grid generation structure to calculate the dynamic carbon emission factor, thereby assessing the marginal carbon emission change brought about by demand response; dynamically calculating the real-time carbon emission factor and dynamically updating EF(t) using grid generation structure data; calculating the baseline and actual carbon emissions based on the baseline load model, adjusted load data, and EF(t), ultimately obtaining the emission reduction effect. Although it achieves dynamic marginal carbon emission factor assessment and high-precision carbon emission reduction quantification, its core lies in the marginal emission change brought about by load adjustment, focusing only on the assessment of the carbon emission reduction effect on the electricity demand side.

[0006] Current technologies 1, 2, and 3 fail to establish a dynamic assessment system for the carbon sink effect tailored to the operational characteristics of hydropower stations. They lack the ability to effectively handle the uncertainties in monitoring data, comprehensively quantify the unique carbon source and sink elements of hydropower stations, and transform the assessment results into intuitive economic value indicators, thus providing a scientific basis for hydropower station carbon asset management and ecological benefit assessment. Therefore, this invention provides a method and system for dynamic assessment of the carbon sink effect during the operation of a hydropower station. Summary of the Invention

[0007] The main objective of this invention is to provide a method and system for dynamic assessment of carbon sequestration effects during the operation of a hydropower station, in order to solve the problem that existing technologies cannot establish a dynamic assessment system for carbon sequestration effects tailored to the operational characteristics of hydropower stations.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A method for dynamically assessing the carbon sink effect during the operation of a hydropower station, comprising the following steps: Several monitoring terminals deployed around the hydropower station continuously collect basic data on the operation of the hydropower station, forming the original dataset. The basic data in the original dataset is cleaned and standardized through an edge computing gateway. In the regional server, the basic data is deeply integrated and probabilistically processed using a hybrid probability distribution model and kernel density estimation method to generate a probability distribution sequence containing key parameters such as power generation, water level and land use conversion probability. The probability distribution sequence is transmitted to the high-performance computing cluster and the following programs are initiated: calculating the carbon emission reduction of hydropower generation according to the IPCC standard, calculating the carbon emissions of reservoir surface according to climate type, quantifying the carbon storage loss of vegetation in the inundation area based on the carbon density table, i.e., dynamically monitoring the net carbon sequestration of vegetation in the inundation area; the four results are repeatedly calculated through Monte Carlo simulation and converged into the probability distribution of net carbon sink of hydropower station. As a further improvement of the present invention, the process of generating a probability distribution sequence containing key parameters such as power generation, water level, and land use conversion probability includes the following steps: Based on hydrodynamic characteristics, continuous basic data are coupled and grouped, and power generation data is associated with corresponding water level observations according to hydrological event types to form hydro-dynamic coupled data groups. Variational inference is used to determine the inherent mixed structure of the hydro-dynamic coupled data groups, identify the probability distribution components corresponding to different operating conditions such as base load power generation, peak shaving operation and extreme events, and establish a mixed probability distribution model. For discrete variables of land use transformation, a land use transformation probability field driven by water level fluctuation is constructed; the transfer frequency between land use types in each water level fluctuation interval is extracted using historical remote sensing sequences; and a continuous response surface of land use transformation probability and water level fluctuation is established under two modes of water level rise and fall through adaptive bandwidth kernel smoothing technology to capture the nonlinear characteristics of vegetation succession during the inundation-outburst process. The parameter space is probabilistically coupled through multiple rounds of random sampling. A mixed probability distribution model is used as the sampling framework to drive the generation of a co-evolutionary parameter sequence of the land use conversion probability field. Each sampling simultaneously obtains the power generation, water level, water area and corresponding land use conversion probability, forming a joint probability distribution sequence that maintains the dynamic correlation between parameters.

[0009] As a further improvement of the present invention, the process of aggregating the probability distribution of net carbon sink of a hydropower station includes the following steps: Using the probability distribution sequences of generated power generation, water level, and land use conversion probability, joint random sampling was performed through Monte Carlo simulation to extract a specific set of parameters for each simulation cycle, and based on this, four results were calculated: carbon emission reduction, reservoir carbon emissions, vegetation carbon storage loss, and vegetation carbon sequestration. The four results obtained from each simulation cycle are algebraically synthesized according to the net carbon sink calculation formula. That is, the carbon emission from the reservoir and the two carbon losses are subtracted from the carbon emission reduction in turn to obtain the net carbon sink value of a specific hydropower station corresponding to this simulation. The Monte Carlo simulation was repeated several times, thereby generating a statistical distribution consisting of a large number of net carbon sink values, which ultimately formed the probability distribution of the net carbon sink of the hydropower station.

[0010] As a further improvement of the present invention, the process of joint random sampling through Monte Carlo simulation includes the following steps: Based on the dynamic dependence between key parameters, a coupled correlation structure is constructed. Using the established joint probability distribution sequence of key parameters, the conditional dependence between power generation, water level and land use conversion probability is analyzed, forming a correlation sampling framework with water level change as the core driving factor. The collaborative generation of parameter combinations is achieved through hierarchical sequential sampling. In the associated sampling framework, a random value is extracted from the water level probability distribution. The random value is used as a conditional input to trigger the associated power generation conditional probability distribution and land use conversion probability response surface. Then, the corresponding power generation value and land use conversion probability value are extracted from the triggered power generation conditional probability distribution. The water level, power generation, and land use conversion probability values ​​obtained from each stratified sequential sampling are combined to form a parameter combination that remains consistent in dynamic relationships. This serves as a complete input instance, providing a data foundation for the parallel calculation of the four results that maintains both randomness and conforms to physical laws.

[0011] As a further improvement of the present invention, the process of extracting the corresponding power generation value and land use conversion probability value from the triggered power generation conditional probability distribution includes the following steps: After a random value is drawn from the water level probability distribution, the operating condition range to which the water level value belongs is identified based on the established mixed probability distribution model, and the specific form of the power generation condition probability distribution to be activated is determined. The water level fluctuation is obtained by comparing the current water level value with the benchmark water level. Based on the sign and magnitude of the water level fluctuation, the corresponding coordinate position is located in the constructed water level fluctuation-driven land use conversion probability field, and all land use conversion probability values ​​at the coordinate position are read. The power generation values ​​extracted from the corresponding operating condition range are combined with the probability values ​​read from the land type conversion response surface to form a set of conditional parameters with the current water level as the core. The set of conditional parameters fully reflects the joint state of each parameter under the constraint of water level conditions.

[0012] As a further improvement of the present invention, the process of reading the conversion probability values ​​of all land types at the coordinate location includes the following steps: The obtained water level amplitude values ​​are corresponding to the horizontal axis coordinates of the established water level amplitude-driven land cover conversion probability field. The response surface for water level rise or fall is selected according to the positive or negative sign of the amplitude. On the selected response curve, locate the vertical axis coordinate that precisely corresponds to the water level fluctuation value. The multiple land use conversion probability values ​​preset at the coordinate position constitute a complete set of land use conversion probabilities. The obtained set of land type conversion probabilities is output as a condition parameter, describing the possibility of conversion between different types of land under the current water level fluctuation, and providing key state transition parameters for parameter combination.

[0013] As a further improvement of the present invention, the process of constructing a complete set of land use conversion probabilities from multiple preset coordinate location land use conversion probability values ​​includes the following steps: By utilizing the land cover conversion frequency extracted from historical remote sensing images, a conversion probability matrix is ​​pre-constructed for each water level amplitude coordinate point; When the coordinate position corresponding to the water level fluctuation value is located on the response curve, the preset complete conversion probability matrix for that position is retrieved, which contains the probability values ​​of mutual conversion between all land types. Expand the retrieved complete conversion probability matrix row by row to form a set containing all possible land use conversion paths and their corresponding probability values.

[0014] As a further improvement of the present invention, the process of pre-constructing a transformation probability matrix for each water level fluctuation coordinate point includes the following steps: By utilizing the land use conversion frequency within different water level fluctuation ranges extracted from historical remote sensing images, the data are grouped and organized according to the water level fluctuation value to form the original land use conversion frequency table corresponding to each fluctuation point. The original land use conversion frequency table for each water level fluctuation point is standardized by row and column processing so that the sum of all conversion frequencies in each row is a uniform standard value. The frequency is converted into probability, and a conversion probability matrix is ​​constructed in which rows represent the original land use type and columns represent the target land use type. Each water level fluctuation value is associated with and stored with its corresponding conversion probability matrix to form a land use conversion probability field driven by water level fluctuation.

[0015] As a further improvement of the present invention, it also includes sending the carbon sink probability distribution to a central processing server, coupling it with real-time carbon trading market price data, automatically aggregating the carbon sink economic value of the entire life cycle of the hydropower station, and generating a visualized carbon sink effect time series curve.

[0016] To achieve the above objectives, the present invention also provides the following technical solution: A dynamic assessment system for the carbon sink effect during the operation period of a hydropower station, applied to the aforementioned dynamic assessment system for the carbon sink effect during the operation period of the hydropower station, comprising: The probability distribution sequence acquisition module is used to continuously collect basic data of hydropower station operation from several monitoring terminals deployed around the hydropower station to form the original dataset. The basic data in the original dataset is cleaned and standardized through the edge computing gateway. In the regional server, the basic data is deeply integrated and probabilistically processed using a hybrid probability distribution model and kernel density estimation method to generate a probability distribution sequence containing key parameters such as power generation, water level and land use conversion probability. The carbon sink aggregation module is used to transmit the probability distribution sequence to the high-performance computing cluster and start the following programs: calculate the carbon emission reduction of hydropower generation according to the IPCC standard, calculate the carbon emissions of reservoir surface according to climate type, quantify the carbon storage loss of vegetation in the inundation area based on the carbon density table, i.e., dynamically monitor the net carbon sequestration of vegetation in the inundation area; the four results are repeatedly calculated through Monte Carlo simulation and aggregated into the probability distribution of net carbon sink of hydropower station. The data visualization module is used to send the probability distribution of carbon sinks to the central processing server, couple it with real-time carbon trading market price data, automatically aggregate the carbon sink economic value of the entire life cycle of the hydropower station, and generate a visualized carbon sink effect time series curve.

[0017] This invention constructs a multi-source data acquisition network by deploying monitoring terminals with wide spatial distribution. The application of edge computing technology enables pre-control of data quality, effectively reducing transmission load and central computing pressure. By employing a hybrid probability distribution model and kernel density estimation method, discrete monitoring data is transformed into a continuous probability distribution, solving the problem of representing the randomness of natural elements such as hydrology and meteorology, and providing a probabilistic input basis for subsequent carbon accounting. The multi-dimensional carbon accounting system based on probability distribution sequences encompasses both the carbon emission reduction benefits of hydropower generation and the complete quantification of the carbon source and sink effects of reservoir ecosystems. Through Monte Carlo simulation and joint probabilistic analysis of four key parameters, the statistical reliability of carbon sink assessment results is significantly improved, effectively addressing the impact of natural condition fluctuations and monitoring data uncertainties. The dynamic coupling mechanism between the carbon sink probability distribution and the carbon trading market enables real-time conversion of ecological benefits into economic value. Visualized time-series curves clearly present the long-term evolution of carbon sink effects, providing a quantitative basis for power plant operation strategy adjustments, carbon asset management, and ecological compensation decisions. The three-tier architecture, from edge computing to high-performance clusters to central servers, optimizes the allocation of data flow and computing resources. The standardized processing procedures ensure the comparability of monitoring data from different periods and regions, providing a scalable technical framework for watershed-scale carbon sequestration assessment. Attached Figure Description

[0018] Figure 1 This is a schematic flowchart of one embodiment of the method for dynamically assessing the carbon sink effect during the operation of a hydropower station according to the present invention. Figure 2 This is a schematic diagram illustrating the steps of generating a probability distribution sequence containing key parameters such as power generation, water level, and land use conversion probability, as an embodiment of the dynamic assessment method for carbon sink effect during the operation period of a hydropower station according to the present invention. Figure 3 This is a schematic diagram of the steps in an embodiment of the dynamic assessment method for carbon sink effect during the operation period of a hydropower station according to the present invention, which aggregates the probability distribution of net carbon sink of the hydropower station. Figure 4 This is a schematic diagram illustrating the steps of generating a visualized time-series curve of carbon sink effect in one embodiment of the dynamic assessment method for carbon sink effect during the operation period of a hydropower station according to the present invention. Figure 5 This is a schematic diagram of the functional modules of an embodiment of the dynamic assessment system for carbon sink effect during the operation of a hydropower station according to the present invention. Figure 6 This is a schematic diagram of the structure of an embodiment of the electronic device of the present invention; Figure 7 This is a schematic diagram of the structure of a storage medium according to an embodiment of the present invention; Figure 8 This is a graph showing the annual power generation variation of the hydropower station according to the present invention; Figure 9This is a BIC / silhouette curve diagram of the present invention; Figure 10 This is a graph of the mixed probability density function of the present invention; Figure 11 This is a PDF schematic diagram of the probability density curves for the main land category conversions in this invention; Figure 12 This is a schematic diagram of the carbon sink distribution during the operation of the hydropower station according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] The terms "first," "second," and "third" used in this invention are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this invention are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the accompanying drawings). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0021] 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 mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] like Figure 1As shown, this embodiment provides an example of a method for dynamically assessing the carbon sink effect during the operation of a hydropower station. Specifically, this method includes the following steps: Step S1: Several monitoring terminals deployed around the hydropower station continuously collect basic data on the operation of the hydropower station to form the original dataset; the basic data in the original dataset is cleaned and standardized through the edge computing gateway, and in the regional server, the basic data is deeply integrated and probabilistically processed using the hybrid probability distribution model and kernel density estimation method to generate a probability distribution sequence containing key parameters such as power generation, water level and land use conversion probability. Step S2: The probability distribution sequence is transmitted to the high-performance computing cluster, and the following programs are started: the carbon emission reduction of hydropower generation is calculated according to the IPCC standard, the carbon emission of reservoir surface is calculated according to climate type, and the carbon storage loss of vegetation in the inundation area is quantified based on the carbon density table, i.e., the net carbon sequestration of vegetation in the inundation area is dynamically monitored; the four results are repeatedly calculated by Monte Carlo simulation and converged into the probability distribution of net carbon sink of hydropower station. Step S3: The probability distribution of carbon sink volume is sent to the central processing server and coupled with real-time carbon trading market price data to automatically aggregate the carbon sink economic value of the entire life cycle of the hydropower station and generate a visualized carbon sink effect time series curve.

[0023] Preferably, this embodiment constructs a multi-source data acquisition network by deploying monitoring terminals with wide spatial distribution. The application of edge computing technology enables pre-control of data quality, effectively reducing transmission load and central computing pressure. A hybrid probability distribution model and kernel density estimation method are used to transform discrete monitoring data into a continuous probability distribution, solving the problem of random characterization of natural elements such as hydrology and meteorology, and providing a probabilistic input basis for subsequent carbon accounting. The multi-dimensional carbon accounting system based on probability distribution sequences covers both the carbon emission reduction benefits of hydropower generation and the complete quantification of the carbon source and sink effects of reservoir ecosystems. Monte Carlo simulation is used to conduct joint probability analysis on four key parameters, significantly improving the statistical reliability of carbon sink assessment results and effectively addressing the impact of natural condition fluctuations and monitoring data uncertainties. The dynamic coupling mechanism between the carbon sink probability distribution and the carbon trading market realizes the real-time conversion of ecological benefits into economic value. Visualized time-series curves clearly present the long-term evolution of carbon sink effects, providing a quantitative basis for power plant operation strategy adjustments, carbon asset management, and ecological compensation decisions. The three-tier architecture from edge computing to high-performance clusters to central servers forms an optimized allocation of data flow and computing resources. The standardized processing procedures ensure the comparability of monitoring data from different periods and regions, providing a scalable technical framework for watershed-scale carbon sequestration assessment.

[0024] The dynamic assessment method for the carbon sink effect during the operation period of a hydropower station in this embodiment includes the following steps: 1) Collect basic data for typical years during the operation of the hydropower station, including data at different times. (Power generation of the unit under different water inflow conditions) Reservoir water level Corresponding water surface area Land area in the reservoir inundation zone Land use types in the inundation area and the area of ​​each type of land wait, ; 2) For continuous variables in the basic data, such as electricity generation Water level, etc., to estimate the mixed weight. and component distribution parameters The optimal number of components is determined using the Bayesian criterion. A mixed probability distribution model, including normal and extreme value distributions, is established for the basic data.

[0025] 3) For discrete variables in the basic data, such as land use type, extract different water level amplitudes based on historical remote sensing images. Land category conversion frequency within the interval The kernel density estimation method is used to... (Rising water level) and (Water level drop) Establish separately and The continuous function relationship.

[0026] 4) Through Monte Carlo simulation joint sampling, sequences of parameters such as hydropower station power generation, water area, land cover conversion probabilities, and corresponding vegetation area in the inundated area were obtained. This is for calculation in the following steps.

[0027] 5) Refer to the IPCC guidelines to calculate different time points. Below, the carbon emission reduction from hydropower generation at hydroelectric power stations , Coal consumption per unit of electricity generated. The carbon content per unit mass of coal.

[0028] 6) Calculate different water inflow conditions Below, carbon emissions from reservoirs , The carbon emission factor per unit area of ​​water surface is selected based on the different climate types where the hydropower station is located.

[0029] 7) Compare land use types before and after water storage at the hydropower station, and use the land use type transition matrix to classify land use types at different water levels. Areas of various land types submerged Transformed into water area ; Calculate different water levels Carbon storage of submerged vegetation in various regions , and The first The aboveground biomass carbon density (carbon in the aboveground living organisms) and belowground biomass carbon density (carbon in the living root system below the surface) of land use types are shown in Table 1.

[0030] Table 1 Carbon density of various land use types

[0031] 8) Measure different water levels Net primary productivity of vegetation communities in submerged areas and soil microbial respiration Calculate the net productivity of the community , recorded as the carbon sequestration by vegetation in the submerged area .

[0032] 9) Based on the above calculations, the probability distribution of the carbon emission reduction of the hydropower station, i.e., the total carbon sink, is obtained. .

[0033] 10) Combine the market value of CO2 per unit mass in the carbon trading market. Calculate the economic value of carbon sinks .

[0034] 11) Calculate the lifespan Total carbon sequestration of inland hydroelectric power stations ,draw ~ The relevant curves were obtained for different lifespans of the hydropower station. The carbon sink effect curve provides support for hydropower station management.

[0035] This embodiment analyzes the dynamic changes of various basic data. For continuous basic data, the EM algorithm is used to fit a Gaussian mixture model (GMM) to determine the mixed probability distribution model, which facilitates the capture of the multimodal distribution characteristics of carbon sinks during the flood / dry season. For discrete basic data, a dynamic transition probability matrix is ​​constructed to determine the probability distribution, solving the problem of fixed land conversion probabilities in traditional methods, and making the calculation of carbon storage in the inundated area dynamically change with the scheduling strategy. The carbon sink distribution during the operation period of the hydropower station at different confidence levels is obtained, improving the accuracy of the calculation and revealing the carbon sink effect at different life stages of the hydropower station, providing support for hydropower station management and carbon market trading. This embodiment considers the dynamic changes of basic data during the operation period of the hydropower station, clarifies the probability distribution of the data, and improves the accuracy of carbon sink effect calculation. Accurate carbon sink calculation helps reduce carbon trading losses and facilitates the management of hydropower stations at different life stages.

[0036] This embodiment collects basic operational data of a hydropower station in typical years, including unit power generation, water surface area at different water levels, reservoir inundation range, land use types in the inundated area, and the area of ​​each land use type. It analyzes the dynamic changes of various basic data. For continuous basic data, the EM algorithm is used to fit a Gaussian mixture probability distribution model (GMM) to determine the mixture probability distribution model. For discrete basic data, a dynamic transition probability matrix is ​​constructed to determine the probability distribution. Through Monte Carlo simulation joint sampling, parameter sequences such as hydropower station power generation, water area, land use type conversion probabilities, and corresponding vegetation area in the inundated area are obtained at different confidence levels. Referring to IPCC guidelines and other relevant materials, the carbon sink of hydropower generation, reservoir carbon emissions, vegetation carbon storage in the inundated area, and vegetation carbon sequestration in the inundated area are calculated to obtain the carbon sink distribution during the hydropower station's operation period. The economic value of the hydropower station's carbon sink is calculated, and the carbon emission coefficient of the hydropower station is calculated based on the carbon sink amount to evaluate the changing pattern of the carbon emission coefficient during different operating periods of the hydropower station.

[0037] Furthermore, such as Figure 2 As shown, step S1, which generates a probability distribution sequence containing key parameters such as power generation, water level, and land use conversion probability, specifically includes the following steps: Step S11: Based on hydrodynamic characteristics, the continuous basic data is coupled and grouped. The power generation data and the corresponding water level observation values ​​are associated according to the hydrological event type to form a hydro-dynamic coupled data group. Variational inference is used to determine the inherent mixed structure of the hydro-dynamic coupled data group, and the probability distribution components corresponding to different operating conditions such as base load power generation, peak shaving operation and extreme events are identified to establish a mixed probability distribution model. Step S12: For the discrete variables of land use transformation, construct a land use transformation probability field driven by water level fluctuation; extract the transfer frequency between land use types in each water level fluctuation interval using historical remote sensing sequences; and establish a continuous response surface of land use transformation probability and water level fluctuation under two modes of water level rise and fall by using adaptive bandwidth kernel smoothing technology to capture the nonlinear characteristics of vegetation succession during the inundation-outburst process. Step S13: Achieve probabilistic coupling of the parameter space through multiple rounds of random sampling. Using a hybrid probability distribution model as the sampling framework, drive the land type conversion probability field to generate a parameter sequence that evolves in a co-evolutionary manner. Each sampling simultaneously obtains the power generation, water level, water area and corresponding land type conversion probability, forming a joint probability distribution sequence that maintains the dynamic correlation between parameters.

[0038] Preferably, this embodiment establishes a multi-parameter joint probability distribution system by coupling continuous hydrodynamic data with discrete land use data. Hydrodynamic grouping and variational inference techniques effectively identify the probabilistic characteristics of different operating conditions, enabling the hybrid probability distribution model to accurately characterize the dynamic correspondence between power generation and water level. The introduction of kernel smoothing technology constructs a continuous response surface for water level fluctuations and land use conversion, accurately quantifying the nonlinear laws of vegetation succession during the inundation-emergence process. Multiple rounds of probabilistic sampling achieve the co-evolution of the parameter space, and the resulting joint probability distribution sequence retains both the stochastic characteristics of parameters such as power generation and water level, and maintains the inherent dynamic correlation between parameters. This coupled modeling method significantly improves the accuracy of joint prediction of power generation scheduling and ecological response under complex hydrological conditions, providing a theoretical basis for multi-objective optimization of water resources systems.

[0039] Furthermore, the process of establishing the mixed probability distribution model in step S11 specifically includes the following steps: Step S111: Construct a potential structure search space for coupled data groups based on the correlation of hydrological events. Integrate power generation and water level observations into multi-dimensional data units according to hydrodynamic processes. Through iterative optimization, approximate the inherent distribution pattern of these data units and identify several typical operating modes implicit in the continuous basic data. Step S112: Confirm the physical meaning of each typical operation mode through posterior membership analysis. For each identified distribution pattern, calculate its matching degree with the known operation state. Based on the matching results, assign each distribution component to the stable operation range of base load power generation, the fluctuation range of peak shaving operation, and the abnormal range of extreme events. The stable operation range shows a concentrated distribution feature, while the abnormal range shows a distribution pattern that emphasizes tail features. Step S113: Construct a hybrid weight constraint mechanism based on physical meaning. Utilize the frequency of occurrence of each typical operating mode in historical basic data and the degree of overlap in the distribution of coupled data groups to determine the contribution weight of each component in the hybrid probability distribution model, and finally form a hybrid probability distribution model that includes both centralized distribution characteristics and tail distribution characteristics.

[0040] Preferably, this embodiment achieves accurate modeling of complex operating conditions by systematically integrating pattern recognition and physical mechanisms. The latent structure search space deeply couples multidimensional data units with hydrodynamic processes, revealing the inherent distribution patterns of the data through iterative optimization and effectively identifying the core characteristics of different operating modes. Posterior membership analysis establishes the correspondence between distribution patterns and physical operating states, accurately representing the stability characteristics of baseload generation, the fluctuation characteristics of peak-shaving operation, and the anomalous characteristics of extreme events in the probability distribution. The hybrid weight constraint mechanism allocates weights based on historical frequency and distribution overlap, ensuring that the model reflects both the concentrated distribution characteristics of normal operation and the tail distribution characteristics of extreme conditions. The modeling method in this embodiment significantly improves the ability of probability distribution models to represent complex operating systems, providing a more reliable mathematical foundation for power system risk analysis and operation optimization.

[0041] Furthermore, the process of calculating the degree of matching between the known operating state and the actual operating state in step S112 specifically includes the following steps: Step S1121: Establish an operational status identification standard based on hydrodynamic characteristics, extract the water level-power generation joint features of three typical states—baseload power generation, peak-shaving operation, and extreme events—from historical operation records, and form a feature boundary set for each state in the coupled data group; Step S1122: Calculate the matching degree through the spatial relationship between distribution patterns and feature boundaries. Perform spatial overlap analysis on the core area of ​​each identified distribution pattern and the feature boundaries of various operating states. Calculate the coverage ratio and deviation distance of each distribution pattern to the three types of feature boundaries to obtain a quantitative matching degree index. The process of calculating the coverage ratio and deviation distance is as follows: Based on the spatial range determined by the feature boundary set, firstly, the proportion of data points in the core area of ​​each distribution pattern that fall within the feature boundary of a certain type of operating state is calculated to obtain the coverage ratio; at the same time, the average of the shortest distances from the center position of the distribution pattern to each benchmark point of the feature boundary of that type is measured to obtain the deviation distance; the coverage ratio reflects the inclusion relationship between the pattern and the state, and the deviation distance describes the geometric difference between the pattern and the state. The two together constitute the matching degree index. Step S1123: Determine the physical correspondence of the distribution patterns according to the optimal matching principle, select each distribution pattern and establish a correspondence with the feature boundary with the highest matching degree among the three types of operating states, identify the pattern with obvious concentrated distribution characteristics and high overlap with the base load power generation feature boundary as the stable operating range, identify the pattern with dispersed distribution and coincidence with the peak shaving operation feature boundary as the fluctuation range, and identify the pattern with significant tail expansion and coincidence with the extreme event feature boundary as the abnormal range.

[0042] Preferably, this embodiment establishes a systematic mapping relationship between hydrodynamic characteristics and operating states, achieving accurate identification and classification of hydropower station operating modes. Based on three typical state feature boundaries extracted from historical data, a quantifiable state discrimination benchmark is constructed. The spatial overlap analysis method transforms abstract distribution patterns into operating intervals with clear physical meaning; the dual indicators of coverage ratio and deviation distance ensure the completeness of the matching degree calculation. The final criteria for determining stable, fluctuating, and abnormal intervals ensure that operating state identification conforms to the physical laws of hydropower generation and is adaptable to complex operating conditions. The multi-feature fusion quantitative discrimination method significantly improves the accuracy and interpretability of operating state classification, providing a reliable technical basis for the safe and economical operation of hydropower stations.

[0043] Furthermore, such as Figure 3 As shown, the process of accumulating the probability distribution of net carbon sink of hydropower stations in step S2 specifically includes the following steps: Step S21: Using the probability distribution sequence of key parameters such as power generation, water level and land use conversion probability, joint random sampling is carried out through Monte Carlo simulation to extract a specific set of parameters for each simulation cycle, and based on this, four results are calculated: carbon emission reduction, reservoir carbon emission, vegetation carbon storage loss and vegetation carbon sequestration. Step S22: Combine the four results obtained from each simulation cycle with the net carbon sink calculation formula, that is, subtract the reservoir carbon emissions and the two carbon losses from the carbon emission reduction in turn to obtain the net carbon sink value of a specific hydropower station corresponding to this simulation. Step S23: Repeat the Monte Carlo simulation several times to generate a statistical distribution consisting of a large number of net carbon sink values, and finally form the probability distribution of the net carbon sink of the hydropower station.

[0044] Preferably, this embodiment employs a Monte Carlo simulation combined with multi-parameter joint random sampling, enabling a probabilistic assessment of the net carbon sink of hydropower stations. Repeated sampling based on the probability distribution sequences of key parameters such as power generation, water level, and land use conversion effectively captures the uncertainties and interactions of each input variable. In each simulation cycle, the system calculates four key indicators: carbon emission reduction, reservoir carbon emissions, vegetation carbon storage loss, and vegetation carbon sequestration, and synthesizes them according to algebraic relationships, ensuring the completeness and theoretical consistency of the net carbon sink calculation. The statistical distribution generated by numerous simulation cycles comprehensively reflects the possible range of net carbon sink values ​​and their probabilities, ultimately providing a robust and reliable quantitative basis for the assessment of hydropower station carbon sinks.

[0045] Furthermore, the joint random sampling process using Monte Carlo simulation in step S21 specifically includes the following steps: Step S211: Based on the dynamic dependence between key parameters, construct a coupled correlation structure, and use the established joint probability distribution sequence of key parameters to analyze the conditional dependence between power generation, water level and land use conversion probability, forming an association sampling framework with water level change as the core driving factor. Step S212: The parameter combination is generated collaboratively through hierarchical sequential sampling. In the associated sampling framework, a random value is extracted from the water level probability distribution. The random value is used as a condition input to trigger the associated power generation condition probability distribution and land type conversion probability response surface. Then, the corresponding power generation value and land type conversion probability value are extracted from the triggered power generation condition probability distribution. Step S213: Combine the water level value, power generation value and land type conversion probability value obtained from each stratified sequential sampling to form a parameter combination that maintains a consistent dynamic relationship. This serves as a complete input instance, providing a data foundation for the parallel calculation of the four results that maintains both randomness and conforms to physical laws.

[0046] Preferably, this embodiment establishes a joint sampling mechanism with water level change as the core driving factor by constructing a coupled correlation structure among key parameters. Based on the dynamic dependencies between parameters, the conditional probability distribution and response surface are analyzed to form a physically consistent correlation sampling framework. A hierarchical sequential sampling method is adopted, using random water level values ​​as input conditions, simultaneously triggering the conditional probability distribution of power generation and the probability response surface of land use conversion, achieving multi-parameter collaborative generation. This ensures that the parameter combinations obtained in each sampling maintain both randomness and conformity to the dynamic laws of the actual system. The final parameter combinations provide a data foundation with both randomness and physical reality for subsequent carbon sink calculations, effectively conveying the uncertainty of the correlation between key parameters.

[0047] Furthermore, the process of extracting the corresponding power generation value and land type conversion probability value from the triggered power generation conditional probability distribution in step S212 specifically includes the following steps: Step S2121: After a random value is drawn from the water level probability distribution, the operating condition range to which the water level value belongs is identified according to the established mixed probability distribution model, and the specific form of the power generation condition probability distribution to be activated is determined. Step S2122: Compare the currently extracted water level value with the benchmark water level to obtain the water level variation. Based on the positive and negative signs and magnitude of the water level variation, locate the corresponding coordinate position in the constructed water level variation-driven land type conversion probability field, and read all land type conversion probability values ​​at the coordinate position. Step S2123: Combine the power generation value extracted from the corresponding operating condition interval with the probability value read from the land type conversion response surface to form a set of conditional parameters with the current water level value as the core. The set of conditional parameters fully reflects the joint state of each parameter under the water level condition constraint.

[0048] Preferably, this embodiment achieves accurate identification of operating condition intervals and activation of corresponding probability distribution patterns through a linkage mechanism between water level probability distribution and power generation conditional probability distribution. Based on the comparison results between water level fluctuation and benchmark water level, the coordinate position is located in the land use conversion probability field driven by water level fluctuation, and a complete land use conversion probability series is extracted. Power generation values ​​are coupled with land use conversion probability values ​​to construct a multi-parameter condition set with water level as the core. This set, through the synergistic effect between parameters, systematically reflects the correlation characteristics between power generation capacity and land use conversion response under water level constraints, providing a quantitative analysis basis for comprehensive watershed water resources management.

[0049] Furthermore, the process of reading the land type conversion probability values ​​of the coordinate location in step S2122 specifically includes the following steps: Step S21221: The obtained water level amplitude values ​​are mapped to the horizontal axis coordinates of the established water level amplitude-driven land cover conversion probability field, and the response surface for water level rise or fall is selected according to the positive or negative sign of the amplitude. Step S21222: On the selected response curve, locate the vertical axis coordinate that precisely corresponds to the water level fluctuation value. The multiple land use conversion probability values ​​preset at the coordinate position constitute a complete set of land use conversion probabilities. Step S21223: Output the obtained set of land type conversion probabilities as conditional parameters to describe the possibility of conversion between different types of land under the current water level fluctuation, providing key state transition parameters for parameter combination.

[0050] Preferably, in this embodiment, the corresponding response surface is determined by mapping the water level fluctuation value with a pre-established water level fluctuation-driven land use conversion probability field. On the selected surface, the vertical coordinate is located based on the water level fluctuation value, and the preset multiple land use conversion probability values ​​at that location are extracted to form a complete probability set. Finally, the probability set is output as a condition parameter to systematically characterize the conversion possibility between different land uses under the current water level conditions, providing a core state transition basis for parameter combination. This enables a refined and quantitative description of the dynamic impact of hydrological changes on land use, improving the accuracy of land conversion simulation and the analytical capability of environmental response mechanisms.

[0051] Furthermore, the process in step S21222 where multiple land use conversion probability values ​​for preset coordinate locations constitute a complete land use conversion probability set specifically includes the following steps: Step S212221: Using the land type conversion frequency extracted from historical remote sensing images, a conversion probability matrix is ​​pre-constructed for each water level amplitude coordinate point. The rows of this matrix represent the current land type, the columns represent the target land type, and the matrix elements are the probabilities of a specific conversion occurring. Step S212222: When the coordinate position corresponding to the water level fluctuation value is located on the response curve, the preset complete conversion probability matrix of that position is retrieved, which contains the probability values ​​of mutual conversion between all land types. Step S212223: Expand the retrieved complete conversion probability matrix row by row to form a set containing all possible land type conversion paths and their corresponding probability values. This set completely describes the probability distribution of any land type converting to all other land types under the current water level fluctuation.

[0052] Preferably, in this embodiment, a conversion probability matrix is ​​constructed by extracting land use conversion frequencies from historical remote sensing data, establishing a quantitative correspondence between water level fluctuation coordinates and land use conversion relationships. When locating specific water level fluctuation coordinates on the response curve, a preset probability matrix is ​​invoked to ensure the acquisition of a complete land use conversion probability system. The matrix is ​​expanded row-wise to form a probability set, systematically presenting the probability distribution of conversions between all land use types. This process achieves precise matching between water level changes and land use conversion probabilities, constructs a complete state transition parameter system, provides comprehensive and reliable probabilistic basis for land use change simulation, and enhances the model's analytical ability and predictive accuracy for land use conversion processes driven by hydrology.

[0053] Furthermore, the process of pre-constructing a transformation probability matrix for each water level fluctuation coordinate point in step S212221 specifically includes the following steps: Step S2122211: Using the land use conversion frequency within different water level fluctuation ranges extracted from historical remote sensing images, group and organize the data according to the water level fluctuation value to form the original land use conversion frequency table corresponding to each fluctuation point. Step S2122212: Perform row and column standardization on the original land use conversion frequency table for each water level fluctuation point so that the sum of all conversion frequencies in each row is a uniform standard value. Convert the frequency into probability and construct a conversion probability matrix in which rows represent the original land use type and columns represent the target land use type. Step S2122213: Associate and store each water level fluctuation value with its corresponding transformation probability matrix to form a land type transformation probability field driven by water level fluctuation, providing a complete data foundation for coordinate positioning.

[0054] Preferably, this embodiment extracts land use conversion frequency data from historical remote sensing images, groups and organizes it according to water level fluctuation values, and establishes an original conversion frequency table corresponding to each fluctuation point. By performing row and column standardization on the frequency table, the frequency data is transformed into a probabilistic form, constructing a conversion probability matrix with a clear row-column correspondence. Each water level fluctuation value is associated with and stored with its corresponding probability matrix, forming a systematic water level fluctuation-driven land use conversion probability field. This process realizes the transformation of historical land use conversion data into a probabilistic expression, establishes a quantitative relationship model between water level changes and land use conversion, provides complete and reliable probabilistic data support for subsequent coordinate positioning, and ensures that land use change simulation has a sufficient historical statistical basis and scientific evidence.

[0055] Furthermore, such as Figure 4 As shown, the process of generating a visualized carbon sink effect time-series curve in step S3 specifically includes the following steps: Step S31: Using the generated probability distribution of net carbon sink of hydropower station as input, introduce the real-time price data stream of carbon trading market to establish the mapping relationship between each probability distribution point and the market value at the corresponding time. Step S32: According to the time series of hydropower station operation, the carbon sink economic value calculated by the value coupling model at each moment is accumulated to generate a cumulative economic value series from the beginning to different operating years, which fully reflects the growth trajectory of carbon sink economic value over time. Step S33: Using the obtained cumulative economic value sequence over the entire life cycle as the vertical axis and the corresponding operating years as the horizontal axis, draw a continuous change trajectory on the visualization interface to form a curve that directly shows the evolution of the carbon sink economic effect over time.

[0056] Preferably, this embodiment establishes a mapping relationship between probability distribution points and market value by coupling the probability distribution of net carbon sinks of hydropower stations with real-time price data streams from the carbon trading market; it accumulates the economic value of carbon sinks according to the operating time series to generate a cumulative economic value sequence reflecting the value growth trajectory throughout the entire life cycle; and it plots a visual curve based on the correspondence between cumulative economic value and operating years, fully presenting the evolution of carbon sink economic effects over time. This process realizes the quantitative conversion of carbon sink ecological benefits into economic value, establishes a systematic mapping mechanism from probability distribution to market value, and forms a visually demonstrable time-series evolution trajectory of carbon sink economic value, providing a complete visual analysis basis for the assessment of carbon sink benefits and the management of carbon assets in hydropower stations.

[0057] like Figure 5 As shown, this embodiment also provides an embodiment of a dynamic assessment system for the carbon sink effect during the operation period of a hydropower station. In this embodiment, the dynamic assessment system for the carbon sink effect during the operation period of a hydropower station is applied to the dynamic assessment method for the carbon sink effect during the operation period of a hydropower station as described in the above embodiment. The dynamic assessment system for the carbon sink effect during the operation period of a hydropower station includes a probability distribution sequence acquisition module 1, a carbon sink amount aggregation module 2, and a data visualization display module 3, which are connected in sequence by electrical connection. The probability distribution sequence acquisition module 1 is used to continuously collect basic data on the operation of the hydropower station from several monitoring terminals deployed around the station, forming the original dataset. The basic data in the original dataset is cleaned and standardized through an edge computing gateway. In the regional server, the basic data is deeply integrated and probabilistically processed using a hybrid probability distribution model and kernel density estimation method to generate a probability distribution sequence containing key parameters such as power generation, water level, and land use conversion probability. The carbon sink aggregation module 2 is used to transmit the probability distribution sequence to the high-performance computing cluster and start the following programs: calculate the carbon emission reduction of hydropower generation according to the IPCC standard, calculate the carbon emissions of the reservoir surface according to the climate type, and quantify the carbon storage loss of vegetation in the inundation area based on the carbon density table, i.e., dynamically monitor the net carbon sequestration of vegetation in the inundation area. The four results are repeatedly calculated through Monte Carlo simulation to aggregate the probability distribution of the net carbon sink of the hydropower station. The data visualization module 3 is used to send the carbon sink probability distribution to the central processing server, couple it with real-time carbon trading market price data, automatically aggregate the carbon sink economic value of the hydropower station throughout its entire life cycle, and generate a visualized carbon sink effect time series curve.

[0058] Preferably, this embodiment achieves real-time acquisition and preprocessing of wide-area spatial data through the cooperation of a multi-monitoring terminal network and an edge computing architecture. The probability distribution sequence acquisition module employs a hybrid probability distribution model and kernel density estimation method to transform discrete monitoring data into statistically significant probability distribution sequences, effectively characterizing the stochastic characteristics of hydrological and meteorological parameters and providing a probabilistic input basis for carbon sink assessment. The carbon sink aggregation module conducts multi-dimensional carbon accounting based on the probability distribution sequence, comprehensively covering key processes such as carbon emission reduction from hydropower generation, carbon emissions from reservoir surfaces, carbon loss from vegetation in flooded areas, and dynamic carbon sequestration. The application of Monte Carlo simulation enables the system to handle multi-source uncertainties and output a statistically significant net carbon sink probability distribution, significantly improving the scientific rigor and reliability of the assessment results. The data visualization module, through the dynamic coupling of the carbon sink probability distribution and the carbon trading market, realizes the transformation analysis of ecological benefits into economic value. The generated carbon sink effect time-series curve intuitively presents the dynamic changes in carbon sinks during system operation, providing continuous technical support for power plant operation management, carbon asset optimization, and ecological compensation decision-making.

[0059] In summary, the modular architecture adopted in this embodiment forms a complete technical chain from data acquisition, processing and analysis to results presentation. The data flow and computing resources between modules are optimized, ensuring the standardization of the evaluation process and the comparability of the results, and providing a systematic solution for the long-term monitoring and evaluation of the carbon sink effect of hydropower stations.

[0060] like Figure 6 As shown, this embodiment provides an embodiment of an electronic device 4, which includes a processor 41 and a memory 42 coupled to the processor 41.

[0061] The memory 42 stores program instructions for implementing the dynamic assessment method for carbon sink effect during the operation period of a hydropower station according to any of the above embodiments.

[0062] The processor 41 is used to execute program instructions stored in the memory 42 to perform dynamic assessment of the carbon sink effect during the operation of the hydropower station.

[0063] The processor 41 can also be referred to as a CPU (Central Processing Unit). The processor 41 may be an integrated circuit chip with signal processing capabilities. The processor 41 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor.

[0064] Furthermore, Figure 7This is a schematic diagram of the structure of a storage medium according to an embodiment of this application. The storage medium 5 of this embodiment stores program instructions 51 capable of implementing all the methods described above. These program instructions 51 can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0065] The application effects of the present invention will be compared and explained below with reference to comparative examples and embodiments: Comparative Example 1 A hydropower station, after commissioning, has an average annual power generation of 23.912 billion kWh. Calculations using the emission factor method yield a hydropower carbon sink of 52.5851 million tons, an increase of 142,300 tons of carbon emissions from the water surface, a carbon storage of 6.5486 million tons from submerged vegetation, and a carbon sequestration of 2.8118 million tons, resulting in a cumulative carbon sink of 43.0824 million tons. However, these figures represent the cumulative carbon sink since impoundment. In reality, annual power generation varies, reservoir water levels fluctuate quarterly, and vegetation growth differs seasonally, leading to variations in the submerged vegetation area and consequently, changes in carbon storage and sequestration. Therefore, it is impossible to assess the relationship between reservoir lifespan and carbon sink.

[0066] Comparative Example 2 A hydropower station, after commissioning, has an average annual power generation of 30.747 billion kWh. Calculations using the emission factor method yield a hydropower carbon sink of 16.619 million tons, with an additional 13,900 tons of carbon emissions from the water surface. The carbon storage and sequestration from submerged vegetation in the reservoir total 3,500 tons, resulting in a total carbon sink of 23.6646 million tons. These figures represent the average annual carbon sink since impoundment. However, since annual power generation varies, and reservoir water levels fluctuate quarterly, the resulting changes in submerged vegetation area lead to variations in carbon storage and sequestration. Therefore, it is impossible to assess the relationship between reservoir lifespan and carbon sink volume.

[0067] Example 1 1. The changes in power generation over ten consecutive years after the hydropower station was put into operation are as follows: Figure 8 As shown. By Figure 8 It is known that the annual power generation varies greatly. Therefore, in order to accurately assess the carbon sink effect of hydropower stations during their operation, these changing factors should be fully considered.

[0068] 2. Based on the power generation and water level data during the hydropower station's operation, a normal distribution is set for 60% of the flood season and 20% for extreme values, while a normal distribution is set for 20% of the non-flood season. Mixed weights and component distribution parameters are estimated for the hydropower station's power generation and water level to determine the optimal number of components and establish a mixed probability distribution model. The BIC criterion and silhouette coefficient are used to determine the optimal number of normally distributed components. The normally distributed components are fitted, extreme points are identified through residual analysis, and the tail data of the extreme value distribution is fitted to display the BIC / silhouette curve. Figure 9 ) and the fitted mixture probability density function plot ( Figure 10 ).Depend on Figure 10 It can be seen that the trend of the BIC indicates that the four components of the normal distribution reasonably represent the distribution characteristics of hydropower generation and water level data. Increasing the number of components leads to overfitting. The silhouette coefficient shows a monotonically decreasing trend. Considering the changes in the BIC, taking 3-4 components can effectively improve accuracy while satisfying strong interpretability. Therefore, the optimal number of components for the normal distribution is 3-4. Figure 10 It can be seen that the values ​​of the mixed probability distribution are relatively concentrated, which greatly improves the accuracy of the next step of carbon sink accounting.

[0069] The obtained normal distribution ( The components are shown in Table 2: Table 2 Normally distributed components

[0070] Generalized extreme value distribution (GEV) components: Shape (c): -0.863 indicates a bounded right tail, with upper limits on both water level and power generation; The central location parameter of the distribution: 198.38m represents the typical value of extreme high water level events (such as peak water level during the flood season). Dispersion of distribution: 19.62m represents the range of fluctuations in extreme events (such as the historical maximum water level difference).

[0071] 3. Based on land use type change data during the operation of the hydropower station, a continuous relationship model between water level fluctuation and land use conversion frequency is established using kernel density estimation. A probability density function for water level rise or fall is established for each land use conversion. Cross-validation is used to automatically select the optimal bandwidth parameter, and probability density curves for major land use conversions are plotted (PDF available). Figure 11 As shown in the figure, the probability density curves for land use conversion differ depending on the magnitude of water level rise and fall. These different land use conversion probabilities affect the vegetation carbon storage and carbon sequestration within the reservoir inundation area.

[0072] 4. Through Monte Carlo simulation and joint sampling, parameter sequences such as power generation, water area, land cover conversion probabilities, and corresponding vegetation area in the inundated area were obtained at different probability levels. Substituting these parameters into the carbon sink model yielded the probability distribution of carbon sink volume during the hydropower station's operation period, as shown below. Figure 12 As shown: the P5 quantile is 1,828,500 tons, the P50 quantile is 3,256,700 tons, and the P95 quantile is 5,989,500 tons. Figure 12 The above values ​​break through the traditional static accounting, taking into account the dynamic changes during the operation of the hydropower station, thus improving the accuracy of carbon accounting.

[0073] 5. The national carbon emission trading market officially launched in July 2021. During the first compliance period (July 16, 2021 to July 14, 2022), the listed price of carbon emission rights fluctuated between RMB 38.50 and RMB 62.29 per ton. Combining this with the market value of CO2 per unit mass in the carbon trading market, the probability distribution of the economic value of carbon sinks was obtained: the P5 quantile is RMB 0.7-1.14 billion, the P50 quantile is RMB 1.25-2.03 billion, and the P95 quantile is RMB 2.31-3.73 billion. These values ​​break through the traditional static accounting and effectively reduce trading losses in the carbon market.

[0074] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0075] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

[0076] The specific embodiments of the invention have been described in detail above, but these are merely examples, and the invention is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this invention. Therefore, all equivalent transformations, modifications, and improvements made without departing from the spirit and principles of this invention should be included within the scope of this invention.

Claims

1. A method for dynamically assessing the carbon sequestration effect during the operation of a hydropower station, characterized in that, The method for dynamically assessing the carbon sequestration effect during the operation of a hydropower station includes the following steps: Several monitoring terminals deployed around the hydropower station continuously collect basic data on the operation of the hydropower station, forming the original dataset. The basic data in the original dataset is cleaned and standardized through an edge computing gateway. In the regional server, the basic data is deeply integrated and probabilistically processed using a hybrid probability distribution model and kernel density estimation method to generate a probability distribution sequence containing the probability of power generation, water level and land use conversion. The probability distribution sequence is transmitted to a high-performance computing cluster to initiate the following procedures: calculating the carbon emission reduction of hydropower generation according to IPCC standards, calculating the carbon emissions of reservoir surface according to climate type, quantifying the carbon storage loss of vegetation in the inundation area based on carbon density table, i.e., dynamically monitoring the net carbon sequestration of vegetation in the inundation area; the four results are repeatedly calculated through Monte Carlo simulation to form the probability distribution of net carbon sink of hydropower station.

2. The method for dynamic assessment of carbon sequestration effect during the operation period of a hydropower station according to claim 1, characterized in that, The process of generating a probability distribution sequence containing key parameters such as power generation, water level, and land use conversion probability includes the following steps: Based on hydrodynamic characteristics, continuous basic data are coupled and grouped, and power generation data is associated with corresponding water level observations according to hydrological event types to form hydro-dynamic coupled data groups. Variational inference is used to determine the inherent mixed structure of the hydro-dynamic coupled data groups, identify the probability distribution components corresponding to different operating conditions such as base load power generation, peak shaving operation and extreme events, and establish a mixed probability distribution model. For discrete variables of land use transformation, a land use transformation probability field driven by water level fluctuation is constructed; the transfer frequency between land use types in each water level fluctuation interval is extracted using historical remote sensing sequences; and a continuous response surface of land use transformation probability and water level fluctuation is established under two modes of water level rise and fall through adaptive bandwidth kernel smoothing technology to capture the nonlinear characteristics of vegetation succession during the inundation-outburst process. The parameter space is probabilistically coupled through multiple rounds of random sampling. A mixed probability distribution model is used as the sampling framework to drive the generation of a co-evolutionary parameter sequence of the land use conversion probability field. Each sampling simultaneously obtains the power generation, water level, water area and corresponding land use conversion probability, forming a joint probability distribution sequence that maintains the dynamic correlation between parameters.

3. The method for dynamic assessment of carbon sequestration effect during the operation period of a hydropower station according to claim 1, characterized in that, The process of accumulating the net carbon sink probability distribution of a hydropower station includes the following steps: Using the probability distribution sequences of generated power generation, water level, and land use conversion probability, joint random sampling was performed through Monte Carlo simulation to extract a specific set of parameters for each simulation cycle, and based on this, four results were calculated: carbon emission reduction, reservoir carbon emissions, vegetation carbon storage loss, and vegetation carbon sequestration. The four results obtained from each simulation cycle are algebraically synthesized according to the net carbon sink calculation formula. That is, the carbon emission from the reservoir and the two carbon losses are subtracted from the carbon emission reduction in turn to obtain the net carbon sink value of a specific hydropower station corresponding to this simulation. The Monte Carlo simulation was repeated several times, thereby generating a statistical distribution consisting of a large number of net carbon sink values, which ultimately formed the probability distribution of the net carbon sink of the hydropower station.

4. The method for dynamic assessment of carbon sequestration effect during the operation period of a hydropower station according to claim 3, characterized in that, The process of joint random sampling using Monte Carlo simulation includes the following steps: Based on the dynamic dependence between key parameters, a coupled correlation structure is constructed. Using the established joint probability distribution sequence of key parameters, the conditional dependence between power generation, water level and land use conversion probability is analyzed, forming a correlation sampling framework with water level change as the core driving factor. The collaborative generation of parameter combinations is achieved through hierarchical sequential sampling. In the associated sampling framework, a random value is extracted from the water level probability distribution. The random value is used as a conditional input to trigger the associated power generation conditional probability distribution and land use conversion probability response surface. Then, the corresponding power generation value and land use conversion probability value are extracted from the triggered power generation conditional probability distribution. The water level, power generation, and land use conversion probability values ​​obtained from each stratified sequential sampling are combined to form a parameter combination that remains consistent in dynamic relationships. This serves as a complete input instance, providing a data foundation for the parallel calculation of the four results that maintains both randomness and conforms to physical laws.

5. The method for dynamic assessment of carbon sequestration effect during the operation period of a hydropower station according to claim 4, characterized in that, The process of extracting the corresponding power generation value and land use conversion probability value from the triggered power generation conditional probability distribution includes the following steps: After a random value is drawn from the water level probability distribution, the operating condition range to which the water level value belongs is identified based on the established mixed probability distribution model, and the specific form of the power generation condition probability distribution to be activated is determined. The water level fluctuation is obtained by comparing the current water level value with the benchmark water level. Based on the sign and magnitude of the water level fluctuation, the corresponding coordinate position is located in the constructed water level fluctuation-driven land use conversion probability field, and all land use conversion probability values ​​at the coordinate position are read. The power generation values ​​extracted from the corresponding operating condition range are combined with the probability values ​​read from the land type conversion response surface to form a set of conditional parameters with the current water level as the core. The set of conditional parameters fully reflects the joint state of each parameter under the constraint of water level conditions.

6. The method for dynamic assessment of carbon sequestration effect during the operation period of a hydropower station according to claim 5, characterized in that, The process of reading the conversion probability values ​​of all land types at coordinate locations includes the following steps: The obtained water level amplitude values ​​are corresponding to the horizontal axis coordinates of the established water level amplitude-driven land cover conversion probability field. The response surface for water level rise or fall is selected according to the positive or negative sign of the amplitude. On the selected response curve, locate the vertical axis coordinate that precisely corresponds to the water level fluctuation value. The multiple land use conversion probability values ​​preset at the coordinate position constitute a complete set of land use conversion probabilities. The obtained set of land type conversion probabilities is output as a condition parameter, describing the possibility of conversion between different types of land under the current water level fluctuation, and providing key state transition parameters for parameter combination.

7. The method for dynamic assessment of carbon sequestration effect during the operation period of a hydropower station according to claim 6, characterized in that, The process of constructing a complete set of land use conversion probabilities from multiple preset land use conversion probabilities at coordinate locations includes the following steps: By utilizing the land cover conversion frequency extracted from historical remote sensing images, a conversion probability matrix is ​​pre-constructed for each water level amplitude coordinate point; When the coordinate position corresponding to the water level fluctuation value is located on the response curve, the preset complete conversion probability matrix for that position is retrieved, which contains the probability values ​​of mutual conversion between all land types. Expand the retrieved complete conversion probability matrix row by row to form a set containing all possible land use conversion paths and their corresponding probability values.

8. The method for dynamic assessment of carbon sequestration effect during the operation period of a hydropower station according to claim 7, characterized in that, The process of pre-constructing a transformation probability matrix for each water level fluctuation coordinate point includes the following steps: By utilizing the land use conversion frequency within different water level fluctuation ranges extracted from historical remote sensing images, the data are grouped and organized according to the water level fluctuation value to form the original land use conversion frequency table corresponding to each fluctuation point. The original land use conversion frequency table for each water level fluctuation point is standardized by row and column processing so that the sum of all conversion frequencies in each row is a uniform standard value. The frequency is converted into probability, and a conversion probability matrix is ​​constructed in which rows represent the original land use type and columns represent the target land use type. Each water level fluctuation value is associated with and stored with its corresponding conversion probability matrix to form a land use conversion probability field driven by water level fluctuation.

9. The method for dynamic assessment of carbon sequestration effect during the operation period of a hydropower station according to claim 1, characterized in that, It also includes sending the probability distribution of carbon sinks to a central processing server, coupling it with real-time carbon trading market price data, automatically aggregating the carbon sink economic value of the entire life cycle of hydropower stations, and generating a visualized carbon sink effect time series curve.

10. A dynamic assessment system for the carbon sink effect during the operation period of a hydropower station, applied to the dynamic assessment system for the carbon sink effect during the operation period of a hydropower station as described in any one of claims 1 to 9, characterized in that, The dynamic assessment system for the carbon sequestration effect during the operation of the hydropower station includes: The probability distribution sequence acquisition module is used to continuously collect basic data on the operation of the hydropower station from several monitoring terminals deployed around the hydropower station, forming the original dataset. The basic data in the original dataset is cleaned and standardized through the edge computing gateway. In the regional server, the basic data is deeply integrated and probabilistically processed using a hybrid probability distribution model and kernel density estimation method to generate a probability distribution sequence containing the probability of power generation, water level and land use conversion. The carbon sink aggregation module is used to transmit the probability distribution sequence to the high-performance computing cluster and start the following programs: calculate the carbon emission reduction of hydropower generation according to the IPCC standard, calculate the carbon emissions of reservoir surface according to climate type, quantify the carbon storage loss of vegetation in the inundation area based on the carbon density table, i.e., dynamically monitor the net carbon sequestration of vegetation in the inundation area; the four results are repeatedly calculated through Monte Carlo simulation and aggregated into the probability distribution of net carbon sink of hydropower station. The data visualization module is used to send the probability distribution of carbon sinks to the central processing server, couple it with real-time carbon trading market price data, automatically aggregate the carbon sink economic value of the entire life cycle of the hydropower station, and generate a visualized carbon sink effect time series curve.

Images