Reservoir sediment dispatching multi-scheme generation method based on generative adversarial network and large language model

Abstract

The invention relates to the technical field of hydrological intelligent prediction and sediment regulation and control, and discloses a reservoir sediment scheduling multi-scheme generation method based on a generative adversarial network and a large language model, and the method comprises the steps: collecting reservoir water and sediment data, carrying out the preprocessing, generating a standardized sample library, constructing a time sequence input feature sequence, and carrying out the feature extraction; and generating multiple sets of future sediment evolution schemes and corresponding regulation and control strategies, determining a final scheme through inspection and risk scoring, generating a quantitative execution evaluation report, and performing iterative optimization. According to the method, the generative adversarial network and the large language model are fused, and data preprocessing, constraint verification, risk quantification and iterative optimization means are combined, so that diversified generation and accurate regulation and control of multiple reservoir sediment scheduling schemes are realized, and scientific and reliable decision support is provided for long-term safe operation of the reservoir and sediment control.

Description

Technical Field

[0001] This invention relates to the field of intelligent hydrological prediction and sediment regulation technology, and more specifically, to a method for generating multiple schemes for reservoir sediment regulation based on generative adversarial networks and large language models. Background Technology

[0002] The formulation of reservoir operation and management plans is a core aspect of reservoir operation and management, and their rationality directly affects the achievement of multiple objectives, including flood control, water supply, power generation, and ecological protection. For river-type reservoirs with significant sedimentation issues, the quality of the operation plan can also influence sedimentation patterns in the reservoir area and downstream scouring, thus determining the long-term operational safety and benefits of the reservoir. Therefore, operation departments typically need to compare and screen various possible plans under different inflow scenarios, operational objectives, and sediment discharge strategies to find the optimal solution that balances safety and economy.

[0003] Currently, reservoir operation mainly relies on hydrodynamic and sediment numerical models for calculation. These models can simulate the evolution of water flow and sediment under specific operating conditions, but they have obvious limitations: on the one hand, model operation depends on manual input of the operation plan (such as gate opening and inflow process), and can only generate a single result each time. Comparison of multiple plans requires repeated construction of inputs and execution one by one, which is inefficient and difficult to meet the needs of rapid response under emergency conditions; on the other hand, numerical simulation is complex and it is difficult to achieve real-time or near-real-time simulation, which limits its flexible application in actual operation.

[0004] To improve efficiency, some studies have introduced data-driven models (such as convolutional neural networks and long short-term memory networks), which can improve prediction accuracy and reduce modeling costs to some extent. However, most methods only output a single prediction result and lack the ability to generate multiple alternatives; at the same time, the generated results are difficult to strictly satisfy physical constraints such as sediment conservation and hydrodynamic laws. In addition, these models mainly focus on prediction accuracy and fail to address the practical needs of multi-alternative selection and decision support during scheduling.

[0005] In actual operation, reservoir managers not only need the results of sediment evolution but also need to transform them into actionable control strategies. However, existing systems lack intelligent strategy generation and optimization capabilities, still relying on human experience for scheme comparison and adjustment, which is highly subjective and poses a risk of decision-making lag under extreme events. Furthermore, existing methods do not fully utilize natural language processing technology; dispatchers cannot analyze, compare, and optimize schemes at the semantic level, resulting in insufficient system interactivity.

[0006] In summary, existing reservoir scheduling technologies have the following core shortcomings: they lack methods to automatically generate multiple sediment evolution schemes, resulting in low scheme comparison efficiency; they lack a generation mechanism that combines physical laws with data-driven models, leading to insufficient rationality of results; and they lack intelligent strategy analysis and recommendation functions, making it difficult to form a closed-loop scheduling support system.

[0007] Therefore, it is necessary to design a multi-scheme generation method for reservoir sediment scheduling based on generative adversarial networks (GANs) and large language models (LLMs). By using GANs to generate diverse sediment evolution schemes under physical constraints, and using LLMs to perform semantic parsing, policy generation, and closed-loop optimization on the generated results, the scheduling schemes can be diversified, rationalized, and intelligent to meet the needs of reservoir operation and management under complex conditions. Summary of the Invention

[0008] In view of this, the present invention proposes a method for generating multiple schemes for reservoir sediment scheduling based on generative adversarial networks and large language models, aiming to solve at least one of the problems in the background technology.

[0009] This invention proposes a method for generating multiple schemes for reservoir sediment management based on generative adversarial networks and large language models, including: Collect water and sediment data on the current hydrodynamic conditions and sediment status of the reservoir, and preprocess the water and sediment data to generate a standardized sample library; A time-series input feature sequence of water and sediment evolution is constructed based on the standardized sample library and continuous time step library observation data. Multiple future sediment evolution schemes are generated based on the time-series input feature sequence and random perturbation vector; Feature extraction and structured representation are performed on the multiple future sediment evolution schemes to generate corresponding control strategies for the multiple future sediment evolution schemes; The aforementioned control strategies are tested and risk-scored, and the final future sediment evolution scheme and its corresponding control strategies are determined based on the risk scores. After obtaining the final future sediment evolution plan and its corresponding regulation strategy, the reservoir water and sediment data are compared with the preset water and sediment data standards, and a quantitative implementation evaluation report is generated based on the comparison results. The final future sediment evolution scheme and its corresponding regulation strategy are used as a new standardized sample library to generate multiple future sediment evolution schemes for iterative optimization.

[0010] Furthermore, water and sediment data on the current hydrodynamic conditions and sediment status of the reservoir will be collected, including: Real-time data collection of reservoir water level, inflow, outflow, inflow sediment concentration, and outflow sediment concentration; Retrieve historical cross-sectional measurement data from the historical database; When there are missing data in the collected data, interpolation methods are used to supplement the missing data; If the collected data is complete, data preprocessing is performed directly.

[0011] Furthermore, the water and sediment data are preprocessed to generate a standardized sample library, including: Outlier identification is performed on the water and sediment data; When the identified data exceeds the preset outlier threshold, the outlier data is removed and replaced with adjacent valid data. If the identified data falls within the preset outlier threshold range, the data is retained. The retained data is normalized. The normalized water and sediment data are classified and stored to generate a standardized sample library.

[0012] Furthermore, based on the standardized sample library and continuous time step observation data, a time-series input feature sequence of water and sediment evolution is constructed, including: Extract water and sediment data parameters corresponding to continuous time step observation data from a standardized sample library; Calculate the correlation coefficients between the corresponding water and sediment data parameters; When the correlation coefficient is greater than the preset correlation threshold, the water and sediment data parameters are stored in the time series input feature sequence. If the correlation coefficient is less than or equal to the preset correlation threshold, then the water and sediment data parameter is excluded. The stored water and sediment data parameters are arranged in chronological order to form a time-series input feature sequence.

[0013] Furthermore, based on the time-series input feature sequence and random perturbation vector, multiple future sediment evolution schemes are generated, including: The time-series input feature sequence and random perturbation vector are used as input samples for the generative adversarial network model to generate multiple future sediment evolution schemes. The generated multiple future sediment evolution schemes are verified according to preset constraints. If the generated future sediment evolution scheme meets the preset constraints, the scheme will be retained. If the generated future sediment evolution scheme does not meet the preset constraints, the range of values ​​for the random disturbance vector is adjusted, and the future sediment evolution scheme is regenerated. The preset constraints are determined by the basic laws of water and sediment movement in reservoirs, including water balance constraints and sediment conservation constraints.

[0014] Furthermore, generating multiple future sediment evolution schemes also includes: For each generated future sediment evolution scheme, the corresponding generation time and input samples are labeled; After generating multiple future sediment evolution schemes, the multiple future sediment evolution schemes are deduplicated. Key indicator data from multiple future sediment evolution schemes were extracted, and the differences in key indicators between each pair of schemes were calculated. When the difference in key indicators between two schemes is less than the preset similarity threshold, the two schemes are determined to be similar, and the scheme generated later is deleted. When the difference in key indicators between two schemes is greater than or equal to the preset similarity threshold, the two schemes are determined to be dissimilar and both are retained. The key indicator data include the annual siltation volume in the reservoir area and the peak value of sediment concentration at the outlet.

[0015] Furthermore, feature extraction and structured representation are performed on the multiple future sediment evolution schemes to generate corresponding control strategies for each future sediment evolution scheme, including: Based on a large language model, feature encoding is performed on the multiple future sediment evolution schemes, key features are extracted and structured representation is performed to generate structured features; Semantic parsing is performed on the structured features to generate a scheme strategy mapping relationship library; Based on the aforementioned scheme strategy mapping relationship library, multiple sets of corresponding control strategies for future sediment evolution schemes are generated.

[0016] Furthermore, the control strategy includes gate opening degree, sand discharge timing and scheduling flow allocation; The regulation strategy is tested, including: When the gate opening adjustment value of the control strategy is within the preset opening range, the gate opening adjustment verification is passed; otherwise, it fails. If the sediment discharge timing of the control strategy does not overlap or conflict with the reservoir's preset functional time period, the sediment discharge timing verification will pass; otherwise, it will fail. If the scheduling flow allocation of the control strategy satisfies the condition that the total inbound flow equals the sum of the outbound flows, then the scheduling flow allocation verification is passed; otherwise, it fails. Only control strategies that pass all three checks will be retained, while other control strategies will be removed.

[0017] Furthermore, a risk score is performed on the aforementioned regulation strategy, and the final future sediment evolution scheme and its corresponding regulation strategy are determined based on the risk score, including: When the control strategy is determined to be feasible, the risk indicators of all feasible control strategies are obtained. The risk indicators include the increase in siltation, the proportion of abnormal flow velocity, and the amplitude of water level fluctuation. The risk score of the control strategy is obtained by weighting and summing the three risk indicators according to the preset weights. All feasible control strategies are ranked according to their risk score values. The control strategy with the lowest risk score is selected as the corresponding strategy for the final future sediment evolution scheme; When the risk scores of the control strategies are equal, the control strategies are sorted from smallest to largest based on the accumulation increment. The control strategy that minimizes the increase in sedimentation is selected as the corresponding strategy for the final future sediment evolution scheme; Furthermore, the sum of the weights for the increase in siltation, the proportion of abnormal flow velocity, and the amplitude of water level fluctuations is 1.

[0018] Furthermore, the reservoir water and sediment data after obtaining the final future sediment evolution plan and its corresponding regulation strategy are compared with preset water and sediment data standards. Based on the comparison results, a quantitative implementation evaluation report is generated, including: Collect actual reservoir water and sediment data after the final control strategy is implemented; Calculate the deviation between the actual reservoir water and sediment data and the preset water and sediment data standard; When the deviation values ​​of all actual reservoir water and sediment data are less than the preset deviation value, the report will mark that the final future sediment evolution scheme and its corresponding control strategy have met the standard, and record the corresponding deviation values ​​of all actual reservoir water and sediment data. When the deviation value of any actual reservoir water and sediment data is greater than or equal to the preset deviation value, the report will mark that the final future sediment evolution scheme and its corresponding control strategy are not up to standard, and record the actual reservoir water and sediment data of the item exceeding the standard, the corresponding deviation value and the cause of the deviation.

[0019] Compared with existing technologies, the beneficial effects of this invention are as follows: The reservoir sediment scheduling multi-scheme generation method based on generative adversarial networks and large language models provided by this invention generates a standardized sample library by preprocessing reservoir water and sediment data, constructs multiple evolution schemes by combining time-series features and random perturbation vectors, generates control strategies by feature extraction, and determines the final scheme by verification and scoring. Then, by comparison, an evaluation report is generated and iterative optimization is performed. This can improve the reliability and standardization of water and sediment data, increase the diversity and pertinence of future sediment evolution schemes, improve the feasibility and adaptability of control strategies, and continuously enhance the dynamic adaptability of schemes and strategies through iterative optimization. This provides multi-dimensional and verifiable decision support for reservoir sediment scheduling, helps to balance problems such as sediment accumulation and sediment discharge efficiency in reservoir operation, and adapts to scheduling needs under different scenarios. Attached Figure Description

[0020] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 The flowchart of the reservoir sediment scheduling multi-scheme generation method based on generative adversarial networks and large language models provided in the embodiments of the present invention Figure 1 ; Figure 2 The flowchart of the reservoir sediment scheduling multi-scheme generation method based on generative adversarial networks and large language models provided in the embodiments of the present invention Figure 2 . Detailed Implementation

[0021] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] See Figure 1-2 As shown in some embodiments of this application, a method for generating multiple schemes for reservoir sediment management based on generative adversarial networks and large language models includes the following steps: S100: Collect water and sediment data on the current hydrodynamic conditions and sediment status of the reservoir, and preprocess the water and sediment data to generate a standardized sample library.

[0023] S200: Construct a time-series input feature sequence of water and sediment evolution based on a standardized sample library and continuous time step library observation data.

[0024] S300: Generates multiple future sediment evolution schemes based on time-series input feature sequences and random perturbation vectors.

[0025] S400: Extract features and structure multiple future sediment evolution schemes to generate corresponding control strategies for multiple future sediment evolution schemes; The control strategies are tested and risk-scored, and the final future sediment evolution scheme and its corresponding control strategies are determined based on the risk scores.

[0026] S500: Obtain reservoir water and sediment data after the final future sediment evolution plan and its corresponding regulation strategy, compare it with the preset water and sediment data standard, and generate a quantitative implementation assessment report based on the comparison results.

[0027] S600: The final future sediment evolution scheme and its corresponding regulation strategy will be used as a new standardized sample library to generate multiple future sediment evolution schemes and conduct iterative optimization.

[0028] It is understandable that the reservoir sediment scheduling multi-scheme generation method based on generative adversarial networks and large language models provided in this embodiment constructs a full-process technical framework from data acquisition to closed-loop optimization by combining generative adversarial networks and large language models. First, a standardized sample library is generated by collecting and preprocessing water and sediment data to ensure the standardization and completeness of the input data, providing a reliable foundation for subsequent models. Second, a time-series input feature sequence is constructed based on continuous time-step observation data to capture the dynamic correlation of water and sediment evolution, enhancing the model's ability to model time-dimensional patterns. Next, by inputting the time-series feature sequence into the generative adversarial network and superimposing random perturbation vectors, diverse sediment evolution schemes are generated under physical constraints. This ensures that the generated results conform to the basic laws of sediment movement and achieves scheme diversity through perturbation injection. Finally, the large language model is used to extract features from the generated schemes and perform analysis. Structured representation transforms complex sediment evolution data into parsable semantic features, which are then mapped to generate control strategies containing specific parameters such as gate opening and sediment discharge timing, thus realizing the transformation from prediction results to executable strategies. Feasibility screening of strategies is conducted by setting verification rules such as water balance and sediment discharge timing conflicts, and scoring and ranking are combined with risk indicators such as sedimentation increment to ensure the rationality and safety of recommended strategies. Finally, the actual water and sediment data after execution are compared with preset standards to generate an evaluation report, and the optimized scheme is fed back to the sample library to form a closed-loop iterative mechanism, continuously improving the adaptability of the scheme and the intelligence level of the scheduling system.

[0029] Specifically, this involves collecting water and sediment data on the current hydrodynamic conditions and sediment status of the reservoir, including: Real-time data collection of reservoir water level, inflow, outflow, inflow sediment concentration, and outflow sediment concentration; Retrieve historical cross-sectional measurement data from the historical database; When there are missing data in the collected data, interpolation methods are used to supplement the missing data; If the collected data is complete, data preprocessing is performed directly.

[0030] Understandably, sensors for water level, flow rate, and sediment concentration are deployed at key monitoring points such as the reservoir inlet section, outlet section, and the central part of the reservoir area. These sensors continuously collect data on water level, inflow rate, outflow rate, inflow sediment concentration, and outflow sediment concentration at a preset sampling frequency (with a fixed data sampling interval of 2 hours). Based on the time range of the currently collected data and the location of the monitoring section, matching historical cross-sectional measurement data is selected from the reservoir's historical monitoring database. This includes cross-sectional morphological parameters, sediment deposition data, and flow velocity distribution data for the corresponding time period. The retrieved historical data is then combined with the real-time collected data to form a complete raw data set.

[0031] The raw data undergoes integrity checks, verifying each data point for water level, inflow, outflow, inflow sediment concentration, outflow sediment concentration, and historical cross-sectional measurement data to determine if any data points are missing due to uncollected data, transmission failures, or recording anomalies. When missing data is detected, an appropriate interpolation method is selected based on the type of missing data (time-series continuous data or cross-sectional discrete data). Supplementary values ​​are calculated based on valid data from sections before, after, or adjacent to the missing data point, and the supplemented complete data is then passed to the subsequent preprocessing stage. When no data is missing, the complete raw data is directly passed to the data preprocessing stage without additional supplementation.

[0032] Understandably, real-time collection of five core parameters—water level, inflow, outflow, inflow sediment concentration, and outflow sediment concentration—covers key indicators of hydrodynamics and sediment conditions, forming a real-time monitoring data source. Historical cross-sectional measurement data is retrieved to supplement long-term evolutionary background information and enhance the temporal continuity of the data. For data gaps, interpolation methods are used to supplement the data, avoiding anomalies in subsequent model inputs due to incomplete data, while retaining a path for direct preprocessing of valid data, balancing data quality and processing efficiency. Through the collaborative collection of real-time dynamic data and historical static data, combined with an intelligent data repair mechanism for missing data, the spatiotemporal integrity and physical consistency of the input data are ensured, providing reliable data support for subsequent sediment evolution modeling.

[0033] Specifically, water and sediment data are preprocessed to generate a standardized sample library, including: Outlier identification in water and sediment data; When the identified data exceeds the preset outlier threshold, the outlier data is removed and replaced with adjacent valid data. If the identified data falls within the preset outlier threshold range, the data is retained. The retained data is normalized. The normalized water and sediment data are classified and stored to generate a standardized sample library.

[0034] Specifically, the first step is to perform statistical analysis on the aggregated water and sediment data, calculating the mean, standard deviation, and other statistical indicators for each parameter. Each data point is then compared with a preset outlier threshold to determine if the data exceeds a reasonable range, thus completing the initial screening and labeling of outliers. For labeled outliers, if their values ​​exceed the preset outlier threshold, they are directly removed. Several adjacent valid data points (e.g., the first three, the last three) are selected, and their mean is calculated or a supplementary value is obtained through linear interpolation. This supplementary value replaces the outlier data. If the data falls within the preset outlier threshold range, it is considered valid and retained for subsequent processing.

[0035] Specifically, all retained valid data are classified and divided into data subsets according to parameter type (flow rate, sediment concentration, water level). For each data subset, a preset normalization transformation formula is used to normalize the original data and convert it into standardized features. After the transformation is completed, the numerical range of each parameter is verified to ensure that there is no data outside the range, thus forming standardized data after normalization.

[0036] Specifically, the preset normalization transformation formula is calculated as follows:

[0037] in, Represents the original feature value of the i-th class. , These are the mean and standard deviation of the corresponding features, respectively. The standard feature value represents the i-th type of feature.

[0038] Understandably, standardized data ensures that the model maintains a uniform scale when processing different types of indicators, avoiding prediction bias caused by differences in magnitude. Simultaneously, this process reduces the impact of outliers on the calculation results, improving the stability and robustness of the features.

[0039] Specifically, the normalized standardized data are classified according to different parameters, such as water level, flow rate, sediment concentration, and historical cross-sectional data; time dimension, such as real-time data, historical data, and different monitoring periods; and monitoring area, such as inflow cross-section, outflow cross-section, and central part of the reservoir area. A data indexing system is established to clarify the storage path and retrieval rules for each type of data. All classified data are stored in a designated database to form a standardized sample library with a clear structure and uniform format.

[0040] Understandably, outlier identification filters out abnormal data that does not conform to physical laws or measurement errors, using preset thresholds as objective criteria to avoid subjective misjudgments. For outlier data exceeding the threshold, adjacent valid data is used as replacements instead of simple deletion, ensuring data continuity while avoiding information loss. Normalization processing after retaining valid data eliminates numerical differences between parameters of different dimensions, providing a unified data scale for subsequent model training. Finally, a standardized sample library is formed through categorized storage, achieving not only orderly data management but also providing a directly accessible structured data source for subsequent time-series feature construction.

[0041] Specifically, a time-series input feature sequence of water and sediment evolution is constructed based on a standardized sample library and continuous time step observation data, including: Extract water and sediment data parameters corresponding to continuous time step observation data from a standardized sample library; Calculate the correlation coefficients between the corresponding water and sediment data parameters; When the correlation coefficient is greater than the preset correlation threshold, the water and sediment data parameters are stored in the time series input feature sequence. If the correlation coefficient is less than or equal to the preset correlation threshold, then the water and sediment data parameter is excluded. The stored water and sediment data parameters are arranged in chronological order to form a time-series input feature sequence.

[0042] Specifically, continuous time-step reservoir observation data refers to real-time monitoring data of the reservoir recorded continuously at fixed time intervals (e.g., 2 hours, 1 day). It reflects the dynamic changes in water and sediment conditions over time and is the core basis for defining the time range of characteristic parameters. The preset correlation threshold is a critical value set based on the laws of water and sediment evolution to determine the correlation of parameters. It is used to distinguish between parameters that have a significant impact on water and sediment evolution and those that do not, and is the core criterion for feature selection. The time-series input feature sequence is an ordered data set formed by arranging parameters strongly correlated with water and sediment evolution in chronological order. It can accurately reflect the time-series evolution laws of water and sediment conditions and is the core input for generating multiple future sediment evolution schemes.

[0043] Specifically, the start and end time range and time interval of the continuous time step database observation data are first clearly defined. Using this time range as the search criteria, all relevant parameters, including water level, inflow, outflow, inflow sediment concentration, outflow sediment concentration, and historical cross-sectional measurement data, are extracted from the standardized sample database to form an initial parameter set, ensuring accurate time matching between the parameters and the observation data. The start and end time range and time interval of the continuous time step database observation data can be determined according to the actual situation.

[0044] Specifically, for each parameter in the initial parameter set, a pairwise correlation coefficient is calculated between it and the core target parameters of water and sediment evolution (such as reservoir sedimentation volume and peak sediment concentration at the outflow). The Pearson correlation coefficient method is used to complete the quantitative calculation, obtaining the numerical value of the correlation strength between each parameter and the core target parameters. The calculated correlation coefficients of each parameter are compared one by one with a preset correlation threshold. When the correlation coefficient of a parameter is greater than the preset correlation threshold, the parameter is determined to have a significant impact on water and sediment evolution and is included in the candidate feature set; when the correlation coefficient is less than or equal to the preset correlation threshold, the parameter is determined to be redundant information and is removed from the initial parameter set.

[0045] Specifically, all parameters in the selected candidate feature set are arranged in order according to the chronological sequence of the observation data in continuous time steps, ensuring that the parameter values ​​corresponding to each time node are accurately matched, forming a well-structured and clearly time-series water and sediment evolution time series input feature sequence, thus completing the construction process.

[0046] Understandably, extracting corresponding water and sediment data parameters from a standardized sample library ensures the integrity and standardization of the input data; by calculating correlation coefficients and setting thresholds for filtering, parameters with weak correlation to continuous observation data are eliminated, avoiding interference from irrelevant features in model generation; and by arranging effective parameters in chronological order to form a temporal feature sequence, the temporal dependence of the water and sediment evolution process is preserved, while the temporal correlation of key parameters is strengthened through a dynamic feature selection mechanism.

[0047] Specifically, multiple future sediment evolution schemes are generated based on the time-series input feature sequence and random perturbation vector, including: By using time-series input feature sequences and random perturbation vectors as input samples for a generative adversarial network model, multiple future sediment evolution schemes are generated. The generated multiple future sediment evolution schemes were verified based on preset constraints. If the generated future sediment evolution scheme meets the preset constraints, the scheme will be retained. If the generated future sediment evolution scheme does not meet the preset constraints, the range of values ​​for the random disturbance vector is adjusted, and the future sediment evolution scheme is regenerated. The preset constraints are determined by the basic laws of water and sediment movement in reservoirs, including water balance constraints and sediment conservation constraints.

[0048] Understandably, the random disturbance vector is a vector composed of multiple independent random variables, used to make minor adjustments to some parameters of the time-series input feature sequence within a reasonable range, introducing moderate uncertainty to generate differentiated schemes. Its value range conforms to the basic laws of reservoir water and sediment movement. Each random variable corresponds to a core water and sediment parameter in the time-series input feature sequence (such as water level, inflow, and inflow sediment concentration). The future sediment evolution scheme is a complete scheme based on current and historical water and sediment characteristics, predicting the state of sediment deposition distribution, transport paths, and sediment concentration changes in the reservoir area within a certain future period. It is the core reference for reservoir sediment scheduling decisions. The preset constraints are restrictive conditions determined based on the basic laws of reservoir water and sediment movement, used to ensure the physical rationality of the generated scheme. The core constraints include water balance constraints and sediment conservation constraints, which are the key criteria for judging the effectiveness of the scheme.

[0049] Understandably, the water balance constraint reflects the water supply and demand relationship of a reservoir, ensuring a balance between the total inflow and outflow of water and the changes in reservoir storage within a certain period. Similarly, the sediment conservation constraint reflects the sediment supply and demand relationship within a reservoir, ensuring a balance between the total inflow and outflow of sediment and the amount of sediment deposited within the reservoir within a certain period.

[0050] Understandably, the time-series input feature sequence provides the fundamental laws governing water and sediment evolution, while the random perturbation vector, by introducing small, reasonable parameter fluctuations, simulates uncertainties in future water and sediment movement. The generator of the generative adversarial network model, based on the fused input, learns the laws of water and sediment evolution and generates diverse schemes. The discriminator, by verifying the fit between the schemes and real water and sediment laws, drives the generator to optimize the scheme quality, generating multiple future sediment evolution schemes.

[0051] It is understandable that the future state of water and sediment in the reservoir will be affected by various uncertain factors such as changes in water and sediment inflow and climate fluctuations. The random perturbation vector simulates the above uncertainties by slightly adjusting some parameters of the input features, so that the generated scheme covers different possible scenarios and avoids the limitations of a single scheme. At the same time, its value range is limited to a physically reasonable range to ensure that the perturbation does not deviate from the actual law.

[0052] Specifically, the constructed temporal input feature sequence and random perturbation vector are fused together and integrated into a complete set of input samples according to the input format required by the generative adversarial network (GAN) model. Each component of the random perturbation vector corresponds to a core parameter in the temporal feature sequence, and the perturbation amplitude is controlled within the range that does not violate the physical operating limits of the reservoir. The integrated input samples are then fed into the GAN model. Based on the temporal patterns and perturbation information in the input samples, the model generator generates multiple differentiated future sediment evolution schemes. Each scheme includes key indicator data such as reservoir sedimentation volume, sedimentation distribution, changes in outflow sediment concentration, and flow velocity distribution within a certain future period. The number of schemes is set according to actual scheduling needs. The water balance data (inflow, outflow, and changes in water storage) and sediment balance data (inflow sediment concentration, outflow sediment concentration, and sedimentation) are extracted from each generated scheme to verify whether they meet the water balance constraints and sediment conservation constraints.

[0053] Understandably, water balance verification is achieved by calculating the difference between the total inflow and outflow of water and the change in water storage, while sediment conservation verification is achieved by calculating the difference between the total inflow and outflow of sediment and the amount of siltation.

[0054] Specifically, when the water volume and sediment balance differences of a given scheme are both within the preset allowable error range, the scheme is deemed to meet the constraints and is retained. When any difference exceeds the preset allowable error range, the scheme is deemed to not meet the constraints and is eliminated. The proportion of eliminated schemes is statistically analyzed. If the proportion exceeds a preset threshold, the range of values ​​for the random disturbance vector is adjusted to reduce the disturbance amplitude that may cause the scheme to violate the constraints. The adjusted vector is then re-fused with the time-series input feature sequence and fed back into the model to generate schemes. This verification and adjustment process is repeated until the number of retained valid schemes meets the requirements.

[0055] Specifically, the process of integrating the collected data to form a complete set of input samples is as follows: Collect historical and real-time data from the reservoir, including: inflow sediment concentration. Inbound flow The distance from the dam corresponding to the current water level Historical cross-sectional measurement data Location of the intrusion point of the density current and current water level All data are continuous time series. The data sampling interval is fixed at 2 hours to ensure the stability and consistency of the time series.

[0056] Core input features were selected from the time-series input feature sequence, including: water level, inflow / outflow rate, inflow / outflow sediment concentration, and historical cross-sectional measurement data. Input samples were constructed using a sliding window technique, and the input sample calculation formula is as follows:

[0057] Where T is the time length and F is the number of features. Indicates at time step The set of input samples at time t; each sample contains data from... to Multidimensional feature sequences within a continuous time window; Indicates the first Each input feature at time step The observed value at that time; It is a feature index, with a value range of 100%. .

[0058] For each training sample, input the hydrodynamic conditions and sediment state of the past T time steps, and generate N sediment scheduling schemes for the next H time steps: in, Indicates coverage of the future A time-step sequence of spatiotemporal sediment evolution;

[0059] Indicates the first The solution will be available in the future. Step (relative to reference time) At spatial grid points The sediment content or siltation value at the location; x, y represent the spatial grid index.

[0060] Specifically, the generator for the generative adversarial network model is designed as follows: The generator G receives a preprocessed historical hydrodynamic condition feature matrix. and set of random perturbation vectors As input, output a sequence of N future sediment evolution schemes for the next H steps: in, For the length of a historical time step, For feature dimensions; The number of generated solutions is H steps in length for each solution sequence; Different random perturbation vectors are used to introduce scheme diversity, with components independently sampled from the standard normal distribution. ; This represents the preprocessed historical hydrodynamic condition feature matrix; Indicates the first The spatiotemporal sediment evolution sequence output by the set generation scheme; Represents a random perturbation vector; This is the generator parameter set, including convolutional kernel weights, fully connected layer weights, and biases.

[0061] The generator internally employs a convolutional neural network (CNN) to extract spatiotemporal features from the historical input matrix. After each convolution, batch normalization and ReLU activation are applied, followed by transpose convolution to restore the original time step length or spatial resolution, ensuring the accuracy of the output scheme.

[0062] Specifically, the discriminator of the generative adversarial network model is designed as follows: Discriminator D is used to distinguish between generated schemes and real data, and outputs the probability of being true or false:

[0063] Among them, input The input is a sediment distribution matrix, which can be based on actual observation data. Or the predicted sediment distribution matrix output by the generator The discriminator structure consists of convolution + pooling + fully connected layers, extracting spatial features and outputting probability values; the set of learnable parameters for the discriminator D is denoted as... The discriminator input includes convolutional kernel weights, fully connected layer weights, bias vectors, and other adjustable network parameters, used to distinguish the sediment scheme output by the generator from real historical sediment distribution data. Or actual sediment observation data The output is a single scalar representing the probability that the input is real data.

[0064] The discriminator is trained to maximize its ability to distinguish between real and generated data, and its adversarial loss function is defined as:

[0065] in, Represents the adversarial loss function; Represents a generator function; Represents the natural logarithm function; This represents the discriminator output function; This represents historical measured data on sediment distribution. This represents the sediment scheme generated by the generator; This represents the historical input feature matrix; T is the time step; F is the number of features; It is a random perturbation vector; This represents the sampling distribution of the measured data; This represents the expectation operator.

[0066] Through the Through optimized training, the discriminator continuously improves its ability to distinguish between real and generated sediment scenarios, thereby driving the generator to generate more physically reasonable and diverse sediment evolution scenarios.

[0067] Specifically, the physical constraint design of the generative adversarial network model is as follows: Introducing physical constraint loss in generator training This measures the deviation between the sediment distribution scheme output by the generator and the theoretical physical calculation results, ensuring that the sediment generated by the generator conforms to hydrodynamic laws in terms of total amount, flow velocity, and deposition pattern, and avoiding physically unreasonable abnormal accumulation or loss. The formula for calculating physical constraint loss is:

[0068] in, The vertical grid number of the reservoir simulation grid (along the dam height or the longitudinal direction of the river channel); This represents the number of horizontal grid points in the reservoir simulation grid (along the width of the reservoir). For vertical grid indexing; Horizontal grid index; This represents the sediment content at the grid point of the first sediment dispatch scheme output by the generator at time step; This represents the theoretical value calculated based on the hydrodynamic model.

[0069] It is understandable that the calculation of this physical constraint loss is used to guide the generation scheme by utilizing prior physical knowledge during the training of the generator, thereby avoiding unrealistic situations such as abnormal sediment accumulation or abnormal flow velocity, and improving the feasibility and reliability of the generated scheme in reservoir regulation.

[0070] Specifically, after setting physical constraints, to ensure the generator can output multiple differentiated sediment evolution schemes, a multi-scheme difference constraint is used to verify the generated multiple future sediment evolution schemes. The mathematical form of the multi-scheme difference constraint is defined as follows:

[0071] in, Indicates the generator at time step The output of the first A sediment management scheme at the grid point The sand content; Indicates the generator at time step The output of the first A sediment management scheme at the grid point The sand content; This indicates the number of sediment scheduling schemes generated by the generator under the same input conditions; This represents the Frobenius norm, used to measure the overall difference in spatial distribution between two schemes.

[0072] Understandably, this multi-scheme difference constraint, under the same hydrodynamic conditions, guides the generator to output multi-scheme results with significant differences, thereby supporting multi-scenario simulation and comparison of control strategies.

[0073] Specifically, to enable the generator to output diverse sediment evolution schemes while ensuring physical rationality, a generator total loss function of the generative adversarial network model is adopted, which combines adversarial loss, physical constraint loss, and multi-scheme difference constraint. The generator total loss function is defined as follows:

[0074] in, This represents the total loss of the generator; This is the adversarial loss between the generator and the discriminator, used to guide the generator to produce sediment schemes that match the real data distribution; To constrain physical losses, based on sediment conservation and hydrodynamic laws, the total sediment volume, flow velocity distribution, and sedimentation pattern of the generated scheme are constrained to ensure that the output results meet the requirements of the hydrodynamic model and actual control. To constrain the differences among multiple schemes, the spatial distribution differences between different generation schemes are calculated. and As adjustable parameters, the weights of physical constraints and scheme diversity constraints in the total loss are controlled separately. This is achieved by minimizing... The generator can output a variety of differentiated sediment solutions while meeting physical rationality requirements, ensuring the authenticity of the solutions and supporting multi-scenario analysis and control strategy optimization.

[0075] Understandably, using time-series input feature sequences as input samples for generative adversarial networks (GANs) can inherit the characteristic expressions of historical water and sediment evolution patterns, ensuring the correlation between generated schemes and the actual operating state of the reservoir. Introducing random perturbation vectors breaks the single prediction path through parameter perturbation mechanisms, enabling the parallel generation of multiple schemes. Verifying the generated schemes by pre-setting water balance and sediment conservation constraints directly eliminates invalid schemes that violate physical conservation laws, avoiding the problem of unreasonable results generated by traditional data-driven models. Adjusting the range of random perturbation vector values ​​when a scheme fails to meet constraints preserves the diversity advantage of GANs while improving the efficiency of generating effective schemes through dynamic parameter space adjustment. The combined application of water balance and sediment conservation constraints ensures that the generated schemes conform to the basic physical laws of water and sediment movement in the reservoir from both mass and momentum conservation dimensions.

[0076] Specifically, generating multiple future sediment evolution scenarios also includes: For each generated future sediment evolution scheme, the corresponding generation time and input samples are labeled; After generating multiple future sediment evolution schemes, the schemes are deduplicated. Key indicator data from multiple future sediment evolution schemes were extracted, and the differences in key indicators between each pair of schemes were calculated. When the difference in key indicators between two schemes is less than the preset similarity threshold, the two schemes are determined to be similar, and the scheme generated later is deleted. When the difference in key indicators between two schemes is greater than or equal to the preset similarity threshold, the two schemes are determined to be dissimilar and both are retained. Key indicators include annual siltation in the reservoir area and peak sediment concentration at the outlet.

[0077] Understandably, after each future sediment evolution scheme is generated and output, its generation time is automatically recorded, and the corresponding input samples are stored in association, namely the complete data of the time sequence input feature sequence and random perturbation vector. The generation time and input sample information are used as metadata and bound to the key indicator data of the scheme, forming a one-to-one correspondence between "scheme-label information" to ensure that the source and generation order of each scheme can be traced.

[0078] Understandably, key indicator data for each of the multiple labeled future sediment evolution schemes are extracted to form a simplified data set of "scheme number - annual sedimentation volume in the reservoir area - peak sediment concentration at the outflow", which facilitates subsequent difference calculation and comparison.

[0079] Specifically, the schemes in the simplified dataset are paired up, and for each pair of schemes to be compared, the absolute difference of two key indicators is calculated, namely, "the absolute value of the annual siltation in the reservoir area of ​​Scheme A - the absolute value of the annual siltation in the reservoir area of ​​Scheme B" and "the absolute value of the peak sediment concentration at the outlet of Scheme A - the absolute value of the peak sediment concentration at the outlet of Scheme B". The two differences are used as the comprehensive difference measurement result of the two schemes.

[0080] Understandably, the overall difference between each pair of schemes is compared with a preset similarity threshold. When the overall difference is less than the preset similarity threshold, the two schemes are considered similar. In this case, the scheme generated earlier is retained, and the redundant scheme generated later is deleted. When the overall difference is greater than or equal to the preset similarity threshold, the two schemes are considered dissimilar, and both are retained for subsequent processes. The above pairwise comparison process is repeated until all schemes are deduplicated, forming a set of future sediment evolution schemes that are free of redundancy and significantly differentiated.

[0081] Understandably, the deduplication process, through key indicator difference calculations and similarity threshold determination, effectively eliminates redundant schemes and retains feasible schemes with significant differences. Among these, the annual siltation volume and peak sediment concentration at the outflow point serve as key indicators, directly reflecting the impact of sediment dispatch on reservoir capacity safety and downstream ecology, ensuring that scheme selection focuses on core business needs. Similar schemes generated later are deleted, while earlier generated schemes are retained, reducing redundant computational resource consumption and avoiding fluctuations in scheme quality due to random perturbation vector adjustments. The introduction of a preset similarity threshold achieves quantitative control over scheme differentiation, balancing the contradiction between scheme diversity and feasibility.

[0082] Specifically, feature extraction and structured representation are performed on multiple future sediment evolution schemes to generate corresponding control strategies for these schemes, including: Based on a large language model, feature encoding is performed on multiple future sediment evolution schemes, key features are extracted and structured representation is performed to generate structured features; Semantic parsing of structured features generates a solution strategy mapping relation library; Based on the scheme strategy mapping relationship library, multiple sets of corresponding control strategies for future sediment evolution schemes are generated.

[0083] Specifically, the raw data from multiple future sediment evolution scenarios, including numerical data such as sedimentation volume and sediment concentration, textual descriptions such as "sedimentation is concentrated in the reservoir tail area," and spatial data such as velocity field distribution, are input into a large language model. The model automatically identifies and extracts key features of each scenario through feature encoding, such as total sedimentation volume, location of high sedimentation areas, peak sediment concentration periods, and areas of abnormal velocity. Subsequently, the extracted key features are organized according to a preset structure, such as "feature type - feature value - feature description," to generate structured features, such as in a table format: "Feature type: sedimentation area; Feature value: reservoir tail; Feature description: area percentage 35%," ensuring that the feature information format of each scenario is consistent.

[0084] Specifically, the generated structured features are analyzed set by set. The large language model, based on pre-trained water and sediment scheduling knowledge, interprets the scheduling requirements reflected by the features, such as "peak sediment concentration at outflow is 1.2 kg / m³". 3 The phrase "appearing in August" is interpreted as "the gate opening degree needs to be optimized before August to control the peak value of sediment content in the outflow". The "feature-demand-strategy" relationship obtained from the analysis, such as "high sedimentation → enhanced sediment discharge → gate opening degree increased by 20%", is processed into rules and stored according to feature type to form a solution strategy mapping relationship library. Each rule in the library contains feature conditions, corresponding strategy content and applicable scenario description.

[0085] Specifically, the large language model searches the scheme strategy mapping relationship library for the structured features of each future sediment evolution scheme, and matches association rules that are completely or highly consistent with the feature conditions. Based on the matched rules, it extracts the corresponding control strategy content, such as gate opening adjustment value, sediment discharge start / end time, and flow distribution ratio of each outlet. The extracted strategy content is integrated according to execution priority, such as "adjust gate opening first, then determine sediment discharge sequence", to generate a complete control strategy exclusive to each scheme, ensuring that the strategy is accurately adapted to the sediment evolution characteristics of the scheme.

[0086] Understandably, feature encoding based on large language models can extract implicit key features from complex sediment evolution schemes, such as physical indicators like sedimentation trends and sediment transport capacity, and transform them into machine-processable structured features. Semantic parsing of these structured features, through the establishment of a mapping database between schemes and strategies, transforms technical parameters into operable scheduling instructions. Control strategies are generated based on this mapping database, ensuring that the generated strategies not only conform to sediment movement patterns but also match actual scheduling needs through semantic-level correlation, such as the coordinated control of gate operation and sediment discharge timing. This process overcomes the limitations of traditional methods that rely on human experience to formulate strategies, significantly improving the rationality of strategy generation and decision-making efficiency through automated feature encoding and semantic parsing.

[0087] Specifically, the control strategies include gate opening, sediment discharge timing, and flow allocation. The control strategy was tested, including: If the gate opening adjustment value of the control strategy is within the preset opening range, the gate opening adjustment verification is passed; otherwise, it fails. If the sediment discharge timing of the regulation strategy does not overlap or conflict with the reservoir's preset functional time period, the sediment discharge timing verification will pass; otherwise, it will fail. If the scheduling flow allocation of the control strategy satisfies the condition that the total inbound flow equals the sum of the outbound flows, then the scheduling flow allocation verification is passed; otherwise, it fails. Only control strategies that pass all three checks will be retained, while other control strategies will be removed.

[0088] Specifically, the preset opening range is an operable range (e.g., "0~80%", where 0 represents fully closed and 80% represents the maximum safe opening) determined based on the gate equipment's technical parameters (such as mechanical structure limits and safe operating thresholds). Adjustments outside this range may lead to equipment damage or operational risks.

[0089] Specifically, the gate opening adjustment value is extracted from the control strategy, the preset opening range of the gate is retrieved, the actual opening after adjustment is calculated, and it is determined whether it falls completely within the preset range. If the adjusted opening is within the range, the verification passes; if it exceeds the range, the verification fails.

[0090] Extract the start and end times of the sediment discharge sequence from the regulation strategy (e.g., "June 10th - June 20th"); retrieve the list of preset functional time periods for the reservoir (e.g., "June 1st - June 30th is the flood control preparation period, requiring low water levels"; "July 1st - September 30th is the main flood season, prohibiting large-scale sediment discharge"); through time interval overlap analysis, determine whether the sediment discharge sequence overlaps with any preset functional time period. If there is no overlap (e.g., the sediment discharge sequence is in October, avoiding all function-specific time periods), the verification passes; if there is overlap (e.g., the sediment discharge sequence June 10th - 20th overlaps with the flood control preparation period), the verification fails.

[0091] Extract specific data on the scheduling flow allocation in the control strategy: Total inflow Qin (e.g., 1200m2) 3 / s), the flow rate at each outlet is Q1 (power generation, 500m³). 3 / s), Q2 (flood discharge, 400m) 3 / s), Q3 (water supply, 300m 3 / s); Calculate the sum of the outflow rates Qout = Q1 + Q2 + Q3 (500 + 400 + 300 = 1200 m³ / s); 3 ( / s); compare the difference between Q-in and Q-out. If the difference is within the preset allowable error range, the verification passes; if the difference exceeds the error range, the verification fails.

[0092] Understandably, control strategies that pass the above three checks are deemed "feasible strategies" and retained; strategies that fail any one of the checks (such as excessive gate opening, sand discharge timing conflicts, or unbalanced flow distribution) are deemed "infeasible strategies" and eliminated. Ultimately, only strategies that pass all three checks are retained for the subsequent risk scoring stage.

[0093] Understandably, the gate opening adjustment verification limits the safety boundary of mechanical operation by setting a preset opening range, avoiding the risk of equipment damage caused by gate over-limit operation; the sediment discharge sequence verification is based on the functional time period division of the reservoir to prevent sediment discharge operation from overlapping with the time periods of water supply, power generation and other functions, and to ensure the realization of the comprehensive benefits of the reservoir; the scheduling flow allocation verification is verified by the water balance equation to ensure the conservation of inflow and outflow flow, and to avoid abnormal water level fluctuations caused by flow allocation imbalance.

[0094] Specifically, risk scores are assigned to regulation strategies, and the final future sediment evolution plan and its corresponding regulation strategies are determined based on the risk scores, including: When the control strategy is determined to be feasible, obtain the risk indicators of all feasible control strategies. The risk indicators include the increase in siltation, the proportion of abnormal flow velocity, and the amplitude of water level fluctuation. The risk score of the control strategy is obtained by weighting and summing the three risk indicators according to the preset weights. All feasible control strategies are ranked according to their risk score values. The control strategy with the lowest risk score is selected as the corresponding strategy for the final future sediment evolution scheme; When the risk scores of the control strategies are equal, the control strategies are sorted from smallest to largest based on the accumulation increment. The control strategy that minimizes the increase in sedimentation is selected as the corresponding strategy for the final future sediment evolution scheme; The sum of the weights for the increase in siltation, the proportion of abnormal flow velocity, and the amplitude of water level fluctuations is 1.

[0095] Understandably, by acquiring three risk indicators—accumulation increment, percentage of abnormal flow velocity, and water level fluctuation—core elements such as sedimentation, flow stability, and water level safety are incorporated into a unified evaluation framework, overcoming the limitations of traditional methods that focus only on a single indicator. Using preset weights to weight and sum the three indicators reflects the differences in the impact of various risk factors on the scheduling strategy, while mathematical constraints (the sum of weights being 1) ensure the objectivity and interpretability of the scoring system. The strategy of selecting the strategy with the highest risk score achieves automatic optimization based on minimizing comprehensive risk, avoiding the inefficiency of manual comparison. For cases with identical scores, a supplementary rule of sorting by sedimentation increment strengthens the core objective of long-term reservoir operation—sediment control—ensuring that strategies with less sedimentation impact are prioritized when risks are comparable. This hierarchical, multi-condition decision-making logic satisfies physical constraints while balancing the economy and safety of the scheduling strategy.

[0096] Specifically, the process of generating corresponding control strategies for multiple future sediment evolution scenarios is as follows: The generator outputs multiple future sediment evolution schemes With the corresponding historical hydrodynamic feature matrix Through feature coding function Convert to vector representation As input to the LLM:

[0097] in, Indicates time step t in the grid The location's sediment content or siltation depth; This represents the hydrodynamic characteristics over the past T time steps (such as water level, inflow / outflow, inflow / outflow sediment concentration, historical cross-sectional measurement data, etc.). For feature dimensions; It is a feature mapping function that transforms high-dimensional spatiotemporal distribution information into structured vectors suitable for language model processing.

[0098] Specifically, based on the coding results, LLM calculates a comprehensive score for each future sediment evolution scheme, and then verifies each scheme based on the comprehensive score. The formula for calculating the comprehensive score is as follows:

[0099] in, The physical rationality scoring function calculates the deviation of the scheme at each grid point based on sediment conservation, hydrodynamic laws, and siltation patterns, ensuring that the generated scheme conforms to the physical prior constraints. To adjust the risk scoring function, assess the potential risks of high water levels, gate overload, or scouring that the sediment dispatching scheme may cause; A scoring function is used to match historical experience, and the feasibility of the scheme is measured by the similarity with historical control schemes and observation data. w1, w2 and w3 are weight coefficients, and w1+w2+w3=1 are used to adjust the relative importance of each scoring index. They can be flexibly set according to specific control objectives.

[0100]

[0101] in, This represents the sediment distribution value output by the generator at time step t, which is a dimensionless value. A high-dimensional vector; These are the theoretical sediment distribution values ​​calculated using a hydrodynamic model. These represent the horizontal and vertical indices of the spatial grid, respectively. To prevent division by zero of small constants; The closer the value is to 1, the more the generation scheme conforms to the laws of physics.

[0102]

[0103] in, As a high water level risk indicator, calculate the probability that the water level before the gate will exceed the safety threshold under the sediment generation scheme; The gate overload risk indicator is calculated based on the ratio of gate flow rate to safe flow rate. As a local scour risk indicator, the potential scour volume is calculated based on the sediment concentration gradient and flow velocity; α1, α2 and α3 are weighting coefficients; The closer the value is to 1, the more the generation scheme conforms to the laws of physics.

[0104]

[0105] in, This refers to historical observation or control scheme data; This represents the Frobenius norm, used to measure the overall difference in spatial distribution between two schemes. This is a scale parameter used to control similarity sensitivity; The closer the value is to 1, the more consistent the generation scheme is with historical experience.

[0106] After generating multiple future sediment evolution schemes and verifying their high consistency with historical experience, LLM (Limited Ledger Model) is based on the characteristics of the encoded schemes. Automatically generate multiple executable control strategies corresponding to future sediment evolution scenarios. :

[0107] in, This is a set of executable scheduling instructions, including gate opening, water level regulation, sediment discharge methods and timing arrangements. This strategy balances reservoir operational safety, sediment discharge efficiency, and downstream ecological water demand, providing direct operational guidance for actual scheduling.

[0108] Understandably, introducing a Large Language Model (LLM) as an intelligent analysis and policy generation engine allows for semantic parsing, comprehensive scoring, and policy adjustment output of multiple solutions generated by Generative Adversarial Networks (GANs), achieving multi-dimensional evaluation and decision support based on "physical rationality + risk control + historical experience matching".

[0109] Specifically, this involves acquiring reservoir water and sediment data after determining the final future sediment evolution plan and its corresponding regulation strategies, comparing it with preset water and sediment data standards, and generating a quantitative implementation assessment report based on the comparison results, including: Collect actual reservoir water and sediment data after the final control strategy is implemented; Calculate the deviation between the actual reservoir water and sediment data and the preset water and sediment data standard; When the deviation values ​​of all actual reservoir water and sediment data are less than the preset deviation value, the report will mark that the final future sediment evolution scheme and its corresponding control strategy have met the standard, and record the corresponding deviation values ​​of all actual reservoir water and sediment data. When the deviation value of any actual reservoir water and sediment data is greater than or equal to the preset deviation value, the report will mark that the final future sediment evolution scheme and its corresponding control strategy are not up to standard, and record the actual reservoir water and sediment data of the item exceeding the standard, the corresponding deviation value and the cause of the deviation.

[0110] Specifically, after the final control strategy is initiated, the data acquisition function of the reservoir monitoring system is simultaneously activated. Core water and sediment data, such as actual reservoir sedimentation, outflow sediment concentration, reservoir water level, total inflow, outflow from each reservoir, and flow velocity at key sections of the reservoir, are continuously collected at a preset sampling frequency (e.g., hourly or daily, adjusted according to parameter importance). After the strategy execution cycle ends, data collection is stopped and all data are aggregated to form a complete actual water and sediment dataset, ensuring data coverage throughout the entire period of strategy execution.

[0111] Specifically, monitoring data such as inflow sediment concentration, water level, outflow, and key section siltation are extracted one by one from the actual water and sediment dataset. These data are then compared with the project indicators predicted by the generator. By calculating the total sediment volume error, section siltation deviation, and gate scheduling safety indicators, a quantitative execution evaluation deviation value is formed. The deviation value calculation method is selected according to the parameter type, and the deviation value for each corresponding parameter is calculated one by one, forming a comparison list of "parameter name - actual value - standard value - deviation value". The deviation values ​​of all parameters in the comparison list are compared with the preset deviation values ​​one by one. If the deviation values ​​of all parameters are less than the preset deviation values, the final future sediment evolution plan and its corresponding control strategy are deemed "compliant"; if the deviation value of any parameter is greater than or equal to the preset deviation value, it is deemed "non-compliant".

[0112] Specifically, if the standard is met, the report should clearly mark the met status and record in detail the names, actual values, standard values, and corresponding deviation values ​​of all parameters in the comparison list, without adding any additional causal analysis. If the standard is not met, the report should mark the non-compliant status and record in detail the names, actual values, standard values, and deviation values ​​of the parameters that exceed the standard. At the same time, the specific causes of the deviations should be analyzed in conjunction with the water and sediment movement patterns, strategy implementation records, and changes in the external environment (such as sediment inflow and rainfall) (e.g., "the deviation of the reservoir sedimentation exceeds the standard, the cause is that the actual sediment inflow exceeds the predicted value by 30%"). Finally, the above content should be organized according to the preset format to form a complete and data-rich quantitative implementation evaluation report.

[0113] Specifically, the deviation value is calculated as follows:

[0114] in, Key indicators of the reservoir observed (inflow sediment content, cross-sectional siltation, etc.); To generate predicted values ​​for the proposed scheme; The control strategy to be implemented; For risk deviation indicators (such as high water level, gate overload, etc.); This is a physical constraint deviation index; , , These are the weighting coefficients.

[0115] Understandably, calculating deviation values ​​visualizes the difference between the execution results and the expected goals, avoiding the ambiguity of qualitative descriptions and improving the objectivity and accuracy of the assessment. Clear rules based on preset deviation values ​​ensure consistency in assessments across different scenarios and personnel, enhancing the reference value of the assessment report. Recording the causes of deviations in substandard situations allows for precise identification of the root causes, making subsequent strategy adjustments or solution optimizations more targeted. Quantitative assessment promptly identifies deviations exceeding standards, preventing risks such as reservoir capacity loss and hydrodynamic imbalances caused by deviations from expected execution results. This deviation-based assessment report generation method not only addresses the lack of execution result verification in traditional methods but also provides a clear direction for iterative optimization of the solution by structurally recording the causes of deviations.

[0116] Specifically, the final future sediment evolution scheme and its corresponding regulation strategy will be used as a new standardized sample library to generate multiple future sediment evolution schemes. During iterative optimization, the optimization objective will be:

[0117]

[0118] in, Indicates the first The parameter set of the generator at the next iteration; Indicates the first The generator parameter set updated in the next iteration; Indicates the first In the iteration, the first step of the scheme evaluation function Each weighting coefficient; Indicates the updated number Each weighting coefficient; The learning rate; Describe the iterative objective function; This represents the gradient of the objective function with respect to the generator parameters; Indicates the objective function for the th The gradient of each weight coefficient; This represents the number of iterations.

[0119] Understandably, based on the evaluation report, the parameters of the generator and the weighting coefficients in the scheme scoring mechanism can be iteratively optimized. This allows the generator to more accurately predict key hydrological indicators in the next round of scheme generation and generate multi-scheme control strategies that conform to physical laws, have controllable risks, and are reasonable based on historical experience. Iterative optimization can be carried out in multiple cycles during each scheduling cycle or key flood season to continuously improve the accuracy of the generated schemes and the safety of strategy execution.

[0120] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0122] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0123] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A reservoir sediment scheduling multi-scheme generation method based on a generative adversarial network and a large language model, characterized in that, The method comprises the following steps: Collecting water and sediment data of current water dynamic conditions and sediment state of the reservoir, and preprocessing the water and sediment data to generate a standardized sample library; According to the standardized sample library and the continuous time step reservoir observation data, a time sequence input feature sequence of water and sediment evolution is constructed; According to the time sequence input feature sequence and the random disturbance vector, multiple future sediment evolution schemes are generated; Feature extraction and structured representation are performed on the multiple future sediment evolution schemes to generate corresponding control strategies of the multiple future sediment evolution schemes; The control strategies are tested and risk scored, and the final future sediment evolution scheme and its corresponding control strategy are determined according to the risk score; After obtaining the water and sediment data of the final future sediment evolution scheme and its corresponding control strategy, the data are compared with the preset water and sediment data standard, and a quantitative execution evaluation report is generated according to the comparison result; The final future sediment evolution scheme and its corresponding control strategy are used as a new standardized sample library to generate multiple future sediment evolution schemes for iterative optimization.

2. The reservoir sediment scheduling multi-scheme generation method based on a generative adversarial network and a large language model according to claim 1, characterized in that, Collecting water and sediment data of current water dynamic conditions and sediment state of the reservoir, including: Real-time collection of water level, inflow, outflow, inflow sediment concentration and outflow sediment concentration data of the reservoir; Accessing historical cross-section measurement data from a historical database; When the collected data is missing, interpolation method is used to supplement the missing data; When the collected data has no missing data, data preprocessing is directly performed.

3. The reservoir sediment scheduling multi-scheme generation method based on a generative adversarial network and a large language model according to claim 2, characterized in that, The water and sediment data are preprocessed to generate a standardized sample library, including: Abnormal value identification is performed on the water and sediment data; When the identified data exceeds the preset abnormal value judgment threshold, the abnormal data is excluded and replaced by adjacent valid data; When the identified data is within the preset abnormal value judgment threshold, the data is retained; The retained data is normalized; The normalized water and sediment data are classified and stored to generate a standardized sample library.

4. The reservoir sediment scheduling multi-scheme generation method based on a generative adversarial network and a large language model according to claim 3, characterized in that, According to the standardized sample library and the continuous time step reservoir observation data, a time sequence input feature sequence of water and sediment evolution is constructed, including: Extracting water and sediment data parameters corresponding to the continuous time step reservoir observation data from the standardized sample library; Calculating the correlation coefficients between the corresponding water and sediment data parameters; When the correlation coefficient is greater than the preset correlation threshold, the water and sediment data parameter is stored in the time sequence input feature sequence; When the correlation coefficient is less than or equal to the preset correlation threshold, the water and sediment data parameter is excluded; The stored water and sediment data parameters are arranged in time sequence to form a time sequence input feature sequence.

5. The reservoir sediment scheduling multi-scheme generation method based on a generative adversarial network and a large language model according to claim 4, characterized in that, According to the time sequence input feature sequence and the random disturbance vector, multiple future sediment evolution schemes are generated, including: The time sequence input feature sequence and the random disturbance vector are used as input samples of a generative adversarial network model to generate multiple future sediment evolution schemes; The generated multiple future sediment evolution schemes are verified according to the preset constraint conditions; When the generated future sediment evolution scheme meets the preset constraint conditions, the scheme is retained; When the generated future sediment evolution scheme does not meet the preset constraint conditions, the value range of the random disturbance vector is adjusted, and the future sediment evolution scheme is re-generated; The preset constraint condition is a constraint condition determined by basic rules of reservoir water and sediment movement, including a water balance constraint and a sediment conservation constraint.

6. The reservoir sediment scheduling multi-scheme generation method based on a generative adversarial network and a large language model according to claim 5, characterized in that, Generating multiple sets of future sediment evolution schemes also includes: Labeling the generated each set of future sediment evolution scheme with the corresponding generation time and input sample; After generating multiple sets of future sediment evolution schemes, the multiple sets of future sediment evolution schemes are processed for deduplication; Extracting key indicator data of multiple sets of future sediment evolution schemes, and calculating the difference of key indicators between each two sets of schemes respectively; When the difference of key indicators of two sets of schemes is less than a preset similarity threshold, it is determined that the two sets of schemes are similar, and the scheme generated later is deleted; When the difference of key indicators of two sets of schemes is greater than or equal to the preset similarity threshold, it is determined that the two sets of schemes are not similar, and all are retained; The key indicator data includes annual deposition amount in the reservoir area and peak outflow sediment concentration.

7. The reservoir sediment scheduling multi-scheme generation method based on a generative adversarial network and a large language model according to claim 6, characterized in that, Feature extraction and structured representation are performed on the multiple sets of future sediment evolution schemes to generate corresponding control strategies of the multiple sets of future sediment evolution schemes, including: Based on a large language model, the multiple sets of future sediment evolution schemes are feature-encoded, key features are extracted and structured representation is performed to generate structured features; Semantic analysis is performed on the structured features to generate a scheme strategy mapping relationship library; According to the scheme strategy mapping relationship library, corresponding control strategies of the multiple sets of future sediment evolution schemes are generated.

8. The reservoir sediment scheduling multi-scheme generation method based on a generative adversarial network and a large language model according to claim 7, characterized in that, The control strategies include gate opening, desilting timing and dispatching flow allocation; The control strategies are tested, including: When the gate opening adjustment value of the control strategy is within the preset opening range, the gate opening adjustment verification is passed, otherwise it is not passed; When the desilting timing of the control strategy has no overlap conflict with the preset function period of the reservoir, the desilting timing verification is passed, otherwise it is not passed; When the dispatching flow allocation of the control strategy satisfies that the total inflow flow is equal to the sum of all outflow flows, the dispatching flow allocation verification is passed, otherwise it is not passed; Only the control strategies that pass all three verifications are retained, and other control strategies are excluded.

9. The reservoir sediment scheduling multi-scheme generation method based on a generative adversarial network and a large language model according to claim 8, characterized in that, Risk scores are assigned to the control strategies, and the final future sediment evolution scheme and its corresponding control strategy are determined according to the risk scores, including: When it is determined that the control strategy is feasible, risk indicators of all feasible control strategies are obtained, the risk indicators include deposition increment, flow rate anomaly proportion and water level fluctuation amplitude; The three risk indicators are weighted and summed according to preset weights to obtain the risk score of the control strategy; All feasible control strategies are sorted according to the numerical value of the risk score; The control strategy with the smallest risk score value is selected as the corresponding strategy of the final future sediment evolution scheme; When the risk score values of the control strategies are equal, the control strategies are sorted according to the deposition increment from small to large; The control strategy with the smallest deposition increment is selected as the corresponding strategy of the final future sediment evolution scheme.

10. The reservoir sediment scheduling multi-scheme generation method based on a generative adversarial network and a large language model according to claim 9, characterized in that, After obtaining the final future sediment evolution scheme and its corresponding control strategy, the reservoir water and sediment data are compared with the preset water and sediment data standard, and a quantitative execution evaluation report is generated according to the comparison result, including: Actual reservoir water and sediment data after execution of the final control strategy are collected; Calculate deviation values of the actual reservoir water and sediment data from the preset water and sediment data standard; When the deviation values of all actual reservoir water and sediment data are less than the preset deviation value, mark the final future sediment evolution scheme and its corresponding regulation strategy as meeting the standard in the report, and record the corresponding deviation values of all actual reservoir water and sediment data; When the deviation value of any actual reservoir water and sediment data is greater than or equal to the preset deviation value, mark the final future sediment evolution scheme and its corresponding regulation strategy as not meeting the standard in the report, and record the actual reservoir water and sediment data that exceeds the standard, the corresponding deviation value and the cause of deviation.

Images