A water conservancy project river management system

Abstract

The present application discloses a water conservancy project river management system, which belongs to the field of water conservancy engineering technology. The system includes the following modules: a sampling point optimization module, which determines the spatial and temporal location point distribution plan of sediment sampling based on the river topography, hydrological conditions and historical sediment data; an intelligent sampling module, which autonomously decides to select the optimal sampling strategy according to the point distribution plan, and implements efficient and accurate unmanned sampling operations; a sediment analysis and prediction module, which conducts in-depth analysis of the spatial and temporal distribution and migration laws of river sediment, and predicts the sediment change trend in the future period; an intelligent scheduling module, which combines sediment prediction results with real-time hydrological conditions, intelligently schedules river management facilities and equipment, optimizes resource allocation, and improves engineering management efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of water conservancy engineering, and more specifically, to a water conservancy engineering river management system. Background Art

[0002] With the continuous advancement of urbanization and the increase in human activities, water resource management and environmental governance of rivers are particularly important. However, in traditional water conservancy engineering river management, there are problems such as complex sediment sampling process, difficulty in selecting sampling points, and insufficient accuracy and real-time performance of sampling data. Current sediment sampling work often relies on manual experience or simple mechanical operations. This method is not only inefficient, but also often fails to fully cover all areas of the river, resulting in low representativeness and accuracy of sampling data. In addition, due to the complexity of the distribution and migration of river sediment, the scheduling of governance facilities also faces great challenges and cannot effectively adapt to sediment change trends and real-time hydrological conditions.

[0003] Therefore, there is an urgent need for a new type of water conservancy project river management system to solve the various problems existing in traditional methods, so as to achieve comprehensive monitoring and management of river sediment and improve water resource utilization efficiency and environmental protection level. Summary of the invention

[0004] In order to overcome a series of defects in the prior art, the purpose of this application is to provide a water conservancy project river management system for the above problems, including:

[0005] The sampling point optimization module determines the spatial and temporal location of sediment sampling based on river topography, hydrological conditions and historical sediment data;

[0006] The intelligent sampling module can independently select the optimal sampling strategy based on the sampling plan and implement efficient and accurate unmanned sampling operations;

[0007] The sediment analysis and prediction module conducts in-depth analysis of the temporal and spatial distribution and migration patterns of river sediment, and predicts the sediment change trend in the future period;

[0008] The intelligent scheduling module combines sediment prediction results with real-time hydrological conditions to intelligently schedule river management facilities and equipment, optimize resource allocation, and improve project management efficiency.

[0009] Furthermore, the sampling point optimization module includes:

[0010] The river terrain data collection submodule is responsible for obtaining the three-dimensional terrain data of the river using drone aerial survey and field surveying methods, providing basic geographic information for site optimization;

[0011] The hydrological condition analysis submodule combines the upstream water supply and meteorological data to analyze the key hydrological parameters of the river and provide hydrodynamic constraints for site optimization.

[0012] The historical data mining submodule conducts statistical analysis on historical sediment sampling data to discover the temporal and spatial laws of sediment distribution and provide empirical references for site optimization.

[0013] The intelligent modeling optimization submodule integrates river topography, hydrological conditions and historical sediment data to construct a mathematical model of river sediment distribution and solves the optimal distribution plan based on a heuristic algorithm;

[0014] The intelligent sampling module includes:

[0015] The strategy generation submodule generates a variety of optional sampling strategy schemes based on the sampling point plan and environmental data, using scenario modeling and decision theory;

[0016] The strategy evaluation submodule evaluates the effectiveness, reliability and economy of each generated sampling strategy to provide decision support for the final strategy selection;

[0017] Autonomous decision-making submodule, used to conduct intelligent decision-making analysis on the evaluation results, autonomously select and optimize the best sampling strategy;

[0018] The hardware control submodule receives strategy instructions and controls the movement and sampling actions of the underwater robot platform and various sampling actuators in real time;

[0019] The status monitoring submodule monitors the operating status, position and working status of the robot platform and the sampling equipment in real time during the sampling process, and feeds the monitoring data back to the decision-making module;

[0020] Sediment analysis and prediction module includes:

[0021] Sediment distribution analysis submodule analyzes the sediment particle size distribution and component content at different spatial and temporal locations in the river channel, revealing the laws of sediment deposition and transportation;

[0022] The transport simulation submodule builds a river hydrodynamic model and a sediment transport model, simulates the riverbed scouring and riverbank erosion process, and analyzes the source, transport path, and destination of sediment;

[0023] The spatiotemporal prediction submodule, based on the results of sediment distribution analysis and transport simulation, combines future climate and hydrological forecasts to establish a time series prediction model to predict the distribution and evolution trend of river sediment in the future.

[0024] The intelligent scheduling module includes:

[0025] The constraint modeling submodule is used to build the performance constraint model, resource limitation model and operation specification model of river management equipment, and quantify various hardware and non-hardware constraints;

[0026] The scheduling optimization submodule combines the sediment change trend prediction, uses optimization algorithms and rule reasoning technology to automatically generate the optimal scheduling plan for the treatment equipment;

[0027] The scheme evaluation submodule comprehensively evaluates the generated scheduling scheme, taking into account indicators such as effectiveness, reliability and economy, and feeds back to the scheduling optimization module for re-optimization when necessary;

[0028] The scheduling execution submodule sends the optimal scheduling plan to each management device, automatically completes the distribution and execution of scheduling commands, and monitors the equipment operation status in real time.

[0029] Furthermore, statistical analysis of historical sediment sampling data was performed to discover the temporal and spatial patterns of sediment distribution, including the following specific steps:

[0030] Collect and organize sediment sampling data obtained at different times and locations in different river channels to establish a unified database;

[0031] In the time dimension, the historical data of the same sampling point are analyzed sequentially to study the multi-year trend of sediment content and find out the periodic law. The formula is: Y(t) = T(t) + S(t) + R(t), where Y(t) is the observation value at time t; T(t) is the long-term trend component, representing the long-term trend; S(t) is the seasonal component, representing the periodic change; R(t) is the residual term, representing the random fluctuation that cannot be explained by the trend and seasonal components;

[0032] In the spatial dimension, the data of different sampling points at the same time were compared and analyzed to find the distribution pattern of sediment in the vertical and horizontal directions of the river. The specific process is as follows: a spatial coordinate system is set, where x represents the horizontal coordinate of the river and y represents the vertical coordinate of the river; for each sampling point (x i ,y i ), measure the sediment content S(x i ,y i ) and use the interpolation method to estimate the sediment distribution of the entire river channel. The specific formula is: Among them, S(x,y) is the sediment content at the predicted point (x,y), λ i It is i The interpolation weights of the sampling points are calculated, where n is the number of sampling points; the spatial distribution statistics of sediment content are calculated to describe the spatial distribution characteristics of sediment; the Moran index is used to evaluate the aggregation or dispersion trend of spatial distribution; and the isoline map or three-dimensional surface map of sediment content is drawn to intuitively display the distribution pattern of sediment in the vertical and horizontal directions of the river channel;

[0033] Combine time series and spatial distribution to construct a four-dimensional data cube model of sediment in time and space, and use data mining and pattern recognition algorithms to automatically discover the implicit temporal and spatial distribution patterns;

[0034] The discovered spatiotemporal distribution patterns were associated with influencing factors, a probability model was established, river topography, hydrological and meteorological parameters were introduced as independent variables, and sediment concentration was used as the dependent variable to explore the intrinsic connection between the spatiotemporal distribution patterns and environmental conditions.

[0035] Furthermore, the river topography, hydrological conditions and historical sediment data are integrated to construct a mathematical model of river sediment distribution, and the optimal distribution plan is solved based on a heuristic algorithm, including the following specific steps:

[0036] Based on the three-dimensional terrain data of the river, the upstream water situation and meteorological data, the river terrain model and hydrological model are established;

[0037] Construct an empirical model of sediment distribution based on the temporal and spatial distribution of sediment;

[0038] The physical mathematical model of river sediment transport is established by organically combining the terrain model, hydrological model and sediment distribution empirical model;

[0039] The sediment sampling point layout problem is formalized into a mathematical optimization problem, with sediment measurement coverage, representativeness and economic cost as optimization targets, and the solution of the physical mathematical model as constraint conditions, to establish a multi-objective optimization model.

[0040] The heuristic algorithm is used to solve the multi-objective optimization model and obtain multiple alternative optimal layout solutions;

[0041] The alternative site layout schemes obtained are comprehensively evaluated and ranked, and the optimal scheme is selected as a reference for the site layout of sediment sampling.

[0042] Furthermore, based on the sampling point plan and environmental data, scenario modeling and decision theory are used to generate a variety of optional sampling strategy plans, including the following specific steps:

[0043] Collect various environmental data that affect the sampling strategy, including river topography, hydrological conditions, meteorological conditions and underwater robot platform performance, establish an environmental scenario model, and build a model of the expected sampling point locations based on the point layout plan;

[0044] Combine the environmental scene model with the sampling point location model to build a digital twin model of the sampling scene to simulate the feasibility and constraints of different sampling paths in the real environment;

[0045] According to decision theory, the optimization goal of the sampling strategy is determined, and the objective function is quantified. At the same time, the constraint model of the sampling strategy is established;

[0046] Combining the digital twin model, optimization objective function and constraint model, designing a solution algorithm and automatically generating multiple initial sampling strategy solutions;

[0047] Conduct simulation evaluation on the generated initial strategy plan, simulate execution in the digital twin environment, evaluate its advantages and disadvantages in different scenarios, and optimize and iterate the strategy based on the evaluation results;

[0048] From the optimized candidate strategy set, several optimal sampling strategy solutions are screened out according to the weight scoring mechanism.

[0049] Furthermore, the effectiveness, reliability and economy of each generated sampling strategy are evaluated, which can be divided into the following specific steps:

[0050] Through simulation analysis, we test whether each strategy can be successfully executed and complete the sampling task in different environmental scenarios, whether the sampling points can cover the key areas, and whether the sampling data is representative and accurate enough;

[0051] Simulate and analyze various abnormal situations and emergencies that may be encountered during the execution of the strategy, test the fault tolerance and backup plan of the strategy, and ensure the continuity of the sampling process and the integrity of the data;

[0052] Cost accounting of various resources required for the strategy, weighing them against expected sampling benefits, and selecting the strategy with the best cost-effectiveness;

[0053] The effectiveness, reliability and economy indicators are quantified and standardized, and reasonable weight values ​​are set, and the various indicators are integrated and calculated to obtain a comprehensive score for the strategy.

[0054] Furthermore, the evaluation results are analyzed through intelligent decision-making, and the best sampling strategy is selected and optimized autonomously, including the following specific steps:

[0055] The historical excellent sampling strategy cases, as well as the manually summarized experience criteria and rules, are modeled using knowledge representation technology to form a structured knowledge base;

[0056] Using supervised learning algorithms, we use the strategy cases in the knowledge base as training sets to build a classification model for determining the pros and cons of strategies.

[0057] The prediction results of the classification model are cross-reasoned with the rules in the knowledge base to obtain preliminary optimal strategy recommendations; at the same time, an unsupervised clustering algorithm is used to analyze the internal patterns of the evaluation results to discover the correlations and differences between strategies;

[0058] Based on the clustering results, a strategy space is constructed, and a heuristic search algorithm is started in the strategy space to iteratively optimize the optimal strategy from the neighborhood to find the global optimal solution. The optimization goal is set to maximize the comprehensive evaluation score.

[0059] Match and verify the optimized optimal strategy with the rules in the knowledge base to ensure its rationality and executability;

[0060] Confirm and output the final strategy, and feed the strategy result back to the knowledge base as a new case instance, continuously train and improve the autonomous decision-making sub-module, and continuously improve the intelligent decision-making ability.

[0061] Furthermore, in the process of strategy optimization, knowledge-based constraint programming is introduced to transform artificial experience knowledge and physical knowledge into prior knowledge constraints, effectively reducing the strategy search space and improving optimization efficiency.

[0062] Furthermore, it is used to construct the performance constraint model, resource limitation model and operation specification model of river management equipment, and quantify various hardware and non-hardware constraints, including the following specific steps:

[0063] Collect technical parameters and performance indicators of various equipment used in river management operations, and establish equipment performance constraint models;

[0064] Count the resource consumption required for river management operations, formulate resource allocation plans, use resource quantity and resource availability time as resource constraints, and build a resource limitation model;

[0065] On the basis of on-site investigation, the standard requirements and operating procedures of river regulation operations are summarized, and normative constraints are made on the operation sequence, operation interval time and safety distance to form an operation standard model;

[0066] Semantically model the qualitative non-hardware constraints and formalize them into the form of logical rules;

[0067] The constructed equipment performance constraint model, resource limitation model, operation specification model and non-hardware constraint conditions in the form of logical rules are integrated, and all constraint conditions are uniformly expressed as constraint items of the objective function using the optimization modeling theory;

[0068] Aiming at the constraint items of the objective function, a solution algorithm is designed to obtain the optimal solution under various constraint conditions, corresponding to the quantified hardware and non-hardware constraints.

[0069] Furthermore, combined with the prediction of sediment change trend, the optimization algorithm and rule reasoning technology are used to automatically generate the optimal scheduling plan for the treatment equipment, including the following specific steps:

[0070] The sediment analysis and prediction module obtains the forecast data of sediment distribution and evolution trend in different areas of the river in the future, and visualizes it in a three-dimensional scene to intuitively analyze the key governance areas;

[0071] According to the geographical location and river conditions of the key treatment areas, the corresponding hardware and non-hardware constraints are extracted and fed into the scheduling optimization submodule. With the maximization of treatment operation efficiency and the minimization of total cost as the objective function, the combinatorial optimization algorithm is applied to automatically generate the initial solutions of multiple alternative scheduling schemes that meet the constraints.

[0072] Based on the operation specification model, the rationality of the scheduling plan is checked and the scheduling plans that do not meet the specifications are eliminated; at the same time, rule reasoning technology is used to further optimize the details of the scheduling plan, improve equipment selection, operation sequence and time arrangement;

[0073] Conduct simulation evaluation on the optimized feasible solutions, simulate execution under the equipment performance constraint model and three-dimensional scenario, analyze and evaluate their actual effects, and optimize and solve the problematic solutions again;

[0074] Repeat the above process until a satisfactory optimal scheduling solution set is generated.

[0075] Compared with the prior art, the beneficial effects of this application are:

[0076] This application achieves efficient and accurate sediment sampling, predicts future sediment change trends, and intelligently dispatches treatment facilities through the synergy of modules such as intelligent sampling, sediment analysis and prediction, and intelligent scheduling, thereby improving engineering management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 This is a structural diagram of a water conservancy project river management system disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical scheme and advantages of the implementation of the present invention clearer, the technical scheme in the embodiment of the present invention will be described in more detail below in conjunction with the drawings in the embodiment of the present invention. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of the present invention, not all of the embodiments.

[0079] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0080] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0081] like Figure 1 As shown, a water conservancy project river management system includes:

[0082] The sampling point optimization module determines the spatial and temporal location of sediment sampling based on the river topography, hydrological conditions and historical sediment data. This module aims to determine the best spatial and temporal location of sediment sampling by analyzing the river topography, hydrological conditions and historical sediment data. By optimizing the location, the comprehensiveness and representativeness of sediment sampling can be ensured, thereby obtaining accurate sediment sample data and providing reliable data support for subsequent sediment analysis and governance work.

[0083] The intelligent sampling module can independently select the optimal sampling strategy according to the sampling point plan and implement efficient and accurate unmanned sampling operations. The main purpose of this module is to independently select the optimal sampling strategy according to the sampling point plan and implement efficient and accurate unmanned sampling operations. Through intelligent sampling operations, human errors in the sampling process can be reduced, the accuracy and efficiency of sampling can be improved, and more reliable sediment sample data can be obtained;

[0084] The sediment analysis and prediction module conducts in-depth analysis on the temporal and spatial distribution and migration law of river sediment, and predicts the sediment change trend in the future. The main purpose of the sediment analysis and prediction module is to conduct in-depth analysis on the temporal and spatial distribution and migration law of river sediment, and predict the sediment change trend in the future by establishing a prediction model. By predicting the sediment change trend, it can timely warn of possible sediment problems and provide scientific basis and guidance for river management work;

[0085] The intelligent scheduling module combines sediment prediction results with real-time hydrological conditions to intelligently schedule river management facilities and equipment, optimize resource allocation, and improve engineering management efficiency. The intelligent scheduling module is designed to combine sediment prediction results with real-time hydrological conditions to intelligently schedule river management facilities and equipment, optimize resource allocation, and improve engineering management efficiency. Through intelligent scheduling, the operating status and working mode of the management facilities can be flexibly adjusted according to actual conditions, thereby maximizing resource utilization efficiency and optimizing management effects, thereby achieving effective management and control of river sediment.

[0086] Furthermore, the sampling point optimization module includes:

[0087] The river channel topography data collection submodule is responsible for obtaining the three-dimensional topographic data of the river channel by using drone aerial survey and field mapping methods, and providing basic geographic information for site optimization. The main purpose of the river channel topography data collection submodule is to obtain the three-dimensional topographic data of the river channel by drone aerial survey and field mapping methods. By obtaining the topographic data of the river channel, we can understand the information such as the terrain undulation, river channel morphology and riverbed characteristics of the river channel, and provide basic geographic information for the subsequent sediment sampling site layout;

[0088] The hydrological condition analysis submodule combines the upstream water supply and meteorological data to analyze the key hydrological parameters of the river, and provides hydrodynamic constraints for site optimization. The main purpose of the hydrological condition analysis submodule is to combine the upstream water supply and meteorological data to analyze the key hydrological parameters of the river, such as flow, flow velocity, water level, etc. By analyzing the hydrological conditions, we can understand the hydrodynamic characteristics and sediment transport laws of the river, and provide hydrodynamic constraints and reference basis for subsequent site optimization;

[0089] The historical data mining submodule conducts statistical analysis on historical sediment sampling data to discover the temporal and spatial laws of sediment distribution and provide empirical references for site optimization. The main purpose of the historical data mining submodule is to conduct statistical analysis on historical sediment sampling data to discover the temporal and spatial laws of sediment distribution. By mining historical data, we can understand the distribution characteristics and change trends of sediment and provide empirical references and basic data support for site optimization.

[0090] The intelligent modeling and optimization submodule integrates river topography, hydrological conditions and historical sediment data to construct a mathematical model of river sediment distribution, and solves the optimal sampling point distribution scheme based on the heuristic algorithm. The main purpose of the intelligent modeling and optimization submodule is to integrate river topography, hydrological conditions and historical sediment data to construct a mathematical model of river sediment distribution. Through intelligent modeling and optimization algorithms, the optimal sampling point distribution scheme can be solved according to the characteristics of river sediment distribution and hydrological conditions, thereby improving the efficiency and accuracy of sampling;

[0091] The intelligent sampling module includes:

[0092] The strategy generation submodule generates a variety of optional sampling strategy schemes based on the sampling point distribution plan and environmental data, using scenario modeling and decision theory. The main purpose of this submodule is to generate a variety of optional sampling strategy schemes based on the given sampling point distribution plan and real-time environmental data, using scenario modeling and decision theory. By generating a variety of strategy schemes, it can provide a variety of options for subsequent strategy selection to cope with different sampling scenarios and needs;

[0093] The strategy evaluation submodule evaluates the effectiveness, reliability and economy of each generated sampling strategy, and provides decision support for the final strategy selection. The main purpose of this submodule is to evaluate each generated sampling strategy, including its effectiveness, reliability and economy. By evaluating the advantages and disadvantages of each strategy, it can provide decision support for the final strategy selection and ensure that the selected strategy can achieve good results in actual operation;

[0094] The autonomous decision-making submodule is used to conduct intelligent decision-making analysis on the evaluation results, autonomously select and optimize the best sampling strategy. The main purpose of this submodule is to conduct intelligent decision-making analysis based on the evaluation results, autonomously select and optimize the best sampling strategy. Through intelligent decision-making analysis, the most suitable sampling strategy can be selected and optimized according to the real-time environment and needs to improve sampling efficiency and accuracy;

[0095] The hardware control submodule receives strategy instructions and controls the movement and sampling actions of the underwater robot platform and various sampling actuators in real time. The main purpose of this submodule is to receive the strategy instructions generated by the strategy generation submodule and the autonomous decision-making submodule, and control the movement and sampling actions of the underwater robot platform and various sampling actuators in real time. Through real-time control, it can ensure that the sampling operation is carried out according to the predetermined strategy, so as to obtain accurate and reliable sample data;

[0096] The status monitoring submodule monitors the operating status, position and working status of the robot platform and the sampling equipment in real time during the sampling process, and feeds the monitoring data back to the decision-making module. The main purpose of this submodule is to monitor the operating status, position and working status of the robot platform and the sampling equipment in real time during the sampling process, and feed the monitoring data back to the decision-making module. Through real-time monitoring, abnormal conditions of the robot platform or sampling equipment can be discovered and handled in time to ensure the smooth progress of the sampling operation;

[0097] Sediment analysis and prediction module includes:

[0098] Sediment distribution analysis submodule analyzes the sediment particle size distribution and component content at different time and space locations in the river channel, and reveals the sediment deposition and transportation rules. The main purpose of this submodule is to analyze the sediment particle size distribution and component content at different time and space locations in the river channel. By analyzing the distribution characteristics of sediment, the sediment deposition and transportation rules in the river channel can be revealed, including the sediment deposition degree and sedimentation rate at different locations, and the distribution of sediments of different particle sizes in the river channel, providing a scientific basis for subsequent sediment control work;

[0099] The transport simulation submodule constructs a river hydrodynamic model and a sediment transport model, simulates the riverbed scouring and riverbank erosion process, and analyzes the source, migration path and destination of sediment. The main purpose of this submodule is to construct a river hydrodynamic model and a sediment transport model, simulate the sediment transport process in the river, and analyze the source, migration path and destination of sediment by simulating the scouring and transportation process of river water flow on sediment, so as to understand the dynamic change law of sediment in the river and provide a basis for sediment control and prediction;

[0100] The spatiotemporal prediction submodule is based on the results of sediment distribution analysis and transport simulation, combined with future climate and hydrological forecasts, to establish a time series prediction model to predict the distribution and evolution trend of river sediment in the future. The main purpose of this submodule is to establish a time series prediction model based on the results of sediment distribution analysis and transport simulation, combined with future climate and hydrological forecasts, to predict the distribution and evolution trend of river sediment in the future. By predicting the distribution and evolution trend of sediment, it is possible to timely warn of possible sediment problems and provide scientific decision-making support for river governance and management;

[0101] The intelligent scheduling module includes:

[0102] The constraint modeling submodule is used to build the performance constraint model, resource limitation model and operation specification model of river management equipment, and quantify various hardware and non-hardware constraints. The main purpose of this submodule is to quantify various hardware and non-hardware constraints and build corresponding constraint models. By modeling the constraints, we can clearly understand the performance limitations, resource limitations and operation specifications of river management equipment, and provide the basis and reference for the constraints for subsequent scheduling optimization;

[0103] The scheduling optimization submodule combines the prediction of sediment change trend with the optimization algorithm and rule reasoning technology to automatically generate the optimal scheduling plan for the treatment equipment. The main purpose of this submodule is to combine the prediction of sediment change trend with the optimization algorithm and rule reasoning technology to automatically generate the optimal scheduling plan for the treatment equipment. By optimizing the scheduling plan, the treatment equipment can respond to the changes in river sediment in the most effective way under the premise of meeting the constraints, thereby improving the efficiency and effect of treatment.

[0104] The scheme evaluation submodule comprehensively evaluates the generated scheduling scheme, taking into account the indicators of effectiveness, reliability and economy, and feeds back to the scheduling optimization module for re-optimization when necessary. The main purpose of this submodule is to comprehensively evaluate the generated scheduling scheme, including indicators such as effectiveness, reliability and economy. By evaluating the scheduling scheme, problems and deficiencies in the scheme can be found, and when necessary, they can be fed back to the scheduling optimization module for re-optimization to ensure that the generated scheduling scheme can achieve the expected effect;

[0105] The scheduling execution submodule sends the optimal scheduling plan to each management device, automates the distribution and execution of scheduling commands, and monitors the operating status of the equipment in real time. The main purpose of this submodule is to send the optimal scheduling plan to each management device, automates the distribution and execution of scheduling commands, and monitors the operating status of the equipment in real time. Through scheduling execution, it can ensure that the scheduling plan can be executed in a timely and effective manner, and monitor and adjust the execution process when necessary to ensure the smooth progress of management work.

[0106] Furthermore, statistical analysis of historical sediment sampling data was performed to discover the temporal and spatial patterns of sediment distribution, including the following specific steps:

[0107] Collect and organize sediment sampling data obtained at different times and different river locations, and establish a unified database so that sediment data at different time points and different river locations can be easily accessed and managed. Such a database can provide data support for subsequent sequence analysis, spatial distribution analysis, and spatiotemporal model construction;

[0108] In the time dimension, the historical data of the same sampling point are analyzed sequentially to study the multi-year trend of sediment content and find out the periodic law. The formula is: Y(t) = T(t) + S(t) + R(t), where Y(t) is the observation value at time t; T(t) is the long-term trend component, representing the long-term trend; S(t) is the seasonal component, representing the periodic change; R(t) is the residual term, representing the random fluctuation that cannot be explained by the trend and seasonal components; By analyzing the historical data of the same sampling point sequentially, we can understand the trend of sediment content over time, including the long-term trend, seasonal change and residual term. Such analysis can reveal the periodic law and long-term trend of sediment content, and provide a basis for subsequent prediction and analysis;

[0109] In the spatial dimension, the data of different sampling points at the same time were compared and analyzed to find the distribution pattern of sediment in the vertical and horizontal directions of the river. The specific process is as follows: a spatial coordinate system is set, where x represents the horizontal coordinate of the river and y represents the vertical coordinate of the river; for each sampling point (x i ,y i ), measure the sediment content S(x i ,y i ) and use the interpolation method to estimate the sediment distribution of the entire river channel. The specific formula is: Among them, S(x,y) is the sediment content at the predicted point (x,y), λ i It is iThe interpolation weights of the sampling points are calculated, where n is the number of sampling points; the spatial distribution statistics of the sediment content are calculated to describe the spatial distribution characteristics of the sediment; the Moran index is used to evaluate the aggregation or dispersion trend of the spatial distribution; the isoline map or three-dimensional surface map of the sediment content is drawn to intuitively display the distribution pattern of the sediment in the vertical and horizontal directions of the river channel; by comparing and analyzing the data of different sampling points at the same time, the distribution law of the sediment in the vertical and horizontal directions of the river channel can be understood, including the spatial interpolation estimation of the sediment content, the calculation of the spatial distribution statistics, the evaluation of the Moran index and the spatial visualization of the sediment content, thereby revealing the spatial distribution characteristics of the sediment in the river channel;

[0110] By combining time series and spatial distribution, a four-dimensional data cube model of sediment in time and space is constructed. By using data mining and pattern recognition algorithms, the implicit time and space distribution law can be automatically discovered. Such a model can provide a basis for subsequent prediction and analysis, and help understand the dynamic change law of sediment in time and space;

[0111] By associating the discovered spatiotemporal distribution patterns with influencing factors and establishing a probability model, river topography, hydrological and meteorological parameters are introduced as independent variables, and sediment concentration is used as a dependent variable to explore the intrinsic connection between spatiotemporal distribution patterns and environmental conditions. Such a model can help understand the formation mechanism of sediment distribution and provide a scientific basis for river management and environmental protection.

[0112] Furthermore, the river topography, hydrological conditions and historical sediment data are integrated to construct a mathematical model of river sediment distribution, and the optimal distribution plan is solved based on a heuristic algorithm, including the following specific steps:

[0113] Based on the three-dimensional terrain data of the river, the upstream water situation and meteorological data, the river terrain model and hydrological model are established to analyze the topographic characteristics, hydrological conditions and water flow dynamics of the river, which can provide the basis and basis for the subsequent establishment of the sediment distribution model;

[0114] Based on the temporal and spatial distribution law of sediment, an empirical model of sediment distribution is constructed. The empirical model of sediment distribution can help understand the migration law and distribution characteristics of sediment and provide a reference for the subsequent establishment of a mathematical model of sediment distribution.

[0115] The topographic model, hydrological model and sediment distribution empirical model are organically combined to establish a physical mathematical model of river sediment transport. The physical mathematical model can comprehensively consider multiple factors such as topography, hydrology and sediment distribution, and accurately describe the sediment transport process in the river.

[0116] The sediment sampling point layout problem is formalized into a mathematical optimization problem. The sediment measurement coverage, representativeness and economic cost are taken as optimization objectives, and the solution results of the physical mathematical model are taken as constraints. A multi-objective optimization model is established. By establishing a multi-objective optimization model, the optimal sampling point layout plan can be found under the premise of considering the coverage, representativeness and cost of the sediment sampling points. Thus, multiple optimization objectives can be comprehensively considered to provide a reasonable decision-making basis for the sediment sampling point layout.

[0117] By using a heuristic algorithm to solve the multi-objective optimization model, multiple alternative optimal point distribution solutions can be obtained. This method can help overcome the complexity and high dimensionality of multi-objective optimization problems and provide more options for decision-making;

[0118] The alternative site layout schemes obtained are comprehensively evaluated and ranked, and the optimal scheme is selected as a reference for the site layout of sediment sampling.

[0119] Furthermore, based on the sampling point plan and environmental data, scenario modeling and decision theory are used to generate a variety of optional sampling strategy plans, including the following specific steps:

[0120] Collect various environmental data that affect the sampling strategy, including river topography, hydrological conditions, meteorological conditions and underwater robot platform performance, establish an environmental scenario model, and build a model of the expected sampling point location based on the point layout plan. By collecting and collating various environmental data, we can fully understand the environmental conditions and characteristics of river management. Establishing an environmental scenario model helps simulate the situation in the real environment and provide a basis for formulating an effective sampling strategy;

[0121] Combine the environmental scenario model with the sampling point location model to build a digital twin model of the sampling scenario to simulate the feasibility and constraints of different sampling paths in the real environment. By building a digital twin model, different sampling scenarios can be simulated in a virtual environment to evaluate the feasibility and effectiveness of sampling strategies, which can help understand the challenges and limitations that different strategies may encounter in actual implementation.

[0122] According to decision theory, the optimization goal of the sampling strategy is determined, and the objective function is quantified. At the same time, a constraint model of the sampling strategy is established. Determining the optimization goal of the sampling strategy helps to clarify the purpose and direction of the strategy. Establishing a constraint model can ensure that the strategy follows various constraints in actual implementation and ensure the effectiveness and rationality of the sampling process.

[0123] Combining the digital twin model, optimization objective function and constraint model, designing a solution algorithm, and automatically generating multiple initial sampling strategy solutions. Designing a solution algorithm helps to generate diverse and effective initial sampling strategy solutions in complex environmental scenarios, thereby making full use of the simulation capabilities of the digital twin model and providing diverse options for subsequent evaluation and optimization;

[0124] Conduct simulation evaluation on the generated initial strategy scheme, simulate execution in the digital twin environment, evaluate its advantages and disadvantages in different scenarios, and optimize and iterate the strategy based on the evaluation results. By simulating and evaluating the initial strategy scheme, we can fully understand its performance and effect in different scenarios. Optimizing and iterating based on the evaluation results will help improve the quality and efficiency of the strategy and improve the feasibility and practicality of the sampling scheme.

[0125] From the optimized candidate strategy set, several optimal sampling strategy schemes are screened out according to the weight scoring mechanism. Through the weight scoring mechanism, the importance of various evaluation indicators is comprehensively considered to screen out the optimal sampling strategy scheme, which can help decision makers better understand the advantages and disadvantages of different schemes and choose the sampling strategy that best suits actual needs.

[0126] Furthermore, the effectiveness, reliability and economy of each generated sampling strategy are evaluated, which can be divided into the following specific steps:

[0127] Through simulation analysis, we can test whether each strategy can be successfully executed and complete the sampling task in different environmental scenarios, whether the sampling points can cover the key areas, and whether the sampling data is sufficiently representative and accurate. Through simulation analysis, we can fully understand the performance of each sampling strategy in actual operation and evaluate whether it meets the requirements of the sampling task, which helps to identify potential problems and risks and provide reference for subsequent optimization and adjustment.

[0128] Simulate and analyze various abnormal situations and emergencies that may be encountered during the execution of the strategy, test the fault tolerance and backup plan of the strategy, ensure the continuity of the sampling process and the integrity of the data, and evaluate the stability and response capabilities of the strategy by simulating and analyzing abnormal situations during the execution of the strategy; to test the effectiveness of the backup plan and fault tolerance mechanism, and ensure that the sampling task can be successfully completed even in the face of emergencies;

[0129] Cost accounting is performed on the various resources required for the strategy, and the cost is weighed against the expected sampling benefits to select the strategy with the best cost performance. Through cost accounting, the economy of the strategy and the efficiency of resource utilization can be fully evaluated. Weighing the cost and benefit helps to find an economically reasonable sampling strategy and ensure the maximum benefit in the sampling task.

[0130] The effectiveness, reliability and economy indicators are quantified and standardized, and reasonable weight values ​​are set. The various indicators are integrated and calculated to obtain a comprehensive score for the strategy. Through the comprehensive score, the advantages and disadvantages of different strategies can be objectively compared and evaluated. Quantifying and standardizing various indicators helps to eliminate the influence of subjective factors and ensure that the evaluation results are objective and credible, thereby providing a scientific basis for the final strategy selection and helping decision makers make reasonable decisions.

[0131] Furthermore, the evaluation results are analyzed through intelligent decision-making, and the best sampling strategy is selected and optimized autonomously, including the following specific steps:

[0132] The excellent historical sampling strategy cases, as well as the manually summarized experience criteria and rules, are modeled using knowledge representation technology to form a structured knowledge base. By establishing a structured knowledge base, the rich experience knowledge can be effectively managed and utilized. The information in the knowledge base includes historical successful sampling strategy cases, experience rules and judgment criteria summarized by experts, providing rich knowledge resources for subsequent intelligent decision-making;

[0133] By using supervised learning algorithms and taking the strategy cases in the knowledge base as training sets, a classification model for determining the pros and cons of strategies is constructed. The classification model constructed by the supervised learning algorithm can automatically determine the pros and cons of new sampling strategies, thereby being able to use the information of historical cases to help the system more accurately evaluate and predict the effects of different strategies, and improve the scientificity and accuracy of decision-making;

[0134] The prediction results of the classification model are cross-reasoned with the rules in the knowledge base to obtain preliminary optimal strategy recommendations. Through cross-reasoning, the system can comprehensively consider the prediction results of the classification model and the expert rules in the knowledge base to obtain more comprehensive and reasonable optimal strategy recommendations, thereby making full use of the knowledge resources in the system and improving the scientificity and reliability of decision-making;

[0135] At the same time, an unsupervised clustering algorithm is used to analyze the internal patterns of the evaluation results and discover the correlations and differences between strategies. Cluster analysis can discover the potential patterns and correlations between strategies, which helps to understand the similarities and differences between different strategies. Such analysis helps to better understand the characteristics and advantages of strategies and provide a basis and direction for strategy optimization.

[0136] Based on the clustering results, a strategy space is constructed, and a heuristic search algorithm is started in the strategy space to iteratively optimize the optimal strategy from the neighborhood to find the global optimal solution. The optimization goal is set to maximize the comprehensive evaluation score.

[0137] Match and verify the optimized optimal strategy with the rules in the knowledge base to ensure its rationality and executability. By matching and verifying with the rules in the knowledge base, it can be ensured that the optimal strategy conforms to the experience and rules of domain experts and has high feasibility and executability, thereby avoiding the optimal strategy from being inconsistent with domain knowledge and improving the actual application effect and operability of the optimal strategy.

[0138] Confirm and output the final strategy, and feed this strategy result back to the knowledge base as a new case instance, continuously train and improve the autonomous decision-making submodule, and continuously improve the intelligent decision-making ability. By outputting the final optimal strategy, an efficient and feasible decision-making plan can be provided for the sampling task; at the same time, feeding the optimal strategy back to the knowledge base as a new case instance can continuously enrich the content of the knowledge base, improve the intelligence level and decision-making ability of the autonomous decision-making submodule, and provide better support and guidance for future decisions.

[0139] Through the comprehensive implementation of the above sub-steps, the system can intelligently select and optimize the best sampling strategy, continuously improve the accuracy and efficiency of decision-making, and thus better cope with complex practical environments and task requirements.

[0140] Furthermore, in the process of strategy optimization, knowledge-based constraint programming is introduced to transform artificial experience knowledge and physical knowledge into prior knowledge constraints, effectively reducing the strategy search space and improving optimization efficiency.

[0141] Furthermore, it is used to construct the performance constraint model, resource limitation model and operation specification model of river management equipment, and quantify various hardware and non-hardware constraints, including the following specific steps:

[0142] Collect the technical parameters and performance indicators of various equipment used in river management operations, and establish equipment performance constraint models; The purpose of this step is to collect the technical parameters and performance indicators of various equipment used in river management operations, such as the horsepower, excavation depth, and operation speed of dredging boats. These data will be used to constrain the selection and use of equipment in the optimization process;

[0143] Statistics on the resource consumption required for river management operations, formulate resource allocation plans, and use the number of resources and the time when resources are available as resource constraints to build a resource constraint model; By counting the consumption of various types of resources required for river management operations, including manpower, time, fuel, materials, etc., a resource allocation plan is formulated, and the number of resources and the time when resources are available will be used as resource constraints to optimize resource allocation and utilization in the model;

[0144] On the basis of on-site investigation, summarize the standard requirements and operating procedures of river management operations, make normative constraints on the operation sequence, operation interval time and safety distance, and form an operation specification model; On the basis of on-site investigation, summarize the standard requirements and operating procedures of river management operations, such as operation sequence, operation interval time, safety distance, etc. These normative constraints will constrain the execution of operations during the optimization process to ensure the safety and effectiveness of operations;

[0145] Semantically model the qualitative non-hardware constraints and formalize them into the form of logical rules;

[0146] The constructed equipment performance constraint model, resource limitation model, operation specification model and non-hardware constraint conditions in the form of logical rules are integrated, and all constraint conditions are uniformly expressed as constraint items of the objective function using optimization modeling theory; these constraint conditions will be used as constraint items of the objective function and uniformly expressed as constraint conditions in the optimization model to ensure that various constraint conditions are met during the optimization process;

[0147] Design a solution algorithm for the constraints of the objective function to obtain the optimal solution under various constraints. Corresponding to the quantified hardware and non-hardware constraints, design an applicable solution algorithm for the constraints of the objective function, such as linear programming, integer programming, mixed integer programming, etc., to obtain the optimal solution under hardware and non-hardware constraints.

[0148] Furthermore, combined with the prediction of sediment change trend, the optimization algorithm and rule reasoning technology are used to automatically generate the optimal scheduling plan for the treatment equipment, including the following specific steps:

[0149] The sediment analysis and prediction module obtains the predicted data of sediment distribution and evolution trend in different areas of the river in the future, and visualizes it in a three-dimensional scene to intuitively analyze the key governance areas; this step aims to understand the changes in river sediment and determine the areas that need to be focused on governance;

[0150] According to the geographical location and river conditions of the key treatment area, the corresponding hardware and non-hardware constraints are extracted and fed into the scheduling optimization submodule. With the maximization of treatment operation efficiency and the minimization of total cost as the objective function, the combinatorial optimization algorithm is applied to automatically generate the initial solutions of multiple alternative scheduling schemes that meet the constraints. This step aims to ensure the maximization of treatment operation efficiency and the minimization of total cost.

[0151] Based on the operation specification model, the rationality of the scheduling plan is checked and the scheduling plans that do not meet the specifications are eliminated; at the same time, rule reasoning technology is used to further optimize the details of the scheduling plan, improve equipment selection, operation sequence and time arrangement to ensure the rationality and feasibility of the plan;

[0152] Conduct simulation evaluation on the optimized feasible solutions, simulate execution under the equipment performance constraint model and three-dimensional scenario, analyze and evaluate their actual effects, and optimize and solve the problematic solutions again. This step aims to verify the feasibility and effectiveness of the solutions and ensure that the expected effects can be achieved in actual implementation.

[0153] Repeat the above process until a satisfactory set of optimal scheduling solutions is generated. This step aims to continuously improve the solutions to make them more in line with actual needs and regulatory requirements, thereby improving the efficiency and cost-effectiveness of governance operations.

Claims

1. A water conservancy project river management system, characterized in that: Includes the following modules: The sampling point optimization module determines the spatial and temporal location of sediment sampling based on river topography, hydrological conditions and historical sediment data; The intelligent sampling module can independently select the optimal sampling strategy based on the sampling plan and implement efficient and accurate unmanned sampling operations; The sediment analysis and prediction module conducts in-depth analysis of the temporal and spatial distribution and migration patterns of river sediment, and predicts the sediment change trend in the future period; The intelligent scheduling module combines sediment prediction results with real-time hydrological conditions to intelligently schedule river management facilities and equipment, optimize resource allocation, and improve engineering management efficiency; The sampling point optimization module includes the following submodules: The river terrain data collection submodule is responsible for obtaining the three-dimensional terrain data of the river using drone aerial survey and field surveying methods, providing basic geographic information for site optimization; The hydrological condition analysis submodule combines the upstream water supply and meteorological data to analyze the key hydrological parameters of the river and provide hydrodynamic constraints for site optimization. The historical data mining submodule conducts statistical analysis on historical sediment sampling data to discover the temporal and spatial laws of sediment distribution and provide empirical references for site optimization. The intelligent modeling optimization submodule integrates river topography, hydrological conditions and historical sediment data to construct a mathematical model of river sediment distribution and solves the optimal distribution plan based on a heuristic algorithm; Statistical analysis of historical sediment sampling data was performed to discover the temporal and spatial patterns of sediment distribution, including the following specific steps: Collect and organize sediment sampling data obtained at different times and locations in different river channels to establish a unified database; In the time dimension, the historical data of the same sampling point are analyzed sequentially to study the multi-year trend of sediment content and find out the periodic law. The formula is: Y(t) = T(t) + S(t) + R(t), where Y(t) is the observation value at time t; T(t) is the long-term trend component, representing the long-term trend; S(t) is the seasonal component, representing the periodic change; R(t) is the residual term, representing the random fluctuation that cannot be explained by the trend and seasonal components; In the spatial dimension, the data of different sampling points at the same time were compared and analyzed to find the distribution pattern of sediment in the vertical and horizontal directions of the river. The specific process is as follows: a spatial coordinate system is set, where x represents the horizontal coordinate of the river and y represents the vertical coordinate of the river; for each sampling point (x i ,y i ), measure the sediment content S(x i ,y i ) and use the interpolation method to estimate the sediment distribution of the entire river channel. The specific formula is: Among them, S(x,y) is the sediment content at the predicted point (x,y), λ i It is i The interpolation weights of the sampling points are calculated, where n is the number of sampling points; the spatial distribution statistics of sediment content are calculated to describe the spatial distribution characteristics of sediment; the Moran index is used to evaluate the aggregation or dispersion trend of spatial distribution; and the isoline map or three-dimensional surface map of sediment content is drawn to intuitively display the distribution pattern of sediment in the vertical and horizontal directions of the river channel; Combine time series and spatial distribution to construct a four-dimensional data cube model of sediment in time and space, and use data mining and pattern recognition algorithms to automatically discover the implicit temporal and spatial distribution patterns; The discovered spatiotemporal distribution patterns were associated with influencing factors, a probability model was established, river topography, hydrological and meteorological parameters were introduced as independent variables, and sediment concentration was used as the dependent variable to explore the intrinsic connection between the spatiotemporal distribution patterns and environmental conditions.

2. A water conservancy project river regulation system according to claim 1, characterized in that: The intelligent sampling module includes the following submodules: The strategy generation submodule generates a variety of optional sampling strategy schemes based on the sampling point plan and environmental data, using scenario modeling and decision theory; The strategy evaluation submodule evaluates the effectiveness, reliability and economy of each generated sampling strategy to provide decision support for the final strategy selection; Autonomous decision-making submodule, used to conduct intelligent decision-making analysis on the evaluation results, autonomously select and optimize the best sampling strategy; The hardware control submodule receives strategy instructions and controls the movement and sampling actions of the underwater robot platform and various sampling actuators in real time; The status monitoring submodule monitors the operating status, position and working status of the robot platform and the sampling equipment in real time during the sampling process, and feeds the monitoring data back to the decision-making module; The sediment analysis and prediction module includes the following submodules: Sediment distribution analysis submodule analyzes the sediment particle size distribution and component content at different spatial and temporal locations in the river channel, revealing the laws of sediment deposition and transportation; The transport simulation submodule builds a river hydrodynamic model and a sediment transport model, simulates the riverbed scouring and riverbank erosion process, and analyzes the source, transport path, and destination of sediment; The spatiotemporal prediction submodule, based on the results of sediment distribution analysis and transport simulation, combined with future climate and hydrological forecasts, establishes a time series prediction model to predict the distribution and evolution trend of river sediment in the future; The intelligent scheduling module includes the following sub-modules: The constraint modeling submodule is used to build the performance constraint model, resource limitation model and operation specification model of river management equipment, and quantify various hardware and non-hardware constraints; The scheduling optimization submodule combines the sediment change trend prediction, uses optimization algorithms and rule reasoning technology to automatically generate the optimal scheduling plan for the treatment equipment; The scheme evaluation submodule comprehensively evaluates the generated scheduling scheme, taking into account indicators such as effectiveness, reliability and economy, and feeds back to the scheduling optimization module for re-optimization when necessary; The scheduling execution submodule sends the optimal scheduling plan to each management device, automatically completes the distribution and execution of scheduling commands, and monitors the equipment operation status in real time.

3. A water conservancy project river regulation system according to claim 2, characterized in that: The river topography, hydrological conditions and historical sediment data are integrated to construct a mathematical model of river sediment distribution, and the optimal distribution scheme is solved based on a heuristic algorithm, including the following specific steps: Based on the three-dimensional terrain data of the river, the upstream water situation and meteorological data, the river terrain model and hydrological model are established; Construct an empirical model of sediment distribution based on the temporal and spatial distribution of sediment; The physical mathematical model of river sediment transport is established by organically combining the terrain model, hydrological model and sediment distribution empirical model; The sediment sampling point arrangement problem is formalized into a mathematical optimization problem, with sediment measurement coverage, representativeness and economic cost as optimization targets, and the solution of the physical mathematical model as constraint conditions, to establish a multi-objective optimization model. The heuristic algorithm is used to solve the multi-objective optimization model and obtain multiple alternative optimal layout solutions; The alternative site layout schemes obtained are comprehensively evaluated and ranked, and the optimal scheme is selected as a reference for the site layout of sediment sampling.

4. A water conservancy project river regulation system according to claim 2, characterized in that: Based on the sampling point plan and environmental data, scenario modeling and decision theory are used to generate a variety of optional sampling strategy plans, including the following specific steps: Collect various environmental data that affect the sampling strategy, including river topography, hydrological conditions, meteorological conditions and underwater robot platform performance, establish an environmental scenario model, and build a model of the expected sampling point locations based on the point layout plan; Combine the environmental scene model with the sampling point location model to build a digital twin model of the sampling scene to simulate the feasibility and constraints of different sampling paths in the real environment; According to decision theory, the optimization goal of the sampling strategy is determined, and the objective function is quantified. At the same time, the constraint model of the sampling strategy is established; Combining the digital twin model, optimization objective function and constraint model, designing a solution algorithm and automatically generating multiple initial sampling strategy solutions; Conduct simulation evaluation on the generated initial strategy plan, simulate execution in the digital twin environment, evaluate its advantages and disadvantages in different scenarios, and optimize and iterate the strategy based on the evaluation results; From the optimized candidate strategy set, several optimal sampling strategy solutions are screened out according to the weight scoring mechanism.

5. A water conservancy project river regulation system according to claim 2, characterized in that: The effectiveness, reliability and economy of each generated sampling strategy can be evaluated in the following specific steps: Through simulation analysis, we test whether each strategy can be successfully executed and complete the sampling task in different environmental scenarios, whether the sampling points can cover the key areas, and whether the sampling data is representative and accurate enough; Simulate and analyze various abnormal situations and emergencies that may be encountered during the execution of the strategy, test the fault tolerance and backup plan of the strategy, and ensure the continuity of the sampling process and the integrity of the data; Cost accounting of various resources required for the strategy, weighing them against expected sampling benefits, and selecting the strategy with the best cost-effectiveness; The effectiveness, reliability and economy indicators are quantified and standardized, and reasonable weight values ​​are set, and the various indicators are integrated and calculated to obtain a comprehensive score for the strategy.

6. A water conservancy project river regulation system according to claim 2, characterized in that: Conduct intelligent decision-making analysis on the evaluation results, and autonomously select and optimize the best sampling strategy, including the following specific steps: The historical excellent sampling strategy cases, as well as the manually summarized experience criteria and rules, are modeled using knowledge representation technology to form a structured knowledge base; Using supervised learning algorithms, we use the strategy cases in the knowledge base as training sets to build a classification model for determining the pros and cons of strategies. The prediction results of the classification model are cross-reasoned with the rules in the knowledge base to obtain preliminary optimal strategy recommendations; at the same time, an unsupervised clustering algorithm is used to analyze the internal patterns of the evaluation results to discover the correlations and differences between strategies; Based on the clustering results, a strategy space is constructed, and a heuristic search algorithm is started in the strategy space to iteratively optimize the optimal strategy from the neighborhood to find the global optimal solution. The optimization goal is set to maximize the comprehensive evaluation score. Match and verify the optimized optimal strategy with the rules in the knowledge base to ensure its rationality and executability; Confirm and output the final strategy, and feed the strategy result back to the knowledge base as a new case instance, continuously train and improve the autonomous decision-making sub-module, and continuously improve the intelligent decision-making ability.

7. A water conservancy project river regulation system according to claim 6, characterized in that: In the process of strategy optimization, knowledge-based constraint programming is introduced to transform artificial experience knowledge and physical knowledge into prior knowledge constraints, which effectively reduces the strategy search space and improves optimization efficiency.

8. A water conservancy project river regulation system according to claim 2, characterized in that: It is used to build river management equipment performance constraint model, resource limitation model and operation specification model, and quantify various hardware and non-hardware constraints, including the following specific steps: Collect technical parameters and performance indicators of various equipment used in river management operations, and establish equipment performance constraint models; Count the resource consumption required for river management operations, formulate resource allocation plans, use resource quantity and resource availability time as resource constraints, and build a resource limitation model; On the basis of on-site investigation, the standard requirements and operating procedures of river regulation operations are summarized, and normative constraints are made on the operation sequence, operation interval time and safety distance to form an operation standard model; Semantically model the qualitative non-hardware constraints and formalize them into the form of logical rules; The constructed equipment performance constraint model, resource limitation model, operation specification model and non-hardware constraint conditions in the form of logical rules are integrated, and all constraint conditions are uniformly expressed as constraint items of the objective function using the optimization modeling theory; Aiming at the constraint items of the objective function, a solution algorithm is designed to obtain the optimal solution under various constraint conditions, corresponding to the quantified hardware and non-hardware constraints.

9. A water conservancy project river regulation system according to claim 2, characterized in that: Combined with the prediction of sediment change trend, the optimization algorithm and rule reasoning technology are used to automatically generate the optimal scheduling plan for treatment equipment, including the following specific steps: The sediment analysis and prediction module obtains the forecast data of sediment distribution and evolution trend in different areas of the river in the future, and visualizes it in a three-dimensional scene to intuitively analyze the key governance areas; According to the geographical location and river conditions of the key treatment areas, the corresponding hardware and non-hardware constraints are extracted and fed into the scheduling optimization submodule. With the maximization of treatment operation efficiency and the minimization of total cost as the objective function, the combinatorial optimization algorithm is applied to automatically generate the initial solutions of multiple alternative scheduling schemes that meet the constraints. Based on the operation specification model, the rationality of the scheduling plan is checked and the scheduling plans that do not meet the specifications are eliminated; at the same time, rule reasoning technology is used to further optimize the details of the scheduling plan, improve equipment selection, operation sequence and time arrangement; Conduct simulation evaluation on the optimized feasible solutions, simulate execution under the equipment performance constraint model and three-dimensional scenario, analyze and evaluate their actual effects, and optimize and solve the problematic solutions again; Repeat the above process until a satisfactory optimal scheduling solution set is generated.

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