Construction method of reservoir sediment dispatching system suitable for sandy river

Abstract

The invention discloses a construction method of a reservoir sediment dispatching system suitable for a sandy river, which improves the fusion efficiency of multi-source heterogeneous data such as hydrology, sediment, engineering operation and the like by establishing a unified data interface standard and a fusion mechanism, and lays a foundation for real-time correlation analysis of the multi-source heterogeneous data. Through a three-dimensional dynamic visualization technology, in combination with a reservoir sediment hydrodynamic model and a reservoir area satellite remote sensing image, the dynamic process of scouring form and sedimentation evolution in the scheduling process is simulated, and the visual display effect of a water and sediment scheduling scheme is improved, so that scheduling personnel can visually grasp the change rule of each index in the scheduling process, and the scheduling efficiency is improved. And a global perspective is provided for evaluating a desilting effect and optimizing a scheduling scheme. Meanwhile, a sediment dispatching knowledge base is further constructed, multi-dimensional intelligent retrieval is supported, intelligent fusion of professional knowledge and dispatching services is achieved, and whole-process digital management of reservoir sediment dispatching is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of sediment dispatching, and is particularly applicable to a method for constructing a reservoir sediment dispatching system suitable for sediment-laden rivers. Background Art

[0002] Reservoir sediment regulation is a critical task for reservoirs on sediment-laden rivers. Its goal is to achieve sediment flushing and silt reduction through scientific scheduling, maintain flood control storage capacity, and achieve flood control, water supply, and other public benefits. Typically, reservoir sediment regulation requires timely monitoring and assessment of the dynamic development of sedimentation patterns in the dam funnel area and reservoir area to ensure the safe operation of the dam and release structures, evaluate sediment removal effectiveness, and optimize sediment regulation strategies in real time.

[0003] Currently, reservoir sediment dispatching systems both domestically and internationally primarily rely on periodic cross-sectional measurements and fixed-point sampling to obtain information on reservoir sedimentation status. Measurement and analysis results are presented in the form of two-dimensional charts and plan views. Even sediment dispatching systems that utilize reservoir sediment hydrodynamic models can only display reservoir sedimentation status information for specific time periods. Dynamic visualization capabilities are insufficient, making it impossible to monitor and assess the dynamic development of sedimentation patterns in the dam funnel area and reservoir area in real time, making it difficult to comprehensively evaluate sediment removal effectiveness. Dispatching decisions are made by experts based on experience, and historical sediment dispatching experience is stored and managed in documents. This makes existing reservoir sediment dispatching systems overly reliant on manual experience and lacks data-driven intelligent support. Key processes such as plan formulation, evaluation, and rehearsal lack intelligent capabilities, limiting the development of intelligent sediment dispatching systems for sediment-laden rivers. These systems are unable to meet the demands of modern reservoir management and hinder the full realization of reservoir functions such as flood control, power generation, and water supply. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for constructing a reservoir sediment scheduling system suitable for sandy rivers, which is used to solve the problems of insufficient dynamic visualization capabilities in existing sediment scheduling systems, inability to monitor and evaluate the dynamic development of the siltation morphology in the funnel area in front of the dam and the reservoir area in real time, difficulty in comprehensively evaluating the sediment discharge effect, excessive reliance on manual experience, and insufficient intelligence level.

[0005] To achieve the above object, the present invention adopts the following technical solutions: The method for constructing a reservoir sediment dispatching system applicable to sediment-laden rivers of the present invention comprises constructing a dispatching decision module, a data fusion module, a reservoir sediment hydrodynamic model, a reservoir sediment analysis module, a knowledge base module, and a three-dimensional visualization module; The data fusion module provides real-time monitoring data and historical test data; the reservoir sediment hydrodynamic model performs dynamic simulation calculations of the scheme; the reservoir sediment analysis module conducts professional analysis of the siltation morphology of the funnel area in front of the dam and the reservoir area; the knowledge base module provides decision-making references through knowledge graphs and intelligent reasoning; the three-dimensional visualization module realizes dynamic preview and multi-dimensional display of the scheduling process; data flow is realized between modules through standardized interfaces, and is uniformly called by the scheduling decision module.

[0006] Furthermore, the data fusion module adopts a layered design architecture, including an interface layer, a data processing layer, a storage layer, a quality control layer, and a service layer; the interface layer converts multi-source heterogeneous data into a unified data format; the data processing layer uses triangulation and surface fitting technology to convert discrete sounding point data into a continuous underwater terrain surface; spatial interpolation and morphological processing methods are used to achieve the fusion of cross-sectional measurement data of different periods and different spacings; boundary constraints and transition area optimization algorithms are used to achieve seamless splicing of surface terrain data and underwater terrain data, and the change detection technology of multi-temporal remote sensing images is combined to extract river evolution characteristics; the The storage layer uses a time series database combined with a three-dimensional indexing mechanism of time, space, and measurement points to store sediment monitoring data; a spatial database is used to manage underwater terrain data; a tiered storage strategy is adopted to store hotspot data in a high-speed cache and historical data in a distributed file system; the quality control layer verifies the validity of sediment data by combining numerical range testing, change trend testing, and spatial correlation testing rules; the consistency of underwater terrain data is checked through terrain feature point matching and cross-section comparison analysis; and a reasonable estimation of missing data is made based on spatiotemporal correlation and physical constraints; the service layer uses distributed caching technology and a data subscription push mechanism to provide multi-dimensional and multi-scale data retrieval and extraction through a standardized data query interface.

[0007] Furthermore, the reservoir sediment hydrodynamic model adopts a one-dimensional non-steady flow water and sediment model, generates a calculation engine based on the water flow and sediment control equations and mathematical discrete methods, reads the large-section data of the reservoir area and the water and sediment entering the reservoir, sets the calculation time step, total calculation time, and calculation section position, obtains the results of the reservoir area scouring and sedimentation changes and sedimentation morphology changes, and calibrates and verifies the model parameters through the measured sediment discharge process over the years, the measured sections of the reservoir area, and the scouring and sedimentation changes along the reservoir area.

[0008] Furthermore, the reservoir sediment analysis module includes a sedimentation morphology analysis unit, a dam front funnel area analysis unit, and a reservoir area river channel evolution comparison unit; the sedimentation morphology analysis unit analyzes the changes in the distance between the dam and the deep point elevation of the large sedimentation section by querying the reservoir area section data at any historical time; according to the determined sediment scheduling method, it queries and draws a comparison chart of the sedimentation morphology changes in different periods; the dam front funnel area analysis unit analyzes the distance between the dam and the elevation data of the dam front funnel area by querying the historical monitoring data of the dam front funnel area; according to the determined sediment scheduling method, it queries and draws a comparison chart of the morphology and elevation changes of the dam front funnel area in different periods; the reservoir area river channel evolution comparison unit extracts river channel characteristics through multi-phase satellite remote sensing image data, uses change detection methods to identify river channel evolution laws, and uses different colors to mark river channel morphology changes.

[0009] Furthermore, the knowledge base module electronically transcribes historical water and sediment regulation plans through OCR technology and text correction algorithm, extracts sediment scheduling domain knowledge from unstructured text through entity recognition and relationship extraction technology; constructs a sediment scheduling domain knowledge graph based on ontology technology to describe the relationship between scheduling objects, scheduling rules, and scheduling plans; uses relational database technology to convert scheduling plans into standardized data records; adopts a combination of semantic retrieval and structural retrieval to locate similar scheduling plans; and then matches similar scheduling plans for current water and sediment conditions through association rule mining and case reasoning.

[0010] Furthermore, the three-dimensional visualization module constructs a digital twin scene of the reservoir based on the UE engine and measured terrain data; according to the water level process data, sediment content data, and sediment transport data dynamically simulated and calculated by the reservoir sediment hydrodynamic model, the digital twin scene is adjusted in real time through dynamic changes in water surface elevation and water body color rendering; and the evolution of siltation morphology is displayed by superimposing cross-sectional measurement data and underwater terrain data from different periods; the elevation difference between adjacent terrains is calculated to display the siltation thickness, and color grading is used to express the siltation intensity distribution.

[0011] Furthermore, the scheduling decision module obtains data resources from the data fusion module according to demand, calls the knowledge base module to obtain the recommended scheduling plan, and calls the reservoir sediment hydrodynamic model to perform water and sediment regulation calculations according to the scheduling plan; calls the reservoir sediment analysis module to analyze and compare the water and sediment regulation calculation results and evaluate the scheduling plan; at the same time, calls the three-dimensional visualization module to dynamically preview the scheduling plan.

[0012] Furthermore, the reservoir sediment hydrodynamic model obtains the results of changes in reservoir scouring and deposition, and sedimentation morphology, including inflow and outflow, sediment content, sediment transport rate, scouring and deposition volume along the reservoir and total scouring and deposition volume, and changes in the water level and water storage capacity in front of the dam.

[0013] Furthermore, the evaluation and scheduling plan includes water and sediment process line analysis, cross-section erosion and deposition comparison, and annual erosion and deposition volume statistics.

[0014] Furthermore, the three-dimensional visualization module is also equipped with annotation tools to display dimension information; it supports free scene roaming and multi-perspective switching, fast positioning and zooming, spatial distance and area measurement tools, heat maps to express sediment distribution characteristics, and timeline playback.

[0015] The advantage of the present invention is that it establishes a unified data interface standard and fusion mechanism, improves the fusion efficiency of multi-source heterogeneous data such as hydrology, sediment, and engineering operation, and lays the foundation for real-time correlation analysis of multi-source heterogeneous data. Through three-dimensional dynamic visualization technology, combined with the reservoir sediment hydrodynamic model and satellite remote sensing images of the reservoir area, the dynamic process of scour morphology and sedimentation evolution in the scheduling process is simulated, and the intuitive display effect of the water and sediment scheduling plan is improved, so that the scheduling personnel can intuitively grasp the changing rules of various indicators in the scheduling process, and provide a global perspective for evaluating the sediment discharge effect and optimizing the scheduling plan. At the same time, the present invention also constructs a sediment scheduling knowledge base, supports multi-dimensional intelligent retrieval, realizes the intelligent integration of professional knowledge and scheduling business, and realizes the digital management of the entire process of reservoir sediment scheduling, providing a systematic technical solution for scientifically formulating sediment scheduling plans, accurately evaluating sediment discharge effects, and effectively maintaining reservoir functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a block diagram of the reservoir sediment scheduling system suitable for sediment-laden rivers according to the present invention. DETAILED DESCRIPTION

[0017] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0018] The method for constructing a reservoir sediment dispatching system suitable for sediment-laden rivers described in the present invention adopts an overall architectural design of "one core, five supports", including the construction of a dispatching decision module, a data fusion module, a reservoir sediment hydrodynamic model, a reservoir sediment analysis module, a knowledge base module, and a three-dimensional visualization module. Among them, the dispatching decision module, as the core of the system, is responsible for the intelligent generation and optimization control of sediment dispatching plans; the data fusion module, the reservoir sediment hydrodynamic model, the reservoir sediment analysis module, the knowledge base module, and the three-dimensional visualization module are the five supporting modules, which work together to provide all-round technical support for dispatching decisions. Figure 1 Shown is the block diagram of the reservoir sediment dispatching system for the sediment-laden river.

[0019] During the scheduling decision-making process, the data fusion module provides real-time monitoring data and historical test information, the reservoir sediment hydrodynamic model performs dynamic simulation calculations of the plan, the reservoir sediment analysis module conducts professional analysis of key factors such as the funnel area in front of the dam and the siltation form of the reservoir area, the knowledge base module provides decision-making references through knowledge graphs and intelligent reasoning, and the three-dimensional visualization module realizes dynamic preview and multi-dimensional display of the scheduling process.

[0020] Standardized interfaces are used between modules to achieve data flow and functional collaboration. The scheduling decision-making module integrates the calculation and analysis results of each supporting module to achieve intelligent support for the entire process of reservoir sediment scheduling, from data acquisition, plan generation, simulation calculation, effect analysis to visual display, and provide an integrated solution for sediment scheduling.

[0021] When constructing the data fusion module, a layered design architecture is adopted, focusing on solving the problem of integrating key data such as underwater topography and siltation morphology in sediment scheduling of sediment-laden river reservoirs.

[0022] The data fusion module is divided into interface layer, data processing layer, storage layer, quality control layer and service layer.

[0023] The interface layer unifies data exchange formats based on WebService and RESTful API standard interface specifications. Configurable data acquisition adapters support multiple communication protocols, such as MQTT and Modbus, enabling the automatic collection of real-time monitoring data such as water level, flow, and sediment content. For multi-source measurement data, a dedicated data parsing module enables standardized conversion of data in different formats, such as multi-beam bathymetry, cross-section measurements, and RTK measurements.

[0024] The data processing layer uses triangulated mesh construction and surface fitting techniques to convert discrete bathymetric data into a continuous underwater topographic surface. Spatial interpolation and morphological processing methods are used to fuse cross-sectional measurement data from different periods and intervals. Boundary constraints and transition region optimization algorithms are used to seamlessly integrate surface and underwater topographic data. Furthermore, change detection techniques using multi-temporal remote sensing imagery are used to extract river channel evolution characteristics.

[0025] The storage layer uses a time series database combined with a three-dimensional indexing mechanism of time, space, and measurement points to store sediment monitoring data; a spatial database is used to manage underwater terrain data; a hierarchical storage strategy is adopted to store hotspot data in a cache and historical data in a distributed file system to build a complete sediment scheduling thematic database.

[0026] The quality control layer verifies the validity of sediment data by combining rules such as numerical range test, change trend test, and spatial correlation test; performs consistency check on underwater terrain data through terrain feature point matching and cross-section comparison analysis; and makes reasonable estimates of missing data based on spatiotemporal correlation and physical constraints.

[0027] The service layer uses distributed caching technology and data subscription push mechanism to improve data access efficiency, realizes multi-dimensional and multi-scale data retrieval and extraction through standardized data query interfaces, and combines data version management to record the entire life cycle information of the data.

[0028] The reservoir sediment hydrodynamic model utilizes a one-dimensional unsteady flow model. The governing equations are constructed using the flow continuity equation, the flow motion equation, the suspended load unbalanced sediment transport equation, and the riverbed deformation equation. The solution process utilizes the Gauss iteration method, with under-relaxation techniques used to enhance iterative stability. The specific steps include assigning an initial water level, solving the momentum equation, updating the water level and flow rate, determining convergence conditions (unit residual mass flow reaching 0.01% of the inlet flow rate and global residual mass flow reaching 0.5% of the inlet flow rate), calculating sediment concentration, solving the bedload transport rate, and updating the riverbed morphology.

[0029] In this paper, a reservoir sediment hydrodynamic model is constructed. A computational engine is generated based on the governing equations for flow and sediment and mathematical discretization methods. Input files such as large-scale reservoir cross-sectional data and the flow and sediment process are then read. Model parameters such as the calculation time step, total calculation duration, and cross-sectional location are set. Finally, output results such as changes in scouring and sedimentation in the reservoir area and changes in sedimentation morphology are calculated. The model parameters are calibrated and validated using field measurements of sediment discharge processes, measured cross-sectional data, and scouring and sedimentation changes along the reservoir in recent years.

[0030] The reservoir sediment analysis module addresses sediment accumulation in reservoirs with high sediment content by proposing a multi-dimensional analysis method based on "deposition morphology, funnel area, and river channel evolution." This module includes a sedimentation morphology analysis unit, a dam-front funnel area analysis unit, and a reservoir-area river channel evolution comparison unit.

[0031] In the sedimentation morphology analysis unit, by querying the reservoir section data at any historical time, the historical sedimentation morphology is simulated in a three-dimensional scene, and the mileage and deep-pool point elevation change data of the large sedimentation section from the dam are displayed in the form of charts; the sediment scheduling plan calculated by the sediment hydrodynamic model is called to query the sedimentation morphology information at different periods, and a sedimentation morphology comparison chart is drawn to show the change process; both merged comparison and split-screen comparison are supported to display the longitudinal section, large section, and scouring and silting volume data of the sedimentation morphology, and the morphology, area, and scouring and silting volume values ​​of any large section along the way can be viewed.

[0032] In the dam front funnel area analysis unit, query the historical monitoring data of the funnel area section, display the morphological changes of the dam front funnel area in a three-dimensional scene, and display the mileage and elevation data of the dam front funnel area; call the sediment scheduling plan calculated by the model, query the morphological and elevation changes of the dam front funnel area in different periods, and draw a funnel area morphological comparison chart to show the change process; it also supports merge comparison and split-screen comparison functions to display relevant section and scouring and sedimentation data.

[0033] The reservoir channel evolution comparison unit supports the management of multi-temporal satellite remote sensing image data, realizes the uploading, importing and exporting of image data, and provides data viewing, comparison, deletion and other operations; extracts river channel characteristics based on remote sensing image interpretation technology, adopts change detection methods to identify the evolution law of river channel, and displays the changes in river channel morphology through different color labels; provides rolling curtain comparison and split-screen comparison methods to realize dynamic comparison of river channel evolution in different periods, supports parallel display of river channel morphology in multiple periods, and intuitively displays the evolution law of reservoir channel before and after sand discharge.

[0034] The knowledge base module adopts a multi-level knowledge representation method to solve the problem of intelligent application of professional knowledge of sediment scheduling in sediment-rich river reservoirs. During construction, it includes a knowledge acquisition layer, a knowledge representation layer, a knowledge retrieval layer, a knowledge application layer, and in some embodiments, also includes a knowledge maintenance layer.

[0035] The knowledge acquisition layer uses optical character recognition (OCR) technology and text correction algorithms to electronically transcribe historical water and sediment control plans. Entity recognition and relationship extraction techniques are then used to extract sediment control knowledge from unstructured text. Based on the characteristics of sediment-rich rivers, a classification system for sediment control plans is designed, encompassing dimensions such as water and sediment control methods, water and sediment conditions, and control objectives, enabling a structured representation of control plans.

[0036] The knowledge representation layer uses ontology technology to construct a knowledge graph for sediment management in sandy rivers. This graph describes core concepts such as management objects, management rules, and management plans, as well as their relationships. A semantic network model is used to represent the logical relationships within management rules, while an attribute graph model is used to store the parameter characteristics of management plans. Incorporating relational database technology, semi-structured management plans are converted into standardized data records.

[0037] The knowledge retrieval layer uses a combination of semantic and structural retrieval. Semantic retrieval calculates concept similarity based on a word vector model and automatically constructs a semantic dictionary using contextual co-occurrence analysis. Structural retrieval uses a K-ary tree index structure to support multi-dimensional combined queries. A fast query algorithm based on keyword matching is designed to quickly locate relevant scheduling solutions by calculating the similarity between query conditions and historical solutions.

[0038] The knowledge application layer provides intelligent services based on the knowledge graph reasoning mechanism. It uses association rule mining technology to analyze empirical patterns in historical scheduling plans. It uses case-based reasoning to match similar historical cases based on current water and sediment conditions. It also uses knowledge graph visualization technology to intuitively display the relationships between scheduling plans.

[0039] The knowledge maintenance layer uses incremental learning to update the knowledge base. Knowledge editing tools are designed to support experts in maintaining knowledge content. A version management mechanism is used to record the evolution of knowledge. A knowledge evaluation mechanism is established to regularly assess the timeliness and usability of knowledge.

[0040] The 3D visualization module uses the UE engine to build a digital twin scene for the reservoir. Specifically, it uses measured terrain data to construct a 3D terrain model of the reservoir, focusing on key areas such as the dam front area and the reservoir river channel. Engineering BIM models of flood discharge structures and sediment drainage facilities are imported, and measured underwater terrain data is loaded to construct the siltation terrain of the dam front funnel area and the reservoir area. The water level process data of the scheduling plan is converted into dynamic changes in water surface elevation. The rendering parameters of the water body color are adjusted in real time based on the sediment content data. The particle effects of sediment transport are set based on the sediment transport data. By loading cross-sectional measurement data and underwater terrain data from different periods, the evolution of the sedimentation morphology is displayed through terrain overlay. Similarly, by importing data from previous dam front funnel area morphology tests, the scour morphology of different periods is compared using terrain overlay. The siltation thickness is displayed by calculating the elevation difference between adjacent terrains, and the siltation intensity distribution is expressed using color grading. The scour depth changes at fixed cross-sectional locations are displayed.

[0041] In addition, the 3D visualization module also provides pause, playback, and progress control functions for the scheduling process; configures annotation tools to display key dimension information; supports free roaming of scenes and multi-perspective switching to achieve rapid positioning and scaling of key areas, configures spatial distance and area measurement tools, and controls the display status of scene elements through layer management; accesses monitoring data such as water level, flow, and sediment content, displays the measurement point location and real-time values ​​in the scene, uses heat maps to express sediment distribution characteristics, and supports timeline playback of historical data.

[0042] After constructing the above modules, the scheduling decision module obtains the data resources of the data fusion module according to the needs, calls the knowledge base module to obtain the recommended scheduling plan, and calls the reservoir sediment hydrodynamic model to perform water and sediment regulation calculations according to the scheduling plan; calls the reservoir sediment analysis module to analyze and compare the water and sediment regulation calculation results and evaluate the scheduling plan; at the same time, calls the three-dimensional visualization module to dynamically preview the scheduling plan.

[0043] The scheduling decision module can be divided into sediment monitoring information query unit, scheduling plan calculation unit, and plan evaluation unit.

[0044] Sediment monitoring information query unit enables query of historical and measured water, sediment, water level and storage data, displays measured cross-sections and longitudinal sections over the years, and real-time reservoir working conditions and reservoir characteristics information; The scheduling plan calculation unit calls the reservoir scheduling plan and reservoir sediment hydrodynamic model in the plan library to perform water and sediment regulation simulation calculations to obtain the inflow and outflow flow, sediment content, sediment transport rate, erosion and sedimentation along the reservoir and the total erosion and sedimentation volume, and the water level in front of the dam and the water storage capacity change process; The scheme evaluation unit evaluates the scheme effect through multiple dimensions such as water and sediment process line analysis, cross-section scouring and silting comparison, and annual scouring and silting volume statistics. At the same time, it can call the three-dimensional visualization module to perform dynamic preview of the scheduling scheme.

[0045] In another implementation method, the scheduling decision module further provides for saving the newly generated scheduling plan into a plan library, and fuzzy querying related scheduling plans through the knowledge base.

[0046] The method for constructing a reservoir sediment dispatching system for sediment-laden rivers, described in this invention, establishes unified data interface standards and fusion mechanisms to enable multi-source data collection, integration, and real-time correlation analysis, providing comprehensive data support for sediment dispatching decisions. At the visualization level, dynamic visualization through a three-dimensional scene allows dispatchers to intuitively grasp the changing patterns of various indicators during the dispatching process, resolving the problem of traditional dispatching schemes being limited to two-dimensional charts and lacking intuitive understanding of the dispatching process. At the analytical level, a three-dimensional visualization of historical dam-front topographic survey data, combined with a reservoir sediment hydrodynamic model, simulates the dynamic changes in scour patterns during the dispatching process, providing effective data support for ensuring dam stability and power station safety. By displaying reservoir sedimentation changes over different historical periods in a three-dimensional scene and simulating sedimentation evolution during the dispatching process using a reservoir sediment hydrodynamic model, this system provides technical support for developing precise scour and sedimentation control plans and maintaining reservoir functionality. By accessing satellite remote sensing images of the reservoir area from different periods and comparing and analyzing the interpreted images in a single image, the system systematically displays the evolution of the reservoir river channel before and after sediment discharge, providing a comprehensive perspective for evaluating sediment discharge effectiveness and optimizing dispatching plans. At the storage and application level of historical knowledge, the sediment scheduling knowledge base constructed by the present invention supports multi-dimensional intelligent retrieval, realizes the deep integration of professional knowledge and scheduling business, and solves the problems of low retrieval efficiency and difficulty in associating with real-time business in traditional sediment scheduling knowledge management methods.

[0047] The present invention is applicable to a method for constructing a reservoir sediment scheduling system for a sediment-laden river, provides a complete method for constructing a reservoir sediment scheduling system, and can realize digital management of the entire process of reservoir sediment scheduling, which has important practical significance for improving the operation and management level of reservoirs on sediment-laden rivers.

Claims

1. A method for constructing a reservoir sediment dispatching system suitable for sediment-laden rivers, characterized by: Construct scheduling decision module, data fusion module, reservoir sediment hydrodynamic model, reservoir sediment analysis module, knowledge base module and 3D visualization module; The data fusion module provides real-time monitoring data and historical test data; the reservoir sediment hydrodynamic model performs dynamic simulation calculations of the scheme; the reservoir sediment analysis module conducts professional analysis of the siltation morphology of the funnel area in front of the dam and the reservoir area; the knowledge base module provides decision-making references through knowledge graphs and intelligent reasoning; the three-dimensional visualization module realizes dynamic preview and multi-dimensional display of the scheduling process; data flow is realized between modules through standardized interfaces, and is uniformly called by the scheduling decision module.

2. The method for constructing a reservoir sediment dispatching system suitable for sediment-laden rivers according to claim 1, characterized in that: The data fusion module adopts a layered design architecture, including an interface layer, a data processing layer, a storage layer, a quality control layer, and a service layer. The interface layer converts multi-source heterogeneous data into a unified data format. The data processing layer uses triangulation and surface fitting technology to convert discrete sounding point data into a continuous underwater terrain surface. Spatial interpolation and morphological processing methods are used to achieve the fusion of cross-sectional measurement data of different periods and different spacings. Boundary constraints and transition area optimization algorithms are used to achieve seamless splicing of surface terrain data and underwater terrain data, and change detection technology of multi-temporal remote sensing images is combined to extract river channel evolution characteristics. The storage layer uses a time series database combined with a three-dimensional indexing mechanism of time, space, and measurement points to store sediment monitoring data. A spatial database is used to manage underwater terrain data. A hierarchical storage strategy is used to store hot data in a cache and historical data in a distributed file system. The quality control layer verifies the validity of sediment data by combining numerical range testing, trend testing, and spatial correlation testing rules. The consistency of underwater terrain data is verified through terrain feature point matching and cross-section comparison analysis; missing data is reasonably estimated based on spatiotemporal correlation and physical constraints; the service layer adopts distributed caching technology and data subscription push mechanism, and provides multi-dimensional and multi-scale data retrieval and extraction through a standardized data query interface.

3. The method for constructing a reservoir sediment dispatching system suitable for sediment-laden rivers according to claim 1, characterized in that: The reservoir sediment hydrodynamic model adopts a one-dimensional non-steady flow water and sediment model, generates a calculation engine based on the water flow and sediment control equations and mathematical discrete methods, reads the large-section data of the reservoir area and the water and sediment entering the reservoir, sets the calculation time step, total calculation time, and calculation section location, obtains the results of the reservoir area scouring and sedimentation changes and sedimentation morphological changes, and calibrates and verifies the model parameters through the measured sediment discharge process over the years, the measured sections of the reservoir area, and the scouring and sedimentation changes along the reservoir area.

4. The method for constructing a reservoir sediment dispatching system suitable for sediment-laden rivers according to claim 1, characterized in that: The reservoir sediment analysis module includes a sedimentation morphology analysis unit, a dam front funnel area analysis unit, and a reservoir area river channel evolution comparison unit; the sedimentation morphology analysis unit analyzes the changes in the distance between the dam and the deep point elevation of the large sedimentation section by querying the reservoir area section data at any historical time; according to the determined sediment scheduling method, it queries and draws a comparison chart of the sedimentation morphology changes in different periods; the dam front funnel area analysis unit analyzes the distance between the dam and the elevation data of the dam front funnel area by querying the historical monitoring data of the dam front funnel area; according to the determined sediment scheduling method, it queries and draws a comparison chart of the morphology and elevation changes of the dam front funnel area in different periods; the reservoir area river channel evolution comparison unit extracts river channel features through multi-phase satellite remote sensing image data, uses change detection methods to identify river channel evolution laws, and uses different colors to mark river channel morphology changes.

5. The method for constructing a reservoir sediment dispatching system suitable for sediment-laden rivers according to claim 1, characterized in that: The knowledge base module electronically transcribes historical water and sediment regulation plans using OCR technology and text correction algorithms, and extracts sediment regulation domain knowledge from unstructured text using entity recognition and relationship extraction technology; Based on ontology technology, a knowledge graph in the field of sediment scheduling is constructed to describe the relationship between scheduling objects, scheduling rules, and scheduling plans; relational database technology is used to convert scheduling plans into standardized data records; a combination of semantic retrieval and structural retrieval is used to locate similar scheduling plans; and through association rule mining and case reasoning, similar scheduling plans are matched to current water and sediment conditions.

6. The method for constructing a reservoir sediment dispatching system suitable for sediment-laden rivers according to claim 1, characterized in that: The three-dimensional visualization module constructs a digital twin scene of the reservoir based on the UE engine and measured terrain data; according to the water level process data, sediment content data, and sediment transport data dynamically simulated and calculated by the reservoir sediment hydrodynamic model, the digital twin scene is adjusted in real time through dynamic changes in water surface elevation and water body color rendering; and the evolution of siltation morphology is displayed by superimposing cross-sectional measurement data and underwater terrain data from different periods; the elevation difference between adjacent terrains is calculated to display the siltation thickness, and color grading is used to express the siltation intensity distribution.

7. The method for constructing a reservoir sediment dispatching system suitable for sediment-laden rivers according to claim 1, characterized in that: The scheduling decision module obtains data resources from the data fusion module according to demand, calls the knowledge base module to obtain the recommended scheduling plan, and calls the reservoir sediment hydrodynamic model to perform water and sediment regulation calculations according to the scheduling plan; calls the reservoir sediment analysis module to analyze and compare the water and sediment regulation calculation results and evaluate the scheduling plan; at the same time, calls the three-dimensional visualization module to dynamically preview the scheduling plan.

8. The method for constructing a reservoir sediment dispatching system suitable for sediment-laden rivers according to claim 3, characterized in that: The reservoir sediment hydrodynamic model obtains the results of reservoir area scouring and silting changes and sedimentation morphology changes, including inflow and outflow, sediment content, sediment transport rate, scouring and silting volume along the reservoir and total scouring and silting volume, and the change process of water level and water storage capacity in front of the dam.

9. The method for constructing a reservoir sediment dispatching system suitable for sediment-laden rivers according to claim 7, characterized in that: The assessment and scheduling plan includes water and sediment process line analysis, cross-section erosion and deposition comparison, and annual erosion and deposition volume statistics.

10. The method for constructing a reservoir sediment dispatching system applicable to sediment-laden rivers according to claim 6, characterized in that: The three-dimensional visualization module is also equipped with a marking tool to display dimension information; It supports free roaming of scenes and multi-perspective switching, fast positioning and zooming, spatial distance and area measurement tools, heat maps to express sediment distribution characteristics, and timeline playback.

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