Large-model-driven intelligent calculation method and system for water conservancy mechanism model

Abstract

The invention discloses a large-model-driven intelligent calculation method and system for a water conservancy mechanism model. According to the method, natural language input, structured conversion and intelligent optimization calculation of a scheduling target are realized by integrating a field-enhanced large language model and a water conservancy professional mechanism model. The method comprises the steps of receiving a calculation target expressed by a user in a natural language, and analyzing and converting the calculation target into a constraint condition and a target function which can be recognized by a water conservancy mechanism model; a hydrological model, a hydraulic model, a hydrodynamic model and other models are called based on a workflow engine, and reverse calculation is carried out by adopting a hybrid optimization strategy of'coarse adjustment-fine adjustment-verification '; synchronously and visually displaying the parameter change and the result convergence state in the calculation process; and outputting a calculation result including parameter adjustment logic, standard conformity analysis and multi-scheme comparison. The system comprises a natural language interaction module, a target conversion module, an intelligent calculation engine module, a visualization module and a result generation module, and supports multiple application scenes such as multi-target scheduling, emergency decision making and ecological guarantee. Compared with a traditional scheme, the method has the advantages that the model use threshold is lowered, the dispatching efficiency and calculation transparency are improved, and the method is suitable for complex hydraulic engineering calculation tasks such as reservoir dispatching, cross-basin water transfer and flood control emergency.

Description

Technical Field

[0001] This invention relates to the field of information technology integration of water conservancy projects and artificial intelligence, and in particular to a large-scale model-driven intelligent calculation method and system for water conservancy mechanism models. Background Technology

[0002] Hydraulic mechanism models, as key tools for decision-making tasks such as water resource regulation, reservoir scheduling, and flood control and disaster reduction, have been widely used in water conservancy project planning and scheduling management. Common hydraulic models include MIKE, HEC-RAS, and SWAT, which have high physical modeling accuracy and engineering adaptability, and can simulate complex hydrological, hydraulic, and hydrodynamic processes, providing technical support for reservoir scheduling and flood control plan preparation.

[0003] However, the application of existing hydraulic mechanism models still faces many practical difficulties, specifically in the following aspects:

[0004] 1. The model has a high barrier to entry and is difficult for non-professionals to master.

[0005] The construction and operation of current water conservancy models typically involve complex parameter configuration, boundary condition setting, and interdisciplinary knowledge (such as runoff generation and concentration calculation, river roughness setting, and flood discharge control). The user interface is highly specialized, and the learning curve is steep. Dispatchers without a water conservancy background often find it difficult to independently complete modeling and trial calculations, which severely restricts the popularization and promotion of models in grassroots units and cross-industry scenarios.

[0006] 2. Traditional computing processes are inefficient and lack intelligent optimization mechanisms.

[0007] In the forward calculation mode, users need to manually try different parameter combinations and go through multiple rounds of trial calculations to obtain results that meet the constraints, resulting in low operational efficiency and long calculation cycles. When facing reverse requirements such as flood control targets and ecological flow constraints, most existing model tools lack effective automatic solution functions. Users need to repeatedly adjust parameters based on experience, which cannot achieve fast and systematic target back-calculation, seriously affecting the efficiency of emergency response and multi-objective optimization.

[0008] 3. The calculation process is a "black box," lacking transparency and interpretability.

[0009] In most modeling tools, the impact of parameter adjustments on results is invisible and uncontrollable, lacking an intuitive representation of the sensitivity and response relationships of key variables. Schedulers often struggle to understand the model's internal operating logic and parameter action paths, affecting the model's persuasiveness and trustworthiness in multi-department collaborative decision-making, further creating obstacles to collaboration in data sharing, scheduling plan review, and other tasks.

[0010] 4. Existing tools struggle to simultaneously achieve professionalism, flexibility, and intelligence.

[0011] While current professional software on the market possesses strong functional completeness and modeling capabilities, it generally suffers from problems such as complex operation, high deployment requirements (e.g., local environment dependence), and high degree of human intervention. Traditional script automation tools, while simplifying some processes, lack flexibility and are difficult to adapt to different types of hydraulic models and dynamic business scenarios. Relying solely on large language model approximate reasoning ignores the physical constraints in hydraulic models, making it difficult to guarantee the engineering usability and security of the results.

[0012] 5. Unable to meet the requirements for rapid response, multi-objective decision-making, and collaborative scheduling.

[0013] In scenarios with high time sensitivity and complex constraints, such as flood control, flood season forecasting, and ecological water transfer, traditional water conservancy models lack intelligent auxiliary mechanisms. It is difficult for dispatchers to complete parameter solutions under multiple constraints within a limited time, which seriously affects the efficiency of emergency response. In multi-department collaboration, the calculation process and parameter logic are difficult to share, resulting in repeated modeling and difficulty in verifying the solution results, which affects the efficiency of collaboration.

[0014] In summary, existing hydraulic mechanism model calculation methods face a typical contradiction: "high level of specialization, high barrier to entry, low efficiency, and opaque results." There is an urgent need for an intelligent, visualized, and accessible solution to improve the efficiency, decision support capabilities, and collaborative friendliness of hydraulic models. The large-scale model-driven intelligent calculation method for hydraulic mechanism models is proposed against this backdrop. By combining natural language understanding capabilities with the physical constraints of hydraulic models, it provides more intelligent, efficient, and interpretable technical support for complex decision-making. Summary of the Invention

[0015] To achieve the above objectives, this invention proposes a large-scale model-driven intelligent computation method and system for hydraulic mechanism models. This method integrates several key technologies, including natural language processing, domain knowledge modeling, hybrid optimization computation, and result visualization. Focusing on the core goals of "lowering the barrier to entry, enhancing computational intelligence, and improving process transparency," it constructs a fully intelligent architecture from user input to computational output through the collaborative operation of multiple functional modules.

[0016] The technical solution of the present invention will be described in detail below with reference to specific embodiments, so as to further clearly illustrate the structural composition, calculation process and functional features of the present invention. Each embodiment can exist independently or can be used in combination.

[0017] In one embodiment of the present invention, a large-model-driven intelligent calculation method for hydraulic mechanism models is provided, the method comprising the following steps:

[0018] S1. Receive and parse the natural language computational objectives input by the user through a large-scale language model with domain enhancement, wherein the computational objectives include single-objective computation and multi-objective optimization;

[0019] S2. Transform the natural language computing objective into executable mathematical constraints for the hydraulic mechanism model, and establish a mapping relationship between the objective function and the constraints;

[0020] S3. The intelligent workflow engine calls the hydraulic mechanism model and uses a hybrid computing strategy to perform goal-oriented reverse calculation;

[0021] S4. Real-time visualization of parameter iteration trajectory and result convergence status during the calculation process;

[0022] S5. Output a calculation report containing parameter adjustment logic, water conservancy engineering specification compliance analysis, and multi-scheme comparison results.

[0023] Furthermore, the domain-enhanced large-scale language model achieves water conservancy domain adaptation in the following manner:

[0024] Based on the dictionary of water conservancy terminology and industry standard documents, we made minor adjustments to the field and established a mapping relationship between natural language expressions and professional terms.

[0025] It has a built-in knowledge graph of water conservancy calculation standards, which can automatically associate the parameter limits and constraint thresholds in the "Reservoir Operation Regulations";

[0026] An integrated error operation identification module can detect and warn users in real time about potential unreasonable or excessive parameter settings in their input.

[0027] In another embodiment of the present invention, the natural language target conversion process includes the following steps:

[0028] S21. Identify the core constraint indicators expressed in natural language through semantic analysis technology, and transform descriptive semantics such as "flood control safety" and "maintaining reservoir water level" into mathematical threshold conditions such as flow rate and water level;

[0029] S22. For optimization problems involving multiple objectives, extract their priority or weight relationships and establish a weighted objective function model;

[0030] S23. Based on semantic recognition results and knowledge graph matching, automatically associate the control parameter dimensions in the water conservancy mechanism model to determine the key parameters that can participate in the optimization calculation, such as gate opening, flood discharge duration, and inflow.

[0031] Furthermore, the hybrid computing strategy includes the following three stages:

[0032] Coarse adjustment stage: The Latin hypercube sampling method is used to generate multiple initial schemes within the feasible region of the preset parameters. The simplified mechanism model is used to perform rapid batch calculations to screen candidate solutions that meet the core constraints.

[0033] Fine-tuning phase: Based on the candidate solutions and combined with the parameter sensitivity analysis results, intelligent optimization algorithms such as NSGA-II are used to perform gradient optimization on key parameters to improve computational accuracy and convergence speed;

[0034] Validation phase: The optimal results are input into multiple physical models such as hydrological model, hydraulic model, and hydrodynamic model for cross-validation. The differences between the results are judged, and the results are considered valid when the deviation rate is ≤5%.

[0035] Preferably, the parameter sensitivity analysis includes:

[0036] Calculate the elasticity coefficients of each adjustable parameter with respect to the objective function to identify the strength of the influence of parameters such as "gate opening" on "downstream flow".

[0037] Construct a parametric-response surface model to help identify highly nonlinear sensitive regions;

[0038] Furthermore, the sensitivity weights are dynamically updated using historical scheduling case data to enhance the global guidance capability of the optimization algorithm.

[0039] Optionally, the visualization module includes the following functional components:

[0040] The real-time iterative curve generation module plots the curve of the objective function value changing with the number of iterations, which is used to present the calculation convergence process;

[0041] The spatial rendering module uses WebGL technology to construct a 3D hydrodynamic visualization scene, dynamically displaying the evolution path of the outflow and the extent of inundation impact.

[0042] The interactive parameter tuning panel allows users to manually adjust parameter sliders and immediately view the results and response changes, enhancing system debugging and comprehension.

[0043] Furthermore, the present invention also includes an anomaly detection and handling mechanism:

[0044] The system monitors the change curves of key parameters (such as flood discharge flow and reservoir water level) in real time during the calculation process. When the sudden change exceeds the set threshold or exceeds the range of engineering experience, it automatically triggers the model reset or rollback calculation steps.

[0045] Abnormal nodes and their processing logic will be automatically written into the final calculation report, enhancing the traceability and reliability of the process.

[0046] In one embodiment of the present invention, a large-model-driven intelligent computing system for hydraulic mechanism models, which is compatible with the above-described method, is also provided. The system includes:

[0047] The natural language interaction module is used to receive and parse the natural language computing objectives input by the user;

[0048] The objective transformation module is used to transform the analytical results into executable constraints and objective functions for the mechanistic model;

[0049] The intelligent computing engine module integrates a hydraulic mechanism model library and a workflow scheduler, which is used to call the model and execute hybrid computing strategies;

[0050] The visualization module is used to dynamically display parameter evolution, intermediate calculation results, and target approximation state;

[0051] The results generation module is used to output structured calculation results reports, including parameter adjustment paths, standard compliance analysis, and multi-scheme comparison information.

[0052] Furthermore, the intelligent computing engine module preferably includes the following sub-components:

[0053] A process template library is used to store standard calculation processes such as reservoir scheduling and flood evolution;

[0054] A dynamic parameter adjuster is used to dynamically adjust the parameter step size and search path based on reinforcement learning algorithms;

[0055] A multi-model coordinator is used to manage the data interfaces and execution synchronization mechanisms between different mechanistic models.

[0056] Alternatively, a computer-readable storage medium is also provided, having stored instruction code thereon, which, when executed by a processor, enables the implementation of all the steps of any of the methods described above.

[0057] In one embodiment of the present invention, the large-model-driven intelligent computation method for hydraulic mechanism models can be widely applied to various typical hydraulic engineering decision support scenarios, including but not limited to the following applications:

[0058] In reservoir flood control scheduling scenarios, the system can quickly determine the optimal flood discharge plan that meets the downstream flood control safety requirements based on the forecast data of the inflow flood through a reverse calculation mechanism, thereby improving the efficiency and scientific nature of the scheduling response.

[0059] In the scenario of beneficial operation and dispatching of hydropower stations, the system automatically optimizes the operating parameters of generator units to maximize power generation benefits while taking into account the reservoir regulation and storage function, under the premise of meeting the ecological flow lower limit constraint.

[0060] In cross-basin water transfer scenarios, the system can coordinate multiple reservoir models to achieve intelligent coordination of joint scheduling parameters, automatically balance the spatiotemporal supply and demand relationship between the water source area and the water receiving area, and improve the overall water resource allocation efficiency.

[0061] In emergency rescue command scenarios, the system can combine real-time rainfall, water conditions and levee risk data to quickly complete the reverse simulation of defense plans, and provide auxiliary decision-making information such as levee reinforcement paths and flood diversion strategies, thus winning critical time for rescue and relief.

[0062] In multi-objective optimization scenarios, the system supports the simultaneous handling of multiple objectives such as flood control, power generation, irrigation, and ecology. Through intelligent optimization algorithms, it coordinates the weights and constraints of various parameters to achieve the automatic output of the globally optimal scheduling scheme.

[0063] Through the above typical applications, the method proposed in this invention not only has technical universality and scenario adaptability, but also significantly improves the intelligence level and decision-making efficiency of water conservancy scheduling, and is suitable for the integrated deployment and extended application of various engineering management platforms.

[0064] Based on the above technical solutions, the large-model-driven intelligent calculation method and system for hydraulic mechanism models of the present invention, by introducing a fusion mechanism between language models and hydraulic mechanism models, realizes the intelligent upgrade of the traditional hydraulic model calculation process, and has the following significant technical effects:

[0065] 1. Lower the barrier to entry for professional users and enhance the system's versatility.

[0066] This invention supports users in inputting scheduling objectives and constraints in natural language through a domain-enhanced large-scale language model. Users do not need to have background knowledge in hydrology, hydraulics, or other disciplines; the system can automatically parse these into mathematical model constraints, significantly reducing the professional requirements of traditional model operations and expanding the system's applicability in scenarios such as grassroots scheduling stations and multi-departmental collaborations.

[0067] 2. Supports goal-oriented reverse computation, optimizing the efficiency of scheduling strategy generation.

[0068] Compared to the traditional "forward trial calculation + manual parameter tuning" process, this invention introduces a reverse calculation core, allowing users to perform calculations based on desired results (such as "downstream flow ≤ 1000m³"). 3 The optimal combination of scheduling parameters is derived by using the method of reverse engineering ( / s”). Through the coordinated operation of sensitivity analysis and intelligent optimization algorithms (such as NSGA-II), the process of automatically reversing from the objective to the solution is realized, which greatly shortens the time for forming a scheduling solution and improves the response capability in emergency scenarios.

[0069] 3. Construct a three-stage hybrid computation mechanism to ensure a balance between computational accuracy and convergence efficiency.

[0070] This invention, combining the characteristics of hydraulic mechanism models, proposes a hierarchical calculation process of "coarse adjustment—fine adjustment—verification": the coarse adjustment stage rapidly narrows the feasible solution space, the fine adjustment stage uses an evolutionary algorithm for global optimization, and the verification stage cross-verifies the rationality of the results through multiple physical models. This strategy balances efficiency and accuracy, effectively solving the dual bottlenecks of "slow computation" and "unreliable results" in traditional schemes.

[0071] 4. Enhance parameter visualization and response surface analysis to improve process transparency and debuggability.

[0072] This invention introduces WebGL 3D visualization capabilities and a parameter-response surface graph construction mechanism into the calculation process, which can dynamically display the impact curves of changes in parameters such as gate opening and flood discharge duration on water level and flow rate, clarify the influence weight of each parameter on the objective function, and transform the calculation process from a "black box" to an "interpretable" one, making it easier for engineers to debug and intervene, and is especially suitable for cross-departmental collaborative scheduling.

[0073] 5. Possesses anomaly detection and self-recovery capabilities, enhancing operational stability.

[0074] To address the problem of model instability or parameter mutations under complex hydrological conditions, the present invention has set up an anomaly detection and handling mechanism. When key parameters change drastically, it can promptly trigger recalculation, rollback, or automatic restart strategies, and record anomalies and countermeasures in the results report, ensuring the safety and controllability of the calculation process and improving the engineering practicality of the system.

[0075] 6. Adaptable to various water conservancy business models, possessing high scalability and integration capabilities.

[0076] Through modular design, the system of this invention supports flexible use of various mechanistic models, including hydrological models, hydraulic models, and hydrodynamic models. It also achieves data decoupling and collaborative operation between models through a workflow scheduler. It can be widely applied to various application scenarios such as reservoir scheduling, flood evolution simulation, and ecological flow guarantee, and has good scalability and industry versatility.

[0077] 7. Produce professional outcome reports to ensure the standardization and traceability of the results.

[0078] After the calculation is completed, the system automatically generates a calculation result report, which includes: parameter adjustment trajectory, target achievement status, comparative analysis of water conservancy standards, and comparative evaluation of multiple schemes. The report has a standardized structure and can be exported, meeting various needs such as project archiving, result review, and collaborative decision-making, thereby improving the digital level of engineering management. Attached Figure Description

[0079] To more clearly illustrate the technical solution of the present invention, several embodiments of the present invention will be further described below with reference to the accompanying drawings. The drawings are for illustrative purposes only and do not constitute a limitation on the scope of protection of the present invention.

[0080] Figure 1 This is the overall architecture diagram of the intelligent computing method and system for hydraulic mechanism models driven by the large model proposed in this invention. It is used to show the logical relationship and information flow path between the various functional modules of the system, including natural language interaction module, target transformation module, intelligent computing engine, visualization module and result output module.

[0081] Figure 2 This invention presents a flowchart of the goal-oriented reverse computation process, which describes how the system completes the computation task through steps such as goal parsing, parameter sensitivity analysis, intelligent optimization, and result verification after the user inputs a natural language goal.

[0082] Figure 3 This is a structural diagram of the intelligent workflow engine in this invention, showing its internal sub-modules such as the process template library, model scheduler, and parameter adjuster, as well as their calling logic and interaction methods during the execution of model calculation tasks.

[0083] Figure 4 This is an example of the visualization interface proposed in this invention. The figure shows the parameter adjustment slider, the objective function convergence curve, the water level-flow response relationship diagram, and the three-dimensional spatial distribution map, which demonstrate the system's ability to visualize the calculation process and results.

[0084] Figure 5 This is a time sequence diagram of the multi-model collaborative computing proposed in this invention. It shows the process sequence of hydrological model, hydraulic model and hydrodynamic model participating in the scheduling calculation in sequence, data interaction and result fusion, reflecting the system's ability to support multi-model collaborative solution. Detailed Implementation

[0085] The implementation methods and systems of the large-model-driven intelligent calculation method and system for hydraulic mechanism models of the present invention will be described in detail below with reference to the accompanying drawings. The present invention is not limited to the following embodiments. Various substitutions or modifications made by those skilled in the art to the structure or process of the present invention without departing from the essential content of the present invention should also be covered within the protection scope of the present invention.

[0086] Example 1: System Structure and Module Composition

[0087] In one embodiment of the present invention, a large-model-driven intelligent computing system for hydraulic mechanism models is provided, the system architecture of which is as follows: Figure 1As shown, the system as a whole includes core functional components such as a natural language interaction module, a target transformation module, an intelligent computing engine module, a visualization presentation module, and a result generation module. These correspond to various technical stages, including user input understanding, target construction, model execution control, result visualization, and scheduling output, and can realize a closed-loop intelligent processing flow from user input intent to professional scheduling solution output.

[0088] In actual operation, users interact with the system through a natural language interaction module, allowing them to propose scheduling goals or control constraints without needing to master technical terminology. For example, a user can input "maximize power generation, but do not allow downstream flow to exceed safe limits," and the system will automatically parse the input semantics and, combined with a water conservancy industry dictionary and standard knowledge graph, transform it into standard technical language. The domain-enhanced large language model on which this module is based possesses the ability to understand technical terms, remember contextual semantics, and recognize computational intent, accurately extracting phrases such as "downstream flow ≤ 1500 m³ / h". 3 Hard constraints and objective functions such as " / s" and "maximizing power generation" provide clear guidance for subsequent modeling and optimization.

[0089] After the natural language interaction is completed, the target transformation module receives the structured semantic content and further parses it into mathematical constraint expressions and objective function forms that can be input into the hydraulic mechanism model. Simultaneously, it matches associated adjustable parameter dimensions, such as gate opening, flood discharge period, and upper and lower water level limits. This module can also automatically set initial parameter boundaries, scheduling cycles, and control precision based on the system's built-in regional reservoir scheduling rule base and historical operating data, thereby improving the quality of the model's initial solution and enhancing the convergence performance of subsequent optimizations.

[0090] After the modeling information is prepared, the system calls the intelligent computing engine module to enter the core solution process.

[0091] like Figure 3 As shown, the intelligent computing engine module consists of sub-modules such as a process template library, a model scheduler, a dynamic parameter adjuster, and a multi-model coordinator. These sub-modules work collaboratively under workflow-driven conditions to achieve functions such as model invocation, parameter adjustment, data transfer, and computational strategy selection. This structure supports automated solving of highly complex scheduling tasks and possesses adaptive optimization and anomaly handling capabilities. The intelligent computing engine integrates a standardized scheduling process template library, a hydrological and hydrodynamic model library, an optimization algorithm manager, and a dynamic parameter adjuster. In the computation process, the system first automatically selects the most suitable process template based on the target scenario (such as routine scheduling, rainstorm emergency response, and ecological water transfer) and loads the appropriate physical model. The model scheduler coordinates and invokes multiple models to ensure consistency in timing and data interfaces for tasks such as hydrological runoff simulation, hydraulic flow calculation, and two-dimensional hydrodynamic propagation processes.

[0092] To avoid the problem of repeated manual calculations during the computation process, the system automatically identifies key variables affecting the objective function through an embedded parameter sensitivity analysis mechanism, dynamically adjusting the optimization dimensions and search strategy. The parameter tuner utilizes reinforcement learning to continuously adjust the step size, sampling density, and variable combination based on historical iteration feedback, thereby improving optimization efficiency and avoiding getting trapped in local optima. The entire process supports switching between various intelligent optimization algorithm interfaces, including NSGA-II, genetic algorithms, and particle swarm optimization, ensuring the system has sufficient adaptability to scenarios with varying levels of complexity.

[0093] During the operation of the intelligent computing engine, all parameter change trajectories and intermediate model output results are simultaneously displayed graphically through the visualization module. Users can view information such as objective function convergence curves, water level change trend graphs, and dispatch control strategy curves on the interface. Based on the watershed model in a 3D GIS scene, they can dynamically observe the evolution of the discharge flow, the expansion of the inundation zone, and the response status of key sections. This module also supports interactive parameter adjustment; users can manually drag parameter sliders, and the system provides real-time feedback on changes in calculation results, facilitating on-site dispatchers to compare and judge different options.

[0094] After the calculation is completed, the results generation module will automatically output a structured scheduling report, which includes information such as the final parameter combination, the achievement of optimization goals, specification compliance analysis, explanation of scheduling logic, and comparison of multiple solutions. The report also integrates records of abnormal nodes and backtracking path descriptions to ensure that the entire calculation process is traceable and verifiable, providing strong technical support for scheduling decisions. The generated results can be exported as PDF, Word, or a structured data interface, supporting various business scenarios such as archiving, reporting, and collaborative analysis.

[0095] In summary, the system described in this embodiment has a complete structure and a closed-loop process, which can effectively support multi-objective computing tasks in complex scenarios such as reservoir scheduling, flood evolution, and ecological protection. Its natural language-driven capability, hybrid computing mechanism, and full-process visualization features constitute the core innovation of this invention.

[0096] Example 2: Goal-oriented reverse computation process

[0097] In another embodiment of the present invention, a user-goal-driven reverse calculation process is provided, which is applicable to water conservancy model scheduling scenarios with clear scheduling goals or constraints, and shows good adaptability and efficiency, especially in calculation tasks such as flood control, ecological flow guarantee, and multi-objective coordination.

[0098] like Figure 2As shown, the reverse computation process provided by this invention includes steps such as natural language target parsing, parameter identification and mapping, sensitivity analysis, three-stage hybrid optimization computation, and result verification. The entire process is goal-oriented, coordinating the invocation of multiple models and data flow through an intelligent workflow engine to construct a closed-loop computation path from user intent input to the output of the optimal scheduling scheme.

[0099] The process is initiated by user natural language input. Users express their decision-making intent through the natural language interaction module, such as "maximize power generation while ensuring that the downstream flow does not exceed 1500 cubic meters per second." The system first performs semantic understanding and structured extraction on the user's input natural language objective, and uses a domain knowledge graph to identify key constraints (such as the downstream flow limit) and optimization directions (such as maximizing power generation). At the same time, it determines that the objective type is a typical "constraint-first + benefit-oriented" reverse scheduling task.

[0100] After semantic parsing, the target transformation module maps the user's expressed language target into a mathematical structure recognizable by the hydraulic mechanism model, constructs a set of constraints and an objective function expression, and extracts associated adjustable parameter dimensions, such as gate opening, flood discharge time period, and initial reservoir water level. During this process, the system also automatically loads suitable computational process templates and model structures based on the target structure and model characteristics, providing a complete execution path for subsequent calculations.

[0101] Upon entering the optimization calculation phase, the system first invokes the intelligent computing engine to perform parameter sensitivity analysis on the current objective. This step uses the local perturbation method to analyze the impact of each type of adjustable parameter, calculates its response elasticity to the objective function, automatically identifies the key parameters most sensitive to the scheduling results, and sets optimization priorities and iteration strategies accordingly. After the sensitivity analysis is completed, the system initializes parameter samples and enters a three-stage hybrid calculation process.

[0102] In the first stage of coarse tuning, the system uses the Latin hypercube sampling method to generate a large number of initial samples within the feasible region of each parameter. A simplified hydraulic model (such as a simplified hydraulics model) is then used to quickly filter these samples, eliminating parameter combinations that violate rigid constraints (such as excessive downstream flow), thus forming the first round of feasible solution sets. This stage is computationally fast and has a wide convergence range, primarily used to narrow the solution space and improve the efficiency of subsequent fine tuning stages.

[0103] The second stage, fine-tuning, then begins, where the system employs the NSGA-II multi-objective evolutionary algorithm to iteratively optimize the feasible solution set. This algorithm, based on a non-dominated sorting mechanism and crowding distance calculation, simultaneously considers both maximizing power generation and satisfying constraints, improving convergence accuracy while preserving objective diversity. During each iteration, the system dynamically adjusts the search step size and crossover / mutation probability of key parameters based on previous sensitivity analysis results, ensuring the optimization path steadily approaches the objective. If abnormal situations such as drastic fluctuations in the objective function, sudden parameter jumps, or model instability occur during calculation, the system automatically activates the anomaly handling mechanism, performing parameter rollback and path correction to prevent error accumulation from affecting the final result.

[0104] In the third stage of verification, such as Figure 5 As shown, the system sequentially calls physical model modules such as the hydrological model, hydraulic model, and hydrodynamic model according to a preset scheduling process, forming a multi-model collaborative operation link. This collaborative mechanism coordinates with the process controller through data interface docking to ensure the consistency of input and output of each model and the closure of the calculation logic, realizing multi-source physical cross-validation of the scheduling scheme results. The system inputs the optimal parameter combination obtained in the fine-tuning stage into the complete physical model system, including the hydrological model, hydraulic model, and hydrodynamic model, to conduct cross-validation. The system compares the key indicators output by different models (such as maximum peak flow and water level at the end of the scheduling period), calculates their deviation rate, and uses 5% as the validity judgment threshold to ensure the reliability of the results in terms of both physical consistency and engineering usability.

[0105] The entire reverse computation process features real-time status recording and visualization feedback within the system. During optimization, the visualization module simultaneously displays the convergence trend of the objective function, the evolution trajectory of key parameters, and the response effect of the current scheduling path in the 3D hydrodynamic model, such as the downstream flood peak propagation path caused by changes in gate opening and the reservoir water level change trend. Furthermore, the system supports interactive scheme adjustments, allowing users to manually adjust a key parameter and view its impact on the objective function and constraint results in real time, thereby improving human-machine collaboration efficiency.

[0106] After the calculations are completed, the results generation module will automatically output a structured scheduling result report. The report includes: the final recommended parameter combination and control strategy, the achievement status of each constraint, statistical analysis of multi-model validation results, power generation benefit evaluation indicators, and visualization charts. Furthermore, if any interruptions, rollbacks, or abnormal corrections occur during the optimization process, the system will also record the relevant nodes, causes, and recovery paths in the report, enhancing the interpretability and credibility of the results.

[0107] Through the aforementioned reverse calculation process, the system can achieve intelligent reasoning capabilities that "start from the target and backtrack to the optimal solution," significantly reducing the manual burden and calculation time in traditional trial-and-error scheduling processes. It is particularly suitable for complex water conservancy engineering application scenarios that require rapid reverse calculation of control strategies, such as joint reservoir scheduling, rainstorm emergency response, and ecological flow control.

[0108] Example 3: Typical Application Scenario—Intelligent Assisted Optimization of Reservoir Scheduling

[0109] In another embodiment of the invention, the system is deployed on the dispatching and command platform of a multi-functional reservoir to support intelligent auxiliary calculations for pre-flood season optimization dispatching tasks. This reservoir is located in a typical mountainous river region and primarily undertakes multiple tasks including flood control, power generation, and ecological water supply. It requires rapid, controllable, and visualized dispatching calculations under uncertain weather conditions.

[0110] In this embodiment, the reservoir dispatcher inputs the following via natural language on the system login interface: "Please generate as much power as possible while ensuring that the downstream flow does not exceed 1500 cubic meters per second, and at the same time meet the ecological flow requirement of no less than 120 cubic meters per second." The system performs semantic parsing on this input through the natural language interaction module, identifying three key points: first, the maximum downstream flow constraint is 1500 cubic meters per second. 3 The constraints are: 1) a hard safety constraint; 2) maximizing power generation as the optimization objective; and 3) ecological base flow as the minimum guarantee condition. The above semantic structure is transformed into a formalized constraint expression and objective function by the system, and the objective transformation module further parses out the associated model control parameters, including gate opening sequence, flood discharge period combination, and reservoir water level control interval.

[0111] After system initialization, a reservoir scheduling process template matching the region is loaded, and a set of mechanistic models suitable for this operating condition is invoked. These include a one-dimensional hydraulic model to simulate the flood discharge process, a rainfall-runoff model to simulate the inflow hydrological situation, and a two-dimensional hydrodynamic model to assess the spatial distribution of the impact of the discharged flood on the downstream area. Subsequently, the system enters the reverse calculation process, and performs scheduling parameter search and solution according to the three-stage hybrid optimization strategy described in Example 2.

[0112] During the coarse-tuning phase, the system uses the Latin hypercube sampling method to generate a large number of initial parameter combinations in two dimensions: gate opening and flood discharge duration. A simplified model is then called to quickly assess whether these combinations satisfy the basic constraints. After screening, the system retains approximately 50 candidate solutions as the initial input for the fine-tuning phase.

[0113] During the fine-tuning phase, based on parameter sensitivity analysis results, the system focuses on optimizing control variables that significantly impact downstream flow, such as gate opening segmentation and flood discharge start / stop timing. A multi-objective optimizer, with the NSGA-II algorithm at its core, performs rapid convergence search based on the nonlinear response of the objective function, continuously generating and filtering the optimal nondominated solution. During iteration, the system automatically monitors the fluctuation trend of the objective function and constraint triggering. Upon detecting sudden jumps or convergence interruption risks, a rollback mechanism is triggered in real time, reverting the parameter search path to near the previous valid solution, resetting the step size, and continuing iteration to ensure overall computational stability.

[0114] During the verification phase, the system inputs the optimal parameter combination obtained from fine-tuning into multiple mechanistic models for consistency verification. The system calculates result indicators from three dimensions: hydrological, hydraulic, and hydrodynamic, such as the maximum flow at the downstream control section, the reservoir water level at the end of the scheduling period, and the ecological flow achievement rate. Deviation analysis is then performed on the core indicators output by different models. Verification results show that the deviation rate between models is controlled within 3.7%, meeting the system's preset engineering tolerance standard of ≤5%, thus determining the scheduling scheme to be physically effective.

[0115] After optimization, the system presents the calculation process and results in multiple ways through a visualization module. For example... Figure 4 As shown, the system's visualization interface includes modules such as parameter adjustment sliders, objective function convergence trend graphs, dynamic curves of scheduling paths, and 3D hydraulic evolution diagrams. Users can intuitively view the calculation process and parameter changes for each round in the visualization interface, and it supports interactive scheduling and real-time feedback of results, improving the transparency of the calculation process and the understandability of the scheduling results. Users can view flood evolution animations through the 3D map interface and analyze the flood peak propagation path and inundation area in real time; they can also view information such as gate opening change curves, water level response diagrams, and ecological flow line graphs through the chart interface. The system also generates three sets of high-quality optional scheduling schemes, which are displayed in a risk-benefit two-dimensional comparison diagram, indicating the optimal solution with the maximum power generation and the minimum risk. Users can choose the recommended scheme based on these results, or make a trade-off choice based on their preferences.

[0116] Finally, the results generation module outputs a scheduling results report, which includes target constraint achievement analysis, parameter control strategies, optimized path descriptions, three-model cross-validation results, and scheduling recommendations. The recommended scheduling scheme is a phased flood discharge control scheme: the gate opening is maintained at 30% for the first 6 hours, increased to 50% for the middle 4 hours, and reduced to 40% for the last 2 hours, with a total flood discharge duration of 12 hours and a maximum downstream flow of 1480 m³ / h. 3 / s, ecological flow continuously meets 120m 3 With a speed of over / s, the power generation reaches 126,000 kWh.

[0117] This embodiment demonstrates that the system provided by the present invention can quickly complete the design of multi-objective scheduling schemes without manual modeling. Especially in complex operating conditions such as flood response, ecological protection, and power generation benefit coordination, it has high responsiveness, strong transparency, and operability, which is significantly better than the traditional "manual modeling + round-by-round trial calculation" method. It is suitable for real-time scheduling, rolling optimization, and cross-departmental collaboration in practical business scenarios.

[0118] Example 4: Exception Handling Mechanism and Security Backtracking Strategy

[0119] In another embodiment of the present invention, in order to deal with abnormal situations such as parameter jumps, model instability, and calculation interruptions that may occur during the iterative calculation of complex hydraulic mechanism models, the system has a built-in multi-level anomaly handling and self-recovery mechanism to ensure the continuity, security and traceability of the entire calculation process.

[0120] During the model execution phase, the system monitors the dynamic changes of key parameters in real time, including variables such as gate opening, reservoir water level, outflow, and power generation, and establishes a trend detection model based on a sliding time window. Building upon this, the system incorporates a historical fluctuation range database and an engineering boundary condition library, setting physical upper limits and empirical fluctuation thresholds for each parameter. For example, if the system detects a gate opening jump exceeding 20% ​​between two iterations in a scheduling calculation, while the historical empirical range is ±10%, the system will immediately classify this behavior as "moderate anomaly" and trigger a rapid warning.

[0121] Furthermore, to identify system-level operational anomalies, the system introduces a model behavior anomaly detection module to jointly monitor model runtime, output stability, and convergence curve characteristics. For example, in a multi-objective optimization computation, during the convergence process of the NSGA-II algorithm, the system detected that the objective function value had significantly retreated by more than 15% over five consecutive iterations, while the diversity of the optimization population had decreased sharply. The model scheduler determined that the current path had entered a "local minimum stagnation zone," thus triggering a high-level anomaly flag.

[0122] Upon triggering an anomaly, the system adopts a tiered response strategy based on different levels. For minor deviations, the system will appropriately reduce the current parameter search step size to decrease the intensity of iterative perturbations. For moderate anomalies, such as single parameter fluctuations exceeding limits but the model not collapsing, the system will automatically revert to the previous valid parameter combination, reset the direction of random perturbations, and re-optimize the solution for that round. For severe anomalies, such as model computation interruption, empty output values, or non-convergence, the system will trigger system-level fault-tolerant logic, including replacing the backup model, switching to a simplified computation path, or temporarily freezing the parameter dimension for local stability recovery.

[0123] To ensure the reproducibility of the anomaly handling process and subsequent auditing capabilities, the system employs a dual protection mechanism of "parameter snapshot + path backtracking." After each optimization iteration, the system automatically solidifies the state of core variables and intermediate results, creating a time-stamped snapshot record stored in the parameter history tree. This tree structure can be used to quickly locate the parameter state at any point in time and supports backtracking several iterations along the historical path to reconstruct the optimization path for new branches.

[0124] In a typical scheduling case, during the 7th round of optimization iteration, the system identified that two parameters, gate opening and flood discharge period, were fluctuating drastically at the same time, causing severe fluctuations in the objective function, with a deviation of up to 24%. The system immediately backtracked to the 5th round snapshot, reset the disturbance range of the two parameters and froze other low-sensitivity parameters, and only performed correction iterations on high-impact factors. Finally, in the 9th round, it converged to an optimal solution that satisfied the constraints, effectively avoiding uncontrollable path propagation.

[0125] In addition to runtime responses, the system also writes all anomaly information into the final deliverables report. This information includes the anomaly detection time, triggering parameters, judgment rules, response strategies, and recovery results, forming a complete "anomaly log table." The report also includes a parameter evolution trend chart, indicating the location of anomaly nodes and correction paths, which aids in manual review and post-event analysis.

[0126] By integrating the above-mentioned anomaly handling mechanisms, this invention not only ensures the stability of the hydraulic mechanism model calculation process and isolates error risks, but also enhances the system's automatic fault tolerance capability in high-frequency iteration and nonlinear complex scenarios, providing highly reliable technical support for key tasks such as reservoir scheduling and flood season emergency response.

[0127] It should be noted that the specific embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. For those skilled in the art, any equivalent substitutions or modifications made without departing from the concept of the present invention should be included within the scope of protection of the present invention.

[0128] The scope of protection of this invention shall be determined by the appended claims. The specific embodiments in the specification are only used to help understand the core ideas and technical solutions of this invention and do not constitute a limitation on the scope of protection of this invention.

Claims

1. A large-scale model-driven intelligent calculation method for hydraulic mechanism models, characterized in that, Includes the following steps: S1. Receive and parse the natural language computational objectives input by the user through a large-scale language model with domain enhancement, wherein the computational objectives include single-objective computation and multi-objective optimization; S2. Transform the natural language computing objective into executable mathematical constraints for the hydraulic mechanism model, and establish a mapping relationship between the objective function and the constraints; S3. The intelligent workflow engine calls the hydraulic mechanism model and uses a hybrid computing strategy to perform goal-oriented reverse calculation; S4. Real-time visualization of parameter iteration trajectory and result convergence status during the calculation process; S5. Output a calculation report containing parameter adjustment logic, water conservancy engineering specification compliance analysis, and multi-scheme comparison results.

2. The method according to claim 1, characterized in that, The domain-enhanced large-scale language model achieves water conservancy domain adaptation in the following ways: Based on the dictionary of water conservancy terminology and industry standard documents, we made minor adjustments to the field and established a mapping relationship between natural language expressions and professional terms. It has a built-in knowledge graph of water conservancy calculation standards and automatically associates the constraint thresholds in the "Reservoir Operation Regulations"; An integrated error operation identification module provides real-time warnings for parameter settings that exceed the engineering safety limits.

3. The method according to claim 1, characterized in that, Step S2 specifically includes: S21. Identify the core constraint indicators in the calculation target through semantic analysis, and transform the description of "flood control safety" into flow threshold constraints; S22. Extract the priority relationships in multi-objective optimization and establish a weighted objective function; S23. Determine the adjustable parameters of gate opening and flood discharge duration by associating the parameter dimensions in the water conservancy mechanism model.

4. The method according to claim 1, characterized in that, The hybrid computing strategy comprises three stages: Coarse adjustment stage: Latin hypercube sampling is used to generate an initial set of solutions within the parameter feasible region, and candidate solutions are quickly screened by simplifying the model; Fine-tuning phase: Based on the parameter sensitivity analysis results, the NSGA-II algorithm is used to perform gradient optimization on key parameters; Validation phase: Cross-validation using hydrological, hydraulic, and hydrodynamic models ensures a result deviation rate of ≤5%.

5. The method according to claim 4, characterized in that, The parameter sensitivity analysis specifically includes: Calculate the elasticity coefficients of each parameter with respect to the objective function, and determine the weight of the influence of the gate opening on the downstream flow. Construct a parameter-result response surface model to identify regions of nonlinear relationships; The sensitivity coefficient is dynamically updated based on historical calculation data.

6. The method according to claim 1, characterized in that, The visualization in step S4 includes: The convergence curve of the objective function value as a function of iterations is displayed in real time. The spatial evolution of the outflow is dynamically rendered using a 3D WebGL model. It provides an interactive parameter adjustment interface and dynamically provides feedback on the impact of parameter adjustments on the results.

7. The method according to claim 1, characterized in that, It also includes an exception handling mechanism: Real-time monitoring of parameter mutations during the calculation process; When the change in key parameters exceeds the historical normal fluctuation range, the model is automatically restarted. Mark the nodes where anomalies occurred and the corresponding handling measures in the calculation report.

8. A large-scale model-driven intelligent computing system for hydraulic mechanism models, characterized in that, include: A large-scale model-driven intelligent computing system for hydraulic mechanism models, characterized in that it includes: The natural language interaction module is used to receive and parse the natural language computing objectives input by the user; The target transformation module is used to transform the natural language computing target into mathematical constraints of the hydraulic mechanism model; The intelligent computing engine module includes a hydraulic mechanism model library and a workflow scheduler, which is used to perform goal-oriented reverse computation based on a hybrid computing strategy; The visualization module is used to dynamically display the parameter iteration process and the convergence of the calculation results; The results generation module is used to output a calculation results report that includes parameter adjustment logic, standard compliance analysis, and multi-scheme comparison.

9. The system according to claim 8, characterized in that, The intelligent computing engine module includes: A process template library stores standardized calculation processes for reservoir scheduling and flood evolution. A dynamic parameter adjuster that optimizes the iteration step size based on a reinforcement learning algorithm; The multi-model coordinator manages the data exchange interface between hydrological and hydraulic models.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

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