Online water quality evaluation treatment optimization method, device, equipment and storage medium

Abstract

The invention discloses an online water quality evaluation and treatment optimization method, device and equipment and a storage medium, and the method comprises the steps: building a hydrodynamic water quality model based on hydrological geographic data of a target river network and a self-defined ecological dynamic water quality sub-model; designing a treatment scheme library, and establishing a mapping rule between the treatment scheme library and the input parameters of the hydrodynamic water quality model; driving the hydrodynamic water quality model to perform batch simulation on all schemes in the treatment scheme library according to the mapping rule, and extracting multiple water quality evaluation indexes; and constructing a comprehensive evaluation function based on the water quality evaluation indexes to screen the treatment schemes in the preset treatment scheme library, and determining an optimal treatment scheme. According to the method, the mapping rule is utilized to drive the hydrodynamic water quality model to perform batch simulation on the scheme library, the coverage and efficiency of scheme evaluation are improved, a comprehensive evaluation function is constructed, and therefore it is guaranteed that the finally selected scheme is a comprehensive optimal solution under the constraint of multiple environment targets.

Description

Technical Field

[0001] This application relates to the field of environmental governance technology, and more specifically, to an online water quality assessment, governance and optimization method, apparatus, equipment and storage medium. Background Technology

[0002] Black and odorous water bodies in river networks are a prominent challenge in current water environment management. Their causes are complex, involving multiple factors such as pollution load input, insufficient hydrodynamic conditions, endogenous release from sediment, and complex microbial geochemical processes. Addressing black and odorous water bodies in river networks requires, first and foremost, a scientific assessment of the water quality status, followed by the development and optimization of remediation plans. Traditional on-site monitoring and experience-based decision-making models are insufficient to meet the needs of dynamic, comprehensive, and predictive management of complex river network systems.

[0003] Currently, conventional techniques in this field mainly rely on a combination of numerical simulation and scenario analysis. The usual approach is to establish a hydrodynamic and water quality model of the target river network to simulate the migration and diffusion of pollutants under the influence of water flow. When developing a treatment plan, technicians pre-determine several limited treatment scenarios based on experience, manually adjust the model input parameters, and run the model to obtain prediction results. The simulation results of these limited scenarios are then manually compared and analyzed to select the relatively optimal treatment plan.

[0004] However, the aforementioned conventional techniques still have some shortcomings. First, the evaluation process for the effectiveness of the schemes lacks systematicity: each preset scheme requires manual configuration of model parameters and individual operation, which is inefficient and makes it difficult to exhaustively explore or efficiently investigate hundreds or thousands of potential scheme combinations. Second, the scheme comparison and decision-making methods are relatively crude: they usually only compare a few core indicators and lack a comprehensive evaluation function that integrates multiple water quality assessment indicators into a unified quantitative score, leading to decisions relying on subjective experience and making it difficult to guarantee the comprehensive optimality of the selected scheme under multi-objective constraints. Summary of the Invention

[0005] To address at least one deficiency or improvement need in the prior art, this invention provides an online water quality assessment and treatment optimization method, apparatus, equipment, and storage medium. This addresses the problem that the evaluation process of the effectiveness of solutions in the prior art requires manual configuration of model parameters and separate operation, which is inefficient. Furthermore, it typically only compares a few core indicators, leading to decision-making relying on subjective experience and making it difficult to ensure that the selected solution is the comprehensive optimal solution under multiple objective constraints.

[0006] To achieve the above objectives, according to a first aspect of the present invention, an online water quality assessment and treatment optimization method is provided, comprising: A hydrodynamic water quality model is established based on the hydrogeographic data of the target river network and a custom ecodynamic water quality sub-model. Design a library of treatment schemes and establish mapping rules between the library of treatment schemes and the input parameters of the hydrodynamic and water quality model; Based on the mapping rules, the hydrodynamic and water quality model is used to perform batch simulations of all the solutions in the treatment solution library and extract multiple water quality assessment indicators. A comprehensive evaluation function is constructed based on water quality assessment indicators to screen treatment schemes in a pre-set treatment scheme library and determine the best treatment scheme.

[0007] In one possible implementation, a hydrodynamic water quality model is established based on the hydrogeographic data of the target river network and a custom ecodynamic water quality sub-model, which also includes: Based on the topographic and hydrological data of the target river network, a hydrodynamic model simulating water flow is constructed. Define multiple water quality state variables that characterize the water body, and construct nonlinear reaction kinetic equations between these multiple water quality state variables; By coupling an ecological dynamics model containing nonlinear reaction kinetic equations with a hydrodynamic model, a hydrodynamic water quality model is formed.

[0008] In one possible implementation, the governance scheme library also includes: Determine the key engineering parameters that affect water quality. These parameters should include at least the pollutant reduction ratio of the interception project, the treatment intensity parameters of the ecological restoration project, and the flow regulation parameters of the water replenishment project. Set the range of values ​​for the technical requirements of each key governance project parameter; The Latin hypercube sampling method is used to uniformly sample within the multidimensional parameter space composed of key governance engineering parameters, generating multiple combinations of governance engineering parameters. Each combination of governance engineering parameters is defined as a governance scheme, and the collection of all governance schemes constitutes a governance scheme library.

[0009] In one possible implementation, establishing the mapping relationship between the treatment engineering parameters and the input parameters of the hydrodynamic water quality model also includes: The pollutant reduction ratio of the interception project is mapped to the proportional reduction coefficient of the point source and non-point source pollution load input data in the hydrodynamic water quality model. The treatment intensity parameters of the ecological restoration project are mapped to the adjustment amount of a specific reaction rate constant of the ecological dynamics module in the hydrodynamic water quality model. The controlled flow of the water replenishment project is mapped to the flow data in the corresponding river boundary condition file in the hydrodynamic and water quality model.

[0010] In one possible implementation, a hydrodynamic and water quality model is used to perform batch simulations of all solutions in the treatment scheme library based on mapping rules, extracting multiple water quality assessment indicators, and also including: Based on the mapping relationship, the governance engineering parameters of each governance scheme in the governance scheme library are converted into configuration files and input data files for the hydrodynamic and water quality model operation; The simulation results output file corresponding to each governance scheme is read in batches based on the configuration file and input data file in parallel. From the simulation results output file, analyze and extract the water quality assessment indicators of the main pollutants at the preset monitoring sections.

[0011] In one possible implementation, a comprehensive evaluation function is constructed based on water quality assessment indicators to screen treatment schemes from a pre-set treatment scheme library and determine the optimal treatment scheme. This also includes: Standardize the data of multiple water quality assessment indicators corresponding to each treatment plan; The objective weight of each water quality assessment indicator is calculated based on the standardized data; The comprehensive score for each treatment plan is calculated based on the objective weight of each water quality assessment indicator; The governance solution with the highest comprehensive score is determined as the optimal governance solution.

[0012] One possible implementation also includes: The selected optimal treatment scheme is then used to simulate and verify the hydrodynamic and water quality model. Detailed information on the optimal governance solution, simulation verification results, and comparative analysis data with other governance solutions are imported into a visual decision support platform for display and analysis.

[0013] According to a second aspect of the present invention, an online water quality assessment and treatment optimization device is also provided, comprising: The model building module is configured to establish a hydrodynamic and water quality model based on the hydrogeographic data of the target river network and a custom ecodynamic water quality sub-model. The rule mapping module is configured to design a treatment scheme library and establish mapping rules between the treatment scheme library and the input parameters of the hydrodynamic and water quality model. The simulation evaluation module is configured to drive the hydrodynamic water quality model to perform batch simulations of all the solutions in the treatment solution library according to the mapping rules, and extract multiple water quality evaluation indicators. The scheme selection module is configured to construct a comprehensive evaluation function based on water quality assessment indicators to select the best treatment scheme from the preset treatment scheme library.

[0014] According to a third aspect of the present invention, an online water quality assessment and treatment optimization device is also provided, which includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the computer program is executed by the processing unit, the processing unit performs the steps of any of the above-described online water quality assessment and treatment optimization methods.

[0015] According to a fourth aspect of the present invention, a storage medium is also provided, which stores a computer program executable by an online water quality assessment, treatment and optimization device, wherein when the computer program is run on the online water quality assessment, treatment and optimization device, the online water quality assessment, treatment and optimization device performs the steps of any of the above-described online water quality assessment, treatment and optimization methods.

[0016] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: This invention provides an online water quality assessment and treatment optimization method. By establishing mapping rules between a treatment scheme library and the input parameters of a hydrodynamic water quality model, the method transforms the previously manual model parameter configuration process into an automated data conversion process. This ensures that each treatment strategy can be seamlessly and accurately converted into boundary conditions or source parameters that the model can recognize, avoiding errors and omissions that may be introduced by manual operation. Using the aforementioned mapping rules to drive the hydrodynamic water quality model to perform batch simulations of the scheme library, the method achieves full-chain automation from scheme analysis, model parameter injection, calculation execution to result extraction. It eliminates the repetitive work of manually setting, running, and recording results for each scheme, improving the coverage and efficiency of scheme evaluation. A comprehensive evaluation function based on multiple water quality assessment indicators is constructed, combining multiple assessment indicators of different dimensions and importance into a measurable comprehensive score. Based on the multi-objective optimization principle, the selection mechanism transforms the decision-making basis from vague, subjectively biased experience-based judgments into clear, transparent, and quantitative calculations, thereby ensuring that the final selected scheme is the comprehensive optimal solution under multiple environmental objective constraints. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating an embodiment of the online water quality assessment and treatment optimization method provided by the present invention; Figure 2 Provided by the present invention Figure 1 A schematic flowchart of an embodiment of step S101; Figure 3 Provided by the present invention Figure 1 A schematic flowchart of an embodiment of step S103; Figure 4 Provided by the present invention Figure 1 A flowchart illustrating an embodiment of step S104; Figure 5 A schematic diagram of an embodiment of the online water quality assessment and treatment optimization device provided by the present invention; Figure 6 A schematic diagram of the structure of the online water quality assessment, treatment and optimization equipment provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0020] The terms "first," "second," "third," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0021] This invention provides an online water quality assessment, treatment, and optimization method, apparatus, equipment, and storage medium, which are described below.

[0022] Please see Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the online water quality assessment, treatment, and optimization method provided by the present invention. In a specific embodiment of the present invention, an online water quality assessment, treatment, and optimization method is disclosed, comprising: S101. Based on the hydrogeographic data of the target river network and a custom ecodynamic water quality sub-model, establish a hydrodynamic water quality model; S102. Design a treatment scheme library and establish mapping rules between the treatment scheme library and the input parameters of the hydrodynamic and water quality model; S103. Drive the hydrodynamic water quality model according to the mapping rules to perform batch simulations of all the schemes in the treatment scheme library and extract multiple water quality assessment indicators. S104. Based on water quality assessment indicators, construct a comprehensive evaluation function to screen the treatment schemes in the preset treatment scheme library and determine the best treatment scheme.

[0023] In the above embodiments, the first step is to collect hydrological and geographical data of the target river network. This data covers basic information such as topography, river network topology, river system distribution, and water level and flow variation patterns, which form the basis for model construction. For example, for a meandering urban river, data on its topographic relief, channel width variations, and tributary confluence locations are obtained, as these factors directly affect hydrodynamic characteristics such as flow velocity and water mixing.

[0024] Simultaneously, a custom ecological dynamics water quality sub-model is developed to describe the migration and transformation patterns of pollutants in water bodies and their interactions with ecological and environmental elements. Taking common organic pollutants as an example, the sub-model needs to consider their degradation process under the action of microorganisms. The degradation rate is affected by various factors such as water temperature, dissolved oxygen content, and microbial population size. Through the custom model, the parameter settings of these influencing factors can be adjusted according to the specific ecological conditions of the target river network, making the model more accurately simulate actual water quality changes. For example, if the microbial activity in the target river network is greatly affected by the season, different microbial degradation rate parameters can be set in the model according to different seasons.

[0025] By combining hydrogeographic data with a custom-designed ecodynamic water quality sub-model and utilizing professional hydrodynamic and water quality simulation software (such as the MIKE series), a hydrodynamic and water quality model reflecting the hydrodynamic and water quality changes of a target river network can be established by setting boundary conditions (such as inflow rate, water quality concentration, outflow conditions, etc.) and initial conditions (such as initial water level, initial pollutant concentration, etc.). For example, when simulating river network water quality changes during urban rainstorms, surface runoff generated by the rainstorms can be input into the model as inflow boundary conditions. Simultaneously, the impact of non-point source pollution from rainwater runoff on water quality can be considered. Running the model allows for the acquisition of the distribution of water level, flow velocity, and water quality indicators (such as COD, ammonia nitrogen, etc.) at different locations within the river network at different times.

[0026] The governance solution library should include various types of governance measures and their combinations. These measures cover engineering measures (such as sewage interception and pipeline laying, river dredging, and ecological slope protection), ecological measures (such as constructed wetland construction, aquatic plant planting, and microbial enhanced remediation), and management measures (such as pollution source control, sewage outlet remediation, and water allocation). For example, for a river network severely polluted by industrial wastewater, the governance solution library could include solutions such as constructing sewage interception pipelines to block direct discharge of industrial wastewater into the river, using constructed wetlands to purify the polluted water, and strengthening the supervision of industrial enterprises' sewage discharge. It could also include combinations of these solutions, such as a solution combining sewage interception and pipeline laying with constructed wetland construction.

[0027] Establishing mapping rules between the treatment scheme library and the input parameters of the hydrodynamic and water quality model is to transform different treatment schemes into changes in input parameters that the model can recognize and simulate. Taking a sewage interception and collection project as an example, after its implementation, the inflow pollution load of the river network will be significantly reduced. The mapping rules need to establish a correlation between the specific parameters of the sewage interception and collection project (such as the length, diameter, and interception efficiency of the interception pipe) and the inflow pollution load input parameters in the hydrodynamic and water quality model. When a specific sewage interception and collection treatment scheme is selected, the inflow pollution load parameters in the model are automatically adjusted according to the mapping rules, enabling the model to simulate the changes in river network water quality after the implementation of the treatment scheme. Similarly, for aquatic plant planting schemes, the mapping rules need to consider the growth characteristics of the plants (such as growth cycle and ability to absorb pollutants), transforming these characteristics into changes in water quality parameters (such as dissolved oxygen and nutrient concentration) in the model, thereby accurately simulating the improvement effect of plant planting on river network water quality.

[0028] Based on established mapping rules, the hydrodynamic and water quality model automatically performs batch simulations of each scheme in the treatment scheme library, achieving high efficiency and automation in the evaluation of treatment schemes and avoiding the tedious and inefficient manual simulation of each scheme individually. During the simulation process, the hydrodynamic and water quality model generates a large amount of data, from which multiple key water quality assessment indicators need to be extracted to comprehensively evaluate the effectiveness of the treatment schemes. Common water quality assessment indicators include, but are not limited to, the Water Quality Index (WQI), Chemical Oxygen Demand (COD), Ammonia Nitrogen (NH3-N), Total Phosphorus (TP), and Dissolved Oxygen (DO).

[0029] The Water Quality Index (WQI) is a comprehensive indicator reflecting the water quality of a body. It is calculated by weighting multiple individual water quality indicators, providing a direct representation of the degree of water quality improvement. For example, after simulating the implementation of a certain treatment plan, the values ​​of COD, NH3-N, TP, DO, and other indicators at different monitoring points are extracted. These values ​​are then calculated using the WQI formula to obtain the WQI value for each point, thereby assessing the overall improvement in water quality across the river network. By extracting these multiple water quality assessment indicators, the impact of the treatment plan on the river network's water quality can be comprehensively analyzed from different perspectives and levels.

[0030] The construction of a comprehensive evaluation function requires consideration of multiple factors, such as water quality improvement effects, treatment costs, engineering implementation difficulty, and environmental impact. The weight of different factors in the evaluation function can be set according to actual needs and objectives. For example, if the focus is on water quality improvement effects, the weight of water quality assessment indicators in the evaluation function can be set relatively high; if treatment costs are considered, cost-related indicators (such as engineering construction costs, operation and maintenance costs, etc.) need to be included in the evaluation function and assigned certain weights.

[0031] To illustrate this with a simple comprehensive evaluation function example, assume the evaluation function is F = a × WQI + b × (1 / treatment cost), where a and b are the weights of water quality improvement and treatment cost in the evaluation function, respectively, and a + b = 1. WQI represents the increase in WQI after the treatment plan is implemented, and treatment cost is the total cost required to implement the plan. By calculating the comprehensive evaluation function value F for each treatment plan, the plans are ranked according to the size of the F value; the larger the F value, the better the comprehensive benefit of the plan. For example, if there are three treatment plans A, B, and C, and their calculated F values ​​are 0.8, 0.6, and 0.7 respectively, then plan A has the best comprehensive benefit and is determined to be the optimal treatment plan.

[0032] In practical applications, the construction of the comprehensive evaluation function can consider more factors and more refined weight allocation. Furthermore, multi-objective optimization algorithms (such as genetic algorithms and particle swarm optimization) can be used to solve for the optimal solution of the comprehensive evaluation function, thus determining the best governance scheme more accurately. For example, a genetic algorithm can be used to search for the combination of schemes that maximizes the comprehensive evaluation function value among multiple governance schemes. Through continuous iterative optimization, the optimal governance scheme that satisfies multiple constraints can ultimately be obtained.

[0033] Compared with existing technologies, this embodiment provides an online water quality assessment and governance optimization method. By establishing mapping rules between a governance scheme library and the input parameters of a hydrodynamic water quality model, the method transforms the previously manual model parameter configuration process into an automated data conversion process. This allows each governance strategy to be seamlessly and accurately converted into boundary conditions or source parameters that the model can recognize, avoiding errors and omissions that may be introduced by manual operation. Using the aforementioned mapping rules to drive the hydrodynamic water quality model to perform batch simulations of the scheme library, the method achieves full-chain automation from scheme analysis, model parameter injection, calculation execution to result extraction. It eliminates the repetitive work of manually setting, running, and recording results for each scheme, improving the coverage and efficiency of scheme evaluation. A comprehensive evaluation function based on multiple water quality assessment indicators is constructed, combining multiple assessment indicators of different dimensions and importance into a measurable comprehensive score. Based on the multi-objective optimization principle, the selection mechanism transforms the decision-making basis from vague, subjectively biased experience-based judgments into clear, transparent, and quantitative calculations, thereby ensuring that the final selected scheme is the comprehensive optimal solution under multiple environmental objective constraints.

[0034] Please see Figure 2 , Figure 2 Provided by the present invention Figure 1 A flowchart illustrating an embodiment of step S101. In some embodiments of the present invention, a hydrodynamic water quality model is established based on the hydrogeographic data of the target river network and a custom ecodynamic water quality sub-model, further including: S201. Based on the topographic and hydrological data of the target river network, construct a hydrodynamic model to simulate water flow. S202. Define multiple water quality state variables that characterize the water body, and construct nonlinear reaction kinetic equations between the multiple water quality state variables. S203. Couple the ecological dynamics model containing nonlinear reaction dynamics equations with the hydrodynamic model to form a hydrodynamic water quality model.

[0035] In the above embodiments, constructing a hydrodynamic model is the foundation of the entire hydrodynamic and water quality model, and its core lies in accurately simulating the movement patterns of water flow in the target river network. First, comprehensive and accurate topographic data of the target river network needs to be collected. This data includes the river network's elevation information, channel cross-sectional shape, bottom slope gradient, etc. The topographic undulations at various locations within the river network can be obtained through field measurements or by utilizing high-precision digital elevation model (DEM) data, as changes in topography directly affect the speed and direction of water flow. For the channel cross-section, parameters such as width and depth at different locations need to be measured to characterize the channel's geometry. Different channel shapes (such as rectangular, trapezoidal, parabolic, etc.) exhibit different resistance characteristics to water flow, thus affecting water flow movement.

[0036] Hydrological data, including rainfall, evaporation, inflow, and outflow, are also crucial inputs for constructing hydrodynamic models. Rainfall data can be obtained through meteorological station monitoring; rainfall of varying intensities and durations generates different runoff processes in the river network, affecting water level and flow rate changes. Evaporation data reflects the loss of surface moisture and significantly impacts the water balance within the river network. Inflow and outflow data clarify the water exchange between the river network and external water bodies, such as inflow from upstream rivers, groundwater replenishment of the river network, and downstream discharge from the river network.

[0037] After acquiring topographic and hydrological data, hydrodynamic simulation software (such as MIKE) is used to build the model. The computational grid is divided according to the actual conditions of the river network. The grid size affects the model's computational accuracy and efficiency. Generally, finer grids are used in critical areas (such as river bends and areas with drastic flow changes), while the grid size can be appropriately increased in open and flat areas. Boundary conditions and initial conditions are set for the model. Boundary conditions include upstream inflow boundaries, downstream outflow boundaries, and lateral inflow boundaries. Initial conditions set the initial water level and flow distribution of the river network, thereby simulating the changes in parameters such as velocity, water level, and flow rate in the target river network over time and space under different hydrological conditions.

[0038] Defining water quality state variables should comprehensively reflect the types and concentrations of pollutants in the water body, as well as the physicochemical properties of the water. Common water quality state variables include chemical oxygen demand (COD), biochemical oxygen demand (BOD), ammonia nitrogen (NH3-N), total phosphorus (TP), dissolved oxygen (DO), pH, and temperature. The migration and transformation of pollutants in water bodies is a complex nonlinear process, with various water quality state variables influencing and interacting with each other. Taking the degradation process of organic pollutants as an example, organic matter decomposes under the action of microorganisms, consuming dissolved oxygen in the water. This process involves multiple water quality state variables such as COD, BOD, and DO. Its reaction kinetic equation can be expressed as follows: the degradation rate of organic matter is related to the concentration of organic matter, the concentration of microorganisms, and the concentration of dissolved oxygen, and exhibits a nonlinear relationship. The specific equation is as follows: ; in C org This refers to the concentration of organic matter. C mic Microbial concentration, k 1 is the degradation rate constant. K s It is the half-saturation constant. DO The dissolved oxygen concentration is denoted as . This equation shows that the degradation rate of organic matter is not only related to the concentration of organic matter and microorganisms, but also constrained by the dissolved oxygen concentration. When the dissolved oxygen concentration is low, the degradation rate decreases, reflecting the nonlinear relationship between various water quality state variables.

[0039] The purpose of coupling ecological dynamics models with hydrodynamic models is to achieve synchronous simulation of water flow and water quality changes, reflecting the interaction between the two. Water flow affects the diffusion, migration, and mixing processes of pollutants, while water quality changes, in turn, affect the physical properties of water bodies such as density and viscosity, thus influencing water flow.

[0040] During the coupling process, the data transfer method and interface between the two models must first be determined. Generally, parameters such as water velocity, water level, and flow rate calculated by the hydrodynamic model are input to the ecodynamic model to calculate the convection and diffusion process of pollutants. For example, under the influence of water velocity, pollutants will migrate with the water flow, and their migration flux is related to the water velocity and pollutant concentration, which can be calculated using the convection and diffusion equation. Simultaneously, changes in water quality state variables (such as dissolved oxygen concentration and pollutant concentration) calculated by the ecodynamic model will affect the physical properties of the water body, and thus feed back to the hydrodynamic model. For example, when the dissolved oxygen concentration in the water body decreases, the water density may change, thereby affecting the water level and velocity distribution.

[0041] Common coupling algorithms include loose coupling and tight coupling. Loose coupling involves running the hydrodynamic and ecodynamic models independently, exchanging data at the end of each time step before proceeding to the next. This method is relatively simple to implement, but the simulation results may contain errors due to untimely data exchange. Tight coupling, on the other hand, solves the equations of both models jointly, considering both water flow and water quality changes within a unified computational framework. This method more accurately reflects the interaction between the two, but it has higher computational complexity and requires more powerful computing resources.

[0042] In practical applications, appropriate coupling methods and algorithms are selected based on factors such as the scale and complexity of the target river network and computational resources. For example, loose coupling can be used for small river networks or situations where computational accuracy requirements are not particularly high; while tight coupling is more suitable for large and complex river networks or situations requiring high-precision simulation. By successfully coupling the ecological dynamics model with the hydrodynamic model, the resulting hydrodynamic water quality model can simultaneously simulate water flow and water quality changes, providing a scientific basis for the assessment, prediction, and management of river network water quality. For example, when simulating sudden pollution incidents in river networks, the hydrodynamic water quality model can accurately simulate the diffusion range, migration speed, and water quality changes over time of pollutants.

[0043] In some embodiments of the present invention, the design governance scheme library further includes: Determine the key engineering parameters that affect water quality. These parameters should include at least the pollutant reduction ratio of the interception project, the treatment intensity parameters of the ecological restoration project, and the flow regulation parameters of the water replenishment project. Set the range of values ​​for the technical requirements of each key governance project parameter; The Latin hypercube sampling method is used to uniformly sample within the multidimensional parameter space composed of key governance engineering parameters, generating multiple combinations of governance engineering parameters. Each combination of governance engineering parameters is defined as a governance scheme, and the collection of all governance schemes constitutes a governance scheme library.

[0044] In the above embodiments, water quality treatment involves various engineering methods, each with its unique mechanism of action and key influencing factors. To design a treatment scheme library, it is necessary to identify the key engineering parameters affecting water quality, and these parameters must at least cover three main aspects: sewage interception projects, ecological restoration projects, and water replenishment projects. Specifically: Wastewater interception projects serve as a defense against external pollution entering water bodies, and their pollutant reduction ratio reflects the effectiveness of these projects in intercepting and removing various pollutants. Different types of interception facilities and different interception methods result in varying reduction ratios for different pollutants. For example, in a combined sewer system interception project in an urban area, by properly configuring interception wells and pipes, the reduction ratio for suspended solids may reach 70%–80%, while the reduction ratio for certain recalcitrant organic compounds may be relatively lower, approximately 30%–50%. Determining this parameter helps assess the specific contribution of wastewater interception projects to improving water quality.

[0045] Ecological restoration projects aim to improve water quality by utilizing the self-regulating and purification capabilities of ecosystems. Treatment intensity parameters reflect the efficiency and extent to which these projects treat pollutants. Taking constructed wetlands as an example, treatment intensity parameters may include hydraulic retention time, plant species and planting density, and the type and method of filling material. A longer hydraulic retention time allows for greater contact time between pollutants and wetland plants and fill material, potentially leading to better treatment results. However, excessively long retention times can increase project costs and land area requirements. Different plants have varying abilities to absorb and transform pollutants; therefore, selecting appropriate plant species and planting densities can improve treatment intensity. The type and method of filling material also affect microbial attachment and growth, thus influencing the degradation of pollutants.

[0046] Water replenishment projects improve water quality by adding clean water to polluted water bodies, increasing water flow and dilution capacity. Flow rate regulation is a key parameter in water replenishment projects, determining the amount of water replenished and the degree of water quality improvement. Insufficient flow may fail to effectively dilute pollutants, resulting in minimal water quality improvement; excessive flow may waste water resources and even impact the aquatic ecosystem. For example, in a lake replenishment project, determining an appropriate flow rate range based on the lake's water balance and water quality targets maintains the lake's water level at a reasonable level while ensuring the water exchange cycle meets ecological requirements, thus contributing to maintaining good water quality.

[0047] After determining the key remediation engineering parameters, it is necessary to set reasonable ranges of technical requirements for each parameter to ensure the effectiveness and feasibility of the remediation project. The setting of these ranges should comprehensively consider multiple factors, including relevant technical standards, engineering experience, environmental capacity, and economic costs. Specific settings can be made according to actual needs, and this invention does not impose further limitations in this regard.

[0048] Latin hypercube sampling (LHS) is an efficient multidimensional parameter sampling method that can perform uniform sampling within the parameter space, ensuring that the generated samples have good representativeness and coverage. Using LHS to generate multiple combinations of governance engineering parameters within a multidimensional parameter space composed of key governance engineering parameters can provide diverse strategy options for the governance solution library.

[0049] First, determine the value range and sampling quantity for each key governance engineering parameter. The sampling quantity should be determined by considering both the needs of the governance scheme library and the limitations of computing resources. Generally, a larger sampling quantity results in a richer variety of governance schemes, but also increases the computational load. Next, divide the value range of each parameter into several intervals with equal probability. Then, perform random sampling within each interval, using different probability distribution functions such as uniform distribution or normal distribution to accommodate different parameter characteristics. Finally, combine the sampled values ​​of each parameter in a specific order to form multiple combinations of governance engineering parameters. Each combination of treatment parameters generated using the Latin hypercube sampling method is defined as a specific treatment scheme, encompassing different combinations of parameters for pollution interception, ecological restoration, and water replenishment. The collection of all treatment schemes constitutes a treatment scheme library. Each treatment scheme should include specific values ​​for key parameters such as the pollutant reduction ratio of the pollution interception project, the treatment intensity parameters of the ecological restoration project, and the controlled flow rate of the water replenishment project. Additionally, based on actual circumstances, other relevant information can be added to each treatment scheme, such as the implementation cost, expected treatment effect, and implementation difficulty, for comprehensive consideration in subsequent scheme evaluation and selection. For example, a treatment scheme could be defined as follows: a pollutant reduction ratio of 75% for the pollution interception project, a hydraulic retention time of 5 days for the ecological restoration project, a controlled flow rate of 1200 cubic meters per day for the water replenishment project, an estimated implementation cost of 5 million yuan, an expected reduction of 60% in nitrogen and phosphorus concentrations in the water body, and a moderate implementation difficulty.

[0050] The governance solution database can be managed in the form of a database, facilitating querying, retrieval, and analysis. When constructing the database, the solutions can be categorized and coded for easier management and use. For example, solutions can be categorized according to the type of governance project, water quality improvement goals, and implementation area, and each solution can be assigned a unique code.

[0051] In some embodiments of the present invention, establishing the mapping relationship between the treatment engineering parameters and the input parameters of the hydrodynamic water quality model further includes: The pollutant reduction ratio of the interception project is mapped to the proportional reduction coefficient of the point source and non-point source pollution load input data in the hydrodynamic water quality model. The treatment intensity parameters of the ecological restoration project are mapped to the adjustment amount of a specific reaction rate constant of the ecological dynamics module in the hydrodynamic water quality model. The controlled flow of the water replenishment project is mapped to the flow data in the corresponding river boundary condition file in the hydrodynamic and water quality model.

[0052] In the above embodiments, the interception project, through the construction of interception facilities and sewage treatment facilities, intercepts and treats sewage entering the water body, thereby reducing pollutant emissions. The pollutant reduction ratio reflects the removal effect of the interception project. Mapping this ratio to a proportional reduction coefficient for point source and non-point source pollution load input data in the hydrodynamic water quality model means that during model operation, the point source and non-point source pollution load data input to the model are reduced proportionally according to the actual reduction ratio of the interception project. For example, if the interception project reduces pollutants by 60%, then in the model, both point source and non-point source pollution load input data will be reduced by 60% to simulate the actual change in pollutant input in the water body after the implementation of the interception project. This mapping allows the hydrodynamic water quality model to more accurately reflect the actual impact of the interception project on the water body's pollution load. Simultaneously, simulating water quality changes under different reduction ratios can also provide a reference for the optimized design and operation management of the interception project, such as determining a reasonable scale and operating parameters for the interception facilities to achieve the best pollution reduction effect and economic benefits.

[0053] Ecological restoration projects, through the construction of artificial wetlands, floating beds, and aquatic plant cultivation, provide suitable living environments for microorganisms and plants in aquatic bodies, promoting their absorption, transformation, and degradation of pollutants. A higher treatment intensity parameter indicates a stronger pollutant treatment capacity of the ecological restoration project, and a correspondingly faster reaction rate in the ecological dynamics process. Therefore, mapping the treatment intensity parameter of the ecological restoration project to an adjustment amount of a specific reaction rate constant in the ecological dynamics module allows the model to better simulate the actual effect of ecological restoration projects on water quality improvement. For example, when the treatment intensity parameter of the ecological restoration project increases, the reaction rate constants of microbial degradation of organic matter and plant absorption of nutrients such as nitrogen and phosphorus in the model will increase accordingly, thus reflecting the accelerated removal rate of pollutants in the water body after the implementation of the ecological restoration project. This mapping relationship enables hydrodynamic and water quality models to more accurately simulate the impact mechanisms of ecological restoration projects on aquatic ecosystems and water quality. By simulating water quality changes under different treatment intensity parameters, the models can predict the governance effects of ecological restoration projects, provide a basis for determining the reasonable scale and layout of ecological restoration projects, and assess the impact of ecological restoration projects on the ecological balance of aquatic bodies, thus avoiding damage to the stability of the ecosystem due to over-restoration.

[0054] The controlled flow rate of a water replenishment project determines the amount of water added to a water body, directly affecting the water level, flow velocity, and water dilution effect. Mapping the controlled flow rate of the water replenishment project to the corresponding flow data in the river boundary condition file of the hydrodynamic and water quality model means that during model operation, the flow data in the river boundary condition file is updated according to the actual controlled flow rate of the water replenishment project to simulate the hydrodynamic and water quality changes of the water body after the implementation of the water replenishment project. For example, when the controlled flow rate of the water replenishment project increases, the inflow flow data in the river boundary condition file will increase accordingly, and the model will simulate the trend of rising water level, increased flow velocity, and enhanced water dilution effect. This mapping relationship enables the hydrodynamic and water quality model to realistically reflect the actual impact of the water replenishment project on the hydrodynamic and water quality conditions of the water body. By simulating the water body changes under different controlled flow rates, the operation and scheduling scheme of the water replenishment project can be optimized, and reasonable water replenishment flow rate and timing can be determined to improve the economic benefits and water quality improvement effect of the water replenishment project.

[0055] Please see Figure 3 , Figure 3 Provided by the present invention Figure 1 A flowchart illustrating an embodiment of step S103. In some embodiments of the present invention, the hydrodynamic water quality model is driven by mapping rules to perform batch simulations of all solutions in the treatment solution library, extracting multiple water quality assessment indicators, and further includes: S301. Based on the mapping relationship, convert the treatment engineering parameters of each treatment scheme in the treatment scheme library into the configuration file and input data file for the hydrodynamic and water quality model operation; S302. Perform batch solutions in parallel based on the configuration file and input data file, and read the simulation result output file corresponding to each governance scheme; S303. From the simulation results output file, analyze and extract the water quality assessment indicators of the main pollutants at the preset monitoring sections.

[0056] In the above embodiments, the governance scheme library contains a variety of different governance schemes, each with its unique governance engineering parameters. These parameters cover relevant information on various governance measures such as sewage interception projects, ecological restoration projects, and water replenishment projects. The hydrodynamic water quality model requires specific configuration files and input data files to define the model's operating environment, boundary conditions, initial conditions, and the physical processes being simulated.

[0057] The engineering parameters for each remediation scheme were extracted from the remediation scheme library. For each scheme, key parameters such as the reduction ratio of pollution interception projects, the treatment intensity of ecological restoration projects, and the controlled flow of water replenishment projects were recorded. Simultaneously, the extracted parameters were organized and categorized, grouped according to mapping relationships for subsequent conversion operations.

[0058] Based on the compiled parameters, use a text editor or programming language to generate the configuration file and input data file required for the hydrodynamic and water quality model. When generating the configuration file, set the basic parameters of the model, such as the running time, time step, and simulation area, according to the characteristics of the treatment scheme. For the input data file, convert the treatment project parameters into a format that the model can recognize according to the mapping relationship, such as converting the reduction ratio of the interception project into the reduction coefficient of point source and non-point source pollution loads, and write it into the corresponding input data file.

[0059] To improve simulation efficiency and save computation time, parallel computing is used to solve all the solutions in the governance scheme library in batches. Parallel computing can utilize multiple computing resources (such as multi-core processors, computer clusters, etc.) to simulate different governance schemes simultaneously, thereby shortening the overall simulation time.

[0060] All solutions in the governance scheme library are distributed across different computing resources for parallel solving according to certain rules. The number of simulation tasks assigned to each computing resource can be determined based on the number of solutions and the performance of the computing resources, aiming to balance the load across resources and avoid situations where some resources are overloaded while others are idle. For example, in a scheme library containing 100 governance solutions, if 10 computing nodes are used for parallel computation, each node can be assigned 10 solutions for simulation.

[0061] Once the simulation calculations for all treatment schemes are completed, the simulation result output file corresponding to each scheme is read. The simulation result output file typically contains the changes in hydrodynamic parameters (such as water level, flow velocity, and flow rate) and water quality parameters (such as dissolved oxygen, chemical oxygen demand, and ammonia nitrogen) of the water body during the simulation period. Using data reading tools or programming languages, the required data is extracted from the output file and stored in a suitable data structure for subsequent extraction and analysis of water quality assessment indicators.

[0062] If the output file is in text format, text processing tools (such as Python's string manipulation functions and regular expressions) can be used to extract the required data. If the output file is in binary format, a dedicated binary data reading library (such as NumPy's binary file reading function) is required to parse the data. When extracting data, accurately extract the corresponding water quality parameter values, such as dissolved oxygen concentration, chemical oxygen demand concentration, and ammonia nitrogen concentration, from the output file according to the monitoring section number and the name of the main pollutant.

[0063] After extracting the water quality parameter values ​​of the main pollutants, water quality assessment indicators are calculated according to relevant water quality assessment standards and calculation methods. Common water quality assessment indicators include average concentration, maximum concentration, and compliance rate. For example, to calculate the average concentration of chemical oxygen demand (COD) at a monitoring section during a simulated time period, the COD concentration values ​​at all time steps of that section can be added together and then divided by the total number of time steps. When calculating the compliance rate, the number of times the COD concentration is lower than the corresponding water quality standard is counted and then divided by the total number of monitoring sessions. By calculating these water quality assessment indicators, the removal effect of the treatment plan on the main pollutants and the degree of improvement in water quality can be intuitively evaluated.

[0064] Please see Figure 4 , Figure 4 Provided by the present invention Figure 1 A flowchart illustrating an embodiment of step S104. In some embodiments of the present invention, a comprehensive evaluation function is constructed based on water quality assessment indicators to screen treatment schemes in a preset treatment scheme library and determine the optimal treatment scheme. The method further includes: S401. Standardize the data of multiple water quality assessment indicators corresponding to each treatment plan; S402. Calculate the objective weight of each water quality assessment indicator based on the standardized data; S403. Calculate the comprehensive score of each treatment plan based on the objective weight of each water quality assessment indicator; S404. The governance solution with the highest comprehensive score is determined as the optimal governance solution.

[0065] In the above embodiments, the dimensions and numerical ranges of different water quality assessment indicators may vary significantly. For example, dissolved oxygen concentration is measured in mg / L, with a typical range of 0-15; while chemical oxygen demand (COD) is also measured in mg / L, but its numerical range can vary from a few to hundreds or even thousands. Without standardization, indicators with large dimensions or wide numerical ranges may have an excessive impact on the results when calculating the comprehensive score, thus masking the role of other indicators. Therefore, it is necessary to standardize the data of multiple water quality assessment indicators corresponding to each treatment scheme to ensure they are within the same order of magnitude and comparable range.

[0066] Common standardization methods include min-max standardization (range standardization) and Z-score standardization (standard deviation standardization). For each treatment scheme in the pre-set treatment scheme library, the corresponding multiple water quality assessment index data are standardized one by one according to the selected standardization method. If the min-max standardization method is used, for the chemical oxygen demand (COD) index of a certain treatment scheme, the minimum COD value x among all treatment schemes is first found. min and maximum value xmax Then, the standardized value x of the COD index of the treatment plan is calculated according to the formula. new The same operation was performed on each indicator to obtain standardized data for all water quality assessment indicators for each treatment plan.

[0067] Objective weights are calculated based on the characteristics and relationships of the data itself. They reflect the importance of each water quality assessment indicator in the comprehensive evaluation. Compared with subjective weights (such as those determined by expert scoring), objective weights are more objective and scientific. Common methods for calculating objective weights include the entropy weight method and the coefficient of variation method. The entropy weight method is based on the concept of information entropy, which measures the uncertainty of information. The smaller the information entropy, the more information the indicator provides, the greater its role in the comprehensive evaluation, and therefore its greater weight. The coefficient of variation method determines the weight based on the degree of variation of the indicator. The larger the coefficient of variation, the greater the difference between different treatment schemes, the more obvious the effect of the indicator in distinguishing different treatment schemes, and therefore its greater weight. This invention does not further limit the selection of appropriate weight calculation methods based on the characteristics of the water quality assessment indicators and the data situation.

[0068] After obtaining the objective weights of each water quality assessment indicator, a comprehensive score for each treatment plan is calculated based on these weights. The comprehensive score comprehensively considers the impact of each indicator on the treatment plan and reflects the overall effectiveness of the treatment plan. The comprehensive evaluation function is a function that weights and sums the standardized values ​​of each water quality assessment indicator with their corresponding objective weights. For each treatment plan in the pre-set treatment plan library, according to the constructed comprehensive evaluation function, the standardized values ​​of each water quality assessment indicator are multiplied by their corresponding objective weights and then summed to obtain the comprehensive score for each treatment plan.

[0069] The comprehensive score reflects the overall performance of the treatment plan across various water quality assessment indicators. A higher comprehensive score indicates a better effect of the treatment plan in improving water quality. Therefore, the treatment plan with the highest comprehensive score is determined as the optimal treatment plan. In some embodiments of the present invention, it further includes: The selected optimal treatment scheme is then used to simulate and verify the hydrodynamic and water quality model. Detailed information on the optimal governance solution, simulation verification results, and comparative analysis data with other governance solutions are imported into a visual decision support platform for display and analysis.

[0070] In the above embodiments, after determining the optimal treatment plan, in order to ensure its effectiveness and feasibility, a hydrodynamic water quality model is used for simulation and verification. A suitable model is selected according to the characteristics of the water body. For example, the MIKE11 model can be selected for rivers, and the MIKE21 model can be selected for lakes.

[0071] Next, model parameters are set, and the geometric, hydrodynamic, and water quality parameters of the water body are determined through field measurements, literature review, and calibration. A simulation scenario is constructed, with an untreated baseline scenario for comparison, and a treated scenario based on the optimal solution. Key parameters are changed, and the simulation is run to extract water quality and hydrodynamic data at different locations and times. The water quality improvement effect and hydrodynamic changes are evaluated. If the solution is insufficient, it is optimized and adjusted based on the simulation results; for example, if the water quality does not meet standards, the treatment capacity is increased or the pipeline layout is optimized.

[0072] The platform compiles detailed information on the optimal solution, including design, investment, and schedule; simulated water quality and hydrodynamic data and analysis charts; and comparative data with other solutions across various indicators. This data is then imported and visualized according to platform requirements, using maps for geographic data, line charts for time-series data, and radar charts for comparative data. The platform's analytical tools are used for statistical and spatial analysis to uncover data patterns. Based on the analysis results, implementation suggestions, adjustment measures, and cost-benefit assessments are provided to decision-makers. Afterward, data is regularly updated, functions are optimized, and the system is maintained to ensure the platform's normal operation.

[0073] To better implement the online water quality assessment and treatment optimization method in this invention embodiment, based on the online water quality assessment and treatment optimization method, please refer to the corresponding... Figure 5 , Figure 5 This is a schematic diagram of an embodiment of the online water quality assessment, treatment, and optimization device provided by the present invention. The embodiment of the present invention provides an online water quality assessment, treatment, and optimization device 500, comprising: Model building module 510 is configured to build a hydrodynamic and water quality model based on the hydrogeographic data of the target river network and a custom ecodynamic water quality sub-model. The rule mapping module 520 is configured to design a treatment scheme library and establish mapping rules between the treatment scheme library and the input parameters of the hydrodynamic and water quality model. The simulation evaluation module 530 is configured to drive the hydrodynamic water quality model to perform batch simulations of all schemes in the treatment scheme library according to the mapping rules, and extract multiple water quality evaluation indicators. The scheme selection module 540 is configured to construct a comprehensive evaluation function based on water quality assessment indicators to select the best treatment scheme from the preset treatment scheme library.

[0074] It should be noted that the device 500 provided in the above embodiments can implement the technical solutions described in the above method embodiments. The specific implementation principles of the above modules or units can be found in the corresponding content in the above method embodiments, and will not be repeated here.

[0075] Please see Figure 6 , Figure 6This is a schematic diagram of the structure of the online water quality assessment, treatment, and optimization device provided in an embodiment of the present invention. Based on the above-described online water quality assessment, treatment, and optimization method, the present invention also provides an online water quality assessment, treatment, and optimization device, which can be a computing device such as a mobile terminal, desktop computer, laptop, handheld computer, or server. The online water quality assessment, treatment, and optimization device 600 includes a processor 610, a memory 620, and a display 630. Figure 6 Only some components of the online water quality assessment and treatment optimization equipment are shown. However, it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0076] In some embodiments, the memory 620 may be an internal storage unit of the online water quality assessment, treatment, and optimization device 600, such as a hard drive or memory of the online water quality assessment, treatment, and optimization device 600. In other embodiments, the memory 620 may be an external storage device of the online water quality assessment, treatment, and optimization device 600, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the online water quality assessment, treatment, and optimization device 600. Furthermore, the memory 620 may include both internal storage units and external storage devices of the online water quality assessment, treatment, and optimization device 600. The memory 620 is used to store application software and various types of data installed on the online water quality assessment, treatment, and optimization device 600, such as the program code installed on the online water quality assessment, treatment, and optimization device 600. The memory 620 may also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 620 stores an online water quality assessment and treatment optimization program 640, which can be executed by the processor 610 to implement the online water quality assessment and treatment optimization methods of the various embodiments of this application.

[0077] In some embodiments, processor 610 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in memory 620 or process data, such as executing online water quality assessment, treatment, and optimization methods.

[0078] In some embodiments, display 630 may be an LED display, a liquid crystal display, a touch-screen liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 630 is used to display information from the online water quality assessment, treatment, and optimization device 600 and to display a visual user interface. Components 610-630 of the online water quality assessment, treatment, and optimization device 600 communicate with each other via a system bus.

[0079] In one embodiment, when the processor 610 executes the online water quality assessment and treatment optimization program 640 in the memory 620, the steps in the online water quality assessment and treatment optimization method described above are implemented.

[0080] This embodiment also provides a computer-readable storage medium storing an online water quality assessment, treatment, and optimization program, which, when executed by a processor, performs the following steps: A hydrodynamic water quality model is established based on the hydrogeographic data of the target river network and a custom ecodynamic water quality sub-model. Design a library of treatment schemes and establish mapping rules between the library of treatment schemes and the input parameters of the hydrodynamic and water quality model; Based on the mapping rules, the hydrodynamic and water quality model is used to perform batch simulations of all the solutions in the treatment solution library and extract multiple water quality assessment indicators. A comprehensive evaluation function is constructed based on water quality assessment indicators to screen treatment schemes in a pre-set treatment scheme library and determine the best treatment scheme.

[0081] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0082] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0083] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of embodiments of this disclosure upon considering the specification and practicing the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

[0084] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An online water quality assessment and treatment optimization method, characterized in that, include: A hydrodynamic water quality model is established based on the hydrogeographic data of the target river network and a custom ecodynamic water quality sub-model. Design a treatment scheme library and establish a mapping rule between the treatment scheme library and the input parameters of the hydrodynamic water quality model; Based on the mapping rules, the hydrodynamic water quality model is driven to perform batch simulations of all the solutions in the treatment solution library, and extract multiple water quality assessment indicators. Based on the water quality assessment indicators, a comprehensive evaluation function is constructed to screen the treatment schemes in the preset treatment scheme library and determine the best treatment scheme.

2. The online water quality assessment and treatment optimization method according to claim 1, characterized in that, The establishment of a hydrodynamic water quality model based on hydrological and geographical data of the target river network and a custom ecodynamic water quality sub-model also includes: Based on the topographic and hydrological data of the target river network, a hydrodynamic model simulating water flow is constructed. Define multiple water quality state variables that characterize the water body, and construct a nonlinear reaction kinetic equation between these multiple water quality state variables; The ecological dynamics model, which includes the nonlinear reaction dynamics equations, is coupled with the hydrodynamic model to form the hydrodynamic water quality model.

3. The method for online water quality assessment and treatment optimization according to claim 1, characterized in that, The design and governance solution library also includes: Determine the key engineering parameters affecting water quality, including at least the pollutant reduction ratio of the interception project, the treatment intensity parameters of the ecological restoration project, and the flow rate regulation of the water replenishment project; Set the range of values ​​for the technical requirements of each key governance project parameter; The Latin hypercube sampling method is used to uniformly sample within the multidimensional parameter space formed by the key governance engineering parameters to generate multiple combinations of governance engineering parameters. Each combination of governance engineering parameters is defined as a governance scheme, and the collection of all governance schemes constitutes the governance scheme library.

4. The method for online water quality assessment and treatment optimization according to claim 3, characterized in that, The process of establishing the mapping relationship between the treatment engineering parameters and the input parameters of the hydrodynamic water quality model further includes: The pollutant reduction ratio of the interception project is mapped to the proportional reduction coefficient of the point source and non-point source pollution load input data in the hydrodynamic water quality model. The treatment intensity parameters of the ecological restoration project are mapped to the adjustment amount of a specific reaction rate constant of the ecological dynamics module in the hydrodynamic water quality model. The controlled flow rate of the water replenishment project is mapped to the flow rate data in the corresponding river boundary condition file in the hydrodynamic and water quality model.

5. The method for online water quality assessment and treatment optimization according to claim 1, characterized in that, The step of driving the hydrodynamic water quality model to perform batch simulations of all schemes in the treatment scheme library according to the mapping rules and extracting multiple water quality assessment indicators also includes: Based on the mapping relationship, the treatment engineering parameters of each treatment scheme in the treatment scheme library are converted into the configuration file and input data file for the hydrodynamic water quality model. The batch solution is performed in parallel with the configuration file and the input data file, and the simulation result output file corresponding to each governance scheme is read. From the simulation results output file, the water quality assessment indicators of the main pollutants at the preset monitoring sections are analyzed and extracted.

6. The method for online water quality assessment and treatment optimization according to claim 1, characterized in that, The step of constructing a comprehensive evaluation function based on the water quality assessment indicators to screen treatment schemes in the preset treatment scheme library and determine the optimal treatment scheme further includes: Standardize the data of multiple water quality assessment indicators corresponding to each treatment plan; The objective weight of each water quality assessment indicator is calculated based on the standardized data; The comprehensive score for each treatment plan is calculated based on the objective weight of each water quality assessment indicator; The governance solution with the highest comprehensive score is determined as the optimal governance solution.

7. The method for online water quality assessment and treatment optimization according to claim 1, characterized in that, Also includes: The selected optimal treatment scheme is then used to drive the hydrodynamic water quality model for simulation verification. The detailed information of the optimal governance scheme, the simulation verification results, and the comparative analysis data with other governance schemes are imported into the visualization decision support platform for display and analysis.

8. An online water quality assessment, treatment, and optimization device, characterized in that, include: The model building module is configured to establish a hydrodynamic and water quality model based on the hydrogeographic data of the target river network and a custom ecodynamic water quality sub-model. The rule mapping module is configured to design a treatment scheme library and establish mapping rules between the treatment scheme library and the input parameters of the hydrodynamic water quality model. The simulation evaluation module is configured to drive the hydrodynamic water quality model to perform batch simulations of all schemes in the treatment scheme library according to the mapping rules, and extract multiple water quality evaluation indicators. The scheme selection module is configured to construct a comprehensive evaluation function based on the water quality assessment indicators to select the best treatment scheme from the preset treatment scheme library.

9. An online water quality assessment, treatment, and optimization device, characterized in that, It includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the computer program is executed by the processing unit, the processing unit performs the steps of the online water quality assessment and treatment optimization method according to any one of claims 1 to 7.

10. A storage medium, characterized in that, It stores a computer program that can be executed by an online water quality assessment, treatment and optimization device. When the computer program is run on the online water quality assessment, treatment and optimization device, the online water quality assessment, treatment and optimization device performs the steps of the online water quality assessment, treatment and optimization method according to any one of claims 1 to 7.

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