Hydrodynamic-water quality-biological coupled simulation method based on biological simulation model

Abstract

The present application relates to the technical field of water resources management and ecological regulation, and discloses a water power-water quality-biology coupling simulation method based on a biological simulation model, which comprises the following steps: establishing a water power model according to the terrain and water flow data of a research area, and determining the water depth and flow rate spatiotemporal distribution field of the research area based on the water power model; constructing a water quality model based on the water depth and flow rate spatiotemporal distribution field of the research area, combining the water quality data of each position, and constructing a biological simulation model combining the target biological attributes and behavior rules; determining a unified grid framework according to the water power model, and performing real-time coupling of the water power model, the water quality model and the biological simulation model based on the unified grid framework and an asynchronous solving algorithm to obtain a multi-model dynamic coupling model; and evaluating a preset regulation scheme based on the multi-model dynamic coupling model to select an optimal regulation scheme. The present application can accurately simulate the dynamic changes of the water depth, flow rate and various water quality indexes of a region through multi-model integrated real-time coupling.

Description

Technical Field

[0001] This invention relates to the field of water resource management and ecological scheduling technology, specifically to a hydrodynamic-water quality-biological coupling simulation method based on a biological simulation model. Background Technology

[0002] River hydrodynamic-water quality-ecology coupled simulation technology is an important technical means for water resource management and ecological scheduling, and it is widely used in the fields of comprehensive management of sluice-controlled rivers, protection of aquatic organism habitats, and improvement of water environment. However, existing hydrodynamic-water quality models mostly focus on simulating physicochemical processes, which can simulate water flow and pollutant transport, but lack effective coupling with the behavior of individual aquatic organisms, failing to truly reflect the dynamic response of fish and other organisms to hydraulic changes. Traditional fish habitat assessment methods use static suitability curves, dividing suitable habitat zones based on fixed water depth and flow velocity thresholds, which cannot simulate the autonomous migration, avoidance, and foraging behaviors of fish in dynamic water flow environments, resulting in a lack of biological basis for ecological scheduling schemes. For rivers with multiple gates, the cascade response mechanism between hydrological processes, environmental factors, and biological behaviors is still unclear under strong disturbances such as gate opening and closing and sudden changes in water level. Existing technologies cannot quantify this complex coupling relationship, restricting the precision of river ecological scheduling. Existing technologies are mostly limited to single objectives (such as flood control and water supply), lacking coupled simulation systems that can comprehensively consider multiple hydrological, water quality, and biological factors, making it difficult to achieve a technological leap from single-quantity water scheduling to multi-objective ecological scheduling.

[0003] Therefore, there is an urgent need for a simulation method and system that can dynamically couple hydrodynamics, water quality and individual biological behavior to provide a scientific decision-making tool for river ecological management. Summary of the Invention

[0004] This invention provides a hydrodynamic-water quality-biological coupling simulation method based on a biological simulation model to address the problems of lack of biological behavior simulation, lack of biological dynamic basis for ecological regulation, and insufficient comprehensive evaluation capabilities.

[0005] In a first aspect, the present invention provides a hydrodynamic-water quality-biological coupling simulation method based on a biological simulation model, the method comprising: A hydrodynamic model is established based on the topography and water flow data of the study area, and the spatiotemporal distribution fields of water depth and flow velocity in the study area are determined based on the hydrodynamic model. Based on the spatiotemporal distribution of water depth and flow velocity in the study area, a water quality model is constructed by combining water quality data from various locations, and a biological simulation model is constructed by combining the target biological attributes and behavioral rules. A unified mesh framework is determined based on the hydrodynamic model, and the hydrodynamic model, water quality model, and biological simulation model are coupled in real time based on the unified mesh framework and asynchronous solution algorithm to obtain a multi-model dynamic coupling model. The preset scheduling scheme is evaluated based on a multi-model dynamic coupling model, and the optimal scheduling scheme is selected.

[0006] The hydrodynamic-water quality-biological coupling simulation method based on a biosimulation model provided by this invention achieves synchronous simulation of physical processes and biological behaviors through the integrated real-time coupling of hydrodynamic models, water quality models, and biosimulation models. It realistically reflects the dynamic evolution of river ecosystems. Relying on a unified grid and asynchronous solution algorithm, it ensures stable and efficient collaborative operation of multiple models, accurately simulates the dynamic changes of regional water depth, flow velocity, and various water quality indicators, and restores the real activity state of organisms by combining biological attributes and behavioral rules. It conducts quantitative evaluation of different dam scheduling schemes, intuitively reflecting the comprehensive impact of scheduling measures on hydrology, water environment, and aquatic organisms. It effectively makes up for the shortcomings of traditional models in terms of single simulation dimension and weak ecological correlation, and provides scientific and reliable technical support for selecting the optimal scheme for river ecological scheduling.

[0007] In one optional implementation, a hydrodynamic model is established based on the topography and flow data of the study area, and the spatiotemporal distribution fields of water depth and flow velocity in the study area are determined based on the hydrodynamic model, including: Extract the boundary coordinates of the study area and divide the study area into an unstructured triangular mesh; The measured topographic data is imported into an unstructured triangular mesh, and the elevation data of the study area is generated by interpolation. Boundary conditions are set based on water flow data, and hydrodynamic model parameters are determined by combining riverbed sediment data. Boundary conditions include: upstream flow boundary, downstream water level boundary, and land boundary. Based on the unstructured triangular mesh, elevation data, boundary conditions, and hydrodynamic model parameters of the study area, a hydrodynamic model is generated and run to obtain the spatiotemporal distribution field of water depth and flow velocity in the study area.

[0008] The hydrodynamic-water quality-biological coupling simulation method based on a biological simulation model provided by this invention adopts an unstructured triangular mesh that can adapt to complex river topography and accurately restore regional geomorphological features. It ensures the authenticity and reliability of elevation data through topographic interpolation, and combines upstream flow, downstream water level, and the combined boundary of land on both sides to fit the actual water flow state of natural rivers. Combined with riverbed sediment calibration model parameters, it further improves the calculation accuracy and stably outputs high-precision spatiotemporal distribution results of water depth and flow velocity.

[0009] In one optional implementation, a water quality model is constructed based on the spatiotemporal distribution of water depth and flow velocity in the study area, combined with water quality data from various locations, including: Water quality state variables are set according to research needs. Based on the spatiotemporal distribution field of water depth and flow velocity in the study area, the changes of water quality state variables at each location are calculated, and a water quality model is constructed. By combining measured water quality data, the key parameters of the water quality model were calibrated, and the corrected water quality model was obtained.

[0010] The hydrodynamic-water quality-biological coupling simulation method based on a biosimulation model provided by this invention calculates water quality indicators by linking hydrodynamic output results, which conforms to the actual laws of water material migration and transformation. By setting water quality state variables as needed, it can flexibly adapt to different research scenarios. Then, by using measured data to complete parameter calibration, it effectively reduces the deviation between theoretical calculations and field conditions, greatly improves the accuracy of water quality simulation, and the corrected model can truly reflect the dynamic changes of regional water quality.

[0011] In one optional implementation, a biological simulation model is constructed based on the spatiotemporal distribution of water depth and flow velocity in the study area, combined with the target organism's attributes and behavioral rules, including: Establish a suitability function for the target organism to water depth and flow velocity based on the target organism's attributes; Construct a motion and growth model of the target organism in response to water depth and flow velocity based on the behavioral rules of the target organism; Based on the spatiotemporal distribution of water depth and flow velocity in the study area, a biological simulation model is constructed by combining the suitability function and the motion growth model.

[0012] The hydrodynamic-water quality-biological coupling simulation method based on biological simulation models provided by this invention constructs a habitat suitability function by combining the biological attributes themselves, accurately determining the quality of the habitat; it builds a movement and growth model based on behavioral rules, realistically restoring the migration, activity and growth patterns of organisms, simulating the autonomous decision-making behavior of organisms in complex water flow environments, and completing the modeling by combining global hydrodynamic data, realizing the dynamic correlation between environmental conditions and biological states, breaking through the limitations of traditional static evaluation, and making the biological simulation results more in line with natural reality.

[0013] In one optional implementation, a unified mesh framework is determined based on the hydrodynamic model, and the hydrodynamic model, water quality model, and biological simulation model are coupled in real time based on the unified mesh framework and asynchronous solution algorithm to obtain a multi-model dynamic coupling model, including: Using unstructured triangular meshes as a unified mesh framework, flow field data are calculated using a hydrodynamic model within the same time step based on the unified mesh framework. Based on flow field data, the concentrations of various water quality indicators are calculated using a water quality model. The current flow field and water quality index concentration difference are mapped to the location of each target organism. The biological simulation model is used to generate individual data for each target organism, which includes behavior, location and physiological parameters.

[0014] In one alternative implementation, flow field data, water quality index concentrations, and individual data of the target organism are transferred between the hydrodynamic model, the water quality model, and the biological simulation model.

[0015] The hydrodynamic-water quality-biological coupling simulation method based on a biosimulation model provided by this invention uses a unified unstructured grid to achieve spatial matching of multiple models, combined with asynchronous time-series solving, to ensure synchronous operation and smooth connection of each module. Data flows and is transferred efficiently between models, forming a complete linkage system. This allows water flow, water quality and biological state to be correlated and mutually influential in real time, ensuring accurate and efficient data transmission, and fully restoring the dynamic evolution process of the river ecosystem, greatly improving the realism and coherence of the overall simulation.

[0016] In one optional implementation, a preset scheduling scheme is evaluated based on a multi-model dynamic coupling model to select the optimal scheduling scheme, including: Multiple preset scheduling schemes are generated based on historical hydrological data; Each preset scheduling scheme is input into the multi-model dynamic coupling model to obtain the ecological effect index of each preset scheduling scheme. The ecological effect index includes: target organism individual trajectory map, population spatial distribution map, habitat adaptation distribution map, effective habitat area change curve and individual growth curve. Compare the ecological effect indicators of each preset scheduling scheme, and determine the optimal scheduling scheme based on research needs.

[0017] The hydrodynamic-water quality-biological coupling simulation method based on a biosimulation model provided by this invention formulates multiple scheduling schemes based on historical hydrological data. After simulation, it can output diversified ecological effect indicators, intuitively showing biological activities, habitats and growth status under different schemes. By comparing various indicators horizontally, the ecological impact of each scheme is quantified. The evaluation dimensions are comprehensive and objective, and the optimal scheduling scheme that meets the needs is accurately selected, providing a strong basis for river ecological management and scientific decision-making.

[0018] Secondly, this invention provides a hydrodynamic-water quality-biological coupling simulation device based on a biological simulation model, the device comprising: The flow field data research module is used to establish a hydrodynamic model based on the topography and flow data of the study area, and to determine the spatiotemporal distribution of water depth and flow velocity in the study area based on the hydrodynamic model. The water quality-biological model construction module is used to construct a water quality model based on the spatiotemporal distribution of water depth and flow velocity in the study area, combined with water quality data from various locations, and to construct a biological simulation model by combining the target biological attributes and behavioral rules. The multi-model dynamic coupling module is used to determine a unified mesh framework based on the hydrodynamic model, and to couple the hydrodynamic model, water quality model and biological simulation model in real time based on the unified mesh framework and asynchronous solution algorithm to obtain a multi-model dynamic coupling model. The scheduling scheme evaluation module is used to evaluate preset scheduling schemes based on a multi-model dynamic coupling model and select the optimal scheduling scheme.

[0019] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method described in the first aspect or any corresponding embodiment thereof.

[0020] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the method described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of the hydrodynamic-water quality-biological coupling simulation method based on a biological simulation model according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the second process of the hydrodynamic-water quality-biological coupling simulation method based on a biological simulation model according to an embodiment of the present invention. Figure 4 This is a structural block diagram of a hydrodynamic-water quality-biological coupling simulation device based on a biosimulation model according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0025] As an optional application scenario of this invention, such as Figure 1 As shown, the hydrodynamic-water quality-biological coupling simulation system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0026] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0027] This invention provides a hydrodynamic-water quality-biological coupling simulation method based on a biosimulation model. By integrating and coupling hydrodynamics, water quality, and biological simulation models in real time, it achieves synchronous simulation of physical processes and biological behaviors, thus realistically reflecting the dynamic evolution of river ecosystems.

[0028] According to an embodiment of the present invention, a hydrodynamic-water quality-biological coupling simulation method based on a biological simulation model is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] This embodiment provides a hydrodynamic-water quality-biological coupling simulation method based on a biological simulation model, which can be used in the aforementioned computer system. Figure 2 This is a flowchart of a hydrodynamic-water quality-biological coupling simulation method based on a biological simulation model according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Establish a hydrodynamic model based on the topography and water flow data of the study area, and determine the spatiotemporal distribution field of water depth and flow velocity in the study area based on the hydrodynamic model.

[0030] Specifically, a hydrodynamic model of the study area is established using two-dimensional hydrodynamic software. Combined with measured data from the study area, mathematical equations are used to simulate the flow patterns. A simulation period is set, with the first 5-10 days serving as a warm-up period. The hydrodynamic model is then run to determine the spatiotemporal distribution of water depth and flow velocity in the study area. Hydrodynamic models can also be constructed using software such as Delft3D, EFDC, TELEMAC, or HEC-RAS, which will not be elaborated upon here.

[0031] Step S202: Based on the spatiotemporal distribution of water depth and flow velocity in the study area, a water quality model is constructed by combining water quality data from various locations, and a biological simulation model is constructed by combining the target organism's attributes and behavioral rules.

[0032] Specifically, based on the hydrodynamic model, pollutant indicators at various locations in the water flow are calculated, and real-time changes in water quality are simulated by combining water temperature and plant growth patterns to generate a water quality model. For target organisms in the river (mainly including typical fish), a bio-simulation (Advanced BioMatrix, ABM) model is established by combining the basic attributes and behavioral rules of the target organisms.

[0033] Step S203: Determine a unified mesh framework based on the hydrodynamic model, and couple the hydrodynamic model, water quality model, and biological simulation model in real time based on the unified mesh framework and asynchronous solution algorithm to obtain a multi-model dynamic coupling model.

[0034] Specifically, when establishing a hydrodynamic model, the study area needs to be divided into grids. The grid division results are used as a unified grid framework. Based on the unified grid framework and asynchronous solution algorithm, the hydrodynamic model, water quality model and biological simulation model are coupled in real time and calculated synchronously in a fixed order. First, the water flow and water quality are calculated. Then, the water flow and water quality data are transmitted to the biological simulation model to update the position and state of the target organism, thereby realizing the dynamic coupling of multiple models.

[0035] Step S204: Evaluate the preset scheduling scheme based on the multi-model dynamic coupling model and select the optimal scheduling scheme.

[0036] Specifically, different dam scheduling schemes are set up (such as dam opening and closing, daily scheduling, ecological water replenishment, and extreme situations of abundant / dry water). The evaluation index of each scheduling scheme is generated by a multi-model dynamic coupling model, and the scheduling scheme is evaluated based on the evaluation index. The optimal scheduling scheme is selected from multiple preset scheduling schemes.

[0037] The hydrodynamic-water quality-biological coupling simulation method based on a biosimulation model provided in this embodiment achieves synchronous simulation of physical processes and biological behaviors through the integrated real-time coupling of hydrodynamic models, water quality models, and biosimulation models. This realistically reflects the dynamic evolution of river ecosystems. Relying on a unified grid and asynchronous solution algorithm, it ensures stable and efficient collaborative operation of multiple models, accurately simulates the dynamic changes of regional water depth, flow velocity, and various water quality indicators, and restores the real activity state of organisms by combining biological attributes and behavioral rules. It conducts quantitative evaluations of different dam and gate scheduling schemes, intuitively reflecting the comprehensive impact of scheduling measures on hydrology, water environment, and aquatic organisms. This effectively makes up for the shortcomings of traditional models in terms of single simulation dimensions and weak ecological correlation, providing scientific and reliable technical support for selecting the optimal scheme for river ecological scheduling.

[0038] This embodiment provides a hydrodynamic-water quality-biological coupling simulation method based on a biological simulation model, which can be used in the aforementioned computer system. Figure 3 This is a flowchart of a hydrodynamic-water quality-biological coupling simulation method based on a biological simulation model according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps: Step S301: Establish a hydrodynamic model based on the topography and water flow data of the study area, and determine the spatiotemporal distribution field of water depth and flow velocity in the study area based on the hydrodynamic model.

[0039] Specifically, step S301 includes: Step S3011: Extract the boundary coordinates of the study area and divide the study area into an unstructured triangular mesh.

[0040] Specifically, the boundary coordinates of the study area are extracted to generate an unstructured triangular mesh. For example, 20,061 meshes and 62,915 meshes are generated in mountainous areas and plains, respectively, with 13,763 and 32,647 nodes. The minimum mesh area is 43.27 m², and the maximum mesh area can be adjusted according to the complexity of the terrain.

[0041] Step S3012: Import the measured topographic data into an unstructured triangular mesh and generate elevation data for the study area through interpolation.

[0042] Specifically, the measured topographic data is generally in .xyz format. It is imported into an unstructured triangular mesh, and the X, Y, and Z three-dimensional information of all measuring points is used to reconstruct the spatial distribution of all measured points in the plane coordinate system. After the points are loaded, the distribution of measuring points is visualized to confirm that the measuring points cover the entire study river section (there are no large areas of blank space in the main channel, beach, bank slope, etc.).

[0043] Due to the limited number and scattered distribution of measured points, while the triangular mesh nodes are numerous and densely distributed, it is impossible to obtain measured elevations for every mesh node. Therefore, a spatial interpolation algorithm is used to calculate the elevation value of each mesh node using the known elevations of surrounding measured points. Inverse distance weighted interpolation, Kriging interpolation, or linear trigonometric interpolation can be selected to adapt to unstructured triangular meshes. The closer a measured point is to a mesh node, the greater its influence on the node's elevation. Using a single triangular mesh node as the calculation unit, the search range around the node is defined, and all measured topographic points within the range are filtered. Weights are assigned based on the spatial distance between the measured point and the node, and the interpolation formula is used to calculate the elevation value of the mesh node. The process is repeated for all nodes within the mesh, ensuring that each triangular mesh node is matched with its corresponding elevation data.

[0044] By combining node elevations, the average elevation / base elevation of each triangular grid cell is calculated simultaneously to fully recreate the topographic undulations of the entire riverbed and bank slope, ensuring the accuracy of topographic representation.

[0045] Step S3013: Set boundary conditions based on water flow data, and determine hydrodynamic model parameters by combining riverbed sediment data. Boundary conditions include: upstream flow boundary, downstream water level boundary, and land boundary.

[0046] Specifically, based on the actual confluence and discharge of rivers and topographic features, boundary rules are defined for each zone, and the data sources are on-site hydrological monitoring, dam operation records, and historical hydrological sequences.

[0047] The upstream starting section of the studied river segment was selected as the upstream flow boundary. Hourly / daily measured flow data were entered and set to flow control mode. The total amount of water flowing into the simulated area per unit time was defined to recreate the dynamic process of upstream inflow, reservoir / dam discharge, and basin confluence. The flow rate changes over time to reflect different operating conditions such as high water, low water, and pulsed water replenishment.

[0048] The downstream water level boundary is set at the end of the river section, and the time series data of the measured downstream water level is entered. A water level control mode is adopted. The backwater and backwater effects formed by downstream dams, main streams, and lakes are simulated to constrain the water surface elevation at the end of the river section and ensure that the water surface line and flow pattern of the entire area are consistent with the actual situation.

[0049] Non-water areas such as riverbanks, beaches, dikes, and isolated islands are uniformly designated as land boundaries. Rules are set to ensure that water flow is impenetrable and has no lateral outflow. At the same time, the characteristics of no-slip boundary are enabled, that is, the water flow velocity at the shoreline approaches 0, simulating real flow states such as backflow and deceleration at the shoreline, thus limiting the simulation range.

[0050] Based on the characteristics of the riverbed sediment, such as particle size, material, and roughness, the key calculation parameters of the model are calibrated. These parameters directly affect the flow resistance, turbulence state, and wet / dry zone determination. The key calculation parameters of the model include Manning coefficient, wet / dry water depth, eddy viscosity coefficient, time step, and evaporation / infiltration rate.

[0051] The Manning coefficient (e.g., 0.03-0.04) is determined based on the riverbed composition (silt, sand, gravel, rock, etc.) to characterize the resistance of the riverbed to water flow; the coarser the riverbed, the larger the coefficient. The dry / wet depth parameter delineates the critical depths for dry, semi-submerged, and fully wet zones. For example, dry depth hdry = 0.005m, submerged depth hflood = 0.05m, and wet depth hwet = 0.1m. This is just an example and not a limitation, used to simulate the exposed and submerged processes of riverbed shoals and beaches as water levels rise and fall. The eddy viscosity coefficient is calculated using Smagori. The nsky formula (with a coefficient of 0.28) is used to characterize the turbulent diffusion capacity of water bodies and is adapted to different flow regimes in rivers. The time step and Courrant-Friedrichs-Lewy (CFL) are set in conjunction with the river flow velocity (minimum time step 0.01s, maximum 60s, CFL within 0.8) to ensure the stability of numerical calculations and avoid computational divergence. Evaporation and seepage are assigned values ​​based on regional meteorology and riverbed infiltration characteristics (e.g., 20-60mm / day) and imported in the form of surface files to reflect the natural water loss of water bodies.

[0052] Step S3014: Based on the unstructured triangular mesh, elevation data, boundary conditions, and hydrodynamic model parameters of the study area, generate a hydrodynamic model, run the hydrodynamic model, and obtain the spatiotemporal distribution field of water depth and flow velocity in the study area.

[0053] Specifically, unstructured triangular meshes, elevation data, boundary conditions, and a complete set of model parameters are linked and bound together to form a complete and computable hydrodynamic model engineering file. All data share the same spatial coordinate system, ensuring complete matching of terrain, mesh, and boundaries.

[0054] A 5-10 day warm-up period is set before starting the operation to eliminate initial value interference and allow the water flow state to gradually approach its natural steady state, avoiding distortion of initial calculation results. Then, the target simulation duration is set, and the model iterates hourly according to the preset time step. Within each time step, the hydrodynamic control equations are solved by combining topographic resistance, boundary inflow and outflow, and turbulence characteristics to calculate the current water depth, longitudinal velocity, and lateral velocity of each grid node / cell. The model continues to operate and stores the time-series results, ultimately generating a spatiotemporal distribution field of water depth and velocity: spatially covering the entire study area and temporally corresponding to the entire simulation cycle, it can intuitively reflect the dynamic changes in water depth and flow speed at different locations and times, providing basic flow field data for subsequent water quality and biological models.

[0055] The hydrodynamic-water quality-biological coupling simulation method based on a biosimulation model provided in this embodiment adopts an unstructured triangular mesh that can adapt to complex river topography and accurately restore regional geomorphological features. It ensures the authenticity and reliability of elevation data through topographic interpolation, and combines upstream flow, downstream water level, and the combined boundary of land on both sides to fit the actual water flow state of natural rivers. Combined with the riverbed sediment calibration model parameters, it further improves the calculation accuracy and stably outputs high-precision spatiotemporal distribution results of water depth and flow velocity.

[0056] Step S302: Based on the spatiotemporal distribution field of water depth and flow velocity in the study area, a water quality model is constructed by combining water quality data at each location, and a biological simulation model is constructed by combining the target organism's attributes and behavioral rules.

[0057] Specifically, step S302 includes: Step S3021: Set water quality state variables according to research needs, calculate the changes in water quality state variables at each location based on the spatiotemporal distribution field of water depth and flow velocity in the study area, and construct a water quality model.

[0058] Specifically, based on the research objectives, the pollution characteristics of the river section, and the needs of ecological protection, water quality indicators to be simulated are set, including but not limited to: biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammonia nitrogen (NH3-N), total phosphorus (TP), dissolved oxygen (DO), and submerged plants (BV), etc. State variables can be flexibly added or removed.

[0059] Using the spatiotemporal distribution fields of global water depth and velocity output from the hydrodynamic model as driving conditions, water quality changes are calculated synchronously with the water flow. Corresponding biochemical reaction control equations are matched for each type of state variable, describing processes such as pollutant degradation, transformation, sediment release, and plant uptake and growth. These biochemical reaction control equations include: (1) BOD degradation equation:

[0060] (2) COD degradation equation:

[0061] (3) Ammonia nitrogen degradation equation:

[0062] (4) Total phosphorus degradation equation:

[0063] (5) Growth equation of submerged plants: .

[0064] Water flow determines the convection, diffusion range, and rate of pollutants, while water temperature, depth, and dissolved oxygen affect the degradation rate. The model embeds these coupled relationships into the equations, and based on a unified grid framework, solves the equations for each grid cell time-series to calculate the concentration changes of various water quality indicators at different locations and times. By integrating state variables, governing equations, and hydrodynamic data, an initial water quality model is formed, enabling the simulation calculation of the dynamic evolution of water quality indicators with water flow.

[0065] Step S3022: Combine measured water quality data to calibrate the key parameters of the water quality model and obtain the corrected water quality model.

[0066] Specifically, field monitoring data from multiple monitoring sections and different time periods of the studied river section were collected, covering all established water quality state variables, as benchmark data for verification. The core parameters corresponding to various indicators include: primary degradation rate of pollutants, temperature correction factor, half-saturated oxygen concentration, sediment pollutant release rate, and aquatic plant growth / respiration coefficient.

[0067] Based on measured data, the key parameters of the calibration model are determined, including: BOD: Primary degradation rate at 20℃ is 0.5 / d, temperature correction factor is 1.07, and half-saturated oxygen concentration is 2 mg / L; COD: Primary degradation rate at 20℃ is 0.1 / d, temperature correction factor is 1.03, and half-saturated oxygen concentration is 2 mg / L; Ammonia nitrogen: 0.08 g / d primary degradation rate at 20℃, 5 mg / L half-saturated oxygen concentration, and 0.03 g / m² release rate in sediment; Total phosphorus: 0.025 g / d at 20℃, half-saturated oxygen concentration 3 mg / L, sediment release rate 0.025 g / m²; Submerged plants: nitrogen-to-carbon ratio 0.137 g N / g C, phosphorus-to-carbon ratio 0.016 g P / g C, respiration rate at 20℃ 0.01 / d, temperature-dependent growth coefficient 1.05.

[0068] The initial water quality model is run using initial parameters, and the simulated concentration results of each monitoring section are output. The simulation results are compared with the measured data of the same period to calculate the deviation. Based on the magnitude of the deviation, the corresponding parameter values ​​are slightly adjusted, and the model is repeatedly tested, compared, and adjusted. The model is continuously iterated until the deviation between the simulated water quality results and the measured data for the entire area and all time periods is controlled within the allowable range, thus obtaining the corrected water quality model.

[0069] The hydrodynamic-water quality-biological coupling simulation method based on a bio-simulation model provided in this embodiment calculates water quality indicators by linking hydrodynamic output results, which conforms to the actual laws of water material migration and transformation. By setting water quality state variables as needed, it can flexibly adapt to different research scenarios. Then, by using measured data to complete parameter calibration, it effectively reduces the deviation between theoretical calculations and field conditions, greatly improves the accuracy of water quality simulation, and the corrected model can truly reflect the dynamic changes of regional water quality.

[0070] In some optional implementations, step S302 above further includes: Step S3023: Establish a suitability function for the target organism to water depth and flow velocity based on the target organism's attributes.

[0071] Specifically, the target organism generally refers to fish, and its individual attributes include: position coordinates (x, y), movement speed (v), and movement direction (x, y). Spatial attributes such as body length (L), weight (W), age (t), and energy state (E); physiological attributes such as search state, migration state, and settlement state; and behavioral state attributes such as search state, migration state, and settlement state.

[0072] Based on measured data and literature, suitability functions for water depth and current velocity for fish were established. For example, the suitable water depth range for the broadfin chub is 0.36~4.2m, the optimal water depth is 1.5~2.5m, the suitable current velocity range is 0.18~2.16m / s, and the optimal current velocity is 0.5~1.2m / s; the suitable water depth range for the blackfin gudgeon is 0.5~1.2m, the optimal water depth is 0.7~1.0m, the suitable current velocity range is 0.1~0.6m / s, and the optimal current velocity is 0.2~0.4m / s. These are just examples and are not intended to limit the fish population.

[0073] Both the water depth suitability function DHSI(d) and the current velocity suitability function VHSI(v) are normalized to the 0-1 range. The closer the value is to 1, the more suitable the environment is for the target organism to live in; conversely, the lower the value, the worse the environment is. The comprehensive habitat suitability index is calculated as: HSI = DHSI × VHSI. This comprehensive index is used to evaluate whether a body of water is a high-quality habitat, thus completing the suitability function system.

[0074] Step S3024: Construct a motion and growth model of the target organism in response to water depth and flow velocity based on the behavioral rules of the target organism.

[0075] Specifically, the behavioral rules of the target organism include: (1) Random search: Individuals move randomly within a 360° range at a normal speed V_normal, continuously monitoring the water depth and current velocity of the surrounding environment. If no better habitat is found within the search radius R_search, they remain in a random wandering state. Broadfin shrike: V_normal = 0.8 m / s, R_search = 150 m; Blackfin gudgeon: V_normal = 0.3 m / s, R_search = 20 m. These are just examples and are not limited to this.

[0076] (2) Target identification: When the suitability index (HSI) of the combination of water depth and current velocity within a radius R_target centered on an individual is higher than the current location threshold. When this location is identified as the target area, the identification threshold is set. It can be set to 1.2 times the current HSI or an absolute value greater than 0.6.

[0077] (3) Rapid migration: After identifying the target, the individual moves to the target area along the shortest path at the maximum speed V_max. During the migration, the environmental information is continuously updated. If a better target appears, the direction is adjusted. Widefin scad: V_max = 1.8 m / s, Blackfin gudgeon: V_max = 0.5 m / s.

[0078] (4) Settlement activities: After arriving at the target area, individuals conduct small-scale activities within the suitable area at a minimum speed V_min, continuously monitoring environmental changes. If the environment deteriorates to below the threshold, Then it re-enters the random search state, and can set the following for the broadfin trevally: V_min = 0.2 m / s. = 0.4, Blackfin Grylloid: V_min = 0.1 m / s, = 0.4.

[0079] By combining environmental and temporal conditions such as water temperature and age, growth equations corresponding to body length and weight are established to quantify the growth patterns of organisms over time and with environmental changes, and to correlate the effects of water temperature and the quality of the living environment on growth rate.

[0080] Body length growth equation based on water temperature:

[0081] In the formula, L is the body length, in mm; max is the maximum body length, taken as 165 mm; t is the age in days (0~30 days); a is a constant, calculated by substituting the maximum body length of the largest blackfin gudgeon into the formula as 0.061; r is an intermediate variable; r opt is a constant, taken as 0.12; n is a constant, taken as 0.0008; Topt The optimal water temperature is 18~26℃, and 22℃ is used in this paper; T is the actual water temperature in ℃, and Fi is a coefficient, taken as 0.015.

[0082] Take the blackfin gudgeon as an example: , .

[0083] Step S3025: Based on the spatiotemporal distribution of water depth and flow velocity in the study area, and combined with the suitability function and motion growth model, construct a biological simulation model.

[0084] Specifically, the spatiotemporal distribution field of water depth and flow velocity output by the hydrodynamic model is invoked, and the environmental data of each grid cell is matched to the biological individuals in the corresponding area based on a unified grid.

[0085] Using real-time water depth and flow velocity as input, the current habitat level is first calculated through a suitability function; then, based on the quality of the habitat, the motion model is driven to update the organism's location and behavioral status; at the same time, combined with conditions such as water temperature, the growth model is used to update physiological parameters such as the organism's body length and weight.

[0086] By connecting all modules of environmental data, suitability assessment, behavioral movement, and individual growth, dynamic calculations of "environmental change → biological behavioral response → physiological state update" are realized, and the biological simulation model is finally completed and simultaneously connected to the subsequent multi-model coupling system.

[0087] The hydrodynamic-water quality-biological coupling simulation method based on biological simulation models provided in this embodiment constructs a habitat suitability function by combining the biological attributes themselves, accurately determining the quality of the habitat; it builds a movement and growth model based on behavioral rules, realistically restoring the migration, activity and growth patterns of organisms, simulating the autonomous decision-making behavior of organisms in complex water flow environments, and completing the modeling by combining global hydrodynamic data, realizing the dynamic correlation between environmental conditions and biological states, breaking through the limitations of traditional static evaluation, and making the biological simulation results more in line with natural reality.

[0088] Step S303: Determine a unified mesh framework based on the hydrodynamic model, and couple the hydrodynamic model, water quality model, and biological simulation model in real time based on the unified mesh framework and asynchronous solution algorithm to obtain a multi-model dynamic coupling model.

[0089] Specifically, step S303 includes: Step S3031: Using the unstructured triangular mesh as a unified mesh framework, and based on the unified mesh framework, the flow field data is calculated using the hydrodynamic model within the same time step.

[0090] Step S3032: Based on the flow field data, calculate the concentration of each water quality indicator using a water quality model.

[0091] Step S3033: The current flow field and water quality index concentration difference are mapped to the location of each target organism. Individual data of each target organism is generated using a biological simulation model. Individual data includes behavior, location, and physiological parameters.

[0092] Specifically, within each time step, the following steps are executed sequentially: (1) Calculate the flow field using a hydrodynamic model and output the water depth H(x,y,t), flow velocity U(x,y,t), and V(x,y,t).

[0093] (2) Calculate the concentrations C(x,y,t) of each water quality index based on the current flow field using the water quality model.

[0094] (3) Interpolate the flow field and water quality information to the location of each individual in the biological simulation model. The ABM model updates the behavior state and location of each individual based on the environmental information, and updates the individual's physiological parameters (body length, weight).

[0095] (4) Proceed to the next time step.

[0096] Depending on the research needs, one-way coupling (environment → organism) or two-way coupling (biological feedback influencing the environment) can be selected.

[0097] In some alternative implementations, flow field data, water quality index concentrations, and individual data of target organisms are transferred between the hydrodynamic model, water quality model, and biological simulation model.

[0098] Specifically, positive propagation refers to the continuous impact of water flow and aquatic environment changes on fish behavior, distribution, and growth, which is the core theme of the simulation. Reverse propagation relies on the feedback interface reserved in the model to transmit the effects of fish feeding, excretion, and schooling activities back to the water quality model: biological metabolites will change the concentration of pollutants in the water, and biological activity disturbing the bottom sediment will also affect the amount of sediment released; water quality changes will then act on hydrodynamics and subsequent biological simulations, forming a realistic closed-loop response of the ecosystem.

[0099] All data is updated globally and synchronously within a unified grid and time step. The three models work together in real time to fully recreate the entire process of interaction and dynamic evolution of water flow, water quality and aquatic organisms in the sluice-controlled river, ensuring the authenticity and continuity of the coupled simulation.

[0100] The hydrodynamic-water quality-biological coupling simulation method based on a biosimulation model provided in this embodiment uses a unified unstructured grid to achieve spatial matching of multiple models. Combined with asynchronous time-series solving, it ensures synchronous operation and smooth connection of each module. Data flows and is transferred efficiently between models, forming a complete linkage system. This allows water flow, water quality and biological status to be correlated and influence each other in real time. It not only ensures accurate and efficient data transmission, but also fully restores the dynamic evolution process of the river ecosystem, greatly improving the realism and coherence of the overall simulation.

[0101] Step S304: Evaluate the preset scheduling scheme based on the multi-model dynamic coupling model and select the optimal scheduling scheme.

[0102] Specifically, step S304 includes: Step S3041: Generate multiple preset scheduling schemes based on historical hydrological data.

[0103] Specifically, the study collects and studies continuous historical hydrological data for the river section over many years, including hourly / daily flow, water level, inflow sequence for different years, and routine operation records of sluice gates and dams. At the same time, it conducts hydrological frequency calculations to classify high-water years, normal-water years, and low-water years, and calculates the routine inflow flow corresponding to a 90% guarantee rate to clarify the natural inflow patterns and engineering regulation capabilities of the river section.

[0104] Option 1: Based on a hydrological guarantee rate of P=90%, this represents the standard operating mode of a river under normal low-water conditions. The discharge volume and opening / closing rules of dams are set according to the flow and water level indicators corresponding to this guarantee rate, simulating the traditional scheduling method primarily focused on water supply and flood control. Option 2: In addition to conventional scheduling, ecological water replenishment is added. Pulsed water replenishment is implemented during the critical periods of fish migration and reproduction in spring and autumn, based on the activity patterns of aquatic organisms. The discharge flow is increased in stages, raising local water levels and creating a dynamic water flow environment specifically for the habitat and reproduction of organisms. Option 3: Divided into two extreme operating conditions: wet years and dry years. Wet years simulate the operation of large-flow discharge during the flood season with dams fully / partially open; dry years simulate the extreme state of extremely low inflow and controlled discharge at dams, used to assess the river's ecological tolerance under extreme hydrological conditions. These are just examples and are not exhaustive.

[0105] Each scheduling scheme is transformed into a time-series boundary condition that the model can recognize. The simulation duration, time step, and gate control timing are uniformly set to ensure that different schemes can be compared and calculated under the same simulation conditions.

[0106] Step S3042: Input each preset scheduling scheme into the multi-model dynamic coupling model to obtain the ecological effect index of each preset scheduling scheme. The ecological effect index includes: target organism individual trajectory map, population spatial distribution map, habitat adaptation distribution map, effective habitat area change curve and individual growth curve.

[0107] Specifically, the flow rate and water level boundary rules of each scheduling scheme are input into the coupled model in sequence. The model iterates according to a predetermined time sequence: first, the global flow field and water quality concentration field are calculated, and then the fish behavior, location and growth status are dynamically simulated to complete the entire simulation cycle.

[0108] Ecological effect indicators were extracted and generated by classification: (1) Target organism trajectory map: The coordinate changes of each fish during the entire simulation period were recorded, and the swimming and migration paths were drawn to intuitively reflect the activity range and migration patterns of organisms under different scheduling conditions; (2) Population spatial distribution map: The number of organisms in the whole area was counted by grid, and a population density distribution map was generated to reflect the spatial distribution characteristics of fish clusters and dispersion; (3) Habitat adaptability distribution map: The habitat suitability index of each grid was calculated based on water depth and flow velocity, and a spatial distribution map was drawn to mark the range of high-quality, general and unsuitable habitat areas; (4) Effective habitat area change curve: The area with a suitability index greater than the threshold was taken as the effective habitat, and the area was counted and the change curve was drawn for each time period to reflect the dynamic fluctuation of habitat size with scheduling conditions; (5) Individual growth curve: The changes in fish body length and weight over time were continuously recorded to generate a growth curve and compare the differences in biological growth rate under different water environments.

[0109] Step S3043: Compare the ecological effect indicators of each preset scheduling scheme, and determine the optimal scheduling scheme according to the research needs.

[0110] Specifically, the three schemes were compared horizontally to analyze all ecological indicators, whether fish migration routes were smooth, population distribution was uniform, effective habitat area size and stability, and biological growth rate were different under different schemes. At the same time, the comprehensive impact of the scheduling scheme on the river ecosystem was judged by combining water quality and water flow status.

[0111] Based on actual research and management needs, the evaluation criteria are set according to the core objectives of the project: if the core objective is ecological protection and biological habitat restoration, priority is given to the scheme with a large effective habitat area and better biological activity and growth status; if the main objective is daily water supply and flood control operation and maintenance, ecological effects, engineering operation costs, safety and stability are taken into account.

[0112] The optimal scheduling scheme is determined by comparing the comprehensive quantitative data with the visualization charts, and the scheduling scheme with the best comprehensive benefits is selected. This scheme can be used as the formal implementation scheme for the actual ecological scheduling and water resource management of dams and gates in the river section.

[0113] The hydrodynamic-water quality-biological coupling simulation method based on a biosimulation model provided in this embodiment formulates multiple scheduling schemes based on historical hydrological data. After simulation, it can output diversified ecological effect indicators, intuitively showing biological activities, habitats and growth status under different schemes. By comparing various indicators horizontally, the ecological impact of each scheme is quantified. The evaluation dimensions are comprehensive and objective, and the optimal scheduling scheme that meets the needs is accurately selected, providing a strong basis for river ecological management and scientific decision-making.

[0114] In one specific embodiment, fish monitoring data, water quality monitoring data, and hydrological data were recorded at multiple cross-sections within a certain study area for a continuous year. Model calibration and validation: Hydrodynamic model: Nash coefficients for the four cross-sections ranged from 0.75 to 0.87; Water quality model: Calibration deviations for BOD, COD, ammonia nitrogen, and total phosphorus were all less than 30%; ABM ​​model: In 20 repeated simulations, the measured values ​​all fell within the simulation range, successfully capturing the migration trend of fish in September and October.

[0115] With a pre-set scheduling plan, an annual net flow of 260 million m³, and spring and autumn pulse water replenishment, the effective habitat area of ​​the blackfin gudgeon reaches 3.49 km², accounting for 22.9% of the water area, even under the condition of minimum water volume; the effective habitat area of ​​the broadfin chub reaches its peak of 4.56 km² and 5.00 km² in March and November, respectively; and the biomass of submerged plants increases by an average of 131.54% within the suitable water depth of 0.5-1.2 m.

[0116] This embodiment also provides a hydrodynamic-water quality-biological coupling simulation device based on a biosimulation model. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0117] This embodiment provides a hydrodynamic-water quality-biological coupling simulation device based on a biological simulation model, such as... Figure 4 As shown, it includes: The flow field data research module 401 is used to establish a hydrodynamic model based on the topography and flow data of the study area, and to determine the spatiotemporal distribution field of water depth and flow velocity in the study area based on the hydrodynamic model.

[0118] The water quality-biological model construction module 402 is used to construct a water quality model based on the spatiotemporal distribution field of water depth and flow velocity in the study area, combined with water quality data at various locations, and to construct a biological simulation model by combining the target biological attributes and behavioral rules.

[0119] The multi-model dynamic coupling module 403 is used to determine a unified mesh framework based on the hydrodynamic model, and to couple the hydrodynamic model, water quality model and biological simulation model in real time based on the unified mesh framework and asynchronous solution algorithm to obtain a multi-model dynamic coupling model.

[0120] The scheduling scheme evaluation module 404 is used to evaluate the preset scheduling schemes based on the multi-model dynamic coupling model and select the optimal scheduling scheme.

[0121] In some alternative implementations, the flow field data study module 401 includes: The grid division unit is used to extract the boundary coordinates of the study area and divide the study area into an unstructured triangular grid.

[0122] The terrain import unit is used to import measured terrain data into an unstructured triangular mesh and generate elevation data for the study area through interpolation.

[0123] The boundary and parameter setting unit is used to set boundary conditions based on water flow data, and at the same time determine the hydrodynamic model parameters by combining riverbed sediment data. The boundary conditions include: upstream flow boundary, downstream water level boundary, and land boundary.

[0124] The hydrodynamic model generation unit is used to generate a hydrodynamic model based on the unstructured triangular mesh, elevation data, boundary conditions, and hydrodynamic model parameters of the study area. By running the hydrodynamic model, the spatiotemporal distribution fields of water depth and flow velocity in the study area can be obtained.

[0125] In some alternative implementations, the water quality-biological model building module 402 includes: The water quality model building unit is used to set water quality state variables according to research needs, calculate the changes of water quality state variables at each location based on the spatiotemporal distribution field of water depth and flow velocity in the study area, and build a water quality model.

[0126] The water quality model correction unit is used to combine measured water quality data to calibrate the key parameters of the water quality model and obtain the corrected water quality model.

[0127] The suitability function establishment unit is used to establish the suitability function of the target organism for water depth and flow velocity by combining the target organism's attributes.

[0128] The motion growth model construction unit is used to construct a motion growth model of the target organism in response to water depth and flow velocity based on the behavioral rules of the target organism.

[0129] The biological simulation model building unit is used to construct a biological simulation model based on the spatiotemporal distribution of water depth and flow velocity in the study area, combined with a suitability function and a motion growth model.

[0130] In some alternative implementations, the multi-model dynamic coupling module 403 includes: The flow field data calculation unit is used to use unstructured triangular meshes as a unified mesh framework. Based on the unified mesh framework, flow field data is calculated using a hydrodynamic model within the same time step.

[0131] The water quality index calculation unit is used to calculate the concentration of various water quality indicators based on flow field data and water quality models.

[0132] The individual data generation unit is used to map the current flow field and water quality index concentration difference to the location of each target organism, and to generate individual data for each target organism using a biological simulation model. The individual data includes behavior, location, and physiological parameters.

[0133] In some optional implementations, the scheduling scheme evaluation module 404 includes: The scheduling scheme generation unit is used to generate multiple preset scheduling schemes based on historical hydrological data.

[0134] The ecological effect index calculation unit is used to input each preset scheduling scheme into the multi-model dynamic coupling model to obtain the ecological effect index of each preset scheduling scheme. The ecological effect index includes: target organism individual trajectory map, population spatial distribution map, habitat adaptation distribution map, effective habitat area change curve and individual growth curve.

[0135] The optimal scheduling scheme selection unit is used to compare the ecological effect indicators of various preset scheduling schemes and determine the optimal scheduling scheme according to research needs.

[0136] The hydrodynamic-water quality-biological coupling simulation device based on a biosimulation model provided in this invention can execute the hydrodynamic-water quality-biological coupling simulation method based on a biosimulation model provided in any embodiment of this invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0137] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0138] The following is a detailed reference. Figure 5The diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from memory 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device. The processor 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0139] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0140] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a memory 508, or installed from a ROM 502. When the computer program is executed by the processor 501, it performs the functions defined in the hydrodynamic-water quality-biological coupling simulation method based on a biosimulation model according to embodiments of the present invention.

[0141] Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0142] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the hydrodynamic-water quality-biological coupling simulation method based on a biological simulation model shown in the above embodiments is implemented.

[0143] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A hydrodynamic-water quality-biological coupling simulation method based on a biological simulation model, characterized in that, The method includes: A hydrodynamic model is established based on the topography and water flow data of the study area, and the spatiotemporal distribution fields of water depth and flow velocity in the study area are determined based on the hydrodynamic model. Based on the spatiotemporal distribution of water depth and flow velocity in the study area, a water quality model is constructed by combining water quality data from various locations, and a biological simulation model is constructed by combining the target biological attributes and behavioral rules. A unified mesh framework is determined based on the hydrodynamic model, and the hydrodynamic model, water quality model, and biological simulation model are coupled in real time based on the unified mesh framework and asynchronous solution algorithm to obtain a multi-model dynamic coupling model. The preset scheduling scheme is evaluated based on the multi-model dynamic coupling model, and the optimal scheduling scheme is selected.

2. The method according to claim 1, characterized in that, A hydrodynamic model is established based on the topography and flow data of the study area, and the spatiotemporal distribution fields of water depth and flow velocity in the study area are determined based on the hydrodynamic model, including: Extract the boundary coordinates of the study area and divide the study area into an unstructured triangular mesh; The measured terrain data is imported into the unstructured triangular mesh, and the elevation data of the study area is generated by interpolation. Boundary conditions are set based on water flow data, and hydrodynamic model parameters are determined by combining riverbed sediment data. The boundary conditions include: upstream flow boundary, downstream water level boundary, and land boundary. Based on the unstructured triangular mesh, elevation data, boundary conditions, and hydrodynamic model parameters of the study area, a hydrodynamic model is generated. By running the hydrodynamic model, the spatiotemporal distribution fields of water depth and flow velocity in the study area are obtained.

3. The method according to claim 1, characterized in that, Based on the spatiotemporal distribution of water depth and flow velocity in the study area, a water quality model is constructed by combining water quality data from various locations, including: Water quality state variables are set according to research needs. Based on the spatiotemporal distribution field of water depth and flow velocity in the study area, the changes of water quality state variables at each location are calculated, and a water quality model is constructed. By combining measured water quality data, the key parameters of the water quality model were calibrated, and the corrected water quality model was obtained.

4. The method according to claim 1, characterized in that, Based on the spatiotemporal distribution of water depth and current velocity in the study area, a biological simulation model is constructed by combining the target organism's attributes and behavioral rules, including: Establish a suitability function for the target organism to water depth and flow velocity based on the target organism's attributes; Construct a motion and growth model of the target organism in response to water depth and flow velocity based on the behavioral rules of the target organism; Based on the spatiotemporal distribution of water depth and flow velocity in the study area, and combined with the suitability function and motion growth model, a biological simulation model is constructed.

5. The method according to claim 2, characterized in that, A unified mesh framework is determined based on the hydrodynamic model, and the hydrodynamic model, water quality model, and biological simulation model are coupled in real time based on the unified mesh framework and asynchronous solution algorithm to obtain a multi-model dynamic coupling model, including: Using the unstructured triangular mesh as a unified mesh framework, flow field data are calculated using a hydrodynamic model within the same time step based on the unified mesh framework. Based on the flow field data, the concentrations of various water quality indicators are calculated using a water quality model. The current flow field and the difference in water quality index concentration are mapped to the location of each target organism. Individual data of each target organism is generated using a biological simulation model. The individual data includes behavior, location, and physiological parameters.

6. The method according to claim 5, characterized in that, The flow field data, water quality index concentrations, and individual data of the target organisms are transferred between the hydrodynamic model, the water quality model, and the biological simulation model.

7. The method according to claim 1, characterized in that, The preset scheduling scheme is evaluated based on the multi-model dynamic coupling model, and the optimal scheduling scheme is selected, including: Multiple preset scheduling schemes are generated based on historical hydrological data; Each preset scheduling scheme is input into the multi-model dynamic coupling model to obtain the ecological effect index of each preset scheduling scheme. The ecological effect index includes: target organism individual trajectory map, population spatial distribution map, habitat adaptation distribution map, effective habitat area change curve and individual growth curve. Compare the ecological effect indicators of each preset scheduling scheme, and determine the optimal scheduling scheme based on research needs.

8. A hydrodynamic-water quality-biological coupling simulation device based on a biological simulation model, characterized in that, The device includes: The flow field data research module is used to establish a hydrodynamic model based on the topography and flow data of the study area, and to determine the spatiotemporal distribution field of water depth and flow velocity in the study area based on the hydrodynamic model. The water quality-biological model construction module is used to construct a water quality model based on the spatiotemporal distribution of water depth and flow velocity in the study area, combined with water quality data from various locations, and to construct a biological simulation model by combining the target biological attributes and behavioral rules. The multi-model dynamic coupling module is used to determine a unified mesh framework based on the hydrodynamic model, and to couple the hydrodynamic model, water quality model and biological simulation model in real time based on the unified mesh framework and asynchronous solution algorithm to obtain a multi-model dynamic coupling model. The scheduling scheme evaluation module is used to evaluate the preset scheduling schemes based on the multi-model dynamic coupling model and select the optimal scheduling scheme.

9. An electronic device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1 to 7.

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