Inland river port hydrodynamic change tracking prediction and evaluation method

Abstract

The invention is suitable for the technical field of inland river ports, and provides an inland river port hydrodynamic change tracking prediction and evaluation method, which comprises the following steps: constructing a quarterly-level elevation change field of a river channel; wherein the quarterly-level elevation change field of the river channel is composed of a plurality of grids, and each grid corresponds to quarterly-level elevation change data of different areas in the river channel; adjusting the grid density in the quarter-level elevation change field of the river channel to form an adjusted quarter-level elevation change field of the river channel; a two-dimensional hydrodynamic model is obtained, and parameters of the two-dimensional hydrodynamic model are updated; and inputting the adjusted quarter-level elevation change field of the river channel into the updated two-dimensional hydrodynamic model to output a hydrodynamic condition prediction result in the water area range of the inland river port, and calculating a hydrodynamic health index in the water area range of the inland river port to measure the hydrodynamic health degree in the water area range of the inland river port. The timeliness and accuracy of the prediction result can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of inland river ports, and in particular to a method for tracking, predicting and evaluating hydrodynamic changes of inland river ports. Background Art

[0002] As the impacts of climate change and human activities (such as river regulation and land use change) intensify, the hydrodynamic conditions of inland ports are becoming increasingly complex and variable. As key nodes in water transportation, the hydrodynamic conditions of inland ports (such as flow velocity, direction, and water level) are directly related to the safety of ship navigation, port operational efficiency, and the surrounding ecological environment. Current inland port hydrodynamic analysis relies primarily on the following methods: Static hydrological observation method: based on statistical analysis of historical data of water level and flow velocity at fixed sites, lacks spatial continuity.

[0003] Traditional hydrodynamic model method: Commercial software such as MIKE21 and Delft3D are used to construct two-dimensional / three-dimensional models. This model can simulate the two-dimensional flow process of water bodies such as rivers and lakes, providing strong support for hydrodynamic analysis. However, it has the following limitations: (1) Insufficient data drive: Boundary conditions are mostly based on the design hydrological year data, without considering the dynamic evolution of river morphology. For example, the existing hydrodynamic model regards the riverbed topography as a static input, ignoring the inter-annual scouring and sedimentation changes in the river around the port (such as deep channel migration and beach development), resulting in a prediction error of more than 30% in the velocity field and sedimentation amount. MIKE21's parameters such as roughness and eddy viscosity coefficient often use empirical values ​​and are not dynamically adjusted according to the river vegetation coverage, ship density, etc. In other words, the traditional hydrodynamic model mainly relies on historical hydrological data and topographic data, lacks the integration and utilization of real-time monitoring data and multi-source heterogeneous data, resulting in a single model input data, which is difficult to accurately reflect the dynamic changes of hydrodynamic conditions. (2) The contradiction between model accuracy and efficiency: High-precision hydrodynamic models require complex computational grids and precise parameter settings, resulting in large computational workload and low efficiency; while simplified models can improve computational efficiency, their accuracy is difficult to meet actual needs. (3) Insufficient consideration of the impact of land use change: Land use changes (such as urbanization, agricultural development, etc.) will significantly change surface runoff, river morphology, etc., thereby affecting hydrodynamic conditions. However, existing technologies rarely couple land use change models with hydrodynamic models, making it difficult to accurately assess the long-term impact of land use changes on hydrodynamics. (4) The problem of balancing prediction timeliness and accuracy: Existing prediction methods are mostly based on historical data extrapolation or simple statistical models, which make it difficult to capture the nonlinear characteristics of hydrodynamic changes, resulting in poor timeliness and low accuracy of prediction results.

[0004] Isolated prediction models: Land-use change models and hydrodynamic models operate independently, lacking a bidirectional coupling mechanism. This means that land-use changes predicted by models like CLUE-S are not fed back into hydrodynamic boundaries (e.g., hardened shorelines increase surface runoff coefficients), resulting in distorted downstream flow calculations.

[0005] Therefore, accurately tracking, predicting and evaluating the hydrodynamic changes of inland ports is of great significance for ensuring the safe operation of ports, optimizing port layout and formulating scientific water resources management strategies. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a method for tracking, predicting and evaluating the hydrodynamic changes of an inland port to solve the problems existing in the above-mentioned background technology.

[0007] The present invention is achieved by providing a method for tracking, predicting and evaluating hydrodynamic changes in an inland port, the method comprising the following steps: Step S1: Obtain the water area of ​​the inland port, determine the range of the river channel based on the water area of ​​the inland port, and then construct a seasonal elevation change field of the river channel; wherein the seasonal elevation change field of the river channel is composed of multiple grids, each grid corresponding to the seasonal elevation change data of a different area in the river channel; Step S2: adjusting the grid density in the seasonal elevation change field of the river channel to form an adjusted seasonal elevation change field of the river channel; Step S3: obtaining a two-dimensional hydrodynamic model, and updating parameters of the two-dimensional hydrodynamic model to obtain an updated two-dimensional hydrodynamic model; Step S4: inputting the adjusted seasonal elevation change field of the river channel into the updated two-dimensional hydrodynamic model to output a prediction result of the hydrodynamic conditions within the inland port waters; Step S5: Calculating a hydrodynamic health index within the inland port water area according to the hydrodynamic condition prediction result within the inland port water area to measure the hydrodynamic health level within the inland port water area.

[0008] As a further solution of the present invention: the step of constructing the seasonal elevation change field of the river channel further includes: Obtain satellite image data of inland ports and rivers in each quarter, and determine the quarterly elevation change data of different areas in the river channel based on the satellite image data of inland ports and rivers in each quarter to construct the quarterly elevation change field of the river channel. , and its calculation formula is:

[0009] in, Represents a moment; each grid Represents seasonal elevation change data for each area in the river channel; Indicates the total number of scenes; Indicates the Reflectance correction coefficient of the near-infrared band under each scene; Indicates the The reflectivity of the near-infrared band under each scene; Indicates the The reflectivity of the red band under each scene; Represents a constant; Indicates the The weight of the impact of each scene is calculated as follows:

[0010] in, Indicates the The cloud cover in each scene.

[0011] As a further solution of the present invention: Step S2 further includes: According to the seasonal elevation change field of the river channel Each grid Corresponding quarterly elevation change data Calculate each grid Corresponding terrain change weight matrix , the calculation formula is:

[0012] in, For each grid The corresponding terrain change weight matrix; is the sedimentation sensitivity coefficient; For each grid Corresponding quarterly elevation change data; is the water gradient weight; For each grid The corresponding unit river length flow change rate, where For traffic, for distance; According to each grid Corresponding terrain change weight matrix , the seasonal elevation change field of the river channel The grid in the is adjusted to form the adjusted seasonal elevation change field of the river channel. .

[0013] As a further solution of the present invention: the updating of the parameters of the two-dimensional hydrodynamic model to obtain an updated two-dimensional hydrodynamic model further includes: (1) Update the surface roughness of the two-dimensional hydrodynamic model: Establish a two-way coupling mechanism between the land use change model and the two-dimensional hydrodynamic model, and the land use change model can output the adjusted seasonal elevation change field of the river channel. The land use type change prediction results within the range are as follows: Since each different land use type change prediction result has its corresponding surface roughness value range, the adjusted quarterly elevation change field of the river channel can be determined by looking up the table. The surface roughness of each grid in the grid is calculated to form the surface roughness field and the surface roughness field inputting the data into the two-dimensional hydrodynamic model to update the surface roughness of the two-dimensional hydrodynamic model in real time; (2) Update the ship dynamic stress tensor field of the two-dimensional hydrodynamic model: Acquire AIS ship trajectory data, and calculate the ship dynamic stress tensor field of the two-dimensional hydrodynamic model based on the AIS ship trajectory data. To update, the specific calculation formula is:

[0014] in, For the The power coefficient of the ship, , For the The main engine power of the ship; For the the draft of the vessel; For the the speed of the vessel; For Grid To the distance between the vessels; is a constant; For the The wake attenuation scale of the ship, ; For the The heading unit vector of the ship; is the total number of ships; (3) Update the channel roughness of the two-dimensional hydrodynamic model: Obtain drone image data of the river, and obtain the vegetation coverage P of the river according to the drone image data of the river, and calculate the river roughness of the two-dimensional hydrodynamic model based on the vegetation coverage P of the river. To update, the specific calculation formula is:

[0015] in, is the updated river channel roughness; It is the roughness of exposed river channel; is the hydraulic impedance coefficient of vegetation; P is the vegetation coverage of the river; is the nonlinear index; is the vegetation type factor.

[0016] As a further solution of the present invention, the updating of the surface roughness of the two-dimensional hydrodynamic model further includes: When the two-dimensional hydrodynamic model outputs the predicted flooding depth of the inland port Exceeding critical submergence depth Generate a development prohibited area space mask when :

[0017] The development prohibited area space mask Input to the land use change model to output a predicted land suitability score , the specific calculation formula is:

[0018] in, Score initial land suitability; is the mask attenuation coefficient; The coupling frequency between the two-dimensional hydrodynamic model and the land use change model is set to annual coupling, that is, the surface roughness of the two-dimensional hydrodynamic model is updated every year according to the land use type change prediction result output by the land use change model in the previous year, and the predicted flooding depth output by the two-dimensional hydrodynamic model in the current year is updated every year. Adjust the land use type change prediction result output by the land use change model in the next year.

[0019] As a further solution of the present invention: the step S5 further includes: The hydrodynamic health index within the waters of the inland port The calculation formula is:

[0020] in, is the dynamic stability index; is the sedimentation risk index; is the ecological disturbance index; 、 、 are the corresponding weights respectively; (1) Dynamic stability index The calculation formula is:

[0021] in, is the standard deviation of the flow velocity in the port waters, which is directly extracted from the prediction results of the hydrodynamic conditions within the inland port waters; is the average flow velocity of the cross section; is the time scale weight; (2) Sedimentation risk index The calculation formula is:

[0022] in, is the change rate of sedimentation area; represents the density of sediment; Indicates the density of water; represents the acceleration due to gravity; It indicates the particle size corresponding to when the cumulative particle size distribution percentage of sediment reaches 50%; and Respectively represent the prediction start time and prediction end time; (3) Ecological disturbance index The calculation formula is:

[0023] in, represents the attenuation coefficient; Indicates abnormal water temperature value; Indicates dissolved oxygen saturation ratio; represents the vorticity intensity; is the tolerance threshold of fish.

[0024] Compared with the prior art, the present invention has the following beneficial effects: (1) Traditional two-dimensional hydrodynamic models use static terrain. This invention uses time-series remote sensing data to achieve quarterly terrain updates, significantly improving the accuracy of the two-dimensional hydrodynamic model. By proposing an adaptive grid densification strategy driven by hydraulic gradients, the grid resolution in highly dynamic areas is increased by an order of magnitude while maintaining computational efficiency. (2) The traditional model separates land use from hydrodynamic processes. The present invention realizes bidirectional coupling, making the port development layout dynamically adaptable to the hydrological risk. By developing a land development feedback algorithm with submergence depth constraints, the mask Dynamically revise the land suitability score of the land use change model to form a closed-loop feedback system; (3) While traditional model parameters are fixed, this invention achieves dynamic parameter adaptation, significantly improving the simulation realism. By proposing a ship wake stress tensor field model, the discrete ship motion is converted into a continuous stress field and embedded into the hydrodynamic equation for the first time, solving the problem of vortex distortion in the harbor. (4) Traditional evaluation only focuses on a single indicator. The present invention establishes a multi-dimensional coupled evaluation function to provide a comprehensive health evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The present invention provides a flow chart of a method for tracking, predicting and evaluating hydrodynamic changes in an inland port. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0027] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0028] like Figure 1 As shown, an embodiment of the present invention provides a method for tracking, predicting and evaluating hydrodynamic changes in an inland port, the method comprising the following steps: Step S1: Obtain the water area of ​​the inland port, determine the range of the river channel based on the water area of ​​the inland port, and then construct a seasonal elevation change field of the river channel; wherein the seasonal elevation change field of the river channel is composed of multiple grids, each grid corresponding to the seasonal elevation change data of a different area in the river channel; Preferably, the step of constructing the seasonal elevation change field of the river channel further includes: Obtain satellite image data of the inland port water area and the river channel in each quarter, and determine the quarterly elevation change data of different areas in the river channel based on the satellite image data of the inland port water area and the river channel in each quarter to construct the quarterly elevation change field of the river channel , and its calculation formula is:

[0029] in, Represents a moment; each grid Represents seasonal elevation change data for each area in the river channel; Indicates the total number of scenes; Indicates the The reflectivity correction coefficient of the near-infrared band under each scene needs to be corrected considering the atmospheric effect and is a dimensionless number; Indicates the The reflectivity of the near-infrared band under each scene, Indicates the The reflectivity of the red band under each scene, Thus, singular values ​​in logarithmic operations are avoided and it is a dimensionless number; Indicates the The weight of the impact of each scene is used to ensure that the low cloud cover image contributes more. The calculation formula is:

[0030] in, Indicates the Cloud coverage in each scene The value ranges from 0 to 1.

[0031] It should be noted that the conventional simulation method of the two-dimensional hydrodynamic model is as follows: First, based on the waters of the inland port, the simulated river channel is clearly defined, including upstream and downstream boundaries, as well as the boundaries between the two banks. A computational mesh is then created using the Mesh Generator tool for the 2D hydrodynamic model. The mesh type is typically triangular or quadrilateral, and the appropriate mesh type should be selected based on the complexity of the river's terrain. Mesh resolution is a trade-off between simulation accuracy and computational efficiency. Typically, only the degree of terrain change is considered; generally, areas with more dramatic terrain changes require a denser mesh.

[0032] Secondly, river elevation data is usually obtained from digital elevation models (DEMs), measured water depth data or historical topographic maps. After collecting the corresponding data, the topographic data needs to be cleaned, interpolated and format converted to ensure data quality; the processed topographic data is imported into the two-dimensional hydrodynamic model as the basis for port hydrodynamic simulation.

[0033] Next, the parameters of the two-dimensional hydrodynamic model are set. The most important parameter in this two-dimensional hydrodynamic model is the river roughness, which is used to reflect the roughness of the river channel. At the same time, the eddy viscosity coefficient needs to be configured to describe the turbulent characteristics of the water flow. In addition, other hydrodynamic parameters such as gravitational acceleration and water density are set as needed.

[0034] Finally, set the boundary and initial conditions. For the upstream and downstream boundaries, set measured inlet and outlet water levels, flow rates, or flow velocities. These boundary conditions can be fixed or time-varying. The initial water level and initial velocity represent the river level and velocity distribution at the start of the simulation. Initial conditions can be based on measured data or generated empirically or through the model's auto-start function.

[0035] After completing the above steps, you can run the two-dimensional hydrodynamic model for simulation calculations. The two-dimensional hydrodynamic model will simulate and calculate the spatiotemporal distribution of hydrodynamic conditions such as water level, flow, flow velocity, and submergence depth of the river according to the set parameters, boundary conditions, and initial conditions.

[0036] Based on the above conventional simulation method, this paper innovatively integrates multi-source remote sensing data to construct a seasonal elevation change field of the river. By fusing multispectral bands from Sentinel-2 and Landsat-8 satellite imagery, we achieve quarterly monitoring of river elevation changes. This requires acquiring Sentinel-2 and Landsat-8 imagery quarterly, covering ports and 10 km upstream of the river, and 5 km downstream. This significantly improves the accuracy of conventional simulations, which use the same water depth data for multiple years.

[0037] Step S2: adjusting the grid density in the seasonal elevation change field of the river channel to form an adjusted seasonal elevation change field of the river channel; Preferably, the step S2 further includes: According to the seasonal elevation change field of the river channel Each grid Corresponding quarterly elevation change data Calculate each grid Corresponding terrain change weight matrix , the calculation formula is:

[0038] in, For each grid The corresponding terrain change weight matrix, dimensionless; is the sedimentation sensitivity coefficient, ranging from 0.6 to 0.9, based on the median particle size of the sediment Dynamic adjustment, , reflecting the sedimentation characteristics of sediments with different particle sizes; It indicates the particle size corresponding to when the cumulative particle size distribution percentage of sediment reaches 50%, also known as the median diameter or median particle size. Its physical meaning is that particles with a diameter larger than and smaller than it each account for 50%, and the unit is mm. For each grid Corresponding quarterly elevation change data; is the water gradient weight, reflecting the scouring potential of water flow, with a value range of 0.3~0.5, and is adjusted according to the river slope S. The larger the S, the The larger the value; For each grid The corresponding rate of change of flow per unit river length can be calculated by numerical differentiation, where is the flow rate, the unit is , is the distance in m; According to each grid Corresponding terrain change weight matrix , the seasonal elevation change field of the river channel The grid in the dynamic densification and sparseness is used to form the adjusted seasonal elevation change field of the river channel. .

[0039] In the embodiment of the present invention, the adjusted seasonal elevation change field of the river channel is The grid resolution in high-weight areas can be increased to 10 meters, while the grid resolution in low-weight areas can be reduced to 100 meters, thus balancing the accuracy and efficiency of the hydrodynamic model. Furthermore, the frequency of terrain updates can be shortened from the traditional five-year cycle in environmental impact assessments to quarterly updates, ensuring that the 2D hydrodynamic model is always running based on the latest terrain data.

[0040] The traditional grid division method is mainly based on the elevation change of the terrain, that is, the grid is denser in areas with large terrain undulations and sparser in flat areas. Although this method can reflect the terrain characteristics to a certain extent, it ignores the characteristics of the hydrodynamic process itself. The hydrodynamic conditions of the river channel are not only determined by the terrain, but also affected by many factors such as water velocity, flow rate, hydraulic gradient, etc. Determining the grid density based solely on the terrain undulation may lead to the following problems: (1) Insufficient characterization of the hydrodynamic process: In areas with small terrain undulations but large hydraulic gradients, such as narrow sections of the river channel, bends, or areas with local obstacles, the water flow is strong and the hydraulic gradient is large. The traditional grid division method may not provide sufficient grid resolution to accurately capture the hydrodynamic details of these areas, resulting in distorted simulation results. (2) Waste of computing resources: In areas with large terrain undulations but small hydraulic gradients, such as floodplains and shallow areas, although the terrain is complex, if the water flow is weak and the hydraulic gradient is small, the traditional grid division method may over-densify the grid, resulting in unnecessary waste of computing resources and reduced computing efficiency.

[0041] The present invention further adopts a hydraulic gradient driven adaptive grid densification strategy to effectively solve the above problem. This strategy no longer relies solely on terrain undulations, but comprehensively considers two key factors: terrain changes and hydraulic gradients, and dynamically guides the densification and sparseness of the grid through the terrain change weight matrix. That is, according to the terrain change weight matrix , in the seasonal elevation change field of the river channel The grid is dynamically encrypted and sparse, and the density of the computational grid is dynamically adjusted. Compared with the seasonal elevation change field of the river channel determined only by the changes in the terrain, The grid density of the present invention is innovative and has significant advantages in the following aspects: (1) It can capture hydrodynamic characteristics more accurately: the hydraulic gradient directly reflects the dynamic intensity and direction change of the water flow. By incorporating the hydraulic gradient into the consideration of grid encryption, the present invention can identify areas with strong water flow dynamics. Even if the terrain in these areas is not very undulating, the grid will be automatically encrypted, thereby more accurately capturing complex hydrodynamic phenomena such as water flow velocity changes, vortices, and backflows. (2) Optimizing the allocation of computing resources: In areas with smaller hydraulic gradients, even if the terrain has certain undulations, the present invention will appropriately sparse the grid to reduce unnecessary calculations and concentrate computing resources on areas that require more detailed simulations, thereby significantly improving computing efficiency while ensuring simulation accuracy. (3) Dynamic adaptability: The present invention can dynamically adjust the grid density according to the real-time changes in the hydraulic gradient during the simulation process. When the water flow conditions change, such as during flood season or dry season, the present invention can automatically adapt to the new hydrodynamic conditions and re-optimize the grid layout to ensure the accuracy and reliability of the simulation results.

[0042] Step S3: obtaining a two-dimensional hydrodynamic model, and updating parameters of the two-dimensional hydrodynamic model to obtain an updated two-dimensional hydrodynamic model; Preferably, the updating of the parameters of the two-dimensional hydrodynamic model to obtain an updated two-dimensional hydrodynamic model further includes: (1) Update the surface roughness of the two-dimensional hydrodynamic model: Establish a two-way coupling mechanism between the land use change model and the two-dimensional hydrodynamic model, and the land use change model can output the adjusted seasonal elevation change field of the river channel. The land use type change prediction results within the range are as follows: Since each different land use type change prediction result has its corresponding surface roughness value range, the adjusted quarterly elevation change field of the river channel can be determined by looking up the table. The surface roughness of each grid in the grid is calculated to form the surface roughness field and the surface roughness field inputting the data into the two-dimensional hydrodynamic model to update the surface roughness of the two-dimensional hydrodynamic model in real time; It should be noted that the land use change model can predict the seasonal elevation change field. The future land use change for each grid in the model is predicted. Each land use type corresponds to a specific surface roughness. Based on the predicted land use change results for each grid, its corresponding surface roughness is determined. Ultimately, the surface roughness of these grids is integrated to generate a surface roughness field file, which serves as the input file for the 2D hydrodynamic model. This surface roughness field file is used to dynamically update the surface roughness parameters in the 2D hydrodynamic model during the simulation process.

[0043] Specifically, the updating of the surface roughness of the two-dimensional hydrodynamic model further includes: When the two-dimensional hydrodynamic model outputs the predicted flooding depth of the inland port Exceeding critical submergence depth Generate a development prohibited area space mask when :

[0044] The development prohibited area space mask Input to the land use change model to output a predicted land suitability score , the specific calculation formula is:

[0045] in, is the initial land suitability score, dimensionless, calculated by the land use change model; is the mask attenuation coefficient, in m -1 , reflecting the impact of flooding depth on land suitability; is the critical submergence depth, in meters, determined based on the port's design water level and safety free height; The coupling frequency between the two-dimensional hydrodynamic model and the land use change model is set to annual coupling, that is, the surface roughness of the two-dimensional hydrodynamic model is updated every year according to the land use type change prediction result output by the land use change model in the previous year, and the predicted flooding depth output by the two-dimensional hydrodynamic model in the current year is updated every year. Adjust the land use type change prediction results output by the land use change model for the next year. For the spatial resolution of the model, the land use change model should use open source 30m resolution land use data to align with the 10-100m variable resolution grid of the 2D hydrodynamic model.

[0046] It should be noted that the conventional simulation method of land use change model is as follows: First, it is necessary to determine the simulation area and clarify the spatial scope of the simulation, which can be an administrative district, river basin or specific port area, etc.

[0047] Secondly, basic data and driving factor data need to be collected. Basic data includes: a map of the current land use / land cover of the port area, typically including types such as cultivated land, forest land, grassland, water area, and construction land. Driving factor data refers to data on factors that affect land use change, such as topography (elevation, slope, aspect), climate (precipitation, temperature, evaporation), socioeconomic factors (population, GDP, policies), and distance factors (distance to rivers, roads, and cities).

[0048] Next, data preprocessing is performed, including: 1. Classifying current land use data and assigning unique codes to facilitate identification and processing by the land-use change model; 2. Standardizing and normalizing the driving factor data to eliminate dimensional differences between different factors and make them comparable; 3. Ensuring that all spatial data (land use data and driving factor data) are spatially aligned with the same coordinate system and resolution. Resampling is performed if necessary to ensure a consistent grid size; 4. Converting the data to a format supported by the land-use change model, such as ASCII Grid.

[0049] Next, model construction and parameter setting mainly include: 1. Based on the historical land-use change trends or future development plans of the study area, a land-use demand document is constructed to specify the area or proportion of each land-use type at the end of the simulation period; 2. A suitability atlas is prepared for each land-use type to reflect the impact of different driving factors on the suitability of that land-use type; 3. Conversion rules between land-use types are set, including the types of permitted conversions, conversion priorities, or restrictions; 4. Model parameters are set, including elasticity coefficients and region-specific parameters. The elasticity coefficient reflects the resistance of a land-use type to change; a larger value indicates a more stable type and less likely to undergo conversion. Region-specific parameters include the conversion matrix between land-use types and spatial interaction parameters.

[0050] Finally, to run the model and perform simulations, you need to: 1. Input the preprocessed data into the land-use change model, set the simulation start and end years, and the time step (usually one year); 2. Start the land-use change model for simulation. The model will simulate land use / land cover change based on the set parameters, driving factor data, and land use requirements document; 3. Monitor the model's performance to ensure the simulation is running smoothly. Adjust model parameters or driving factor data as needed to optimize the simulation results.

[0051] (2) Update the ship dynamic stress tensor field of the two-dimensional hydrodynamic model: Acquire AIS ship trajectory data, and calculate the ship dynamic stress tensor field of the two-dimensional hydrodynamic model based on the AIS ship trajectory data. Updated to achieve accurate simulation of ship wake effects, the tensor field It can be directly added as a source term to the two-dimensional shallow water equations of the two-dimensional hydrodynamic model to realize the real-time impact of ship disturbance on the flow field. The specific calculation formula is:

[0052] in, For the The power factor of the ship, in kW·s² / m 4 , , For the The main engine power of the ship, in kW; For the The draft of the ship is obtained from the ship draft information in the AIS data, in meters; For the The speed of the ship is obtained from the speed information in the AIS data, in m / s; For Grid To The distance between ships, if the small constant To avoid singularities, the unit is m; is a constant; For the The wake attenuation scale of the ship, , reflects the range of wake influence, unit is m; For the The heading unit vector of the ship is obtained from the heading information in the AIS data and is a dimensionless number; is the total number of ships; (3) Update the channel roughness of the two-dimensional hydrodynamic model: Obtain drone image data of the river, and use the YOLOv5 deep learning model to identify the vegetation type in the drone image data, and establish the vegetation coverage P and river roughness The nonlinear relationship between the vegetation coverage P of the river channel and the river roughness of the two-dimensional hydrodynamic model is To update, The calculation results can be used to correct the initial value of the river roughness in the two-dimensional hydrodynamic model in real time, reflecting the dynamic changes in vegetation resistance to water flow. Among them, drone images should be flown once a month to obtain high-resolution vegetation coverage. The specific calculation formula is:

[0053] in, is the updated river channel roughness; The roughness of the exposed river channel is determined according to the type of riverbed, and the unit is s / m 1 / 3 ; is the hydraulic impedance coefficient of vegetation, and different vegetation types have different values ​​(reed can be 0.12, shrubs can be 0.08); P is the vegetation coverage of the river; It is a nonlinear index reflecting the effect of vegetation coverage P on river roughness. The nonlinear effect of , the value is 1.5; is the vegetation type factor, with 1 for submerged plants and 1.3 for emergent plants, reflecting the differences in water flow resistance of different vegetation types.

[0054] Step S4: inputting the adjusted seasonal elevation change field of the river channel into the updated two-dimensional hydrodynamic model to output a prediction result of the hydrodynamic conditions within the inland port waters; It should be noted that the traditional two-dimensional hydrodynamic model uses static DEM data. The present invention uses the time series remote sensing terrain fusion technology to transform the adjusted seasonal elevation change field of the river channel into a real-time dynamic model. Replace traditional static DEM data The input of the two-dimensional hydrodynamic model can realize the real-time update of underwater terrain, providing more accurate and reliable terrain data support for the tracking, prediction and evaluation of hydrodynamic changes in inland ports. Therefore, it is necessary to develop a data interface, that is, to develop the API interface of MIKE21 to realize the elevation change field. The interface supports NetCDF format data transmission to ensure data compatibility.

[0055] Step S5: Calculating a hydrodynamic health index within the inland port water area according to the hydrodynamic condition prediction result within the inland port water area to measure the hydrodynamic health level within the inland port water area.

[0056] Preferably, the step S5 further includes: The hydrodynamic health index within the waters of the inland port It can provide intuitive hydrodynamic health evaluation results. The calculation formula is:

[0057] in, is the dynamic stability index; is the sedimentation risk index; is the ecological disturbance index; 、 、 are the corresponding weights respectively; (1) Dynamic stability index The calculation formula is:

[0058] in, The standard deviation of the flow velocity in the port waters reflects the uniformity of the flow field and is directly extracted from the prediction results of the hydrodynamic conditions within the inland port waters. The unit is m / s. is the average flow velocity of the cross section, which is calculated by numerical integration and the unit is m / s; is the time scale weight, which is determined according to the demand and reflects the influence of the time variation rate of flow velocity on stability. It is a dimensionless number with a value of 0.1~0.3; (2) Sedimentation risk index The calculation formula is:

[0059] in, is the rate of change of sedimentation area, obtained through remote sensing image interpretation, in m 2 / day; It represents the density of sediment, which is determined based on sediment particle analysis experiments and is expressed in kg / m³; Indicates the density of water, taking the constant value as 1000, the unit is kg / m³; Represents the acceleration due to gravity, taking the constant value 9.81, the unit is m / s 2 ; It indicates the particle size corresponding to when the cumulative particle size distribution percentage of sediment reaches 50%, also known as the median diameter or median particle size. Its physical meaning is that particles with a diameter larger than and smaller than it each account for 50%; and Respectively represent the prediction start time and prediction end time; (3) Ecological disturbance index The calculation formula is:

[0060] in, Represents the attenuation coefficient, used to quantify water temperature anomalies The degree of stress or disturbance on the ecosystem also determines the sensitivity of water temperature deviations to exponential decay. The larger the value, the more likely it is that even a small deviation in water temperature will cause a significant negative impact on the ecosystem. The smaller the value, the more tolerant the ecosystem is to water temperature changes; Indicates the abnormal value of water temperature, which is calculated by the difference between the measured water temperature and the multi-year average water temperature, in °C; It represents the dissolved oxygen saturation ratio, which reflects the degree of eutrophication of water bodies and is a dimensionless number; represents the vorticity intensity, which is calculated by numerical differentiation and has the unit of 1 / s; is the fish tolerance threshold, determined based on ecotoxicology experiments, and the unit is 1 / s.

[0061] The method for tracking, predicting and evaluating the hydrodynamic changes of inland ports provided by the embodiment of the present invention is specifically a method for tracking, predicting and evaluating the hydrodynamic changes of inland ports that integrates multi-source data, an improved two-dimensional hydrodynamic model (built using commercial software such as MIKE 21) and a land use change model (CLUE-S model). (1) Through multi-source data fusion technology, multi-source heterogeneous data such as real-time monitoring data, remote sensing images, and land use planning maps are aligned and fused in time and space to construct a high-precision, multi-dimensional river hydrodynamic database, providing richer input information for the model. (2) Unstructured grid technology and adaptive parameter calibration algorithm are used to optimize the model grid structure and parameter settings, thereby improving the model calculation efficiency and accuracy; at the same time, dynamic adjustment and optimization of the model are achieved through model coupling and real-time monitoring data feedback. (3) The land use change model is coupled with the two-dimensional hydrodynamic model to achieve a linkage simulation of land use change and hydrodynamic change, and accurately evaluate the long-term impact of land use change on hydrodynamics. (4) Based on the two-dimensional hydrodynamic model and real-time monitoring data, combined with the prediction results of land use type changes, dynamic prediction of future hydrodynamic conditions is carried out; at the same time, through model coupling and multi-source data fusion, the timeliness and accuracy of the prediction results are improved.

[0062] MIKE 21 is a professional engineering software package designed for simulating currents, waves, sediments, and other environments in rivers, lakes, estuaries, bays, coasts, and oceans. It provides a comprehensive and efficient design environment for engineering applications, coastal management, and planning. Its advanced graphical user interface and efficient computational engine make MIKE 21 an indispensable tool for professional estuarine and coastal engineers worldwide. The inclusion of the ECO LAB environmental module further enhances MIKE 21's effectiveness as an environmental simulation and assessment tool. Numerous engineers and scientists worldwide rely on MIKE 21 as their key simulation tool.

[0063] It should be understood that the CLUES (Conversion of Land Use and its Effects) model is a process-based spatial simulation model of land use change, which is mainly used to simulate the land use change process and its driving forces, and predict future trends. (1) The CLUES model consists of three main parts: Data layer: Integrates topographic, soil, climate, socio-economic and other data to describe the natural and socio-economic characteristics of the study area. Conversion layer: Establishes land use type conversion rules through multivariate regression analysis, comprehensively considering natural factors, socio-economic factors and policy influences. Output layer: Generates land use spatial distribution and change trend prediction results. (2) The main functions of the CLUES model: The model can simulate the spatiotemporal evolution of land use / cover changes, evaluate changes in ecological benefits, and provide decision support for regional sustainable development. Its advantage lies in the comprehensive consideration of multiple driving factors (including natural, economic, policy, etc.), but it needs to rely on high-precision data support. (3) Application scenarios of the CLUES model: Applicable to national / regional land use planning. The latest versions (such as the Dyna-CLUE model) optimize small-scale data representation and accuracy issues and support global land use type spatial configuration.

[0064] The above is only a detailed description of the preferred embodiments of the present invention, which 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 in the scope of protection of the present invention.

[0065] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0066] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When executed, the program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0067] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered merely as exemplary, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A method for tracking, predicting and evaluating hydrodynamic changes in an inland port, characterized in that: The method for tracking, predicting and evaluating the hydrodynamic changes of an inland port comprises the following steps: Step S1: Obtain the water area of ​​the inland port, determine the range of the river channel based on the water area of ​​the inland port, and then construct a seasonal elevation change field of the river channel; wherein the seasonal elevation change field of the river channel is composed of multiple grids, each grid corresponding to the seasonal elevation change data of a different area in the river channel; Step S2: adjusting the grid density in the seasonal elevation change field of the river channel to form an adjusted seasonal elevation change field of the river channel; Step S3: obtaining a two-dimensional hydrodynamic model, and updating parameters of the two-dimensional hydrodynamic model to obtain an updated two-dimensional hydrodynamic model; Step S4: inputting the adjusted seasonal elevation change field of the river channel into the updated two-dimensional hydrodynamic model to output a prediction result of the hydrodynamic conditions within the inland port waters; Step S5: Calculating a hydrodynamic health index within the inland port water area according to the hydrodynamic condition prediction result within the inland port water area to measure the hydrodynamic health level within the inland port water area.

2. The method for tracking, predicting and evaluating the hydrodynamic changes of an inland port according to claim 1 is characterized in that: The construction of the seasonal elevation change field of the river channel also includes: Obtain satellite image data of inland ports and rivers in each quarter, and determine the quarterly elevation change data of different areas in the river channel based on the satellite image data of inland ports and rivers in each quarter to construct the quarterly elevation change field of the river channel. , and its calculation formula is: ； in, Represents a moment; each grid Represents seasonal elevation change data for each area in the river channel; Indicates the total number of scenes; Indicates the Reflectance correction coefficient of the near-infrared band under each scene; Indicates the The reflectivity of the near-infrared band under each scene; Indicates the The reflectivity of the red band under each scene; Indicates a constant; Indicates the The weight of the impact of each scene is calculated as follows: ； in, Indicates the The cloud cover in each scene.

3. The method for tracking, predicting and evaluating the hydrodynamic changes of an inland port according to claim 2, characterized in that: The step S2 further includes: According to the seasonal elevation change field of the river channel Each grid Corresponding quarterly elevation change data Calculate each grid Corresponding terrain change weight matrix , the calculation formula is: ； in, For each grid The corresponding terrain change weight matrix; is the sedimentation sensitivity coefficient; For each grid Corresponding quarterly elevation change data; is the water gradient weight; For each grid The corresponding unit river length flow change rate, where For traffic, for distance; According to each grid Corresponding terrain change weight matrix , the seasonal elevation change field of the river channel The grid in the is adjusted to form the adjusted seasonal elevation change field of the river channel. .

4. The method for tracking, predicting and evaluating the hydrodynamic changes of an inland port according to claim 3 is characterized in that: The updating of the parameters of the two-dimensional hydrodynamic model to obtain an updated two-dimensional hydrodynamic model further includes: (1) Update the surface roughness of the two-dimensional hydrodynamic model: Establish a two-way coupling mechanism between the land use change model and the two-dimensional hydrodynamic model, and the land use change model can output the adjusted seasonal elevation change field of the river channel. The land use type change prediction results within the range are as follows: Since each different land use type change prediction result has its corresponding surface roughness value range, the adjusted quarterly elevation change field of the river channel can be determined by looking up the table. The surface roughness of each grid in the grid is calculated to form the surface roughness field and the surface roughness field inputting the data into the two-dimensional hydrodynamic model to update the surface roughness of the two-dimensional hydrodynamic model in real time; (2) Updating the ship dynamic stress tensor field of the two-dimensional hydrodynamic model: Acquire AIS ship trajectory data, and calculate the ship dynamic stress tensor field of the two-dimensional hydrodynamic model based on the AIS ship trajectory data. To update, the specific calculation formula is: ； in, For the The power coefficient of the ship, , For the The main engine power of the ship; For the the draft of the vessel; For the the speed of the vessel; For Grid To the distance between the vessels; is a constant; For the The wake attenuation scale of the ship, ; For the The heading unit vector of the ship; is the total number of ships; (3) Update the river channel roughness of the two-dimensional hydrodynamic model: Obtain drone image data of the river, and obtain the vegetation coverage P of the river according to the drone image data of the river, and calculate the river roughness of the two-dimensional hydrodynamic model based on the vegetation coverage P of the river. To update, the specific calculation formula is: ； in, is the updated river channel roughness; It is the roughness of exposed river channel; is the hydraulic impedance coefficient of vegetation; P is the vegetation coverage of the river; is the nonlinear index; is the vegetation type factor.

5. The method for tracking, predicting and evaluating the hydrodynamic changes of an inland port according to claim 4, characterized in that: The updating of the surface roughness of the two-dimensional hydrodynamic model further includes: When the two-dimensional hydrodynamic model outputs the predicted flooding depth of the inland port Exceeding critical submergence depth Generate a development prohibited area space mask when : ； The development prohibited area space mask Input to the land use change model to output a predicted land suitability score , the specific calculation formula is: ； in, Score initial land suitability; is the mask attenuation coefficient; The coupling frequency between the two-dimensional hydrodynamic model and the land use change model is set to annual coupling, that is, the surface roughness of the two-dimensional hydrodynamic model is updated every year according to the land use type change prediction result output by the land use change model in the previous year, and the predicted flooding depth output by the two-dimensional hydrodynamic model in the current year is updated every year. Adjust the land use type change prediction result output by the land use change model in the next year.

6. The method for tracking, predicting and evaluating the hydrodynamic changes of an inland port according to claim 1, characterized in that: The step S5 further includes: The hydrodynamic health index within the waters of the inland port The calculation formula is: ； in, is the dynamic stability index; is the sedimentation risk index; is the ecological disturbance index; 、 、 are the corresponding weights respectively; (1) Dynamic stability index The calculation formula is: ； in, is the standard deviation of the flow velocity in the port waters, which is directly extracted from the prediction results of the hydrodynamic conditions within the inland port waters; is the average flow velocity of the cross section; is the time scale weight; (2) Sedimentation risk index The calculation formula is: ； in, is the change rate of sedimentation area; represents the density of sediment; Indicates the density of water; represents the acceleration due to gravity; It indicates the particle size corresponding to when the cumulative particle size distribution percentage of sediment reaches 50%; and Respectively represent the prediction start time and prediction end time; (3) Ecological disturbance index The calculation formula is: ； in, represents the attenuation coefficient; Indicates abnormal water temperature value; Indicates dissolved oxygen saturation ratio; represents the vorticity intensity; is the tolerance threshold of fish.

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