Evaluation method and system for determining safe navigation channel based on non-structural grid digital flow field

Abstract

The invention discloses an evaluation method and system for determining a safe navigation channel based on a non-structural grid digital flow field, and the method comprises the steps: building a non-structural grid digital flow field model based on historical topographic mapping and hydrological data; generating real-time flow field data based on the non-structural grid digital flow field; processing the flow field data of the non-structural grid digital flow field model into a non-structural triangular grid with three-dimensional characteristics; generating flow field data and importing the flow field data into a Cesium platform for identification; determining effective water flow indexes and weights thereof in the to-be-evaluated channel; establishing a navigation safety index evaluation model based on each water flow index and the weight thereof; and carrying out navigation safety assessment on the to-be-assessed channel, and outputting a visual assessment result. The method has the remarkable effects that a channel navigation evaluation system containing five core indexes including channel gradient, transverse flow velocity, flow state characteristics and the like is constructed, and dynamic analysis and navigation safety quantitative evaluation of complex channel water flow characteristics are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of waterway navigation safety assessment, and in particular to an assessment method and system for determining a navigation safety waterway based on an unstructured grid digital flow field. Background Art

[0002] Internationally, waterway flow analysis technology is developing in two major directions. European and American countries have built basin-level intelligent analysis systems based on mature hydrodynamic models (such as Delft3D and HEC-RAS). However, these systems primarily focus on macro-scale hydrodynamic simulation and lack the ability to analyze detailed flow patterns at the waterway scale. Furthermore, these systems are expensive to acquire (with license fees exceeding 200,000 yuan for a single software package), making them difficult to adapt to the unique needs of my country's mountainous rivers. Furthermore, some countries have developed GIS-based dynamic waterway flow assessment platforms, but these platforms employ a single metric and lack quantitative analysis of key navigation risk factors such as lateral velocity and localized flow patterns.

[0003] At the same time, domestic related research has long relied on secondary development of foreign software, but there are three major technical bottlenecks: 1) Mathematical model post-processing technology lags behind, and traditional manual extraction methods take 3-5 days to process millions of grid data; 2) The flow field analysis dimension is missing. Existing systems can only output basic parameters such as water level and flow velocity, and lack intelligent recognition modules for special flow patterns such as vortex and scissor flow. 3) The navigation evaluation system is imperfect. The current "Inland Waterway Navigation Standards" have not yet established a quantitative threshold system for key indicators such as lateral flow velocity.

[0004] In summary, the existing waterway peer safety assessment technology has the following defects: (1) Technical level The data analysis efficiency of the existing mathematical model post-processing system is low and cannot meet the needs of high-frequency and high-density flow field analysis in mountainous waterways; there is a lack of dynamic flow field coupling analysis technology for ship navigation trajectories, making it difficult to quantify the real-time impact of water flow on ship maneuverability; traditional evaluation methods ignore the temporal and spatial correlation between lateral flow velocity and local flow state, resulting in blind spots in navigation safety warnings.

[0005] (2) Application level The cost of localizing and adapting foreign systems is high, and the core algorithms are not open, which restricts technological innovation; The industry lacks a standardized waterway flow regime database, and existing data is severely fragmented, with a utilization rate of less than 30%; Waterway management departments rely on experience and judgment, and the coverage rate of quantitative decision support tools is less than 15%.

[0006] In order to solve the above defects, there is an urgent need for an assessment method and system for safe navigation channels that can break through the technical bottleneck of intelligent analysis of water flow in the channel. Summary of the Invention

[0007] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide an evaluation method and system for determining safe navigation channels based on unstructured grid digital flow fields. Following the technical path of "three-dimensional reconstruction-intelligent analysis-dynamic evaluation", the three-dimensional flow field base is constructed using Delaunay triangulation, and the channel characteristic parameters are extracted through a vector decomposition algorithm. A full-process technical system of "data preprocessing → feature intelligent identification → navigation safety assessment → three-dimensional visualization" is established to achieve a closed-loop transformation of complex water flow characteristics from numerical simulation to navigation decision-making.

[0008] In order to achieve the above object, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention proposes an evaluation method for determining a safe navigation channel based on an unstructured grid digital flow field, the key of which is that it includes the following steps: Step 1: Based on historical topographic mapping and hydrological data, an unstructured grid digital flow field model is established; Step 2: using a Delaunay triangulation algorithm to process the flow field data of the unstructured grid digital flow field model into an unstructured triangular grid with three-dimensional characteristics; Step 3: Generate flow field data based on the unstructured flow field data grid obtained in step 2 and import it into the Cesium platform for recognition; Step 4: Determine the effective water flow indicators in the waterway to be evaluated, evaluate and analyze the water flow characteristics, and determine the weights of each water flow indicator; Step 5: Based on various water flow indicators and their weights, a navigation safety index evaluation model is established; Step 6: Use the navigation safety index evaluation model and set thresholds to perform navigation safety evaluation on the waterway to be evaluated, and output a visual evaluation result.

[0009] Furthermore, the establishment of an unstructured grid digital flow field based on historical hydrological data in step 1 specifically includes: Step 1.1: Collect and calculate the flow and water level data of the upstream and downstream hydrological stations of the river section, and establish a one-dimensional water flow mathematical model based on the continuity equation; Step 1.2: Based on the calculation results of the one-dimensional water flow mathematical model, obtain the flow-water level relationship at the upstream and downstream boundaries of the two-dimensional water flow model; Step 1.3: Based on the historical hydrological data set, the characteristic flow level is determined as the core analysis indicator, and an unstructured grid digital flow field model is established through model calculation.

[0010] Furthermore, the establishment of a one-dimensional water flow mathematical model based on the continuity equation described in step 1.1 specifically includes: Step 1.1.1. Use the public data of hydrological stations and the automatic collection program to obtain the flow and water level data of the hydrological stations above and below the calculated river reach; Step 1.1.2: Collect historical topographic maps, match the optimal topographic maps based on hydrological data, and divide river section information; Step 1.1.3: Calculate the flow-water relationship at each river section using the water-level-flow relationship at the hydrological station, and establish a one-dimensional flow mathematical model based on the continuity equation. The calculation results of the one-dimensional water flow mathematical model described in step 1.2 are used to obtain the flow-water level relationship at the upstream and downstream boundaries of the two-dimensional water flow model, specifically including: Step 1.2.1. Select the calculation results of the one-dimensional water flow mathematical model and analyze the rationality of the boundary conditions of the two-dimensional water flow model; Step 1.2.2: Import boundary conditions into the two-dimensional water flow model for calculation; Step 1.2.3: Use the measured water gauge and hydrological section to calibrate the roughness field of the two-dimensional water flow model, construct a flow-water level-roughness field correlation model, and iteratively verify and optimize the roughness parameters through the finite element calculation program.

[0011] Furthermore, the Delaunay triangulation algorithm used in step 2 to process the flow field data into an unstructured triangular mesh with three-dimensional characteristics specifically includes: Step 2.1, summarizing the flow field calculation results of the two-dimensional water flow mathematical model described in step 1, and extracting the flow field data into discrete data sets; Step 2.2: Use the Delaunay triangulation algorithm to obtain a two-dimensional temporary triangular mesh based on the plane coordinate relationship; Step 2.3: Record the grid corner point numbers of the Delaunay triangulation, assign corresponding values, and generate a six-dimensional unstructured triangular mesh dataset containing plane coordinates, water level, vertical component of flow velocity, and horizontal component of flow velocity to obtain the triangulated flow field data grid.

[0012] Furthermore, the calculation and generation of flow field data based on the unstructured grid digital flow field results in step 3 and importing it into the Cesium platform for identification specifically include: Step 3.1: Implement cross-section probe coordinate interpolation based on the Cesium platform to obtain interpolated flow field data; Step 3.2: Generate two-dimensional grid data using inverse distance weighted interpolation; Step 3.3: Based on the two-dimensional grid data, draw a gradient color cloud map with transparency through Canvas; Step 3.4: Use SingleTileImageryProvider to implement terrain-fitting rendering and obtain the unstructured grid digital flow field of the Cesium platform.

[0013] Furthermore, the effective water flow indicators in the waterway to be evaluated in step 4 include: water level and gradient indicators along the waterway, lateral flow velocity indicators along the waterway, absolute value indicators of flow velocity in the waterway, and local flow state complexity indicators.

[0014] Furthermore, the expression of the navigation safety index evaluation model in step 5 is: The expression of the navigation safety index evaluation model in step 5 is: in, NSI is the evaluation model for the aviation safety index. It is the water level and gradient index along the waterway. is the lateral flow velocity index along the process, is the absolute value index of the flow velocity in the channel, is the local flow complexity index, are the weights of each indicator respectively.

[0015] In a second aspect, the present invention proposes an evaluation system for determining safe navigation channels based on an unstructured grid digital flow field, comprising: The flow field model building module is used to build an unstructured grid digital flow field model based on historical topographic mapping and hydrological data; A mesh processing module, configured to process the flow field data of the unstructured mesh digital flow field model into an unstructured triangular mesh with three-dimensional characteristics by using a Delaunay triangulation algorithm; The flow field data generation module generates flow field data based on the obtained unstructured flow field data grid calculation and imports it into the Cesium platform for recognition; The indicator weight determination module is used to determine the effective water flow indicators in the waterway to be evaluated, evaluate and analyze the water flow characteristics, and determine the weights of each water flow indicator; Evaluation model building module, used to establish a navigation safety index evaluation model based on various water flow indicators and their weights; The safety assessment module is used to use the navigation safety index evaluation model and the set threshold to perform navigation safety assessment on the waterway to be assessed, and output a visual assessment result.

[0016] In a third aspect, the present invention provides a computer device, comprising: one or more processors; Memory; and One or more programs stored in a memory, wherein the one or more programs include steps for executing the method according to the first aspect.

[0017] In a fourth aspect, the present invention provides a computer-readable storage medium comprising one or more programs for execution by one or more processors, wherein the one or more programs comprise steps for executing the method as described in the first aspect.

[0018] The remarkable effects of the present invention are: (1) Three-dimensional reconstruction and dynamic analysis Following a technical approach of "3D reconstruction - intelligent analysis - dynamic assessment," the system constructs a 3D flow field base using Delaunay triangulation technology, and combines it with a vector decomposition algorithm to extract channel characteristic parameters, forming a complete technical chain from data preprocessing to navigation safety assessment. Furthermore, the system innovatively employs Delaunay triangulation technology to achieve 3D topological reconstruction of finite element meshes, establishing a flow field data interpolation algorithm based on unstructured meshes. This supports real-time flow field reconstruction for millions of mesh nodes, increasing computational efficiency by 15 times compared to traditional methods.

[0019] (2) Analysis of multi-dimensional indicators Based on the water flow characteristics of the waterway, a waterway navigation evaluation system was constructed, which includes five core indicators such as channel gradient, lateral flow velocity, and flow state characteristics. It breaks through the limitations of traditional two-dimensional flow field analysis and realizes the dynamic analysis of complex waterway flow characteristics and quantitative assessment of navigation safety.

[0020] (3) Dynamic coupling analysis For special flow states such as rapids, shallows, and whirlpools, the space vector decomposition algorithm and dynamic path integration technology are used to realize the dynamic coupling analysis of ship navigation trajectories and water flow elements, forming a complete set of waterway navigation safety assessment solutions. The accuracy rate of typical adverse flow state identification reaches 92%, which solves the problem of insufficient analysis ability of traditional methods for complex flow states. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a method flow chart of the method of the present invention; Figure 2 It is a structural diagram of the system of the present invention; Figure 3 It is a schematic diagram of the cross-sectional flow velocity distribution and vertical average flow velocity at multiple flow levels of the system of the present invention; Figure 4 It is a schematic diagram of the flow direction of the system of the present invention at a certain flow level; Figure 5 This is a diagram showing the system of the present invention in an unfavorable flow state; Figure 6 is a schematic diagram of the digital-analog buoy trace of the system of the present invention; Figure 7 It is a cloud diagram of the flow field of the watershed of the system of the present invention; Figure 8It is the flow field output diagram in the CAD of the system of the present invention; Figure 9 is a schematic diagram of an indicator chart of the system of the present invention; Figure 10 is a schematic diagram of the visual evaluation results of the system of the present invention; Figure 11 It is a principle block diagram of the device described in the present invention. DETAILED DESCRIPTION

[0022] The specific implementation manner and working principle of the present invention will be further described in detail below with reference to the accompanying drawings.

[0023] Example 1: like Figure 1 As shown, this embodiment proposes an evaluation method for determining a safe navigation channel based on an unstructured grid digital flow field. The specific steps are as follows: Step 1: Based on historical topographic mapping and hydrological data, an unstructured grid digital flow field model is established, wherein the digital flow field model includes a one-dimensional flow field model and a two-dimensional flow field model; In some embodiments, the establishment of the unstructured grid digital flow field model can be achieved by the following steps: Step 1.1: Collect and calculate the flow and water level data of the upstream and downstream hydrological stations of the river section, and establish a one-dimensional water flow mathematical model based on the continuity equation; The specific implementation process of the one-dimensional water flow mathematical model is: Step 1.1.1. Use the public data of hydrological stations and the automatic collection program to obtain the flow and water level data of the hydrological stations above and below the calculated river reach; Step 1.1.2: Collect historical topographic maps, match the optimal topographic maps based on hydrological data, and divide river section information; Step 1.1.3: Calculate the flow-water relationship of each river section through the water level-flow relationship calculation of the hydrological station, and establish a one-dimensional water flow mathematical model based on the continuity equation.

[0024] Step 1.2: Based on the calculation results of the one-dimensional water flow mathematical model, obtain the flow-water level relationship at the upstream and downstream boundaries of the two-dimensional water flow model; In this embodiment, the specific implementation process of the flow-water level relationship at the upstream and downstream boundaries of the two-dimensional water flow model is as follows: Step 1.2.1. Select the calculation results of the one-dimensional water flow mathematical model and analyze the rationality of the boundary conditions of the two-dimensional water flow model; Step 1.2.2: Import boundary conditions into the two-dimensional water flow model for calculation; Step 1.2.3: Use the measured water gauge and hydrological section to calibrate the roughness field of the two-dimensional water flow model, construct a flow-water level-roughness field correlation model, and iteratively verify and optimize the roughness parameters through the finite element calculation program.

[0025] The finite element calculation program of the planar two-dimensional water flow model is placed on the back-end server. The back-end server will perform multi-flow field calculations based on the historical hydrological data (including long-term model boundary flow and water level conditions). While calculating, it will verify the water level, cross-sectional flow velocity and other measurement data determined in a certain period between the upstream and downstream boundaries, and continuously iteratively adjust the flow field roughness to reduce the error between the measured points of the numerical model and the model calculation points. When the water level error between the measured points and the calculation points is controlled within 1 cm, and the flow velocity value error is controlled within 20%, the back-end server will end the iterative calculation of the flow field, and save the roughness field of a certain flow-water level correspondence relationship. This roughness flow field is used as the characteristic roughness field of this flow-water level relationship, and the roughness parameters are repeatedly verified and optimized through the finite element calculation program.

[0026] Step 1.3: Based on the historical hydrological data set, the characteristic flow level is determined as the core analysis indicator, and an unstructured grid digital flow field model is established through model calculation. The specific implementation process is as follows: Step 1.3.1. Determine the characteristic flow levels of low water, moderate water, and flood water according to the design water level of the beach section and the beach leveling method for good river sections; Step 1.3.2: Perform model calculation based on the characteristic flow level.

[0027] In some embodiments, the specific implementation process of using the Delaunay triangulation algorithm in step 2 to process the flow field data into an unstructured triangular mesh with three-dimensional characteristics is as follows: Step 2.1, summarize the flow field calculation results of the two-dimensional water flow mathematical model described in step 1, and extract the flow field data into a discrete data set including six-dimensional data such as x, y, z, h, u, v (x coordinate, y coordinate, riverbed elevation, calculated water surface elevation, vertical component of flow velocity, horizontal component of flow velocity); In this example, the flow field calculations were performed using an unstructured grid. This refers to a grid whose cells have no fixed shape, size, or connectivity, and whose node distribution follows no specific pattern. Unstructured grids can be composed of a mix of cells of various shapes, such as triangles, quadrilaterals, and polygons. This allows them to better fit complex boundary shapes and topography, making them suitable for simulating flows with irregular boundaries or complex geometries.

[0028] The calculated flow-water level values and the corresponding characteristic roughness field are stored in the database, and the calculation results are exported. Because the flow field finite element mesh is an unstructured mesh, the Delaunay algorithm is used to perform Delaunay triangulation on the spatial plane coordinates (XY) of the calculation points, forming a triangular mesh as the basis. The corresponding mesh nodes have three layers of associated data: water level, vertical component of flow direction, and horizontal component of flow direction.

[0029] Step 2.2: Use the Delaunay triangulation algorithm to obtain a two-dimensional temporary triangular mesh based on the plane coordinate relationship; The specific steps of using the Delaunay triangulation algorithm to grid the calculation points are as follows: Step 2.2.1, determine the positional relationship between the point and the triangle based on the cross multiplication method; Cross multiplication: The core idea is to determine the relative position of a point to each side of a triangle using vector cross products. Arrange the vertices of the triangle counterclockwise (e.g., ABC) and calculate the vector cross products of point P with each side (AB, BC, CA). If point P is on the same side of all sides (e.g., the left side), then P is inside the triangle; otherwise, it is outside. In practice, the condition is satisfied if the cross products have the same sign (either all positive or all negative).

[0030] Step 2.2.2: Use the barycentric coordinate algorithm to perform linear interpolation of discrete flow level data; After iteratively querying the triangular mesh number corresponding to each coordinate P in the cross-section coordinate point probe array, the corner point coordinates in the triangular mesh number are extracted, and the barycentric coordinate algorithm is used to perform linear interpolation of the water level, vertical component of flow direction, and horizontal component of flow direction on point P to obtain the water level and flow direction data of point P.

[0031] It should be noted that the above-mentioned barycentric coordinate algorithms are all existing technologies and will not be described in detail here.

[0032] Step 2.3: Record the grid corner numbers from the Delaunay triangulation and assign corresponding values to generate a six-dimensional unstructured triangular mesh dataset containing plane coordinates, water level, vertical velocity component, and horizontal velocity component, resulting in a pre-triangulated flow field data grid. Then, export the water level and flow direction data for each point on the cross-section to the echart plugin to visualize the cross-sectional data of the waterway flow field. The same principle can be used to construct a gridded query for any scattered point data in a three-dimensional spatial coordinate system.

[0033] In some embodiments, the calculation and generation of flow field data based on the unstructured grid digital flow field results and the importation of the flow field data into the Cesium platform for recognition in step 3 are mainly achieved by the following steps: Step 3.1: Implement cross-section probe coordinate interpolation based on the Cesium platform to obtain interpolated flow field data. The specific steps are as follows: Step 3.1.1. Based on the coordinate point provided by the user, determine the positional relationship between the point and the triangle in the Delaunay grid based on the cross multiplication method, and find the triangle grid number where the coordinate point is located in the flow field data grid. Step 3.1.2: Obtain the flow field data of the three corner points based on the sequence number, and use the barycentric coordinate algorithm to perform linear interpolation on the user coordinate points to obtain the interpolated flow field data.

[0034] Step 3.2: Generate data (water level, vertical component of flow velocity, horizontal component of flow velocity) using inverse distance weighted interpolation; That is, the scattered data is interpolated into a two-dimensional grid using algorithms such as inverse distance weighted (IDW).

[0035] Step 3.2: Based on the data obtained in step 3.2, a gradient color cloud with transparency is drawn through Cesium.Canvas, that is, the interpolated grid data is mapped to color and drawn to the Canvas element.

[0036] Step 3.4: Use SingleTileImageryProvider to implement terrain fitting rendering and obtain the unstructured grid digital flow field; that is, overlay Canvas as an image layer on the terrain surface and use Cesium's ImageryLayer to achieve fitting.

[0037] Data interpolation: Establishing digital flow field on unstructured grid

[0038] Color mapping: Draw gradient colors through Canvas with transparency to blend with the base map.

[0039] Terrain fitting: Use SingleTileImageryProvider to add images as layers, and Cesium automatically handles terrain adaptation.

[0040] Through steps 1-3 above, this embodiment establishes discrete flow-level data, corresponding to water level, flow rate, characteristic roughness field, and calculation results, based on historical hydrological data. After the front-end inputs flow field prediction instructions, the back-end server performs polynomial interpolation based on the measured boundary conditions within the discrete flow field database based on the daily measured boundary water level-flow data, generating real-time flow field data. After data processing, the real-time flow field is returned to the front-end and presented in the digital twin earth using the aforementioned visualization method, allowing real-time viewing of cross-sectional flow velocity and direction data.

[0041] This embodiment uses the above-mentioned unstructured grid digital flow field construction method, integrates the plane two-dimensional water flow model and the digital twin scene, and innovatively adopts the Delaunay triangulation algorithm of the unstructured grid, combined with the flow field iterative calculation, characteristic roughness field construction, multi-source data interpolation and other technologies to achieve dynamic visualization and real-time prediction of the inland waterway flow field. The present invention solves the problems of poor adaptability of traditional structured grids to complex boundaries, low flow field calculation efficiency, and difficulty in real-time prediction. It can be applied to waterway regulation design, flood evolution simulation, ship navigation safety warning and other fields. The established unstructured grid digital flow field has been applied to the flow field optimization of the upper Yangtze River waterway regulation project and the construction management and control system of the Chongqing Yangtze River Tunnel waterway maintenance emergency repair project of the Chongqing-Guizhou Railway. The implementation effect shows that the flow field calculation efficiency is improved by 40%, and the prediction accuracy reaches 89.7%. At the same time, this embodiment innovatively adopts the Delaunay triangulation technology to realize the three-dimensional topological reconstruction of the finite element grid, establishes a flow field data interpolation algorithm based on the unstructured grid, supports real-time flow field reconstruction of millions of grid nodes, and the calculation efficiency is improved by 15 times compared with the traditional method.

[0042] Step 4: Because the main characteristics of the water flow in the upper Yangtze River are large gradients, rapid currents, vortices, and sliding beams, effective comprehensive water flow indicators within the waterway are required for waterway flow analysis. This allows for evaluation and analysis of water flow characteristics. This allows us to identify the appropriate indicators based on the flow field dataset and determine the weights of each flow indicator to effectively eliminate noise. 1) Water level and gradient along the waterway: Water level directly determines vessel draft and effective water depth. River sections with steep gradients experience rapid currents, requiring greater propulsion for upstream passages and controlled speed for downstream passages to prevent loss of control. Accurate water level and gradient data optimizes vessel route planning. By inputting the coordinates of the channel centerline or the actual route of a navigable vessel, the centerline gradient and water depth data can be obtained, serving as a metric for evaluating channel conditions.

[0043] 2) Along-course transverse velocity: In the rapids and shoals of the upper Yangtze River, the along-course transverse velocity (i.e., the velocity component perpendicular to the channel direction) of a ship is a key indicator affecting navigation safety and maneuverability. In rapids and shoals, the interaction between the mainstream and the shoreline or reefs often creates oblique or transverse currents (such as backflow and vortexes). If the transverse velocity is too high (e.g., exceeding 0.3 m / s), a ship, even while traveling forward, will be pushed off course and may run aground or even run aground. The transverse velocity can counteract the rudder force, especially when sailing at low speeds (such as upstream ships), requiring the ship to significantly adjust the rudder angle or even rely on tugboat assistance, otherwise it will be difficult to maintain course. In terms of shallow channel passability, rapids and shoals are often accompanied by undulating riverbeds, and the transverse velocity may scour out a deep trough on one side and shallow silt on the other (such as a "scissor water" topography). If a ship is pushed into shallow water by a crosscurrent, it is very likely to run aground. At the same time, the crosscurrent creates asymmetric pressure on the sides of the hull, which can cause the ship to list, especially at a deep draft (such as a fully loaded cargo ship), posing a risk of capsizing. By inputting the coordinates of the centerline of the channel or the actual route of a navigable ship, the gradient and water depth of the centerline can be obtained as a criterion for evaluating channel conditions. Based on the flow field data of the UV component, vector conversion is used to calculate the angle between the perpendicular line of the ship's route and the direction of the water flow, and the flow velocity component perpendicular to the channel direction is obtained as a criterion for evaluating channel flow patterns.

[0044] 3) Absolute Value of Channel Velocity: The average channel velocity is a key indicator affecting ship safety, maneuverability, and navigation efficiency. It directly determines the ship's navigation resistance and power requirements, impacting its maneuverability and track stability. It also, along with the depth of shallow water, restricts navigation conditions. The absolute value of the velocity is obtained by calculating the modulus of the UV component. This is used to evaluate the velocity of the water flow within the channel. This can be used as an assessment criterion for determining whether a river section has become a rapids section, determining ship upstream performance, and evaluating the effectiveness of regulation.

[0045] 4) Local Flow Complexity Index: Compared to the lateral velocity index, the local flow complexity index provides a two-dimensional view of the velocity and direction distribution at various points in the flow field. This allows for a more intuitive assessment of channel passability for undesirable flow patterns such as backflow and vortexes. Flow characteristics directly determine ship power requirements and maneuvering strategies. Based on UV component flow field data, flow direction and velocity values at each grid point are displayed on the platform and can be transferred to CAD as needed.

[0046] This implementation addresses special flow regimes such as rapids, shallows, and swirling waters by employing a space vector decomposition algorithm and dynamic path integration technology. This enables dynamic coupled analysis of ship trajectories and current elements, resulting in a comprehensive solution for waterway navigation safety assessment. Based on this intelligent recognition algorithm for characteristic flow regimes (such as swirling waters and scissor currents), the accuracy rate for identifying typical adverse flow regimes reaches 92%, addressing the inability of traditional methods to analyze complex flow regimes.

[0047] Step 5: Based on various water flow indicators and their weights, a navigation safety index evaluation model is established to evaluate water flow characteristics; This implementation utilizes a technical approach combining three-dimensional reconstruction, intelligent analysis, and dynamic assessment to transform numerical flow simulation data into quantifiable navigation safety indicators. The model utilizes standardized scoring, weighted multi-indicator synthesis, and fuzzy logic processing to create a dynamic and visual safety index scoring system.

[0048] In the evaluation model, the exp function is usually used to perform nonlinear scaling or normalization on the parameters. , each parameter G i It is converted into a standardized score between [0,1], where 1 represents the best and 0 represents the worst.

[0049] Therefore, the standardized functions of the five indicators, namely, the water level and gradient along the channel, the lateral velocity along the channel, the absolute value of the velocity in the channel, the local flow pattern complexity index, and the digital model buoy trace indicator light, are determined as follows: 1) Water level and gradient indicators along the route G 1: Where ΔH / L is the water level gradient per unit length, k 1 is the empirical parameter; 2) Transverse flow velocity index G 2: Where, for, is the velocity vector perpendicular to the heading, is the unit vector perpendicular to the heading, is the critical cross-flow index of the river section; 3) Absolute value of flow rate G 3: Where, is the absolute value of flow velocity, is the flow velocity perpendicular to the heading, is the flow velocity along the heading direction, k 3 is the empirical parameter; 4) Local flow complexity index G 4: Where, k 4 is the empirical parameter, is the local directional standard deviation or vortex strength, for, is the velocity vector perpendicular to the heading; Based on the standard functions of the above indicators, the navigation safety index evaluation model described in this embodiment is NSI The expression is: in, NSI is the evaluation model for the aviation safety index. It is the water level and gradient index along the waterway. is the lateral flow velocity index along the process, is the absolute value index of the flow velocity in the channel, is the local flow complexity index, are the weights of each indicator respectively.

[0050] From the expression of the navigation safety index evaluation model, we can see that NSI ∈[0, 1], so the threshold can be set (such as NSI <0.4 is a high-risk segment), so according to the calculated NSI The value of is used to evaluate and judge the navigation safety.

[0051] Therefore, this embodiment constructs a Navigational Safety Index (NSI) evaluation model based on standardized scoring, multi-index weighted synthesis, and fuzzy logic processing to achieve quantitative indicator output for waterway passability assessment.

[0052] Step 6: Use the navigation safety index evaluation model and set thresholds to perform navigation safety evaluation on the waterway to be evaluated, and output a visual evaluation result.

[0053] In a specific implementation process, the visual evaluation result may include at least one of the following items: Heat map output: By building a 3D dynamic flow field visualization engine, the Navigation Safety Index (NSI) value is projected onto the channel profile to form a color gradient heat map, which intuitively displays high-risk areas; Risk Warning: High risk (low NSI value) area; Optimization suggestions: Provide route adjustment suggestions.

[0054] It can be seen that this embodiment follows the technical path of "three-dimensional reconstruction-intelligent analysis-dynamic evaluation". It constructs a three-dimensional flow field base through Delaunay triangulation technology and extracts channel characteristic parameters in combination with vector decomposition algorithm, forming a complete technical chain from data preprocessing to navigation safety assessment. In addition, based on the water flow characteristics of the complex waterway in the upper reaches of the Yangtze River, a waterway navigation evaluation system was constructed, which includes five core indicators such as channel gradient, lateral flow velocity, and flow state characteristics. It breaks through the limitations of traditional two-dimensional flow field analysis, realizes the dynamic analysis of complex waterway flow characteristics and quantitative assessment of navigation safety, and automatically generates navigation risk heat maps and optimization plans, providing dynamic decision-making support for waterway management departments and promoting the transformation of the industry from "empirical judgment" to "data-driven".

[0055] Example 2: See attached Figure 2 This embodiment proposes an evaluation system for determining a safe navigation channel based on an unstructured grid digital flow field for implementing the method described in Example 1. The system specifically includes: The flow field model building module is used to build an unstructured grid digital flow field model based on historical topographic mapping and hydrological data; A mesh processing module, configured to process the flow field data of the unstructured mesh digital flow field model into an unstructured triangular mesh with three-dimensional characteristics by using a Delaunay triangulation algorithm; The flow field data generation module generates flow field data based on the obtained unstructured flow field data grid calculation and imports it into the Cesium platform for recognition; The indicator weight determination module is used to determine the effective water flow indicators in the waterway to be evaluated, evaluate and analyze the water flow characteristics, and determine the weights of each water flow indicator; Evaluation model building module, used to establish a navigation safety index evaluation model based on various water flow indicators and their weights; The safety assessment module is used to use the navigation safety index evaluation model and the set threshold to perform navigation safety assessment on the waterway to be assessed, and output a visual assessment result.

[0056] Developed in C#, this system platform targets the complex flow characteristics of the upper Yangtze River and integrates flow field reconstruction, multi-dimensional indicator analysis, and dynamic visualization into an intelligent assessment platform. It innovatively employs Delaunay triangulation technology to achieve three-dimensional topological reconstruction of finite element meshes and establishes a flow field data interpolation algorithm based on unstructured meshes. The system has pioneered a waterway navigation evaluation system encompassing five core indicators: channel gradient, lateral velocity, and flow pattern characteristics. It has also developed specialized functional modules such as dynamic simulation of buoy tracks and flow field vector interpolation. Specifically targeting special flow regimes such as rapids, shallows, and swirling waters, the system utilizes a spatial vector decomposition algorithm and dynamic path integration technology to dynamically couple ship navigation trajectories with flow elements, forming a complete solution for waterway navigation safety assessment. The system supports integrated CAD platform applications, forming a complete technical chain from mathematical model output to navigation safety assessment, achieving a leap from "data visualization" to "intelligent decision-making."

[0057] In the actual application of the system in the channel regulation project of Fufeng section in the upper reaches of the Yangtze River, the interface of the cross-sectional velocity distribution and vertical average velocity under multiple flow levels of the system is as follows: Figure 3 As shown; the flow interface of this system at a certain flow level is as follows Figure 4 As shown; the system shows the effect under bad flow conditions as shown Figure 5 As shown; the digital-analog buoy trace of this system is as follows Figure 6 As shown; the basin flow field cloud diagram of this system is as follows Figure 7 As shown; the flow field output results of this system in CAD are as follows Figure 8 As shown; the different indicator charts of this system are as follows Figure 9 As shown in Figure 9, where 9(a) shows the average cross-flow index in the channel, 9(a) shows the average flow velocity index in the channel, 9(a) shows the channel centerline elevation index, and 9(a) shows the channel centerline gradient. The schematic diagram of the navigation safety visualization evaluation results of the system for the two locations is shown in Figure 9. Figure 10 shown.

[0058] Example 3: like Figure 11 As shown, an embodiment of the present invention provides a computer device, including: one or more processors; Memory; and One or more programs stored in a memory, wherein the one or more programs include steps for executing the method according to embodiment 1.

[0059] Of course, the device may also have components such as a wired or wireless network interface, a keyboard, and an input / output interface for input and output. The device may also include other components for realizing the functions of the device, which will not be described in detail here.

[0060] Example 4: An embodiment of the present invention provides a computer-readable storage medium, comprising one or more programs for execution by one or more processors, wherein the one or more programs include steps for executing the method described in Example 1.

[0061] This invention breaks through the limitations of traditional two-dimensional flow field analysis, achieving for the first time the dynamic analysis of complex waterway flow characteristics and quantitative assessment of navigation safety. Compared with traditional manual analysis methods, work efficiency is increased by more than 20 times, and the accuracy of indicator calculations is improved by 40%. It has been successfully applied in the waterway regulation projects in the Chaofu and Fufeng sections of the upper Yangtze River, the emergency repair of the Yangtze River Tunnel on the Chongqing-Guizhou Railway, and the emergency repair of the Yangtze River Tunnel on Chongqing Rail Line 27. It is driving the transformation and upgrading of the water transport industry from experience-based management to data-driven decision-making, and providing core technical support for the development of intelligent shipping.

[0062] This invention deeply integrates numerical simulation technology with shipping safety requirements, achieving a leapfrog development from "data visualization" to "intelligent decision-making." It provides key technical support for building "digital waterways" and has significant social and economic benefits and industry promotion value: 1. Accelerate project launch Efficient assessment supports decision-making: This invention significantly shortens the time required for early project feasibility studies and solution demonstrations through rapid and accurate flow field reconstruction and navigation safety assessment. For example, in the Fufeng Channel Regulation Project in the upper Yangtze River, the system reduced the traditional flow field data processing time from months to hours, shortening the project assessment cycle by 70% and providing strong support for the rapid launch of the project.

[0063] Reduced decision-making risk: By quantifying navigation safety indicators, the implementation of this invention provides a scientific basis for project decision-making, reducing errors caused by empirical judgment. For example, in the emergency channel reopening project of the Yangtze River Tunnel on the Chongqing-Guizhou Railway, the system optimized the construction plan through dynamic simulation and risk assessment, ensuring the smooth progress of the project.

[0064] 2. Optimize project resource allocation: Precise Investment Planning: This invention provides a quantitative basis for project investment through the Navigation Safety Index (NSI) model. For example, in the Fufeng section waterway regulation project, the system improved resource utilization efficiency by identifying high-risk areas.

[0065] Improving project success rates: Utilizing dynamic path integration technology and ship-water dynamic coupling analysis, the system can proactively identify potential risks and optimize project designs. For example, during the emergency waterway reopening project for the Yangtze River Tunnel on Chongqing Metro Line 27, the system simulated ship trajectories to help adjust construction plans and ensure successful project implementation.

[0066] 3. Indirect benefits of quantifying adverse flow indicators Improve shipping safety and accurately identify adverse flow patterns: The system has developed an intelligent recognition algorithm for characteristic flow patterns (such as vortex flow, scissor water, etc.), with an accuracy rate of 92% for identifying typical adverse flow patterns. By quantifying key indicators such as lateral velocity and local flow patterns, the system can provide early warnings for high-risk areas and reduce the occurrence of shipping accidents. For example, in the Fufeng section waterway regulation project, the system helped optimize the waterway design by identifying adverse flow patterns such as vortex flow and scissor water, significantly reducing the risk of ships running aground and hitting reefs. In the emergency channel opening project of the Yangtze River Tunnel on the Chongqing-Guizhou Railway, the system helped reduce the potential risks to ships caused by adverse flow patterns through dynamic simulation and risk assessment.

[0067] Optimized route planning: The system provides intuitive navigation advice to ships through dynamic flow field visualization and Navigation Safety Index (NSI) heat maps. For example, during the Fufeng section waterway regulation project, the system output heat maps of high-risk areas to help ships adjust their routes, reducing navigation risks caused by adverse flow patterns.

[0068] Improving the overall industry level: By promoting quantitative assessment technology, this invention has improved the overall technical level of the water transport industry. For example, in multiple waterway regulation projects in the upper Yangtze River, the system has helped the industry transition from experience-based management to data-driven decision-making by providing standardized assessment methods and tools.

[0069] IV. Social, Economic and Ecological Benefits Ensuring shipping safety: By quantifying adverse flow indicators and optimizing waterway design, the implementation of this invention significantly improves the navigation safety of the upper Yangtze River waterway and protects the lives and property of the people.

[0070] Improve the industry image: The promotion and application of this invention has promoted the modernization and intelligent development of the water transport industry, and improved the overall image and social recognition of the industry.

[0071] Promoting sustainable development: The promotion and application of this invention promotes the greening and sustainable development of the water transport industry and provides technical support for the ecological protection of the Yangtze River Economic Belt.

[0072] The technical solution provided by the present invention is introduced in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, the present invention can also be improved and modified in a number of ways, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. An evaluation method for determining safe navigation channels based on unstructured grid digital flow fields, characterized in that: The steps include: Step 1: Based on historical topographic mapping and hydrological data, an unstructured grid digital flow field model is established; Step 2: using a Delaunay triangulation algorithm to process the flow field data of the unstructured grid digital flow field model into an unstructured triangular grid with three-dimensional characteristics; Step 3: Generate flow field data based on the unstructured flow field data grid obtained in step 2 and import it into the Cesium platform for recognition; Step 4: Determine the effective water flow indicators in the waterway to be evaluated, evaluate and analyze the water flow characteristics, and determine the weights of each water flow indicator; Step 5: Based on various water flow indicators and their weights, a navigation safety index evaluation model is established; Step 6: Use the navigation safety index evaluation model and set thresholds to perform navigation safety evaluation on the waterway to be evaluated, and output a visual evaluation result.

2. The evaluation method for determining safe navigation channels based on unstructured grid digital flow fields according to claim 1 is characterized in that: The establishment of the unstructured grid digital flow field model described in step 1 specifically includes: Step 1.1: Collect and calculate the flow and water level data of the upstream and downstream hydrological stations of the river section, and establish a one-dimensional water flow mathematical model based on the continuity equation; Step 1.2: Based on the calculation results of the one-dimensional water flow mathematical model, obtain the flow-water level relationship at the upstream and downstream boundaries of the two-dimensional water flow model; Step 1.3: Based on the historical hydrological data set, the characteristic flow level is determined as the core analysis indicator, and an unstructured grid digital flow field model is established through model calculation.

3. The evaluation method for determining safe navigation channels based on unstructured grid digital flow fields according to claim 2 is characterized in that: The establishment of a one-dimensional water flow mathematical model based on the continuity equation described in step 1.1 specifically includes: Step 1.1.

1. Use the public data of hydrological stations and the automatic collection program to obtain the flow and water level data of the hydrological stations above and below the calculated river reach; Step 1.1.2: Collect historical topographic maps, match the optimal topographic maps based on hydrological data, and divide river section information; Step 1.1.3: Calculate the flow-water relationship at each river section using the water-level-flow relationship at the hydrological station, and establish a one-dimensional flow mathematical model based on the continuity equation. Based on the calculation results of the one-dimensional water flow mathematical model described in step 1.2, the flow-water level relationship at the upstream and downstream boundaries of the two-dimensional water flow model is obtained, which specifically includes: Step 1.2.

1. Select the calculation results of the one-dimensional water flow mathematical model and analyze the rationality of the boundary conditions of the two-dimensional water flow model; Step 1.2.2: Import boundary conditions into the two-dimensional water flow model for calculation; Step 1.2.3: Use the measured water gauge and hydrological section to calibrate the roughness field of the two-dimensional water flow model, construct a flow-water level-roughness field correlation model, and iteratively verify and optimize the roughness parameters through the finite element calculation program.

4. The evaluation method for determining safe navigation channels based on unstructured grid digital flow fields according to claim 1 is characterized in that: The Delaunay triangulation algorithm used in step 2 to process the flow field data into an unstructured triangular mesh with three-dimensional characteristics specifically includes: Step 2.1, summarizing the flow field calculation results of the two-dimensional water flow mathematical model described in step 1, and extracting the flow field data into discrete data sets; Step 2.2: Use the Delaunay triangulation algorithm to obtain a two-dimensional temporary triangular mesh based on the plane coordinate relationship; Step 2.3: Record the grid corner point numbers of the Delaunay triangulation, assign corresponding values, and generate a six-dimensional unstructured triangular mesh dataset containing plane coordinates, water level, vertical component of flow velocity, and horizontal component of flow velocity to obtain the triangulated flow field data grid.

5. The evaluation method for determining safe navigation channels based on unstructured grid digital flow fields according to claim 1 is characterized in that: The calculation of flow field data based on the unstructured grid digital flow field results in step 3 and importing it into the Cesium platform for identification specifically include: Step 3.1: Implement cross-section probe coordinate interpolation based on the Cesium platform to obtain interpolated flow field data; Step 3.2: Generate two-dimensional grid data using inverse distance weighted interpolation; Step 3.3: Based on the two-dimensional grid data, draw a gradient color cloud map with transparency through Canvas; Step 3.4: Use SingleTileImageryProvider to implement terrain-fitting rendering and obtain the unstructured grid digital flow field of the Cesium platform.

6. The evaluation method for determining safe navigation channels based on unstructured grid digital flow fields according to claim 1 is characterized in that: The effective water flow indicators in the waterway to be evaluated in step 4 include: water level and gradient indicators along the waterway, lateral flow velocity indicators along the waterway, absolute value indicators of flow velocity in the waterway, and local flow state complexity indicators.

7. The evaluation method for determining safe navigation channels based on unstructured grid digital flow fields according to claim 1 is characterized in that: The expression of the navigation safety index evaluation model in step 5 is: in, NSI is the evaluation model for the aviation safety index. It is the water level and gradient index along the waterway. is the lateral flow velocity index along the process, is the absolute value index of the flow velocity in the channel, is the local flow complexity index, are the weights of each indicator respectively.

8. An evaluation system for determining safe navigation channels based on unstructured grid digital flow fields, used to implement the steps of the method according to any one of claims 1 to 7, characterized in that: include: The flow field model building module is used to build an unstructured grid digital flow field model based on historical topographic mapping and hydrological data; A mesh processing module, configured to process the flow field data of the unstructured mesh digital flow field model into an unstructured triangular mesh with three-dimensional characteristics by using a Delaunay triangulation algorithm; The flow field data generation module generates flow field data based on the obtained unstructured flow field data grid calculation and imports it into the Cesium platform for recognition; The indicator weight determination module is used to determine the effective water flow indicators in the waterway to be evaluated, evaluate and analyze the water flow characteristics, and determine the weights of each water flow indicator; Evaluation model building module, used to establish a navigation safety index evaluation model based on various water flow indicators and their weights; The safety assessment module is used to use the navigation safety index evaluation model and the set threshold to perform navigation safety assessment on the waterway to be assessed, and output a visual assessment result.

9. A computer device, characterized in that: include: one or more processors; Memory; and One or more programs stored in a memory, said one or more programs comprising steps for executing the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The method comprises one or more programs for execution by one or more processors, wherein the one or more programs comprise steps for executing the method according to any one of claims 1 to 7.

Images