Method, device, equipment and medium for indexing water depth data of inbound and outbound channels

Abstract

The present application provides a method, device, equipment and medium for indexing water depth data of waterways entering and leaving the port, including: obtaining measurement data, the measurement data including measurement engineering metadata and water depth measurement data; constructing multi-level grid data based on the water depth measurement data, the multi-level grid data including waterway data, the waterway data including segment data, and the segment data including segment data; associating the measurement data with the multi-level grid data to obtain a multi-level spatiotemporal index; querying the measurement data corresponding to the target query request from the multi-level spatiotemporal index as the query result. In this way, the effective organization of water depth data of waterways entering and leaving the port is achieved, and at the same time, the efficient retrieval of water depth data of waterways entering and leaving the port is achieved, providing technical means for the business application and data mining of waterway depth, and serving the preventive maintenance and safety management of waterways.

Description

Technical Field

[0001] The present application relates to the field of geographic information processing technology, and in particular to a method, device, equipment and medium for indexing water depth data of inbound and outbound waterways. Background Art

[0002] Ports play a crucial role in international trade and logistics. The water depth and underwater topography of access channels significantly impact ship navigation safety and port operational efficiency. Due to both natural and human factors, access channels are long and narrow, making knowledge of these channels crucial. Advances in marine surveying and mapping technology have enabled efficient and accurate data acquisition. However, the resulting massive amounts of data pose stringent requirements for storage, management, and analysis, necessitating urgent research into technologies for constructing spatiotemporal indexes.

[0003] Common indexing methods include R-tree, KD-tree, grid tree, and combined indexes. R-trees excel at processing complex spatial queries, but suffer from spatial overlap and data redundancy, which reduce query efficiency. Grid trees offer a simple structure and efficient queries, but perform poorly when processing data with uneven spatial distribution. KD-trees can improve search efficiency for multidimensional spatial data and have low time complexity, but their performance significantly decreases in high-dimensional spaces. They are also sensitive to data distribution, and unbalanced tree structures can reduce query efficiency. However, due to the long and narrow geometry of inbound and outbound channels, their varying widths, and their complex water depth distribution, common indexing methods struggle to accurately represent their complex characteristics. They are unable to adapt to the long, irregular distribution of inbound and outbound channels, and are unable to cope with higher data density, more frequent data updates, and more complex query scenarios.

[0004] Therefore, how to effectively manage the water depth data of the inlet and outlet channels and improve the data retrieval efficiency is a technical problem that technical personnel in this field urgently need to solve. Summary of the Invention

[0005] In view of this, the embodiments of the present application provide a method, device, equipment and medium for indexing water depth data of inbound and outbound channels, aiming to effectively manage water depth data of the channel and improve data retrieval efficiency.

[0006] In a first aspect, an embodiment of the present application provides a method for indexing water depth data of an inbound and outbound channel, comprising:

[0007] Acquiring measurement data, wherein the measurement data includes measurement engineering metadata and water depth measurement data;

[0008] constructing multi-level grid data based on the water depth measurement data, wherein the multi-level grid data includes channel data, the channel data includes segment data, and the segment data includes segment data;

[0009] Associating the measurement data with the multi-level grid data to obtain a multi-level spatiotemporal index;

[0010] The measurement data corresponding to the target query request is searched from the multi-level spatiotemporal index as a query result.

[0011] Optionally, querying the multi-level spatiotemporal index for measurement data corresponding to the target query request as a query result includes:

[0012] Obtaining a target query request, wherein the target query request includes a query formula and a data scale;

[0013] The query is ;

[0014] R st is the query result, Q st is the spatiotemporal query function, Geo is the query space range, T start is the query start time, T end is the query end time, GridSet is the query grid set, ProjSet is the query project set, and ResType is the output format of the query result;

[0015] Wherein, the query grid set ;

[0016] Grid1 is the first grid, Grid2 is the second grid, Grid n is the nth grid, where n is a positive integer;

[0017] The i-th grid ;

[0018] Level i For the grid level, Id i is the grid number, Geo i is the grid range, Attrs i is the grid attribute, i is a positive integer;

[0019] Query project collection ;

[0020] Proj1 is the first project, Proj2 is the second project, Proj k is the k-th project, k is a positive integer;

[0021] The jth project ;

[0022] Id j is the project number, Geo j is the scope of the project, T j For engineering time, Attrsj is the engineering attribute, j is a positive integer;

[0023] According to the query formula, the measurement data corresponding to the data scale is queried from the multi-level spatiotemporal index as a query result.

[0024] Optionally, querying the measurement data corresponding to the data scale from the multi-level spatiotemporal index according to the query formula as a query result includes:

[0025] In response to the query spatial range being a point, the query spatial range is used as a target point, and the following target point query operations are performed: in response to the data scale being lower than a preset precision value, the mean or maximum value of the measurement data in the multi-level grid data corresponding to the target point is used as a query result; in response to the data scale being not lower than the preset precision value, an underwater terrain model is constructed based on the measurement data in the multi-level grid data corresponding to the target point; and based on the underwater terrain model, a water depth value of the target point is calculated using an inverse distance interpolation method as a query result;

[0026] In response to the query space range being a line, the query space range is discretized into a plurality of target points; for each of the target points, the target point query operation is performed to obtain query results corresponding to the plurality of target points; and based on the query results corresponding to the plurality of target points, a query result corresponding to the query space range is obtained;

[0027] In response to the query spatial range being a surface, multi-level grid data intersecting the query spatial range is obtained as query grid data; and measurement data in the query grid data is obtained as a query result.

[0028] Optionally, constructing the underwater terrain model according to the measurement data in the multi-level grid data corresponding to the target point includes:

[0029] Performing Delaunay triangulation on the measurement data in the multi-level grid data corresponding to the target point to construct an initial triangulated network, wherein the initial triangulated network includes a plurality of triangles;

[0030] generating an underwater terrain boundary line corresponding to the target point by using an adaptive boundary extraction method based on an alpha shape algorithm;

[0031] The centroid topological mapping method is used to remove redundant triangles outside the underwater terrain boundary line from the initial triangulated network to obtain an underwater terrain model.

[0032] Optionally, the multi-level spatiotemporal index includes a spatial index and a temporal index, and associating the measurement data with the multi-level grid data to obtain the multi-level spatiotemporal index includes:

[0033] Associating the bathymetric data with the multi-level grid data to obtain a spatial index;

[0034] The time data in the measurement engineering metadata is associated with the spatial index to obtain a time index.

[0035] Optionally, associating the water depth measurement data with the multi-level grid data to obtain a spatial index includes:

[0036] Obtaining the maximum side length of a measurement grid of the water depth measurement data;

[0037] constructing a concave hull polygon of the bathymetric data according to the maximum side length of the measurement grid;

[0038] Acquire multi-level grid data intersecting with the concave hull polygon as target grid data;

[0039] Associating the bathymetric data with the target grid data to obtain a spatial index;

[0040] Alternatively, associating the bathymetric data with the multi-level grid data to obtain a spatial index includes:

[0041] associating the bathymetric data with the segmented data;

[0042] The water depth measurement data associated with the segment data is synchronized with the segment data and the channel data in sequence.

[0043] Optionally, associating the time data in the measurement engineering metadata with the spatial index to obtain the time index includes:

[0044] According to the correspondence between the surveying engineering metadata and the water depth measurement data, the engineering time in the surveying engineering metadata is annotated into the surveying data of the spatial index as a time index.

[0045] In a second aspect, an embodiment of the present application provides a device for indexing water depth data of a port entry and exit channel, comprising:

[0046] An acquisition module, configured to acquire measurement data, wherein the measurement data includes measurement engineering metadata and water depth measurement data;

[0047] A network construction module, configured to construct multi-level grid data based on the water depth measurement data, wherein the multi-level grid data includes waterway data, the waterway data includes segment data, and the segment data includes segment data;

[0048] An index building module, configured to associate the measurement data with the multi-level grid data to obtain a multi-level spatiotemporal index;

[0049] The query module is used to query the measurement data corresponding to the target query request from the multi-level spatiotemporal index as the query result.

[0050] In a third aspect, an embodiment of the present application provides a device for indexing water depth data of an inlet and outlet channel of a port, the device comprising a memory and a processor, the memory being used to store instructions or codes, the processor being used to execute the instructions or codes so that the device executes the method for indexing water depth data of an inlet and outlet channel of a port as described in any one of the first aspects above.

[0051] In a fourth aspect, an embodiment of the present application provides a computer storage medium, in which a code is stored. When the code is executed, the device executing the code implements the method for indexing water depth data of an entry and exit channel as described in any one of the first aspects above.

[0052] The embodiment of the present application provides a method, device, equipment and medium for indexing water depth data of waterways entering and leaving the port. When executing the method, the measurement data is first obtained, and the measurement data includes measurement engineering metadata and water depth measurement data; then, based on the water depth measurement data, multi-level grid data is constructed, and the multi-level grid data includes waterway data, and the waterway data includes segment data, and the segment data includes segment data; then, the measurement data is associated with the multi-level grid data to obtain a multi-level spatiotemporal index; finally, the measurement data corresponding to the target query request is queried from the multi-level spatiotemporal index as the query result. In this way, the effective organization of water depth data of waterways entering and leaving the port is achieved, which is conducive to efficient retrieval, provides technical means for business applications and data mining of waterway depth, and serves the preventive maintenance and safety management of waterways. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in this embodiment or the prior art, the following briefly introduces the drawings required for use in the embodiment or the prior art description. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 A flow chart of a method for indexing water depth data of inbound and outbound channels provided in an embodiment of the present application;

[0055] Figure 2 A schematic diagram of the waterway grid division provided in an embodiment of the present application;

[0056] Figure 3 A schematic diagram of a multi-level spatiotemporal index construction method provided in an embodiment of the present application;

[0057] Figure 4 Another method flow chart of the method for indexing water depth data of inbound and outbound channels provided in an embodiment of the present application;

[0058] Figure 5 A schematic diagram of a sparse query using the center point distance proximity method provided in an embodiment of the present application;

[0059] Figure 6 A schematic diagram of redundant triangles in triangulation provided in an embodiment of the present application;

[0060] Figure 7 A schematic diagram of a reasonable triangulated network provided in an embodiment of the present application;

[0061] Figure 8 Schematic diagram of the contour extraction algorithm mechanism provided in the embodiment of the present application;

[0062] Figure 9 A schematic diagram comparing redundant triangle calculations provided in an embodiment of the present application;

[0063] Figure 10 A schematic diagram of the statistics of the surveying project in the research area provided in the embodiment of the present application;

[0064] Figure 11 Graphs illustrating experimental results provided in the examples of this application;

[0065] Figure 12 A schematic diagram of water depth and shallow point extraction provided in an embodiment of the present application;

[0066] Figure 13 This is a diagram of the waterway cross-section analysis results provided in the embodiment of the present application;

[0067] Figure 14 A diagram of the dredging analysis calculation process provided in an embodiment of the present application;

[0068] Figure 15 This is a diagram of the waterway evolution analysis results provided in the embodiment of this application;

[0069] Figure 16 A schematic structural diagram of a water depth data indexing device for an inbound and outbound channel provided in an embodiment of the present application. DETAILED DESCRIPTION

[0070] With the rapid development of the global economy, ports, as key nodes for international trade and logistics, are becoming increasingly important. As vital pathways connecting ports and the ocean, the water depth and underwater topography of port access channels are directly related to ship navigation safety and port operational efficiency. Over their long-term use, port access channels are subject to the dual influences of natural factors such as siltation and water erosion, as well as human factors such as engineering construction and ship navigation. These channels exhibit characteristics such as a long and narrow shape, high traffic density, significant tidal influences, high maintenance standards, and frequent topographic changes. Accurate and timely access to channel water depth data and underwater topography information plays a supporting role in ensuring safe navigation of ships, optimizing port operations, and implementing preventive maintenance measures.

[0071] In recent years, ocean surveying and mapping technology has continued to advance, with technologies such as multi-beam bathymetry systems and laser scanning being widely used, making the acquisition of water depth data for inbound and outbound channels increasingly efficient and accurate. The accumulation of massive water depth data provides a basis for quickly and accurately obtaining waterway spatiotemporal information for preventive waterway maintenance, while also placing higher demands on data storage, management, and analysis. Given the characteristics of waterway depth, such as large data volumes, dense distribution, multi-scale, and spatiotemporal continuity, there is an urgent need to conduct research on the construction of spatiotemporal indexes for water depth data for inbound and outbound channels, to achieve efficient organization, rapid retrieval, and precise analysis of massive spatiotemporal data, and to provide technical support for the management and maintenance of inbound and outbound waterways.

[0072] At present, research on spatiotemporal indexing of water depth data mainly focuses on how to efficiently organize and retrieve massive spatiotemporal data. Common indexing methods include R-tree, KD-tree, grid tree, and combined index. R-tree is a hierarchical spatial index structure that performs well in processing complex spatial queries, but it needs to solve the problem of reduced query efficiency caused by spatial overlap and data redundancy. The grid tree has a simple structure and efficient query, but it performs poorly when processing data with uneven spatial distribution. KD-tree improves the efficiency of multidimensional spatial data search by dividing the data space into different dimensions. It has low time complexity, but its performance decreases significantly in high-dimensional space and is sensitive to data distribution. Unbalanced tree structure will reduce query efficiency.

[0073] However, most existing methods for constructing water depth data indexes are designed for areas with continuous and uniform water depth distribution, such as wide sea areas and inland waterways. How to adapt to the long, irregular distribution characteristics of port entry and exit channels and cope with the challenges brought by higher data density, more frequent data updates and more complex query scenarios are issues that need to be urgently addressed in current research.

[0074] To address the above issues, this application proposes the following solutions. Obviously, the embodiments described are only a portion of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0075] See also Figure 1 , Figure 1 A method flow chart of a method for indexing water depth data of inbound and outbound channels provided in an embodiment of the present application includes:

[0076] Step S101: Acquire measurement data.

[0077] The waterway in and out of the port relies on surveying projects to collect and update water depth data. Surveying data includes surveying project metadata and water depth measurement data. Surveying project metadata includes project time, type, and content. Project time includes pre-dredging and post-dredging survey times.

[0078] It should be noted that raw bathymetric data may have inconsistent data sources, inconsistent data standards, and significant differences in data structure. Before acquiring the data, it is necessary to standardize the raw bathymetric data, convert it into a unified data format, and use coordinate transformation to unify it into a standardized coordinate system. Data quality issues such as missing values ​​and noise points should also be checked and corrected. The results of data preprocessing are measurement project metadata and bathymetric data.

[0079] Step S102: constructing multi-level grid data based on the water depth measurement data.

[0080] The construction of spatiotemporal index of water depth data of inbound and outbound channels needs to comprehensively consider the multi-scale characteristics of water depth data, the geometric characteristics of the channel, and the needs of channel maintenance and management and safe navigation of ships, so as to achieve efficient data management and query.

[0081] Therefore, in response to the retrieval needs of multi-scale water depth data, the inlet and outlet channels are spatially divided, and a multi-level grid data of channel-section-segment is established, which is conducive to the subsequent design of corresponding data indexing strategies for different spatial scales.

[0082] Multi-level grid data includes channel data, channel data includes segment data, and segment data includes segment data. Multi-level grids are the main carrier of spatiotemporal indexing, and contain grids at three levels: channel, segment, and segment. The grid division of channels and segments is determined based on the spatial morphology of the channels and data application requirements, and the segments can be further divided into multiple levels of grids based on actual conditions. Grid classification affects the smoothness of data transitions. The more levels there are, the smoother the transition, but the indexing efficiency may decrease. The grid size affects the level of sophistication of water depth data management, and its determination needs to consider the measurement accuracy and application accuracy requirements of water depth data. In the absence of functional changes in the inlet and outlet channels, the stability of the multi-level grid should be maintained as much as possible to ensure the accuracy of data indexing.

[0083] See also Figure 2To ensure full coverage of the navigable area, this embodiment uses the spatial extent of waterways as the basic division unit and constructs an R+ tree-based spatial index to store the minimum bounding rectangle and attribute information of the waterways. Due to the limited number of waterways, the impact of R+ tree imbalance caused by uneven spatial distribution of waterways on query performance can be ignored.

[0084] Based on the channel index and incorporating geometric features, the channel is divided into multiple segments based on mileage intervals. Mileage-specific naming conventions are defined for each segment to ensure unique mileage. An R+ tree-based spatial index is constructed, associating attributes such as mileage intervals and associated channels to facilitate spatial queries between adjacent locations or at different scales. For complex or wide channels, the channel centerline is used as the boundary when dividing the segments to ensure consistency with actual channel management practices.

[0085] Each route segment is further divided into multiple levels of grids, using the channel centerline as the horizontal reference and the perpendicular to the channel center as the vertical reference. The level and size of the grid divisions depend on the area of ​​the route segment. A top-down quad-tree index is established on the multi-level grids to enable fast data query and update.

[0086] In this way, corresponding management strategies can be adopted for water depth data of different scales:

[0087] For the channel scale, the spatial distribution and attribute information of the channel are organized and managed based on the R+ tree; for the segment scale, considering the narrow and long geometric characteristics of the inlet and outlet channels, the channel is divided into multiple segments along the channel centerline according to the channel mileage, and naming rules with spatial location semantics are defined for the segments, and a spatial index based on the R+ tree is constructed; for the segment scale, multi-level grids are further divided, and the quadtree index is used to achieve efficient retrieval of spatial data in cross-scale scenarios.

[0088] Step S103: Associating the measurement data with the multi-level grid data to obtain a multi-level spatiotemporal index.

[0089] The multi-level spatiotemporal index includes spatial index and temporal index.

[0090] See also Figure 3 ,The multi-level spatiotemporal index based on spatial division adopts a bottom-up ,construction approach. Based on the survey engineering, the spatial ,indexes of water depth, grid, section, and channel are constructed in sequence, and ,associated with the time index.

[0091] Specifically, for multi-temporal bathymetric data, the bathymetric data are associated with the engineering data using the engineering time as a temporal reference, and the bathymetric data and multi-level grids are associated with the engineering scope as a spatial reference.

[0092] On the time scale, based on the multi-temporal measurement project, with the measurement time as the time reference and the project scope as the spatial reference, the spatiotemporal association between the project scope and the multi-level grid is established through spatial relationship judgment, and a chain time index of "project information-project scope-multi-level grid-water depth point" based on the time version is constructed. This chain structure takes the project attributes as the starting point and sequentially associates the project scope, multi-level grid, and water depth point. It can not only accurately reflect the temporal changes of water depth data, but also efficiently integrate data of different spatial scales.

[0093] In combination with the actual characteristics of waterway maintenance projects, engineering attributes are integrated on the basis of spatial index, and the association between engineering water depth data and multi-level spatial index is established, thereby forming a multi-level spatiotemporal index mechanism based on spatial division.

[0094] Step S104: Query the multi-level spatiotemporal index for the measurement data corresponding to the target query request as the query result.

[0095] The target query request is used to specify the data scope of this query. Since the multi-level spatiotemporal index includes both time and space dimensions, the target query request can selectively query from these dimensions. From the time dimension, it is necessary to specify the start time, end time, or other specific time period; from the spatial dimension, considering that the multi-level spatiotemporal index includes a multi-layer grid of channels, sections, and segments, the data scale can be limited in the target query request, making the measurement data retrieved from the multi-level spatiotemporal index more accurate.

[0096] In summary, this embodiment uses the above-mentioned method to realize spatial indexing based on multi-level grids and temporal indexing based on the time of the surveying project, and establishes the spatiotemporal association between the multi-level grids and the multi-phase surveying project with the help of the surveying project scope, forming a multi-level spatiotemporal indexing mechanism based on spatial division, which helps to adapt to the long strip-like irregular distribution characteristics of the inbound and outbound waterways. This mechanism uses the surveying project as the data processing unit and the engineering metadata and water depth data as the input data. While conforming to the characteristics of the inbound and outbound waterway data, it also meets the actual needs of waterway maintenance and safety management. At the same time, the multi-level spatiotemporal index can cope with the challenges brought by higher data density, more frequent data updates and more complex query scenarios.

[0097] In the embodiment of the present application, the above Figure 1 There are many possible implementations of the steps, which are described below. It should be noted that the implementations given in the following description are only for illustrative purposes and do not represent all implementations of the embodiments of the present application.

[0098] See also Figure 4 , which is another method flow chart of the method for indexing water depth data of inbound and outbound channels provided in an embodiment of the present application, including:

[0099] Step S401: Acquire measurement data.

[0100] Step S402: construct multi-level grid data based on the water depth measurement data.

[0101] Step S403: Associating the measurement data with the multi-level grid data to obtain a multi-level spatiotemporal index.

[0102] Spatial index construction aims to establish a relationship between multi-level grid data and the sounding points in the bathymetry data. Traditional methods achieve this by traversing the sounding point data and calculating the spatial relationship between the grid and the sounding data level by level. However, this approach suffers from high computational complexity and low efficiency. To improve construction efficiency, the following two strategies can be adopted:

[0103] The first is to reduce the amount of data involved in the calculation, and use the maximum edge length constraint method to calculate the concave hull polygon of the water depth measurement data, that is, the actual measurement range. Then, the actual measurement range is superimposed and analyzed with the maximum level grid to obtain the intersecting grid, and then the intersecting grid is traversed to index the water depth data.

[0104] Second, grid-association calculations replace grid-spatial relationship calculations. Instead of performing calculations on all levels of grids, the chain-like relationships of the grids are used to sequentially derive information on the corresponding segments, sections, and access channels. Based on the grid index, the perpendicular distance from the water depth point to the centerline of the access channel, as well as the mileage of the perpendicular point within the section, is calculated.

[0105] The time index's time information is derived from the survey project time. This allows for rapid batch updates through the association between project metadata and water depth data. Waterway engineering projects are conducted through conventional surveying and dredging surveying. Dredging surveying includes data from before and after dredging, which are close in time. If time accuracy is not critical, the post-dredging time is used as the basis for the time index.

[0106] Specifically, as a possible implementation, step S403 may include step S4031 and step S4032:

[0107] Step S4031: Associating the water depth measurement data with the multi-level grid data to obtain a spatial index.

[0108] Step S4032: Associating the time data in the measurement engineering metadata with the spatial index to obtain the time index.

[0109] In this way, spatial indexing is performed first, and then time is associated, and a multi-level spatiotemporal index is constructed layer by layer.

[0110] As a possible implementation, step S4031 may correspond to the two strategies mentioned above.

[0111] When corresponding to the first strategy, step S4031 may include steps S40311 to S40314:

[0112] Step S40311: Obtain the maximum side length of the measurement grid of the water depth measurement data.

[0113] Analyze the distribution of bathymetric data to understand the average distance between data points and the density of the data. Determine an appropriate maximum side length parameter based on the data density and measurement accuracy requirements. This value determines the compactness of the concave hull polygon. If it is too large, the hull may be too rough and may not accurately reflect the actual measurement range. If it is too small, the calculation process may be overly complex.

[0114] Step S40312: Construct a concave hull polygon of the water depth measurement data according to the maximum side length of the measurement grid.

[0115] The concave hull polygon can be constructed by first constructing a Delaunay triangulation based on the maximum edge length constraint, and then traversing all triangles to check whether their three edges all meet the maximum edge length constraint. If a side of a triangle exceeds the maximum edge length, the triangle is marked as a non-compliant triangle. All triangles marked as non-compliant are removed from the triangulation. After removing the non-compliant triangles, the remaining triangles will form one or more closed areas. The boundaries of these areas are the boundaries of the concave hull polygon. By traversing the edges of the triangles, those edges that belong to only one triangle are found. These edges are the boundary edges of the concave hull polygon, and their endpoints are the boundary points. The extracted boundary points and boundary edges are connected in sequence to form one or more closed polygons, which are the desired concave hull polygons. These concave hull polygons can tightly wrap the water depth measurement data points and accurately reflect the actual measurement range.

[0116] Step S40313: Acquire multi-level grid data intersecting with the concave hull polygon as target grid data.

[0117] The calculated concave hull polygon is overlaid with the maximum-level grid to determine which grids intersect the actual measurement range. By determining spatial relationships, grids intersecting the concave hull polygon are identified. These intersecting grids are the grids containing the bathymetric data, and subsequent operations such as bathymetric data indexing are performed based on these grids.

[0118] Step S40314: Associating the water depth measurement data with the target grid data to obtain a spatial index.

[0119] The grids that intersect the concave hull polygon are traversed to extract the water depth data points within these grids. The traversed water depth data points are indexed with the corresponding grids to quickly locate and retrieve the relevant water depth data in subsequent queries and analysis.

[0120] For the second strategy, step S4031 may include steps S40315 to S40316:

[0121] Step S40315: Associating the water depth measurement data with the segmented data.

[0122] Step S40316: Synchronize the water depth measurement data associated with the segmented data to the segment data and channel data in sequence.

[0123] The above two strategies can improve the efficiency of building multi-level spatiotemporal indexes and reduce the amount of calculation.

[0124] As a possible implementation, step S4032 may include: based on the correspondence between the surveying engineering metadata and the water depth measurement data, marking the project time in the surveying engineering metadata to the spatially indexed surveying data as a time index.

[0125] In summary, steps 401 to 403 implement the construction of a multi-level spatiotemporal index, and subsequent query operations can be performed on the multi-level spatiotemporal index.

[0126] Step S404: Obtain a target query request.

[0127] The target query request consists of a query type and a data scale. Multi-level grid queries can flexibly meet query requirements at different data scales. When querying, the appropriate data scale must be selected based on the application scenario. The data scale reflects the level of detail and resolution of the data and is closely related to the display scale. Different display scales correspond to different levels of the multi-level grid. After determining the grid level, the query type is used, combined with the grid number, spatial range, or project number, to select the set of grids to be queried.

[0128] Query ;

[0129] Among them, R st is the query result; Q st is a spatiotemporal query function, which is used to represent that multiple parameters can be combined arbitrarily. At the same time, the value of each parameter can be empty; Geo is the query space range; T start is the query start time; T endis the query end time; GridSet is the query grid set; ProjSet is the query project set; ResType is the output format of the query result, which can be a grid set, a water depth point set, or both types of data sets.

[0130] Because each grid contains multiple parameters, querying grid sets and combining different grid parameters allows for diverse query operations based on multi-level grids. This flexibility enables the system to adapt to diverse and complex data retrieval requirements, thereby improving the efficiency and accuracy of data management and query.

[0131] Querying a Grid Collection ;

[0132] Grid1 is the first grid, Grid2 is the second grid, Grid n is the nth grid, where n is a positive integer.

[0133] The i-th grid ;

[0134] Among them, Level i For the grid level, Id i is the grid number, Geo i is the grid range, Attrs i is the grid attribute, i is a positive integer.

[0135] Query project collection ;

[0136] Proj1 is the first project, Proj2 is the second project, Proj k is the kth project, where k is a positive integer.

[0137] The jth project ;

[0138] Among them, Id j is the project number, Geo j is the scope of the project, T j For engineering time, Attrs j is the engineering attribute, and j is a positive integer.

[0139] Through the query formula, query requirements at different levels of time and space can be integrated, and according to the query formula, the measurement data of the area in the query formula can be matched.

[0140] Step S405: According to the query formula, the measurement data corresponding to the data scale is searched from the multi-level spatiotemporal index as the query result.

[0141] For the query type's corresponding set of water depth points within the grid, thinning methods can be combined to select the most representative water depth data, achieving efficient and accurate queries. Thinning rules can be tailored to the specific situation, such as center point distance proximity, depth peak, and random selection. These methods convert spatial relationship calculations into standard SQL queries, which can be implemented through nested queries, thereby improving query efficiency. Figure 5 A schematic diagram of a sparse query using the center point distance proximity method is shown. Triangles represent points filtered based on the time-recentness principle, crosses represent points eliminated by the sparseness algorithm, and circles represent the final query results.

[0142] For different query spatial ranges, the measurement data corresponding to the data scale queried from the multi-level spatiotemporal index are also different.

[0143] On the one hand, from the perspective of spatial query, it can be divided into point query, line query and surface query.

[0144] Among them, point query is to interpolate the water depth value of any point from discrete water depth points.

[0145] The conventional method is to use underwater terrain models to achieve this, but the query efficiency is poor.

[0146] This implementation improves efficiency by using a multi-level grid. First, a specific level of grid is queried based on data accuracy requirements, and then data extraction and analysis are performed based on this grid. When the query accuracy requirement is low, the mean or maximum value of the water depth data within the grid is directly taken. When the query accuracy requirement is high, an underwater terrain model is first constructed for the grid's water depth point set, and then the water depth value of the target point is calculated using inverse distance interpolation.

[0147] In this way, on the one hand, by controlling the data accuracy, the amount of data involved in the calculation is indirectly controlled; on the other hand, spatial queries and interpolation operations are converted into data statistical calculations, which significantly improves query efficiency.

[0148] Line query is to query the water depth change curve along the line from discrete water depth points.

[0149] In this embodiment, the query line is first discretized into a set of multiple points, and then a point query calculation is performed on each point. Finally, the water depth variation curve along the line is obtained by fitting the water depth value of each point.

[0150] The polygon query is used to retrieve the water depth data set within the polygon range.

[0151] Conventional methods rely on spatial inclusion analysis, which is less efficient.

[0152] This embodiment first determines the multi-level grid set of the specified level that intersects with the query polygon according to the data accuracy requirement, and then obtains the corresponding water depth point set through an associated query.

[0153] In this way, complex spatial queries are simplified into grid queries and relational queries, thereby effectively improving query efficiency.

[0154] As a possible implementation, the above step S405 may include steps S4051 to S4053:

[0155] Step S4051: In response to the query space range being a point, the query space range is used as the target point, and the target point query operation of steps a to b is performed:

[0156] Step a: In response to the data scale being lower than a preset accuracy value, the mean or maximum value of the measurement data in the multi-level grid data corresponding to the target point is used as the query result.

[0157] The preset accuracy value can be set according to needs and is not limited here.

[0158] Step b: In response to the data scale being no less than a preset accuracy value, an underwater terrain model is constructed based on the measurement data in the multi-level grid data corresponding to the target point; based on the underwater terrain model, the water depth value of the target point is calculated using the inverse distance interpolation method as the query result.

[0159] Optionally, in step b, constructing the underwater terrain model based on the measurement data in the multi-level grid data corresponding to the target point may include the following steps ① to ③:

[0160] Step ①: Perform Delaunay triangulation on the measurement data in the multi-level grid data corresponding to the target point to construct the initial triangulated network.

[0161] The initial triangulation consists of multiple triangles. Delaunay triangulation is often used to construct underwater irregular triangulation. Unlike wide waters, the channel in and out of the port is narrow and long. Its entrance, turning point and port connection are mostly curved, resulting in irregular strip distribution of water depth measurement data. Direct application of Delaunay triangulation is prone to produce outside the edge. Figure 6 Redundant triangles. Constrained Delaunay triangulation is a triangulation method that introduces constraints on the basis of Delaunay triangulation. It can effectively retain the boundary information and geometric features of the original data by forcing specific line segments (such as boundary lines, feature lines, etc.) as the edges of the triangulation, avoiding the appearance of unreasonable triangles, and thus generating Figure 7 The triangulated network shown is more consistent with the actual terrain characteristics.

[0162] Step ②: Generate the underwater terrain boundary line corresponding to the target point using the adaptive boundary extraction method based on the alpha shape algorithm.

[0163] Alpha shape is a classic point set contour extraction algorithm. It introduces an adjustable parameter α based on the construction of the convex hull of an unordered point set to control the accuracy of the contour information. Its construction process can be vividly understood as: a ball with a radius of α rolls on the surface of the point set, and the points touched by the ball are connected to form a surface. This mechanism is similar to Figure 8 The following example sets a length limit for the polygon's edges. A larger α value results in a more convex boundary, while a smaller α value results in a more concave boundary at certain locations.

[0164] Determining the parameter α is crucial for accurate boundary extraction. Excessively large α affects boundary accuracy, while too small a parameter cannot guarantee boundary integrity. The value of α is closely related to the distribution density of the bathymetric data and query accuracy. The distribution density depends on the accuracy of the bathymetric measurements, and the maximum valid value of α corresponds to the distance between bathymetric points within the given bathymetric accuracy. Due to variations in bathymetric accuracy, the value of α must be dynamically determined based on the data. Therefore, studying the relationship between bathymetric data and parameter α and developing an adaptive dynamic α adjustment strategy are crucial.

[0165] Specifically, when the α value is 1 to 2 times the average point spacing, the resulting point set shape is both relatively complete and not overly fragmented. Given that the density of water depth points is directly related to the mapping scale used for the measurement, the range of distances between adjacent points corresponding to different scales can be estimated. The mean error limits for positioning points at different mapping scales are clearly defined. Mapping scale accuracy reflects the actual horizontal distance corresponding to a distance on the topographic map. Based on this, the actual distance limits for points at different mapping scales, i.e., the maximum point spacing, can be calculated, thereby determining the reference range for the α value.

[0166] Table 1

[0167]

[0168] Table 1 above lists the reference range of α values ​​corresponding to different mapping scales. Once the α value is determined, the α-shape algorithm can be used to generate the concave hull polygon.

[0169] Step 3: Using the centroid topological mapping method, remove the redundant triangles outside the underwater terrain boundary line from the initial triangulated network to obtain the underwater terrain model.

[0170] from Figure 9 It can be seen that redundant triangles usually have a narrow and long shape and contain sharp angles or wide obtuse angles. The calculation process of the conventional method is shown in Figure 9Above, this feature is used to identify redundant triangles by setting thresholds for triangle area, side length, and interior angle. Since the target boundary is a subset of the initial triangulation, the traversal starts from the outermost triangle of the triangulation, and the triangles that do not meet the constraints are deleted one by one, finally obtaining the required boundary.

[0171] However, this method has obvious limitations: first, all triangles need to be judged one by one, resulting in low computational efficiency; second, a fixed threshold makes it difficult to distinguish between sharp angles and wide obtuse angles, and erroneous extraction is prone to occur in areas with complex boundary shapes, thus affecting the extraction accuracy.

[0172] This embodiment uses the centroid-triangle mapping relationship to achieve redundant triangle filtering based on topological relationship based on the adaptive boundary. The calculation process is as follows: Figure 9 As shown below. First, the centroid of each triangle is calculated, and the relationship between the triangles, vertices and centroids of the triangulated network is established. This relationship is stored with the help of data caching technology to improve computational efficiency. Then, the intersection operation between the adaptive boundary extraction result and the centroid is used to quickly retrieve redundant polygons outside the boundary. Compared with conventional methods, this method innovatively transforms the analysis object from the edges of the triangulated network to the centroid of the triangulated network, retrieves the centroid set through spatial query, and then filters out redundant triangles through associative query. The entire process does not require traversing the triangulated network, effectively reducing the number of operations and significantly improving computational efficiency.

[0173] Optionally, in step b, the water depth value of the target point is calculated using the inverse distance interpolation method based on the underwater terrain model as the query result. Specifically, the following steps may be performed: Based on the location of the target point and the query requirements, determine the scope of the interpolation area. Determine the number of adjacent measured points to be considered in the interpolation calculation. Typically, several points close to the target point are selected to ensure the accuracy of the interpolation result. Select a power parameter for the inverse distance interpolation method. This parameter controls the rate at which the weight changes with distance. A larger power value increases the influence of closer points. Within the constructed Delaunay triangulation, use a spatial query to locate the closest measured points to the target point. Calculate corresponding weights based on the distance between the target point and each adjacent measured point. The weights are inversely proportional to the power of the distance, meaning that closer points have greater weights. Using the calculated weights, perform a weighted average of the water depth values ​​of the adjacent measured points to obtain the water depth value of the target point. Compare the interpolation result with the actual values ​​of points with known water depths to evaluate the accuracy and reliability of the interpolation method. Quantitative analysis can be performed by calculating error metrics (such as root mean square error). Based on the verification results, the number of neighboring points and power parameters are adjusted to optimize the interpolation process and improve the accuracy of the interpolation results. The calculated target point water depth value is integrated with other query-related information (such as the target point coordinates and the grid to which it belongs) to form a complete query result set and output the query results.

[0174] Step S4052: In response to the query space range being a line, the query space range is discretized into multiple target points; for each target point, a target point query operation is performed to obtain query results corresponding to the multiple target points; and based on the query results corresponding to the multiple target points, a query result corresponding to the query space range is obtained.

[0175] The case where the spatial range is a line is discretized into multiple target points, and then the query results of multiple target points are integrated to obtain the query results corresponding to the query spatial range.

[0176] Step S4053: In response to the query spatial range being a surface, multi-level grid data intersecting the query spatial range is obtained as query grid data; and measurement data in the query grid data is obtained as a query result.

[0177] When the spatial scope is a polygon, you can retrieve multi-level grid data that intersects the query spatial scope. Based on the intersecting multi-level grid data, extract all water depth data points within these grids. This step requires traversing the intersecting grids and collecting the water depth data within them. The extracted water depth data points are filtered and sorted to remove duplicate or invalid data, and data format conversion or additional calculations are performed as needed.

[0178] The filtering and sorting process can be combined with the above-mentioned sparseness method to screen the most representative water depth data and achieve efficient and accurate query.

[0179] On the other hand, from the perspective of time query, time attributes are usually stored in survey projects, grids, and depth points.

[0180] When processing large datasets, query efficiency using a single time dimension can significantly decrease. Therefore, nested queries are often combined with other query conditions. For specific application requirements, it's important to comprehensively consider data accuracy, data content, and query efficiency to select the appropriate query dimension and fusion strategy.

[0181] The survey engineering dimension is suitable for acquiring large-scale, long-term engineering data, such as cross-year channel evolution analysis or comparison of the effects of multiple dredging projects. Survey engineering projects within a specific time period can be queried using measurement time, and the corresponding water depth point sets can be retrieved by association. The grid cell dimension is suitable for analyzing changes in a specific area over a certain period of time, such as regional sedimentation monitoring. Grid time can be used to query grid sets that have updated data within a certain time period, and the corresponding water depth point sets can be retrieved by association. The water depth point dimension is suitable for precise query and analysis, allowing direct query of water depth data within a specific time period or time slice.

[0182] On the other hand, from the perspective of comprehensive query, the query method of this embodiment can flexibly meet the query requirements of different data scales, and combined with the sparsification rule, more representative query results can be obtained.

[0183] In summary, this embodiment addresses the spatiotemporal characteristics of water depth data in port access channels, such as their long, dense distribution and frequent updates. A multi-level spatiotemporal indexing method based on spatial partitioning was designed. This method constructs a multi-level spatial index of channel, segment, and segment, enabling efficient organization of massive water depth data. Based on this multi-level spatiotemporal index, a spatiotemporal query method combining time, space, and grids was developed to meet the diverse data query requirements of port access channels. Furthermore, a method for constructing underwater terrain that integrates α-shape and constrained Delaunay triangulation (CDT) was proposed, enabling the construction of underwater terrain models that conform to the characteristics of port access channels. This method effectively organizes and efficiently retrieves water depth data for port access channels, providing technical support for operational applications and data mining of water depth data, and facilitating preventive maintenance and safety management of waterways.

[0184] To illustrate the efficiency of this embodiment, a specific case is provided below:

[0185] Ships in a certain port area enter the harbor through a 400,000-ton deep-water channel, and then reach the various port operating areas through the harbor's internal channels. The total length of the channel is about 57.8 kilometers, with significant long channel characteristics. This area is a muddy coast located in the open sea, with significant wave action, resulting in more serious channel siltation. Although the port area adopts an embracing breakwater design to reduce internal siltation, the tidal movement near the mouth has become more complicated. In order to accurately grasp the underwater topography and its changes, the waterway management department conducts regular comprehensive measurements and conducts supplementary measurements from time to time according to actual navigation and maintenance needs. Regular measurements are carried out on a quarterly basis, and the entire channel is covered by multiple measurements of different areas; while supplementary measurements are targeted at specific areas, and their measurement range and frequency are more flexible, with no obvious pattern.

[0186] This example is based on bathymetric data from a study area over a specific time period. Regular measurements primarily target the main channel, using single-beam bathymetry technology. The measurement cycle is quarterly, and the measurement range is relatively fixed. Supplementary measurements are primarily concentrated in harbors, berths, and some waterway areas, primarily focusing on dredging measurements. Data output is stored in as-built documentation by project, in formats including CAD, XYH files, and PDF. These data suffer from issues such as inconsistent formats, inconsistent spatial references, mismatched measurement scales, and overlapping data ranges. To address data inconsistencies, this example first preprocesses the data, unifying the data format into a standard XYH file and unifying the coordinate system. CGCS2000 (China Geodetic Coordinate System 2000) is used as the plane control, and the local theoretical lowest tide level is used as the elevation control benchmark, thereby ensuring data consistency and accuracy. Figure 10 The statistics of surveying works in the study area after data processing are shown.

[0187] The storage of massive amounts of data is a prerequisite for achieving unified management of water depth data. Compared to directly storing data in the database, building a spatiotemporal index will sacrifice some data storage efficiency but significantly improve the efficiency of subsequent data queries.

[0188] In order to evaluate the efficiency of constructing a multi-level spatiotemporal index based on spatial partitioning, the efficiency of data entry based on relational databases and spatial databases was compared.

[0189] The experiment uses conventional surveying projects as processing units and tests the impact of spatiotemporal index construction on storage efficiency under different data scales and different grid division accuracies. Figure 11 Experimental results are presented for six scenarios at different data scales: relational databases, spatial databases, 1:2k grids, 1:5k grids, 1:10k grids, and 1:20k grids. These experiments cover 96 conventional survey projects with a total of 360,609 water depth points. The statistical metric is the sum of the time required to read data, build the index, and store data, measured in milliseconds. It can be seen that:

[0190] 1) For single-beam measurement projects, the data size processed in a single run is small (less than 6,000 points), and the overall time required to build the index is short (less than 5 seconds), which meets the actual business requirements of water depth data management.

[0191] 2) As data size increases, the time required to build an index increases linearly. The order of increase is, from highest to lowest, 1:2k grid resolution, 1:5k grid resolution, 1:10k grid resolution, 1:20k grid resolution, spatial databases, and relational databases. The time required to build a multi-level spatiotemporal index exceeds the time required to store data in spatial and relational databases, and the finer the grid resolution, the longer the index construction time.

[0192] 3) The index construction time for 1:10k and 1:20k grids is similar, and the growth trend is similar. This is mainly due to the fact that the mapping scale of the study area is mostly 1:2000 or 1:5000. At this larger spatial scale, the spatial distribution characteristics of the data are relatively small.

[0193] 4) The index construction time is generally stable. When the grid accuracy is 1:2k, the index construction time fluctuates slightly. This may be due to the fluctuation of system resource usage caused by changes in the complexity of data spatial distribution or increased computational workload.

[0194] Experimental results show that while index construction has a certain impact on data storage efficiency, given that data storage is performed on a project-by-project basis, with a short overall time consumption and high storage frequency, its significant improvement in query and analysis efficiency makes this impact negligible in practical applications. Furthermore, multi-level gridding must be considered in conjunction with data distribution and application scale. High-precision grids facilitate capturing details, while coarse-precision grids reduce computational overhead. By properly adjusting the scale, a balance can be achieved between construction efficiency and application effectiveness.

[0195] Furthermore, to validate the effectiveness of multi-level spatiotemporal indexing based on spatial partitioning in spatiotemporal fusion queries and underwater terrain construction, representative application scenarios for port entry and exit channels were selected, including shallow point analysis, dredging analysis, profile analysis, and evolution analysis. Through in-depth discussion of these specific application scenarios, the specific implementation methods of multi-level spatiotemporal indexing were analyzed in detail, and compared with traditional methods based on relational and spatial databases, thereby comprehensively evaluating the advantages and effectiveness of multi-level spatiotemporal indexing in practical applications.

[0196] In the maritime field, shallow water points refer to areas where the water depth is lower than a certain value, especially those shallow points that do not meet the channel design requirements or are lower than the ship's draft. These areas are the focus of attention for the safety of navigation in and out of the port. The relational database-based method uses SQL statements to filter out shallow water points according to the set water depth value conditions, and has high retrieval efficiency during global analysis. However, due to the differences in the judgment criteria for shallow water points in different areas of the channel, relying solely on relational databases is difficult to effectively handle complex spatial relationships. The spatial database-based method, with the help of spatial relationship judgment, can efficiently retrieve water depth points in a specific area, but when faced with massive water depth data, the query efficiency will be significantly reduced.

[0197] The method proposed in this embodiment effectively improves the efficiency of shallow point analysis by converting relational queries and spatial operations on water depth points into calculations on multi-level grids. Specifically, based on the distribution characteristics of inbound and outbound waterways, regional query requirements are converted into queries for shallow point analysis units (i.e., waterways, sections, and multi-level grids). First, a set of multi-level grids at a specified level that intersects the target polygon is queried. Next, SQL queries are used to further filter by grid water depth value range to obtain a set of grids that meet the criteria. Finally, nested SQL statements are used to implement shallow point analysis for the water depth point set within each grid. Figure 12 The water depth and shallow points obtained by analyzing the method of this embodiment are displayed. The extraction results include data such as the shallow point location, the grid to which it belongs, and depth information.

[0198] Profile analysis is used to determine water depth variations along a specified profile line. It is widely used in waterway maintenance and can be categorized by profile type into transverse, longitudinal, and arbitrary profiles. Longitudinal profiles are used to observe longitudinal variations in underwater topography along the channel centerline, while transverse profiles are used to observe lateral variations perpendicular to the channel centerline. Profile analysis is typically performed by discretizing the profile line and simulating water depth variations along the profile line using continuous sampling points. Profile analysis methods based on relational databases perform this analysis by querying water depth data points within a certain coordinate range around the sampling points. This method is simple, flexible, and efficient, but lacks a mechanism for handling the continuity of underwater topography, resulting in low query accuracy. Profile analysis methods based on spatial databases use spatial interpolation to obtain water depth values ​​at any location, thereby achieving profile analysis. This method offers high query accuracy, but query efficiency decreases significantly when the data volume is large or the interpolation algorithm is complex.

[0199] The method proposed in this embodiment first performs a buffer analysis on the section line to generate a buffer polygon, which is used as the input parameter Geo of the query formula, and combined with T start 、T end, ProjSet and other parameters are used to accurately obtain the set of water depth points around the profile line, ensuring that the number of input discrete water depth point sets is limited and valid; then, the sampling points and the output water depth point set are spatially interpolated to obtain the water depth value of each sampling point; finally, the data of each sampling point are combined and fitted into a continuous profile line. Figure 13 The results of the channel cross-section analysis are presented.

[0200] Dredging analysis compares water depth data before and after dredging to understand water depth distribution and dredging workload, providing decision support for waterway maintenance projects. Triangulated irregular networks (TRIANs) are often used for dredging analysis due to their efficient storage, simple structure, and ability to accurately represent terrain features. However, methods based on relational databases lack spatial data management capabilities, making dredging analysis difficult. Spatial database-based methods, while highly accurate, construct uniform grids or irregular triangulated networks for dredging analysis. However, these methods are inefficient when dealing with large data volumes and struggle to dynamically thin data to meet accuracy requirements.

[0201] The method proposed in this embodiment combines a multi-level grid with an irregular triangulated network (ITN) to implement dredging analysis. First, an adaptive boundary extraction method based on the alpha shape algorithm is used to generate the pre- and post-dredging data ranges, and the intersection region is obtained by intersecting. Next, an appropriate grid level is selected based on accuracy requirements. The intersection region is used as the input parameter Geo and the grid level as the input parameter ProjSet. A query is used to retrieve the pre- and post-dredging depth data points within the intersection region. Finally, an irregular triangulated network (ITN) for pre- and post-dredging depth data is constructed using a redundant triangulated network filtering method based on centroid topological mapping. Finally, an overlay analysis is performed to calculate water depth distribution, dredging depth, and volume. Figure 14 The calculation process of dredging analysis is demonstrated.

[0202] Dredging and silting alter channel topography, making understanding its evolution crucial for channel maintenance. Channel evolution analysis, based on multi-period bathymetric data, includes topographic change analysis and erosion and deposition change analysis. Topographic change analysis focuses on the spatial distribution and historical changes of underwater topography, while erosion and deposition change analysis focuses on the distribution patterns and trends of erosion and deposition. While the implementation logic of evolution analysis is largely similar to that of dredging analysis, it faces the challenge of larger data volumes, making it difficult for relational and spatial database-based approaches to meet the requirements for efficiency and accuracy.

[0203] The method proposed in this embodiment realizes evolution analysis by using irregular triangulated network based on multi-level grid. First, according to the time range parameter (T start 、T end) and analysis area (Geo) to filter out the surveying project set (ProjSet), select the appropriate grid level (Level) according to the analysis area (Geo) and data accuracy, and use the query method to obtain the multi-level grid set and water depth data set; then, construct a multi-period water depth measurement data set based on time series according to the measurement time; finally, generate an irregular triangulated network according to a unified scale, and perform superposition analysis on the multi-period water depth measurement data based on the irregular triangulated network to calculate the water depth distribution, dredging depth, dredging volume and other information. Figure 15 The results of the waterway evolution analysis are presented.

[0204] In summary, this example proposes a method for constructing a spatiotemporal index of water depth for inbound and outbound channels based on a multi-level grid, integrating α-shape and constrained Delaunay triangulation for underwater terrain model construction. Experimental verification yields the following main conclusions:

[0205] Spatiotemporal Index Construction: The multi-level spatiotemporal index constructed efficiently organizes and manages water depth data for inbound and outbound channels, significantly improving data query efficiency. Experiments show that this method performs well across various data sizes, with a particularly significant advantage when processing large datasets.

[0206] Underwater terrain construction: The underwater terrain construction method that integrates α-shape and constrained Delaunay triangulation can effectively generate underwater terrain models that conform to the characteristics of the port entrance and exit channels, avoiding the problem of redundant triangles in traditional methods and improving the accuracy and practicality of the model.

[0207] Application Scenario Verification: The proposed method has been validated in practical applications using typical scenarios such as shallow point analysis, dredging analysis, profile analysis, and evolution analysis. This method can provide a scientific basis for preventive waterway maintenance and safety management, supporting relevant decision-making and management activities.

[0208] The above are some specific implementations of the method provided in the embodiment of the present application. Based on this, the present application also provides a corresponding device. The device provided in the embodiment of the present application will be introduced from the perspective of functional modularization.

[0209] See also Figure 16 The structure diagram of the device for indexing water depth data of inbound and outbound channels is shown, and the device includes an acquisition module 1701, a network construction module 1702 and an index construction module 1703.

[0210] The acquisition module 1701 is used to acquire measurement data, including measurement engineering metadata and water depth measurement data;

[0211] A network construction module 1702 is configured to construct multi-level grid data based on the bathymetric data, wherein the multi-level grid data includes waterway data, the waterway data includes segment data, and the segment data includes segment data;

[0212] An index building module 1703 is configured to associate the measurement data with the multi-level grid data to obtain a multi-level spatiotemporal index, wherein the multi-level spatiotemporal index includes a spatial index and a temporal index;

[0213] The query module 1704 is configured to query the multi-level spatiotemporal index for measurement data corresponding to the target query request as a query result.

[0214] As a possible implementation manner, the device further includes:

[0215] A query formula acquisition module, configured to acquire a target query request, wherein the target query request includes a query formula and a data scale;

[0216] The query is ;

[0217] R st is the query result, Q st is the spatiotemporal query function, Geo is the query space range, T start is the query start time, T end is the query end time, GridSet is the query grid set, ProjSet is the query project set, and ResType is the output format of the query result;

[0218] Wherein, the query grid set ;

[0219] Grid1 is the first grid, Grid2 is the second grid, Grid n is the nth grid, where n is a positive integer;

[0220] The i-th grid ;

[0221] Level i For the grid level, Id i is the grid number, Geo i is the grid range, Attrs i is the grid attribute, i is a positive integer;

[0222] Query project collection ;

[0223] Proj1 is the first project, Proj2 is the second project, Proj k is the k-th project, k is a positive integer;

[0224] The jth project ;

[0225] Id j is the project number, Geo j is the scope of the project, T j For engineering time, Attrs j is the engineering attribute, j is a positive integer;

[0226] A query module is used to query the measurement data corresponding to the data scale from the multi-level spatiotemporal index according to the query formula, as a query result.

[0227] Optionally, the query module includes:

[0228] The point query unit is configured to, in response to the query spatial range being a point, use the query spatial range as a target point and perform the following target point query operations: in response to the data scale being lower than a preset precision value, use the mean or maximum value of the measurement data in the multi-level grid data corresponding to the target point as a query result; in response to the data scale being not lower than the preset precision value, construct an underwater terrain model based on the measurement data in the multi-level grid data corresponding to the target point; and based on the underwater terrain model, calculate the water depth value of the target point using an inverse distance interpolation method as a query result;

[0229] a line query unit configured to, in response to the query space range being a line, discretize the query space range into a plurality of target points; perform the target point query operation on each target point to obtain query results corresponding to the plurality of target points; and obtain a query result corresponding to the query space range based on the query results corresponding to the plurality of target points;

[0230] The surface query unit is used for obtaining multi-level grid data intersecting with the query space range as query grid data in response to the query space range being a surface; and obtaining measurement data in the query grid data as query results.

[0231] Optionally, the point query unit includes:

[0232] A triangulation component is used to perform Delaunay triangulation on the measurement data in the multi-level grid data corresponding to the target point to construct an initial triangulation network, wherein the initial triangulation network includes a plurality of triangles;

[0233] A boundary setting component is used to generate an underwater terrain boundary line corresponding to the target point using an adaptive boundary extraction method based on an alpha shape algorithm;

[0234] The redundancy removal component is used to remove redundant triangles outside the underwater terrain boundary line from the initial triangulation network by using a centroid topological mapping method to obtain an underwater terrain model.

[0235] Optionally, the multi-level spatiotemporal index includes a spatial index and a temporal index, and the index construction module 1703 includes:

[0236] A spatial construction unit, configured to associate the bathymetric data with the multi-level grid data to obtain a spatial index;

[0237] The time construction unit is used to associate the time data in the measurement engineering metadata with the spatial index to obtain a time index.

[0238] Optionally, the space construction unit includes:

[0239] a positioning construction component for obtaining the maximum side length of a measurement grid of the water depth measurement data; constructing a concave hull polygon of the water depth measurement data based on the maximum side length of the measurement grid; obtaining multi-level grid data intersecting with the concave hull polygon as target grid data; and associating the water depth measurement data with the target grid data to obtain a spatial index;

[0240] Components are constructed layer by layer to associate the water depth measurement data with the segmented data; and the water depth measurement data associated with the segmented data is synchronized with the segment data and the channel data in sequence.

[0241] Optionally, the time construction unit includes:

[0242] The time construction component is used to mark the engineering time in the surveying engineering metadata to the surveying data of the spatial index as a time index according to the corresponding relationship between the surveying engineering metadata and the water depth measurement data.

[0243] The embodiments of the present application also provide corresponding devices and computer storage media for implementing the solutions provided by the embodiments of the present application.

[0244] The device includes a memory and a processor, the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device executes the method for indexing water depth data of the port entry and exit channel described in any embodiment of the present application.

[0245] The computer storage medium stores a code. When the code is executed, the device executing the code implements the method for indexing water depth data of the port entry and exit channels described in any embodiment of the present application.

[0246] The “first” and “second” in the names such as “first” and “second” (if any) mentioned in the embodiments of this application are only used as name identifiers and do not mean the first or second in order.

[0247] Through the description of the above embodiments, it can be seen that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment methods can be implemented by means of software plus a general hardware platform. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as a read-only memory (ROM) / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in various embodiments or certain parts of the embodiments of the present application.

[0248] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple. For the relevant parts, refer to the partial description of the method embodiment. Some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment. Those of ordinary skill in the art can understand and implement it without paying any creative work.

[0249] The above description is merely an exemplary embodiment of the present application and is not intended to limit the scope of protection of the present application.

Claims

1. A method for indexing water depth data of inbound and outbound channels, characterized in that: The method comprises: Acquiring measurement data, wherein the measurement data includes measurement engineering metadata and water depth measurement data; constructing multi-level grid data based on the water depth measurement data, wherein the multi-level grid data includes channel data, the channel data includes segment data, and the segment data includes segment data; Associating the measurement data with the multi-level grid data to obtain a multi-level spatiotemporal index; Obtaining a target query request, wherein the target query request includes a query formula and a data scale; The query is ; R st is the query result, Q st is the spatiotemporal query function, Geo is the query space range, T start is the query start time, T end is the query end time, GridSet is the query grid set, ProjSet is the query project set, and ResType is the output format of the query result; Wherein, the query grid set ; Grid1 is the first grid, Grid2 is the second grid, Grid n is the nth grid, where n is a positive integer; The i-th grid ; Level i For the grid level, Id i is the grid number, Geo i is the grid range, Attrs i is the grid attribute, i is a positive integer; Query project collection ; Proj1 is the first project, Proj2 is the second project, Proj k is the k-th project, k is a positive integer; The jth project ; Id j is the project number, Geo j is the scope of the project, T j For engineering time, Attrs j is the engineering attribute, j is a positive integer; According to the query formula, querying the measurement data corresponding to the data scale from the multi-level spatiotemporal index as a query result; and The query formula is used to query the measurement data corresponding to the data scale from the multi-level spatiotemporal index, and the query result includes: In response to the query spatial range being a point, the query spatial range is used as a target point, and the following target point query operations are performed: in response to the data scale being lower than a preset precision value, the mean or maximum value of the measurement data in the multi-level grid data corresponding to the target point is used as a query result; in response to the data scale being not lower than the preset precision value, an underwater terrain model is constructed based on the measurement data in the multi-level grid data corresponding to the target point; and based on the underwater terrain model, the water depth value of the target point is calculated using an inverse distance interpolation method as a query result; In response to the query space range being a line, the query space range is discretized into a plurality of target points; for each of the target points, the target point query operation is performed to obtain query results corresponding to the plurality of target points; and based on the query results corresponding to the plurality of target points, a query result corresponding to the query space range is obtained; In response to the query spatial range being a surface, obtaining multi-level grid data intersecting the query spatial range as query grid data; obtaining measurement data in the query grid data as a query result; and The step of constructing an underwater terrain model based on the measurement data in the multi-level grid data corresponding to the target point includes: Performing Delaunay triangulation on the measurement data in the multi-level grid data corresponding to the target point to construct an initial triangulated network, wherein the initial triangulated network includes a plurality of triangles; generating an underwater terrain boundary line corresponding to the target point by using an adaptive boundary extraction method based on an alpha shape algorithm; The centroid topological mapping method is used to remove redundant triangles outside the underwater terrain boundary line from the initial triangulated network to obtain an underwater terrain model.

2. The method according to claim 1, characterized in that The multi-level spatiotemporal index includes a spatial index and a temporal index. Associating the measurement data with the multi-level grid data to obtain the multi-level spatiotemporal index includes: Associating the bathymetric data with the multi-level grid data to obtain a spatial index; The time data in the measurement engineering metadata is associated with the spatial index to obtain a time index.

3. The method according to claim 2, characterized in that The step of associating the bathymetric data with the multi-level grid data to obtain a spatial index includes: Obtaining the maximum side length of a measurement grid of the water depth measurement data; constructing a concave hull polygon of the bathymetric data according to the maximum side length of the measurement grid; Acquire multi-level grid data intersecting with the concave hull polygon as target grid data; Associating the bathymetric data with the target grid data to obtain a spatial index; Alternatively, associating the bathymetric data with the multi-level grid data to obtain a spatial index includes: associating the bathymetric data with the segmented data; The water depth measurement data associated with the segment data is synchronized with the segment data and the channel data in sequence.

4. The method according to claim 2, characterized in that The step of associating the time data in the measurement engineering metadata with the spatial index to obtain the time index includes: According to the correspondence between the surveying engineering metadata and the water depth measurement data, the engineering time in the surveying engineering metadata is annotated into the surveying data of the spatial index as a time index.

5. A device for indexing water depth data of waterways entering and leaving a port, characterized in that: The device comprises: An acquisition module, configured to acquire measurement data, wherein the measurement data includes measurement engineering metadata and water depth measurement data; A network construction module, configured to construct multi-level grid data based on the water depth measurement data, wherein the multi-level grid data includes waterway data, the waterway data includes segment data, and the segment data includes segment data; An index building module, configured to associate the measurement data with the multi-level grid data to obtain a multi-level spatiotemporal index; A query formula acquisition module, configured to acquire a target query request, wherein the target query request includes a query formula and a data scale; The query is ; Rst is the query result, Qst is the spatiotemporal query function, Geo is the query spatial range, Tstart is the query start time, Tend is the query end time, GridSet is the query grid set, ProjSet is the query project set, and ResType is the output format of the query result; Wherein, the query grid set ; Grid1 is the first grid, Grid2 is the second grid, Gridn is the nth grid, and n is a positive integer; The i-th grid ; Leveli is the grid level, Idi is the grid number, Geoi is the grid range, Attrsi is the grid attribute, and i is a positive integer; Query project collection ; Proj1 is the first project, Proj2 is the second project, Projk is the kth project, and k is a positive integer; The jth project ; Idj is the project number, Geoj is the project scope, Tj is the project time, Attrsj is the project attribute, and j is a positive integer; a query module, configured to query the measurement data corresponding to the data scale from the multi-level spatiotemporal index according to the query formula, as a query result; and The query module includes: The point query unit is configured to, in response to the query spatial range being a point, use the query spatial range as a target point and perform the following target point query operations: in response to the data scale being lower than a preset accuracy value, use the mean or maximum value of the measurement data in the multi-level grid data corresponding to the target point as a query result; in response to the data scale being not lower than the preset accuracy value, construct an underwater terrain model based on the measurement data in the multi-level grid data corresponding to the target point; and based on the underwater terrain model, calculate the water depth value of the target point using an inverse distance interpolation method as a query result; a line query unit configured to, in response to the query space range being a line, discretize the query space range into a plurality of target points; perform the target point query operation on each target point to obtain query results corresponding to the plurality of target points; and obtain a query result corresponding to the query space range based on the query results corresponding to the plurality of target points; A surface query unit is configured to, in response to the query space range being a surface, obtain multi-level grid data intersecting the query space range as query grid data; and obtain measurement data in the query grid data as a query result; and The point query unit includes: A triangulation component is used to perform Delaunay triangulation on the measurement data in the multi-level grid data corresponding to the target point to construct an initial triangulation network, wherein the initial triangulation network includes a plurality of triangles; A boundary setting component is used to generate an underwater terrain boundary line corresponding to the target point using an adaptive boundary extraction method based on an alpha shape algorithm; The redundancy removal component is used to remove redundant triangles outside the underwater terrain boundary line from the initial triangulation network by using a centroid topological mapping method to obtain an underwater terrain model.

6. A water depth data indexing device for inbound and outbound channels, characterized in that: The device includes a memory and a processor, the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device executes the method for indexing water depth data of inbound and outbound channels as described in any one of claims 1 to 4.

7. A computer storage medium, characterized in that The computer storage medium stores a code. When the code is executed, the computer storage device executing the code implements the method for indexing water depth data of an inlet and outlet channel according to any one of claims 1 to 4.

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