Harbor entry and exit channel water depth data indexing method, device, equipment and medium

Abstract

The invention provides a port entering and leaving channel water depth data indexing method, device and equipment and a medium, and the method comprises the steps: obtaining measurement data which comprises measurement engineering metadata and water depth measurement data; according to the water depth measurement data, multi-level grid data is constructed, the multi-level grid data comprises channel data, the channel data comprises leg data, and the leg data comprises segment data; associating the measurement data to the multi-level grid data to obtain a multi-level spatial-temporal index; and querying the measurement data corresponding to the target query request from the multi-level spatio-temporal index as a query result. Thus, effective organization of the water depth data of the port entering and leaving channel is achieved, meanwhile, efficient retrieval of the water depth data of the port entering and leaving channel is achieved, a technical means is provided for service application and data mining of the water depth of the channel, and the method serves preventative maintenance and safety management of the channel.

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: 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; Query the measurement data corresponding to the target query request from the multi-level spatio-temporal index as the query result.

[0007] Optionally, the querying the measurement data corresponding to the target query request from the multi-level spatio-temporal index as the query result includes: Obtain a target query request, where the target query request includes a query formula and a data scale; Among them, the query formula is ; R st is the query result, Q st is a spatio-temporal 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 form of the query result; Among them, the query grid set ; Grid1 is the first grid, Grid2 is the second grid, Grid n is the nth grid, and n is a positive integer; The ith grid ; Level i is the grid level, Id i is the grid number, Geo i is the grid range, Attrs i is the grid attribute, and i is a positive integer; The query project set ; Proj1 is the first project, Proj2 is the second project, Proj k is the kth project, and k is a positive integer; The jth project ; Id j is the project number, Geo j is the project range, T j is the project time, Attrs j is the project attribute, and j is a positive integer; Query the measurement data corresponding to the data scale from the multi-level spatio-temporal index according to the query formula as the query result.

[0008] Optionally, the querying the measurement data corresponding to the data scale from the multi-level spatio-temporal index according to the query formula as the query result includes: In response to the query space range being a point, taking the query space range as the target point, perform the following target point query operations: In response to the data scale being lower than the preset accuracy value, taking the mean or maximum value of the measurement data in the multi-level grid data corresponding to the target point as the 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; Based on the underwater terrain model, use the inverse distance interpolation method to calculate the water depth value of the target point as the query result; In response to the query space range being a line, discretize the query space range into multiple target points; For each of the target points, perform the target point query operation to obtain the query results corresponding to the multiple target points; According to the query results corresponding to the multiple target points, obtain the query result corresponding to the query space range; In response to the query space range being a surface, obtain the multi-level grid data intersecting with the query space range as the query grid data; Obtain the measurement data in the query grid data as the query result.

[0009] Optionally, the constructing an underwater terrain model based on the measurement data in the multi-level grid data corresponding to the target point includes: Perform Delaunay triangulation on the measurement data in the multi-level grid data corresponding to the target point to construct an initial triangulation network, where the initial triangulation network includes multiple triangles; Use an adaptive boundary extraction method based on the alpha shape algorithm to generate the underwater terrain boundary line corresponding to the target point; Use the centroid topological mapping method to remove the redundant triangles outside the underwater terrain boundary line from the initial triangulation network to obtain the underwater terrain model.

[0010] Optionally, the multi-level spatio-temporal index includes a spatial index and a time index. The associating the measurement data with the multi-level grid data to obtain the multi-level spatio-temporal index includes: Associate the water depth measurement data with the multi-level grid data to obtain a spatial index; Associate the time data in the measurement engineering metadata with the spatial index to obtain a time index.

[0011] Optionally, the associating the water depth measurement data with the multi-level grid data to obtain a spatial index includes: Obtain the maximum side length of the measurement grid of the water depth measurement data; Construct a concave hull polygon of the water depth measurement data according to the maximum side length of the measurement grid; Obtain the multi-level grid data intersecting with the concave hull polygon as the target grid data; Associate the water depth measurement data with the target grid data to obtain a spatial index; Alternatively, the associating the water depth measurement data with the multi-level grid data to obtain a spatial index includes: Associate the water depth measurement data with the segmented data; Synchronize the water depth measurement data associated with the segmented data to the voyage segment data and the waterway data in sequence.

[0012] Optionally, the associating the time data in the measurement project metadata with the spatial index to obtain a time index includes: According to the correspondence between the measurement project metadata and the water depth measurement data, label the project time in the measurement project metadata to the measurement data of the spatial index as the time index.

[0013] In a second aspect, an embodiment of the present application provides a device for indexing water depth data of an approach channel, including: An acquisition module, configured to acquire measurement data, where the measurement data includes measurement project metadata and water depth measurement data; A network construction module, configured to construct multi-level grid data according to the water depth measurement data, where the multi-level grid data includes waterway data, the waterway data includes voyage segment data, and the voyage segment data includes segmented data; An index construction module, configured to associate the measurement data with the multi-level grid data to obtain a multi-level spatio-temporal index; A query module, configured to query the measurement data corresponding to a target query request from the multi-level spatio-temporal index as a query result.

[0014] In a third aspect, an embodiment of the present application provides a device for indexing water depth data of an approach channel. 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 an approach channel according to any one of the foregoing first aspects.

[0015] In a fourth aspect, an embodiment of the present application provides a computer storage medium. Codes are stored in the computer storage medium. When the codes are run, the device running the codes implements the method for indexing water depth data of an approach channel according to any one of the foregoing first aspects.

[0016] The embodiments of the present application provide a method, device, equipment and medium for indexing water depth data of in-out port channels. When executing the method, measurement data is first obtained, and the measurement data includes measurement engineering metadata and water depth measurement data; then, according to the water depth measurement data, multi-level grid data is constructed, and the multi-level grid data includes channel data, the channel data includes section data, and the section data includes segmented data; then, the measurement data is associated with the multi-level grid data to obtain a multi-level spatio-temporal index; finally, the measurement data corresponding to the target query request is queried from the multi-level spatio-temporal index as the query result. In this way, the effective organization of the water depth data of the in-out port channels is realized, which is beneficial to realizing efficient retrieval, provides technical means for the business application and data mining of the water depth of the channels, and serves the preventive maintenance and safety management of the channels. Description of the Drawings

[0017] To more clearly illustrate the technical solutions in the embodiments or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a flowchart of a method for indexing water depth data of in-out port channels provided by the embodiments of the present application; Figure 2 It is a schematic diagram of the channel grid division provided by the embodiments of the present application; Figure 3 It is a schematic diagram of a construction method of a multi-level spatio-temporal index provided by the embodiments of the present application; Figure 4 It is another flowchart of a method for indexing water depth data of in-out port channels provided by the embodiments of the present application; Figure 5 It is a schematic diagram of the sparse query of the center point distance proximity method provided by the embodiments of the present application; Figure 6 It is a schematic diagram of redundant triangles in triangulation provided by the embodiments of the present application; Figure 7 It is a schematic diagram of a reasonable triangular mesh in triangulation provided by the embodiments of the present application; Figure 8 It is a schematic diagram of the contour extraction algorithm mechanism provided by the embodiments of the present application; Figure 9 It is a schematic diagram of the calculation and comparison of redundant triangles provided by the embodiments of the present application; Figure 10 It is a schematic diagram of the statistical situation of the measurement project in the research area provided by the embodiments of the present application; Figure 11 Schematic diagram of experimental results provided by an embodiment of the present application; Figure 12 Schematic diagram of extraction of shallow and deep points of water depth provided by an embodiment of the present application; Figure 13 Schematic diagram of analysis results of channel cross-section provided by an embodiment of the present application; Figure 14 Schematic diagram of dredging analysis calculation process provided by an embodiment of the present application; Figure 15 Schematic diagram of analysis results of channel evolution provided by an embodiment of the present application; Figure 16 Schematic diagram of a structure of a device for indexing water depth data of approach channels provided by an embodiment of the present application. Detailed implementation manners

[0019] With the rapid development of the global economy, ports, as key nodes in international trade and logistics transportation, have become increasingly important. As an important channel connecting ports and the ocean, the water depth conditions and underwater terrain of approach channels are directly related to the navigation safety of ships and the operation efficiency of ports. During the long-term use of approach channels, they are affected by natural factors such as sediment deposition and water flow scouring, as well as human factors such as engineering construction and ship navigation, showing characteristics such as long and narrow shapes, high navigation density, significant tidal effects, high maintenance standards, and frequent terrain changes. Timely and accurately grasping the water depth data and underwater terrain information of the channel plays a supporting role in ensuring the safe navigation of ships, optimizing port operations, and implementing preventive maintenance measures.

[0020] In recent years, marine surveying and mapping technologies have continued to progress, and technologies such as multibeam sounding systems and laser scanning have been widely used, making the acquisition of water depth data of approach channels increasingly efficient and accurate. The accumulation of massive water depth data provides a basis for quickly and accurately obtaining the spatio-temporal information of the channel for preventive maintenance of the channel. At the same time, it also poses higher requirements for the storage, management, and analysis of data. Given the characteristics of large data volume, dense distribution, multi-scale, and spatio-temporal continuity of channel water depth, it is urgent to carry out research on the construction technology of spatio-temporal indexing of approach channel water depth data to achieve the efficient organization, rapid retrieval, and accurate analysis of massive spatio-temporal data, providing technical support for the management and maintenance of approach channels.

[0021] At present, the research on the spatio-temporal index of water depth data mainly focuses on how to efficiently organize and retrieve massive spatio-temporal data. Common indexing methods include R-trees, KD-trees, grid trees, and combined indexes, etc. The R-tree is a hierarchical spatial index structure, which performs well in dealing with 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 queries, but it performs poorly when dealing with unevenly distributed spatial data. The KD-tree improves the search efficiency of multi-dimensional spatial data by dividing the data space in different dimensions, with a low time complexity, but its performance significantly degrades in high-dimensional spaces, and it is sensitive to data distribution. An unbalanced tree structure will reduce the query efficiency.

[0022] However, most of the existing methods for constructing water depth data indexes are designed for areas with continuous and uniform water depth distributions such as wide seas and inland river channels. How to adapt to the long strip-shaped and irregular distribution characteristics of the water depth in the approach channel, and cope with the challenges brought by higher data density, more frequent data updates, and more complex query scenarios is an urgent problem to be solved in the current research.

[0023] To solve the above problems, the present application proposes the following solutions. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0024] See Figure 1 , Figure 1 which is a flowchart of a method for the water depth data index of the approach channel provided by the embodiment of the present application, including: Step S101: Obtain measurement data.

[0025] The approach channel relies on measurement projects to carry out the collection and update of water depth data. The measurement data includes measurement project metadata and water depth measurement data. The measurement project metadata includes project time, type, content, etc. The project time includes pre-dredging measurement time, post-dredging measurement time, etc.

[0026] It should be noted that the original water depth measurement data may have characteristics such as inconsistent data sources, inconsistent data standards, and large differences in data structures. Before obtaining the measurement data, it is necessary to first standardize the original water depth measurement data, convert it into a unified data format, unify it to the agreed coordinate system through coordinate transformation, and at the same time check and correct data quality problems such as missing values and noise points. The result of data preprocessing is measurement project metadata and water depth measurement data.

[0027] Step S102: Construct multi-level grid data according to the water depth measurement data.

[0028] The construction of a spatio-temporal index for the water depth data of the approach channels needs to comprehensively consider the multi-scale characteristics of the water depth data, the geometric morphological characteristics of the channels, and the requirements of channel maintenance management and safe navigation of ships, so as to achieve efficient data management and query.

[0029] Therefore, aiming at the retrieval requirements of multi-scale water depth data, spatially partitioning the approach channels and establishing a multi-level grid data of channel-segment-sub-segment is beneficial to designing corresponding data index strategies for different spatial scales subsequently.

[0030] The multi-level grid data includes channel data, the channel data includes segment data, and the segment data includes sub-segment data. The multi-level grid is the main carrier of the spatio-temporal index, containing grids at three levels: channel, segment, and sub-segment. The grid divisions of the channel and segment are determined according to the spatial morphology of the channel and the data application requirements. The sub-segment can be further divided into multi-level grids according to the actual situation. The grid grading affects the smoothness of data transition. The more levels there are, the smoother the transition, but the indexing efficiency may decrease; the grid size affects the fineness of water depth data management, and its determination needs to consider the measurement accuracy and application accuracy requirements of the water depth data. When there is no functional change in the approach channels, the stability of the multi-level grid should be maintained as much as possible to ensure the accuracy of data indexing.

[0031] See Figure 2 , to ensure full coverage of the navigable area, in this embodiment, the spatial range of the channel is used as the basic division unit to construct a spatial index based on the R+-tree, storing the minimum bounding rectangle and attribute information of the channel. Since the number of channels is limited, the impact of the imbalance of the R+-tree caused by the uneven spatial distribution of the channels on the query performance can be ignored.

[0032] Based on the channel index, combined with the geometric morphological characteristics, the channel is divided into multiple segments according to the mileage interval, a naming rule with mileage semantics is defined for the segments to ensure the uniqueness of the mileage semantics of each segment, and a spatial index based on the R+-tree is constructed to associate attribute information such as mileage intervals and the affiliated channels, so as to handle spatial queries at adjacent positions or different scales. For compound channels or channels with a large width, when dividing the segments, the center line of the channel is used as the boundary to ensure compliance with the actual channel management.

[0033] For each segment, using the center line of the channel as the horizontal reference and the perpendicular line to the center of the channel as the vertical reference, further divide the multi-level grid to form sub-segments. The level and size of the grid division depend on the area size of the segment. A top-down Quad - Tree index is established for the divided multi-level grid to achieve fast data query and update.

[0034] In this way, for water depth data at different scales, corresponding management strategies can be adopted respectively: 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 long and narrow geometric morphological characteristics of the approach channel, the channel is divided into multiple segments along the center line of the channel according to the channel mileage, a naming rule with spatial position semantics is defined for the segments, and a spatial index based on the R+ tree is constructed; for the sub-segment scale, multi-level grids are further divided, and a quadtree index is used to achieve efficient retrieval of spatial data in cross-scale scenarios.

[0035] Step S103: Associate the measurement data with the multi-level grid data to obtain a multi-level spatio-temporal index.

[0036] The multi-level spatio-temporal index includes a spatial index and a time index.

[0037] See Figure 3 , the multi-level spatio-temporal index based on spatial partitioning adopts a bottom-up construction method. Based on the measurement project, the spatial indexes of water depth, grid, segment, and channel are constructed in sequence, and are associated with the time index.

[0038] Specifically, for multi-temporal water depth measurement data, the measurement project data is associated with the time reference of the project time, and the water depth measurement data and the multi-level grid are associated with the spatial reference of the project scope.

[0039] On the time scale, based on the multi-temporal measurement project, taking the measurement time as the time reference and the project scope as the spatial reference, through spatial relationship judgment, the spatio-temporal association between the project scope and the multi-level grid is established, and a chain time index of "project information - project scope - multi-level grid - water depth point" based on time version is constructed. This chain structure starts with the project attributes and associates the project scope, multi-level grid, and water depth point in sequence, which can not only accurately reflect the time change of water depth data but also efficiently integrate data of different spatial scales. [[ID=I8]]

[0040] Combined with the actual characteristics of the channel maintenance project, on the basis of the spatial index, the project attributes are integrated to establish the association between the project water depth data and the multi-level spatial index, and then a multi-level spatio-temporal index mechanism based on spatial partitioning is formed.

[0041] Step S104: Query the measurement data corresponding to the target query request from the multi-level spatio-temporal index as the query result.

[0042] The target query request is used to describe the data range of the current query. Since the multi-level spatio-temporal index includes two dimensions of time and space, the target query request can perform selective queries from the above dimensions. From the time dimension, it is necessary to describe the start time, end time, or other specific time periods; from the space dimension, considering that the multi-level spatio-temporal index includes multiple layers of grids such as waterway-segment-sub-segment, the data scale can be limited in the target query request, so that the measurement data queried from the multi-level spatio-temporal index is more accurate.

[0043] In summary, through the above method, this embodiment realizes the spatial index based on the multi-level grid and the time series index based on the measurement project time, and establishes the spatio-temporal association between the multi-level grid and the multi-temporal measurement project by means of the measurement project range, forming a multi-level spatio-temporal index mechanism based on spatial division, which helps to adapt to the long strip irregular distribution characteristics of the approach channel. This mechanism uses the measurement project as the data processing unit and the engineering metadata and water depth data as the input data, which meets the actual needs of channel maintenance and safety management while conforming to the data characteristics of the approach channel. At the same time, the multi-level spatio-temporal index can cope with the challenges brought by higher data density, more frequent data updates, and more complex query scenarios.

[0044] In the embodiment of the present application, the above Figure 1 The described steps have multiple possible implementation methods, which will be introduced separately below. It should be noted that the implementation methods given in the following introduction are only for illustrative purposes and do not represent all implementation methods of the embodiment of the present application.

[0045] See Figure 4 , which is another flowchart of the method for indexing the water depth data of the approach channel provided by the embodiment of the present application, including: Step S401: Obtain measurement data.

[0046] Step S402: Construct multi-level grid data according to the water depth measurement data.

[0047] Step S403: Associate the measurement data with the multi-level grid data to obtain a multi-level spatio-temporal index.

[0048] The construction of the spatial index aims to establish the association between the multi-level grid data and the water depth points of the water depth measurement data. The traditional method realizes it by traversing the water depth point data and calculating the spatial relationship between the grid and the water depth data level by level, but this method has the problems of large calculation amount and low efficiency. To improve the construction efficiency, the following two strategies can be adopted: One is to reduce the amount of data participating in the calculation. Use the maximum side length constraint method to calculate the concave hull polygon of the water depth measurement data, that is, the actual measurement range, and then superimpose and analyze the actual measurement range with the maximum level grid to obtain the intersecting grid, and then traverse the intersecting grid for water depth data indexing.

[0049] Second, instead of calculating the grid space relationship, calculate through the grid association relationship. Instead of calculating all levels of grids, use the chain association relationship of grids to obtain corresponding information such as each level of segmentation, flight segment, and approach and departure channels in turn. Based on the grid index, calculate the perpendicular distance from the water depth point to the center line of the approach and departure channel and the mileage of the perpendicular point in the flight segment.

[0050] The time information of the time index comes from the measurement project time, and batch fast update can be realized through the association relationship between the project metadata and the water depth data. Since the channel project is divided into two methods: conventional measurement and dredging measurement, and the dredging measurement includes data before and after dredging, and the two times are close. When the requirement for time accuracy is not high, take the time after dredging as the basis for the time index.

[0051] Specifically, as a possible implementation manner, step S403 may include step S4031 and step S4032: Step S4031: Associate the water depth measurement data with the multi-level grid data to obtain a space index.

[0052] Step S4032: Associate the time data in the measurement project metadata with the space index to obtain a time index.

[0053] In this way, first perform a space index, then associate the time, and construct a multi-level spatio-temporal index layer by layer.

[0054] As a possible implementation manner, step S4031 may correspond to the above two strategies.

[0055] When corresponding to the first strategy, step S4031 may include step S40311 to step S40314: Step S40311: Obtain the maximum side length of the measurement grid of the water depth measurement data.

[0056] Analyze the distribution of the water depth measurement data to understand the average distance between data points and the density of the data. According to the data distribution density and the measurement accuracy requirement, determine a suitable maximum side length parameter value. This value determines the tightness of the concave hull polygon. If it is too large, the concave hull may be too rough to accurately reflect the actual measurement range; if it is too small, the calculation process may be too complex.

[0057] Step S40312: Construct a concave hull polygon of the water depth measurement data according to the maximum side length of the measurement grid.

[0058] The concave hull polygon can be constructed by first building a Delaunay triangulation based on the maximum side length constraint, then traversing all the triangles to check whether all three of their sides satisfy the maximum side length constraint condition. If a certain side of a triangle exceeds the maximum side length, then mark this triangle as a non-conforming triangle. Remove all the triangles marked as non-conforming from the triangulation. After removing the non-conforming triangles, the remaining triangles will form one or more closed regions. The boundaries of these regions are the boundaries of the concave hull polygon. By traversing the sides of the triangles, find those sides that belong to only one triangle. These sides are the boundary sides of the concave hull polygon, and their endpoints are the boundary points. Connect the extracted boundary points and boundary sides in sequence to form one or more closed polygons, which are the required concave hull polygons. These concave hull polygons can tightly wrap the bathymetric measurement data points and accurately reflect the actual measurement range.

[0059] Step S40313: Obtain the multi-level grid data intersecting with the concave hull polygon as the target grid data.

[0060] Overlay and analyze the calculated concave hull polygon with the maximum-level grid to determine the grids intersecting with the actual measurement range. Through spatial relationship judgment, find the grids intersecting with the concave hull polygon. These intersecting grids are the grids containing the bathymetric measurement data, and subsequent operations such as bathymetric data indexing will be carried out based on these grids.

[0061] Step S40314: Associate the bathymetric measurement data with the target grid data to obtain a spatial index.

[0062] Traverse the grids intersecting with the concave hull polygon, and extract the bathymetric data points within these grids. Establish an index relationship between the traversed bathymetric data points and the corresponding grids so that relevant bathymetric data can be quickly located and retrieved in subsequent queries and analyses.

[0063] Corresponding to the second strategy, step S4031 may include steps S40315 to S40316: Step S40315: Associate the bathymetric measurement data with the segmented data.

[0064] Step S40316: Sequentially synchronize the bathymetric measurement data associated with the segmented data to the leg data and the waterway data.

[0065] Both of the above two strategies can improve the construction efficiency of the multi-level spatio-temporal index and reduce the amount of calculation.

[0066] As a possible implementation, step S4032 may include: According to the correspondence between the measurement project metadata and the bathymetric measurement data, annotate the project time in the measurement project metadata to the measurement data in the spatial index as the time index.

[0067] In summary, steps 401 to 403 achieve the construction of a multi-level spatio-temporal index. For the multi-level spatio-temporal index, subsequent query operations can be performed.

[0068] Step S404: Obtain the target query request.

[0069] The target query request includes a query formula and a data scale. The multi-level grid query can flexibly meet the query requirements for different data scales. When querying, it is necessary to select an appropriate data scale according to the application scenario. The data scale reflects the 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, according to the query formula, combined with the grid number, spatial range, or project number, the set of grids to be queried is filtered out.

[0070] Query formula ; where R st is the query result; Q st is a spatio-temporal query function, 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 spatial range; T start is the query start time; T end is the query end time; GridSet is the set of grids to be queried; ProjSet is the set of projects to be queried; ResType is the output form of the query result, and the set of grids, the set of water depth points, or both types of data sets can be returned simultaneously.

[0071] Since each grid contains multiple parameters, by combining different grid parameters through the set of grids to be queried, diverse query operations based on the multi-level grid can be realized. This flexibility enables the system to adapt to various complex data retrieval requirements, thereby improving the efficiency and accuracy of data management and query.

[0072] Set of grids to be queried ; Grid1 is the first grid, Grid2 is the second grid, Grid n is the nth grid, and n is a positive integer.

[0073] The ith grid ; where Level i is the grid level, Id i is the grid number, Geo i is the grid range, Attrs i is the grid attribute, and i is a positive integer.

[0074] Set of projects to be queried ; Proj1 is the first project, Proj2 is the second project, and Proj k is the kth project, where k is a positive integer.

[0075] The jth project ; Among them, Id j is the project number, Geo j is the project scope, T j is the project time, Attrs j is the project attribute, and j is a positive integer.

[0076] Through the query formula, the query requirements at different levels of time and space can be integrated. According to the query formula, the measurement data of the area in the query formula can be corresponding queried.

[0077] Step S405: According to the query formula, query the measurement data corresponding to the data scale from the multi-level spatio-temporal index as the query result.

[0078] For the set of water depth points within the grid corresponding to the query formula, the sparse method can be combined to screen the most representative water depth data to achieve efficient and accurate query. The sparse rules can be formulated according to specific situations, such as the central point distance proximity method, the depth peak method, the random selection method, etc. These methods can convert the spatial relationship calculation into a standard SQL query statement and be realized through nested queries, thereby improving the query efficiency. Figure 5 Shows a schematic diagram of the sparse query by the central point distance proximity method. Triangles represent the points filtered according to the principle of the latest time, crosses represent the points eliminated by the sparse algorithm, and circles are the final query results.

[0079] For different query space ranges, the measurement data corresponding to the data scale queried from the multi-level spatio-temporal index is also different.

[0080] On the one hand, from the spatial query, it can be divided into point query, line query and surface query.

[0081] Among them, the point query is to interpolate the water depth value of any point from the discrete water depth points.

[0082] The conventional method is to achieve it by means of an underwater terrain model, and the query efficiency is poor.

[0083] In this embodiment, the efficiency is improved based on the multi-level grid. First, the grid of a specific level is queried according to the data accuracy requirements, and then the data extraction and analysis are performed based on the grid. When the query accuracy requirement is low, the mean or maximum value of the water depth data within the grid is directly taken; while when the query accuracy requirement is high, an underwater terrain model is first constructed for the set of water depth points in the grid, and then the water depth value of the target point is calculated by the inverse distance interpolation method.

[0084] In this way, on the one hand, by controlling the data precision, the amount of data participating in the calculation is indirectly controlled; on the other hand, the spatial query and interpolation operations are transformed into data statistical calculations, significantly improving the query efficiency.

[0085] The line query is to query the water depth change curve along the midline from discrete water depth points.

[0086] In this embodiment, the query line is first discretized into a set of multiple points, then point query calculations are performed on each point, and finally the water depth change curve along the line is obtained by fitting the water depth values of each point.

[0087] The surface query is used to retrieve the set of water depth data within the polygon range.

[0088] The conventional method relies on spatial inclusion analysis and has low efficiency.

[0089] In this embodiment, first, according to the data precision requirement and the query polygon, the set of specified-level multi-level grids intersecting with it is determined, and then the corresponding set of water depth points is obtained through an association query.

[0090] In this way, the complex spatial query is simplified into a grid query and a relationship query, thus effectively improving the query efficiency.

[0091] As a possible implementation manner, the above step S405 may include step S4051 to step S4053: Step S4051: In response to the query space range being a point, taking the query space range as the target point, perform the following target point query operations of steps a to b: Step a: In response to the data scale being lower than the preset precision value, taking the mean or maximum value of the measurement data in the multi-level grid data corresponding to the target point as the query result.

[0092] The preset precision value can be set according to requirements and is not limited here.

[0093] Step b: In response to the data scale not being 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; based on the underwater terrain model, use the inverse distance interpolation method to calculate the water depth value of the target point as the query result.

[0094] Optionally, constructing an underwater terrain model based on the measurement data in the multi-level grid data corresponding to the target point in step b may include the following steps ① to ③: Step ①: Perform Delaunay triangulation on the measurement data in the multi-level grid data corresponding to the target point to construct an initial triangular network.

[0095] The initial triangular mesh consists of multiple triangles. Delaunay triangulation is often used to construct an underwater irregular triangular mesh. Different from wide waters, the approach channels for entering and leaving ports are long and narrow in shape, and their entrances, turning points, and port connections are mostly curved, resulting in the bathymetric data being distributed in an irregular strip shape. Direct application of Delaunay triangulation is likely to generate redundant triangles as shown in Figure 6 Constrained Delaunay triangulation is a triangular mesh construction method that introduces constraints on the basis of Delaunay triangulation. By forcing specific line segments (such as boundary lines, feature lines, etc.) to be the edges of the triangular mesh, it can effectively retain the boundary information and geometric features of the original data, avoid the appearance of unreasonable triangles, and thus generate Figure 7 a triangular mesh that is more in line with the actual terrain features as shown in

[0096] Step ②: Use an adaptive boundary extraction method based on the alpha shape algorithm to generate the underwater terrain boundary line corresponding to the target points.

[0097] Alpha shape is a classic point set contour extraction algorithm. It introduces an adjustable parameter α on the basis of constructing 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 sphere with a radius of α rolls on the surface of the point set, and connection relationships are established between the points in contact with the sphere, thus forming a surface. This mechanism is similar to Figure 8 setting a length limit for the sides of the polygon as shown in

[0098] The determination of the parameter α is the key to accurately extracting the boundary. If the parameter α is too large, it will affect the accuracy of the extracted boundary; if the parameter α is too small, the integrity of the boundary cannot be guaranteed. The value of α is closely related to the distribution density and query accuracy of the bathymetric data. Among them, the distribution density depends on the bathymetric measurement accuracy, and the maximum effective value of α corresponds to the distance between bathymetric points under the bathymetric measurement accuracy. Due to the differences in bathymetric measurement accuracy, it is necessary to dynamically determine the value of α according to the data situation. Therefore, it is particularly crucial to study the corresponding relationship between the bathymetric data and the parameter α and formulate an adaptive dynamic α adjustment strategy.

[0099] Specifically, when the α value is 1 to 2 times the average point spacing, the shape of the obtained point set is relatively complete and not overly fragmented. Given that the water depth point density is directly related to the mapping scale used in the measurement, the value range of the adjacent point distance corresponding to different scales can be estimated. There are clear regulations on the error limit of the positioning point position corresponding to different mapping scales. The mapping scale accuracy reflects the actual horizontal distance corresponding to a distance on the topographic map. Based on this, the limit value of the actual distance of the positioning point corresponding to different mapping scales, that is, the maximum point spacing, can be calculated, and then the reference range of the α value can be determined.

[0100] Table 1 <9999999>

[0101] The above Table 1 lists the reference range of the α value corresponding to different mapping scales. After determining the α value, the concave hull polygon can be generated using the α-shape algorithm.

[0102] Step ③: Using the centroid topological mapping method, redundant triangles outside the underwater terrain boundary line are removed from the initial triangular mesh to obtain an underwater terrain model.

[0103] From Figure 9 It can be seen that redundant triangles usually have a long and narrow shape and contain sharp angles or wide obtuse angles. The calculation process of the conventional method is shown in Figure 9 above. Using this feature, redundant triangles are identified by setting thresholds for triangle area, side length, and interior angle. Since the target boundary is a subset of the initial triangular mesh, starting from the outermost triangle of the triangular mesh and traversing one by one, triangles that do not meet the constraint conditions are deleted, and finally the required boundary is obtained.

[0104] However, this method has obvious limitations: one is that it is necessary to judge each triangle one by one, resulting in low calculation efficiency; the other is that it is difficult for fixed thresholds to distinguish between sharp angles and wide obtuse angles, and mis-extraction is likely to occur in areas with complex boundary shapes, thus affecting the extraction accuracy.

[0105] In this embodiment, based on the adaptive boundary, the centroid-triangle mapping relationship is used to achieve the filtering of redundant triangles driven by topological relationships. The calculation process is as Figure 9 shown below. First, calculate the centroid point of each triangle, establish the association relationship between the triangles, vertices, and centroids of the triangular mesh, and store this association relationship with the help of data caching technology to improve the calculation efficiency. Then, using the intersection operation of the adaptive boundary extraction result and the centroid, the redundant polygons outside the boundary are quickly retrieved. Compared with the conventional method, this method innovatively changes the analysis object from the side line of the triangular mesh to the centroid of the triangular mesh, retrieves the centroid set through spatial query, and then realizes the filtering of redundant triangles through associated query. The whole process does not require traversing the triangular mesh, effectively reducing the number of operations and significantly improving the calculation efficiency.

[0106] Optionally, based on the underwater terrain model in step b, the inverse distance interpolation method is used to calculate the water depth value of the target point as the query result. The specific operation can be as follows: Determine the range of the area to be interpolated according to the position of the target point and the query requirements. Determine the number of adjacent measured points to be considered in the interpolation calculation. Usually, several points closer to the target point are selected to ensure the accuracy of the interpolation result. Select the power value parameter of the inverse distance interpolation method. This parameter controls the rate at which the weight changes with distance. The larger the power value, the greater the influence of the nearby points. In the constructed Delaunay triangulation, find several measured points closest to the target point through spatial query. Calculate the corresponding weights according to the distances between the target point and each adjacent measured point. The weight value is inversely proportional to the power of the distance, that is, the closer the point, the greater the weight. Use the calculated weights to perform weighted averaging on 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 value of the known water depth point to evaluate the accuracy and reliability of the interpolation method. Quantitative analysis can be carried out by calculating error indicators (such as root mean square error, etc.). According to the verification results, adjust the number of adjacent points and power value parameters, etc., to optimize the interpolation process and improve the accuracy of the interpolation result. Integrate the calculated water depth value of the target point with other query-related information (such as target point coordinates, belonging grid, etc.) to form a complete query result set and output the query result.

[0107] Step S4052: In response to the query space range being a line, discretize the query space range into multiple target points; for each target point, perform the target point query operation to obtain the query results corresponding to the multiple target points; according to the query results corresponding to the multiple target points, obtain the query result corresponding to the query space range.

[0108] Discretize the case where the space range is a line into multiple target points, and then integrate the query results of the multiple target points to obtain the query result corresponding to the query space range.

[0109] Step S4053: In response to the query space range being a surface, obtain the multi-level grid data intersecting with the query space range as the query grid data; obtain the measurement data in the query grid data as the query result.

[0110] When the space range is a surface, multi-level grid data intersecting with the query space range can be obtained. According to the intersecting multi-level grid data, extract all the water depth data points within these grids. This step requires traversing the intersecting grids and collecting the water depth data therein. Filter and organize the extracted water depth data points, remove duplicate or invalid data, and perform data format conversion or supplementary calculation as needed.

[0111] The process of filtering and organizing can combine the sparsification method mentioned above to screen the most representative water depth data and achieve efficient and accurate query.

[0112] On the other hand, in terms of time query, time attributes are usually stored in measurement projects, grid cells, and depth points.

[0113] When dealing with large-scale data sets, the query efficiency of a single time dimension will significantly decrease. Therefore, nested queries are usually used in combination with other query conditions. For specific application requirements, it is necessary to comprehensively consider data accuracy, data content, and query efficiency to select appropriate query dimensions and fusion strategies.

[0114] The measurement project dimension is suitable for obtaining large-scale engineering data with a long time span, such as cross-year channel evolution analysis or comparison of the effects of multi-phase dredging projects. Specific measurement projects within a certain time period can be queried through the measurement time, and then the corresponding set of depth points can be retrieved by association. The grid cell dimension is suitable for analyzing the changes in a specific area within a certain time, such as regional siltation monitoring. The set of grids with data updates within a certain time period can be queried based on the grid time, and then the corresponding set of depth points can be retrieved by association. The depth point dimension is suitable for precise query and analysis, and can directly query the depth data within a specific time period or at a specific time slice.

[0115] On the other hand, in terms of comprehensive query, the query method of this embodiment can flexibly meet the query requirements of different data scales. Combining with the sparsification rule, a more representative query result can be obtained.

[0116] In summary, in view of the long-strip distribution, high-density distribution, and frequent update spatio-temporal characteristics of the water depth data of the in-out port channel, this embodiment designs a multi-level spatio-temporal index method based on spatial partitioning, constructs a multi-level spatial index of channel-segment-sub-segment, and realizes the efficient organization of massive water depth data; based on the multi-level spatio-temporal index, studies spatio-temporal query methods combining multiple methods such as time, space, and grid to meet the diverse data query needs of the in-out port channel; proposes an underwater terrain construction method integrating α-shape and constrained Delaunay triangulation (CDT) to realize the construction of an underwater terrain model conforming to the characteristics of the in-out port channel. Using the above methods, the effective organization and efficient retrieval of the water depth data of the in-out port channel are realized, providing technical means for the business application and data mining of the channel water depth, and serving the preventive maintenance and safety management of the channel.

[0117] To facilitate the description of the efficiency of this embodiment, a specific case is described below: Vessels in a certain port area enter the harbor through a 400,000 - ton deep - water channel, and then reach each port operation area via the internal channel of the harbor. The total length of the channel is about 57.8 kilometers, featuring a significant long - channel characteristic. This area belongs to a muddy coast and is located in an open sea area with significant wave action, resulting in relatively prominent channel siltation. Although the port area adopts an enclosed breakwater design to reduce internal sediment siltation, the tidal current movement near the entrance has become more complex. To accurately grasp the underwater topography and its changes, the channel management department conducts comprehensive surveys regularly and supplementary surveys irregularly according to the actual navigation and maintenance needs. The regular surveys are carried out quarterly, covering the entire channel area through multiple surveys of different regions; while the supplementary surveys are targeted at specific areas, with their measurement scope and frequency being relatively flexible and without obvious rules.

[0118] This embodiment is based on the bathymetric measurement data of a certain research area during a certain period. The regular surveys mainly target the main channel, using single - beam sounding technology, with a measurement period of once per quarter and a relatively fixed measurement range. The supplementary surveys mainly focus on the port basin, berths, and some channel areas, mainly for dredging surveys. The data results are stored in the completion data in units of projects, and their formats include CAD, XYH files, and PDF, etc. These data have problems such as inconsistent formats, inconsistent spatial references, mismatched measurement scales, and overlapping data ranges. To solve the problem of data non - uniformity, this embodiment first pre - processes the data, unifies the data format into a standard XYH file, and unifies the coordinate system, using CGCS2000 (China Geodetic Coordinate System 2000) as the plane control and the local theoretical lowest tide level as the elevation control benchmark, so as to ensure the consistency and accuracy of the data. Figure 10 It shows the statistical situation of the measurement projects in the research area after data processing.

[0119] Storing a large amount of data in the database is the premise for realizing the unified management of bathymetric data. Compared with directly storing data in the database, although constructing a spatio - temporal index will sacrifice a certain data - storage efficiency, it can significantly improve the subsequent data - query efficiency.

[0120] To evaluate the efficiency of constructing a multi - level spatio - temporal index based on space partitioning, its data - storage efficiency is compared with that based on a relational database and a spatial database.

[0121] The experiment takes regular measurement projects as processing units, and tests the impact of spatio - temporal index construction on data - storage efficiency under different data scales and different grid - partitioning precisions. Figure 11Shows the experimental results for six scenarios including relational databases, spatial databases, 1:2k precision grids, 1:5k precision grids, 1:10k precision grids, and 1:20k precision grids under different data scales, covering 96 conventional survey projects with a total of 360,609 water depth points. The statistical metric is the sum of data reading, index construction, and data storage time, in milliseconds. It can be seen that: 1) For single-beam survey projects, the data scale processed each time is relatively small (less than 6,000 points), and the overall time-consuming for index construction is relatively short (less than 5 seconds), which meets the actual business requirements of water depth data management.

[0122] 2) As the data scale expands, the time-consuming for index construction shows a linear growth. Arranged in descending order of the growth rate, they are 1:2k precision grid, 1:5k precision grid, 1:10k precision grid, 1:20k precision grid, spatial database, and relational database. The time-consuming for constructing a multi-level spatio-temporal index exceeds the data storage time of spatial databases and relational databases, and the finer the grid division, the more time-consuming for index construction.

[0123] 3) The time-consuming for index construction of 1:10k precision grid and 1:20k precision grid is similar, and the growth trends are similar. This is mainly due to the fact that the mapping scales in the study area are mostly 1:2,000 or 1:5,000. Under a larger spatial division scale, the differences in the spatial distribution characteristics of the data are smaller.

[0124] 4) The construction time of the index is generally stable. When the grid precision is 1:2k, there is a slight fluctuation in the time-consuming for index construction, which may be caused by the fluctuation of system resource occupancy due to changes in the complexity of data spatial distribution or an increase in the amount of calculation.

[0125] The experimental results show that although index construction has a certain impact on the data storage efficiency, considering that data storage is carried out in units of projects, with a short overall time-consuming and a high storage frequency, its significant improvement in query analysis efficiency makes this impact negligible in practical applications. At the same time, multi-level grid division needs to be combined with data distribution and application scales. High-precision grids are conducive to capturing details, and coarse-precision grids can reduce calculation consumption. By reasonably adjusting the scale division, the construction efficiency and application effect can be effectively balanced.

[0126] In addition, to verify the effectiveness of the multi-level spatio-temporal index based on spatial division in spatio-temporal fusion query and underwater terrain construction, representative application scenarios of in and out port channels are selected, including shallow point analysis, dredging analysis, profile analysis, and evolution analysis. Through in-depth discussions in these specific application scenarios, the specific implementation methods of the multi-level spatio-temporal index are analyzed in detail, and it is compared with traditional methods such as relational databases and spatial databases, so as to comprehensively evaluate the advantages and effectiveness of the multi-level spatio-temporal index in practical applications.

[0127] In the maritime field, shallow water depth points refer to areas where the water depth is lower than a certain specific value, especially those that do not meet the requirements of the channel design or are lower than the draft of the ship. Such areas are the key objects that need to be focused on for the navigational safety of the approach channel. The method based on the relational database filters out shallow water depth points through SQL statements according to the set water depth value conditions, and has a relatively high retrieval efficiency in global analysis. However, due to the differences in the judgment criteria for shallow water depth points in different areas of the channel, it is difficult to effectively process complex spatial relationships simply relying on the relational database. The method based on the spatial database can efficiently retrieve the water depth points in a specific area by means of spatial relationship judgment, but when faced with a large amount of water depth data, the query efficiency will decrease significantly.

[0128] The method proposed in this embodiment effectively improves the efficiency of shallow point analysis by converting the relational query and spatial operation of water depth points into calculations on multi-level grids. Specifically, according to the distribution characteristics of the approach channel, the regional query requirements are transformed into queries on shallow point analysis units (i.e., channels, sections, multi-level grids). First, query the set of multi-level grids at the specified level that intersect with the target polygon. Then, use SQL query to further filter according to the water depth value range of the grids to obtain the set of grids that meet the conditions. Finally, for the set of water depth points in each grid, nested SQL statements are used to implement shallow point analysis. Figure 12 The shallow water depth points analyzed by using the method of this embodiment are shown, and the extraction results include data such as the positions of the shallow points, the grids to which they belong, and the depth information.

[0129] Profile analysis is used to obtain the water depth changes on a specified profile line and is widely used in channel maintenance. It can be divided into cross-section, longitudinal-section, and arbitrary-section analysis according to the profile type. The longitudinal section is used to observe the longitudinal changes of the underwater terrain along the center line of the channel, and the cross section is used to observe the lateral changes of the underwater terrain perpendicular to the center line of the channel. Profile analysis is usually realized by discretizing the profile line and simulating the water depth changes along the profile line with continuous sampling points. The profile analysis method based on the relational database is realized by querying the water depth data points within a certain coordinate range around the sampling points. This method is simple, flexible, and efficient, but due to the lack of a processing mechanism for the continuity of the underwater terrain, the query accuracy is relatively low. The profile analysis method based on the spatial database realizes profile analysis by means of spatial interpolation to obtain the water depth value at any position. This method has a relatively high query accuracy, but when the data volume is large or the interpolation algorithm is complex, the query efficiency decreases significantly.

[0130] The method proposed in this embodiment first performs buffer analysis on the profile line to generate a buffer polygon, which is used as the input parameter Geo of the query formula, and combines it with T start 、T endParameters such as ProjSet are used to accurately obtain the set of water depth points around the section line, ensuring that the number of discrete water depth point sets input is limited and effective. Then, spatial interpolation calculations are performed between the sampling points and the output water depth point set to obtain the water depth values of each sampling point. Finally, the data of each sampling point is combined and fitted into a continuous section line. Figure 13 Shows the results of the channel cross-section analysis.

[0131] Dredging analysis compares the water depth data before and after dredging to understand the water depth distribution and the amount of dredging work, providing decision-making support for channel maintenance projects. Triangulated Irregular Network (TIN) is often used in dredging analysis because of its efficient storage, simple structure, and ability to accurately represent terrain features. However, the method based on relational databases lacks the ability to manage spatial data and is difficult to implement dredging analysis. The method based on spatial databases constructs a uniform grid or TIN for dredging analysis. Although it has high accuracy, it is inefficient when dealing with large amounts of data and difficult to dynamically thin data to meet the accuracy requirements.

[0132] The method proposed in this embodiment realizes dredging analysis by combining TIN on the basis of a multi-level grid. First, an adaptive boundary extraction method based on the alpha shape algorithm is used to generate the data ranges before and after dredging, and their intersection is obtained. Then, according to the accuracy requirements, an appropriate grid level is selected. Using the intersection area as the input parameter Geo and the grid level as the input parameter ProjSet, query the sets of water depth data points before and after dredging within the intersection area. Then, use the redundant TIN filtering method based on centroid topological mapping to construct the TINs of the water depth data before and after dredging respectively. Finally, calculate information such as water depth distribution, dredging depth, and volume through overlay analysis. Figure 14 Shows the calculation process of dredging analysis.

[0133] Channel dredging and silting will change the channel topography. Mastering its evolution law is crucial for channel maintenance. Channel evolution analysis is based on multi-period water depth measurement data, including terrain change analysis and erosion and deposition change analysis. Terrain change analysis focuses on the spatial distribution and historical changes of the underwater terrain, while erosion and deposition change analysis focuses on the distribution law and change trend of erosion and deposition. The implementation logic of evolution analysis is roughly similar to that of dredging analysis, but it faces the challenge of larger amounts of data. The methods based on relational databases and spatial databases are difficult to meet the requirements of high efficiency and accuracy.

[0134] The method proposed in this embodiment realizes evolution analysis by using TIN on the basis of a multi-level grid. First, according to the time range parameter (T start 、T end) and the analysis area (Geo) to filter out the measurement project set (ProjSet), select an appropriate grid level (Level) according to the analysis area (Geo) and data accuracy, and use query-based methods to obtain multi-level grid sets and bathymetric data sets; then, construct a multi-period bathymetric measurement data set based on time series according to the measurement time; finally, generate an irregular triangular network according to a unified scale, and perform superposition analysis on the multi-period bathymetric measurement data based on this irregular triangular network to calculate information such as bathymetric distribution, dredging depth, and dredging volume. Figure 15 Shows the results of the channel evolution analysis.

[0135] In summary, this embodiment proposes a method for constructing a spatio-temporal index of the water depth of the approach channel based on multi-level grids, and integrates α-shape and constrained Delaunay triangulation for underwater terrain model construction. Through experimental verification, the following main conclusions are obtained: Spatio-temporal index construction: The constructed multi-level spatio-temporal index can efficiently organize and manage the water depth data of the approach channel, significantly improving the data query efficiency. Experiments show that under different data scales, this method shows good performance, especially when dealing with large-scale data sets, the advantages are more obvious.

[0136] Underwater terrain construction: The underwater terrain construction method that integrates α-shape and constrained Delaunay triangulation can effectively generate an underwater terrain model that conforms to the characteristics of the approach channel, avoiding the problem of redundant triangles in traditional methods and improving the accuracy and practicality of the model.

[0137] Application scenario verification: Through the verification of typical application scenarios such as shallow point analysis, dredging analysis, profile analysis, and evolution analysis, the effectiveness of the proposed method in practical applications is proved. This method can provide a scientific basis for channel preventive maintenance and safety management, and support relevant decision-making and management activities.

[0138] The above are some specific implementation methods of the method provided by the embodiments of the present application. Based on this, the present application also provides a corresponding device. The device provided by the embodiments of the present application will be introduced from the perspective of functional modularization below.

[0139] See Figure 16 The structural schematic diagram of the approach channel water depth data indexing device shown. The device includes an acquisition module 1701, a network construction module 1702, and an index construction module 1703.

[0140] Among them, the acquisition module 1701 is used to acquire measurement data, and the measurement data includes measurement project metadata and bathymetric measurement data; A network construction module 1702, configured to construct multi-level grid data according to the water depth measurement data, where the multi-level grid data includes waterway data, the waterway data includes segment data, and the segment data includes sub-segment data; An index construction module 1703, configured to associate the measurement data with the multi-level grid data to obtain a multi-level spatio-temporal index, where the multi-level spatio-temporal index includes a spatial index and a time index; A query module 1704, configured to query the measurement data corresponding to a target query request from the multi-level spatio-temporal index as a query result.

[0141] As a possible implementation manner, the apparatus further includes: A query-based acquisition module, configured to acquire a target query request, where the target query request includes a query formula and a data scale; Wherein, the query formula is ; R st is the query result, Q st is a spatio-temporal query function, Geo is the query spatial 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 form 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, and n is a positive integer; The ith grid ; Level i is the grid level, Id i is the grid number, Geo i is the grid range, Attrs i is the grid attribute, and i is a positive integer; The query project set ; Proj1 is the first project, Proj2 is the second project, Proj k is the kth project, and k is a positive integer; The jth project ; Id j is the project number, Geo j is the project range, T j is the project time, Attrs j is the project attribute, and j is a positive integer; A query module, configured to query measurement data corresponding to the data scale from the multi-level spatio-temporal index according to the query formula, as a query result.

[0142] Optionally, the query module includes: A point query unit, configured to, in response to the query space range being a point, use the query space range as a target point, and then 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 the 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; based on the underwater terrain model, use the inverse distance interpolation method to calculate the water depth value of the target point as the query result; A line query unit, configured to, in response to the query space range being a line, discretize the query space range into multiple target points; for each of the target points, perform the target point query operation to obtain query results corresponding to the multiple target points; and obtain a query result corresponding to the query space range according to the query results corresponding to the multiple target points; A surface query unit, configured to, in response to the query space range being a surface, obtain multi-level grid data intersecting with the query space range as query grid data; and obtain the measurement data in the query grid data as the query result.

[0143] Optionally, the point query unit includes: A triangulation construction component, configured 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, where the initial triangulation network includes multiple triangles; A boundary setting component, configured to use an adaptive boundary extraction method based on the alpha shape algorithm to generate an underwater terrain boundary line corresponding to the target point; A redundancy removal component, configured to use a centroid topology mapping method to remove redundant triangles outside the underwater terrain boundary line from the initial triangulation network to obtain an underwater terrain model.

[0144] Optionally, the multi-level spatio-temporal index includes a spatial index and a temporal index, and the index construction module 1703 includes: A spatial construction unit, configured to associate the water depth measurement data with the multi-level grid data to obtain a spatial index; A temporal construction unit, configured to associate the time data in the measurement engineering metadata with the spatial index to obtain a temporal index.

[0145] Optionally, the spatial construction unit includes: A positioning construction component is used to obtain the maximum side length of the measurement grid for the water depth measurement data; according to the maximum side length of the measurement grid, a concave hull polygon of the water depth measurement data is constructed; multi-level grid data intersecting with the concave hull polygon is obtained as target grid data; the water depth measurement data is associated with the target grid data to obtain a spatial index. A layer-by-layer construction component is used to associate the water depth measurement data with the segmented data; the water depth measurement data associated with the segmented data is sequentially synchronized to the voyage data and the waterway data.

[0146] Optionally, the time construction unit includes: A time construction component is used to label the project time in the measurement project metadata to the measurement data of the spatial index according to the corresponding relationship between the measurement project metadata and the water depth measurement data as a time index.

[0147] 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.

[0148] Among them, 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 the water depth data of the approach channel according to any embodiment of the present application.

[0149] The computer storage medium stores codes. When the codes are run, the device running the codes implements the method for indexing the water depth data of the approach channel according to any embodiment of the present application.

[0150] In the embodiments of the present application, the "first", "second" (if any) in the names such as "first" and "second" are only used as name identifiers and do not represent the first and second in order.

[0151] From the description of the above embodiments, those skilled in the art can clearly understand that all or part of the steps in the above embodiment methods can be implemented by means of software plus a general hardware platform. Based on such an 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 read-only memory (ROM) / RAM, magnetic disk, optical disc, etc., and includes several instructions to enable 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 some parts of the embodiments of the present application.

[0152] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device embodiments, since they are basically similar to the method embodiments, they are described relatively simply. For the relevant parts, reference can be made to the descriptions in the method embodiments. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative work.

[0153] The above description is only an exemplary embodiment of the present application and is not intended to limit the protection scope of the present application.

Claims

1. A method for indexing water depth data of an approach channel, characterized in that, The method includes: Obtaining measurement data, where the measurement data includes measurement engineering metadata and bathymetric measurement data; Constructing multi-level grid data according to the bathymetric measurement data, where the multi-level grid data includes channel data, the channel data includes segment data, and the segment data includes subsection data; Associating the measurement data with the multi-level grid data to obtain a multi-level spatio-temporal index; Querying the measurement data corresponding to a target query request from the multi-level spatio-temporal index as a query result.

2. The method according to claim 1, characterized in that The querying the measurement data corresponding to a target query request from the multi-level spatio-temporal index as a query result includes: Obtaining a target query request, where the target query request includes a query formula and a data scale; wherein, the query formula is ; R st is the query result, Q st is the spatio-temporal query function, Geo is the query spatial 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, ResType is the output form of the said query result; Among them, the query grid set ; Grid1 is the first grid, Grid2 is the second grid, and Grid n is the nth grid, where n is a positive integer; The i-th grid ; Level i is the grid level, Id i is the grid number, Geo i is the grid range, Attrs i is the grid attribute, where i is a positive integer; Query engineering set ; Proj1 is the first project, Proj2 is the second project, and Proj k is the k-th project, where k is a positive integer; The j-th project ; Id j is the project number, Geo j is the project scope, T j is the project time, Attrs j is the project attribute, where j is a positive integer; Querying the measurement data corresponding to the data scale from the multi-level spatio-temporal index according to the query formula as a query result.

3. The method according to claim 2, characterized in that The querying the measurement data corresponding to the data scale from the multi-level spatio-temporal index according to the query formula as a query result includes: In response to the query space range being a point, taking the query space range as a target point and performing the following target point query operation: In response to the data scale being lower than a preset accuracy value, taking the mean or maximum value of the measurement data in the multi-level grid data corresponding to the target point as the query result; In response to the data scale not being lower than the preset accuracy value, constructing an underwater terrain model according to the measurement data in the multi-level grid data corresponding to the target point; Based on the underwater terrain model, using the inverse distance interpolation method to calculate the water depth value of the target point as the query result; In response to the query space range being a line, discretizing the query space range into multiple target points; For each of the target points, performing the target point query operation to obtain query results corresponding to the multiple target points; Obtaining the query result corresponding to the query space range according to the query results corresponding to the multiple target points; In response to the query space range being a surface, obtaining the multi-level grid data intersecting with the query space range as query grid data; Obtaining the measurement data in the query grid data as the query result.

4. The method according to claim 3, wherein The constructing an underwater terrain model according to 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 triangulation network, where the initial triangulation network includes multiple triangles; Generating an underwater terrain boundary line corresponding to the target point by using an adaptive boundary extraction method based on the alpha shape algorithm; Using the centroid topology mapping method to remove redundant triangles outside the underwater terrain boundary line from the initial triangulation network to obtain an underwater terrain model.

5. The method according to claim 1, wherein The multi-level spatio-temporal index includes a spatial index and a time index. The associating the measurement data with the multi-level grid data to obtain a multi-level spatio-temporal index includes: Associating the bathymetric measurement data with the multi-level grid data to obtain a spatial index; Associating the time data in the measurement engineering metadata with the spatial index to obtain a time index.

6. The method according to claim 5, characterized in that, Associating the water depth measurement data with the multi-level grid data to obtain a spatial index includes: Obtaining the maximum side length of the measurement grid of the water depth measurement data; Constructing a concave hull polygon of the water depth measurement data according to the maximum side length of the measurement grid; Obtaining the multi-level grid data that intersects with the concave hull polygon as target grid data; Associating the water depth measurement data with the target grid data to obtain a spatial index; Alternatively, associating the water depth measurement data with the multi-level grid data to obtain a spatial index includes: Associating the water depth measurement data with the segmented data; Sequentially synchronizing the water depth measurement data associated with the segmented data to the voyage section data and the waterway data.

7. The method according to claim 5, wherein Associating the time data in the measurement project metadata with the spatial index to obtain a time index includes: According to the correspondence between the measurement project metadata and the water depth measurement data, annotating the project time in the measurement project metadata to the measurement data in the spatial index as a time index.

8. An indexing device for the water depth data of the approach channel, characterized in that, The device includes: An acquisition module for acquiring measurement data, where the measurement data includes measurement project metadata and water depth measurement data; A network construction module for constructing multi-level grid data according to the water depth measurement data, where the multi-level grid data includes waterway data, the waterway data includes voyage section data, and the voyage section data includes segmented data; An index construction module for associating the measurement data with the multi-level grid data to obtain a multi-level spatio-temporal index; A query module for querying the measurement data corresponding to a target query request from the multi-level spatio-temporal index as a query result.

9. An index device for the water depth data of the approach channel, 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 the water depth data of the approach channel according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The computer storage medium stores codes. When the codes are run, the computer storage device running the codes implements the method for indexing the water depth data of the approach channel according to any one of claims 1 to 7.

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