Water depth measurement precision inspection method combining vertical reference conversion and dynamic terrain discrimination

Abstract

The invention provides a water depth measurement precision inspection method combining vertical reference conversion and dynamic terrain discrimination, and relates to the technical field of marine surveying and mapping, and the method comprises the steps: building a depth reference data set of theoretical depth reference grid points, and building a sonar measurement point data set containing main measurement line data and inspection line data; structuring the depth reference data set to generate a grid storage unit with a unique identification code; carrying out Hash index positioning on the sonar measuring point data set by adopting an identification code, and determining a grid storage unit to which each sonar measuring point belongs; and extracting coordinates and L values of four corners of the determined grid storage unit, and calculating the L value at each sonar measuring point through bilinear interpolation to form a sonar measuring point L value data set. According to the invention, the water depth comparison strategy can be optimized, the rationality and accuracy of the water depth measurement precision check are improved, and the automatic processing of the check process is realized.

Description

Technical Field

[0001] This invention relates to the field of marine surveying technology, and in particular to a method for checking the accuracy of water depth measurement that combines vertical datum conversion and dynamic topography discrimination. Background Technology

[0002] Water depth measurement is a core technical link in marine surveying. The accuracy of its measurement results largely determines the quality of work in the marine field, such as navigation safety, marine resource development, and engineering construction. Water depth measurement accuracy inspection, as a key means of quality control of measurement results, is a core step to ensure the reliability and application value of water depth measurement data.

[0003] Currently, the mainstream approach to depth measurement accuracy checks in the industry is to use the nearest point matching principle for depth comparison. This involves selecting only the point with the closest spatial geometric distance between the check point and the measurement point for depth value comparison and analysis. The technical flaws of this method are: it fails to incorporate the spatial characteristics of the seabed topography to construct an adaptive discrimination mechanism, using an indiscriminate single nearest point comparison strategy to complete the depth accuracy check across the entire survey area, completely ignoring the actual undulations of the seabed topography. This deficiency leads to an inaccurate reflection of the actual spatial characteristics of the topography in complex areas with drastic seabed topographic changes, such as steep slopes, trenches, and seamounts. This results in a significant distortion between the depth comparison results and the actual measurement accuracy. Furthermore, the distortion of the comparison results further leads to a high gross error rate and an insufficient number of effective comparison points during the depth accuracy assessment process. This makes it difficult to meet the actual technical requirements of marine surveying for depth measurement accuracy checks under complex topographic conditions, as the quality control of depth measurement results lacks reliable judgment criteria. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for checking the accuracy of water depth measurement by combining vertical reference conversion and dynamic terrain discrimination. This method can optimize the water depth comparison strategy, improve the rationality and accuracy of water depth measurement accuracy checking, and at the same time realize the automation of the checking process.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: A method for checking the accuracy of water depth measurements by combining vertical datum conversion and dynamic terrain discrimination, the method comprising: Establish a depth benchmark dataset of theoretical depth benchmark grid points, and establish a sonar measurement point dataset containing main survey line data and inspection line data; The depth benchmark dataset is structured to generate grid storage units with unique identifiers; the identifiers are used to perform hash indexing on the sonar measurement point dataset to determine the grid storage unit to which each sonar measurement point belongs. The coordinates and L values ​​of the four corner points of the determined grid storage unit are extracted and used to calculate the L value at each sonar measurement point through bilinear interpolation, forming a sonar measurement point L value dataset. Using the sonar measurement point L-value dataset, the normal height of the sonar measurement points in the main measurement line and the check line is uniformly converted into the water depth below the theoretical lowest tide level, and the water depth results of the main measurement point and the check point are obtained respectively. Based on the water depth results at the checkpoints, the radius of the comparison window is dynamically determined according to the measurement scale, with each checkpoint as the center. Spatial retrieval is then performed within this radius to obtain all the main measurement points contained in each comparison window. For each comparison window, the window is determined to be a flat or undulating terrain area by comparing the maximum difference in water depth at the main measuring point within the window with the corresponding water depth interval threshold. For windows identified as flat terrain areas and windows identified as undulating terrain areas, calculate their water depth discrepancies respectively; An evaluation is conducted based on all discrepancies in water depth, and qualified and unqualified points are counted. The mean error of depth measurement is calculated, and the overall quality of the measurement results is judged to be qualified.

[0006] Furthermore, a depth benchmark dataset of theoretical depth benchmark grid points is established, and a sonar measurement point dataset containing master survey line data and check line data is also established, including: Obtain the raw data of the theoretical depth benchmark grid points, the raw data including the coordinates of each grid point and its L value; The acquired raw data is parsed to extract and organize the coordinates and L values ​​of each grid point in order to construct a computer-recognizable depth benchmark dataset. Obtain normal elevation data of sonar measurement points based on the 1985 National Elevation Datum, classify this data into main survey lines and inspection lines, and obtain the classified main survey line data and inspection line data. The classified main survey line data and check line data are organized and stored separately to form a structured sonar measurement point dataset.

[0007] Furthermore, the depth benchmark dataset is structured to generate grid storage units with unique identifiers; the identifiers are then used to perform hash indexing on the sonar measurement point dataset to determine the grid storage unit to which each sonar measurement point belongs, including: The depth benchmark dataset is structured, and based on the spatial distribution of the grid points in the depth benchmark dataset, a grid storage unit defined by the coordinates of the four corner points and the L value is constructed. Based on the coordinates of the four corner points of each grid storage unit, a grid identifier code with spatial uniqueness is generated according to a preset encoding rule; Using the grid identifier as the key, a hash table is established, and each grid storage unit is associated with the corresponding grid identifier for storage, thus constructing a hash index structure; Based on the coordinates of the sonar measurement points in the dataset, the grid identifier code corresponding to the spatial location of each sonar measurement point is determined by querying the constructed hash index structure, thereby locating the grid storage unit to which each sonar measurement point belongs.

[0008] Furthermore, the coordinates and L values ​​of the four corner points of the determined grid storage unit are extracted and used to calculate the L values ​​at each sonar measurement point through bilinear interpolation, forming a sonar measurement point L value dataset, including: Based on the grid storage unit to which each sonar measurement point belongs, extract the coordinates of the four corner points and the L value of the corresponding grid storage unit to obtain the corner point data set of each sonar measurement point. Based on the corner data set of each sonar measurement point, bilinear interpolation is performed on each sonar measurement point. The precise L value of each sonar measurement point at its specific coordinate position is obtained through the bilinear interpolation calculation. The precise L values ​​of all sonar measurement points are collected and integrated to form a unified dataset of sonar measurement point L values.

[0009] Furthermore, using the sonar measurement point L-value dataset, the normal elevations of the sonar measurement points in the main measurement line and the check line are uniformly converted to the water depth below the theoretical lowest tide level, obtaining the water depth results for the main measurement points and the check points, including: For each sonar measurement point in the main measurement line dataset and the inspection line dataset, extract the corresponding L value of the measurement point from the sonar measurement point L value dataset. Based on the normal height and extracted L value of each sonar measuring point, the water depth value of each sonar measuring point below the theoretical lowest tide level is obtained by conversion calculation. For all sonar measurement points in the main measurement line dataset and the check line dataset, the water depth values ​​calculated by each sonar measurement point below the theoretical lowest tide level are summarized to form the corresponding water depth results for the main measurement point and the water depth results for the check point.

[0010] Furthermore, based on the water depth results at the checkpoints, the radius of the comparison window is dynamically determined according to the measurement scale, with each checkpoint as the center. Spatial retrieval is then performed within this radius to obtain all the main measurement points included in each comparison window, including: For each checkpoint in the water depth results, the corresponding comparison window radius is dynamically calculated according to the predetermined measurement scale. Spatial retrieval is performed in the water depth results of the main measurement points, with each checkpoint as the center and its corresponding comparison window radius as the spatial range. By performing the spatial retrieval, all main test points falling within the window range corresponding to each checkpoint are identified and obtained; A spatial association is established between each checkpoint and all the acquired main test points, thereby determining the set of all main test points contained within each comparison window.

[0011] Furthermore, for each comparison window, based on the comparison between the maximum difference in water depth at the main measuring point within the window and the corresponding water depth interval threshold, it is determined whether the window belongs to a flat terrain area or a terrain undulating area, including: For each comparison window, the average water depth of all main measuring points within each comparison window is calculated to obtain the reference water depth for each comparison window; The water depth range to which it belongs is determined based on the reference water depth, and the preset threshold corresponding to the water depth range is obtained; Calculate the difference between the maximum and minimum water depths at all main measuring points within the same comparison window to obtain the maximum difference; The maximum difference is compared with a preset threshold, and the terrain category of the same comparison window is determined based on the comparison result. If the maximum difference does not exceed the preset threshold, the window is determined to be a flat terrain area; if the maximum difference exceeds the preset threshold, the window is determined to be a undulating terrain area.

[0012] Furthermore, for windows identified as flat terrain areas and windows identified as undulating terrain areas, the water depth discrepancy values ​​are calculated separately, including: Obtain the terrain category determination result for each comparison window, whereby the terrain category includes flat terrain or undulating terrain. For windows identified as flat terrain areas, the nearest comparison method is used to select the main measuring point within the window that is geometrically closest to the check point. The water depth discrepancy value of this window is calculated based on the water depth values ​​of the main measuring point and the check point. For windows identified as areas with undulating terrain, the inverse distance weighted comparison method is used. The weights are defined based on the geometric distances between each main measuring point and the check point within the window, and the water depth discrepancy value of this window is calculated by combining the water depth values ​​of all main measuring points and check points.

[0013] Furthermore, based on all discrepancies in water depth, an evaluation is conducted, points of acceptance and rejection are statistically analyzed, and the mean square error of the depth measurement is calculated. Based on this, a pass / fail judgment is made on the overall quality of the measurement results, including: Obtain all calculated water depth discrepancies; Each water depth discrepancy is compared with its corresponding water depth range threshold to determine whether each point is a qualified comparison point, and the number of qualified points and the number of unqualified points are counted. Based on the water depth discrepancies at all qualified comparison points, the depth sounding error is calculated. Based on the ratio of unqualified points to qualified points obtained from statistics, and the mean square error of the depth measurement, the overall quality of the water depth measurement results is finally judged as qualified according to the preset specifications.

[0014] The above-described solution of the present invention has at least the following beneficial effects: Because it employs a grid-based structure combined with hash indexes for rapid positioning, achieves accurate vertical benchmark conversion through bilinear interpolation, sets up a dynamic comparison window, and establishes a terrain-adaptive discrimination mechanism, it uses differentiated methods to calculate water depth discrepancies for flat and undulating terrains and completes quantitative quality assessment. Therefore, it effectively overcomes the technical problems of traditional methods that ignore seabed topographic undulations by comparing a single nearest point, resulting in distorted water depth comparison results and a lack of reliable basis for accuracy assessment. This improves the accuracy and rationality of water depth measurement accuracy inspection, increases inspection efficiency, and provides a reliable and effective basis for quality control of marine surveying and mapping results. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a method for checking the accuracy of water depth measurement that combines vertical datum conversion and dynamic terrain discrimination, provided by an embodiment of the present invention. Figure 2 This is a flowchart illustrating another method for checking the accuracy of water depth measurement by combining vertical reference conversion and dynamic terrain discrimination, provided in the specific implementation of an embodiment of the present invention. Figure 3 This is a schematic diagram of vertical reference conversion in a water depth measurement accuracy check method that combines vertical reference conversion and dynamic terrain discrimination, provided in a specific implementation process of an embodiment of the present invention. Figure 4 This is a schematic diagram of water depth discrepancy comparison provided in the specific implementation process of an embodiment of the present invention, which is a water depth measurement accuracy check method combining vertical reference conversion and dynamic terrain discrimination. Figure 5 This is an example diagram of the depth measurement accuracy check result report of a depth measurement accuracy check method that combines vertical reference conversion and dynamic terrain discrimination, provided in the specific implementation process of an embodiment of the present invention; Detailed Implementation

[0016] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0017] like Figure 1As shown, an embodiment of the present invention proposes a method for checking the accuracy of water depth measurement by combining vertical datum conversion and dynamic terrain discrimination. The method includes the following steps: Step 1: Establish a depth benchmark dataset of theoretical depth benchmark grid points, and establish a sonar measurement point dataset containing main survey line data and inspection line data; Step 2: Structure the depth benchmark dataset to generate grid storage units with unique identifiers; use the identifiers to perform hash indexing on the sonar measurement point dataset to determine the grid storage unit to which each sonar measurement point belongs. Step 3: Extract the coordinates and L values ​​of the four corner points of the determined grid storage unit, which are used to calculate the L values ​​at each sonar measurement point through bilinear interpolation to form a sonar measurement point L value dataset. Step 4: Using the sonar measurement point L-value dataset, the normal height of the sonar measurement points in the main measurement line and the check line is uniformly converted into the water depth below the theoretical lowest tide level, and the water depth results of the main measurement point and the check point are obtained respectively. Step 5: Based on the water depth results of the checkpoints, take each checkpoint as the center, dynamically determine the radius of the comparison window according to the measurement scale, and perform spatial retrieval within this radius to obtain all the main measurement points contained in each comparison window. Step 6: For each comparison window, determine whether the window belongs to a flat terrain area or a undulating terrain area by comparing the maximum difference in water depth at the main measuring point within the window with the corresponding water depth interval threshold. Step 7: Calculate the water depth discrepancy value for windows identified as flat terrain areas and windows identified as undulating terrain areas, respectively. Step 8: Evaluate all water depth discrepancies, count the qualified and unqualified points, calculate the depth measurement error, and make a qualification judgment on the overall quality of the measurement results.

[0018] In this embodiment of the invention, precise conversion of vertical reference can be achieved, adaptive discrimination can be completed based on seabed topographic features, and water depth discrepancies can be calculated in a differentiated manner for different topographic regions. Furthermore, quantitative evaluation and overall qualification judgment of measurement results can be carried out. The method effectively improves the accuracy and rationality of water depth measurement accuracy inspection, adapts to the accuracy inspection needs of complex terrain, reduces human intervention, and realizes automated processing of the inspection process. It not only improves the efficiency of inspection work, but also provides accurate and reliable judgment basis for the quality control of marine surveying and mapping results.

[0019] In a preferred embodiment of the present invention, step 1 above may include: Step 1.1: Obtain the raw data of the theoretical depth benchmark grid points. The raw data includes the coordinates and L-values ​​of each grid point. Specifically, this involves: first, accurately delineating the geographical boundary of the survey area to be checked for depth measurement accuracy using latitude and longitude values, and determining the maximum and minimum latitude and longitude values ​​of the survey area to lock in the complete geographical range to be processed; the theoretical depth benchmark grid points are regular spatial grid nodes constructed based on the theoretical depth benchmark surface in marine mapping. This step uses a standard 1'×1' grid resolution by default, but supports expansion to other grid resolutions. Each grid point corresponds to a unique latitude and longitude location within the survey area and is a fundamental geographic spatial node representing the spatial distribution of the seabed depth benchmark, providing accurate data for subsequent vertical benchmark conversion. The spatial location reference; the depth benchmark feature L value is a key vertical conversion parameter connecting the 1985 National Elevation Datum and the theoretical lowest tide level. It represents the vertical distance from the 1985 National Elevation Datum to the theoretical lowest tide level at the corresponding grid point, and is the core numerical basis for converting water depth results between different vertical benchmarks. Based on the defined geographical scope of the survey area, the original data of the matching theoretical depth benchmark grid points are accurately retrieved from the authoritative theoretical depth benchmark grid data resource library of the marine surveying and mapping industry. This ensures that in the retrieved original data, each theoretical depth benchmark grid point is completely and accurately associated with the corresponding latitude and longitude coordinate information and the depth benchmark feature L value, without any invalid records with missing data items, coordinate offsets, or abnormal L value values.

[0020] Step 1.2 involves parsing the acquired raw data, extracting and organizing the coordinates and L values ​​of each grid point to construct a computer-recognizable depth benchmark dataset. Specifically, this includes: firstly, performing multi-dimensional pre-verification on the retrieved theoretical depth benchmark grid point raw data, sequentially completing data integrity verification, coordinate rationality verification, and L value validity verification, removing null values, duplicate grid point data, invalid data with latitude and longitude coordinates exceeding the range of the area to be checked, and abnormal data with abnormal L values; then, performing structured parsing on each grid point of the verified raw data, accurately extracting the latitude and longitude coordinates and corresponding depth benchmark L values ​​for each grid point; subsequently, organizing the parsed data according to the spatial increasing pattern of latitude and longitude coordinates: that is, increasing the longitude from west to east. The spatial pattern of increasing latitude from south to north was used to arrange all extracted grid data points in an orderly manner, ensuring that the order of the grid data points corresponds one-to-one with their actual geographical distribution, thus guaranteeing the spatial correlation of the data. Finally, the organized grid data points were converted into a geospatial vector data format that can be directly read and processed by computers. Each grid point was matched with an independent spatial vector element, and each vector element was fully associated with the latitude and longitude coordinates and depth benchmark L-value of the corresponding grid point. This achieved integrated storage of spatial location and attribute data, allowing computers to directly identify the spatial characteristics and attribute information of the data. This completed the construction of a depth benchmark dataset covering the entire survey area, and the dataset was stored in a local dedicated data directory for easy retrieval and use in subsequent steps.

[0021] Step 1.3: Obtain normal elevation data of sonar measurement points based on the 1985 National Height Datum. Classify this data into main survey lines and inspection lines to obtain the classified main survey line data and inspection line data. Specifically, this includes: first, extracting normal elevation data of all sonar measurement points based on the 1985 National Height Datum within the entire area to be inspected. During the extraction process, all operational survey lines in the survey area are covered to ensure no measurement point data is missed. Each extracted measurement point data completely includes the measurement point's latitude and longitude coordinates, normal elevation value under the 1985 National Height Datum, on-site operation survey line number, measurement point acquisition time, and operation equipment identification, etc. Basic information was verified, with no missing or abnormal information items. Next, the formal survey line planning technical document, prepared and approved before the water depth measurement operation, was retrieved. This document clearly defines the numbering rules, attribute classification standards, and corresponding operational scope of the main survey lines and check lines, serving as the sole basis for determining survey line attributes. Subsequently, the survey line number of each sonar measurement point was checked one by one, matching the measurement point's survey line number with the main and check line numbering rules in the planning document to accurately determine the survey line attribute of each measurement point and mark it accordingly. A second verification was conducted after verification to avoid misjudgments or omissions in survey line attribute determination. Finally, strictly adhering to the principle of one verification per measurement point, without duplication or omission, all normal high-yield data from all sonar measurement points were accurately and completely classified into two independent categories: main survey line data and check line data, based on the marked survey line attributes. These categories were then collected separately, retaining all core basic information for each measurement point.

[0022] Step 1.4 involves organizing and storing the classified main survey line data and check line data to form a structured sonar measurement point dataset. Specifically, this includes: refining and organizing the classified main survey line data and check line data at the single survey line and single measurement point dimensions; adding attribute identifiers such as survey line type, survey line number, and start and end latitude and longitude to each survey line; adding unique identifiers such as a unique measurement point code, survey line affiliation, and data collection timestamp to each measurement point; and removing invalid measurement point records with missing coordinates or abnormally high values ​​from both types of data. Then, following the geospatial data... The industry standard for structured storage establishes separate relational database storage modules for main survey line data and inspection line data. The main survey line data storage module uses the main survey line measurement point information table as its core data table. The table is designed with core fields such as unique measurement point code, survey line number, measurement point latitude and longitude coordinates, normal height value under the 1985 National Height Datum, collection time, and operation equipment identification as needed. The field types match the attribute characteristics of the corresponding data. For example, latitude and longitude are set to high-precision floating point type, and various numbers are set to character type. Combined indexes are established for the survey line number and measurement point latitude and longitude coordinate fields to improve data retrieval efficiency.

[0023] The inspection line data storage module uses the inspection line measurement point information table as its core data table. The table structure is completely consistent with the core data table of the main inspection line data storage module in terms of field dimensions and field types. Only a comparison priority auxiliary field is added for priority determination in the subsequent water depth comparison process. Similarly, a combined index is established for the inspection line number and measurement point latitude and longitude coordinate fields. The two storage modules are physically deployed independently and their data does not overlap, ensuring the independence and security of the management of the main and inspection line data. Subsequently, unified latitude and longitude coordinate retrieval fields and measurement point number retrieval fields are set for the two modules, and a spatial index is built. Finally, the two independent storage modules are logically linked and integrated to form a structured sonar measurement point dataset with clear measurement line classification, complete identification information, and rich retrieval dimensions.

[0024] In a preferred embodiment of the present invention, step 2 above may include: Step 2.1 involves structuring the depth benchmark dataset and constructing a grid storage unit defined by the coordinates of the four corner points and the L value based on the spatial distribution of the grid points in the depth benchmark dataset. Specifically, this includes: first, confirming that the basic grid resolution of the depth benchmark dataset is 1'×1'; then, clarifying the geographic spatial row and column distribution logic of all grid points in the dataset to ensure that the grids within the survey area are continuously arranged according to latitude and longitude, without regional overlap or geographic gaps; next, performing targeted spatial structuring processing on the depth benchmark dataset, retaining the core information of the grid point's latitude and longitude coordinates and the depth benchmark L value while removing redundant auxiliary information; subsequently, based on the actual geographic spatial distribution pattern of the grid points, dividing the survey area into independent geographic regions grid by grid according to the latitude and longitude range corresponding to the resolution, with each divided grid region corresponding to the actual geographic distribution of the survey area. The system first defines a dedicated sub-region within the survey area. Then, for each independent grid region, four vertices are precisely marked as the four corner points of the grid based on its geographic spatial boundary, ensuring that the positions of the four corner points perfectly match the actual latitude and longitude boundaries of the grid region. Next, the latitude and longitude coordinates and depth benchmark L value corresponding to each four corner point are precisely extracted from the depth benchmark dataset one by one, and the extracted information is simultaneously checked against the original records in the dataset to ensure that there are no mismatches, missing information, or anomalies in the coordinates and L values ​​of the four corner points. Finally, the latitude and longitude coordinates of the four corner points and the corresponding depth benchmark L values ​​of each grid region are integrated to establish a dedicated set of spatial location and attribute parameter information for each grid region. With this information set as the core, a grid storage unit is constructed that is completely defined by the coordinates of the four corner points and the L value and is precisely matched with the actual geographic space of the survey area.

[0025] Step 2.2: Based on the coordinates of the four corner points of each grid storage unit, generate a spatially unique grid identifier code according to a preset encoding rule. Specifically, this includes: performing a secondary verification of the positions of the four corner points of each grid storage unit to confirm that the spatial positions of the lower left, upper right, upper left, and lower right corners completely match the geographical boundaries of the grid area, with no mislabeling. Then, standardize the latitude and longitude coordinates of the four corner points, unifying the number of decimal places retained for the coordinate values, removing redundant symbols and spaces, and ensuring that the coordinate character expression format of all grid storage units is consistent, thus ensuring the standardization of coordinate information. Subsequently, strictly following the preset encoding rule of lower left longitude + latitude - upper right longitude + latitude - upper left longitude + latitude - lower right longitude + latitude, extract the complete latitude and longitude coordinate values ​​of each corner point in this fixed order. The plus sign (+) is used as a connector for the latitude and longitude coordinates of a single corner point, and the minus sign (-) is used as a separator between different corner points. Coordinate information is sequentially concatenated to generate the initial grid identifier code for each grid storage unit. After generating the initial grid identifier codes for the entire survey area, a global traversal comparison is used for uniqueness verification. All initial identifier codes are compared pairwise to check for duplicate codes. If a duplicate code is found, the corresponding grid storage unit is immediately traced, the four corner point coordinates are recalibrated, and a new identifier code is generated again according to the rules until no duplicate codes exist. Finally, all verified grid identifier codes undergo a format uniformity check to ensure that the character length, connector usage, and numerical expression are completely consistent. The final grid identifier code is then linked one-to-one with the corresponding grid storage unit, completing the synchronous archiving of the identifier information and the grid storage unit.

[0026] Step 2.3: Using the grid identifier as the key, establish a hash table, associating each grid storage unit with its corresponding grid identifier, and constructing a hash index structure. Specifically, this includes: first, building the basic storage architecture of the hash table, precisely dividing the hash table into two core parts: key fields and attribute fields; setting unique storage constraints for the grid identifier for the key fields; and storing all core data of the grid storage unit according to information type in the attribute fields, including the latitude and longitude coordinates of the four corner points, the depth baseline L value of the four corner points, and the geographical spatial boundary value of the grid area. Then, formulating unified storage rules for the hash table and determining the character storage of the grid identifier. The data entry specifications for format and attribute fields, as well as the one-to-one mapping relationship between keywords and attribute information, were established. Then, following the arrangement order of the grid storage units in the survey area, each grid identifier was matched one-to-one with the classification attribute information of the corresponding grid storage unit and entered into the corresponding field of the hash table. Real-time information verification was performed during the entry process to avoid omissions of identifiers or mismatches of attribute information. After all grid information for the entire survey area was entered, a full-domain consistency verification was conducted to check for duplicate identifiers in key fields and for missing or abnormal information in attribute fields. Finally, using the grid identifier as the core retrieval key, a hierarchical hash index structure was constructed based on the verified hash table. The hierarchical hash index structure uses the geographical scope of the survey area as the top-level division basis, dividing the entire survey area into several first-level index partitions according to latitude and longitude intervals. Each first-level partition is further subdivided into second-level index sub-partitions according to a 1'×1' grid resolution. Each level of the index uses the grid identifier code as the core retrieval key, forming a three-level hierarchical architecture of large survey area partitions, small grid partitions, and specific grid units. This hierarchical retrieval method can quickly narrow the query scope and improve the retrieval efficiency of the grid storage unit. At the same time, it determines the fast retrieval mapping rules of the index (i.e., first extract the latitude and longitude feature segments representing the first-level partitions of the survey area from the grid identifier code to be retrieved, and match them to...). The corresponding first-level index partition is then used to extract the latitude and longitude feature segments representing the second-level sub-regions from the identifier code, locate the second-level index sub-regions under the first-level partition, and finally accurately match the corresponding grid storage units within the second-level sub-regions using the complete grid identifier code (achieving a direct mapping from the identifier code to the attribute information of the grid storage unit). This allows the index to be directly mapped to all attribute information of the corresponding grid storage unit through the identifier code, and performs basic retrieval performance optimization on the hash index structure. Finally, the hash index structure is exclusively associated and bound to the hash table to ensure the synchronization between index retrieval and data in the table, completing the construction of the overall hash index structure.

[0027] Step 2.4: Based on the coordinates of the sonar measurement points in the dataset, the grid identifier code corresponding to the spatial location of each sonar measurement point is determined by querying the constructed hash index structure. This then locates the grid storage unit to which each sonar measurement point belongs. Specifically, this includes: performing standardized preprocessing on the latitude and longitude coordinates of all measurement points in the sonar measurement point dataset, unifying the numerical retention precision and coordinate character representation format to be completely consistent with the grid storage unit and hash index structure. Simultaneously, abnormal and invalid data exceeding the geographical range of the measurement point coordinates are screened and removed. Then, according to the structured storage order of the sonar measurement point dataset, spatial location retrieval operations are performed on each individual measurement point. The preprocessed latitude and longitude coordinates of the measurement points are substituted into the retrieval rules of the hash index structure. Through precise matching of the coordinates with the geographical spatial range of the grid storage unit, the spatial location of the measurement point is selected. The corresponding unique grid identifier is then used. The retrieved grid identifier undergoes a double validity check: first, it is verified that the identifier exists in the key fields of the hash table and is a valid code; second, it is verified that the latitude and longitude coordinates of the measurement point are completely within the geographic spatial boundary of the grid storage unit corresponding to the identifier, ensuring no cross-grid or grid matching deviations. If the verification fails, a spatial search is performed again to check for coordinate matching issues. Once the identifier verification is successful, the valid grid identifier is used as the retrieval basis to accurately retrieve all core information of the corresponding grid storage unit from the hash table, completing the precise positioning of the grid storage unit to which a single sonar measurement point belongs. Finally, the positioning results of all measurement points are batch archived according to the measurement point number, retaining a unique association record between the measurement point and the corresponding grid storage unit to ensure that this association information can be quickly retrieved in subsequent steps.

[0028] In a preferred embodiment of the present invention, step 3 above may include: Step 3.1: Based on the grid storage unit to which each sonar measurement point belongs, extract the coordinates of the four corner points and the L value of the corresponding grid storage unit to obtain the corner point data set of each sonar measurement point. Specifically, this includes: first, retrieving the unique association and positioning record between the archived sonar measurement point and the grid storage unit. This record contains the unique code of the measurement point and the grid identification code of the corresponding grid storage unit. Each sonar measurement point is accurately matched based on its unique code, and then its grid storage unit is located using the grid identification code. Subsequently, from the attribute database of the grid storage unit, all parameters required for bilinear interpolation calculation are extracted in the spatial order of the grid points: confirming the grid point index sequence. i , j The corresponding row and column positions of the grid points in the survey area, and the x-coordinates of the grid points. x i (Horizontal coordinate of the grid in the lower left corner) x i+1 (Horizontal coordinates of the grid in the lower right corner) and vertical coordinates y j (Lower left corner grid coordinates) yj+1 (Top-left grid ordinate) must meet the accuracy requirements of a 1'×1' grid resolution, with latitude and longitude retained to three decimal places after the second. Simultaneously, extract the depth reference L values ​​corresponding to the four corner grid points: L i,j (L value of grid point in the lower left corner) L i+1,j (L value of grid point in the lower right corner) L i,j+1 (L value of the top left grid point) L i+1,j+1 (L value of grid point in the upper right corner), and simultaneously extract the horizontal and vertical coordinates of the current sonar measurement point. x , y (Maintaining consistent accuracy with grid point coordinates); During the extraction process, each parameter needs to be double-checked: firstly, the grid point index needs to be checked. i , j With coordinates x i 、x i+1 、y j 、y j+1 First, the spatial correspondence is verified to ensure no misalignment of subscripts and coordinates. Second, the binding relationship between the coordinates of each grid point and the corresponding L value is checked to avoid L value mismatch. Finally, taking a single sonar measurement point as an independent unit, all the verified parameters are organized into a structured corner point data set. The set is classified and stored in the order of grid point subscript, coordinate, L value, and measurement point coordinate, providing complete and accurate parameter input for subsequent bilinear interpolation calculations.

[0029] Step 3.2: Based on the corner data set of each sonar measurement point, perform bilinear interpolation calculation for each sonar measurement point. Specifically, this includes: preprocessing and verifying parameters for the corner data set of each sonar measurement point: the first step is to verify the coordinate range and confirm the coordinates of the sonar measurement points. x In x i and x i+1 between, y In y j and y j+1 The first step is to ensure that the measuring point is within the grid storage unit; if it is outside the range, return to relocate the grid unit. The second step is to verify the parameter accuracy, checking whether the numerical accuracy of all coordinates and L values ​​is consistent. Coordinates are retained to three decimal places after the second, and L values ​​are retained to the millimeter level. If the accuracy is inconsistent, normalization is performed. The third step is to verify the rationality of the L value and make a preliminary judgment. L i,j , L i+1,j ,L i,j+1 , L i+1,j+1 Whether it falls within the normal range of the depth benchmark L value for the survey area, excluding obvious outliers; after parameter verification, perform bilinear interpolation calculation: first calculate the denominator term. and Based on a 1'×1' grid resolution, with a fixed latitude and longitude difference in the denominator, pre-calculation can be performed to reduce redundant calculations; then, the four weighting coefficients are calculated separately as follows: , ; , ; The calculation should be rounded to six decimal places to avoid error accumulation; finally, the weighting coefficients and the corresponding grid point L values ​​should be substituted into the overall formula. Multiplication and addition operations are performed step by step, and the values ​​are checked for abnormalities after each step to ensure that the calculation process is traceable.

[0030] Step 3.3: Through bilinear interpolation, obtain the precise L value for each sonar measurement point at its specific coordinate position. This specifically includes: extracting the final value from the calculation results; this value is the sonar measurement point's coordinates at its specific horizontal and vertical coordinates. x, y The precise L value at ) L First, a double verification is performed on the precise L value: The first verification checks the calculation logic, verifying whether the sum of the four weight coefficients is 1, allowing a calculation deviation of ±0.000001. If the deviation exceeds this range, the weight calculation process is backtracked for inspection, focusing on whether there are any errors in the calculation of coordinate differences and denominator terms; The second verification verifies the rationality of the value, taking the maximum and minimum values ​​of the L values ​​at the four corner points of the grid storage unit to confirm the precise L value. L If the value is within this range, it is considered abnormal if it exceeds the range, and the parameter input and calculation steps need to be rechecked; after successful verification, the precise L value will be obtained. L A one-to-one association is established with the core information of the sonar measurement point: the association information includes the unique code of the measurement point, latitude and longitude coordinates ( x,y The system records the grid identification code and survey line type (main survey line or inspection line), and simultaneously records key intermediate data during the bilinear interpolation calculation process, including four weighting coefficients and L-values ​​at the four corner points. After association and binding, a survey point-L-value association record table is generated. The record table is stored in a structured format, such as a table, with each row corresponding to a survey point and each column corresponding to an item of information, which facilitates subsequent data traceability and anomaly investigation. Finally, the precise L-value and association information of each survey point are archived separately to a temporary database to ensure that the information of a single survey point is stored independently and is not confused with other survey points.

[0031] Step 3.4: Summarize the precise L values ​​of all sonar measurement points and integrate them to form a unified sonar measurement point L value dataset. Specifically, this includes: first, according to the classification criteria of the main measurement line and the inspection line, and combined with the unique coding order of the measurement points, batch retrieve the associated information of all sonar measurement points, including unique codes and coordinates. x,y The process involves recording survey line types, grid identification codes, precise L-values, and weight calculation records, followed by multi-dimensional data verification: data integrity verification ensures no missing information items, L-value validity verification excludes outliers exceeding reasonable ranges, weight correlation verification confirms the sum of weights meets requirements, and all unqualified survey point data is removed. Subsequently, qualified data is standardized and organized, and the precision of the precise L-values ​​(6 decimal places) and character expression format are unified. Finally, in accordance with geospatial data storage specifications, the core information of the survey points and the precise L-values ​​are integrated and archived to construct a unified sonar survey point L-value dataset that covers the entire survey area, is clearly categorized, and has complete information. This provides core parameter support for the subsequent vertical datum conversion from the 1985 National Height Datum to the theoretical lowest tide level.

[0032] In a preferred embodiment of the present invention, step 4 above may include: Step 4.1: For each sonar measurement point in the main survey line dataset and the inspection line dataset, extract the corresponding L value from the sonar measurement point L value dataset. Specifically, this includes: first, traversing each sonar measurement point record in the main survey line dataset and the inspection line dataset, extracting the unique code of the measurement point in each record. This code is the unique identifier of the measurement point within the entire survey area. Then, using this unique code as the precise retrieval key, perform an exact matching query in the sonar measurement point L value dataset. During the matching process, a matching rule of complete equality is used, and fuzzy matching or approximate matching is not performed to ensure the uniqueness of the matching results. After the query is completed, the matching results are verified. If the number of matched L value records is 0, the measurement point is marked as an L value missing anomaly. If the number of matched L value records is greater than 1, the measurement point is marked as an L value duplicate. If an anomaly is found, and the number of matched L-value records is 1, the precise L-value of that record is extracted. Simultaneously, the extracted L-value is validated to ensure it falls within the normal range of L-values ​​for the survey area depth baseline, without null values, negative values, or extreme values ​​exceeding a reasonable range. If an L-value is abnormal, the survey point is marked as having an abnormal L-value. After all validations pass, the extracted precise L-value is associated with the corresponding survey point's unique code, survey line type, survey point coordinates, and other core information to generate a survey point-L-value association table. Each record in the table corresponds to a sonar survey point, clearly recording the survey point's unique code, survey line type, survey point coordinates, precise L-value, and extraction status. Finally, the survey point-L-value association table is batch-reviewed, and the number of missing, duplicate, and abnormal survey points is counted to generate an extraction validation report, providing reliable L-value input for subsequent steps.

[0033] Step 4.2: Based on the normal height and extracted L value of each sonar measuring point, the water depth value below the theoretical lowest tide level for each sonar measuring point is obtained through conversion calculation. Specifically, this includes: first determining the vertical datum conversion formula, that is, the formula for calculating the water depth value below the theoretical lowest tide level is: D = -(H + L) in, D This indicates the water depth at the sonar measurement point below the theoretical lowest tide level, in meters. H This indicates the normal elevation of the measuring point under the 1985 National Height Datum, in meters. L This represents the precise L-value of the depth benchmark corresponding to the measuring point, in meters; then, iterate through each record in the measuring point-L-value association table to extract the normal elevation of the corresponding measuring point. H With the already associated precise L value L Simultaneous verification during extraction H and L The numerical precision is ensured to maintain consistent decimal places for both methods, avoiding calculation errors caused by inconsistencies in precision. After verification, the parameters are substituted into the conversion formula for calculation, using double-precision floating-point arithmetic to retain sufficient intermediate precision and avoid the accumulation of errors caused by rounding. After calculation, the obtained water depth value is... D A multi-dimensional rationality check is performed, with the first check being the sign of the numerical value to confirm its validity. D The values ​​taken conform to the physical meaning of the water depth in the survey area, and there are no negative or extreme values ​​that obviously violate the actual situation. The second verification of the numerical range confirms... D If the water depth falls within the standard range of historical water depth data for the survey area, and exceeds this range, it is marked as a water depth anomaly. A third layer of verification calculation logic is then applied for review. H and L The order of substitutions and operators must be carefully checked to ensure no formula misuse or parameter mismatch; after all checks pass, the calculated water depth value will be... D The core information of the corresponding measuring point is associated and bound to generate a measuring point-water depth association table, which records the unique code of the measuring point, the type of measuring line, and the normal elevation. H Precise L value L water depth value D The calculation status is then checked, and finally, the measurement point-water depth correlation table is reviewed in batches. The number of measurement points with calculation anomalies is counted, and a calculation verification report is generated to provide reliable water depth data for subsequent results compilation.

[0034] Finally, it should be noted that the theoretical lowest tide level is the unified reference surface for calculating sea depth in hydrographic surveys. The vertical depth value calculated downwards from this reference is the water depth value below the theoretical lowest tide level. This water depth value eliminates the influence of dynamic changes in the sea surface such as tides and water levels. It is a standardized and comparable unified expression of sea depth measurement results, and it is also the unified calculation benchmark and comparison basis for the water depth results of the main measuring point and the check point in this invention.

[0035] Step 4.3: For all sonar measurement points in the main survey line dataset and the check line dataset, summarize the water depth values ​​calculated by each sonar measurement point below the theoretical lowest tide level to form the corresponding main survey point water depth results and check point water depth results. Specifically, this includes: grouping and collecting the records in the measurement point-water depth association table according to the survey line type (main survey line and check line). The main survey line group collects all measurement point records of the survey line type as the main survey line, and the check line group collects all measurement point records of the survey line type as the check line. During the collection process, the records in each group are sorted in ascending order of the survey line number, and within the same survey line, they are sorted in ascending order of the measurement point number to ensure the orderliness of the data. After the collection is completed, the integrity of the two sets of data is checked to confirm that the number of measurement points in the main survey line group is consistent with the number of measurement points in the main survey line dataset, and the number of measurement points in the check line group is consistent with the number of measurement points in the check line dataset, with no measurement points missing or duplicated.

[0036] Subsequently, the water depth values ​​in the two sets of data were analyzed. D Multi-dimensional verification was conducted, including data integrity verification (no null or missing water depth values), numerical validity verification (no extreme values ​​exceeding the normal water depth range of the survey area), and correlation consistency verification (no mismatches between measuring points and water depth values). All abnormal measuring point records that failed the verification were removed, and the reasons for the abnormalities and the handling methods were recorded. After the verification was completed, the qualified water depth values ​​were standardized and unified. D The decimal places and character representation format were carefully selected to ensure data standardization. Finally, in accordance with the specifications for geospatial data outputs, two datasets were constructed: one for the main measurement point and one for the checkpoints. Each dataset includes a unique measurement point code, a survey line number, the latitude and longitude coordinates of the measurement point, and normal elevation. H Precise L value L water depth value D The system collects core information such as calculation time and generates a statistical report, which includes the maximum, minimum, average, and standard deviation of the water depth results at the main measurement points, the corresponding statistical information of the water depth results at the check points, and statistical analysis of abnormal data, forming standardized water depth results that can be directly used for subsequent quality inspection, results acceptance, and application.

[0037] In a preferred embodiment of the present invention, step 5 above may include: Step 5.1: For each checkpoint in the water depth results, dynamically calculate the corresponding comparison window radius according to the predetermined measurement scale. Specifically, this includes using the dynamic comparison window radius calculation formula based on GB12327-2022 "Specifications for Hydrographic Surveying". The formula is as follows: R =1.0mm× M ,in M To measure the denominator of the scale, RTo compare the actual geographic radius of the window and to perform unit standardization, 1.0 mm on the map is converted to the actual geographic length in meters. R =10 -3 × M (Unit: meters), then iterate through each checkpoint in the water depth results, retrieve the predetermined measurement scale of the survey area to which the checkpoint belongs from the basic archive of the survey task, and accurately extract the denominator of the scale. M And verify the validity of the values, exclude invalid or erroneous scale data, and then submit the verified data. M Substitute into the formula to complete the comparison window radius R The calculation, after calculation, for R A rationality check was performed, and the radius value was confirmed to be neither too large nor too small, taking into account the terrain features and the distribution density of the measuring points in the survey area, to ensure that the search range was suitable for subsequent terrain discrimination requirements. Finally, the unique code of each checkpoint was compared with the calculated exclusive comparison window radius. R Perform one-to-one association binding to generate a standardized checkpoint-comparison window radius association table, and complete the assignment and archiving of all checkpoint radius parameters.

[0038] Step 5.2: Using each checkpoint as the center and its corresponding comparison window radius as the spatial range, perform a spatial search in the water depth results of the main measurement points. This specifically includes: retrieving the checkpoint-comparison window radius association table and the core information of each checkpoint in the water depth results, including the unique code of the checkpoint, the latitude and longitude coordinates under the unified coordinate system of the survey area, the water depth value below the theoretical lowest tide level, and the associated comparison window radius. R During the retrieval process, the validity of the checkpoint coordinates is verified to ensure that the coordinates do not exceed the geographical range of the survey area. It is then confirmed that the coordinate system of the main measurement point's water depth results is completely consistent with that of the checkpoints, avoiding spatial retrieval deviations caused by differences in coordinate benchmarks. Subsequently, based on the geospatial index structure already constructed from the main measurement point's water depth results, a circular retrieval is performed with the latitude and longitude coordinates of each checkpoint as the center and the corresponding comparison window radius as the reference. R To determine the search range radius, a targeted spatial neighborhood search was conducted in the water depth results of the main measurement points. During the search process, a search threshold matching the accuracy of the measurement area coordinates was set to ensure the accuracy of the search. An independent spatial search operation was performed for each checkpoint one by one, and the search status of each checkpoint was recorded simultaneously to provide a basis for subsequent result verification.

[0039] Step 5.3 involves performing spatial retrieval to identify and acquire all main measurement points falling within the window range corresponding to each checkpoint. Specifically, this includes: conducting an initial screening of the data validity of the preliminary spatial retrieval results for each checkpoint, removing main measurement point data that has missing coordinates, coordinates exceeding the measurement area, abnormally negative water depth values, or invalid unique measurement point codes; then, precisely verifying the actual geographic straight-line distance between each of the remaining main measurement points after the initial screening and their corresponding checkpoints, ensuring consistent coordinate accuracy during verification, and confirming that the calculated actual distance is less than or equal to the comparison window radius. R This process thoroughly eliminates misjudged data caused by retrieval bias. Next, the uniqueness of verified main measuring points is checked to avoid duplicate extraction of the same main measuring point. Then, all core associated information for these valid main measuring points is extracted, including unique codes, latitude and longitude coordinates under the unified coordinate system of the survey area, the main measuring line number, and the water depth below the theoretical lowest tide level. Finally, using the unique code of each checkpoint as an identifier, a temporary data collection unit is established for each checkpoint, and all corresponding valid main measuring point information is completely entered into the unit. This achieves classified temporary storage of all valid main measuring point information, ensuring no omissions or mismatches.

[0040] Step 5.4 establishes a spatial association between each checkpoint and all acquired master measurement points, thereby determining the set of all master measurement points included in each comparison window. Specifically, this involves: first, using the unique code of each checkpoint as the unique primary key, establishing an independent standardized spatial association file for it, dividing the file into two dedicated areas: a checkpoint basic information area and a master measurement point association information area; then, first, setting the checkpoint's unique code, latitude and longitude coordinates, water depth below the theoretical lowest tide level, and comparison window radius... R Once the core basic information is fully entered into the corresponding area, all valid master measurement point information in the temporary data collection unit of the checkpoint is entered one by one into the master measurement point association information area. During the entry process, the number of master measurement points entered is verified to be completely consistent with the number of temporary collections to ensure that no master measurement points are missed. Then, all master measurement points in the master measurement point association information area are organized in order of their actual geographical straight-line distance from the corresponding checkpoint, from closest to farthest, and the master measurement line number to which each master measurement point belongs is marked. Subsequently, a full-dimensional verification is carried out on the spatial association files of all checkpoints to verify that there are no missing basic information of checkpoints and no mismatches in master measurement point association information. At the same time, empty window checkpoints that have no valid master measurement points after retrieval are marked and archived separately. Finally, all spatial association files are systematically integrated according to the unique code of the checkpoints to formally determine the complete and valid set of master measurement points contained in each comparison window, and construct a standardized comparison window-master measurement point spatial association dataset to provide structured and directly callable spatial association data for the subsequent terrain adaptive discrimination process.

[0041] In a preferred embodiment of the present invention, step 6 above may include: Step 6.1: For each comparison window, calculate the average water depth of all main measuring points within that window to obtain the reference water depth for each window. This includes: retrieving the constructed spatial association dataset of comparison windows and main measuring points; extracting the set of valid main measuring points corresponding to each window according to the unique code of the comparison window; then accurately extracting the water depth value of each valid main measuring point below the theoretical lowest tide level from the set, ensuring a one-to-one correspondence between the extraction and the unique code of the main measuring point to avoid omissions or errors; subsequently, performing a second validity verification on the extracted water depth values, removing null values, negative values, and extreme outliers exceeding the reasonable range of water depth in the measurement area. Count and record the number of effective water depth values ​​participating in the calculation within each window. Then calculate the reference water depth for that window, i.e., reference water depth = sum of all effective water depth values ​​÷ number of effective main measuring points participating in the calculation. During the calculation, maintain the same decimal precision as the water depth values ​​and verify the accuracy of the sum calculation. Finally, associate and bind the calculated reference water depth, the number of effective main measuring points participating in the calculation, and the unique code and main measuring point set information of the corresponding comparison window. At the same time, perform a preliminary screening of the reference water depth to ensure that it is within the normal water depth range of the measurement area. Complete the calculation and structured archiving of the reference water depth for all comparison windows.

[0042] Step 6.2: Determine the water depth range to which the water depth belongs based on the reference water depth, and obtain the preset threshold corresponding to the water depth range. Specifically, this includes: first, solidifying a standardized water depth range-preset threshold comparison table based on the water depth measurement accuracy requirements in GB12327-2022 "Specifications for Hydrographic Surveying". This comparison table is a two-dimensional structured table, which contains three core fields: water depth range, preset threshold type, and preset threshold value / calculation formula. The specific contents are as follows: water depth range 0 to 20m, threshold type is fixed threshold, preset threshold value ±0.5m; water depth range 20 to 30m, threshold type is fixed threshold, preset threshold value ±0.6m; water depth range 30 to 50m, threshold type is fixed threshold, preset threshold value ±0.7m; water depth range 50 to 100m, threshold type is fixed threshold, preset threshold value ±1.5m; water depth range >100m, threshold type is dynamically calculated threshold, and the preset threshold calculation formula is ±Z×3%m, where Z is the reference water depth of the comparison window in meters.

[0043] Simultaneously, the rules for assigning interval boundary values ​​in the reference table are determined. Critical values ​​such as 20m, 30m, 50m, and 100m are all assigned to the next lower water depth interval. For example, a reference water depth of 20m is assigned to the 0-20m interval, and a reference water depth of 100m is assigned to the 50-100m interval. Then, the reference water depths archived in each comparison window are retrieved one by one. First, the validity and accuracy of the reference water depth values ​​are verified, excluding reference water depths with invalid values, negative values, or calculation errors. Then, based on the magnitude of the reference water depth values, the specific water depth interval to which it belongs is precisely matched against the water depth interval-preset threshold reference table. For comparison windows matching intervals >100m, the reference water depth Z is substituted into the reference table. The calculation formula ±Z×3%m is used to calculate the specific preset threshold value, retaining the same decimal precision as the fixed threshold during calculation. For comparison windows that match the fixed threshold range, the corresponding preset threshold value in the reference table is directly retrieved. Subsequently, the threshold type of each comparison window is marked as fixed or dynamic calculation. The unique code of the comparison window, the reference water depth, the water depth range, the corresponding preset threshold value, and the threshold type are uniformly associated and bound to generate a standardized comparison window, reference water depth, and preset threshold association table. After generation, 10% of the data in the table is randomly sampled for review to verify the accuracy of water depth range matching and threshold value retrieval / calculation, ensuring no mismatches or calculation errors.

[0044] Step 6.3: Calculate the difference between the maximum and minimum water depth values ​​of all main measuring points within the same comparison window to obtain the maximum difference. Specifically, this includes: for each set of valid main measuring points corresponding to each comparison window, extracting all valid main measuring point water depth values ​​after secondary verification, ensuring that the decimal retention precision of all water depth values ​​is consistent; then sorting the group of water depth values ​​in ascending order of value; confirming the sorting results a second time and extracting the water depth value with the largest and smallest values ​​to avoid errors from manual selection; and simultaneously recording the unique code and latitude / longitude of the main measuring point corresponding to the maximum and minimum values. The coordinates and actual water depth values ​​facilitate subsequent data traceability and anomaly verification. Then, the maximum difference in water depth of the main measuring points within the same comparison window is calculated by subtracting the minimum value from the maximum value. During the calculation, the decimal precision is maintained consistent with the water depth values, and it is confirmed that the calculation result is not negative. Finally, the maximum difference, the maximum water depth, the minimum water depth, and the core identification information of the corresponding main measuring points are associated and bound with the unique code of the comparison window and the reference water depth to complete the calculation and temporary storage of the maximum difference of all comparison windows. At the same time, the maximum difference is checked for reasonableness to ensure that it does not have extreme values ​​that obviously violate the topographic features of the survey area.

[0045] Step 6.4: Compare the maximum difference with a preset threshold, and determine the terrain category of the same comparison window based on the comparison result. If the maximum difference does not exceed the preset threshold, the window is determined to be a flat terrain area; if the maximum difference exceeds the preset threshold, the window is determined to be a terrain undulating area. Specifically, this includes: synchronously retrieving the comparison window, reference water depth, preset threshold association table, and temporary maximum difference data; performing precise matching according to the unique code of the comparison window to ensure that the maximum difference of each window corresponds one-to-one with the corresponding preset threshold without mismatch; after matching, unifying the decimal precision of both; and then comparing the maximum difference with the preset threshold numerically, strictly following the judgment rule: if the maximum difference is less than or equal to the preset threshold, it is determined to not exceed the threshold; if the maximum difference is greater than the preset threshold, it is determined to exceed the threshold. For cases without a valid master test... In addition to marking the points as undetermined, the comparison windows are also included in the abnormal data ledger and the reasons are recorded. Then, the terrain category of each valid comparison window is determined based on the numerical comparison results. If the maximum difference does not exceed the preset threshold, it is determined to be a flat terrain area; if the maximum difference exceeds the preset threshold, it is determined to be a undulating terrain area. Subsequently, the terrain category determination results, numerical comparison results, and determination criteria (maximum difference, preset threshold) are uniformly associated and bound with the unique code of the comparison window, the reference water depth, and the water depth interval to which it belongs. Finally, the determination information of all comparison windows is fully verified to check whether the determination logic is reasonable and whether the information is complete and without missing information. After removing invalid comparison windows, a standardized comparison window and terrain category association dataset is constructed, providing accurate and complete determination criteria for the selection of water depth mismatch comparison methods.

[0046] In a preferred embodiment of the present invention, step 7 above may include: Step 7.1: Obtain the terrain category determination result for each comparison window. The terrain category includes flat terrain or undulating terrain. Specifically, this involves: first, retrieving the constructed standardized comparison window-terrain category association dataset, fully extracting the unique code and terrain category determination result (flat terrain or undulating terrain) for each valid comparison window, and simultaneously retrieving the core information of the checkpoints corresponding to each comparison window (unique code, water depth value below the theoretical lowest tide level), and the information of the main measuring point set within the window (unique code of each main measuring point, water depth value below the theoretical lowest tide level, and actual geometric distance to the checkpoint). During the extraction process, a full-dimensional verification is carried out to remove comparison window data that has not completed terrain category determination, has missing water depth values ​​for checkpoints or main measuring points, or has invalid geometric distance data. Then, the unique code of each valid comparison window is precisely associated and bound one-to-one with the corresponding terrain category determination result, full information of checkpoints, and complete information of the main measuring point set. A structured association table of comparison window, terrain category, and measuring point basic information is generated and temporarily stored to ensure that the terrain category of each comparison window matches the corresponding measuring point information and geometric distance data without mismatch or omission.

[0047] Step 7.2: For windows identified as flat terrain areas, the nearest neighbor comparison method is used to select the main measuring point within the window that is geometrically closest to the check point. Based on the water depth values ​​of the main measuring point and the check point, the water depth discrepancy value for this window is calculated. Specifically, this includes: first determining the formula for calculating the water depth discrepancy value using the nearest neighbor comparison method for flat terrain areas: In the formula i For the index sequence of checkpoints and comparison windows, k The index of the main test point that is geometrically closest to the check point within the window. For the first i The water depth at each checkpoint below the theoretical lowest tide level. For the first k The water depth value below the theoretical lowest tide level at the nearest main measuring point. For the first i If the water depth comparison values ​​of the comparison windows do not match, then extract all comparison windows that are determined to be flat terrain from the association table of comparison windows, terrain categories, and basic information of measuring points, and retrieve the water depth values ​​of the checkpoints corresponding to each window one by one. Water depth values ​​of all main measuring points within the window After verifying the accuracy of the geometric distance data between each main measuring point and the check point, the single main measuring point with the smallest geometric distance from the check point was accurately selected, and the water depth value of this main measuring point was verified again. The validity and rationality of the test will be confirmed after no abnormalities are found. and Accurately substitute the values ​​into the calculation formula to determine the water depth discrepancy. The calculation is performed, and a basic check is conducted on the discrepancies in the results to confirm that there are no errors in the operation symbols or parameter substitutions. Finally, the calculated water depth discrepancies are returned. It is associated and bound with the unique code of the corresponding comparison window, the unique code of the check point, the unique code of the nearest main measurement point, and related water depth and geometric distance data.

[0048] Step 7.3: For windows identified as terrain undulation areas, the inverse distance weighted comparison method is used. Weights are defined based on the geometric distances between each main measuring point and the check point within the window. The water depth discrepancy value for this window is calculated by combining the water depth values ​​of all main measuring points and check points. Specifically, this includes: determining the formula for calculating the water depth discrepancy value using the inverse distance weighted comparison method for terrain undulation areas: ; In the formula i For the index sequence of checkpoints and comparison windows, k The index sequence of the main measurement points within the window. n This represents the number of valid main measurement points within the window. For the first iThe water depth at each checkpoint below the theoretical lowest tide level. For the first k The water depth values ​​at each main measuring point below the theoretical lowest tide level. For the first k The first main measuring point and the first i The actual geometric distance between each checkpoint For the first i The water depth comparisons of the comparison windows showed discrepancies. Then, from the association table of comparison windows, terrain categories, and basic information of measuring points, all comparison windows identified as terrain undulation areas were extracted, and the water depth values ​​of the checkpoints corresponding to each window were retrieved one by one. Water depth values ​​of all main measuring points within the window and the actual geometric distance between each main measuring point and the inspection point First, the retrieved data is validated to ensure geometric distance. With no zero values ​​and all water depth values ​​considered valid data, weights are defined based on geometric distance, meaning the weight of each main measuring point is... Calculate the numerator terms in sequence With denominator After verifying the accuracy of the summation results, the weighted average water depth value of the main measuring points within the window is calculated, and then the weighted average water depth value is compared with the water depth value of the checkpoint. Substitute into the formula to complete the water depth discrepancy value The calculation maintains numerical precision consistent with the water depth value throughout the process. After the calculation, double verification is performed to check the correctness of the weight calculations, summation operations, and formula substitutions. Finally, any discrepancies in the calculated water depth are corrected. The data is fully associated and bound with the unique codes of the corresponding comparison window, checkpoints, all main measurement points within the window, and related water depth, geometric distance, and weight data.

[0049] In a preferred embodiment of the present invention, step 8 above may include: Step 8.1: Obtain all calculated water depth discrepancies, specifically including: retrieving all calculated water depth discrepancy data from all comparison windows, and fully extracting the unique code, checkpoint unique code, and water depth discrepancy value Δ corresponding to each comparison window. h ik The data extraction process involves a comprehensive verification of data validity, including the water depth range, the preset threshold for the corresponding water depth range, and the terrain category determination results. Invalid data with missing water depth discrepancies, mismatched preset thresholds, or incomplete checkpoint identification information is removed. Then, the water depth discrepancy value Δ for each valid data point is calculated. h ikA one-to-one precise association and binding is established between the corresponding comparison window, checkpoint, water depth range, and preset threshold to construct a standardized structured dataset of water depth discrepancies and associated information, providing complete and accurate basic data for subsequent qualification judgment and mean error calculation.

[0050] Step 8.2 compares each water depth discrepancy value with its corresponding water depth interval threshold to determine whether each point is a qualified comparison point, and counts the number of qualified points and the number of unqualified points. Specifically, this includes: retrieving the water depth discrepancy value Δ for each valid comparison point one by one from the water depth discrepancy value-association information structured dataset. h ik And the preset threshold for the corresponding water depth range, the clear judgment rule is to compare the absolute value of the water depth discrepancy with the corresponding preset threshold, if If the value is less than or equal to a preset threshold, the comparison point is considered a qualified comparison point. If the value exceeds a preset threshold, the comparison point is determined to be an unqualified comparison point. Then, each comparison point is assessed for compliance according to this rule, and the assessment result of each comparison point is recorded simultaneously during the assessment process. Subsequently, stratified statistical work is carried out. On the one hand, the total number of all valid comparison points, the total number of qualified comparison points, and the total number of unqualified comparison points are counted. On the other hand, the statistics are further subdivided according to the water depth ranges of 0 to 20m, 20 to 30m, 30 to 50m, 50 to 100m, and >100m. The total number of comparison points, the number of qualified points, and the number of unqualified points in each water depth range are recorded. At the same time, the proportion of unqualified points in the total and each water depth range to the corresponding total number of comparison points is calculated, and a standardized comparison point compliance statistical ledger is generated.

[0051] Step 8.3, based on the water depth discrepancies of all qualified comparison points, calculate the mean depth sounding error, specifically including: first, determining the formula for calculating the mean depth sounding error as follows: ; in i The subscript sequence of qualified comparison points that does not exceed the limit is used for comparison; N This represents the total number of qualified comparison points that did not exceed the limit. For the first i The water depth discrepancies at each qualified comparison point, marked with positive or negative signs, are consistent with the definitions in steps 7.2 and 7.3. To account for the error in water depth measurement, all qualified comparison point data are precisely extracted from the comparison point qualification statistics ledger. All unqualified comparison points exceeding limits and abnormal data with missing water depth values ​​are removed to ensure that only valid qualified comparison points are included in the calculation. Simultaneously, the total number of qualified comparison points participating in the calculation is counted. N Check N To ensure the accuracy of numerical values ​​and prevent omissions or miscalculations.

[0052] Subsequently, the marked water depth discrepancies at each valid and qualified comparison point were analyzed. Perform the squaring operations sequentially, maintaining the same decimal precision as the water depth values ​​during the calculations to avoid errors caused by rounding. After completing all the squaring operations, calculate the sum of all the squared values. Then, the accuracy of the sum was checked again to ensure there were no calculation errors; next, the sum was substituted into the formula, and the denominator operation was performed first, that is, the 2 was calculated. N The value is then divided by the numerator to obtain the result. Finally, the square root of the result is calculated to obtain the mean depth sounding error. During the calculation, at least three decimal places are retained to ensure the accuracy of the mean square error calculation; after the calculation is completed, a double-core evaluation is performed on the entire operation: the accuracy of the squaring operation and the summation value in the first core is checked, and the accuracy of the denominator 2 in the second core is checked. N After confirming that the calculation steps for the square root conform to the formula requirements and there are no calculation errors, record the mean square error of the depth sounding. The final result involves calculating the corresponding depth sounding error step by step according to different water depth intervals, and then comparing the overall depth sounding error, the depth sounding error of each water depth interval, and the total number of corresponding qualified comparison points. N The data of sum of squares and squares are linked and bound together to complete the structured archiving of all calculation results.

[0053] Step 8.4: Based on the ratio of unqualified points to qualified points obtained from statistics, and the mean square error of the depth measurement, a final qualification judgment is made on the overall quality of the depth measurement results based on the preset standard. Specifically, this includes: first confirming that the preset standard is GB12327-2022 "Specifications for Hydrographic Surveying". The quality judgment rule stipulated in this standard is: if the proportion of unqualified points to the total number of points participating in the comparison exceeds 10%, or the calculated mean square error of the depth measurement exceeds 10%, the quality judgment rule is: If the error exceeds the standard error limit for the corresponding water depth range in the specification, the overall inspection of the water depth measurement results is deemed unqualified. If both indicators meet the standard requirements, the overall inspection is deemed qualified. Then, retrieve the percentage of unqualified points from the comparison point qualification statistics log, and retrieve the calculated depth measurement standard error from step 8.3. First, verify whether the overall percentage of unqualified points is ≤10%, then calculate the mean square error of the depth sounding. The depth measurement error was compared one by one with the depth measurement error limits for each depth range in the aforementioned standard. Simultaneously, the quality of each section was assessed based on the pass / fail statistics for each depth range. Finally, the overall percentage of non-compliant points and the depth measurement error were considered together. The verification results and the judgment status of each water depth interval are used to complete the final qualification judgment of the overall quality of the water depth measurement results. The judgment basis, the values ​​of each core indicator and the judgment results of each section are recorded simultaneously to form a standardized water depth measurement result quality judgment report.

[0054] The specific implementation process of the embodiments also includes the following steps and methods, such as... Figure 2 As shown ( Figure 2 This embodiment fully describes the entire process of checking the accuracy of water depth measurement during its implementation. This embodiment applies the application of multibeam bathymetry accuracy inspection to a complex nearshore topography area in a certain sea area. This sea area features flat seabed areas, steep underwater slopes, and small trenches, with dramatic changes in seabed topography. If traditional bathymetry accuracy inspection methods are used, the results are easily unreliable due to single L-value conversion errors and nearest-point matching distortion. Therefore, a bathymetry accuracy inspection method combining vertical benchmark conversion and dynamic topography discrimination is adopted to effectively solve the application defects of traditional methods in this complex topographic sea area, achieving high-precision and automated bathymetry accuracy inspection. The specific implementation content of this invention is as follows: First, based on theoretical depth benchmark grid data with a resolution of 1'×1', bathymetry accuracy inspection is carried out in the complex topographic sea area near the Yellow Sea, realizing the entire process from the establishment of depth benchmark dataset to the quantitative evaluation of measurement quality. The specific implementation steps are as follows: Step 1, Depth Benchmark Dataset Establishment: This method takes theoretical depth benchmark grid point data with a resolution of 1'×1' as an example (supports extensions to other grid sizes) to establish a computer-recognizable dataset containing grid point coordinates and L values, providing basic data support for subsequent calculations.

[0055] Step 2, Grid storage unit structuring: Based on the depth benchmark dataset, construct grid storage units with complete corner point information, assign a unique identifier to each unit, and realize the structured organization of spatial data.

[0056] Step 3, Grid identification code encoding rules: The encoding format is as follows: lower left longitude + latitude - upper right longitude + latitude - upper left longitude + latitude - lower right longitude + latitude, to ensure that each grid unit has a unique identifier.

[0057] Step 4, Establishment of sonar measurement point dataset: The normal elevation results of measurement points under the 1985 National Elevation Datum are classified and stored according to the main measurement line and the inspection line to form a dual dataset structure, which facilitates subsequent comparison and analysis.

[0058] Step 5, Hash Index for Fast Location: Using a hash table algorithm, the grid identifier code is used as the key to quickly locate the grid cell where the sonar measurement point is located, thereby improving spatial retrieval efficiency.

[0059] Step 6, Grid Corner Data Extraction: Automatically obtain the coordinates of the four corner points and L value of the positioning grid to provide parameter input for bilinear interpolation calculation.

[0060] Step 7, Calculation of L-value at sonar measurement point: The L-value at the sonar measurement point is accurately calculated using a bilinear interpolation algorithm, such as... Figure 3 As shown ( Figure 3 The left side illustrates the theoretical depth benchmark grid data and the retrieval and location of grid cells; the right side illustrates the calculation of the L value at sonar measurement points based on the bilinear interpolation algorithm, realizing a continuous expression of the theoretical depth benchmark and establishing a dataset of sonar measurement point L values. The specific calculation formula is as follows: ； ， ； ， ； In the formula, , For the grid point index sequence; , The x-coordinate of the grid point; , The ordinate of the grid point; , , , The L value of the grid points; , The coordinates of the sonar measurement points are: x and y. The value of L is the sonar measurement point.

[0061] Step 8, Vertical datum conversion: Using the results of the dual datasets of the main survey line and the inspection line, along with the L-value dataset, the normal elevation of the sonar measurement points under the 1985 National Height Datum is converted into the water depth results of the theoretical lowest tide level.

[0062] Step 9, Dynamic Comparison Window Settings: Based on the requirements of GB12327-2022 "Specifications for Hydrographic Surveying", follow the formula... ( To measure the denominator of the scale, the radius of the comparison window is dynamically calculated to ensure that the distance on the map does not exceed 1.0 mm.

[0063] Step 10, Spatial Neighborhood Retrieval: Using the checkpoints of the sounding line as the center, search all main test points within the dynamic radius search window to construct a spatial association dataset.

[0064] Step 11, Adaptive Terrain Classification: First, threshold setting is performed, referring to the requirements for discrepancies in water depth comparisons in GB12327-2022 "Specifications for Hydrographic Surveying". The thresholds are set as follows: 0 to 20m: ±0.5m; 20 to 30m: ±0.6m; 30 to 50m: ±0.7m; 50 to 100m: ±1.5m; >100m: ±Z·3%m, where Z is the water depth value. Second, terrain classification is performed. The average water depth of all main measuring points within the comparison window is used as the reference value for that window. The water depth range of the window is determined based on this reference value. The difference between the maximum and minimum water depth values ​​of the main measuring points within the comparison window is calculated. If the difference does not exceed the threshold, the window is classified as a flat terrain area; if the difference exceeds the threshold, the window is classified as a undulating terrain area.

[0065] Step 12, Water Depth Discrepancy Comparison: For flat terrain areas, the nearest neighbor comparison method is used to calculate the water depth discrepancies within the window. This involves selecting the point geometrically closest to the check point for water depth measurement comparison. Figure 4 As shown ( Figure 4 This diagram illustrates the dynamic comparison window in water depth discrepancy comparison. The relevant parameters of the calculation models for the nearest neighbor comparison method and the inverse distance weighted comparison method are shown in the figure. It is assumed that a total of [number missing] values ​​are retrieved within the comparison window. n The main measurement points, of which the first i Geometric distance between each main measuring point and the check point d i If the minimum value is found, the formula for calculating the water depth discrepancy within the comparison window is as follows: ; In the formula, i Main measurement point subscript sequence j For the checkpoint and comparison window index sequence, To check the water depth value, The water depth value at the main measuring point. For areas with undulating terrain, a reverse distance-weighted comparison method is used to calculate the discrepancies in water depth within the comparison window. This method defines weights based on the geometric distances between the main measuring points and check points within the comparison window. All main measuring points participate in the water depth measurement comparison according to their respective weights. The formula for calculating the discrepancy in water depth within the comparison window is as follows: ; Step 13, Quantitatively assess the inspection quality: Points with discrepancies exceeding the threshold in water depth comparison are considered unqualified; otherwise, they are considered qualified. All comparison results are automatically summarized, and the total number of comparison points, the number of unqualified points, the proportion of unqualified points, and the mean square error of depth measurement are calculated. The mean square error calculation excludes measurement points exceeding the limit and is calculated using the following formula: ; In the formula, k To compare the index sequence, m To compare the total number of points that did not exceed the limit, The depth measurement error is considered. The quality of the depth measurement results is judged according to GB12327-2022 "Specifications for Hydrographic Surveying". If the number of unqualified points exceeds 10% of the total number of points in the comparison, or if the depth measurement error exceeds the limit, the overall inspection is deemed unqualified; otherwise, the inspection is deemed qualified. An example of a depth measurement accuracy inspection report is shown below. Figure 5 As shown, where Figure 5 To quantify and evaluate the final accuracy inspection results report in the quality inspection process, this paper presents statistics on the overall inspection, the total number of comparison points in different water depth ranges, the number of unqualified points, the proportion of unqualified points, and the depth measurement error, and provides a conclusion on whether the accuracy inspection is qualified.

[0066] Brief Explanation of Formal Differences in the Specification: Individual formal differences in formula variations, symbol designations, and abbreviations in this invention specification do not constitute substantial changes to the technical solution. They are merely elaborations of the core technical content from different angles and dimensions, aiming to facilitate understanding of the technical principles, calculation logic, and implementation steps by those skilled in the art. All formal differences correspond to highly consistent technical implications; there are no disagreements or disputes regarding calculation results or implementation requirements. Specific explanations are as follows: For differences in formula transformations, such as different expressions of water depth discrepancies, they are mathematically equivalent derivations. One emphasizes the logical decomposition of step-by-step calculations, while the other emphasizes the integration of overall calculations. The core calculation rules and parameter relationships are consistent, and those skilled in the art can refer to them flexibly according to their understanding habits without affecting actual implementation. For differences in symbol identification, such as different annotations of bilinear interpolation weighting coefficients and water depth discrepancy subscript symbols, these are merely differences in symbolic designations. The technical meanings and corresponding parameters referred to by the symbols are completely consistent and can be clearly identified by combining the textual definitions in the context, without any ambiguity in understanding. For differences in abbreviations, such as the 1985 National Height Datum sometimes being abbreviated as the Normal Height Datum, these are common expressions in the field of marine surveying. The connotations of the abbreviation and the full name are completely consistent, and they appear in context throughout the process. This is a common expression habit in the industry and can be clearly understood without additional definition.

[0067] All the above-mentioned adjustments are intended to assist in explaining the technical solution of the present invention from multiple perspectives, so that those skilled in the art with different understanding habits can accurately grasp the core technical points. The technical solution of the entire document remains highly consistent, and all differences are undisputed and do not affect the actual implementation and execution of the method of the present invention.

[0068] This invention provides a scientific method for checking the accuracy of water depth measurements. Its beneficial effects include: by combining vertical datum transformation with dynamic terrain discrimination, it avoids the problem of high gross error rates caused by abrupt terrain changes, significantly increases the number of effective comparison points, makes accuracy assessment more reliable, and improves the quality control level of measurement results under complex terrain conditions. This method greatly improves inspection efficiency, reduces manpower and time consumption, and is adaptable to various measurement scenarios such as single-beam and multi-beam surveying, meeting regulatory requirements. Its results can provide reliable data support for many fields such as marine surveying, resource development, and disaster early warning, with broad application prospects and significant practical value. It can meet the dual requirements of accuracy and efficiency in modern marine surveying, combining technological innovation with engineering practicality.

[0069] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for checking the accuracy of water depth measurement by combining vertical datum conversion and dynamic terrain discrimination, characterized in that, The method includes: Establish a depth benchmark dataset of theoretical depth benchmark grid points, and establish a sonar measurement point dataset containing main survey line data and inspection line data; The depth benchmark dataset is structured to generate grid storage units with unique identifiers; the identifiers are used to perform hash indexing on the sonar measurement point dataset to determine the grid storage unit to which each sonar measurement point belongs. The coordinates and L values ​​of the four corner points of the determined grid storage unit are extracted and used to calculate the L value at each sonar measurement point through bilinear interpolation, forming a sonar measurement point L value dataset. Using the sonar measurement point L-value dataset, the normal height of the sonar measurement points in the main measurement line and the check line is uniformly converted into the water depth below the theoretical lowest tide level, and the water depth results of the main measurement point and the check point are obtained respectively. Based on the water depth results at the checkpoints, the radius of the comparison window is dynamically determined according to the measurement scale, with each checkpoint as the center. Spatial retrieval is then performed within this radius to obtain all the main measurement points contained in each comparison window. For each comparison window, the window is determined to be a flat or undulating terrain area by comparing the maximum difference in water depth at the main measuring point within the window with the corresponding water depth interval threshold. For windows identified as flat terrain areas and windows identified as undulating terrain areas, calculate their water depth discrepancies respectively; An evaluation is conducted based on all discrepancies in water depth, and qualified and unqualified points are counted. The mean error of depth measurement is calculated, and the overall quality of the measurement results is judged to be qualified.

2. The method for checking the accuracy of water depth measurement by combining vertical datum conversion and dynamic terrain discrimination according to claim 1, characterized in that, A depth benchmark dataset was established using theoretical depth benchmark grid points, and a sonar measurement point dataset containing master survey line data and check line data was also established, including: Obtain the raw data of the theoretical depth benchmark grid points, the raw data including the coordinates of each grid point and its L value; The acquired raw data is parsed to extract and organize the coordinates and L values ​​of each grid point in order to construct a computer-recognizable depth benchmark dataset. Obtain normal elevation data of sonar measurement points based on the 1985 National Elevation Datum, classify this data into main survey lines and inspection lines, and obtain the classified main survey line data and inspection line data. The classified main survey line data and check line data are organized and stored separately to form a structured sonar measurement point dataset.

3. The method for checking the accuracy of water depth measurement by combining vertical datum conversion and dynamic terrain discrimination according to claim 2, characterized in that, The depth benchmark dataset is structured to generate grid storage units with unique identifiers; The sonar measurement point dataset is located using a hash index based on the identification code, determining the grid storage unit to which each sonar measurement point belongs, including: The depth benchmark dataset is structured, and based on the spatial distribution of the grid points in the depth benchmark dataset, a grid storage unit defined by the coordinates of the four corner points and the L value is constructed. Based on the coordinates of the four corner points of each grid storage unit, a grid identifier code with spatial uniqueness is generated according to a preset encoding rule; Using the grid identifier as the key, a hash table is established, and each grid storage unit is associated with the corresponding grid identifier for storage, thus constructing a hash index structure; Based on the coordinates of the sonar measurement points in the dataset, the grid identifier code corresponding to the spatial location of each sonar measurement point is determined by querying the constructed hash index structure, thereby locating the grid storage unit to which each sonar measurement point belongs.

4. The method for checking the accuracy of water depth measurement by combining vertical datum conversion and dynamic terrain discrimination according to claim 3, characterized in that, The coordinates and L values ​​of the four corner points of the determined grid storage unit are extracted and used to calculate the L values ​​at each sonar measurement point through bilinear interpolation, forming a sonar measurement point L value dataset, including: Based on the grid storage unit to which each sonar measurement point belongs, extract the coordinates of the four corner points and the L value of the corresponding grid storage unit to obtain the corner point data set of each sonar measurement point. Based on the corner data set of each sonar measurement point, bilinear interpolation is performed on each sonar measurement point. The precise L value of each sonar measurement point at its specific coordinate position is obtained through the bilinear interpolation calculation. The precise L values ​​of all sonar measurement points are collected and integrated to form a unified dataset of sonar measurement point L values.

5. The method for checking the accuracy of water depth measurement by combining vertical datum conversion and dynamic terrain discrimination according to claim 4, characterized in that, Using the sonar measurement point L-value dataset, the normal elevations of the sonar measurement points in the main measurement line and the check line are uniformly converted to the water depth below the theoretical lowest tide level, and the water depth results of the main measurement points and the check points are obtained respectively, including: For each sonar measurement point in the main measurement line dataset and the inspection line dataset, extract the corresponding L value of the measurement point from the sonar measurement point L value dataset. Based on the normal height and extracted L value of each sonar measuring point, the water depth value of each sonar measuring point below the theoretical lowest tide level is obtained by conversion calculation. For all sonar measurement points in the main measurement line dataset and the check line dataset, the water depth values ​​calculated by each sonar measurement point below the theoretical lowest tide level are summarized to form the corresponding water depth results for the main measurement point and the water depth results for the check point.

6. The method for checking the accuracy of water depth measurement by combining vertical datum conversion and dynamic terrain discrimination according to claim 5, characterized in that, Based on the water depth data at each checkpoint, the radius of the comparison window is dynamically determined according to the measurement scale, with each checkpoint as the center. Spatial retrieval is then performed within this radius to obtain all the main measurement points included in each comparison window, including: For each checkpoint in the water depth results, the corresponding comparison window radius is dynamically calculated according to the predetermined measurement scale. Spatial retrieval is performed in the water depth results of the main measurement points, with each checkpoint as the center and its corresponding comparison window radius as the spatial range. By performing the spatial retrieval, all main test points falling within the window range corresponding to each checkpoint are identified and obtained; A spatial association is established between each checkpoint and all the acquired main test points, thereby determining the set of all main test points contained within each comparison window.

7. The method for checking the accuracy of water depth measurement by combining vertical datum conversion and dynamic terrain discrimination according to claim 6, characterized in that, For each comparison window, based on the comparison between the maximum difference in water depth at the main measuring point within the window and the corresponding water depth interval threshold, it is determined whether the window belongs to a flat terrain area or a terrain undulating area, including: For each comparison window, the average water depth of all main measuring points within each comparison window is calculated to obtain the reference water depth for each comparison window; The water depth range to which it belongs is determined based on the reference water depth, and the preset threshold corresponding to the water depth range is obtained; Calculate the difference between the maximum and minimum water depths at all main measuring points within the same comparison window to obtain the maximum difference; The maximum difference is compared with a preset threshold, and the terrain category of the same comparison window is determined based on the comparison result. If the maximum difference does not exceed the preset threshold, the window is determined to be a flat terrain area; if the maximum difference exceeds the preset threshold, the window is determined to be a undulating terrain area.

8. The method for checking the accuracy of water depth measurement by combining vertical datum conversion and dynamic terrain discrimination according to claim 7, characterized in that, For windows identified as flat terrain and windows identified as undulating terrain, calculate their water depth discrepancies, including: Obtain the terrain category determination result for each comparison window, whereby the terrain category includes flat terrain or undulating terrain. For windows identified as flat terrain areas, the nearest comparison method is used to select the main measuring point within the window that is geometrically closest to the check point. The water depth discrepancy value of this window is calculated based on the water depth values ​​of the main measuring point and the check point. For windows identified as areas with undulating terrain, the inverse distance weighted comparison method is used. The weights are defined based on the geometric distances between each main measuring point and the check point within the window, and the water depth discrepancy value of this window is calculated by combining the water depth values ​​of all main measuring points and check points.

9. The method for checking the accuracy of water depth measurement by combining vertical datum conversion and dynamic terrain discrimination according to claim 8, characterized in that, An evaluation is conducted based on all discrepancies in water depth, and the points that meet or fail are statistically analyzed. The mean square error of the depth measurement is then calculated, and based on this, a judgment is made on the overall quality of the measurement results, including: Obtain all calculated water depth discrepancies; Each water depth discrepancy is compared with its corresponding water depth range threshold to determine whether each point is a qualified comparison point, and the number of qualified points and the number of unqualified points are counted. Based on the water depth discrepancies at all qualified comparison points, the depth sounding error is calculated. Based on the ratio of unqualified points to qualified points obtained from statistics, and the mean square error of the depth measurement, the overall quality of the water depth measurement results is finally judged as qualified according to the preset specifications.

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