Coastal design high tide level accurate calculation method based on multi-source data fusion

Abstract

The invention relates to a coastal design high tide level accurate calculation method based on multi-source data fusion. The method is suitable for tide level data calculation. The method comprises the following steps: acquiring tide level data acquired by each monitoring station in a coastal target area; preprocessing the data, and constructing data individuals based on monitoring sites to form a data set; dividing data individuals in the data set into reliable data and unreliable data; based on each data individual in the reliable data, determining a design high tide level at each corresponding monitoring station in combination with the combination model; taking a station corresponding to the unreliable data as a target station, selecting a monitoring station corresponding to the reliable data adjacent to each target station as a base station, and determining the design high tide level of the target station based on the design high tide level of the base station and in combination with a difference ratio coefficient between the target station and the base station; and checking and correcting the designed high tide level of each station, and outputting the designed high tide level of each station in the coastal target area.

Description

A Precise Calculation Method for Coastal Design High Tide Levels Based on Multi-Source Data Fusion Technical Field

[0001] This invention relates to a method for accurately calculating coastal design high tide levels based on multi-source data fusion. It is applicable to the field of tide level data calculation. Background Technology

[0002] Coastal design high tide level calculation is a key technology in port engineering, coastal protection, and other fields. The core objective is to estimate the tidal baseline value for a specific return period by combining measured data with statistical models. Currently, mainstream methods include those based on a single type of probability distribution model, such as the P-III type distribution, the Gumbel type I distribution, and the generalized extreme value (GEV) distribution, which obtain the design value by fitting an annual extreme tidal level sequence. Simultaneously, optimized models such as the traditional maximum entropy distribution (TMED) and the improved maximum entropy distribution (MMED) have been developed to adapt to the bimodal characteristics of tidal duration curves.

[0003] For stations with sufficient measured data, the statistical standard of 10% of the cumulative high tide frequency or 1% of the cumulative duration frequency is adopted; for newly built stations with less than one year of data, the mainstream approach is the "short-term synchronization difference ratio method", which calculates the design tide level by performing correlation analysis with the synchronization data of neighboring benchmark stations.

[0004] However, existing technologies have the following core limitations: ① Strong data dependence and insufficient adaptability: Single statistical models have extremely high requirements for data quality. When the observation period is less than 10 years or the data integrity is less than 90%, the fitting results are significantly biased. Moreover, traditional models are difficult to adapt to the topographic differences and runoff effects of different regions at the same time, and their adaptability to complex coastal environments is weak.

[0005] ② The model is too simplistic and does not make full use of multi-source information: Existing methods rely on single tide data and do not effectively integrate influencing factors such as typhoons, topography, and meteorology, resulting in insufficient accuracy of tide prediction under extreme weather conditions; even the improved distributed model has not formed a multi-model collaborative computing architecture.

[0006] ③ The difference ratio method has strict constraints: The short-term synchronous difference ratio method requires the reference station and the target station to meet three conditions: similar tidal properties, geographical proximity, and similar runoff influence. It is difficult to find a suitable reference station in areas with complex terrain and variable tidal patterns, which limits the applicability of the method. Summary of the Invention

[0007] The technical problem to be solved by this invention is to provide a method for accurate calculation of coastal design high tide levels based on multi-source data fusion, addressing the aforementioned problems.

[0008] The technical solution adopted in this invention is: a method for accurately calculating coastal design high tide levels based on multi-source data fusion, comprising: acquiring tide level data collected by each monitoring station in the coastal target area, topographic data corresponding to each monitoring station, historical typhoon data, and meteorological forecast data; preprocessing the tide level, topography, typhoon, and meteorological data, and constructing data individuals based on the monitoring stations to form a dataset; dividing the data individuals in the dataset into reliable data and unreliable data based on the data observation duration, measurement accuracy, and data integrity; determining the design high tide level at each corresponding monitoring station based on each data individual in the reliable data, combined with a combined model, wherein the combined model includes a P-III type distribution and an extreme value I type distribution; taking the station corresponding to the unreliable data as the target station, selecting the monitoring station corresponding to the reliable data adjacent to each target station as its reference station, and determining the design high tide level of the target station based on the design high tide level of the reference station and the difference ratio coefficient between the target station and the reference station; verifying and correcting the design high tide level of each station, and outputting the design high tide level of each station in the coastal target area.

[0009] Based on the data observation duration, measurement accuracy, and data integrity, the data individuals in the dataset are divided into reliable data and unreliable data, including: data individuals with actual measured data that have an observation duration of ≥30 years and data integrity of ≥95% are classified as reliable data.

[0010] The potential coefficient and skewness coefficient of the P-III type distribution are corrected by topographic factors, which are determined based on topographic data.

[0011] The P-III type distribution and the extreme value I type distribution are combined into a combined model through weighted fusion.

[0012] The difference ratio between the target station and the reference station is determined based on the topographic similarity and tidal correlation between the target station and the reference station.

[0013] The difference ratio coefficient is calculated using a multiple linear regression model based on the longitude difference, latitude difference, terrain slope, and shoreline type between the target station and the reference station.

[0014] The verification and correction of the design high tide level of each site includes: verification of historical extreme events, verification of consistency with neighboring sites, and verification of similarity of planning results.

[0015] A precise calculation device for coastal design high tide levels based on multi-source data fusion includes: a data acquisition module for acquiring tide level data, topographic data corresponding to each monitoring station within a coastal target area, historical typhoon data, and meteorological forecast data; a preprocessing module for preprocessing the tide level, topography, typhoon, and meteorological data, and constructing data individuals based on the monitoring stations to form a dataset; a data classification module for classifying data individuals in the dataset into reliable and unreliable data based on data observation duration, measurement accuracy, and data integrity; a tide level calculation module I for determining the design high tide level at each corresponding monitoring station based on each data individual in the reliable data, combined with a combined model including P-III type distribution and extreme value type I distribution; a tide level calculation module II for selecting the monitoring stations corresponding to the unreliable data as target stations, selecting the reliable data corresponding monitoring stations adjacent to each target station as reference stations, and determining the design high tide level of the target station based on the design high tide level of the reference station and the difference ratio coefficient between the target station and the reference station; and a verification and correction module for verifying and correcting the design high tide level of each station and outputting the design high tide level of each station within the coastal target area.

[0016] A storage medium storing a computer program executable by a processor, wherein the computer program, when executed, implements the steps of the method for accurately calculating the coastal design high tide level.

[0017] A device for accurately calculating coastal design high tide levels includes a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, it implements the steps of the method for accurately calculating coastal design high tide levels.

[0018] The beneficial effects of this invention are: this invention divides individual data in the dataset into reliable data and unreliable data, and calculates the design high tide level of the site corresponding to the reliable data based on the reliable data and the combined model, and then determines the design high tide level of the remaining sites by combining the difference ratio coefficient.

[0019] This invention establishes a data grading system based on data observation duration, measurement accuracy, and data integrity, and formulates differentiated processing strategies for data of different quality (such as direct modeling of reliable data and optimization using reference data combined with interpolation methods) to solve the problem of inconsistent data quality.

[0020] This invention is based on a combined model of P-III type distribution and extreme value type I distribution. By optimizing the Cv and Cs parameter fitting algorithm of P-III type distribution (introducing terrain factor correction), it improves the dynamic adjustment mechanism of the root mean square error of extreme value type I distribution, and then achieves complementary advantages through equal weight fusion.

[0021] For stations lacking long-term measured data (unreliable data), an optimized process of "baseline station selection - multi-factor difference coefficient calculation - target value estimation" is constructed, incorporating longitude difference, terrain similarity, and other parameters into the parameter system to improve the adaptability of the difference method.

[0022] This invention integrates three dimensions: "historical extreme event verification, neighboring site consistency verification, and planning result similarity verification". It optimizes the results through weighted average correction, thus solving the problem of the single verification method in traditional methods. Attached Figure Description

[0023] Figure 1 is a flowchart of an embodiment. Detailed Implementation

[0024] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0025] In the description of this invention, "multiple" means two or more. The use of "first" and "second" is for distinguishing technical features only and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or the order of the indicated technical features. Furthermore, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

[0026] Example 1: As shown in Figure 1, this example is a method for accurate calculation of coastal design high tide level based on multi-source data fusion. Specifically, it includes the following steps: S100, acquiring tide level data collected by each monitoring station in the coastal target area, topographic data corresponding to each monitoring station, as well as historical typhoon data and meteorological forecast data.

[0027] This embodiment collects measured tide data (including long-term observation data from national basic stations and telemetry stations), historical typhoon data (path, intensity, impact range, etc.), topographic data (longitude, latitude, topographic slope, shoreline type, etc.) and meteorological forecast data (precipitation, wind direction, air pressure, etc.) from various monitoring stations within the coastal target area.

[0028] The impact of typhoons on tides is primarily achieved through storm surges. The strong winds and sudden drops in air pressure brought by typhoons cause abnormal seawater rise, which, when superimposed on astronomical tides, forms a composite extreme tide level of "typhoon-induced water increase + astronomical tide." The core of quantification is separating the storm surge component and establishing a quantitative relationship between typhoon parameters and storm surge amplification. The impact of meteorological data (precipitation, wind direction, air pressure) on tides is divided into direct impacts (air pressure, wind) and indirect impacts (precipitation). The core of quantification is separating meteorological-driven tide fluctuations and dynamically adjusting model parameters.

[0029] S200 preprocesses tidal, topographic, typhoon, and meteorological data, and constructs individual data based on monitoring stations to form a dataset.

[0030] In this example, the 3σ principle is used to remove outliers, missing values ​​are supplemented by interpolation of data from neighboring sites, all data are standardized into a uniform format, and individual data are constructed according to the monitoring sites to form a standardized dataset.

[0031] S300. Based on data observation duration, measurement accuracy, and data integrity, individual data points in the dataset are divided into reliable data and unreliable data (basically reliable data and reference data).

[0032] In this embodiment, the dataset is divided into three levels based on the observation duration, measurement accuracy, and data integrity: Reliable data: measured data with an observation duration of ≥30 years and data integrity of ≥95%; Basically reliable data: measured or surveyed data with an observation duration of 10-30 years and data integrity of 90%-95%; Reference data: interpolated or surveyed data with an observation duration of <10 years or data integrity of <90%.

[0033] S400. Based on individual data points in the reliable data and combined with the combined model, determine the design high tide level at each corresponding monitoring station in the reliable data. The combined model includes P-III type distribution and extreme value I type distribution.

[0034] In this embodiment, the combined model is constructed based on the P-III type distribution and the extreme value type I distribution. In the P-III type distribution, the fitting algorithm for the deviation coefficient (Cv) and skewness coefficient (Cs) is modified by introducing a topographic factor, which is determined based on topographic data. In the extreme value type I distribution, a root mean square error (s) dynamic adjustment factor is added to adjust the model parameters according to the tidal fluctuation characteristics of the area where the station is located. The P-III type distribution and the extreme value type I distribution complement each other through weight fusion.

[0035] Topographic factors are parameters that comprehensively reflect the influence of topography on tidal propagation and backwater effect, denoted as... The value ranges from 0.8 to 1.2 (the more complex the terrain, the higher the value). The further away from 1 (the greater the deviation). Among them, the terrain slope factor ( Measured in radians, the steeper the slope, the more pronounced the backwatering at high tide. Larger); shoreline morphology factor Quantified based on shoreline curvature radius (straight shoreline = 1.0, curved shoreline = 1.05~1.1, estuary shoreline = 1.1~1.2); Land-sea transition factor. Calculated based on offshore distance d (when d < 10km) When d≥10km (Nearshore topography has a more significant impact on tidal levels). Incorporating topographic factors into the P-III type distribution parameters, the final corrected parameters are: , .

[0036] The dynamic adjustment factor is a parameter that adjusts the root mean square error s in real time based on the tidal fluctuation characteristics of the area where the station is located. It is denoted as... The value ranges from 0.9 to 1.3. The core logic is that the more drastic the fluctuation, the better. The larger, The larger the value, the higher the probability weight of extreme tide levels. Dynamically adjusted mean squared error. Update scale parameters The design value is inferred from the distribution function based on the return period. .

[0037] S500. Taking each corresponding monitoring station in the unreliable data as the target station, select the reliable data corresponding monitoring station near each target station as its reference station. Based on the design high tide level of the reference station obtained in step S400, and combined with the difference ratio coefficient between the target station and the reference station, determine the design high tide level of the target station.

[0038] This embodiment determines the difference ratio coefficient by considering the topographic similarity and tidal level correlation between the target station and the reference station, and calculates the design high tide level of the target station based on the calculation results of the reference station. Taking into account the difference in longitude, latitude, topographic slope, and shoreline type, the difference ratio coefficient is calculated using a multiple linear regression model.

[0039] S600 verifies and corrects the design high tide level of each station, outputs design high tide level data for each station in the coastal target area with different return periods (5 years, 10 years, 20 years, 30 years, 50 years, 100 years, 200 years), and generates a result report containing station information, calculation parameters, and reliability rating.

[0040] In this embodiment, the verification integrates three dimensions: "historical extreme event verification, neighboring site consistency verification, and planning result similarity verification." The historical extreme event verification compares the measured tide levels during historical strong typhoons (such as Typhoon "9711") to verify the rationality of the calculation results. The neighboring site consistency verification compares the calculation results of the target site with those of nearby reliable sites to ensure the consistency of the regional tide level distribution. The planning result similarity verification refers to the published coastal design high tide level planning report and compares the differences in the results. Based on the differences in the results of each verification, the design high tide level calculated in steps S400 and S500 is adjusted by weighted average to achieve result optimization.

[0041] The following is a specific example: S100 collects measured tide level data from 1950 to 2013 from 18 stations along the Ningbo coast (including Ganpu, Linhaipu, Ningbo, Zhenhai, Shipu marine stations, etc.), and simultaneously collects historical typhoon data such as "9711" and "0509", as well as longitude, latitude, and topographic slope data of each station, and long-term meteorological observation data from the Ningbo Meteorological Department.

[0042] S200. The abnormal tide level data of Zhenhai Station in 1998 were removed by using the 3σ principle. The missing data of Fenshuijiao Station from 1985 to 1987 were interpolated by the correlation between the contemporaneous data of Maojiao Station and Beilun Marine Station. All data were standardized into an "annual highest tide level" sequence.

[0043] S300: Ningbo Station (64 years of observation data) and Zhenhai Station (63 years of observation data) are determined to be reliable data; Linhaipu Station (40 years of observation data) and Gaobeipu Station (43 years of observation data) are determined to be basically reliable data; the interpolated portion of Maojiao Station (34 years of observation data) is determined to be reference data.

[0044] S400. Based on reliable data, the design high tide level for Ningbo Station during a 200-year return period was calculated to be 3.63m using the P-III type distribution model, and 3.60m using the extreme value type I distribution model. The design high tide level was calculated to be 3.61m by weighted averaging (50% weight each).

[0045] S500. For Beilun Marine Station (no long-term measured data), Zhenhai Station is selected as the benchmark station. The difference ratio coefficient is calculated to be 1.05. Combined with the 200-year return period design high tide level of Zhenhai Station of 3.62m, the initial value of Beilun Marine Station is estimated to be 3.80m.

[0046] S600, Historical Event Verification: The measured tide level at Zhenhai Station during Typhoon "9711" was 3.28m, which is in good agreement with the calculated 10-year return period design high tide level of 2.88m; Verification with neighboring stations: The difference between the 200-year return period design high tide level at Ningbo Station and Zhenhai Station is 0.01m, which is consistent with the regional tide level distribution pattern; Verification with planning results: The deviation from the results in the "Research and Analysis Report on Design High Tide Levels along the Coast of Zhejiang Province" is ≤3%; Based on the differences in the verification results, the corrected 200-year return period design high tide level is 3.62m at Ningbo Station and 3.82m at Beilun Marine Station.

[0047] The 200-year return period design high tide level of Ningbo Station calculated in this embodiment is 3.62m, which deviates by 4.6% from 3.46m in the "Yongjiang River Basin Flood Control and Drainage Plan" and by only 0.5% from 3.64m in the "Technical Regulations for Seawall Engineering in Zhejiang Province". This verifies the accuracy and reliability of this embodiment.

[0048] Example 2: This example is a precise coastal design high tide level calculation device based on multi-source data fusion, comprising: a data acquisition module for acquiring tide level data collected by various monitoring stations within the coastal target area, topographic data corresponding to each monitoring station, historical typhoon data, and meteorological forecast data; a preprocessing module for preprocessing the tide level, topography, typhoon, and meteorological data, and constructing data individuals based on the monitoring stations to form a dataset; a data classification module for classifying data individuals in the dataset into reliable data and unreliable data based on data observation duration, measurement accuracy, and data integrity; and a tide level calculation module. Module I is used to determine the design high tide level at each corresponding monitoring station based on individual data points in the reliable data and combined with a combined model, where the combined model includes the P-III type distribution and the extreme value I type distribution; Module II is used to select the monitoring stations corresponding to unreliable data as target stations, select the monitoring stations corresponding to reliable data adjacent to each target station as reference stations, and determine the design high tide level of the target station based on the design high tide level of the reference station and the difference ratio coefficient between the target station and the reference station; Module III is used to verify and correct the design high tide level of each station and output the design high tide level of each station in the coastal target area.

[0049] Example 3: This example is a storage medium that stores a computer program that can be executed by a processor. When the computer program is executed, it implements the steps of the method for accurately calculating the coastal design high tide level described in Example 1.

[0050] Example 4: This example is a device for accurately calculating the coastal design high tide level. It has a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, it implements the steps of the method for accurately calculating the coastal design high tide level described in Example 1.

[0051] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0052] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0053] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0054] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0055] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0056] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0057] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for accurately calculating coastal design high tide levels based on multi-source data fusion, characterized in that, include: Acquire tidal data collected by each monitoring station in the coastal target area, topographic data corresponding to each monitoring station, as well as historical typhoon data and meteorological forecast data; The data on tide level, topography, typhoon, and meteorology are preprocessed, and individual data are constructed based on monitoring stations to form a dataset. Based on the data observation duration, measurement accuracy, and data integrity, the individual data in the dataset are divided into reliable data and unreliable data. Based on each individual data in the reliable data, the design high tide level at each corresponding monitoring station is determined by combining a combined model, which includes the P-III type distribution and the extreme value I type distribution. The station corresponding to the unreliable data is taken as the target station, and the monitoring station corresponding to the reliable data adjacent to each target station is selected as the reference station. Based on the design high tide level of the reference station, combined with the difference ratio coefficient between the target station and the reference station, the design high tide level of the target station is determined. The design high tide levels of each station are verified and corrected, and the design high tide levels of each station within the coastal target area are output.

2. The method for accurate calculation of coastal design high tide levels based on multi-source data fusion according to claim 1, characterized in that, Based on the data observation duration, measurement accuracy, and data integrity, the data individuals in the dataset are divided into reliable data and unreliable data, including: data individuals with actual measured data that have an observation duration of ≥30 years and data integrity of ≥95% are classified as reliable data.

3. The method for accurate calculation of coastal design high tide levels based on multi-source data fusion according to claim 1, characterized in that, The potential coefficient and skewness coefficient of the P-III type distribution are corrected by topographic factors, which are determined based on topographic data.

4. The method for accurate calculation of coastal design high tide level based on multi-source data fusion according to claim 1 or 3, characterized in that, The P-III type distribution and the extreme value I type distribution are combined into a combined model through weighted fusion.

5. The method for accurate calculation of coastal design high tide levels based on multi-source data fusion according to claim 1, characterized in that, The difference ratio between the target station and the reference station is determined based on the topographic similarity and tidal correlation between the target station and the reference station.

6. The method for accurate calculation of coastal design high tide levels based on multi-source data fusion according to claim 5, characterized in that, The difference ratio coefficient is calculated using a multiple linear regression model based on the longitude difference, latitude difference, terrain slope, and shoreline type between the target station and the reference station.

7. The method for accurate calculation of coastal design high tide level based on multi-source data fusion according to claim 1, characterized in that, The verification and correction of the design high tide level of each site includes: verification of historical extreme events, verification of consistency with neighboring sites, and verification of similarity of planning results.

8. A precise calculation device for coastal design high tide levels based on multi-source data fusion, characterized in that, include: The data acquisition module is used to acquire tide data collected by each monitoring station in the coastal target area, topographic data corresponding to each monitoring station, as well as historical typhoon data and meteorological forecast data. The preprocessing module is used to preprocess tidal, topographic, typhoon, and meteorological data, and to build individual data based on monitoring stations to form a dataset; The data classification module is used to classify individual data points in a dataset into reliable data and unreliable data based on data observation duration, measurement accuracy, and data integrity. Tide level calculation module I is used to determine the design high tide level at each corresponding monitoring station based on individual data points in the reliable data and combined with a combined model. The combined model includes the P-III type distribution and the extreme value I type distribution. The tide level calculation module II is used to select the monitoring stations with reliable data corresponding to each target station as the reference stations, and determine the design high tide level of the target station based on the design high tide level of the reference station and the difference ratio coefficient between the target station and the reference station. The verification and correction module is used to verify and correct the design high tide level of each station and output the design high tide level of each station in the coastal target area.

9. A storage medium having a computer program stored thereon that can be executed by a processor, characterized in that, When the computer program is executed, it implements the steps of the method for accurately calculating the coastal design high tide level as described in any one of claims 1 to 7.

10. A precise calculation device for coastal design high tide levels, comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, characterized in that, When the computer program is executed, it implements the steps of the method for accurately calculating the coastal design high tide level as described in any one of claims 1 to 7.

Images