Efficient processing and report generation method, system and equipment for tide level data and storage medium

Abstract

The invention provides an efficient processing and report generation method, system and equipment for tide level data and a medium. The method comprises the following steps: S1, obtaining original tide level data and carrying out data continuity analysis; s2, de-noising processing is carried out on the tide level data to obtain processed tide level data; s3, performing interpolation processing on the processed tide level data to obtain interpolated tide level data; s4, extracting tide level characteristic parameters from the normalized tide level data after interpolation correction; s5, performing identification and lunar calendar conversion on related Gregorian calendar dates; and S6, generating a standardized report based on the extracted tide characteristic parameters. Through automatic data denoising, interpolation, feature extraction and other processing, the processing efficiency and accuracy of the tide level data are greatly improved, and the defects of manual processing are effectively overcome; meanwhile, by means of an intelligent report generation mode, the analysis result of the tide level data is more visual and readable, and a user can conveniently and rapidly obtain needed key information.

Description

Technical Field

[0001] The present invention belongs to the technical field of marine hydrographic data processing, and specifically relates to a method, system, device and storage medium for efficient processing and report generation of tide level data. Background Art

[0002] Tide level data is important basic data in fields such as marine scientific research, engineering construction, and shipping scheduling. However, the original tide level data usually contains noise, outliers, etc., and needs to be preprocessed and analyzed to obtain effective characteristic parameters and statistical results for reference in related applications.

[0003] Traditional methods for processing tide level data mainly rely on manual work. From data cleaning, feature extraction to report compilation, a large amount of time and manpower are often required, resulting in low work efficiency. Moreover, it is difficult to ensure the accuracy and consistency of the results manually, and subjective errors are easily introduced. Therefore, there is an urgent need for a new, efficient, intelligent and standardized method for processing tide level data to improve the level of data processing and application.

[0004] At present, there have been some attempts to analyze tide level data using computer technology, but there are generally deficiencies such as imperfect analysis processes and lack of standardized report output. How to achieve integrated and automated processing from tide level data acquisition to report generation has become an urgent problem in this field. Summary of the Invention

[0005] The first object of the present invention is to provide a method for efficient processing and report generation of tide level data in view of the above-mentioned problems.

[0006] To achieve the above object of the present invention, the following technical solutions are adopted:

[0007] A method for efficient processing and report generation of tide level data includes the following steps:

[0008] S1. Obtain the original tide level data and perform data continuity analysis;

[0009] S2. Denoise the tide level data to obtain the processed tide level data;

[0010] S3. Interpolate the processed tide level data to obtain the interpolated tide level data;

[0011] S4. Extract tide level characteristic parameters from the normalized tide level data after interpolation and correction;

[0012] S5. Identify and convert the Gregorian dates involved to lunar dates;

[0013] S6. Generate a standardized report based on the extracted tidal characteristic parameters.

[0014] While adopting the above technical solution, the present invention can also adopt or combine the following technical solutions:

[0015] As a preferred technical solution of the present invention: the data continuity analysis in step S1 further includes the following sub-steps:

[0016] S11. Sort the tide level data according to the time series;

[0017] S12. Detect the time interval between data. When the time interval between adjacent data points is greater than 2 hours, mark this place as a discontinuous point;

[0018] S13. Divide the data into two categories: continuous data and discontinuous data according to the discontinuous points;

[0019] S14. For continuous data, further divide it into two situations: less than 2 months and greater than or equal to 2 months.

[0020] As a preferred technical solution of the present invention: step S2 further includes the following sub-steps:

[0021] S21. Perform outlier detection on the tide level data. Calculate the median feature of the data by using a moving window, judge the data through a set outlier detection threshold factor, and mark the data points exceeding the threshold range as outliers;

[0022] S22. Replace the outlier data to obtain the preliminarily processed tide level data.

[0023] As a preferred technical solution of the present invention: step S3 further includes the following sub-steps:

[0024] S31. Check the continuity of the tide level data. When the time interval between adjacent data points is greater than 0.5 hours, mark this place as a discontinuous point;

[0025] S32. Perform interpolation processing on each continuous data segment respectively by using the cubic spline interpolation method to unify the data time interval to 1 minute;

[0026] S33. Verify the continuity of the interpolated data;

[0027] S34. Fill the identified discontinuous intervals with the value 999.

[0028] As a preferred technical solution of the present invention: step S4 further includes the following sub-steps:

[0029] S41. Identify the peaks of the normalized tide level data process line to obtain the high tide time and tide height;

[0030] S42. Multiply the water level data sequence by -1 and then perform peak detection to obtain the low tide time and water level;

[0031] S43. Based on the identified high and low tide feature points, calculate the flood tide duration, ebb tide duration, tidal range sequence, average tidal range, maximum tidal range, minimum tidal range, average high water level, and average low water level.

[0032] As a preferred technical solution of the present invention: Step S5 further includes the following sub-steps:

[0033] S51. Identify the Gregorian date of each water level record to obtain the year, month, and day information;

[0034] S52. Use astronomical algorithms and solar term tables to convert the Gregorian date into the corresponding lunar date;

[0035] S53. Display both Gregorian date and lunar date information in the final report.

[0036] As a preferred technical solution of the present invention: Step S6 further includes the following sub-steps:

[0037] S61. For continuous data periods less than 2 months, generate a single report;

[0038] S62. For continuous data periods greater than or equal to 2 months, generate reports by month;

[0039] S63. Adopt a unified standard header format, including basic information such as station name, station code, longitude, latitude, and water level unit;

[0040] S64. Display tidal level characteristic parameters according to the requirements of the standard data format.

[0041] The second object of the present invention is to provide an efficient processing and report generation system for water level data, including the following modules:

[0042] Data acquisition and continuity analysis module, which is used to acquire original water level data and perform data continuity analysis;

[0043] Data denoising processing module, which is used to perform denoising processing on water level data;

[0044] Data interpolation processing module, which is used to perform interpolation processing on the processed water level data;

[0045] Tidal level characteristic parameter extraction module, which is used to identify high and low tide feature points and calculate tidal characteristic parameters;

[0046] A date conversion module, which is used to identify the Gregorian date and convert it into the corresponding lunar date;

[0047] A report generation module, which is used to generate a standardized report of tide level data.

[0048] The third object of the present invention is to provide an electronic device, which includes a processor, a communication interface, a memory, and a communication bus. The processor, the communication interface, and the memory complete mutual communication through the communication bus. Its characteristics are as follows:

[0049] A memory, which is used to store computer programs;

[0050] A processor, which is used to execute the computer programs stored on the memory to implement the method steps of the efficient processing and report generation of tide level data as described above.

[0051] Another object of the present invention is to provide a non-volatile storage medium, in which an executable program is stored. When the executable program is executed by a processor, the method steps of the efficient processing and report generation of tide level data as described above are implemented.

[0052] Compared with the prior art, the present invention has the following beneficial effects: By adopting an automated data preprocessing method, including outlier detection and elimination, data smoothing, etc., the noise removal and quality improvement of tide level data can be quickly and accurately completed, greatly reducing the workload and time cost of manual processing;

[0053] By introducing a variety of interpolation methods to interpolate the tide level data to a 1-minute interval and performing continuity checks and repairs, a more complete, continuous, and reliable tide level data sequence can be obtained, laying a good data foundation for subsequent feature extraction and analysis;

[0054] Automatically extract tide level characteristic parameters, including tide time, tide height, tidal range, flood and ebb tide durations, etc., which can comprehensively and accurately describe the key characteristics of tide level changes, providing important reference information for tidal prediction, ocean engineering, etc.

[0055] Through automated data denoising, interpolation, feature extraction, etc., the processing efficiency and accuracy of tide level data are greatly improved, effectively overcoming the defects of manual processing; at the same time, through an intelligent report generation method, the analysis results of tide level data are more intuitive and readable, facilitating users to quickly obtain the required key information. Description of the Drawings

[0056] Figure 1 It is a flowchart of the method for the efficient processing and report generation of tide level data provided by the present invention.

[0057] Figure 2 It is a flowchart for data preprocessing.

[0058] Figure 3 It is a flowchart for feature parameter extraction.

[0059] Figure 4 It is a schematic diagram for generating a standardized report. Specific implementation manners

[0060] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0061] The method for processing tide level data and generating a report provided by the present invention is computer-implemented, using MATLAB as the development platform. MATLAB has powerful numerical calculation and data processing capabilities, with a rich built-in mathematical function library and signal processing toolbox, which is particularly suitable for processing time series data.

[0062] As Figure 1-2 shown, an efficient method for processing tide level data and generating a report specifically includes the following steps:

[0063] S1. Obtain the original tide level data and perform data continuity analysis:

[0064] Read the data file through the readtable function of MATLAB. The data file contains timestamps and tide level observations;

[0065] S11. Sort the tide level data in time series;

[0066] S12. Detect the time interval between data. When the time interval between adjacent data points is greater than 2 hours, mark this place as a discontinuous point;

[0067] S13. Divide the data into two categories: continuous data and discontinuous data according to the discontinuous points;

[0068] S14. For continuous data, further divide it into two cases: less than 2 months and greater than or equal to 2 months.

[0069] Calculate the absolute value of the change value between every two adjacent tide level data. The timestamps are stored in the datenum format of MATLAB. Use the diff function to calculate the time difference between adjacent records. When the time difference exceeds 0.5 / 24 (i.e., 0.5 hours, because one day in MATLAB is represented as 1), mark this place as a discontinuous point. The setting of the segmentation standard mainly considers ensuring the capture of the complete tidal change process and guaranteeing the reliability of the hourly tide level report data;

[0070] The system uses the logical indexing function of MATLAB to divide the data into continuous data segments and discontinuous data segments. For continuous data, the time span is judged by calculating the difference between the first and last records of the time series. If the span is less than 2 months (i.e., 60 days), it is treated as a whole processing unit; if the span is greater than or equal to 2 months, the datetime function is used to extract the month information for segmented processing.

[0071] S2. Denoise the tide level data to obtain the processed tide level data:

[0072] S21. Detect outliers in the tide level data. The moving window method is used for outlier detection. The window size can be set, with a default of 30 data points. Two outlier detection methods, the median method (movmedian) and the moving average method (movmean), are provided for selection, and the data is judged based on the set threshold factor (default value is 1);

[0073] The outlier detection methods include the moving median method and the moving average method. The moving median method calculates the median feature of the data using a moving window, judges the data through the set outlier detection threshold factor, and marks the data points outside the threshold range as outliers; the moving average method calculates the mean feature of the data using a moving window, judges the data through the set outlier detection threshold factor, and marks the data points outside the threshold range as outliers.

[0074] The outlier detection parameter settings include:

[0075] The value range of the threshold factor is 0.1 - 5, and the default value is 1;

[0076] The value range of the moving window size is 1 - 100, and the default value is 30 data points.

[0077] S22. Replace the outlier data to obtain the preliminarily processed tide level data.

[0078] The preliminarily processed tide level data is smoothed. Multiple filtering algorithms can be selected to smooth and denoise the data after removing outliers, including but not limited to moving median filtering, Gaussian filtering, local linear regression (Lowess), local quadratic regression (Loess), robust Lowess, robust Loess, and Savitzky - Golay polynomial filtering (default method), etc.

[0079] The moving median filter is implemented through the medfilt1 function; the Gaussian filter uses the combination of the gausswin and filter functions; the local regression - type algorithms use the smooth function and set different method parameters; the Savitzky - Golay filter is implemented through the sgolayfilt function. Each method can optimize the processing effect by adjusting parameters such as the window length and polynomial order.

[0080] Which filtering algorithm to specifically adopt can be determined according to the data characteristics and application requirements. The window size parameter is default set to 330 and can be adjusted according to the data characteristics.

[0081] S3. Interpolate the processed tide level data to obtain the interpolated tide level data:

[0082] S31. Check the continuity of the tide level data. When the time interval between adjacent data points is greater than 0.5 hours, mark this place as a discontinuous point, and accordingly divide the data sequence into several continuous data segments;

[0083] S32. Respectively use the cubic spline interpolation method for interpolation processing on each continuous data segment, supporting multiple interpolation methods such as spline, linear, nearest, pchip, makima, etc., and unify the data time interval to 1 minute;

[0084] S33. Verify the continuity of the interpolated data. Calculate the first - order difference sequence of the interpolation result. If there are data points with difference values greater than the preset threshold, use the linear interpolation method to repair these points to ensure the smoothness of the interpolation result;

[0085] S34. For the identified discontinuous intervals (greater than 0.5 hours), fill them with the value 999 to identify the data missing areas in subsequent processing.

[0086] Use the filloutliers function to detect outliers;

[0087] Fill the detected outliers using the linear interpolation method;

[0088] Generate a visual display of the processing results: display the original data as gray lines; display the cleaned data as blue lines; mark the positions of outliers with red "×"; mark the filling results with green "○"; display the threshold boundary as gray dashed lines.

[0089] The interpolation process adopts a segmented strategy. For each continuous data segment, the spline function is used to construct a cubic spline interpolation. The cubic spline interpolation is chosen because the spline function in MATLAB implements natural boundary conditions, which can ensure good smoothness of the interpolation results at the nodes. A uniform time series is generated by the linspace function, and the time interval is set to 1 / (24*60) (i.e., 1 minute).

[0090] To ensure the reliability of the interpolation results, the first-order difference is calculated by the gradient function for continuity verification. When the difference value exceeds the preset threshold, the interp1 function is used for linear interpolation correction. For discontinuous intervals (greater than 0.5 hours), the ones function is used to generate 999 values for filling.

[0091] S4. Extract tidal level characteristic parameters from the interpolated and corrected normalized tidal level data, such as Figure 3 shown below:

[0092] S41. Identify the peaks of the normalized tidal level data process line to obtain the high tide time and height. Set the minimum detection distance (default value 300) and the high tide threshold ratio (default value 0.1). Adopt the local extreme value detection method to identify the high and low tide points, and calculate and record the time and tidal level values of each characteristic point;

[0093] S42. Screen the characteristic points through time interval constraints and amplitude thresholds. Multiply the tidal level data sequence by -1 and then perform peak detection to obtain the low tide time and height;

[0094] During the feature recognition process, data points with a value of 999 are automatically skipped;

[0095] S43. Based on the identified high and low tide characteristic points, calculate the flood duration, ebb duration, tidal range sequence, average tidal range, maximum tidal range, minimum tidal range, average high tide level, and average low tide level.

[0096] Calculate the flood duration by the time difference between adjacent high tide times and low tide times;

[0097] Calculate the ebb duration by the time difference between adjacent low tide times and high tide times;

[0098] Calculate the difference between all high tide levels and adjacent low tide levels to obtain the tidal range sequence;

[0099] Calculate the average value of the tidal range sequence to obtain the average tidal range;

[0100] Calculate the maximum value of the tidal range sequence to obtain the maximum tidal range;

[0101] Calculate the minimum value of the tidal range sequence to obtain the minimum tidal range;

[0102] The average high tide level is obtained by averaging all high tide levels;

[0103] Averaging all low tide levels gives the mean low tide level.

[0104] The findpeaks function is used to identify the high and low tide features of the processed tide data. The minimum detection distance parameter is set through the MinDistanceSpinner control, and the default value is 300. The high tide threshold ratio parameter is set through the PeakThresholdSpinner control, and the default value is 0.1. For the identification of low tide, the current data sequence is multiplied by -1 and then the findpeaks function is called again. For the detected feature points, their position index, tide value and corresponding time information are recorded. At the same time, the feature points are screened according to the set time interval constraints and amplitude thresholds to ensure the reliability of the recognition results. The screening results are analyzed and evaluated to ensure the high tide point-high tide point, high tide point-low tide point, high tide point-low tide point time intervals and whether high and low tides appear alternately to ensure the accuracy of feature point detection.

[0105] Based on the detected feature points, MATLAB array operations and statistical functions are used to calculate tidal parameters. The diff function is used to calculate the time difference to obtain the duration of the tide; the max, min, mean and other functions are used to calculate the statistical characteristics of the tidal range. All calculation processes make full use of MATLAB's matrix operation capabilities.

[0106] S5. Identify the Gregorian calendar dates involved and convert them to the lunar calendar:

[0107] S51, identifying the Gregorian calendar date of each tide level record, and obtaining year, month, and day information;

[0108] S52, converting the Gregorian calendar date into the corresponding lunar calendar date using an astronomical algorithm and a solar term table;

[0109] S53. The final report displays both the Gregorian calendar date and the lunar calendar date information.

[0110] The lunar calendar information data including leap month information, year days and leap month days are obtained by table lookup; the cumulative number of days from the date to be converted to January 31, 1900 is calculated; the lunar year is determined by combining the cumulative number of days with the number of days in the lunar year; the specific lunar month and day are calculated based on the number of days in the month and leap month information; wherein the lunar calendar information table supports conversion within the range of 1900-2100, the function input parameters are year, month and day, and the output is the corresponding lunar month and day values.

[0111] S6. Generate standardized reports based on the extracted tidal characteristic parameters. The report style is as follows: Figure 4 As shown, it should be noted that Figure 4Only for demonstrating the style of the report, not for showing specific numerical results:

[0112] S61. For continuous data with a duration less than 2 months, generate a single report;

[0113] S62. For continuous data with a duration greater than or equal to 2 months, generate reports by month, and store the data for each month in an independent Sheet;

[0114] S63. Adopt a unified standard header format, including basic information such as station name, station code, longitude, latitude, and tide level unit;

[0115] Indicate the tide level reference plane information;

[0116] Indicate the year and month of observation;

[0117] Report data format specification:

[0118] The tide time adopts the 24-hour system and is accurate to the minute;

[0119] The unit of the tide height value is centimeter, and the integer is reserved;

[0120] The duration of flood and ebb tides is accurate to the minute;

[0121] The unit of the tidal range value is centimeter, and the integer is reserved;

[0122] The average value data is reserved as an integer.

[0123] S64. Display the tide level characteristic parameters according to the specified data format requirements.

[0124] Data format requirements:

[0125] All numerical values are centered and displayed;

[0126] The time data adopts a unified format;

[0127] Display the value 999 as a null value;

[0128] When calculating the missing tide time of the characteristic tide level according to different tidal characteristics, fill it with "-".

[0129] The report content is arranged in chronological order, and the corresponding Gregorian and lunar dates are marked for the convenience of users to view and use the tide level analysis results.

[0130] The implementation of the report generation process uses the COM interface of Excel to write the processing results into the report file, which specifically includes the following steps:

[0131] First, create an Excel server object and set it to an invisible state; create worksheets by month, with the worksheet names in the format of year - month string; set a unified report format in each worksheet. Merge cells A1:AK1 and write the title "***Hourly Tide Level Observation Report of Tide Gauge Station"; set basic information such as station name, tide level datum, and usage time in the second row; set date column headers in A3:C3, tide level data column headers in D3:AA3, and statistical value and high - low tide characteristic value column headers in AB3:AK3; set 24 - hour time column headers and tide time - tide level column headers in the 4th - 5th rows; starting from the 6th row, fill in tide level data in date order, uniformly set the numerical format and alignment of corresponding cells, and replace the value of 999 with a blank value to represent missing values; set monthly statistical information in the last three rows, including statistical data such as the highest high tide, lowest low tide, tidal range, and flood - ebb tide duration; the report format also includes unified standard settings for cell font, border, column width, and row height.

[0132] In terms of report format control in this embodiment, the unified table size is set by setting the column width and row height of the Range object; the table border lines are set using the Borders property; relevant cells are merged using the MergeCells property; and the alignment of cell content is set through the HorizontalAlignment and VerticalAlignment properties to ensure the standardization and aesthetics of the report layout.

[0133] The present invention also provides an efficient processing and report generation system for tide level data, including the following modules:

[0134] Data acquisition and continuity analysis module, which is used to acquire original tide level data and perform data continuity analysis;

[0135] Data denoising processing module, which is used to perform denoising processing on tide level data;

[0136] Data interpolation processing module, which is used to perform interpolation processing on the processed tide level data;

[0137] Tide level characteristic parameter extraction module, which is used to identify high - low tide characteristic points and calculate tidal characteristic parameters;

[0138] Date conversion module, which is used to identify the Gregorian date and convert it into the corresponding lunar date;

[0139] Report generation module, which is used to generate a standardized report of tide level data.

[0140] The present invention also provides an electronic device, which includes a processor, a communication interface, a memory, and a communication bus. The processor, the communication interface, and the memory communicate with each other through the communication bus.

[0141] The memory is used to store a computer program.

[0142] The processor is used to execute the computer program stored on the memory to implement the steps of the method for efficient processing of tide level data and report generation as described above.

[0143] The present invention also provides a non-transitory readable storage medium, which is a non-volatile storage medium. The non-volatile storage medium stores an executable program. When the executable program is executed by a processor, it can implement the steps of the method for efficient processing of tide level data and report generation as described above.

[0144] So far, the technical solution of the present invention has been described in combination with the specific experimental process shown in the drawings. However, the protection scope of the present invention is not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. An efficient method for processing tidal level data and generating reports, characterized in that, It includes the following steps: S1. Obtain the original tide level data and conduct data continuity analysis; S2. Denoise the tide level data to obtain the processed tide level data; S3. Interpolate the processed tide level data to obtain the interpolated tide level data; S4. Extract tide level characteristic parameters from the normalized tide level data after interpolation and correction; S5. Identify the Gregorian calendar dates involved and perform lunar calendar conversion; S6. Generate a standardized report based on the extracted tidal characteristic parameters.

2. The method according to claim 1, characterized in that: The data continuity analysis in step S1 further includes the following sub-steps: S11. Sort the tide level data in time series; S12. Detect the time intervals between data. When the time interval between adjacent data points is greater than 2 hours, mark this place as a discontinuous point; S13. Divide the data into two categories: continuous data and discontinuous data according to the discontinuous points; S14. For continuous data, further divide it into two cases: less than 2 months and greater than or equal to 2 months.

3. The method according to claim 1, wherein: Step S2 further includes the following sub-steps: S21. Detect outliers in the tide level data. Calculate the median feature of the data using a moving window, judge the data through a set outlier detection threshold factor, and mark the data points exceeding the threshold range as outliers; S22. Replace the outlier data to obtain the preliminarily processed tide level data.

4. The method according to claim 1, characterized in that: Step S3 further includes the following sub-steps: S31. Check the continuity of the tide level data. When the time interval between adjacent data points is greater than 0.5 hours, mark this place as a discontinuous point; S32. Perform interpolation processing on each continuous data segment using the cubic spline interpolation method respectively to unify the data time interval to 1 minute; S33. Verify the continuity of the interpolated data; S34. Fill the identified discontinuous intervals with the value 999.

5. The method according to claim 1, characterized in that: Step S4 further includes the following sub-steps: S41. Identify the peaks of the normalized tide level data process line to obtain the high tide time and tide height; S42. Detect the peaks after multiplying the tide level data sequence by -1 to obtain the low tide time and tide height; S43. Based on the identified high and low tide characteristic points, calculate the flood tide duration, ebb tide duration, tidal range sequence, average tidal range, maximum tidal range, minimum tidal range, average high tide level, and average low tide level.

6. The method according to claim 1, characterized in that: Step S5 further includes the following sub-steps: S51. Identify the Gregorian calendar date of each tide level record to obtain the year, month, and day information; S52. Convert the Gregorian calendar date to the corresponding lunar calendar date using astronomical algorithms and the solar term table; S53. Display both the Gregorian calendar date and the lunar calendar date information in the final report.

7. The method according to claim 1, characterized in that: Step S6 further includes the following sub-steps: S61. Generate a single report for continuous data with a duration of less than 2 months; S62. Generate reports separately by month for continuous data with a duration of greater than or equal to 2 months; S63. Adopt a unified standardized header format, including basic information such as station name, station code, longitude, latitude, and tide level unit; S64. Display the tide level characteristic parameters according to the requirements of the standardized data format.

8. An efficient processing and report generation system for tide level data, characterized in that, It includes the following modules: Data acquisition and continuity analysis module, which is used to obtain the original tide level data and conduct data continuity analysis; A data denoising processing module, which is used to perform denoising processing on tide level data; A data interpolation processing module, which is used to perform interpolation processing on the processed tide level data; A tide level feature parameter extraction module, which is used to identify high and low tide feature points and calculate tide feature parameters; A date conversion module, which is used to identify the Gregorian date and convert it into the corresponding lunar date; A report generation module, which is used to generate a standardized report of tide level data.

9. An electronic device, which includes a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory complete mutual communication through the communication bus. It is characterized in that: A memory, which is used to store computer programs; A processor, which is used to execute the computer programs stored on the memory to implement the method steps for efficient processing and report generation of tide level data as described in any one of claims 1-7.

10. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores an executable program. When the executable program is executed by the processor, it implements the method steps for efficient processing and report generation of tide level data as described in any one of claims 1-7.

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