Wave measuring instrument comparison error analysis method, device, equipment and medium

Abstract

The present application discloses a method, apparatus, equipment, and medium for analyzing the comparative measurement errors of wave measuring instruments, relating to the field of ocean observation. The method comprises: obtaining original wave spectrum data of the instrument under test and the standard instrument, and standardizing them into first standard spectrum data and second standard spectrum data; performing time synchronization matching processing on the first standard spectrum data and the second standard spectrum data to obtain a wave eigenvalue sequence; constructing a wave height-period two-dimensional matrix, and mapping the wave eigenvalue sequence to the corresponding grid of the wave height-period two-dimensional matrix to determine a valid target grid; for each target grid, determining the error statistical parameters of the instrument under test relative to the standard instrument; calculating the sampling variability variance based on the second standard spectrum data, and correcting it to obtain the corrected standard deviation of the instrument under test; generating an error heat map based on the error statistical parameters and the corrected standard deviation, and performing a comprehensive analysis to obtain the comparative measurement results. The present application improves the accuracy of comparative measurement error analysis of wave measuring instruments.

Description

Technical Field

[0001] The present application relates to the field of ocean observation technology, and in particular to a method, device, equipment and medium for analyzing comparative measurement errors of wave measuring instruments. Background Art

[0002] As a key parameter in ocean dynamics research and numerous marine engineering applications, accurate measurement of waves is crucial. The high randomness and wide frequency-domain energy distribution of waves make their measurement a challenging task. In practical applications, wave observation data is extensively used to validate ocean wave forecast models, providing fundamental data support for improving the accuracy of wave forecasts; for satellite remote sensing calibration, ensuring the reliability of satellite ocean wave monitoring; and for ocean energy resource assessment, wave data is the core basis for evaluating the potential for ocean energy development.

[0003] The metrological performance of traditional wave measuring instruments, such as buoys, acoustic instruments, and radar instruments, is difficult to verify directly through laboratory calibration due to their operating principles and the influence of the complex ocean environment. Laboratory environments often struggle to fully simulate the complex and changing wave conditions found in the ocean, including varying wave heights, periods, wave spectrum energy distribution, and the combined effects of field environmental factors such as currents and anchor systems on instrument measurements. Therefore, synchronous comparative measurements between field instruments and standard instruments (such as the Wave Rider buoy) have become a common method for evaluating the metrological performance of traditional wave measuring instruments. To optimize marine engineering design and promote the development of marine science research, the analysis of wave instrument comparative measurement errors is particularly important.

[0004] Currently, wave instrument comparison error analysis in related technologies relies on statistical evaluation of the average error and root mean square error of wave characteristic parameters. However, the error characteristics of wave instruments are affected by multiple factors, such as wave height, period, wave spectral energy distribution, instrument operating principle, and field environment (such as currents and anchor systems). A single statistical parameter cannot reflect its dynamic performance under different wave heights and periods, and the correction of random errors of standard instruments is relatively crude, resulting in low accuracy in wave measurement instrument comparison error analysis. Summary of the Invention

[0005] The purpose of this application is to provide a method, device, equipment and medium for analyzing the comparative measurement errors of wave measuring instruments.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for analyzing comparative measurement errors of a wave measuring instrument, comprising:

[0008] Acquire original wave spectrum data of the instrument under test and standardize it into first standard spectrum data, and acquire original wave spectrum data of a standard instrument and standardize it into second standard spectrum data;

[0009] Performing time synchronization matching processing on the first standard spectrum data and the second standard spectrum data to obtain a wave characteristic value sequence; the wave characteristic value sequence includes a plurality of wave characteristic values, and the wave characteristic values ​​include significant wave heights and corresponding average periods;

[0010] Constructing a wave height-period two-dimensional matrix, mapping the wave eigenvalue sequence to a corresponding grid of the wave height-period two-dimensional matrix, and determining a valid target grid in the grid;

[0011] For each target grid, determining a statistical parameter of an error between the instrument under test and a standard instrument;

[0012] Calculating the sampling variability variance according to the second standard spectrum data, and correcting the error statistical parameter according to the sampling variability variance to obtain a corrected standard deviation of the instrument under test;

[0013] An error heat map is generated based on the error statistical parameters and the corrected standard deviation, and a comprehensive analysis is performed on the instrument to be tested to obtain a comparative test result.

[0014] Optionally, performing time synchronization matching processing on the first standard spectrum data and the second standard spectrum data to obtain a wave eigenvalue sequence includes:

[0015] Determine first spectral moment information of the first standard spectrum data, determine a first comparative measurement parameter based on the first spectral moment information, determine second spectral moment information of the second standard spectrum data, and determine a second comparative measurement parameter based on the second spectral moment information; the first comparative measurement parameter includes the significant wave height and average period of the instrument under test, and the second comparative measurement parameter includes the significant wave height and average period of the standard instrument;

[0016] The effective wave height and average period of the instrument under test and the effective wave height and average period of the standard instrument are subjected to time synchronization matching processing to obtain the wave characteristic value sequence.

[0017] Optionally, constructing a wave height-period two-dimensional matrix, mapping the wave eigenvalue sequence to a corresponding grid of the wave height-period two-dimensional matrix, and determining a valid target grid in the grid includes:

[0018] Determining a wave height interval and an average period interval based on the acquired historical wave statistical characteristics and the amount of wave characteristic value data in the wave characteristic value sequence;

[0019] Dividing the grid boundary range of the wave height-period two-dimensional matrix, and constructing a corresponding grid of the wave height-period two-dimensional matrix according to the wave height interval, the average period interval and the grid boundary range;

[0020] Classifying the wave eigenvalue sequence into corresponding grids of the wave height-period two-dimensional matrix according to the measurement values ​​of the standard instrument, and counting the data volume of each grid;

[0021] Delete all grids whose data amount is less than a preset threshold to obtain the target grid.

[0022] Optionally, the error statistical parameters include an error mean and an error standard deviation;

[0023] For each target grid, determining the error statistical parameters of the instrument under test relative to the standard instrument includes:

[0024] For each target grid, generating an error sequence between the instrument under test and the standard instrument;

[0025] The error mean is calculated based on the error sequence, and the error standard deviation is determined according to the error mean.

[0026] Optionally, calculating the sampling variability variance according to the second standard spectrum data, and correcting the error statistical parameter according to the sampling variability variance to obtain the corrected standard deviation of the instrument under test, includes:

[0027] For each target grid, calculating the significant wave height variance and the average period variance according to the second standard spectrum data;

[0028] The random phase method is used to simulate the wave sequence and calibrate the correction coefficient;

[0029] Correcting the significant wave height variance and the mean period variance according to the correction coefficient to obtain the sampling variability variance of the standard instrument;

[0030] The sampling variability variance is separated from the error standard deviation to obtain the corrected standard deviation of the instrument under test.

[0031] Optionally, the error heat map includes: a two-dimensional distribution map of the relative error mean and the relative standard deviation, and a two-dimensional distribution map of the error mean and the corrected standard deviation;

[0032] Generating an error heat map based on the error statistical parameter and the corrected standard deviation includes:

[0033] Normalizing the error mean and the corrected standard deviation to obtain a relative error mean and a relative standard deviation;

[0034] With the wave height as the horizontal axis and the average period as the vertical axis, a two-dimensional distribution diagram of the relative error mean and the relative standard deviation, and a two-dimensional distribution diagram of the error mean and the corrected standard deviation are drawn respectively; the two-dimensional distribution diagram of the relative error mean and the relative standard deviation, and the two-dimensional distribution diagram of the error mean and the corrected standard deviation both use color coding to mark different error ranges.

[0035] Optionally, the comparison test results include: system deviation index, random fluctuation intensity, abnormal sea condition adaptability result and frequency band sensitivity comparison test result;

[0036] Perform a comprehensive analysis on the instrument to be tested to obtain comparative test results, including:

[0037] Determining a systematic deviation index based on the relative error mean, and determining a random fluctuation intensity based on the relative standard deviation; and / or,

[0038] Determining a key grid from the target grid, determining the excess error ratio based on the error mean and relative error mean in the key grid, and obtaining the abnormal sea condition adaptability result; and / or,

[0039] The frequency band sensitivity comparison measurement result is determined according to the obtained relative error mean and relative standard deviation of the preset frequency band.

[0040] In a second aspect, the present application provides a wave measuring instrument comparison error analysis device, comprising:

[0041] A standardization module is used to obtain the original wave spectrum data of the instrument under test and standardize it into first standard spectrum data, and to obtain the original wave spectrum data of the standard instrument and standardize it into second standard spectrum data;

[0042] a time synchronization module, configured to perform time synchronization matching processing on the first standard spectrum data and the second standard spectrum data to obtain a wave characteristic value sequence; the wave characteristic value sequence includes a plurality of wave characteristic values, each of which includes a significant wave height and a corresponding average period;

[0043] A grid determination module is used to construct a wave height-period two-dimensional matrix, map the wave eigenvalue sequence to a corresponding grid of the wave height-period two-dimensional matrix, and determine a valid target grid in the grid;

[0044] A parameter determination module, configured to determine, for each target grid, an error statistical parameter of the instrument under test relative to a standard instrument;

[0045] a correction module, configured to calculate the sampling variability variance based on the second standard spectrum data, and correct the error statistical parameter based on the sampling variability variance to obtain a corrected standard deviation of the instrument under test;

[0046] The analysis module is used to generate an error heat map based on the error statistical parameters and the corrected standard deviation, and to perform a comprehensive analysis on the instrument to be tested to obtain a comparative test result.

[0047] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the wave measurement instrument comparison error analysis method described in any one of the above.

[0048] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the wave measurement instrument comparison error analysis method described in any one of the above are implemented.

[0049] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0050] The present application provides a method, apparatus, equipment and medium for analyzing the comparative measurement errors of wave measuring instruments, which obtains the original wave spectrum data of the instrument to be measured and normalizes it into first standard spectrum data, and obtains the original wave spectrum data of the standard instrument and normalizes it into second standard spectrum data; performs time synchronization matching processing on the first standard spectrum data and the second standard spectrum data to obtain a wave eigenvalue sequence; constructs a wave height-period two-dimensional matrix, and maps the wave eigenvalue sequence to the corresponding grid of the wave height-period two-dimensional matrix to determine the effective target grid in the grid; for each target grid, determines the error statistical parameters of the instrument to be measured relative to the standard instrument; calculates the sampling variability variance based on the second standard spectrum data, and corrects the error statistical parameters based on the sampling variability variance to obtain the corrected standard deviation of the instrument to be measured; generates an error heat map based on the error statistical parameters and the corrected standard deviation, and performs a comprehensive analysis on the instrument to be measured to obtain a comparative measurement result.

[0051] Compared with the existing technology, on the one hand, this scheme breaks through the single statistical limitation of traditional characteristic parameters by standardizing the original spectrum data of the instrument under test and the standard instrument into first standard spectrum data and second standard spectrum data respectively. The original spectrum standardization retains the frequency domain energy distribution details, providing a data basis for multi-factor error tracing, and performs time synchronization processing on the standard spectrum data, which can ensure that the two sets of spectrum data are compared under the same temporal and spatial sea conditions, avoiding errors caused by temporal and spatial misalignment. Then, a wave height-period two-dimensional matrix is ​​constructed, and the wave eigenvalue sequence is mapped to the corresponding grid of the two-dimensional matrix, which can more comprehensively reflect the dynamic laws of different grid intervals. On the other hand, by determining the error statistical parameters and sampling variability error, the uncertainty of the standard instrument is incorporated into the error analysis system, which improves the credibility of the assessment. The error statistical parameters are corrected by the sampling variability variance, which can more carefully restore the true error of the instrument under test. Based on the error statistical parameters and the corrected standard deviation, an error heat map is generated, which can intuitively visualize the error to present the error sensitive area, and then a comprehensive analysis of the instrument under test can be performed, and the comparison result can be accurately determined, greatly improving the accuracy of the comparison error analysis of wave measurement instruments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0053] Figure 1 This is a structural diagram of a comparative measurement error analysis system for a wave measuring instrument in one embodiment of the present application;

[0054] Figure 2 A schematic flow chart of a method for analyzing comparative measurement errors of a wave measuring instrument provided in one embodiment of the present application;

[0055] Figure 3 A schematic flow chart of a method for determining a valid target grid in a grid provided in one embodiment of the present application;

[0056] Figure 4 A schematic diagram of the grid structure corresponding to the wave height-period two-dimensional matrix provided in one embodiment of the present application;

[0057] Figure 5 A flowchart of a method for determining a corrected standard deviation of an instrument under test provided in one embodiment of the present application;

[0058] Figure 6 A heat map of effective wave height error provided in one embodiment of the present application;

[0059] Figure 7 A schematic diagram of the functional modules of a comparative measurement error analysis device for a wave measuring instrument provided in an embodiment of the present application;

[0060] Figure 8 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0062] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0063] In related technologies, wave instrument comparison error analysis is based on statistical evaluation of the average error and root mean square error of wave characteristic parameters. However, the error characteristics of wave instruments are affected by multiple factors, such as wave height, period, wave spectral energy distribution, instrument operating principle, and field environment (such as currents and anchor systems). A single statistical parameter cannot reflect its dynamic performance under different wave heights and periods, and the correction of random errors of standard instruments is relatively rough, resulting in low accuracy in wave measurement instrument comparison error analysis.

[0064] Based on the above-mentioned defects, this application provides a method for analyzing the comparative measurement errors of wave measuring instruments. Compared with the existing technology, on the one hand, this scheme breaks through the single statistical limitation of traditional characteristic parameters by standardizing the original spectrum data of the instrument under test and the standard instrument into first standard spectrum data and second standard spectrum data respectively. The original spectrum standardization retains the frequency domain energy distribution details, providing a data basis for multi-factor error tracing, and performs time synchronization processing on the standard spectrum data, which can ensure that the two sets of spectrum data are compared under the same temporal and spatial sea conditions, avoiding errors caused by temporal and spatial misalignment. Then, a wave height-period two-dimensional matrix is ​​constructed, and the wave eigenvalue sequence is mapped to the corresponding grid of the two-dimensional matrix, which can more comprehensively reflect the dynamic laws of different grid intervals. On the other hand, by determining the error statistical parameters and sampling variability error, the uncertainty of the standard instrument is incorporated into the error analysis system, which improves the credibility of the assessment. The error statistical parameters are corrected by the sampling variability variance, which can more carefully restore the true error of the instrument under test. Based on the error statistical parameters and the corrected standard deviation, an error heat map is generated, which can intuitively visualize the error to present the error sensitive area, and then a comprehensive analysis of the instrument under test can be performed, and the comparison result can be accurately determined, greatly improving the accuracy of the comparison error analysis of wave measurement instruments.

[0065] The wave measurement instrument comparison error analysis method provided in the embodiment of the present application can be applied to Figure 1 The wave measuring instrument comparison error analysis system shown. The wave measuring instrument comparison error analysis system includes: a terminal 102, a server 104 and a data storage system. The terminal 102 communicates with the server 104 through a grid. The data storage system can store data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the acquired original wave spectrum data of the instrument under test and the original wave spectrum data of the standard instrument to the server 104. After receiving the original wave spectrum data of the instrument under test and the original wave spectrum data of the standard instrument, the server 104 performs standardization processing, time synchronization matching processing, and constructs a wave height-period two-dimensional matrix and error correction processing to perform a comprehensive analysis of the instrument under test and obtain a comparison result. In addition, in some embodiments, the wave measurement instrument comparison error analysis method can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform normalization processing and time synchronization matching processing based on the acquired raw wave spectrum data of the instrument under test and the raw wave spectrum data of the standard instrument, and construct a two-dimensional wave height-period matrix and perform error correction processing to perform a comprehensive analysis of the instrument under test and obtain a comparison result. In particular, the terminal 102 can pre-store a data processing algorithm to facilitate the wave measurement instrument comparison error analysis.

[0066] Terminal 102 may include, but is not limited to, various desktop computers, laptops, smartphones, tablet computers, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers, or may be a cloud server.

[0067] It should be noted that the wave measurement instrument comparison error analysis method in this embodiment establishes a wave energy-frequency two-dimensional error distribution model and quantifies the random error of the standard instrument to achieve a refined evaluation of the measurement performance of the instrument to be measured, providing good data information for instrument optimization, data instruction control and observation scenario adaptation analysis, and is applied to different comparison scenarios, such as long-term performance evaluation, short-term acceptance testing, etc., and supports error analysis of multiple types of wave instruments (such as gravity acceleration, GNSS, acoustics, radar, pressure type, etc.), and has wide industrial applicability.

[0068] In an exemplary embodiment, Figure 2 As shown, a method for analyzing the comparison error of a wave measuring instrument is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, and can also be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the process, including the following steps S201 to S206.

[0069] Step S201: obtaining original wave spectrum data of the instrument to be tested and normalizing it into first standard spectrum data, and obtaining original wave spectrum data of a standard instrument and normalizing it into second standard spectrum data.

[0070] It should be noted that the aforementioned raw wave spectrum data is a function that describes the distribution of wave energy as a function of frequency or period, essentially reflecting the distribution of wave energy among different frequency components. Raw wave spectrum data refers to the original record of frequency domain energy distribution calculated by the instrument using sensor data. The raw wave spectrum data of the instrument under test (e.g., the buoy, acoustic, or radar instrument being evaluated) is directly collected through sensors by the wave measuring instrument under test (e.g., the buoy, acoustic, or radar instrument being evaluated). After preliminary processing such as hardware filtering and analog-to-digital conversion, the raw frequency domain data of wave energy distribution as a function of frequency is output. The raw wave spectrum data of a standard instrument refers to the raw frequency domain data of wave energy distribution collected and initially processed by a standard wave measuring instrument (e.g., a high-precision instrument like the Wave Rider buoy) and used as a benchmark for comparison. The raw wave spectrum data of the instrument under test has complex error sources, including instrument principles and environmental interference. The raw wave spectrum data of a standard instrument is primarily composed of random errors.

[0071] For example, if the instrument under test is an acoustic wave meter and the standard instrument is a Wave Rider buoy, the raw wave spectrum data of the instrument under test can be time-domain wave data acquired by a sensor. This time-domain wave data includes pressure fluctuations, acceleration, etc., and then generated by FFT transformation. The standard instrument can simultaneously collect similar data based on the data collected by the instrument under test.

[0072] After obtaining the original wave spectrum data of the instrument under test and the original wave spectrum data of the standard instrument, the original wave spectrum data of the instrument under test and the original wave spectrum data of the standard instrument can be unified into a standardized frequency domain range and frequency resolution through an interpolation algorithm. The standardized frequency domain range can include 0.05Hz~0.5Hz. The standardized frequency domain range can cover the typical wave energy concentration frequency band, and the frequency resolution can be 0.01Hz, so that 46 frequency points of standard spectrum data can be generated, and then the first standard spectrum data can be obtained. and the second standard spectrum data The interpolation algorithm can be a cubic spline interpolation algorithm. It is understood that the lower limit of 0.05 Hz in the standardized frequency domain corresponds to a wave period of 20 seconds, covering the surge component, and the upper limit of 0.5 Hz corresponds to a period of 2 seconds, covering the high frequency band of wind and waves. The resolution of 0.01 Hz is used to balance the calculation accuracy and data volume requirements.

[0073] In this embodiment, by standardizing the original wave spectrum data of the instrument to be tested and the original wave spectrum data of the standard instrument, the data of instruments with different principles and different sampling characteristics can be unified into a comparable benchmark, providing good data guidance information for subsequent multi-dimensional error analysis.

[0074] Step S202, performing time synchronization matching processing on the first standard spectrum data and the second standard spectrum data to obtain a wave characteristic value sequence; the wave characteristic value sequence includes multiple wave characteristic values, and the wave characteristic value includes a significant wave height and a corresponding average period.

[0075] The above-mentioned wave characteristic value sequence is used to characterize the wave characteristic value after time synchronization matching processing, and may include the effective wave height after time synchronization and the corresponding average period.

[0076] After obtaining the first standard spectrum data and the second standard spectrum data, the first spectral moment information of the first standard spectrum data can be determined first, and the first comparison parameter can be determined based on the first spectral moment information. The second spectral moment information of the second standard spectrum can also be determined, and the second comparison parameter can be determined based on the second spectral moment information.

[0077] The first spectral moment information refers to the spectral moment information of the instrument under test, which may include multi-order spectral moment information; the second spectral moment information refers to the spectral moment information of the standard instrument, which may include multi-order spectral moment information; the first comparative measurement parameter refers to the comparative measurement parameter of the instrument under test, including the significant wave height and average period of the instrument under test; the second comparative measurement parameter refers to the comparative measurement parameter of the standard instrument, including the significant wave height and average period of the standard instrument.

[0078] Specifically, after obtaining the second standard spectrum data, the wave energy n-order spectral moment of the standard instrument is calculated according to the second standard spectrum data using the following formula:

[0079] (1)

[0080] in, is the wave spectrum energy value of the standard instrument at the i-th frequency point in the second standard spectrum data, is the nth-order spectral moment, =0.01.

[0081] After obtaining the nth order spectral moment of the wave energy of the above standard instrument, the second comparative measurement parameter can be calculated, including the effective wave height of the standard instrument and averaging period , effective wave height It can be expressed by the following formula:

[0082] (2)

[0083] in, is the zero-order spectral moment of the wave energy of the standard instrument, which is calculated by formula (1) by setting n = 0.

[0084] After determining the effective wave height of the standard instrument, the average period can be calculated based on the zero-order spectral moment and the second-order spectral moment of the standard instrument. , expressed by the following formula:

[0085] (3)

[0086] in, is the second-order spectral moment of the wave energy of the standard instrument, which is calculated by formula (1) with n=2.

[0087] Similarly, the above method can be used to calculate the nth-order spectral moment of the wave energy of the instrument under test, and calculate the zeroth-order spectral moment and second-order spectral moment of the instrument under test. Then, the effective wave height of the instrument under test is calculated based on the zeroth-order spectral moment, and the average period of the instrument under test is calculated based on the zeroth-order spectral moment and second-order spectral moment.

[0088] After obtaining the significant wave height and average period of the instrument under test and the significant wave height and average period of the standard instrument, the timestamp of the standard instrument can be used as a reference. The significant wave height and average period of the instrument under test and the significant wave height and average period of the standard instrument can then be aligned based on the timestamp. The time deviation between the standard instrument and the instrument under test is calculated and compared with a preset threshold. Data with a time deviation greater than the preset threshold is eliminated, and the synchronized significant wave height and average period data is selected. The preset threshold can be customized according to actual needs.

[0089] For example, taking the time deviation of 5 minutes as an example, and taking the time of the standard instrument measurement data as the benchmark, search for data in the first comparative parameter of the instrument to be tested to see if there is data with a measurement time within ±5 minutes of the measurement time in the second comparative parameter of the standard instrument. If there is, a set of synchronous measurement pairs is obtained. The data is a wave characteristic value sequence, including the effective wave height and the corresponding average period. The standard instrument measurement data sequence can be checked one by one to obtain the effective wave height and average period sequence after time synchronization.

[0090] In this embodiment, by respectively determining the first spectral moment information of the first standard spectrum data, the effective wave height and average period of the instrument to be tested can be accurately determined. The effective wave height can directly reflect the overall energy intensity of the wave height. The average period can more accurately characterize the main energy period characteristics of the wave by combining the zero-order spectral moment and the second-order spectral moment compared with the single time domain period calculation. Through frequency domain energy structured analysis, time domain noise interference is avoided, and a frequency domain feature basis is provided for instrument error tracing. The first comparison parameter and the second comparison parameter are time-synchronized and matched, which can upgrade the wave instrument comparison from "non-real-time comparison" to "quasi-synchronous analysis", providing a basic data pair that is consistent in time and space for subsequent error statistics and correction.

[0091] Step S203: construct a wave height-period two-dimensional matrix, map the wave eigenvalue sequence to the corresponding grid of the wave height-period two-dimensional matrix, and determine the valid target grid in the grid.

[0092] It can be understood that the aforementioned two-dimensional wave height-period matrix is ​​a mathematical model that transforms ocean wave characteristics into a spatial grid structure. Using wave height as the vertical axis and period as the horizontal axis, it divides the continuous range of wave parameter values ​​into discrete grid cells. Each grid corresponds to a specific wave height-period combination interval and is used to count the wave data volume and characteristics within that interval. This is a spatial modeling process that transforms abstract wave parameters into a visual grid. The aforementioned target grid is the grid obtained by screening all grids for data validity.

[0093] In this embodiment, by constructing a wave height-period two-dimensional matrix and mapping the wave eigenvalue sequence to the corresponding grid of the wave height-period two-dimensional matrix, the dynamic change law of instrument error with wave energy and frequency can be more clearly revealed, avoiding misjudgment caused by a single indicator.

[0094] In one embodiment, the present application also provides a specific implementation method for determining a valid target grid in a grid, see Figure 3 As shown, the method includes:

[0095] Step S301, determining the wave height interval and the average period interval based on the acquired historical wave statistical characteristics and the data volume of the wave characteristic values ​​in the wave characteristic value sequence.

[0096] Step S302 : dividing the grid boundary range of the wave height-period two-dimensional matrix, and constructing the corresponding grid of the wave height-period two-dimensional matrix according to the wave height interval, the average period interval and the grid boundary range.

[0097] Step S303: classify the wave eigenvalue sequence into corresponding grids of the wave height-period two-dimensional matrix according to the measurement values ​​of the standard instrument, and count the data volume of each grid.

[0098] Step S304: Delete all grids whose data volume is less than a preset threshold to obtain a target grid.

[0099] Optionally, the historical wave statistical features may be obtained by calling a long-term wave observation database, importing from an external device, or obtaining from a blockchain or database. This embodiment does not impose any restrictions on the method of obtaining the historical wave statistical features. The long-term wave observation database may include ERA or NCEP reanalysis data, buoy historical records, etc.

[0100] Specifically, after obtaining the historical wave statistical characteristics and wave eigenvalue sequence, the data volume of the wave eigenvalue in the wave eigenvalue sequence can be determined, and then the effective wave height of the two-dimensional matrix is ​​defined according to the data volume of the historical wave statistical characteristics and wave eigenvalue sequence. The wave height interval Δ and averaging period The average period interval Δ , for example, the wave height interval Δ It can be 0.1m, 0.25m or 0.5m, and the average period interval Δ It can be 0.25s or 0.5s. The above wave height interval and average period interval can be customized according to the data volume of the wave characteristic value sequence.

[0101] After determining the wave height interval and the average period interval, divide the grid boundary range of the wave height-period two-dimensional matrix according to the historical wave statistical characteristics, so as to construct the corresponding grid of the wave height-period two-dimensional matrix. Suppose a total of A (wave height direction) × B (average period direction) groups of grids are obtained. Among them, ( , ) represents the i-th in the wave height direction and the j-th grid in the average period direction. The corresponding wave height interval range is , and the average period interval range is .

[0102] Optionally, when the total amount of data > 3000 groups, a high-resolution grid with Δ = 0.1m and Δ = 0.25s can be used; when the total amount of data < 1000 groups, a low-resolution grid with Δ = 0.5m and Δ = 1.0s is used.

[0103] After constructing the corresponding grid of the wave height-around two-dimensional matrix, then classify each wave characteristic value in the wave characteristic value sequence into the corresponding grid of the wave height-period two-dimensional matrix according to the measured value of the standard instrument. The wave characteristic value includes the data pair of the significant wave height and the corresponding average period, and judge the interval in the grid where each data pair exists. If the ( , ) of a certain data pair is within the interval of the grid ( , ), then classify it into this grid, and then count the data volume of each grid, compare the data volume of each grid with the preset threshold Nmin, and delete the grid with the data volume < Nmin to obtain the effective target grid. Among them, the preset threshold can be custom-set according to actual needs. For example, Nmin can be 10. The grid with the data volume < Nmin is an invalid low-density grid, and the target grid is an effective high-density grid. Please refer to Figure 4 shown, Figure 4 is a schematic diagram of the corresponding grid structure of the constructed wave height-period two-dimensional matrix. The horizontal axis is the significant wave height, the vertical axis is the average period, and the numbers in the grid represent the number of synchronous observation data pairs of each grid. Grid M is the determined effective target grid.

[0104] The dynamic matrix construction method in this embodiment achieves adaptive optimization of grid division by integrating historical statistics and real-time data features, which can significantly improve the pertinence and accuracy of wave instrument error analysis; by combining historical wave statistical characteristics with real-time data volume to divide wave height and period intervals, the matrix grid division is made to fit the actual wave distribution. In typhoon-prone sea areas, the high wave height interval can be automatically expanded to accurately capture extreme wave data; in nearshore areas, the period interval is compressed to focus on short-period waves, avoiding invalid grid interference analysis and adapting to different sea environments. The target grid is screened based on the data volume to ensure that the analysis is based on reliable samples, and grids with insufficient data volume are eliminated to avoid statistical deviations caused by too few samples, making the comparison results closer to the actual situation and providing a reliable basis for instrument performance evaluation; and by deleting invalid grids, redundant data processing is reduced, reducing computing resource consumption and analysis time costs. When processing massive wave data, this method can significantly improve computing efficiency and make the analysis process more efficient, especially suitable for real-time monitoring and rapid assessment scenarios.

[0105] Step S204: for each target grid, determine the error statistical parameters of the instrument under test relative to the standard instrument.

[0106] It should be noted that the error statistical parameters of the above-mentioned instrument under test relative to the standard instrument are the core indicators for evaluating the performance of the instrument under test. By performing statistical analysis on the data within each target grid, the error characteristics of the instrument under characteristic sea conditions can be quantified.

[0107] For each target grid, the error statistical parameters of the instrument under test relative to the standard instrument are determined, including: for each target grid, generating an error sequence of the instrument under test and the standard instrument; calculating an error mean based on the error sequence, and determining an error standard deviation based on the error mean.

[0108] Specifically, for a valid grid ( , ), assuming that the amount of data in the effective grid is N, the error sequence between the instrument under test and the standard instrument can be defined. In the process of determining the error sequence between the instrument under test and the standard instrument, the effective wave height and average period sequence of the instrument under test can be defined as , the effective wave height and average period sequence of the standard instrument are ,in, is the Nth measurement value of the instrument under test, is the Nth measurement value of the standard instrument, which includes the significant wave height and the corresponding average period.

[0109] After determining the effective wave height and average period sequence of the instrument under test and the standard instrument, the error sequence D of each effective grid can be calculated, which is expressed by the following formula:

[0110] (4)

[0111] in, is the Nth measurement value of the instrument under test, is the Nth measurement value of the standard instrument.

[0112] Then, for each valid grid, the error mean is calculated using the following formula:

[0113] (5)

[0114] After determining the mean error, the standard deviation of the error can be calculated using the following formula:

[0115] (6)

[0116] Where k is the kth value in the current grid error sequence, N is the amount of data for the significant wave height-average period, is the mean error of the kth measurement value of the standard instrument, is the kth measurement value of the instrument under test, which is used to characterize the systematic deviation, and the error standard deviation is used to characterize the random deviation.

[0117] In this embodiment, for each target grid, an error sequence of the instrument under test and the standard instrument is generated, which retains the time continuity of the original measurement error, can accurately characterize the error distribution, capture the error fluctuation pattern and outliers, and separate systematic errors from random errors, clarifying the direction of instrument deviation and stability shortcomings; improve the credibility of error analysis, and provide a quantitative basis for instrument calibration and scene adaptation through statistical testing and standard instrument error correction.

[0118] Step S205 , calculating the sampling variability variance based on the second standard spectrum data, and correcting the error statistical parameter based on the sampling variability variance to obtain the corrected standard deviation of the instrument under test.

[0119] It should be noted that the sampling variability variance mentioned above refers to the variance of measurement fluctuations caused by the randomness of wave data sampling, reflecting the degree of dispersion when the standard instrument performs repeated measurements under the same sea conditions. The corrected standard deviation of the instrument under test is a statistic that reflects the true measurement error of the instrument under test after removing the influence of the sampling variability of the standard instrument from the original error standard deviation of the instrument under test. This metric eliminates the interference of "wave randomness" on error assessment and can more accurately reflect the measurement stability of the instrument under test, such as inherent defects such as sensor noise and algorithm errors.

[0120] In one embodiment, in order to more accurately evaluate the error of the instrument under test, it is necessary to calculate the sampling variability variance based on the second standard spectrum data, and correct the error statistical parameters based on the sampling variability variance to obtain the corrected standard deviation of the instrument under test. Figure 5 As shown, the method includes the following steps S401 to S404:

[0121] Step S401: For each target grid, calculate the significant wave height variance and the average period variance according to the second standard spectrum data.

[0122] It's understandable that wave measurement errors generally consist of two components: natural wave variability and inherent instrument error. Natural wave variability refers to the fluctuation in multiple measurements of a standard instrument under the same wave conditions caused by the randomness of the waves themselves (i.e., sampling variability). Intrinsic instrument error, on the other hand, refers to measurement deviations caused by inherent performance flaws in the instrument under test (such as sensor accuracy and algorithm bias). Traditional error statistics fail to distinguish between these two components, potentially misinterpreting "natural wave fluctuations" as "instrument error," leading to distorted assessments.

[0123] Specifically, taking the standard instrument as the Wave Knight buoy as an example, the Wave Knight buoy uses the periodogram method to calculate the wave spectrum, and its effective wave height and averaging period The sampling variability of can be determined by the spectral moment covariance. First, the effective wave height variance and the average period variance are calculated based on the second spectral moment information determined by the second standard spectrum data of the standard instrument. The second spectral moment information can include the zero-order spectral moment and the second-order spectral moment of the wave energy of the standard instrument. The above-mentioned effective wave height variance can be expressed by the following formula:

[0124] (7)

[0125] The above average period variance can be expressed by the following formula:

[0126] (8)

[0127] in, is the zero-order spectral moment of wave energy of the standard instrument, is the covariance of the second-order spectral moment, p-order spectral moment and q-order spectral moment of the wave energy of the standard instrument It can be expressed by the following formula:

[0128] (9)

[0129] in, is the observation time, is the kth frequency point, For The wave spectrum energy value at the frequency, is the frequency resolution, K is the total number of frequency points, is the p-order spectral moment of the wave energy of the standard instrument, is the q-order spectral moment of the wave energy of the standard instrument. When p is 0 and q is 2, the covariance can be obtained by substituting it into the above formula (9): , when p is 2 and q is 2, substituting into the above formula (9) we can get , when p is 0 and q is 0, substituting into the above formula (9) we can get .

[0130] For each valid grid, the second standard spectrum data of the Wave Rider buoy is determined. After that, the corresponding second spectral moment information is determined, and then the corresponding effective wave height variance is calculated according to the above formula (7), and the corresponding average period variance is calculated according to the above formula (8).

[0131] Step S402: Use the random phase method to simulate the wave sequence and calibrate the correction coefficient.

[0132] Step S403: Correct the significant wave height variance and the mean period variance according to the correction coefficient to obtain the sampling variability variance of the standard instrument.

[0133] Step S404: Separate the sampling variability variance from the error standard deviation to obtain the corrected standard deviation of the instrument under test.

[0134] It should be noted that for the systematic deviations introduced by different segmentation strategies, windowing functions and overlap rates of the periodogram method, the correction coefficient can be calculated by simulating the wave sequence through the random phase method. First, a wave height time series that meets the simulation target spectrum (such as the JONSWAP spectrum) is generated, a random phase is added, and the Wave Rider buoy supporting software is used to calculate the simulation sequence. and , and repeat n times (n ≥ 1000), and then count and Standard deviation and compare it with the theoretical value , Compare and calculate the correction coefficient a, and then for each valid target grid, substitute the correction coefficient a into the above formula (7) and formula (8) to obtain the sampling variability variance of the standard instrument , including the corrected significant wave height variance and the modified mean period variance , can be expressed by the following formula:

[0135] (10)

[0136] in, is the effective wave height variance, is the average period variance, and a is the correction coefficient.

[0137] After determining the sampling variability variance, for each valid grid, separate the sampling variability variance of the standard instrument from the error standard deviation of the instrument to be tested to obtain the corrected standard deviation of the instrument to be tested. The corrected standard deviation is an accurate estimate of the random error of the instrument to be tested itself, which can be expressed by the following formula:

[0138] (11)

[0139] in, is the standard deviation of error, is the sampling variation variance of the standard instrument.

[0140] It can be understood that the above formulas (7)-(11) The calculation is applicable to any comparison scenario using the Wave Rider buoy as the standard instrument. If the standard instrument needs to be replaced, the sampling variability model can be re-derived based on its spectral estimation method.

[0141] In this embodiment, for each target grid, the variance is calculated using the second standard spectrum data and the random phase method is used to simulate ocean waves. This allows for precise separation of natural wave variation from inherent instrument error. The calculated results are then optimized using correction coefficients to obtain the variance of the standard instrument sampling variability. This variance is separated from the standard deviation of the error to obtain the corrected standard deviation of the instrument under test. This effectively avoids misinterpreting the random error of the standard instrument as a defect in the instrument under test, improving the accuracy and comparability of instrument error assessment under different sea conditions and providing a reliable basis for instrument calibration, selection, and marine engineering design.

[0142] Step S206: Generate an error heat map based on the error statistical parameters and the corrected standard deviation, and perform a comprehensive analysis on the instrument to be tested to obtain a comparative test result.

[0143] It should be noted that the error heatmap described above is a visualization tool that uses color gradients to visually display the error distribution characteristics of the instrument under test under different wave conditions or parameter dimensions. Based on the corrected standard deviation as the core data, combined with raw error statistical parameters (such as mean deviation and variance), it transforms the spatial or parameter dependence of instrument measurement errors into a visual map, facilitating the rapid identification of error-sensitive areas and abnormal patterns.

[0144] In one of the embodiments of the present application, in order to observe the error mean and relative standard deviation, the error mean and the corrected standard deviation intuitively and clearly, they can be visualized. An error heat map is generated based on the error statistical parameters and the corrected standard deviation, including: normalizing the error mean and the corrected standard deviation to obtain the relative error mean and the relative standard deviation; using the wave height as the horizontal axis and the period as the vertical axis, respectively drawing a two-dimensional distribution diagram of the relative error mean and the relative standard deviation, and a two-dimensional distribution diagram of the error mean and the corrected standard deviation; the two-dimensional distribution diagram of the relative error mean and the relative standard deviation and the two-dimensional distribution diagram of the error mean and the corrected standard deviation use color coding to mark different error ranges.

[0145] Specifically, in order to eliminate the influence of wave height energy level differences on error assessment, the error mean and error standard deviation can be normalized to obtain the relative error mean and relative standard deviation. The relative error mean is used to characterize the systematic deviation ratio, and the relative standard deviation is used to standardize the random deviation fluctuation intensity. The relative error mean can be expressed by the following formula:

[0146] (12)

[0147] The relative standard deviation can be expressed by the following formula:

[0148] (13)

[0149] Among them, N is the amount of data, D k is the kth error value, is the kth measurement value of the standard instrument, is the mean error, is the corrected standard deviation.

[0150] After obtaining the relative error mean and relative standard deviation, the relative error mean can be distributed and plotted with the wave height as the horizontal axis and the average period as the vertical axis. and relative standard deviation The two-dimensional distribution graph can use color coding to represent different intervals. Different colors represent different relative error mean intervals. Figure 6 As shown, Figure 6 The effective wave height error heat map provided in the embodiment of this application has the horizontal axis as the effective wave height and the vertical axis as the average period, wherein the effective wave height error distribution characteristics are displayed in a color scale manner. For example, the blue area a represents , the green area b represents , the yellow area c represents .

[0151] It is understandable that the error of the average period is less affected by the magnitude of the wave height. The absolute error index can be used to generate the corresponding heat map, with the wave height as the horizontal axis and the average period as the vertical axis, and the error mean is plotted respectively. and the corrected standard deviation The two-dimensional distribution graph can use color coding to represent different intervals. Different colors represent different error mean intervals. For example, blue represents , green represents , yellow represents , red represents .

[0152] This example constructs an error heatmap and visualizes the error distribution, transforming complex error statistics into an interpretable knowledge graph, achieving a leap from "numerical calculation" to "mechanistic understanding." This not only provides an intuitive basis for evaluating the performance of the instrument under test but also supports the optimized design of ocean observation systems and engineering application decisions through spatial and parameter correlation analysis of error patterns.

[0153] In another embodiment of the present application, a specific implementation method for performing a comprehensive analysis of the instrument under test to obtain a comparative test result is also provided. In the process of performing a comprehensive analysis of the instrument under test to obtain the comparative test result, a system deviation index can be determined based on the relative error mean, and the random fluctuation intensity can be determined based on the relative standard deviation; and / or a key grid can be determined from the target grid, and the proportion of excess errors can be determined based on the error mean and relative error mean in the key grid to obtain an abnormal sea condition adaptability result; and / or a frequency band sensitivity comparative test result can be determined based on the relative error mean and relative standard deviation obtained for a preset frequency band.

[0154] The above-mentioned comparison test results include: systematic deviation index, random fluctuation intensity level, abnormal sea condition adaptability results, and frequency band sensitivity comparison test results. The systematic deviation index quantifies the degree of systematic deviation between the measured value of the instrument under test and the true value (the standard instrument measurement value). Its core function is to reflect whether the instrument has fixed offset (such as sensor zero error or calibration parameter deviation) and is a basic indicator for evaluating instrument measurement accuracy. The random fluctuation intensity level is a level of dispersion of instrument measurement values ​​based on the corrected standard deviation. It is used to characterize the level of random error introduced by the instrument's inherent noise or algorithm after eliminating natural wave variation. The abnormal sea condition adaptability results are used to evaluate the measurement reliability of the instrument under test under extreme wave conditions (such as high significant wave height, short-period breaking waves, and abnormal waves). The frequency band sensitivity comparison test results are used to analyze how the error of the instrument under test varies with wave frequency (or period) and locate specific frequency bands with significant error (such as swell frequency bands and high-frequency wind-driven wave bands).

[0155] As an implementable method, after obtaining the relative error mean, the system deviation index can be calculated using the following formula:

[0156] (14)

[0157] After obtaining the relative standard deviation, the random fluctuation intensity can be expressed by the following formula:

[0158] (15)

[0159] in, is the mean relative error, is the relative standard deviation, N total The amount of data.

[0160] In this embodiment, the system deviation index and random fluctuation intensity can support standardized horizontal comparison of instrument performance, and by constructing a visual heat map, the performance boundaries of the instrument under different sea conditions can be more intuitively displayed, providing data support for the layout of the ocean observation network (such as the deployment of high-precision instruments in key sea areas).

[0161] As another possible implementation method, key grids can be determined from target grids. For example, grids with wave heights greater than a preset wave height threshold and average periods greater than an average period threshold can be determined as key grids. When the preset wave height threshold is 2.5m and the average period threshold is 10s, the grids with wave heights greater than a preset wave height threshold and average period threshold can be determined as key grids. >2.5m is considered as high wave height, If the period is >10s, the grids with high wave height and long period are counted and used as key grids. Then, the error ratio exceeding the standard is determined based on the error mean and relative error mean in the key grids, and the adaptability result of abnormal sea conditions is obtained. For example, the error exceeding the standard in the statistical grid ( or ) ratio.

[0162] As another possible implementation, a preset frequency band is determined from all frequency bands. The preset frequency band may include a low frequency band, a medium frequency band, and a high frequency band. The low frequency band represents low-frequency swells, the medium frequency band represents medium wind waves, and the high frequency band represents high-frequency breaking waves. The relative error mean of different frequency bands is used to calculate the relative error of the frequency bands. and relative standard deviation , identify the sensitive frequency band of the instrument under test. The low frequency band can be 0.05~0.1Hz, the medium frequency band can be 0.1~0.3Hz, and the high frequency band can be 0.3~0.5Hz.

[0163] In this embodiment, the system deviation index and random fluctuation intensity level are defined by the relative error mean and standard deviation, which separates the accuracy and stability issues of the instrument and can accurately quantify the system and random errors. The key grids are screened to calculate the error ratio exceeding the standard, and the performance attenuation area of ​​the instrument under extreme wave conditions is identified, thereby locating the shortcomings of abnormal sea conditions. Based on the relative error parameters of the preset frequency bands, the error-sensitive frequency bands are locked, providing direction for sensor or algorithm optimization and realizing frequency band-level error tracing. The abstract errors are converted into quantifiable indicators to assist in instrument selection, calibration and adaptation to marine engineering scenarios, and realize decision support visualization.

[0164] For example, a synchronous observation setup is first performed. A certain type of gravity acceleration wave buoy (abbreviated as wave buoy) and a standard wave rider buoy (Waverider MKIII) are deployed in a certain offshore waters with a water depth of 50m. The horizontal spacing between the two instruments is ≤200m. The synchronous observation lasts for 24 days, and a set of data is measured every half hour to obtain the original wave spectrum data of the wave buoy and the original wave spectrum data of the wave rider buoy Waverider MKIII. Then, the wave spectrum data is preprocessed. By performing spectrum unification processing on it, the wave spectrum data measured by the wave buoy and Waverider MKIII are interpolated to the standardized frequency domain range (0.05~0.5Hz, resolution 0.01Hz), generating standard spectrum data of 46 frequency points. Then, the effective wave height of the wave buoy and Waverider MKIII is calculated according to formulas (1)-(3) With the average period After acquiring this data set, time synchronization matching can be performed. Specifically, based on GPS time, data with a measurement time difference of ±5 minutes are selected, and missing data caused by communication interruptions are removed. The time-synchronized significant wave height and average period series are obtained, totaling 1134 data sets.

[0165] After time synchronization matching, the grid division parameters can be determined by obtaining historical statistical data, which includes: Range (0~5m) and The wave height range is determined based on the acquired historical wave statistical characteristics and the amount of wave characteristic value data in the wave characteristic value sequence (0~8s): Δ =0.25m, divide into grids ; Cycle interval: Δ = 0.5s, divide the grid ;Total number of grids: 20 ( direction) × 16 ( Direction) = 320 grids. Data mapping and screening were then performed, mapping the 1134 data pairs to the corresponding grids described above based on the values ​​measured by the standard instrument (Waverider MKIII). The data volume of each grid was counted. Assuming a threshold of N_min = 10, grids with data volumes less than N_min were eliminated, ultimately retaining 21 valid target grids.

[0166] After constructing the wave height-period two-dimensional matrix, error calculation and correction operations are performed. For each valid target grid ( , ), the mean error can be calculated (systematic deviation of the wave buoy relative to Waverider MKIII), and based on the mean error Calculate the standard deviation of error (the raw random deviation of the wave buoy). For example, for a target grid ( =1.75~2.0m, =4.5~5.0s), the data volume N=37, the determined =0.0298m, =0.1503m, =0.128s, =0.1413s. Then, the standard instrument error correction is performed. According to the observation time of each set of WaveriderMKIII data (1600s, sampling rate 1.28Hz, number of sampling points 2048) and the spectrum estimation smoothing window (every 200s, non-overlapping Welch periodogram method, Turkey window function), the random phase method is used to simulate and obtain the spectrum estimation correction coefficient a=1.414 of WaveriderMKIII. Then, according to the above formulas (7) and (8), the uncorrected variance of the sampling variability of the significant wave height and average period of the standard instrument is calculated. Assuming that the target grid ( =1.75~2.0m, =4.5~5.0s) and the results are =0.0675m, =0.0999s, and then substitute the correction parameter into the above formula (10) to obtain the corrected Waverider MKIII sampling variability variance, which is =0.0955m, =0.1413s. Then, the corrected variance of the standard instrument is separated from the total error of the instrument under test by formula (11), and the random error variance of the instrument under test itself (corrected standard deviation) is obtained. The corrected standard deviation can be =0.116m, =0.0993s.

[0167] After the standard instrument error correction, the error visualization and performance evaluation can be performed. Using the normalized error index, the average period ( ) Using the absolute error index, the relative error mean and relative standard deviation of the target grid are obtained as follows: =2, =6. Then take the wave height as the horizontal axis (0.0~5.0m), average period is the vertical axis (0.0~8.0s), draw the effective wave height error heat map; and draw the average period error heat map, different color-level codes can be represented by different colors.

[0168] After generating the two-dimensional error heat map, a comprehensive performance evaluation can be performed. The system deviation index is determined to be 0.425 by the above formula (14), and the random fluctuation intensity is calculated to be 8.675 by the above formula (15). Scenario applicability analysis can also be performed by analyzing wind waves of different frequencies. For medium wind waves (0.1~0.3Hz): the mean relative error of effective wave height is within 5%, the relative standard deviation is basically less than 10%, the mean error of average period is within 0.3s, and the standard deviation of error is basically within 0.15s. For high-frequency breaking waves (0.3~0.5Hz): the mean relative error of effective wave height in the range of average period 2s to 2.5s is about 10%, the relative standard deviation of effective wave height in the range of average period 2s to 3s is basically greater than 10%, the mean error of average period is greater than 0.3s, and some reach 0.7s. The standard deviation of error is basically greater than 0.2s, especially in low sea conditions.

[0169] In this embodiment, frequency band sensitivity analysis can identify fundamental defects (such as how the integral error of gravitational acceleration amplifies low-frequency noise and zero-point drift), providing data guidance information for subsequent analysis to facilitate targeted improvements.

[0170] The present application provides a method for analyzing the comparative measurement errors of wave measuring instruments. On the one hand, by standardizing the original spectrum data of the instrument to be measured and the standard instrument into the first standard spectrum data and the second standard spectrum data respectively, it breaks through the single statistical limitation of the traditional characteristic parameters, retains the frequency domain energy distribution details through the original spectrum standardization, provides a data basis for multi-factor error tracing, and performs time synchronization processing on the standard spectrum data, which can ensure that the two sets of spectrum data are compared under the same time and space sea conditions, avoiding errors caused by time and space misalignment, and then constructs a wave height-period two-dimensional matrix, maps the wave eigenvalue sequence to the corresponding grid of the two-dimensional matrix, which can It can more comprehensively reflect the dynamic laws of different grid intervals; on the other hand, by determining the error statistical parameters and sampling variability error, the uncertainty of the standard instrument is incorporated into the error analysis system, which improves the credibility of the assessment. By correcting the error statistical parameters through the sampling variability variance, the true error of the instrument to be tested can be restored more carefully, and the error heat map is generated based on the error statistical parameters and the corrected standard deviation, which can intuitively visualize the error to present the error sensitive area, and then conduct a comprehensive analysis of the instrument to be tested, which can accurately determine the comparison result, greatly improving the accuracy of the comparison error analysis of wave measuring instruments.

[0171] Based on the same inventive concept, embodiments of the present application further provide a device for implementing the aforementioned wave measurement instrument comparative error analysis. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the wave measurement instrument comparative error analysis device provided below can be found in the aforementioned limitations of the wave measurement instrument comparative error analysis method, and will not be further elaborated here.

[0172] In an exemplary embodiment, Figure 7 As shown, a device for analyzing comparative measurement errors of a wave measuring instrument is provided, comprising:

[0173] The standardization module 510 is used to obtain the original wave spectrum data of the instrument under test and normalize it into first standard spectrum data, and obtain the original wave spectrum data of the standard instrument and normalize it into second standard spectrum data;

[0174] A time synchronization module 520 is configured to perform time synchronization matching processing on the first standard spectrum data and the second standard spectrum data to obtain a wave characteristic value sequence; the wave characteristic value sequence includes a plurality of wave characteristic values, and the wave characteristic value includes a significant wave height and a corresponding average period;

[0175] The grid determination module 530 is used to construct a wave height-period two-dimensional matrix, map the wave eigenvalue sequence to the corresponding grid of the wave height-period two-dimensional matrix, and determine the valid target grid in the grid;

[0176] A parameter determination module 540 is used to determine, for each target grid, the error statistical parameters of the instrument under test relative to the standard instrument;

[0177] Correction module 550, configured to calculate the sampling variability variance based on the second standard spectrum data, and correct the error statistical parameter based on the sampling variability variance to obtain a corrected standard deviation of the instrument under test;

[0178] The analysis module 560 is used to generate an error heat map based on the error statistical parameters and the corrected standard deviation, and perform a comprehensive analysis on the instrument to be tested to obtain a comparative test result.

[0179] As an optional implementation, the time synchronization module 520 is specifically configured to:

[0180] Determine first spectral moment information of first standard spectrum data, determine a first comparative measurement parameter based on the first spectral moment information, determine second spectral moment information of second standard spectrum data, and determine a second comparative measurement parameter based on the second spectral moment information; the first comparative measurement parameter includes the significant wave height and average period of the instrument under test, and the second comparative measurement parameter includes the significant wave height and average period of the standard instrument;

[0181] The significant wave height and average period of the instrument under test and the significant wave height and average period of the standard instrument are time-synchronized and matched to obtain a wave characteristic value sequence.

[0182] As an optional implementation, the grid determination module 530 is specifically configured to:

[0183] Determine the wave height interval and average period interval based on the acquired historical wave statistical characteristics and the amount of wave characteristic value data in the wave characteristic value sequence;

[0184] Divide the grid boundary range of the wave height-period two-dimensional matrix, and construct the corresponding grid of the wave height-period two-dimensional matrix according to the wave height interval, the average period interval and the grid boundary range;

[0185] Classify the wave eigenvalue sequence into the corresponding grid of the wave height-period two-dimensional matrix according to the measurement values ​​of the standard instrument, and count the data volume of each grid;

[0186] Delete all grids whose data volume is less than the preset threshold to obtain the target grid.

[0187] As an optional implementation, the parameter determination module 540 is specifically configured to:

[0188] For each target grid, generate the error sequence of the instrument under test and the standard instrument;

[0189] The error mean is calculated based on the error sequence, and the error standard deviation is determined based on the error mean.

[0190] As an optional implementation, the correction module 550 is specifically configured to:

[0191] For each target grid, the significant wave height variance and mean period variance are calculated based on the second standard spectrum data;

[0192] The random phase method is used to simulate the wave sequence and calibrate the correction coefficient;

[0193] The significant wave height variance and the mean period variance are corrected according to the correction coefficient to obtain the sampling variability variance of the standard instrument;

[0194] The sampling variability variance is separated from the error standard deviation to obtain the corrected standard deviation of the instrument under test.

[0195] As an optional implementation, the analysis module 560 is specifically configured to:

[0196] Normalize the error mean and the corrected standard deviation to obtain the relative error mean and relative standard deviation;

[0197] With wave height as the horizontal axis and average period as the vertical axis, two-dimensional distribution diagrams of relative error mean and relative standard deviation, and two-dimensional distribution diagrams of error mean and corrected standard deviation are drawn respectively; different error ranges are marked by color coding in the two-dimensional distribution diagrams of relative error mean and relative standard deviation, and the two-dimensional distribution diagrams of error mean and corrected standard deviation.

[0198] As an optional implementation, the analysis module 560 is further configured to:

[0199] Comprehensively analyze the instrument to be tested and obtain comparative test results, including:

[0200] Determine the systematic deviation index based on the relative error mean and determine the random fluctuation intensity based on the relative standard deviation; and / or,

[0201] Determine the key grid from the target grid, determine the error ratio exceeding the standard based on the error mean and relative error mean in the key grid, and obtain the adaptability result for abnormal sea conditions; and / or,

[0202] The frequency band sensitivity comparison test result is determined based on the obtained relative error mean and relative standard deviation of the preset frequency band.

[0203] Among them, the wave measuring instrument comparison error analysis device provided in the embodiment of the present application, on the one hand, breaks through the single statistical limitation of traditional characteristic parameters by standardizing the original spectrum data of the instrument to be measured and the standard instrument into the first standard spectrum data and the second standard spectrum data respectively, retains the frequency domain energy distribution details through the original spectrum standardization, provides a data basis for multi-factor error tracing, and performs time synchronization processing on the standard spectrum data, which can ensure that the two sets of spectrum data are compared under the same time and space sea conditions, avoiding errors caused by time and space misalignment, and then constructs a wave height-period two-dimensional matrix, and maps the wave eigenvalue sequence to the corresponding grid of the two-dimensional matrix. , which can more comprehensively reflect the dynamic laws of different grid intervals; on the other hand, by determining the error statistical parameters and sampling variability error, the uncertainty of the standard instrument is incorporated into the error analysis system, which improves the credibility of the evaluation. The error statistical parameters are corrected by the sampling variability variance, which can more carefully restore the real error of the instrument to be tested, and the error heat map is generated based on the error statistical parameters and the corrected standard deviation, which can intuitively visualize the error to present the error sensitive area, and then conduct a comprehensive analysis of the instrument to be tested, which can accurately determine the comparison result, greatly improving the accuracy of the comparison error analysis of wave measuring instruments.

[0204] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store video tag processing data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a grid connection. When the computer program is executed by the processor, a method for analyzing the comparison error of a wave measuring instrument is implemented.

[0205] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0206] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0207] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0208] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0209] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0210] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0211] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0212] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0213] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for analyzing comparative measurement errors of wave measuring instruments, characterized in that: The wave measuring instrument comparative measurement error analysis method comprises: Acquire original wave spectrum data of the instrument under test and standardize it into first standard spectrum data, and acquire original wave spectrum data of a standard instrument and standardize it into second standard spectrum data; Performing time synchronization matching processing on the first standard spectrum data and the second standard spectrum data to obtain a wave characteristic value sequence; the wave characteristic value sequence includes a plurality of wave characteristic values, and the wave characteristic values ​​include significant wave heights and corresponding average periods; Constructing a wave height-period two-dimensional matrix, mapping the wave eigenvalue sequence to a corresponding grid of the wave height-period two-dimensional matrix, and determining a valid target grid in the grid; For each target grid, determining a statistical parameter of an error between the instrument under test and a standard instrument; Calculating the sampling variability variance according to the second standard spectrum data, and correcting the error statistical parameter according to the sampling variability variance to obtain a corrected standard deviation of the instrument under test; An error heat map is generated based on the error statistical parameters and the corrected standard deviation, and a comprehensive analysis is performed on the instrument to be tested to obtain a comparative test result.

2. The wave measurement instrument comparative measurement error analysis method according to claim 1, characterized in that: Performing time synchronization matching processing on the first standard spectrum data and the second standard spectrum data to obtain a wave eigenvalue sequence, including: Determine first spectral moment information of the first standard spectrum data, determine a first comparative measurement parameter based on the first spectral moment information, determine second spectral moment information of the second standard spectrum data, and determine a second comparative measurement parameter based on the second spectral moment information; the first comparative measurement parameter includes the significant wave height and average period of the instrument under test, and the second comparative measurement parameter includes the significant wave height and average period of the standard instrument; The effective wave height and average period of the instrument under test and the effective wave height and average period of the standard instrument are subjected to time synchronization matching processing to obtain the wave characteristic value sequence.

3. The wave measurement instrument comparative measurement error analysis method according to claim 2, characterized in that: Constructing a wave height-period two-dimensional matrix, mapping the wave eigenvalue sequence to a corresponding grid of the wave height-period two-dimensional matrix, and determining a valid target grid in the grid, including: Determining a wave height interval and an average period interval based on the acquired historical wave statistical characteristics and the amount of wave characteristic value data in the wave characteristic value sequence; Dividing the grid boundary range of the wave height-period two-dimensional matrix, and constructing a corresponding grid of the wave height-period two-dimensional matrix according to the wave height interval, the average period interval and the grid boundary range; Classifying the wave eigenvalue sequence into corresponding grids of the wave height-period two-dimensional matrix according to the measurement values ​​of the standard instrument, and counting the data volume of each grid; Delete all grids whose data amount is less than a preset threshold to obtain the target grid.

4. The method for analyzing the comparative measurement error of a wave measuring instrument according to claim 1, characterized in that: The error statistical parameters include error mean and error standard deviation; For each target grid, determining the error statistical parameters of the instrument under test relative to the standard instrument includes: For each target grid, generating an error sequence between the instrument under test and the standard instrument; The error mean is calculated based on the error sequence, and the error standard deviation is determined according to the error mean.

5. The method for analyzing the comparative measurement error of a wave measuring instrument according to claim 4, characterized in that: Calculating the sampling variability variance according to the second standard spectrum data, and correcting the error statistical parameter according to the sampling variability variance to obtain the corrected standard deviation of the instrument under test, including: For each target grid, calculating the significant wave height variance and the average period variance according to the second standard spectrum data; The random phase method is used to simulate the wave sequence and calibrate the correction coefficient; Correcting the significant wave height variance and the mean period variance according to the correction coefficient to obtain the sampling variability variance of the standard instrument; The sampling variability variance is separated from the error standard deviation to obtain the corrected standard deviation of the instrument under test.

6. The method for analyzing the comparative measurement error of a wave measuring instrument according to claim 4, characterized in that: The error heat map includes: a two-dimensional distribution map of the relative error mean and the relative standard deviation, and a two-dimensional distribution map of the error mean and the corrected standard deviation; Generating an error heat map based on the error statistical parameter and the corrected standard deviation includes: Normalizing the error mean and the corrected standard deviation to obtain a relative error mean and a relative standard deviation; With the wave height as the horizontal axis and the average period as the vertical axis, a two-dimensional distribution diagram of the relative error mean and the relative standard deviation, and a two-dimensional distribution diagram of the error mean and the corrected standard deviation are drawn respectively; the two-dimensional distribution diagram of the relative error mean and the relative standard deviation, and the two-dimensional distribution diagram of the error mean and the corrected standard deviation both use color coding to mark different error ranges.

7. The method for analyzing comparative measurement errors of wave measuring instruments according to claim 6, characterized in that: The comparative test results include: system deviation index, random fluctuation intensity, abnormal sea condition adaptability results and frequency band sensitivity comparative test results; Perform a comprehensive analysis on the instrument to be tested to obtain comparative test results, including: Determining a systematic deviation index based on the relative error mean, and determining a random fluctuation intensity based on the relative standard deviation; and / or, Determining a key grid from the target grid, determining the excess error ratio based on the error mean and relative error mean in the key grid, and obtaining the abnormal sea condition adaptability result; and / or, The frequency band sensitivity comparison measurement result is determined according to the obtained relative error mean and relative standard deviation of the preset frequency band.

8. A wave measuring instrument comparative measurement error analysis device, characterized in that: The wave measuring instrument comparison error analysis device comprises: A standardization module is used to obtain the original wave spectrum data of the instrument under test and standardize it into first standard spectrum data, and to obtain the original wave spectrum data of the standard instrument and standardize it into second standard spectrum data; a time synchronization module, configured to perform time synchronization matching processing on the first standard spectrum data and the second standard spectrum data to obtain a wave characteristic value sequence; the wave characteristic value sequence includes a plurality of wave characteristic values, each of which includes a significant wave height and a corresponding average period; A grid determination module is used to construct a wave height-period two-dimensional matrix, map the wave eigenvalue sequence to a corresponding grid of the wave height-period two-dimensional matrix, and determine a valid target grid in the grid; A parameter determination module, configured to determine, for each target grid, an error statistical parameter of the instrument under test relative to a standard instrument; a correction module, configured to calculate the sampling variability variance based on the second standard spectrum data, and correct the error statistical parameter based on the sampling variability variance to obtain a corrected standard deviation of the instrument under test; The analysis module is used to generate an error heat map based on the error statistical parameters and the corrected standard deviation, and to perform a comprehensive analysis on the instrument to be tested to obtain a comparative test result.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and runnable on the processor, characterized in that the processor executes the computer program to implement the steps of the wave measurement instrument comparison error analysis method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for analyzing the comparative measurement error of a wave measuring instrument according to any one of claims 1 to 7 is implemented.

Images