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

Abstract

The invention discloses a wave measuring instrument comparison measurement error analysis method, device and equipment and a medium, and relates to the field of ocean observation, and the method comprises the steps: obtaining original wave spectrum data of a to-be-measured instrument and a standard instrument, and carrying out the standardization processing of the original wave spectrum data 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 characteristic value sequence; constructing a wave height-period two-dimensional matrix, mapping the wave characteristic value sequence to a corresponding grid of the wave height-period two-dimensional matrix, and determining an effective target grid; for each target grid, determining error statistical parameters of the to-be-tested instrument relative to the standard instrument; calculating a sampling variability variance according to the second standard spectrum data, and performing correction processing to obtain a corrected standard deviation of the to-be-tested instrument; an error thermodynamic diagram is generated based on the error statistical parameters and the corrected standard deviation, and a comparison and measurement result is obtained through comprehensive analysis. According to the invention, the comparison measurement error analysis accuracy of the wave measurement instrument is improved.

Description

Technical Field

[0001] The present application relates to the field of ocean observation technologies, and particularly to a method, device, equipment and medium for analyzing the comparison error of wave measurement instruments. Background Art

[0002] As a key parameter in ocean dynamics research and many ocean engineering applications, the accurate measurement of waves is of crucial importance. The strong randomness and wide frequency-domain energy distribution of waves make their measurement a highly challenging task. Among them, in practical applications, wave observation data is widely used in the verification of wave forecasting models to provide basic data support for improving the accuracy of wave forecasting; for satellite remote sensing calibration to ensure the reliability of satellite monitoring of ocean waves; in the assessment of ocean energy resources, wave data is the core basis for evaluating the exploitable potential of ocean energy.

[0003] For traditional wave measurement instruments, such as buoy-type, acoustic-type, radar-type, etc., due to their working principles and the influence of complex ocean environments, it is difficult to directly verify their metrological performance solely through laboratory calibration. The laboratory environment often cannot fully simulate the complex and changeable wave conditions in the ocean, including the combined effects of different wave heights, periods, wave spectrum energy distributions, and on-site environmental factors such as water flow and mooring on instrument measurement. Therefore, synchronous comparison with a standard instrument (such as a Wave Rider buoy) in the field has become a common means to evaluate the metrological performance of traditional wave measurement instruments. In order to optimize ocean engineering designs and promote the development of ocean science research, how to analyze the comparison error of wave instruments is particularly important.

[0004] Currently, in related technologies, the analysis of the comparison error of wave instruments is statistically evaluated based on the average error and root mean square error of wave characteristic parameters. However, the error characteristics of wave instruments are affected by various factors, such as wave height, period, wave spectrum energy distribution, instrument working principle, and on-site environment (such as water flow and mooring). A single statistical parameter cannot reflect their dynamic performance under different wave heights and periods, and the correction of the random error of the standard instrument is relatively rough, resulting in a low accuracy of the analysis of the comparison error of wave measurement instruments. Summary of the Invention

[0005] The purpose of the present application is to provide a method, device, equipment and medium for analyzing the comparison error of wave measurement instruments.

[0006] To achieve the above purpose, the present application provides the following solutions: In a first aspect, the present application provides a method for analyzing the comparison error of wave measurement instruments, including: Obtaining the original wave spectrum data of the instrument to be measured and standardizing it into first standard spectrum data, and obtaining the original wave spectrum data of the standard instrument and standardizing it into second standard spectrum data; Perform time synchronization matching processing on the first standard spectrum data and the second standard spectrum data to obtain a wave eigenvalue sequence; the wave eigenvalue sequence includes a plurality of wave eigenvalues, and the wave eigenvalues include the significant wave height and the corresponding mean period; 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 grids in the grid; For each of the target grids, determine the error statistical parameters of the instrument under test relative to the standard instrument; Calculate the sampling variability variance according to the second standard spectrum data, and correct the error statistical parameters according to the sampling variability variance to obtain the corrected standard deviation of the instrument under test; Generate an error heat map based on the error statistical parameters and the corrected standard deviation, and perform comprehensive analysis on the instrument under test to obtain the comparison measurement result.

[0007] 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: Determine the first spectral moment information of the first standard spectrum data, determine the first comparison measurement parameter according to the first spectral moment information, determine the second spectral moment information of the second standard spectrum data, and determine the second comparison measurement parameter according to the second spectral moment information; the first comparison measurement parameter includes the significant wave height and the mean period of the instrument under test, and the second comparison measurement parameter includes the significant wave height and the mean period of the standard instrument; Perform time synchronization matching processing on the significant wave height and the mean period of the instrument under test, and the significant wave height and the mean period of the standard instrument to obtain the wave eigenvalue sequence.

[0008] Optionally, constructing a wave height - period two - dimensional matrix, mapping the wave eigenvalue sequence to the corresponding grid of the wave height - period two - dimensional matrix, and determining the valid target grids in the grid includes: Determine the wave height interval and the mean period interval according to the obtained historical wave statistical characteristics and the data volume of the wave eigenvalues in the wave eigenvalue sequence; 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 mean period interval and the grid boundary range; Classify the wave eigenvalue sequence into the corresponding grids of the wave height - period two - dimensional matrix according to the measured values of the standard instrument, and count the data volume of each grid; Delete the grids with data volume less than the preset threshold in all grids to obtain the target grids.

[0009] Optionally, the error statistical parameter includes an error mean value and an error standard deviation; For each of the target grids, determining the error statistical parameter of the instrument under test relative to the standard instrument, including: For each of the target grids, generating an error sequence of the instrument under test and the standard instrument; Calculating the error mean value based on the error sequence, and determining the error standard deviation according to the error mean value.

[0010] Optionally, calculating a 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, including: For each of the target grids, calculating an effective wave height variance and an average period variance according to the second standard spectrum data; Simulating a sea wave sequence by using a random phase method and calibrating a correction coefficient; Correcting the effective wave height variance and the average period variance according to the correction coefficient to obtain the sampling variability variance of the standard instrument; Separating the sampling variability variance from the error standard deviation to obtain the corrected standard deviation of the instrument under test.

[0011] Optionally, the error heat map includes: a two-dimensional distribution map of a relative error mean value and a relative standard deviation, and a two-dimensional distribution map of the error mean value and the corrected standard deviation; Generating an error heat map based on the error statistical parameter and the corrected standard deviation, including: Performing normalization processing on the error mean value and the corrected standard deviation to obtain a relative error mean value and a relative standard deviation; Taking the wave height as the horizontal axis and the average period as the vertical axis, and respectively plotting a two-dimensional distribution map of the relative error mean value and the relative standard deviation, and a two-dimensional distribution map of the error mean value and the corrected standard deviation; different error ranges are marked by using a color scale coding method in both the two-dimensional distribution map of the relative error mean value and the relative standard deviation and the two-dimensional distribution map of the error mean value and the corrected standard deviation.

[0012] Optionally, the comparison test result includes: a system deviation index, a random fluctuation intensity, an abnormal sea condition adaptability result, and a frequency band sensitivity comparison test result; Performing comprehensive analysis on the instrument under test to obtain a comparison test result, including: Determining a system deviation index based on the relative error mean value, and determining a random fluctuation intensity based on the relative standard deviation; and / or, Determine the key grids from the target grids, determine the over-standard error ratio based on the mean error and the relative mean error in the key grids, and obtain the abnormal sea condition adaptability result; and / or, Determine the frequency band sensitivity ratio measurement result according to the relative mean error and the relative standard deviation of the acquired preset frequency band.

[0013] In a second aspect, the present application provides a comparison error analysis device for a wave measurement instrument, including: A standardization module, configured to acquire the original wave spectrum data of the instrument to be measured and standardize it into first standard spectrum data, and acquire 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 feature value sequence; the wave feature value sequence includes multiple wave feature values, and the wave feature values include the significant wave height and the corresponding mean period; A grid determination module, configured to construct a wave height - period two-dimensional matrix, map the wave feature value sequence to the corresponding grids of the wave height - period two-dimensional matrix, and determine the effective target grids in the grids; A parameter determination module, configured to determine, for each of the target grids, the error statistical parameters of the instrument to be measured relative to the standard instrument; A correction module, configured to calculate the sampling variability variance according to the second standard spectrum data, and correct the error statistical parameters according to the sampling variability variance to obtain the corrected standard deviation of the instrument to be measured; An analysis module, configured 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 measured to obtain a comparison result.

[0014] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where 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.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and 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.

[0016] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application: The present application provides a method, device, equipment and medium for analyzing the comparison error of a wave measurement instrument. By obtaining the original wave spectrum data of the instrument to be measured and standardizing it into the first standard spectrum data, and obtaining the original wave spectrum data of the standard instrument and standardizing it into the 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, mapping the wave eigenvalue sequence to the corresponding grid of the wave height - period two - dimensional matrix, and determining the effective target grids in the grid; for each target grid, determining the error statistical parameters of the instrument to be measured relative to the standard instrument; calculating the sampling variability variance according to the second standard spectrum data, and correcting the error statistical parameters according to the sampling variability variance to obtain the corrected standard deviation of the instrument to be measured; generating an error heat map based on the error statistical parameters and the corrected standard deviation, and comprehensively analyzing the instrument to be measured to obtain the comparison result.

[0017] Compared with the prior art, on the one hand, in this solution, the original spectrum data of the instrument to be measured and the standard instrument are respectively standardized into the first standard spectrum data and the second standard spectrum data, breaking through the single - statistical limitation of traditional characteristic parameters. By standardizing the original spectrum, the details of the frequency - domain energy distribution are retained, providing a data basis for multi - factor error tracing. And by performing time - synchronization processing on the standard spectrum data, it can ensure the comparison of the two groups of spectrum data under the same spatio - temporal sea conditions, avoiding errors caused by spatio - temporal misalignment. Then, by constructing a wave height - period two - dimensional matrix and mapping the wave eigenvalue sequence to the corresponding grid of this two - dimensional matrix, it can more comprehensively reflect the dynamic laws in different grid intervals. On the other hand, by determining the error statistical parameters and the sampling variability error, the uncertainty of the standard instrument is incorporated into the error analysis system, improving the evaluation credibility. By correcting the error statistical parameters with the sampling variability variance, it can more precisely restore the true error of the instrument to be measured. And by generating an error heat map based on the error statistical parameters and the corrected standard deviation, it can visually visualize the error to present the error - sensitive areas. Furthermore, by comprehensively analyzing the instrument to be measured, it can accurately determine the comparison result, greatly improving the accuracy of the comparison error analysis of the wave measurement instrument. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic structural diagram of a wave measurement instrument comparison error analysis system in an embodiment of the present application; Figure 2Schematic flow chart of the comparison error analysis method for wave measurement instruments provided by an embodiment of the present application; Figure 3 Schematic flow chart of the method for determining valid target grids in a grid provided by an embodiment of the present application; Figure 4 Schematic diagram of the corresponding grid structure of the wave height - period two - dimensional matrix provided by an embodiment of the present application; Figure 5 Schematic flow chart of the method for determining the corrected standard deviation of the instrument to be measured provided by an embodiment of the present application; Figure 6 Thermal map of the significant wave height error provided by an embodiment of the present application; Figure 7 Schematic diagram of the functional modules of a comparison error analysis device for wave measurement instruments provided by an embodiment of the present application; Figure 8 Schematic diagram of the structure of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0020] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0021] To make the above - mentioned objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0022] In the related art, the comparison error analysis of wave instruments is statistically evaluated based on the average error and root - mean - square error of wave characteristic parameters. However, the error characteristics of wave instruments are affected by various factors, such as wave height, period, wave spectrum energy distribution, instrument working principle, and on - site environment (such as water flow, mooring system), etc. A single statistical parameter cannot reflect its dynamic performance under different wave heights and periods, and the random error correction of the standard instrument is relatively rough, resulting in a low accuracy of the comparison error analysis of wave measurement instruments.

[0023] Based on the above defects, the present application provides a method for analyzing the comparison error of wave measurement instruments. Compared with the prior art, on the one hand, by normalizing 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, this solution breaks through the single statistical limitation of traditional characteristic parameters. By normalizing the original spectrum, the details of the frequency-domain energy distribution are retained, providing a data basis for multi-factor error tracing. And by performing time synchronization processing on the standard spectrum data, it can ensure the comparison of the two sets of spectrum data under the same spatio-temporal sea conditions, avoiding errors caused by spatio-temporal misalignment. Then, a wave height-period two-dimensional matrix is constructed, and the wave characteristic value 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 errors, the uncertainty of the standard instrument is incorporated into the error analysis system, improving the evaluation credibility. By correcting the error statistical parameters with the sampling variability variance, the true error of the instrument to be measured can be restored more precisely. And based on the error statistical parameters and the corrected standard deviation, an error heat map is generated, which can visually visualize the error to present the error-sensitive area, and then comprehensively analyze the instrument to be measured, accurately determining the comparison result, greatly improving the accuracy of the comparison error analysis of wave measurement instruments.

[0024] The method for analyzing the comparison error of wave measurement instruments provided by the embodiments of the present application can be applied to a Figure 1 comparison error analysis system of wave measurement instruments as shown. The comparison error analysis system of wave measurement instruments includes: a terminal 102, a server 104, and a data storage system. Among them, the terminal 102 communicates with the server 104 through a grid. The data storage system can store the data that the server 104 needs to process. The data storage system can be set separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the original wave spectrum data of the instrument to be measured and the original wave spectrum data of the standard instrument it obtains to the server 104. After receiving the original wave spectrum data of the instrument to be measured and the original wave spectrum data of the standard instrument, the server 104 performs normalization processing, time synchronization matching processing, constructs a wave height-period two-dimensional matrix, and error correction processing to comprehensively analyze the instrument to be measured and obtain the comparison result. In addition, in some embodiments, the method for analyzing the comparison error of wave measurement instruments can also be implemented separately by the server 104 or the terminal 102. For example, the terminal 102 can directly perform normalization processing, time synchronization matching processing, construct a wave height-period two-dimensional matrix, and error correction processing based on the original wave spectrum data of the instrument to be measured and the original wave spectrum data of the standard instrument it obtains to comprehensively analyze the instrument to be measured and obtain the comparison result. Among them, data processing algorithms can be pre-stored in the terminal 102 to facilitate the analysis of the comparison error of wave measurement instruments.

[0025] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0026] It should be noted that the method for analyzing the comparison error of the wave measurement instrument in this embodiment realizes the refined evaluation of the measurement performance of the instrument to be measured by establishing a wave energy-frequency two-dimensional error distribution model and quantifying the random error of the standard instrument, provides good data information for the analysis of instrument optimization, data instruction control, and observation scenario adaptation, and is applied to different comparison scenarios, such as long-term performance evaluation, short-term acceptance test, etc., and supports the error analysis of multiple types of wave instruments (such as gravity acceleration, GNSS, acoustic, radar, pressure type, etc.), and has broad industrial practicability.

[0027] In an exemplary embodiment, as Figure 2 shown, a method for analyzing the comparison error of a wave measurement instrument is provided. This method is executed by a computer device, and specifically can be executed alone by a computer device such as a terminal or a server, or jointly executed by a terminal and a server. In the embodiment of the present application, taking this method applied to Figure 1 the server 104 in as an example for description, it includes the following steps S201 to step S206. Among them: Step S201, obtain the original wave spectrum data of the instrument to be measured and standardize it into the first standard spectrum data, and obtain the original wave spectrum data of the standard instrument and standardize it into the second standard spectrum data.

[0028] It should be noted that the above original wave spectrum data is a function describing the distribution of wave energy with frequency or period, and essentially reflects the distribution of wave energy in different frequency components. The original wave spectrum data refers to the original record of the frequency-domain energy distribution calculated by the instrument through sensor data collection. Among them, the original wave spectrum data of the instrument to be measured is directly collected by the wave measurement instrument to be measured (such as a buoy type, acoustic type, or radar type instrument to be evaluated), and after preliminary processing such as hardware filtering and analog-to-digital conversion, the original frequency-domain data of the wave energy distribution with frequency is output. The original wave spectrum data of the standard instrument refers to the original data of the wave energy frequency-domain distribution collected and preliminarily processed by the standard wave measurement instrument (such as a high-precision instrument such as a wave rider buoy) and used as a comparison benchmark. The error sources of the original wave spectrum data of the instrument to be measured are complex and can include instrument principles, environmental interference, etc. The original wave spectrum data of the standard instrument is mainly random error.

[0029] Taking the acoustic wave gauge as the instrument to be measured and the Wave Rider buoy as the standard instrument as an example, the original wave spectrum data of the instrument to be measured can be the time-domain wave data obtained through sensors. The time-domain wave data includes pressure fluctuations, accelerations, etc., and then the original wave spectrum data is generated through FFT transformation. The above standard instrument can synchronously collect the same type of data on the basis of the data collected by the instrument to be measured.

[0030] After obtaining the original wave spectrum data of the instrument to be measured and the original wave spectrum data of the standard instrument, the original wave spectrum data of the instrument to be measured and the original wave spectrum data of the standard instrument can be unified to the standardized frequency domain range and frequency resolution through an interpolation algorithm. The standardized frequency domain range can include 0.05 Hz to 0.5 Hz. This standardized frequency domain range can cover the typical sea wave energy concentration frequency band, and the frequency resolution can be 0.01 Hz. Thus, standard spectrum data of 46 frequency points can be generated, and then the first standard spectrum data and the second standard spectrum data can be obtained. Among them, the above interpolation algorithm can be a cubic spline interpolation algorithm. It can be understood that the lower limit of 0.05 Hz in the above standardized frequency domain range corresponds to a wave period of 20 s, covering the swell component, and the upper limit of 0.5 Hz corresponds to a period of 2 s, covering the high-frequency band of wind waves. The resolution of 0.01 Hz is used to balance the calculation accuracy and the data volume requirement.

[0031] In this embodiment, by standardizing both the original wave spectrum data of the instrument to be measured and the original wave spectrum data of the standard instrument, the instrument data with different principles and different sampling characteristics can be unified to a comparable benchmark, providing good data guidance information for subsequent multi-dimensional error analysis.

[0032] Step S202: Perform time synchronization and matching processing on the first standard spectrum data and the second standard spectrum data to obtain a wave eigenvalue sequence; the wave eigenvalue sequence includes multiple wave eigenvalues, and the wave eigenvalues include the significant wave height and the corresponding mean period.

[0033] The above wave eigenvalue sequence is used to characterize the wave eigenvalues after time synchronization and matching processing, and can include the significant wave height and the corresponding mean period after time synchronization.

[0034] After obtaining the first standard spectrum data and the second standard spectrum data, it can be to first determine the first spectral moment information of the first standard spectrum data, determine the first comparison parameter according to the first spectral moment information, determine the second spectral moment information of the second standard spectrum, and determine the second comparison parameter according to the second spectral moment information.

[0035] Among them, the above first spectral moment information refers to the spectral moment information of the instrument to be measured, which may include multi-order spectral moment information, and 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 comparison parameter refers to the comparison parameter of the instrument to be measured, including the significant wave height and average period of the instrument to be measured, and the second comparison parameter refers to the comparison parameter of the standard instrument, including the significant wave height and average period of the standard instrument.

[0036] Specifically, after obtaining the second standard spectral data, the n-th order spectral moment of the wave energy of the standard instrument is calculated according to the second standard spectral data through the following formula: (1) Where is the wave spectral energy value of the standard instrument at the i-th frequency point in the second standard spectral data, is the n-th order spectral moment, = 0.01.

[0037] After obtaining the above n-th order spectral moment of the wave energy of the standard instrument, the second comparison parameter can be calculated, including the significant wave height and average period , the significant wave height can be expressed by the following formula: (2) Where is the zero-th order spectral moment of the wave energy of the standard instrument, which is obtained by setting n = 0 and calculating through formula (1).

[0038] After determining the significant wave height of the standard instrument, the average period can be calculated according to the zero-th order spectral moment and the second-order spectral moment of the standard instrument, (3) Where is the second-order spectral moment of the wave energy of the standard instrument, which is obtained by setting n = 2 and calculating through formula (1).

[0039] Similarly, the n-th order spectral moment of the wave energy of the instrument to be measured can be calculated by the above method, and the zero-th order spectral moment and the second-order spectral moment of the instrument to be measured can be calculated. Then, the significant wave height of the instrument to be measured is calculated according to the zero-th order spectral moment, and the average period of the instrument to be measured is calculated according to the zero-th order spectral moment and the second-order spectral moment.

[0040] After obtaining the significant wave height, average period of the instrument to be measured, and the significant wave height, average period of the standard instrument, it is possible to use the timestamp of the standard instrument as a reference, and then align the significant wave height, average period of the instrument to be measured, the significant wave height, average period of the standard instrument according to the timestamp, calculate the time deviation between the standard instrument and the instrument to be measured, compare the time deviation with a preset threshold, and eliminate the data with a time deviation greater than the preset threshold, and screen out the synchronized data of significant wave height and average period. Among them, the preset threshold can be customized according to actual needs.

[0041] For example, taking a time deviation of 5 minutes as an example, based on the time of the measurement data of the standard instrument, check whether there is data in the measurement time of the first comparison parameter data of the instrument to be measured that differs from the measurement time of the second comparison parameter data of the standard instrument by within ±5 minutes. If there is, a set of synchronized measurement pairs is obtained. This data is a sequence of wave characteristic values, including the significant wave height and the corresponding average period. The measurement data sequence of the standard instrument can be checked one by one to obtain the sequence of significant wave height and average period after time synchronization.

[0042] In this embodiment, by respectively determining the first spectral moment information of the first standard spectrum data, the significant wave height and average period of the instrument to be measured can be accurately determined. The significant wave height can directly reflect the overall energy intensity of the wave height. The average period, by combining the zero-order spectral moment and the second-order spectral moment, can more accurately characterize the main energy period characteristics of the wave compared with single-time domain period calculation. Through frequency-domain energy structured analysis, time-domain noise interference is avoided, providing a frequency-domain characteristic basis for instrument error tracing. By performing time synchronization matching processing on the first comparison parameter and the second comparison parameter, the wave instrument comparison can be upgraded from "non-real-time comparison" to "quasi-synchronous analysis", providing a spatio-temporally consistent basic data pair for subsequent error statistics and correction.

[0043] Step S203: Construct a wave height - period two-dimensional matrix, map the sequence of wave characteristic values to the corresponding grid of the wave height - period two-dimensional matrix, and determine the effective target grid in the grid.

[0044] It can be understood that the above wave height - period two-dimensional matrix is a mathematical model that transforms ocean wave characteristics into a spatial grid structure. It uses wave height as the vertical axis and period as the horizontal axis, divides the continuous wave parameter value range into discrete grid cells, and each grid corresponds to a specific wave height - period combination interval, which is used to statistically analyze the wave data volume and characteristics within this interval. It is to transform abstract wave parameters into visual grids through spatial modeling. The above target grid is the grid obtained by screening the data validity of all grids.

[0045] In this embodiment, by constructing a wave height - period two - dimensional matrix and mapping the wave feature value sequence to the corresponding grid of the wave height - period two - dimensional matrix, the dynamic variation law of instrument error with wave energy and frequency can be revealed more clearly, avoiding misjudgment caused by a single index.

[0046] In one of the embodiments, the present application also provides a specific implementation method for determining the valid target grids in the grid. Please refer to Figure 3 As shown, the method includes: Step S301: Determine the wave height interval and the average period interval according to the obtained historical wave statistical characteristics and the data volume of the wave feature values in the wave feature value sequence.

[0047] Step S302: 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.

[0048] Step S303: Classify the wave feature value sequence into the corresponding grid of the wave height - period two - dimensional matrix according to the measured values of the standard instrument, and count the data volume of each grid.

[0049] Step S304: Delete the grids with data volume less than the preset threshold in all grids to obtain the target grids.

[0050] Optionally, the above - mentioned historical wave statistical characteristics can be obtained by calling a long - term wave observation database, imported from an external device, or obtained from a blockchain or a database. This embodiment does not limit the acquisition method of historical wave statistical characteristics. Among them, the long - term wave observation database can include: ERA or NCEP re - analysis data, buoy historical records, etc.

[0051] Specifically, after obtaining the historical wave statistical characteristics and the wave feature value sequence, the data volume of the wave feature values in the wave feature value sequence can be determined, and then according to the historical wave statistical characteristics and the data volume of the wave feature value sequence, the effective wave height of the two - dimensional matrix is defined of the wave height interval Δ and the average period of the average period interval Δ , for example, the wave height interval Δ can be 0.1m, 0.25m or 0.5m, and the average period interval Δ can be 0.25s or 0.5s. Among them, the above - mentioned wave height interval and average period interval can be custom - set according to the data volume of the wave feature value sequence.

[0052] After determining the wave height range and the average period range, the grid boundary range of the wave height - period two - dimensional matrix is divided 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 range is , and the average period range is .

[0053] Optionally, when the total amount of data > 3000 groups, high - resolution grids with Δ = 0.1m and Δ = 0.25s can be used; when the total amount of data < 1000 groups, low - resolution grids with Δ = 0.5m and Δ = 1.0s are used.

[0054] After constructing the corresponding grid of the wave height - period two - dimensional matrix, then each wave characteristic value in the wave characteristic value sequence is classified 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. 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 it is classified into this grid, and then the data volume of each grid is counted . Compare the data volume of each grid with the preset threshold Nmin, and delete the grids with data volume < Nmin to obtain the effective target grids. Among them, the preset threshold can be custom - set according to actual needs. For example, Nmin can be 10. The grids with data volume < Nmin are invalid low - density grids, and the target grids are effective high - density grids. Please refer to Figure 4 shown in Figure 4 is a schematic diagram of the corresponding grid structure of the wave height - period two - dimensional matrix. The abscissa is the significant wave height, the ordinate is the average period, and the numbers in the grids represent the number of synchronous observation data pairs of each grid. Grid M is the determined effective target grid.

[0055] The dynamic matrix construction method in this embodiment realizes the adaptive optimization of grid division by integrating historical statistics and real-time data characteristics, which can significantly improve the pertinence and accuracy of wave instrument error analysis; by combining historical wave statistics characteristics and real-time data volume to divide wave height and period intervals, the matrix grid division fits the actual wave condition distribution. In the typhoon-prone sea area, the high wave height interval can be automatically expanded to accurately capture extreme wave data; in the nearshore area, the period interval is compressed to focus on short-period waves, avoiding the interference of invalid grids on the analysis and adapting to different sea area environments. And the target grids are selected according to the data volume to ensure that the analysis is based on reliable samples, eliminating the grids with insufficient data volume and avoiding statistical deviations caused by too few samples, making the comparison results closer to the real situation and providing a reliable basis for instrument performance evaluation; and by deleting invalid grids, redundant data processing is reduced, and the consumption of computing resources and the analysis time cost are reduced. When dealing with a large amount of wave data, this method can significantly improve the computing efficiency, making the analysis process more efficient, especially suitable for real-time monitoring and rapid evaluation scenarios.

[0056] Step S204, for each target grid, determine the error statistical parameters of the instrument under test relative to the standard instrument.

[0057] It should be noted that the above error statistical parameters of the instrument under test relative to the standard instrument are the core indicators for evaluating the performance of the instrument under test. By statistically analyzing the data in each target grid, the error characteristics of the instrument under characteristic sea conditions can be quantified.

[0058] 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 the error sequences of the instrument under test and the standard instrument; calculating the error mean based on the error sequences, and determining the error standard deviation according to the error mean.

[0059] Specifically, for a certain effective grid ( , ), assuming the data volume of this effective grid is N, then the error sequences of the instrument under test and the standard instrument can be defined. In the process of determining the error sequences of the instrument under test and the standard instrument, it can be to first define the effective wave height and average period sequences of the instrument under test as , and the effective wave height and average period sequences of the standard instrument as , where is the Nth measurement value of the instrument under test, is the Nth measurement value of the standard instrument, and this measurement value includes the effective wave height and the corresponding average period.

[0060] After determining the effective wave height and average period sequences of the instrument under test and the standard instrument, the error sequence D of each effective grid can be calculated, which is represented by the following formula: (4) Among them, is the Nth measurement value of the instrument to be measured, is the Nth measurement value of the standard instrument.

[0061] Then, for each valid grid, calculate the mean error through the following formula: (5) After determining the mean error, the standard deviation of the error can be calculated through the following formula: (6) Among them, k is the kth value in the error sequence of the current grid, N is the data volume of the effective 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 to be measured, which is used to characterize the systematic deviation, and the standard deviation of the error is used to characterize the random deviation.

[0062] In this embodiment, for each target grid, an error sequence of the instrument to be measured and the standard instrument is generated, which retains the time continuity of the original measurement error, can accurately depict the error distribution, capture the error fluctuation law and outliers, and separate the systematic error and random error, clarify the instrument deviation direction and stability short - board; improve the credibility of error analysis, and provide a quantitative basis for instrument calibration and scenario adaptation through statistical tests and standard instrument error correction.

[0063] Step S205: Calculate the sampling variability variance according to the second standard spectrum data, and correct the error statistical parameters according to the sampling variability variance to obtain the corrected standard deviation of the instrument to be measured.

[0064] It should be noted that the above - mentioned sampling variability variance refers to the variance of the measurement value fluctuations caused by the randomness of wave data sampling, which reflects the dispersion degree of the standard instrument during repeated measurements under the same sea conditions. The corrected standard deviation of the instrument to be measured is a statistic that reflects the true measurement error of the instrument to be measured after removing the influence of the standard instrument sampling variability from the original error standard deviation of the instrument to be measured. This index excludes the interference of "the randomness of the wave itself" on error assessment and can more truly reflect the measurement stability of the instrument to be measured, such as inherent defects like sensor noise and algorithm errors.

[0065] In one of the embodiments, in order to more accurately evaluate the error of the instrument to be measured, it is necessary to calculate the sampling variability variance according to the second standard spectrum data, and correct the error statistical parameters according to the sampling variability variance to obtain the corrected standard deviation of the instrument to be measured, as shown in Figure 5 The method includes the following steps S401 to step S404: Step S401: For each target grid, calculate the variance of the significant wave height and the variance of the mean period according to the second standard spectral data.

[0066] It can be understood that the wave measurement error generally consists of two parts, namely the natural variability of the wave and the inherent error of the instrument. The natural variability of the wave refers to the fluctuation of the measured values of the standard instrument due to the randomness of the wave itself under the same wave condition (i.e., sampling variability); while the inherent error of the instrument refers to the measurement deviation caused by the performance defects of the instrument to be measured (such as sensor accuracy, algorithm deviation, etc.). Traditional error statistics do not distinguish between the two, and may misjudge the "natural wave fluctuation" as the "instrument error", resulting in distorted evaluation.

[0067] Specifically, taking the Wave Rider buoy as an example of the standard instrument, the Wave Rider buoy uses the periodogram method to calculate the wave spectrum, and its significant wave height and mean period The sampling variability can be determined by the spectral moment covariance. First, calculate the variance of the significant wave height and the variance of the mean period according to the second spectral moment information determined from the second standard spectral data of the standard instrument. The second spectral moment information can include the zero-order spectral moment of the wave energy and the second-order spectral moment of the wave energy of the standard instrument. The above variance of the significant wave height can be expressed by the following formula: (7) The above variance of the mean period can be expressed by the following formula: (8) Where, is the zero-order spectral moment of the wave energy of the standard instrument, is the second-order spectral moment of the wave energy of the standard instrument, and the covariance of the p-th spectral moment and the q-th spectral moment can be expressed by the following formula: (9) Where, is the observation duration, is the k-th frequency point, is at The wave spectrum energy value at the frequency, is the frequency resolution, K is the total number of frequency points, is the p-th spectral moment of the wave energy of the standard instrument, is the q-th spectral moment of the wave energy of the standard instrument. When p is 0 and q is 2, substituting into the above formula (9) can obtain the covariance , when p is 2 and q is 2, substituting into the above formula (9) can obtain , when p is 0 and q is 0, substituting into the above formula (9) can obtain .

[0068] For each valid grid, after determining the second standard spectrum data of the wave rider buoy the corresponding second spectral moment information is determined, and then the corresponding significant wave height variance is calculated according to the above formula (7), and the corresponding mean period variance is calculated according to the above formula (8).

[0069] Step S402: Simulate the sea wave sequence by the random phase method and calibrate the correction coefficient.

[0070] 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.

[0071] Step S404: Separate the sampling variability variance from the error standard deviation to obtain the corrected standard deviation of the instrument under test.

[0072] It should be noted that for the systematic deviations introduced by different segmentation strategies, window functions, and overlap rates of the periodogram method, the correction coefficient can be calculated by simulating the sea wave sequence by the random phase method. First, generate a wave height time series that conforms to the simulated target spectrum (such as the JONSWAP spectrum), add random phases, and use the software supporting the wave rider buoy to calculate the and of the simulated sequence, and repeat n times (n≥1000), and then statistically analyze the and standard deviation and compare it with the theoretical value , to calculate the correction coefficient a. 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 corrected mean period variance , which can be expressed by the following formula: (10) where is the significant wave height variance, is the mean period variance, and a is the correction coefficient.

[0073] 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 under test to obtain the corrected standard deviation of the instrument under test. This corrected standard deviation is an accurate estimate of the random error of the instrument under test itself, and it can be expressed by the following formula: (11) where is the error standard deviation, is the sampling variance of the standard instrument.

[0074] It can be understood that in the above formulas (7)-(11) the calculations are applicable to any comparison measurement scenario with the Wave Knight buoy as the standard instrument. If the standard instrument needs to be replaced, the sampling variability model can be re-derived according to its spectral estimation method.

[0075] In this embodiment, for each target grid, by calculating the variance using the second standard spectral data and simulating ocean waves by the random phase method, the natural variation of waves and the inherent error of the instrument can be accurately separated. And the calculation results are optimized using the correction coefficient to obtain the sampling variability variance of the standard instrument. Stripping it from the error standard deviation to obtain the corrected standard deviation of the instrument under test can effectively avoid misjudging the random error of the standard instrument as a defect of the instrument under test, improving the accuracy and comparability of instrument error evaluation under different sea conditions, and providing a reliable basis for instrument calibration, selection, and ocean engineering design.

[0076] Step S206, generate an error heat map based on the error statistical parameters and the corrected standard deviation, and conduct a comprehensive analysis of the instrument under test to obtain the comparison measurement result.

[0077] It should be noted that the above error heat map is a visualization tool that intuitively presents the error distribution characteristics of the instrument under test in different wave conditions or parameter dimensions through color gradients. It takes the corrected standard deviation as the core data basis and combines the original error statistical parameters (such as mean deviation, variance, etc.) to transform the spatial or parameter dependence of the instrument measurement error into a visualization map, facilitating the rapid positioning of error-sensitive regions and abnormal patterns.

[0078] In one embodiment of the present application, in order to visually and clearly observe the error mean and relative standard deviation, and the error mean and the corrected standard deviation, they can be visualized. Generating an error heat map based on the error statistical parameters and the corrected standard deviation includes: normalizing the error mean and the corrected standard deviation to obtain the relative error mean and relative standard deviation; taking wave height as the horizontal axis and period as the vertical axis, respectively plotting two-dimensional distribution maps of the relative error mean and relative standard deviation, and two-dimensional distribution maps of the error mean and the corrected standard deviation; and using a color scale coding method to mark different error ranges in both the two-dimensional distribution maps of the relative error mean and relative standard deviation and the two-dimensional distribution maps of the error mean and the corrected standard deviation.

[0079] Specifically, in order to eliminate the influence of wave height energy level differences on error evaluation, 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 proportion of systematic deviation, and the relative standard deviation is used to standardize the fluctuation intensity of random deviation. The relative error mean can be expressed by the following formula: (12) The relative standard deviation can be expressed by the following formula: (13) 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.

[0080] 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 .

[0081] 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 .

[0082] 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.

[0083] In another embodiment of the present application, a specific implementation manner for comprehensively analyzing the instrument to be measured and obtaining the comparison measurement result is also provided. In the process of comprehensively analyzing the instrument to be measured and obtaining the comparison measurement result, the system deviation index can be determined based on the mean relative error, and the random fluctuation intensity can be determined based on the relative standard deviation; and / or, the key grid can be determined from the target grid, and the over-standard error ratio can be determined based on the mean error and the mean relative error in the key grid to obtain the abnormal sea condition adaptability result; and / or, according to the mean relative error and the relative standard deviation of the acquired preset frequency band, the frequency band sensitivity comparison measurement result can be determined.

[0084] The above comparison measurement results include: system deviation index, random fluctuation intensity level, abnormal sea condition adaptability result, and frequency band sensitivity comparison measurement result. The system deviation index quantifies the systematic deviation degree between the measured value of the instrument to be measured and the true value (the measured value of the standard instrument), and its core function is to reflect whether there is a fixed deviation in the instrument (such as sensor zero error, calibration parameter deviation), which is a basic index for evaluating the measurement accuracy of the instrument. The random fluctuation intensity level is the level of the discrete degree of the measured value of the instrument divided based on the corrected standard deviation, and is used to characterize the random error level introduced by its own noise or algorithm after excluding the natural variation of the waves. The abnormal sea condition adaptability result is used to evaluate the measurement reliability of the instrument to be measured under extreme wave conditions (such as high significant wave height, short-period breaking waves, abnormal waves). The frequency band sensitivity comparison measurement result is used to analyze the variation law of the error of the instrument to be measured with the wave frequency (or period), and locate the specific frequency band with significant errors (such as the swell frequency band, high-frequency wind-generated wave frequency band).

[0085] As an implementable manner, after obtaining the mean relative error, the system deviation index can be calculated through the following formula: (14) After obtaining the relative standard deviation, the random fluctuation intensity can be expressed through the following formula: (15) Wherein, is the mean relative error, is the relative standard deviation, N total is the data volume.

[0086] In this embodiment, the system deviation index and the random fluctuation intensity can support the standardized horizontal comparison of the instrument performance, and by constructing a visual heat map, the performance boundary 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 deploying high-precision instruments in key sea areas).

[0087] As another implementable approach, key grids can be determined from the target grids. For example, grids with a wave height greater than a preset wave height threshold and an average period greater than an average period threshold can be determined as key grids. When the preset wave height threshold is 2.5 m and the average period threshold is 10 s, then > 2.5 m is considered as a high wave height, and > 10 s is considered as a long period. Then, grids with high wave heights and long periods are counted and used as key grids. Then, based on the mean error and relative mean error in the key grids, the proportion of exceeding-standard errors is determined to obtain the abnormal sea condition adaptability result. For example, the proportion of exceeding-standard errors ( or ) in the grids is counted.

[0088] As yet another implementable approach, a preset frequency band is determined from all frequency bands. The preset frequency band can include a low-frequency band, a middle-frequency band, and a high-frequency band. The low-frequency band represents low-frequency surges, the middle-frequency band represents middle-frequency wind waves, and the high-frequency band represents high-frequency breaking waves. According to the relative mean error and relative standard deviation in different frequency bands, the sensitive frequency band of the instrument to be measured is identified. Among them, the above-mentioned low-frequency band can be 0.05 - 0.1 Hz, the middle-frequency band can be 0.1 - 0.3 Hz, and the high-frequency band can be 0.3 - 0.5 Hz.

[0089] In this embodiment, the system deviation index and random fluctuation intensity level are defined by the relative mean error and standard deviation, separating the instrument accuracy and stability problems, and being able to accurately quantify the systematic and random errors; and screening key grids to calculate the proportion of exceeding-standard errors, identifying the performance attenuation area of the instrument under extreme wave conditions, thereby locating the short board of abnormal sea conditions; and based on the relative error parameters of the preset frequency band, locking the error-sensitive frequency band, providing a direction for sensor or algorithm optimization, realizing error traceability at the frequency band level; and converting the abstract error into a quantifiable index, assisting in instrument selection, calibration, and adaptation to ocean engineering scenarios, and realizing the visualization of decision support.

[0090] 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.

[0091] 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.

[0092] 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 Calculation of error standard deviation (Original random deviation of the wave buoy). For example, for a certain target grid ( = 1.75 - 2.0 m, = 4.5 - 5.0 s), with the data volume N = 37, the determined = 0.0298 m, = 0.1503 m, = 0.128 s, = 0.1413 s. Then perform the standard instrument error correction. According to the observation duration of each group of data of Waverider MKIII (1600 s, sampling rate 1.28 Hz, number of sampling points 2048) and the spectral estimation smoothing window (each segment is 200 s, non - overlapping Welch periodogram method, Turkey window function), use the random phase method to simulate, and obtain the spectral estimation correction coefficient a = 1.414 of Waverider MKIII. Furthermore, calculate the uncorrected variances of the significant wave height and the average period of the sampling variability of the standard instrument according to the above formulas (7) and (8). Assume that for the target grid ( = 1.75 - 2.0 m, = 4.5 - 5.0 s), the obtained results are respectively = 0.0675 m, = 0.0999 s. Then substitute the correction parameters into the above formula (10) to obtain the corrected sampling variability variance of Waverider MKIII, that is, = 0.0955 m, = 0.1413 s. Then separate the corrected variance of the standard instrument from the total error of the instrument to be measured through formula (11) to obtain the random error variance (corrected standard deviation) of the instrument to be measured itself. This corrected standard deviation can be = 0.116 m, = 0.0993 s.

[0093] After performing the standard instrument error correction, error visualization and performance evaluation processing can be carried out. For the significant wave height adopt the normalized error index, and for the average period ( ) adopt the absolute error index. The mean relative error and the relative standard deviation of this target grid are respectively: = 2, = 6. Then use the wave height as the horizontal axis (0.0 - 5.0 m), the average period as the vertical axis (0.0 - 8.0 s) to draw the heat map of the significant wave height error; and draw the heat map of the average period error, and different color levels can be represented by different colors.

[0094] After generating the two-dimensional error heat map, its comprehensive performance can be evaluated. The system deviation index is determined to be 0.425 through the above formula (14), and the random fluctuation intensity is calculated to be 8.675 through the above formula (15). Scenario applicability analysis can also be carried out. By analyzing wind waves of different frequencies, for medium wind waves (0.1 - 0.3 Hz): the mean relative error of the significant wave height is within 5%, the relative standard deviation is basically less than 10%, the mean error of the average period is within 0.3 s, and the error standard deviation is basically within 0.15 s. For high-frequency breaking waves (0.3 - 0.5 Hz): the mean relative error of the significant wave height in the range of the average period from 2 s to 2.5 s is about 10%, the relative standard deviation of the significant wave height in the range of the average period from 2 s to 3 s is basically greater than 10%, the mean error of the average period is greater than 0.3 s, and some reach 0.7 s, and the error standard deviation is basically greater than 0.2 s, especially in low sea conditions.

[0095] In this embodiment, through frequency band sensitivity analysis, principle defects can be identified (such as the integration error of gravitational acceleration will amplify low-frequency noise and zero drift), providing data guiding information for subsequent analysis to facilitate targeted improvement.

[0096] This application provides a method for analyzing the comparison error 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 traditional characteristic parameters. By standardizing the original spectrum, the details of the frequency domain energy distribution are retained, providing a data basis for multi-factor error traceability. After time synchronization processing of the standard spectrum data, it can ensure the comparison of the two groups of spectrum data under the same spatio-temporal sea conditions, avoiding errors caused by spatio-temporal dislocation. Then, a wave height - period two-dimensional matrix is constructed, and the wave characteristic value sequence is mapped to the corresponding grid of this 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 errors, the uncertainty of the standard instrument is incorporated into the error analysis system, improving the evaluation credibility. By correcting the error statistical parameters with the sampling variability variance, the true error of the instrument to be measured can be restored more meticulously. And based on the error statistical parameters and the corrected standard deviation, an error heat map is generated, which can visually visualize the error to present the error-sensitive area, and then conduct a comprehensive analysis of the instrument to be measured, accurately determining the comparison result, greatly improving the accuracy of the analysis of the comparison error of wave measuring instruments.

[0097] Based on the same inventive concept, an embodiment of the present application further provides a device for analyzing the comparison error of a wave measurement instrument involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the wave measurement instrument comparison error analysis device provided below can refer to the limitations on the wave measurement instrument comparison error analysis method in the above text, and will not be repeated here.

[0098] In an exemplary embodiment, as Figure 7 shown, a device for analyzing the comparison error of a wave measurement instrument is provided, including: A normalization module 510, configured to obtain the original wave spectrum data of the instrument to be measured and normalize it into the first standard spectrum data, and obtain the original wave spectrum data of the standard instrument and normalize it into the second standard spectrum data; A time synchronization module 520, configured to perform time synchronization matching processing on the first standard spectrum data and the second standard spectrum data to obtain a wave feature value sequence; the wave feature value sequence includes multiple wave feature values, and the wave feature values include the significant wave height and the corresponding mean period; A grid determination module 530, configured to construct a wave height - period two - dimensional matrix, map the wave feature value sequence to the corresponding grid of the wave height - period two - dimensional matrix, and determine the effective target grid in the grid; A parameter determination module 540, configured to determine the error statistical parameter of the instrument to be measured relative to the standard instrument for each target grid; A correction module 550, configured to calculate the sampling variability variance according to the second standard spectrum data, and correct the error statistical parameter according to the sampling variability variance to obtain the corrected standard deviation of the instrument to be measured; An analysis module 560, configured to generate an error heat map based on the error statistical parameter and the corrected standard deviation, and perform comprehensive analysis on the instrument to be measured to obtain the comparison result.

[0099] As an optional implementation manner, the time synchronization module 520 is specifically configured to: Determine the first spectral moment information of the first standard spectrum data, determine the first comparison parameter according to the first spectral moment information, determine the second spectral moment information of the second standard spectrum data, and determine the second comparison parameter according to the second spectral moment information; the first comparison parameter includes the significant wave height and the mean period of the instrument to be measured, and the second comparison parameter includes the significant wave height and the mean period of the standard instrument; Perform time synchronization matching processing on the significant wave height and the mean period of the instrument to be measured, the significant wave height and the mean period of the standard instrument to obtain a wave feature value sequence.

[0100] As an optional implementation manner, the grid determination module 530 is specifically configured to: Determine the wave height interval and the average period interval according to the obtained historical wave statistical characteristics and the amount of data of the wave characteristic values in the wave characteristic value sequence; 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; Classify the wave characteristic value sequence into the corresponding grid of the wave height - period two - dimensional matrix according to the measured values of the standard instrument, and count the amount of data in each grid; Delete the grids with the amount of data less than the preset threshold in all grids to obtain the target grids.

[0101] As an optional implementation manner, the parameter determination module 540 is specifically used for: For each target grid, generate an error sequence of the instrument to be measured and the standard instrument; Calculate the error mean based on the error sequence, and determine the error standard deviation according to the error mean.

[0102] As an optional implementation manner, the correction module 550 is specifically used for: For each target grid, calculate the effective wave height variance and the average period variance according to the second standard spectrum data; Simulate the sea wave sequence by the random phase method and calibrate the correction coefficient; Correct the effective wave height variance and the average period variance according to the correction coefficient to obtain the sampling variability variance of the standard instrument; Separate the sampling variability variance from the error standard deviation to obtain the corrected standard deviation of the instrument to be measured.

[0103] As an optional implementation manner, the analysis module 560 is specifically used for: Normalize the error mean and the corrected standard deviation to obtain the relative error mean and the relative standard deviation; Taking the wave height as the horizontal axis and the average period as the vertical axis, respectively draw the two - dimensional distribution diagrams of the relative error mean and the relative standard deviation, and the two - dimensional distribution diagrams of the error mean and the corrected standard deviation; the different error ranges are marked by the color scale coding method in both the two - dimensional distribution diagrams of the relative error mean and the relative standard deviation and the two - dimensional distribution diagrams of the error mean and the corrected standard deviation.

[0104] As an optional implementation manner, the analysis module 560 is also used for: Conduct a comprehensive analysis on the instrument to be measured to obtain the comparison measurement result, including: Determine the system deviation index based on the relative error mean, and determine the random fluctuation intensity based on the relative standard deviation; and / or, Determine the key grids from the target grids, determine the proportion of excessive errors based on the mean error and the mean relative error in the key grids, and obtain the abnormal sea condition adaptability result; and / or, Determine the frequency band sensitivity ratio measurement result according to the mean relative error and the relative standard deviation of the acquired preset frequency band.

[0105] Among them, for the wave measurement instrument ratio error analysis device provided in the embodiments of the present application, 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, this device breaks through the single statistical limitation of traditional characteristic parameters. By standardizing the original spectrum, the details of the frequency domain energy distribution are retained, providing a data basis for multi-factor error traceability. And by performing time synchronization processing on the standard spectrum data, it can ensure the comparison of the two sets of spectrum data under the same spatio-temporal sea conditions, avoiding errors caused by spatio-temporal misalignment. Then, a wave height-period two-dimensional matrix is constructed, and the wave characteristic value sequence is mapped to the corresponding grids of this 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 the sampling variability error, the uncertainty of the standard instrument is incorporated into the error analysis system, improving the evaluation credibility. By correcting the error statistical parameters with the sampling variability variance, the true error of the instrument to be measured can be restored more meticulously. And by generating an error heat map based on the error statistical parameters and the corrected standard deviation, the error can be visually visualized to present the error-sensitive area, and then a comprehensive analysis of the instrument to be measured can be carried out to accurately determine the ratio measurement result, greatly improving the accuracy of the wave measurement instrument ratio error analysis.

[0106] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 8 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, 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 the 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 external devices. The communication interface of the computer device is used to communicate with external terminals through a grid. When the computer program is executed by the processor, it implements a wave measurement instrument ratio error analysis method.

[0107] Those skilled in the art can understand,Figure 8 The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. Specifically, the computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0108] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0109] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0110] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0111] 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 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 need to comply with relevant regulations.

[0112] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing 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 embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0113] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0114] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope described in this specification.

[0115] In this article, specific examples are used to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for analyzing the comparison error of a wave measurement instrument, characterized in that, The method for analyzing the comparison measurement error of the wave measurement instrument includes: Obtaining the original wave spectrum data of the instrument to be measured and standardizing it into the first standard spectrum data, and obtaining the original wave spectrum data of the standard instrument and standardizing it into the 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; the wave eigenvalue sequence includes multiple wave eigenvalues, and the wave eigenvalues include the significant wave height and the corresponding mean period; Constructing a wave height-period two-dimensional matrix, mapping the wave eigenvalue sequence to the corresponding grid of the wave height-period two-dimensional matrix, and determining the effective target grid in the grid; For each of the target grids, determining the error statistical parameters of the instrument to be measured relative to the standard instrument; Calculating the sampling variability variance according to the second standard spectrum data, and correcting the error statistical parameters according to the sampling variability variance to obtain the corrected standard deviation of the instrument to be measured; Generating an error heat map based on the error statistical parameters and the corrected standard deviation, and comprehensively analyzing the instrument to be measured to obtain the comparison measurement result.

2. The method for analyzing the comparison error of the wave measurement instrument according to claim 1, wherein Performing time synchronization matching processing on the first standard spectrum data and the second standard spectrum data to obtain a wave eigenvalue sequence, including: Determining the first spectral moment information of the first standard spectrum data, determining the first comparison measurement parameter according to the first spectral moment information, determining the second spectral moment information of the second standard spectrum data, and determining the second comparison measurement parameter according to the second spectral moment information; the first comparison measurement parameter includes the significant wave height and the mean period of the instrument to be measured, and the second comparison measurement parameter includes the significant wave height and the mean period of the standard instrument; Performing time synchronization matching processing on the significant wave height and mean period of the instrument to be measured and the significant wave height and mean period of the standard instrument to obtain the wave eigenvalue sequence.

3. The method for analyzing the comparison error of the wave measurement instrument according to claim 2, wherein, Constructing a wave height-period two-dimensional matrix, mapping the wave eigenvalue sequence to the corresponding grid of the wave height-period two-dimensional matrix, and determining the effective target grid in the grid, including: Determining the wave height interval and the mean period interval according to the obtained historical wave statistical characteristics and the data volume of the wave eigenvalues in the wave eigenvalue sequence; 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 mean period interval and the grid boundary range; Classifying the wave eigenvalue sequence into the corresponding grid of the wave height-period two-dimensional matrix according to the measured values of the standard instrument, and counting the data volume of each grid; Deleting the grids with data volume less than the preset threshold in all grids to obtain the target grid.

4. The method for analyzing the comparison error of the wave measurement instrument according to claim 1, characterized in that, The error statistical parameters include the error mean and the error standard deviation; For each of the target grids, determining the error statistical parameters of the instrument to be measured relative to the standard instrument, including: For each of the target grids, generating an error sequence of the instrument to be measured and the standard instrument; Calculating the error mean based on the error sequence, and determining the error standard deviation according to the error mean.

5. The method for analyzing the comparison error of the wave measurement 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 to be measured, including: For each of the target grids, calculating the significant wave height variance and the mean period variance according to the second standard spectrum data; Simulating a sea wave sequence using the random phase method and calibrating a 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; Separating the sampling variability variance from the error standard deviation to obtain the corrected standard deviation of the instrument to be measured.

6. The method for analyzing the comparison error of the wave measurement instrument according to claim 4, wherein The error heat map includes: a two-dimensional distribution map of the mean relative error 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, including: Normalizing the error mean and the corrected standard deviation to obtain the mean relative error and the relative standard deviation; Taking the wave height as the horizontal axis and the mean period as the vertical axis, respectively plotting the two-dimensional distribution maps of the mean relative error and the relative standard deviation, and the two-dimensional distribution maps of the error mean and the corrected standard deviation; different error ranges are marked by a color scale coding method in the two-dimensional distribution maps of the mean relative error and the relative standard deviation, and the two-dimensional distribution maps of the error mean and the corrected standard deviation.

7. The method for analyzing the comparison error of the wave measurement instrument according to claim 6, characterized in that, The comparison test results include: a system deviation index, a random fluctuation intensity, an abnormal sea condition adaptability result, and a frequency band sensitivity comparison test result; Performing a comprehensive analysis on the instrument to be measured to obtain the comparison test results, including: Determining the system deviation index based on the mean relative error and determining the random fluctuation intensity based on the relative standard deviation; and / or, Determining key grids from the target grids, and determining the proportion of exceeded standard errors based on the error mean and the mean relative error in the key grids to obtain the abnormal sea condition adaptability result; and / or, Determining the frequency band sensitivity comparison test result according to the mean relative error and the relative standard deviation of a preset frequency band obtained.

8. An error analysis device for comparing wave measurement instruments, characterized in that, The wave measurement instrument comparison test error analysis device includes: A standardization module, configured to obtain the original wave spectrum data of the instrument to be measured and standardize it into the first standard spectrum data, and obtain the original wave spectrum data of the standard instrument and standardize it into the 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 feature value sequence; the wave feature value sequence includes a plurality of wave feature values, and the wave feature value includes the significant wave height and the corresponding mean period; A grid determination module, configured to construct a wave height-period two-dimensional matrix, map the wave feature value sequence to the corresponding grid of the wave height-period two-dimensional matrix, and determine the effective target grids in the grid; A parameter determination module, configured to determine, for each of the target grids, the error statistical parameter of the instrument to be measured relative to the standard instrument; A correction module, configured to calculate a sampling variability variance according to the second standard spectrum data, and correct the error statistical parameter according to the sampling variability variance to obtain a corrected standard deviation of the instrument to be measured; An analysis module, configured to generate an error heat map based on the error statistical parameter and the corrected standard deviation, and perform a comprehensive analysis on the instrument to be measured to obtain a comparison measurement result.

9. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for analyzing the comparison measurement error of the wave measurement instrument according to any one of claims 1-7.

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

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