GNSS-R sea surface height inversion quality control method, device and terminal equipment

Abstract

The present invention discloses a GNSS-R sea surface height inversion quality control method, device, and terminal device. The quality control method includes: step 1, selecting a plurality of target height data from a preset sea surface effective reflection height dataset; step 2, calculating the average height data and its standard deviation for each observation period as a fitting dataset; step 3, fitting the fitting formula to obtain model parameters and the standard deviation of the fitting residual to construct a prediction model; step 4, predicting the predicted height data for the current observation period; step 5, detecting whether the sea surface effective reflection height data for the current observation period is abnormal; step 6, if not, updating the fitting dataset and re-executing steps 3 to 5 based on the next observation period until quality control of each observation period is completed; if so, deleting the sea surface effective reflection height data and re-executing steps 4 to 5 based on the next observation period until quality control of each observation period is completed. The present invention can effectively improve the accuracy of sea surface height inversion results.

Description

Technical Field

[0001] The present invention belongs to the technical field of sea surface elevation monitoring, and in particular relates to a GNSS-R sea surface elevation inversion quality control method, device and terminal equipment. Background Art

[0002] Global Navigation Satellite System Reflectometry (GNSS-R) is a significant innovation in the field of Global Navigation Satellite Systems (GNSS) and has been widely used in monitoring land / sea surface elevation, soil moisture, and vegetation changes. Ground-based single-antenna GNSS-R technology can be divided into two approaches: GNSS interferometric reflectometry and interferometric mode. Both GNSS-R modes have been successfully applied to monitor sea surface elevation changes, achieving good results. In 2013, Larson et al. first attempted to invert sea level elevation changes using a single geodetic GPS receiver based on GNSS interferometric reflectometry, achieving results that were consistent with those from tide gauges. However, due to the complexity of the reflected signal and the interferometric process, GNSS-R technology often exhibits anomalies, resulting in low accuracy in the effective reflecting surface elevation measurements.

[0003] To improve the accuracy of effective reflector inversion results, existing technologies typically utilize quality control measures such as setting a minimum amplitude for peak intensity in the signal-to-noise ratio spectrum, a minimum ratio of peak intensity to background intensity, and varying the effective reflector area. However, these quality control measures primarily operate during the initialization phase of the inversion algorithm and cannot guarantee the accuracy of effective reflector inversion results. This problem is particularly prominent in real-time sea surface height inversion, as the arc segment duration is relatively short, and brief anomalies can significantly impact the results for the entire arc segment. Summary of the Invention

[0004] One object of the present invention is to address the shortcomings of the existing technology and provide a GNSS-R sea surface height inversion quality control method to solve the problem that the existing quality control method cannot effectively improve the accuracy of the sea surface height inversion results.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0006] A GNSS-R sea surface height inversion quality control method comprises the following steps:

[0007] Step 1: Obtain a sea surface effective reflection height dataset, and select a number of sea surface effective reflection height data within a preset time period before the current observation period from the sea surface effective reflection height dataset;

[0008] Step 2: Based on the selected sea surface effective reflection height data and their corresponding observation periods, calculate the average reflection height data and the standard deviation of the average reflection height data corresponding to each observation period, and use the average reflection height data and the standard deviation corresponding to each observation period as the fitting data set;

[0009] Step 3: Preset a fitting formula, fit the fitting formula using the current fitting data set, obtain several model parameters in the fitting formula and the standard deviation of the fitting residual, substitute the several model parameters into the fitting formula to obtain the sea surface reflection height prediction model;

[0010] Step 4: Using the time information of the current observation period as the input of the sea surface reflection height prediction model, the sea surface reflection height is predicted by the sea surface reflection height prediction model to obtain the predicted sea surface effective reflection height data for the current observation period;

[0011] Step 5: Based on the predicted reflection height data and the standard deviation of the fitting residual, detect whether the effective sea surface reflection height data in the current observation period is abnormal reflection height data;

[0012] Step 6: When the sea surface effective reflection height data is non-abnormal reflection height data, the fitting data set is updated using the sea surface effective reflection height data, and the next observation period of the current observation period is used as the new current observation period, and steps 3 to 5 are re-executed until the quality control of all observation periods is completed;

[0013] When the sea surface effective reflection height data is abnormal reflection height data, the sea surface effective reflection height data is deleted, and the next observation period of the current observation period is used as the new current observation period, and steps 4 to 5 are re-executed until the quality control of all observation periods is completed.

[0014] Furthermore, the specific method for calculating the average reflection height data and its standard deviation corresponding to each observation period in step 2 is:

[0015] Calculating, based on the observation periods corresponding to the plurality of target reflection height data, first average reflection height data of a plurality of sea surface effective reflection height data within each observation period and its corresponding standard deviation;

[0016] Determine a data retention range based on the first average reflection height data corresponding to each observation period and its corresponding standard deviation;

[0017] Deleting a number of sea surface effective reflection height data whose values are outside the data retention range in each observation period, and obtaining a number of retained reflection height data in each observation period;

[0018] The second average reflection height data and the standard deviation of the retained reflection height data in each observation period are calculated, and the second average reflection height data and the standard deviation corresponding to each observation period are respectively used as the average reflection height data and the standard deviation corresponding to each observation period.

[0019] Furthermore, the data retention value range is [H1-3*STD1, H1+3*STD1]; wherein H1 represents the first average reflection height data corresponding to any observation period; STD1 represents the standard deviation of the first average reflection height data.

[0020] Furthermore, the fitting formula preset in step 3 is specifically:

[0021] H m =a0+a1cos2πt+b1sin2πt+a2cos4πt+b2sin4πt+a3cos6πt+b3sin6πt;

[0022] Among them, H m represents the average reflection height data corresponding to any observation period; t represents the time of the observation period; a0, a1, b1, a2, b2, a3 and b3 represent the first model parameter, the second model parameter, the third model parameter, the fourth model parameter, the fifth model parameter, the sixth model parameter and the seventh model parameter, respectively.

[0023] Furthermore, the parameter t in the fitting formula is the number of hours in a day or the number of days per year with a decimal.

[0024] Furthermore, in step 3, based on the fitting formula, the fitting formula is fitted using the current fitting data set, and the weighted least squares method is used to calculate and obtain the standard deviations of several model parameters and fitting residuals in the fitting formula.

[0025] Furthermore, the specific method for detecting whether the effective sea surface reflection height data in the current observation period is abnormal reflection height data is as follows:

[0026] Calculate the difference between the effective sea surface reflection height data and the predicted reflection height data;

[0027] When the difference between the effective sea surface reflection height data and the predicted reflection height data is less than or equal to three times the standard deviation of the fitting residual, the effective sea surface reflection height data is determined to be non-abnormal reflection height data;

[0028] When the difference between the effective sea surface reflection height data and the predicted reflection height data is greater than three times the standard deviation of the fitting residual, the effective sea surface reflection height data is determined to be abnormal reflection height data.

[0029] Furthermore, in step 6, the method for updating the fitting data set using the sea surface effective reflection height data is:

[0030] The effective sea surface reflection height data that are non-abnormal reflection height data in the current observation period are added to the fitting data set, and the average reflection height data with the earliest observation time is deleted from the fitting data set to obtain an updated fitting data set.

[0031] Another object of the present invention is to provide a quality control device for implementing the above-mentioned GNSS-R sea surface height inversion quality control method, comprising:

[0032] A data selection module is used to execute step 1, based on the pre-acquired sea surface effective reflection height data set, select a number of target reflection height data within a preset time period before the current observation period from the sea surface effective reflection height data set;

[0033] A first data processing module is configured to execute step 2, calculate average reflection height data corresponding to each observation period and a standard deviation of the average reflection height data based on the plurality of target reflection height data and the observation periods corresponding to the plurality of target reflection height data, and use the average reflection height data and the standard deviation corresponding to each observation period as a fitting data set;

[0034] A model fitting module is used to execute step 3, based on a preset fitting formula, fit the fitting formula using the current fitting data set, obtain a number of model parameters and standard deviations of fitting residuals, and obtain a sea surface reflection height prediction model based on the model parameters;

[0035] A prediction module is used to execute step 4, use the current observation period as the input of the sea surface reflection height prediction model, predict the sea surface reflection height through the sea surface reflection height prediction model, and obtain the predicted reflection height data for the current observation period;

[0036] A data detection module is used to execute step 5, detecting whether the effective sea surface reflection height data in the current observation period is abnormal reflection height data based on the predicted reflection height data and the standard deviation of the fitting residual;

[0037] A second data processing module is configured to, when the sea surface effective reflection height data is non-abnormal reflection height data, update the fitting data set using the sea surface effective reflection height data in step 6, and use the next observation period of the current observation period as the new current observation period, and re-execute steps 3, 4, and 5 respectively through the model fitting module, the prediction module, and the data detection module until quality control of all observation periods is completed;

[0038] The third data processing module is used to execute step 6, when the sea surface effective reflection height data is abnormal reflection height data, delete the sea surface effective reflection height data, and use the next observation period of the current observation period as the new current observation period, and re-execute steps 4 and 5 respectively through the prediction module and the data detection module until the quality control of all observation periods is completed.

[0039] The third object of the present invention is to provide a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned GNSS-R sea surface height inversion quality control method when executing the computer program.

[0040] Compared with the existing technology, the beneficial effect of the present invention is: the present invention can effectively improve the accuracy of the sea surface height inversion results by simultaneously utilizing the acquired sea surface effective reflection height data set and the predicted reflection height data of the current observation period to perform quality control on the sea surface effective reflection height data of all observation periods, thereby solving the problem that the quality control method in the existing technology cannot effectively improve the accuracy of the sea surface height inversion results. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 1 is a flow chart of a GNSS-R sea surface height inversion quality control method in an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of the one-hour sea surface height inversion results for January 2021 obtained by the SC02 station using the GNSS-R sea surface height inversion quality control method provided in an embodiment of the present invention;

[0043] Figure 3 3 is a schematic structural diagram of a GNSS-R sea surface height inversion quality control device in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0045] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0046] The present invention will be further described below with reference to specific examples, but they are not intended to limit the present invention.

[0047] See also Figure 1 A first aspect of an embodiment of the present invention provides a GNSS-R sea surface height inversion quality control method, comprising the following steps:

[0048] Step 1: pre-acquire a sea surface effective reflection height dataset, and select a number of sea surface effective reflection height data within a preset time period before the current observation period from the sea surface effective reflection height dataset;

[0049] In this step, the pre-acquired sea surface effective reflection height dataset is specifically a single-arc sub-daily scale sea surface effective reflection height dataset acquired based on ground-based GNSS-R. Several target reflection height data within a preset time period before the current observation period are selected from the pre-acquired sea surface reflection height dataset for subsequent model fitting. By way of example, this embodiment selects several target reflection height data 3 days before the current observation period from the sea surface reflection height dataset.

[0050] Step 2: Based on the selected sea surface effective reflection height data and their corresponding observation periods, calculate the average reflection height data corresponding to each observation period and its corresponding standard deviation, and use the average reflection height data corresponding to each observation period and its standard deviation as the fitting data set;

[0051] In this step, in order to establish a fitting data set for constructing a sea surface reflection height prediction model, this embodiment calculates the average reflection height data corresponding to each observation period and its corresponding standard deviation based on the target reflection height data selected in step 1 and the corresponding observation periods, as a fitting data set. Exemplarily, the observation period of a single arc segment sea surface effective reflection height is usually no more than 75 minutes. Preferably, in this embodiment, each observation period is 60 minutes, that is, the average reflection height data corresponding to each hour and its corresponding standard deviation are calculated.

[0052] According to the reflection height data of several targets and their corresponding observation periods, the average reflection height data and its standard deviation corresponding to each observation period are calculated. The specific method is as follows:

[0053] Calculating, based on the observation periods corresponding to the plurality of target reflection height data, first average reflection height data and a standard deviation of the first average reflection height data of the plurality of target reflection height data in each observation period;

[0054] The data retention range is determined based on the first average reflection height data corresponding to each observation period and the standard deviation of the first average reflection height data. In this embodiment, the data retention range is specifically [H1-3*STD1, H1+3*STD1]; wherein H1 represents the first average reflection height data corresponding to any observation period; STD1 represents the standard deviation of the first average reflection height data;

[0055] Deleting a number of sea surface effective reflection height data whose values are outside the data retention range in each observation period, and obtaining a number of retained reflection height data in each observation period;

[0056] The second average reflection height data and the standard deviation of the retained reflection height data in each observation period are calculated, and the second average reflection height data and the standard deviation corresponding to each observation period are respectively used as the average reflection height data and the standard deviation corresponding to each observation period.

[0057] Specifically, when calculating the average reflection height data and its standard deviation corresponding to each observation period, this embodiment also identifies anomalies of several sea surface effective reflection height data within each observation period according to a preset anomaly judgment standard. First, the first average reflection height data and its corresponding standard deviation of several sea surface effective reflection height data within each observation period are calculated, and then the data retention range is determined. Using the data retention range as the anomaly judgment standard, several sea surface effective reflection height data within each observation period whose values are outside the data retention range are deleted to obtain several retained reflection height data within each observation period, thereby realizing anomaly identification of several sea surface effective reflection height data. Finally, the second average reflection height data and its standard deviation of several retained reflection height data within each observation period are calculated as the average reflection height data and its corresponding standard deviation corresponding to each observation period.

[0058] It is worth noting that, since the fitting to obtain the sea surface reflection height prediction model needs to be based on the average reflection height data corresponding to each observation period and its corresponding standard deviation, and the predicted reflection height data output by the sea surface reflection height prediction model and the standard deviation of the fitting residual need to be used to perform quality control on the sea surface effective reflection height data of each observation period, the accuracy of the predicted reflection height data and the standard deviation of the fitting residual plays a very important role in the effect of quality control, and the accuracy of the predicted reflection height data and the standard deviation of the fitting residual depends on the prediction accuracy of the sea surface reflection height prediction model, and the prediction accuracy of the sea surface reflection height prediction model depends on the accuracy of the fitting data set during the fitting process. Based on this, this embodiment can improve the accuracy of the fitting data set by identifying anomalies of several sea surface effective reflection height data in each observation period, thereby improving the effectiveness of quality control.

[0059] Step 3: Preset a fitting formula, fit the fitting formula using the current fitting data set, obtain several model parameters in the fitting formula and the standard deviation of the fitting residual, substitute the several model parameters into the fitting formula to obtain the sea surface reflection height prediction model;

[0060] This embodiment presets a fitting formula, fits the fitting formula using the current fitting data set, thereby determining several model parameters in the fitting formula and the standard deviation of the fitting residuals, and then substitutes the obtained several model parameters into the fitting formula to obtain a sea surface reflection height prediction model;

[0061] In this embodiment, the preset fitting formula is specifically:

[0062] H m =a0+a1cos2πt+b1sin2πt+a2cos4πt+b2sin4πt+a3cos6πt+b3sin6πt;

[0063] Among them, H m represents the average reflection height data corresponding to any observation period; t represents the observation period; a0, a1, b1, a2, b2, a3 and b3 represent the first model parameter, the second model parameter, the third model parameter, the fourth model parameter, the fifth model parameter, the sixth model parameter and the seventh model parameter, respectively.

[0064] It is worth noting that the parameter t in the formula is the number of hours in a day or the cumulative number of days in a year with decimals.

[0065] As a preferred solution, based on the fitting formula, the fitting formula is fitted using a fitting data set consisting of time information, average reflection height data and corresponding standard deviation data of several observation periods before the current observation period, and the weighted least squares method is used to calculate several model parameters (i.e., the above-mentioned a0, a1, b1, a2, b2, a3, b3) and the standard deviation of the fitting residuals.

[0066] Step 4: Using the time information of the current observation period as the input of the sea surface reflection height prediction model, the sea surface reflection height is predicted by the sea surface reflection height prediction model to obtain the predicted reflection height data of the current observation period;

[0067] After obtaining the sea surface reflection height prediction model (i.e. the preset fitting formula after fitting), the time information of the current observation period is input into the model as input, and the predicted reflection height data of the current observation period can be predicted.

[0068] Step 5: Based on the predicted reflection height data and the standard deviation of the fitting residual, detect whether the effective sea surface reflection height data in the current observation period is abnormal reflection height data;

[0069] The predicted reflection height data and the standard deviation of the fitting residual are used to detect anomalies in the effective sea surface reflection height data of the current observation period. It can be understood that the effective sea surface reflection height data of the current observation period is obtained based on ground-based GNSS-R.

[0070] Check whether the effective sea surface reflection height data in the current observation period is abnormal reflection height data. The specific method is as follows:

[0071] Calculate the difference between the effective sea surface reflection height data and the predicted reflection height data;

[0072] When the difference between the effective sea surface reflection height data and the predicted reflection height data is less than or equal to three times the standard deviation of the fitting residual, the effective sea surface reflection height data is determined to be non-abnormal reflection height data;

[0073] When the difference between the effective sea surface reflection height data and the predicted reflection height data is greater than three times the standard deviation of the fitting residual, the effective sea surface reflection height data is determined to be abnormal reflection height data.

[0074] Step 6: When the sea surface effective reflection height data is non-abnormal reflection height data, the sea surface effective reflection height data is used to update the fitting data set, that is, the time information, effective reflection height data and its standard deviation of the current observation period are included in the fitting data set, the oldest data in the fitting data set is removed, and the next observation period is used as the new current observation period. Steps 3 to 5 are re-executed until the quality control of all observation periods is completed;

[0075] When the sea surface effective reflection height data is abnormal reflection height data, the sea surface effective reflection height data is deleted, and the next observation period of the current observation period is used as the new current observation period, and steps 4 to 5 are re-executed until the quality control of all observation periods is completed.

[0076] In this step, if the sea surface effective reflection height data for the current observation period is non-anomalous reflection height data, the sea surface effective reflection height data is retained and used to update the current fitting dataset, thereby continuously improving the accuracy of the fitting dataset and continuously optimizing the sea surface reflection height prediction model. The next observation period after the current observation period is used as the new current observation period, and steps 3 to 5 are re-executed until quality control of all observation periods is completed.

[0077] When the effective reflection height data of the sea surface in the current observation period is abnormal reflection height data, it indicates that the effective reflection height data of the sea surface is a gross error and should be deleted. The next observation period of the current observation period should be used as the new current observation period, and steps 4 to 5 should be re-executed until the quality control of all observation periods is completed.

[0078] As a preferred solution, the sea surface effective reflection height data is used to update the fitting dataset. The specific method is as follows:

[0079] The effective sea surface reflection height data, which is non-abnormal reflection height data, is added to the fitting dataset, that is, the time information, effective reflection height data and its standard deviation of the current observation period are included in the fitting dataset, and the earliest data in the fitting dataset are removed to obtain an updated fitting dataset. This is equivalent to updating the fitting dataset using the sliding time window method in the observation time series. The time window slides over time, and each time it moves backward to supplement new data, removing the earliest data corresponding to the current observation period.

[0080] It is worth noting that adding the effective sea surface reflection height data that has been confirmed to be normal to the fitting data set and deleting the average reflection height data with the earliest observation time corresponding to the current observation period from the fitting data set can continuously improve the accuracy of the data in the fitting data set, and the sample size in the data set will not increase, so there is no need to increase the fitting time when updating the sea surface reflection height prediction model based on the updated fitting data set.

[0081] An embodiment of the present invention provides a GNSS-R sea surface height inversion quality control method, which can effectively improve the accuracy of the sea surface height inversion results by simultaneously utilizing the acquired sea surface effective reflection height dataset and the predicted reflection height data of the current observation period to perform quality control on the sea surface effective reflection height data of all observation periods.

[0082] For example, see Figure 2 , which is the one-hour sea surface height inversion result in January 2021 obtained by the SC02 station using the GNSS-R sea surface height inversion quality control method provided by an embodiment of the present invention. It can be seen from the figure that some abnormal reflection height data have been eliminated through quality control. Therefore, the embodiment of the present invention can effectively improve the accuracy of the sea surface height inversion results.

[0083] See also Figure 3 A second aspect of an embodiment of the present invention provides a quality control device for implementing a GNSS-R sea surface height inversion quality control method, comprising:

[0084] The data selection module 301 is configured to execute step 1, based on a pre-acquired sea surface effective reflection height dataset, select a plurality of target reflection height data within a preset time period before a current observation period from the sea surface effective reflection height dataset;

[0085] The first data processing module 302 is configured to execute step 2, calculate the average reflection height data and its standard deviation corresponding to each observation period based on the plurality of target reflection height data and the observation periods corresponding to the plurality of target reflection height data, and use the average reflection height data and its standard deviation corresponding to each observation period as a fitting data set;

[0086] The model fitting module 303 is used to execute step 3, based on a preset fitting formula, use the current fitting data set fitting formula to perform fitting, obtain a number of model parameters and standard deviations of fitting residuals, and obtain a sea surface reflection height prediction model based on the number of model parameters;

[0087] The prediction module 304 is configured to execute step 4, use the current observation period as the input of the sea surface reflection height prediction model, predict the sea surface reflection height using the sea surface reflection height prediction model, and obtain predicted reflection height data for the current observation period;

[0088] The data detection module 305 is used to execute step 5, and detect whether the effective sea surface reflection height data in the current observation period is abnormal reflection height data based on the predicted reflection height data and the standard deviation of the fitting residual;

[0089] The second data processing module 306 is configured to execute step 6. When the sea surface effective reflection height data is non-abnormal reflection height data, the fitting data set is updated using the sea surface effective reflection height data, and the observation period next to the current observation period is used as the new current observation period. Steps 3, 4, and 5 are respectively re-executed by the model fitting module 303, the prediction module 304, and the data detection module 305 until quality control of all observation periods is completed;

[0090] The third data processing module 307 is used to execute step 6. When the sea surface effective reflection height data is abnormal reflection height data, the sea surface effective reflection height data is deleted, and the next observation period of the current observation period is used as the new current observation period. Steps 4 and 5 are re-executed through the prediction module 304 and the data detection module 305 respectively until the quality control of all observation periods is completed.

[0091] As a preferred solution, the data detection module 305 is used to detect whether the effective sea surface reflection height data in the current observation period is abnormal reflection height data based on the predicted reflection height data and the standard deviation of the fitting residual, specifically including:

[0092] Calculating the difference between the effective sea surface reflection height data and the predicted sea surface reflection height data;

[0093] When the difference between the effective sea surface reflection height data and the predicted reflection height data is less than or equal to three times the standard deviation of the fitting residual, it is determined that the effective sea surface reflection height data is not the abnormal reflection height data;

[0094] When the difference between the effective sea surface reflection height data and the predicted reflection height data is greater than three times the standard deviation of the fitting residual, it is determined that the effective sea surface reflection height data is the abnormal reflection height data.

[0095] As a preferred solution, the second data processing module 306 is used to update the fitting data set using the sea surface effective reflection height data, specifically including:

[0096] The sea surface effective reflection height data is added to the fitting data set, and the average reflection height data with the earliest observation time corresponding to the observation period and the current observation period is deleted from the fitting data set to obtain an updated fitting data set.

[0097] As a preferred solution, the preset fitting formula is specifically:

[0098] H m =a0+a1cos2πt+b1sin2πt+a2cos4πt+b2sin4πt+a3cos6πt+b3sin6πt;

[0099] Among them, H m represents the average reflection height data corresponding to any observation period; t represents the observation period; a0, a1, b1, a2, b2, a3 and 00623 represent the first model parameter, the second model parameter, the third model parameter, the fourth model parameter, the fifth model parameter, the sixth model parameter and the seventh model parameter, respectively.

[0100] As a preferred solution, the model fitting module 303 is used to fit the fitting formula based on a preset fitting formula using the current fitting data set to obtain several model parameters and standard deviations of fitting residuals, specifically including:

[0101] Based on the fitting formula, the fitting formula is fitted using the current fitting data set, and the weighted least squares method is used to calculate and obtain the standard deviations of several model parameters and fitting residuals.

[0102] As a preferred solution, the first data processing module 302 is configured to calculate, based on the plurality of target reflection height data and the observation periods corresponding to the plurality of target reflection height data, the average reflection height data corresponding to each observation period and the standard deviation of the average reflection height data, specifically including:

[0103] Calculating, based on the observation periods corresponding to the plurality of target reflection height data, first average reflection height data of the plurality of target reflection height data in each observation period and a standard deviation of the first average reflection height data;

[0104] determining a data retention value range according to the first average reflection height data corresponding to each observation period and a standard deviation of the first average reflection height data;

[0105] Deleting a number of target reflection height data in each observation period whose values are not within the data retention value range, to obtain a number of retained reflection height data in each observation period;

[0106] Calculate the standard deviation of the second average reflection height data and the second average reflection height data of several retained reflection height data in each observation period, and use the standard deviation of the second average reflection height data and the second average reflection height data corresponding to each observation period as the average reflection height data and the standard deviation of the average reflection height data corresponding to each observation period, respectively.

[0107] As a preferred solution, the data retention value range is specifically [H1-3*STD1, H1+3*STD1]; wherein H1 represents the first average reflection height data corresponding to any observation period; STD1 represents the standard deviation of the first average reflection height data.

[0108] It should be noted that the GNSS-R sea surface height inversion quality control device provided in an embodiment of the present invention can implement all the processes of the GNSS-R sea surface height inversion quality control method described in any of the above embodiments. The functions of each module in the device and the technical effects achieved are respectively the same as the functions and technical effects achieved by the GNSS-R sea surface height inversion quality control method described in the above embodiments, and will not be repeated here.

[0109] A third aspect of an embodiment of the present invention provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the GNSS-R sea surface height inversion quality control method as described in any embodiment of the first aspect is implemented.

[0110] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device may include, but is not limited to, a processor and a memory. The terminal device may also include input and output devices, network access devices, a bus, etc. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting the various parts of the entire terminal device using various interfaces and lines. The memory may be used to store the computer programs and / or modules. The processor implements the various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory may include a high-speed random access memory and may also include a non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0111] The above are only preferred embodiments of the present invention and do not limit the implementation mode and protection scope of the present invention. For those skilled in the art, it should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the contents of the present invention specification should be included in the protection scope of the present invention.

Claims

1. A GNSS-R sea surface height inversion quality control method, characterized in that: The steps include: Step 1: Obtain a sea surface effective reflection height dataset, and select a number of sea surface effective reflection height data within a preset time period before the current observation period from the sea surface effective reflection height dataset; Step 2: Based on the selected sea surface effective reflection height data and their corresponding observation periods, the average reflection height data and the standard deviation of the average reflection height data corresponding to each observation period are calculated, and the average reflection height data and the standard deviation corresponding to each observation period are used as the fitting data set; Step 3: Preset a fitting formula, fit the fitting formula using the current fitting data set, obtain several model parameters in the fitting formula and the standard deviation of the fitting residual, substitute the several model parameters into the fitting formula to obtain the sea surface reflection height prediction model; Step 4: Using the time information of the current observation period as the input of the sea surface reflection height prediction model, the sea surface reflection height is predicted by the sea surface reflection height prediction model to obtain the predicted sea surface effective reflection height data for the current observation period; Step 5: Based on the predicted reflection height data and the standard deviation of the fitting residual, detect whether the effective sea surface reflection height data in the current observation period is abnormal reflection height data; Step 6: When the sea surface effective reflection height data is non-abnormal reflection height data, the fitting data set is updated using the sea surface effective reflection height data, and the next observation period of the current observation period is used as the new current observation period, and steps 3 to 5 are re-executed until the quality control of all observation periods is completed; When the sea surface effective reflection height data is abnormal reflection height data, the sea surface effective reflection height data is deleted, and the next observation period of the current observation period is used as the new current observation period, and steps 4 to 5 are re-executed until the quality control of all observation periods is completed.

2. The GNSS-R sea surface height inversion quality control method according to claim 1, characterized in that: The specific method for calculating the average reflection height data and its standard deviation corresponding to each observation period in step 2 is: Based on the observation periods corresponding to the selected plurality of sea surface effective reflection height data, calculating first average reflection height data of a plurality of target reflection height data in each observation period and its corresponding standard deviation; Determining a data retention range based on the first average reflection height data corresponding to each observation period and a standard deviation of the first average reflection height data; Deleting a number of sea surface effective reflection height data whose values are outside the data retention range in each observation period, and obtaining a number of retained reflection height data in each observation period; The second average reflection height data and the standard deviation of the retained reflection height data in each observation period are calculated, and the second average reflection height data and the standard deviation corresponding to each observation period are respectively used as the average reflection height data and the standard deviation corresponding to each observation period.

3. The GNSS-R sea surface height inversion quality control method according to claim 2, characterized in that: The data retention value range is [H1-3*STD1, H1+3*STD1]; wherein H1 represents the first average reflection height data corresponding to any observation period; STD1 represents the standard deviation of the first average reflection height data.

4. The GNSS-R sea surface height inversion quality control method according to claim 1, characterized in that: The fitting formula preset in step 3 is: H m =a0+a1cos2πt+b1sin2πt+a2cos4πt+b2sin4πt+a3cos6πt+b3sin6πt; Among them, H m represents the average reflection height data corresponding to any observation period; t represents the time of the observation period; a0, a1, b1, a2, b2, a3 and b3 represent the first model parameter, the second model parameter, the third model parameter, the fourth model parameter, the fifth model parameter, the sixth model parameter and the seventh model parameter, respectively.

5. The GNSS-R sea surface height inversion quality control method according to claim 4, characterized in that: The parameter t in the fitting formula is the number of hours in a day or the number of days per year with decimals.

6. The GNSS-R sea surface height inversion quality control method according to claim 1 or 4, characterized in that: In step 3, based on the fitting formula, the fitting formula is fitted using the current fitting data set, and the weighted least squares method is used to calculate several model parameters in the fitting formula and the standard deviation of the fitting residuals.

7. The GNSS-R sea surface height inversion quality control method according to claim 1, characterized in that: In step 5, the specific method for detecting whether the effective sea surface reflection height data in the current observation period is abnormal reflection height data is: Calculate the difference between the effective sea surface reflection height data and the predicted reflection height data; When the difference between the effective sea surface reflection height data and the predicted reflection height data is less than or equal to three times the standard deviation of the fitting residual, the effective sea surface reflection height data is determined to be non-abnormal reflection height data; When the difference between the effective sea surface reflection height data and the predicted reflection height data is greater than three times the standard deviation of the fitting residual, the effective sea surface reflection height data is determined to be abnormal reflection height data.

8. The GNSS-R sea surface height inversion quality control method according to claim 1, characterized in that: The method for updating the fitting data set using the sea surface effective reflection height data in step 6 is: The effective sea surface reflection height data that are non-abnormal reflection height data in the current observation period are added to the fitting data set, and the average reflection height data with the earliest observation time is deleted from the fitting data set to obtain an updated fitting data set.

9. A quality control device for implementing the GNSS-R sea surface height inversion quality control method according to any one of claims 1 to 8, characterized in that: include: A data selection module is used to execute step 1, based on the pre-acquired sea surface effective reflection height data set, select a number of target reflection height data within a preset time period before the current observation period from the sea surface effective reflection height data set; A first data processing module is configured to execute step 2, calculate average reflection height data corresponding to each observation period and a standard deviation of the average reflection height data based on the plurality of target reflection height data and the observation periods corresponding to the plurality of target reflection height data, and use the average reflection height data and the standard deviation corresponding to each observation period as a fitting data set; A model fitting module is used to execute step 3, based on a preset fitting formula, fit the fitting formula using the current fitting data set, obtain a number of model parameters and standard deviations of fitting residuals, and obtain a sea surface reflection height prediction model based on the model parameters; A prediction module is used to execute step 4, use the current observation period as the input of the sea surface reflection height prediction model, predict the sea surface reflection height through the sea surface reflection height prediction model, and obtain the predicted reflection height data for the current observation period; A data detection module is used to execute step 5, detecting whether the effective sea surface reflection height data in the current observation period is abnormal reflection height data based on the predicted reflection height data and the standard deviation of the fitting residual; A second data processing module is configured to, when the sea surface effective reflection height data is non-abnormal reflection height data, update the fitting data set using the sea surface effective reflection height data in step 6, and use the next observation period of the current observation period as the new current observation period, and re-execute steps 3, 4, and 5 respectively through the model fitting module, the prediction module, and the data detection module until quality control of all observation periods is completed; The third data processing module is used to execute step 6, when the sea surface effective reflection height data is abnormal reflection height data, delete the sea surface effective reflection height data, and use the next observation period of the current observation period as the new current observation period, and re-execute steps 4 and 5 respectively through the prediction module and the data detection module until the quality control of all observation periods is completed.

10. A terminal device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for controlling the quality of GNSS-R sea surface height inversion according to any one of claims 1 to 8 is implemented.

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