River, lake and reservoir ice thickness data processing method, device and equipment based on radar waves

Abstract

The invention discloses a river-lake-reservoir ice thickness data processing method, device and equipment based on radar waves, and relates to the technical field of ice condition prediction, and the method comprises the steps: controlling an unmanned plane to fly along a to-be-detected river-lake-reservoir ice surface area, transmitting radar waves according to a preset interval, and receiving lake ice radar echo data; lake ice radar echo data are obtained through a vector network analyzer, interpolation processing and wavelet decomposition processing are carried out, and reference radar wave data are obtained; performing adaptive processing on the reference radar wave data by adopting a dynamic threshold value and a sliding window to obtain target radar wave data; and calculating the ice thickness of each measuring point based on the reflection main peak time difference of the air-ice interface and the ice-water interface in the target radar wave data in combination with a preset dielectric constant, and performing adjacent peak mutation detection and mutation point correction to obtain target ice thickness data. According to the method, the problem of inaccurate measurement caused by sparse data, multi-path interference, false peaks and sudden ice thickness change when the river-lake-reservoir ice thickness is measured by existing radar waves is solved.

Description

Technical Field

[0001] This application relates to the field of ice condition prediction technology, and in particular to methods, devices and equipment for processing river, lake and reservoir ice thickness data based on radar waves. Background Technology

[0002] Of the approximately 117 million lakes worldwide, more than half are covered by seasonal or perennial lake ice. Lake ice is sensitive to climate change and serves as an indicator of the climate system; its observational data can reflect climate change. The Qinghai-Tibet Plateau has numerous lakes, accounting for 50% of my country's total lake area, making the study of lake ice's impact on climate change significant. However, pumped-storage power station reservoirs in high-altitude and cold regions experience frequent water level adjustments, leading to ice layers that break up, float, and vary in thickness, putting pressure on panel dams and other structures. Furthermore, due to the rapid changes in reservoir ice, engineers cannot reach the reservoir surface for measurements, rendering traditional manual measurements ineffective. Currently, there is a lack of research both domestically and internationally on measuring the thickness of such dynamically breaking ice masses.

[0003] With the development of detection equipment and technologies such as drones, airborne radar has gained increasing attention and application in ice measurement work due to its advantages such as non-contact operation, wide measurement range, and strong environmental adaptability. However, current research on the processing of radar wave data in ice measurement work is still insufficient, making it difficult to effectively cope with multi-source interference and signal distortion problems in complex ice environments. This includes not only interference from surface undulations, internal bubbles, and ice layering in conventional lake ice layers, but also scenarios involving dynamically broken ice bodies such as pumped storage reservoirs. Furthermore, the vibration noise and electromagnetic interference from drone platforms place higher demands on the processing and calculation of radar wave data.

[0004] Currently, the main methods for measuring ice thickness using radar waves involve simple processing using existing software or calculation formulas. For example: (1) Calculations are made by combining the two-way travel time of radar waves in the ice layer with the dielectric constant of the ice layer. This method calculates based on the time difference between the main peaks of the waveform, but it cannot address some measurement error points caused by factors such as ice layer undulations or bubbles. (2) Existing commercial third-party radar wave processing software is used for calculation, and ice thickness data can be obtained with one click. However, it still cannot effectively address the error effects under different measurement environments. For example, third-party software can effectively filter out surrounding noise interference, but it cannot effectively handle errors such as those caused by the ice layer structure at some measurement points. Therefore, the inaccuracy of existing radar wave measurements of river-lake-reservoir ice thickness due to data sparsity, multipath interference, false peaks, and sudden changes in ice thickness has become an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this application is to provide a method, apparatus and equipment for processing river, lake and reservoir ice thickness data based on radar waves, in order to solve the technical problems of inaccurate measurement of river-lake-reservoir ice thickness caused by data sparsity, multipath interference, false peaks and sudden changes in ice thickness when using existing radar waves.

[0006] To achieve the above objectives, this application proposes a radar wave-based method for processing river, lake, and reservoir ice thickness data. The method is applied to a radar wave ice-measuring device, which includes a vector network analyzer, a microcomputer, and a lithium battery. The radar wave ice-measuring device is fixed to the top of a drone, which is equipped with a Vivaldi antenna. The method includes: The drone is controlled to fly along the ice surface area of ​​the river-lake-reservoir to be measured, and to transmit radar waves at preset intervals and receive lake ice radar echo data. The lake ice radar echo data is acquired by the vector network analyzer, and the lake ice radar echo data is interpolated and decomposed to obtain reference radar wave data. The reference radar wave data is adaptively processed using dynamic thresholding and a sliding window to obtain the target radar wave data. Based on the time difference of the main reflection peaks of the air-ice interface and ice-water interface in the target radar wave data, the ice thickness at each measuring point is calculated in combination with the preset dielectric constant to obtain the initial ice thickness data. The initial ice thickness data is subjected to adjacent peak abrupt change detection and abrupt change point correction to obtain the target ice thickness data.

[0007] In one embodiment, the step of interpolating and decomposing the lake ice radar echo data to obtain reference radar wave data includes: Linear interpolation is performed on the lake ice radar echo data to obtain the augmented time-domain data. The linear interpolation formula is as follows: in, and These are respectively from the lake ice radar echo data and The time-domain signal amplitude at time t. For interpolation time points, ; The augmented time-domain data is subjected to wavelet decomposition to obtain reference radar wave data.

[0008] In one embodiment, the step of performing wavelet decomposition on the augmented time-domain data to obtain reference radar wave data includes: Based on a preset wavelet basis and a preset number of decomposition levels, the augmented time-domain data is decomposed using wavelet decomposition to obtain approximation coefficients and detail coefficients, as shown in the following formulas: in, This refers to the approximation coefficient. This refers to the detail coefficients. It refers to a low-pass filter. It refers to a high-pass filter. This refers to the preset number of decomposition layers. , This refers to the expanded time-domain data; The fitness threshold is determined based on the mean and standard deviation of the detail coefficients and the preset threshold coefficient, using the following formula: in, For the first The mean of the layer detail coefficients, For the first Standard deviation of layer detail factor The preset threshold coefficient; The detail coefficients are thresholded based on the adaptive threshold to obtain the processed detail coefficients, as shown in the following formula: in, For the processed first Layer detail factor, For the first The adaptive threshold of a layer; Based on the approximation coefficients and the processed detail coefficients, the expanded time-domain data is reconstructed to obtain reference radar wave data.

[0009] In one embodiment, the step of adaptively processing the reference radar wave data using a dynamic threshold and a sliding window to obtain the target radar wave data includes: The mean of the approximation coefficients is taken as the amplitude of the main peak, as shown in the following formula: in, The amplitude of the main peak, For the first The number of layer approximation coefficients; According to the preset sliding window length, the detail coefficients are traversed through the sliding window, and the local noise level quantization value is calculated based on the mean and standard deviation of the detail coefficients in each window; Based on the main peak amplitude, the quantized value of the local noise level, the preset main peak weight, and the preset noise weight, the dynamic detection threshold of the main peak is calculated using the following formula: in, The threshold value for dynamic detection of the main peak is... This represents the quantized value of the local noise level. and Let be the mean and standard deviation of the detail coefficients within the sliding window. The preset threshold coefficient, The preset main peak weight; The preset noise weight; The target radar wave data is obtained by removing signal points in the reference radar wave data that are below the main peak dynamic detection threshold.

[0010] In one embodiment, the formulas for calculating the mean and standard deviation of the detail coefficients are as follows: in, The preset sliding window length, and They are the first Layer Detail level within a sliding window The mean and standard deviation.

[0011] In one embodiment, the step of calculating the ice thickness at each measuring point based on the time difference of the main reflection peaks of the air-ice interface and the ice-water interface in the target radar wave data, combined with a preset dielectric constant, to obtain the initial ice thickness data includes: Waveform analysis was performed on the target radar wave data to identify the first main reflection peak corresponding to the air-ice interface and the second main reflection peak corresponding to the ice-water interface. The time difference between the first and second main reflection peaks is obtained by measuring the time interval on the time axis. The ice thickness at a single measuring point is calculated based on the vacuum light speed, the preset dielectric constant, and the main peak time difference, using the following formula: in, This is the ice thickness value. It is the speed of light in a vacuum. It is the time difference of the main peak. It is the preset dielectric constant; By traversing all measurement points along the flight path of the UAV, the ice thickness value at each measurement point is calculated to obtain the initial ice thickness data.

[0012] In one embodiment, the step of performing adjacent peak abrupt change detection and abrupt change point correction on the initial ice thickness data to obtain the target ice thickness data includes: Arrange the initial ice thickness data in order of measurement point location to determine the target measurement point corresponding to each ice thickness value; Calculate the normal ice thickness range based on the initial ice thickness data; When the ice thickness value of the target measuring point is not within the normal ice thickness range, the target measuring point is regarded as a sudden change point; Find the first and second normal measuring points corresponding to the two nearest normal ice thickness values ​​before and after the mutation point; Based on the ice thickness values ​​and locations of the first and second normal measuring points, the corrected ice thickness value of the abrupt change point is calculated; The target ice thickness data is obtained by replacing the ice thickness value at the mutation point in the initial ice thickness data with the corrected ice thickness value.

[0013] In one embodiment, the step of calculating the normal ice thickness range based on the initial ice thickness data includes: Extract the ice thickness values ​​of several preset neighboring points before and after each target measuring point to obtain the target measuring point neighborhood data; Calculate the mean and standard deviation of the neighborhood data of the target measurement point to obtain the neighborhood mean and neighborhood standard deviation; The normal ice thickness range is calculated based on the neighborhood mean, the neighborhood standard deviation, and the preset anomaly determination coefficient.

[0014] Furthermore, to achieve the above objectives, this application also proposes a radar wave-based river, lake, and reservoir ice thickness data processing device, the device comprising: The data acquisition module is used to control the UAV to fly along the ice surface area of ​​the river-lake-reservoir to be measured, and to transmit radar waves at preset intervals and receive lake ice radar echo data. The preprocessing module is used to acquire the lake ice radar echo data through a vector network analyzer, and to perform interpolation and wavelet decomposition on the lake ice radar echo data to obtain reference radar wave data. The false peak suppression module is used to adaptively process the reference radar wave data using dynamic thresholds and sliding windows to obtain the target radar wave data. The ice thickness calculation module is used to calculate the ice thickness at each measuring point based on the time difference of the main reflection peaks of the air-ice interface and ice-water interface in the target radar wave data, combined with a preset dielectric constant, to obtain the initial ice thickness data. The mutation correction module is used to detect adjacent peak mutations and correct mutation points in the initial ice thickness data to obtain the target ice thickness data.

[0015] In addition, to achieve the above objectives, this application also proposes a radar wave-based river, lake, and reservoir ice thickness data processing device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the radar wave-based river, lake, and reservoir ice thickness data processing method described above.

[0016] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the radar wave-based river, lake, and reservoir ice thickness data processing method described above.

[0017] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the radar wave-based river, lake, and reservoir ice thickness data processing method described above.

[0018] One or more technical solutions proposed in this application have at least the following technical effects: The system controls a drone to fly along the ice surface of the river-lake-reservoir under test at preset intervals, transmitting radar waves and receiving high-density lake ice radar echo data in real time. This allows for rapid large-scale raw sampling without landing on the ice. After acquiring the data using a vector network analyzer, interpolation processing is performed in a microcomputer to encrypt the time-domain sampling. Wavelet decomposition is then performed to filter out high-frequency noise and multipath clutter, resulting in reference radar wave data with high time resolution and clear weak peaks. Dynamic thresholding and sliding windows are used to adaptively determine the reference radar wave data, automatically adjusting the detection sensitivity based on local statistics. The method retains only the true main peaks at the air-ice and ice-water interfaces, thus eliminating false peaks in the target radar wave data. Based on the time difference between the main peaks of the two interfaces in the target radar wave data, and combined with the preset dielectric constant, the ice thickness is calculated point by point to generate initial ice thickness data corresponding to the flight track, achieving centimeter-level automatic thickness measurement. Adjacent peak abrupt change detection and abrupt change point correction are performed on the initial ice thickness data in spatial order to obtain continuous and reasonable final target ice thickness data. This solves the problem of inaccurate measurement caused by data sparsity, multipath interference, false peaks, and ice thickness abrupt changes when measuring ice thickness in rivers, lakes, and reservoirs using existing radar waves. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating an embodiment of the radar wave-based river, lake, and reservoir ice thickness data processing method of this application. Figure 2This is a flowchart illustrating Embodiment 2 of the radar wave-based river, lake, and reservoir ice thickness data processing method of this application; Figure 3 A schematic diagram illustrating the measurement and calculation optimization effect of the radar wave-based river, lake, and reservoir ice thickness data processing method provided in Embodiment 2 of this application; Figure 4 This is a schematic diagram of the module structure of the radar wave-based river, lake, and reservoir ice thickness data processing device according to an embodiment of this application; Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the radar wave-based river, lake, and reservoir ice thickness data processing method in the embodiments of this application.

[0022] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0024] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0025] It should be noted that the executing entity of this application embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer or personal computer, or an electronic device or radar wave ice measuring device capable of the above functions. The following description uses a radar wave ice measuring device as an example to illustrate this embodiment and the subsequent embodiments.

[0026] Based on this, embodiments of this application provide a method for processing river, lake, and reservoir ice thickness data based on radar waves, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the radar wave-based river, lake, and reservoir ice thickness data processing method of this application.

[0027] In this embodiment, the radar wave-based river, lake, and reservoir ice thickness data processing method is applied to a radar wave ice measurement device. The radar wave ice measurement device includes a vector network analyzer, a microcomputer, and a lithium battery. The radar wave ice measurement device is fixed to the top of a drone, which is equipped with a Vivaldi antenna. The method includes steps S10 to S50: Step S10: Control the UAV to fly along the ice surface area of ​​the river-lake-reservoir to be tested, transmit radar waves at preset intervals and receive lake ice radar echo data.

[0028] It should be noted that the Vivaldi antenna is used to generate electromagnetic wave radiation facing the ice-covered ground; the vector network analyzer is used to realize real-time time-domain observation of the antenna and collect 10 time-domain data points per second; the microcomputer is used to run the professional software accompanying the vector network analyzer and run the radar wave data acquisition program based on Python; the UAV carries the Vivaldi antenna on its belly and fixes the radar wave ice measuring equipment in the equipment box on the top of the UAV.

[0029] The preset spacing refers to the horizontal interval between two adjacent radar wave trigger points on the survey line direction (e.g., 8-10 cm) to ensure that the spatial sampling density is sufficient to capture centimeter-level ice thickness changes. Lake ice radar echo data refers to the time-domain amplitude sequence recorded by the receiving antenna after the radar waves are reflected at various dielectric interfaces of the air-ice-water body. Its waveform main peak carries information about the ice thickness and internal structure.

[0030] Understandably, the radar wave ice measurement equipment controls the drone to fly close to the ice surface at a stable speed, and the navigation trajectory completely covers the river-lake-reservoir area to be measured. At each preset interval point, it automatically triggers the ultra-wideband radar to transmit pulses. The electromagnetic waves penetrate the ice layer downwards and are reflected at each dielectric interface. The airborne receiving antenna collects the returned radar echo signals in real time and continuously records the time domain waveform data until the measurement line ends.

[0031] Step S20: Obtain the lake ice radar echo data through the vector network analyzer, and perform interpolation and wavelet decomposition processing on the lake ice radar echo data to obtain reference radar wave data.

[0032] It should be noted that the reference radar wave data refers to the lake ice radar echo sequence after interpolation expansion and wavelet decomposition denoising. Its time resolution is improved and high-frequency interference is suppressed, which can more clearly present the true reflection characteristics of the air-ice-water interface and is used for subsequent peak detection and ice thickness calculation.

[0033] Understandably, the radar wave ice measurement equipment uses its own vector network analyzer to collect lake ice radar echo data received by the Vivaldi antenna in real time during the UAV's flight. The data is then transmitted to its own microcomputer, which first performs interpolation processing on the lake ice radar echo data to increase the time series density, and then performs wavelet decomposition processing to separate and suppress high-frequency noise and multipath interference. Finally, it outputs enhanced and purified reference radar wave data.

[0034] Step S30: Adaptive processing of the reference radar wave data is performed using dynamic threshold and sliding window to obtain target radar wave data.

[0035] It should be noted that the target radar wave data refers to the reference radar wave data segment retained after joint discrimination by dynamic threshold and sliding window. Only the main peak signal that is determined to be the reflection of the real air-ice and ice-water interface is retained. False peaks and residual noise have been removed and can be directly used to calculate ice thickness.

[0036] Understandably, after obtaining the reference radar wave data, the radar wave ice measuring equipment immediately updates the dynamic threshold in real time within each sliding window based on the local mean and standard deviation, suppressing all peaks with amplitudes below the threshold and retaining only the main peak segment determined to be the true interface reflection, outputting pure target radar wave data for subsequent thickness calculation.

[0037] Step S40: Based on the time difference of the main reflection peaks of the air-ice interface and ice-water interface in the target radar wave data, and combined with the preset dielectric constant, calculate the ice thickness at each measuring point to obtain the initial ice thickness data.

[0038] It should be noted that the time difference between the main reflection peaks refers to the arrival time interval between the main reflection peaks at the air-ice interface and the ice-water interface in the target radar wave data. The preset dielectric constant refers to a fixed relative dielectric constant (e.g., 3.5) pre-defined for the lake ice material, used to convert electromagnetic wave travel time into geometric thickness. The initial ice thickness data refers to the set of ice thicknesses at each measuring point, calculated point-by-point using the time difference between the main reflection peaks and the preset dielectric constant, without any spatial correction.

[0039] As an example, the step of calculating the ice thickness at each measuring point based on the time difference of the main reflection peaks at the air-ice interface and ice-water interface in the target radar wave data, combined with a preset dielectric constant, to obtain the initial ice thickness data includes: performing waveform analysis on the target radar wave data to identify the first main reflection peak corresponding to the air-ice interface and the second main reflection peak corresponding to the ice-water interface; measuring the time interval between the first and second main reflection peaks on the time axis to obtain the main peak time difference; and calculating the ice thickness value at a single measuring point based on the vacuum light speed, the preset dielectric constant, and the main peak time difference, using the following formula: in, This is the ice thickness value. It is the speed of light in a vacuum. It is the time difference of the main peak. The preset dielectric constant is used; all measuring points along the flight path of the UAV are traversed, and the ice thickness value at each measuring point is calculated to obtain the initial ice thickness data.

[0040] The first reflection peak refers to the strong amplitude peak in the target radar wave data caused by a dielectric abrupt change when the air enters the ice surface. A strong amplitude peak refers to a local maximum region in the waveform where the amplitude is significantly higher than the surrounding background, corresponding to the strong reflection energy generated by the electromagnetic wave at the interface of the dielectric constant abrupt change. The second reflection peak refers to the strong amplitude peak in the target radar wave data caused by another dielectric abrupt change when the ice layer enters the water body from the bottom. The peak time difference refers to the arrival interval between the first and second reflection peaks on the time axis. A measurement point refers to an independent measurement location corresponding to each preset interval when the UAV triggers radar waves along its flight path. The ice thickness value refers to the geometric thickness of the ice layer at that measurement point, calculated using the peak time difference, the speed of light in vacuum, and a preset dielectric constant.

[0041] First, the radar wave ice measurement equipment uses its own microcomputer to perform peak retrieval on each channel of the target radar wave data. Using the sliding window gradient method, it identifies the position with the highest amplitude and steepest slope change at the initial time period as the first main reflection peak of the air-ice interface. Then, within the time window corresponding to the estimated ice thickness, it continues to search for the second highest peak with the same polarity as the second main reflection peak of the ice-water interface, ensuring that both peaks originate from the real interface and are not spurious peaks. Second, it accurately extracts the time indices corresponding to the apex of the two peaks, calculates their difference to obtain the main peak time difference, and multiplies this difference by the speed of light in vacuum and divides it by twice the square root of the preset dielectric constant to convert the ice thickness value at that measurement point. Finally, the above identification and calculation process is repeated cyclically along the UAV's flight path, writing the ice thickness values ​​of all measurement points into an array in flight path order to form complete initial ice thickness data, providing a foundation for subsequent spatial continuity correction.

[0042] Step S50: Perform adjacent peak abrupt change detection and abrupt change point correction on the initial ice thickness data to obtain the target ice thickness data.

[0043] It should be noted that the target ice thickness data refers to the final ice thickness result obtained after detecting abrupt changes in adjacent peaks and correcting abrupt change points from the initial ice thickness data. It eliminates local abnormal jumps caused by noise, false peaks, or calculation errors, and maintains the continuity and physical rationality of the ice layer along the spatial direction. It can be directly used for ice condition analysis, engineering safety assessment, and numerical simulation input.

[0044] As an example, the step of performing adjacent peak abrupt change detection and abrupt change point correction on the initial ice thickness data to obtain target ice thickness data includes: arranging the initial ice thickness data in order of measurement point location to determine the target measurement point corresponding to each ice thickness value; calculating the normal ice thickness range based on the initial ice thickness data; when the ice thickness value of the target measurement point is not within the normal ice thickness range, the target measurement point is regarded as an abrupt change point; finding the first normal measurement point and the second normal measurement point corresponding to the two nearest normal ice thickness values ​​before and after the abrupt change point; calculating the corrected ice thickness value of the abrupt change point based on the ice thickness values ​​and positions of the first normal measurement point and the second normal measurement point; replacing the ice thickness value of the abrupt change point in the initial ice thickness data with the corrected ice thickness value to obtain the target ice thickness data.

[0045] The target measurement point refers to the location and ice thickness value of a single measurement point currently undergoing abrupt change testing. The normal ice thickness range refers to the allowable fluctuation range of ice thickness calculated using neighborhood statistics, used to determine whether the target measurement point is abnormal. A mutation point is a measurement point whose ice thickness value exceeds the normal ice thickness range and is therefore marked as abnormal. The two closest normal ice thickness values ​​before and after the mutation point refer to the two valid ice thickness values ​​adjacent to the mutation point along the flight path that are not marked as abnormal. The first normal measurement point is the location of the measurement point corresponding to the nearest normal ice thickness value in front of the mutation point. The second normal measurement point is the location of the measurement point corresponding to the nearest normal ice thickness value behind the mutation point. The corrected ice thickness value is the new ice thickness value assigned to the mutation point after linear interpolation using the ice thickness values ​​and locations of the first and second normal measurement points.

[0046] First, the initial ice thickness data is arranged in order of measurement point location, ensuring a one-to-one correspondence between each ice thickness value and a corresponding target measurement point. Then, a predetermined number of neighboring points are selected around each target measurement point to calculate the mean and standard deviation. The mean ± a predetermined multiple of the standard deviation is used as the normal ice thickness range. If the ice thickness value of a target measurement point exceeds this range, it is identified as an abrupt change point. Next, the path is searched forward and backward to find the two closest normal ice thickness values ​​that are not marked as anomalous. Their positions are recorded as the first and second normal measurement points, respectively. Then, linear interpolation is performed using the ice thickness values ​​and mileage of the two normal measurement points as a benchmark to calculate the corrected ice thickness value at the abrupt change point. Finally, the corrected ice thickness value replaces the original value of the abrupt change point in the initial ice thickness data. After traversing all measurement points, continuous, non-abrupt target ice thickness data is obtained. The anomalous point correction strategy combines neighboring point data to ensure that the corrected ice thickness conforms to local continuity. Specifically, a linear interpolation correction method is used, as follows: in, This refers to the target measurement point. It is a point Calculate the ice thickness value. It is a guide Corrected ice thickness value, , The two points are the two nearest normal value points (the first normal measurement point and the second normal measurement point) near the target point. The abnormal points are corrected by the linear relationship between the normal points before and after, and the processed ice thickness data is obtained.

[0047] As an example, the step of calculating the normal ice thickness range based on the initial ice thickness data includes: extracting the ice thickness values ​​of several preset neighboring points before and after each target measuring point to obtain the target measuring point's neighboring data; calculating the mean and standard deviation of the target measuring point's neighboring data to obtain the neighboring mean and standard deviation; and calculating the normal ice thickness range based on the neighboring mean, the neighboring standard deviation, and a preset anomaly determination coefficient.

[0048] The preset neighboring points refer to the number of measuring points symmetrically selected before and after each target measuring point for statistical calculations (e.g., 5-10). The target measuring point's neighborhood data refers to the set of ice thickness values ​​from all measuring points within the preset neighboring points. The neighborhood mean is the arithmetic mean of all ice thickness values ​​in the target measuring point's neighborhood data. The neighborhood standard deviation is a measure of the dispersion of all ice thickness values ​​in the target measuring point's neighborhood data relative to the neighborhood mean. The preset anomaly determination coefficient is a multiplier parameter used to multiply by the neighborhood standard deviation to determine the width of the normal ice thickness range (e.g., 1.5-2.5).

[0049] First, taking the target measuring point as the center, collect ice thickness values ​​from several preset neighboring measuring points both forward and backward, summarizing them into neighborhood data for the target measuring point. Then, calculate the arithmetic mean of this data segment to obtain the neighborhood mean, and calculate the deviation of each value from the neighborhood mean to obtain the neighborhood standard deviation. Finally, subtract the product of the preset anomaly judgment coefficient and the neighborhood standard deviation from the neighborhood mean to obtain the lower limit, and add the same product to obtain the upper limit, thus defining the normal ice thickness range, used to determine whether the target measuring point is abnormal. If the difference between the ice thickness of the target point and the neighborhood mean exceeds... If the value exceeds one standard deviation (neighborhood standard deviation), it is considered an outlier. This refers to the preset anomaly judgment coefficient.

[0050] This embodiment provides a radar wave-based method for processing river, lake, and reservoir ice thickness data. It controls a drone to fly along the ice surface of the river-lake-reservoir area to be measured at preset intervals, transmitting radar waves and receiving high-density lake ice radar echo data in real time. This allows for rapid large-scale raw sampling without ice access. After acquiring the data using a vector network analyzer, interpolation processing is performed in a microcomputer to encrypt the time-domain sampling. Wavelet decomposition is then performed to filter out high-frequency noise and multipath clutter, resulting in reference radar wave data with high time resolution and clear weak peaks. Dynamic thresholding and a sliding window are used to adaptively determine the reference radar wave data based on... The system automatically adjusts the detection sensitivity of local statistics, retaining only the true main peaks at the air-ice and ice-water interfaces, thus obtaining target radar wave data free of false peaks. Based on the time difference between the main peaks of reflection from the two interfaces in the target radar wave data, and combined with the preset dielectric constant, the ice thickness is calculated point by point to generate initial ice thickness data corresponding to the flight track, achieving centimeter-level automatic thickness measurement. The initial ice thickness data is then processed in spatial order to detect abrupt changes in adjacent peaks and correct abrupt change points, resulting in continuous and reasonable final target ice thickness data. This solves the problem of inaccurate measurement caused by data sparsity, multipath interference, false peaks, and abrupt changes in ice thickness when measuring ice thickness in rivers, lakes, and reservoirs using existing radar waves.

[0051] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the radar wave-based river, lake, and reservoir ice thickness data processing method of this application. Step S20 of the radar wave-based river, lake, and reservoir ice thickness data processing method includes steps S21 to S22: Step S21: Perform linear interpolation on the lake ice radar echo data to obtain the augmented time-domain data. The linear interpolation formula is as follows: in, and These are respectively from the lake ice radar echo data and The time-domain signal amplitude at time t. For interpolation time points, .

[0052] It should be noted that the expanded time-domain data refers to time-domain waveform data with higher time sampling density and finer intervals between adjacent points obtained by linear interpolation of lake ice radar echo data, which is used to more accurately identify the location of reflection peaks.

[0053] Step S22: Perform wavelet decomposition on the expanded time-domain data to obtain reference radar wave data.

[0054] As an example, the step of performing wavelet decomposition on the augmented time-domain data to obtain reference radar wave data includes: performing wavelet decomposition on the augmented time-domain data according to a preset wavelet basis and a preset number of decomposition levels to obtain approximation coefficients and detail coefficients, as shown in the following formula: in, This refers to the approximation coefficient. This refers to the detail coefficients. It refers to a low-pass filter. It refers to a high-pass filter. This refers to the preset number of decomposition layers. , This refers to the expanded time-domain data; the adaptive threshold is determined based on the mean and standard deviation of the detail coefficients and the preset threshold coefficient, using the following formula: in, For the first The mean of the layer detail coefficients, For the first Standard deviation of layer detail factor The preset threshold coefficient is used; the detail coefficient is then subjected to threshold processing based on the adaptive threshold to obtain the processed detail coefficient, as shown in the following formula: in, For the processed first Layer detail factor, For the first The adaptive threshold of the layer; based on the approximation coefficient and the processed detail coefficient, the expanded time-domain data is reconstructed to obtain reference radar wave data.

[0055] The preset wavelet basis refers to the pre-selected wavelet function type, used to determine the filter characteristics and waveform matching capability of wavelet decomposition. The preset decomposition level refers to the pre-set wavelet decomposition level, used to control the number of frequency bands and time resolution into which the signal is divided. The approximation coefficients are the coefficient sequence that retains low-frequency information after wavelet decomposition, representing the main contours of the signal. The detail coefficients are the coefficient sequence that retains high-frequency information after wavelet decomposition, containing noise and subtle features. The preset threshold coefficients are pre-given multipliers used to adjust the threshold strength to control the degree of noise reduction. The adaptive threshold is a threshold dynamically calculated based on the mean and standard deviation of the detail coefficients, used to quantize the noise reduction threshold.

[0056] The specific process of signal reconstruction is as follows: the approximation coefficients and the processed detail coefficients are fed into the wavelet reconstruction filter in the order of decomposition. First, the deepest layer coefficients are upsampled and convolved with low-pass and high-pass filters. Then, the result is merged with the detail coefficients of the previous layer and the upsampling and convolution process is repeated until the preset number of reverse operations of the decomposition layer is completed. The output is a reconstruction sequence with the same length as the expanded time domain data, which is the reference radar wave data that retains the true interface reflection for noise reduction.

[0057] As an example, the step of adaptively processing the reference radar wave data using dynamic thresholding and a sliding window to obtain the target radar wave data includes: taking the mean of the approximation coefficients as the main peak amplitude, as shown in the following formula: in, The amplitude of the main peak, For the first The number of layer approximation coefficients; based on the preset sliding window length, the detail coefficients are traversed through a sliding window, and the local noise level quantization value is calculated based on the mean and standard deviation of the detail coefficients within each window; based on the main peak amplitude, the local noise level quantization value, the preset main peak weight, and the preset noise weight, the main peak dynamic detection threshold is calculated, as follows: in, The threshold value for dynamic detection of the main peak is... This represents the quantized value of the local noise level. and The mean and standard deviation of the detail coefficients within the sliding window. The preset threshold coefficient, The preset main peak weight; The preset noise weight is used to remove signal points in the reference radar wave data that are below the main peak dynamic detection threshold, thereby obtaining the target radar wave data.

[0058] The main peak amplitude refers to the signal intensity representative value of the main peak reflected from the air-ice interface, using the mean of the approximate coefficients. The preset sliding window length refers to the pre-set number of sample points (e.g., 7-15 points) used for sliding calculations across continuous detail coefficients. The local noise level quantization value is the upper limit of noise at the current location, obtained by adding a preset multiple of the standard deviation to the mean of the detail coefficients within the sliding window. The preset main peak weight is a pre-given weighting factor for the main peak amplitude in threshold calculation (e.g., 0.3-0.7). The preset noise weight is a pre-given weighting factor for the local noise level quantization value in threshold calculation (e.g., 0.5-0.8). The main peak dynamic detection threshold is an adaptive threshold obtained by linearly combining the main peak amplitude and the local noise level quantization value according to the preset main peak weight and preset noise weight, used to determine whether to retain or discard signal points.

[0059] and The weights of the main peak amplitude and local noise in the threshold calculation are controlled separately, and their values ​​need to be dynamically adjusted according to the ice scene. The settings should be based on the ice thickness at the current measuring point. If it is in a shallow ice area, the radar wave path is short, the received signal is strong, and the main peak amplitude is large. A smaller value should be chosen to avoid a threshold that is too high and leads to missed detection of weak main peaks; if located in a deep ice area, the radar wave path is long and the loss is large, resulting in a weak received signal and a small main peak amplitude. A larger value should be selected to ensure that the threshold is high enough to suppress noise.

[0060] Similarly, the noise weight should be set with reference to the flatness or undulation of the ice surface at the current measuring point. If it is in a flat ice surface area, the local noise is relatively small. A smaller value should be chosen so that the threshold is dominated by the main peak, avoiding the missed detection of weak main peaks; if it is in a rough ice surface area, the local noise is relatively large, then A larger value should be selected to raise the threshold of the noise component and suppress interference.

[0061] Specifically as follows: This ensures the "adaptability" of the main peak threshold under different scenarios, preserving the main peak information while suppressing noise interference, which is a key support for high-precision ice thickness measurement.

[0062] As an example, the formulas for calculating the mean and standard deviation of the detail coefficients are as follows: in, The preset sliding window length, and They are the first Layer Detail level within a sliding window The mean and standard deviation.

[0063] This embodiment first performs linear interpolation on the lake ice radar echo data, inserting new amplitudes at equal intervals between adjacent sampling points to double the time-domain sequence density and obtain expanded time-domain data, thereby shortening the time step to the sub-nanosecond level and avoiding peak misalignment caused by sparse sampling. Subsequently, the expanded sequence is decomposed according to a preset wavelet basis and a preset number of decomposition levels to obtain reference radar wave data, which retains the true interface reflection and has a clean background, providing a high-resolution, high signal-to-noise ratio waveform basis for subsequent main peak detection.

[0064] For example, to help understand the implementation process of the radar wave-based river, lake, and reservoir ice thickness data processing method obtained in this embodiment combined with the above-described embodiment one, please refer to... Figure 3 , Figure 3 A schematic diagram illustrating the measurement and calculation optimization effect of a radar wave-based method for processing river, lake, and reservoir ice thickness data is provided. Specifically: The top left image is a schematic diagram of a drone, showing the drone carrying radar equipment ready to perform a measurement task; the middle left image is a schematic diagram of a drone carrying radar flying in the field, showing the scene of the drone flying over the ice surface and collecting radar data; the bottom left image is a schematic diagram of the drilling points in the field, showing the points where drilling measurements are performed on the ice surface to verify radar data; the top right image is a schematic diagram of the radar waveform at the field measurement points, showing the radar wave waveform at specific measurement points for analyzing the ice structure; the middle right image is a schematic diagram of the radar wave along the field measurement path, presenting the radar waveform data collected along the measurement path. These data reflect the continuous changes in the ice layer. The two continuous curves with the highest brightness in the image represent the upper and lower surfaces of the ice layer. The initial ice thickness data is obtained by calculating the distance between each point between the two curves; the bottom right image is a comparison diagram of the ice thickness calculation results after optimization of the field measurement path, showing the ice thickness data after data processing and calculation method optimization. Compared with the initial data, the optimized data is smoother and better reflects the true thickness changes of the ice layer. This application effectively reduces calculation errors and corrects some measurement points with large errors. These illustrations collectively verify the effectiveness and accuracy of the radar wave-based river, lake, and reservoir ice thickness data processing method proposed in this application.

[0065] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the radar wave-based river, lake and reservoir ice thickness data processing method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0066] This application also provides a radar wave-based device for processing river, lake, and reservoir ice thickness data; please refer to [reference needed]. Figure 4The radar wave-based river, lake, and reservoir ice thickness data processing device includes: Data acquisition module 10 is used to control the UAV to fly along the ice surface area of ​​the river-lake-reservoir to be measured, transmit radar waves at preset intervals and receive lake ice radar echo data; The preprocessing module 20 is used to acquire the lake ice radar echo data through a vector network analyzer, and to perform interpolation and wavelet decomposition processing on the lake ice radar echo data to obtain reference radar wave data. The false peak suppression module 30 is used to adaptively process the reference radar wave data using a dynamic threshold and a sliding window to obtain the target radar wave data. Ice thickness calculation module 40 is used to calculate the ice thickness at each measuring point based on the time difference of the main reflection peaks of the air-ice interface and ice-water interface in the target radar wave data, combined with a preset dielectric constant, to obtain the initial ice thickness data. The mutation correction module 50 is used to perform adjacent peak mutation detection and mutation point correction on the initial ice thickness data to obtain the target ice thickness data.

[0067] The radar-wave-based river, lake, and reservoir ice thickness data processing device provided in this application, employing the radar-wave-based river, lake, and reservoir ice thickness data processing method described in the above embodiments, can solve the technical problems of inaccurate measurement caused by data sparsity, multipath interference, false peaks, and abrupt changes in ice thickness when measuring river-lake-reservoir ice thickness using existing radar waves. Compared with the prior art, the beneficial effects of the radar-wave-based river, lake, and reservoir ice thickness data processing device provided in this application are the same as those of the radar-wave-based river, lake, and reservoir ice thickness data processing method provided in the above embodiments, and other technical features in the radar-wave-based river, lake, and reservoir ice thickness data processing device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0068] This application provides a radar wave-based river, lake, and reservoir ice thickness data processing device. The radar wave-based river, lake, and reservoir ice thickness data processing device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the radar wave-based river, lake, and reservoir ice thickness data processing method in the first embodiment described above.

[0069] The following is for reference. Figure 5The diagram illustrates a structural schematic of a radar-wave-based river, lake, and reservoir ice thickness data processing device suitable for implementing embodiments of this application. The radar-wave-based river, lake, and reservoir ice thickness data processing device in the embodiments of this application may include, but is not limited to, mobile terminals such as laptops, PDAs (Personal Digital Assistants), PADs (Portable Application Description), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as desktop computers. Figure 5 The radar wave-based ice thickness data processing device for rivers, lakes, and reservoirs shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0070] like Figure 5 As shown, the radar wave-based river, lake, and reservoir ice thickness data processing device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in ROM (Read Only Memory) 1002 or the program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the radar wave-based river, lake, and reservoir ice thickness data processing device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, LCDs (Liquid Crystal Displays), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the radar wave-based river, lake, and reservoir ice thickness data processing equipment to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a radar wave-based river, lake, and reservoir ice thickness data processing equipment with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0071] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0072] The radar-wave-based river, lake, and reservoir ice thickness data processing device provided in this application, employing the radar-wave-based river, lake, and reservoir ice thickness data processing method described in the above embodiments, can solve the technical problems of inaccurate measurement caused by data sparsity, multipath interference, false peaks, and abrupt changes in ice thickness when measuring river-lake-reservoir ice thickness using existing radar waves. Compared with the prior art, the beneficial effects of the radar-wave-based river, lake, and reservoir ice thickness data processing device provided in this application are the same as those of the radar-wave-based river, lake, and reservoir ice thickness data processing method provided in the above embodiments, and other technical features in this radar-wave-based river, lake, and reservoir ice thickness data processing device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0073] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0074] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0075] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the radar wave-based river, lake, and reservoir ice thickness data processing method in the above embodiments.

[0076] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0077] The aforementioned computer-readable storage medium may be included in a radar wave-based river, lake, and reservoir ice thickness data processing device; or it may exist independently and not be assembled into a radar wave-based river, lake, and reservoir ice thickness data processing device.

[0078] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the radar-wave-based river, lake, and reservoir ice thickness data processing equipment, the equipment performs the following actions: controls a drone to fly along the ice surface area of ​​the river-lake-reservoir to be measured, transmits radar waves at preset intervals, and receives lake ice radar echo data; acquires the lake ice radar echo data through a vector network analyzer, and performs interpolation and wavelet decomposition processing on the lake ice radar echo data to obtain reference radar wave data; adaptively processes the reference radar wave data using dynamic thresholds and sliding windows to obtain target radar wave data; calculates the ice thickness at each measuring point based on the time difference of the main reflection peaks at the air-ice interface and ice-water interface in the target radar wave data, combined with a preset dielectric constant, to obtain initial ice thickness data; and performs adjacent peak abrupt change detection and abrupt change point correction on the initial ice thickness data to obtain target ice thickness data.

[0079] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0080] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0081] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0082] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described radar wave-based river, lake, and reservoir ice thickness data processing method. This solves the technical problem of inaccurate measurements of river-lake-reservoir ice thickness caused by data sparsity, multipath interference, spurious peaks, and abrupt changes in ice thickness when using radar waves. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the radar wave-based river, lake, and reservoir ice thickness data processing method provided in the above embodiments, and will not be elaborated upon here.

[0083] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the radar wave-based river, lake, and reservoir ice thickness data processing method described above.

[0084] The computer program product provided in this application can solve the technical problems of inaccurate measurement of river-lake-reservoir ice thickness caused by data sparsity, multipath interference, spurious peaks, and abrupt changes in ice thickness when using existing radar waves. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the radar wave-based river-lake-reservoir ice thickness data processing method provided in the above embodiments, and will not be repeated here.

[0085] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for processing river, lake, and reservoir ice thickness data based on radar waves, characterized in that, The method is applied to a radar wave icing measurement device, which includes a vector network analyzer, a microcomputer, and a lithium battery. The radar wave icing measurement device is fixed to the top of a drone, which is equipped with a Vivaldi antenna. The method includes: The drone is controlled to fly along the ice surface area of ​​the river-lake-reservoir to be measured, and to transmit radar waves at preset intervals and receive lake ice radar echo data. The lake ice radar echo data is acquired by the vector network analyzer, and the lake ice radar echo data is interpolated and decomposed to obtain reference radar wave data. The reference radar wave data is adaptively processed using dynamic thresholding and a sliding window to obtain the target radar wave data. Based on the time difference of the main reflection peaks of the air-ice interface and ice-water interface in the target radar wave data, the ice thickness at each measuring point is calculated in combination with the preset dielectric constant to obtain the initial ice thickness data. The initial ice thickness data is subjected to adjacent peak abrupt change detection and abrupt change point correction to obtain the target ice thickness data.

2. The method as described in claim 1, characterized in that, The steps of interpolating and decomposing the lake ice radar echo data to obtain reference radar wave data include: Linear interpolation is performed on the lake ice radar echo data to obtain the augmented time-domain data. The linear interpolation formula is as follows: in, and These are respectively from the lake ice radar echo data and The time-domain signal amplitude at time t. For interpolation time points, ; The augmented time-domain data is subjected to wavelet decomposition to obtain reference radar wave data.

3. The method as described in claim 2, characterized in that, The step of performing wavelet decomposition on the augmented time-domain data to obtain reference radar wave data includes: Based on a preset wavelet basis and a preset number of decomposition levels, the augmented time-domain data is decomposed using wavelet decomposition to obtain approximation coefficients and detail coefficients, as shown in the following formulas: in, This refers to the approximation coefficient. This refers to the detail coefficients. It refers to a low-pass filter. It refers to a high-pass filter. This refers to the preset number of decomposition layers. , This refers to the expanded time-domain data; The fitness threshold is determined based on the mean and standard deviation of the detail coefficients and the preset threshold coefficient, using the following formula: in, For the first The mean of the layer detail coefficients, For the first Standard deviation of layer detail factor The preset threshold coefficient; The detail coefficients are thresholded based on the adaptive threshold to obtain the processed detail coefficients, as shown in the following formula: in, For the processed first Layer detail factor, For the first The adaptive threshold of a layer; Based on the approximation coefficients and the processed detail coefficients, the expanded time-domain data is reconstructed to obtain reference radar wave data.

4. The method as described in claim 3, characterized in that, The step of adaptively processing the reference radar wave data using dynamic thresholds and sliding windows to obtain the target radar wave data includes: The mean of the approximation coefficients is taken as the amplitude of the main peak, as shown in the following formula: in, The amplitude of the main peak, For the first The number of layer approximation coefficients; According to the preset sliding window length, the detail coefficients are traversed through the sliding window, and the local noise level quantization value is calculated based on the mean and standard deviation of the detail coefficients in each window; Based on the main peak amplitude, the quantized value of the local noise level, the preset main peak weight, and the preset noise weight, the dynamic detection threshold of the main peak is calculated using the following formula: in, The threshold value for dynamic detection of the main peak is... This represents the quantized value of the local noise level. and The mean and standard deviation of the detail coefficients within the sliding window. The preset threshold coefficient, The preset main peak weight; The preset noise weight; The target radar wave data is obtained by removing signal points in the reference radar wave data that are below the main peak dynamic detection threshold.

5. The method as described in claim 4, characterized in that, The formulas for calculating the mean and standard deviation of the detail coefficients are as follows: in, The preset sliding window length, and They are the first Layer Detail level within a sliding window The mean and standard deviation.

6. The method as described in claim 1, characterized in that, The step of calculating the ice thickness at each measuring point based on the time difference of the main reflection peaks of the air-ice interface and ice-water interface in the target radar wave data, combined with a preset dielectric constant, to obtain the initial ice thickness data includes: Waveform analysis was performed on the target radar wave data to identify the first main reflection peak corresponding to the air-ice interface and the second main reflection peak corresponding to the ice-water interface. The time difference between the first and second main reflection peaks is obtained by measuring the time interval on the time axis. The ice thickness at a single measuring point is calculated based on the vacuum light speed, the preset dielectric constant, and the main peak time difference, using the following formula: in, This is the ice thickness value. It is the speed of light in a vacuum. It is the time difference of the main peak. It is the preset dielectric constant; By traversing all measurement points along the flight path of the UAV, the ice thickness value at each measurement point is calculated to obtain the initial ice thickness data.

7. The method as described in claim 1, characterized in that, The step of performing adjacent peak abrupt detection and abrupt point correction on the initial ice thickness data to obtain the target ice thickness data includes: Arrange the initial ice thickness data in order of measurement point location to determine the target measurement point corresponding to each ice thickness value; Calculate the normal ice thickness range based on the initial ice thickness data; When the ice thickness value of the target measuring point is not within the normal ice thickness range, the target measuring point is regarded as a sudden change point; Find the first and second normal measuring points corresponding to the two nearest normal ice thickness values ​​before and after the mutation point; Based on the ice thickness values ​​and locations of the first and second normal measuring points, the corrected ice thickness value of the abrupt change point is calculated; The target ice thickness data is obtained by replacing the ice thickness value at the mutation point in the initial ice thickness data with the corrected ice thickness value.

8. The method as described in claim 7, characterized in that, The step of calculating the normal ice thickness range based on the initial ice thickness data includes: Extract the ice thickness values ​​of several preset neighboring points before and after each target measuring point to obtain the target measuring point neighborhood data; Calculate the mean and standard deviation of the neighborhood data of the target measurement point to obtain the neighborhood mean and neighborhood standard deviation; The normal ice thickness range is calculated based on the neighborhood mean, the neighborhood standard deviation, and the preset anomaly determination coefficient.

9. A radar wave-based data processing device for river, lake, and reservoir ice thickness, characterized in that, The device includes: The data acquisition module is used to control the UAV to fly along the ice surface area of ​​the river-lake-reservoir to be measured, and to transmit radar waves at preset intervals and receive lake ice radar echo data. The preprocessing module is used to acquire the lake ice radar echo data through a vector network analyzer, and to perform interpolation and wavelet decomposition on the lake ice radar echo data to obtain reference radar wave data. The false peak suppression module is used to adaptively process the reference radar wave data using dynamic thresholds and sliding windows to obtain the target radar wave data. The ice thickness calculation module is used to calculate the ice thickness at each measuring point based on the time difference of the main reflection peaks of the air-ice interface and ice-water interface in the target radar wave data, combined with a preset dielectric constant, to obtain the initial ice thickness data. The mutation correction module is used to detect adjacent peak mutations and correct mutation points in the initial ice thickness data to obtain the target ice thickness data.

10. A radar wave-based data processing device for river, lake, and reservoir ice thickness, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the radar wave-based river, lake, and reservoir ice thickness data processing method as described in any one of claims 1 to 8.

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