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

Abstract

The application discloses a kind of river lake reservoir ice thickness data processing method, device and equipment based on radar wave, it is related to ice condition prediction technical field, the method comprises: control unmanned aerial vehicle flies along the ice surface area of river-lake-reservoir to be measured, launches radar wave and receives lake ice radar echo data according to preset interval;Lake ice radar echo data is obtained by vector network analyzer, and interpolation processing and wavelet decomposition processing are carried out, to obtain reference radar wave data;Adaptive processing is carried out to reference radar wave data using dynamic threshold and sliding window, to obtain target radar wave data;Based on the reflection main peak time difference of air-ice interface, ice-water interface in target radar wave data, calculate the ice thickness of each measuring point in combination with preset dielectric constant, and carry out adjacent peak mutation detection and mutation point correction, to obtain target ice thickness data.The application solves the problem that existing radar wave measurement river-lake-reservoir ice thickness is not accurate due to sparse data, multipath interference, false peak and ice thickness mutation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ice condition prediction, in particular to a lake and reservoir ice thickness data processing method and device based on radar waves and equipment. BACKGROUND

[0002] Among the approximately 117 million lakes in the world, more than half have seasonal or perennial lake ice cover, and lake ice is sensitive to climate change and is an indicator of the climate system. Its observation data can reflect climate change. The Qinghai-Tibet Plateau has numerous lakes, accounting for 50% of the total area of lakes in China. Therefore, studying lake ice is of great significance to climate change. However, the pumped storage power station reservoir in the alpine and high-altitude region is frequently adjusted in water level, which leads to broken and floating ice layer, uneven thickness and repeated growth and decline, and brings certain pressure to the face dam. At the same time, due to the rapid change of reservoir ice, engineering personnel cannot measure on the reservoir surface, and traditional manual measurement is invalid. At present, there is a research gap in the measurement of ice thickness data of such dynamic broken ice bodies at home and abroad.

[0003] With the development of detection equipment and unmanned aerial vehicles, airborne radar has been paid more and more attention and applied in ice measurement work due to its non-contact, wide measurement range and strong environmental adaptability. However, the current processing of radar wave data in ice measurement work still lacks certain research, and it is difficult to effectively deal with the multi-source interference and signal distortion problem in the complex ice layer environment - not only including the surface undulation, internal bubbles and ice stratification of the conventional lake ice layer, but also involving the scene of dynamic broken ice body of pumped storage reservoir, superimposing the vibration noise and electromagnetic interference of unmanned aerial vehicle platform, which puts forward higher requirements for the processing and calculation of radar wave data.

[0004] At present, the method of radar wave measurement of ice thickness mainly uses existing software or calculation formula for simple processing, such as: (1) combining the radar wave travel time double pass in the ice layer and the dielectric constant in the ice layer for calculation. This kind of method calculates according to the time difference between the main peak waves of the waveform, but cannot deal with some measurement error points caused by ice layer undulation or bubbles and other factors. (2) Using existing commercial third-party radar wave processing software for calculation, one-key acquisition of ice thickness data, but still cannot effectively deal with the error influence in different measurement environments, such as the third-party software can effectively filter out the surrounding noise interference, but the error of some measurement points caused by ice layer structure cannot be effectively processed. Therefore, the existing radar wave measurement of river-lake-reservoir ice thickness is not accurate due to data sparseness, multipath interference, false peaks and ice thickness mutation, which becomes a problem to be solved. SUMMARY

[0005] The present application aims to provide a lake and reservoir ice thickness data processing method and device based on radar waves and equipment, which aims to solve the technical problem of inaccurate measurement of existing radar wave measurement of river-lake-reservoir ice thickness due to data sparseness, multipath interference, false peaks and ice thickness mutation.

[0006] To achieve the above object, the application provides a lake and reservoir ice thickness data processing method based on radar waves. The method is applied to a radar wave ice measuring device, which comprises a vector network analyzer, a microcomputer and a lithium battery. The radar wave ice measuring device is fixed on the top of a UAV, and the UAV is equipped with a Vivaldi antenna. The method comprises the following steps:

[0007] Controlling the UAV to fly along the ice surface area of the river, lake and reservoir to be measured, emitting radar waves at a predetermined interval and receiving lake ice radar echo data;

[0008] Obtaining the lake ice radar echo data by the vector network analyzer, and performing interpolation processing and wavelet decomposition processing on the lake ice radar echo data to obtain reference radar wave data;

[0009] Performing adaptive processing on the reference radar wave data by using a dynamic threshold and a sliding window to obtain target radar wave data;

[0010] Calculating the ice thickness of each measuring point based on the reflection 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 to obtain initial ice thickness data;

[0011] Performing adjacent peak mutation detection and mutation point correction on the initial ice thickness data to obtain target ice thickness data.

[0012] In an embodiment, the step of performing interpolation processing and wavelet decomposition processing on the lake ice radar echo data to obtain reference radar wave data comprises:

[0013] Performing linear interpolation on the lake ice radar echo data to obtain expanded time domain data, and the linear interpolation formula is as follows:

[0014]

[0015] wherein, and are the time domain signal amplitudes of the lake ice radar echo data at times and , is an interpolation time point, ;

[0016] Performing wavelet decomposition processing on the expanded time domain data to obtain reference radar wave data.

[0017] In an embodiment, the step of performing wavelet decomposition on the expanded time domain data to obtain reference radar wave data comprises:

[0018] 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:

[0019]

[0020]

[0021] 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;

[0022] 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:

[0023]

[0024] in, For the first The mean of the layer detail coefficients, For the first Standard deviation of layer detail factor The preset threshold coefficient;

[0025] The detail coefficients are thresholded based on the adaptive threshold to obtain the processed detail coefficients, as shown in the following formula:

[0026]

[0027] in, For the processed first Layer detail factor, For the first The adaptive threshold of a layer;

[0028] Based on the approximation coefficients and the processed detail coefficients, the expanded time-domain data is reconstructed to obtain reference radar wave data.

[0029] 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:

[0030] The mean of the approximation coefficients is taken as the amplitude of the main peak, as shown in the following formula:

[0031]

[0032] wherein, is the main peak amplitude, is the first layer approximation coefficient,

[0033] According to the preset sliding window length, the detail coefficients are traversed in a sliding window, and a local noise level quantization value is calculated according to the mean and standard deviation of the detail coefficients in each window;

[0034] According to the main peak amplitude, the local noise level quantization value, a preset main peak weight, and a preset noise weight, a main peak dynamic detection threshold is calculated, and the formula is as follows:

[0035]

[0036] wherein, is the main peak dynamic detection threshold, represents the local noise level quantization value, and is the mean and standard deviation of the detail coefficients in the sliding window, is the preset threshold coefficient, is the preset main peak weight; is the preset noise weight;

[0037] The signal points in the reference radar wave data that are lower than the main peak dynamic detection threshold are removed to obtain target radar wave data.

[0038] In an embodiment, the formula for calculating the mean and standard deviation of the detail coefficients is:

[0039]

[0040]

[0041] wherein, is the preset sliding window length, and are the mean and standard deviation of the detail coefficients in the i-th sliding window of the j-th layer, respectively.

[0042] In an embodiment, the step of calculating the ice thickness of each measurement point based on the time difference of the reflection main peaks of the air-ice interface and the ice-water interface in the target radar wave data, combined with the preset dielectric constant, to obtain the initial ice thickness data includes:

[0043] Performing waveform analysis on the target radar wave data, identifying a first reflection main peak corresponding to the air-ice interface and a second reflection main peak corresponding to the ice-water interface; ​​​

[0044] measuring interval of the first reflection main peak and the second reflection main peak on time axis to obtain main peak time difference;

[0045] calculating ice thickness value of single measuring point according to vacuum light speed, preset dielectric constant and the main peak time difference, formula as follows:

[0046]

[0047] wherein, is the ice thickness value, is vacuum light speed, is the main peak time difference, is the preset dielectric constant;

[0048] traversing all measuring points on flight path of the unmanned aerial vehicle, calculating the ice thickness value of each measuring point to obtain initial ice thickness data.

[0049] In an embodiment, the step of performing adjacent peak mutation detection and mutation point correction on the initial ice thickness data to obtain target ice thickness data comprises:

[0050] arranging the initial ice thickness data in order of measuring point position to determine target measuring point corresponding to each ice thickness value;

[0051] calculating normal ice thickness range according to the initial ice thickness data;

[0052] when ice thickness value of the target measuring point is not within the normal ice thickness range, regarding the target measuring point as a mutation point;

[0053] finding first normal measuring point and second normal measuring point corresponding to two nearest normal ice thickness values before and after the mutation point;

[0054] calculating correction ice thickness value of the mutation point based on ice thickness values and positions of the first normal measuring point and the second normal measuring point;

[0055] replacing ice thickness value of the mutation point in the initial ice thickness data with the correction ice thickness value to obtain target ice thickness data.

[0056] In an embodiment, the step of calculating normal ice thickness range according to the initial ice thickness data comprises:

[0057] extracting ice thickness values of each preset number of neighborhood points before and after the target measuring point to obtain target measuring point neighborhood data;

[0058] calculating mean value and standard deviation of the target measuring point neighborhood data to obtain neighborhood mean value and neighborhood standard deviation;

[0059] Calculate a normal ice thickness range according to the neighborhood mean, the neighborhood standard deviation, and a preset abnormality determination coefficient.

[0060] In addition, to achieve the above object, the application further provides a lake and reservoir ice thickness data processing device based on radar waves.

[0061] The data acquisition module is configured to control the unmanned aerial vehicle to fly along the ice surface area of the lake to be measured, emit radar waves at a preset interval, and receive lake ice radar echo data.

[0062] The preprocessing module is configured to acquire the lake ice radar echo data by using a vector net analyzer, and perform interpolation processing and wavelet decomposition processing on the lake ice radar echo data to obtain reference radar data.

[0063] The false peak suppression module is configured to perform adaptive processing on the reference radar data by using a dynamic threshold and a sliding window to obtain target radar data.

[0064] The ice thickness calculation module is configured to calculate the ice thickness of each measuring point based on the time difference of the reflection main peaks of the air-ice interface and the ice-water interface in the target radar data, and in combination with a preset dielectric constant to obtain initial ice thickness data.

[0065] The mutation correction module is configured to perform adjacent peak mutation detection and mutation point correction on the initial ice thickness data to obtain target ice thickness data.

[0066] In addition, to achieve the above object, the application further provides a lake and reservoir ice thickness data processing device based on radar waves, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the lake and reservoir ice thickness data processing method based on radar waves as described above.

[0067] In addition, to achieve the above object, the application further provides a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the lake and reservoir ice thickness data processing method based on radar waves as described above.

[0068] In addition, to achieve the above object, the application further provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the steps of the lake and reservoir ice thickness data processing method based on radar waves as described above.

[0069] The one or more technical solutions provided by the application have at least the following technical effects:

[0070] The unmanned aerial vehicle flies along the ice surface area of the river-lake-reservoir to be measured at a preset interval and emits radar waves, and high-density lake ice radar echo data are received in real time, so that original sampling in a large range can be quickly completed without ice climbing; after the data are obtained by the vector network analyzer, interpolation processing is performed in the microcomputer to encrypt the time domain sampling, wavelet decomposition processing is performed to filter high-frequency noise and multi-path clutter, reference radar wave data with high time resolution and clear weak peaks are obtained; the dynamic threshold and the sliding window are used for adaptive threshold discrimination of the reference radar wave data, the detection sensitivity is automatically adjusted according to the local statistical quantity, only the real main peaks of the air-ice and ice-water interfaces are reserved, and target radar wave data with false peaks removed are obtained; based on the time difference between the reflection main peaks of the two interfaces in the target radar wave data, the ice thickness is calculated point by point combined with the preset dielectric constant, the initial ice thickness data corresponding to the track are generated, and the centimeter-level automatic thickness measurement is realized; the initial ice thickness data are sequentially subjected to adjacent peak mutation detection and mutation point correction, and the continuous and reasonable final target ice thickness data are obtained, so that the problems of inaccurate measurement caused by sparse data, multi-path interference, false peaks and ice thickness mutation in the existing radar wave measurement of river-lake-reservoir ice thickness are solved. BRIEF DESCRIPTION OF DRAWINGS

[0071] The drawings incorporated into the specification and constituting a part of the specification show embodiments consistent with the present application and, together with the specification, serve to explain the principles of the present application.

[0072] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0073] Figure 1 A flowchart provided by the first embodiment of the river-lake-reservoir ice thickness data processing method based on radar waves of the present application;

[0074] Figure 2 A flowchart provided by the second embodiment of the river-lake-reservoir ice thickness data processing method based on radar waves of the present application;

[0075] Figure 3 A measurement and calculation optimization effect diagram of the river-lake-reservoir ice thickness data processing method based on radar waves of the second embodiment of the present application;

[0076] Figure 4 A module structure diagram of the river-lake-reservoir ice thickness data processing device based on radar waves of the present application;

[0077] Figure 5 A device structure diagram of the hardware running environment involved in the river-lake-reservoir ice thickness data processing method based on radar waves in the embodiment of the present application.

[0078] The purposes, functional features and advantages of the present application will be further illustrated in conjunction with the embodiments, with reference to the accompanying drawings. DETAILED DESCRIPTION

[0079] It should be understood that the specific embodiments described herein are merely intended to explain the technical solutions of the present application, and are not intended to limit the present application.

[0080] In order to better understand the technical solutions of the present application, the following will be described in detail in conjunction with the drawings and specific embodiments.

[0081] It should be noted that the execution subject of the embodiments of the present application can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, etc., or an electronic device capable of realizing the above functions, a radar wave ice measuring device, etc. The following will take the radar wave ice measuring device as an example to describe the embodiments and the following embodiments.

[0082] Based on this, the present application provides a river-lake-pond ice thickness data processing method based on radar waves, which is described in detail with reference to Figure 1 , Figure 1 is a flowchart of the first embodiment of the river-lake-pond ice thickness data processing method based on radar waves of the present application.

[0083] In the present embodiment, the river-lake-pond ice thickness data processing method based on radar waves 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 UAV, the UAV is equipped with a Vivaldi antenna, and the method includes steps S10-S50:

[0084] Step S10, control the UAV to fly along the ice surface area of the river-lake-pond to be measured, emit radar waves at a predetermined interval and receive lake ice radar echo data.

[0085] It should be noted that the Vivaldi antenna is used to generate electromagnetic wave radiation facing the ice layer 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 of 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 device in the equipment box on the top of the UAV.

[0086] The preset interval refers to the horizontal interval (for example, 8-10 cm) between adjacent radar wave trigger points in the direction of the measuring line of the unmanned aerial vehicle, so as to ensure that the spatial sampling density is sufficient to capture centimeter-level ice thickness changes. The lake ice radar echo data refer to the time-domain amplitude sequence recorded by the receiving antenna after the radar wave is reflected at the air-ice-ice-underwater interface. The main peak of the waveform carries the ice layer thickness and internal structure information.

[0087] It can be understood that the radar wave ice measuring device controls the unmanned aerial vehicle to fly stably at a speed close to the ice surface, and the navigation trajectory completely covers the river-lake-reservoir area to be measured. The ultra-wideband radar pulse is automatically triggered at each preset interval point. The electromagnetic wave penetrates the ice layer downward and is reflected at each dielectric interface. The airborne receiving antenna collects the returned radar echo signal in real time, and records the time-domain waveform data continuously until the end of the measuring line.

[0088] In step S20, the lake ice radar echo data are obtained by the vector network analyzer, and the lake ice radar echo data are subjected to interpolation processing and wavelet decomposition processing to obtain reference radar wave data.

[0089] It should be noted that the reference radar wave data refer to the lake ice radar echo sequence after interpolation expansion and wavelet decomposition denoising. The time resolution is improved, the high-frequency interference is suppressed, and the real reflection characteristics of the air-ice-water interface can be more clearly presented, which is used for subsequent peak detection and ice thickness calculation.

[0090] It can be understood that the radar wave ice measuring device collects the lake ice radar echo data received by the Vivaldi antenna in real time during the flight of the unmanned aerial vehicle through the vector network analyzer of the device, and transmits the data to the microcomputer of the device. The microcomputer first performs interpolation processing on the lake ice radar echo data to increase the time sequence density, and then performs wavelet decomposition processing to separate and suppress high-frequency noise and multipath interference, and finally outputs the reference radar wave data after enhancement and purification.

[0091] In step S30, the reference radar wave data are subjected to adaptive processing by using a dynamic threshold and a sliding window to obtain target radar wave data.

[0092] It should be noted that the target radar wave data refer to the reference radar wave data segments retained after joint judgment by the dynamic threshold and the sliding window. Only the main peak signals judged as real air-ice and ice-water interface reflections are retained, and false peaks and residual noise are removed, which can be directly used for calculating the ice thickness.

[0093] It can be understood that the radar wave ice measuring device updates the dynamic threshold in real time according to the local mean value and the standard deviation in each sliding window immediately after obtaining the reference radar wave data, suppresses all peaks with an amplitude lower than the threshold, and only retains the main peak segment determined as a real interface reflection, and outputs pure target radar wave data for subsequent thickness calculation.

[0094] In step S40, the ice thickness of each measuring point is calculated based on the time difference of the reflection main peaks of the air-ice interface and the ice-water interface in the target radar wave data, and in combination with a preset dielectric constant, to obtain initial ice thickness data.

[0095] It should be noted that the time difference of the reflection main peaks refers to the time interval between the arrival of the reflection main peak of the air-ice interface and the reflection main peak of the ice-water interface in the target radar wave data. The preset dielectric constant refers to a fixed value (for example, 3.5) of the relative dielectric constant of the lake ice material, which is used to convert the electromagnetic wave travel time into geometric thickness. The initial ice thickness data refers to a set of ice layer thicknesses of each measuring point calculated by using the time difference of the reflection main peaks and the preset dielectric constant without any spatial correction.

[0096] As an example, the step of calculating the ice thickness of each measuring point based on the time difference of the reflection main peaks of the air-ice interface and the ice-water interface in the target radar wave data in combination with a preset dielectric constant to obtain initial ice thickness data includes: performing waveform analysis on the target radar wave data to identify a first reflection main peak corresponding to the air-ice interface and a second reflection main peak corresponding to the ice-water interface; measuring the interval of the first reflection main peak and the second reflection main peak on the time axis to obtain a main peak time difference; and calculating the ice thickness value of a single measuring point according to the vacuum light speed, the preset dielectric constant, and the main peak time difference, with the formula as follows:

[0097]

[0098] wherein, is the ice thickness value, is the vacuum light speed, is the main peak time difference, is the preset dielectric constant; and the ice thickness value of each measuring point is calculated to obtain the initial ice thickness data.

[0099] The first reflection main peak refers to a strong amplitude peak in the target radar wave data caused by a dielectric constant mutation when air enters the ice surface. The strong amplitude peak refers to a local maximum value region in the waveform, which is significantly higher than the surrounding background, corresponding to the strong reflection energy of electromagnetic waves at the dielectric constant mutation interface. The second reflection main peak refers to a strong amplitude peak in the target radar wave data caused by a dielectric constant mutation when the ice layer bottom enters the water body. The main peak time difference refers to the arrival interval of the first reflection main peak and the second reflection main peak on the time axis. The measuring point refers to an independent measuring position corresponding to the radar wave triggered by the unmanned aerial vehicle at each preset interval on the flight path. The ice thickness value refers to the geometric thickness of the ice layer at the measuring point calculated by the main peak time difference, the vacuum light speed and the preset dielectric constant.

[0100] Firstly, the radar wave ice measuring equipment performs peak value retrieval on the target radar wave data channel by channel through its own microcomputer, and uses the sliding window gradient method to lock the position with the highest amplitude and the most abrupt slope change at the starting period as the first reflection main peak of the air-ice interface. Then, the second reflection main peak of the ice-water interface is searched in the time window corresponding to the estimated ice thickness as the second reflection main peak of the ice-water interface, which ensures that both peaks come from the real interface rather than false peaks. Secondly, the time index corresponding to the two peak vertices is accurately extracted, the difference value is calculated to obtain the main peak time difference, and the difference value is multiplied by the vacuum light speed and then divided by twice the square root of the preset dielectric constant to complete the conversion of the ice thickness value at the measuring point. Finally, the above identification and calculation process is executed along the flight path of the unmanned aerial vehicle in a loop, and the ice thickness values of all measuring points are written into an array in the order of the flight path to form complete initial ice thickness data, which provides a basis for subsequent spatial continuity correction.

[0101] In step S50, adjacent peak mutation detection and mutation point correction are performed on the initial ice thickness data to obtain target ice thickness data.

[0102] It should be noted that the target ice thickness data refers to the final ice thickness result obtained after the adjacent peak mutation detection and mutation point correction of the initial ice thickness data, which eliminates local abnormal jumps caused by noise, false peaks or calculation errors, maintains the continuity and physical reasonableness of the ice layer along the spatial direction, and can be directly used for ice condition analysis, engineering safety evaluation and numerical simulation input.

[0103] As an example, the step of performing adjacent peak mutation detection and mutation point correction on the initial ice thickness data to obtain target ice thickness data includes: arranging the initial ice thickness data in order of measuring point position, determining a target measuring point corresponding to each ice thickness value; calculating a normal ice thickness range according to the initial ice thickness data; regarding the target measuring point as a mutation point when the ice thickness value of the target measuring point is not within the normal ice thickness range; finding a first normal measuring point and a second normal measuring point corresponding to the nearest two normal ice thickness values before and after the mutation point; calculating a corrected ice thickness value of the mutation point based on the ice thickness values and positions of the first normal measuring point and the second normal measuring point; and replacing the ice thickness value of the mutation point in the initial ice thickness data with the corrected ice thickness value to obtain target ice thickness data.

[0104] The target measuring point refers to a single measuring point position and its ice thickness value that is currently being subjected to mutation detection. The normal ice thickness range refers to an ice thickness fluctuation interval calculated in a neighborhood statistical manner, used to determine whether the target measuring point is abnormal. The mutation point refers to a measuring point whose ice thickness value exceeds the normal ice thickness range and is marked as abnormal. The nearest two normal ice thickness values before and after the mutation point refer to the two valid ice thickness values that are closest to the mutation point and are not marked as abnormal along the flight path direction. The first normal measuring point refers to the measuring point position corresponding to the nearest normal ice thickness value before the mutation point. The second normal measuring point refers to the measuring point position corresponding to the nearest normal ice thickness value after the mutation point. The corrected ice thickness value refers to a new ice thickness value assigned to the mutation point by linear interpolation using the ice thickness values and positions of the first normal measuring point and the second normal measuring point.

[0105] First, the initial ice thickness data is arranged in order of measuring point position, so that each ice thickness value corresponds to a target measuring point. Then, the mean and standard deviation are calculated by selecting a preset number of neighborhood points around each target measuring point. The mean ± a preset multiple of the standard deviation is used as the normal ice thickness range. If the ice thickness value of a target measuring point exceeds this range, it is determined to be a mutation point. Next, the nearest two normal ice thickness values that are closest to the mutation point and are not marked as abnormal are found along the path, and their positions are recorded as the first normal measuring point and the second normal measuring point. Then, the corrected ice thickness value at the mutation point is calculated by linear interpolation based on the ice thickness values and positions of the two normal measuring points. Finally, the original value of the mutation point in the initial ice thickness data is replaced with the corrected ice thickness value, and the target ice thickness data is obtained after traversing all measuring points. The correction strategy for abnormal points combines neighborhood point data to ensure that the corrected ice thickness meets local continuity. Linear interpolation correction is used, as follows:

[0106]

[0107] wherein, is the target measuring point, is the calculated ice thickness value of point , 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.

[0108] 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.

[0109] 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).

[0110] 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 detection coefficient.

[0111] The embodiment provides a lake and reservoir ice thickness data processing method based on radar waves, controls a UAV to fly along a lake and reservoir ice surface area to be measured at a preset interval and emit radar waves, and receives high-density lake ice radar echo data in real time, so that original sampling in a large range can be quickly completed without climbing the ice; after data is acquired by a vector network analyzer, interpolation processing is performed in a microcomputer to encrypt time domain sampling, wavelet decomposition processing is performed to filter high-frequency noise and multi-path clutter, reference radar wave data with high time resolution and clear weak peaks are obtained; dynamic threshold and a sliding window are used to perform adaptive threshold discrimination on the reference radar wave data, detection sensitivity is automatically adjusted according to local statistics, only real main peaks of air-ice and ice-water interfaces are reserved, and target radar wave data with false peaks removed are obtained; based on a time difference between reflection main peaks of the two interfaces in the target radar wave data, ice thickness is calculated point by point in combination with a preset dielectric constant, initial ice thickness data corresponding to a flight path are generated, and centimeter-level automatic thickness measurement is realized; adjacent peak mutation detection and mutation point correction are performed on the initial ice thickness data in a spatial order, and continuous and reasonable final target ice thickness data are obtained, so that the problem that measurement is inaccurate due to sparse data, multi-path interference, false peaks and ice thickness mutation when lake and reservoir ice thickness is measured by using the radar wave is solved.

[0112] Based on the first embodiment of the application, the same or similar contents as the above embodiment one can be referred to the above introduction, and subsequent details will not be repeated. On this basis, please refer to Figure 2 , Figure 2 The flowchart of the second embodiment of the lake and reservoir ice thickness data processing method based on radar waves is shown in the figure, and the steps S20 of the lake and reservoir ice thickness data processing method based on radar waves include steps S21-S22.

[0113] Step S21, linear interpolation is performed on the lake ice radar echo data to obtain expanded time domain data, and the linear interpolation formula is as follows:

[0114]

[0115] Wherein, and are time domain signal amplitudes at t and t+1 moments in the lake ice radar echo data, is an interpolation time point, . It should be noted that the expanded time domain data refers to time domain waveform data with higher time sampling density and finer adjacent point interval obtained after linear interpolation is performed on the lake ice radar echo data, which is used for more accurate identification of reflection peak positions.

[0116] It should be noted that the expanded time domain data refers to time domain waveform data with higher time sampling density and finer adjacent point interval obtained after linear interpolation is performed on the lake ice radar echo data, which is used for more accurate identification of reflection peak positions.

[0117] ​Step S22: Perform wavelet decomposition on the expanded time-domain data to obtain reference radar wave data.

[0118] 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:

[0119]

[0120]

[0121] 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:

[0122]

[0123] 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:

[0124]

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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:

[0129]

[0130] 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:

[0131]

[0132] in, The threshold value for dynamic detection of the main peak is [value]. 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 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Specifically as follows:

[0137]

[0138] 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.

[0139] As an example, the formulas for calculating the mean and standard deviation of the detail coefficients are as follows:

[0140]

[0141]

[0142] in, The preset sliding window length, and They are the first Layer Detail level within a sliding window The mean and standard deviation.

[0143] 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.

[0144] 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:

[0145] 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.

[0146] It should be noted that the above examples are only used for understanding the present application and do not constitute a limitation on the ice thickness data processing method based on radar waves of rivers, lakes and reservoirs based on the technical concept. More forms of simple transformation are within the protection scope of the present application.

[0147] The present application also provides a radar wave-based river, lake and reservoir ice thickness data processing device, which is described in detail as follows. Figure 4 The radar wave-based river, lake and reservoir ice thickness data processing device comprises:

[0148] A data acquisition module 10 is configured to control a UAV to fly along an ice surface area of a river, lake or reservoir to be measured, emit radar waves at a preset interval, and receive lake ice radar echo data.

[0149] A preprocessing module 20 is configured to acquire the lake ice radar echo data through a vector net analyzer, and perform interpolation processing and wavelet decomposition processing on the lake ice radar echo data to obtain reference radar wave data.

[0150] A false peak suppression module 30 is configured to perform adaptive processing on the reference radar wave data by using a dynamic threshold and a sliding window to obtain target radar wave data.

[0151] An ice thickness calculation module 40 is configured to calculate the ice thickness of each measurement point based on the time difference of the reflection main peak of the air-ice interface and the ice-water interface in the target radar wave data, in combination with a preset dielectric constant, to obtain initial ice thickness data.

[0152] A mutation correction module 50 is configured to perform adjacent peak mutation detection and mutation point correction on the initial ice thickness data to obtain target ice thickness data.

[0153] The radar wave-based river, lake and reservoir ice thickness data processing device provided by the present application adopts the radar wave-based river, lake and reservoir ice thickness data processing method in the above embodiments, which can solve the technical problem of inaccurate measurement caused by sparse data, multipath interference, false peaks and ice thickness mutation when measuring the ice thickness of rivers, lakes and reservoirs by using radar waves. Compared with the prior art, the radar wave-based river, lake and reservoir ice thickness data processing device provided by the present application has the same beneficial effects as the radar wave-based river, lake and reservoir ice thickness data processing method provided by 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 the features disclosed in the above method embodiments, which will not be repeated here.

[0154] The application provides a river lake reservoir ice thickness data processing device based on radar waves, which comprises at least one processor and a memory in communication connection with the at least one processor; 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 river lake reservoir ice thickness data processing method based on radar waves in the above embodiment one.

[0155] Reference will be made to the following Figure 5 which shows a structural schematic diagram of the river lake reservoir ice thickness data processing device based on radar waves suitable for being used to implement the embodiments of the application. The river lake reservoir ice thickness data processing device based on radar waves in the embodiments of the application can include but is not limited to mobile terminals such as notebook computers, PDAs (Personal Digital Assistant, personal digital assistants), PADs (Portable Application Description, tablet computers), vehicle-mounted terminals (for example, vehicle-mounted navigation terminals) and the like, and fixed terminals such as desktop computers and the like. Figure 5 The river lake reservoir ice thickness data processing device based on radar waves shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the application.

[0156] As Figure 5As shown, the radar wave-based lake ice thickness data processing device can include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to programs stored in a ROM (Read Only Memory) 1002 or programs loaded from a storage device 1003 into a RAM (Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the radar wave-based lake ice thickness data processing device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. In general, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, an LCD (Liquid Crystal Display), a speaker, a vibrator, etc.; the storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the radar wave-based lake ice thickness data processing device to communicate wirelessly or by wire with other devices to exchange data. Although the radar wave-based lake ice thickness data processing device with various systems is shown in the figure, it should be understood that all the systems shown are not required to be implemented or possessed. More or fewer systems can be alternatively implemented or possessed.

[0157] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present disclosure 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 through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are performed.

[0158] The river-lake-reservoir ice thickness data processing device based on radar waves provided by the application adopts the river-lake-reservoir ice thickness data processing method based on radar waves in the above embodiment, and can solve the technical problem of inaccurate measurement caused by sparse data, multipath interference, false peaks and sudden changes in ice thickness when measuring the river-lake-reservoir ice thickness by using radar waves. Compared with the prior art, the beneficial effects of the river-lake-reservoir ice thickness data processing device based on radar waves provided by the application are the same as those of the river-lake-reservoir ice thickness data processing method based on radar waves provided by the above embodiment, and other technical features in the river-lake-reservoir ice thickness data processing device based on radar waves are the same as those disclosed in the previous embodiment method, which will not be repeated here.

[0159] It should be understood that various parts of the present application can be realized by 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 one or more embodiments or examples in a suitable manner.

[0160] The above is merely specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0161] The present application provides a computer readable storage medium having stored thereon computer readable program instructions (i.e. computer programs) for executing the river-lake-reservoir ice thickness data processing method based on radar waves in the above embodiment.

[0162] The computer readable storage medium provided in the application may be, for example, a U disk, but is not limited to an electric, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination of the above. More specific examples of the computer readable storage medium may include, but are not limited to, an electric connection with one or more conductive wires, a portable computer disk, a hard disk, a RAM (Random Access Memory), a ROM (Read Only Memory), an EPROM (Erasable Programmable Read Only Memory or flash memory), an optical fiber, a CD-ROM (CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the embodiment, the computer readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system, or device. The program code contained on the computer readable storage medium can be transmitted by any suitable medium, including but not limited to an electric wire, an optical cable, an RF (Radio Frequency), and the like, or any suitable combination of the above.

[0163] The computer readable storage medium described above may be contained in the lake and reservoir ice thickness data processing device based on radar waves; or may exist separately and not be assembled into the lake and reservoir ice thickness data processing device based on radar waves.

[0164] The computer readable storage medium described above carries one or more programs, when the one or more programs are executed by the lake and reservoir ice thickness data processing device based on radar waves, the lake and reservoir ice thickness data processing device based on radar waves: controls the unmanned aerial vehicle to fly along the ice surface area to be measured of the river-lake-reservoir, emits radar waves at a preset interval and receives lake ice radar echo data; obtains the lake ice radar echo data through a vector net analyzer, and performs interpolation processing and wavelet decomposition processing on the lake ice radar echo data to obtain reference radar data; performs adaptive processing on the reference radar data by using a dynamic threshold and a sliding window to obtain target radar data; calculates the ice thickness of each measurement point based on the time difference of the reflection main peak of the air-ice interface and the ice-water interface in the target radar data, in combination with a preset dielectric constant to obtain initial ice thickness data; performs adjacent peak mutation detection and mutation point correction on the initial ice thickness data to obtain target ice thickness data.

[0165] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0166] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0167] The modules involved in the embodiments of the present application can be implemented in software or hardware. In some cases, the names of the modules do not constitute a limitation on the modules themselves.

[0168] The readable storage medium provided by the application is a computer readable storage medium, and the computer readable storage medium stores computer readable program instructions (i.e., a computer program) for executing the above-mentioned river-lake-reservoir ice thickness data processing method based on radar waves, and can solve the technical problem of inaccurate measurement caused by sparse data, multipath interference, false peaks and ice thickness mutation when the existing radar wave measures the river-lake-reservoir ice thickness. Compared with the prior art, the computer readable storage medium provided by the application has the same beneficial effects as the river-lake-reservoir ice thickness data processing method based on radar waves provided by the above-mentioned embodiments, and will not be repeated here.

[0169] The application also provides a computer program product comprising a computer program, which, when executed by a processor, implements the steps of the river-lake-reservoir ice thickness data processing method based on radar waves as described above.

[0170] The computer program product provided by the application can solve the technical problem of inaccurate measurement caused by sparse data, multipath interference, false peaks and ice thickness mutation when the existing radar wave measures the river-lake-reservoir ice thickness. Compared with the prior art, the computer program product provided by the application has the same beneficial effects as the river-lake-reservoir ice thickness data processing method based on radar waves provided by the above-mentioned embodiments, and will not be repeated here.

[0171] The above-mentioned is only part of the embodiments of the application, and does not limit the patent scope of the application, and any equivalent structural transformation made by using the content of the application specification and drawings, or directly / indirectly applied to other related technical fields under the technical concept of the application is included in the patent protection scope of the application.

Claims

1. A radar wave-based river, lake and reservoir ice thickness data processing method, characterized in that, The method is applied to a radar wave ice measuring device, the radar wave ice measuring device comprising a vector net analyzer, a microcomputer and a lithium battery, the radar wave ice measuring device being fixed to the top of a UAV, the UAV being equipped with a Vivaldi antenna, the method comprising: controlling the UAV to fly along an ice surface area of a river-lake-reservoir to be measured, emitting radar waves at a preset interval and receiving lake ice radar echo data; acquiring the lake ice radar echo data by the vector net analyzer, and performing interpolation processing and wavelet decomposition processing on the lake ice radar echo data to obtain reference radar wave data; performing adaptive processing on the reference radar wave data by using a dynamic threshold and a sliding window to obtain target radar wave data; calculating the ice thickness of each measuring point based on the time difference of reflection peaks of the air-ice interface and the ice-water interface in the target radar wave data, in combination with a preset dielectric constant to obtain initial ice thickness data; arranging the initial ice thickness data in order of measuring point positions to determine a target measuring point corresponding to each ice thickness value; calculating a normal ice thickness range according to the initial ice thickness data; when the ice thickness value of the target measuring point is not within the normal ice thickness range, regarding the target measuring point as a mutation point; finding a first normal measuring point and a second normal measuring point corresponding to the nearest two normal ice thickness values before and after the mutation point; calculating a corrected ice thickness value of the mutation point based on the ice thickness values and positions of the first normal measuring point and the second normal measuring point; replacing the ice thickness value of the mutation point in the initial ice thickness data with the corrected ice thickness value to obtain target ice thickness data; the wavelet decomposition processing specifically comprises: performing wavelet decomposition on the lake ice radar echo data according to a preset wavelet base and a preset decomposition level to obtain approximation coefficients and detail coefficients, and the formula is as follows: wherein, denotes the approximation coefficients, denotes the detail coefficients, denotes a low-pass filter, denotes a high-pass filter, denotes the preset decomposition layer number, , denotes the lake ice radar echo data; determining an adaptive threshold value according to the mean and standard deviation of the detail coefficients and a preset threshold coefficient, and the formula is as follows: wherein, is the first average of the layer detail coefficients, is the first standard deviation of the layer detail coefficients, is the preset threshold coefficient; performing threshold processing on the detail coefficients according to the adaptive threshold value to obtain processed detail coefficients, and the formula is as follows: wherein, is the processed 1st layer detail coefficient, is the processed 1st layer adaptive threshold; performing signal reconstruction on the lake ice radar echo data according to the approximation coefficients and the processed detail coefficients to obtain reference radar wave data; the step of performing adaptive processing on the reference radar wave data by using a dynamic threshold and a sliding window to obtain target radar wave data comprises: taking the mean of the approximation coefficients as a peak amplitude, and the formula is as follows: wherein, is the main peak amplitude, is the first number of layer approximation coefficients; performing sliding window traversal on the detail coefficients according to a preset sliding window length, and calculating a local noise level quantization value according to the mean and standard deviation of the detail coefficients in each window; calculating a peak dynamic detection threshold value according to the peak amplitude, the local noise level quantization value, a preset peak weight and a preset noise weight, and the formula is as follows: wherein, is the main peak dynamic detection threshold, denotes the local noise level quantization value, and is the mean and standard deviation of the detail coefficients within the sliding window, is the preset threshold coefficient, is the preset main peak weight; is the preset noise weight; eliminating signal points in the reference radar wave data that are lower than the peak dynamic detection threshold value to obtain target radar wave data.

2. The method of claim 1, wherein, the step of performing interpolation processing and wavelet decomposition processing on the lake ice radar echo data to obtain reference radar wave data comprises: performing linear interpolation on the lake ice radar echo data to obtain expanded time domain data, and the linear interpolation formula is as follows: wherein, and are the time domain signal amplitudes at the time instances and of the lake ice radar echo data, is the interpolation time point, ; The wavelet decomposition processing is performed on the extended time domain data to obtain reference radar wave data.

3. The method of claim 1, wherein, The calculation formula of the mean and standard deviation of the detail coefficients is: wherein, is the preset sliding window length, and are the average value and the standard deviation of the detail coefficients in the first layer the first sliding window, respectively. ​ 4. The method of claim 1, wherein, The step of 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 and combining the preset dielectric constant to obtain the initial ice thickness data comprises: Performing waveform analysis on the target radar wave data to identify the first reflection main peak corresponding to the air-ice interface and the second reflection main peak corresponding to the ice-water interface; Measuring the interval of the first reflection main peak and the second reflection main peak on the time axis to obtain a main peak time difference; According to the vacuum light speed, the preset dielectric constant and the main peak time difference, the ice thickness value of a single measuring point is calculated, and the formula is as follows: wherein, is the ice thickness value, is the vacuum light speed, is the main peak time difference, is the preset dielectric constant; Traversing all the measuring points on the flight path of the unmanned aerial vehicle, the ice thickness value of each measuring point is calculated to obtain the initial ice thickness data.

5. The method of claim 1, wherein, The step of calculating the normal ice thickness range according to the initial ice thickness data comprises: Extracting the ice thickness values of a plurality of measuring points before and after each target measuring point to obtain target measuring point neighborhood data; Calculating the mean and standard deviation of the target measuring point neighborhood data to obtain a neighborhood mean and a neighborhood standard deviation; According to the neighborhood mean, the neighborhood standard deviation and a preset abnormality judgment coefficient, a normal ice thickness range is calculated.

6. A radar wave-based lake and river ice thickness data processing device, characterized by, The device comprises: A data acquisition module configured to control an unmanned aerial vehicle to fly along a river-lake-reservoir ice surface area to be measured, emit radar waves at a preset interval, and receive lake ice radar echo data; A preprocessing module configured to obtain the lake ice radar echo data through a vector network analyzer, perform interpolation processing and wavelet decomposition processing on the lake ice radar echo data, and obtain reference radar wave data; the wavelet decomposition processing specifically comprises: performing wavelet decomposition on the lake ice radar echo data according to a preset wavelet basis and a preset decomposition level to obtain approximation coefficients and detail coefficients, and the formula is as follows: wherein, denotes the approximation coefficients, denotes the detail coefficients, denotes a low-pass filter, denotes a high-pass filter, denotes the preset decomposition layer number, , denotes the lake ice radar echo data; an adaptive threshold is determined according to a mean and a standard deviation of the detail coefficients and a preset threshold coefficient, and a formula is as follows: wherein, is the first is the mean of the detail coefficients of the layer, is the first is the standard deviation of the detail coefficients of the layer, is the preset threshold coefficient; the detail coefficients are thresholded according to the adaptive threshold to obtain processed detail coefficients, and the formula is as follows: wherein, is the processed first layer detail coefficient, is the processed first layer adaptive threshold; and performing signal reconstruction on the lake ice radar echo data according to the approximation coefficient and the processed detail coefficient to obtain reference radar wave data. A false peak suppression module configured to perform adaptive processing on the reference radar wave data by using a dynamic threshold and a sliding window to obtain target radar wave data; the adaptive processing on the reference radar wave data by using the dynamic threshold and the sliding window to obtain the target radar wave data comprises: taking the mean of the approximation coefficients as a main peak amplitude, and the formula is as follows: wherein, is the main peak amplitude, is the first the number of layer approximation coefficients; according to a preset sliding window length, the detail coefficients are traversed by a sliding window, and a local noise level quantization value is calculated according to the mean and standard deviation of the detail coefficients in each window; according to the main peak amplitude, the local noise level quantization value, a preset main peak weight and a preset noise weight, a main peak dynamic detection threshold is calculated, and the formula is as follows: wherein, is the main peak dynamic detection threshold, represents the local noise level quantization value, and is the mean and standard deviation of the detail coefficients in the sliding window, is the preset threshold coefficient, is the preset main peak weight; is the preset noise weight; and the signal points in the reference radar wave data that are lower than the main peak dynamic detection threshold are removed to obtain target radar wave data. An ice thickness calculation module configured to calculate 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 and combining a preset dielectric constant to obtain initial ice thickness data; A mutation correction module configured to arrange the initial ice thickness data in order of measuring point positions, determine a target measuring point corresponding to each ice thickness value, calculate a normal ice thickness range according to the initial ice thickness data, regard the target measuring point as a mutation point when the ice thickness value of the target measuring point is not within the normal ice thickness range, find a first normal measuring point and a second normal measuring point corresponding to the nearest two normal ice thickness values before and after the mutation point, calculate a corrected ice thickness value of the mutation point based on the ice thickness values and positions of the first normal measuring point and the second normal measuring point, and replace the ice thickness value of the mutation point in the initial ice thickness data with the corrected ice thickness value to obtain target ice thickness data.

7. A radar wave-based river, lake, and reservoir ice thickness data processing device, characterized by, The device comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the radar wave-based lake and river reservoir ice thickness data processing method according to any one of claims 1 to 5.

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