A radar level meter and data correction method thereof

Abstract

The present application relates to the field of liquid level measurement technology, and specifically to a radar level meter and a data correction method thereof, the method comprising: obtaining liquid level data, temperature and pressure data corresponding to the radar level meter of the material at each moment in the canning process; determining the potential influence coefficient based on the data change trend and data discreteness of the temperature data and pressure data in the canning process, as well as the correlation between temperature and pressure; determining the disordered randomness of the liquid level subsequence by analyzing the change distribution of the liquid level data in the liquid level subsequence and the length of the liquid level subsequence, combined with the potential influence coefficient at each moment, and then obtaining the liquid level stability reliability according to the autocorrelation characteristics of the liquid level sequence; obtaining the filter window size based on the liquid level stability reliability, and using a filter algorithm to correct the liquid level data. The present application can correct the measured liquid level data and improve the detection accuracy of the radar level meter.

Description

Technical Field

[0001] The present application relates to the field of liquid level measurement technology, and in particular to a radar level meter and a data correction method thereof. Background Art

[0002] A radar level meter is a highly accurate, non-contact measurement device that determines the level of a medium by emitting and receiving microwave pulses. This instrument is widely used in complex industrial environments, such as those involving flammable, explosive, and highly corrosive media. Accurate level measurement is crucial for industrial process control, helping to ensure production efficiency, safety, and product quality while reducing resource waste and environmental risks.

[0003] Real-time monitoring of material levels is crucial in industrial production. Conventionally, radar level meters are used to directly measure material levels. However, due to fluctuations in temperature and pressure within the tank, as well as interference from the vaporization of the medium, the collected level data may contain a certain degree of random noise and complex variations, resulting in insufficient detection accuracy for radar level meters. To improve monitoring accuracy, appropriate correction measures are required to reduce the impact of these errors. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a radar level meter and a data correction method thereof. The technical solutions adopted are as follows:

[0005] The present invention provides a method for calibrating radar level meter data, including the following steps:

[0006] Obtain the liquid level data, temperature and pressure data corresponding to the material radar level meter at each moment in the filling process, and normalize them to form liquid level series, temperature series and pressure series respectively;

[0007] Based on the data change trend and data dispersion of temperature and pressure data during the filling process, as well as the correlation between the temperature series and the pressure series, the potential impact coefficient of the liquid level data at each moment is determined;

[0008] The liquid level sequence is divided into multiple liquid level subsequences according to the peak points in the liquid level sequence. The randomness of the liquid level subsequences is determined by analyzing the distribution of the liquid level data in the liquid level subsequences and the length of the liquid level subsequences, and combining the product of the liquid level data at each moment and its potential influence coefficient.

[0009] The liquid level stability reliability is obtained based on the average level of disorder randomness of all liquid level subsequences combined with the autocorrelation characteristics of the liquid level sequence;

[0010] The filter window size is obtained based on the liquid level stability reliability, and the liquid level data is corrected using a filtering algorithm.

[0011] Optionally, the determination of the potential influence coefficient of the liquid level data at each moment further includes:

[0012] According to the trend test statistics and the coefficient of variation of the temperature data at each moment in the local window of each moment in the temperature sequence, the trend dispersion index of the temperature data at each moment is obtained, and accordingly, the trend dispersion index of the pressure data at each moment is obtained;

[0013] Analyze the sum of the trend dispersion index of the temperature data at each moment and the trend dispersion index of the pressure data, and obtain the correlation coefficient between all temperature data and all pressure data corresponding to the local window at each moment; fuse the summation result with the correlation coefficient as the potential influence coefficient of the liquid level data at each moment.

[0014] Optionally, the trend dispersion index of the temperature data at each moment is the product of the trend test statistic of the temperature data at all moments in the local window at each moment and the coefficient of variation.

[0015] Optionally, the potential influence coefficient of the liquid level data at each moment is the product of the addition result and the correlation coefficient.

[0016] Optionally, determining the disorder randomness of each liquid level subsequence further includes:

[0017] The liquid level change disorder of each liquid level subsequence is determined based on the distribution of liquid level data in each liquid level subsequence and the length of each liquid level subsequence; the potential influence coefficient of the liquid level data at each moment in the liquid level subsequence is multiplied by the liquid level data to form a product sequence corresponding to the liquid level subsequence; and the disorder randomness of each liquid level subsequence is obtained by combining the liquid level change disorder of each liquid level subsequence and the fractal dimension of the product sequence.

[0018] Optionally, the liquid level change disorder of each liquid level subsequence is the ratio of the sum of all liquid level data in each liquid level subsequence to the length of each liquid level subsequence.

[0019] Optionally, the disorder randomness of each liquid level subsequence is the product of the disorder of the liquid level change and the fractal dimension.

[0020] Optionally, the calculation formula for the liquid level stability reliability is: Where, represents the reliability of liquid level stability, M is the absolute value of the autocorrelation coefficient of the liquid level sequence, and L is the mean of the disorder randomness of all liquid level subsequences.

[0021] Optionally, the calculation formula corresponding to the filter window size is: ; Where Q is the approximate window size, is the rounding function, It is an exponential function with a natural constant as the base, where the odd number closest to Q is used as the filtering window size.

[0022] An embodiment of the present application further provides a radar level meter, the system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0023] As can be seen from the above, the radar level meter and data correction method provided by this application have at least the following beneficial effects:

[0024] This application considers that during LNG feeding or storage, the fluctuations in temperature and pressure within the tank, as well as the interference of vaporization on the radar level meter, may cause a certain degree of random noise and complex changes in the collected liquid level data, resulting in the radar level meter's insufficient detection accuracy. Therefore, this application analyzes the trend characteristics and change consistency characteristics of the temperature and pressure data within the LNG storage tank to calculate the potential impact coefficient on the liquid level data. The beneficial effect is that it can enhance the ability to identify abnormal liquid level data;

[0025] The liquid level sequence is further divided. Based on the peak characteristics of the liquid level subsequences and the random variation characteristics of the characteristic subsequences, the chaotic randomness of the subsequences is calculated. Combining the chaotic randomness with the autocorrelation of the liquid level sequence, the liquid level stability reliability of the liquid level sequence is calculated. This method has the beneficial effect of reflecting the stationary characteristics of the liquid level data and the amount of random noise contained. The filter window size of the Savitzky-Golay algorithm is determined based on the liquid level stability reliability, which is used to correct the measured liquid level data. This improves the detection accuracy of the radar level meter. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0027] Figure 1 A flowchart of the steps of a radar level meter data correction method provided in this application;

[0028] Figure 2 A schematic diagram of the process provided for this application;

[0029] Figure 3 A flowchart for obtaining the potential impact coefficient of the liquid level data at each moment provided in this application;

[0030] Figure 4 This is a flow chart for obtaining the disordered randomness of each liquid level subsequence provided in this application. DETAILED DESCRIPTION

[0031] To further illustrate the technical means and effectiveness of this application's implementation of the intended invention, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a radar level meter and its data correction method, including its specific implementation, structure, features, and effectiveness. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0032] Unless otherwise specified and limited, terms such as "comprises", "includes" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device comprising a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the article or device comprising the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs.

[0033] The following describes in detail a radar level meter and a data correction method thereof provided by the present application with reference to the accompanying drawings.

[0034] See also Figure 1 , which shows a flowchart of a radar level meter data correction method provided by an embodiment of the present application, including the following steps:

[0035] Step 1: Obtain the liquid level data, temperature, and pressure data corresponding to the material radar level meter at each moment in the filling process, and normalize them to form a liquid level sequence, temperature sequence, and pressure sequence.

[0036] This embodiment uses the liquid level detection of liquefied natural gas (LNG) storage tanks as an example to calibrate the obtained liquid level data. LNG filling requires liquefying the gaseous natural gas at extremely low temperatures and storing it in dedicated LNG tanks. LNG tanks are typically equipped with temperature and pressure gauges to monitor the LNG storage environment in real time. This embodiment collects temperature and pressure data using the temperature and pressure gauges within the tanks. A radar level meter installed on the top of the tank collects LNG level data within the tank.

[0037] The schematic diagram of the core components of the radar level meter of this embodiment is as follows Figure 2 As shown, Figure 2 In the figure, number 1 is the shell component of the level meter, number 2 contains the measurement module, whose main function is to realize the core liquid level measurement. In this embodiment, a data correction module is added to the measurement module for the purpose of correcting the measured liquid level data. Number 3 is the waveguide tube, and number 4 is the radar antenna.

[0038] In this embodiment, the data collection interval is set to 0.1 seconds, and the collection duration is 1 minute. The collected temperature data, pressure data, and liquid level data are normalized using the Z-Score algorithm, and the resulting sequences in ascending time order are recorded as the temperature sequence, pressure sequence, and liquid level sequence, respectively. The Z-Score method is well known in the art, and the specific process will not be repeated here.

[0039] Step 2: Based on the data change trend and data dispersion of the temperature data and pressure data during the filling process, as well as the correlation between the temperature series and the pressure series, determine the potential impact coefficient of the liquid level data at each moment.

[0040] Liquid level detection is a critical step in the gas storage facility's production process, crucial for the stable and safe operation of the entire production system's interlocking control system. First, the data correction module processes and analyzes the collected data. Because liquefied natural gas (LNG) must be processed and filled at extremely low temperatures to maintain its liquid state, pressure fluctuations can affect its boiling point. The pressure and temperature of LNG storage tanks affect liquid level data primarily in the following two ways.

[0041] Temperature changes within LNG storage tanks affect the physical state of LNG, thereby affecting its density and volume. Rising temperatures cause LNG to evaporate, generating gas and causing the liquid level to drop. Conversely, falling temperatures increase the density of LNG, potentially causing the liquid level to rise. LNG storage tanks are stored in a sealed and insulated environment. Increased pressure within the tank makes it more difficult for LNG to maintain its liquid state, potentially causing it to evaporate into gas. This reduces the volume of the liquid and, consequently, the liquid level.

[0042] From the above analysis, it can be seen that changes in temperature and pressure in the tank may cause fluctuations in the position of the liquid level. Therefore, the degree of change in temperature and pressure is analyzed below to determine the interference effect of the changes in temperature and pressure on the liquid level. Since the changes in temperature and pressure of LNG tanks are significant and prone to large changes in a short period of time, in this embodiment, it is preferred to set the i-th moment as the center. The window is the local window at the i-th moment. Further, the variation characteristics of the temperature and pressure sequences within the local windows are obtained. It should be noted that for missing data in the local windows of the first 7 and last 7 data points in the sequence, that is, when the data within the local window is insufficient, this embodiment uses a mirror filling method to fill the missing data. In actual applications, implementers can also use a mean filling method, which is not specifically limited in this embodiment.

[0043] First, the Mann-Kendall trend test algorithm is used to obtain the trend change of the data in each local window in the temperature sequence. Taking the local window of the temperature sequence at the i-th moment as an example, the input of the algorithm is the temperature sequence corresponding to the local window at the i-th moment, and the output is the trend test statistic. The absolute value of the statistic reflects the significance of the upward or downward trend of the temperature sequence in the window. The coefficient of variation of the temperature sequence in the local window is calculated, and the size of the coefficient of variation reflects the degree of dispersion of the temperature sequence in the window. Then, the product of the absolute value of the statistic and the coefficient of variation in the local window at the i-th moment is calculated, and used as the trend dispersion index of the temperature data corresponding to the i-th moment, recorded as . Income The larger it is, the greater the instantaneous change of the temperature series in the local window.

[0044] For the pressure series, the same method can be used to calculate the trend dispersion index of the pressure data corresponding to the i-th moment, which is recorded as . Income The larger the value, the greater the instantaneous change in the pressure sequence within that local window. Furthermore, there is a certain correlation between the temperature and pressure within an LNG tank. Generally, when the temperature within an LNG tank rises, the thermal expansion of the LNG causes the volume of the liquid natural gas within the tank to increase, potentially causing an increase in the tank pressure and, in turn, causing fluctuations in the liquid level data.

[0045] In order to obtain the influence of the approximate degree of temperature and pressure changes on the liquid level data, the correlation coefficient between all temperature data and all pressure data corresponding to the local window at each moment is analyzed. Preferably, in this embodiment, the absolute value of the Pearson correlation coefficient between all temperature data and all pressure data corresponding to the local window at the i-th moment is calculated and recorded as . Income The larger it is, the closer the changing characteristics of temperature and pressure in the LNG tank are.

[0046] Furthermore, the sum of the trend dispersion index of the temperature data at each moment and the trend dispersion index of the pressure data is analyzed, and the result of fusing the sum with the correlation coefficient is used as the potential influence coefficient of the liquid level data at each moment. It should be noted that the fusion in this implementation is the addition relationship and the multiplication relationship between the variables. Preferably, in this embodiment, the calculation formula for the potential influence coefficient of the liquid level data at each moment is:

[0047] Where, Represents the potential influence coefficient of the liquid level data at the i-th moment, and the obtained The larger the value is, the greater the instantaneous change in temperature and pressure in the LNG tank at that moment, and the greater the impact on the liquid level data in the tank.

[0048] For details, please refer to Figure 3 , Figure 3 The flowchart for obtaining the potential influence coefficient of liquid level data at each moment is shown.

[0049] So far, according to the above process of this embodiment, the potential influence coefficient corresponding to the liquid level data at each moment can be obtained.

[0050] Step 3: Divide the liquid level sequence into multiple liquid level subsequences according to the peak points in the liquid level sequence. By analyzing the change distribution of the liquid level data in the liquid level subsequence and the length of the liquid level subsequence, combined with the product of the liquid level data at each moment and its potential influence coefficient, the disordered randomness of the liquid level subsequence is determined.

[0051] During natural gas feeding and storage, significant vaporization may occur within LNG tanks. Because radar level meters rely on the emission and reflection of electromagnetic waves to determine liquid level, the gas generated by this vaporization within the tank can interfere with the normal propagation of radar waves, reducing measurement accuracy. Specifically, the liquid level sequence may exhibit irregular peaks and random noise. Therefore, we further analyzed the peak characteristics of the liquid level sequence and the randomness of the noise data.

[0052] This embodiment adopts the automatic multiscale-based peak detection algorithm (AMPD) to obtain the peak position in the liquid level sequence. AMPD input is the liquid level sequence within the acquisition time length, and outputs all the peak points in the liquid level sequence. The liquid level sequence is divided into multiple liquid level subsequences according to the peak points in the liquid level sequence. In this embodiment, all the peak positions are used as segmentation points to divide the liquid level sequence into multiple liquid level subsequences. The higher the peak value, the more unstable the liquid level change, and the smaller the peak interval, the greater the frequency of the liquid level change, where the peak interval is the length of the liquid level subsequence. Therefore, for the jth liquid level subsequence, the ratio of the sum of all liquid level data in the liquid level subsequence to the length of the liquid level subsequence is taken as the liquid level change disorder, which is recorded as , income The larger the value is, the more frequent the irregular changes in the liquid level are during the time period of the subsequence.

[0053] Further analysis of the magnitude of random noise in the liquid level data reveals that, in addition to affecting the liquid level data, temperature and pressure fluctuations can also, to a certain extent, cause variations in the vaporization phenomenon within the tank, affecting the level meter's detection accuracy. Therefore, a product sequence corresponding to the liquid level subsequence, consisting of the product of the potential influence coefficient of the liquid level data at each moment in the liquid level subsequence and the liquid level data, is recorded as this sequence. This helps enhance sensitivity to abnormal liquid level data. For example, if the liquid level data at a particular moment contains random noise and a high potential influence coefficient, it indicates a higher probability of an abnormal liquid level at that moment.

[0054] Then, in this embodiment, the Higuchi algorithm is used to obtain the fractal dimension corresponding to each product sequence, and the fractal dimension is recorded as . The larger the value, the greater the randomness of the product sequence. Combining the fractal dimension and the disorder of the liquid level change, the randomness of the disorder of the liquid level subsequence is calculated as follows:

[0055] Where, Represents the disordered randomness of the j-th liquid level subsequence. The larger the value is, the greater the impact of the gasification phenomenon of the LNG storage tank on the measurement of the radar level meter is, and the greater the random noise contained in the liquid level sequence may be.

[0056] For details, please refer to Figure 4 , Figure 4 The flowchart for obtaining the disordered randomness of each liquid level subsequence is shown.

[0057] Step 4: Based on the average level of disorder randomness of all liquid level subsequences and the autocorrelation characteristics of the liquid level sequence, the liquid level stability reliability is obtained.

[0058] The above analysis reveals the randomness of the disorder associated with each level subsequence. Each level subsequence corresponds to a time interval, and the magnitude of this value reflects the degree to which the radar level meter's measurement process is affected by the tank's temperature, pressure, and vaporization phenomena during each time interval. The lower the degree of interference from these factors within a given time interval, the more likely the radar level meter's measurement data is to be close to the true level data. Furthermore, the stronger the autocorrelation of the level sequence, the more similar the level sequence is at different time points, indicating that the radar level meter's measurement data is more stable.

[0059] Therefore, this embodiment will analyze the liquid level stability reliability of the radar level meter measurement results. The specific process is as follows:

[0060] First, calculate the mean value of the randomness of all liquid level subsequences, denoted as L. The smaller the obtained L, the less interference the measured data is caused by the temperature, pressure and gasification phenomenon in the tank;

[0061] Furthermore, the autocorrelation of the liquid level sequence corresponding to the acquisition period is analyzed to analyze the change of the liquid level sequence. In this embodiment, the autocorrelation coefficient of the liquid level sequence is calculated, and the absolute value of the autocorrelation coefficient is recorded as M. The larger the obtained M, the more stable the data measured by the radar level meter;

[0062] Therefore, the liquid level stability reliability is calculated based on the average level of disorder randomness of all liquid level subsequences and the autocorrelation coefficient of the liquid level sequence. The calculation formula is as follows:

[0063] Where, It indicates the reliability of liquid level stability. The larger the P is, the smoother the measured liquid level sequence is.

[0064] Step 5: Obtain the filter window size based on the liquid level stability reliability, and use a filtering algorithm to correct the liquid level data.

[0065] This embodiment analyzes the temperature, pressure, and liquid level data in the LNG tank and calculates the liquid level stability reliability characteristics of the liquid level sequence before correction. The liquid level stability reliability reflects the stationary characteristics of the liquid level data and the amount of random noise contained. Furthermore, in this embodiment, the liquid level sequence is corrected in combination with the Savitzky-Golay filtering algorithm. If the calculated liquid level stability reliability P is larger, it indicates that the collected liquid level sequence is smoother and the corresponding noise is less, and the window size required for Savitzky-Golay filtering is smaller to avoid overfitting and retain the detailed information of the liquid level sequence as much as possible. If the calculated liquid level stability reliability P is smaller, it indicates that the collected liquid level sequence is more unstable and its changes are more complex, requiring a larger window for filtering to reduce noise interference.

[0066] Specifically, in this embodiment, the calculation method of the approximate value of the Savitzky-Golay filtering algorithm window size is: ; where Q is the approximate window size, is the rounding function, is an exponential function with a natural constant as its base. Considering that the window size of the Savitzky-Golay filter algorithm is usually not too large or too small, this embodiment limits the window size to a value of 10 when calculating the approximate value of the filter window size. In actual application, the implementer can set it according to the actual application scenario. Since the window size needs to be an odd number, the odd number closest to Q is used as the filter window size. The polynomial order of the Savitzky-Golay filter algorithm is 3. The liquid level sequence is used as the input of the Savitzky-Golay filter algorithm, and the filtered liquid level sequence is output to obtain the corrected liquid level data, thereby correcting the data of the radar level meter.

[0067] Based on the same inventive concept as the above method, an embodiment of the present application further provides a radar level meter, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned radar level meter data correction methods are implemented.

[0068] It should be understood that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0070] The above content is only an implementation method of the present application and is not intended to limit the scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the scope of protection of the present application.

Claims

1. A radar level meter data correction method, characterized in that: The following steps are involved: Obtain the liquid level data, temperature and pressure data corresponding to the radar level meter of the material at each moment in the filling process, and normalize them to form a liquid level sequence, a temperature sequence and a pressure sequence respectively; Based on the data change trend and data dispersion of temperature data and pressure data during the filling process, as well as the correlation between the temperature series and the pressure series, the potential influence coefficient of the liquid level data at each moment is determined; The liquid level sequence is divided into multiple liquid level subsequences according to the peak points in the liquid level sequence. The randomness of the disorder of the liquid level subsequence is determined by analyzing the change distribution of the liquid level data in the liquid level subsequence and the length of the liquid level subsequence, and combining the product of the liquid level data at each moment and its potential influence coefficient. Based on the average level of disorder randomness of all liquid level subsequences combined with the autocorrelation characteristics of the liquid level sequence, the liquid level stability reliability is obtained; The size of the filtering window is obtained based on the liquid level stability reliability, and the liquid level data is corrected using a filtering algorithm.

2. A radar level meter data correction method as claimed in claim 1, characterized in that: The determination of the potential influence coefficient of the liquid level data at each moment further includes: According to the trend test statistics and the coefficient of variation of the temperature data at all moments in the local window of the temperature sequence, the trend dispersion index of the temperature data at each moment is obtained, and correspondingly, the trend dispersion index of the pressure data at each moment is obtained; Analyze the addition result of the trend dispersion index of the temperature data at each moment and the trend dispersion index of the pressure data, and obtain the correlation coefficient between all temperature data and all pressure data corresponding to the local window at each moment; fuse the addition result with the correlation coefficient as the potential influence coefficient of the liquid level data at each moment.

3. A radar level meter data correction method as claimed in claim 2, characterized in that: The trend dispersion index of the temperature data at each moment is the product of the trend test statistic of the temperature data at all moments in the local window at each moment and the coefficient of variation.

4. A radar level meter data correction method as claimed in claim 2, characterized in that: The potential influence coefficient of the liquid level data at each moment is the product of the addition result and the correlation coefficient.

5. The radar level meter data correction method according to claim 1, characterized in that: The determination of the disorder randomness of each liquid level subsequence further includes: The liquid level change disorder of each liquid level subsequence is determined according to the distribution of the liquid level data in each liquid level subsequence and the length of each liquid level subsequence; the product of the potential influence coefficient of the liquid level data at each moment in the liquid level subsequence and the liquid level data is used to form a product sequence corresponding to the liquid level subsequence; and the disorder randomness of each liquid level subsequence is obtained by combining the liquid level change disorder of each liquid level subsequence and the fractal dimension of the product sequence.

6. A radar level meter data correction method as claimed in claim 5, characterized in that: The liquid level change disorder of each liquid level subsequence is the ratio of the sum of all liquid level data in each liquid level subsequence to the length of each liquid level subsequence.

7. A radar level meter data correction method as claimed in claim 5, characterized in that: The disorder randomness of each liquid level subsequence is the product of the disorder of the liquid level change and the fractal dimension.

8. A radar level meter data correction method as claimed in claim 1, characterized in that: The calculation formula of the liquid level stability reliability is: ; In the formula, It represents the reliability of liquid level stability, M is the absolute value of the autocorrelation coefficient of the liquid level sequence, and L is the mean value of the disorder randomness of all liquid level subsequences.

9. A radar level meter data correction method as claimed in claim 1, characterized in that: The corresponding calculation formula for the filter window size is: ; Where Q is the approximate window size, is the rounding function, is an exponential function with a natural constant as the base, where the odd number closest to Q is used as the filter window size.

10. A radar level meter, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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