CN118817038B - Radar level gauge and data correction method thereof - Google Patents
Radar level gauge and data correction method thereof Download PDFInfo
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- CN118817038B CN118817038B CN202411303315.3A CN202411303315A CN118817038B CN 118817038 B CN118817038 B CN 118817038B CN 202411303315 A CN202411303315 A CN 202411303315A CN 118817038 B CN118817038 B CN 118817038B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F23/00—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F23/00—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm
- G01F23/22—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm by measuring physical variables, other than linear dimensions, pressure or weight, dependent on the level to be measured, e.g. by difference of heat transfer of steam or water
- G01F23/28—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm by measuring physical variables, other than linear dimensions, pressure or weight, dependent on the level to be measured, e.g. by difference of heat transfer of steam or water by measuring the variations of parameters of electromagnetic or acoustic waves applied directly to the liquid or fluent solid material
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Abstract
The application relates to the technical field of liquid level measurement, in particular to a radar level gauge and a data correction method thereof, wherein the method comprises the steps of acquiring liquid level data, temperature data and pressure data corresponding to the radar level gauge of materials at all moments in a canning process; determining potential influence coefficients based on data change trend and data discrete degree of temperature data and pressure data in the canning process and correlation between temperature and pressure, determining the turbulence randomness of the liquid level subsequence by analyzing change distribution of the liquid level data in the liquid level subsequence and the length of the liquid level subsequence and combining the potential influence coefficients at all moments, further acquiring liquid level stability and reliability according to autocorrelation characteristics of the liquid level sequence, acquiring a filter window size based on the liquid level stability and reliability, and performing positive effect on the liquid level data by adopting a filter algorithm. The application can correct the measured liquid level data and improve the detection precision of the radar level gauge.
Description
Technical Field
The application relates to the technical field of liquid level measurement, in particular to a radar level gauge and a data correction method thereof.
Background
Radar level gauges are a high-precision non-contact measuring device that determines the level of a medium by emitting and receiving microwave pulses. The instrument can be widely applied to various complex industrial environments, such as inflammable, explosive, highly corrosive media and the like. Accurate level measurement is critical to control of industrial processes, which helps to ensure production efficiency, safety and product quality while reducing resource waste and environmental risks.
In industrial production, the real-time monitoring of the material height is important, and the conventional means is to directly acquire the material height according to a radar level gauge. However, due to fluctuation of temperature and pressure in the tank and interference of gasification phenomenon of the medium, a certain degree of random noise and complex change may exist in the collected liquid level data, and further, the defect of insufficient detection precision of the radar level gauge exists. To improve the accuracy of the monitoring, corresponding corrective measures are required to reduce the effects of these errors.
Disclosure of Invention
In order to solve the technical problems, the application aims to provide a radar level gauge and a data correction method thereof, and the adopted technical scheme is as follows:
the embodiment of the application provides a radar level count correction method, which comprises the following steps of:
acquiring liquid level data, temperature data and pressure data corresponding to a radar level meter of the material at each moment in the canning process, and respectively normalizing the liquid level data, the temperature data and the pressure data to form a liquid level sequence, a temperature sequence and a pressure sequence;
determining potential influence coefficients of liquid level data at each moment based on data change trend and data discrete degree of temperature data and pressure data in the canning process and correlation between a temperature sequence and a pressure sequence;
Dividing the liquid level sequence into a plurality of liquid level subsequences according to each peak point in the liquid level sequence, and determining the disorder randomness of the liquid level subsequences by analyzing the change distribution of the liquid level data in the liquid level subsequences and the length of the liquid level subsequences and combining the product result of the liquid level data at each moment and the potential influence coefficient thereof;
acquiring the stability and reliability of the liquid level based on the average level of the disorder randomness of all the liquid level subsequences and the autocorrelation characteristic of the liquid level sequence;
And acquiring the size of a filtering window based on the liquid level stability and reliability, and performing positive effect on the liquid level data by adopting a filtering algorithm.
Optionally, the determining of the potential influence coefficient of the liquid level data at each moment further includes:
Obtaining trend discrete indexes of the temperature data at each moment according to trend test statistics and variation coefficients of the temperature data at all moments in a local window at each moment in a temperature sequence, and correspondingly obtaining trend discrete indexes of the pressure data at each moment;
Analyzing the addition result of the trend discrete index of the temperature data at each moment and the trend discrete index of the pressure data, acquiring the correlation coefficients between all the temperature data and all the pressure data corresponding to the local window at each moment, and fusing the addition result and the correlation coefficients to obtain the potential influence coefficient of the liquid level data at each moment.
Optionally, the trend discrete index of the temperature data at each time is the product of the trend test statistic and the variation coefficient of the temperature data at all times in the local window at each time.
Optionally, the potential influence coefficient of the liquid level data at each moment is a product of the addition result and the correlation coefficient.
Optionally, the determining of the randomness of the fluid level sub-sequences further comprises:
Determining the liquid level change disorder of each liquid level sub-sequence according to the distribution of the liquid level data in each liquid level sub-sequence and the length of each liquid level sub-sequence, forming a product sequence corresponding to the liquid level sub-sequence by multiplying the potential influence coefficient of the liquid level data at each moment in the liquid level sub-sequence by the liquid level data, and obtaining the disorder randomness of each liquid level sub-sequence by combining the liquid level change disorder of each liquid level sub-sequence and the fractal dimension of the product sequence.
Optionally, the liquid level change turbulence of each liquid level sub-sequence is the ratio of the sum value of all liquid level data in each liquid level sub-sequence to the length of each liquid level sub-sequence.
Optionally, the randomness of the turbulence of each liquid level sub-sequence is a product between the turbulence of the liquid level variation and the fractal dimension.
Optionally, the calculation formula of the liquid level stability and reliability is: in the formula (I), in the formula (II), The liquid level stability and reliability are represented, M is the absolute value of the autocorrelation coefficient of the liquid level sequence, and L is the average value of the disorder randomness of all liquid level subsequences.
Optionally, the calculation formula of the filter window size corresponds to: wherein Q is a window size approximation, In order to round up the rounding function,Is an exponential function with a natural constant as the base, where the odd number closest to Q is taken as the filter window size.
The embodiment of the application also provides a radar level gauge, the system comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor realizes the steps of the method in any one of the above steps when executing the computer program.
From the above, the radar level gauge and the data correction method thereof provided by the application have at least the following beneficial effects:
According to the method, the defect that the radar level gauge detection precision is insufficient due to the fact that the random noise and the complex change to a certain extent possibly occur in the collected liquid level data due to the fluctuation influence of the temperature and the pressure in the tank and the interference of the gasification phenomenon on the radar level gauge in the LNG feeding or storage process is considered, so that potential influence coefficients on the liquid level data are calculated by analyzing trend characteristics and change consistency characteristics of the temperature and the pressure data in the LNG storage tank. The method has the beneficial effects that the identification capability of abnormal liquid level data can be enhanced;
The liquid level sequence is further divided, the disorder randomness of the subsequence is calculated based on the peak value characteristic of the liquid level subsequence and the change random characteristic of the characteristic subsequence, and the liquid level stability and reliability of the liquid level sequence are calculated by combining the disorder randomness and the autocorrelation of the liquid level sequence. The liquid level data processing device has the beneficial effects that the stability characteristic of the liquid level data and the random noise are reflected. And obtaining the size of a filtering window of the Savitzky-Golay algorithm based on the stability and reliability of the liquid level, so as to correct the measured liquid level data. The detection precision of the radar level gauge is improved.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the steps of a method for calibrating a radar level gauge count according to the present application;
FIG. 2 is a schematic flow chart provided by the present application;
FIG. 3 is a flow chart of the acquisition of potential influence coefficients of liquid level data at various moments provided by the application;
FIG. 4 is a flow chart for obtaining the turbulence randomness of each liquid level sub-sequence provided by the application.
Detailed Description
In order to further describe the technical means and effects adopted by the present application to achieve the preset purpose, the following detailed description refers to the specific implementation, structure, characteristics and effects of a radar level gauge and a data correction method thereof according to the present application with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless specified and limited otherwise, terms such as "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such article or apparatus. Without further limitation, the statement "comprises an" or "comprising" does not exclude that an additional identical element is present in an article or device comprising the element. In addition, the term "and/or" as used herein includes any and all combinations of one or more of the associated listed items. All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
The following specifically describes a specific scheme of the radar level gauge and the data correction method thereof provided by the application with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for correcting radar level gauge data according to an embodiment of the present application is shown, including the following steps:
Step one, acquiring liquid level data, temperature and pressure data corresponding to a radar level meter of the material at each moment in the canning process, and respectively normalizing the liquid level data, the temperature sequence and the pressure sequence to form a liquid level sequence, a temperature sequence and a pressure sequence.
In this embodiment, liquid level detection of a Liquefied Natural Gas (LNG) tank is taken as an example, and the obtained liquid level data is corrected. The filling of LNG requires that gaseous natural gas be liquefied at very low temperatures and stored in dedicated LNG tanks. LNG tanks are typically equipped with temperature and pressure gauges to detect in real time the LNG storage environment within the tank. The present embodiment collects temperature data and pressure data through a temperature meter and a pressure meter within the tank. And a radar level gauge is arranged at the top of the storage tank to collect liquid level data of liquefied natural gas in the tank.
The schematic diagram of the core component of the radar level gauge in this embodiment is shown in fig. 2, the number 1 is a shell component of the level gauge, the number 2 includes a measurement module, the main function is to realize core liquid level measurement, a data correction module is added in the measurement module, the purpose is to correct the measured liquid level data, the number 3 is a waveguide, and the number 4 is a radar antenna.
In this embodiment, the time interval for data acquisition is set to 0.1 seconds, and the acquisition duration is set to 1 minute. And normalizing the acquired temperature data, pressure data and liquid level data through a Z-Score algorithm, and respectively marking sequences obtained in time ascending sequence as a temperature sequence, a pressure sequence and a liquid level sequence. The Z-Score method is a well-known technique, and the detailed process is not repeated.
And step two, determining potential influence coefficients of liquid level data at each moment based on the data change trend and the data discrete degree of temperature data and pressure data in the canning process and the correlation between the temperature sequence and the pressure sequence.
The liquid level detection is a key step in the gas production process of the gas storage, and the linkage control of the whole set of gas production device is stable, safe and stable. First, each item of collected data is processed and analyzed in a data correction module. Because LNG is handled and filled at very low levels to maintain its liquid state, pressure changes can affect the boiling point of LNG. The effect of LNG tank pressure and temperature on the liquid level data is mainly reflected in the following two aspects.
Temperature variations within LNG tanks can affect the physical state of LNG and thus its density and volume. An increase in temperature may cause the LNG to vaporize, generating gas, resulting in a decrease in liquid level, whereas a decrease in temperature may increase the density of the LNG, possibly resulting in an increase in liquid level. LNG storage tanks are stored in a sealed and thermally insulated environment, and the increase in pressure within the tank can make it more difficult for LNG to maintain a liquid state, potentially causing the LNG to vaporize into a gas, which reduces the volume of liquid and thus reduces the liquid level data.
From the above analysis, it is known that the temperature and pressure changes in the tank may cause the position of the liquid level to fluctuate, and therefore, the degree of the temperature and pressure changes is analyzed below to determine the interference effect of the two changes on the liquid level. Since the temperature and pressure of the LNG tank are significantly changed and are liable to be greatly changed in a short time, it is preferable to set the temperature and pressure in the LNG tank around the i-th time in the present embodimentIs the local window at the i-th instant. Further acquiring the change characteristics of the temperature sequence and the pressure sequence in the local window. It should be noted that, for missing data of the partial window of the first 7 and the last 7 data in the sequence, that is, when the data in the partial window is insufficient, the filling is performed by adopting a mirror image filling mode in the embodiment, and an implementer can also adopt a mean filling mode in the actual application process, which is not limited in particular in the embodiment.
Firstly, a Mann-Kendall trend test algorithm is adopted to obtain the trend variation of the data in each local window in the temperature sequence. Taking the local window at the ith moment of the temperature sequence as an example, the input of the algorithm is the temperature sequence corresponding to the local window at the ith moment, and the output is trend test statistics. The magnitude of the absolute value of the statistic reflects the degree of significance of the rising or falling trend of the temperature sequence within the window. And calculating the variation coefficient of the temperature sequence in the local window, wherein the variation coefficient reflects the discrete degree of the temperature sequence in the window. Then, calculating the product of the absolute value of the statistic and the variation coefficient in the local window at the ith moment and taking the product as a trend discrete index of the temperature data corresponding to the ith moment as. The obtained productThe larger the transient change in the sequence of temperatures within the local window, the greater the transient change.
For the pressure sequence, the trend discrete index of the pressure data corresponding to the ith moment can be calculated by adopting the same method and is recorded as. The obtained productThe greater the transient change in pressure sequence within the local window, the greater the degree of transient change. Furthermore, there is a certain correlation characteristic between the temperature and the pressure within the LNG storage tank. In general, when the temperature in the LNG tank increases, the liquid natural gas volume in the tank increases due to thermal expansion of LNG, which may cause the pressure in the tank to increase, thereby causing fluctuation in the liquid level data.
In order to obtain the influence of the approximation degree of the temperature and the pressure change on the liquid level data, the correlation coefficients between all the temperature data and all the pressure data corresponding to the local window at each moment are analyzed, preferably, in this embodiment, the absolute values of the pearson correlation coefficients between all the temperature data and all the pressure data corresponding to the local window at the ith moment are calculated as. The obtained productThe larger the variation characteristics that account for the temperature and pressure within the LNG tank, the closer.
Further, the addition result of the trend discrete index of the temperature data and the trend discrete index of the pressure data at each time is analyzed, and the result of the addition result and the correlation coefficient are fused, as the potential influence coefficient of the liquid level data at each time, it should be noted that the fusion in the present embodiment is the addition relationship and multiplication relationship between variables, and preferably, in the present embodiment, the calculation formula of the potential influence coefficient of the liquid level data at each time is:
in the formula (I), in the formula (II), Representing potential influence coefficients of the liquid level data at the ith moment, and obtainingThe larger the temperature and pressure in the LNG tank at this point, the larger the instantaneous change in temperature and pressure, and the larger the influence on the tank liquid level data.
Specifically, referring to fig. 3, fig. 3 shows a flowchart for acquiring potential influence coefficients of liquid level data at each moment.
Thus, according to the embodiment, the process can acquire the potential influence coefficient corresponding to the liquid level data at each moment.
Dividing the liquid level sequence into a plurality of liquid level subsequences according to each peak point in the liquid level sequence, and determining the turbulence randomness of the liquid level subsequences by analyzing the change distribution of the liquid level data in the liquid level subsequences and the length of the liquid level subsequences and combining the product result of the liquid level data at each moment and the potential influence coefficients thereof.
During the feeding or storage of natural gas, significant vaporization may occur within the LNG storage tank. Since the radar level gauge relies on the principle of emission and reflection of electromagnetic waves to determine the liquid level, the gas generated by vaporization in the tank may interfere with the normal propagation of radar waves, which may reduce the measurement accuracy. In particular, the liquid level sequence may exhibit irregular peaks and random noise. Thus, the peak characteristics of the liquid level sequence and the randomness of the occurrence of the noise data are further analyzed.
The present embodiment employs an automatic multiple low grade peak finding algorithm (Automatic multiscale-based peak detection, AMPD) to obtain peak positions in the liquid level sequence. AMPD input is a liquid level sequence within the acquisition duration, outputting all peak points in the liquid level sequence. According to the peak points in the liquid level sequence, the liquid level sequence is divided into a plurality of liquid level subsequences, and in the embodiment, all the peak positions are used as dividing points, so that the liquid level sequence is divided into a plurality of liquid level subsequences. The higher the peak value, the less stable the liquid level change, and the smaller the peak value interval, the greater the frequency of the liquid level change, wherein the peak value interval is the length of the liquid level subsequence. Therefore, for the jth liquid level sub-sequence, the ratio of the sum of all liquid level data in the liquid level sub-sequence to the length of the liquid level sub-sequence is taken as the liquid level change disorder, and is recorded asThe obtainedThe larger the indication is, the more frequent the irregular variation of the liquid level during the time the sub-sequence is.
The size of random noise in the liquid level data is further analyzed, and the temperature and pressure changes can influence the difference of gasification phenomena in the storage tank to a certain extent besides the liquid level data, so that the detection precision of the level meter is influenced. Therefore, a sequence formed by the product result 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 a product sequence corresponding to the liquid level subsequence, so that the sensitivity to abnormal liquid level data is enhanced. For example, if random noise exists in the liquid level data at a certain moment, and the potential influence coefficient is higher, the higher the abnormal possibility of the liquid level at the moment is.
Then, in this embodiment, a Higuchi algorithm is adopted to obtain a fractal dimension corresponding to each product sequence, and the fractal dimension is recorded as。The larger the sequence of products, the more random the variation. Combining the fractal dimension and the turbulence of the liquid level change, calculating the turbulence randomness of the liquid level subsequence as follows:
in the formula (I), in the formula (II), Indicating the random nature of the disorder of the j-th liquid level sub-sequence. The obtained productThe larger the LNG storage tank is, the greater the influence degree of the gasification phenomenon of the LNG storage tank on the radar level gauge measurement is, and the larger the random noise contained in the liquid level sequence is likely to be.
Specifically, referring to fig. 4, fig. 4 shows a flow chart for obtaining the randomness of the fluid level sub-sequences.
And step four, acquiring the stable and reliable liquid level based on the average level of the disorder randomness of all liquid level sub-sequences and the autocorrelation characteristic of the liquid level sequences.
Through the analysis, the turbulence randomness corresponding to each liquid level sub-sequence is obtained, each liquid level sub-sequence corresponds to a time interval, and the value reflects the interference degree of the radar level gauge measuring process under different time intervals due to the temperature, pressure and gasification phenomenon in the tank. If the interference degree of the factors is smaller in a certain time interval, the measurement data of the radar level gauge in the time interval is more likely to be close to the real liquid level data. And the stronger the autocorrelation of the liquid level sequence, the higher the similarity of the liquid level sequence at different time points is, and the more stable the change of the measured data of the radar level gauge is.
Therefore, the present embodiment will analyze the level stability reliability of the radar level gauge measurement. The specific process is as follows:
First, the average value of the randomness of all the liquid subsequences is calculated and is marked as L. The smaller the L is, the less the measured data is interfered by the temperature, pressure and gasification phenomena in the tank;
Further, the autocorrelation of the liquid level sequence corresponding to the acquisition period is analyzed to analyze the change condition 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 is, the more stable the data measured by the radar level gauge is;
From this, according to the average level of the turbulence randomness of all liquid level subsequences and the autocorrelation coefficient of the liquid level sequence, the liquid level stability reliability is calculated, and the calculation formula is as follows:
in the formula (I), in the formula (II), The liquid level stability and reliability are represented, and the larger the obtained P is, the more gentle the liquid level sequence obtained through measurement is.
And fifthly, acquiring the size of a filtering window based on the liquid level stability and reliability, and correcting the liquid level data by adopting a filtering algorithm.
According to the embodiment, the liquid level stability and reliability characteristics of the liquid level sequence before correction are obtained through calculation by analyzing temperature, pressure and liquid level data in the LNG tank. The stability and reliability of the liquid level reflect the stability characteristics of the liquid level data and the amount of random noise, and further, the liquid level sequence is effectively positive by combining a Savitzky-Golay filtering algorithm in the embodiment. If the calculated liquid level stability and reliability P is larger, the collected liquid level sequence is flatter, the corresponding noise is smaller, and the window size required by Savitzky-Golay filtering is smaller, so that excessive fitting is avoided, and detailed information of the liquid level sequence is reserved as much as possible. If the calculated liquid level stability and reliability P is smaller, the acquired liquid level sequence is unstable, the change is more complex, and a larger window is needed for filtering, so that the interference of noise is reduced.
Specifically, in this embodiment, the method for calculating the window size approximation of the Savitzky-Golay filtering algorithm includes: wherein Q is a window size approximation, In order to round up the rounding function,Is an exponential function with a base of natural constant. Considering that the window size of the Savitzky-Golay filtering algorithm is not easy to be too large or too small under the normal condition, in the embodiment, the window size is limited by a value of 10 when the approximate value of the filtering window size is calculated, and in the practical application process, an implementer can set the window size according to the practical application scene. Because the window size needs to be odd, the odd number closest to Q is taken as the filtering window size, the polynomial order of the Savitzky-Golay filtering algorithm is 3, the liquid level sequence is taken as the input of the Savitzky-Golay filtering algorithm, and the filtered liquid level sequence is output so as to acquire corrected liquid level data, so that the data of the radar level meter are corrected.
Based on the same inventive concept as the above method, the embodiment of the application further provides a radar level gauge, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to realize the steps of any one of the above-mentioned radar level gauge data correction methods.
It should be understood that the foregoing sequence of the embodiments of the present application is only for illustration, and does not represent the advantages or disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing is merely an embodiment of the present application, and is not intended to limit the scope of the present application, and all equivalent structures or equivalent processes using the descriptions and the drawings of the present application or direct or indirect application in other related technical fields are included in the scope of the present application.
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