Low-temperature environment LNG (Liquefied Natural Gas) metering sensor drift compensation method and system

Abstract

The invention discloses a low-temperature environment LNG (Liquefied Natural Gas) metering sensor drift compensation method and system, and particularly relates to the technical field of sensor drift compensation. Output data of a sensor are collected and processed, and a temperature compensation model of the sensor is established; in the working temperature range, digital filtering processing is carried out on data of displacement change of each measuring range point, and then analysis and mining are carried out; a data longitudinal interval in a temperature range is refined by using a table look-up method, so that the data interval is reduced. And calculating the G value according to the residual error obtained by the table look-up method and the numerical value change condition in each temperature section. If the G value is greater than a preset threshold value of the system, selecting a proper fitting method, such as a least square method or Legendre orthogonal polynomial fitting, according to the distribution of the G value and the data interval when curve fitting is used, so as to further optimize the temperature compensation model; and if the G value is smaller than the system preset threshold value, no subsequent operation is carried out.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sensor drift compensation, and more particularly to a low-temperature LNG metering sensor drift compensation method and system. BACKGROUND

[0002] Liquefied natural gas is usually in an ultra-low temperature environment below-162℃ during storage and transportation, which puts high requirements on the stability and accuracy of sensors. Drift in sensors is a relatively complex and inevitable phenomenon, and there are many factors causing drift, such as aging and poisoning of sensors, changes in temperature and humidity in the environment, and delay in data transmission.

[0003] Sensor drift refers to the shift or change in the output signal of a sensor over time due to various factors, resulting in inaccurate measurement results or errors between the true value. Drift usually occurs gradually over time and can affect measurement results and system performance. In the prior art, the accuracy is further compensated using curve fitting based on two-dimensional lookup table method, which may cause overfitting to some extent, making the originally accurate data more deviate from the actual value, and causing larger sensor errors. In order to solve the above defects, the present application provides a technical solution. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a low-temperature LNG metering sensor drift compensation method and system to solve the problems raised in the background art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0006] The low-temperature LNG metering sensor drift compensation method comprises the following steps:

[0007] Collecting sensor data, processing the data, establishing a temperature compensation model of the sensor, analyzing and mining the displacement change data of each range point in the working temperature range after digital filtering processing, and using lookup table method to divide and narrow the data range vertically in the temperature range;

[0008] According to the residual error after the lookup table method and the numerical change in each temperature section, G is calculated, and whether the curve fitting method is used to further compensate the temperature compensation model is determined according to G;

[0009] If G is greater than the system preset threshold, the least square method or Legendre orthogonal polynomial fitting is used to further compensate the temperature compensation model by G value and data interval distribution when using curve fitting method; if G is less than the system preset threshold, no subsequent operation is performed.

[0010] In a preferred embodiment, the data acquisition needs to obtain the data changes of the strain gauge at different temperatures, and a temperature sensing device is added near the internal resistance strain gauge, sensing circuit and measuring circuit of the sensor during design, and the real-time internal environment temperature data and displacement data are uploaded at the same time.

[0011] In a preferred embodiment, according to the residual error after the lookup table method and the numerical change in each temperature segment, G is calculated by logistic regression.

[0012] In a preferred embodiment, the method for obtaining the residual error after the lookup table method comprises the following steps:

[0013] Step A1: Constructing an ideal reference data matrix; obtaining the ideal output value matrix of the measured system , wherein and are discrete points of input variables, covering all grid points of the compensation table;

[0014] Step A2: Collecting the actual output data after compensation; measuring the system after applying two-dimensional lookup table compensation, and recording the output value matrix after compensation , ensuring that the test conditions are consistent with the ideal data acquisition conditions;

[0015] Step A3: Calculating error value point by point; for each grid point , calculate the residual error: ; if the error needs to be normalized, it can be further converted to percentage or ratio relative to full scale;

[0016] Step A4: Global error index calculation; mean square error: ; the mean square error represents the residual error after two-dimensional lookup table compensation.

[0017] In a preferred embodiment, the numerical change in each temperature segment determines the numerical change in each temperature segment, judges the proportion of the continuity of the compensation value between adjacent temperature segments; the negative number of the proportion of the continuity of the compensation value between adjacent temperature segments represents the numerical change in each temperature segment; the proportion of the continuity of the compensation value between adjacent temperature segments is: dividing the working temperature range into multiple temperature segments, counting the number of adjacent temperature segments whose compensation value difference at the segment boundary is less than the preset threshold δ, and the proportion of the number to the logarithm of all adjacent temperature segments.

[0018] In a preferred embodiment, when G is greater than the system preset threshold, the curve fitting method is used to compensate the temperature compensation model based on the two-dimensional lookup table method, otherwise the curve fitting method is not used to compensate the temperature compensation model.

[0019] In a preferred embodiment, the G is determined, the data interval distribution is calculated by weighted summation to calculate the selection coefficient; the data interval distribution refers to the fluctuation degree of the compensation voltage data in each small interval in the working temperature interval after the look-up table method processing, and the standard deviation of each small interval compensation value is calculated, and the average value is obtained.

[0020] In a preferred embodiment, the calculation selection coefficient is determined, and when the value of the selection coefficient is greater than the system preset threshold value, the least square method is used for curve fitting, and when the value of the selection coefficient is less than the system preset threshold value, the curve fitting is performed by using the Legendre orthogonal polynomial fitting.

[0021] In a preferred embodiment, the temperature compensation model construction module, the dynamic compensation decision module, and the curve fitting execution module are provided.

[0022] The temperature compensation model construction module is used for longitudinally dividing the working temperature range into base segments and subsegments, storing the compensation voltage corresponding to the temperature segments, and constructing a segmented data table through the reference voltage and the compensation voltage formula; the temperature drift amplitude is compressed to a smaller range through the look-up table method, and the subsequent fitting complexity is reduced.

[0023] The dynamic compensation decision module is used for calculating G according to the residual error after the look-up table method and the numerical change in each temperature segment, and judging whether the curve fitting mode needs to be used to further compensate the temperature compensation model according to G.

[0024] The curve fitting execution module is used for determining the least square method or the Legendre orthogonal polynomial fitting to further compensate the temperature compensation model by using the G value and the data interval distribution when the curve fitting mode is used.

[0025] In a preferred embodiment, the dynamic compensation decision module comprises a residual error evaluation module, and the residual error evaluation module is used for calculating the residual error by point-by-point calculation of the ideal reference data matrix and the actual output matrix.

[0026] The technical effects and advantages of the present application are as follows:

[0027] The application collects sensor output data, processes the data, establishes a temperature compensation model of the sensor, analyzes and mines the displacement change data of each range point in the working temperature range after digital filtering processing, uses a table lookup method to reduce the longitudinal interval of the data in the temperature range, calculates G according to the residual error after the table lookup method and the numerical change in each temperature section, judges whether the temperature compensation model needs to be further compensated using a curve fitting method according to G, avoids overfitting, so that the originally accurate data becomes more deviated from the actual value, if G is greater than the system preset threshold, the least square method or Legendre orthogonal polynomial fitting is used to further compensate the temperature compensation model when using the curve fitting method, the dynamic adaptive fitting method makes the fitted data more close to the actual value, and if G is less than the system preset threshold, no subsequent operation is performed. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to facilitate those skilled in the art to understand, the application will be further described below with reference to the drawings;

[0029] Figure 1 The flowchart of the low-temperature environment LNG metering sensor drift compensation method of the application is shown.

[0030] Figure 2 The structure diagram of the low-temperature environment LNG metering sensor drift compensation system of the application is shown. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0032] The application collects sensor output data, processes the data, establishes a temperature compensation model of the sensor, analyzes and mines the displacement change data of each range point in the working temperature range after digital filtering processing, uses a table lookup method to reduce the longitudinal interval of the data in the temperature range, calculates G according to the residual error after the table lookup method and the numerical change in each temperature section, judges whether the temperature compensation model needs to be further compensated using a curve fitting method according to G, avoids overfitting, so that the originally accurate data becomes more deviated from the actual value, if G is greater than the system preset threshold, the least square method or Legendre orthogonal polynomial fitting is used to further compensate the temperature compensation model when using the curve fitting method, the dynamic adaptive fitting method makes the fitted data more close to the actual value, and if G is less than the system preset threshold, no subsequent operation is performed.

[0033] Example 1

[0034] The low-temperature environment LNG metering sensor drift compensation method, as shown in the specification, comprises the following steps: Figure 1

[0035] Collecting sensor output data; processing the data; establishing a temperature compensation model for the sensor; after digital filtering processing of the displacement change data of each range point in the working temperature range, analyzing and mining, and using the table lookup method to separate and reduce the longitudinal interval of the data in the temperature range;

[0036] According to the residual error after the table lookup method and the numerical change in each temperature section, calculate G, and according to G, determine whether to use curve fitting to further compensate the temperature compensation model;

[0037] If G is greater than the system preset threshold, when using curve fitting, determine to use least square method or Legendre orthogonal polynomial fitting to further compensate the temperature compensation model through G value and data interval distribution; if G is less than the system preset threshold, do not perform subsequent operations.

[0038] Specifically:

[0039] Establish a temperature compensation model for a high-precision displacement sensor. Place the sensor in a slowly changing temperature field, set the temperature change range of the temperature field to the product design working temperature range, and collect displacement data of the zero range point, full range point, and multiple range points (according to the situation) through measurement analysis and correction software. After digital filtering processing of the displacement change data of each range point in the working temperature range, analyze and mine, use the table lookup method to separate and reduce the longitudinal interval of the data in the temperature range, and combine appropriate curve fitting to linearly fit the data, obtain regularly changing data, and construct a compensation model to finally correct the measurement.

[0040] In the study of strain gauge temperature characteristics, it is found that the changing displacement data will have large amplitude fluctuations without obvious rules and linear relationships without digital filtering processing and table lookup method processing. The amplitude of the processed data will retain the original data characteristics, reduce data redundancy and data calculation amount, and easily obtain regularly changing and locally linearly related data. When collecting data, the data change of the strain gauge at different temperatures needs to be obtained, and temperature sensing devices are added near the internal resistance strain gauge, sensing circuit, and measurement circuit in the sensor during design, and real-time internal environment temperature data and displacement data are uploaded at the same time, which is convenient for obtaining data changes at different temperatures. Through data change analysis and mining, a digital expression or a curve approximating the data change is obtained. When collecting data, the displacement sensor is displaced to a certain position and fixed, and the sampling frequency of the displacement sensor can be set to 10 Hz. The baseline value of the displacement data of the sensor at this time is recorded as ​, the temperature is T_N, every 0.1℃, 10 displacement data are collected. The median value average filtering method is used to obtain the data fitting value of every 0.1℃, and the output voltage data drift distribution is obtained. According to the analysis of the measured data, the value of the cumulative displacement data is larger as the temperature rises. After digital filtering processing, the segmented data table is constructed, including the base temperature, the sub-temperature and the corresponding output voltage. The data value collected every 0.1℃ is filled in the corresponding position, and the processed data amplitude retains the characteristics of the original data, to a certain extent, avoiding the points with large data error and improving the compensation accuracy of the high-precision displacement sensor.

[0041] The reference voltage is the compensation voltage representing the corresponding temperature segment at the current temperature, which is obtained by subtracting the set temperature reference voltage value from the voltage value at the current temperature. The current compensation voltage increases on the basis of the reference voltage every 0.1℃, and the reference voltage is obtained as follows: ; wherein VN is the reference value of a certain temperature segment; Vi is the actual voltage at the current temperature; V_REF is the set temperature reference voltage; V_B is the compensation voltage of every 0.1℃ change in the temperature segment, that is, the offset voltage caused by temperature change.

[0042] Using the look-up table method can effectively reduce the amplitude of the displacement result deviation caused by temperature drift, compensate the data with large transformation amplitude to a smaller range, and the compensation accuracy depends on the reference voltage and the accurate compensation of 0.1℃ in the temperature segment. A two-dimensional look-up table compensation method is formed to improve the efficiency of the ordinary look-up table method.

[0043] Meanwhile, on the basis of the two-dimensional look-up table method, the curve fitting method is used to make up for the deficiencies of the temperature compensation model in flexibility and compensation accuracy, and to increase the reliability of the temperature compensation of the high-precision displacement sensor. However, in some cases, the curve fitting method is not needed to further process the compensation accuracy; excessive compensation will make the compensated value deviate from the normal value; according to the residual error after the look-up table method and the value change in each temperature segment, G is calculated, and according to G, it is judged whether the curve fitting method is needed to further compensate the temperature compensation model.

[0044] Further, the method steps for obtaining the residual error after the two-dimensional look-up table compensation are as follows:

[0045] Step A1: constructing an ideal reference data matrix; obtaining the ideal output value matrix of the measured system through high-precision instruments or theoretical models , wherein and are the discrete points of the input variables (such as sensor input voltage, temperature, etc.), covering all grid points of the compensation table.

[0046] Step A2: collecting the actual output data after compensation; recording the output value matrix after compensation for the system that has applied the two-dimensional look-up table compensation Ensure that the test conditions are consistent with the ideal data acquisition conditions (such as the same ambient temperature, sampling frequency, etc.).

[0047] Step A3: Calculate the error value point by point; for each grid point , calculate the residual error: ; if the error needs to be normalized, it can be further converted to a percentage or a ratio relative to the full scale.

[0048] Step A4: Global error indicator calculation; root mean square error: ; use the root mean square error to represent the residual error after two-dimensional table compensation.

[0049] The two-dimensional table method stores the compensation voltage by temperature segmentation, but the actual temperature may span multiple segments, and segmented compensation may cause discontinuity of compensation values between adjacent temperature segments; determine the value variation in each temperature segment and the proportion of continuous compensation values between adjacent temperature segments; the proportion of continuous compensation values between adjacent temperature segments is: divide the working temperature range into multiple temperature segments, count the number of adjacent temperature segments whose compensation value difference at the segment boundary is less than a preset threshold δ, and the proportion of the number to the logarithm of all adjacent temperature segments. The lower the proportion of continuous compensation values between adjacent temperature segments, the more continuous functions should be fitted in each temperature segment to achieve accurate compensation during temperature changes, avoid segmented jumps, and enhance compensation continuity. The negative number of the proportion of continuous compensation values between adjacent temperature segments represents the value variation in each temperature segment, and the greater the proportion of continuous compensation values between adjacent temperature segments, the smaller the value variation in each temperature segment.

[0050] Further, to determine the residual error after the table lookup method and the value variation in each temperature segment, first normalize the residual error after the table lookup method and the value variation in each temperature segment; specifically, use the following formula to normalize the data to the range [0, 1]: ; where is the normalized value, and are the minimum and maximum values of the data , respectively.

[0051] Then calculate G according to the logistic regression formula, and the specific formula is as follows: ; where wc represents the residual error after the table lookup method, the greater the residual error after the table lookup method, the greater the G, and vice versa; bh represents the value variation in each temperature segment, the greater the value variation in each temperature segment, the greater the G, and vice versa; α and β are the logistic regression coefficients of the residual error after the table lookup method and the value variation in each temperature segment, respectively; e is the natural base.

[0052] Determine the value of G, according to G to determine whether to use curve fitting to further compensate for temperature compensation model. When G is greater than the system preset threshold, it indicates that the temperature compensation model is insufficient in flexibility and compensation accuracy, and the curve fitting method is needed to make up for the deficiency of the temperature compensation model in flexibility and compensation accuracy on the basis of the two-dimensional lookup table method, and to increase the reliability of the temperature compensation of the high-precision displacement sensor.

[0053] Further, when using the curve fitting method, the selection coefficient is calculated by weighted summation of G and data interval distribution, and the least squares method or Legendre orthogonal polynomial fitting is determined according to the value of the selection coefficient.

[0054] The data interval distribution refers to the fluctuation degree of the compensation voltage data in each small interval (for example, every 0.1℃) in the working temperature interval after the lookup table method is processed, which is obtained by calculating the standard deviation of the compensation value in each small interval and taking the average value.

[0055] Specifically, G and data interval distribution are determined by the following formula: F=a*G+b*fb; wherein F represents the selection coefficient; fb represents the data interval distribution; a and b are the weight coefficients of G and data interval distribution respectively, both of which are greater than zero. The selection coefficient is determined, and when the value of the selection coefficient is greater than the system preset threshold, the least squares method is used for curve fitting, and when the value of the selection coefficient is less than the system preset threshold, the Legendre orthogonal polynomial fitting is used for curve fitting.

[0056] The smaller the value of G is, the smaller the range to be fitted is, and the least squares method is less resistant to overfitting than the Legendre orthogonal polynomial fitting, so the Legendre orthogonal polynomial fitting is needed when G is small. If the data distribution is in a fixed interval, the Legendre orthogonal polynomial fitting is used, because the Legendre orthogonal polynomial fitting is based on orthogonal basis functions, and has high numerical stability, which is suitable for non-linear high-precision approximation in a fixed interval.

[0057] On the basis of the two-dimensional lookup table method, the curve fitting method is used to make up for the deficiency of the temperature compensation model in flexibility and compensation accuracy, and to increase the reliability of the temperature compensation of the high-precision displacement sensor. The curve fitting method is as follows. The least squares method is a mathematical optimization technique, which is used to find the best regression function by regression analysis of related variables with nonlinear relationship according to scattered data. Its goal is to minimize the sum of squares of residuals between observed data and fitted curve, and to find parameters that make the fitted model as close as possible to the actual observation. The unknown data can be easily obtained, and this method is simple in theory and small in calculation amount, and has been widely used in curve fitting, regression analysis and data processing.

[0058] The Legendre orthogonal polynomial fitting is based on the weight function The orthogonal polynomial sequence. Its orthogonality is shown as follows: if m is not equal to n, then ; if m is equal to n, then

[0059] The recursive formula is: , , .

[0060] In the fitting of discrete data, the Legendre polynomial approximates a function by the least square method, and its orthogonality can reduce the condition number of the equation group and improve the numerical stability, and is especially suitable for high-order fitting.

[0061] Embodiment 2

[0062] The low-temperature environment LNG metering sensor drift compensation system, as shown in Figure 2 , comprises the following modules: a temperature compensation model construction module, a residual error evaluation module, a dynamic compensation decision module, and a curve fitting execution module.

[0063] The temperature compensation model construction module is used to longitudinally divide a working temperature range into a base section and a sub-section, store compensation voltages corresponding to the temperature sections, and construct a segmented data table through a reference voltage and a compensation voltage formula; the temperature drift amplitude is compressed to a smaller range through a table lookup method, and the subsequent fitting complexity is reduced.

[0064] The residual error evaluation module calculates residual errors point by point through an ideal reference data matrix and an actual output matrix.

[0065] The dynamic compensation decision module is used to calculate G according to the residual errors after the table lookup method and the numerical change in each temperature section, and determine whether the temperature compensation model needs to be further compensated by using a curve fitting method according to G.

[0066] The curve fitting execution module is used to determine to use the least square method or the Legendre orthogonal polynomial fitting to further compensate the temperature compensation model by using the G value and the data interval distribution when the curve fitting method is used.

[0067] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0068] ​In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the embodiments of the device described above are merely schematic, and the division of the units is merely logical function division. There can be other division manners in actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0069] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0070] In addition, each functional unit in the embodiments of the present application can be integrated in a processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit.

[0071] The above describes only specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered in 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.

Claims

1. A method for compensating for drift of LNG metering sensors in low-temperature environments, characterized in that, Includes the following steps: Collect sensor data; process the data; establish a temperature compensation model for the sensor; analyze and mine the displacement change data of each range point within the operating temperature range after digital filtering, and use the lookup table method to narrow down the vertical intervals of the data within the temperature range. G is calculated based on the residual error after the table lookup method and the numerical changes in each temperature range. Based on G, it is determined whether curve fitting is needed to further compensate the temperature compensation model. If G is greater than the system's preset threshold, when using curve fitting, the least squares method or Legendre orthogonal polynomial fitting will be used to further compensate the temperature compensation model based on the G value and the data interval distribution; if G is less than the system's preset threshold, no further operations will be performed.

2. The method for compensating for drift of LNG metering sensors in low-temperature environments according to claim 1, characterized in that: The data acquisition process requires obtaining the data changes of strain gauges at different temperatures. During the design, temperature sensing devices are added near the resistance strain gauges, sensing circuits, and measurement circuits inside the sensor, and real-time internal ambient temperature data and displacement data are uploaded simultaneously.

3. The drift compensation method for LNG metering sensors in low-temperature environments according to claim 1, characterized in that: Based on the residual error after the table lookup method and the numerical changes in each temperature range, G is calculated through logistic regression.

4. The method for compensating for drift of LNG metering sensors in low-temperature environments according to claim 3, characterized in that: The steps for obtaining the residual error after the table lookup method are as follows: Step A1: Construct an ideal reference data matrix; obtain the ideal output value matrix of the system under test. ,in and The discrete points of the input variables cover all grid points of the compensation table; Step A2: Collect the actual output data after compensation; perform actual measurements on the system that has applied two-dimensional lookup table compensation, and record the output value matrix after compensation. Ensure that the test conditions are consistent with the ideal data acquisition conditions; Step A3: Calculate the error value point by point; for each grid point Calculate the residual error: If the error needs to be normalized, it can be further converted into a percentage or a proportion relative to full scale. Step A4: Calculation of global error index; Root mean square error: ; The root mean square error is used to represent the residual error after two-dimensional lookup table compensation.

5. The method for compensating for drift of LNG metering sensors in low-temperature environments according to claim 3, characterized in that: The numerical changes within each temperature range are determined, and the proportion of continuous compensation values ​​between adjacent temperature ranges is judged. A negative proportion of the continuous compensation values ​​between adjacent temperature ranges represents the numerical changes within each temperature range. The proportion of continuous compensation values ​​between adjacent temperature ranges is calculated by dividing the working temperature range into multiple temperature ranges and counting the number of adjacent temperature ranges where the difference in compensation values ​​at the boundary of the segments is less than a preset threshold δ, which is the proportion of the total number of adjacent temperature ranges.

6. The method for compensating for drift of LNG metering sensors in low-temperature environments according to claim 3, characterized in that: When G is greater than the system's preset threshold, curve fitting is needed to compensate the temperature compensation model based on the two-dimensional lookup table method; otherwise, curve fitting is not needed to compensate the temperature compensation model.

7. The method for compensating for drift of LNG metering sensors in low-temperature environments according to claim 6, characterized in that: Determine G and the data interval distribution, and calculate the selection coefficient by weighted summation; the data interval distribution refers to the degree of fluctuation of the compensation voltage data between each cell within the working temperature range after the table lookup method is processed, which is obtained by calculating the standard deviation of the compensation value between each cell and taking the average value.

8. The method for compensating for drift of a cryogenic LNG metering sensor according to claim 7, characterized in that: The selection coefficients are determined. When the value of the selection coefficient is greater than the system preset threshold, the least squares method is used for curve fitting. When the value of the selection coefficient is less than the system preset threshold, Legendre orthogonal polynomial fitting is used for curve fitting.

9. A low-temperature environment LNG metering sensor drift compensation system, used to implement the low-temperature environment LNG metering sensor drift compensation method according to any one of claims 1-8, characterized in that, It includes the following modules: temperature compensation model construction module, dynamic compensation decision module, and curve fitting execution module; The temperature compensation model construction module is used to divide the operating temperature range vertically into base segments and sub-segments, store the compensation voltage for the corresponding temperature segments, and construct a segmented data table through the base voltage and compensation voltage formulas; the temperature drift amplitude is compressed to a smaller range by using the table lookup method, thereby reducing the complexity of subsequent fitting. The dynamic compensation decision module is used to calculate G based on the residual error after the lookup table method and the numerical changes in each temperature range. Based on G, it determines whether curve fitting is needed to further compensate the temperature compensation model. The curve fitting execution module is used to determine whether to use least squares or Legendre orthogonal polynomial fitting to further compensate the temperature compensation model when using curve fitting, based on the G value and data interval distribution.

10. The LNG metering sensor drift compensation system for cryogenic environments according to claim 9, characterized in that: The dynamic compensation decision module includes a residual error evaluation module, which is used to calculate the residual error point by point using the ideal reference data matrix and the actual output matrix.

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