Method and System for Drift Compensation of LNG Metering Sensors in Low-Temperature Environments

Abstract

This invention discloses a method and system for drift compensation of LNG metering sensors in cryogenic environments, specifically relating to the field of sensor drift compensation technology. The invention collects and processes sensor output data to establish a temperature compensation model for the sensor. Within the operating temperature range, the data on displacement changes at each measurement point are digitally filtered, followed by analysis and data mining. A lookup table method is used to refine the vertical data intervals within the temperature range, thereby narrowing the data range. Based on the residual error obtained from the lookup table method and the numerical changes within each temperature segment, the G value is calculated. If the G value is greater than a system-preset threshold, when using curve fitting, a suitable fitting method is selected based on the G value and the distribution of the data interval, such as least squares or Legendre orthogonal polynomial fitting, to further optimize the temperature compensation model. If the G value is less than the system-preset threshold, no further operations are performed.

Description

Technical Field

[0001] This invention relates to the field of sensor drift compensation technology, and more specifically, to a method and system for drift compensation of LNG metering sensors in cryogenic environments. Background Technology

[0002] Liquefied natural gas is typically stored and transported in ultra-low temperature environments below -162°C, which places extremely high demands on the stability and accuracy of sensors. Sensor drift is a relatively complex and unavoidable phenomenon, caused by many factors, such as sensor aging and poisoning, changes in environmental temperature and humidity, and data transmission delays.

[0003] Sensor drift refers to the phenomenon where the sensor output signal shifts or changes over time due to various factors, leading to inaccurate measurement results or errors compared to the true value. Drift typically occurs gradually over time and can affect measurement results and system performance. Existing technologies use curve fitting to further compensate for accuracy based on two-dimensional lookup tables, which can easily lead to overfitting, making the originally accurate data even more detached from reality and increasing sensor error. To address these shortcomings, a technical solution is provided. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method and system for drift compensation of LNG metering sensors in cryogenic environments to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for compensating for LNG metering sensor drift in cryogenic environments includes the following steps:

[0007] 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.

[0008] 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.

[0009] 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.

[0010] In a preferred embodiment, the data acquisition requires obtaining the data changes of strain gauges at different temperatures. During the design, a temperature sensing device is added near the internal resistance strain gauge, sensing circuit, and measurement circuit of the sensor, and the real-time internal ambient temperature data and displacement data are uploaded simultaneously.

[0011] In a preferred embodiment, G is calculated by logistic regression based on the residual error after the lookup table method and the numerical changes in each temperature range.

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

[0013] 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;

[0014] 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;

[0015] 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.

[0016] 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.

[0017] In a preferred embodiment, the numerical changes within each temperature segment are determined, and the proportion of continuous compensation values ​​between adjacent temperature segments is judged. A negative number of the proportion of continuous compensation values ​​between adjacent temperature segments represents the numerical changes within each temperature segment. The proportion of continuous compensation values ​​between adjacent temperature segments is calculated by dividing the working temperature range into multiple temperature segments and counting the number of segments where the difference in compensation values ​​at the segment boundaries is less than a preset threshold δ, which is the proportion of the total number of adjacent temperature segments.

[0018] In a preferred embodiment, when G is greater than the system preset threshold, curve fitting is used 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.

[0019] In a preferred embodiment, G is determined, and the data interval distribution is calculated by weighted summation. The data interval distribution refers to the degree of fluctuation of the compensation voltage data between each cell within the operating temperature range after the lookup table method is processed. It is obtained by calculating the standard deviation of the compensation value between each cell and taking the average value.

[0020] In a preferred embodiment, a selection coefficient is determined. When the value of the selection coefficient is greater than a 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.

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

[0022] 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.

[0023] 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.

[0024] 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.

[0025] In a preferred embodiment, the dynamic compensation decision module includes a residual error evaluation module, which is used to calculate the residual error point by point using an ideal reference data matrix and an actual output matrix.

[0026] The technical effects and advantages of this invention are as follows:

[0027] This invention collects sensor output data; processes the data; establishes a temperature compensation model for the sensor; analyzes and mines the displacement change data of each range point within the operating temperature range after digital filtering, and uses a lookup table method to narrow down the vertical intervals of the data within the temperature range; calculates G based on the residual error after the lookup table method and the numerical changes within each temperature segment, and uses G to determine whether curve fitting is needed to further compensate the temperature compensation model; this avoids overfitting, which makes the originally accurate data more detached from the actual value; if G is greater than the system preset threshold, when using curve fitting, the least squares method or Legendre orthogonal polynomial fitting is used to further compensate the temperature compensation model based on the G value and the data interval distribution; the dynamic adaptation fitting method makes the fitted data more closely match the actual value; if G is less than the system preset threshold, no further operations are performed. Attached Figure Description

[0028] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0029] Figure 1 This is a schematic flowchart of the LNG metering sensor drift compensation method in a low-temperature environment according to the present invention.

[0030] Figure 2 This is a schematic diagram of the drift compensation system for LNG metering sensors in a low-temperature environment according to the present invention. Detailed Implementation

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

[0032] This invention collects sensor output data; processes the data; establishes a temperature compensation model for the sensor; analyzes and mines the displacement change data of each range point within the operating temperature range after digital filtering, and uses a lookup table method to narrow down the vertical intervals of the data within the temperature range; calculates G based on the residual error after the lookup table method and the numerical changes within each temperature segment; uses G to determine whether curve fitting is needed to further compensate the temperature compensation model; if G is greater than a system preset threshold, when using curve fitting, the least squares method or Legendre orthogonal polynomial fitting is used to further compensate the temperature compensation model based on the G value and the data interval distribution; if G is less than a system preset threshold, no further operations are performed.

[0033] Example 1

[0034] The present invention provides a method for compensating for the drift of LNG metering sensors in low-temperature environments, such as... Figure 1 As shown, it includes the following steps:

[0035] Collect sensor output 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.

[0036] 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.

[0037] 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.

[0038] Specifically:

[0039] A temperature compensation model for a high-precision displacement sensor is established. The sensor is placed in a slowly changing temperature field, with the temperature range set to the product's designed operating temperature range. Displacement data is collected at the zero-scale point, full-scale point, and multiple range points in between (depending on the situation) using measurement analysis and calibration software. The displacement change data at each range point within the operating temperature range are digitally filtered, analyzed, and mined. A lookup table method is used to narrow down the vertical intervals of the data within the temperature range. Then, appropriate curve fitting is combined to perform linear fitting on the data, obtaining data with regular variations. A compensation model is then constructed, and finally, the measurements are corrected.

[0040] When studying the temperature characteristics of strain gauges, it was found that without digital filtering and lookup table processing, the changing displacement data exhibited significant fluctuations and lacked clear patterns and linear relationships. Processed data retains its original characteristics, reduces data redundancy and computational load, and easily yields data with regular patterns and locally linear relationships. Data acquisition requires capturing strain gauge data changes at different temperatures. The design incorporates temperature sensing devices near the internal resistance strain gauge, sensing circuit, and measurement circuit of the sensor, and simultaneously uploads real-time internal ambient temperature data along with displacement data to facilitate the acquisition of data changes at different temperatures. Through data change analysis and mining, numerical expressions or curves approximating the data changes are obtained. During data acquisition, the displacement sensor is moved to a fixed position, and the sampling frequency of the displacement sensor can be set to 10Hz. The baseline value of the sensor's displacement data at this point is recorded. At a temperature of T_N, 10 displacement data points are collected for every 0.1℃ increase. The median average filtering method is used to calculate the fitted value for each 0.1℃ point, obtaining the output voltage data drift distribution. Analysis of the measurement data shows that the cumulative displacement data value increases with increasing temperature. After digital filtering, a segmented data table is constructed, including the base temperature, sub-segment temperature, and corresponding output voltage. The data values ​​collected for each 0.1℃ point are then filled into the corresponding positions. The processed data amplitude retains the characteristics of the original data, to some extent avoiding points with large data errors and improving the compensation accuracy of the high-precision displacement sensor.

[0041] The reference voltage represents the compensation voltage for the corresponding temperature range at the current temperature. It is obtained by subtracting the set temperature reference voltage value from the current temperature voltage value. For every 0.1℃ increase in temperature, the current compensation voltage is increased by the reference voltage, resulting in: Where VN is the reference value for a certain temperature range; Vi is the actual voltage at the current temperature; V_REF is the reference voltage for the set temperature; and V_B is the compensation voltage for every 0.1℃ change within the temperature range, i.e., the offset voltage caused by temperature changes.

[0042] Using a lookup table method can effectively reduce the magnitude of displacement deviation caused by temperature drift, and compensate data with large changes to a smaller range. The compensation accuracy depends on the reference voltage and the accurate compensation within the temperature range of 0.1℃, forming a two-dimensional lookup table compensation method, which improves the efficiency of the ordinary lookup table method.

[0043] Simultaneously, based on the two-dimensional lookup table method, curve fitting is used to compensate for the shortcomings of the temperature compensation model in terms of flexibility and compensation accuracy, thereby increasing the reliability of temperature compensation for high-precision displacement sensors. However, in some cases, further processing of compensation accuracy using curve fitting is unnecessary; excessive compensation can cause the compensated value to deviate further from the normal value. Based on the residual error after the lookup table method and the numerical changes within each temperature range, G is calculated, and G is used to determine whether further compensation of the temperature compensation model using curve fitting is necessary.

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

[0045] Step A1: Construct an ideal reference data matrix; obtain the ideal output value matrix of the system under test using high-precision instruments or theoretical models. ,in and For discrete points of input variables (such as sensor input voltage, temperature, etc.), cover all grid points of the compensation table.

[0046] 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 (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 into a percentage or a proportion relative to full scale.

[0048] 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.

[0049] The two-dimensional lookup table method stores the compensation voltage in temperature segments. However, the actual temperature may span multiple segments, and segmented compensation may lead to discontinuities in compensation values ​​between adjacent temperature segments. This method determines the numerical changes within each temperature segment and assesses the proportion of continuous compensation values ​​between adjacent temperature segments. This proportion is calculated by dividing the operating temperature range into multiple temperature segments and counting the number of segments where the difference in compensation value at the segment boundary is less than a preset threshold δ, relative to the logarithm of all adjacent temperature segments. A lower proportion of continuous compensation values ​​between adjacent temperature segments necessitates fitting a continuous function within each temperature segment to achieve accurate compensation during temperature changes, avoid segment jumps, and enhance compensation continuity. A negative proportion of continuous compensation values ​​between adjacent temperature segments indicates varying numerical values ​​within each temperature segment; a higher proportion indicates less variation in numerical values ​​within each temperature segment.

[0050] Furthermore, to determine the residual error after the table lookup method and the numerical variations within each temperature range, the residual error and numerical variations within each temperature range are first normalized. Specifically, the following formula is used to normalize the data. Normalized to the range [0,1]: ;in The value is the normalized value. and Data respectively The minimum and maximum values.

[0051] Then, G is calculated using the logistic regression formula, as follows: Where wc represents the residual error after the lookup table method, the larger the residual error after the lookup table method, the larger G is, and vice versa; bh represents the numerical change in each temperature range, the larger the numerical change in each temperature range, the larger G is, and vice versa; α and β are the logistic regression coefficients of the residual error after the lookup table method and the numerical change in each temperature range, respectively; e is the natural base.

[0052] Determine the value of G, and use G to determine whether curve fitting is needed to further compensate the temperature compensation model. When G is greater than the system's preset threshold, it indicates that the temperature compensation model is insufficient in terms of flexibility and accuracy. In this case, curve fitting should be used on top of the two-dimensional lookup table method to compensate for the shortcomings in flexibility and accuracy of the temperature compensation model, thereby increasing the reliability of temperature compensation for high-precision displacement sensors.

[0053] Furthermore, when using curve fitting, the selection coefficients are calculated by weighted summation using G and the data interval distribution. The value of the selection coefficients determines whether to use the least squares method or Legendre orthogonal polynomial fitting.

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

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

[0056] A smaller G value indicates a smaller range to be fitted. Since the least squares method is less effective at preventing overfitting than Legendre orthogonal polynomial fitting, Legendre orthogonal polynomial fitting is necessary when G is small. Legendre orthogonal polynomial fitting is used if the data is distributed within a fixed interval because it is based on orthogonal basis functions, has high numerical stability, and is suitable for high-precision nonlinear approximation within a fixed interval.

[0057] Based on the two-dimensional lookup table method, curve fitting is used to compensate for the shortcomings of temperature compensation models in terms of flexibility and accuracy, thereby increasing the reliability of temperature compensation for high-precision displacement sensors. Two curve fitting methods are employed. Least squares is a mathematical optimization technique that uses scattered data to perform regression analysis on related variables with nonlinear relationships to find the optimal regression function. Its goal is to find the parameters that make the fitted model as close as possible to the actual observations by minimizing the sum of squared residuals between the observed data and the fitted curve. This method can easily obtain unknown data, is theoretically simple, and has low computational cost, making it widely used in curve fitting, regression analysis, and data processing.

[0058] Legendre orthogonal polynomial fitting is a function of weights on the interval [-1, 1]. An orthogonal sequence of polynomials. Its orthogonality is expressed as follows: if m ≠ n, then... If m equals n, then ;

[0059] The recursive formula is: , , .

[0060] In discrete data fitting, Legendre polynomials approximate functions using the least squares method. Their orthogonality can reduce the condition number of the system of equations and improve numerical stability, making them particularly suitable for high-order fitting.

[0061] Example 2

[0062] The present invention provides a drift compensation system for LNG metering sensors in cryogenic environments, such as... Figure 2 As shown, it includes the following modules: temperature compensation model construction module, residual error evaluation module, dynamic compensation decision module, and curve fitting execution module;

[0063] 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.

[0064] The residual error assessment module calculates the residual error point by point using the ideal reference data matrix and the actual output matrix;

[0065] 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.

[0066] 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.

[0067] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented 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 implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0068] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0069] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0070] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0071] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

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. Based on the residual error after the table lookup method and the numerical changes within each temperature range, G is calculated using logistic regression, with the specific formula as follows: ; where wc represents the residual error after the lookup method, the larger the residual error after the lookup method, the larger G is, and vice versa; bh represents the numerical change in each temperature range, the larger the numerical change in each temperature range, the larger G is, and vice versa; α and β are the logistic regression coefficients of the residual error after the lookup method and the numerical change in each temperature range, respectively; e is the natural base. 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. Based on G, it is determined whether curve fitting is needed to further compensate the temperature compensation model; the numerical changes in each temperature segment are represented by the negative number of the proportion of continuous compensation values ​​between adjacent temperature segments. The proportion of continuous compensation values ​​between adjacent temperature segments is: the working temperature range is divided into multiple temperature segments, and the number of segments in which the difference in compensation values ​​at the segment boundary is less than a preset threshold δ is counted, which is the proportion of the total number of adjacent temperature segments. 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. 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. It is obtained by calculating the standard deviation of the compensation value between each cell and taking the average value. If G is less than the system's preset threshold, no further operation 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 method for compensating for drift of LNG metering sensors in low-temperature environments according to claim 1, 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.

4. The drift compensation method for LNG metering sensors in low-temperature environments according to claim 1, characterized in that: Determine G and the data interval distribution, calculate the selection coefficient by weighted summation, and use the least squares method for curve fitting when the value of the selection coefficient is greater than the system preset threshold, and use Legendre orthogonal polynomial fitting when the value of the selection coefficient is less than the system preset threshold.

5. 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 1-4, 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 a base segment and sub-segments, store the compensation voltage for the corresponding temperature segment, 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 the lookup table method, 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 segment. Based on G, it is determined whether curve fitting is needed to further compensate the temperature compensation model. The numerical changes in each temperature segment are represented by the negative number of the proportion of continuous compensation values ​​between adjacent temperature segments. The proportion of continuous compensation values ​​between adjacent temperature segments is calculated by dividing the working temperature range into multiple temperature segments and counting the number of segments where the difference in compensation values ​​at the segment boundaries is less than a preset threshold δ, which is the proportion of the total number of adjacent temperature segments. The curve fitting execution module is used to determine whether to use the least squares method 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. 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 lookup table method is processed. It is obtained by calculating the standard deviation of the compensation value between each cell and taking the average value.

6. The cryogenic environment LNG metering sensor drift compensation system according to claim 5, 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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