Low-temperature Coriolis mass flowmeter temperature monitoring system based on fiber grating sensing

Abstract

The invention discloses a low-temperature Coriolis mass flowmeter temperature monitoring system based on fiber grating sensing, and relates to the field of low-temperature flow measurement. Comprising the following steps: signal acquisition and multi-band decomposition: acquiring wavelength drift and vibration acceleration signals and performing frequency domain segmentation; the thermal vibration coupling feature extraction module is used for calculating a coupling energy operator and generating an entropy change modulation factor; the thermal vibration entanglement generation module is used for fusing the characteristics of each frequency band to obtain the thermal vibration entanglement; the multi-state manifold recognition and construction module is used for dividing states according to the thermal vibration entanglement degree and extracting statistical characteristics to construct a temperature manifold; the ice entropy projection temperature estimation module is used for correcting the temperature projection value to obtain real temperature change; and the quantum tunneling phase compensation module is used for constructing a compensation model based on the temperature change and obtaining a net phase difference. By constructing a thermal vibration coupling model and introducing a thermal vibration entanglement index and a quantum tunneling compensation mechanism, the interaction between temperature disturbance and structural vibration is quantified, and the stability and accuracy of phase measurement in a low-temperature environment are improved.

Description

Technical Field

[0001] This invention relates to the field of low-temperature flow measurement, specifically to a temperature monitoring system for a low-temperature Coriolis mass flow meter based on fiber optic grating sensing. Background Technology

[0002] Cryogenic Coriolis mass flow meters offer advantages such as high precision and the absence of moving parts in the transport and measurement of cryogenic liquids. However, their measurement accuracy is susceptible to the nonlinear coupling effect of environmental temperature disturbances and structural vibrations, exhibiting significant signal drift and dynamic response hysteresis, especially under extreme low-temperature conditions. To improve the accuracy of flow measurement and the real-time performance of temperature compensation, it is necessary to perform precise sensing and dynamic correction of key physical quantities in the flow meter.

[0003] Fiber Bragg gratings (FBGs) have been widely used for temperature monitoring in low-temperature environments due to their excellent electromagnetic interference resistance and sensitive temperature response characteristics. However, the wavelength drift signal of FBGs and the structural vibration signal exhibit complex frequency domain coupling characteristics in low-temperature fields. Traditional temperature demodulation methods cannot effectively distinguish the interaction effects between thermal disturbances and mechanical interferences, thus limiting further improvements in temperature correction accuracy.

[0004] Existing methods mostly rely on fixed-band filtering, linear regression models, or static compensation strategies, which are difficult to adapt to the non-stationary characteristics of multi-source interference in cryogenic flow systems and cannot achieve dynamic estimation of real temperature changes or in-depth compensation of the original phase signal. Summary of the Invention

[0005] Based on the shortcomings of the prior art described above, the purpose of this invention is to provide a temperature monitoring system for a low-temperature Coriolis mass flow meter based on fiber optic grating sensing, so as to solve the above-mentioned technical problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a low-temperature Coriolis mass flow meter temperature monitoring system based on fiber Bragg grating sensing, comprising: Signal acquisition and multi-band decomposition module: Acquires wavelength drift signal and vibration acceleration signal of fiber optic grating in low temperature Coriolis mass flow meter, and performs frequency domain segmentation through multi-band signal decomposition; Thermal vibration coupling feature extraction module: In each frequency band, the coupling energy operator is calculated based on the wavelength drift signal and vibration acceleration signal, and the entropy-change modulation factor is generated by combining the signal complexity difference; Thermal entanglement generation module: Based on the coupling energy operator and entropy-change modulation factor of each frequency band, a weighted normalization strategy is used to generate thermal entanglement. Multi-state manifold identification and construction module: Based on the thermal vibration entanglement degree, the multi-state manifold of the temperature sensing signal is divided. Within different state manifolds, the wavelength drift signal is statistically extracted using a local sliding window to construct a temperature manifold projection operator. Ice entropy projection temperature estimation module: Combining temperature location parameters and ice entropy correction coefficients, the projection value of the wavelength drift signal on the temperature manifold projection operator is weighted and corrected to obtain a dynamic estimate of the actual temperature change.

[0007] Quantum tunneling phase compensation module: Based on the real temperature change and the original phase difference signal, a quantum tunneling compensation model is constructed to obtain the net phase difference.

[0008] The present invention is further configured such that the signal acquisition and multi-band decomposition module includes: The wavelength drift signal and the vibration acceleration signal coupled to the fiber optic grating sensor deployed in the low-temperature Coriolis mass flow meter are collected, and the wavelength drift signal and vibration acceleration signal are decomposed into multi-band signals. By constructing a continuously differentiable frequency domain window function, frequency domain filtering and segmentation are performed on signals of different frequency bands to extract wavelength drift signal components and vibration acceleration signal components in different frequency bands.

[0009] The present invention is further configured such that the thermal vibration coupling feature extraction module includes: Based on the wavelength drift signal components and vibration acceleration signal components in different frequency bands, calculate the coupling energy operator of the complex signal composed of wavelength drift signal and vibration acceleration signal in each frequency band; The signal complexity is calculated based on the entropy measurement of wavelength drift signals and vibration acceleration signals in each frequency band, and the complexity difference between wavelength drift signals and vibration acceleration signals is obtained. Based on the coupling energy operator and the complexity difference, an entropy-change modulation factor is generated.

[0010] The present invention is further configured such that the thermal vibration entanglement degree generation module includes: Multi-band temperature disturbance signals are acquired using a distributed fiber optic grating array, and a coupling efficiency function is constructed by modeling the coupling energy operator and entropy-change modulation factor for each frequency band. Within a preset time window, the frequency normalized density weight is calculated based on the coupling effectiveness function, and the coupling response of each frequency band is fused using a nonlinear weighted normalization strategy to obtain the thermal entanglement degree that reflects the thermal coupling strength.

[0011] The present invention is further configured such that the multi-state manifold identification and construction module includes: Based on the thermal vibration entanglement degree, the temperature sensing signal is divided into multiple state manifolds to generate multiple state manifolds, each of which corresponds to a different thermal vibration coupling strength range. Within each state manifold, a local sliding window is used to extract statistical features of the wavelength drift signal, including the amplitude, rate of change of time, and nonlinear entropy of the wavelength drift signal. Principal component analysis is applied to the extracted statistical features to generate a temperature manifold projection operator.

[0012] The present invention is further configured such that the ice entropy projection temperature estimation module includes: The temperature sensing signal is normalized based on the normalized temperature location parameters to obtain a standardized temperature location representation. By combining the normalized temperature location parameter and the ice entropy correction coefficient, the projection value of the wavelength drift signal on the temperature manifold projection operator is corrected by weighting. By weighting and correcting the projected values ​​of the wavelength drift signal, a dynamic estimate of the actual temperature change is obtained.

[0013] The present invention is further configured such that the quantum tunneling phase compensation module includes: A quantum tunneling compensation model is constructed based on the real temperature change signal and the original phase difference signal, and the quantum tunneling compensation coefficient is obtained. The dynamic integral weight is calculated by combining the quantum tunneling compensation coefficient and the temperature change rate. By using quantum tunneling compensation coefficient and dynamic integral weight to weight and correct the original phase difference signal, the net phase difference after quantum tunneling compensation is obtained, which further improves the temperature monitoring accuracy of the low-temperature Coriolis mass flow meter.

[0014] The present invention is further configured to input the net phase difference signal as the corrected dynamic response quantity into the mass flow analysis model of the Coriolis mass flow meter, and perform a trade-off calculation through the instrument coefficient and temperature-related calibration function to achieve continuous estimation of the actual mass flow.

[0015] The present invention is further configured such that the system also includes a visualization module for: Frequency domain spectra of the multi-band decomposition results of wavelength drift signals and vibration acceleration signals are displayed. Tensor diagrams, thermograms, or 3D projection diagrams are used to display the evolution of coupling energy operators, entropy-change modulation factors, and thermal entanglement in each frequency band. Multi-dimensional state diagrams and embedded manifold diagrams are used to display the multi-state manifolds of temperature sensing signals and their corresponding statistical characteristics. Trend graphs and residual analysis graphs are presented to show the net phase difference signal after quantum tunneling compensation and its impact on mass flow rate estimation.

[0016] This invention provides a temperature monitoring system for a low-temperature Coriolis mass flow meter based on fiber Bragg grating sensing. The system comprises a signal acquisition and multi-band decomposition module: acquiring wavelength drift and vibration acceleration signals from the fiber Bragg grating in the low-temperature Coriolis mass flow meter and performing frequency domain segmentation through multi-band signal decomposition; a thermo-vibration coupling feature extraction module: calculating coupling energy operators based on wavelength drift and vibration acceleration signals within each frequency band, and generating entropy-variable modulation factors by combining signal complexity differences; and a thermo-vibration entanglement generation module: generating thermo-vibration entanglement degree based on the coupling energy operators and entropy-variable modulation factors of each frequency band using a weighted normalization strategy. The multi-state manifold identification and construction module classifies the temperature sensing signal into multi-state manifolds based on thermal entanglement. Within different manifolds, a local sliding window is used to extract statistical features of the wavelength drift signal, constructing a temperature manifold projection operator. The ice entropy projection temperature estimation module combines temperature location parameters and ice entropy correction coefficients to weighted correct the projection values ​​of the wavelength drift signal onto the temperature manifold projection operator, obtaining a dynamic estimate of the true temperature change. The quantum tunneling phase compensation module constructs a quantum tunneling compensation model based on the true temperature change and the original phase difference signal, obtaining the net phase difference. The beneficial effects include: 1. Constructing a coupled quantification mechanism for thermal vibration interaction: A thermal vibration coupling modeling method linking coupled energy operator and entropy-change modulation factor is proposed to quantify the energy transfer and structural complexity differences between multi-band signals, providing a computable physical basis for the interaction between temperature disturbance and structural vibration. 2. A characterization index for thermal vibration entanglement is proposed: By constructing a coupling effectiveness function and integrating the coupling characteristics of each frequency band, a new concept of thermal vibration entanglement is proposed to measure the coupling strength between temperature disturbance and vibration response, thereby enhancing the ability to perceive complex temperature dynamic characteristics. 3. Integrating quantum tunneling mechanism to correct phase drift error: Establishing a quantum tunneling compensation model based on temperature change rate to dynamically correct the original phase difference signal, suppressing phase distortion caused by microscale energy barrier perturbation under low temperature environment, and improving the stability and repeatability of phase difference measurement.

[0017] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a structural diagram of a cryogenic Coriolis mass flow meter temperature monitoring system based on fiber optic grating sensing, as an exemplary embodiment of the present invention. Detailed Implementation

[0019] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0020] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0021] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0022] Temperature monitoring system based on fiber optic grating sensing for cryogenic Coriolis mass flow meter, such as Figure 1 As shown, it includes: Signal acquisition and multi-band decomposition module: Acquires wavelength drift signal and vibration acceleration signal of fiber optic grating in low temperature Coriolis mass flow meter, and performs frequency domain segmentation through multi-band signal decomposition; Thermal vibration coupling feature extraction module: In each frequency band, the coupling energy operator is calculated based on the wavelength drift signal and vibration acceleration signal, and the entropy-change modulation factor is generated by combining the signal complexity difference; Thermal entanglement generation module: Based on the coupling energy operator and entropy-change modulation factor of each frequency band, a weighted normalization strategy is used to generate thermal entanglement. Multi-state manifold identification and construction module: Based on the thermal vibration entanglement degree, the multi-state manifold of the temperature sensing signal is divided. Within different state manifolds, the wavelength drift signal is statistically extracted using a local sliding window to construct a temperature manifold projection operator. Ice entropy projection temperature estimation module: Combining temperature location parameters and ice entropy correction coefficients, the projection value of the wavelength drift signal on the temperature manifold projection operator is weighted and corrected to obtain a dynamic estimate of the actual temperature change. Quantum tunneling phase compensation module: Based on the real temperature change and the original phase difference signal, a quantum tunneling compensation model is constructed to obtain the net phase difference.

[0023] The present invention is further configured such that the signal acquisition and multi-band decomposition module includes: The wavelength drift signal and the vibration acceleration signal coupled to the fiber optic grating sensor deployed in the low-temperature Coriolis mass flow meter are collected, and the wavelength drift signal and vibration acceleration signal are decomposed into multi-band signals. By constructing a continuously differentiable frequency domain window function, frequency domain filtering and segmentation are performed on signals of different frequency bands to extract wavelength drift signal components and vibration acceleration signal components at different frequency bands; specifically, a fiber optic grating sensor deployed in a low-temperature Coriolis mass flow meter is used to collect data over time. Wavelength drift signal This signal is closely related to temperature changes. The working principle of the fiber Bragg grating sensor is based on the wavelength drift caused by temperature changes. Therefore, the wavelength drift signal can be used to monitor the temperature in real time, while simultaneously acquiring the vibration acceleration signal coupled to the position of the fiber Bragg grating sensor. This signal is used to monitor acceleration fluctuations caused by mechanical vibration or changes in hydrodynamics; by constructing a frequency band set Multi-band signal decomposition is performed on wavelength drift signals and vibration acceleration signals, breaking them down into multiple sub-signals. Each sub-signal contains only frequency components within its designated band, improving signal processing accuracy and increasing bandwidth. The specific manifestations are as follows: ,in, For frequency band set Total number of frequency bands For frequency band index variables, For the first The lower limit frequency of each frequency band, For the first The upper limit frequency of each frequency band and The first The lowest frequency at the start of the first frequency band and the first The highest frequency at the end of each band; the band division is based on the fine segmentation of the signal spectrum using multi-resolution analysis methods, with upper and lower frequency limits for each band. and Automatically generated by the signal analysis process to ensure that the frequency domain decomposition of the signal does not overlap and has a clear physical meaning, with the lower and upper limits of each frequency band satisfying the required values. and To ensure no overlap between adjacent frequency bands and avoid signal interference between different frequency bands, the upper and lower frequency limits for each band are set. and This is used to determine the frequency domain decomposition range of the signal, and in subsequent signal processing, the signal components corresponding to each frequency band will be analyzed independently to avoid cross-band interference; in order to perform accurate frequency domain segmentation, for each frequency band... Define a continuously differentiable frequency domain window function to filter signals in each frequency band. The purpose of the window function is to ensure a smooth transition of the signal and avoid edge effects introduced by frequency truncation. The specific form of the frequency domain window function is: ,in, For frequency domain window functions, For frequency variables; by shifting the signal for each wavelength and vibration acceleration signal Perform Fourier transform The frequency domain representation of the signal is obtained as follows: and , Frequency domain representation of wavelength-shifted signals Frequency domain representation of the vibration acceleration signal; using a frequency domain window function. The wavelength drift signal and vibration acceleration signal, represented in the frequency domain, are subjected to frequency domain filtering, and the signals are then segmented in the frequency domain to obtain wavelength drift signal components and vibration acceleration signal components in different frequency bands. The calculation logic for the wavelength drift signal components is as follows: , For the first Wavelength drift signal components in the frequency band The calculation logic for the vibration acceleration signal components, based on the inverse Fourier transform, is as follows: , For the first The vibration acceleration signal components in the specified frequency bands are extracted. Through the above frequency domain filtering process, the wavelength drift signal components and vibration acceleration signal components in each frequency band are extracted from the original wavelength drift signal and vibration acceleration signal, respectively. and It reflects temperature and vibration information in different frequency ranges; by constructing a continuously differentiable frequency domain window function for frequency domain filtering, frequency domain overlap or signal fragmentation problems can be avoided, thereby ensuring the independence of frequency band signals and the integrity of information.

[0024] The present invention is further configured such that the thermal vibration coupling feature extraction module includes: Based on the wavelength drift signal components and vibration acceleration signal components in different frequency bands, calculate the coupling energy operator of the complex signal composed of wavelength drift signal and vibration acceleration signal in each frequency band; The signal complexity is calculated based on the entropy measurement of wavelength drift signals and vibration acceleration signals in each frequency band, and the complexity difference between wavelength drift signals and vibration acceleration signals is obtained. Based on the coupling energy operator and complexity differences, an entropy-change modulation factor is generated. Specifically, the frequency components of the wavelength drift signal and vibration acceleration signal in each frequency band are constructed as complex signals. These complex signals are used to describe the amplitude and phase information of the signal. The calculation logic for the complex signals is as follows: , For the first Complex signals in each frequency band The unit is the imaginary unit. For each frequency band of complex signal, a coupling energy operator is calculated. This operator captures the coupling strength between signals in different frequency bands by nonlinearly weighting the amplitude of the complex signal, reflecting the interaction between wavelength drift signals and vibration acceleration signals. The calculation logic of the coupling energy operator is as follows: , For the first Each frequency band at time Coupled energy operator, For the first Each frequency band at time Complex signals, For nonlinear energy index, For exponential decay coefficient, This is the width of the sliding time window; The weights used to enhance the large-scale coupling effect have a value range of [1.5, 3]. The signal complexity is used to reflect the time delay effect of temperature changes or vibration acceleration changes on the signal, with a value range of [0.5, 10]. The signal complexity of each frequency band is calculated based on a nonlinear entropy metric. Signal complexity measures the complexity of signal changes within a certain time window, reflecting the non-stationarity of the signal structure. The entropy metric reflects the time-varying structural complexity of the signal. The calculation logic for signal complexity is as follows: , For the first Each frequency band corresponds to a signal component at time... signal complexity, For the first Each frequency band at time signal components, For entropy gain exponent, It is a constant; A metric used to enhance signal complexity, with values ​​ranging from [1.5, 3]. Used to prevent logarithmic singularity, its value range is [0.000001, 0.001]; The entropy kernel function is used to measure the complexity of how the signal amplitude changes over time. It can be a wavelength drift signal component. or vibration acceleration signal components The complexity difference reflects the degree of dynamic change of wavelength drift signals and vibration acceleration signals within the frequency band. A large complexity difference indicates a strong interaction between the two. The calculation logic for the complexity difference is as follows: , For the first Each frequency band corresponds to the wavelength drift signal component and the vibration acceleration signal component at time... Differences in complexity For the first The wavelength drift signal component corresponding to each frequency band at time... signal complexity, For the first Each frequency band corresponds to the vibration acceleration signal component at time 1 The signal complexity; the entropy-variable modulation factor combines the difference between coupling energy and signal complexity, and can dynamically adjust the signal strength and compensate for the nonlinear characteristics of the system. When the coupling effect is strong or the complexity difference is large, the entropy-variable modulation factor will increase, thereby enhancing the response to signal changes. The calculation logic of the entropy-variable modulation factor is as follows: , For the first Each frequency band at time Entropy-change modulation factor It is a time constant; The sensitivity of the adjustment factor to the difference in coupling energy and complexity is measured in the range of [0.01, 10]. Through the synergistic effect of the coupling energy operator, complexity difference and entropy modulation factor, the precise capture and adaptive adjustment of nonlinear coupling and dynamic changes of the signal are achieved, thereby improving the accuracy of temperature monitoring by the low-temperature Coriolis mass flow meter.

[0025] The present invention is further configured such that the thermal vibration entanglement degree generation module includes: Multi-band temperature disturbance signals are acquired using a distributed fiber optic grating array, and a coupling efficiency function is constructed by modeling the coupling energy operator and entropy-change modulation factor for each frequency band. Within a preset time window, frequency-normalized density weights are calculated based on the coupling effectiveness function, and a nonlinear weighted normalization strategy is used to fuse the coupling responses of each frequency band to obtain the thermal entanglement degree, which reflects the thermal coupling strength. Specifically, the coupling effectiveness function is a quantitative representation of the coupling strength in each frequency band, which can accurately describe the coupling response of the signal in each frequency band. Combined with the influence of coupling energy and entropy-change modulation factor, it reflects the influence of temperature disturbances in different frequency bands. The construction logic of the coupling effectiveness function is as follows: , For the first Each frequency band at time Coupling efficiency function and For weighted index, For adjustment coefficient, To adjust the parameters; and Used to control the relative contributions of the coupling energy operator and the entropy-change modulation factor to the coupling efficiency function, with a value range of [0,5]. Used to control the difference sensitivity between the coupled energy operator and the entropy modulation factor, with a value range of [0,10]; Used to reflect the synchronization of coupling effectiveness and modulation factor, the value range is [0.5, 3]. The frequency normalized density weight calculated based on the coupling effectiveness function can adjust the weight distribution of the frequency band according to the contribution of the coupling effectiveness function of each frequency band, ensuring that the contribution of each frequency band in the coupling strength calculation is reasonable, and avoiding the signal of certain frequency bands dominating the overall calculation. The calculation logic of the frequency normalized density weight is as follows: , For the first Each frequency band at time Frequency normalized density weights For the first Each frequency band at time Coupling efficiency function For the first Each frequency band at time Coupling efficiency function For frequency band index variables, For nonlinear exponents, It is a constant; The weights used to enhance the high-response frequency band have a value range of [1.5, 3]. To prevent the denominator from being zero, the value range is [0.0000001, 0.001]. Thermal entanglement is used to represent the coupling strength between signals in different frequency bands and the thermal-vibrational coordination characteristics of the system. It can quantify the coupling response of signals in different frequency bands, thus providing a basis for subsequent temperature estimation and system monitoring. The calculation logic of thermal entanglement is as follows: , For a moment Thermal entanglement For the first Each frequency band at time Coupling efficiency function For nonlinear amplification index, Normalized suppression index, It is a constant; Used to control the nonlinear amplification effect of the coupling efficiency function in each frequency band in the denominator, with a value range of [1.5, 3]; Used to control the degree of inhibition of the numerator response by the denominator as a whole, with a value range of [0,1]; To prevent the denominator from being 0, the value range is [0.0000001, 0.001]. Through the above logic, the coupling strength of signals in different frequency bands can be effectively calculated, and thermal entanglement degree can be generated through frequency normalization weight and nonlinear weighted normalization strategy, so as to realize temperature monitoring and system performance optimization of low temperature Coriolis mass flow meter.

[0026] The present invention is further configured such that the multi-state manifold identification and construction module includes: Based on the thermal vibration entanglement degree, the temperature sensing signal is divided into multiple state manifolds to generate multiple state manifolds, each of which corresponds to a different thermal vibration coupling strength range. Within each state manifold, a local sliding window is used to extract statistical features of the wavelength drift signal, including the amplitude, rate of change of time, and nonlinear entropy of the wavelength drift signal. Principal component analysis was applied to the extracted statistical features to generate a temperature manifold projection operator; specifically, based on the thermal entanglement degree, the temperature sensing signal was divided into multiple state manifolds. By dividing the manifold into multiple states, each manifold representing a different range of thermal-vibration coupling strength, the variation characteristics of temperature sensing signals under different physical states can be captured, facilitating subsequent feature extraction and construction of temperature manifold projection operators. According to the preset interval The state is divided into M state manifolds, where M is the total number of state manifolds. The specific form of each state manifold is as follows: , For the first A state manifold, and The first The lower and upper bounds of each state manifold; in each state manifold The sliding window is used to extract local features of the wavelength drift signal. The specific form of the sliding window is as follows: , A sliding window is used to calculate the local characteristics of the temperature sensing signal, ensuring that only the local properties of the signal near the current time point are considered; for each sliding window... Statistical features are extracted, including the amplitude, rate of change over time, and nonlinear entropy of the wavelength drift signal. Amplitude is used to capture temperature fluctuations; the rate of change over time measures the rate of change of the signal, revealing the dynamic characteristics of the wavelength drift signal and reflecting the instantaneous rate of temperature change; nonlinear entropy quantifies the dynamic complexity of the signal, with higher values ​​indicating more complex and unpredictable signal changes, while lower values ​​indicate a more stable signal; amplitude includes a maximum and a minimum value, and the calculation logic for the maximum amplitude is as follows: , In the sliding window The maximum amplitude of the internal wavelength drift signal For a moment The logic for calculating the minimum amplitude of the wavelength drift signal at a given location is as follows: , In the sliding window The minimum amplitude of the internal wavelength drift signal. Indicates a sliding window The time point within the period; the calculation logic for the rate of change over time is as follows: , In the sliding window Instantaneous rate of change of the internal wavelength drift signal In the sliding window The absolute value of the internal wavelength drift signal. The calculation logic is as follows: The calculation logic for nonlinear entropy is as follows: , In the sliding window Nonlinear entropy of internal wavelength drift signal At any moment Wavelength drift signal at the location It is the entropy gain exponent; This is used to enhance the contribution of signal amplitude to entropy measurement through nonlinear amplification, with a value range of [1,3]. By extracting the above statistical features, the local dynamic changes, amplitude changes, and complexity of the wavelength drift signal under different state manifolds can be captured. The temperature manifold projection operator is generated by principal component analysis. The temperature manifold projection operator is used to project the features of the wavelength drift signal from a high-dimensional space to a low-dimensional space to achieve a simplified representation of the signal. The calculation logic of the temperature manifold projection operator is as follows: , For the first A state manifold at time 1 Temperature manifold projection operator, The projection matrix to be optimized; the projection matrix Principal component analysis (PCA) is used to obtain the following steps: First, wavelength drift signal data is extracted within a set time window to construct a sample matrix; then, the data is zero-mean normalized, its covariance matrix is ​​calculated, and eigenvalue decomposition is performed on the matrix to extract the eigenvectors corresponding to the first r largest eigenvalues, thus constructing a projection matrix. This is used to perform linear projection on the original signal, preserving its main feature components. This is an existing technology and will not be elaborated here. By generating a temperature manifold projection operator, high-dimensional signal features can be simplified into low-dimensional representations, which facilitates subsequent temperature estimation and improves the system's response speed and accuracy.

[0027] The present invention is further configured such that the ice entropy projection temperature estimation module includes: The temperature sensing signal is normalized based on the normalized temperature location parameters to obtain a standardized temperature location representation. By combining the normalized temperature location parameter and the ice entropy correction coefficient, the projection value of the wavelength drift signal on the temperature manifold projection operator is corrected by weighting. By weighting and correcting the projected values ​​of the wavelength drift signal, a dynamic estimate of the actual temperature change is obtained. Specifically, the actual temperature value of the temperature sensing signal is mapped to a standardized range by normalizing the temperature location parameter. Normalization can standardize the temperature sensing signal within a fixed range, allowing different temperature sensing signal values ​​to be compared on the same scale, avoiding the influence of temperature deviations from different devices and environments on the results. The calculation logic of the normalized temperature location parameter is as follows: , For normalized temperature and location parameters, The current temperature sensor signal value, and These represent the minimum and maximum values ​​of the temperature sensing signal, respectively. The ice entropy correction coefficient is used to correct the nonlinear distortion between the temperature sensing signal and its manifold projection. The temperature sensing signal may undergo nonlinear distortion due to thermal vibration coupling effects, therefore, it needs to be compensated for by the ice entropy correction coefficient. The calculation logic of the ice entropy correction coefficient is as follows: , For the ice entropy correction coefficient, For the benchmark correction factor, For adjustment coefficient, The median of the temperature sensing signal, The standard deviation of the temperature sensor signal value. This is the error function, used to describe a smooth nonlinear transition process; The reference amplitude used to adjust the output of the error function has a value range of [0.5, 2]. Used to control the overall correction gain, with a value range of [-1, 1]; wavelength drift signal projection operator on temperature manifold. When projecting onto the surface, the projected value of the wavelength drift signal is weighted and corrected by combining the normalized temperature position parameter and the ice entropy correction coefficient. The calculation logic for the weighted correction is as follows: , The weighted corrected projection value of the wavelength drift signal is used to ensure that the signal representation in low-dimensional space reflects both the true characteristics of temperature change and compensates for distortion caused by nonlinear effects. Using the weighted corrected projection value of the wavelength drift signal, combined with the dynamic characteristics of temperature change, a dynamic estimate of the true temperature change is obtained. This estimate is based not only on the current wavelength drift signal but also incorporates the effects of the temperature manifold projection operator and the ice entropy correction coefficient, providing more accurate and dynamic temperature monitoring results. The calculation logic for the estimate of the true temperature change is as follows: , For real temperature change signals, The above calculation logic can improve the accuracy of temperature monitoring by low-temperature Coriolis mass flow meters, reduce the interference of temperature changes on measurement results, and provide stable dynamic temperature estimation, especially in the case of strong thermal vibration coupling effect or rapid temperature change.

[0028] The present invention is further configured such that the quantum tunneling phase compensation module includes: A quantum tunneling compensation model is constructed based on the real temperature change signal and the original phase difference signal, and the quantum tunneling compensation coefficient is obtained. The dynamic integral weight is calculated by combining the quantum tunneling compensation coefficient and the temperature change rate. The original phase difference signal is weighted and corrected using quantum tunneling compensation coefficients and dynamic integral weights to obtain the net phase difference after quantum tunneling compensation, further improving the temperature monitoring accuracy of the low-temperature Coriolis mass flow meter. Specifically, in low-temperature environments, the phase difference signal of the Coriolis mass flow meter is affected by temperature changes. Temperature changes cause wavelength drift of the fiber optic grating, thus affecting the phase difference measurement results. This effect is usually nonlinear, especially under low-temperature conditions, requiring quantum tunneling compensation to correct the phase deviation caused by temperature changes. A quantum tunneling compensation model is constructed based on the real temperature change signal and the original phase difference signal to obtain the quantum tunneling compensation coefficients. The calculation of the quantum tunneling compensation coefficients can correct the phase error caused by temperature changes, taking into account the nonlinear relationship between temperature changes and phase difference, which helps to improve the measurement accuracy of phase difference under low-temperature conditions. The calculation logic of the quantum tunneling compensation coefficients is as follows: , For quantum tunneling compensation coefficient, For temperature characteristic parameters, An empirical adjustment factor; Used to determine the weight of temperature change in the compensation coefficient, with a value range of [0.1, 10], and the unit is degrees Celsius; The attenuation effect of the temperature change rate on the phase difference is used to quantify the effect of temperature change rate on phase difference, with a value range of [0.3, 0.9]. The dynamic integral weight is used to reflect the cumulative effect of temperature change and compensation coefficient over time, and combined with the effect of temperature on the elastic modulus of the material, further enhances the accuracy of phase correction. The calculation logic of the dynamic integral weight is as follows: , For dynamic integral weights, quantum tunneling compensation coefficient, Temperature sensing signal Corresponding elastic modulus; elastic modulus It can be estimated through experimental measurements, literature searches, or based on physical models; the calculation logic for the net phase difference is as follows: , For net phase difference, The original phase difference signal is used. Through the above steps, the quantum tunneling compensation model can correct the phase deviation caused by temperature changes and improve the accuracy of temperature monitoring, especially in low-temperature environments.

[0029] The present invention is further configured to use the net phase difference signal as a corrected dynamic response quantity, input into the mass flow rate analytical model of the Coriolis mass flow meter, and perform a trade-off calculation using the instrument coefficient and temperature-related calibration function to achieve continuous estimation of the actual mass flow rate; specifically, the calculation logic of the actual mass flow rate is as follows: , For a moment Actual mass flow rate Temperature sensing signal The instrument coefficient below; Used to convert net phase difference into mass flow rate. The calculation logic is as follows: , Instrument coefficient at reference temperature, This is a compensation function for the temperature sensing signal; The calculation logic is as follows: , For calibrating temperature, Temperature compensation intensity factor, The standard deviation related to sensitivity to temperature changes; Used to reflect the degree of influence of temperature on the instrument coefficient, with a value range of [0,1]; Used to control the impact of temperature variation on the compensation function, with a value range of [0.5, 10], in degrees Celsius; through temperature compensation and correction of the instrument coefficient, the estimation of mass flow rate is ensured to remain highly accurate under temperature variations, which helps to accurately reflect the performance of the flow meter under different working environments.

[0030] The present invention is further configured such that the system also includes a visualization module for: Frequency domain spectra of the multi-band decomposition results of wavelength drift signals and vibration acceleration signals are displayed. Tensor diagrams, thermograms, or 3D projection diagrams are used to display the evolution of coupling energy operators, entropy-change modulation factors, and thermal entanglement in each frequency band. Multi-dimensional state diagrams and embedded manifold diagrams are used to display the multi-state manifolds of temperature sensing signals and their corresponding statistical characteristics. The system displays trend charts and residual analysis graphs for the net phase difference signal after quantum tunneling compensation and its impact on mass flow rate estimation. Specifically, the visualization module graphically displays and analyzes various data such as temperature sensing signals, wavelength drift signals, and vibration acceleration signals, enabling users to monitor, analyze, and optimize key indicators such as system status, thermal-vibration coupling degree, and mass flow rate in real time. Multi-band decomposition of wavelength drift and vibration acceleration signals is performed, and the power spectral density or energy distribution of each band is displayed through frequency domain spectra, helping to analyze the contribution of different frequency bands to system performance. This clearly presents the signal characteristics of each band and helps identify the frequency components in the system and their relationship with factors such as temperature and vibration. The system dynamically displays the changes in coupling energy operators, entropy modulation factors, and thermal-vibration entanglement degrees for each frequency band, using tensor diagrams and heat maps to represent changes in system status. Color depth or curve changes intuitively show the dynamic changes in thermal-vibration coupling characteristics, which helps in analyzing each frequency band. The coupling strength between the signals and its change over time is demonstrated; the distribution and evolution of temperature sensing signals in multi-state manifolds are shown, intuitively reflecting the dynamic changes of temperature sensing signals and the transitions between different states. Multi-state manifolds of temperature sensing signals are divided according to thermal entanglement, and statistical features such as amplitude, rate of change over time, and nonlinear entropy are extracted within each manifold. Multi-dimensional state diagrams and embedded manifold diagrams are used to display the multi-dimensional characteristics of temperature changes, effectively revealing the multi-state characteristics of temperature sensing signals, helping to analyze signal changes under different states, and contributing to improved temperature monitoring accuracy. The net phase difference after quantum tunneling compensation is displayed to help assess the impact of the compensated phase difference on mass flow estimation. Trend graphs show the real-time impact of the corrected phase difference on flow estimation, while residual analysis graphs show the error changes between the compensated phase difference and the mass flow estimation results. The graphical display of the changes in various signals helps technicians monitor and analyze the system status in real time and quickly identify potential problems.

[0031] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0032] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0033] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0034] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0035] 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.

[0036] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0037] In the several embodiments provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the device 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 devices or units may be electrical, mechanical, or other forms.

[0038] 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.

[0039] 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.

[0040] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0041] 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 technical scope 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 temperature monitoring system for a low temperature Korma mass flow meter based on fiber Bragg grating sensors, characterized in that, include: Signal acquisition and multi-band decomposition module: Acquires wavelength drift signal and vibration acceleration signal of fiber optic grating in low temperature Coriolis mass flow meter, and performs frequency domain segmentation through multi-band signal decomposition; Thermal vibration coupling feature extraction module: In each frequency band, the coupling energy operator is calculated based on the wavelength drift signal and vibration acceleration signal, and the entropy-change modulation factor is generated by combining the signal complexity difference; Thermal entanglement generation module: Based on the coupling energy operator and entropy-change modulation factor of each frequency band, a weighted normalization strategy is used to generate thermal entanglement. Multi-state manifold identification and construction module: Based on the thermal vibration entanglement degree, the multi-state manifold of the temperature sensing signal is divided. Within different state manifolds, the wavelength drift signal is statistically extracted using a local sliding window to construct a temperature manifold projection operator. Ice entropy projection temperature estimation module: Combining temperature location parameters and ice entropy correction coefficients, the projection value of the wavelength drift signal on the temperature manifold projection operator is weighted and corrected to obtain a dynamic estimate of the real temperature change. Quantum tunneling phase compensation module: Based on the real temperature change and the original phase difference signal, a quantum tunneling compensation model is constructed to obtain the net phase difference.

2. The fiber grating sensor based cryogenic klystron mass flow meter temperature monitoring system of claim 1, wherein, The signal acquisition and multi-band decomposition module includes: The wavelength drift signal and the vibration acceleration signal coupled to the fiber optic grating sensor deployed in the low-temperature Coriolis mass flow meter are collected, and the wavelength drift signal and vibration acceleration signal are decomposed into multi-band signals. By constructing a continuously differentiable frequency domain window function, frequency domain filtering and segmentation are performed on signals of different frequency bands to extract wavelength drift signal components and vibration acceleration signal components in different frequency bands.

3. The fiber grating sensor based cryogenic klystron mass flow meter temperature monitoring system of claim 1, wherein, The thermal vibration coupling feature extraction module includes: Based on the wavelength drift signal components and vibration acceleration signal components in different frequency bands, calculate the coupling energy operator of the complex signal composed of wavelength drift signal and vibration acceleration signal in each frequency band; The signal complexity is calculated based on the entropy measurement of wavelength drift signals and vibration acceleration signals in each frequency band, and the complexity difference between wavelength drift signals and vibration acceleration signals is obtained. Based on the coupling energy operator and the complexity difference, an entropy-change modulation factor is generated.

4. The fiber grating sensor based cryogenic klystron mass flow meter temperature monitoring system of claim 1, wherein, The thermal entanglement degree generation module includes: Multi-band temperature disturbance signals are acquired using a distributed fiber optic grating array, and a coupling efficiency function is constructed by modeling the coupling energy operator and entropy-change modulation factor for each frequency band. Within a preset time window, the frequency normalized density weight is calculated based on the coupling effectiveness function, and the coupling response of each frequency band is fused using a nonlinear weighted normalization strategy to obtain the thermal entanglement degree that reflects the thermal coupling strength.

5. The fiber grating sensor based cryogenic klystron mass flow meter temperature monitoring system of claim 1, wherein, The multi-state manifold identification and construction module includes: Based on the thermal vibration entanglement degree, the temperature sensing signal is divided into multiple state manifolds to generate multiple state manifolds, each of which corresponds to a different thermal vibration coupling strength range. Within each state manifold, a local sliding window is used to extract statistical features of the wavelength drift signal, including the amplitude, rate of change of time, and nonlinear entropy of the wavelength drift signal. Principal component analysis is applied to the extracted statistical features to generate a temperature manifold projection operator.

6. The fiber grating sensor based cryogenic klystron mass flow meter temperature monitoring system of claim 1, wherein, The ice entropy projection temperature estimation module includes: The temperature sensing signal is normalized based on the normalized temperature location parameters to obtain a standardized temperature location representation. By combining the normalized temperature location parameter and the ice entropy correction coefficient, the projection value of the wavelength drift signal on the temperature manifold projection operator is corrected by weighting. By weighting and correcting the projected values ​​of the wavelength drift signal, a dynamic estimate of the actual temperature change is obtained.

7. The fiber grating sensor based cryogenic klystron mass flow meter temperature monitoring system of claim 1, wherein, The quantum tunneling phase compensation module includes: A quantum tunneling compensation model is constructed based on the real temperature change signal and the original phase difference signal, and the quantum tunneling compensation coefficient is obtained. The dynamic integral weight is calculated by combining the quantum tunneling compensation coefficient and the temperature change rate. By using quantum tunneling compensation coefficient and dynamic integral weight to weight and correct the original phase difference signal, the net phase difference after quantum tunneling compensation is obtained, which further improves the temperature monitoring accuracy of the low-temperature Coriolis mass flow meter.

8. The fiber grating sensor based cryogenic klystron mass flow meter temperature monitoring system of claim 7, wherein, The net phase difference signal is used as the corrected dynamic response quantity and input into the mass flow analysis model of the Coriolis mass flow meter. By balancing the calculations using the instrument coefficient and the temperature-related calibration function, continuous estimation of the actual mass flow rate is achieved.

9. The temperature monitoring system for a low-temperature Coriolis mass flow meter based on fiber optic grating sensing according to claim 1, characterized in that, The system also includes a visualization module for: Frequency domain spectra of the multi-band decomposition results of wavelength drift signals and vibration acceleration signals are displayed. Tensor diagrams, thermograms, or 3D projection diagrams are used to display the evolution of coupling energy operators, entropy-change modulation factors, and thermal entanglement in each frequency band. Multi-dimensional state diagrams and embedded manifold diagrams are used to display the multi-state manifolds of temperature sensing signals and their corresponding statistical characteristics. Trend graphs and residual analysis graphs are presented to show the net phase difference signal after quantum tunneling compensation and its impact on mass flow rate estimation.

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