Gas density measuring method based on gas densitometer

Abstract

The invention relates to the technical field of intelligent control of gas sensing and measurement, and particularly discloses a gas density measurement method based on a gas densitometer, which comprises the following steps of: acquiring sound velocity data and molecular adsorption dynamic response time data of gas in real time in a measurement process, and respectively extracting a gas compressibility fluctuation characteristic value and a response time adsorption hysteresis characteristic value; and constructing a comprehensive stability feature vector as a key index for evaluating a measurement state. By establishing a gas measurement reliability evaluation model based on a random forest algorithm, quantitative scoring of the credibility of each measurement is realized, and parameters such as sampling frequency and measurement period are automatically adjusted according to a scoring result to form a closed-loop feedback mechanism, so that the accuracy and stability of gas density measurement are improved, and the reliability of gas density measurement is improved. The method enhances the adaptability and robustness of the system under complex working conditions, has the advantages of high intelligent degree, high response speed, wide application range and the like, and is suitable for multi-component gas on-line monitoring and high-precision density detection scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control for gas sensing and measurement, and particularly to a gas density measurement method based on a gas densitometer. Background Art

[0002] Gas density measurement has important application values in fields such as industrial process control, environmental monitoring, and energy metering. Traditional gas density measurement methods mainly include the buoyancy method, vibrating densitometer, and sound velocity method, etc. These methods have their own advantages and disadvantages under different working conditions. Among them, the gas densitometer based on the sound velocity principle is widely used due to its advantages such as non-contact and fast response. Its basic principle is to calculate the gas density by measuring the propagation speed of sound waves in the gas. However, in practical applications, gas density measurement is easily affected by environmental factors (such as temperature and pressure changes), gas component fluctuations, and the adsorption effect on the sensor surface, resulting in measurement result deviations. Existing technologies usually adopt single-parameter calibration or static compensation methods to improve measurement accuracy, but in a dynamically changing gas environment, these methods are difficult to effectively cope with complex real-time interferences, leading to a decline in measurement reliability.

[0003] The existing technologies have the following deficiencies: In existing gas density measurement technologies, a serious problem affecting measurement accuracy is the coupled interference between the dynamic adsorption hysteresis effect of gas molecules on the sensor surface and the sound velocity fluctuation. The specific manifestation is as follows: When gas molecules adsorb or desorb on the sensor surface, their dynamic response time will produce non-linear delays due to changes in gas components or environmental conditions, and this process will further affect the stability of sound wave propagation, resulting in composite deviations in the measured sound velocity values that are difficult to separate by conventional methods. Traditional methods usually treat sound velocity data and adsorption effects as independent factors, ignoring their synergistic effects in the time domain and frequency domain, thus being unable to accurately identify the hidden instabilities in the measurement process. In addition, existing technologies lack means for joint quantitative analysis of gas compressibility fluctuations and adsorption hysteresis characteristics, resulting in the system being unable to dynamically evaluate measurement credibility and adaptively adjust parameters. The existence of this problem makes it difficult to guarantee the long-term stability and reliability of gas density measurement under complex working conditions. Summary of the Invention

[0004] The purpose of the present invention is to provide a gas density measurement method based on a gas densitometer to solve the problems in the above background.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A gas density measurement method based on a gas densitometer includes the following steps: S1: During the gas density measurement process, collect the sound velocity data of the gas and the dynamic response time data of molecular adsorption in real time; S2: Process the sound velocity data of the gas, calculate the characteristic value of gas compressibility fluctuation according to the deviation degree of the sound velocity measurement value, and use it to evaluate the deviation degree of the gas during the measurement process; S3: Process the data of the dynamic response time of molecular adsorption of the gas, calculate the characteristic value of adsorption hysteresis of the response time according to the delay degree of the dynamic response time of molecular adsorption, and use it to evaluate the interaction strength between gas molecules and the densitometer; S4: Construct a comprehensive stability characteristic vector from the characteristic value of gas compressibility fluctuation and the characteristic value of adsorption hysteresis of the response time, and input it into the gas measurement reliability evaluation model for analysis; S5: According to the analysis result, output the credibility level of the current gas density measurement, and automatically adjust the measurement parameters according to the credibility level.

[0006] As a further solution of the present invention: The evaluation of the deviation degree of the gas during the measurement process specifically includes: During the gas density measurement process, obtain the sound velocity data of the gas, process the sound velocity data of the gas, calculate the characteristic value of gas compressibility fluctuation according to the deviation degree of the sound velocity measurement value, and judge whether the characteristic value of gas compressibility fluctuation is greater than or equal to the preset threshold. If so, the deviation of the gas during the measurement process is in the abnormal range; if not, the deviation of the gas during the measurement process is in the normal range.

[0007] As a further solution of the present invention: The process of obtaining the characteristic value of gas compressibility fluctuation is as follows: During the gas density measurement process, obtain the sound velocity data of the gas, integrate the sound velocity data into a sound velocity data sequence of the gas, calculate the average value of all sound velocity measurement values, calculate the difference between the sound velocity measurement value of each measurement point and the average value, and calculate the ratio of the difference calculation result to the average value of all sound velocity measurement values to obtain the relative deviation degree value of each measurement point; Form all the relative deviation degree values into a set of time series data, and perform a fast Fourier transform on the time series data of the relative deviation degree values to obtain the complex value form of the frequency domain signal; Calculate the energy spectral density corresponding to each frequency component in the frequency domain signal, and calculate the ratio of the mean value to the variance of the energy spectral density of all frequency components to obtain the characteristic value of gas compressibility fluctuation.

[0008] As a further solution of the present invention: The evaluation of the interaction strength between gas molecules and the densitometer specifically includes: During the gas density measurement, obtain the molecular adsorption dynamic response time data of the gas, process the molecular adsorption dynamic response time data of the gas, calculate the response time adsorption hysteresis eigenvalue according to the delay degree of the molecular adsorption dynamic response time, and determine whether the response time adsorption hysteresis eigenvalue is greater than or equal to a preset threshold. If so, the interaction between the gas molecules and the densitometer is abnormal; if not, the interaction between the gas molecules and the densitometer is normal.

[0009] As a further solution of the present invention: the process of obtaining the response time adsorption hysteresis eigenvalue is as follows: During the gas density measurement, obtain the molecular adsorption dynamic response time data of the gas; Standardize the molecular adsorption dynamic response time data of the gas to obtain a standardized matrix; Based on the standardized data matrix, construct a covariance matrix and perform eigenvalue decomposition on the covariance matrix to obtain a set of eigenvalues and a corresponding set of unit orthogonal eigenvectors; Select the eigenvectors corresponding to the first largest eigenvalues to form a projection matrix, and project the standardized data matrix into the dimensionality-reduced space; Calculate the standard deviation of the projection values on the first principal component of the principal component score matrix to obtain the response time adsorption hysteresis eigenvalue.

[0010] As a further solution of the present invention: construct the gas compressibility fluctuation eigenvalue and the response time adsorption hysteresis eigenvalue into a comprehensive stability eigenvector and input it into the gas measurement reliability evaluation model for analysis, which specifically includes: During the gas density measurement, obtain the gas compressibility fluctuation eigenvalue and the response time adsorption hysteresis eigenvalue, construct the gas compressibility fluctuation eigenvalue and the response time adsorption hysteresis eigenvalue into a comprehensive stability eigenvector, use it as the input of the gas measurement reliability evaluation model, minimize the error between the predicted credibility score and the actual credibility score, train the gas measurement reliability evaluation model with this as the training target, and output the credibility score during the bulk density measurement process according to the trained gas measurement reliability evaluation model. The gas measurement reliability evaluation model is a random forest model.

[0011] As a further solution of the present invention: the training process of the gas measurement reliability evaluation model is as follows: During the model training process, multiple collected measurement samples, their corresponding feature vectors, and actual credibility scores are divided into a training dataset and a test dataset; the hyperparameters of the number of decision trees, splitting depth, and minimum number of samples per node in the random forest model are tuned using the training dataset, and the training objective is to minimize the error between the predicted credibility score output by the model and the actual credibility score; the mean square of the difference between the predicted credibility score and the actual credibility score is used as the optimization objective function, and the prediction accuracy and generalization ability of the model are improved through continuous iteration.

[0012] As a further solution of the present invention: the output of the credibility level of the current gas density measurement specifically includes: During the gas density measurement process, it is judged whether the credibility score of the current gas density measurement is greater than or equal to a preset threshold. If so, the current gas density measurement is credible; if not, the current gas density measurement is not credible.

[0013] As a further solution of the present invention: the automatic adjustment of the measurement parameters according to the credibility level specifically includes: If the current gas density measurement is not credible, it is determined that the current measurement state is unstable, and the parameter adaptive adjustment mechanism is triggered. When the gas compressibility fluctuation eigenvalue is greater than or equal to the preset threshold, the system automatically increases the sampling frequency; when the response time adsorption hysteresis eigenvalue is greater than or equal to the preset threshold, the system extends the measurement period and enhances the data acquisition of the adsorption response process.

[0014] The beneficial effects of the present invention: (1) By introducing a multi-physical field collaborative sensing mechanism, the present invention constructs a gas density measurement method based on the combination of acoustic characteristics and molecular adsorption kinetics, significantly improving the accuracy, stability, and environmental adaptability of the measurement system. Specifically, during the measurement process, the system synchronously collects the sound velocity data and the molecular adsorption dynamic response time data of the gas, and respectively extracts the eigenvalue that can reflect the gas compressibility fluctuation characteristics and the hysteresis eigenvalue that reflects the interaction strength between the gas molecules and the sensor interface. Among them, the gas compressibility fluctuation eigenvalue is obtained by performing frequency-domain energy analysis on the sound velocity time series, which can effectively characterize the non-linear compression behavior and transient fluctuation characteristics of the gas under different pressure or component conditions; while the response time adsorption hysteresis eigenvalue quantifies and characterizes the time delay of the adsorption process based on the principal component analysis technology, thereby revealing the dynamic hysteresis effect of the gas molecules during the adsorption / desorption process on the sensor surface and its impact on the measurement stability.

[0015] From the two aspects of macroscopic acoustic response and microscopic molecular behavior, the above two characteristic values construct a comprehensive stability evaluation system with clear physical meaning and rigorous mathematical expression, realizing the dynamic quality monitoring of the whole process of gas density measurement. This method can real-time identify abnormal fluctuations caused by gas composition changes, environmental disturbances or sensor adsorption effects, thereby effectively improving the accuracy and repeatability of gas density measurement results and avoiding data errors caused by unstable measurement.

[0016] (2) Based on the extracted gas compressibility fluctuation characteristic value and response time adsorption hysteresis characteristic value, the present invention constructs a comprehensive stability eigenvector with physical interpretability and statistical robustness, and uses this as the input feature to train and optimize the gas measurement reliability evaluation model by using the random forest algorithm. Through multi-dimensional feature learning, this model establishes a non-linear mapping relationship from the measurement stability feature to the credibility score, and can output a highly interpretable quantitative credibility score for each gas density measurement process. In practical applications, the system dynamically adjusts key measurement parameters according to the scoring results. For example, when high fluctuations or strong adsorption hysteresis are detected, it automatically increases the sampling frequency, extends the measurement period or enhances the data filtering intensity, thereby realizing the real-time optimization of the measurement strategy. This closed-loop feedback mechanism not only endows the measurement system with the ability of intelligent diagnosis and adaptive adjustment, but also significantly enhances its operation robustness and environmental adaptability under complex and changeable working conditions, breaking through the technical limitations of traditional gas density measurement methods that rely on static calibration and lack state perception ability, and providing a new technical path and system solution for high-precision and continuous stable on-line gas density monitoring. Brief Description of the Drawings

[0017] The present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 is a flow block diagram of the gas density measurement method based on a gas densitometer of the present invention. Detailed Embodiments

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Please refer to Figure 1 as shown, the present invention is a gas density measurement method based on a gas densitometer, including the following steps: S1: During the gas density measurement process, the sound velocity data of the gas and the molecular adsorption dynamic response time data are collected in real time; S2: Process the sound velocity data of the gas, and calculate the gas compressibility fluctuation characteristic value according to the deviation degree of the sound velocity measurement value, which is used to evaluate the deviation degree of the gas during the measurement process; S3: Process the molecular adsorption dynamic response time data of the gas, and calculate the response time adsorption hysteresis characteristic value according to the delay degree of the molecular adsorption dynamic response time, which is used to evaluate the interaction strength between the gas molecules and the densitometer; S4: Construct a comprehensive stability characteristic vector from the gas compressibility fluctuation characteristic value and the response time adsorption hysteresis characteristic value, and input it into the gas measurement reliability evaluation model for analysis; S5: According to the analysis result, output the credibility level of the current gas density measurement, and automatically adjust the measurement parameters according to the credibility level.

[0021] In S1, during the gas density measurement process, the sound velocity data and the molecular adsorption dynamic response time data of the gas are collected in real time, specifically including: During the gas density measurement process, the specific process of collecting the sound velocity data and the molecular adsorption dynamic response time data of the gas in real time is as follows: First, a specially designed sound velocity sensor module and an adsorption response detection module are configured in the gas density measurement system. The sound velocity sensor module is installed at both ends of the closed measurement cavity through a pair of ultrasonic transmitting and receiving probes, which is used to continuously transmit and receive high-frequency acoustic wave signals passing through the gas, and calculate the time difference between each transmission and reception to determine the gas sound velocity; At the same time, the adsorption response detection module uses a highly sensitive piezoelectric crystal to monitor the frequency change caused by the adsorption and desorption of gas molecules on the sensor surface, and records the time required for each adsorption event to reach a stable state as the molecular adsorption dynamic response time.

[0022] The obtained sound velocity data and molecular adsorption dynamic response time data are transmitted to the data processing unit in real time, and each group of data is marked with an accurate timestamp for subsequent analysis. The sound velocity data is obtained by calculating the acoustic wave propagation time difference at each sampling point, while the molecular adsorption dynamic response time is determined by monitoring the stable time of the frequency change curve during the adsorption event. This process ensures that the gas compressibility fluctuation characteristic value and the response time adsorption hysteresis characteristic value can be accurately extracted, providing solid data support for the subsequent construction of the comprehensive stability characteristic vector and the evaluation of the gas density measurement reliability.

[0023] In S2, process the sound velocity data of the gas, and calculate the gas compressibility fluctuation characteristic value according to the deviation degree of the sound velocity measurement value, which is used to evaluate the deviation degree of the gas during the measurement process, specifically including: During the gas density measurement process, the sound speed data of the gas is obtained, the sound speed data of the gas is processed, and according to the deviation degree of the measured sound speed value, the gas compressibility fluctuation characteristic value is calculated. It is judged whether the gas compressibility fluctuation characteristic value is greater than or equal to a preset threshold value. If so, the deviation of the gas during the measurement process is within the abnormal range. If not, the deviation of the gas during the measurement process is within the normal range.

[0024] The process of obtaining the gas compressibility fluctuation characteristic value is as follows: During the gas density measurement process, the sound speed data of the gas is obtained, the sound speed data is integrated into a sound speed data sequence of the gas, the average value of all measured sound speed values is calculated, the difference between the measured sound speed value at each measurement point and the average value is calculated, and the ratio of the difference calculation result to the average value of all measured sound speed values is calculated to obtain the relative deviation degree value of each measurement point; All the relative deviation degree values are formed into a set of time series data, and the time series data of the relative deviation degree values is subjected to a fast Fourier transform to obtain the complex value form of the frequency domain signal; Calculate the energy spectral density corresponding to each frequency component in the frequency domain signal. The calculation expression is: ; where represents the energy spectral density of the frequency component , represents the th frequency component, represents the frequency component in the complex value form. Calculate the ratio of the mean value to the variance of the energy spectral density of all frequency components to obtain the gas compressibility fluctuation characteristic value.

[0025] It should be noted that: by performing time-domain normalization processing on the gas sound speed measurement data, extracting the relative deviation degree of each measurement point relative to the average value, converting the obtained time series data into a frequency domain signal, obtaining the spectral information in the complex value form by using the fast Fourier transform, further calculating the energy spectral density of each frequency component, and finally using the ratio of the mean value to the variance of the energy spectral density as the gas compressibility fluctuation characteristic value; in this implementation scheme, the statistical characteristics of the frequency domain energy distribution are first applied to the gas compressibility fluctuation analysis, which can not only effectively reflect the non-stationarity and complexity of the sound speed fluctuation of the gas during the measurement process, but also realize the quantitative evaluation of the abnormal fluctuation of the gas compressibility through mathematical modeling, improve the sensitivity and accuracy of the gas density measurement system in judging the stability of the measurement state, and thus enhance the intelligent level and adaptive ability of the overall measurement method.

[0026] In S3, the molecular adsorption dynamic response time data of the gas is processed. According to the delay degree of the molecular adsorption dynamic response time, the response time adsorption hysteresis characteristic value is calculated to evaluate the interaction strength between the gas molecules and the densitometer. Specifically, it includes: During the gas density measurement process, obtain the molecular adsorption dynamic response time data of the gas, process the molecular adsorption dynamic response time data of the gas, calculate the response time adsorption hysteresis characteristic value according to the delay degree of the molecular adsorption dynamic response time, and determine whether the response time adsorption hysteresis characteristic value is greater than or equal to a preset threshold. If so, the interaction between the gas molecules and the densitometer is abnormal; if not, the interaction between the gas molecules and the densitometer is normal.

[0027] The process for obtaining the response time adsorption hysteresis characteristic value is as follows: During the gas density measurement process, obtain the molecular adsorption dynamic response time data of the gas; Perform standardization processing on the molecular adsorption dynamic response time data of the gas to obtain a standardized matrix. The calculation expression for the standardization processing is: ; where represents the standardized adsorption response time data, represents the th sample at the th time point of the original adsorption response time, represents the number of collected sample data, represents the number of collection time points, represents the average adsorption response time of all samples at the th time point, represents the th time point of the standard deviation of all samples; Based on the standardized data matrix, construct a covariance matrix. The calculation expression is: ; where represents the covariance matrix, represents the standardized matrix, represents the transpose operation, represents the total number of collected sample data. Perform eigenvalue decomposition on the covariance matrix to obtain an eigenvalue set and a corresponding set of unit orthogonal eigenvectors, and satisfy: where represents the unit orthogonal eigenvector, represents the eigenvalue; Select the eigenvectors corresponding to the first largest eigenvalues to form a projection matrix, project the standardized data matrix into the reduced-dimensional space, and obtain the principal component score matrix. The calculation expression is: ; where represents the principal component score matrix, represents the projection matrix composed of eigenvectors; Calculate the standard deviation of the projection value on the first principal component of the principal component score matrix to obtain the response time adsorption hysteresis eigenvalue.

[0028] It should be noted that: The technical solution for calculating the response time adsorption hysteresis eigenvalue through the dynamic response time data of molecular adsorption has the following technical effects and innovation points: This method standardizes the dynamic response time data during the adsorption process of gas molecules on the sensor surface, and extracts the projection standard deviation in the direction of the first principal component as the response time adsorption hysteresis eigenvalue based on principal component analysis, thereby realizing the quantitative evaluation of the interaction strength between gas molecules and the densitometer; Compared with the traditional judgment method that only relies on the mean or maximum value of a single response time, this solution fully explores the potential change trends in multi-dimensional time series data and improves the sensitivity to the hysteresis and non-linear behavior of the adsorption process; This method not only effectively removes the influence of data dimension and fluctuation amplitude under different measurement conditions, but also enhances the interpretability and robustness of data features through dimensionality reduction means, providing the gas density measurement system with the ability to accurately identify the stability state of gas adsorption behavior, further improving the reliability of measurement results and the self-diagnosis level of the device.

[0029] In S4, construct the gas compressibility fluctuation eigenvalue and the response time adsorption hysteresis eigenvalue into a comprehensive stability feature vector and input it into the gas measurement reliability evaluation model for analysis, specifically including: During the gas density measurement process, obtain the gas compressibility fluctuation eigenvalue and the response time adsorption hysteresis eigenvalue, construct the gas compressibility fluctuation eigenvalue and the response time adsorption hysteresis eigenvalue into a comprehensive stability feature vector as the input of the gas measurement reliability evaluation model, minimize the error between the predicted credibility score and the actual credibility score, and use it as the training objective to train the gas measurement reliability evaluation model. According to the trained gas measurement reliability evaluation model, output the credibility score during the bulk density measurement process. The gas measurement reliability evaluation model is a random forest model.

[0030] During the model training process, divide the collected multiple measurement samples and their corresponding feature vectors and actual credibility scores into a training data set and a test data set; use the training data set to optimize the hyperparameters of the number of decision trees, splitting depth, and minimum number of samples per node in the random forest model. The training objective is to minimize the error between the predicted credibility score output by the model and the actual credibility score; Specifically, use the mean square of the difference between the predicted score and the true score as the optimization objective function, and continuously iterate to improve the prediction accuracy and generalization ability of the model; Finally, when the model training reaches the set convergence condition or the maximum number of training rounds, save the model version with the optimal performance and deploy it to the gas density measurement system; in the actual application stage, input the comprehensive stability feature vector extracted during the new measurement process into the trained gas measurement reliability evaluation model, and the model automatically outputs the credibility score of this measurement, which serves as a quantitative basis for measuring the reliability of the current gas density measurement result, thereby realizing the quality control and dynamic evaluation of the entire process of gas density measurement.

[0031] During the gas density measurement process, determine whether the credibility score of the current gas density measurement is greater than or equal to the preset threshold. If so, the current gas density measurement is credible; if not, the current gas density measurement is not credible.

[0032] In S5, according to the analysis result, output the credibility level of the current gas density measurement and automatically adjust the measurement parameters according to the credibility level, specifically including: If the current gas density measurement is not credible, it is determined that the current measurement state is unstable, triggering the parameter adaptive adjustment mechanism. When the gas compressibility fluctuation eigenvalue is greater than or equal to the preset threshold, the system automatically increases the sampling frequency and data processing accuracy to capture the rapidly changing fluctuation characteristics; when the response time adsorption hysteresis eigenvalue is greater than or equal to the preset threshold, the system extends the measurement period and enhances the data acquisition of the adsorption response process to improve the adaptability and stability of the measurement system.

[0033] Working principle of the present invention: By collecting the sound velocity data and the molecular adsorption dynamic response time data of the gas during the measurement process, the gas compressibility fluctuation eigenvalue and the response time adsorption hysteresis eigenvalue are calculated respectively, so as to construct a comprehensive eigenvector reflecting the measurement stability, and input it into the gas measurement reliability evaluation model trained based on the random forest algorithm to realize the quantitative evaluation of the current measurement credibility. This method first uses the ultrasonic sensor module to accurately obtain the gas sound velocity data, combines the fast Fourier transform to extract the frequency domain energy distribution characteristics, and calculates the gas compressibility fluctuation eigenvalue through the ratio of the mean value and variance of the energy spectral density, which can effectively identify the non-stationary fluctuation behavior of the gas during the measurement process; at the same time, a high-sensitivity piezoelectric crystal detection module is used to collect the time series data of the gas molecular adsorption process, and the principal component analysis technology is introduced to extract the standard deviation of the first principal component projection as the response time adsorption hysteresis eigenvalue to quantitatively evaluate the interaction strength and adsorption stability between the gas molecules and the sensor. Finally, the above two eigenvalues are fused into a comprehensive stability eigenvector, an intelligent evaluation model is constructed, the model training is completed by minimizing the error between the predicted score and the actual score, and the reliability of the measurement state is automatically judged according to the output credibility score. When abnormal fluctuations or adsorption instability are detected, the system can adaptively adjust the key parameters such as the sampling frequency and measurement period, so as to realize the dynamic quality monitoring and intelligent control of the whole process of gas density measurement. This solution significantly improves the robustness, accuracy and self-regulation ability of the gas density measurement system, and is applicable to the real-time density detection scenario of multi-component gases under complex working conditions.

[0034] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0035] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of 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, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. 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 by wired or wireless (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more sets of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0036] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context before and after.

[0037] It should be understood that in various embodiments of the present application, the sequence numbers of the above processes do not indicate the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0038] The above has described in detail one embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. Any equivalent changes and improvements made within the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A gas density measurement method based on a gas densitometer, characterized in that, It includes the following steps: S1: During the gas density measurement, the sound speed data and the molecular adsorption dynamic response time data of the gas are collected in real time; S2: Process the sound speed data of the gas, and calculate the gas compressibility fluctuation characteristic value according to the deviation degree of the sound speed measurement value, which is used to evaluate the deviation degree of the gas during the measurement process; S3: Process the molecular adsorption dynamic response time data of the gas, and calculate the response time adsorption hysteresis characteristic value according to the delay degree of the molecular adsorption dynamic response time, which is used to evaluate the interaction strength between the gas molecules and the densitometer; S4: Construct a comprehensive stability characteristic vector from the gas compressibility fluctuation characteristic value and the response time adsorption hysteresis characteristic value, and input it into the gas measurement reliability evaluation model for analysis; S5: According to the analysis result, output the credibility level of the current gas density measurement, and automatically adjust the measurement parameters according to the credibility level.

2. The gas density measurement method based on a gas densitometer according to claim 1, wherein The evaluation of the deviation degree of the gas during the measurement process specifically includes: During the gas density measurement process, obtain the sound speed data of the gas, process the sound speed data of the gas, calculate the gas compressibility fluctuation characteristic value according to the deviation degree of the sound speed measurement value, and judge whether the gas compressibility fluctuation characteristic value is greater than or equal to the preset threshold. If so, the deviation of the gas during the measurement process is in the abnormal range. If not, the deviation of the gas during the measurement process is in the normal range.

3. The gas density measurement method based on a gas densitometer according to claim 2, characterized in that, The process of obtaining the gas compressibility fluctuation characteristic value is as follows: During the gas density measurement process, obtain the sound speed data of the gas, integrate the sound speed data into a sound speed data sequence of the gas, calculate the average value of all sound speed measurement values, take the difference between the sound speed measurement value of each measurement point and the average value, and calculate the ratio of the difference calculation result to the average value of all sound speed measurement values to obtain the relative deviation degree value of each measurement point; Form all the relative deviation degree values into a set of time series data, and perform a fast Fourier transform on the time series data of the relative deviation degree values to obtain the complex number form of the frequency domain signal; Calculate the energy spectral density corresponding to each frequency component in the frequency domain signal, and calculate the ratio of the mean value to the variance of the energy spectral density of all frequency components to obtain the gas compressibility fluctuation characteristic value.

4. The gas density measurement method based on a gas densitometer according to claim 1, wherein, The evaluation of the interaction strength between the gas molecules and the densitometer specifically includes: During the gas density measurement process, obtain the molecular adsorption dynamic response time data of the gas, process the molecular adsorption dynamic response time data of the gas, calculate the response time adsorption hysteresis characteristic value according to the delay degree of the molecular adsorption dynamic response time, and judge whether the response time adsorption hysteresis characteristic value is greater than or equal to the preset threshold. If so, the interaction between the gas molecules and the densitometer is abnormal. If not, the interaction between the gas molecules and the densitometer is normal.

5. The gas density measurement method based on a gas densitometer according to claim 4, characterized in that, The process of obtaining the response time adsorption hysteresis characteristic value is as follows: During the gas density measurement process, obtain the molecular adsorption dynamic response time data of the gas; Standardize the molecular adsorption dynamic response time data of the gas to obtain a standardized matrix; Based on the standardized data matrix, construct a covariance matrix, and for the covariance matrix perform eigenvalue decomposition to obtain a set of eigenvalues and the corresponding set of unit orthogonal eigenvectors; Before selection The eigenvectors corresponding to the largest eigenvalues form a projection matrix, and the standardized data matrix is projected onto the dimensionality-reduced space; Calculate the standard deviation of the projection value on the first principal component of the principal component score matrix to obtain the response time adsorption hysteresis characteristic value.

6. The gas density measurement method based on a gas densitometer according to claim 1, characterized in that, Constructing the gas compressibility fluctuation eigenvalue and the response time adsorption hysteresis eigenvalue into a comprehensive stability feature vector and inputting it into the gas measurement reliability evaluation model for analysis, specifically including: During the gas density measurement process, obtain the gas compressibility fluctuation eigenvalue and the response time adsorption hysteresis eigenvalue, construct the gas compressibility fluctuation eigenvalue and the response time adsorption hysteresis eigenvalue into a comprehensive stability feature vector, and use it as the input of the gas measurement reliability evaluation model. Minimize the error between the predicted credibility score and the actual credibility score, and use it as the training objective to train the gas measurement reliability evaluation model. According to the trained gas measurement reliability evaluation model, output the credibility score during the bulk density measurement process. The gas measurement reliability evaluation model is a random forest model.

7. The gas density measurement method based on a gas densitometer according to claim 6, wherein The training process of the gas measurement reliability evaluation model is as follows: During the model training process, divide the collected multiple measurement samples and their corresponding feature vectors and actual credibility scores into a training data set and a test data set; use the training data set to optimize the hyperparameters of the number of decision trees, splitting depth, and minimum number of samples at nodes in the random forest model. The training objective is to minimize the error between the predicted credibility score output by the model and the actual credibility score; the mean square of the difference between the predicted credibility score and the actual credibility score is used as the optimization objective function, and the prediction accuracy and generalization ability of the model are improved through continuous iteration.

8. The gas density measurement method based on a gas densitometer according to claim 1, wherein Outputting the credibility level of the current gas density measurement, specifically including: During the gas density measurement process, determine whether the credibility score of the current gas density measurement is greater than or equal to the preset threshold. If so, the current gas density measurement is credible; if not, the current gas density measurement is not credible.

9. The gas density measurement method based on a gas densitometer according to claim 1, characterized in that, Automatically adjusting the measurement parameters according to the credibility level, specifically including: If the current gas density measurement is not credible, it is determined that the current measurement state is unstable, and the parameter adaptive adjustment mechanism is triggered. When the gas compressibility fluctuation eigenvalue is greater than or equal to the preset threshold, the system automatically increases the sampling frequency; when the response time adsorption hysteresis eigenvalue is greater than or equal to the preset threshold, the system extends the measurement period and enhances the data collection of the adsorption response process.

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