A real-time wave phase chromatographic data monitoring and alarm system

Abstract

This invention relates to the field of chromatographic detection and data processing technology, specifically a real-time wave-phase chromatographic data monitoring and alarm system. It includes modules for data acquisition, virtual mapping, wave-phase analysis, and dynamic monitoring. The system acquires the physical parameters of the chromatographic column and real-time voltage, generating a dimensionless scalar. Its core is to construct a wave-phase stability index based on virtual viscosity and signal changes, and to calculate a dynamic alarm threshold. By comparing the index with the threshold, it determines in real-time whether the flow is normal laminar or abnormal turbulent. This invention utilizes nonlinear mapping to suppress baseline drift and noise, overcoming the limitations of fixed threshold values, and achieving intelligent identification of true chromatographic peaks, significantly reducing the false alarm rate while maintaining high sensitivity.

Description

Technical Field

[0001] This invention relates to the field of chromatographic detection and data processing technology, specifically a real-time wave phase chromatographic data monitoring and alarm system. Background Technology

[0002] With the continuous advancement of chromatographic analysis technology, the requirements for sensitivity and accuracy in the detection of trace substances are increasing, and the signal environment faced by chromatographic detection is becoming increasingly complex. Currently, traditional chromatographic data monitoring mainly relies on setting fixed voltage thresholds or manually checking the chromatograms periodically. However, this conventional method has significant limitations when dealing with dynamically changing signal flow fields. In high-sensitivity detection mode, existing technologies often struggle to effectively distinguish between true abnormal chromatographic peaks and random fluctuations caused by instrument baseline drift or background noise. This leads to an inherent and irreconcilable contradiction between high sensitivity and high false alarm rate during the detection process. Traditional methods cannot suppress false alarms while improving detection accuracy, seriously affecting detection efficiency and the reliability of results.

[0003] Therefore, how to maintain high sensitivity to minute abnormal signals while effectively avoiding baseline drift and noise interference, and achieve accurate real-time monitoring of the chromatographic flow field state, has become an urgent problem to be solved in this field. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a real-time wave phase chromatography data monitoring and alarm system. Specifically, the technical solution of this invention includes:

[0005] The data acquisition module is used to acquire the physical length of the chromatographic column, the particle size of the packing material, the porosity correction factor, the real-time voltage signal, the upper limit voltage of the detector range, and the sampling time interval.

[0006] The virtual space mapping module is used to generate virtual feature diameters based on the physical length of the chromatographic column, the particle size of the packing material, and the porosity correction factor; to generate a dimensionless voltage scalar based on the real-time voltage signal and the upper limit voltage of the detector range; and to calibrate the virtual dynamic viscosity based on background noise data.

[0007] The wave phase analysis module is used to calculate virtual shear stress based on dimensionless voltage scalar, sampling time interval, and virtual dynamic viscosity; and to construct the wave phase stability index by combining virtual fluid density.

[0008] The dynamic monitoring module is used to calculate the dynamic alarm threshold based on the virtual characteristic diameter; compare the wave phase stability index with the dynamic alarm threshold; if the wave phase stability index is less than or equal to the dynamic alarm threshold, the monitoring state is determined to be normal laminar flow; if the wave phase stability index is greater than the dynamic alarm threshold, the monitoring state is determined to be abnormal turbulent flow.

[0009] Preferably, the virtual space mapping module performs the following steps:

[0010] Adjust the column physical length, packing particle size, and porosity correction factors;

[0011] Introduce unit conversion factors;

[0012] Calculate the product of the physical length of the chromatographic column and the unit conversion factor to generate the converted length;

[0013] Calculate the ratio of the converted length to the filler particle size;

[0014] Calculate the product of the ratio and the porosity correction factor to generate a virtual characteristic diameter.

[0015] Preferably, the virtual space mapping module further performs the following steps:

[0016] Call the real-time voltage signal and the upper limit voltage of the detector range;

[0017] Calculate the quotient of the real-time voltage signal and the upper limit voltage of the detector range to generate a dimensionless voltage scalar;

[0018] Obtain the normalized signal sequence of the system during the blank solvent operation phase;

[0019] Calculate the statistical variance of the rate of change of adjacent sampling points in a normalized signal sequence;

[0020] The statistical variance is defined as the virtual dynamic viscosity.

[0021] Preferably, the wave phase analysis module performs the following steps:

[0022] Call upon the dimensionless voltage scalar, sampling time interval, and virtual dynamic viscosity;

[0023] Calculate the difference between the dimensionless voltage scalar at the current moment and the dimensionless voltage scalar at the previous moment;

[0024] Calculate the quotient of the difference and the sampling time interval to generate the real-time signal change rate;

[0025] The virtual shear stress is generated by calculating the product of the virtual dynamic viscosity and the rate of change of the real-time signal.

[0026] Preferably, the wave phase analysis module further performs the following steps:

[0027] Invoke virtual fluid density, dimensionless voltage scalar, and real-time signal rate of change;

[0028] Calculate the product of the virtual fluid density, the dimensionless voltage scalar, and the square of the rate of change of the real-time signal to generate the inertial force term;

[0029] Virtual dynamic viscosity is defined as a viscous force term;

[0030] Calculate the ratio of the inertial force term to the viscous force term to generate the wave phase stability index.

[0031] Preferably, the dynamic monitoring module performs the following steps:

[0032] The arithmetic mean of the wave phase stability index during the system's blank operation phase is called and defined as the baseline stability value;

[0033] Call the preset sensitivity adjustment coefficient and virtual feature diameter;

[0034] Calculate the natural logarithm of the virtual feature diameter;

[0035] Calculate the product of the natural logarithm and the sensitivity adjustment coefficient to generate the geometric correction.

[0036] The sum of the baseline stable value and the geometric correction is calculated to generate a dynamic alarm threshold.

[0037] Preferably, it also includes a health prediction module for performing the following steps:

[0038] Obtain the total duration of a single run, the ideal phase curve and the limit dissipation value during the operation of the standard product;

[0039] Calculate the difference between the wave phase stability index and the ideal wave phase curve;

[0040] The difference is integrated over the total duration of a single run and divided by the total duration of a single run to generate the average dissipation rate.

[0041] Calculate the ratio of the average dissipation rate to the limiting dissipation value;

[0042] The health index is generated by subtracting the ratio from the value 1.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. This system constructs a wave phase stability index and utilizes the nonlinear mapping relationship between dimensionless voltage scalar, real-time signal change rate, and virtual dynamic viscosity to achieve intelligent identification of signal characteristics. This index can effectively suppress baseline drift, which is characterized by large amplitude and low rate of change, as well as background noise, which is characterized by small amplitude and high frequency. The index only increases exponentially when a real abnormal chromatographic peak appears. This mechanism overcomes the limitations of the traditional fixed threshold method in processing dynamic flow fields, and while maintaining high sensitivity to capture trace substances, it significantly reduces the false alarm rate caused by environmental interference.

[0045] 2. This system uses a virtual space mapping module to convert the physical length of the chromatographic column, the particle size of the packing material, and the porosity correction factor into a virtual characteristic diameter, and calculates the dynamic alarm threshold accordingly. For long chromatographic columns with high column efficiency and sharp peak shapes, the system automatically raises the alarm threshold to prevent false alarms triggered by normal high-frequency signals. For short columns with lower column efficiency, the system automatically lowers the threshold to maintain monitoring sensitivity. This adaptive adjustment mechanism based on geometric properties ensures that the system maintains a consistent and accurate monitoring standard under different hardware specifications.

[0046] 3. This system abandons simple statistical analysis and, by introducing unit conversion factors and virtual fluid density, maps abstract voltage signals and background noise into virtual shear stress, inertial force terms, and viscous force terms. It evaluates the flow field state by calculating the ratio of virtual kinetic energy to background potential energy, thus establishing a direct correlation between signal fluctuations and the physical damping characteristics of the system. This processing method, which gives physical meaning to the signal, makes the judgment of abnormal turbulent states more robust and can more accurately reflect the real changes inside the flow path.

[0047] 4. This system integrates a health prediction module, which calculates the difference between the real-time phase stability index and the ideal phase curve, and then integrates the result to generate the average dissipation rate. This index physically characterizes the energy loss caused by increased micro-turbulence in the flow path due to packing collapse or contamination. The health index generated by the system can intuitively and quantitatively reflect the remaining service life of the chromatographic column, helping users to plan maintenance or replacement in advance and effectively avoid affecting the reliability of detection data due to sudden column failure. Attached Figure Description

[0048] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0049] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0051] Example 1:

[0052] Please see Figure 1 A real-time wave phase chromatography data monitoring and alarm system, comprising:

[0053] The data acquisition module is used to acquire the physical length of the chromatographic column, the particle size of the packing material, the porosity correction factor, the real-time voltage signal, the upper limit voltage of the detector range, and the sampling time interval.

[0054] The virtual space mapping module is used to generate virtual feature diameters based on the physical length of the chromatographic column, the particle size of the packing material, and the porosity correction factor; to generate a dimensionless voltage scalar based on the real-time voltage signal and the upper limit voltage of the detector range; and to calibrate the virtual dynamic viscosity based on background noise data.

[0055] The wave phase analysis module is used to calculate virtual shear stress based on dimensionless voltage scalar, sampling time interval, and virtual dynamic viscosity; and to construct the wave phase stability index by combining virtual fluid density.

[0056] The dynamic monitoring module is used to calculate the dynamic alarm threshold based on the virtual characteristic diameter; compare the wave phase stability index with the dynamic alarm threshold; if the wave phase stability index is less than or equal to the dynamic alarm threshold, the monitoring state is determined to be normal laminar flow; if the wave phase stability index is greater than the dynamic alarm threshold, the monitoring state is determined to be abnormal turbulent flow.

[0057] This embodiment provides a real-time wave phase chromatography data monitoring and alarm system. The system solves the inherent contradiction between high sensitivity and high false alarm rate in traditional chromatographic detection by constructing a virtual fluid dynamics model. The system mainly consists of a data acquisition module, a virtual space mapping module, a wave phase analysis module, and a dynamic monitoring module.

[0058] The data acquisition module is configured to acquire the physical length of the chromatographic column, the particle size of the packing material, the porosity correction factor, the real-time voltage signal, the upper limit voltage of the detector range, and the sampling time interval. Among the above parameters, the physical length of the chromatographic column and the particle size of the packing material are obtained from the manufacturer's specifications of the chromatographic column. The real-time voltage signal is read in real time by the high-precision analog-to-digital converter of the chromatograph and converts the analog continuous signal into a discrete time series signal. The sampling time interval is determined by the main clock frequency of the system.

[0059] The virtual space mapping module is configured to establish the mapping relationship between physical entities and mathematical space. This module calculates and generates virtual feature diameters based on the physical length of the chromatographic column, the particle size of the packing material, and the porosity correction factor. At the same time, it calculates and generates dimensionless voltage scalars based on real-time voltage signals and the upper limit voltage of the detector range. It also calibrates virtual dynamic viscosity based on the background noise data collected by the system during the blank operation phase.

[0060] The wave phase analysis module is configured to solve the flow field state in real time. Based on the dimensionless voltage scalar, sampling time interval and virtual dynamic viscosity, the module calculates the virtual shear stress and constructs the wave phase stability index in combination with the preset virtual fluid density. The wave phase stability index is a dimensionless criterion used to quantify whether the current signal flow field is in a stable laminar state or a divergent turbulent state.

[0061] The dynamic monitoring module is configured to execute the judgment logic. This module calculates the dynamic alarm threshold based on the virtual feature diameter and compares the real-time calculated wave phase stability index with the dynamic alarm threshold. If the wave phase stability index is less than or equal to the dynamic alarm threshold, the system determines that the current monitoring state is a normal laminar flow state, and the corresponding signal fluctuation is judged as baseline drift or background noise. If the wave phase stability index is greater than the dynamic alarm threshold, the system determines that the monitoring state is an abnormal turbulent flow state and triggers an alarm signal, indicating that a potential abnormal chromatographic peak or ghost peak has been detected. Through this hydrodynamic mapping mechanism, this application can effectively avoid false alarms caused by baseline drift while maintaining high sensitivity to trace anomalies.

[0062] Example 2:

[0063] The virtual space mapping module performs the following steps:

[0064] Adjust the column physical length, packing particle size, and porosity correction factors;

[0065] Introduce unit conversion factors;

[0066] Calculate the product of the physical length of the chromatographic column and the unit conversion factor to generate the converted length;

[0067] Calculate the ratio of the converted length to the filler particle size;

[0068] Calculate the product of the ratio and the porosity correction factor to generate a virtual characteristic diameter.

[0069] This embodiment details the process of generating virtual feature diameters in the virtual space mapping module. The physical geometric properties of the chromatographic column determine the flow stability of the fluid within it. To transform these physical properties into geometric constraints recognizable by the algorithm, this embodiment uses the following steps to generate virtual feature diameters. ;

[0070] The virtual space mapping module accesses the physical length of the chromatographic column stored in the system database. , filler particle size and porosity correction factor ;in The unit is millimeters. The unit is micrometer. A dimensionless constant characterizing the packing density, for example, with a value of 0.4;

[0071] To eliminate dimensional differences, the module introduces a unit conversion factor. Its value is set to 1000; the module calculates the physical length of the chromatographic column. Unit conversion factor The product of these values ​​generates the converted length, and this step unifies the unit of column length to the micrometer level.

[0072] Module calculates filler particle size With porosity correction factor The product of the terms is used as the denominator; the converted length is used as the numerator; the ratio of the numerator to the denominator is calculated to generate the virtual feature diameter. The specific calculation formula is as follows:

[0073] ;

[0074] Among them, the virtual feature diameter As a dimensionless geometric constraint parameter, it is used in subsequent steps to define the boundary conditions of fluid flow, ensuring that chromatographic columns of different specifications can be mapped to a unified virtual fluid dynamics space.

[0075] Example 3:

[0076] The virtual space mapping module also performs the following steps:

[0077] Call the real-time voltage signal and the upper limit voltage of the detector range;

[0078] Calculate the quotient of the real-time voltage signal and the upper limit voltage of the detector range to generate a dimensionless voltage scalar;

[0079] Obtain the normalized signal sequence of the system during the blank solvent operation phase;

[0080] Calculate the statistical variance of the rate of change of adjacent sampling points in a normalized signal sequence;

[0081] The statistical variance is defined as the virtual dynamic viscosity.

[0082] This embodiment provides a detailed explanation of the signal normalization and virtual dynamic viscosity calibration process in the virtual space mapping module. In order to comply with the principle of physical dimension consistency, the system needs to perform dimensionless processing on the voltage signal and physicalize the background noise into the viscous resistance of the fluid.

[0083] The virtual space mapping module calls upon the real-time voltage signals collected by the sensors. and detector range upper limit voltage The module calculates real-time voltage signals. Voltage above the detector range The quotient generates a dimensionless voltage scalar in the range of 0 to 1. :

[0084] ;

[0085] The system has a preset baseline calibration mode, which the user activates during the preparation phase before sample testing. The module acquires the normalized signal sequence from the blank solvent run phase, denoted as... The sequence was acquired under conditions of no sample injection and only mobile phase operation, and therefore only contains background noise and inherent pulsation of the system.

[0086] The module calculates the normalized signal sequence. The statistical variance of the rate of change of adjacent sampling points is defined as the virtual dynamic viscosity. Specifically, the total number of sampling points during the blank operation phase is set to... , No. The signal value at each sampling point is The sampling time interval is Then the virtual dynamic viscosity The calculation formula is:

[0087] ;

[0088] in, It is the arithmetic mean of the rates of change; this parameter This characterizes the inherent damping properties of the system under laminar flow conditions. Higher background noise results in a higher calculated virtual viscosity, thus providing stronger fluctuation suppression capabilities in the algorithm model. If the calculated... If the value is 0, the system will automatically set it to a very small positive number to prevent errors in subsequent calculations where the denominator is zero.

[0089] Example 4:

[0090] The wave phase analysis module performs the following steps:

[0091] Call upon the dimensionless voltage scalar, sampling time interval, and virtual dynamic viscosity;

[0092] Calculate the difference between the dimensionless voltage scalar at the current moment and the dimensionless voltage scalar at the previous moment;

[0093] Calculate the quotient of the difference and the sampling time interval to generate the real-time signal change rate;

[0094] The virtual shear stress is generated by calculating the product of the virtual dynamic viscosity and the rate of change of the real-time signal.

[0095] This embodiment provides a detailed explanation of the process of calculating virtual shear stress in the wave phase analysis module; virtual shear stress characterizes the frictional resistance generated inside the fluid due to the velocity gradient and is a key intermediate variable for determining whether the flow field is distorted;

[0096] The wave phase analysis module calls the dimensionless voltage scalar at the current moment. The dimensionless voltage scalar of the previous moment Sampling time interval and the virtual dynamic viscosity calibrated in the preceding steps ;

[0097] The module calculates the dimensionless voltage scalar at the current moment. dimensionless voltage scalar at the previous moment The difference is calculated, and the difference is used to determine the sampling time interval. The quotient generates the real-time signal change rate. This rate of change corresponds, in a physical sense, to the velocity gradient of the virtual fluid.

[0098] The module constructs a virtual mapping model based on the discretized form of Newton's law of internal friction to calculate the virtual dynamic viscosity. With real-time signal change rate The product of these factors generates virtual shear stress. :

[0099] ;

[0100] By introducing virtual dynamic viscosity As a coefficient, this step transforms the simple rate of change of the signal into a shear stress with mechanical significance, thus establishing a direct physical relationship between the amplitude of the signal fluctuation and the background noise level of the system.

[0101] Example 5:

[0102] The wave phase analysis module also performs the following steps:

[0103] Invoke virtual fluid density, dimensionless voltage scalar, and real-time signal rate of change;

[0104] Calculate the product of the virtual fluid density, the dimensionless voltage scalar, and the square of the rate of change of the real-time signal to generate the inertial force term;

[0105] Virtual dynamic viscosity is defined as a viscous force term;

[0106] Calculate the ratio of the inertial force term to the viscous force term to generate the wave phase stability index.

[0107] This embodiment provides a detailed explanation of the process of constructing the wave phase stability index in the wave phase analysis module; wave phase stability index It is a dimensionless criterion based on the ratio of fluid kinetic energy to viscous dissipation, used to assess in real time whether there are abnormal disturbances in the flow field;

[0108] The wave phase analysis module calls the preset virtual fluid density. The dimensionless voltage scalar at the current moment and the real-time signal change rate calculated from the preceding steps. In this embodiment, to adhere to the principle of dimensionless modeling, the virtual fluid density is... It is set to a standard constant of 1.0; or in another implementation, the system reads the current mobile phase configuration information and sets it to... The density is set as the relative ratio of the mixed solvent density to the water density. To quantify the virtual kinetic energy contained in the signal abrupt change, the module calculates the virtual fluid density based on the kinetic energy theorem. Dimensionless voltage scalar With real-time signal change rate The product of the squares generates the virtual kinetic energy term. :

[0109] ;

[0110] In this formula, for Squaring not only eliminates the influence of the signal change direction, but also gives the term a virtual dimension characteristic analogous to energy density;

[0111] It should be noted that when the system is in gradient elution mode, changes in the mobile phase ratio not only alter the virtual fluid density. This can also cause the background noise baseline to drift; therefore, in this embodiment, the system further calls the solvent viscosity-ratio curve stored in the database to generate a real-time viscosity correction coefficient. In subsequent calculations, the corrected virtual dynamic viscosity was used. Replace the original fixed constant To maintain the background potential term Dynamic consistency with the physical properties of the current flowing phase;

[0112] Meanwhile, in order to quantify the viscous potential energy represented by the system background noise, the module directly calls the virtual dynamic viscosity calibrated in the previous steps. ;because In the preceding definition, it is the statistical variance of the rate of change of the normalized signal, which itself already possesses... Since they share the same dimensional properties, they are directly defined as the background potential energy term. :

[0113] ;

[0114] Module calculates virtual kinetic energy term With background potential term The ratio of the two values ​​generates the wave phase stability index. :

[0115] ;

[0116] Among them, the index As a strictly dimensionless number, it cleverly establishes a nonlinear mapping relationship for the signal-to-noise ratio: when the signal exhibits baseline drift, although the amplitude... It may be large, but the square of the rate of change Extremely small, the denominator Suppression; when the signal exhibits high-frequency noise, although the rate of change is large, the amplitude... The value is extremely small, and the numerator product remains low; only when a true abnormal chromatographic peak appears do both the amplitude and the square of the rate of change increase significantly, causing the numerator to be much larger than the denominator. It exhibits an exponential increase, thereby enabling precise capture of abnormal signals.

[0117] Example 6:

[0118] The dynamic monitoring module performs the following steps:

[0119] The arithmetic mean of the wave phase stability index during the system's blank operation phase is called and defined as the baseline stability value;

[0120] Call the preset sensitivity adjustment coefficient and virtual feature diameter;

[0121] Calculate the natural logarithm of the virtual feature diameter;

[0122] Calculate the product of the natural logarithm and the sensitivity adjustment coefficient to generate the geometric correction.

[0123] The sum of the baseline stable value and the geometric correction is calculated to generate a dynamic alarm threshold.

[0124] This embodiment provides a detailed explanation of the process for calculating the dynamic alarm threshold in the dynamic monitoring module; in order to adapt to the physical characteristics of different chromatographic columns, the alarm threshold needs to be adaptively adjusted according to the hardware parameters;

[0125] The dynamic monitoring module calls the wave phase stability index sequence calculated by the system during the blank operation phase, calculates its arithmetic mean, and defines it as the benchmark stability value. This value represents the average energy level of the flow field under the current system environment.

[0126] The module calls the preset sensitivity adjustment coefficient. and the virtual feature diameter generated in the preceding steps Among them, the sensitivity adjustment coefficient Based on statistics The empirical constant set by the criterion, for example, is 3.0; the module calculates the virtual feature diameter. natural logarithm And calculate the natural logarithm and the sensitivity adjustment coefficient. The product of these factors generates the geometric correction. It should be noted that, because the column length after unit conversion is numerically much larger than the packing particle size, the virtual characteristic diameter is ensured. Thus making The value is always positive, ensuring that the positive gain logic of the dynamic alarm threshold based on the reference value holds true;

[0127] Module calculates baseline stability value With geometric correction The sum of these values ​​generates a dynamic alarm threshold. :

[0128] ;

[0129] This formula establishes a dynamic threshold model based on logarithmic relationships; for long chromatographic columns with high column efficiency and sharp peak shapes, its If the value is too high, the system will automatically raise the alarm threshold. This prevents false alarms caused by normal high-frequency signal components; conversely, for short columns with low column efficiency, the system will automatically lower the threshold to maintain sufficient monitoring sensitivity.

[0130] Example 7:

[0131] It also includes a health prediction module, which performs the following steps:

[0132] Obtain the total duration of a single run, the ideal phase curve and the limit dissipation value during the operation of the standard product;

[0133] Calculate the difference between the wave phase stability index and the ideal wave phase curve;

[0134] The difference is integrated over the total duration of a single run and divided by the total duration of a single run to generate the average dissipation rate.

[0135] Calculate the ratio of the average dissipation rate to the limiting dissipation value;

[0136] The health index is generated by subtracting the ratio from the value 1.

[0137] This embodiment also includes a health prediction module, which is used to quantitatively assess the health status of the chromatographic column based on rheological principles;

[0138] The health prediction module obtains the total duration of a single run. Ideal wave phase curve of pre-stored standard products during operation and the limit dissipation value when the chromatographic column is scrapped. ;in, It is an empirical threshold derived from statistical analysis of a large amount of historical failure data; the specific steps of the statistical analysis are: selecting... For chromatographic columns of the same model that have been confirmed to be ineffective through column efficiency testing; obtain the average dissipation rate of these ineffective columns during a single run. Set; calculate the arithmetic mean and standard deviation of the set. ;set up It equals the arithmetic mean minus Double standard deviation This serves as a conservative energy boundary for determining column failure.

[0139] Using dynamic time warping or maximum cross-correlation algorithms, with the goal of minimizing the Euclidean distance, the wave phase stability index calculated in real time is used. Phase curve of ideal wave Feature alignment is performed on the time axis to eliminate phase errors caused by retention time drift; based on the aligned data sequence, the absolute value of the difference between the wave phase stability index and the ideal wave phase curve is calculated; specifically, the real-time wave phase stability index sequence is calculated using a dynamic time warping algorithm. With ideal wave phase curve sequence Minimum regularization path between Based on this regularized path The real-time wave phase stability index sequence The time axis is mapped onto the time axis of the ideal wave phase curve to generate a time-aligned calibration sequence. At this point, calculating the difference is equivalent to calculating... and On the same ideal timeline The corresponding point difference;

[0140] During the detection process, the module calculates the current wave phase stability index in real time. Phase curve of ideal wave The difference at the corresponding moment; the module's response to this difference over the total duration of a single run. The integral is calculated and the result is divided by the total duration of a single run. Generate average dissipation rate :

[0141] ;

[0142] This average dissipation rate physically characterizes the additional energy loss caused by increased micro-turbulence in the flow path due to column packing collapse or contamination.

[0143] Module calculates average dissipation rate With limit dissipation value The ratio of the two values ​​is used to calculate the health index, which is generated by subtracting the ratio from the value 1. :

[0144] ;

[0145] in, The function is used to ensure that the health index is non-negative; this health index The value is between 0 and 1, which intuitively reflects the remaining lifespan of the chromatographic column, allowing users to plan maintenance or replacement in advance based on this index, and avoid affecting the detection quality due to sudden column failure.

[0146] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A real-time wave phase chromatography data monitoring and alarm system, characterized in that, include: The data acquisition module is used to acquire the physical length of the chromatographic column, the particle size of the packing material, the porosity correction factor, the real-time voltage signal, the upper limit voltage of the detector range, and the sampling time interval. The virtual space mapping module is used to generate virtual feature diameters based on the physical length of the chromatographic column, the particle size of the packing material, and the porosity correction factor. A dimensionless voltage scalar is generated based on the real-time voltage signal and the upper limit voltage of the detector range. Based on background noise data, the virtual dynamic viscosity is calibrated; The wave phase analysis module is used to calculate virtual shear stress based on dimensionless voltage scalar, sampling time interval, and virtual dynamic viscosity; and to construct the wave phase stability index by combining virtual fluid density. The dynamic monitoring module is used to calculate the dynamic alarm threshold based on the virtual characteristic diameter; compare the wave phase stability index with the dynamic alarm threshold; if the wave phase stability index is less than or equal to the dynamic alarm threshold, the monitoring state is determined to be normal laminar flow; if the wave phase stability index is greater than the dynamic alarm threshold, the monitoring state is determined to be abnormal turbulent flow. The virtual space mapping module performs the following steps: Adjust the column physical length, packing particle size, and porosity correction factors; Introduce unit conversion factors; Calculate the product of the physical length of the chromatographic column and the unit conversion factor to generate the converted length; Calculate the ratio of the converted length to the filler particle size; Calculate the product of the ratio and the porosity correction factor to generate a virtual feature diameter; The virtual space mapping module also performs the following steps: Call the real-time voltage signal and the upper limit voltage of the detector range; Calculate the quotient of the real-time voltage signal and the upper limit voltage of the detector range to generate a dimensionless voltage scalar; Obtain the normalized signal sequence of the system during the blank solvent operation phase; Calculate the statistical variance of the rate of change of adjacent sampling points in a normalized signal sequence; The statistical variance is defined as the virtual dynamic viscosity.

2. The real-time wave phase chromatography data monitoring and alarm system according to claim 1, characterized in that, The wave phase analysis module performs the following steps: Call upon the dimensionless voltage scalar, sampling time interval, and virtual dynamic viscosity; Calculate the difference between the dimensionless voltage scalar at the current moment and the dimensionless voltage scalar at the previous moment; Calculate the quotient of the difference and the sampling time interval to generate the real-time signal change rate; The virtual shear stress is generated by calculating the product of the virtual dynamic viscosity and the rate of change of the real-time signal.

3. The real-time wave phase chromatography data monitoring and alarm system according to claim 2, characterized in that, The wave phase analysis module also performs the following steps: Invoke virtual fluid density, dimensionless voltage scalar, and real-time signal rate of change; Calculate the product of the virtual fluid density, the dimensionless voltage scalar, and the square of the rate of change of the real-time signal to generate the inertial force term; Virtual dynamic viscosity is defined as a viscous force term; Calculate the ratio of the inertial force term to the viscous force term to generate the wave phase stability index.

4. The real-time wave phase chromatography data monitoring and alarm system according to claim 1, characterized in that, The dynamic monitoring module performs the following steps: The arithmetic mean of the wave phase stability index during the system's blank operation phase is called and defined as the baseline stability value; Call the preset sensitivity adjustment coefficient and virtual feature diameter; Calculate the natural logarithm of the virtual feature diameter; Calculate the product of the natural logarithm and the sensitivity adjustment coefficient to generate the geometric correction. The sum of the baseline stable value and the geometric correction is calculated to generate a dynamic alarm threshold.

5. The real-time wave phase chromatography data monitoring and alarm system according to claim 1, characterized in that, It also includes a health prediction module, which performs the following steps: Obtain the total duration of a single run, the ideal phase curve and the limit dissipation value during the operation of the standard product; Calculate the difference between the wave phase stability index and the ideal wave phase curve; The difference is integrated over the total duration of a single run and divided by the total duration of a single run to generate the average dissipation rate. Calculate the ratio of the average dissipation rate to the limiting dissipation value; The health index is generated by subtracting the ratio from the value 1.

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