An analysis method and system for improving the quality of mass spectrometry

Abstract

The present invention discloses an analysis method and system for improving the quality of mass spectrometers, and relates to the technical field of mass spectrometer systems. Specifically, the present invention discloses a method and system for improving the quality of mass spectrometers, and relates to the technical field of mass spectrometer systems. The method and system are as follows: a high-precision three-dimensional mass spectrometer model is constructed to simulate the actual working process of the mass spectrometer; a normal and abnormal mass spectrometer database is established using historical experimental data, and a simulated mass spectrometer corresponding to the historical data is obtained through virtual simulation; the consistency of the simulated mass spectrometer and the historical mass spectrometer is verified by calculating a similarity scale to form an effective corresponding group; the mass spectrometer obtained in real time is matched with the effective corresponding group to achieve optimized correction or abnormal alarm of the mass spectrometer. The above technical scheme of the present invention realizes the accuracy correction of the mass spectrometer and improves the quality of the mass spectrometer.

Description

Technical Field

[0001] The present invention relates to the technical field of mass spectrometer systems, and in particular to an analysis method and system for improving the quality of mass spectrometer graphs. Background Art

[0002] In the field of mass spectrometry, it is a crucial task to accurately distinguish fixed sample groups with specific equivalent characteristics. Such sample groups may originate from the same biological pathway, chemical synthesis path or have similar physical and chemical properties, and the slight differences between them often contain important scientific information or application value. However, due to the high similarity between samples, traditional mass spectrometry methods face great challenges in distinguishing these samples. As the core output of mass spectrometry, the quality of the mass spectrum is directly related to the accuracy and reliability of sample differentiation.

[0003] In actual operation, the quality of mass spectra is affected by many complex factors, including but not limited to contamination during sample processing, efficiency fluctuations during ionization, performance limitations of the mass spectrometer itself, and deficiencies in data analysis methods. These factors may lead to problems such as low signal-to-noise ratio, peak overlap, and reduced resolution in mass spectra, which seriously restricts the ability to identify subtle differences between samples. Summary of the invention

[0004] The object of the present invention is to provide a mass spectrum quality analysis method and system capable of improving the quality of mass spectra.

[0005] The present invention discloses an analysis method for improving the quality of a mass spectrum, comprising:

[0006] Obtaining a design drawing of a mass spectrometer model, determining a core structure in the design drawing, constructing a plurality of three-dimensional virtual components, and combining the three-dimensional virtual components to construct a three-dimensional mass spectrometer model;

[0007] Establish a virtual sample model, grid the three-dimensional virtual components, define the influence of each component grid on the virtual sample model based on the working principle of each three-dimensional virtual component, and construct a virtual simulation mass spectrometer;

[0008] Acquire historical experimental data, and divide the historical experimental data based on the data records to obtain a number of normal historical experimental monomer data and a number of abnormal historical experimental monomer data, analyze each normal historical experimental monomer data to determine a normal historical mass spectrum, and analyze each abnormal historical experimental monomer data to determine an abnormal historical mass spectrum;

[0009] The virtual simulation mass spectrometer is configured based on the normal historical experimental monomer data and the abnormal historical experimental monomer data, and the virtual simulation mass spectrometer is driven to perform simulation operations to obtain a number of normal simulation mass spectra and a number of abnormal simulation mass spectra, and the corresponding normal simulation mass spectra and normal historical mass spectra are combined to form a normal mass spectra corresponding group, and the corresponding abnormal simulation mass spectra and abnormal historical mass spectra are combined to form an abnormal mass spectra corresponding group;

[0010] Analyze the normal mass spectrum corresponding group and the abnormal mass spectrum corresponding group respectively, calculate the similarity scale between the mass spectra, and if the similarity scale is greater than or equal to a preset value, identify the normal mass spectrum corresponding group as a valid normal mass spectrum corresponding group, or identify the abnormal mass spectrum corresponding group as a valid abnormal mass spectrum corresponding group;

[0011] The real-time mass spectrum acquired in real time is matched for consistency using a valid normal mass spectrum corresponding group or a valid abnormal mass spectrum corresponding group. If a valid normal mass spectrum corresponding group is matched, the real-time mass spectrum is optimized and corrected using the valid normal mass spectrum corresponding group. If a valid abnormal mass spectrum corresponding group is matched, a mass spectrometer abnormality alarm is issued, and the abnormal historical experimental monomer data corresponding to the valid abnormal mass spectrum corresponding group are analyzed. The sample data therein is used as a search label, and the corresponding normal historical experimental monomer data is retrieved from several normal historical experimental monomer data. Based on the normal historical mass spectrum corresponding group corresponding to the retrieved normal historical experimental monomer data, a corrected mass spectrum of the real-time mass spectrum is determined.

[0012] In an embodiment of the present invention, the influence relationship of each component network on the virtual sample model is defined, and the method for constructing a virtual simulation mass spectrometer includes:

[0013] A mass parameter interval and a charge parameter interval are defined for a virtual sample model, wherein the virtual sample model includes a plurality of virtual sample particles, a travel trajectory is configured for each virtual sample particle, and a travel speed is configured for each virtual sample particle based on the obtained mass parameter and charge parameter;

[0014] Based on the working principle of each three-dimensional virtual component, the component grid of the corresponding area is configured with direction influencing parameters and speed influencing parameters for the virtual sample particles, and the component network block adjusts the trajectory and speed of the virtual sample particles passing through its own area;

[0015] The component grid that the virtual sample particles finally reach is recorded, and a simulated mass spectrum is constructed based on the recorded relative position of the component grid and the number of virtual sample particles that terminate at the component grid.

[0016] In an embodiment of the present invention, the three-dimensional virtual component includes a three-dimensional virtual ion source component, a three-dimensional mass analyzer component, and a three-dimensional detector component;

[0017] The three-dimensional virtual ion source component determines the number of different virtual sample particles split, the mass parameter of each virtual sample particle, and the charge parameter of each virtual sample particle based on the input parameters of the virtual sample model;

[0018] The three-dimensional mass analyzer component determines the direction influencing parameters and the speed influencing parameters of the virtual sample particles based on the mass parameters and the charge parameters of the virtual sample particles;

[0019] The three-dimensional detector component determines the abundance of different virtual sample particles based on the end positions of the virtual sample particles and the number of virtual sample particles at the same end position.

[0020] In an embodiment of the present invention, a method for calculating a similarity scale between mass spectra of a corresponding group of mass spectra includes:

[0021] Performing a mixed feature analysis on several simulated mass spectra to determine a first mass spectrum weight mapping template corresponding to the simulated mass spectrum, performing a mixed feature analysis on several historical mass spectra to determine a second mass spectrum weight mapping template corresponding to the historical mass spectrum, and averaging the first mass spectrum weight mapping template and the second mass spectrum weight mapping template to obtain a third mass spectrum weight mapping template;

[0022] Based on the attention weights corresponding to nodes at different positions in the third mass spectrum weight mapping template and the difference characteristics between the mass spectra of the corresponding group of mass spectra, the similarity scale between the mass spectra of the corresponding group of mass spectra is determined.

[0023] In an embodiment of the present invention, a method for performing mixed characteristic analysis on a simulated mass spectrum or a historical mass spectrum includes:

[0024] Determine the abundance mapping points of nodes at different positions on each simulated mass spectrum or historical mass spectrum, and connect the abundance mapping points to obtain an abundance expression line;

[0025] A basic mass spectrum weight mapping template is constructed, and a number of abundance expression broken lines are mapped onto the basic mass spectrum weight mapping template, and the abundance expression broken lines are classified for consistency, and the abundance expression broken lines in each classification group are analyzed, and based on the analysis results, a category abundance expression broken line is constructed;

[0026] The expressions of the abundance expression lines of several categories on the basic mass spectrum weight mapping template are identified as the first mass spectrum weight mapping template or the second mass spectrum weight mapping template.

[0027] In an embodiment of the present invention, the method for classifying the consistency of the abundance expression line includes:

[0028] The abundance expression polyline is first screened and classified using the horizontal axis node corresponding to each abundance mapping point of the abundance expression polyline as the first screening condition, and the abundance expression polyline after the first screening and classification is second screened and classified using the vertical axis node corresponding to each abundance mapping point of the abundance expression polyline as the second screening condition, wherein when the second screening condition is used for classification, the vertical axis interval to which the vertical axis node corresponding to each abundance mapping point belongs is determined, and several abundance mapping points belonging to the same vertical axis interval are identified as the same type of abundance mapping points, and the abundance expression polyline is second screened and classified using the condition that each abundance mapping point is mutually the same type of abundance mapping point;

[0029] The abundance expression broken line of the second screening classification is averaged to obtain the category abundance expression broken line. The method of averaging includes averaging the vertical axis value and the horizontal axis value of each relative turning point to obtain a new abundance mapping point, and connecting the abundance mapping points.

[0030] In an embodiment of the present invention, a method for averaging the first mass spectrum weight mapping template and the second mass spectrum weight mapping template includes:

[0031] The abundance expression broken lines in the first mass spectrum weight mapping template and the second mass spectrum weight mapping template are aligned, and the vertical axis value and the horizontal axis value of each relative turning point are averaged to obtain a new abundance mapping point, and the abundance mapping points are connected to obtain an average abundance expression broken line.

[0032] In an embodiment of the present invention, based on the attention weights corresponding to the nodes at different positions in the third mass spectrum weight mapping template and the difference characteristics between the mass spectra of the corresponding group of mass spectra, a method for determining the similarity scale between the mass spectra of the corresponding group of mass spectra includes:

[0033] Determine each turning point of the category abundance expression polyline, and construct several levels of weight range circles for each turning point of the category abundance expression polyline. Each level of weight range circle corresponds to a preset weight coefficient. The radii of weight range circles of different levels are different. The longer the radius, the lower the weight coefficient corresponding to the weight range circle.

[0034] Determine the abundance expression broken lines in the simulated mass spectra and the historical mass spectra of the mass spectrum corresponding group, determine the position node of each abundance mapping point in the abundance expression broken lines, and combine each relative abundance mapping point between the abundance expression broken lines to record as the abundance mapping point corresponding group;

[0035] Determine the average abundance expression line in the simulated mass spectra and the historical mass spectra in the corresponding group of mass spectra, and retrieve the most consistent category abundance expression line from a number of category abundance expression lines, including calculating the relative distance between the average abundance expression line and each abundance mapping point in the category abundance expression line, calculating the total distance of all relative distances, and identifying the category abundance expression line with the lowest total distance as the most consistent category abundance expression line;

[0036] Determine the weight range circle to which the abundance mapping point in each abundance mapping point corresponding group belongs in the category abundance expression polyline, and select the most suitable weight range circle with the smallest radius in the weight range circle to which it belongs, and determine the attention weight corresponding to the abundance mapping point corresponding group based on the determined most suitable weight range circle;

[0037] Determining the distance between the abundance mapping points in the corresponding group of abundance mapping points, and determining the similarity scale between the mass spectra of the corresponding group of mass spectra in combination with the attention weight;

[0038] Among them, the expression for calculating the similarity scale is:

[0039] ;

[0040] Among them, S is the similarity scale, is the preset maximum similarity scale, is the pre-judgment function of the abundance mapping point corresponding group. If the distance between the abundance mapping points in the abundance mapping point corresponding group is less than or equal to the preset value, then Output 1, otherwise output 0. is the influence adjustment coefficient of the pre-judgment of the distance between abundance mapping points, c is the influence adjustment constant of the pre-judgment of the distance between abundance mapping points, is the attention weight of the group corresponding to the i-th abundance mapping point, is the distance between the abundance mapping points of the corresponding group between the i-th abundance mapping points, and n is the total number of corresponding groups between abundance mapping points.

[0041] In an embodiment of the present invention, a method for matching the real-time mass spectra acquired in real time using the valid normal mass spectra corresponding group or the valid abnormal mass spectra corresponding group includes:

[0042] Determine the abundance mapping points of nodes at different positions on each simulated mass spectrum or historical mass spectrum in the corresponding group of valid normal mass spectra or the corresponding group of valid abnormal mass spectra, connect the abundance mapping points to obtain an abundance expression broken line, average the abundance expression broken lines between the simulated mass spectrum and the historical mass spectrum to obtain an average abundance expression broken line, and determine the real-time abundance expression broken line in the real-time mass spectrum in the same manner as above;

[0043] Based on the matching degree between the real-time abundance expression line and the average abundance expression line, a corresponding group of valid normal mass spectra or a corresponding group of valid abnormal mass spectra is selected;

[0044] The method for determining the degree of matching between the real-time abundance expression line and the average abundance expression line includes:

[0045] Align the real-time abundance expression line and the average abundance expression line, and gradually scan the area between the two lines, and determine the positive area change rate of the area between the lines in real time. If the positive area change rate is greater than or equal to a preset value, the corresponding horizontal axis point is used as the recording starting point, and the line difference segment is recorded until the positive area change rate is less than the preset value;

[0046] Determine the degree of match between the real-time abundance expression polyline and the average abundance expression polyline based on the area between the polylines and the length ratio of the polyline difference segment to the polyline horizontal axis mapping segment;

[0047] The expression for calculating the matching degree is:

[0048] ;

[0049] Among them, P is the matching degree, V is the area between the broken lines, is the segment length of the x-th polyline difference segment, is the segment length of the segment mapped on the horizontal axis of the broken line, N is the number of broken line difference segments, J is the segment length ratio impact adjustment coefficient, and d is the segment length ratio impact adjustment constant.

[0050] In an embodiment of the present invention, an analysis system for improving the quality of a mass spectrum is also disclosed, comprising:

[0051] The first module is used to obtain the design drawings of the mass spectrometer model, determine the core structure in the design drawings, construct a number of three-dimensional virtual components, and combine the three-dimensional virtual components to construct a three-dimensional mass spectrometer model;

[0052] The second module is used to establish a virtual sample model, grid the three-dimensional virtual components, define the influence relationship of each component grid on the virtual sample model based on the working principle of each three-dimensional virtual component, and construct a virtual simulation mass spectrometer;

[0053] The third module is used to obtain historical experimental data, and divide the historical experimental data based on the data records to obtain a number of normal historical experimental monomer data and a number of abnormal historical experimental monomer data, analyze each normal historical experimental monomer data to determine a normal historical mass spectrum, and analyze each abnormal historical experimental monomer data to determine an abnormal historical mass spectrum;

[0054] The fourth module is used to configure the virtual simulation mass spectrometer based on the normal historical experimental monomer data and the abnormal historical experimental monomer data, and drive the virtual simulation mass spectrometer to perform simulation operations to obtain a number of normal simulation mass spectra and a number of abnormal simulation mass spectra, combine the corresponding normal simulation mass spectra with the normal historical mass spectra to form a normal mass spectra corresponding group, and combine the corresponding abnormal simulation mass spectra with the abnormal historical mass spectra to form an abnormal mass spectra corresponding group;

[0055] The fifth module is used to analyze the normal mass spectrum corresponding group and the abnormal mass spectrum corresponding group respectively, calculate the similarity scale between the mass spectra, and if the similarity scale is greater than or equal to a preset value, the corresponding normal mass spectrum corresponding group is identified as a valid normal mass spectrum corresponding group, or the abnormal mass spectrum corresponding group is identified as a valid abnormal mass spectrum corresponding group;

[0056] The sixth module is used to use the effective normal mass spectrum corresponding group or the effective abnormal mass spectrum corresponding group to perform consistency matching on the real-time mass spectrum acquired in real time. If the effective normal mass spectrum corresponding group is matched, the real-time mass spectrum is optimized and corrected using the effective normal mass spectrum corresponding group. If the effective abnormal mass spectrum corresponding group is matched, the mass spectrometer abnormality alarm is issued, and the abnormal historical experimental monomer data corresponding to the effective abnormal mass spectrum corresponding group are analyzed, and the corresponding normal historical experimental monomer data are retrieved from several normal historical experimental monomer data using the sample data therein as the retrieval label, and the corrected mass spectrum of the real-time mass spectrum is determined based on the normal historical mass spectrum corresponding group corresponding to the retrieved normal historical experimental monomer data.

[0057] The present invention discloses an analysis method for improving the quality of a mass spectrometer, and relates to the technical field of mass spectrometer systems. Specifically, the present invention discloses a method for improving the quality of a mass spectrometer, and relates to the technical field of mass spectrometer systems. The method comprises the following steps: constructing a high-precision three-dimensional mass spectrometer model to simulate the actual working process of the mass spectrometer; establishing a normal and abnormal mass spectrometer database using historical experimental data, and obtaining a simulated mass spectrometer corresponding to the historical data through virtual simulation; verifying the consistency of the simulated mass spectrometer with the historical mass spectrometer by calculating a similarity scale, and forming an effective corresponding group; matching the mass spectrometer obtained in real time with the effective corresponding group, and realizing optimized correction or abnormal alarm of the mass spectrometer. The above technical scheme of the present invention realizes the accuracy correction of the mass spectrometer and improves the quality of the mass spectrometer.

[0058] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A method step diagram of an analysis method for improving the quality of a mass spectrum disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0061] The following will be combined with the accompanying drawings and specific embodiments to clearly and completely describe the technical solution of the present invention. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and cannot be understood as limiting the scope of protection of the present invention. Those skilled in the art in this field can make some non-essential improvements and adjustments based on the content of the present invention described below. In the present invention, unless otherwise clearly specified and limited, the technical terms used in the present invention should be the common meanings understood by the technical personnel described in the present invention. Example

[0062] The present invention discloses an analytical method for improving the quality of mass spectrometry. Figure 1 ,include:

[0063] Step S100, obtaining a design drawing of a mass spectrometer model, determining the core structure in the design drawing, constructing a plurality of three-dimensional virtual components, and combining the three-dimensional virtual components to construct a three-dimensional mass spectrometer model.

[0064] In this step, technicians first build a virtual sample model that represents the actual sample. Then, they mesh the previously built three-dimensional virtual components, a process that subdivides the components into smaller, easier-to-calculate units. Based on the actual working principle of each component in the mass spectrometer, technicians define how these grid cells interact with the virtual sample model to affect the output of the mass spectrometer. Through this sophisticated simulation process, technicians are able to build a virtual simulation mass spectrometer that can simulate the working process of an actual mass spectrometer in a virtual environment, providing strong support for subsequent experimental simulation and data analysis.

[0065] Step S200, establish a virtual sample model, grid the three-dimensional virtual components, define the influence relationship of each component grid on the virtual sample model based on the working principle of each three-dimensional virtual component, and construct a virtual simulation mass spectrometer.

[0066] This step involves in-depth mining and analysis of past experimental data of the mass spectrometer. The technicians first collected a large amount of historical experimental data, which included the performance of the mass spectrometer under different experimental conditions, both normal and abnormal. Through careful data recording and analysis, the technicians were able to distinguish between normal and abnormal historical experimental monomer data. For each set of normal data, they extracted a mass spectrum representing the normal behavior of the mass spectrometer; for abnormal data, they extracted a mass spectrum reflecting the abnormal state of the mass spectrometer. These mass spectra provide an important reference for simulation operations and anomaly detection in subsequent steps.

[0067] In an embodiment of the present invention, the influence relationship of each component network on the virtual sample model is defined, and the method for constructing a virtual simulation mass spectrometer includes:

[0068] Step S201, defining a mass parameter range and a charge parameter range for a virtual sample model, wherein the virtual sample model includes a plurality of virtual sample particles, configuring a travel trajectory for each virtual sample particle, and configuring a travel speed for each virtual sample particle based on the obtained mass parameter and charge parameter.

[0069] In this step, technicians first define the mass parameter range and charge parameter range for the virtual sample model. These parameters are crucial for simulating the behavior of sample particles in the mass spectrometer because they directly affect the trajectory and speed of the particles. The virtual sample model consists of several virtual sample particles, each of which represents a sample molecule or atom with a specific mass and charge.

[0070] Next, the technician configures the trajectory for each virtual sample particle. This is done by simulating the initial state of the particle in the mass spectrometer (such as position, velocity, etc.) and the external forces it is subjected to (such as electric field force, magnetic field force, etc.). Based on the acquired mass parameters and charge parameters, the technician can also configure the travel speed for each particle, which will change continuously as the particle moves in the mass spectrometer.

[0071] Step S202, based on the working principle of each three-dimensional virtual component, the component grid of the corresponding area is configured with direction influencing parameters and speed influencing parameters for the virtual sample particles, and the component network block adjusts the trajectory and speed of the virtual sample particles passing through its own area.

[0072] In this step, the technician configures the direction and speed influencing parameters of the virtual sample particles for the component grid of the corresponding area based on the working principle of each three-dimensional virtual component. These parameters are used to simulate the influence of the component grid on the virtual sample particles passing through its area, such as changing the movement direction and speed of the particles.

[0073] When the virtual sample particles enter the component grid area, the component grid block will adjust the particle's trajectory and speed according to its configured influencing parameters. This process simulates the effects of actual components in the mass spectrometer on sample particles, such as ion acceleration in the electric field and deflection in the magnetic field.

[0074] Step S203, recording the component grid that the virtual sample particles finally reach, and constructing a simulated mass spectrum based on the recorded relative position of the component grid and the number of virtual sample particles that terminate at the component grid.

[0075] In this step, technicians record the component grid locations that the virtual sample particles ultimately reach. These locations are critical for constructing simulated mass spectra because they represent the final distribution of the sample particles in the mass spectrometer.

[0076] Based on the recorded relative positions of the component grids and the number of virtual sample particles that terminate in these grids, a simulated mass spectrometer can be constructed. This image shows the distribution of particles of different masses and charges in the mass spectrometer, and has similar features and structures to the actual mass spectrometer. By comparing the simulated mass spectrometer with the actual mass spectrometer, the accuracy of the virtual simulated mass spectrometer can be evaluated and optimized and improved.

[0077] In an embodiment of the present invention, the three-dimensional virtual component includes a three-dimensional virtual ion source component, a three-dimensional mass analyzer component, and a three-dimensional detector component;

[0078] Among them, the three-dimensional virtual ion source component determines the number of different virtual sample particles split, the mass parameter of each virtual sample particle, and the charge parameter of each virtual sample particle based on the input parameters of the virtual sample model. The three-dimensional virtual ion source component is the starting point of the virtual simulation mass spectrometer. It is responsible for generating virtual sample particles with specific mass and charge according to the input parameters of the virtual sample model. These input parameters may include the chemical composition, concentration, ionization energy, etc. of the sample. The ion source component first determines the number of different virtual sample particles split. This number depends on the complexity of the sample and the analysis requirements. Then, it assigns a mass parameter and charge parameter to each virtual sample particle. These parameters are based on the chemical composition and ionization process of the sample and determine the movement behavior of the particles in the mass spectrometer. By simulating the working process of the ion source, the three-dimensional virtual ion source component can generate a series of virtual sample particles with different masses and charges, providing a basis for subsequent mass analysis and detection.

[0079] The three-dimensional mass analyzer component determines the direction-influencing parameters and speed-influencing parameters of the virtual sample particles based on the mass parameters and charge parameters of the virtual sample particles. The three-dimensional mass analyzer component is the core component of the virtual simulation mass spectrometer. It is responsible for separating and analyzing the particles according to the mass parameters and charge parameters of the virtual sample particles. This component simulates the working principle of the actual mass analyzer in the mass spectrometer, such as the effects of the electric field, magnetic field or a combination of the two. The mass analyzer component determines the direction-influencing parameters and speed-influencing parameters of the particles by calculating the motion trajectory of each virtual sample particle in the electric field or magnetic field. These parameters determine the motion path and speed of the particles in the analyzer, thereby achieving effective separation of particles of different masses. By simulating the working process of the mass analyzer, the three-dimensional mass analyzer component can accurately predict the behavior of the virtual sample particles in the mass spectrometer and provide accurate data for subsequent particle detection and abundance calculation.

[0080] The three-dimensional detector component determines the abundance of different virtual sample particles based on the end position of the virtual sample particles and the number of virtual sample particles at the same end position. The three-dimensional detector component is the end point of the virtual simulation mass spectrometer. It is responsible for detecting the virtual sample particles separated by the mass analyzer and determining the abundance of different particles. The detector component simulates the working principle of the actual detector in the mass spectrometer, such as the electron multiplier, photomultiplier, etc. When the virtual sample particles reach the detector, the detector component records the end position of the particles and the number of particles at the same end position. These data are used to calculate the abundance of different particles, that is, the relative content of each particle in the sample. By simulating the working process of the detector, the three-dimensional detector component can accurately measure the abundance of virtual sample particles and provide key data support for mass spectrometry analysis. At the same time, these data can also be used to verify the accuracy and reliability of the virtual simulation mass spectrometer.

[0081] Step S300, obtain historical experimental data, and divide the historical experimental data based on data records to obtain a number of normal historical experimental monomer data and a number of abnormal historical experimental monomer data, analyze each normal historical experimental monomer data to determine a normal historical mass spectrum, and analyze each abnormal historical experimental monomer data to determine an abnormal historical mass spectrum.

[0082] This step involves in-depth mining and analysis of past experimental data of the mass spectrometer. The technicians first collected a large amount of historical experimental data, which included the performance of the mass spectrometer under different experimental conditions, both normal and abnormal. Through careful data recording and analysis, the technicians were able to distinguish between normal and abnormal historical experimental monomer data. For each set of normal data, they extracted a mass spectrum representing the normal behavior of the mass spectrometer; for abnormal data, they extracted a mass spectrum reflecting the abnormal state of the mass spectrometer. These mass spectra provide an important reference for simulation operations and anomaly detection in subsequent steps.

[0083] Step S400, configure the virtual simulation mass spectrometer based on the normal historical experimental monomer data and the abnormal historical experimental monomer data, and drive the virtual simulation mass spectrometer to perform simulation operations to obtain a number of normal simulation mass spectra and a number of abnormal simulation mass spectra, combine the corresponding normal simulation mass spectra and the normal historical mass spectra to form a normal mass spectra corresponding group, and combine the corresponding abnormal simulation mass spectra and the abnormal historical mass spectra to form an abnormal mass spectra corresponding group.

[0084] In this step, technicians use the previously extracted normal and abnormal historical experimental monomer data to configure the parameters of the virtual simulation mass spectrometer. They adjust the settings of the virtual mass spectrometer so that it can simulate the conditions of these historical experiments. Subsequently, by driving the virtual simulation mass spectrometer to perform simulation operations, technicians can obtain simulated mass spectra corresponding to normal and abnormal historical experiments. These simulated mass spectra are further combined with the corresponding historical mass spectra into normal and abnormal mass spectra corresponding groups. This step provides a key data set for subsequent mass spectrometry analysis and optimization correction.

[0085] Step S500, analyze the normal mass spectrum corresponding group and the abnormal mass spectrum corresponding group respectively, calculate the similarity scale between the mass spectra, and if the similarity scale is greater than or equal to a preset value, the corresponding normal mass spectrum corresponding group is identified as a valid normal mass spectrum corresponding group, or the abnormal mass spectrum corresponding group is identified as a valid abnormal mass spectrum corresponding group.

[0086] The focus of this step is to conduct an in-depth analysis of the corresponding groups of normal and abnormal mass spectra. The accuracy of the virtual simulation mass spectrometer is evaluated by comparing the similarity between the simulated mass spectra and the historical mass spectra. They use the similarity scale as a quantitative indicator to measure the similarity between the two groups of mass spectra. If the similarity scale is greater than or equal to the preset threshold, it means that the simulated mass spectrum can better reflect the characteristics of the historical mass spectrum, and is therefore identified as a valid corresponding group. This step provides a reliable basis for subsequent real-time mass spectrum optimization correction and anomaly detection.

[0087] In an embodiment of the present invention, a method for calculating a similarity scale between mass spectra of a corresponding group of mass spectra includes:

[0088] Step S501, perform characteristic analysis on several mixed simulated mass spectra to determine a first mass spectrum weight mapping template corresponding to the simulated mass spectrum, perform characteristic analysis on several mixed historical mass spectra to determine a second mass spectrum weight mapping template corresponding to the historical mass spectrum, average the first mass spectrum weight mapping template and the second mass spectrum weight mapping template to obtain a third mass spectrum weight mapping template.

[0089] The core of this step is to build a unified reference standard, the third mass spectrum weight mapping template, by mixing and averaging the features of the simulated mass spectra and the historical mass spectra. First, a mixed feature analysis is performed on several simulated mass spectra. The simulated mass spectra are data generated based on theoretical models or simulation experiments, and they can reflect the mass spectrum characteristics under ideal conditions. Through feature analysis, the first mass spectrum weight mapping template corresponding to the simulated mass spectrum can be determined, and this template represents the characteristic distribution of the simulated mass spectrum. Next, a mixed feature analysis is performed on several historical mass spectra. The historical mass spectra are real data obtained through actual experiments, and they contain various variables and interference factors in the experimental process. Through feature analysis, the second mass spectrum weight mapping template corresponding to the historical mass spectrum can be determined, and this template represents the characteristic distribution of the historical mass spectrum. Finally, the first mass spectrum weight mapping template and the second mass spectrum weight mapping template are averaged to obtain a third mass spectrum weight mapping template that combines the characteristics of the two types of mass spectra. This template, as a unified reference standard, will be used for subsequent calculations of similarity scales between mass spectra.

[0090] Step S502, based on the attention weights corresponding to the nodes at different positions in the third mass spectrum weight mapping template and the difference characteristics between the mass spectra of the corresponding group of mass spectra, determine the similarity scale between the mass spectra of the corresponding group of mass spectra.

[0091] The goal of this step is to calculate the similarity scale between mass spectra of the corresponding group of mass spectra based on the third mass spectrum weight mapping template. First, pay attention to the attention weights corresponding to the nodes at different positions in the third mass spectrum weight mapping template. These weights reflect the importance of different positions in the mass spectrum and are instructive for calculating the similarity scale. Next, analyze the difference features between the mass spectra of the corresponding group of mass spectra. These difference features may include peak position shift, intensity change, shape difference, etc., which are important bases for calculating the similarity scale. Finally, consider the attention weights and difference features comprehensively, and use appropriate algorithms (to calculate the similarity scale between mass spectra. This similarity scale is a numerical value that reflects the degree of similarity between mass spectra. By comparing the similarity scales of different mass spectra, their similarities and differences can be quantitatively evaluated, thereby providing strong support for the analysis and comparison of mass spectra.

[0092] In an embodiment of the present invention, a method for performing mixed characteristic analysis on a simulated mass spectrum or a historical mass spectrum includes:

[0093] Step S5011, determining the abundance mapping points of nodes at different positions on each simulated mass spectrum or historical mass spectrum, and connecting the abundance mapping points to obtain an abundance expression line.

[0094] The core of this step is to extract abundance information from the mass spectrum and express it intuitively in the form of a broken line. The mass spectrum, as a chart showing the relationship between the mass-to-charge ratio (m / z) of ions or molecules and relative abundance (usually ion current intensity), contains a large amount of chemical information. In step S5011, each simulated mass spectrum or historical mass spectrum is first processed to determine the abundance mapping points of nodes at different positions on the graph (i.e., positions with different mass-to-charge ratios). These mapping points are actually a direct reflection of the abundance of each ion or molecule on the mass spectrum. Then, these abundance mapping points are connected in the order of mass-to-charge ratio to obtain one or more abundance expression broken lines. These broken lines not only intuitively show the changing trend of abundance on the mass spectrum, but also provide basic data for subsequent feature analysis. Through such processing, the complex information in the mass spectrum can be simplified into a broken line form that is easy to understand and compare.

[0095] Step S5012, construct a basic mass spectrum weight mapping template, and map several abundance expression lines onto the basic mass spectrum weight mapping template, and classify the abundance expression lines according to their consistency, and analyze the abundance expression lines in each classification group, and construct a category abundance expression line based on the analysis results.

[0096] The goal of this step is to classify and analyze the abundance expression polylines to extract a set of polylines with similar features and construct a category abundance expression polyline. To achieve this goal, a basic mass spectrum weight mapping template is first constructed. This template can be regarded as a standardized mass spectrum framework for mapping and comparing different abundance expression polylines. Then, several abundance expression polylines are mapped to this template, and their conformity is classified by comparing their shapes, trends, key node abundances and other features. Each group of polylines after classification represents a mass spectrum or a set of compounds with similar features. Next, the abundance expression polylines in each classified group are deeply analyzed to extract the common features and differences of the category abundance expression polylines. Finally, based on these analysis results, category abundance expression polylines are constructed, which reflect the typical features of the category mass spectra. Through such classification and analysis, the similarities and differences between mass spectra can be better understood, providing strong support for subsequent applications such as feature matching and anomaly detection.

[0097] Step S5013: The expression of the abundance expression lines of several categories on the basic mass spectrum weight mapping template is identified as the first mass spectrum weight mapping template or the second mass spectrum weight mapping template.

[0098] The purpose of this step is to determine the final weight mapping template for subsequent feature analysis and comparison. After completing the classification and analysis of the abundance expression polylines, several category abundance expression polylines are obtained. The expression of these polylines on the basic mass spectrum weight mapping template reflects the characteristics of mass spectra of different categories. In order to determine the final weight mapping template, these category abundance expression polylines are mapped again to the basic mass spectrum weight mapping template, and evaluated according to the mapping results and actual needs. Based on the evaluation results, the most representative and practical template can be selected as the final weight mapping template. This template may represent a typical mass spectrum pattern of a certain class of compounds, or have a specific chemical meaning. By determining such a weight mapping template, a unified reference standard can be provided for subsequent feature analysis, compound identification and other applications.

[0099] In an embodiment of the present invention, the method for classifying the consistency of the abundance expression line includes:

[0100] Step S50121, using the horizontal axis node corresponding to each abundance mapping point of the abundance expression line as the first screening condition, performing the first screening and classification on the abundance expression line, using the vertical axis node corresponding to each abundance mapping point of the abundance expression line as the second screening condition, performing the second screening and classification on the abundance expression line after the first screening and classification, wherein when classifying with the second screening condition, the vertical axis interval to which the vertical axis node corresponding to each abundance mapping point belongs is determined, and several abundance mapping points belonging to the same vertical axis interval are identified as the same type of abundance mapping points, and the abundance expression line is subjected to the second screening and classification under the condition that each abundance mapping point is mutually the same type of abundance mapping point.

[0101] Step S50122, average calculation is performed on the abundance expression line of the second screening classification to obtain the category abundance expression line, and the method of performing the average calculation includes performing the average calculation of the vertical axis value and the horizontal axis value of each relative turning node to obtain a new abundance mapping point, and connecting the abundance mapping points.

[0102] In an embodiment of the present invention, a method for averaging the first mass spectrum weight mapping template and the second mass spectrum weight mapping template includes:

[0103] Step S5014, align the abundance expression lines in the first mass spectrum weight mapping template and the second mass spectrum weight mapping template, and average the vertical axis value and the horizontal axis value of each relative turning node to obtain a new abundance mapping point, and connect the abundance mapping points to obtain an average abundance expression line.

[0104] In an embodiment of the present invention, based on the attention weights corresponding to the nodes at different positions in the third mass spectrum weight mapping template and the difference characteristics between the mass spectra of the corresponding group of mass spectra, a method for determining the similarity scale between the mass spectra of the corresponding group of mass spectra includes:

[0105] Step S5021, for each turning node of the category abundance expression polyline, determine and construct several levels of weight range circles for each turning node of the category abundance expression polyline. Each level of weight range circle corresponds to a preset weight coefficient, and the radii of weight range circles of different levels are different. The longer the radius, the lower the weight coefficient corresponding to the weight range circle.

[0106] Step S5022, determine the abundance expression lines in the simulated mass spectra and the historical mass spectra of the mass spectrum corresponding group, determine the position node of each abundance mapping point in the abundance expression lines, and combine each relative abundance mapping point between the abundance expression lines to record them as the abundance mapping point corresponding group.

[0107] Step S5023, determine the average abundance expression line in the simulated mass spectrum and the historical mass spectrum in the corresponding group of mass spectra, and retrieve the most consistent category abundance expression line from several category abundance expression lines, including calculating the relative distance between the average abundance expression line and each abundance mapping point in the category abundance expression line, calculating the total distance of all relative distances, and identifying the category abundance expression line with the lowest total distance as the most consistent category abundance expression line.

[0108] Step S5024, determine the weight range circle to which the abundance mapping point in each abundance mapping point corresponding group belongs in the category abundance expression curve, and select the most suitable weight range circle with the smallest radius from the corresponding weight range circle, and determine the attention weight corresponding to the abundance mapping point corresponding group based on the determined most suitable weight range circle.

[0109] Step S5025, determining the distance between the abundance mapping points in the corresponding group of abundance mapping points, and determining the similarity scale between the mass spectra in the corresponding group of mass spectra in combination with the attention weight.

[0110] Among them, the expression for calculating the similarity scale is:

[0111] ;

[0112] Among them, S is the similarity scale, is the preset maximum similarity scale, is the pre-judgment function of the abundance mapping point corresponding group. If the distance between the abundance mapping points in the abundance mapping point corresponding group is less than or equal to the preset value, then Output 1, otherwise output 0. is the influence adjustment coefficient of the pre-judgment of the distance between abundance mapping points, c is the influence adjustment constant of the pre-judgment of the distance between abundance mapping points, is the attention weight of the group corresponding to the i-th abundance mapping point, is the distance between the abundance mapping points of the corresponding group between the i-th abundance mapping points, and n is the total number of corresponding groups between abundance mapping points.

[0113] Step S600, use the effective normal mass spectrum corresponding group or the effective abnormal mass spectrum corresponding group to perform consistency matching on the real-time mass spectrum acquired in real time. If the effective normal mass spectrum corresponding group is matched, the real-time mass spectrum is optimized and corrected using the effective normal mass spectrum corresponding group. If the effective abnormal mass spectrum corresponding group is matched, the mass spectrometer abnormality alarm is performed, and the abnormal historical experimental monomer data corresponding to the effective abnormal mass spectrum corresponding group are analyzed, and the sample data therein is used as a search label to retrieve the corresponding normal historical experimental monomer data from several normal historical experimental monomer data, and based on the normal historical mass spectrum corresponding group corresponding to the retrieved normal historical experimental monomer data, the corrected mass spectrum of the real-time mass spectrum is determined.

[0114] In this step, the technician uses the previously determined valid normal and abnormal mass spectra corresponding groups to match the mass spectra acquired in real time. If the real-time mass spectrum matches the valid normal mass spectrum corresponding group, it means that the mass spectrometer is currently in normal working condition. However, in order to further improve the accuracy of the data, the technician may use the normal corresponding group to make subtle optimization corrections to the real-time mass spectrum. If the real-time mass spectrum matches the valid abnormal mass spectrum corresponding group, the abnormal alarm mechanism of the mass spectrometer is immediately triggered. At this time, the technician will deeply analyze the abnormal historical experimental monomer data corresponding to the abnormal corresponding group to find out the cause of the abnormality. By using the sample data in the abnormal data as the retrieval label, the corresponding normal data is retrieved in the normal historical data, and the corrected mass spectrum of the real-time mass spectrum is determined based on these data, thereby providing clear guidance for subsequent experimental operations or mass spectrometer maintenance.

[0115] In an embodiment of the present invention, a method for matching the real-time mass spectra acquired in real time using the valid normal mass spectra corresponding group or the valid abnormal mass spectra corresponding group includes:

[0116] Step S601, determine the abundance mapping points of nodes at different positions on each simulated mass spectrum or historical mass spectrum in the corresponding group of valid normal mass spectra or the corresponding group of valid abnormal mass spectra, connect the abundance mapping points to obtain the abundance expression line, and average the abundance expression line between the simulated mass spectrum and the historical mass spectrum to obtain the average abundance expression line, and determine the real-time abundance expression line in the real-time mass spectrum in the same manner as above.

[0117] Step S602, based on the matching degree between the real-time abundance expression line and the average abundance expression line, select a corresponding group of valid normal mass spectra or a corresponding group of valid abnormal mass spectra.

[0118] The method for determining the degree of matching between the real-time abundance expression line and the average abundance expression line includes:

[0119] Step S6011, align the real-time abundance expression line and the average abundance expression line, and gradually scan the area between the two lines, and determine the positive area change rate of the area between the lines in real time. If the positive area change rate is greater than or equal to the preset value, the corresponding horizontal axis point is used as the recording starting point, and the line difference segment is recorded until the positive area change rate is less than the preset value.

[0120] Step S6012, determining the matching degree between the real-time abundance expression polyline and the average abundance expression polyline based on the area between the polylines and the length ratio of the polyline difference segment to the polyline horizontal axis mapping segment;

[0121] The expression for calculating the matching degree is:

[0122] ;

[0123] Among them, P is the matching degree, V is the area between the broken lines, is the segment length of the x-th polyline difference segment, is the segment length of the segment mapped on the horizontal axis of the broken line, N is the number of broken line difference segments, J is the segment length ratio impact adjustment coefficient, and d is the segment length ratio impact adjustment constant.

[0124] In an embodiment of the present invention, an analysis system for improving the quality of a mass spectrum is also disclosed, comprising:

[0125] The first module is used to obtain the design drawings of the mass spectrometer model, determine the core structure in the design drawings, construct a number of three-dimensional virtual components, and combine the three-dimensional virtual components to construct a three-dimensional mass spectrometer model;

[0126] The second module is used to establish a virtual sample model, grid the three-dimensional virtual components, define the influence relationship of each component grid on the virtual sample model based on the working principle of each three-dimensional virtual component, and construct a virtual simulation mass spectrometer;

[0127] The third module is used to obtain historical experimental data, and divide the historical experimental data based on the data records to obtain a number of normal historical experimental monomer data and a number of abnormal historical experimental monomer data, analyze each normal historical experimental monomer data to determine a normal historical mass spectrum, and analyze each abnormal historical experimental monomer data to determine an abnormal historical mass spectrum;

[0128] The fourth module is used to configure the virtual simulation mass spectrometer based on the normal historical experimental monomer data and the abnormal historical experimental monomer data, and drive the virtual simulation mass spectrometer to perform simulation operations to obtain a number of normal simulation mass spectra and a number of abnormal simulation mass spectra, combine the corresponding normal simulation mass spectra with the normal historical mass spectra to form a normal mass spectra corresponding group, and combine the corresponding abnormal simulation mass spectra with the abnormal historical mass spectra to form an abnormal mass spectra corresponding group;

[0129] The fifth module is used to analyze the normal mass spectrum corresponding group and the abnormal mass spectrum corresponding group respectively, calculate the similarity scale between the mass spectra, and if the similarity scale is greater than or equal to a preset value, the corresponding normal mass spectrum corresponding group is identified as a valid normal mass spectrum corresponding group, or the abnormal mass spectrum corresponding group is identified as a valid abnormal mass spectrum corresponding group;

[0130] The sixth module is used to use the effective normal mass spectrum corresponding group or the effective abnormal mass spectrum corresponding group to perform consistency matching on the real-time mass spectrum acquired in real time. If the effective normal mass spectrum corresponding group is matched, the real-time mass spectrum is optimized and corrected using the effective normal mass spectrum corresponding group. If the effective abnormal mass spectrum corresponding group is matched, the mass spectrometer abnormality alarm is issued, and the abnormal historical experimental monomer data corresponding to the effective abnormal mass spectrum corresponding group are analyzed, and the corresponding normal historical experimental monomer data are retrieved from several normal historical experimental monomer data using the sample data therein as the retrieval label, and the corrected mass spectrum of the real-time mass spectrum is determined based on the normal historical mass spectrum corresponding group corresponding to the retrieved normal historical experimental monomer data.

[0131] The present invention discloses an analysis method for improving the quality of a mass spectrometer, and relates to the technical field of mass spectrometer systems. Specifically, the present invention discloses a method for improving the quality of a mass spectrometer, and relates to the technical field of mass spectrometer systems. The method comprises the following steps: constructing a high-precision three-dimensional mass spectrometer model to simulate the actual working process of the mass spectrometer; establishing a normal and abnormal mass spectrometer database using historical experimental data, and obtaining a simulated mass spectrometer corresponding to the historical data through virtual simulation; verifying the consistency of the simulated mass spectrometer with the historical mass spectrometer by calculating a similarity scale, and forming an effective corresponding group; matching the mass spectrometer obtained in real time with the effective corresponding group, and realizing optimized correction or abnormal alarm of the mass spectrometer. The above technical scheme of the present invention realizes the accuracy correction of the mass spectrometer and improves the quality of the mass spectrometer.

[0132] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. An analytical method for improving the quality of a mass spectrum, characterized in that: include: Obtaining a design drawing of a mass spectrometer model, determining a core structure in the design drawing, constructing a plurality of three-dimensional virtual components, and combining the three-dimensional virtual components to construct a three-dimensional mass spectrometer model; Establish a virtual sample model, grid the three-dimensional virtual components, define the influence of each component grid on the virtual sample model based on the working principle of each three-dimensional virtual component, and construct a virtual simulation mass spectrometer; Acquire historical experimental data, and divide the historical experimental data based on the data records to obtain a number of normal historical experimental monomer data and a number of abnormal historical experimental monomer data, analyze each normal historical experimental monomer data to determine a normal historical mass spectrum, and analyze each abnormal historical experimental monomer data to determine an abnormal historical mass spectrum; The virtual simulation mass spectrometer is configured based on the normal historical experimental monomer data and the abnormal historical experimental monomer data, and the virtual simulation mass spectrometer is driven to perform simulation operations to obtain a number of normal simulation mass spectra and a number of abnormal simulation mass spectra, and the corresponding normal simulation mass spectra and normal historical mass spectra are combined to form a normal mass spectra corresponding group, and the corresponding abnormal simulation mass spectra and abnormal historical mass spectra are combined to form an abnormal mass spectra corresponding group; Analyze the normal mass spectrum corresponding group and the abnormal mass spectrum corresponding group respectively, calculate the similarity scale between the mass spectra, and if the similarity scale is greater than or equal to a preset value, identify the normal mass spectrum corresponding group as a valid normal mass spectrum corresponding group, or identify the abnormal mass spectrum corresponding group as a valid abnormal mass spectrum corresponding group; The real-time mass spectrum acquired in real time is matched for consistency using a valid normal mass spectrum corresponding group or a valid abnormal mass spectrum corresponding group. If a valid normal mass spectrum corresponding group is matched, the real-time mass spectrum is optimized and corrected using the valid normal mass spectrum corresponding group. If a valid abnormal mass spectrum corresponding group is matched, a mass spectrometer abnormality alarm is issued, and the abnormal historical experimental monomer data corresponding to the valid abnormal mass spectrum corresponding group are analyzed. The sample data therein is used as a search label, and the corresponding normal historical experimental monomer data is retrieved from several normal historical experimental monomer data. Based on the normal historical mass spectrum corresponding group corresponding to the retrieved normal historical experimental monomer data, a corrected mass spectrum of the real-time mass spectrum is determined.

2. The method for improving the quality of mass spectrometry according to claim 1, characterized in that: The method of defining the influence relationship of each component network on the virtual sample model and constructing a virtual simulation mass spectrometer includes: A mass parameter interval and a charge parameter interval are defined for a virtual sample model, wherein the virtual sample model includes a plurality of virtual sample particles, a travel trajectory is configured for each virtual sample particle, and a travel speed is configured for each virtual sample particle based on the obtained mass parameter and charge parameter; Based on the working principle of each three-dimensional virtual component, the component grid of the corresponding area is configured with direction influencing parameters and speed influencing parameters for the virtual sample particles, and the component network block adjusts the trajectory and speed of the virtual sample particles passing through its own area; The component grid that the virtual sample particles finally reach is recorded, and a simulated mass spectrum is constructed based on the recorded relative position of the component grid and the number of virtual sample particles that terminate at the component grid.

3. The method for improving the quality of mass spectrometry according to claim 2, characterized in that: The three-dimensional virtual components include a three-dimensional virtual ion source component, a three-dimensional mass analyzer component, and a three-dimensional detector component; The three-dimensional virtual ion source component determines the number of different virtual sample particles split, the mass parameter of each virtual sample particle, and the charge parameter of each virtual sample particle based on the input parameters of the virtual sample model; The three-dimensional mass analyzer component determines the direction influencing parameters and the speed influencing parameters of the virtual sample particles based on the mass parameters and the charge parameters of the virtual sample particles; The three-dimensional detector component determines the abundance of different virtual sample particles based on the end positions of the virtual sample particles and the number of virtual sample particles at the same end position.

4. The method for improving the quality of mass spectrometry according to claim 1, characterized in that: Methods for calculating similarity measures between mass spectra of corresponding groups of mass spectra include: Performing a mixed feature analysis on several simulated mass spectra to determine a first mass spectrum weight mapping template corresponding to the simulated mass spectrum, performing a mixed feature analysis on several historical mass spectra to determine a second mass spectrum weight mapping template corresponding to the historical mass spectrum, and averaging the first mass spectrum weight mapping template and the second mass spectrum weight mapping template to obtain a third mass spectrum weight mapping template; Based on the attention weights corresponding to nodes at different positions in the third mass spectrum weight mapping template and the difference characteristics between the mass spectra of the corresponding group of mass spectra, the similarity scale between the mass spectra of the corresponding group of mass spectra is determined.

5. The method for improving the quality of mass spectrometry according to claim 4, characterized in that: Methods for performing mixed feature analysis on simulated mass spectra or historical mass spectra include: Determine the abundance mapping points of nodes at different positions on each simulated mass spectrum or historical mass spectrum, and connect the abundance mapping points to obtain an abundance expression line; A basic mass spectrum weight mapping template is constructed, and a number of abundance expression broken lines are mapped onto the basic mass spectrum weight mapping template, and the abundance expression broken lines are classified for consistency, and the abundance expression broken lines in each classification group are analyzed, and based on the analysis results, a category abundance expression broken line is constructed; The expressions of the abundance expression lines of several categories on the basic mass spectrum weight mapping template are identified as the first mass spectrum weight mapping template or the second mass spectrum weight mapping template.

6. The method for improving the quality of mass spectrometry according to claim 5, characterized in that: Methods for classifying the consistency of abundance expression curves include: The abundance expression polyline is first screened and classified using the horizontal axis node corresponding to each abundance mapping point of the abundance expression polyline as the first screening condition, and the abundance expression polyline after the first screening and classification is second screened and classified using the vertical axis node corresponding to each abundance mapping point of the abundance expression polyline as the second screening condition, wherein when the second screening condition is used for classification, the vertical axis interval to which the vertical axis node corresponding to each abundance mapping point belongs is determined, and several abundance mapping points belonging to the same vertical axis interval are identified as the same type of abundance mapping points, and the abundance expression polyline is second screened and classified using the condition that each abundance mapping point is mutually the same type of abundance mapping point; The abundance expression broken line of the second screening classification is averaged to obtain the category abundance expression broken line. The method of averaging includes averaging the vertical axis value and the horizontal axis value of each relative turning point to obtain a new abundance mapping point, and connecting the abundance mapping points.

7. The method for improving the quality of mass spectrometry according to claim 5, characterized in that: The method for averaging the first mass spectrum weight mapping template and the second mass spectrum weight mapping template comprises: The abundance expression broken lines in the first mass spectrum weight mapping template and the second mass spectrum weight mapping template are aligned, and the vertical axis value and the horizontal axis value of each relative turning point are averaged to obtain a new abundance mapping point, and the abundance mapping points are connected to obtain an average abundance expression broken line.

8. The method for improving the quality of mass spectrometry according to claim 7, characterized in that: Based on the attention weights corresponding to the nodes at different positions in the third mass spectrum weight mapping template and the difference characteristics between the mass spectra of the corresponding group of mass spectra, the method for determining the similarity scale between the mass spectra of the corresponding group of mass spectra includes: Determine each turning point of the category abundance expression polyline, and construct several levels of weight range circles for each turning point of the category abundance expression polyline. Each level of weight range circle corresponds to a preset weight coefficient. The radii of weight range circles of different levels are different. The longer the radius, the lower the weight coefficient corresponding to the weight range circle. Determine the abundance expression broken lines in the simulated mass spectra and the historical mass spectra of the mass spectrum corresponding group, determine the position node of each abundance mapping point in the abundance expression broken lines, and combine each relative abundance mapping point between the abundance expression broken lines to record as the abundance mapping point corresponding group; Determine the average abundance expression line in the simulated mass spectra and the historical mass spectra in the corresponding group of mass spectra, and retrieve the most consistent category abundance expression line from a number of category abundance expression lines, including calculating the relative distance between the average abundance expression line and each abundance mapping point in the category abundance expression line, calculating the total distance of all relative distances, and identifying the category abundance expression line with the lowest total distance as the most consistent category abundance expression line; Determine the weight range circle to which the abundance mapping point in each abundance mapping point corresponding group belongs in the category abundance expression polyline, and select the most suitable weight range circle with the smallest radius in the weight range circle to which it belongs, and determine the attention weight corresponding to the abundance mapping point corresponding group based on the determined most suitable weight range circle; Determining the distance between the abundance mapping points in the corresponding group of abundance mapping points, and determining the similarity scale between the mass spectra of the corresponding group of mass spectra in combination with the attention weight; Among them, the expression for calculating the similarity scale is: ； Among them, S is the similarity scale, is the preset maximum similarity scale, is the pre-judgment function of the abundance mapping point corresponding group. If the distance between the abundance mapping points in the abundance mapping point corresponding group is less than or equal to the preset value, then Output 1, otherwise output 0. is the influence adjustment coefficient of the pre-judgment of the distance between abundance mapping points, c is the influence adjustment constant of the pre-judgment of the distance between abundance mapping points, is the attention weight of the group corresponding to the i-th abundance mapping point, is the distance between the abundance mapping points of the corresponding group between the i-th abundance mapping points, and n is the total number of corresponding groups between abundance mapping points.

9. The method for improving the quality of mass spectrometry according to claim 1, characterized in that: The method of using the effective normal mass spectrum corresponding group or the effective abnormal mass spectrum corresponding group to perform consistency matching on the real-time mass spectrum acquired in real time includes: Determine the abundance mapping points of nodes at different positions on each simulated mass spectrum or historical mass spectrum in the corresponding group of valid normal mass spectra or the corresponding group of valid abnormal mass spectra, connect the abundance mapping points to obtain an abundance expression broken line, average the abundance expression broken lines between the simulated mass spectrum and the historical mass spectrum to obtain an average abundance expression broken line, and determine the real-time abundance expression broken line in the real-time mass spectrum in the same manner as above; Based on the matching degree between the real-time abundance expression line and the average abundance expression line, a corresponding group of valid normal mass spectra or a corresponding group of valid abnormal mass spectra is selected; The method for determining the degree of matching between the real-time abundance expression line and the average abundance expression line includes: Align the real-time abundance expression line and the average abundance expression line, and gradually scan the area between the two lines, and determine the positive area change rate of the area between the lines in real time. If the positive area change rate is greater than or equal to a preset value, the corresponding horizontal axis point is used as the recording starting point, and the line difference segment is recorded until the positive area change rate is less than the preset value; Determine the degree of match between the real-time abundance expression polyline and the average abundance expression polyline based on the area between the polylines and the length ratio of the polyline difference segment to the polyline horizontal axis mapping segment; The expression for calculating the matching degree is: ； Among them, P is the matching degree, V is the area between the broken lines, is the segment length of the x-th polyline difference segment, is the segment length of the segment mapped on the horizontal axis of the broken line, N is the number of broken line difference segments, J is the segment length ratio impact adjustment coefficient, and d is the segment length ratio impact adjustment constant.

10. An analysis system for improving the quality of a mass spectrum, characterized in that: include: The first module is used to obtain the design drawings of the mass spectrometer model, determine the core structure in the design drawings, construct a number of three-dimensional virtual components, and combine the three-dimensional virtual components to construct a three-dimensional mass spectrometer model; The second module is used to establish a virtual sample model, grid the three-dimensional virtual components, define the influence relationship of each component grid on the virtual sample model based on the working principle of each three-dimensional virtual component, and construct a virtual simulation mass spectrometer; The third module is used to obtain historical experimental data, and divide the historical experimental data based on the data records to obtain a number of normal historical experimental monomer data and a number of abnormal historical experimental monomer data, analyze each normal historical experimental monomer data to determine a normal historical mass spectrum, and analyze each abnormal historical experimental monomer data to determine an abnormal historical mass spectrum; The fourth module is used to configure the virtual simulation mass spectrometer based on the normal historical experimental monomer data and the abnormal historical experimental monomer data, and drive the virtual simulation mass spectrometer to perform simulation operations to obtain a number of normal simulation mass spectra and a number of abnormal simulation mass spectra, combine the corresponding normal simulation mass spectra with the normal historical mass spectra to form a normal mass spectra corresponding group, and combine the corresponding abnormal simulation mass spectra with the abnormal historical mass spectra to form an abnormal mass spectra corresponding group; The fifth module is used to analyze the normal mass spectrum corresponding group and the abnormal mass spectrum corresponding group respectively, calculate the similarity scale between the mass spectra, and if the similarity scale is greater than or equal to a preset value, the corresponding normal mass spectrum corresponding group is identified as a valid normal mass spectrum corresponding group, or the abnormal mass spectrum corresponding group is identified as a valid abnormal mass spectrum corresponding group; The sixth module is used to use the effective normal mass spectrum corresponding group or the effective abnormal mass spectrum corresponding group to perform consistency matching on the real-time mass spectrum acquired in real time. If the effective normal mass spectrum corresponding group is matched, the real-time mass spectrum is optimized and corrected using the effective normal mass spectrum corresponding group. If the effective abnormal mass spectrum corresponding group is matched, the mass spectrometer abnormality alarm is issued, and the abnormal historical experimental monomer data corresponding to the effective abnormal mass spectrum corresponding group are analyzed, and the corresponding normal historical experimental monomer data are retrieved from several normal historical experimental monomer data using the sample data therein as the retrieval label, and the corrected mass spectrum of the real-time mass spectrum is determined based on the normal historical mass spectrum corresponding group corresponding to the retrieved normal historical experimental monomer data.

Images