A sample injection control method and system for a mass spectrometer

Abstract

The present invention relates to a sample introduction control method and system for a mass spectrometer. The method includes: identifying a sample to be tested and obtaining a sample identification result; determining sample introduction parameters for the sample to be tested based on the sample identification result; controlling the introduction of the sample to be tested according to the sample introduction parameters, and introducing the sample to be tested into the mass spectrometer for analysis. This application solves the technical problem that the sample introduction process in existing mass spectrometers requires manual intervention, resulting in low mass spectrometry analysis efficiency, and achieves the technical effect of realizing automatic control of sample introduction through automatic sample identification and intelligent parameter matching, thereby improving the overall efficiency of mass spectrometry analysis.

Description

Technical Field

[0001] The present invention relates to the field of mass spectrometer injection control, and particularly to an injection control method and system for a mass spectrometer. Background Art

[0002] In the process of mass spectrometry analysis, the sample injection link is a key prerequisite step in the entire analysis process, and its accuracy and efficiency directly affect the quality of subsequent mass spectrometry analysis results. In the prior art, the injection control of a mass spectrometer mainly relies on manual operation. The operator sets injection parameters such as injection temperature, injection rate, split ratio, etc. according to the properties, types and other characteristics of the sample to be measured, and then the sample can be introduced into the mass spectrometer for analysis. In this injection method, the operator needs to have rich professional knowledge and experience to determine the most suitable injection parameters for a specific sample. Frequent manual participation greatly reduces the efficiency of mass spectrometry analysis, especially in scenarios where a large number of samples need to be analyzed, resulting in a serious bottleneck in work efficiency. Therefore, the existing mass spectrometers have the defect that the injection process requires manual intervention, leading to low efficiency of mass spectrometry analysis. Summary of the Invention

[0003] In view of the technical problem that the injection process in the existing mass spectrometer requires manual intervention, resulting in low efficiency of mass spectrometry analysis, the present invention provides an injection control method and system for a mass spectrometer to solve this problem.

[0004] The technical solutions of the present invention for solving the above technical problems are as follows:

[0005] In a first aspect, the present invention provides an injection control method for a mass spectrometer, including: identifying a sample to be measured and obtaining a sample identification result; determining injection parameters of the sample to be measured based on the sample identification result; and controlling the injection of the sample to be measured according to the injection parameters, and introducing the sample to be measured into the mass spectrometer for analysis.

[0006] Optionally, identifying the sample to be measured and obtaining a sample identification result includes: collecting an image of the sample to be measured to obtain an image of the sample to be measured; performing image analysis on the image of the sample to be measured according to a preset feature set to obtain multiple features of the sample to be measured; and using the multiple features of the sample to be measured as the sample identification result of the sample to be measured.

[0007] Optionally, determining the injection parameters of the sample to be measured based on the sample identification result includes: constructing a measurement sample space according to an archived measurement sample record and multiple feature dimensions; extracting multiple features of the sample to be measured in the sample identification result; determining multiple neighboring reference samples of the sample to be measured in the measurement sample space based on the multiple features of the sample to be measured; and determining the injection parameters of the sample to be measured according to the multiple neighboring reference samples.

[0008] Optionally, constructing a measurement sample space based on the archived measurement sample records and multiple feature dimensions includes: generating multiple feature dimensions according to the preset feature set, and generating a basic feature space based on the multiple feature dimensions; interacting with the historical measurement database to obtain archived measurement sample records in the historical measurement database according to a preset time period; traversing the archived measurement sample records to obtain a first archived measurement sample; extracting multiple first sample features of the first archived measurement sample, determining a first sample position in the basic feature space according to the multiple first sample features, and updating the basic feature space based on the first sample position; sequentially processing the remaining archived measurement samples in the archived measurement sample records, and continuously updating the basic feature space until all archived measurement samples are traversed to obtain a measurement sample space.

[0009] Optionally, determining multiple neighboring reference samples of the sample to be measured in the measurement sample space based on the multiple features of the sample to be measured includes: determining a target position of the sample to be measured in the measurement sample space according to the multiple features of the sample to be measured; determining a neighboring distance in the measurement sample space based on the target position according to a neighboring distance determination formula; constructing a neighboring space of the sample to be measured with the target position as the center and the neighboring distance as the radius; extracting samples within the neighboring space to obtain multiple neighboring reference samples.

[0010] Optionally, the neighboring distance determination formula is:

[0011]

[0012] where is the basic reference distance, is the average sample density of the measurement sample space, is the local sample density around the target position, is the preset minimum number of reference samples, is the number of samples within the basic reference distance range with the target position as the center, is the smoothing factor.

[0013] Optionally, determining the sample injection parameters of the sample to be measured according to the multiple neighboring reference samples includes: obtaining the sample injection parameters of the multiple neighboring reference samples and their corresponding mass spectrometry analysis effect scores; screening the sample injection parameters of the multiple neighboring reference samples based on the mass spectrometry analysis effect scores, eliminating the sample injection parameters with mass spectrometry analysis effect scores lower than a preset threshold, and obtaining a set of preferred sample injection parameters; performing weighted averaging on the sample injection parameters in the set of preferred sample injection parameters according to their corresponding mass spectrometry analysis effect scores to obtain the sample injection parameters of the sample to be measured.

[0014] In a second aspect, the present invention provides a sample introduction control system for a mass spectrometer, comprising: a sample identification module configured to identify a sample to be measured and obtain a sample identification result; a parameter determination module configured to determine sample introduction parameters for the sample to be measured based on the sample identification result; and a sample introduction control module configured to perform sample introduction control on the sample to be measured according to the sample introduction parameters and introduce the sample to be measured into the mass spectrometer for analysis.

[0015] The beneficial effects of the present invention are as follows:

[0016] By identifying the sample to be measured and obtaining the sample identification result, it is possible to automatically obtain basic characteristic information such as the nature and type of the sample to be measured, laying a foundation for the subsequent determination of sample introduction parameters. Based on the sample identification result, determining the sample introduction parameters for the sample to be measured can automatically determine the most suitable sample introduction parameters for the current sample to be measured, such as sample introduction temperature, sample introduction rate, split ratio, etc., without manual intervention. Performing sample introduction control on the sample to be measured according to the sample introduction parameters and introducing the sample to be measured into the mass spectrometer for analysis. Through this step, the sample introduction operation is performed according to the automatically determined parameters, ensuring that the sample is introduced into the mass spectrometer for analysis under optimal conditions.

[0017] Through the above technical solutions, the present invention realizes the full-process automation from sample identification to parameter determination and then to sample introduction control, effectively solving the technical problem of low mass spectrometry analysis efficiency caused by manual intervention in the sample introduction process of existing mass spectrometers, achieving the technical effect of realizing automatic sample introduction control through automatic sample identification and intelligent parameter matching and improving the overall efficiency of mass spectrometry analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a schematic flowchart of a sample introduction control method for a mass spectrometer provided by the present invention;

[0019] Figure 2 is a schematic structural diagram of a sample introduction control system for a mass spectrometer provided by the present invention.

[0020] In the drawings, the components represented by the reference numerals are as follows:

[0021] sample identification module 11, parameter determination module 12, sample introduction control module 13. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0023] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0024] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be practiced without these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0025] Example 1, as Figure 1 shown, the embodiment of the present invention provides a sample injection control method for a mass spectrometer, which is applied to a sample injection control system. The method includes:

[0026] S100: Identify the sample to be tested and obtain the sample identification result;

[0027] S200: Based on the sample identification result, determine the injection parameters of the sample to be tested;

[0028] S300: Control the injection of the sample to be tested according to the injection parameters, and introduce the sample to be tested into the mass spectrometer for analysis.

[0029] Specifically, first, the sample to be tested entering the sample injection control system is subjected to feature extraction and identification analysis through an automatic recognition technology, so as to obtain a sample recognition result that can characterize the sample to be tested. This sample recognition process can be implemented in various ways, such as but not limited to optical recognition, barcode scanning, radio frequency identification technology, image processing and other technical means. In a preferred embodiment, the recognition process uses image acquisition and analysis technology to automatically recognize the appearance characteristics, color, shape, size, etc. of the sample to be tested. The sample recognition result contains sufficient information to distinguish different samples, providing a basis for determining subsequent injection parameters. The sample recognition result can be a feature vector containing feature parameters in multiple dimensions, and these feature parameters together constitute the identification information of the sample to be tested. By obtaining the sample recognition result, the automatic recognition of the sample to be tested is realized, and the sample recognition result can be obtained without manual intervention, laying a foundation for subsequent parameter matching and injection control, thereby improving the automation degree and efficiency of mass spectrometry analysis.

[0030] Subsequently, the obtained sample recognition result is used to determine the optimal injection parameters for the sample to be tested. Among them, the injection parameters include but are not limited to key parameters that affect the mass spectrometry analysis effect, such as injection rate, injection volume, injection temperature, solvent ratio, ion source parameters, detector settings, etc. For example, a proximity reference matching algorithm based on the sample space is adopted. By constructing a multi-dimensional feature space, reference samples similar to the characteristics of the sample to be tested are searched in the historical measurement samples, and based on the injection parameters of these reference samples and their corresponding mass spectrometry analysis effects, the optimal injection parameters for the sample to be tested are comprehensively determined. The injection parameter determination process can automatically adjust the injection parameters according to the characteristic differences of the samples, without manual experience judgment or manual setting, thus avoiding the problems of sample waste and low analysis efficiency caused by improper parameter setting in traditional mass spectrometry analysis.

[0031] After that, according to the determined injection parameters, the sample to be tested is controlled for injection, and the sample to be tested is accurately introduced into the mass spectrometer for analysis. Through the control unit of the injection control system, the determined injection parameters are received and converted into specific operation control signals for the injection device. The control unit precisely regulates each execution component of the injection device, including but not limited to the syringe drive mechanism, sample transfer channel, temperature control device, pressure regulator, etc., to make it strictly execute the sample injection operation according to the determined injection parameters. During the injection process, the execution status of each control parameter is monitored in real time to ensure the stability and reliability of the injection process. When the sample to be tested is successfully introduced into the mass spectrometer, the mass spectrometry analysis is automatically started, and a series of analysis processes such as ionization, mass separation, and detection of the sample are carried out. Through the seamless connection from parameter determination to actual injection, the injection operation link that requires manual intervention in mass spectrometry analysis is completely eliminated, significantly improving the automation degree, accuracy, and repeatability of sample analysis, thereby effectively enhancing the overall efficiency of mass spectrometry analysis. At the same time, due to the adoption of optimized injection parameters, it is also possible to reduce sample consumption, improve analysis sensitivity and accuracy, and provide a solid guarantee for the reliability of mass spectrometry analysis results.

[0032] Through the above mass spectrometer injection control method, the problem of low efficiency caused by manual intervention in the injection process in existing mass spectrometry analysis is solved, realizing full-process automation from sample entry to analysis completion, reducing manual operation time and errors, and improving the accuracy, repeatability, and reliability of mass spectrometry analysis; at the same time, due to the automatic configuration of optimal injection parameters for different samples, the waste of samples and the decline in analysis quality caused by improper parameter setting are avoided, improving the overall efficiency and resource utilization rate of mass spectrometry analysis.

[0033] As an optional implementation manner, the identifying the sample to be tested and obtaining a sample identification result includes:

[0034] S110: Collect an image of the sample to be tested to obtain an image of the sample to be tested;

[0035] S120: Perform image analysis on the image of the sample to be tested according to a preset feature set to obtain multiple features of the sample to be tested;

[0036] S130: Use the multiple features of the sample to be tested as the sample identification result of the sample to be tested.

[0037] Specifically, when identifying a sample to be tested and obtaining the sample identification result, first, an image acquisition device set in the sample injection control system is used to acquire an image of the sample to be tested, so as to obtain a high-definition image of the sample to be tested. The image acquisition device can be, but is not limited to, devices such as a high-resolution digital camera, an industrial camera, or an optical scanner, which is installed at an appropriate position where the sample enters the sample injection control system to ensure that a clear and complete image of the sample to be tested can be obtained. The image acquisition process can be carried out under different lighting conditions to obtain more comprehensive visual feature information of the sample.

[0038] Secondly, image analysis is performed on the acquired image of the sample to be tested according to a preset feature set. The preset feature set is a set of optimized and selected feature parameters, including but not limited to various visual features such as the color distribution, geometric shape, size, surface texture, transparency, liquid level height, etc. of the sample, which can be extracted from the image. By using image processing algorithms, such as edge detection, color segmentation, texture analysis and other technical means, multiple quantitative or qualitative features of the sample to be tested are extracted from the image of the sample to be tested, and these features together constitute a feature set that can accurately characterize the sample to be tested. Subsequently, the multiple features of the sample to be tested obtained are used as the sample identification result of the sample to be tested. The sample identification result provides a basis for determining the subsequent sample injection parameters.

[0039] By obtaining the sample identification result, the automatic and non-contact identification of the sample to be tested is realized, avoiding the subjective errors and low efficiency problems that may exist in the traditional manual identification method, and laying a solid foundation for the automatic sample injection control of mass spectrometry analysis.

[0040] As an alternative implementation manner, determining the sample injection parameters of the sample to be tested based on the sample identification result includes:

[0041] S210: Construct a measurement sample space according to the archived measurement sample records and multiple feature dimensions;

[0042] S220: Extract multiple features of the sample to be tested in the sample identification result;

[0043] S230: Based on the multiple features of the sample to be tested, determine multiple neighboring reference samples of the sample to be tested in the measurement sample space;

[0044] S240: Determine the sample injection parameters of the sample to be tested according to the multiple neighboring reference samples.

[0045] Specifically, first, a measurement sample space is constructed using historical analysis data. Specifically, based on the archived measurement sample records accumulated during the historical operation of the mass spectrometer and combined with multiple predefined feature dimensions, a multi-dimensional feature space is established to form the measurement sample space. This measurement sample space contains various types of samples analyzed in the past, their corresponding feature information, and injection parameter information, and is the basic data structure for matching. This space can be updated in real time and continuously expanded and optimized as new samples are analyzed. Secondly, from the obtained sample identification results, multiple characteristic parameters of the sample to be measured are extracted. These characteristic parameters are consistent with the feature dimensions used when constructing the measurement sample space to ensure that the sample to be measured can be accurately mapped into the established measurement sample space. These characteristic parameters are standardized to meet the requirements for position positioning in the measurement sample space.

[0046] Then, based on the characteristics of the sample to be measured extracted, the sample to be measured is mapped to a specific position in the measurement sample space, and historical samples similar to the characteristics of the sample to be measured are searched in this space to determine multiple neighboring reference samples. These neighboring reference samples are the historical samples closest to the sample to be measured in the feature space, and the characteristics of the neighboring reference samples are highly similar to those of the sample to be measured. After that, based on the determined multiple neighboring reference samples, their historical injection parameters, and analysis effects, the optimal injection parameters for the sample to be measured are determined. For example, by comprehensively considering factors such as the similarity of the neighboring reference samples, historical analysis effects, and parameter sensitivity, the optimal injection parameters applicable to the current sample to be measured are generated.

[0047] Through the intelligent mapping from sample characteristics to injection parameters, the optimal injection parameters can be automatically determined for different types of samples without manual experience judgment, improving the automation level and analysis efficiency of mass spectrometry analysis. This method has high adaptability and scalability, can handle various types of samples, and continuously optimizes the accuracy of parameter matching as historical data accumulates.

[0048] As an alternative implementation, the construction of the measurement sample space according to the archived measurement sample records and multiple feature dimensions includes:

[0049] S211: Generate multiple feature dimensions according to the preset feature set and generate a basic feature space based on the multiple feature dimensions;

[0050] S212: Interact with the historical measurement database to obtain archived measurement sample records in the historical measurement database according to a preset time period;

[0051] S213: Traverse the archived measurement sample records to obtain the first archived measurement sample;

[0052] S214: Extract multiple first sample features of the first archived measurement sample, determine a first sample position in the basic feature space according to the multiple first sample features, and update the basic feature space based on the first sample position;

[0053] S215: Process the remaining archived measurement samples in the archived measurement sample record in sequence, and continuously update the basic feature space until all archived measurement samples are traversed to obtain a measurement sample space.

[0054] Specifically, when constructing a measurement sample space, first, generate multiple feature dimensions according to the preset feature set used. The feature dimensions in the preset feature set are the coordinate axes of the measurement sample space, and each dimension corresponds to a feature parameter of the sample, such as color, shape, size, etc. Secondly, initialize a multi-dimensional space based on these feature dimensions to obtain a basic feature space, which is initially empty and will be gradually filled with historical sample data through subsequent steps. The number and type of dimensions of the basic feature space directly determine the accuracy and efficiency of subsequent sample matching. Therefore, features with significant discrimination ability should be selected as the space dimensions. Then, interact with the historical measurement database, and query and obtain archived measurement sample records that meet the conditions according to the preset time period parameters. The preset time period can be a fixed historical period, such as the most recent month, three months, or one year, or a time range dynamically adjusted according to the sample type. These archived measurement sample records contain key data such as the feature information of historical analysis samples, injection parameter settings, and analysis result evaluations.

[0055] Then, start traversing the obtained archived measurement sample records. Each time, obtain an archived measurement sample as the first archived measurement sample. Next, extract the first sample features of the first archived measurement sample (i.e., multiple feature parameters of the first archived measurement sample), and map the first sample features into the basic feature space to determine the first sample position of the sample in the space (i.e., the position coordinates of the first archived measurement sample in the basic feature space). Subsequently, update the basic feature space based on the first sample position, and store the position information of the sample and the associated injection parameters and analysis effect data into the basic feature space to achieve the first mapping of historical sample data to the feature space. After that, process the remaining sample data in the archived measurement sample record in the same way as processing the first archived measurement sample, and continuously update the basic feature space. Each time a historical sample is processed, the feature space is expanded and optimized once. When the traversal and processing of all archived measurement samples are completed, the basic feature space is transformed into a measurement sample space containing complete historical data, and each point in this space represents a historical analysis sample and its related information.

[0056] Through the automated construction of the measurement sample space, this space integrates historical analysis experience and sample characteristic information, providing a data basis for subsequent intelligent parameter matching. With the continuous use of the mass spectrometer, this space can be continuously updated and optimized, continuously improving the accuracy and efficiency of parameter matching.

[0057] As an alternative implementation, determining a plurality of neighboring reference samples of the sample to be measured in the measurement sample space based on the plurality of characteristics of the sample to be measured includes:

[0058] S231: Determine the target position of the sample to be measured in the measurement sample space according to the plurality of characteristics of the sample to be measured;

[0059] S232: Determine the proximity distance in the measurement sample space based on the target position according to the proximity distance determination formula;

[0060] S233: Construct the proximity space of the sample to be measured with the target position as the center and the proximity distance as the radius;

[0061] S234: Extract the samples within the proximity space to obtain a plurality of neighboring reference samples.

[0062] Specifically, when obtaining a plurality of neighboring reference samples, first, according to the plurality of characteristics of the sample to be measured extracted from the sample identification result, map the sample to be measured to a specific position coordinate in the measurement sample space, that is, determine the target position of the sample to be measured in this multi-dimensional feature space. This target position is the precise positioning of the sample to be measured in the feature space, reflecting the characteristics of the sample to be measured in each feature dimension and serving as the starting point for subsequent neighboring sample search. Use the same feature mapping method as when constructing the measurement sample space to ensure that the sample to be measured is compared with historical samples in the same coordinate system. Second, according to the specially designed proximity distance determination formula, calculate an appropriate proximity distance in the measurement sample space based on the target position of the sample to be measured. This proximity distance is a key parameter for finding reference samples, and its size directly affects the number and similarity of reference samples. For example, consider the overall sample distribution density of the measurement sample space and the local sample density around the target position, and adaptively determine the optimal proximity distance to ensure both obtaining a sufficient number of reference samples and ensuring that these samples have a high similarity to the sample to be measured.

[0063] Then, with the target position of the sample to be measured as the center and the determined adjacent distance as the radius, a hyperspherical adjacent space is constructed in the measurement sample space. This adjacent space defines the range where the reference samples will be searched, and its geometric shape and size are automatically adjusted according to the characteristics of the feature space and the adjacent distance determination formula to adapt to the sample distribution characteristics in different regions. After that, historical samples are extracted from the constructed adjacent space to obtain multiple adjacent reference samples. These adjacent reference samples are the historical samples that are closest to the sample to be measured in the feature space, and their characteristics are highly similar to those of the sample to be measured. Record information such as the positions, characteristics, injection parameters, and historical analysis effects of these adjacent reference samples to provide data support for subsequent parameter determination.

[0064] By automatically searching for adjacent reference samples based on feature similarity in the measurement sample space, the subjectivity and uncertainty of relying on manual experience judgment are avoided, providing an objective and reliable reference basis for determining the injection parameters of the sample to be measured.

[0065] As an optional implementation manner, the adjacent distance determination formula is:

[0066]

[0067] Wherein, is the basic reference distance, is the average sample density of the measurement sample space, is the local sample density around the target position, is the preset minimum number of reference samples, is the number of samples within the basic reference distance range when the target position is the center, is the smoothing factor.

[0068] Specifically, this adjacent distance determination formula comprehensively considers the global distribution characteristics and local distribution characteristics of the sample space, and can adaptively determine the optimal adjacent distance for samples to be measured at different positions.

[0069] Wherein, is the basic reference distance, which is the preset initial search radius and serves as the benchmark value for adjacent distance calculation. This basic reference distance can be preset according to the overall size and number of dimensions of the measurement sample space, and usually a distance value that can contain a certain number of historical samples is selected. is the average sample density of the measurement sample space, which reflects the distribution density of historical samples in the entire feature space. This parameter is obtained by calculating the total number of samples in the entire measurement sample space divided by the space volume, and is a global statistical characteristic of the sample distribution. is the local sample density around the target position, reflecting the historical sample concentration degree in the area where the sample to be measured is located. This parameter is obtained by calculating the number of samples within a preset range centered on the target position divided by the volume of this range, and characterizes the local characteristics of the sample distribution. is the preset minimum reference sample number, which is the minimum number of neighboring reference samples expected to be obtained. This parameter ensures that a sufficient number of reference samples are obtained to improve the reliability and stability of parameter matching. is the basic reference distance when centered on the target position is the number of samples within the range, reflecting the number of reference samples that can be obtained within the basic search range. is the smoothing factor, used to avoid the case of division by zero, and smooth the calculation result of the neighboring distance, improving the robustness and stability of the formula.

[0070] Through this neighboring distance determination formula, the adaptive calculation of the neighboring distance can be realized. Through item, the adaptive adjustment of the local sample density is realized; when the local density is higher than the average density, the neighboring distance is correspondingly reduced to avoid including too many irrelevant samples; when the local density is lower than the average density, the neighboring distance is correspondingly increased to ensure obtaining sufficient reference samples. Through item, the adaptive adjustment of the number of samples within the basic range is realized; when the number of samples within the basic range is insufficient, the neighboring distance is appropriately increased; when the number of samples within the basic range is sufficient, the neighboring distance basically remains unchanged. By comprehensively considering global and local factors, the determination of the neighboring distance not only considers the overall sample distribution characteristics but also adapts to local sample distribution differences, so that an appropriate number and quality of neighboring reference samples can be obtained in various complex sample distribution situations.

[0071] The above neighboring distance determination formula enables the flexible adjustment of the search strategy in different sample density regions, improves the quality and relevance of the neighboring reference samples, and provides a more reliable data basis for the subsequent determination of the sample injection parameters.

[0072] As an optional implementation manner, determining the sample injection parameters of the sample to be measured according to the multiple neighboring reference samples includes:

[0073] S241: Obtain the sample injection parameters of the multiple neighboring reference samples and the corresponding mass spectrometry analysis effect scores;

[0074] S242: Based on the mass spectrometry analysis effect scores, screen the sample injection parameters of the multiple neighboring reference samples, eliminate the sample injection parameters with mass spectrometry analysis effect scores lower than the preset threshold, and obtain the preferred sample injection parameter set;

[0075] S243: Weighted average the sample injection parameters in the set of preferred sample injection parameters according to the corresponding mass spectrometry analysis effect scores to obtain the injection parameters of the sample to be tested.

[0076] Specifically, when determining the injection parameters of the sample to be tested, first, obtain the historical injection parameters of multiple determined neighboring reference samples and their corresponding mass spectrometry analysis effect scores. The sample injection parameters include, but are not limited to, key parameters that affect the mass spectrometry analysis effect, such as injection rate, injection volume, injection temperature, solvent ratio, ion source parameters, etc. The mass spectrometry analysis effect score is a comprehensive evaluation index that reflects the overall quality of the mass spectrometry analysis under specific injection parameters. This score can be calculated comprehensively by an expert group based on multiple analysis indicators such as peak shape, signal-to-noise ratio, sensitivity, and repeatability. Extract the injection parameter setting records of these neighboring reference samples and their corresponding analysis effect scores from the historical measurement database as the basic data for parameter determination.

[0077] Secondly, screen and optimize the sample injection parameters of multiple neighboring reference samples based on the mass spectrometry analysis effect scores. Specifically, set a preset threshold, which represents the acceptable minimum mass spectrometry analysis effect standard. Eliminate the sample injection parameters of the neighboring reference samples with mass spectrometry analysis effect scores lower than this preset threshold, and only retain the sample injection parameters with higher analysis effect scores, thereby forming a set of preferred sample injection parameters. This screening process ensures that only those injection parameters that performed well in historical analyses are used for subsequent parameter calculations, effectively avoiding the negative impact of poor parameter settings on the determination of the parameters of the sample to be tested. After that, perform a weighted average calculation on the sample injection parameters in the set of preferred sample injection parameters to obtain the final injection parameters of the sample to be tested. During the weighted average process, use the mass spectrometry analysis effect scores of each neighboring reference sample as the weight coefficients, so that the reference samples with better analysis effects have a greater influence on the final parameter determination. Through this weighted average method, the experience of multiple high-quality reference samples can be comprehensively considered to generate the optimal injection parameter settings for the sample to be tested.

[0078] By introducing the mass spectrometry analysis effect score as the basis for parameter screening and weighting, it is ensured that the parameter determination process pays more attention to the historical analysis effect, thereby improving the reliability and effectiveness of the determined parameters. By eliminating low-score parameters and performing weighted average on high-score parameters, the optimization and refinement of historical experience are realized, avoiding the influence of bad parameters that may be introduced by simple averaging, and making the finally determined parameters more optimized. The entire parameter determination process is completely automated, without manual intervention and empirical judgment, improving the efficiency and accuracy of mass spectrometry analysis. This method can not only automatically determine the optimal injection parameters for various types of samples, but also continuously optimize the accuracy of parameter determination with the accumulation of historical data.

[0079] The sample introduction control method of a mass spectrometer provided by an embodiment of the present invention has at least the following technical effects:

[0080] Identify the sample to be tested, obtain the sample identification result, and provide basic data for subsequent parameter settings. Based on the sample identification result, determine the sample introduction parameters of the sample to be tested, automatically match the most suitable sample introduction parameters for the identified sample identification result, such as sample introduction temperature, sample introduction rate, split ratio, etc., eliminating the need for manual parameter setting based on experience and avoiding human errors. Control the sample introduction of the sample to be tested according to the sample introduction parameters, introduce the sample to be tested into the mass spectrometer for analysis, control the sample introduction device according to the automatically determined parameters, and accurately introduce the sample into the mass spectrometer, ensuring that the sample is introduced into the analysis system under optimal conditions and improving the accuracy and repeatability of the analysis.

[0081] Through the sample introduction control method of this mass spectrometer, automation is achieved from sample identification to parameter determination and then to actual sample introduction operation, effectively eliminating the manual intervention link, not only significantly reducing the work burden and human errors of the operator, but also shortening the cycle time from sample preparation to analysis. At the same time, since the sample introduction parameters are automatically optimized and determined based on accurate sample identification results, the sample can be analyzed under optimal conditions, ensuring the stability and reliability of the analysis results, and achieving a significant improvement in the overall efficiency and accuracy of mass spectrometry analysis. Especially in application scenarios that require processing a large number of samples, this efficiency improvement is particularly obvious.

[0082] Embodiment 2, as Figure 2 shown, based on the same inventive concept as the sample introduction control method of a mass spectrometer provided in Embodiment 1, an embodiment of the present invention further provides a sample introduction control system of a mass spectrometer, including:

[0083] A sample identification module 11, configured to identify the sample to be tested and obtain the sample identification result;

[0084] A parameter determination module 12, configured to determine the sample introduction parameters of the sample to be tested based on the sample identification result;

[0085] A sample introduction control module 13, configured to control the sample introduction of the sample to be tested according to the sample introduction parameters and introduce the sample to be tested into the mass spectrometer for analysis.

[0086] Further, the sample identification module 11 includes the following execution steps:

[0087] Collect an image of the sample to be tested to obtain an image of the sample to be tested;

[0088] Perform image analysis on the image of the sample to be tested according to a preset feature set to obtain multiple features of the sample to be tested;

[0089] Take the multiple characteristics of the sample to be measured as the sample identification result of the sample to be measured.

[0090] Further, the parameter determination module 12 includes the following execution steps:

[0091] Construct a measurement sample space according to the archived measurement sample records and multiple feature dimensions;

[0092] Extract multiple characteristics of the sample to be measured in the sample identification result of the sample to be measured;

[0093] Based on the multiple characteristics of the sample to be measured, determine multiple neighboring reference samples of the sample to be measured in the measurement sample space;

[0094] Determine the sample injection parameters of the sample to be measured according to the multiple neighboring reference samples.

[0095] Further, the parameter determination module 12 further includes the following execution steps:

[0096] Generate multiple feature dimensions according to the preset feature set, and generate a basic feature space based on the multiple feature dimensions;

[0097] Interact with the historical measurement database, and obtain archived measurement sample records in the historical measurement database according to a preset time period;

[0098] Traverse the archived measurement sample records to obtain the first archived measurement sample;

[0099] Extract multiple first sample characteristics of the first archived measurement sample, determine the first sample position in the basic feature space according to the multiple first sample characteristics, and update the basic feature space based on the first sample position;

[0100] Process the remaining archived measurement samples in the archived measurement sample records in sequence, and continuously update the basic feature space until all archived measurement samples are traversed to obtain a measurement sample space.

[0101] Further, the parameter determination module 12 further includes the following execution steps:

[0102] Determine the target position of the sample to be measured in the measurement sample space according to the multiple characteristics of the sample to be measured;

[0103] According to the adjacent distance determination formula, determine the adjacent distance in the measurement sample space based on the target position;

[0104] With the target position as the center and the adjacent distance as the radius, construct the adjacent space of the sample to be measured;

[0105] Extract the samples in the adjacent space to obtain multiple neighboring reference samples.

[0106] Further, the adjacent distance determination formula is as follows:

[0107]

[0108] where is the basic reference distance, is the average sample density of the measurement sample space, is the local sample density around the target position, is the preset minimum reference sample number, is the number of samples within the basic reference distance range when centered on the target position, is the smoothing factor.

[0109] Further, the parameter determination module 12 further includes the following execution steps:

[0110] Obtain the sample injection parameters of the multiple adjacent reference samples and the corresponding mass spectrometry analysis effect scores;

[0111] Based on the mass spectrometry analysis effect scores, screen the sample injection parameters of the multiple adjacent reference samples, eliminate the sample injection parameters with mass spectrometry analysis effect scores lower than the preset threshold, and obtain a set of preferred sample injection parameters;

[0112] Perform weighted averaging on the sample injection parameters in the set of preferred sample injection parameters according to the corresponding mass spectrometry analysis effect scores to obtain the injection parameters of the sample to be measured.

[0113] It should be noted that in the above embodiments, the descriptions of the various embodiments have their own emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0114] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0115] The present invention will be described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general purpose computer, special purpose computer, embedded computer or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one or more of the flows Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0116] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one or more of the flows Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0118] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn of the basic inventive concept.

[0119] Obviously, those skilled in the art can make various changes and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for controlling the injection of a mass spectrometer, characterized in that: Applied to an automatic sampling system, the method comprises: Identify the sample to be tested and obtain the sample identification result; Determining the injection parameters of the sample to be tested based on the sample identification result; Controlling the sample to be tested according to the injection parameters, and introducing the sample to be tested into a mass spectrometer for analysis; Wherein, based on the sample identification result, determining the injection parameters of the sample to be tested includes: Constructing a measurement sample space based on archived measurement sample records and multiple feature dimensions; Extracting multiple features of the sample to be tested from the sample identification result; Based on the multiple characteristics of the samples to be tested, determining multiple neighboring reference samples of the sample to be tested in the measurement sample space; Determining the injection parameters of the sample to be tested according to the multiple adjacent reference samples; Among them, the measurement sample space is constructed according to the archived measurement sample records and multiple feature dimensions, including: Generate multiple feature dimensions according to a preset feature set, and generate a basic feature space based on the multiple feature dimensions; Interact with a historical measurement database, and obtain archived measurement sample records in the historical measurement database according to a preset time period; Traversing the archived measurement sample records to obtain a first archived measurement sample; extracting a plurality of first sample features of the first archived measurement sample, determining a first sample position in the basic feature space according to the plurality of first sample features, and updating the basic feature space based on the first sample position; The remaining archived measurement samples in the archived measurement sample records are processed in sequence, and the basic feature space is continuously updated until all archived measurement samples are traversed to obtain a measurement sample space.

2. The method according to claim 1, characterized in that The step of identifying the sample to be tested and obtaining the sample identification result includes: Capturing an image of the sample to be tested to obtain an image of the sample to be tested; Performing image analysis on the image of the sample to be tested according to a preset feature set to obtain a plurality of features of the sample to be tested; The multiple features of the samples to be tested are used as sample identification results of the samples to be tested.

3. The method according to claim 1, characterized in that The step of determining a plurality of adjacent reference samples of the sample to be tested in the measurement sample space based on the plurality of features of the sample to be tested comprises: Determining a target position of the sample to be tested in the measurement sample space according to the multiple characteristics of the sample to be tested; Determine the proximity distance in the measurement sample space based on the target position according to a proximity distance determination formula; Taking the target position as the center and the proximity distance as the radius, constructing a proximity space of the sample to be tested; Samples in the adjacent space are extracted to obtain a plurality of adjacent reference samples.

4. The method according to claim 3, characterized in that: The formula for determining the proximity distance is: in, is the basic reference distance, To measure the average sample density of the sample space, is the local sample density around the target location, is the preset minimum number of reference samples, is the number of samples within the basic reference distance range centered on the target position, is the smoothing factor.

5. The method according to claim 1, characterized in that The step of determining the injection parameters of the sample to be tested according to the plurality of adjacent reference samples comprises: Obtaining sample injection parameters and corresponding mass spectrometry analysis effect scores of the plurality of adjacent reference samples; Based on the mass spectrometry analysis effect score, the sample injection parameters of the multiple adjacent reference samples are screened, and the sample injection parameters whose mass spectrometry analysis effect scores are lower than a preset threshold are eliminated to obtain a set of preferred sample injection parameters; The sample injection parameters in the preferred sample injection parameter set are weighted averaged according to the corresponding mass spectrometry analysis effect scores to obtain the injection parameters of the sample to be tested.

6. A sample injection control system for a mass spectrometer, characterized in that: A method for controlling the injection of a mass spectrometer according to any one of claims 1 to 5, the system comprising: The sample identification module is used to identify the sample to be tested and obtain the sample identification result; A parameter determination module, used to determine the injection parameters of the sample to be tested based on the sample identification result; The injection control module is used to control the injection of the sample to be tested according to the injection parameters, and introduce the sample to be tested into the mass spectrometer for analysis.

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