Mass spectrometer control method and device, mass spectrometer and readable storage medium

Abstract

The invention discloses a mass spectrometer control method and device, a mass spectrometer and a readable storage medium, and relates to the field of mass spectrometry. The control method comprises the following steps: moving a target plate into a vacuum cavity from a sample inlet under the condition that a target entering condition is met; starting vacuum treatment on the vacuum cavity; analyzing the image of each target point in the target plate to generate a sample point recognition result; determining an identifier in the sample point identification result as an identifier of a target sample point under the condition of an automatic proofing mode; under the condition that vacuum processing is completed, sample detection is conducted on all the target sample points, and a detection result is obtained; and generating a detection report based on the detection result of each target sample point. According to the technical scheme, the detection efficiency of the mass spectrometer can be improved.

Description

Technical Field

[0001] The present application belongs to the technical field of mass spectrometry analysis, and in particular, relates to a control method and a control device for a mass spectrometer, a mass spectrometer, and a readable storage medium. Background Art

[0002] A mass spectrometer is an instrument that separates and detects the composition of substances according to the mass differences of atoms, molecules or molecular fragments of substances based on the principle that charged particles can move in electric or magnetic fields. Mass spectrometers are widely used in the field of biomedicine for proteomics research, discovery of disease markers, drug metabolism analysis and clinical diagnosis.

[0003] The sample detection process mainly includes: taking a sample (colony) from a culture dish, placing (dropping or smearing) the sample on the target point of a target plate (also called a sample plate or sample plate), the target point where the sample is placed is called a sample point or sample point, and then applying a matrix to the sample in the target point, drying it, and then sending the target plate into a mass spectrometer; the mass spectrometer detects the sample crystals in the target point. The target plate usually includes multiple target points. For example, a target plate includes 24, 48 or more target points. In actual use, the user (i.e., the tester) may only place samples on some of the target points in the target plate. If a mass spectrometer is used to detect all the target points in the target plate, more resources will be consumed and the detection time will be longer.

[0004] In view of the above situation, the current solution is: the user inputs the information of the target where the sample is placed into the mass spectrometer, and the mass spectrometer detects only the target indicated by the information input by the user based on the above information. However, this solution has the following problems: manual input of information is prone to errors (for example, multiple selections or missed selections of targets), which leads to low sample detection efficiency. For example, when the user selects multiple targets (that is, the target does not have a sample placed, but the user selects the target), the mass spectrometer will perform invalid detection on the multiple selected targets. In the event of missed selection (that is, the target has a sample placed, but the user does not select the target), the missed targets need to be re-inspected, resulting in low detection efficiency. When the number of target plates is large, the above problems will be more serious. Summary of the invention

[0005] In view of the above problems, the present application provides a control method, a control device, a mass spectrometer and a readable storage medium to improve detection efficiency.

[0006] To achieve the above objectives, this application provides the following technical solutions:

[0007] A first aspect of the present application provides a method for controlling a mass spectrometer, comprising:

[0008] When the target entry conditions are met, the target plate is moved from the injection port into the vacuum chamber;

[0009] Starting vacuum treatment of the vacuum chamber;

[0010] Obtaining images of each target point in the target plate;

[0011] Analyzing the images of the respective target points to identify whether sample crystals exist at the respective target points, and generating sample point identification results, wherein the sample point identification results include identifications of the sample points, wherein target points for which the identification results indicate that sample crystals exist are determined as sample points;

[0012] In the case of the automatic proofing mode, determining the identifier in the sample point recognition result as the identifier of the target sample point;

[0013] When the vacuum degree of the vacuum chamber reaches a preset vacuum degree threshold, performing sample detection on each target sample point to obtain a detection result of each target sample point;

[0014] Generate a test report based on the test results of each target sample point;

[0015] Wherein, each step in the control method is performed in an automated manner.

[0016] In a possible implementation, the method further includes:

[0017] In the case of manual proofing mode, obtaining a sample point selection result, wherein the sample point selection result includes an identifier of a sample point selected by a user;

[0018] comparing the sample point identification result and the sample point selection result;

[0019] When the sample point recognition result and the sample point selection result are consistent, determining the identifier in the sample point selection result as the identifier of the target sample point;

[0020] When the sample point recognition result and the sample point selection result are inconsistent, generating prompt information and outputting the prompt information;

[0021] When the user selects the sample point selection result, or when the user does not perform a selection operation within a predetermined time, the identifier in the sample point selection result is determined as the identifier of the target sample point; when the user selects the sample point recognition result, the identifier in the sample point recognition result is determined as the identifier of the target sample point.

[0022] In a possible implementation, when the sample point recognition result and the sample point selection result are inconsistent, generating prompt information includes:

[0023] When the sample point selection result includes all identifiers in the sample point identification result, and at least one identifier in the sample point selection result does not exist in the sample point identification result, determining that a multiple selection exception occurs, and generating first prompt information;

[0024] When the sample point identification result includes all identifiers in the sample point selection result, and at least one identifier in the sample point identification result does not exist in the sample point selection result, determining that an omission exception occurs, and generating second prompt information;

[0025] When at least one identifier in the sample point selection result does not exist in the sample point identification result, and when at least one identifier in the sample point identification result does not exist in the sample point selection result, it is determined that a multiple selection anomaly and a missed selection anomaly occur, and a third prompt information is generated.

[0026] In a possible implementation, in the manual proofing mode, sample detection is performed on each target sample point respectively, including:

[0027] When the sample point identification result and the sample point selection result are consistent, during the sample detection process for any target sample point, sample detection is performed on a specific site in the target sample point;

[0028] When the sample point identification result and the sample point selection result are inconsistent and the user selects the sample point identification result, during the sample detection process for any target sample point, sample detection is performed on a specific site in the target sample point;

[0029] In the case that the sample point identification result and the sample point selection result are inconsistent, and the user selects the sample point selection result, in the process of performing sample detection on any target sample point, the type of the target sample point is determined, wherein if the identifier of the target sample point exists in the sample point identification result, the target sample point is of the first type, and if the identifier of the target sample point only exists in the sample point selection result, the target sample point is of the second type; in the case that the target sample point is of the first type, sample detection is performed on a specific site in the target sample point; in the case that the target sample point is of the second type, sample detection is performed on multiple sites in the target sample point.

[0030] In a possible implementation, in the automatic proofing mode, sample detection is performed on each target sample point respectively, including:

[0031] During the sample detection process for any target sample point, sample detection is performed on a specific site in the target sample point.

[0032] In a possible implementation, the method further includes: when the identification result of the target point is that sample crystallization exists, determining an effective crystallization site in the target point;

[0033] The performing sample detection on a specific site in the target sample site includes: performing sample detection on an effective crystallization site in the target sample site.

[0034] In a possible implementation, the method further includes: for each target sample point, determining whether a detection result of the target sample point meets a preset requirement;

[0035] If the detection result of the target sample point meets the preset requirement, the detection result is determined as the qualified detection result of the target sample point;

[0036] If the detection result of the target sample point does not meet the preset requirement, the target sample point is tested again until the detection result of the target sample point meets the preset requirement, and the detection result that meets the preset requirement is determined as the qualified detection result of the target sample point, or until the preset number of detections is reached, the detection result with the best quality is determined as the qualified detection result of the target sample point.

[0037] In a possible implementation, analyzing the image of each target point to identify whether there is sample crystallization at the target point includes:

[0038] Converting the image of the target into a grayscale image;

[0039] Performing denoising on the grayscale image;

[0040] Perform gradient detection on the denoised grayscale image to obtain a gradient amplitude image;

[0041] Binarization is performed on the gradient amplitude image, wherein when the value of a pixel point in the gradient amplitude image is greater than a preset first threshold, the value of the pixel point is set to a first value, and when the value of the pixel point in the gradient amplitude image is less than or equal to the first threshold, the value of the pixel point is set to a second value;

[0042] Determine the number of target pixel points in the binary image, wherein the value of the target pixel point is the first value;

[0043] It is determined whether sample crystallization exists at the target point based on the number of the target pixel points.

[0044] A second aspect of the present application provides a control device for a mass spectrometer, comprising:

[0045] A target feeding control module is used to move the target plate from the injection port to the vacuum chamber when the target feeding conditions are met;

[0046] A vacuum starting module, used for starting vacuum processing of the vacuum chamber;

[0047] A target image acquisition module, used to obtain images of each target in the target plate;

[0048] A sample point identification module is used to analyze the image of each target point to identify whether there is sample crystallization at each target point, and generate a sample point identification result, wherein the sample point identification result includes an identifier of the sample point, wherein a target point with an identification result of the presence of sample crystallization is determined as a sample point;

[0049] A first processing module, configured to determine, in an automatic proofing mode, an identifier in the sample point recognition result as an identifier of a target sample point;

[0050] A detection control module, used for performing sample detection on each target sample point respectively to obtain the detection result of each target sample point when the vacuum degree of the vacuum chamber reaches a preset vacuum degree threshold;

[0051] The report generation module is used to generate a test report based on the test results of each target sample point.

[0052] A third aspect of the present application provides a mass spectrometer, comprising an ion source, a mass analyzer and a control device, wherein the control device is used to implement any one of the above control methods.

[0053] A fourth aspect of the present application provides a readable storage medium, which carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device implements any one of the above-mentioned control methods.

[0054] A fifth aspect of the present application provides a computer program product, comprising computer-readable instructions, which, when executed on an electronic device, enables the electronic device to implement any one of the above-mentioned control methods.

[0055] It can be seen that the beneficial effects of this application are:

[0056] The technical solution disclosed in the present application is to start the vacuum treatment of the vacuum chamber after moving the target plate from the injection port to the vacuum chamber. While the vacuum chamber is being vacuum treated, the image of each target point in the target plate is analyzed to identify whether there is sample crystallization at each target point, and generate a sample point identification result. When in the automatic proofing mode, the identifier in the sample point identification result is determined as the identifier of the target sample point. When the vacuum degree of the vacuum chamber reaches a preset vacuum degree threshold, sample detection is performed on each target sample point respectively, and a detection report is generated based on the detection results of each target sample point. In the technical solution disclosed in the present application, when the mass spectrometer is in the automatic proofing mode, the image of each target point is analyzed to identify whether there is sample crystallization at each target point. Subsequently, sample detection is performed on the target point whose identification result is the presence of sample crystallization, which can reduce the possibility of multiple selection of targets and missed selection of targets, thereby improving the detection efficiency of the mass spectrometer.

[0057] Moreover, after the target plate is moved from the injection port into the vacuum chamber, the vacuum treatment of the vacuum chamber is started. During the vacuum treatment of the vacuum chamber, the operation of determining the target sample point is performed simultaneously, which can shorten the time consumed in the entire detection process.

[0058] In addition, based on the technical solution disclosed in the present application, the target plate advancement control, the vacuum treatment of the vacuum chamber, the determination of the target sample point, the sample detection for the target sample point, and the generation of the detection report are all performed in an automated manner, which significantly improves the automation level of the mass spectrometer. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 A flow chart of a control method for a mass spectrometer disclosed in the present application;

[0061] Figure 2 A flow chart of another mass spectrometer control method disclosed in the present application;

[0062] Figure 3 A flow chart of a method for identifying whether a sample crystal exists at a target disclosed in the present application;

[0063] Figure 4 A schematic diagram of the structure of a control device for a mass spectrometer disclosed in the present application;

[0064] Figure 5This is a schematic structural diagram of another mass spectrometer control device disclosed in this application. DETAILED DESCRIPTION

[0065] The present application discloses a control method and a control device of a mass spectrometer, a mass spectrometer and a readable storage medium to improve detection efficiency.

[0066] The following describes the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. The terms used in the implementation method section of the present application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application.

[0067] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable in appropriate circumstances, which is merely a way of distinguishing the objects of the same attributes when describing them in the embodiments of the present application. In addition, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only include those elements, but also include other elements that are not clearly listed, or also include elements inherent to such process, method, article or equipment. In the absence of more restrictions, the elements limited by the statement "include one..." do not exclude the existence of other identical elements in the process, method, article or equipment including the elements.

[0068] See also Figure 1 , Figure 1 This is a flow chart of a control method of a mass spectrometer disclosed in the present application. The control method is executed by a control device of the mass spectrometer, and includes:

[0069] S101: When the target feeding conditions are met, the target plate is moved from the injection port into the vacuum chamber.

[0070] During the sample processing stage, take a sample from the culture dish, place (drop or smear) the sample on the target point of the target plate, and then apply the matrix to the sample in the target point, and dry the target plate. It should be noted that after the drying treatment, the sample in the target point forms sample crystals. After the drying treatment is completed, the target plate is sent to the injection port of the mass spectrometer. When it is determined that the target feeding conditions are met, the target plate is moved from the injection port to the vacuum chamber. It should be noted that the target plate is placed on a carrying mechanism, which has the ability to move in the vacuum chamber, and the target plate is moved from the injection port to the vacuum chamber by controlling the carrying mechanism.

[0071] Optionally, the human-machine interaction device of the mass spectrometer is provided with a physical button for triggering the target approach operation. Accordingly, the target approach condition is: the physical button for triggering the target approach operation in the human-machine interaction device is pressed.

[0072] Optionally, the human-computer interaction interface of the mass spectrometer displays a control for triggering the target approach operation. Accordingly, the target approach condition is: the control for triggering the target approach operation in the human-computer interaction interface is selected.

[0073] Optionally, the mass spectrometer has a gesture control function. Accordingly, the target approach condition is: detecting a gesture performed by a user for triggering a target approach operation.

[0074] Optionally, the mass spectrometer has a voice control function. Accordingly, the target entry condition is: detecting a voice for triggering the target entry operation.

[0075] S102: Start vacuum processing of the vacuum chamber.

[0076] Optionally, after the target plate is moved from the injection port into the vacuum chamber, the vacuum treatment of the vacuum chamber is automatically started immediately. During the vacuum treatment of the vacuum chamber, the control device simultaneously performs subsequent steps to determine the target sample points. After the vacuum degree of the vacuum chamber reaches the preset requirement, sample detection can be immediately performed on each target sample point.

[0077] It can be understood that the vacuum processing of the vacuum chamber and the operation of the control device to determine the target sample point are performed simultaneously, which can shorten the time consumed in the entire detection process.

[0078] S103: Obtaining images of each target point in the target plate.

[0079] The image acquisition device acquires the image of each target point in the target plate and transmits the image of each target point to the control device.

[0080] There are multiple solutions for collecting images of each target point in the target plate by using an image acquisition device.

[0081] In the first solution, the image acquisition device is located in the external space of the mass spectrometer. After the target plate is dried, the image of each target point in the target plate is acquired by the image acquisition device.

[0082] As an optional solution, the position of the image acquisition device is adjusted so that the image acquisition device can acquire images of various target points.

[0083] The second solution is to arrange an image acquisition device in the vacuum chamber of the mass spectrometer, and after the target plate is moved into the vacuum chamber, the image of each target point in the target plate is acquired by the image acquisition device.

[0084] As an optional solution, the target plate's carrying mechanism has the ability to move in the vacuum chamber. When the image acquisition device is fixed in position, the carrying mechanism drives the target plate to move so that the image acquisition device can acquire images of each target point.

[0085] It should be noted that the space inside the mass spectrometer has a high degree of cleanliness. When the target plate is located in the vacuum chamber, image acquisition is performed, which makes it very unlikely that the target plate and the image acquisition device will be disturbed by impurities such as dust, and can reduce noise in the image of the target point.

[0086] S104: Analyze the image of each target point to identify whether there is sample crystallization at each target point, and generate a sample point identification result.

[0087] The sample point recognition result includes the identification of the sample point. In this step, the target point whose recognition result indicates that there is sample crystallization is determined as the sample point.

[0088] That is to say, for each target point in the target plate, the following is performed: Analyze the image of the target point to determine whether there is sample crystallization at the target point. Generate sample point recognition results based on the recognition results of each target point in the target plate. The sample point recognition results only include the identification of the target point whose recognition result is that there is sample crystallization.

[0089] S105: In the case of the automatic proofing mode, determining the identifier in the sample point recognition result as the identifier of the target sample point.

[0090] S106: When the vacuum degree of the vacuum chamber reaches a preset vacuum degree threshold, sample detection is performed on each target sample point to obtain detection results of each target sample point.

[0091] Optionally, in the automatic proofing mode, sample detection is performed on each target sample point respectively, and the following scheme is adopted: during the sample detection process of any target sample point, sample detection is performed on a specific site in the target sample point. The selection of the specific site in the target sample point is described in detail below.

[0092] S107: Generate a test report based on the test results of each target sample point.

[0093] As an optional solution, the test report is automatically pushed to a designated client. As an optional solution, the control device of the mass spectrometer sends the test report to a printing device, and the printing device automatically prints the test report.

[0094] It should be noted that the above steps are performed in an automated manner.

[0095] The technical solution disclosed in the present application is to immediately start the vacuum treatment of the vacuum chamber after the target plate is moved from the injection port to the vacuum chamber. While the vacuum chamber is being vacuum treated, the image of each target point in the target plate is analyzed to identify whether there is sample crystallization at each target point, and generate a sample point identification result. When in the automatic proofing mode, the identifier in the sample point identification result is determined as the identifier of the target sample point. When the vacuum degree of the vacuum chamber reaches a preset vacuum degree threshold, sample detection is performed on each target sample point respectively, and a detection report is generated based on the detection results of each target sample point. In the technical solution disclosed in the present application, when the mass spectrometer is in the automatic proofing mode, by analyzing the image of each target point to identify whether there is sample crystallization at each target point, sample detection is subsequently performed on the target point whose identification result is the presence of sample crystallization, which can reduce the possibility of multiple target selection and missed target selection, thereby improving the detection efficiency of the mass spectrometer.

[0096] Moreover, after the target plate is moved from the injection port into the vacuum chamber, the vacuum treatment of the vacuum chamber is started. During the vacuum treatment of the vacuum chamber, the operation of determining the target sample point is performed simultaneously, which can shorten the time consumed in the entire detection process.

[0097] In addition, based on the technical solution disclosed in the present application, the target plate advancement control, the vacuum treatment of the vacuum chamber, the determination of the target sample point, the sample detection for the target sample point, and the generation of the detection report are all performed in an automated manner, which significantly improves the automation level of the mass spectrometer.

[0098] The above mainly introduces the control process in the automatic proofing mode. In addition to the automatic proofing mode, the mass spectrometer in this application also has a manual proofing mode, and the following focuses on the control process in the manual proofing mode.

[0099] See also Figure 2 , Figure 2 This is a flow chart of a control method of a mass spectrometer disclosed in the present application. The control method is executed by a control device of the mass spectrometer, and includes:

[0100] S201: When the target feeding conditions are met, the target plate is moved from the injection port into the vacuum chamber.

[0101] S202: Start vacuum processing of the vacuum chamber.

[0102] S203: Obtaining images of each target point in the target plate.

[0103] S204: Analyze the image of each target point to identify whether there is sample crystallization at each target point, and generate a sample point identification result.

[0104] The sample point recognition result includes the identification of the sample point. In this step, the target point whose recognition result indicates that there is sample crystallization is determined as the sample point.

[0105] S205: In the case of the automatic proofing mode, determining the identifier in the sample point recognition result as the identifier of the target sample point.

[0106] Optionally, the human-computer interaction interface of the mass spectrometer displays a prompt message for selecting a proofing mode. If the user selects manual proofing mode, the manual proofing mode is entered; if the user selects automatic proofing mode, or the user fails to perform a mode selection operation within a predetermined time, or the user fails to perform a sample point selection operation within a predetermined time, the automatic proofing mode is entered.

[0107] S206: In the manual proofing mode, obtain a sample point selection result.

[0108] The sample point selection result includes the identifier of the sample point selected by the user. That is, the sample point selection result is generated according to the target point selected by the user. The sample point selection result only includes the identifier of the target point selected by the user.

[0109] It is understandable that the identification of each target point in the target plate is set in advance, and the identification of different targets is different, that is, the identification of the target point is unique. If the control device identifies a target point as a sample point, then the identification of the sample point is the identification of the target point. If the user selects a target point as a sample point, then the identification of the sample point is the identification of the target point.

[0110] Optionally, the identification of the target is the number of the target or the position information of the target in the target plate.

[0111] S207: Compare the sample point recognition result and the sample point selection result.

[0112] S208: When the sample point recognition result and the sample point selection result are consistent, determine the identifier in the sample point selection result as the identifier of the target sample point.

[0113] S209: When the sample point recognition result and the sample point selection result are inconsistent, generate prompt information and output the prompt information.

[0114] S210: When the user selects a sample point selection result, or when the user does not perform a selection operation within a predetermined time, determine an identifier in the sample point selection result as an identifier of a target sample point.

[0115] S211: When the user selects a sample point recognition result, determine the identifier in the sample point recognition result as the identifier of the target sample point.

[0116] If the mass spectrometer is in manual sampling mode, compare the sample point identification result with the sample point selection result. Based on the comparison result, perform the subsequent steps. Specifically:

[0117] In the first case, if the sample point identification result is consistent with the sample point selection result, the identifier in the sample point selection result is determined as the identifier of the target sample point, that is, the target point selected by the user is determined as the target sample point, and the mass spectrometer subsequently performs sample detection on these target points.

[0118] It is understandable that, since the sample point recognition result and the sample point selection result are consistent, the "determining the identifier in the sample point selection result as the identifier of the target sample point" in step S208 can also be replaced by "determining the identifier in the sample point recognition result as the identifier of the target sample point".

[0119] In the second case, the sample point recognition result is inconsistent with the sample point selection result. This may be caused by the following reasons: the sample recognition result is accurate, but the user selects multiple targets or misses them; the user selects the target correctly, but the sample recognition result is biased. Generally, the first case is more likely to occur.

[0120] In the case where the sample point identification result and the sample point selection result are inconsistent, a prompt message is generated and outputted, so that the user is informed that the sample point identification result and the sample point selection result are inconsistent. If the user selects the sample point selection result, or the user does not perform the selection operation within a predetermined time, the identifier in the sample point selection result is determined as the identifier of the target sample point, that is, the target point selected by the user is determined as the target sample point; if the user selects the sample point identification result, the identifier in the sample point identification result is determined as the identifier of the target sample point, that is, the identified target point with sample crystallization is determined as the target sample point.

[0121] Based on the above-mentioned comparison of sample point identification results and sample point selection results, a prompt is issued to the user when the two are inconsistent, and the user then makes a selection. This solution can reduce the possibility of deviation between the target sample point and the actual situation. In other words, it can reduce the possibility of target points without samples being determined as target sample points, and the possibility of target points with samples being omitted, thereby reducing the possibility of invalid detection and re-inspection by the mass spectrometer, thereby improving the detection efficiency of the mass spectrometer.

[0122] S212: When the vacuum degree of the vacuum chamber reaches a preset vacuum degree threshold, sample detection is performed on each target sample point to obtain detection results of each target sample point.

[0123] S213: Generate a test report based on the test results of each target sample point.

[0124] This application Figure 2 The control method shown is similar to Figure 1 Compared with the control method shown in the figure, a manual proofing mode is further added. In the case of the manual proofing mode, a sample point selection result generated based on the user selection is obtained, and the sample point identification result and the sample point selection result are compared. If the two are consistent, the sample point identification result or the identifier in the sample point selection result is determined as the identifier of the target sample point. If the two are inconsistent, a prompt message is output. After that, based on the user's operation, the identifier in the sample point identification result or the sample point selection result is determined as the identifier of the target sample point. Based on the above-mentioned comparison of the sample point identification result and the sample point selection result, a prompt is issued to the user when the two are inconsistent, and then the user makes a selection. The scheme can reduce the possibility of deviation between the target sample point and the actual situation, that is, it can reduce the possibility of a target point without a sample being determined as a target sample point, and the possibility of a target point with a sample being omitted, thereby reducing the possibility of invalid detection and supplementary detection by the mass spectrometer, thereby improving the detection efficiency of the mass spectrometer.

[0125] In another embodiment of the present application, the description focuses on a solution for generating prompt information when the sample point recognition result and the sample point selection result are inconsistent.

[0126] This includes the following steps:

[0127] A1: when the sample point selection result includes all the identifiers in the sample point identification result, and at least one identifier in the sample point selection result does not exist in the sample point identification result, it is determined that a multiple selection exception occurs, and a first prompt information is generated;

[0128] A2: when the sample point identification result includes all the identifiers in the sample point selection result, and at least one identifier in the sample point identification result does not exist in the sample point selection result, it is determined that an omission exception occurs, and a second prompt information is generated;

[0129] A3: When at least one identifier in the sample point selection result does not exist in the sample point identification result, and at least one identifier in the sample point identification result does not exist in the sample point selection result, it is determined that a multiple selection anomaly and a missed selection anomaly occur, and a third prompt information is generated.

[0130] As an optional solution, the first prompt information includes: information indicating that a multiple selection anomaly has occurred; and identifiers of the multiple selected sample points.

[0131] As an optional solution, the second prompt information includes: information prompting that an omission abnormality has occurred; and an identifier of the omission sample point.

[0132] As an optional solution, the third prompt information includes: information prompting the occurrence of multiple selection anomalies and missed selection anomalies; identifiers of multiple selected sample points; and identifiers of missed selection sample points.

[0133] Based on the above scheme, users can not only know that multiple selection anomalies and missed selection anomalies have occurred, but also know the specific sample points that have been multiple selected or missed, which helps users make correct choices in sample point selection results and sample point identification results.

[0134] It is understandable that the identifiers of the multiple selected sample points are specifically identifiers that exist in the sample point selection result but not in the sample point recognition result. The identifiers of the missed sample points are specifically identifiers that exist in the sample point recognition result but not in the sample point selection result.

[0135] In another embodiment of the present application, the focus is on describing a solution for performing sample detection on each target sample point separately when in manual proofing mode.

[0136] The program includes the following steps:

[0137] B1: When the sample point identification result and the sample point selection result are consistent, during the sample detection process of any target sample point, the sample detection is performed on a specific site in the target sample point;

[0138] B2: When the sample point identification result and the sample point selection result are inconsistent and the user selects the sample point identification result, during the sample detection process for any target sample point, the sample detection is performed on a specific site in the target sample point;

[0139] B3: When the sample point identification result and the sample point selection result are inconsistent and the user selects the sample point selection result, in the process of performing sample detection on any target sample point, the type of the target sample point is determined, wherein if the identifier of the target sample point exists in the sample point identification result, the target sample point is of the first type; if the identifier of the target sample point only exists in the sample point selection result, the target sample point is of the second type; when the target sample point is of the first type, sample detection is performed on a specific site in the target sample point; when the target sample point is of the second type, sample detection is performed on multiple sites in the target sample point.

[0140] That is, if the sample point identification result is consistent with the sample point selection result, then in the process of performing sample detection on each target sample point, sample detection is performed on a specific site in the target sample point.

[0141] If the sample point identification result and the sample point selection result are inconsistent, and the user subsequently selects the sample point identification result, then during the process of performing sample detection on each target sample point, sample detection is performed on a specific site in the target sample point.

[0142] If the sample point recognition result and the sample point selection result are inconsistent, and the user subsequently selects the sample point selection result, then the target sample point may have multiple situations: first, the identifier of the target sample point exists in both the sample point recognition result and the sample point selection result, and the target sample point is determined to be the first type; second, the identifier of the target sample point exists in the sample point selection result but not in the sample point recognition result, and the target sample point is a multiple-selected sample point, and the target sample point is determined to be the second type.

[0143] During the sample detection process for the first type of target sample point, sample detection is performed on a specific site in the target sample point. Regarding the second type of target sample point, it may be caused by user error, or it may be caused by a sample placed at the target point, but it is misjudged as not having sample crystallization. Considering that the user is prompted with a multiple selection anomaly, the user still selects the sample point selection result, which indicates that the second type of target sample point may have a sample placed, but the crystallization effect of the sample is not good. Therefore, during the sample detection process for the second type of target sample point, sample detection is performed on multiple sites in the target sample point.

[0144] Here is a brief description of the working process of the mass spectrometer. The mass spectrometer includes an ion source and a mass analyzer. The ion source is used to irradiate the crystals formed by the sample and the matrix in the target, and the components in the sample are ionized to generate charged ions with different mass-to-charge ratios. The charged ions enter the mass analyzer; under the action of the electric field, the charged ions pass through the flight pipe to reach the detector. The mass analyzer forms a corresponding mass spectrum peak diagram based on the time when the charged ions reach the detector and the number of charged ions.

[0145] In the above scheme, performing sample detection on a specific site in the target sample point can be understood as: controlling the laser device in the ion source to irradiate the specific site in the target sample point. Performing sample detection on multiple sites in the target sample point can be understood as: controlling the laser device in the ion source to sequentially irradiate multiple sites in the target sample point. Optionally, the carrier mechanism of the target plate is controlled to move horizontally and / or vertically so that different sites in the target sample point move to the position corresponding to the laser device.

[0146] In the above disclosed solution, in the manual proofing mode or in the automatic proofing mode, during the process of performing sample detection on a specific site in the target sample point, the specific site can be determined by a variety of solutions.

[0147] The first solution is to determine the pre-set site as a specific site, that is, to perform sample detection on the pre-set site in the target sample point.

[0148] The second solution: when the identification result of the target point is that there is sample crystallization, the effective crystallization site in the target point is determined, and accordingly, the effective crystallization site in the target point is determined to be the aforementioned specific site. In other words, sample detection is performed on the effective crystallization site in the target sample point.

[0149] The effective crystallization site of the target refers to: the area where the sample crystals are evenly distributed. The effective crystallization site of the target is determined by image recognition technology, and the existing technology can be used to identify the effective crystallization site in the target.

[0150] The second solution disclosed above performs sample detection on effective crystallization points in target sample points, that is, controls the laser device in the ion source to irradiate the effective crystallization points in target sample points, which can improve the quality of the detection results.

[0151] In another embodiment of the present application, the following steps are further arranged:

[0152] For each target sample point, determine whether the detection result of the target sample point meets the preset requirements;

[0153] If the test result of the target sample point meets the preset requirements, the test result is determined as the qualified test result of the target sample point;

[0154] If the test result of the target sample point does not meet the preset requirements, the target sample point is tested again until the test result of the target sample point meets the preset requirements, and the test result that meets the preset requirements is determined as the qualified test result of the target sample point, or until the preset number of tests is reached, the test result with the best quality is determined as the qualified test result of the target sample point.

[0155] Accordingly, a test report is generated based on the test results of each target sample point, specifically: a test report is generated based on the qualified test results of each target sample point.

[0156] Optionally, the above-mentioned preset requirements include: the resolution, signal-to-noise ratio and peak height of the characteristic peak in the spectrum (ie, the detection result) all reach corresponding thresholds.

[0157] It should be noted that, when the sample detection for the target sample point reaches the preset number of detection times but the detection result that meets the preset requirements is still not obtained, the detection result with the best quality is selected from the multiple detection results.

[0158] Optionally, the quality of the detection result is evaluated based on the resolution, signal-to-noise ratio and peak height of the characteristic peaks in the detection result.

[0159] Optionally, a characteristic peak in the test result is determined, a first score is determined based on the resolution of the characteristic peak, a second score is determined based on the signal-to-noise ratio of the characteristic peak, a third score is determined based on the peak height of the characteristic peak, and then a comprehensive score of the test result is determined based on the first score, the second score and the third score. For example, the sum of the first score, the second score and the third score is used as the comprehensive score of the test result. For another example, the first score, the second score and the third score are weighted and summed, and the result of the weighted summation is used as the comprehensive score of the test result. For multiple test results of the target sample point, the comprehensive scores of each test result are determined respectively, and the test result with the highest comprehensive score is selected as the qualified test result.

[0160] In the above embodiment, sample detection is performed on the target sample point to obtain the detection result, and it is further determined whether the detection result meets the preset requirements. If the preset requirements are met, a detection report is generated based on the detection result. If the detection result does not meet the preset requirements, sample detection is performed on the target sample point again until the obtained detection result meets the preset requirements, or the preset number of detection times of the sample detection on the target sample point is reached, and a detection report is generated based on the inspection result that meets the preset requirements, or a detection report is generated based on the detection result with the best quality. A detection report with better quality can be obtained without the participation of the user.

[0161] In another embodiment of the present application, a scheme for analyzing an image of any target point to identify whether there is sample crystallization at the target point is introduced.

[0162] See also Figure 3 , Figure 3 This is a flow chart of a method for identifying whether a sample crystal exists at a target disclosed in the present application. The method includes:

[0163] S301: Convert the target image into a grayscale image.

[0164] The image of the target point obtained by the image acquisition device is an RGB image. In order to simplify subsequent processing, the image of the target point is converted into a grayscale image.

[0165] Optionally, convert the target image to a grayscale image using the following scheme:

[0166] For each pixel in the target image, the grayscale value of the pixel is determined according to the value of the red channel, the value of the green channel and the value of the blue channel of the pixel to generate a grayscale image.

[0167] In practice, the grayscale value of a pixel can be calculated according to the following formula:

[0168] I gray =0.299×IR +0.587×I G +0.114×I B

[0169] Among them, I R is the value of the red channel of the pixel, I G is the value of the green channel of the pixel, I B is the value of the blue channel of the pixel, I gray is the gray value of the pixel.

[0170] That is to say, for each pixel in the target image, perform the following steps: weighted sum of the red channel value, green channel value and blue channel value of the pixel according to the above formula to obtain the gray value of the pixel. The gray values ​​of all pixels constitute the gray image of the target.

[0171] S302: De-noising the grayscale image of the target.

[0172] During the process of acquiring images of the target, noise may be introduced due to shooting conditions, sensor defects or other interference factors. The grayscale image of the target is denoised to remove the noise in the grayscale image, thereby improving the quality and clarity of the grayscale image and reducing the difficulty of identifying colonies in the image later.

[0173] Optionally, Gaussian filtering is performed on the grayscale image of the target.

[0174] Gaussian filtering is a linear smoothing filter that has a good inhibitory effect on noise that obeys the normal distribution and can reduce Gaussian noise in the image.

[0175] The essence of Gaussian filtering on the grayscale image of the target is to perform convolution operation on the Gaussian kernel and the grayscale image of the target. In other words, the pixel value of each pixel in the grayscale image after Gaussian filtering is obtained by weighted average of its own pixel value and the pixel values ​​of other pixels in its neighborhood.

[0176] Optionally, a two-dimensional Gaussian function is used to generate a Gaussian kernel used for Gaussian filtering of the grayscale image of the target.

[0177] The two-dimensional Gaussian function is:

[0178] Among them, (x, y) is the relative coordinate of the pixel point, indicating the distance between the pixel point and the center point; G(x, y) is the weight of the pixel point with a relative position of (x, y); σ is the standard deviation, which determines the smoothness of the image after Gaussian filtering.

[0179] S303: Performing gradient detection on the denoised grayscale image to obtain a gradient amplitude image.

[0180] The gradient magnitude indicates the intensity of the grayscale value change in the image, which can help identify the edges and boundaries in the image. The larger the gradient magnitude, the more dramatic the grayscale change in the image in that area, usually corresponding to the edge or boundary; conversely, the area with a smaller gradient magnitude indicates a smoother image.

[0181] In this scheme, if a sample is placed on the target, the image of the target includes an image formed by the colonies in the sample. The image formed by the colonies has a large difference in grayscale value compared with the images in other areas. Based on this, in this scheme, gradient detection is performed on the denoised grayscale image to obtain the gradient amplitude image of the target, and then the presence of colonies is determined based on the gradient amplitude image.

[0182] Optionally, gradient detection is performed on the denoised grayscale image to obtain a gradient amplitude image using the following scheme:

[0183] C1: Determine the horizontal gradient value of each pixel in the denoised grayscale image;

[0184] C2: Determine the vertical gradient value of each pixel in the denoised grayscale image;

[0185] C3: For each pixel in the denoised grayscale image, the gradient value of the pixel in the horizontal direction and the gradient value in the vertical direction are combined to obtain the gradient amplitude of the pixel to form a gradient amplitude image.

[0186] That is to say, for each pixel in the denoised grayscale image, the following operations are performed: determining the horizontal gradient value and the vertical gradient value of the pixel, merging the horizontal gradient value and the vertical gradient value of the pixel, and obtaining the gradient amplitude of the pixel. The gradient amplitudes of all pixels constitute the gradient amplitude image of the target point.

[0187] As an optional solution, the gradient value of each pixel in the denoised grayscale image in the horizontal direction is determined respectively, including: performing a convolution operation on the horizontal Sobel operator with the denoised grayscale image to obtain the gradient of each pixel in the denoised grayscale image in the horizontal direction.

[0188] The Sobel operator in the horizontal direction is:

[0189] Here, a pixel point in the denoised grayscale image (the pixel point located at the 2nd row and the 2nd column) is taken as an example to illustrate the scheme for determining the gradient value of the pixel point in the horizontal direction.

[0190]

[0191] Where: G (2,2)x is the horizontal gradient value of the pixel located at the 2nd row and the 2nd column in the denoised grayscale image; f(i,j) is the grayscale value of the pixel located at the ith row and the jth column in the denoised grayscale image, i=1,2,3…,M, j=1,2,3…,N, M is the number of rows of pixels in the denoised grayscale image, and N is the number of columns of pixels in the denoised grayscale image.

[0192] As an optional solution, the vertical gradient of each pixel in the denoised grayscale image is determined separately, including: performing a convolution operation on the vertical Sobel operator and the denoised grayscale image to obtain the vertical gradient of each pixel in the denoised grayscale image.

[0193] The Sobel operator in the vertical direction is:

[0194] Here, a pixel point in the denoised grayscale image (the pixel point located at the 2nd row and the 2nd column) is taken as an example to illustrate the scheme for determining the gradient value of the pixel point in the horizontal direction.

[0195]

[0196] Among them, G (2,2)y is the vertical gradient value of the pixel located at the 2nd row and the 2nd column in the denoised grayscale image; f(i,j) is the grayscale value of the pixel located at the ith row and the jth column in the denoised grayscale image, i=1,2,3…,M, j=1,2,3…,N, M is the number of rows of pixels in the denoised grayscale image, and N is the number of columns of pixels in the denoised grayscale image.

[0197] It should be noted that the Sobel operator assigns different weights to pixels at different positions in the neighborhood of the center point. Based on this, the Sobel operator is used to determine the gradient values ​​of each pixel in the denoised grayscale image in the horizontal and vertical directions. Subsequently, the edge points can be more accurately identified based on the gradient amplitude image, thereby more accurately determining whether there are colonies at the target point.

[0198] Of course, other solutions may also be used to determine the gradient value of each pixel in the denoised grayscale image in the horizontal and vertical directions.

[0199] As another optional solution, the gradient value of each pixel in the denoised grayscale image in the horizontal direction is determined separately, including: performing a convolution operation on the horizontal prewitt operator and the denoised grayscale image to obtain the gradient of each pixel in the denoised grayscale image in the horizontal direction.

[0200] The prewitt operator in the horizontal direction is:

[0201] As an optional solution, the vertical gradient of each pixel in the denoised grayscale image is determined separately, including: convolving the vertical prewitt operator with the denoised grayscale image to obtain the vertical gradient of each pixel in the denoised grayscale image.

[0202] The prewitt operator in the vertical direction is:

[0203] As an optional solution, the gradient value of the pixel in the horizontal direction and the gradient value in the vertical direction are combined to obtain the gradient amplitude of the pixel, including:

[0204] According to the formula Calculate the gradient magnitude of a pixel.

[0205] As another optional solution, the gradient value of the pixel point in the horizontal direction and the gradient value in the vertical direction are combined to obtain the gradient amplitude of the pixel point, including:

[0206] According to the formula G=|G x |+|G y |Calculate the gradient magnitude of a pixel.

[0207] In the above two formulas, G x is the gradient value of the pixel in the horizontal direction, G y is the gradient value of the pixel in the vertical direction, and G is the gradient amplitude of the pixel.

[0208] S304: Binarization processing is performed on the gradient amplitude image.

[0209] When the value of a pixel in the gradient amplitude image is greater than a preset first threshold, the value of the pixel is set to a first value; when the value of a pixel in the gradient amplitude image is less than or equal to the first threshold, the value of the pixel is set to a second value.

[0210] Optionally, when the value of a pixel in the gradient amplitude image is greater than a preset first threshold, the value of the pixel is set to 255, and when the value of a pixel in the gradient amplitude image is less than or equal to the first threshold, the value of the pixel is set to 0. That is, if the value of a pixel in the gradient amplitude image is greater than the first threshold, the pixel is processed as a point with a gray value of 255 (i.e., a white point), and if the value of a pixel in the gradient amplitude image is less than or equal to the first threshold, the pixel is processed as a point with a gray value of 0 (i.e., a black point).

[0211] Optionally, when the value of a pixel in the gradient amplitude image is greater than a preset first threshold, the value of the pixel is set to 0, and when the value of a pixel in the gradient amplitude image is less than or equal to the first threshold, the value of the pixel is set to 255. That is, if the value of a pixel in the gradient amplitude image is greater than the first threshold, the pixel is processed as a point with a gray value of 0 (i.e., a black point), and if the value of a pixel in the gradient amplitude image is less than or equal to the first threshold, the pixel is processed as a point with a gray value of 255 (i.e., a white point).

[0212] The above are two examples of the first value and the second value. In practice, the first value and the second value may also be other values, as long as the first value and the second value have a large difference.

[0213] The first threshold may be an empirical value calibrated through multiple tests.

[0214] S305: Determine the number of target pixel points in the binary image (ie, the image after binarization), wherein the value of the target pixel point is a first value.

[0215] That is to say, the number of pixels whose values ​​are the first value in the binary image is determined.

[0216] S306: Determine whether there is sample crystallization at the target point based on the number of target pixel points.

[0217] If a sample is placed at the target, the image of the target includes an image formed by the colonies in the sample, and the image after binarization will contain more target pixels. Therefore, based on the number of target pixels contained in the binary image, it can be determined whether there are colonies at the target. Furthermore, if there are colonies at the target, it is determined that there are sample crystals at the target.

[0218] As an optional solution, determining whether there is sample crystallization at the target point based on the number of target pixels includes:

[0219] According to the number of target pixels and the number of pixels contained in the binary image, the proportion of the target pixels in the binary image is determined;

[0220] When the ratio is greater than a preset second threshold, it is determined that sample crystals exist at the target point; and when the ratio is less than or equal to the second threshold, it is determined that sample crystals do not exist at the target point.

[0221] The second threshold may be an empirical value calibrated through multiple tests.

[0222] As another optional solution, determining whether there is sample crystallization at the target point based on the number of target pixel points includes:

[0223] Comparing the number of target pixels with a preset third threshold;

[0224] If the number of target pixel points is greater than the third threshold, it is determined that sample crystallization exists at the target point; if the number of target pixel points is less than or equal to the third threshold, it is determined that sample crystallization does not exist at the target point.

[0225] The third threshold may be an empirical value calibrated through multiple tests.

[0226] The method for identifying whether sample crystallization exists at a target disclosed above in the present application is that, when a sample is placed at the target, the image of the target includes an image formed by colonies in the sample. The image formed by the colonies is significantly different from images of other areas. In the binary image obtained by processing the image of the target, there will be more target pixels. Based on the number of target pixels, it is possible to accurately determine whether colonies exist at the target, thereby accurately identifying whether sample crystallization exists at the target.

[0227] The present application discloses a control method of a mass spectrometer as mentioned above. Accordingly, the present application also discloses a control device of a mass spectrometer. The descriptions of the two in the specification can refer to each other.

[0228] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of a control device for a mass spectrometer disclosed in this application. The control device includes:

[0229] The target entry control module 401 is used to move the target plate from the injection port to the vacuum chamber when the target entry conditions are met;

[0230] A vacuum starting module 402 is used to start vacuum processing of the vacuum chamber;

[0231] The target image acquisition module 403 is used to obtain the image of each target in the target plate;

[0232] The sample point identification module 404 is used to analyze the image of each target point to identify whether there is sample crystallization at each target point, and generate a sample point identification result, which includes an identifier of the sample point, wherein the target point with the identification result of the sample crystallization is determined as the sample point;

[0233] The first processing module 405 is used to determine the identifier in the sample point recognition result as the identifier of the target sample point when in the automatic proofing mode;

[0234] The detection control module 406 is used to perform sample detection on each target sample point when the vacuum degree of the vacuum chamber reaches a preset vacuum degree threshold, and obtain the detection result of each target sample point;

[0235] The report generation module 407 is used to generate a detection report based on the detection results of each target sample point.

[0236] As an optional solution, the detection control module 406 includes a first control submodule. The first control submodule is used to: in the automatic proofing mode, during the sample detection process for any target sample point, perform sample detection on a specific site in the target sample point.

[0237] As an optional solution, the control device of the mass spectrometer further includes a second processing module 408, such as Figure 5 The second processing module 408 is used to: in the case of manual proofing mode, obtain a sample point selection result, the sample point selection result includes the identifier of the sample point selected by the user; compare the sample point recognition result and the sample point selection result; if the sample point recognition result and the sample point selection result are consistent, determine the identifier in the sample point selection result as the identifier of the target sample point; if the sample point recognition result and the sample point selection result are inconsistent, generate prompt information and output the prompt information; if the user selects the sample point selection result, or the user does not perform the selection operation within the predetermined time, determine the identifier in the sample point selection result as the identifier of the target sample point; if the user selects the sample point recognition result, determine the identifier in the sample point recognition result as the identifier of the target sample point.

[0238] As an optional solution, the second processing module 408 generates prompt information when the sample point recognition result and the sample point selection result are inconsistent, specifically:

[0239] When the sample point selection result includes all identifiers in the sample point identification result, and at least one identifier in the sample point selection result does not exist in the sample point identification result, determining that a multiple selection anomaly occurs, and generating first prompt information;

[0240] When the sample point identification result includes all the identifiers in the sample point selection result, and at least one identifier in the sample point identification result does not exist in the sample point selection result, it is determined that an omission exception occurs, and a second prompt information is generated;

[0241] When at least one identifier in the sample point selection result does not exist in the sample point identification result, and at least one identifier in the sample point identification result does not exist in the sample point selection result, it is determined that multiple selection anomaly and missed selection anomaly occur, and third prompt information is generated.

[0242] As an optional solution, the detection control module 406 further includes a second control submodule. The second control submodule is used to:

[0243] When the sample point identification result and the sample point selection result are consistent, during the sample detection process for any target sample point, the sample detection is performed on a specific site in the target sample point;

[0244] When the sample point identification result and the sample point selection result are inconsistent and the user selects the sample point identification result, during the sample detection process for any target sample point, the sample detection is performed on a specific site in the target sample point;

[0245] In the case that the sample point identification result and the sample point selection result are inconsistent, and the user selects the sample point selection result, in the process of performing sample detection on any target sample point, the type of the target sample point is determined, wherein if the identifier of the target sample point exists in the sample point identification result, the target sample point is of the first type, and if the identifier of the target sample point only exists in the sample point selection result, the target sample point is of the second type; in the case that the target sample point is of the first type, sample detection is performed on a specific site in the target sample point; in the case that the target sample point is of the second type, sample detection is performed on multiple sites in the target sample point.

[0246] As an alternative, Figure 4 and Figure 5 On the basis of the control device shown, a crystallization site identification module is further provided. The crystallization site identification module is used to: determine the effective crystallization site in the target point when the identification result of the target point is that there is sample crystallization. Accordingly, the first processing module 405 and the second processing module 408 perform sample detection on a specific site in the target sample point, specifically: perform sample detection on the effective crystallization site in the target sample point.

[0247] As an optional solution, based on the control device disclosed above in the present application, a detection result evaluation module is further provided.

[0248] The detection result evaluation module is used to: for each target sample point, determine whether the detection result of the target sample point meets the preset requirements; if the detection result of the target sample point meets the preset requirements, determine the detection result as the target sample point's qualified detection result; if the detection result of the target sample point does not meet the preset requirements, trigger the detection control module 406 to perform sample detection on the target sample point again, until the detection result of the target sample point meets the preset requirements, determine the detection result that meets the preset requirements as the target sample point's qualified detection result, or, until the preset number of detections is reached, determine the detection result with the best quality as the target sample point's qualified detection result. Correspondingly, the report generation module 407 generates a detection report based on the detection results of each target sample point, specifically: generates a detection report based on the qualified detection results of each target sample point.

[0249] As an optional solution, the sample point identification module 404 includes:

[0250] An image conversion submodule, used to convert the image of the target into a grayscale image;

[0251] Denoising submodule, used to perform denoising on grayscale images;

[0252] The gradient detection submodule is used to perform gradient detection on the denoised grayscale image to obtain a gradient amplitude image;

[0253] A binarization processing submodule, used to perform binarization processing on the gradient amplitude image, wherein when the value of a pixel point in the gradient amplitude image is greater than a preset first threshold, the value of the pixel point is set to a first value, and when the value of a pixel point in the gradient amplitude image is less than or equal to the first threshold, the value of the pixel point is set to a second value;

[0254] A pixel number determination submodule is used to determine the number of target pixel points in the binary image, wherein the value of the target pixel point is a first value;

[0255] The target recognition submodule is used to determine whether there is sample crystallization at the target based on the number of target pixels.

[0256] As an optional solution, the target recognition submodule includes: a ratio determination unit, which is used to determine the ratio of target pixels in the binary image based on the number of target pixels and the number of pixels contained in the binary image; a target recognition unit, which is used to determine that sample crystallization exists at the target when the ratio is greater than a preset second threshold, and to determine that sample crystallization does not exist at the target when the ratio is less than or equal to the second threshold.

[0257] As an optional solution, the image conversion submodule is specifically used to: for each pixel in the target image, determine the grayscale value of the pixel according to the value of the red channel, the value of the green channel and the value of the blue channel of the pixel to generate a grayscale image.

[0258] As an optional solution, the denoising submodule is specifically used to perform Gaussian filtering on the grayscale image.

[0259] As an optional solution, the gradient detection submodule includes: a horizontal gradient determination unit, which is used to determine the gradient value of each pixel in the denoised grayscale image in the horizontal direction; a vertical gradient determination unit, which is used to determine the gradient value of each pixel in the denoised grayscale image in the vertical direction; and a gradient merging unit, which is used to merge the gradient value of the pixel in the horizontal direction and the gradient value in the vertical direction for each pixel in the denoised grayscale image to obtain the gradient amplitude of the pixel so as to form a gradient amplitude image.

[0260] As an optional solution, the horizontal gradient determination unit is specifically used to: perform a convolution operation on the Sobel operator in the horizontal direction and the denoised grayscale image to obtain the gradient value of each pixel in the denoised grayscale image in the horizontal direction.

[0261] As an optional solution, the vertical gradient determination unit is specifically used to: perform a convolution operation on the Sobel operator in the vertical direction and the denoised grayscale image to obtain the gradient value of each pixel in the denoised grayscale image in the vertical direction.

[0262] As an optional solution, the gradient merging unit is specifically used to: According to the formula Calculate the gradient amplitude of the pixel; where G x is the gradient value of the pixel in the horizontal direction, G y is the gradient value of the pixel in the vertical direction, and G is the gradient amplitude of the pixel.

[0263] In the sample point recognition module disclosed above in the present application, when a sample is placed at a target point, the image of the target point includes an image formed by colonies in the sample. The image formed by the colonies is significantly different from the images of other areas. In the binary image obtained by processing the image of the target point, there will be more target pixels. Based on the number of target pixels, it is possible to accurately determine whether there are colonies at the target point, thereby accurately identifying whether there are sample crystals at the target point.

[0264] The application also discloses a mass spectrometer.

[0265] The mass spectrometer disclosed in the present application comprises an ion source, a mass analyzer and a control device, wherein the ion source and the mass analyzer adopt existing structures, and the control device is used to implement any one of the control methods disclosed in the present application.

[0266] The present application also discloses a readable storage medium, which carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device implements any control method disclosed in the embodiments of the present application.

[0267] The present application also discloses a computer program product including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements any one of the control methods provided in the embodiments of the present application.

[0268] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. The technical features in each embodiment can be arranged and combined to form a new embodiment. For the control device, mass spectrometer, readable storage medium and computer program product of the mass spectrometer disclosed in the embodiment, since it corresponds to the control method of the mass spectrometer disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0269] In addition, it should be noted that the device embodiments described above are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, and may be located in one place or distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines.

[0270] Through the description of the above implementation methods, the technicians in the relevant field can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course, it can also be implemented by special hardware including special integrated circuits, special CPUs, special memories, special components, etc. In general, all functions performed by computer programs can be easily implemented with corresponding hardware, and the specific hardware structures used to implement the same function can also be various, such as analog circuits, digital circuits or special circuits. However, for the present application, software program implementation is a better implementation method in more cases. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, a U disk, a mobile hard disk, a ROM, a RAM, a disk or an optical disk, etc., including a number of instructions to enable a computer device to execute the methods described in each embodiment of the present application.

[0271] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0272] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a training device, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, training device, or data center. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device, a data center, etc. that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

Claims

1. A method for controlling a mass spectrometer, characterized in that: include: When the target entry conditions are met, the target plate is moved from the injection port into the vacuum chamber; Starting vacuum treatment of the vacuum chamber; Obtaining images of each target point in the target plate; Analyzing the images of the respective target points to identify whether sample crystals exist at the respective target points, and generating sample point identification results, wherein the sample point identification results include identifications of the sample points, wherein target points with identification results indicating that sample crystals exist are determined as sample points; In the case of the automatic proofing mode, determining the identifier in the sample point recognition result as the identifier of the target sample point; When the vacuum degree of the vacuum chamber reaches a preset vacuum degree threshold, performing sample detection on each target sample point to obtain a detection result of each target sample point; Generate a test report based on the test results of each target sample point; Wherein, each step in the control method is performed in an automated manner.

2. The control method according to claim 1, characterized in that: Also includes: In the case of manual proofing mode, obtaining a sample point selection result, wherein the sample point selection result includes an identifier of a sample point selected by a user; comparing the sample point identification result and the sample point selection result; When the sample point recognition result and the sample point selection result are consistent, determining the identifier in the sample point selection result as the identifier of the target sample point; When the sample point recognition result and the sample point selection result are inconsistent, generating prompt information and outputting the prompt information; When the user selects the sample point selection result, or when the user does not perform a selection operation within a predetermined time, determining an identifier in the sample point selection result as an identifier of a target sample point; When the user selects the sample point recognition result, the identifier in the sample point recognition result is determined as the identifier of the target sample point.

3. The control method according to claim 2, characterized in that: When the sample point recognition result and the sample point selection result are inconsistent, generating prompt information includes: When the sample point selection result includes all identifiers in the sample point identification result, and at least one identifier in the sample point selection result does not exist in the sample point identification result, determining that a multiple selection exception occurs, and generating first prompt information; When the sample point identification result includes all identifiers in the sample point selection result, and at least one identifier in the sample point identification result does not exist in the sample point selection result, determining that an omission exception occurs, and generating second prompt information; When at least one identifier in the sample point selection result does not exist in the sample point identification result, and when at least one identifier in the sample point identification result does not exist in the sample point selection result, it is determined that a multiple selection anomaly and a missed selection anomaly occur, and a third prompt information is generated.

4. The control method according to claim 2, characterized in that: In the manual proofing mode, sample detection is performed on each target sample point respectively, including: When the sample point identification result and the sample point selection result are consistent, during the sample detection process for any target sample point, sample detection is performed on a specific site in the target sample point; When the sample point identification result and the sample point selection result are inconsistent and the user selects the sample point identification result, during the sample detection process for any target sample point, sample detection is performed on a specific site in the target sample point; In the case that the sample point identification result and the sample point selection result are inconsistent, and the user selects the sample point selection result, in the process of performing sample detection on any target sample point, the type of the target sample point is determined, wherein if the identifier of the target sample point exists in the sample point identification result, the target sample point is of the first type, and if the identifier of the target sample point only exists in the sample point selection result, the target sample point is of the second type; in the case that the target sample point is of the first type, sample detection is performed on a specific site in the target sample point; in the case that the target sample point is of the second type, sample detection is performed on multiple sites in the target sample point.

5. The control method according to claim 1, characterized in that: In the automatic proofing mode, sample detection is performed on each target sample point respectively, including: During the sample detection process for any target sample point, sample detection is performed on a specific site in the target sample point.

6. The control method according to claim 4 or 5, characterized in that: Also includes: When the identification result of the target is that sample crystals exist, determining effective crystallization sites in the target; The performing sample detection on a specific site in the target sample site includes: performing sample detection on an effective crystallization site in the target sample site.

7. The control method according to claim 1, characterized in that: Also includes: For each target sample point, determining whether the detection result of the target sample point meets the preset requirements; If the detection result of the target sample point meets the preset requirement, the detection result is determined as the qualified detection result of the target sample point; If the detection result of the target sample point does not meet the preset requirement, the target sample point is tested again until the detection result of the target sample point meets the preset requirement, and the detection result that meets the preset requirement is determined as the qualified detection result of the target sample point, or until the preset number of detections is reached, the detection result with the best quality is determined as the qualified detection result of the target sample point.

8. The control method according to claim 1, characterized in that: The images of each target are analyzed to identify whether sample crystals exist at the target, including: Converting the image of the target into a grayscale image; Performing denoising on the grayscale image; Perform gradient detection on the denoised grayscale image to obtain a gradient amplitude image; Binarization is performed on the gradient amplitude image, wherein when the value of a pixel point in the gradient amplitude image is greater than a preset first threshold, the value of the pixel point is set to a first value, and when the value of the pixel point in the gradient amplitude image is less than or equal to the first threshold, the value of the pixel point is set to a second value; Determine the number of target pixel points in the binary image, wherein the value of the target pixel point is the first value; It is determined whether sample crystallization exists at the target point based on the number of the target pixel points.

9. A control device for a mass spectrometer, characterized in that: include: A target feeding control module is used to move the target plate from the injection port to the vacuum chamber when the target feeding conditions are met; A vacuum starting module, used for starting vacuum processing of the vacuum chamber; A target image acquisition module, used to obtain images of each target in the target plate; A sample point identification module is used to analyze the image of each target point to identify whether there is sample crystallization at each target point, and generate a sample point identification result, wherein the sample point identification result includes an identifier of the sample point, wherein a target point with an identification result of the presence of sample crystallization is determined as a sample point; A first processing module, configured to determine, in an automatic proofing mode, an identifier in the sample point recognition result as an identifier of a target sample point; A detection control module, used for performing sample detection on each target sample point respectively to obtain the detection result of each target sample point when the vacuum degree of the vacuum chamber reaches a preset vacuum degree threshold; The report generation module is used to generate a test report based on the test results of each target sample point.

10. A mass spectrometer, comprising an ion source, a mass analyzer and a control device, characterized in that: The control device is used to implement the control method as described in any one of claims 1 to 8.

11. A readable storage medium, characterized in that: The readable storage medium carries one or more computer programs, and when the one or more computer programs are executed by an electronic device, the electronic device implements the control method as claimed in any one of claims 1 to 8.

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