Mass spectrometry automated sample making positioning method and system

Abstract

This invention discloses an automated sample preparation and positioning method and system for mass spectrometry, relating to the field of mass spectrometry sample preparation technology. The method includes: acquiring substrate image data and identifying reference marker points; calibrating the coordinates of the reference marker points to generate substrate coordinate data; based on the sample preparation positioning map, employing a dual-domain offset determination method to analyze the spatial offset difference between the theoretical and actual positions, and calculating the offset direction and degree of each point to generate positioning offset data; using a hotspot back-calculation method to perform local mass spectrometry probing on the positioning offset data to obtain intensity distribution data, determining the effective response center based on the intensity distribution data, and generating feedback positioning data; performing path repositioning on the feedback positioning data to obtain a compensation path sequence, and synchronously correcting and adjusting the sample preparation path and detection execution position in the compensation path sequence to generate a sample preparation positioning scheme. This improves positioning accuracy and enhances dynamic adaptability.

Description

Technical Field

[0001] This invention relates to the field of mass spectrometry sample preparation technology, and in particular to an automated mass spectrometry sample preparation and positioning method and system. Background Technology

[0002] With the widespread application of mass spectrometry in proteomics, metabolomics, and high-throughput biodetection, the accuracy of sample preparation and site deposition has gradually become a key factor affecting the stability and repeatability of detection results. In existing methods, automated mass spectrometry sample preparation usually relies on a set coordinate template or a site control method based on mechanical positioning. By dividing the substrate carrier into a regular grid and performing sample liquid deposition according to a predetermined path, the automated processing of batch samples is completed. Some methods further combine machine vision methods to acquire substrate images and identify marker points to complete the preliminary calibration of the substrate coordinate system, thereby improving the consistency of sample preparation positions.

[0003] Traditional methods often focus on static path planning and one-time offset correction based on the initial coordinate system. They lack fine-grained characterization and closed-loop feedback mechanisms for the dynamic offset relationship between the actual response position and the theoretical position after sample deposition. Especially when there are local response inhomogeneities, signal drift, or microscale positional deviations, it is difficult to achieve accurate correction through a single spatial domain information. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an automatic mass spectrometry sample preparation and positioning method that solves the problems of insufficient dynamic offset relationship characterization and insufficient positioning correction accuracy.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides an automatic sample preparation and positioning method for mass spectrometry, comprising: acquiring substrate image data and identifying reference marker points; calibrating the coordinates of the reference marker points to generate substrate coordinate data; performing spatial layout calculations on the substrate coordinate data to obtain target point data; and performing path planning on the target point data to generate a sample preparation path sequence; performing point-by-point sample deposition according to the sample preparation path sequence, and simultaneously acquiring images of the deposition area; performing center identification on the deposition area images to generate a sample preparation positioning map; based on the sample preparation positioning map, employing a dual-domain offset determination method to analyze the spatial offset difference between the theoretical position and the actual position, and calculating the offset direction and degree of each point to generate positioning offset data; performing local mass spectrometry probing on the positioning offset data using a hotspot back-inference method to obtain intensity distribution data; determining the effective response center based on the intensity distribution data to generate feedback positioning data; performing path repositioning on the feedback positioning data to obtain a compensation path sequence; and synchronously correcting and adjusting the sample preparation path and detection execution position in the compensation path sequence to generate a sample preparation positioning scheme.

[0007] As a preferred embodiment of the mass spectrometry automatic sample positioning method of the present invention, the specific steps of acquiring substrate image data and identifying reference marker points, calibrating the coordinates of the reference marker points, and generating substrate coordinate data are as follows: Gray-level unification and noise suppression are performed on the base image data to obtain preprocessed image data. Then, gray-level abrupt change regions are highlighted on the preprocessed image data to expand the contour expression and generate labeled contour data. Filter and match the shape constraints along the connected regions of the marked contour data to obtain reference marker candidate data, and determine the center position of each reference marker candidate data to generate reference marker points; Based on the reference marker points, we establish spatial-physical mapping data, perform proportional conversion and coordinate unification on the spatial-physical mapping data, obtain unified coordinate data, and perform overall coordinate alignment on the unified coordinate data to generate base coordinate data.

[0008] As a preferred embodiment of the mass spectrometry automatic sample positioning method of the present invention, the specific steps for performing spatial layout calculations on the base coordinate data to obtain target point data are as follows: The base coordinate data is divided into regions and arranged in order according to spatial location to obtain an ordered coordinate set. The relative spacing and adjacency relationship of each position are extracted from the ordered coordinate set to generate spatial correlation data. The spatial correlation data is constrained and laid out according to rules to obtain a set of target points. The target points are then uniformly numbered and sequentially marked to generate target point data.

[0009] As a preferred embodiment of the mass spectrometry automatic sample location method of the present invention, the specific steps of performing path planning on the target point data to generate a sample path sequence are as follows: Establish path connection relationships based on target point data, and expand and organize the initial path data according to the path connection relationships to obtain path sequence data. Then, perform continuity correction on the path sequence data to generate path connection data. The path connection data is rearranged as a whole and adjusted without cross constraints to generate a sample path sequence.

[0010] As a preferred embodiment of the automated mass spectrometry sample localization method of the present invention, the specific steps of performing point-by-point sample deposition according to the sample path sequence and simultaneously acquiring images of the deposition area are as follows: Based on the sampling path sequence, the execution location and execution order of each target point are determined one by one, and deposition scheduling data is generated; Based on the sedimentation scheduling data, point-by-point sample liquid deposition is performed at each target location to obtain sedimentation state data, and corresponding images of the sedimentation state data are acquired synchronously to generate sedimentation area images.

[0011] As a preferred embodiment of the automatic mass spectrometry sample localization method of the present invention, the specific steps of center identification of the deposition area image and generation of the sample localization map are as follows: The depositional regions in the depositional region image are located and analyzed item by item. The location of the response concentration is determined in each depositional region, the depositional center data is obtained, and the actual depositional location of each target point is uniformly mapped according to the depositional center data to generate location mapping data. Based on the sampling path sequence, the location mapping data is sequentially integrated to generate a sampling location map.

[0012] As a preferred embodiment of the automatic sample positioning method for mass spectrometry described in this invention, the step of using a dual-domain offset determination method based on the sample positioning map to analyze the spatial offset difference between the theoretical and actual positions, and calculating the offset direction and degree of each point to generate positioning offset data, is as follows: The actual deposition location is extracted from the sampling location map, and the actual deposition location is combined with the target point data and matched with the numbers to generate theoretical and actual alignment data; Along the sampling path sequence, adjacent points are selected from the theoretical and actual alignment data as reference points, and the spacing directional characteristics between the current point and the reference point under the theoretical and actual positions are established. A dual-domain offset determination method is adopted to compare the center offset difference, the spacing offset difference, and the direction angle difference between the center position offset and the adjacent relationship offset in the spacing direction feature to obtain offset difference data. The offset difference data is continuously merged to generate offset determination data. The degree of offset in the offset determination data is expressed in a hierarchical manner to obtain offset level data. The offset level data is combined with the spacing direction features to determine the offset direction of each point and generate offset description data. The offset direction and degree of the offset description data are sequentially integrated and continuously merged to generate positioning offset data.

[0013] As a preferred embodiment of the automatic mass spectrometry sample localization method of the present invention, the step of performing local mass spectrometry probing on the localization offset data through the hotspot back-inference method to obtain intensity distribution data, determining the effective response center based on the intensity distribution data, and generating feedback localization data, is as follows: By using the hotspot reverse inference method, the offset feature data of each point is read item by item from the positioning offset data, and the offset feature data is extended in the offset direction to generate directional extension data. The probe direction is determined based on the directional extension data, and the degree of offset of the determined probe direction is converted to determine the probe range and generate probe guidance data. Based on the trial guidance data, a local mass spectrometry trial area is constructed around each point, and trial sampling positions are set up in the local mass spectrometry trial area in a ring-domain progression manner from the inside to the outside to generate trial response data. The response intensity at each sampling location in the trial response data is spatially ordered to obtain intensity distribution data. Based on the intensity distribution data, the intensity adjacency feature data of each sampling location is back-analyzed to generate hotspot candidate data. The spatial location of each sampling location within the candidate area in the hot spot candidate data is centrally located to determine the effective response center and generate feedback positioning data.

[0014] As a preferred embodiment of the mass spectrometry automatic sample positioning method of the present invention, the specific steps of relocating the feedback positioning data to obtain a compensation path sequence, and synchronously correcting and adjusting the sample positioning path and the detection execution position in the compensation path sequence to generate a sample positioning scheme are as follows: The feedback positioning data is read sequentially according to the sampling path sequence to obtain sequential positioning data. The spatial correspondence between the effective response center position and the corresponding target point position of each point is extracted from the sequential positioning data to generate position correction data. The location correction data is replaced and the location is reconstructed item by item to obtain path relocation data, and the connection order relationship is established based on the path relocation data; Based on the connection order relationship, the access order of each point is checked for continuity and rearranged to generate a compensation path sequence. The sampling paths in the compensation path sequence are matched one-to-one with the detection execution positions to obtain the corresponding data, and the corresponding data is corrected to generate the position correction execution data. The position correction execution data is synchronously adjusted and uniformly expressed to generate a sample positioning scheme.

[0015] Secondly, the present invention provides an automated mass spectrometry sample preparation and positioning system, comprising: The marker calibration module is used to acquire base image data and identify reference marker points, calibrate the coordinates of the reference marker points, and generate base coordinate data; The layout planning module is used to perform spatial layout calculations on the base coordinate data, obtain target point data, and perform path planning on the target point data to generate a sampling path sequence. The path deposition module is used to perform point-by-point sample liquid deposition according to the sample preparation path sequence, and simultaneously acquire images of the deposition area, perform center identification on the deposition area images, and generate a sample preparation location map. The offset determination module is used to analyze the spatial offset difference between the theoretical position and the actual position based on the sampled positioning map and the dual-domain offset determination method, and to calculate the offset direction and degree of each point to generate positioning offset data. The hotspot probing module is used to perform local mass spectrometry probing on the positioning offset data using the hotspot back-inference method, obtain intensity distribution data, determine the effective response center based on the intensity distribution data, and generate feedback positioning data. The path correction module is used to relocate the path of the feedback positioning data, obtain the compensation path sequence, and synchronously correct and adjust the sampling path and detection execution position in the compensation path sequence to generate a sampling positioning scheme.

[0016] The beneficial effects of this invention are as follows: a unified coordinate benchmark is established through substrate image calibration, and an orderly sampling path is generated by combining spatial layout calculation and path planning. During execution, the actual deposition location is obtained through deposition image recognition and a sampling positioning map is constructed. Furthermore, a dual-domain offset determination method is used to perform multi-dimensional analysis on the offset between the theoretical and actual locations to complete a fine offset characterization. The effective response center is determined by combining the hotspot back-inference method with the mass spectrometry intensity distribution to complete the positioning correction. A closed-loop adjustment is formed through path repositioning and synchronous correction, so that the sampling path continuously matches the actual response location, thereby improving the positioning accuracy and enhancing the dynamic adaptability. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of an automated sample localization method for mass spectrometry.

[0019] Figure 2 A schematic diagram of an automated sample preparation and positioning system for mass spectrometry.

[0020] Figure 3 Flowchart of the method for generating base coordinate data.

[0021] Figure 4 This is a flowchart of the dual-domain offset determination method. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides an automated mass spectrometry sample localization method, comprising the following steps: S1. Acquire base image data and identify reference marker points, calibrate the coordinates of the reference marker points, and generate base coordinate data.

[0026] S1.1 Perform grayscale unification and noise suppression on the base image data to obtain preprocessed image data, and highlight grayscale abrupt change areas on the preprocessed image data to expand contour expression and generate labeled contour data.

[0027] It should be noted that the gray values ​​at each location in the base image data are converted according to the brightness distribution to ensure that the gray value expression of different regions is consistent, thus obtaining preprocessed image data. The gray value difference and continuous change of adjacent locations in the preprocessed image data are compared point by point to weaken the scattered abrupt changes to complete noise suppression. Then, the gray value jump intensity of each location in the preprocessed image data is calculated, and the locations with continuous concentration of jump intensity are connected to highlight the gray value abrupt change areas to expand the contour expression and generate marked contour data.

[0028] S1.2. Filter and match the shape constraints along the connected regions of the marker contour data to obtain candidate reference marker data, and determine the center position of each candidate reference marker to generate reference marker points.

[0029] It should be noted that the contour structure features of each connected region in the marked contour data are read item by item, connected regions with abnormal area are removed, and the remaining connected regions are compared with the morphological constraints one by one. The morphological constraints are matched according to the shape ratio, contour integrity and central clustering to obtain reference marker candidate data. Symmetry analysis is performed on the boundary position of each candidate region in the reference marker candidate data. By the distance distribution from each boundary position to the internal clustering position of the region, the position with the most balanced distance distribution is determined as the center position, and the reference marker point is generated.

[0030] S1.3. Based on the reference marker points, establish the empty-real mapping data, perform proportional conversion and coordinate unification on the empty-real mapping data, obtain unified coordinate data, and perform overall coordinate alignment on the unified coordinate data to generate the base coordinate data.

[0031] It should be noted that a one-to-one correspondence between the image position and the actual position is established based on the positional order and mutual spacing of each point in the reference marker, generating spatial-physical mapping data; the spacing ratio of each corresponding position in the spatial-physical mapping data is calculated item by item, the coordinate values ​​under the image position are converted to a unified scale according to the corresponding ratio, and the horizontal and vertical positions are uniformly expressed according to the same coordinate reference to obtain unified coordinate data; the positional deviation of each reference marker in the unified coordinate data is compared to determine the overall offset direction and rotation relationship, and all positions are synchronously translated and rotated according to the determined unified direction to generate base coordinate data.

[0032] S2. Perform spatial layout calculations on the base coordinate data to obtain target point data, and perform path planning on the target point data to generate a sampling path sequence.

[0033] S2.1 Divide and arrange the base coordinate data into regions according to spatial location to obtain an ordered coordinate set, and extract the relative spacing and adjacency relationship of each position from the ordered coordinate set to generate spatial correlation data.

[0034] It should be noted that the horizontal and vertical distribution of each coordinate position in the base coordinate data is partitioned and arranged according to spatial location. Coordinate positions within similar ranges are grouped into the same area and arranged sequentially from top to bottom and from left to right to obtain an ordered coordinate set. Adjacent coordinate positions are read item by item from the ordered coordinate set, the horizontal and vertical intervals and positional order between the preceding and following coordinate positions are calculated, and the proximity connection between each coordinate position within the same area and across areas is determined to generate spatial correlation data.

[0035] S2.2. Perform rule constraints and layout expansion on the spatial correlation data to obtain the target point set, and uniformly number and sequentially mark the target point set to generate target point data.

[0036] It should be noted that the relative spacing, adjacency relationship, and regional order of each location in the spatial correlation data are read item by item. Locations with coordinated adjacent spacing, continuous adjacency relationship, and consistent arrangement direction are retained as locations, while locations with abnormal spacing, broken adjacency, or conflicting order are removed or adjusted to complete the rule constraints. Then, the layout is expanded according to the regional distribution and order relationship of the retained locations to obtain the target point set. The target point set is uniformly numbered according to the predetermined arrangement order and a corresponding sequence mark is attached to generate the target point data.

[0037] S2.3. Establish path connection relationships based on target point data, and expand and organize the initial path data according to the path connection relationships to obtain path sequence data. Perform continuity correction on the path sequence data to generate path connection data.

[0038] It should be noted that, based on the numbering order, spatial location, and adjacency of each target point in the target point data, the preceding and following connection positions are determined one by one to establish path connection relationships; the connection order between each target point is unfolded sequentially according to the path connection relationships, the connection process from the starting position to the ending position is arranged in series, and the forward direction of each connection segment is uniformly organized to obtain path sequence data; the positions of interruption of preceding and following connections, sequence jumps, and direction reversals in the path sequence data are checked one by one, and discontinuous positions are reconnected and corrected according to adjacent connection relationships to generate path connection data.

[0039] S2.4. Perform overall rearrangement and adjustment of the path connection data without cross constraints to generate a sample path sequence.

[0040] It should be noted that the start and end positions, connection order and turning relationship of each connection segment in the path connection data are read as a whole. The spatial crossing between adjacent connection segments are compared item by item. The order or turning direction of connection segments with cross risk are adjusted. All the adjusted connection segments are continuously connected and rearranged as a whole to generate a sample path sequence.

[0041] It should also be noted that adjustment without cross-constraints refers to comparing the spatial orientation of each connecting segment in the path connection relationship item by item. By judging whether there is a path intersection or crossing relationship between adjacent or cross-segment connections, the connecting segments with intersection trends are rearranged in order or their directions are adjusted so that the path remains continuous and does not have intersection conflicts during the spatial unfolding process.

[0042] The sampling path sequence is an ordered execution path formed based on the spatial distribution and connection relationship of the target points. It is used to guide the point-by-point operation sequence and movement trajectory of sample liquid deposition. The sampling path sequence can complete the continuous connection and path optimization of the deposition process, reduce invalid movement and path conflict, thereby improving sampling efficiency and ensuring positioning consistency.

[0043] S3. Perform point-by-point sample liquid deposition according to the sample preparation path sequence, and simultaneously acquire images of the deposition area. Perform center identification on the deposition area images to generate a sample preparation location map.

[0044] S3.1. Determine the execution location and execution order of each target point according to the sampling path sequence, and generate sedimentation scheduling data.

[0045] It should be noted that, based on the sequential positions of each target point in the sampling path sequence, the arrival position and execution order of each target point in the entire path are determined item by item, and the execution position, execution order and connection relationship of each target point are organized accordingly to generate deposition scheduling data.

[0046] It should also be noted that the sedimentation scheduling data is used to describe the execution location, execution order, and sequential relationship of each target point, in order to guide the specific execution arrangement of the sample sedimentation process. Through the sedimentation scheduling data, each point can be operated in an orderly manner according to a predetermined order, ensuring the continuity and location matching of the sedimentation process, thereby improving the stability and consistency of the sample preparation process.

[0047] S3.2. Based on the sedimentation scheduling data, perform point-by-point sample liquid deposition at each target point to obtain sedimentation state data, and simultaneously acquire images of the corresponding areas in the sedimentation state data to generate sedimentation area images.

[0048] It should be noted that, based on the execution location and execution order of each target point in the sedimentation scheduling data, the sample liquid sedimentation actions are sequentially mapped to each target point, and the sedimentation time, sedimentation location, and sedimentation completion status of each target point are recorded to obtain sedimentation state data. From the sedimentation state data, the corresponding areas of each target point are extracted one by one, and the image content of the corresponding areas is synchronously collected according to the sedimentation time. The image content of each corresponding area is then associated and organized with the sedimentation location to generate a sedimentation area image.

[0049] S3.3 Perform item-by-item location analysis on each sedimentary region in the sedimentary region image, determine the response concentration location in each sedimentary region, obtain sedimentary center data, and uniformly map the actual sedimentary location of each target point based on the sedimentary center data to generate location mapping data.

[0050] It should be noted that the boundary range, grayscale distribution, and location clustering of each sedimentary region in the sedimentary region image are read item by item. The grayscale changes and regional shrinkage trends at each location are compared in an outward-to-inward manner to determine the region with the highest grayscale concentration and location clustering. Within the corresponding region, the distance distribution from each location to the surrounding boundary and the grayscale concentration centroid are calculated to determine the response concentration location and obtain the sedimentation center data. The sedimentation center data and target point data are matched item by item in the execution order, and the actual sedimentation location of each target point is uniformly mapped and associated with a number to generate location mapping data.

[0051] S3.4. Based on the sampling path sequence, sequentially integrate the location mapping data to generate a sampling positioning map.

[0052] It should be noted that, according to the order of each target point in the sampling path sequence, the actual deposition locations in the location mapping data are read and arranged item by item. The sequence identifier, actual deposition location and connection relationship of each target point are continuously integrated according to the path order, and the connection relationship between adjacent locations is uniformly expressed to form a sampling location map that includes the path order and the correspondence between actual locations.

[0053] S4. Based on the sample positioning map, the dual-domain offset determination method is adopted to analyze the spatial offset difference between the theoretical position and the actual position, and to calculate the offset direction and degree of each point to generate positioning offset data.

[0054] S4.1 Extract the actual deposition location from the sampling location map, combine the actual deposition location with the target point data and match them by number to generate theoretical and actual alignment data.

[0055] It should be noted that the actual depositional locations corresponding to each target point are read sequentially from the sampling location map according to the path, and the coordinate representation, sequence identifier, and connection relationship of the actual depositional locations are extracted; the actual depositional locations are compared one-to-one with the target point numbers, target point positions, and arrangement order in the target point data, and the numbering is matched according to the same sequence identifier and corresponding position relationship; the matched actual depositional locations and target point positions are uniformly organized according to the same number to generate theoretical and practical alignment data.

[0056] S4.2. Select adjacent points from the theoretical and actual alignment data one by one along the sampling path sequence as reference points, and establish the distance direction characteristics between the current point and the reference point in the theoretical and actual positions.

[0057] It should be noted that, following the sequential order of the sampling path sequence, the current point and its directly connected adjacent points are read item by item from the theoretical and actual alignment data, and the adjacent points are selected as reference points. The lateral, longitudinal, and connecting directions of the current point and the reference point in the theoretical position are calculated respectively, and the lateral, longitudinal, and connecting directions of the current point and the reference point in the actual position are also calculated. The corresponding intervals and directions in the theoretical and actual positions are grouped and organized to establish the spacing and direction characteristics of the current point and the reference point in the theoretical and actual positions.

[0058] S4.3. Using a dual-domain offset determination method, the center position offset and the adjacent relationship offset in the spacing direction feature are compared by center offset difference, spacing offset difference and direction angle difference to obtain offset difference data. The offset difference data are continuously merged to generate offset determination data.

[0059] It should be noted that a dual-domain offset determination method is adopted. The corresponding positional relationships between the current point and the reference point in the spacing direction feature are compared item by item under both theoretical and actual positions. The positional difference between the theoretical and actual positions of the current point is calculated, as is the positional difference between the theoretical and actual positions of the reference point. These two are then used to form the center position offset and the adjacency offset. The center position offset is compared using center offset difference calculations. The corresponding spacing between the current point and the reference point under both theoretical and actual positions is compared using spacing offset difference calculations. The angle between the theoretical and actual connecting directions is also compared using directional angle difference calculations to obtain offset difference data. Following the sequential order in the sampling path sequence, the offset difference data of adjacent points that change continuously and exhibit consistent offset behavior are sequentially spliced ​​and continuously merged to generate offset determination data.

[0060] It should also be noted that the dual-domain offset determination method performs corresponding calculations for the same set of points in two domains: theoretical position and actual position. By establishing positional correspondence point by point, the difference in center position, the difference in distance between adjacent points, and the difference in the angle of the connecting line direction are calculated separately. The three types of differences are synchronously compared and jointly compared, and then the continuous change characteristics are integrated along the path sequence, thereby achieving a comprehensive determination of the offset direction and offset degree.

[0061] Offset determination data is used to describe the direction, degree, and continuity of the offset between the theoretical and actual positions of each point, and to characterize the deviation of a single point and the changes in adjacent relationships. Offset determination data can be used to achieve a unified expression of the overall offset distribution, providing a basis for subsequent offset analysis and position correction, thereby improving the accuracy and consistency of positioning adjustments.

[0062] The expression for calculating the position difference between the theoretical and actual positions of the current point is: ; in, This represents the positional difference between the theoretical and actual positions of the current location. This represents the horizontal coordinate value of the current point in its theoretical position. This represents the vertical coordinate value of the current point at its theoretical location. This represents the horizontal coordinate of the current point in its actual location. This represents the vertical coordinate value of the current point in its actual location. This is the current location's index. These are the coordinate values ​​of the actual location; These are the coordinate values ​​at the theoretical position.

[0063] S4.4. The degree of offset in the offset determination data is expressed in a hierarchical manner, offset level data is obtained, the offset level data is combined with the spacing direction feature, the offset direction of each point is determined, and offset description data is generated.

[0064] It should be noted that the center offset difference, spacing offset difference, and direction angle difference corresponding to each point in the offset determination data are read item by item, and the data are segmented and classified according to the distribution interval of the offset amplitude from low to high. The offset performance in the same interval is assigned the same level expression to obtain offset level data. The offset level data is matched with the theoretical connection direction, actual connection direction, and adjacent position relationship in the spacing direction feature item by item. The offset direction of each point is determined by the deflection direction of the theoretical connection direction and the actual connection direction. The offset direction and offset level data are then uniformly organized to generate offset description data.

[0065] S4.5. Sequentially integrate and continuously merge the offset direction and degree of the offset description data to generate positioning offset data.

[0066] It should be noted that the offset direction and degree of each point in the offset description data are read item by item in the order of the sampling path sequence, and the content with the same offset direction, similar offset degree and continuous change in adjacent points is integrated sequentially; the content after sequential integration is connected according to the path direction, the offset expressions that appear repeatedly in continuous segments are merged and sorted, and the starting position, ending position and continuous change relationship of each segment are retained to generate positioning offset data.

[0067] S5. Using the hotspot back-inference method, perform local mass spectrometry probing on the positioning offset data to obtain intensity distribution data. Based on the intensity distribution data, determine the effective response center and generate feedback positioning data.

[0068] S5.1 Using the hotspot reverse calculation method, the offset feature data of each point is read from the positioning offset data item by item, and the offset feature data is extended in the offset direction to generate directional extension data.

[0069] It should be noted that, using the hotspot back-inference method, the offset direction, offset degree, starting position, and continuous change relationship of each point are read item by item from the positioning offset data according to the sampling path sequence. The offset direction of each point is used as the extension benchmark, the offset degree of each point is used as the extension length, and the continuous change relationship of adjacent points is used to determine whether the extension direction is consistent. The position is gradually expanded outward from the starting position of each point along the corresponding offset direction. The position of each step is recorded sequentially, and the position range after the shift is unified with the corresponding offset direction to generate directional extension data.

[0070] It should also be noted that the hotspot back-inference method is used to reverse-infer and expand the range of locations where there may be effective responses based on the offset direction and degree in the positioning offset data. The hotspot back-inference method can transform offset information into spatial guidance for potential response areas, thereby narrowing the detection range and improving the positioning accuracy of the effective response center.

[0071] S5.2 Determine the test direction based on the directional extension data, calculate the degree of offset of the determined test direction, determine the test range, and generate test guidance data.

[0072] It should be noted that, based on the extension direction, extension length, and location expansion range corresponding to each point in the directional extension data, the direction with the consistent extension direction and the largest continuous expansion length is selected as the test direction; the degree of offset in the directional extension data is segmented and converted according to the position step size relationship, and the degree of offset is corresponding to the position level and outward expansion distance that need to be covered along the test direction. The larger the degree of offset, the more position levels and the longer the outward expansion distance, thus determining the test range; the test direction, position level, and outward expansion distance are organized according to the point order to generate test guidance data.

[0073] S5.3 Based on the trial guidance data, construct a local mass spectrometry trial area around each point, and set up trial sampling positions in the local mass spectrometry trial area in a ring-domain progression manner from the inside to the outside, and generate trial response data.

[0074] It should be noted that, based on the test direction, position level, and outward expansion distance corresponding to each point in the test guidance data, the position range of different outward expansion levels is gradually delineated along the test direction and surrounding adjacent directions, using the current position of each point as the center, to construct a local mass spectrometry test area; following a ring-domain progression method from the inside out, the positions of each layer within the local mass spectrometry test area are sequentially arranged, with the inner layer positions arranged first and the outer layer positions arranged later, and the spatial position and hierarchical order corresponding to each test sampling position are recorded item by item; the mass spectrometry response of each test sampling position is collected and sequentially organized to generate test response data.

[0075] It should also be noted that the local mass spectrometry test area is used to define an ordered and controllable detection range around the target point, so that the test sampling can be carried out layer by layer around the offset direction; through the local mass spectrometry test area, the potential response location can be partitioned and gradually approached, thereby improving the accuracy of the effective response center location and reducing the invalid detection range.

[0076] S5.4. Spatially organize the response intensity of each test sampling location in the test response data to obtain intensity distribution data. Based on the intensity distribution data, perform reverse analysis on the intensity adjacency feature data of each sampling location to generate hotspot candidate data.

[0077] It should be noted that the response intensity, spatial location, and hierarchical order of each test sampling location in the test response data are read item by item, arranged according to the ring domain order from the inside out and the adjacent position relationship in the same layer, and the response intensity of each test sampling location is matched with its corresponding spatial location to obtain intensity distribution data; based on the intensity distribution data, the response intensity level, intensity change direction, and intensity continuous aggregation between each sampling location and its adjacent sampling locations are compared item by item to identify areas where the response intensity continuously increases and is concentrated in adjacent locations, and back-analysis is performed along the intensity change direction to generate hotspot candidate data.

[0078] S5.5. The spatial location of each sampling location within the candidate area in the hot spot candidate data is centrally located to determine the effective response center and generate feedback positioning data.

[0079] It should be noted that the spatial location, response intensity, and adjacency clustering relationship of each sampling location within each candidate region in the hotspot candidate data are read item by item. Sampling locations with high response intensity and continuous and concentrated spatial distribution are selected. Then, the retained sampling locations are grouped according to spatial proximity, and the distance distribution from each sampling location to the candidate region boundary and surrounding sampling locations is compared to determine the location range with the highest degree of spatial clustering. Within the corresponding location range, the spatial location of each sampling location is centrally located to determine the effective response center. The sequence identifiers and positional relationships of the effective response center and the corresponding points are uniformly organized to generate feedback positioning data.

[0080] It should also be noted that the effective response center refers to the location within the test area where the response intensity is most concentrated and the spatial aggregation is highest, serving as the optimal location point for characterizing the actual detection signal.

[0081] S6. Perform path relocation on the feedback positioning data, obtain the compensation path sequence, and synchronously correct and adjust the sampling path and detection execution position in the compensation path sequence to generate a sampling positioning scheme.

[0082] S6.1. Read the feedback positioning data sequentially according to the sampling path sequence to obtain sequential positioning data, and extract the spatial correspondence between the effective response center position of each point and the corresponding target point position from the sequential positioning data to generate position correction data.

[0083] It should be noted that, according to the sequential order of each point in the sampling path sequence, the effective response center position, sequence identifier, and corresponding point relationship in the feedback positioning data are read and arranged item by item to obtain sequential positioning data; the effective response center position of each point is extracted from the sequential positioning data, and compared one-to-one with the target point position; the lateral position difference, longitudinal position difference, and relative offset direction between the effective response center position and the corresponding target point position are calculated to establish spatial correspondence; the spatial correspondence of each point is uniformly organized according to the sequence identifier to generate position correction data.

[0084] S6.2. Replace and reconstruct the position correction data item by item to obtain path relocation data, and establish the connection order relationship based on the path relocation data.

[0085] It should be noted that the effective response center position, target point position, and spatial correspondence of each point in the position correction data are read item by item. Each target point position is replaced according to the corresponding effective response center position, and the horizontal and vertical positions of each point after replacement are rearranged according to the spatial correspondence to complete the position reconstruction and obtain the path relocation data. Based on the reconstructed position distribution and sequence identifier of each point in the path relocation data, the connection sequence relationship and adjacent connection relationship between the preceding and following points are determined item by item, and the connection sequence relationship is established.

[0086] S6.3. Based on the connection order relationship, perform continuity verification and order rearrangement of the access order of each point to generate a compensation path sequence.

[0087] It should be noted that, based on the sequential relationships, adjacent connections, and sequence identifiers among the points in the connection sequence, the access order of each point is read item by item. It checks whether there are any connection interruptions, sequence jumps, or backtracking conflicts between the current point and the previous or next position, and marks the positions that do not meet the continuous connection requirement. The marked positions are then adjusted according to the adjacent connection relationship so that the previous position can continuously transition to the current point and the current point can continue to connect to the next position, thus completing the sequence rearrangement. Subsequently, all the rearranged access sequences are continuously connected and organized again to generate a compensation path sequence.

[0088] S6.4. Match the sampling path and the detection execution position in the compensation path sequence one by one, obtain the position corresponding data, perform position correction on the position corresponding data, and generate position correction execution data.

[0089] It should be noted that the path order, path position, and corresponding connection relationship of each point in the compensation path sequence are read item by item, and the sampling path of each point is matched one by one with the corresponding detection execution position according to the same order identification and spatial correspondence to obtain position correspondence data; the horizontal position difference, vertical position difference, and offset direction between the sampling path position and the detection execution position of each point in the position correspondence data are calculated item by item, and the sampling path position of each point is corrected according to the calculated corresponding difference to make the sampling path position of each point consistent with the detection execution position, and position correction execution data is generated.

[0090] S6.5. Synchronously adjust and unify the expression of the position correction execution data to generate a sample positioning scheme.

[0091] It should be noted that the corrected coordinates, path order, and detection execution position of each point in the position correction execution data are read item by item. First, the corrected coordinates and detection execution positions of each point are synchronously matched according to the same order. Through the position connection and sequence succession relationship between the points, the contents with position deviation or inconsistency in sequence are synchronously adjusted according to the corresponding relationship. Then, the positions, path order, and detection execution positions of each point after synchronous adjustment are centrally organized according to a unified coordinate expression method to generate a sample positioning scheme.

[0092] This embodiment also provides an automated mass spectrometry sample preparation and positioning system, including: The marker calibration module is used to acquire base image data and identify reference marker points, calibrate the coordinates of the reference marker points, and generate base coordinate data; The layout planning module is used to perform spatial layout calculations on the base coordinate data, obtain target point data, and perform path planning on the target point data to generate a sampling path sequence. The path deposition module is used to perform point-by-point sample liquid deposition according to the sample preparation path sequence, and simultaneously acquire images of the deposition area, perform center identification on the deposition area images, and generate a sample preparation location map. The offset determination module is used to analyze the spatial offset difference between the theoretical position and the actual position based on the sampled positioning map and the dual-domain offset determination method, and to calculate the offset direction and degree of each point to generate positioning offset data. The hotspot probing module is used to perform local mass spectrometry probing on the positioning offset data using the hotspot back-inference method, obtain intensity distribution data, determine the effective response center based on the intensity distribution data, and generate feedback positioning data. The path correction module is used to relocate the path of the feedback positioning data, obtain the compensation path sequence, and synchronously correct and adjust the sampling path and detection execution position in the compensation path sequence to generate a sampling positioning scheme.

[0093] In summary, this invention establishes a unified coordinate benchmark through substrate image calibration, generates an ordered sampling path by combining spatial layout calculation and path planning, obtains the actual deposition location through deposition image recognition and constructs a sampling positioning map during execution, further employs a dual-domain offset determination method to perform multi-dimensional analysis of the offset between the theoretical and actual locations to complete fine offset characterization, and determines the effective response center by combining the hotspot back-inference method with mass spectrometry intensity distribution to complete positioning correction, and forms a closed-loop adjustment through path repositioning and synchronous correction to ensure that the sampling path continuously matches the actual response location, thereby improving positioning accuracy and enhancing dynamic adaptability.

[0094] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A mass spectrometry auto-sampling positioning method, characterized by, include: Acquire base image data and identify reference marker points, perform coordinate calibration on the reference marker points, and generate base coordinate data; Spatial layout calculations are performed on the base coordinate data to obtain target point data, and path planning is performed on the target point data to generate a sampling path sequence; The sample liquid is deposited point by point according to the sampling path sequence, and images of the deposition area are acquired simultaneously. The center of the deposition area image is identified to generate a sampling location map. Based on the sampled positioning map, a dual-domain offset determination method is used to analyze the spatial offset difference between the theoretical position and the actual position, and to calculate the offset direction and degree of each point to generate positioning offset data. By using the hotspot back-inference method, local mass spectrometry is performed on the positioning offset data to obtain intensity distribution data. Based on the intensity distribution data, the effective response center is determined and feedback positioning data is generated. The feedback positioning data is used to relocate the path, obtain the compensation path sequence, and synchronously correct and adjust the sampling path and detection execution position in the compensation path sequence to generate a sampling positioning scheme.

2. The mass spectrometry auto-sampling positioning method of claim 1, wherein, The specific steps for acquiring base image data, identifying reference marker points, calibrating the coordinates of the reference marker points, and generating base coordinate data are as follows: Gray-level unification and noise suppression are performed on the base image data to obtain preprocessed image data. Then, gray-level abrupt change regions are highlighted on the preprocessed image data to expand the contour expression and generate labeled contour data. Filter and match the shape constraints along the connected regions of the marked contour data to obtain reference marker candidate data, and determine the center position of each reference marker candidate data to generate reference marker points; Based on the reference marker points, we establish spatial-physical mapping data, perform proportional conversion and coordinate unification on the spatial-physical mapping data, obtain unified coordinate data, and perform overall coordinate alignment on the unified coordinate data to generate base coordinate data.

3. The automated mass spectrometry sample preparation and positioning method as described in claim 2, characterized in that, The specific steps for performing spatial layout calculations on the base coordinate data to obtain the target point data are as follows: The base coordinate data is divided into regions and arranged in order according to spatial location to obtain an ordered coordinate set. The relative spacing and adjacency relationship of each position are extracted from the ordered coordinate set to generate spatial correlation data. The spatial correlation data is constrained and laid out according to rules to obtain a set of target points. The target points are then uniformly numbered and sequentially marked to generate target point data.

4. The automated mass spectrometry sample preparation and positioning method as described in claim 3, characterized in that, The specific steps for path planning of the target point data and generating a sampling path sequence are as follows: Establish path connection relationships based on target point data, and expand and organize the initial path data according to the path connection relationships to obtain path sequence data. Then, perform continuity correction on the path sequence data to generate path connection data. The path connection data is rearranged as a whole and adjusted without cross constraints to generate a sample path sequence.

5. The automated mass spectrometry sample preparation and positioning method as described in claim 4, characterized in that, The specific steps for performing point-by-point sample deposition according to the sample path sequence, and simultaneously acquiring images of the deposition area, are as follows: Based on the sampling path sequence, the execution location and execution order of each target point are determined one by one, and deposition scheduling data is generated; Based on the sedimentation scheduling data, point-by-point sample liquid deposition is performed at each target location to obtain sedimentation state data, and corresponding images of the sedimentation state data are acquired synchronously to generate sedimentation area images.

6. The automated mass spectrometry sample preparation and positioning method as described in claim 5, characterized in that, The specific steps for center identification of the sedimentation area image and generation of the sample location map are as follows: The depositional regions in the depositional region image are located and analyzed item by item. The location of the response concentration is determined in each depositional region, the depositional center data is obtained, and the actual depositional location of each target point is uniformly mapped according to the depositional center data to generate location mapping data. Based on the sampling path sequence, the location mapping data is sequentially integrated to generate a sampling location map.

7. The automated mass spectrometry sample preparation and positioning method as described in claim 6, characterized in that, Based on the sampled positioning map, a dual-domain offset determination method is used to analyze the spatial offset difference between the theoretical and actual positions, and to calculate the offset direction and degree of each point to generate positioning offset data. The specific steps are as follows: The actual deposition location is extracted from the sampling location map, and the actual deposition location is combined with the target point data and matched with the numbers to generate theoretical and actual alignment data; Along the sampling path sequence, adjacent points are selected from the theoretical and actual alignment data as reference points, and the spacing directional characteristics between the current point and the reference point under the theoretical and actual positions are established. A dual-domain offset determination method is adopted to compare the center offset difference, the spacing offset difference, and the direction angle difference between the center position offset and the adjacent relationship offset in the spacing direction feature to obtain offset difference data. The offset difference data is continuously merged to generate offset determination data. The degree of offset in the offset determination data is expressed in a hierarchical manner to obtain offset level data. The offset level data is combined with the spacing direction features to determine the offset direction of each point and generate offset description data. The offset direction and degree of the offset description data are sequentially integrated and continuously merged to generate positioning offset data.

8. The automated mass spectrometry sample preparation and positioning method as described in claim 7, characterized in that, The method of hotspot back-inference involves performing local mass spectrometry probing on the positioning offset data to obtain intensity distribution data, determining the effective response center based on the intensity distribution data, and generating feedback positioning data. The specific steps are as follows: By using the hotspot reverse method, the offset feature data of each point is read item by item from the positioning offset data, and the offset feature data is extended in the offset direction to generate directional extension data. The probe direction is determined based on the directional extension data, and the degree of offset of the probe direction is calculated to determine the probe range and generate probe guidance data. Based on the trial guidance data, a local mass spectrometry trial area is constructed around each point, and trial sampling positions are set up in the local mass spectrometry trial area in a ring-domain progression manner from the inside to the outside to generate trial response data. The response intensity of each sampling location in the trial response data is spatially ordered to obtain intensity distribution data. Based on the intensity distribution data, the intensity adjacency feature data of each sampling location is back-analyzed to generate hotspot candidate data. The spatial location of each sampling location within the candidate area in the hot spot candidate data is centrally located to determine the effective response center and generate feedback positioning data.

9. The automated mass spectrometry sample preparation and positioning method as described in claim 8, characterized in that, The specific steps for relocating the feedback positioning data, obtaining a compensation path sequence, and synchronously correcting and adjusting the sampling path and detection execution position in the compensation path sequence to generate a sampling positioning scheme are as follows: The feedback positioning data is read sequentially according to the sampling path sequence to obtain sequential positioning data. The spatial correspondence between the effective response center position and the corresponding target point position of each point is extracted from the sequential positioning data to generate position correction data. The location correction data is replaced and the location is reconstructed item by item to obtain path relocation data, and the connection order relationship is established based on the path relocation data; Based on the connection order relationship, the access order of each point is checked for continuity and rearranged to generate a compensation path sequence. The sampling paths in the compensation path sequence are matched one-to-one with the detection execution positions to obtain the corresponding data, and the corresponding data is corrected to generate the position correction execution data. The position correction execution data is synchronously adjusted and uniformly expressed to generate a sample positioning scheme.

10. An automated mass spectrometry sample positioning system, based on the automated mass spectrometry sample positioning method according to any one of claims 1 to 9, characterized in that, include: The marker calibration module is used to acquire base image data and identify reference marker points, calibrate the coordinates of the reference marker points, and generate base coordinate data; The layout planning module is used to perform spatial layout calculations on the base coordinate data, obtain target point data, and perform path planning on the target point data to generate a sampling path sequence. The path deposition module is used to perform point-by-point sample liquid deposition according to the sample preparation path sequence, and simultaneously acquire images of the deposition area, perform center identification on the deposition area images, and generate a sample preparation location map. The offset determination module is used to analyze the spatial offset difference between the theoretical position and the actual position based on the sampled positioning map and the dual-domain offset determination method, and to calculate the offset direction and degree of each point to generate positioning offset data. The hotspot probing module is used to perform local mass spectrometry probing on the positioning offset data using the hotspot back-inference method, obtain intensity distribution data, determine the effective response center based on the intensity distribution data, and generate feedback positioning data. The path correction module is used to relocate the path of the feedback positioning data, obtain the compensation path sequence, and synchronously correct and adjust the sampling path and detection execution position in the compensation path sequence to generate a sampling positioning scheme.

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